Impact of Foreign Direct Investment on Income Distribution in China 9781844642366, 9781844642359

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The Impact of Foreign Direct Investment on Income Distribution in China

Liu Shiguo

Contents

CONTENTS CHAPTER 1 OVERVIEW ......................................................................................................................... 1 1.1 QUESTIONS AND SIGNIFICANCE OF RESEARCH.............................................................................................. 1 1.2 RESEARCH THEORY, METHOD AND DATA .................................................................................................... 5 1.3 MAIN INNOVATIONS AND WEAKNESSES ..................................................................................................... 8 1.4 STRUCTURE OF THE BOOK...................................................................................................................... 10 CHAPTER 2 OVERVIEW OF RESEARCH ON THE EFFECTS OF FDI ON THE INCOME GAP AMONG RESIDENTS OF THE HOST COUNTRY.................................................................................................... 18 2.1 HOW FDI AFFECTS THE INCOME GAP AMONG RESIDENTS OF THE HOST COUNTRY ........................................... 19 2.2 EMPIRICAL MODEL SETTINGS AND MEASUREMENT METHODS REGARDING THE EFFECTS OF FDI ON THE INCOME GAP .............................................................................................................................................................. 25 2.3 INCOME-GAP AND FDI MEASUREMENT.................................................................................................... 28 2.4 SUMMARY.......................................................................................................................................... 35 CHAPTER 3 A THEORETICAL MODEL OF THE EFFECTS OF FDI ON INCOME DISTRIBUTION AMONG RESIDENTS OF THE HOST COUNTRY.................................................................................................... 37 3.1 WORKS REGARDING FDI’S GROWTH EFFECTS ........................................................................................... 37 3.2 THEORETICAL MODEL OF FOREIGN CAPITAL’S EFFECTS ON INCOME DISTRIBUTION ............................................ 40 3.3 FURTHER DISCUSSION OF THE THEORETICAL MODEL................................................................................... 48 3.4 SUMMARY.......................................................................................................................................... 52 CHAPTER 4 THE DYNAMIC PANEL DATA ANALYSIS METHOD ............................................................... 54 4.1 THE GENERAL MODEL FOR PANEL DATA ANALYSIS ...................................................................................... 54 4.2 THE GENERAL MODEL FOR DYNAMIC PANEL DATA ANALYSIS ........................................................................ 59 4.3 ESTIMATION METHODS FOR DYNAMIC MODELS OF PANEL DATA ................................................................... 60 4.4 ASSUMPTIONS OF DYNAMIC MODELS OF PANEL DATA AND MOMENT CONDITIONS FOR GMM ESTIMATES ........... 63 4.5 DYNAMIC MODELS OF PANEL DATA: ESTIMATING THE INTERDEPENDENCE BETWEEN INDIVIDUALS ........................ 66 4.6 SUMMARY.......................................................................................................................................... 70 CHAPTER 5 EMPIRICAL ANALYSIS OF THE EFFECTS OF FDI ON THE INCOME OF CORPORATE EMPLOYEES IN CHINA ........................................................................................................................ 71 5.1 OVERVIEW OF THE EFFECTS OF FDI ON THE INCOME OF CORPORATE EMPLOYEES IN THE HOST COUNTRY .............. 71 5.2 THE EFFECTS OF FDI ON EMPLOYEE INCOME: EMPIRICAL MODELS, VARIABLES, AND DATA................................. 73 5.3 EMPIRICAL CONCLUSIONS...................................................................................................................... 79 5.4 SUMMARY.......................................................................................................................................... 85 CHAPTER 6 EMPIRICAL ANALYSIS OF THE EFFECTS OF FDI ON EMPLOYMENT IN CHINESE ENTERPRISES .......................................................................................................................................................... 97

6.1 OVERVIEW OF THE EFFECTS OF FDI ON EMPLOYMENT IN THE HOST COUNTRY ................................................. 97 6.2 AN EMPIRICAL MODEL OF FDI’S EMPLOYMENT EFFECTS ON CHINESE ENTERPRISES ........................................ 106 6.3 MAIN CONCLUSIONS .......................................................................................................................... 108 6.4 SUMMARY........................................................................................................................................ 117 CHAPTER 7 ESTIMATING A SIMULTANEOUS EQUATION MODEL OF THE EFFECTS OF FDI ON WAGE RATES AND EMPLOYMENT AT CHINESE ENTERPRISES ....................................................................... 118 7.1 EMPIRICAL SETTING OF THE SIMULTANEOUS EQUATION MODEL OF WAGE RATES AND EMPLOYMENT ................. 118 7.2 EMPIRICAL CONCLUSIONS REGARDING THE EFFECTS OF FDI ON WAGE RATES AT ENTERPRISES........................... 121 7.3 EMPIRICAL CONCLUSIONS ON THE EFFECTS OF FDI ON EMPLOYMENT AT ENTERPRISES .................................... 125 7.4 SUMMARY........................................................................................................................................ 128 CHAPTER 8 MEASURING THE EFFECTS OF FDI ON INCOME DISTRIBUTION IN CHINA ........................ 134 8.1 METHODOLOGY FOR MEASURING INCOME DISTRIBUTION ......................................................................... 134 8.2 DECOMPOSITION OF THE THEIL INDEX.................................................................................................... 137 8.3 MEASUREMENT RESULTS OF THE EFFECTS OF FDI ON THE INCOME GAP ....................................................... 142 8.4 SUMMARY........................................................................................................................................ 154 CHAPTER 9 MAIN CONCLUSIONS, POLICY RECOMMENDATIONS, AND FOLLOW-UP RESEARCH ........ 156 9.1 MAIN CONCLUSIONS .......................................................................................................................... 156 9.2 POLICY IMPLICATIONS ......................................................................................................................... 160 9.3 FOLLOW-UP RESEARCH ....................................................................................................................... 161 REFERENCES ..................................................................................................................................... 162 POSTSCRIPT ..................................................................................................................................... 174

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Chapter 1 Overview 1.1 Questions and Significance of Research 1.1.1 Questions The income gap in China has been widening since this country began reform and opening in 1978. With regard to the incomes of Chinese residents, the Gini coefficient reached 0.496 in 2006 (Ru Xin et al, 2006), which was 86.6% higher than in 1978, with an annual average growth rate of 2.25% (see Fig. 1-1 and Table 1-1)ķ. On a stage-by-stage basis, the income Gini coefficient had an annual average growth rate of 3.03% for the period 1980-1989, 2.66% for 1990-1999, and 2.49% for 2000-2005. million USD, current price

0.50

140000 GINI Coef.˄RHS˅

FDI (LHS)

0.45 120000 0.40 100000

0.35 0.30

80000

0.25 60000

0.20 0.15

40000

0.10 20000 0.05 0 1978

1981

1984

1987

1990

1993

1996

1999

2002

2005

2008

0.00 2011 (year)

Fig. 1-1 Foreign Direct Investment (FDI) Flows and the Income Gini Coefficient in China, 1978-2012 Source: Data from Tables 1-1 and 1-4 in this chapter

The income gap between Chinese residents has reached a rather high level compared with other countries around the world. In 2005, the country with the highest Gini coefficient was Namibia at 0.743 (1993), while the lowest was Azerbaijan at 0.19 (2002) among 127 countries and regions whose most recent Gini coefficients were available, according to data from the Human ķ The income Gini coefficient for China by period: 0.53 in 2004 (Ru Xin et al, 2006, p. 376); 0.536 in 2006 (Li Peilin et al, 2008, p. 67-68); close to or even greater than 0.484 in 2007 (Ru Xin et al, 2008, p. 219 and 340); about 0.47 in 2009 (Yang Yiyong, 2010), and 0.474 in 2012 (NBS, 2013). Scholars and the public are also focused on issues such as “gray income” or “hidden income” relating to the income gap in China. The income gap between the richest 10% households and the poorest 10%, with hidden incomes included: An increase to 31 times from nine times in urban areas, and to 55 times from 21 times across China in 2005 (Wang Xiaolu, 2007); and an increase to 26 times from nine times in urban areas, and to 65 times from 23 times across China in 2008 (Wang Xiaolu, 2010). This is described in Section 3, Chapter 2 of this book.

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Development Report (HDR) published by the United Nations Conference on Trade and Development (UNCTAD, 2006).In descending order, China took 35th place (0.447, 2001) or the 34th place (0.46, 2005), making it second only to the Philippines in Asia, higher than all the European countries and the same as some Latin American countries. The household Gini coefficients from the perspective of reserves (i.e., wealth) distribution: 0.55 (China) and 0.892 (the world) in 2000 (based on amounts converted at the official exchange rate); 0.686 (China) in 2006 (Li Peilin et al, 2008). Please see Tables 1-1 and 1-2 for the comparison between China and other countries in terms of income and wealth gaps. Wages/salaries or labor remuneration have long been among the most important sources of income for Chinese households. The income of a resident is therefore highly dependent upon his/her employment status. A great many jobs in China are offered by companies with different forms of ownership, including foreign-invested enterprises (FIEs). Foreign direct investment (FDI) has been increasing and FIEs have been growing since China began reform and opening. They have become an important part of the Chinese economy. FDI in China was only 80,000 US dollars in 1979, before it grew year after year and reached 72.4 billion US dollars in 2005, or 905,000 times that of 1979, with an annual average growth rate of nearly 70%, as is shown in Fig. 1-1 and Table 1-4. Adding up the annual FDI inflows, we can see that the total amount of FDI in China in this 27-year period was 633.5 billion US dollars, without considering statistical issues such as changes in the value of the US dollar and in the prices of capital goods. The ratio of FDI to fixed-capital formation in China was only 1/1,000,000 in 1978, before it rose to 8.05% in 2004, with a maximum of 17.3% (1994) and an average of 9.2% over this period. The ratio of the FDI inflow to China’s GDP was 3/10,000,000 in 1978, before it topped 1% for the very first time in 1991 and reached 3.7% in 2004, with a maximum of 6.2% (1994) and an average of 3.3% in this period. In 2004, businesses in China’s secondary sector combined to receive 7.06 trillion yuan worth of capital, of which foreign capital represented 22.7% (1.6 trillion yuan); and 97.3% of the registered foreign capital was with FIEs (i.e., companies invested by Hong Kong, Macao, Taiwanese and/or foreign companies)ķ. Sample data in this study also shows a similar pattern (see Table 1-3). Chinese residents have received considerable benefits as the amount of foreign capital in China has increased. In 2004, the annual average number of employees in the secondary sector was 93.04 million in China, including 39.83 million in FIEs, or 42.8% of the total. For these employees, total labor remuneration was 1.2 trillion yuan, including 624.3 billion yuan from FIEs, or 52% of the total, according to China Economic Census Yearbook 2004. Moreover, the World Bank (2006) found in a survey of 12,400 businesses in 120 Chinese cities that FIEs have a pre-tax capital return as high as 22% in China. Since local investors represent 26.47% of the total registered capital of FIEs and foreign investors represent 0.89% of that of domestic-invested enterprises (DIEs), local investors as Chinese residents have also received a corresponding share of the earnings of foreign capital in Chinaĸ. FIEs, which claim to be more efficient than DIEs, or local businesses, already account for more than a third of China’s exports, value added, taxes and jobs created. We might say that the increasing penetration of FDI into the Chinese economy and a widening income gap among residents are two remarkable phenomena that appeared almost at the same time in this economy after China began reform and opening. People are therefore prone to correlate the two phenomena and ask: Is there a certain correlation between FDI and the widening income gap in ķ

Accordingly, a population of FIEs is fundamentally equivalent to that of FDI from the analytical perspective. The numbers in this paragraph were calculated on the basis of Table 1-A-1, the Secondary Sector Volume (Part 1), China Economic Census Yearbook 2004. ĸ

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China? If there is, how does the strength of this correlation evolve? What strength has it reached so far? How did it come into being? As an example, FIEs already account for about 60% of China’s international goods trade and have become an important force pushing China’s reliance on international trade to 70%, an all-time high, in 2004. This has triggered broad concerns that China is susceptible to foreign impact on its economic security. Given FIEs’ focus on the processing trade, however, are they helpful for adding jobs and increasing incomes for China’s low-skill labor force (especially the massive surplus rural labor force)? Is the growing group of high-position, high-income employees in FIEs becoming the main part of the emerging middle class in China, which in turn is expected to play a leading role in stabilizing the Chinese economy and society? Do the huge net inflows of foreign capital and the increasing international trade surplus mean that China’s export-oriented economic sector has received more economic opportunities than its domestic market-oriented sector, thereby impacting the latter’s production and income growth (similar to the position of Huang, 2002)? Will the joint objection of large multinational companies (MNCs) with operations in China to the Chinese government’s unification of income taxes paid by DIEs and FIEs weaken its ability to redistribute income among residents? The high ratio of round-trip FDI to the total inflow of FDI allows local investors rather than foreign investors to receive various incentives such as those relevant to taxes, but since they lack the competitive advantages in technologies, management, and global market networks of true MNCs, will they cause the spillover effects of economic growth to be much lower than what is theoretically expectable and, thus, be largely responsible for the widening income gap among Chinese residents? To address these questions and to narrow the income gap among Chinese residents, it is necessary to consider further policy questions. Does China need to provide better industrial and regional guidance on the inflow of foreign capital? Should it cancel the tax incentives for FIEs so that DIEs and FIEs are fundamentally equal in terms of national treatment, thereby helping DIEs, especially state-owned enterprises (SOEs), compete with FIEs in a fair market environment? Furthermore, if there is a certain correlation between FDI growth and changes in the income gap among Chinese residents, is this correlation systematic and inevitable? How can we explain, identify and verify this correlation through economic theories and econometric methods? If a certain systematic correlation indeed is verified to exist, how do we understand and assess this correlation in the context of China’s economic transition? To narrow the income gap in China, will it be efficient to do this by changing FDI-relevant policies? How can we properly balance the sharing of FDI benefits for the Chinese economy and the narrowing of the income gap among Chinese residents? These questions obviously need to be answered as soon as possible. 1.1.2 The Significance of Research Does FDI “equal” FIEs? Of course not. In reality, they are not fully consistent with each other, as is mentioned in the summary of statistical issues relevant to FDI and FIEs in Chapter 2. Foreign capital represented 1.8% of the total registered capital of DIEs in the 1998-2006 period, while local capital represented 28.2% of the total registered capital of FIEs in the same period, according to data (see Table 1-3) about enterprises above a given size in China’s secondary sector (for the 1998-2006 period, they refer to: All the SOEs; and all non-state-owned enterprises whose annual income from their main business is at least five million yuan). To research the effects of FDI, therefore, new methods must be employed instead of equating the registered capital of FIEs with the capital invested by foreigners. This study happens to aim at using scientific methods to identify the real correlation between

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FDI and income distribution among residents and at trying to provide more reasonable theoretical explanations for this correlation so as to explore measures for narrowing the income gap among Chinese residents and contributing to a healthier Chinese economy and society. (1) By analyzing FDI as an important economic force to identify the reasons for a widening income gap among Chinese residents, we may be able to find an effective approach to alleviating the increasing resistance to the sustainable growth of the Chinese economy that this gap causes. It took China only fewer than three decades to transform from the world’s “fairest” country to one of the world’s “most unfair” countries. The uneven income distribution has sparked conflicts and even turmoil in Chinese society. All the problems, regardless of the theoretical Gini coefficient or real-world conflicts, focus on a tough reality in Chinese society: wealth is unfairly distributed. To understand the real income gap in China, it is necessary to examine the process of income distribution in this country. Since income distribution is a process, its fairness means that this process is fair, including the fairness of all the steps of income creation (i.e., the start, the course and the result). Among them, there is a close cause-and-effect relationship between the fairness of the result and that of the start and of the course. With regard to its formation and evolution, the income distribution pattern will be affected by every step of the entire process of income distribution. Accordingly, the impact of FDI on the income gap among Chinese residents starts from the process of income creation by FDI. We should pay particular attention to research on the income gap because a widening income gap will certainly affect long-term economic growth. The existing literature, such as a book co-authored by Fabio-Cesare Bagliano and Giuseppe Bertola (2004), generally explains how the income gap affects economic growth from a micro-perspective. Since low-income households differ from high-income ones in terms of marginal propensity of consumption (savings), the income gap will affect total consumption, then the structure of total demand, and then investment allocation, especially with respect to human resources. The income gap also affects the redistribution policy and its effectiveness. The income gap will affect economic growth through political-economic effects, such as in Alesina (1994), Rodrik (1994) and Berytola (1993), and the effects of an imperfect capital market, such as in Galor (1993) and Aghion (1997), according to some of the other literature. In addition, inequality will also cause political and social instability (Perotti, 1996), add to the pressure on the social and political systems, reduce their effectiveness in handling external impacts, and bring about various types of social violence (Rodrik, 1997; Fajnzylberetal, 1998), thereby increasing the burden on economic and social development (Bourguigon, 1998). Given the current stage of economic and social development in China, a severe income-gap problem has become a special source of a potential “middle income trap.” In 2006, China’s per-capita GDP reached US$2,042, that is, it was among countries with low and middle incomes (US$826 to US$3,255) as defined by the World Bank. As a common phenomenon observed by scholars, however, a great many social problems, especially a rapidly widening income gap, will occur in a country whose per-capita GDP is somewhere between 1,000 and 3,000 US dollars. In fact, some literature studies the correlation between FDI and income distribution from the perspective of the human capital gap. None of the aforementioned theories is obviously useless despite that none is able to fully explain how FDI affects the income gap among residents of the host country. Developing further theories on their foundation is part of the intention of this book. (2) We try to use modern micro econometric methods on the basis of the latest massive statistics to research the correlation between FDI and the income gap among residents and, thus, to identify the effects of FDI from among numerous factors affecting the income gap.

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Empirically, there are still a great many methodological challenges in quantitatively identifying the effects of FDI from among the numerous factors affecting the income gap among residents. There is no precise theory available for identifying these factors. What exactly are the correlations between the income distribution mechanism for FDI and factors relevant to economic development, such as geographical distribution, resources, population and its changes, the per-capita income threshold, the labor force’s physical structure (e.g., gender, age, ethnic group, or race) and social structure (e.g., skills mix, existing human capital mix, household burden coefficient, or employment status), industrial structure, and government policies (e.g., labor force flow, education/training, infrastructures construction, trade promotion, industry development, education extension, political democracy, and investment incentives)? Does FDI have short-term or long-term effects on the income gap in the host country? Does the host country have cyclic characteristics? Do the source countries and capital portfolio types of FDI also constitute one of the distribution mechanisms for FDI? What is the relationship between FDI’s growth effects and distribution effects? What are the redistribution effects of FDI? All these need more scientific examination from an academic angle. To identify the effects of each of these factors on the income gap among Chinese residents, we must rely on more precise theoretical models, accurate measurement of relevant statistical indicators, and more realistic econometric methods. The income gap is the core issue that relates to realizing social justice in China: building a scientific, fair, and just income distribution system has become a top priority in building a harmonious society. Economic growth, technological advances, social modernization, etc., fall into the sphere of instruments, whereas human development and welfare are the ultimate goals of social development. We can effectively narrow the gap between rich and poor and promote harmonious growth only by seeking policies related to FDI or their combinations that enable the use of FDI’s growth effects with no side effects on distribution of income and by realizing both fairness and efficiency in the process of initial distribution of income during production by FIEs. To alleviate the problem of a widening income gap in China, therefore, we should ascertain, from theoretical and empirical perspectives, how FDI affects the income gap among Chinese residents, what its final effects are and how to handle its effects. These are doubtlessly issues that need immediate, in-depth research.

1.2 Research Theory, Method and Data The research on the correlation between FDI and income distribution described in this book is made from the perspective of economics. First of all, a general equilibrium model determined by employee incomes and employment is designed. On this basis, single-equation empirical models determined by employee incomes or employment are designed; all the SOEs and non-state-owned enterprises above a given size in the secondary sector in the 1998-2006 period are taken as samples; and dynamic panel data econometrics is employed to estimate how FDI affects income gap among employees of Chinese enterprises in the secondary sector. 1.2.1 Theory A general equilibrium model determined by employee incomes and employment is designed in this book. This model uses a social planning framework in which society comprises the consumer and producer sectors. The consumer sectors aim at maximizing the consumer’s utility and also act as the suppliers of labor. The producer sectors take raw labor, human capital, physical capital, and

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technology as the factors of production and aim at maximizing profits. The labor force consists of skilled and unskilled labor forces. The conversion of the unskilled labor force into a skilled one requires capital and investment by the skilled labor force, of which the latter is realized in the “skill-building sector.” The producer sectors consist of the agricultural sector and modern sectors represented by the secondary sector, which is further divided into foreign-invested and domestic-invested modern subsectors (“FIS” and “DIS” respectively). Among them, the FIS will act as one of the engines of economic growth. The concept of endogenous growth is at the core of this model, with a few specific manifestations. First, the buildup of human capital of the host country, which is one of the sources of endogenous growth, is realized through the “skill-building sector.” Human capital requirements vary with producer sectors: The agricultural sector needs only an unskilled labor force, whereas modern sectors require a skilled labor force. The unskilled labor force can become skilled only through human-capital buildup in the skill-building sector, which in turn takes the unskilled labor force and domestic capital as the factors of production. It is the operation of the skill-building sector that reflects the host country’s response to challenges posed by FDI’s need for a local skilled labor force. This is helpful for explaining what the host country’s government does amid the effects of FDI on the income gap among residents in the host country. Second, comprehensive competition between FDI and domestic capital ultimately will likely push them both forward. And competition is one of the sources of endogenous growth. Third, the growing human capital of the host country satisfies the needs of the producer sectors on the one hand and allows laborers to receive higher pay thanks to generally higher productivity on the other. This means that an expanding market of the host country has become another source of endogenous growth. Fourth, skill-bias characteristics of the FIS constitutes one of the critical factors that affect the employment and income structures of the host country and, hence, the income distribution pattern. This theoretical model has assumptions that will be as close to reality as possible; and relevant conclusions drawn under the aforementioned framework will be conducive to explaining how FDI affects the income gap among residents in the host country. 1.2.2 Method This book employs dynamic panel data econometrics to investigate the given model. Compared with other methods, this method can meet the research needs of this book because of the following characteristics. The income gap among enterprises is determined by differences at the macroscopic, mesoscopic and microscopic levels. The macroscopic decisive factor for corporate income refers to the position of the national economy in the cycle. The mesoscopic decisive factor for corporate income refers to the economic conditions inside and outside the region and industry in which the enterprise resides. The microscopic decisive factor for corporate income refers to the characteristics of each enterprise relative to the others. Panel data is therefore most suitable for describing the income gap among enterprises, because it includes observations about the characteristics of individual enterprises and thus enables this research to examine both the heterogeneity among microscopic units of different types and the differences and similarities among dynamic changes in the characteristics of these units. By comparison, time-series data conceals microeconomic dynamics because it is macroscopic and contains a general deviation. The “stickiness” of variables such as income and employment dictates that the measurement techniques must be able to reflect the dynamics of these variables. Past events will affect future events, as the hysteresis of actions is a common phenomenon in social sciences. The term

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“stickiness” is mainly used in social sciences, especially economics. It is intended to describe the resistance of something to change. Nominal wages are usually believed to be sticky. Market forces may have reduced the real value of laborers in a particular industry, but wages are still inclined to remain the same as before. This may result from institutional factors, such as price regulation, legal obligations (e.g., formal rental and employment contracts), trade unions, human stubbornness, or self-interest. Economists have pointed out that price stickiness may be caused by menu cost, money illusion, incomplete information on price changes, and attention to fairness. Wages, prices, and the employment rate indeed are all sticky. The value of a variable usually varies with market conditions, but after stickiness is introduced into the system, such variation tends to move in a single direction, thereby making this variable exhibit characteristics of “crawling,” which is also known as “ratchet effect.” Cross-sectional data is observations of one or more variables of each unit at a single time point, making it impossible to estimate the dynamic correlations between time points. In addition, the panel data model has a larger number of degrees of freedom over the cross-section or time-series data model, and thus is more likely to avoid multi-collinearity and an excessively small number of degrees of freedom, thereby enabling the creation of more complex behavioral hypotheses (Hsiao, 2001). For more information on methods of analyzing panel data, please read Section 1, Chapter 4 of this book. 1.2.3 Data Basic data for this research is comprised of two parts: production and operation data about all the SOEs and non-state-owned enterprises above a given size in China’s secondary sector (hereafter referred to as “industrial data”), as well as provincial price indices, national input/output tables, and data relevant to road-travel distances between cities at the county level in China. Industrial data comes from the National Bureau of Statistics of China (NBSC). This data covers all SOEs and non-state-owned enterprises above a given size in China’s secondary sector in the 1998-2006 period. The corresponding database covers about 2,900 administrative divisions at the county level, more than 530 manufacturing industries (Industrial Classification and Codes for National Economic Activities (GBT4754-1994) with four-digit codes), 23 types of registered enterprises (three-digit codes) and three corporate sizes, with the number of enterprises being somewhere between 156,000 and 300,000. This database includes four qualitative indicators and 45 quantitative ones. For other information on the sample enterprises, please refer to Tables 1-5 through 1-8. Since prices change between the time series of the data, it is necessary to eliminate the effects of price changes on all the value indicators. Given a lack of price indices by industry and county-level areas, we use provincial consumer price indices (CPIs) to reduce the income indicator in each corresponding province, provincial producer price indices (PPIs) to reduce the production indicator in each corresponding province, provincial fixed-asset investment price indices (IPIs) to reduce the investment indicator in each corresponding province, and provincial GDP reduction indices to reduce the value-added indicator in each corresponding province. In addition, given a lack of provincial PPI data, we use IPI data to approximate PPI data. The provincial CPI and IPI data come from CEIC databases, while the GDP reduction indexes were calculated on the basis of provincial current-price GDP and annual GDP growth rates available from the CEIC. The input-output coefficient, especially the total input coefficient, is the best indicator for measuring technical connections between industries. Enterprises that specialize in processing trade activities represent a very large share of FIEs in the secondary sector, and their technical connections with DIEs are obviously weaker than those between DIEs themselves. With the

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input-output tables published by the Chinese government, however, it is impossible to gain an insight into the specific technical connections between FIEs and DIEs, and auxiliary materials necessary for deducing the reality of such connections are also unavailable. As a result, this book has to assume that the input-output connections between FIEs and DIEs are identical to those between DIEs themselves, despite that this will definitely exaggerate the spillover effects of FIEs. Travelling distance is one of the basic factors that affect how enterprises carry out production. Geographical distances fall into two types – straight-line and traveling distances. The former refers to the straight-line distance between the source and destination cities on the map, while the latter refers to the real-world traveling distance between them. Straight-line distances are like those available from the 1:4,000,000 provincial or county-level administrative division map published by the National Geomatics Center of China (NGCC).They are used in empirical literature such as “The Correlation Between Economic Growth and the Income Gap” (e.g., Wu Yuming, 2005). Straight-line distances are measured with two basic assumptions: travels between two places are made on straight-line paths, and all places are at the same geographical level. Unfortunately, neither of these assumptions is consistent with the reality of travel between most Chinese cities. Fortunately, we found a database closer to reality. We use road travel distances to measure the distances between the administrative divisions. Compared with the networks of railways, waterways and airways, the network of roads covers the widest geographical areas in China – there are 2,862 cities at the county-level in China, including 2,859 cities accessible by road. Road travel distances are used as the geographical distances between cities on the basis of the following assumptions. First, all roads are homogeneous, and roads at all levels are identical to each other from the perspective of users. Second, all the roads are identical to each other in terms of the costs of use, such as tolls. The two assumptions are of course inconsistent with reality in many aspectsķ, but the most important thing is that compared with the 1:4,000,000 straight-line travel distance measurements, the real-world road travel distance data is the best travel distance data that is currently available.

1.3 Main Innovations and Weaknesses 1.3.1 Main Innovations From the perspective of the level of individuals and entities across which income is distributed, this book is the first among the few pieces of worldwide literature to research the effects of FDI on the income gap among residents in the host country at the level of enterprises. With regard to the other relevant literature, the research subjects are at the level of national totals, provinces or eastern/central/western regions. The finer of the categories of individuals and entities across which income is distributed, the more accurate are the measurements of the income gap. The opposite is also true, as is described in the overview of such measurements in Chapter 2 of this book. As for the theoretical model, a general equilibrium model is designed that contains characteristics of endogenous growth that have not been seen in other worldwide literature of the same topic. With real simulation and precise deduction of the reality of the Chinese economy, this model explains how FDI generally affects the income gap among residents in the host country, and reflects many ķ

Roads in China are divided into five levels (Road Level Codes (GB/T919-2002)), for example. Roads have quality and functions that vary with their levels; roads at the same level in different regions are not necessarily the same in terms of tolls and may play very different roles in transportation in each region.

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qualities of China in terms of this issue. With regard to setting an empirical model, this book employs a two-step method as opposed to the one-step method in the other literature of the same topic. First, behavioral equations are set for the per-capita wage rate (or labor remuneration per capita) at a business and the number of employees, respectively, in accordance with the economic theory. Their independent variables include FDI. In this step, since there are a great many factors that affect the wage rate, or the labor remuneration per capita, at a business, control variables in this book are added in strict accordance with the derivation of the theoretical model, thereby avoiding the randomness of this addition, which is nearly ignored in many other pieces of literature. Second, the indicator of the wage or compensation gap between enterprises is calculated and, on the basis of the calculation results in Step 1, the effects of FDI on the per-capita wage rate (or compensation per capita) at an enterprise and on the number of employees are yielded from this indicator and used to calculate FDI’s contribution to the income gap among residents. By comparison, the other literature of the same topic generally finishes the setting in one step, that is, taking the income gap indicator as the explained variable and FDI, etc., as the explaining variables. Obviously, this practice is not based upon any precise economic theory, and control variables are usually added at will or missing. There are so many variables that affect the income gap in society. With regard to measurement methods, this book employs dynamic panel data, which is the first time this has been done among worldwide literature of the same topic. Most such literature uses the time-series method, while the remaining uses the cross-section or panel data analysis method. Almost none of those that use the panel data analysis method consider dynamic characteristics, and they all assume that the observed basic units are independent from each other. But this book has taken a step forward on this issue, as it formally addresses cross-section interdependency in the empirical section. 1.3.2 Main Weaknesses The weaknesses of this book mainly result from the raw data. First, information on basic characteristics such as the sources of foreign capital is unavailable from the production and operation data regarding FIEs. As for the effects of FDI on the income gap with respect to enterprises, control variables other than those mentioned herein are not included in this research. Within FIEs, for example, capital from different countries typically has apparent country-specific characteristics, according to some literature. Information on the sources of foreign capital will appear in the approval documents of business administrations and in corporate registration documents, but these two sources of information are controlled by business administrations and industry and commerce administrations, respectively. Production and operation (excluding imports and exports) statistics about FIEs are compiled by statistics bureaus, while import/export statistics about these enterprises are compiled by the General Administration of Customs of the People’s Republic of China (GACC). Since these official statistics are not compiled in a unified manner, no systematic statistics that combine FIE registration information, product/operation statistics, and international trade statistics are available. Second, China’s input-output statistics do not distinguish foreign inputs from domestic ones, so it is impossible to make in-depth analysis of how FIEs affect the income gap among Chinese residents through the characteristics of processing trade and local procurement. Third, there may be certain problems with the industrial data in terms of sample representativeness. The industrial data used in this book includes all the SOEs and non-state-owned enterprises above a given size in the secondary sector, but most such enterprises are in central and

10

eastern China, with a relatively small number of them being in western China. Moreover, some enterprises are not necessarily eligible at all time points in the sample period as a result of fluctuations in their operations. If these enterprises become ineligible at particular time points and no other “eligible” enterprises are found in their regions, then the representativeness of the samples will be affected to a certain extent. Fourth, the research subjects of this book are confined to the secondary sector and do not include agriculture, the construction industry, or the tertiary sector, the latter having one of the major fields where China uses FDI. Fifth, this book researches only the distribution of income among laborers, not that of capital gains among domestic and foreign capitalists, especially among domestic capitalists who are operating under the banner of “foreign capitalists.” This is obviously incomplete. Sixth, this book researches the wage (or labor remuneration) gap among enterprises, not among employees within an enterprise, that is, not the effects of FDI on the income gap among individual Chinese residents. Information and data about the income gap among individual employees within particular FIEs were secured through a survey made for this research. Given their weak representativeness, however, such information and data are not used in this research, much less serve as the base for extrapolation. The aforementioned flaws in the raw data not only make it impossible for this paper to gain a deeper understanding of this topic, but may also have certain effects on the universality of its conclusions.

1.4 Structure of the Book With regard to the subsequent content of this book, Chapter 2 outlines the research described in relevant literature from perspectives such as theories, methods, and conclusions. Chapter 3 proposes a theoretical model and analyzes how FDI affects the income gap among residents in the host country. Chapter 4 presents dynamic panel data analysis to lay a methodological basis for the empirical decision model that later estimates the wage rate (or the labor remuneration per capita) and the number of employees at an enterprise. On the basis of outlining the relevant content, Chapters 5 and 6 start from the derivation relevant to the theoretical model in Chapter 3 and set empirical models for the effects of FDI on the wage rate (or labor remuneration) of employees at DIEs and on employment in the host country. These chapters also present data processing for particular variables in these models. Chapter 7 builds a small system of simultaneous equations to determine the correlations between the wage rate/employment and FDI decisions. Since it is still impossible to estimate dynamic panel simultaneous equation models using existing Stata tools for estimating dynamic panel data, we turn to the three-stage least squares (3SLS) method to estimate this system of simultaneous equations. On the basis of the estimation results described in Chapters 5 through 7, Chapter 8 breaks down FDI’s contribution to the income gap by breaking down the income-gap index. Chapter 9 presents a summary of the whole book, policy recommendations and an agenda for future research.

11

Table 1-1 The Gini Coefficient of Chinese Residents’ Income, 1978-2006: an Overview of Estimation Results from Some Research Year

(1)

1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

0.2016 0.2168 0.2200 0.2212 0.2156 0.2268 0.2384 0.2492 0.2684 0.2736 0.2868 0.2800 0.2940 0.2936 0.3012 0.3100 0.3240 0.3280 0.3750 0.3790 0.3860 0.3970

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

0.330 0.25

0.29 0.288

0.30

0.3306 0.3399 0.3453 0.3595 0.3568 0.3624

0.376 0.375 0.415 0.390

0.2780 0.2494 0.2641 0.2684 0.2656 0.2968 0.3052 0.3133 0.3214 0.3063 0.3240 0.3396 0.3592 0.3621 0.3515

0.382

0.452

0.2635 0.2525 0.2865 0.2705 0.2875 0.2875 0.2924 0.2961

0.3650

0.30

0.3497 0.3697 0.3469 0.3690 0.3772 0.4017 0.4356 0.4191 0.4058 0.4027 0.4026 0.4129

0.3228 0.3365 0.3228 0.3462 0.3628 0.3800 0.3921 0.3839 0.3618 0.3627 0.3716 0.4019

0.35

0.389 0.375 0.379 0.386 0.397 0.417 0.442

0.39

0.46

(13) 0.2658 0.2168 0.2533 0.2627 0.2392 0.2770 0.2834 0.2869 0.3031 0.3070 0.3266 0.3315 0.3175 0.3332 0.3514 0.3652 0.3785 0.3882 0.3815 0.3809 0.3866 0.4022 0.4385 0.4470 0.4420 0.4790 0.4730 0.4850 0.4870 0.4840 0.4910 0.4900 0.4810 0.4770 0.4740

Sources: (1) Quan Heng (2004), p. 247. Raw data: The raw data in the 1978-1995 period comes from Huang Dan (1999), and the raw data in the 1996-1999 period comes from Qiu Xiaohua (2000). (2) The income distribution research project team of Nankai University (1990). (3) Liu Xiaodong and Lu Qing (1991). (4) World Bank (1995). (5) Research Office of the State Council (1997). (6) Xiang Shujian (1998). (7) Zhao Renwei et al (1999), p. 100, the calculated Gini coefficient of Chinese residents’ disposable income on Page 223 of this book is 0.4621. (8) Chen Zongsheng (1991, 1997). (9), (10) Chen Zongsheng and Zhou Yunbo (2002), Appendix I Table 1, p. 46-47. (11) Wei Zhong (2006). (12) Krongkaew (2003). (13) Each number in this column is the arithmetic average of the numbers in the same row from Columns (1) through (11); among them, the Gini coefficients for 2001 come from UNDP (2006)˗Gini coefficient for 2003-2012 in Columns (13) from NBS (2013), and that for 2005 also can be referred from Lou Jiwei et al (2006), the World Bank stated it was 0.45, and the research institute of the National Development and Reform Commission (NDRC)

12

believed it was 0.4; the 2006 also can be referred from Ru Xin et al (2006).

Table 1-2 World Wealth Gap among Households and in Selected Countries, 2000 Gini Coefficient

Gini Coefficient

Gini Coefficient

World

0.892

Germany

0.671

Pakistan

0.697

China

0.550

India

0.669

Russia

0.698

Argentina

0.740

Indonesia

0.763

Spain

0.565

Australia

0.622

Italy

0.609

Switzerland

0.803

Bengal

0.658

Japan

0.547

Thailand

0.709

Brazil

0.783

South Korea

0.579

Turkey

0.717

Canada

0.663

Mexico

0.748

Britain

0.697

Taiwan

0.654

Holland

0.649

United States

0.801

France

0.730

Nigeria

0.735

Vietnam

0.680

Note: These are the calculation results after the units of the wealth of all the countries are unified in accordance with official exchange rates. The Gini coefficient of the world wealth gap among households would be 0.802 if it was calculated using purchasing power parity (PPP) measurements. Source: Davies et al (2006).

Table 1-3 Foreign Capital among Capital Received by the Sample Businesses, 1998-2006 1998

1999

2000

2001

2002

2003

2004

2005

2006

Total

Received Capital (Yuan in billions) DIEs

1,848.9

2,058.4

2,282.4

2,546.1

2,719.3

2,851.3

3,569.4

3,668.7

4,092.7

25,637.2

FIEs

705.7

777.0

865.7

1,013.5

1,138.3

1,277.5

1,680.7

1,835.3

2,120.4

11,414.2

All Enterprises

2,554.5

2,835.4

3,148.1

3,559.6

3,857.6

4,128.8

5,250.1

5,504.0

6,213.1

37,051.4

Received Foreign Capital (Yuan in billions) DIEs

48.3

50.9

53.0

51.8

56.6

52.1

34.1

53.8

56.4

456.9

FIEs

455.8

513.4

590.6

709.4

808.2

922.5

1,231.8

1,365.2

1,603.1

8,200.1

All Enterprises

504.1

564.3

643.6

761.2

864.8

974.6

1,265.9

1,419.0

1,659.5

8,657.0

Ratio of Foreign Capital to Received Capital (%) DIEs

2.6

2.5

2.3

2.0

2.1

1.8

1.0

1.5

1.4

1.8

FIEs

64.6

66.1

68.2

70.0

71.0

72.2

73.3

74.4

75.6

71.8

All Enterprises

19.7

19.9

20.4

21.4

22.4

23.6

24.1

25.8

26.7

23.4

Note: FIEs include companies invested by Hong Kong, Macao, Taiwanese and/or foreign companies. Source: A database containing production and operation data about enterprises above a given size in China’s secondary sector.

13

Table 1-4 Used Foreign Capital (Flows) and Its Ratios to Investment, Year 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Ratio of FDI to Foreign Ratio of Foreign Capital to Ratio of FDI to Net Increase FDI Inflow (USD Ratio of FDI to Investment in Fixed Assets Total Fixed-asset Investment in of Total Fixed Asset Value in in millions) GDP (%) (%) China (%) China (%) N/A N/A N/A N/A N/A N/A N/A N/A 0.000 0.000 57.00 N/A N/A 0.065 0.019 265.00 12.427 3.78 0.360 0.092 430.00 13.449 4.92 0.544 0.148 916.00 27.194 4.65 1.061 0.298 1,419.00 46.731 3.86 1.549 0.460 1,956.00 62.790 3.60 2.177 0.654 2,243.73 56.421 4.40 2.499 0.764 2,313.53 47.322 4.80 2.300 0.730 3,193.68 43.177 5.92 2.569 0.808 3,392.57 43.883 6.60 2.948 0.777 3,487.11 58.605 6.30 3.522 0.910 4,366.34 72.888 5.70 3.911 1.092 11,007.51 129.522 5.80 7.292 2.345 27,514.95 166.137 7.30 12.210 4.594 33,766.50 164.518 9.92 17.268 6.234 37,520.53 136.476 11.19 15.433 5.355 41,725.52 126.270 11.73 14.858 5.074 45,257.04 139.787 10.63 14.915 5.009 45,462.75 143.824 9.11 13.624 4.765 40,318.71 166.321 6.74 11.326 4.038 40,714.81 198.706 5.12 10.334 3.773 46,877.59 224.186 4.56 10.544 3.937 52,742.86 209.380 4.63 10.418 4.047 53,505.00 170.374 4.43 8.645 3.646 60,630.00 152.730 4.41 8.051 3.677 72,406.00 149.102 4.21 7.657 3.240

Notes: (1) According to the “International Economy and Trade” section in relevant issues of the China Statistical Yearbook published by the NBSC, FDI inflows were US$1.658 billion in 1985, US$1.874 billion in 1986, and US$60.325 billion in 2005. They differ from the numbers in this table that came from Source (1), but the numbers for the remaining years in the 1978-2005 period are identical in the two sources. (2) The value of foreign capital in fixed-asset investment began to be greater than the foreign capital inflow from 1992 onward, because the pattern of China’s use of foreign capital changed suddenly in this year, that is, the share of non-FDI foreign capital (e.g., foreign loans) decreased while that of FDI increased suddenly. (3) Unlike previous issues, the China Statistical Yearbook 2005 no longer defines the value of foreign capital invested in fixed assets by the forms of corporate ownership. Instead, it defines this value by urban and rural areas. It is surprising, however, that the sum of foreign capital and capital from Hong Kong, Macao and Taiwan in urban areas is even greater than the sum of foreign capital in urban areas and that in rural areas, according to Table 6.14 in this yearbook. (4) Since the China Statistical Yearbook 2006 and the 2005 FDI data from UNCTAD are inconsistent in terms of the ratio of foreign capital to the total value of fixed assets in China, this table uses 4.21% from the former. Sources: (1) The FDI inflows come from the UNCTAD FDI database, http://unctadstat.unctad.org/ReportFolders/reportFolders.aspx. (2) The RMB exchange rates come from the “International Trade and Economic Cooperation” section of relevant issues of the China Statistical Yearbook published by the NBSC; (3) The data about foreign capital in fixed-asset investment comes from the “Fixed-asset Investment” section of relevant issues of the China Statistical Yearbook published by the NBSC; (4) The ratio of foreign capital to total fixed-asset investment in China comes from Table 6-4 in the China Statistical Yearbook 2005 published by the NBSC.

14

Table 1-5 Data about the Registered Capital of Enterprises above a Given Size in the Secondary Sector: Averages in the 1998-2006 Period (By the Form of Corporate Ownership) Unit: % Ratio to TotalRatio to TotalRatio toRatio of FDI to Number ofValue of ReceivedTotal FDIReceived Form of Corporate Ownership Enterprises Capital Value Capital State-owned 15.2 24.2 0.9 0.9 Collective 13.6 4.0 0.6 3.7 Joint-Stock Cooperative 4.4 1.2 0.1 2.4 State-owned Associated 0.2 0.3 0.0 0.4 Collective Associated 0.3 0.1 0.0 1.3 State-owned & Collective 0.3 0.2 0.0 1.2 Associated Other Associated 0.2 0.1 0.0 4.6 Solely State-owned 0.6 8.1 0.5 1.6 Other Limited Liability Companies 11.6 12.9 1.1 2.0 Company Limited by Shares 2.8 10.3 1.4 3.2 Private Solely-owned 8.8 1.4 0.1 2.0 Private Partnership 1.9 0.3 0.0 1.4 Private Limited Liability Company 19.6 5.5 0.3 1.4 Private Company Limited by 1.3 0.5 0.0 1.1 Shares Other DIEs 0.2 0.1 0.0 6.1 Joint Ventures with Hong Kong, Macao and/or Taiwanese4.4 5.1 10.9 49.4 Companies Cooperative Businesses with Hong Kong, Macao and/or Taiwanese0.9 1.1 3.0 65.5 Companies Solely-owned by Hong Kong, 4.8 5.4 22.4 97.0 Macao or Taiwanese Companies Companies Limited by Shares Invested by Hong Kong, Macao0.1 0.7 1.3 41.5 and/or Taiwanese Companies Sino-foreign Joint Venture 4.4 9.7 23.2 56.2 Sino-foreign Cooperative Business 0.6 1.0 3.0 67.9 FIEs 3.8 7.1 29.6 97.8 Companies Limited by Shares 0.1 0.7 1.4 44.2 Invested by Foreign Companies Remarks: 80.9 69.2 5.3 1.8 DIEs FIEs 19.1 30.8 94.7 71.8 Source: A database containing production and operation data about enterprises above a given size in China’s secondary sector.

15

Table 1-6 Data about the Registered Capital of Enterprises above a Given Size in the Secondary Sector: Averages in the 1998-2006 Period (By Province-level Administrative Division) Unit: % Ratio to Total NumberRatio to Total ValueRatio to TotalRatio of FDI to of Enterprises of Received Capital FDI Value Received Capital Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Xinjiang

2.27 2.73 4.01 1.87 0.88 3.93 1.32 1.33 5.52 12.08 12.57 2.08 4.21 1.79 8.51 4.94 3.13 2.94 12.36 1.57 0.27 1.01 2.82 1.08 1.08 0.14 1.27 1.14 0.21 0.24 0.70

5.04 3.21 3.84 2.38 1.59 5.69 2.04 2.82 8.76 9.31 6.81 2.15 3.52 1.21 6.92 3.55 3.66 1.74 12.42 1.25 0.35 1.23 2.84 1.06 1.41 0.11 1.64 1.29 0.37 0.36 1.43

3.22 4.56 1.80 0.33 0.25 3.93 0.82 0.47 14.82 15.70 6.38 1.23 7.44 0.45 4.61 0.86 1.46 0.61 27.44 0.74 0.29 0.65 0.80 0.15 0.30 0.00 0.35 0.08 0.02 0.07 0.17

14.90 33.17 10.95 3.21 3.60 16.12 9.41 3.85 39.51 39.40 21.90 13.38 49.34 8.67 15.56 5.65 9.33 8.18 51.62 13.85 19.92 12.31 6.58 3.40 4.96 1.01 4.97 1.51 1.56 4.79 2.80

Source: A database containing production and operation data about enterprises above a given size in China’s secondary sector.

16

Table 1-7 Data about the Registered Capital of Enterprises above a Given Size in the Secondary Sector: Averages in the 1998-2006 Period (By Enterprise Size) Unit: % Enterprise Size

Ratio to Total NumberRatio to Total ValueRatio to TotalRatio of FDI to of Enterprises of Received Capital FDI Value Received Capital

Large

2.62

41.51

24.92

14.03

Medium

9.40

25.79

31.46

28.50

Small

87.97

32.70

43.62

31.17

Source: A database containing production and operation data about enterprises above a given size in China’s secondary sector.

Table 1-8 Data about the Registered Capital of Enterprises above a Given Size in the Secondary Sector: Averages in the 1998-2006 Period (By Industry Category with Two-digit Codes) Unit: % Ratio to Ratio of Ratio to Total Ratio to Total Value FDI to Code Name Number of Total FDI of Received Received Enterprises Value Capital Capital Non-metallic Mineral Mining & 10 0.93 0.45 0.10 5.3 Separation 11 Other Mining 0.01 0.00 0.00 8.3 13 Non-staple Food Processing 5.93 2.40 2.61 25.4 14 Food Manufacturing 2.41 1.59 3.05 44.8 15 Beverage Manufacturing 1.66 1.93 3.08 37.4 16 Tobacco Products 0.13 0.84 0.03 0.8 17 Textiles 7.58 4.39 5.80 30.8 Apparel, Footwear and Headgear 18 4.39 1.57 3.11 46.3 Manufacturing Leather, Fur, Feather (Fuzz) and 19 2.19 0.87 1.99 53.0 Their Products Wood Processing and Wooden, 20 1.81 0.66 0.86 30.4 Bamboo, Rattan, Palm-fiber and Grass Products 21 Furniture Manufacturing 1.04 0.44 0.88 46.9 22 Papermaking and Paper Products 2.92 2.05 3.22 36.7 Printing Industry and Copying 23 2.03 0.93 1.09 27.5 from/to Recording Media Cultural/Education/Sports 24 1.19 0.54 1.51 65.2 Product Manufacturing Petroleum Processing, Coking 25 0.67 2.66 0.72 6.3 and Nuclear Fuel Processing

17

26 27 28 29 30 31 32 33 34 35 36 37 39 40

41 42 43 44 45 46

Chemical Material and Product Manufacturing Pharmaceutical Chemical Fiber Manufacturing Rubber Products Plastic Products Non-metallic Mineral Products Ferrous Metal Smelting and Rolling Non-ferrous Metal Smelting and Rolling Metallic Products General-purpose Equipment Manufacturing Special-purpose Equipment Manufacturing Transportation Equipment Manufacturing Electric Machinery and Equipment Manufacturing Manufacturing of Communications Equipment, Computer and Other Electronics Instrument, Meter and Cultural/Office Equipment Manufacturing Manufacturing of Handicraft and Others Recycling and Processing of Discarded Resources and Used Materials Power and Heat Generation and Supply Gas Production and Supply Water Production and Supply

6.83

6.70

6.55

22.8

1.97 0.46 1.09 4.19 8.01

2.32 1.24 0.87 2.13 4.93

2.00 1.46 1.61 4.29 4.83

20.2 27.5 43.3 47.0 22.9

2.24

6.36

2.16

7.9

1.74

2.28

1.28

13.2

4.88

2.11

3.53

39.0

6.71

3.79

4.44

27.3

3.84

2.66

2.26

19.8

4.19

6.37

6.56

24.1

5.34

4.18

6.58

36.8

2.98

6.12

15.58

59.5

1.30

0.98

1.96

46.8

2.06

0.66

1.29

45.8

0.08

0.02

0.03

34.0

2.45

14.04

4.27

7.1

0.18 1.16

0.76 1.59

0.27 0.30

8.4 4.4

Source: A database containing production and operation data about enterprises above a given size in China’s secondary sector.

18

Chapter 2 Overview of Research on the Effects of FDI on the Income Gap among Residents of the Host Country The correlation between FDI and income distribution essentially epitomizes the correlation between economic globalization and economic development. Globally, the relationships between the incomes of individual residents can be divided into the following levels: income distribution among nations, income distribution among regions within each nation, and income distribution among residents within each region of the nation. Today, most literature focuses on the research on the correlation between FDI and the income gap among nationsķ, whereas only a small part of it relates to the correlation between FDI and the income gap within a nation, and there is nearly no literature relating to the correlation between FDI and the income gap among residents. The empirical research on the correlation between FDI and the income gap within the host country started to appear worldwide in the 1970s and in China probably in 2002, with only a small amount of relevant literature availableĸ. As stated by Aaron and Andaya (1998), worldwide relevant pieces of literature have made no consistent conclusions regarding the effects of FDI on income distribution within a host country. FDI has widened not only the income gap among host countries, but also that within each host country, according to most relevant literature published outside of China. Chase-Dunn (1975), Tsai (1995), Chen, Chang and Zhang (1995: China), Ikemoto and Uehara (2000: Thailand in the 1980s), Beer and Buswell (2002), Fijita and Hu (2001), Velde (2003), Basu and Guraiglia (2003), Choi (2006) and others have all found out that MNCs penetrate their respective host countries through FDI and assistance, thereby widening the income gap within these countries. Herkenrath and Bornschier (2003) have verified that MNCs are the dominant force in the field of FDI, but that host country reliance on them has not accelerated domestic economic growth and, instead, has widened the income gap within these countries. MNCs take away most gains from FDI, allow a small elite group in the host country to share the remainder, a small portion of the gains, and leave few or no gains to others, who have even been deprived of potential opportunities to receive a portion of the gains. Grosse (1998) made a small-scale sample survey among FDI projects in Venezuela and stated that we can’t say that FDI will widen the income gap within the host country. Still, there are some pieces of literature, such as works written by Gustafsson and Johansson (1997), Feenstra and Hanson (1997), Alderson and Nielson (1999), Bussmann et al (2002), Milanovic (2002, 2003), Borraz and Lopez-Cordova (2004), Bhandari (2005), and Sylwester (2005), which argue that FDI has narrowed the income gap within the host country or has had no systematic relationship with the income gap among residents in this country. Krause (1999:14) ķ Such as Chase-Dunn (1975a, 1975b), Bornschier, Chase-Dunn and Rubinson (1978), London and Robinson (1989), Tsai (1995), Burkhauser and Poupore (1997), Gustafsson and Johansson (1997), Alderson and Nielson (1999, 2005), Firebaugh (1999), Brian (2001), Cooper (2001), Wade (2001), Darity and Davis (2005), etc. ĸ Literature in China is represented by works written by Chen Limin and Xie Huaizhu (2004), Cheng Xinzhang (2005), Fan Yanhui and Duan Junshan (2003), Fang Hui (2005), Fang Hui and Liu Hongjie (2005), Li Shiguang (2004), Li Xuehui and Xu Luodan (2002), Lou Haimiao et al (2004), Luo Peng (2005), Pan Yixing (2003), Wan Guanghua (2006), Wu Jian (2004), Xie Xiaoqing (2005), Xuan Ye and Zhao Shudong (2005), Zhang Haoguang and Jiang Xiulan (2004), Zhu Tingxing (2006) and Zhou Hua (2006). Zhao Lingdi and Liu Xiupeng (2007) wrote a very good summary of the effects of FDI on the income gap but made no mention of its effects on employment.

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asserted that FDI has positive effects on income distribution – it will narrow the income gap within the host country – in countries that have special regulations and institutions to attract FDI. With regard to cases about China as a host country, most relevant pieces of literature argue that FDI has widened the income gap in China, while a few pieces of literature have different conclusions. Li Xuehui and Xu Luodan (2002), Wu Jian (2004), Fu (2004), Karbura and Zhang (2004), Xing and Zhang (2004), Wan et al(2004), Xuan Ye, and Zhao Shudong (2005), as well as Tang and Selvanathan (2005) have found that FDI is one of the causes of the widening income gap among residents or regions of China. Zhou Hua (2006) found that FDI could narrow the income gap within the host country in a short period of time, but that it would widen this gap in the long run as a result of its effects with respect to technological advances. And Zhang and Zhang (2003) broke down data about the overall situation in China to see that foreign capital has nearly no effect on the income gap among Chinese residents. The inconsistent research conclusions regarding the correlation between FDI and income distribution actually reflect numerous issues: theories about this correlation have yet to be unified, there are severe flaws in relevant measurement methods, there is a severe shortage of relevant data, and there are research gaps that have yet to be filled. From the perspective of research, FDI’s distribution effects are actually an extension of its growth effects. The distribution effects of FDI as a factor of production are mainly about functional distribution, that is, all market players share the income created by FDI in accordance with the extents to which they participate in the production process involving FDI, thereby forming the pattern of initial income distribution among these players. FDI also affects income distribution among residents of the host country through financial subsidies. Such subsidies generally are paid in two ways – direct and indirect. The direct way refers to income redistribution that FIEs realize directly by means such as donations to residents of the host country. The indirect way refers to income redistribution that FIEs realize by paying taxes to the government of the host country, which in turn pays them to residents of the country. These redistributive effects of FIEs reflect their social responsibility in the host country. There are few academic works exclusively about how direct the donations of foreign-invested organizations to China and taxes paid there affect income distribution in the country.ķ This book focuses on analyzing the initial income distribution among FIEs. The following part of this chapter is divided into four sections. Section 1 outlines how FDI affects the income gap among residents of the host country. Section 2 outlines the empirical model settings and measurement methods regarding the effects of FDI on the income gap. Section 3 provides an overview of income-gap and FDI measurement. Section 4 summarizes this chapter.

2.1 How FDI Affects the Income Gap among Residents of the Host Country The effects of FDI on income distribution, as we have discussed previously, are mainly functional, as they are about the initial distribution of income created during production by FIEs among all the owners of factors of production, or all the participants in this production process. Factors of ķ There are some non-academic works concerning this issue. The Southern Weekend, for example, published the List of the Best Investors in China among the World’s Top500 Companies on December 6, 2005. It employed measures that covered five aspects such as contribution to society and to the region. Among them, contribution to society was assessed through four measures such as the FIE donations to the Chinese Mainland, contribution to the region involving taxes that the FIE paid to the host country, etc. A great many academic works regarding the FDI debate, such as Jiang (2003), cover the latter issue.

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production involved in this process include capital and the labor force, while participants in income distribution mainly include capital owners and employees. Income from capital is in the form of profits; that of the labor force, wages/salaries or labor remuneration. Accordingly, we can analyze how FDI affects the income gap among residents of the host country on the basis of clues such as the distribution of profits and wages/salaries between capital owners and employees, that of profits among capitalists, and that of wages/salaries or labor remuneration among employees. This section focuses on analyzing income distribution between capitalists and employees, as well as among employees, but this does not mean that the distribution of profits among capitalists is not important. In reality, the distribution of direct return on investment (ROI) among foreign companies in China is extraordinarily important because of the forms in which production activities by FIEs are registered and because of the issue of round-tripping FDI. The forms of direct, foreign-invested production organizations in China include FIEs, such as solely foreign-owned enterprises, joint ventures, cooperative enterprises and limited companies, as well as non-FIEs (enterprises in which FDI represents less than 10% of the registered capital are not FIEs). As a result, income from FDI is distributed between foreign and local capital owners. Round-tripping FDI involves money being invested by a resident entity in a particular economy into a resident entity in a second economy and then being invested into another entity in the former economy. Among these, the entity in the second economy carries out only limited operations. The ultimate investment destination and the ultimate host country of round-tripping FDI are the same (IMF, 2008; OECD, 2008), and its owner is essentially still a resident of the host country. As a result, the recipient of income from round-tripping FDI is the capitalist of the host country rather than the foreign one. Nonetheless, since it now statistically difficult to identify round-tripping FDI, the distribution of income from FDI among capitalists is not covered by the framework of empirical analysis described in this book. You may refer to the works of Liu Shiguo (2007, 2008) for an overview of the estimation of the rate of return on FDI, round-tripping FDI statistics, and the effects of round-tripping FDI on the income gap among residents of the host country. Now that FDI organizes production activities in the forms of FIEs and non-FIEs, its effects on income distribution among residents of the host country can be analyzed in three aspects. First is income distribution within the group of FIEs. This group has its own characteristics in terms of income distribution, which are influenced by factors such as the source of foreign capital, regional and industrial distributions, as well as preferences in terms of factors of production. Second, income distributions on the side of FIEs and that on the side of DIEs are interactive rather than independent from each other, and such interactions are shown by spillover effects such as wage spillover, employment spillover, and inter-industry linkage effects. Third, the characteristics and evolution of income distribution within the group of FIEs, which constitute a portion of all the enterprises in the host country, will necessarily become part of the direct causes for changes in the overall income distribution among enterprises in China. 2.1.1 How FDI Inflow Affects the Income Gap between Employees and Capitalists in the Host Country To this day, only a few works have devised theoretical models for the correlation between FDI and income distribution among residents, such as Basu and Guraiglia (2003), Chen (2003), Bhandari (2005), Zhao Ying (2003), etc. These works categorize recipients of income into two groups – employees and capitalists. This is obviously the primary step in analyzing how FDI affects income distribution, because income generated by FDI is first distributed between these two groups. At the

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heart of these theoretical models are industrial relations – an ancient issue with proven theoretical guidance – that generally prove that FDI inflow will narrow the income gap between employees and capitalists in the host country and, hence, facilitates the narrowing of the overall income gap in the country. Developing countries are characterized by insufficient capital and relatively abundant labor resources, which lead to very high ROICs but very low wages/salaries. The inflow of foreign capital such as FDI leads to lower ROICs (or interest rates) and higher wage rates in the host country, thereby narrowing the functional-income gap between employees and capitalists. Note that the concept of income in this context focuses on marginal income. An early analysis by Mundell (1957) indicates that capital flow has the same effects on wages/salaries as the effects of the import of goods – that is, the formation of a global capital market may narrow the income gap between employees and capitalists in a capital importer. The theoretical model and empirical conclusions of Bhandari (2005) also show that FDI will narrow the income gap between employees and capitalists. This model studies the effects of FDI on the prices of the labor force and capital owners, with a conclusion that the inflow of FDI will lead to higher wages/salaries for employees in the host country and a lower income from capital leases, thereby narrowing the income gap between two groups, employees and capitalists, in the host country. Empirical research from U.S. state panel data with the 1982-1997 period as the sample found that the inflow of FDI indeed had narrowed the income gap in the United States. Nonetheless, such a correlation has not been generally confirmed from an empirical perspective. Rama (2003:6) provided evidence that FDI has positive effects on the average pay in the short term but that these effects disappear rapidly in the medium term. Nonetheless, the aforementioned conclusions may only be partially correct if the market of the host country is severely distorted: for example, if the host country does not protect the right of local employees of FIEs to normal pay increases; if it provides FIEs with better treatment than domestic companies in aspects such as market access, intellectual property protection, taxation, the use of land and resources, the acquisition of local strategic assets, etc. Any of these would cause long-term excessively low wages/salaries of local employees as opposed to, again long-term, excessive profits for foreign capital, thereby severely widening the income gap among residents of the host country. Alternatively, FIEs may cheaply and excessively use various natural resources, such as minerals, land, water, and forests, as well as the labor resources of the host country such that its economy becomes unsustainable. This would widen the inter-generational income gap in the host country. After studying the distribution of income created by FDI between employees and capitalists, it is necessary to study further the income distribution among employees and among capitalists. 2.1.2 How FDI Affects the Income Gap Among Employees in the Host Country The effects of FDI are approximated, in most relevant works, with the similar effects of FIEs. These authors hold that FIEs affect the income gap among employees in the host country as follows: First, wage rates at FIEs are almost always higher than those at DIEs in the host country (Lipsey, 2002), according to most relevant works. This causes a widening income gap between employees of FIEs and those of DIEs in the developing host country. And to encourage foreign investment, the government of the host country provides FIEs with policies such as tax relief, allowing them to remit their profits to other countries/regions, underpaying local employees, prohibiting their employees (typically local ones) from establishing trade unions and/or going on strikes, etc. This

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further widens the income gap in the country (London and Robinson, 1989). But some, such as Chen (2003: developing and developed countries), Freeman et al (2001: developing countries) as well as Graham and Krugman (1995: the United States), hold that there is no systematic relationship between FDI and the pay gap in the host country. Although FIEs almost always offer higher wage rates than those offered by DIEs, it is difficult to conclude that, for the same labor force, FIEs pay higher wages/salaries than DIEs (this indeed is the conclusion of a very small number of works). To measure the pure effects of foreign capital on the wage rates in the host country, therefore, it is necessary to remove “impure” factors (i.e., those that are not necessarily the inherent differences between the foreign and host countries). Second, FIEs have wage spillover effects on DIEs. FIEs may affect DIEs in terms of wages/salaries whether the former actually pay higher wages/salaries than those paid by the latter or not. This is the so-called wage spillover effect (Lipsey, 2002), which may exist most of the time in most host countries. Lewis (1954) and Findlay (1978) both believed that wage spillover effects would occur as long as the labor-force supply curve of the host country is not horizontal (i.e., its labor force supply is infinite). Lipsey (2002) found that the wage spillover effects of FIEs might reflect the transfer of excellent employees from DIEs to FIEs. If FIEs offer higher wage rates than DIEs, then FIEs will attract excellent employees from DIEs. To retain these employees, DIEs will be forced to increase their wage rates. The wage spillover effects of FDI may also occur through increased demand for labor. Even if no labor moves to FIEs from DIEs and there is no difference in wage rates between DIEs and FIEs when FIEs enter the host country, the overall wage rate available to the labor force (including the labor force at DIEs) also will increase as a result of increased demand for the labor force of the host country caused by the entry and expansion of FIEs, provided that there is sufficient competition in the labor market. Third, the distributive effects of FDI also depend on its characteristics relative to the other types of capital (Hiemenz, 1990: 90). FDI is more than simple capital transfer, as it also includes the overall transfer of human capital, knowledge, and technology. Accordingly, it affects not only changes in the labor force with respect to relative ROIC, but also changes in capitalists’ competence requirements on workers, before ultimately affecting income distribution among individuals. The inflow of FDI with the transfer of more technologies will increase the need for high-quality human capital in the host country and, hence, increase the rewards for skilled labor, whereas it will decrease the need for unskilled labor and, hence, decrease the rewards for it (i.e., FDI is skills biased). The ultimate effects on the income gap among individuals in the host country depend on the balance between these opposing forces (Feenstra and Hanson, 1995; Nunnenkamp and Spatz, 2001; te Velde, 2003). 2.1.3 How FDI Affects Employment in the Host Country The measurement of income distribution is comprised of the level of income and the number of income recipients, according to Cowell (1995). Nonetheless, almost all works relevant to the correlation between FDI and income distribution focus on the correlation between FDI and the gap between levels of income while overlooking that between FDI and the number of income recipients. As a result, there are much fewer works about the effects of FDI on employment in the host country than those about its effects on the level of income received by factors of production. In fact, increasing the quality and quantity of jobs in the host country through the introduction of FDI is among the major objectives of policies in developing economies (UNCTAD 1999: 257, 258).The effects of FIEs on employment are influenced by the behavior of their parent companies since they are production organizations invested directly by these MNCs. In addition, the effects

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of FDI on employment are influenced by factors such as the characteristics of FDI, the labor market of the host country, how and how deep it is integrated into the host country’s economy, etc. In summary, employment at FIEs is influenced by the following factors: First is the relative position of the FIE in the value creation chain of its parent enterprise (UNCTAD 1994: 166-172; UNCTAD 1999: 258-261). An FIE will copy most value-chain activities of its parent enterprise if it mainly serves the markets of the host country and its neighbors and is not highly related to fellow companiesķ nearby. Jobs created by the FIE mostly belong to the host country, where the quantity of jobs depends on market demand, the size of the subsidiary, and competition in the local market. Such jobs are characterized by being relatively stable, safe, and diversified (with exception to the highest skills such as the R&D and headquarters functions).The level of specialty and the improvement rate of job skills depend on market competition and relevant policies of the host country. Second, the quantity and quality of jobs at the FIE depend on the characteristics of the host country’s location advantages if the FIE’s production activities constitute only one of several links of the value chain of its parent enterprise’s production activities and are intended to provide its parent enterprise with specific inputs or products so as for the latter to carry out production for the next link at a more competitive location. Third, the effects of the FIE on employment depend on the role of its production activities in maximizing the performance of the MNC system if: the FIE specializes in manufacturing a particular type of products or carrying out a particular type of processing or function, and such products, processing or function is already organically integrated into its parent enterprise’s regional or global production network, there are closer business ties among more subsidiaries in larger areas, the places where the end products are made or consumed may be farther from each other, and the international market strategy is more about seeking particular assets. Second, is the ways of that FDI enters the host country (UNCTAD; 2000: 180-188). These ways include mergers & acquisitions (M&A) and green field investments. There are obvious differences in the direct employment effects between these two methods, especially from the short-term perspective. Greenfield FDI will increase the need for employees directly and rapidly, while M&A FDI has much more complex effects on employment. M&A FDI generally will not create new jobs and probably will reduce jobs in the short term. From a long-term perspective, however, the FIE will offer more jobs if the M&A and the subsequent consolidation become successful. The effects of M&A FDI on employment usually vary with the motive behind the M&A and characteristics of the company being merged or acquired. Generally, there is no systematic difference between the two methods in terms of the direct and indirect overall employment effects in the long term. For a more detailed overview of this issue, please refer to Liu Shiguo (2007). Third is labor intensity and preferences for labor skills in the production activities of FDI (UNCTAD 1994: 166). These are critical factors determining whether FDI can produce positive employment effects or not. Industries with higher labor intensity employ more laborers and increase the share of employees in income distribution, thereby narrowing the income gap between employees and investors. This is consistent with the view of Fiala (1987): Investment will produce very great employment effects in a developing country if this country aggressively expands the labor-intensive secondary sector and the low-skill service sector, thereby further narrowing its income gap. The production process of export-oriented FDI, especially production ķ Fellow companies are subsidiaries that belong to the same parent enterprise but that are not holding companies of each other; the parent enterprise must be the holding company of one of these subsidiaries.

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activities in manufacturing, will create a great many jobs for the unskilled labor force. Some industries in mining and manufacturing, such as telecom equipment manufacturing, are capital intensive and generally require employees to have high skillsķ, but create fewer jobs, as opposed to labor-intensive industries in manufacturing, such as furniture making. It is difficult to determine whether the service sector is capital- or labor-intensive. The labor force required by MNCs includes the absolutely and relatively skilled labor forces. This will increase skill bonuses, widen the corresponding wage gap, and ultimately, may cause the shutdown of some DIEs unless the labor force supply in the developing economy is able to fully satisfy these different needs (International Labor Office, 2002). Fourth is the linkage effect between FIEs and DIEs (UNCTAD 1994: 2006). This is a very critical factor for the indirect effects of FDI on employment. Linkage effects refer to the production linkages between FIEs and local entities in the host country, including backward linkage (or sourcing), forward linkage, and horizontal linkage (UNCTAD 2001: 126). Among them, backward linkage effects, especially local sourcing, are the most important factor in the effect of FDI on employment, and their positivity or negativity is mainly affected by factors including: how much and how DIEs are protected (the linkage effects between them and FIEs will be weakened if they are excessively protected; otherwise, these effects will be strengthened), the sizes of FIEs and their market influence (linkage with an FIE as an oligarch will hinder competition and be unfair to local suppliers), the quality, reliability, flexibility, and similarity of local suppliers relative to the other suppliers, as well as technologies and added value provided in the process of FDI creating benefits. From the perspective of industry categories, linkage effects usually have limited coverage in the primary and tertiary sectors, as opposed to much wider coverage in the manufacturing sector but with great differences among industries. In addition, horizontal linkage effects between FIEs and DIEs reflect the possible competitive or complementary relationships between them, and greatly influence the indirect employment effects in the host country. Fifth are the host country’s location advantages, the openness of its trade and industry policies, the efficiency of its labor market, etc. If the host country has an abundant, low-cost labor force, for example, then FDI is willing to come in and carry out export-oriented activities and, hence, may produce greater (direct and indirect) effects on job creation. If the host country encourages the replacement of imports with locally produced goods and has a very large market, then FDI is highly willing to come in and, hence, is able to promote job creation. But if the local market is always highly protected, then local job growth will slow. In addition, the quality of jobs created by FDI may be higher and on-job training for employees more and better if the host country’s labor market is more effective with higher skills being available (UNCTAD 2000; 181). Generally, the effects of FDI on income distribution in the host country include effects on the level of income and on employment. Of these, the effects on the level of income include the effects of FDI on income distribution between employees (their income is in the form of wages/salaries) and capitalists (their income is in the form of profits) in the host country and on effects on their respective sub-groups. Employment effects include the effects of FDI on the quantity, categories and quality of jobs. All these effects can be observed from direct or indirect ķ The mining sector is characterized by higher employee pay and ROIC – higher pay widens the wage gap between mining and the other sectors of the country, while higher ROIC also affects the income gap. The government may levy higher taxes, for example, to increase the income of the poor and, hence, to narrow the income gap. Otherwise, the income gap between capitalists and relevant employees/irrelevant residents will be widened.

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and short-term or long-term perspectives. These effects depend not only on factors at the microscopic level, such as capital and laborers, but also on those at the macroscopic level, such as the host country’s market environment (e.g., location advantages, market size, and enterprise protection), and economic development strategies (e.g., market liberalization and industrial development strategies).

2.2 Empirical Model Settings and Measurement Methods Regarding the Effects of FDI on the Income Gap Methods for empirical research on the correlation between FDI and income distribution among residents of the host country refer to the ways of setting the model, the selection of the applied econometric methods, as well as methods for setting indicators for relevant theoretical variables and for statistically measuring these variables. This section covers the first two parts, while the next section covers the remaining one. 2.2.1 An Overview of Empirical Model Settings Most models regarding the effects of FDI are non-structural ones, that is, single-equation economic models. Models can be structural or non-structural, according to Goldberger (1972). Structural models express cause-and-effect relationships, while non-structural ones simply describe statistical relationships. Compared with single-equation model settings, structural models obviously can systematically define the inherent and intricate behavioral relationships and connections in the economic system, including mutual cause-and-effect relationships, rather than the one-way characteristics of a particular behavioral relationship or a particular group of behavioral relationships in this system. Since employees’ wages/salaries and jobs in enterprises are a pair of variables that have a mutual cause-and-effect relationship, it is incomplete to simulate behavioral decisions on either wages/salaries or jobs. Instead, structural models must be employed. Unfortunately, however, no works regarding this topic have been found. With regard to existing relevant pieces of literature, the most common models regarding the effects of FDI on income distribution are non-structural. In other words, FDI inflow is correlated directly with the measurement of the income gap among regions, industries or residents (or even some of the residents) of the host country to form the FDI-income gap relationship, with relevant assumptions being proposed – that is, the income gap is regarded as the dependent variable and the FDI inflow the independent variable (or one of the independent variables) – such as Bussmann et al (2002), Fan Yanhui and Duan Junshan (2003), Hemmer et al (2005), Sylwester (2005), etc. The most common single-equation model is set as follows: The income gap = f (FDI; …), where the Gini coefficient is usually used as the income gap variable, and the existing amount of FDI or the ratio of this amount to the GDP is used as the FDI variable. Please refer to Rubinson (1976), Tsai (1995), Alderson and Nielson (1999), or Hemmer et al (2005). The biggest weakness of this model specification is that support from economic theories is unavailable. FDI affects income distribution in a very complex process, with a very long transmission chain. There is no direct causal relationship between the two things, and existing economic theories have not yet to build a proven theoretical model for their relationship. As discussed in Chapter 1, none of the existing theories, whether the world-systems or dependency theory in sociology, or the development or modernization theory in economics, is fully able to

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explain the causes for changes in the international or domestic income gap. With the rise of the endogenous growth theory in macroeconomics since the mid-1980s, we may have strong support and guidance on the correlation between FDI and the income gap. As a factor of production, FDI first functions in the production process, before affecting the distribution and redistribution of outputs – the former is exhibited as FDI’s growth effects and the latter as its distribution effects – in accordance with how it interacts with the other factors of production in that process. There are causal relationships between FDI and the level of income of laborers, between FDI and the quantity & quality of laborer jobs, as well as between the level of income of laborers and the quantity & quality of their jobs in the production process. The aforementioned single-equation method obviously cannot reflect all three causal relationships and, hence, cannot reveal how FDI affects the income gap among residents of the host country. Such an economic phenomenon seems reckless for seeking theoretical guidance and is prone to make mistakes in terms of measurement methods, such as missing variables, if a single-equation, empirical causal relationship is established with only two variables, each at either end of this long transmission chain, with the specific transmission mechanism in between being overlooked. In addition, both FDI and the income gap as two variables are time series in most relevant empirical works, but they are measured on different grounds. FDI is measured on the basis of the highly generalized time series, whereas the income gap is measured on the basis of panel data. In other words, these two variables are not equivalent in terms of statistical levels. When making empirical analysis of the effects of FDI on the income gap in China, for example, common works group the “distribution” of income either by administrative division or industry, or by urban/rural areas, and calculate the variable of income gap on the basis of such grouping. By comparison, the measurement of FDI as a variable has nothing to do with such grouping. From the perspective of the variables’ statistical levels, FDI is a level variable, whereas the variable of income gap is the difference between group-specific incomes, which are another level variable, that is, it is derived from this level variable. If an economic relationship established between economic variables that are not equivalent in terms of statistical levels is to be set as an economic proposition, then this proposition must meet at least two conditions. First and theoretically, this proposition must have been explained directly by a proven economic theory or be based on a precise deduction from such a theory. Second and econometrically, the explaining and explained variables must all be stationary variables or have the same order of integration (i.e., they are co-integrated). Under these principles, the factor that decides the income gap should be the difference in the distributed income between all the factors that decide income, not these factors themselves. Overlooking the match between the dependent and independent variables in terms of statistical levels in the process of empirical investigation will be very likely to cause flaws like the one pointed out by Voitchovsky (2005): using a single statistic, i.e., the income gap, to explain the effects of income distribution on economic growth may end up describing only its average effects on growth while covering up the complexity of the pertinent relationships. The method developed by Bhandari (2005) is more consistent with economic theory, but similar works are not common nowadays. In his work, the effects of FDI on the levels of income of all participants in the production process are researched before the difference in the level of income between these participants is calculated. The former part has proven economic theories as the basis for model setting, while the latter part is flawless with respect to how the income gap is measured. Combine them and we can see how FDI affects the income gap. Up to now, there has been no work that establishes a system of simultaneous equations regarding the effects of FDI on the level of income and the quantity of jobs, not to mention one that calculates its effects on income

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distribution. In addition, empirical model settings are often faced with the issue of adding control variables. Tsai (1995) found that empirical equations have conclusions very sensitive to whether or not regional dummy variables are added. If no regional dummy variable is added in the process of empirical investigation, then we will see that FDI has widened the income gap in the host country. Otherwise, we will see that FDI has narrowed the income gap. This means that the correlation between FDI and the income gap results more from interregional differences than from FDI itself. Hemmer et al (2005) found that measurement results depend on whether and which control variables are added. Unfortunately, existing works make no sufficient methodological discussions and reach no agreement regarding the definition of control variables, the principles by which to identify these variables, or the specific ways of operating them. As a result, control variables are selected with a high degree of randomness in the process of empirical investigation. This apparently is one of the major factors that affect the conclusion of the proposition of the correlation between FDI and the income gap. 2.2.2 An Overview of Measurement Methods Most of the existing empirical works on the correlation between FDI and the income gap employ the time series method during analysis, such as Wei and Wu (2001), Zhang Haoguang and Jiang Xiulan (2004), etc. A time series is a group of observations about something, or the observed object, at a particular temporal interval (this interval is usually consistent in a series) and is intended to capture changes in this observed object in a certain period of time. Time series analysis is about describing the process in which data points in the series are generated or predicting unknown data at subsequent time points on the basis of this mechanism and process. Accordingly, time series data are suitable only for studying characteristics of temporal changes in a particular observed object. Since participants in income distribution may have different individual characteristics, these characteristics have obvious effects on the levels of income of the participants and, hence, on the income gap between them. Nonetheless, since the highly generalized time series method obviously overlooks differences in characteristics between the generalized individuals, its conclusions may naturally be biased. A small number of relevant works employ the cross-section data method, such as Benabou (1996) and Sylwester (2005). Cross-section data is observations about multiple observed objects (e.g., persons, companies, countries or regions) at a particular time point and constitutes a one-dimensional (i.e., the dimension of objects) database. It is therefore suitable only for static comparison and research on characteristics of multiple objects. A problem with the cross-section data method lies in analyzing the heterogeneity of the observed objects, as it is difficult to make clear the roles of their unobservable characteristics. One of the solutions is, for example, that when we investigate factories that have been bought by foreign companies (e.g., Conyon et al (1999)), we may also investigate companies that have been bought by foreign and domestic companies respectively with their forms of ownership remaining unchanged after they were bought. The wage relationships between companies that have been bought by foreign and domestic companies respectively, as well as time-varying characteristics when and after they were purchased, are able to reflect differences between these two types of companies in terms of whether they are owned by foreign or domestic companies. In addition, employees’ incomes, especially wages/salaries, are characterized by stickiness, the effects of which can usually be reflected by their incomes in the lag period. Since data about the observed individuals at different time points is unavailable for

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cross-section data analysis, however, it is impossible to estimate such dynamic effects. This is a flaw inherent in cross-section data. There are a growing number of works that employ the panel data method, such as Deininger and Squire (1996), Alderson and Nielsen (1999), te Velde and Morrissey (2002), te Velde (2003) and Wan et al (2004). Panel data is a group of observations about changes in multiple objects in a certain period of time and constitutes a two-dimensional (i.e., the dimensions of time and objects) or multidimensional database. Panel data analysis often examines time-variable characteristics of objects and compares differences in these dynamic characteristics. We can see, therefore, that cross-section data analysis is only static while time-series and panel-data analyses are dynamic and that time-series analysis is only about the temporal characteristics of the researched objects and cross-section data can only be used to research structural differences among multiple researched objects, while panel data analysis has advantages over the former two – it can be used to analyze structural differences among multiple objects on the one hand and to research their time-variable characteristics on the other. Empirical works indicate that for the same economic proposition, research conclusions may differ from each other since different measurement methods are used. Empirical conclusions about FDI’s wage spillover effects come with systematic differences simply because of differences in the measurement methods employed. Regarding research on wage spillover effects, the majority of works that employ panel data conclude that they are negative, whereas those that employ cross-section data conclude that they are positive, according to Gorg and Greenaway (2001). But there has been no case where panel and cross-section data are employed simultaneously to analyze the same country. In addition, I doubt that most cross-section data research has missed some unknown corporate characteristics. But Lipsey (2002) stated that panel data analysis alone is sufficient to prove positive wage spillover effects and that what truly deserve further consideration are different environments, as well as corporate, industrial and national policies that affect spillover. The panel data method may be the best method for researching the correlation between FDI and the income gap. The income gap is an issue regarding the income structure among income recipients, and the effects of FDI on this gap in a certain period of time are apparently dynamic. The income gap is therefore highly relevant to characteristics of the income recipients. The accuracy of income-gap measurements, as is subsequently analyzed in this book, is highly relevant to the granularity of the categories of income recipients: the more detailed the categories, the more accurate the measurements. Accordingly, the more detailed the analysis of the characteristics of income recipients, the more reliable the income-gap measurements and, hence, the deeper and more reliable the exploration into factors that affect the income gap and how much they affect it. In addition, by observing the effects of FDI on the levels of income of different recipients in a certain period of time, we are able to identify differences in the extents to which these recipients are affected by FDI. Compared with time-series and cross-section data analysis, therefore, the panel data method is more suitable for researching the correlation between FDI and the income gap.

2.3 Income-gap and FDI Measurement The reason why a great many works cannot reach consistent empirical conclusions on the correlation between FDI and the incomes of the host country’s residents relates to the

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aforementioned different model settings, on the one hand, and widely varying indicators set for critical theoretical variables in this correlation and the accuracy of statistical measurements on the other. Critical theoretical variables in this proposition include income, income gap and FDI. 2.3.1 Income-gap Measurement Some say that all empirical research on the income gap has to address issues such as the definition and measurement of incomes and the income gap. (1) Income Measurement Incomes can be roughly categorized into stocks and flows. Terms such as wealth, GDP, gross national income (GNI, i.e., the traditional GNP), wages/salaries, functional income, disposable income, etc., have appeared in empirical works. The “wealth” in national economic accounting belongs to stocks. With regard to official statistics in China, the most common measures of individual residents’ wealth include cash, savings, and securities as financial assets, whereas there are nearly no official statistics about their non-productive assets such as fixed assets. Generally, works that analyze the effects of FDI on the wealth gap among residents account for only a tiny share of existing works about the correlation between FDI and income distribution. GDP or GNP comprises depreciation of fixed assets, labor remuneration, net taxes on production, and operating surplus and belongs to flows. The GDP or GNP per capita is the average income calculated on the basis of the total number of all the nationals of a country and is suitable for measuring the average level of income of a country’s residents. Neither GDP nor GNP is suitable for comparison between residents, because not all of its contents constitute the disposable income of every individual: only a small number of residents have operating surplus and fixed assets depreciation. The vast majority of them receive only labor remuneration. On average, labor remuneration, depreciation of fixed assets, net taxes on production, and operating surplus account for 50.5%, 14.4%, 13.9%, and 20.9%of China’s GDP respectively, according to China’s input/output tables (1992, 1997, and 2002). From the perspective of residents, disposable income is the best statistical measure for their incomes. The disposable income of a resident refers to the income freely disposable by him/her that equals his/her functional or original income minus ordinary taxes such as income and asset taxes, social contributions, and other current transfers. It includes the wages that a resident receives from his/her employer, net property income, social welfare, other net incomes as current transfers, net incomes from self-employment, etc., but does not include income from loans. Labor remuneration typically constitutes the most important and steady part of the disposable income of residents: from 1992-2004, labor remuneration, property income, and net incomes as current transfers represented 88% - 93.4%, 5.3% - 8.9% and 1.6% - 4.2% of the aforementioned disposable income, respectively (Kong Jingyuan, 2005). Labor remuneration, also known as employee compensation, is defined as "the total remuneration, in cash or in kind, payable by an enterprise to an employee in return for work done by the latter during the accounting period." It is mainly comprised of two parts – remuneration paid in cash or in kind and actual social contributions payable by the employer to social security schemes, for example, or imputed social contributions by employers providing unfunded social benefits (SNA 1993, Chinese translation, p. 170-171). In China’s national economic accounting, employee compensation, or labor remuneration, comprises wages/salaries, bonuses, and allowances, reimbursable medical and health expenses, as well as travel allowances to and from

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work, social security contributions payable by employers, and pensions directly paid by former employers (China Statistical Yearbook). For most Chinese residents, wages/salaries are the primary part of labor remuneration. Wages/salaries are in cash and in kind (SNA 1993). Wages/salaries in cash include: (i) wages or salaries paid at regular temporal intervals, including payments based on the result or quantity of work done, additional payments or special allowances due to working outside regular work hours, allowances for working away from the office or in harsh, dangerous environments, allowances for working overseas, etc., (ii) regularly paid special-purpose allowances, such as those for housing or travelling to and from work, (iii) wages or salaries paid to employees who are on short-term leaves due to vacations or production suspension, (iv) special-purpose bonuses paid under an incentive system or other special payments made in accordance with the overall business performance,(v) commissions, rewards, or tips received by employees. Among wages and salaries in kind, what is especially noteworthy includes housing services or a particular type of board and lodging that employers provide to their employees for use by all the members of the households to which the employees belong. Employees of the organs of the Chinese government or the Communist Party of China (CPC), institutions, and SOEs usually have access to housing services funded by national budgets and/or provided by their organizations. Nonetheless, these services are not included into current income statistics, especially those of nominal wages/salaries. In addition, another important issue is, as te Velde and Morrissey (2002) pointed out, that for income recipients in more developed regions or industries, the wage gap may be able to well represent the income gap, but for those in regions or industries where agriculture accounts for a relatively high share of all the jobs or where informal jobs are critical, there may be no strong correlation between the wage gap and the income gap. In China, disposable incomes of individuals include “gray incomes,” “hidden incomes,” or “illegal, abnormal incomes,” in addition to “legal” incomes (Chen Zongsheng and Zhou Yunbo, 2002; Wang Xiaolu, 2010; Wang Xiaolu et al, 2010). As a remarkable characteristic of incomes of Chinese residents, gray or hidden incomes are extra-wage incomes received by residents and include legal and illegal extra-wage incomes received inside and outside the organizations where they work. For organizations such as the organs of the government and the CPC, institutions, SOEs, and collective enterprises, employee incomes include not only formal, monetary wages/salaries but also benefits or allowances with respect to foods, medical care, schooling of their children, housing, sunstroke prevention, heating, subscriptions to publications, etc., many of which are not paid in cash but are usually much higher than monetary wages/salaries. Although incomes within these organizations are increasingly distributed under the market pricing system, monetary wages/salaries still represent a small part of the real incomes of their employees. No official statistics are available on figures about gray incomes in China, and only a small number of academic works include rough estimates. The four main kinds of illegal, abnormal incomes, if combined, would increase China’s Gini coefficient in the 1988-1999 period to 0.461 from 0.391 and would account for about 15% of the total income gap, according to estimates by Chen Zongsheng and Zhou Yunbo (2002, p. 360-389). Wang Xiaolu (2007, 2010) found out that the total disposable hidden income of Chinese urban residents was 4.8 and 9.3 trillion yuan in 2005 and 2008 respectively, most of which was “gray income” (it was 5.4 trillion yuan in 2008), especially among high-income individuals. If hidden incomes were included, then the income gap between China’s richest 10% urban households and the poorest 10% would be 31 and 26 times in 2005 and 2008 respectively, as opposed to the official figures – nine times in both years – which did not include such incomes. The income gap across China would have been55 and 65 times in

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2005 and 2008 respectively, as opposed to the official figures – 21 and 23 times – which again did not include hidden incomes. Gray incomes accounted for 15% of GNI in 2008 and over 13% in 2005. China’s Gini coefficient, if including hidden incomes, would obviously be beyond the range of 0.47 through 0.50 calculated by some worldwide experts in recent years. In addition, someone has proposed the concept of “full income,” which combines per-capita GDP with life spans of individuals, as he believes that although per-capita GDP is usually regarded as an approximation of the quality of lives of individuals in different countries, welfare also includes the quality of lives of individuals (such as their life spans). It is also necessary to consider differences in the purchasing power of incomes between periods of time and between regions. Since China is a developing economy with great interregional differences, there are large, natural differences between regions in terms of the purchasing power of money. Given the unavailability of regional data about purchasing power, however, people usually have to assume that in the same period, incomes of residents in different regions have the same purchasing power. This will obviously result in some errors. Jiang Xiaojuan and Li Hui (2005), for example, verified that the differences in real incomes of residents among Chinese regions would be smaller than those of nominal incomes if the level of income were reduced with the price index in each region. The differences in the purchasing power of money among different periods in the same region can be removed through price index smoothing since the corresponding time series of the price index are available. (2) Income Gap Measurement Correct answers to questions such as, “Is income more evenly distributed?” depend on whether the correct measures for the income gap are selected or not (Wade, 2001) and how accurate the actual measurements are. In works regarding the income gap, the most common income-gap measures include the Gini coefficient (e.g., Tsai, 1995; Hemmer et al, 2005), variance, the coefficient of variation (CV) of income, the logarithm of income variance, the variance of the logarithm of income, the Atkinson index, the Dalton index, the Theil index, the Herfindahl index, etc. Income gap, or income inequality, may also be categorized into persistent or permanent inequality or transitory inequality (Durlauf 1992; Burkhauser and Poupore 1997). Cowell (1995), Deininger and Squire (1998), Chakravarty (1999) and Fields (2001) argue that the following four criteria are typically used to assess the income gap, including “population homogeneity” (or “population independence”), “principle of transfers” (or “Pigou Dalton condition”, “income homogeneity” (or “scale independence” or “normalization”), and “decomposability.” With regard to the above-listed income-gap measures, only the CV, the Theil index and the Atkinson index satisfy all four criteria simultaneously. The Gini coefficient satisfies all but the criteria of decomposability, but a great many Chinese works often resolve it into the sum of the urban, rural and urban-rural Gini coefficients. Income distribution contains information in two respects: the number of income recipients and the level of income. Take the Theil index, a classic income-gap measure, for example. This index, or T, is:

Where i=1, … , n and represents the ith group of income recipients, whether they are affected by FDI or not; is the ratio of each income group to the population; yi and y are the average per-capita incomes of this group and the population respectively.

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With regard to , many works on the income gap assume that income recipients at each level of income account for an equal share of the population, that is, the sizes of all the groups of income recipients equal each other. By so doing, they simplify the issue of the income gap among residents into that of differences in the levels of income among residents. In reality, however, it is almost impossible for the sizes of income groups to equal each other. In addition, the granularity of the categories of income recipients, or the amount of n, is another factor that affects the accuracy of income-gap measurements. In other words, the reliability of measurements is in direct proportion to the granularity of income groups or categories (Wade, 2001). Measurements in the unit of region, industry/section/segment, enterprise, or even household can only roughly reflect the income gap among residents (for pertinent discussions, please refer to Husbands and Money, 1970; Kuznets, 1957, 1963; Cutright, 1967a; Fiala, 1987). It is obvious that the effects of FDI on the income gap among residents of the host country are an extension of its effects on the income gaps among regions and industries/sections/segments in the country. A great many works focus on researching the income gap within urban or rural Chinese residents, but they are far from making sufficient research on the measurement of the income gap among all the Chinese residents. Much research on the income gap among all the Chinese residents is done on the basis of time points or periods, as opposed to a small volume of research on the basis of long-term, continuous time series. We have summarized, on the basis of an overview by Chen Zongsheng and Zhou Yunbo (2002, p. 45-58), the results of some research on the income gap among all the Chinese residents into Table 1 in Chapter 1. Given a lack of relevant data, no calculation of China’s Gini coefficient is now available. Some researchers have published their research results, but most of them are based on nationwide average per-capita incomes and ratios to the total population resulting from rough grouping of residents (e.g., urban vs. rural, city/province, eastern/central/western China, etc.). These results deserve discussion since none of them considers the effects of the income overlaps between these rough groups on the Gini coefficient. 2.3.2 FDI Measurement Relevant works, regardless of how their models are set, may deal with the variable of FDI in different manners – they may take its flow or stock (e.g., Tsai, 1995), or the ratio of its stock to GDP (e.g., Hemmer et al, 2005). It is obviously necessary to clarify the measurement of this variable. (1) FDI Flow Measurement Existing FDI statistics are hardly suitable for international comparison. Foreign investment that accounts for 10% or more of an enterprise’s stock is FDI, according to the OECD Benchmark Definition of Foreign Direct Investment, Fourth Edition, 2008. FDI comes in three forms: equity capital, inter-enterprise debt, and reinvestment of earnings. With regard to the collection of FDI statistics, different countries deal with these three forms in different ways. First, some countries do not adopt the 10% threshold, as their investment laws provide that this threshold is 20% instead. Second, there are misunderstandings about the definition of FDI: FDI does not necessarily mean control over an enterprise, because a 10% stake is sufficient to establish a direct investment relationship. FDI does not include any “10% ownership” held by any “unrelated” investor group living in the same foreign country, as FDI refers to either one investor or an investor group

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relevant to investments in one or more countries; FDI is based not on the investor’s nationality, but on where the direct investor lives. Money that a direct investment company borrows from an unrelated foreign party with a guarantee from the direct investor is not FDI (Patterson et al, 2004). Third, only some countries report both short-term and long-term intra-enterprise loans, where most other countries report only long-term intra-enterprise loans. There are also countries that report intra-enterprise short-term loans and trade credit (some countries maintain that the latter does not belong to FDI). Fourth and lastly, some countries are now still unable to report reinvestment of earnings since it is difficult to obtain enterprises’ data reports or data about balance of payments (BOP). Even countries that are able to report this data generally do it with a long time lag. Moreover, it is impossible to make an international comparison of the calculation results regarding this measure since the definition of “operating income” differs from country to country. With regard to FDI flow statistics; there are differences between the home and host countries in terms of the ways of collecting data, FDI criteria, periods in which FDI transactions are recorded and the ways of dealing with round-tripping FDI and special-purpose entities (SPEs). For possible reasons for this, you may refer to Liu Shiguo (2009). Below is an excerpt of the parts of his work that are most relevant to the theme of this book. This paragraph is about the service implications of FDI transactions and the statistical categorization of FDI involved in international trade services. An FDI stock, in different sectors of an economy, may not represent the same or even similar value of capital. This is caused mainly by the service implications of this FDI transaction. This issue will become increasingly prominent because of a growing importance of services in the overall activities of MNCs, on the one hand, and the tradability of these services, on the other. If assets of an FIE have increasingly high financial value but have not created any real-economy-relevant activity in this process, then they will add to the total FDI stock of a country. The increasing foreign investments made by Japan in the 1980s, for example, was largely attributable to the fact that its financial service sector was larger than its manufacturing sector (Bellak, 1997). In addition, a lot of overseas affiliates of US companies are categorized as trading companies, even though a very large part of these affiliates’ businesses fall under manufacturing activities. A great many US-based affiliates are also usually categorized as trading companies, whereas many US-based affiliates of Japanese companies have a large number of manufacturing activities. These issues regarding statistical categorization have greatly distorted the estimation of FDI stocks and, hence, made macroeconomic and sectoral statistics greatly misleading, especially because of the growing importance of trading within MNCs (Edwards, 2006). Determining whether FDI is made through M&As or green field investments is also critical for assessing its economic impact. Direct investments in controlled companies or SPEs may conceal all the economic effects on the ultimate beneficiary country (Edwards, 2006). Nonetheless, efforts to accomplish this objective are hindered by a lack of information on the activities of controlled companies and SPEs, M&As, and green field investments, as well as the economic effects of FDI. (2) FDI Stock Measurement For the severe negative effects of existing FDI stock statistics on empirical research on economic growth and income inequality, you may refer to de Mello (1997), Yeung (1999), Lipsey (2001, 2006 and 2007) and Liu Shiguo (2009). FDI stock is the value of the share of capital and reserves attributable to the parent enterprise, plus the net indebtedness of affiliates to the parent enterprise. Today, FDI stock is calculated in two ways: accumulated annual BOP flows from a particular year onward to get the cumulative

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total, or deducing the “book value” from the balance sheet. These two ways are called the accumulation method and the book value method, respectively. Bellak (1997) found that the book value method is faced with difficulties including: (i) there are always differences based on whether the book value is from the accounting books of the parent enterprise or its subsidiaries; (ii) book value is calculated by three methods, including the historical cost method, the replacement cost (constant or current price) method, and the market cost method, from which FDI statistics compilers must select one method; (iii) if relevant data is available, then debits and credits between the parent enterprise and its subsidiaries should also be included into FDI stock statistics to reflect the overall changes in the value of the company’s overseas assets. It is necessary to emphasize that both transaction value and book value are inclined to underestimate the real level of FDI; (iv) it is inadvisable to think that book value equals the cumulative total of FDI flows plus reinvested earnings, because this algorithm excludes the other value assessment variables and similar problems exist with this cumulative total itself. Problems with the method of estimating the book value of FDI stock result from diverse data sources. Book value is derived from the balance sheet and will go through multiple value changes in the lifecycle of FDI: exchange-rate changes, value changes after assets are depreciated, profit and loss, liquidity, divestment, expropriation or confiscation, changes in accounting standards, etc. There are necessarily significant differences between the values of historical and replacement costs, of which the former is smaller than the latter. Problems with FDI stock measurement caused by differences in research assumptions and methodologies do not occur with the value assessment of domestic capital stock. Naturally, it is impossible to compare domestic-capital and FDI stocks. Accordingly, the result of value reassessment, even if it is necessary and desirable operations that are made, at best shows that the resulting numbers are accurate to a certain extent. An alternative method for reassessing the value of assets is to deduce the market value of FDI stock. The market value of capital stock (or equity) is assessed by summing up FDI flows weighted by relevant stock price indices. The problem with share price indices lies in that they largely reflect financial phenomena more than economic phenomena. FDI, if assessed on the basis of its market value, is an alternative variable not for the extent to which production is internationalized, but for corporate value. In addition, replacement value does not include intangible assets, but market value does. (3) FIE Statistics Compilation Based on his judgment on US data, Lipsey (2007) found that flow and stock data only roughly reflect the distribution of FDI sources and destinations and that they do not even qualify as rough data about the industrial distribution of FDI, much less time-series data about its industrial and source distributions. There is only a pitiably low correlation between the industrial distribution of FDI stock and the production distribution of FDI, as there is between changes in capital stock and in production. In fact, Kindleberger (1969) and Dunning (1970) realized a long time ago that as the dominant force in direct investment, the international activities of MNCs are much more valuable than relevant international capital flows. Gao Minxue (2008) explored the internal logic: registered capital is only the initial startup capital for business operations, as modern enterprises typically borrow money to operate – they raise funds in financial markets, whose value may be much greater than that of their respective registered capital and, hence, make the value of their assets, debts, equities, etc., much greater than that of their respective registered capital. As Yeung (1999) found, therefore, FDI statistics should also cover the extents to which investment companies are integrated into the local economy. To properly measure FDI, we must understand

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what it means for ownership, control, and capital respectively. Unfortunately, traditional statistics about BOP and international investments do not include the statistics regarding MNCs’ affiliates in terms of operations, such as production, sale, export, jobs, and assets. These statistics are usually called “foreign affiliate trade statistics.” Most users of FDI data need to measure operating data of FIEs, including statistics regarding jobs, payrolls, capital inputs/outputs, profits, taxes, etc. Obviously, there is a wide gap between existing FDI statistics and those needed by users. And this indeed is an obstacle to the research described in this book. Please refer to Chapter 1 for the deficiencies of this research.

2.4 Summary The correlation between FDI and income distribution is essentially the epitome of the correlation between economic globalization and economic development. Empirical research on the correlation between FDI and income distribution in the host country started in the 1970s outside China and probably in 2002 in China, where only a very small number of relevant works are available. Globally, relevant works have yet to reach an agreement on the effects of FDI on income distribution in the host country. This actually reflects problems such as the lack of a unified theory about the correlation between FDI and income distribution, severe flaws in the relevant measurement methods, a severe shortage of relevant data, areas that have yet to be researched, etc. The effects of FDI on income distribution are mainly functional, as they are about the initial distribution of incomes generated in production involving foreign capital among the owners of factors of production, or the participants in production. This summary focuses on analyzing income distribution between capitalists and employees as well as within the group of employees, but this does not mean that profit distribution within the group of capitalists is unimportant. Interest rates in developing countries must be high since they have insufficient capital. But these countries are characterized by relatively abundant labor resources, which lead to very high ROICs but very low wages/salaries. The inflow of foreign capital such as FDI leads to lower ROICs (or interest rates) and higher wage rates in the host country, thereby narrowing the functional-income gap between employees and capitalists. If the market of the host country is severely distorted, however, this conclusion may not be completely correct. With regard to the situation among employees in the host country, most relevant works have found that employees of FIEs almost always have higher wage rates than do their counterparts within DIEs. Higher wage rates available from FIEs may attract excellent employees to from DIEs, and/or the entry and expansion of FIEs increase the total demand for the host country’s labor force while producing spillover effects on the wage rates available from DIEs. In addition, characteristics of FDI relative to the other types of capital will also influence its distribution effects. Nearly all works regarding the correlation between FDI and income distribution focus on the correlation between FDI and differences in the levels of income, without simultaneously studying the differences between FDI and the number of income recipients. There are much fewer works regarding the effects of FDI on employment in the host country than those regarding its effects on the levels of income received by factors of production. Since FIEs are how MNCs organize production through FDI, their employment effects are influenced by the behavior of their parent companies. In addition, the employment effects of FDI employment effects are also influenced by factors such as its own characteristics, the host country’s labor market, how and how deep it is

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integrated into the host country’s economy, etc. With regard to empirical methods, most models with respect to FDI’s distributive effects are non-structural, that is, single-equation economic models, which cannot systematically describe the intricate behavioral and connection relationships inherent in the economic system. Existing works set empirical models by taking the income gap as the explained variable and FDI as the explaining one. This method lacks strong support from economic theories and equivalence in terms of statistical levels, so it is apparently difficult to reach any reliable conclusion. And if we describe the correlation between FDI and the income gap with the time-series or cross-section data method, then we cannot disclose the dynamic influence from structural changes in the income gap. Also, there are problems with existing works when it comes to measuring the income gap and FDI.

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Chapter 3 A Theoretical Model of the Effects of FDI on Income Distribution among Residents of the Host Country Generally, social justice is comprised of justice at the start, in the process, and at the end. Justice at the start means that income recipients have an equal chance to participate in production activities. Justice in the process means that all the participants create incomes in a fair process (e.g., the market mechanism).Justice at the end refers to equality in income distribution. And justice at the end is closely tied to justice at the start and in the process. Income inequality is formed in a process where incomes are created and distributed, and income distribution is the continuation of the process of income creation. To research income distribution, we need to begin with income creation before examining justice in income distribution. Before researching the effects of FDI on income distribution, therefore, we need to research FDI’s growth effects. This chapter is comprised of four sections. Section 1 outlines FDI’s growth effects; Section 2 proposes a theoretical model of the effects of FDI on income distribution among residents of the host country; Section 3 provides further discussion on this model; Section 4 summarizes this chapter.

3.1 Works regarding FDI’s Growth Effects Before the mid-1980s, economic theories were unable to fully explain the correlation between FDI and the income gap among residents. The endogenous growth theory has since emerged in the field of macroeconomics and may be able to support research on this correlation. When it comes to models of the effects of FDI on endogenous growth, equilibrium models are typically built upon the Cobb Douglas production function. FDI’s growth effects generally include: growth effects of FDI as capital that is identical in quality to local capital, FDI’s growth effects resulting from the spill-over effects of technological advances, human capital, and the other factors of production in the host country. The latter are usually more important than the former. Existing relevant works differ greatly in terms of how they describe and empirically analyze these effects. It is undeniable that these differences, together with those in sampling periods, sample makeup, and empirical measurement methods, have led to widely different conclusions. 3.1.1 FDI’s Growth Effects: a Theoretical Debate How FDI enters a growth modelķ depends on how much we know about the properties of FDI. Growth theories have evolved from growth models represented by the Solow-Swan-Ramsey model to endogenous models represented by that developed by Romer (1986, 1990). Based on different understandings of capital as a factor critical for growth, these models have changed the position of ķ For works regarding the correlation between FDI and economic growth, you may refer to de Mello (1997, 1999), Carkovic and Levine (2003), Chowdhury and Mavrotas (2006), Hanson (2001), Gorg and Greenaway (2004), OECD (2002); for works regarding the correlation between FDI and domestic investments, you may refer to Mody and Murshid (2002); for works regarding the correlation between FDI and technology transfer, you may refer to Blomstrom and Kokko (1998); for works regarding decisive factors for FDI, you may refer to Aseidu (2002), Charkrabarti (2001) and Tai (1994).

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FDI in growth models. Before this new growth theory appeared, the authors of quite a many works believed that FDI was identical in quality to domestic capital. In many developing host countries, the inflow of FDI may alleviate the problem of insufficient domestic savings or of inefficiency in fund allocation within their financial sectors (OECD, 2002). In addition, FDI has higher stability than other types of foreign capital, such as loans and investment in short-term securities, and this is one of its growth mechanisms (Lensink and Morrissey, 2001; OECD, 2002). In the Solow-Swan-Ramsey growth model, therefore, FDI has a decreasing return, only affects the level of income, and will not change the long-term growth rate. Nevertheless, this model only explains FDI’s growth effects when it is identical in quality to domestic capital and gives no explanation for its growth effects when it functions as the carrier of knowledge and technology transfer and differs from domestic capital in nature: FDI may increase return on production through externality and productivity spill-over and, hence, endogenously affect economic growth. According to Romer (1993), for example, the main obstacle facing developing countries in terms of receiving more benefits from developed ones is knowledge or thought gap, not capital gap. Moreover, proportions are used in empirical analysis to measure the relative size of FDI, such as the share of FDI or FIEs of total amounts such total investment or total number of employees in the host country, or the ratio of FDI to the GDP or domestic capital (please refer to Hansen and Rand, 2006). Since these proportions fail to fully reflect FDI’s ties with knowledge and its structural effects, it is impossible for the growth model to reflect how the transfer or spread of new knowledge and technologies represented by FDI affect economic growth. The endogenous growth theory, which starts logically from the concept of increasing returns and the differentiation between internal and external economies, holds that endogenous growth results from technological advances, especially knowledge innovation, the spread of new knowledge, the presence of fixed capital, and particular market forces, size of the labor force, market size, and competition (Chandra and Sandilands, 2005)ķ. This theory also holds that FDI is a combination of capital stock, knowledge, and technologies (de Mello, 1997) and, hence, differs essentially from local capital. The effects of FDI on the endogenous growth of the host country’s economy are mainly at the commercial level: FDI integrates commercial activities in the host country into the global trade system; it has spill-over effects, including the spread of knowledge and technologies as well as the improvement of the host country’s human capital; it has efficiency effects, i.e., stimulating competition and improving management and corporate governance in the host country. Next, we will discuss some of these growth effects. For other relevant information, please refer to Liu Shiguo (2007). 3.1.2 The Effects of FDI on Endogenous Growth: The Spread of Technologies and the Cultivation of Human Capital FDI affects economic growth of the host country by affecting knowledge stock, according to de Mello (1997). Such knowledge stock exhibits itself as technological advances and is a major factor for long-term income growth. Technological advances are realized through capital deepening. They ķ Endogenous growth models are still faced with criticism despite that they have become the mainstream of contemporary growth theories. As an example, modern growth theories still look at growth only from the perspective of suppliers while missing the core message – “Division of labor and market size are the leading factors for economic growth.” They overlook the market’s more effective use of existing knowledge and technologies and wrongly take fixed costs, market forces or economies of scale as necessary conditions for increasing returns. In reality, market competition is a sufficient condition for increasing returns from scale (please refer to Chandra and Sandilands, 2005).

39

either push the improvement of the quality of various products (Schumpeterian Model of Quality Ladders) (Aghion and Howitt, 1992) or exhibit themselves as additions to product types in the host country (Romer, 1987, 1990; Rodriguez-Clare, 1996; Fishman and Simhon, 2001; Alfaro and Rodriguez-Clare, 2004; Dai Qian and Bie Zhaoxia, 2006). FDI brings the host country new knowledge and technologies that are then materialized into new intermediate inputs and production technologies and are spread into local commercial sectors in various ways, thereby pushing the upgrade of production technologies on the side of local suppliers (Barro and Sala-i-Martin, 2003: 368-370; OECD, 2002). The linkage effects of MNCs on the host country are welcome only when they use a great many intermediate products in production, there are high communications costs between the headquarters and plants, and there are no great differences between the intermediate products of the host country and of the MNC’s home country. Otherwise, the host country’s economy will be damaged. It is necessary for the host country to support advanced technologies represented by FDI with competent and sufficient human capital. There are relevant works such as Borensztein et al (1998), Xu (2000), Shen Kunrong and Geng Qiang (2001), Cheng Huifang (2002), Alfaro et al (2002), Dai Qian and Bie Zhaoxia (2006), Fishman and Simhon (2001), Basu et al (2003), Driffiel and Taylor (2002) and Lai Mingyong et al (2005). The authors of these works generally hold that cross-border investments will have higher productivity only when human capital of the host country exceeds a particular threshold. In this situation, the host country’s factor endowment and its structure decide both the amount of inward FDI and the extent of FDI’s growth effects after it works in combination with human capital. In fact, some types of human capital are closely tied with technological advances – if there are no technological advances, then the return on such human capital cannot remain unchanged, and endogenous growth can be anything but real (Barro and Sala-i-Martin, 2003: 285). Accordingly, the host country will receive corresponding spillover effects from the inflow of FDI when it adopts policies to improve technologies and human capital at the same time (OECD, 2002). A great many empirical works also indicate that human capital stock, when it appears separately in a growth equation, tends to be either statistically insignificant or statistically significant but has weak explanatory power. One possible reason is that human capital stock’s critical effects on the growth process generally lie in its externality and productivity spillover, not human capital accumulation itself. But it is usually difficult to capture the former two in standard growth accounting. Human capital as a separate variable is hardly seen in FIE-relevant production functions in works that research the effects of FDI on production. The failure of existing works to reach an agreement on the measurement of human capital may of course be one of the reasons for the variable of human capital being insignificant in growth accounting. On the other hand, foreign capital plays a relatively unimportant role in cultivating the host country’s human capital (de Mello, 1997). FDI’s contribution to the cultivation of human capital is reflected mainly by in that FIEs provide more training to employees than do DIEs and produce demonstrative effects on the direction to which the host country’s education system will develop. Nevertheless, most contents of such training are purpose-specific and are intended to add to employees’ skills rather than replace them with new ones. This makes such training much different from general education. Although employees of FIEs will produce very limited spillover effects by job-hopping to DIEs (Yuan Cheng and Lu Ting, 2005), it is very difficult for human capital cultivation provided by FIEs to spill over to unrelated fields. Unfortunately, many works tend to neglect this reality and, instead, correlate FDI with the host country’s overall human capital. As an example, they may simultaneously put FDI and overall human capital as two variables in the aggregate production function or simply introduce their interaction term. This will probably

40

exaggerate the interactions between foreign capital and human capital. The aforementioned effects of FDI on endogenous growth will become the core, or the basis, of how the effects of FDI on the income gap are designed in this book.

3.2 Theoretical Model of Foreign Capital’s Effects on Income Distribution To analyze the effects of foreign capital on the income gap among residents using economic theories, it is necessary to do so within a social planning framework. Society comprises the consumer sectors, which are also the labor supplier sectors, which aim at maximizing consumer utility, and the producer sectors, which take raw labor, human capital, physical capital, and technology as the factors of production and aim at maximizing profits. The labor force supply consists of the skilled and unskilled labor forces; the conversion of the unskilled labor force into a skilled one requires both physical capital and skilled labor force as the inputs, and such conversion is realized in the “skill-building sector.” The producer sectors consist of the agricultural sector and modern sectors represented by the secondary sector, of which the latter are further divided into the FIS and the DIS. The FIS act as one of the engines for economic growth. This model assumes the following: The low-skilled labor force is massive and is dominant in the overall labor force, and the labor force can flow freely. Capital is insufficient relative to the labor force. After it enters the producer sectors, foreign capital as a factor of production has changed the inter-sectoral distribution of resources and caused changes in the conversion of labor types and in the relative price of labor. Together with its characteristics in distribution (e.g., industrial and regional distributions), tendency toward processing or trade and other respects, foreign capital affects the pattern and the evolution of income distribution among residents of the host country. 3.2.1 Factor Endowment (1)Human Capital Let the total labor force of an economy be

and every person’s potential or innate ability

(Dinopoulos et al, 2002) be

in terms of δ and d

. Express

ķ

.

Individuals’ goal of maximizing lifetime income decides laborers’ status when they seek jobs in the labor market, that is, low-skilled labor or high-skilled labor, which are expressed in terms of L and S respectively. Accordingly, the total labor force can be expressed as:

Assume that Laborer i is employed in Sector Z, whose productivity is αiδi (αi>0). Then the average wage in this sector, , where is the average productivity of all labors in this sector whose

ķ

capabilities are average

wage

, of all

is the output of this sector, and

employees and

in

the

entire

economy

is

is its labor input. The ,

where

. Note that sectors probably have different

minimal labor skill requirements but that none of them has constraints over the highest level of skills. This assumption is economically realistic because every sector may need a certain amount of high-skilled labor. This also means that there may be no overlap on the left end of the income distribution curves of laborers in different economic sectors but that there are probably overlaps in the other parts of these curves.

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(3.1) Let the wage rate of high-skilled labor, S, be proportional to its capability δ and that of low-skilled labor be irrelevant to its capability δ. Then there must be a capability level so that individuals whose

are low-skilled labor L when they enter the labor market and those

are high-skilled labor S. Next, let the average wage rates per efficiency unit of whose low-skilled labor and high-skilled labor be wL and wS respectively and wL 0, then we say that changes in the output per efficiency unit, y, are “high-skill labor biased” or “low-skilled labor saving.” The economic idea behind it is: with given prices of factors, if β> 0, then a higher y causes a relative increase in the need for high-skilled labor; if β< 0, then an expanding y causes a relative increase in the need for low-skilled labor; if β = 0, then an expanding y causes equal changes in the needs for both types of labor, and it is in this situation that we say changes in the output are neutral to those in the needs for factors. Since y is essentially a concept of productivity per efficiency unit, and the factors studied herein are S and L, more accurately speaking, βreflects the skill bias of technological advances. A great many empirical works, such as Idson and Oi (1999:106) and Haltiwanger et al (1999:97), have proven that there is a positive correlation between business size and the skill intensity of production: if an enterprise increases in size, then the skill intensity of its production will also increase provided that the prices of factors remain unchanged. In other words, capacity expansion by the company is high-skilled-labor-biased, i.e., β> 0. It is necessary to emphasize that since the prices of factors are endogenous and changes in them and in the output can be in the same or opposite direction or independent of each other, β> 0 does not necessarily mean that skill upgrading will occur in the equilibrium state. For a sector, the size of its output is the sum of the outputs of all . β> 0 (i.e., skill upgrading) becomes possible only when the enterprises in it, or expansion of this sector’s output is realized by an increase in the average output of enterprises in it. If this sector expands its output by simply implementing a large number of similar or even identical projects, then β> 0 will not occur since such expansion has nothing to do with technological advances. The agricultural sector as well as the non-agricultural DIS and FIS all have their respective measures of skills bias with respect to technological advances:

The agricultural sector’s measure of skills bias with respect to technological advances is . When the L needed for the production of unit output in the agricultural sector absolutely decreases, ( ), means that technological advances in this sector are low-skilled-labor-saving. However, its unit output increases, bringing about the necessity of shifting the resulting surplus labor into other sectors.

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The time-varying characteristics of intermediate inputs in the FIS are mostly from overseas in the early stage of foreign capital entry into the host country, when FIEs’ production in the host country is usually in the form of simple activities such as importing intermediate inputs for processing and assembly, plus less need for high-skilled labor than for low-skilled labor. As a result, , and the increase of relative to is slower than that of relative to . and, hence, < 0. As the FIS becomes more familiar with the operating So environment of the host country and there is a narrowing technological gap between the FIS and the DIS that supports it (it is assumed herein that the FIS is technologically higher than the DIS), the FIS shifts more production activities into the host country and there is a decreasing share of imported intermediate inputs. This is because the skill-building sector is affected and the local labor force, especially its high-skilled portion, becomes increasingly skilled. The increase of relative to

becomes faster than that of

to become positive (

relative to

is not necessarily greater than

, so

increases and exceeds 0

). Obviously, if the import of

is also a function of t, , intermediate inputs is a decreasing function of time, t, then which is an increasing function of t, provided that the level of labor skills in the host country evolves under certain conditions (these will be explained in the subsequent text):

We will see in the subsequent analysis that

will be a critical factor for foreign capital’s

effects on income distribution among residents of the host country. Assume that just as , is greater than zero in the long term, i.e., technological advances in the non-agricultural DIS are also high-skilled-labor-biased. The maximization of profits occurs when marginal income equals marginal cost, that is: (3.13) Marginal income and cost are on the left and right sides of this equation, respectively. The following relationship between average cost and price holds when a fully competitive market environment reaches an equilibrium state: (3.14) From the above-listed equations, we obtain Autarky equilibrium: (3.15) It is denoted as:

(the pricing condition)

where The condition for labor market clearing: (3.16) Total demand for and total supply of S and L are on the left and right sides of the corresponding equation listed above. Since , is a function of . The unit output’s relative demand for high-skilled and low-skilled labor, for example, will increase and decrease,

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respectively, as the relative wage rate, , increases. Take the definition of the relative endowment , then:

of

high-skilled

labor

and

let

(3.17) That is, when the relative endowment of high-skilled labor (i.e., the left side of the equation), S, equals its relative demand (i.e., the right side of the equation), the labor market reaches an equilibrium state and the entire economy also operates in an equilibrium manner. Otherwise, it is impossible for the labor market to reach such a state. > 0 suggests that with a given factor price , the expansion of outputs Condition in a sector will cause a relative increase in demand for S, that is,

. In addition,

with an unchanged output size, an increase in the relative wage rate, ω, of high-skilled labor will necessarily make the relative demand for it decrease and that for low-skilled labor increase, that is, .

3.3 Further Discussion of the Theoretical Model Assume that investors of the host country hold a portion of the FIS and that the ratio of this share to . Accordingly, of capital return in the FIS belongs to residents of the host the total is country while the rest is remitted out of the country. Laborers in the FIS are all residents of the host country. The national income of residents of the host country then is: (3.18) Introduce the technological level, aji, and the relative importance of the output, ai, of each sector, to change the above-listed equation to: (3.18.1) Terms on the right side of this equation are organized by factor. The differences among the three terms reflect the pattern of income distribution among low-skilled labor, high-skilled labor, domestic capital owners and foreign companies. If the terms on the right side are organized by sector, then this equation further changes to:

(3.18.2) where

; let

and

. The ratios of the

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three terms on the right side of this equation reflect the differences in income distribution among the agricultural sector and the non-agricultural DIS and FIS. If we divide human capital, then we can break down the terms with respect to L and S in Equations (3.18.1) and (3.18.2) so as to more accurately measure the income gap at the level of the self-employed. Assume that the factor price remains unchanged in the period , then (i = 21, 22) remains unchanged. I,

,

and Y are all functions of time, t, and are at least twice

differentiable with respect to t. Let

, then: (3.19)

As is mentioned above, an absolute decrease in the unit agricultural output’s need for L means that . As modern sectors, the non-agricultural DIS and FIS both come with technological advances that are high-skilled-labor-biased. Rural low-skilled labor will either turn into high-skilled labor in the skill-building sector or still work as low-skilled labor in the modern sectors. We assume herein that the high-skilled labor cultivated in the skill-building sector is able to satisfy the additional need for such labor caused by the expansion of the non-agricultural sectors (or the price of high-skilled labor will definitely rise). Since technological advances are high-skilled-labor-biased ( > 0, i = 21, 22), . Since > 0 within the non-agricultural DIS (this has been proven above) and the entire economy almost always expands, . In addition, since technological advances are always capital-intensive or labor-saving, . Economic modernization leads to a decreasing ratio of agriculture to the entire economy on the one hand and an increasing ratio of non-agricultural sectors on the other. In non-agricultural sectors, economic globalization and liberalization lead to increasing ratios of the non-agricultural FIS and DIS to the entire economy, but the ratio of the FIS increases faster since it is a new sector. As a result, and . Next, we will further analyze how the skill bias of technological advances in the FIS affects income distribution among residents of the host country. First, assume that the entire labor force is fully employed ( , where Le is the number of and (i = 21, 22) remain unchanged, the latter of which means that employees), and both (i= 21, 22) remains unchanged. In the period (t0, t1), the FIS expands its output by : since technological increasing the output per efficiency unit, then advances in the FIS are high-skilled-labor (S)-biased, the relative demand for S will increase. Given the definition of β, this will make the value of

increase. In addition, the expansion of the

output in the FIS will increase the demand for low-skilled labor L. Ultimately, while the total labor force in the FIS is increasing, the ratio of high-skilled labor increases, and ratio of low-skilled labor decreases. and , and the Since the entire labor force is fully employed, FIS’ additional demand for high-skilled labor will, if the total supply of such labor remains unchanged, be satisfied by decreasing high-skilled labor in the non-agricultural DIS. The additional

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demand for low-skilled labor will be satisfied by attracting such labor from the non-agricultural DIS and the agricultural sector. As the cost of training low-skilled labor in the non-agricultural DIS is lower than that of training labor in the agricultural sector, a considerable portion of such additional demand will most likely be satisfied first by attracting labor from the non-agricultural DIS. In the state of full employment, the FIS competes with the DIS for labor resources, especially high-skilled labor, so as to expand. The DIS, especially its non-agricultural part, is relatively disadvantaged. To address this challenge, the DIS has to attract laborers from the agricultural sector and train them to improve their skills until its production needs are satisfied. The low-skilled labor in the agricultural sector becomes the ultimate “pool” that continuously satisfies additional demand for labor force caused by the production expansion of non-agricultural sectors. This results in a decreasing absolute number of laborers in the agricultural sector. As a result, the expansion of the FIS leads to a ladder-like path – labor is shifted from the agricultural sector to the non-agricultural DIS and then to the FIS – in terms of skill and factor price. Some S and L laborers with lower wages move along this path into the FIS that offers higher wages. In this process, the group of low-income laborers in the agricultural sector continuously shrinks, the number of middle-income laborers in the non-agricultural DIS decreases and then increases, and the number of high-income laborers in the FIS continuously increases. In the meantime, the size of the agricultural sector may shrink relatively, that of the non-agricultural DIS may remain unchanged, and that of the FIS will continuously increase. Connecting n periods (t0, t1) characterized by the same industrial expansion as is mentioned above, we see how the income of residents of the host country increases in the long term, as is shown in Fig. 3-1.

Fig. 3-1 How the Skills Bias of Technological Advances in the FIS Dynamically Affects Income Distribution among Residents of the Host Country Second, assume that the host country’s labor force is not fully employed, that is: where

,

and

are unemployment rates, the latter two of which are the unemployment

rates of low-skilled labor and high-skilled labor respectively. (i= 21, 22) remains unchanged. In the period (t0, t1), the FIS expands production and has higher demand for both S and L, but its technological advances are S-biased, that is, . The FIS’ additional demand for S and L will be satisfied by attracting the unemployed S and L in the non-agricultural DIS. Such

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demand, if high enough, will fully absorb the unemployed in the non-agricultural DIS so that the latter’s S and L become fully employed. If such demand exceeds the number of the unemployed in the non-agricultural DIS, then the unemployed in the agricultural sector will also be attracted into the FIS. This process then turns immediately into the aforementioned scenario of full employment. Third, assume that L of the host country is fully employed while S is not: where ; (i = 21, 22) remains unchanged. In the period (t0, t1), the FIS expands production and , thus competing directly with the non-agricultural DIS for S while attracting surplus L from the DIS and the agricultural sector. This will decrease unemployment in the entire economy, but S will be undersupplied. The only way to alleviate this problem is to rely on the skill-building sector’s capability of upgrading a sufficient amount of L to S. When foreign capital enters the host country in the form of green field direct investment, the FIS will immediately exhibit a trend of expanding production and affect income distribution among residents of the host country as mentioned previously. When foreign capital enters the host country by means of M&A, the FIS will typically be characterized by an unchanged output size in the short term. In this case, the FIS will affect income distribution among residents of the host country in a different manner from the former cases. Given an unchanged output size, the FIS will usually adjust production by increasing the relative wage rate of to increase . An unchanged output size of the FIS means that its technological level is unchanged and that a higher relative wage rate, , between S and L within the FIS will make its demand for S and L decrease and increase, respectively. This will lead to an oversupply of S and an undersupply of L. Accordingly, S will exit the FIS, which will absorb L from other sectors. This will favor higher average incomes for L but will bring about overall lower incomes for S, before ultimately narrowing the income gap between S and L as the two main groups of laborers. We can draw the following conclusions on the basis of the aforementioned facts. On the one hand, if the prices of factors remain unchanged, the overall income of low-skilled labor in the agricultural sector will decrease over time, whereas those of both low-skilled laborers employed in non-agricultural sectors and high-skilled labor will increase. The returns on both domestic capital and foreign capital will increase as technological advances become increasingly capital-intensive. Although the overall income of low-skilled laborers will increase, high-skilled laborers will have an increasing share of the total labor force and of total wage income since technological advances are low-skilled-labor-saving or high-skilled-labor-biased. Accordingly, there will be a widening income gap between the groups of S and L. On the other hand, an increasing size will lead to an increasing capital input and, hence, to an increasing total return on domestic and foreign capital. Since technological advances are labor-saving (regardless of S or L), the income gap between the groups of laborers and capitalists will become wider. The effects of foreign capital inflow on the income gap among residents of the host country can of course be measured more accurately if this gap is examined in the unit of laborer and investor instead of the groups of laborers and capitalists, that is, if the group of laborers is dealt with in the unit of laborer’s potential as is described at the beginning of this chapter. The aforementioned conclusion is obviously dependent upon the answer to this question: Is the expansion of FIS output based upon an increase in productivity? Whether FIS demand for S and L is satisfied or not, and whether competition between the FIS and the DIS for labor resources will cause the shrinkage of the latter fundamentally depends upon the total endowment of such

52

resources and the relative endowment of . As analyzed previously, depends upon its initial level the skill-building sector’s capability of upgrading L to S. This capability is important especially when S has a low initial value. As Lin et al (2004) found, the migration of agricultural labor into non-agricultural sectors is not fast enough to narrow the existing income gap in China since and opening. This is because of the household registration system and the rapid development of China’s coastal regions. This has caused the coexistence of the flow of labor and a widening income gap between rural and urban areas.

3.4 Summary This chapter presents a general equilibrium model of an open economy. This economy comprises the agricultural sector and non-agricultural modern sectors, the latter in turn being comprised of the DIS and FIS. Factors of production include low-skilled and high-skilled labor as well as domestic and foreign capital. Factors of production include domestic capital and low-skilled labor for the agricultural sector and low-skilled and high-skilled labor and domestic capital for the non-agricultural DIS. Factors of production for the non-agricultural FIS include low-skilled and high-skilled labor and foreign capital. These sectors make agricultural and non-agricultural products (including finished products and services), respectively. In addition, low-skilled labor is upgraded to high-skilled labor in the skill-building sector. High-skilled labor is the end product of the skill-building sector but is a factor of production in the non-agricultural sectors. The condition for general equilibrium of this economy is demand for high-skilled labor relative to low-skilled labor equaling their relative endowments. Assume that technological advances fuel the expansion of output in the non-agricultural sectors. Such advances are then all high-skilled-labor-biased rather than low-skilled-labor-biased. In other words, technological advances in the non-agricultural FIS and DIS make increase the amount of high-skilled labor required for output expansion faster than the amount of low-skilled labor required. Assuming that the FIS is technologically more advanced than the non-agricultural DIS, then the former is more skills-biased than the latter. As a result, the expansion of the FIS increases the need for low-skilled labor and high-skilled labor simultaneously, especially the latter. Since high-skilled labor accounts for a smaller share of the total supply of labor, the expansion of the FIS, with a given total supply of high-skilled labor, will cause competition with the non-agricultural DIS for high-skilled labor and then for low-skilled labor. By comparison, if the total supply of low-skilled labor is more abundant, the expansion of the non-agricultural sectors will require less low-skilled labor than high-skilled labor and, in particular, the agricultural sector can provide a lot of low-skilled labor, which can satisfy the job requirements of the non-agricultural sectors with little need for training from the skill-building sector. Even if the total labor force is fully employed, therefore, it is easier for low-skilled labor to satisfy the output expansion of the non-agricultural sectors, especially the FIS. By comparison, it is not that easy to satisfy the additional demand for high-skilled labor created by the expansion of the non-agricultural sectors, especially when such additional demand is sufficiently high. As for the relative demand for high-skilled labor created by the expansion of the FIS, it is less easy to satisfy this demand when the labor market is in a state of full employment than otherwise. The role of the skill-building sector in upgrading low-skilled labor to high-skilled labor is critical for balancing between the relative demand for high-skilled labor caused by the FIS and its relative endowment, especially when the labor market is in a state of full employment. In this process, since the wage rate is higher in the non-agricultural DIS (and the non-agricultural

53

FIS) than in the agricultural sector (and the non-agricultural DIS), the group of laborers with lower wage rates will shrink, while the group of labors with higher wage rates will expand as a result of the shift of low-skilled labor to the non-agricultural DIS and FIS from the agricultural sector, plus that of high-skilled labor to the non-agricultural FIS from the non-agricultural DIS. This will lead to a higher average wage rate for the entire labor force. But the wage gap between low-skilled and high-skilled labor will widen even if we assume that their respective average wage rates remain unchanged. This is because technological advances in the FIS are more high-skilled-labor-biased and, thus, will bring a faster increase in such labor. The aforementioned analysis is based on the assumption that technological advances fuel the expansion of output in the FIS. But if this expansion is fueled not by technological advances but by a large number of similar or even identical projects at relatively low technological levels, then it will make the demand for low-skilled labor increase faster than the demand for high-skilled labor. Accordingly, more low-skilled labor will shift to the FIS, which offers a higher wage rate, from the agricultural sector and the non-agricultural DIS, which offer lower wage rates, whereas less high-skilled labor will shift to the FIS, which offers a higher wage rate, from the non-agricultural DIS, which offers a lower wage rate. As a result, the average wage rate for the entire labor force will increase but at a lower growth rate if fueled by technological advances in the FIS. There will also be a narrowing wage gap between high-skilled and low-skilled labor. In this scenario, the skill-building sector plays a less significant role in the general equilibrium of the economy. The aforementioned conclusion is based upon the assumption that there is no obstacle to the inter-sectoral flow of labor, whether technological advances fuel the output expansion of the FIS or not. The migration of agricultural labor into non-agricultural sectors is not fast enough to narrow the existing income gap in China since reform and opening. This is because of the household registration system and the rapid development of China’s coastal regions. This has caused the coexistence of the flow of labor and a widening income gap between rural and urban areas.

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Chapter 4 The Dynamic Panel Data Analysis Method We have mentioned in the overview of empirical methods in Chapter 2 that dynamic panel data analysis is the method most suitable for researching microscopic, individual behavior. Essentially, panel data focuses on characteristics of individual behavior. In addition, the dynamic panel data analysis method can reflect the stickiness of income and of employment. The next sections will present empirical research on enterprises’ average wage and employment behavior using the dynamic panel data analysis method. It is therefore first necessary to introduce this method. With regard to the contents of this chapter, Section 1 presents the basics of panel data analysis; Sections 2 through 6 explain dynamic panel data analysis from different perspectives, including the general model (Section 2), estimation methods (Section 3), moment conditions for the generalized method of moments (GMM) (Section 4), and how to deal with the interdependence between individuals (Section 5); Section 6 summarizes this chapter.

4.1 The General Model for Panel Data Analysis The general model for panel data analysis is: (4.1) where y is the explained variable, to be estimated,

is the observable explaining variable,

is the disturbance term, Subscript

is the parameter

is any observed individual in the panel, and

t is the observation period. Compared with the times-series data model, is an extra subscript for all the variables in the panel data model. As for characteristics of individuals in panel data, number of choices are available for setting the panel data model. Among them, a very important choice is: how to address the effects of the error term of each individual, , on model estimation and check if this term is heteroscedastic? There are generally three methods for this. First, condition variables can be added to make up for such effects, but such variables may not exist. And even if they do, they may also cause problems such as excessive loss of degrees of freedom and multi-collinearity. Second, a varying-parameter model can be divided into three parts: , which can be employed (Hsiao, 2003: Chapter 6). Third, varies with individuals, not with time; and which varies with both individuals and time:

, which varies with time, not with individuals;

,

(4.2) Where is a random disturbance term? is called unobserved effects ķ since it is unobservable. And Model (4.2) is called an unobserved-effects model (UEM). In most applications, the primary reason for collecting panel data is considering the correlation as unobserved effects and as an observable explaining variable (Wooldridge, 2000: between 407). It is possible to further refine Model (4.2) on the basis of this correlation. When the observed ķ

Unobserved effects, have many other names in applications, such as unobserved component (this is because is part of the error term, , of Model (4.1)), latent variable, unobserved heterogeneity, individual effect or individual heterogeneity.

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individuals are regarded as random samples in the same population or – this case,

is not correlated with

, Model (4.2) is called a random-effect model (REM). In can be used as a component of the random error term and, when estimation is made,

can be included into the random disturbance term with no need to estimate it separately. The REM is advantageous in that it has a fixed number of parameters and that random-effect estimators are valid as long as the model settings are correct. It is necessary during inference to make assumptions about the distribution of the effects: if the model is non-linear, then it is more necessary to make special assumptions, and, to obtain consistent estimates, it is usually necessary to employ maximum-likelihood estimation (MLE).If the model is linear, the independent variables are used as conditions and individuals are regarded as being sampled from the same population, then it is assumed that the effects are independently distributed and that the means are the same as the variance-covariance matrix. Generalized least squares (GLS) are relatively simple to use and, if relevant characteristics of the residual are known, then it can be inferred that GLS is asymptotically efficient. When T is fixed, N approaches infinity and all the units on the cross-section are independently distributed, the general method can also be used – i.e., accumulate the behavioral equations of all the individuals in a given model to get T equations at all the time points in Period T, before estimating the common parameters using the procedures that determine the minimal distance among the observations on the cross-section. When the error structure is unknown, these procedures allow serial correlation to be arbitrary and the form of heterogeneity to be certain, thereby obtaining consistent and significant estimates. Moreover, consistent estimates of the variance-covariance matrices of T equations (or equation series) can be obtained with no need to make particular assumptions about characteristics of serial interdependence. This allows particular assumptions about the distribution of the error terms to be checked. Nonetheless, it has a weakness in that when there is a correlation between random effects (RE), vit, and the explaining variable, , the RE estimators are biased. When the observed individuals are from a heterogeneous population, or –

is correlated with

, Model (4.2) is called a fixed effect model (FEM), where

is still a random variable, is not correlated with the disturbance term, , and needs to be estimated separately. Obviously, unobserved effects are not identical to fixed effects (Wooldridge, 2000: 407). The FEM is advantageous in that it not only allows differences to exist among individuals and thus makes it unnecessary to make assumptions about the distribution of certain , and effects of individuals, but also allows correlation to exist between the explaining variable, the fixed effects, , with no need to explain the specific pattern of this correlation. The FEM has a weakness in that it brings about the classic incidental parameters problem, that is, the number of unknown parameters, and , increases as N and T increaseķ. Consequently, this makes it impossible to estimate parameters of variables that do not vary with time and individuals (More and may result in insufficient degrees of freedom) and, thus, makes inference less efficient. Otherwise, we have to set a constraint: different individuals exhibit only tiny changes in different periods of time. ķ That is, αi and λt have N and T parameters respectively; increasing N and T will increase their respective parameters.

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In real-world applications, not all panels are standard, such as unbalanced panels, incomplete panels, panels with large N and T, and panels with a multi-layer structure. For panels with large N and T, it is necessary to make a unit root test before estimating them. Errors in variable measurement affect estimation. As for measurement processing techniques regarding these issues, very good recommendations can be found in works such as Wooldridge (2002) and Hsiao (2003). Although panel data contains the cross-section and time-series dimensions simultaneously, the most common scenario is that either dimension (usually the time-series dimension) has fewer observations while the other one (usually the cross-section dimension) has a great many observations. To obtain consistent estimates of unknown parameters, it is necessary to increase the dimensions of samples so as to increase information on relevant parameters. It is critical, therefore, to determine whether we should make either or both of N and T of the panels approach infinity. Only on this basis can we determine for which parameters we can obtain consistent estimates from the given panels. Panels with large N and T have been receiving more attention recently, but when T becomes larger, the unchanged assumptions about certain effects of individuals no longer seem that realistic. It is necessary, therefore, to make assumptions more consistent with the real process of data generation. Generally, the panel data analysis method has the following advantages over cross-section, time-series, or pooled cross-section data (Hsiao, 2003). First, the panel data model has N x T data points as opposed to the1 x T data points of time series. This greatly increases the degrees of freedom of estimates while decreasing possible collinearity problems in data. Second, panel data can provide microscopic foundations for aggregate data analysis. Aggregate data analysis often involves the “representative agent” assumption, but if microscopic units are not homogeneous, then not only will aggregate data differently from microscopic data in terms of time-series properties, but policy assessment obtained on the basis of aggregate data may also be totally misleading. Moreover, aggregate data is not as accurate as microscopic data in terms of forecast. Third, panel data is helpful for discriminating economic assumptions and identifying economic models. To identify economic theories and/or assumptions that compete with each other, it is necessary to examine microscopic properties. Obviously, aggregate time-series data cannot provide these properties, nor can individual time-series data provide information on differences among individuals. Cross-section data does contain information on differences among microscopic individuals, but it cannot be used to model dynamics or causal ordering among individuals, unless it has access to data of control variables that reflect these differences and explicitly set them in the model. Panel data provide series observations for individuals and thus enable the discrimination of differences among and within individuals. Moreover, the proper addition of extra sources of changes can provide very useful information to discriminate individual and average behavior or to identify models that were otherwise unidentified. Also, with the cross-section dimension in addition to the time dimension, the model will be more likely to identify both serial correlation in the residual and adjustment lag when condition variables change, with no need to make a priori settings for parameters or to identify a particular model under measure error restrictions. Fourth, panel data enables the reduction or elimination of estimation errors. Usually, whether a model is properly set or not will be reflected in its residual term, indicating that there is a correlation between variables and the error term. This correlation is typically from four possibilities: omitted variables are correlated with existing explaining variables; the lagged terms of explained variables are correlated with the error term; the simultaneity of the model causes all the explained variables to be simultaneously correlated with the error term; explaining variables come with

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measurement errors. The panel data analysis method deals with these four types of errors as follows. Errors caused by omitted variables can be eliminated with one of the following three methods if their effects on a given individual remain unchanged throughout the period or are identical for all individuals in a given period. These methods include: differentiating sample observations to eliminate effects of particular individuals and/or times; using dummy variables to describe individual-invariant (i.e., fixed) and/or time-invariant effects; for given exogenous variables, assuming the conditional distribution of unobserved effects (random effects). All three methods apply to linear regression models. Moreover, if the error term has a makeup that varies with individuals and time, then slope coefficients estimated by the dummy variable (fixed effects) and random-effect methods have equal variances. Under the other assumptions, the estimated variances of slope coefficients may be invalid, but they are still unbiased and consistent. The fixed-effect method is therefore extremely important in empirical research. Unfortunately, the results of linear models are, after all, not universal and usually do not apply to non-linear models. In non-linear models, the fixed-effect and random-effect methods use different estimators, and the Neyman-Scott principle generally does not apply. Widely used in linear models, this principle is intended to estimate common coefficients – that is, parameters across individuals throughout the period and also known as structural parameters – separately from certain effect estimates. If the number of unknown certain effects increases with the sample size at the same rate, then the estimation of certain effects will bring about the incidental parameters problem. Accordingly, the fixed-effect method may not bring consistent estimates of common coefficients. For general, non-linear models with fixed effects, it seems that no universal analysis framework exists to obtain consistent estimates of structural parameters. Instead, non-parametric modeling methods must be explored to obtain such estimatesķ. On the other hand, the random-effect method replaces the conditional probability distribution of explained variables. In this method, the probability distribution function takes only explaining variables as its conditions, and the probability distribution of explained variables has certain effects and exogenous variables as its conditions. The distribution function of effects generally relies only on a finite number of parameters and, thus, avoids the incidental parameters problem. Nonetheless, there are important differences between linear and non-linear models. For linear models, it is necessary to make certain assumptions, as it is necessary only to divide certain effects into projections on the observed exogenous variables and those on orthogonal residuals. For non-linear models, it is usually necessary to assume that certain effects are actually linear with respect to the conditional means of the observed exogenous variables. Distribution parameters of effects can be set when explaining variables are given. These are all restrictive assumptions and loosening them will produce decisive effects. As for errors caused by the dynamic structure of the model, it is necessary to discriminate two sources of such errors. One error occurs when persistence is ignored, that is, the correlation between errors and the lagged terms of explained variables are ignored. The other occurs when initial observations are wrongly modeled. The length of time (T) of observations does not affect the correlation between the residual and the lagged terms of explained variables, but a very small T ķ There are three most common nonparametric methods: (i) the conditional method, that is, making statistics of effects be minimally sufficient; (ii) the semiparametric method, that is, exploring the linear structure hidden in the model; (iii) model reparameterization – after being reparameterized, individual effects and structural parameters will have information matrices that become uncorrelated with one another. But these methods are not widely applicable to general non-linear models. Whether they will obtain consistent estimators or not depends on the studied cases.

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value or a lack of information on initial values will bring about the initial value problem. When T is very large, initial values have a very small or even negligible weight in the likelihood function, and it is thus unnecessary to consider the initial value problem. When T is very small, it is advisable to regard initial values as random ones. Whether errors caused by the correlation between initial values and the residual term can be correctly eliminated or not depends on specific characteristics of the serial correlation in the error term. If the model is linear and the persistence error is the sum of two parts – one part is individually time-invariant and the other independently distributed – then the time-invariant effects of individuals can be eliminated by differentiating serial observations of individuals. After the model is modified, the lagged terms (the order is sufficiently high) of the explained variable can be used as an instrumental variable to solve the initial value problem and the problem of serial correlation in the residual. If serial correlation in the error term is arbitrary, then it is sufficient to maintain the assumption that individuals are independent from one another. For all T observations in a given model, the behavioral equations of particular individuals can be set as T equations and all observed exogenous variables are taken as the conditions for initial values. Using simultaneous equation estimation, can obtain consistent estimates of coefficients and serial variance matrices. If the model is non-linear, then we must make certain assumptions about initial values and the error process. Usually, when model properties are given, the estimation of model coefficients cannot be made separately from the estimation that describes the parametric characteristics of the error process. To obtain consistent estimates, we must set the error process accurately or in the general manner – i.e., by encompassing this basic process – and will likely use the maximum likelihood estimator. In addition, before doing various likelihood ratio tests, we must obtain the maximum likelihood estimates of unknown parameters of the error process under various assumptions. Regarding simultaneity deviation, the instrumental variable method (IVM) is the standard method of eliminating such errors. If there is no constraint over cross-section equation correlation in errors, then exogenous variables excluded from the equation can be used as the instrumental variables of the explained variable. If cross-section equation correlation derives from common, omitted, invariant variables, then variables already included in the equation can be used as instrumental variables in addition to the aforementioned method. Regarding deviation caused by measurement errors, the differentiation method, which aims to eliminate individual effects, may increase deviation from one source when eliminating deviation from another. And this leads to a greater deviation than that from the simple least-squares estimator. Nonetheless, various data transformations will cause changes in parameters, and on the basis of these changes, we can determine the importance of measurement errors and obtain consistent estimates of parameters of interest. Measurement problems brought about by characteristics of panel data, however, are usually brand-new and hard to handle, especially in non-linear models. Since a great many observations are available, panel data analysis considers more the efficiency of estimation than the consistency of estimates. If the model is correctly set, then ordinary least-squares (OLS) estimation should produce consistent results despite that it ignores the random coefficient assumption. In practice, estimation results are elusive. GLS takes into account stochastic properties of cross-section units and produces estimation results that are opposite those of OLS estimation. Note that panel data is not a panacea despite its advantages. The power of panel data analysis strictly depends on the compatibility between the assumptions of statistical tools and the data generating process, or misleading inferences will follow.

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4.2 The General Model for Dynamic Panel Data Analysis The so-called generality of dynamic models of panel data refers to the fact that such models consider the following characteristics generally involved in a great many works: coefficients are time-varying; the following information is unclear – the lag periods of lagged dependent variables, the time that possible structural break points of parameters occur, whether independent variables are predetermined or strictly exogenous, whether independent variables are correlated with individual effects. They can encompass particular dynamic models of panel data involved in well-known works. Andrews and Lu (2001) made a very good summary of the general model for dynamic panel data analysis, and the subsequent contents of this section are from this summary unless otherwise stated. Consider the general dynamic model of panel data: (4.3) Where

and

are the observed variables,

the unobserved idiosyncratic error, and

is the unobserved individual effects,

is

is an unknown parameter to be estimated. The

and are not set. All random variables in the model are assumed to be distributions of independent from the Individual i. Vector includes L lagged terms of the dependent variable, that is, , and L’s real value,

, may be unknown. Assume that the initial observations of y,

, are known. The other observed variables that the regression vector includes can and time-varying vector : , and the be grouped into time-invariant vector two vectors may of course include other variables that do not enter the regression function, such as instrumental variables. Depending on the types of specific relations between variables and , and

may include variables whose types can be expressed as: (4.4) (4.5)

Where, with respect to

, variables

,p

and

exogenous variable, predetermined variable and endogenous variable respectively. are uncorrelated with

, but

, and

are strictly the and

are correlated with it. In practice, people

and do not include may not know which of the above-listed type zit or fi belongs to. If variables other than the aforementioned ones, then Model (4.3) can be rewritten as:

(4.6) , and . If contains constants, Where then this model will include intercept parameters. Allowing the intercept to be time varying is critical for most applications (Wooldridge, 2000:407). can vary with time. Accordingly, people can examine models with In Model (4.3), Parameter

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structural break points, the time of whose occurrence may be known or unknown. If structural before the occurrence will differ from the value after it. break points occur, then the value of Parameterize such a model as follows: (4.7) Where is the deviation between and . In the most general model, (4.7) allows to have a different value at each t, so the parameter vector can be defined as: (4.8) In practice, however, people usually put different restrictions over (4.7) and, thus, create “restricted models” on the basis of a sufficiently general Model (4.3). It is based on these distinctive restricted models that Model (4.3) will stay highly general to summarize numerous characteristics of different empirical research. In pure dynamic models of panel data (i.e., models that contain no independent variables other than the lagged terms of dependent variables), for example, it is critical to consider the scenario where the lag periods of dependent variables are unknown. In rational-expectation models, it is also critical to discriminate sub-variables in as predetermined or strictly exogenous variables depending on the correlation between dependent variables and . In a lot of applications, it is also critical to discriminate analysis models as correlated and standard REMs depending on characteristics of the correlation between variables in and . Considering structural break points in parameters provides a new approach, besides panel unit root models, to non-stationary modeling in dynamic models of panel data. We do not expect, of course, that all these characteristics will be present or important in every single piece of empirical research. Model (4.3) is a simple structure, but it includes numerous restricted sub-models, which are of interest to a great many pieces of applied research.

4.3 Estimation Methods for Dynamic Models of Panel Data In reality, panel data has a most common scenario – the panel has a very small T and a very large N. Many estimation methods for panel data models are built upon this data reality. Panel data is a type of data widely used in dynamic measurement models. Do all estimation methods commonly used in statistical inference, such as the least-squares method (LSM), MLE, and IVM, generally apply to estimating dynamic models of panel data? If not, which one is the most suitable? 4.3.1 Applicability of Common Statistical Inference Methods for Estimation for Dynamic Models of Panel Data (1) OLS One assumption for applying OLS is that there is no correlation between the disturbance term and explaining variables. In dynamic models of panel data, however, if individual effects are random, then they are necessarily correlated with the lagged terms of y unless their distribution is degenerate. In addition, if idiosyncratic errors are assumed to have no serial correlation, then the two conditions combine to mean that OLS estimates of lag parameters of y are inconsistent. This is because the

61

combined error term is necessarily positively correlated with the lagged terms of y owing to the presence of individual effects, and this correlation will not disappear as N or T increases. With the presence of omitted variables, standard estimation results indicate that at least in large samples, the OLS level estimators are biased upward. Now that individual effects will cause inconsistent OLS estimators of dynamic models of panel data, will these estimators become consistent if such effects are eliminated? After the initial sequence is centralized around Individual i, the new sequence is the deviation of the initial sequence from the means of all variables of individuals in T periods (this data transformation process is called “intra-group transformation”). This can eliminate individual effects, but since T is very small, there are still non-negligible correlations (the two negative correlations combine into a positive

correlation)

between

(and

)

and

(and

in

the

transformed

lagged

term

) in the transformed error term

, and these correlations will not disappear as sample size N reaches its maximum, but will disappear as T increases. For a model with the aforementioned transformation, the OLS estimator is called “the intra-groups estimator,” which is also inconsistent for the aforementioned reason. With the presence of omitted variables, standard estimation results indicate that at least in large samples, the intra-groups estimator is biased downward. Here, the OLS level estimator and the intra-groups estimator constitute an interval of alternative consistent estimators: these consistent estimators should at least be neither significantly greater than the OLS estimator nor significantly smaller than the intra-groups estimator. (2)MLE One of the difficulties in using MLE to estimate dynamic models of panel data is that since T is very small, the distribution of (t = 2, 3, … , T) relies on that of and such reliance is non-negligible. is generated in many possible processes, as it may or may not be stochastic, correlated with individual effects, etc. Setting it in different ways will lead to different likelihood functions. Inconsistent likelihood estimation results will follow unless it is properly set. (3) IVM The instrumental variable estimator has weaker requirements on than does MLE. The first-order differential two-stage least squares (2SLS) can be employed. The first-order differential transformation of the original sequence is intended to eliminate individual effects. This is similar to data preprocessing by the intra-group estimation methodķ. ’s reliance on means that OLS estimates of Parameter

are inconsistent and that the inconsistency is downward.

Nonetheless, if we introduce instrumental variables correlated with with

, then we can obtain consistent estimates of

but uncorrelated

using the 2SLS method. Assume that

ķ The two methods differ in that: the intra-group estimation method centralizes initial data in a way that involves the entire time series of every variable. The first-order difference involves only two neighboring time points of , … , ) of the every variable, so, unlike the former, it will not introduce the entire time series ( , , of the transformation equation. idiosyncratic error into the error term,

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is uncorrelated with the sequence {

: t = 2, 3, … , T}, that is,

assumption, together with the assumption that uncorrelated with

is predetermined. This

has no serial correlation, means that

is

and thus can be used as an instrumental variable of first-order differential

equations to obtain consistent 2SLS estimates in panels with large N and fixed T. When

,

parameters of can be identified. When , more instrumental variables can be obtained, i.e., there is more than one instrumental variable in this situation, where the dynamic model of panel data is over-identified. If the assumption that has no serial correlation is still correct but has first-order serial correlation, then the obtained 2SLS estimates are still asymptotically efficient even if all the available instrumental variables can be used in estimation. And in this situation, we can obtain asymptotically efficient estimates using the GMM (Hansen 1982, Holtz Eakin et al 1988, Arellano and Bond 1991). 4.3.2 The GMM for Dynamic Models of Panel Data The (ordinary and generalized) least-squares methods are moment estimation methods and require that moment equations be as numerous as parameters, that is, equations should be just enough to identify parameters. In some cases, however, there are more moment equations than parameters, that is, parameters are over-identified. To obtain consistent estimates of parameters in this case, moment methods require that all pieces of information be consistent with one another. The GMM is a method that meets this requirement. It is the best instrument that synthesizes sample information using the best method (Darnell, 1994), and we may say that it is now the mainstream method for estimating dynamic models of panel data (Baltagil 1995; Mátyás and Sevestre, 1996). For a precise overview of GMM estimators, you may refer to Arellano and Honore (2001) or Blundell et al (2000). Assume that the GMM is applied to a pure dynamic model of panel data. At the core of this method is a matrix of moment variables as follows:

All the rows correspond to the first-order differential equations of I at t=3, 4, … , T and the moment condition is: where . Generally, an asymptotically efficient GMM estimator based on this moment condition should minimize the following criterion:

where

and

is the first-order difference of the residual when

the consistent estimators are first estimated. GMM estimators obtained like this can therefore be called “two-step GMM estimators.” If has homoscedasticity, then GMM estimators of

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first-order differential models can be obtained in one step (i.e., “one-step GMM estimators”) and , where H is a T-2 square

the corresponding weight matrix is changed to

matrix that has twos on the main diagonal, negative ones on the first off-diagonals and zeros elsewhere. We can see that W1N does not rely on any estimated parameter. This at least suggests that it is reasonable to use one-step GMM estimators of W1N as the initial consistent estimators and that it can be further used to calculate the optimal weight matrix and then calculate two-step GMM estimators. In practice, a great deal of GMM application research pays more attention to one-step estimators than to two-step estimators for at least two reasons. First, simulation research indicates that two-step estimators are far from efficient even if heteroscedasticity exists. Second and more important, the weight matrix in the two-step method relies on estimated parameters, which reduces the reliability of the general approximate asymptotic distribution of two-step estimators (Windmeijer, 2000). When T>3 and the model is over-identified, we can use the standard GMM over-identifying restriction statistic, JN, to do the Sargan test, so as to obtain the validity of the hypotheses for the aforementioned moment conditions (Sargan, 1958, 1988; Hansen, 1982). The null hypothesis for JN is that these moment conditions are all valid. Under this null hypothesis, JN follows an distribution. Under such a framework, that no serial correlation exists in becomes a critical hypothesis in identification and, in this situation, second-order serial correlation does not exist in the first-order residual, or the null hypothesis is rejected. The aforementioned GMM procedures do not discuss hypotheses about the other explaining variables. If the other explaining variables (i.e., X, the current term, and/or the lagged terms) are included, then the moment conditions of the GMM should also consider the correlations between these explaining variables and the two components of the error term. In addition, it is necessary to determine whether X is: (i) endogenous – i.e., is correlated with and its lagged terms but uncorrelated with its future terms; (ii) predetermined – i.e.,

is

at any time point. If X is endogenous, then treat it and the lagged terms of Y

in the same way: in the first-order differential equations of t = 3, … , T,

,

all valid IVs. If X is predetermined, then in the first-order differential equations of t, , … ,

’s lagged

and its future terms; (iii) strictly exogenous – i.e.,

terms but uncorrelated with uncorrelated with

is correlated with

,…,

are

,

,

are all valid IVs. If X is strictly exogenous, then in the first-order differential

equations of any time,

,

,

,…,

, are all valid IVs.

4.4 Assumptions of Dynamic Models of Panel Data and Moment Conditions for GMM Estimates Regarding the significance of estimation in dynamic models of panel data, Arellano and Bond (1991), Arellano and Bover (1995), as well as Ahn and Schmidt (1995) researched this issue by studying the number of available moment conditions under some assumptions about the relation between the initial condition, Yi1, and the error term. Once these moment conditions are confirmed, with the addition of exogenous assumptions about the other independent variables in the regression model, we can use the GMM to obtain significant estimates. These GMM estimates are significant

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as long as moment conditions are correctly set. Moment conditions used for GMM estimators are determined with assumptions of dynamic models of panel data. Various assumptions can be made for dynamic models of panel data, and moment conditions correspond to these assumptions. Andrew and Lu (2001) enumerated combinations of different assumptions that lead to different models, but it is unnecessary to impose all assumptions on a dynamic model of panel data. Adding correct moment conditions can substantially increase the efficiencies of estimators in certain situations (Blunder and Bond, 1995). Moreover, both the identification of particular parameters and the consistency of estimators depend on the correctness of moment conditions. On the other hand, using incorrect moment conditions will naturally bring about inconsistent estimators. Table 4-1 depicts all the assumptions of Model (4.3), a dynamic model of panel data. In this table, , , , and (i = 1, … , N). Assumptions P1(a) through P1(c) are specific to the structures of error components and, in dynamic model of panel data where independent variables include only lagged dependent variables, are called “standard assumptions” (Ahn and Schmidt, 1995).Pa(1) requires that the mean be zero and that the terms and be uncorrelated with each other. of the error P1(b) requires that

has no serial correlation. P1(c) requires that

existing observations of y. P1(d) requires that that all variables in least with respect to

, except for

be uncorrelated with

be uncorrelated with all lagged terms of

and

as an endogenous variable, are predetermined variables at

(i.e., the simultaneous correlation coefficient between these variables and

is zero). Assumptions P1(a) through P1(d) are the lowest restrictions for (5.1) and relying only on these restrictions may be insufficient to identify the parameter

of

as a constant

. independent variable. Assumption P2 sets a strict exogenous variable with respect to Assumptions P3 and P4 aim to set the correlations between variables and unobserved individual effects, . Among them, variables set by the former are uncorrelated with , whereas those set by the latter are correlated with . Assumption P5 sets variances of idiosyncratic error terms to be time-invariant, but it does not further set whether there are differences between these variances or not. In relevant works, the time-invariant nature of idiosyncratic errors and the homoscedasticity of idiosyncratic errors between individuals are two necessary conditions for obtaining GLS estimators of REMs and 3SLS estimators of relevant REMs. Assumption P5 helps the GMM estimate and add new moment conditions. Assumption P6 sets the “steady state” of the initial condition, , … , . It requires that the correlation between the initial condition and equal the correlation between the dependent variable and

at t = 1. If this assumption does not hold, then it

suggests that the initial condition differs from in terms of the process of data generation. Table 4-1 depicts the moment conditions and their numbers corresponding to all the aforementioned assumptions. There are many kinds of GMM estimators of dynamic models of panel data, and the key difference among them lies in that different moment conditions come with different orthogonal conditions. It is difficult to analyze which estimate has a smaller asymptotic variance and thus to determine that this estimate is better. Moreover, GMM significant estimate clusters are difficult to use in large databases. Park et al (2007) found a new path by constructing significant semi-parametric estimators. It has been proven by using the Monte Carlo method in finite samples

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that such estimators are more efficient than parametric ones. This book focuses on GMM as the mainstream estimation method. Table 4-1 Common Assumptions of Dynamic Models of Panel Data and Moment Conditions Corresponding to Them Assumption

Moment Condition

Number of Moment Conditions

P1(a)

T

P1(b)

L(T –1) + (T –1)(T –2)/2

P1(c)

T–2

P1(d)

P2

, P3

P4

P5

T–1

P6

L

Notes: (i) Δ is the first-order differential operator of the next variable, such as

; dw

; 1T is a T-th order vector whose elements are ones; is a is the dimension of Vector w; T-th order vector whose first t elements are zeros and other elements are ones. (ii) Assume that the moment conditions corresponding to P3 and P4 are actually those corresponding to combined assumptions P3+P1(d) and P4+P1(d). (iii) Let the parametric vector of the regression function be

or θ(b), then, by replacing

in the

aforementioned moment conditions with the difference between and this regression function, we can constitute GMM estimators based on these moment conditions. We may know, therefore, that the moment conditions corresponding to Assumptions P1(a), P1(b) and P1(c) are such that the estimation equation is linear with respect to the parameters, whereas the moment condition corresponding to Assumption P1(d) is such that the estimation equation is non-linear with respect to the parameters. Source: Section 5.3 in Andrew and Lu (2001).

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4.5 Dynamic Models of Panel Data: Estimating the Interdependence between Individuals 4.5.1 The Interdependence between Individuals: Explanations from Economic Theories Most panel data analyses assume that, in addition to the possible individual-invariant but time-varying effects, the distribution of the effects of omitted variables is uncorrelated with the observed individuals in the panel. The spatial interdependence in cross-section or panel data has been much discussed in a great many works over the past few years. In such a cross-section, observations have obvious absolute or relative positions in a particular form of coordinate system or distance measurements, such as enterprises belonging to a particular geographical area or industry, and the interdependence between geographical positions or industries can be reflected by modeling. Such interdependence is usually called “spatial interdependence” which is rooted in the implicit functional relationship between individuals in cross-section data. For theoretical or empirical spatial issues, you may refer to Anselin (1988), Case (1991), Conley (1999), Delong and Summers (1991), Dubin (1988), Fishback et al (2002), Kelejian and Robinson (1999), Quah (1992), Topa (1996), Druska (2003), as well as Pesaran and Tosetti (2007). These works underline important phenomena such as spatial aggregation, infrastructure effects, and economic spillover. The interdependence between agents may be a particular combination of the following sources: geographical proximity, proximity of blood, cultural similarities, climatic homogeneity, technological correlations, etc. Not all economic analyses support such a point of view, of course. In the 1950s, economic analyses were believed to have taken place in “a wonderland of no spatial dimensions” (Isard, 1990). All standard, mainstream economic theories assume, explicitly or implicitly, that space is homogenous and that the regional distribution of all economic agents is even in a given space. On the basis of assumptions that sizes and returns are unchanged and that competition is sufficient, for example, it is believed in the field of traditional economic geography that, with no interregional difference, economic activities will ultimately be evenly distributed in the geographical space. In reality, however, it is often seen that economic activities at different levels may be highly concentrated or scattered in a given space. As a matter of fact, since factors are not evenly distributed in the geographical space, their flows also involve interregional transportation costs, and one of the decisive factors for this cost is the geographical distance between regions. On the side of new theories of economic geography, it is believed that economic activities are clustered or scattered in the geographical space out of considerations such as geographical distances, increasing returns, a market environment with insufficient competition (Krugman, 1991a, 1991b), and externalityķ (Marshall, 1920). Assume that there are large enough economies of scale, that there are technological correlations between industries (i.e., the “forward linkage” and “backward linkage” effects), and that transportation costs (including ordinary transportation costs and interregional non-tariff barriers) constitute part of every product or service. To minimize transportation costs, every enterprise will select a region with strong local demand. Nonetheless, strong local demand will appear only when a significant number of enterprises are located in the same region. In addition, the flow of labor force within and between regions must be highly elastic ķ

Externality comprises temporal and spatial externalities. Among them, spatial externality refers to additional profits and losses caused by a particular economic activity to economic entities around it in a certain space (Wu Yuming, 2005, p. 53).

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with respect to the wage rate. This will lead to industry clusters. Once industry clusters appear in a region, the process of clustering will last as long as there is no external disturbance. This is so-called “path dependence” of clustering (Arthur, 1996; Wu Yuming, 2005, p. 55-57). Accordingly, the spatial clustering and evolution of economic activities are non-linear and constitute an accumulative, dynamic mechanism characterized by circular causality. Factors such as distance and transportation cost are still affecting economic development despite that such effects are decreasing as opposed to increasing effects of economic globalization and information technology that follows Moore’s Law. The dissemination of some information still relies on the movement of human beings and physical carriers. Most production and services still require face-to-face communication, and human flow is much more expensive than the transportation of goods (Glaeser and Kohlhase, 2004). The flow of labor force is easier than before, but it is still rather restricted in terms of geographical space (Cheshire and Malecki, 2004). For the part of developing countries and regions, in particular, distance still has a great effect on the transportation of goods, human flow, and information dissemination owing to less developed transportation infrastructure (McCann and Shefer, 2004).Geographical space still has obvious effects and places restrictions on economic development (Wu Yuming, 2005, p. 58). Geographical spillover effects of economic growth are a “positive knowledge externality” and include global and local effects. In contrast to its local counterpart, global geographical spillover will not strengthen the clustering process or regional convergence. The relative powers of geographical spillover within and between regions dictate whether regional growth is balanced or not (Wu Yuming, 2005, p. 160). Economic analyses target these spatial characteristics and have led to some theoretical models, such as the social interaction model (Akerlof, 1997), the evolution of trading structures (Ioannides, 1990, 1997), proximity spillover effects (Durlauf, 1994; Glaeser, Sacerdote and Scheinkman, 1996), etc. Examples include analyses made by Arthur (1989), Krugman (1991a, 1991b, 1995, 1998), etc., of Marshall Externalities, economies of agglomeration and other spatial effects of spillover effects. In summary, the interdependence between the observed individuals in panel data plays an important role in the growth differentiation or convergence mechanism and must be emphasized in econometric research, whether such interdependence is from geographical space or inter-industry linkage effects. Unfortunately, such interdependence has been overlooked in most previous research on panel data categorized by geographical area and/or industry (Wu Yuming, 2005, p. 27-28). 4.5.2 How to Set the Interdependence Between Individuals in Panel Data It is easy to solve the issue of temporal autocorrelation in panel data models, whereas support from other theories and methods is needed to solve issues in the dimension of regional space, such as spatial autocorrelation effects and spatial non-uniformity. Standard econometric techniques usually cannot be used to deal with issues such as spatial autocorrelation and non-uniformity, which generally occur in geographic data sets. Back in the 1970s, Europe carried out spatial econometric research. As a branch of econometrics, spatial econometrics aims to research techniques for dealing with spatial autocorrelation and non-uniformity in cross-section and panel data. Spatial non-uniformity typically occurs along with spatial autocorrelation, especially on a single cross-section, where they may be identical to each other. Spatial non-uniformity is essentially the instability of spatial structures and can be expressed with a covariance matrix between observed units in the spatial distribution (Frees, 1995; Driscoll and Kraay, 1998). Relevant works indicate that there are three methods of constructing this

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covariance matrix: the spatial random process method, the direct expression method based on the covariance structure, and the non-parametric method (Anselin, 1999). Before explaining these three methods, it is necessary to introduce the concept of the spatial weighting matrix. The timestamps of time-series data provide a natural ordering and structure, whereas it is difficult to create the dependence between observed units or cross sections. Econometricians therefore have to rely on very strong parametric assumptions, such as “spatial contiguity” (or “spatial lag”), to measure the extents to which observed units in cross-section or panel data relate to each other. Every observed unit has its own neighbor set and its contiguity with each unit in this set is always measurable, according to the definition of the neighbor set (Cliff and Ord, 1973; Goodchild, 1986). The measurements of the spatial contiguity between every unit and the units in its neighbor set constitute a spatial contiguity matrix, that is, a spatial weighting matrix. Spatial weights can be selected from among measures such as geographical distance (Anselin, 1992), the structure of a social network (Doreian, 1980), economic distance (e.g., environmental or weather differences such as air quality; Case, Rosen and Hines, 1993), empirical flow matrices (Aten, 1996), etc. The interdependence between cross-section units can usually be estimated using time series-like parametric or non-parametric methods (Conley, 1999; Hall, Fisher and Holfman, 1994; Newey and West, 1987). Assume that the random distribution of a random variable at a series of points is a function of the spatial distance between points. The population that individuals belong to, in particular, can be assumed to belong to a low-dimensional Euclidean space, R2, where Individual i is at Si and the spatial distance between Si and Sj is wij. The correlation between yi and yj is a function of wij and has nothing to do with the absolute position of Si or Sj. The spatial lag of , where is Variable y at Unit i is expressed as: a spatial weighting matrix, wij is non-zero, non-random and exogenous, and . Like time-series analysis, spatial random processes fall under spatial autoregressive (SAR) and spatial moving average (SMA) processes, which are defined as follows:

SAR and SMA processes differ from temporal characteristics in that diagonal elements of the covariance matrix are not constant and that the y process has no constant covariance (Anselin, 1999). The spatial interdependence is directly expressed as follows:

where

and

are

decreasing

functions

on

distance,

that

is,

. In panel data models, non-parametric methods are commonly used to estimate elements of the spatial covariance matrix (Fiebig, 1999). Spatial linear models of panel data have a general form (Anselin, 1988) as follows:

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where z is an exogenous variable. This model

This satisfies

is also known as the spatial error model (SEM). If different assumptions are made for parameters and

,

several

common

SAR

models

can

be

obtained,

such

, as

. In practice, most spatial regression models use a single spatial weighting matrix. Higher-order models are theoretically possible, such as high-order SAR models (Blommestein, 1983, 1985), SAR models, SARMA models (Huang, 1984) and models that include both an SAR error process and a spatial lagged dependent variable (Case, 1992). Note that the weighting matrix W in a high-order model is unique and orthogonal and that all coefficients can be determined (Anselin and Bera, 1998). 4.5.3 Estimating and Checking the Interdependence between Individuals in Panel Data Models The presence of spatial effects markedly influences the estimation and checking of regression coefficients. Moreover, standard econometric techniques are no longer applicable owing to the bi-directionality or multi-directionality of spatial correlation. Consequently, characteristics of OLS estimation of models that have lagged dependent variables or serial residual correlation cannot be shifted directly into spatial models. Instead, it is usually necessary to employ MLE (Ord, 1975) and suitable non-linear optimization procedures to estimate regression coefficients or spatial parameters of spatial lag models (SLMs) and SEMs (Anselin, 1999; Ord and Getis, 2001), thereby formally merging spatial correlation into the joint probability density of observations. In the meantime, we can derive the Wald test or the asymptotic t-test on the basis of the estimated asymptotic variance matrix (Anselin, 1999) so as to check whether spatial correlation is caused by substantial correlation or error autocorrelation. Note that each of the aforementioned three spatial interdependent models contains Si as an independent term. Similar to the individual’s location effect term in panel data models, this is how interregional differences are expressed with regional dummy variables. When geographical properties of observed units are involved, traditional panel data models generally will set this effect but without including the following spatial interdependence term, since both of them essentially assume that regions are independent from each other. Nonetheless, a spatial correlation test is required to see whether regions are independent from each other or not. If this test indicates that they are not independent from each other, then relevant OLS estimates are unreliable. There are six common spatial autocorrelation test statistics in the relevant works, including Moran’s I, LMLAG, R-LMLAG, LMERR, R-LMERR, and LM. Moran’s I cannot be used for model selection; LMLAG and LMERR check SAR models and SEMs respectively; R-LMLAG and R-LMERR are their respective robust versions (Anselin and Rey, 1991). In real-world applications, people need to select from among the three models on the basis of spatial autocorrelation tests. The specific judgment rule is: if LMLAG>LMERR, R-LMLAG is significant and R-LMERR is not, then select an SAR model; otherwise, select an SEM (Anselin and Florax, 1995). If spatial interdependence exists, the MLE or IVM can be used to estimate SAR models, the LSM to estimate SMA models, and the MLE or GMM to estimate SEMs.

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4.6 Summary There is probably heteroscedasticity between individual behaviors and this poses a huge challenge for estimating and checking such behaviors. The panel data analysis method is one of the effective solutions to this challenge. According to correlations between the “characters” of the researched individuals and their observed variables, panel data implicitly contains all the possible combined models of REMs and FEMs. Current mainstream panel data analysis methods are mostly developed for “standard panels” with large N and small T. But there is still great potential outside these “standard” panel data analysis methods so as to meet diverse real-world needs. Panel data analysis methods have a lot of advantages and, of course, disadvantages when compared with the cross-section data method or the time-series analysis method. Dynamic models of panel data are a method with extensive potential applications, as they enable effective analysis of characteristics such as coefficients varying with time, the time when structural break points of parameters occur being unknown, whether independent variables are predetermined or endogenous, and whether individual effects are correlated with independent variables. Among methods for statistical inference, the GMM is the one most suitable for dynamic panel data analysis. With the relation between initial conditions and the error term being the precondition, the significance of GMM estimates relates to the selected number of moment conditions. Moreover, GMM estimates are very sensitive to how dynamic models of panel data are set and what moment conditions are selected. Consistent model and moment selection criteria (MMSC) and a downward testing procedure assure that the right model and moment conditions are selected. MMSC are criteria based on the J statistic and resemble a (negative) logarithmic likelihood function. One of these criteria aims to balance the numbers of parameters and of moment conditions. After studying widely used model selection criteria such as BIC, AIC and HQIC, we found that MMSC-BIC and the downward testing procedure work better than do MMSC-AIC and MMSC-HQIC. The common interdependence between individuals in panel data plays an important role in their behavioral mechanism, whether such interdependence is from geographical space or inter-industry linkage effects. It can be measured by constructing a covariance matrix among individual units. With regard to the spatial autocorrelation among individual units in panel data, we may set up a spatial weighting matrix and employ the SAR, SMA and non-parametric methods to construct a covariance matrix among individual units. Most spatial regression models use a single spatial weighting matrix to express a particular interdependence among individuals. We may select a suitable statistic from among Moran’s I, LMLAG, R-LMLAG, LMERR, R-LMERR, LM, etc., to check spatial autocorrelation while employing the MLE, IVM, LSM, or GMM for estimation. Stata statistical software deals with linear dynamic panel techniques using statements mainly including xtabond, xtdpdsys, and xtdpd, all of which apply to analyzing panel data with large N and small T. Both xtabond and xtdpdsys are suitable for panel data models in which there is no autocorrelation in a series of idiosyncratic errors, but the xtabond estimator produces a poorer result than does xtdpdsys if the autoregression coefficient or the ratio of the covariance of panel-level effects to that of idiosyncratic errors is too large. And xtdpd is suitable for panel data models in which there may be low-order moving average correlation in a series of idiosyncratic errors or in which predetermined variables have a structure more complex than those for which xtabond and xtdpdsys are suitable.

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Chapter 5 Empirical Analysis of the Effects of FDI on the Income of Corporate Employees in China With relevant theories explained in Chapter 3 and empirical methods prepared in Chapter 4, we will begin empirical analysis in this chapter. Empirical analysis is divided into “income (wage),” “employment,” and “income distribution,” which are the contents of this chapter. This chapter aims to make empirical analysis of the effects of FDI on the income level in the host country – China. It starts with an overview of relevant works, before setting an empirical model, explaining variables and relevant data issues, and drawing conclusions.

5.1 Overview of the Effects of FDI on the Income of Corporate Employees in the Host Country Chapter 2 already outlines how FDI affects the income of corporate employees in the host country. This section provides further details on this foundation. Since FDI is identical to domestic capital from the perspective of capital itself, it will necessarily increase the total amount of capital in the host country after it enters this country. This will lead to a higher ratio of this total amount to the total number of laborers in the host country and, ultimately, a lower ROIC and a higher average wage rate among its laborers. Mundell (1957) and Bhandari (2005) explored this issue. In this sense, foreign and domestic capital should have the same effects on labor remuneration and ROIC. But this trend is no more than theoretical reasoning. In empirical research, this trend may rely on some realities. First, this trend may be only a short-term phenomenon and may not be obvious in the medium and long terms (Rama, 2003). Second, it also depends on whether the host country’s market is distorted or not, whether FIEs receive long-term, better-than-domestic treatment or not, whether the government properly protects the interests of FIE employees or not, etc. FDI differs from domestic capital in terms of the integrated human capital, knowledge, technology and managerial experience, and this may lead to a higher wage rate in FIEs than in DIEs. If FDI flows into the host country together with the transfer of more technology and knowledge, as is discussed in Chapter 3, it will increase demand for excellent local human capital and thus its income, on the one hand, and decrease demand for unskilled labor and thus its income, on the other. By comparison, an equal increase in domestic capital invested in the host country, even if it is identical to FDI in terms of all the other relevant properties, will add less to demand for excellent laborers and more to demand for non-excellent laborers because it is technologically inferior to FDI. The two types of capital have significantly different effects on the wages of different laborers. In this sense, foreign capital should have a greater effect on labor remuneration and ROIC than does domestic capital. FDI also affects differences in wage rates between FIEs and DIEs because of the heterogeneity between them. FIEs are the primary form in which FDI organizes production, as is discussed in Chapter 1. First, all enterprises in the host country are divided into two populations – FIEs and DIEs. The distributions of the first population in terms of basic properties such as location of headquarters, years in business, locations, business size, and industry are unlikely to be identical to

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those of the otherķ. Second, from the perspective of individual enterprises in the two populations, FIEs may differ from DIEs – even if they are identical to each other in the aforementioned basic aspects – in terms of individual properties such as capital intensity, R&D intensity, the structure of intermediate inputs (Lipsey and Sjoholm, 2001, 2004), the age distribution of employees, education levels, the ratio of trade-union members to all employees, the ratio of female employees, occupational structure, and the ratio of skilled employees to unskilled ones. Differences in any of these properties between FIEs and DIEs may affect differences in wage rate between them. FDI also affects wage rates at DIEs through FIEs’ wage-rate spillover effects (Lipsey, 2002). There are two mechanisms with respect to wage-rate spillover, according to the overview in Chapter 2. First, FIEs attract excellent employees of DIEs through higher wage rates than those of DIEs and force them to increase their wage rates. Second, FIEs increase aggregate demand for the labor force of the host country by expanding in the country and, thus, lead to a higher average wage rate across the country. The two mechanisms reflect changes in the structure and total volume of demand in the host country’s labor market respectively. The strength of the wage-rate spillover effects of FIEs mainly depends on their inter-industry linkage and geographical proximity effects, of which the former includes vertical and horizontal linkage (UNCTAD, 1994: p. 192, Table IV.12). FIEs’ vertical inter-industry linkage refers to the input or output technical relationships between FIEs and the downstream or upstream enterprises across their industry chains. Horizontal inter-industry linkage refers to the input or output technical relationships between FIEs and enterprises across production chains inside or outside the same sector/industry. For every enterprise that has inter-industry linkage with a particular FIE, its wage ķ From the perspective of the secondary sector, for example, FIE employees have a higher average wage/salary than that of their DIE counterparts. In 2004, the average labor remuneration in this sector was 12,910 yuan. Specifically, enterprises invested by Hong Kong, Macao, and Taiwanese companies, FIEs and DIEs saw average labor remuneration of 13,615, 17,920, and 12,157 yuan, respectively, of which the former two were 12% and 47.4% higher than the latter. Among more specific categories of enterprises, enterprises wholly owned by Hong Kong, Macao, and Taiwanese companies, enterprises wholly owned by foreign companies, SOEs and private enterprises are more important ones. In 2004, these four types of enterprises in the secondary sector saw average labor remuneration at 12,714, 17,249, 17,986 and 9,310 yuan, respectively. SOEs took the first place, whereas enterprises wholly owned by foreign companies, enterprises wholly owned by Hong Kong, Macao, and Taiwanese companies and private enterprises were 96%, 71%, and 52% as high as SOEs, respectively. This conclusion also holds in the case of enterprises above a given size in China’s secondary sector. As a result, it is not that FIEs generally have higher average labor remuneration per capita than that of DIEs. We can also see from the above-listed data that even within the group of FIEs, this average also varies with the sources of FDI. In 2004, the average labor remuneration per capita at enterprises invested by Hong Kong, Macao, and Taiwanese companies was 26% lower than that of FIEs. This difference had great effects on the overall income gap in China since Hong Kong, Macao, and Taiwanese companies accounted for 37.4% of the total FDI in this country in 2004. In industries above a given size in China’s secondary sector (“major industries” for short) in that year, small-, medium and large-sized enterprises invested by foreign, Hong Kong, Macao, and Taiwanese companies (“FIEs” for short) were higher than private enterprises in terms of average wage/salary and benefits per capita, whereas they were 0.95, 0.94 and 1.08 times as high as their state-controlled counterparts, respectively. In other words, only small-sized FIEs were higher than small-sized state-controlled enterprises. Among forty major industries, there were twenty-seven industries where FIEs were higher than state-controlled enterprises in terms of average labor remuneration per capita and thirty-seven industries where FIEs were higher than the entirety of large industries in terms of the same measure. From the perspective of major industries by province (municipality and autonomous region), FIEs were higher than private enterprises in terms of average wage/salary and benefits per capita in all provinces, municipalities, and autonomous regions, but were higher than state-controlled enterprises in only eight provinces, municipalities, and autonomous regions. Within the group of FIEs, enterprises invested by foreign companies were higher than those invested by Hong Kong, Macao, and Taiwanese companies in terms of average wage/salary and benefits per capita in twenty-four provinces, municipalities, and autonomous regions.

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rate will be pulled by that of the FIE if it is upstream relative to the industry where this FIE is, pushed by that of this FIE if it is downstream relative to the industry where this FIE is, and affected by competition with the FIE if it is in the same industry. Such pull, push, or competitive effects are all manifestations of the FIE’s wage-rate spillover effects. The inter-industry input/output linkage discussed in this context is based on the average level of linkage among all enterprises in the host country. But FIEs are unique in that they rely more heavily on the rest of the world than do DIEs in terms of input or output. In other words, larger shares of FIEs’ input factors and output are imported and exported, respectively. To carry out empirical research on the wage-rate effects of FIEs, therefore, we must combine the correlation between their industries and other industries with their reliance on the rest of the world. Geographical proximity effects are also one of the channels for FIEs’ wage-rate spillover effects. Traditional economic theories usually assume that regions are independent from each other in terms of economic activities. But this is obviously not in line with reality according to theories of spatial or regional economics. In reality, enterprises that are economically correlated with each other will usually form industry clusters in a particular region, such as the Yangtze River Delta, Pearl River Delta, Bohai Sea Rim, and Chengdu-Chongqing economic zones in China, Ruhr Industrial Region in Germany, or the Great Lakes economic zone and the Silicon Valley in the United States. These economic zones typically span a number of administrative divisions, which are closely tied and frequently interact with each other in terms of economic activities, as opposed to much weaker connections with other administrative divisions outside the economic zone. Contemporary theoretical economic models typically research how interactions between agents lead to behaviors of groups and how these behaviors constitute the aggregated behavior of groups. These models have received much attention recently and are reflected in the development of some theoretical frameworks; they are usually used to explain peer effects, neighborhood effects, spatial spillovers and other similar effects (Anselin, 2006). Physical and virtual distances have had much weaker effects on economic and social development owing to the continuous development of modern transportation/information/communications technologies, but they still have obvious effects on the movement of human resources and production materials as well as the production and warehousing of tangible products. Accordingly, research involving interregional interactions must address regional interdependence.

5.2 The Effects of FDI on Employee Income: Empirical Models, Variables, and Data 5.2.1 Deduction of Empirical Models Given a lack of consumer information, we assume that consumers are also labor suppliers in the process of production at enterprises and that these laborers are homogeneous in terms of age, physical conditions, education levels, humanistic qualities, as well as skill structure and proficiency. Accordingly, we need only to set a model from the perspective of the producer’s behavior. Assume that the producer has typical characteristic as follows: (5.1) where Y, A, K, L and H denote the total output, total factor productivity (TFP), capital stock, raw labor, and human capital respectively. Given a lack of relevant data, L is not discriminated by the level of skill in this context, but will be case-studied in subsequent parts of this book. Let the

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function F be in the Cobb-Douglas form, then: (5.2) K comprises domestic capital, Kd, and foreign capital, Kf. Since the vast majority of foreign capital in China in the sample period is FDI, Kf is approximated by the capital formed by FDI stock in this context. Let Kd and Kf be in the total capital stock as follows: (5.3) where μ and 1 – μ are the ratios of domestic and foreign capital to K, respectively. Assume that the TFP, A, is decided by openness, x, and foreign capital and that A has a natural growth rate, δ0, in every time frame: (5.4) where the openness, x, of the real sector equals EX/Y. Human capital, H, is decided by domestic and foreign capital and is in the following form: (5.5) where γ is the ratio of foreign capital to the human capital stock. Substitute Equations (5.3), (5.4) and (5.5), which define K, H and A, respectively, into Equation (5.2) and we have: (5.6) Transform the equation into the following form: (5.7) The equation of the producer’s cost is: (5.8) where B is the producer’s cost, and w, r, and r* are the wage rate, return on domestic capital, and return on foreign capital respectively. The equation of the producer’s profit is: П=Y – B (5.9) Given the maximization of the producer’s profit, we have: (5.10) Transform it into the following equation:

(5.11)

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where

,

,

, and

.

This model is set such that it becomes linearly additive after logarithmic transformation. Accordingly, models in the following texts are all in the form of log-linear functions unless otherwise stated. When using this empirical model for panel data, it is necessary to add a panel dimension identifier to each variable: (5.12) where the superscripts and subscripts have the following meanings: p denotes regions (six-digit codes for county-level cities); q denotes industries (three-digit codes); j denotes business size; i = d,f, that is, the registered type of an enterprise. To simplify the expression, the time dimension, t, is not added. When i is d or f, the aforementioned equation is:

The values of the aforementioned variables of sample enterprises are shown in Table 5-6. 5.2.2 Variables When using Empirical Equation (5.12) for microscopic panel data, it is necessary to add control variables into the corresponding empirical model. Control variables are explaining variables – in addition to the explaining and explained variables of the cause-and-effect relationship set in the theoretical model – which affect the explained variables. In other words, control variables are explaining variables but are not those set in the theoretical model. These variables are set in strict accordance with a relevant overview in Chapter 2, the theoretical model described in Chapter 3, and the overview in this chapter. (1) The wage-rate spillover variable To allow for possible wage-rate spillover effects between FIEs and DIEs (such effects may be greater than, equal to, or smaller than zero), we must add corresponding wage-rate control variables. When the explained variable on the left side of the equation is the wage rate of DIEs (or FIEs), it is necessary to add the wage-rate variable of FIEs (or DIEs) to its right side. Such spillover effects may exist between DIEs and FIEs inside or outside a particular industry or inside or outside the same region. The average wage rates, average labor remuneration, and employment shares of DIEs and FIEs in Industry q in Region p are

and

, respectively;

The average wage rates, average labor remuneration, and employment shares of DIEs and FIEs outside Industry q in Region p are

and

, respectively;

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The average wage rates, average labor remuneration, and employment shares of DIEs and FIEs in Industry q are

and

respectively;

The average wage rates, average labor remuneration, and employment shares of DIEs and FIEs in industries other than Industry q are

and

, respectively;

The average wage rates, average labor remuneration, and employment shares of DIEs and FIEs in Industry q outside Region p are

and

respectively;

The average wage rates, average labor remuneration, and employment shares of DIEs and FIEs outside Industry q outside Region p are

and

,

respectively. Panels described in this book have four qualitative dimensions, including region, industry, ownership type, and size. These dimensions themselves are control variables, which are denoted as ap, aq, ai, and aj, respectively. The wage rates, average labor remuneration, and FIE employment shares of wholly foreign-invested enterprises and foreign-invested joint-stock companies (the registered type of enterprise is 230, 240, 330, or 340 and is denoted as

) in Industry q are

and

,

respectively; The wage rates, average labor remuneration, and FIE employment shares of medium- and large-sized enterprises (the registered type of business size is 1 or 2 and is denoted as ) are and

, respectively.

(2) Control Variables Based on Geographical Proximity Effects Another dimension in this panel data is region. Like industries, regions are not independent from one another. Relations between regions are usually measured with spatial regression models, which fall under SLMs and SEMs. After being conceptualized, SLMs are usually in the empirical form of “strategic interactions” equilibrium solutions, that is, a spatial reaction function. Strategic interactions come with two different behavioral mechanisms – spillover and resource flow. The ultimate reaction function is the same regardless of the mechanism. SLMs make great sense in terms of economic mechanism, but they cannot discriminate the two mechanisms for themselves. Unlike SLMs, SEMs are not based on economic theoretical models, but are built to deal with data issues. In other words, correlation is caused by the cross-section property of data, not necessarily by the “spatial” property of models. In practice, therefore, error autocorrelation is more likely to occur than lagged correlation regardless of spatial or non-spatial models. In a great many cross-section data analyses, a particular statistical process to be researched has a spatial dimension that does not match the spatial unit of specific observed units. This will necessarily cause systematic changes in measurement errors in the entire space. In addition, integrated data (observations on different spatial dimensions are merged) and spatial interpolation are very likely to cause patterns of spatial correlation. These issues have yet to be further researched in spatial

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econometrics. Simultaneity and endogeneity derived from spatial correlation have brought about a number of identification problems, of which the “reaction problem” is best known – i.e., parameters of social/spatial interaction modelsķ can only be identified under strict conditions. In SLMs, this problem can be avoided if an SWM is properly set. A lot of discussions on social interactions employ the individual agent model (IAM), but carry out estimation using the spatial aggregation of individual observations. This will cause “ecological fallacy” or “cross-level bias problem,” which believes “individuals necessarily have behavioral characteristics of the population.” When individual and contextual characteristics are included in the same regression model, contextual estimation does not take into account the separate identification of individual effects and exogenous contextual effects. Moreover, it is impossible to systematically estimate individual-level SLM coefficients in a spatial aggregate lag model of individuals. We should therefore be very careful when setting and explaining spatial and social interaction models. A spatial random process model (SRPM) is one of the means of dealing with space-relevant cases. The spatial random process, or the spatial random domain, is a set of random variables, which are marked by regions. A set of spatial regions is either a continuous surface or a finite set of discrete regions. Setting the spatial process of the regression error term will cause a certain covariance structure or pattern of spatial autocorrelation. A critical issue for such setting is determining the neighborhood structure, that is, how to formally express regions in the spatially lagged term on the right side of the regression equation. This geographical connection structure is denoted by an SWM. An SWM is an n x n positive matrix W, which sets a “neighborhood set” for every observation. A region (i.e., an observation) appears in a row and a column of the matrix simultaneously; an element of the matrix, wij, denotes the neighborhood relationship of Regions i (a row) and j (a column); when i ≠j, wij> 0; when i = j, wij = wii = wjj = 0, that is, the neighborhood value of a region itself does not exist. A weighting matrix is usually in a row-standardized form, that is, . After the rows of the matrix are standardized, the weights are interpreted as an average of the neighborhood values, that is, the so-called “spatial lag operator.” In spatial econometric regression models, spatial lag operators are set in multiple forms, such as Wy, WX, and Wε. There has yet to be a formal opinion on how to select “right” spatial weights to set a model. The classical definition of neighborhood is based on geographical standards such as two polygons having a common edge (i.e., adjacency) or common points within a critical distance interval. The other geographical standards include a combination of adjacency and edge lengths or the k-nearest neighbors (k-NN). Spatial weights are of course not necessarily confined to geographical standards, as they may also be based on other standards such as social networks or “economies.” The spatial weights constructed in this book are based on road travel distances among cities. The average wage rate and average labor remuneration based on road travel distances between the other regions s (the source) and p (the destination) are:

ķ Manski (1993) considered three social interaction models, including endogenous effects (interactions among individual agents), contextual effects, and correlated effects (they exist between observed and unobserved characteristics, which are shared by individuals).

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and denotes the destination [ where s = 1, 2, 3, … and denotes the source; p = 1, 2, 3, … , is the number of Regions j between which and Region i the road travel distance is greater than 1; (i = 1, 2, 3, …) are very likely to differ from each other]; M is the distance. is the weighted average of the wage rates in cities other than City i, and the weights are the coefficients of distances between these cities and City i, that is, the values in the bigger parentheses of the aforementioned equations. These coefficients indicate that all the other cities have effects on the wage rate in City i and that cities with shorter road travel distances to City i have a greater effect on its wage rate, that is, they have greater weights in this average wage rate. Also, as discussed above, the ratio of intermediate inputs is added as a control variable:

IIN, VAD, and VAT are intermediate inputs, value added, and value-added tax payable, respectively. 5.2.3 The Empirical Model Added with Control Variables Added with these control variables, the empirical model changes into: (1) Let units in dimensions such as region, industry, ownership type, and business size be independent from each other:

(2) Let regions be not independent from each other:

(3) Assume that industries are not independent from each other in terms of the wage rate (or average labor remuneration) and that the strengths of their mutual effects depend on the extent to which they are technologically correlated with each other (the higher the extent, the greater the effects).Industries with higher wage rates (or average labor remuneration) will cause other industries with lower wage rates (or average labor remuneration) to increase their respective wage rates, whereas the latter industries in turn will cause the former to decrease their wage rates:

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(4) Let foreign capital’s spillover effects inside and outside industries be confined within the region:

(5) Let foreign capital’s spillover effects inside and outside industries exist inside and outside the region:

(6) Let wholly foreign-invested enterprises and foreign-invested joint-stock companies inside and outside the region have spillover effects inside and outside industries:

(7) Let medium- and large-sized FIEs inside and outside the region have spillover effects inside and outside industries:

(8) Let spillover effects exist across China regardless of industry, region, ownership type or business size:

Note that for the purpose of simplification, none of the above-listed equations of the model marks the time property of the variables (the subscript is t or t-n, n = 1, 2, …), but this does not mean that the corresponding empirical model does not take into account their temporal characteristics. For the calculation of relevant variables, please refer to Table 5-4.

5.3 Empirical Conclusions The effects of foreign capital on the average wage rate/labor remuneration of corporate employees will be analyzed and summarized in terms of employment, wage-rate spillover, and other indicators. To present estimation results intuitively, GMM estimation results are not separately listed in this book unless it is necessary to do so. Instead, these results are visualized together with all other results when the former are apparently consistent with the latter.

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5.3.1 The Effects of Employment on the Average Wage Rate/Labor Remuneration of Corporate Employees In addition to effects of employment in an enterprise itself, the average wage rate in this enterprise is affected by employment in other enterprises (including DIEs and FIEs) inside and outside its industry and region. (1) Effects of Employment within FIEs The growth of employment in FIEs or DIEs (F05) will necessarily cause a decrease in the average income per employee. Fig. 5-1 shows that among fifty-six estimates about the wage rate or labor remuneration, forty-eight come with a statistically significant coefficient of F05 and include forty-five negative estimates. These estimates are distributed between -0.13565 and 0.00735, with a simple arithmetical average of -0.0487, a standard deviation of 0.0482, and a dispersion coefficient of -1.0096. These estimation results demonstrate the economic theory that there is a quantitatively inverse relationship between wage rate and employment as two variables.

Fig. 5-1 Effects of Employment in an Enterprise (F05) on Its Average Wage Rate or Labor Remuneration: the Distribution of Significant Estimates (2) Effects of Employment in Other Enterprises on the Level of Income in FIEs The level of income in an enterprise is necessarily affected by employment in other enterprises inside and outside its industry and region, including FIEs and DIEs. The empirical results are as shown in Fig. 5-2. The average wage rate among enterprises in a particular industry in a region may not be affected by the expansion of local DIEs and FIEs in the same industry, but be pushed upward by that of DIEs and FIEs in other industries in other regions (F28) and downward by that of local DIEs and FIEs in other industries (F25) and of DIEs and FIEs in the same industry in other regions (F31). All the significant estimates of the coefficient of Variable F28 are positive; most of the significant estimates of the coefficient of F25 are negative; F31 is less significant and its only significant estimate is negative; F34 is totally insignificant in relevant equations.

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Fig. 5-2 Effects of Employment in DIEs and FIEs on the Average Wage Rate/Labor Remuneration among Enterprises in a Particular Industry in a Region: the Distribution of Significant Estimates Note: F13 is employment in local FIEs in the same industry; F16 is employment in non-local DIEs in other industries; F19 is employment in non-local DIEs in the same industry; F22 is employment in local DIEs in the same industry; F25 is employment in local FIEs in other industries; F28 is employment in non-local DIEs and FIEs in other industries; F31 is employment in non-local FIEs in the same industry.

The pull of FIEs with respect to the wage rate in a particular industry in a region is partially from the expansion of local medium- and large-sized FIEs in other industries (F37). In contrast, the increase of the same wage rate will be suppressed by the expansion of local medium- and large-sized FIEs (F46) or wholly foreign-invested companies and foreign-invested joint-stock companies in the same industry (F58) and wholly foreign-invested companies and foreign-invested joint-stock companies in other industries (F49), as well as that of non-local medium- and large-sized FIEs (F43) or wholly foreign-invested companies and foreign-invested joint-stock companies in the same industry (F55) and medium- and large-sized FIEs in other industries (F40). All the significant estimates of these variables are negative in the relevant equations. (2) Income Effects of Employment in FIEs: Comparison with the Same Effects of DIEs Effects of employment in DIEs on the average wage rate/labor remuneration among enterprises in a particular industry in a region can be divided into positive and negative effects. Regarding the positive effects, the expansion (i.e., employment growth) of local DIEs in the same industry (F22) will promote growth of the average wage rate or labor remuneration among enterprises in this industry. Regarding the negative effects, the expansion of local DIEs in other industries (F13), as well as non-local DIEs in other industries (F16) and DIEs in the same industry (F19), will decrease the average wage rate or labor remuneration among enterprises in this industry in this region. This will offset the positive effects of F22 to a certain extent. As is shown in Fig. 5-2, all significant estimates of the coefficient of Variable F22 are positive; all ten significant estimates of F13 and five significant estimates of F16 are negative; seven significant estimates of the coefficient of F19 are positive or negative, but with a negative simple arithmetic average. This indicates that there is a more competitive than complementary relationship between employment in other industries, whether local or not, or in the same industry in other regions, and income in that industry in that

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region. Quantitatively, such competition mainly occurs between that industry in that region and other industries regardless of local or not ( or or ). With regard to the effects of the expansion of FIEs in different scopes on the wage rate in a particular industry in a region, statistically significant estimates obtained with most methods of most set equations are much fewer than those of variables with respect to DIEs of the same type, as is shown in Fig. 5-2. From the perspective of the simple arithmetic averages of all the significant estimates of variables in the relevant equations, the expansion of non-local FIEs in other industries (F28) will bring a significant increase (the coefficient of F28 is 0.37232) in the wage rate in that industry in that region, but that of non-local DIEs in other industries (F16) will cause an even more significant decrease (the simple arithmetic average of its coefficient is -0.53705) in the same wage rate. We may roughly say that the effects of employment in FIEs, regardless of their statistical types, on the wage rate in a particular industry in a region are of rather limited statistical significance and that its positive effects, if any, are offset by the negative ones caused by competition among DIEs. 5.3.2 The Wage-Rate Spillover Effects of FIEs The wage-rate or labor-remuneration spillover effects between neighboring regions are the most important factor for an increase in the wage rate or labor remuneration in a region. Estimation results indicate that the weighted wage rate and labor remuneration in the neighboring region or upstream industry (F71-F74) are all statistically significant, that estimates of their effects are all positive, and that they are the maximal effects among estimates of the effects of all variables. The simple arithmetic averages of significant estimates of F71-F74 are 0.71131, 0.6414, 0.77473 and 0.73254, respectively. Other empirical models focus on examining the wage-rate/labor-remuneration spillover effects between correlated enterprises in the industry. (1) The wage rate or labor remuneration in FIEs has positive spillover effects, which are statistically significant inside and outside the industry in the region, with the intra-industry ones being at least three times as significant as the inter-industry ones. Interregional and inter-industry positive spillover effects may also exist. Among the eight estimation results in Models IV and V, the wage rates in local FIEs in other industries (F23) and local FIEs in the same industry (F32) both have significant and positive coefficients. This shows that the wage rates in FIEs, whether they are in this industry or not, always have positive spillover effects in this region. None of the four types of estimates of the interregional and inter-industry spillover effects of the wage rates in FIEs (F26) in Model IV is significant, whereas all four types of estimates in Model V are significant and the coefficients are positive. (F24) and (F33) both have estimation results in Model IV that are similar to the aforementioned ones. (2)The wage-rate or labor-remuneration spillover effects of FIEs on enterprises in a particular industry in a region are mostly from local wholly-owned foreign-invested companies and foreign-invested, joint-stock companies in the same industry as well as non-local companies in other industries, whereas such effects from local companies in other industries and non-local companies in the same industry are much weaker.

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Fig. 5-3 Effects of Wage-Rate Spillover (Half) and Employment (Right) on the Wage Rate: the Distribution of Significant Estimates Note: F11 is the average wage rate among local DIEs in other industries; F13 is the ratio of local DIEs to employment in other industries; F14 is the average wage rate among non-local DIEs in other industries; F17 is the average wage rate among non-local DIEs in the same industry; F19 is the ratio of non-local DIEs to employment in the same industry; F20 is the average wage rate among local DIEs in the same industry; F22 is the ratio of local DIEs to employment in the same industry; F23 is the average wage rate among local FIEs in other industries; F25 is the average wage rate among local FIEs in other industries; F26 is the average wage rate among non-local FIEs in other industries; F28 is the ratio of non-local FIEs to employment in other industries; F31 is the ratio of non-local FIEs to employment in the same industry; F32 is the average wage rate among local FIEs in the same industry.

The wage rates in local wholly foreign-invested companies and foreign-invested joint-stock companies in other industries (F47), those in non-local wholly foreign-invested companies and foreign-invested joint-stock companies in other industries (F50), and those in local wholly foreign-invested companies and foreign-invested joint-stock companies in the same industry (F56) are all significant variables under all four estimation methods in Model VI. Among them, F56 and F47 have the largest and smallest coefficients, respectively. The wage rates in non-local wholly foreign-invested companies and foreign-invested joint-stock companies in the same industry (F53) have significant estimates only under the MLE method, but their values are smaller than those of F47. With regard to the estimation of labor remuneration (F02), the weighted average labor remuneration inside and outside the industry in the region (F57 and F48) as well as the weighted average labor remuneration inside and outside the industry outside the region (F54 and F51) have results similar to those mentioned previously. (3)The wage-rate or labor-remuneration spillover effects of medium- and large-sized FIEs will push the increase of the average wage rate or labor remuneration among enterprises in the same industry, but will go against its increase among FIEs in the same industry in other regions. Their spillover effects on wage rates or labor remuneration in local FIEs in the same industry are not necessarily that significant and important. Among the wage rates in local medium- and large-sized FIEs in all industries (F35), those in all industries in other regions (F38), those in non-local medium- and large-sized FIEs in the same industry (F41), and those in local medium- and large-sized FIEs in the same industry (F44), F41 and F44 always have significant estimates under all four estimation methods in Model VII. F35 and F38 have significant estimates only under GLS, RE and MLE. F44, F41 and F35 have positive effects, whereas F38 has negative effects. Similar estimation with labor remuneration being the dependent variable produces similar results.

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5.3.3 Effects of Other Factors on the Average Income of Corporate Employees (1) Effects of Capital Changes on the Average Wage Rate Among Enterprises a) Investment increases by enterprises themselves (F03, F04) will fuel the increase of the average wage rate among enterprises, and foreign investment has a greater effect than domestic investment does. Foreign capital (per capita) has a greater effect than does local capital (per capita) in terms of pushing the increase of the wage rate or labor remuneration. An increase in unit investment will push the wage rate upward and foreign capital (F04) has a greater effect than local capital (F03) does, as is shown in the upper left quarter of Fig. 5-4. There are seven model settings for the wage rate or labor remuneration, all the 54 estimation results of these settings under GLSFE, GLSRE, MLE and A Bond are highly consistent with exception to those of the first setting under GLSFE: F03 and F04 both have positive coefficients, of which the one of F04 is always greater than that of F03. b) The Position of Foreign Capital in Corporate Capital Stock (F09) As foreign capital has a rising position in corporate capital stock (F09), the income gap measured by wage rate or labor remuneration will narrow. Forty-six out of fifty-six estimation results of F09 are statistically significant and all forty-six coefficients are negative, according to the lower right quarter of Fig. 5-4. Since the position of local capital in corporate capital stock (F08) always has a negative correlation with F09 (F08 = 1 – F09), the higher the ratio of local capital to corporate capital stock, the higher the wage rate or labor remuneration in enterprises.

Fig. 5-4 Effects of Capital and Tendency toward Processing/Trade on the Average Wage Rate/Labor Remuneration among Enterprises: the Distribution of Significant Estimates Note: F03 and F04 are the domestic and foreign capital per capita (yuan/person) in enterprises, respectively; F06 is the ratio of intermediate inputs to enterprises’ total output; F07 is the ratio of exports to enterprises’ total product; F09 is the ratio of foreign capital (including capital from Hong Kong, Macao and Taiwan) to corporate capital stock; F10 is the ratio of the places where enterprises are based to the total employment in a particular industry.

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(2) Effects of Tendency toward Processing/Trade on the Average Wage Rate/Labor Remuneration among Enterprises a) The higher FIEs’ tendency toward processing, the lower the wage rate or labor remuneration. F06 is the ratio of intermediate inputs to enterprises’ total output. Many of the FIEs in China’s manufacturing industry specialize in production as part of the processing trade, with a very high ratio of intermediate inputs to the total product. With regard to F06 estimates, fifty-three out of fifty-six estimation results pertaining to wage rate or labor remuneration are statistically significant and negative (as is shown in the lower left quarter of Fig. 5-4). We can infer that the higher FIEs’ tendency toward the processing business, the more likely they are to narrow the income gap. Overall, the lower DIEs’ tendency toward processing is than FIEs, the higher their wage rates or labor remuneration. b) The effects of tendency toward international trade on the wage rate are unclear. Despite higher tendency toward exporting products (F07), FIEs’ effects on the income (wage rate or labor remuneration) gap among laborers are unclear. With regard to the coefficient of F07, thirty-two out of fifty-six estimation results pertaining to wage rate or labor remuneration are statistically significant, including fifteen positive ones and seventeen negative ones; the remaining twenty-four estimates are not statistically significant, as is shown in the lower left quarter of Fig. 5-4. c) The higher the concentration of a particular industry in a particular region, the easier to push the wage rate or labor remuneration upward in this industry in this region. Regarding the concentration of an industry in a region (F10), thirty-two out of fifty-six estimates in seven set models with fourteen equations are significant, including twenty-nine positive ones; F10-relevant estimates in Models I (Equation 1), V (Equation 5) and VI (Equation 6) are all significant, as is shown in the upper right quarter of Fig. 5-4. Positive coefficient point estimates are distributed in [0.13502, 2.15554] and, among these, F10 estimates obtained with xtabond are all statistically significant and greater than one.

5.4 Summary In this chapter, we set the wage rate and employment behavior models of enterprises on the basis of the overview in Chapter 2 and the inference result obtained from a theoretical model in Chapter 3. And we employ dynamic panel data analysis to simulate the effects of FDI on the average wage rate and labor remuneration among enterprises. The results show that the average wage rate of corporate employees will decrease as enterprises increase the headcount and that it will also receive “spill-in” effects from employment changes in other enterprises. We use an empirical model to simulate such “spill-in” effects produced by employment in FIEs and DIEs, respectively, before finding out that inside and outside the industry and region where an enterprise is located, FIEs may have positive or negative spillover effects on the wage rate of this enterprise. Among them, FIEs with positive spillover effects are from other industries outside the region (but medium- and large-sized FIEs have negative effects), while FIEs with negative spillover effects are from other industries in the region (but medium- and large-sized FIEs have positive effects) and non-local FIEs in the same industry (including medium- and large-sized FIEs).Positive spillover effects of employment in DIEs are from local enterprises in the same industry, while negative ones are from local enterprises in other industries and non-local enterprises inside and outside the industry. Overall, the effects of employment in FIEs, regardless of their statistical types, on the wage rate in a

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particular industry in a region are of rather limited statistical significance, and their positive effects, if any, are offset by the negative ones caused by competition among DIEs. The average wage rate of corporate employees is also subject to the wage-rate spillover effects of other enterprises. Such effects are from the weighted wage rate in neighboring regions or upstream industries (including interregional and inter-industry spillover, which is positive) and FIEs in the same region (the spillover is always positive whether local FIEs are in the same industry or not, but the intra-industry spillover effects are at least three-times as strong as the inter-industry ones). In addition, both investment increases by enterprises themselves and higher industry concentration in a region will increase the average wage rate of enterprises. Both a higher position of foreign capital in corporate capital stock and a higher ratio of intermediate inputs in enterprises will decrease the same wage rate. Table 5-1 Labor Remuneration per Capita among Enterprises in the Secondary Sector by Registered Type, 2004

12,910 13,615

Wage/Salary & Benefits per Capita in Industries Above a Given Size 16,561 15,458

14,894 13,632 12,714 22,292 17,920

16,685 15,475 14,389 26,612 20,698

Labor Remuneration Capita in All Industries Average Enterprises Invested by Hong Kong, Macao and Taiwanese companies Joint Ventures Cooperative Businesses Solely-owned Businesses Companies Limited by Shares Enterprises Invested by Foreign Companies Joint Ventures Cooperative Businesses Solely-owned Businesses Companies Limited by Shares DIEs SOEs Collective Enterprises Joint-stock Companies Associated Enterprises State-owned Associated Collective Associated State-owned & Collective Associated Others Limited Liability Companies Solely State-owned Others Companies Limited by Shares Private Enterprises Solely-owned Cooperative Limited Liability Companies Companies Limited by Shares Others Source: calculations based on Tables 1-A-1 through Economic Census Yearbook 2004.

per

18,334 21,342 16,799 19,439 17,249 19,733 26,528 30,881 12,157 16,030 17,986 20,639 9,103 11,882 10,022 12,418 14,166 19,734 26,028 33,571 9,484 12,951 12,971 15,641 10,295 13,057 14,714 17,523 20,743 23,817 13,024 15,500 16,800 19,843 9,310 11,817 8,253 10,491 8,469 10,751 10,167 12,237 9,197 11,095 7,841 10,295 1-A-7, the Secondary Sector Volume (Part 2), China

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Table 5-2 Labor Remuneration per Capita (in yuan) of Enterprises in the Secondary Sector by Size, Industry, and Registered Type, 2004

Average Large-sized Enterprises Medium-sized Enterprises

12,910 21,812 14,851

Wage/Salary & Benefits per Capita Industries above a Given Size Enterprises Invested The Other Stateby Hong Kong, Private Industries controlled Enterprises Macao,& Taiwanese Enterprises Companies 16,561 22,343 11,817 18,033 8,274 24,781 26,993 13,667 25,772 N/A 16,654 19,863 12,613 18,618 N/A

Small-sized Enterprises Mining Coal Mining & Washing Oil & Gas Extraction Ferrous Metal Mineral Mining & Separation Nonferrous Metal Mineral Mining & Separation Nonmetallic Mineral Mining & Separation Other Mining Manufacturing Non-staple Food Processing Food Manufacturing Beverage Manufacturing Tobacco Products Textiles Apparel, Footwear & Headgear Leather, Fur, Feather (Fuzz) & Their Products Wood Processing & Wooden, Bamboo, Rattan, Palm-fiber and Grass Products Furniture Paper & Its Products Printing Industry and Copying from/to Recording Media Cultural, Education & Sports Products Petroleum Processing, Coking and Nuclear Fuel Processing Chemical Materials and Products Pharmaceutical Chemical Fibers Rubber Products

10,080 14,834 14,013 29,363 11,811

12,857 19,116 17,205 33,500 15,676

14,220 21,359 18,308 33,637 22,340

11,566 12,462 13,398 10,000 11,802

15,340 22,071 20,000 90,000 14,000

N/A 9,419 10,276 24,820 8,462

11,796

14,953

17,545

12,628

34,310

7,931

9,295

12,458

13,871

10,566

14,643

8,161

9,117 12,233 9,113

19,412 15,594 11,123

10,000 21,124 11,994

12,500 11,786 9,664

10,000 17,808 13,393

7,594 8,144 7,262

11,370 11,286 47,513 9,758 10,706

14,499 14,825 59,643 11,434 12,688

15,135 14,248 62,207 10,348 11,817

9,653 9,510 5,000 11,525 11,960

21,424 25,865 29,063 13,346 13,523

7,526 7,315 7,473 8,296 8,438

10,395

11,900

10,623

11,391

12,215

8,079

8,306

10,448

10,339

9,654

12,667

7,114

10,592 10,363 11,725

12,987 12,933 16,565

18,333 14,174 19,636

11,353 10,467 12,883

14,108 18,120 19,402

8,258 7,888 8,572

10,585

12,389

15,062

11,725

12,595

7,978

18,781

23,038

32,206

10,776

30,160

8,667

12,991

16,793

19,748

11,981

28,514

7,988

14,903 13,508 11,107

17,894 16,012 13,410

20,814 20,595 15,333

11,780 11,882 10,947

27,442 22,544 15,684

9,020 8,638 8,129

Labor Remuneration per Capita in All Subtotal Industries

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Plastic Products Nonmetallic Mineral Products Ferrous Metal Smelting & Rolling Nonferrous Metal Smelting & Rolling Metallic Products General-purpose Equipment Special-purpose Equipment Transportation Equipment Electric Machinery and Equipment Communications Equipment, Computers and Other Electronics Instruments, Meters and Cultural/Office Equipment Handicraft and Others Recycling and Processing of Discarded Resources and Used Materials Power, Heat, Gas & Water Production and Supply Power & Heat Production & Supply Gas Production & Supply Water Production & Supply

10,911 8,942

13,909 12,119

15,448 13,613

11,898 10,738

15,832 17,029

8,246 7,040

19,147

23,014

31,067

11,988

21,905

8,559

14,319

17,501

22,009

12,567

18,664

8,520

11,326 12,473

14,403 16,221

17,393 19,413

12,616 12,994

17,690 23,955

8,678 8,889

13,340 15,850 13,348

16,797 20,218 16,166

17,776 24,864 19,762

13,362 12,498 13,233

23,157 29,434 17,651

9,307 8,924 8,801

18,161

21,397

26,863

14,083

21,741

9,960

14,513

17,879

19,941

14,452

18,985

9,857

10,259 10,755

12,549 14,762

18,335 21,000

11,654 13,152

12,442 15,641

7,961 8,602

23,620

28,668

29,313

13,277

41,397

9,538

25,852

31,065

31,980

12,561

43,473

9,574

17,786 14,747

21,359 18,156

21,608 17,839

15,417 16,809

24,094 42,762

9,553 9,461

Source: calculations based on Tables 1-A-1 through 1-A-7, the Secondary Sector Volume (Part 2), China Economic Census Yearbook 2004.

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Table 5-3 Labor Remuneration or Wage/Salary & Benefits per Capita (in yuan) by Region and Registered Type, 2004 All

Average

Above a Given Size State-controlled Private

Labor Wage/Salary & Benefits Remuneration Heilongjiang 12,097 16,193 18,359 Shanghai 20,937 26,717 38,115 Jiangsu 13,090 16,890 25,079 Zhejiang 13,092 16,066 32,053 Anhui 11,193 15,178 19,318 Fujian 12,612 14,779 22,843 Jiangxi 10,179 13,225 16,305 Shandong 11,107 13,812 22,241 Henan 9,570 12,860 17,449 Hubei 12,633 16,045 21,671 Hunan 11,650 14,914 18,734 Guangdong 13,705 16,673 34,400 Guangxi 11,700 15,882 21,419 Hainan 13,849 17,595 19,108 Chongqing 12,007 15,531 19,507 Sichuan 11,771 15,313 21,149 Guizhou 13,081 16,550 18,853 Yunnan 13,675 19,394 24,849 Tibet 15,573 19,180 20,128 Shaanxi 12,743 17,459 19,129 Gansu 13,723 19,125 22,690 Qinghai 15,578 20,621 24,420 Ningxia 13,859 18,132 23,238 Xinjiang 18,140 22,466 26,575 Nationwide 12,910 16,561 22,343

9,067 15,435 13,045 13,650 9,846 12,414 9,699 9,904 8,043 9,309 11,245 13,097 10,091 11,591 10,993 9,451 11,612 11,145 16,000 10,294 8,917 10,735 10,417 12,568 11,817

Enterprises Invested by Hong Kong/Macao/Taiwanese & Foreign Companies By Hong Kong/Macao/Taiwanese Companies By Foreign Companies

13,836 30,708 19,718 17,484 15,333 14,677 11,760 13,001 13,669 19,275 14,893 16,140 15,404 20,313 23,307 16,777 13,957 19,845 10,000 23,190 19,055 24,828 17,687 18,018 18,033

11,012 24,380 17,410 16,134 13,056 13,860 8,975 11,280 14,747 12,536 13,350 14,523 10,428 16,102 26,515 15,217 12,857 18,931 N/A 16,409 19,077 37,931 13,000 15,833 15,458

14,554 33,739 21,345 18,816 16,800 16,115 15,907 13,465 12,857 22,734 16,941 19,770 19,176 21,818 21,390 17,471 14,424 20,732 10,000 27,527 19,032 11,724 18,065 19,683 20,698

Source: Calculations based on Tables 1-B-1 through 1-B-7, the Secondary Sector Volume (Part 2), China Economic Census Yearbook 2004.

Table 5-4 Explanations of the Calculation of Relevant Variables in Empirical Analysis Subscripts in this table mean as follows: p is region (six-digit codes, or county-level cities); q is industry (three-digit codes); i is registered type of enterprise and equals 110, 120, … , 340; when i = 210, 220, 230, 240, 310, 320, 330, 340, the total value represents FIEs and is denoted as i = f; i = f34 means that i = 230, 240, 330, 340; when i = 110, 120, 130, 141, 142, 143, 149, 151, 159, 160, 171, 172, 173, 174, 190, the total value represents DIEs and is denoted as i = d; j is business size and equals 1, 2, 3, which denote large, medium and small sizes respectively; j = j12 means medium and large sizes; t = 1998, 1999, … , 2006 and denotes year.

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Code

F01

F02 F03 F04 F05 F06

F07

F08 F09

F10

F11

F12

F13

F14

F15

F16

F17

F18

F19

F20

F21

Equation

91

F22

F23

F24

F25

F26

F27

F28

F29

F30

F31

F32

F33

F34

F35

F36

F37

F38

F39

92

F40

F41

F42

F43

F44

F45

F46

F47

F48

F49

F50

F51

F52

F53

F54

F55

F56

F57

93

F58

F59

F60

F61

F62

F63

F64

F65

F66

F67

F68

F69

F70

F71 F72 F73 F74

(i.e., the distance-weighted average wage) (i.e., the distance-weighted average remuneration) (i.e., the IO coefficient-weighted backward average wage) (i.e., the IO coefficient-weighted backward average labor remuneration)

Notes: (i) for F03, when t = 1998, t– 1 = 1997 does not exist in the database, so let 1NV pqij,1997 = 0; (ii) F71 and F72 are the travel distance-weighted wage rate, w, and labor remuneration, c, respectively; F73 and F74 are the input/output coefficient-weighted w and c respectively; (iii) the calculation results of these 74 equations are named after their respective codes; (iv) for the indicators referred to in these equations, please see Table 5-5.

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Table 5-5 List of Database Indicators No.

Code

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

t REG SRE SEC SIZ HOU AGM KPU KFH ACI INV AFT AFO DAC DEC AFN AIN ATO ECR RPO EOP EAD TAX IPW ILU FCH IEX POP PTO WPT WAP WEP WPO VAT IIN MDI IIM IIA IIO

Name Year Administrative division code (six digits) Registered type (three digits) Industry code (four digits) Business size (single digit) Housing provident fund & allowance Pension & medical insurance premiums Paid-up capital Foreign capital Total current assets Inventory Total fixed assets Original fixed assets Accumulated depreciation Depreciation in the year Annual average of net fixed assets Intangible assets Total assets Total owners’ equity Revenue from the primary operation Operating expenses Administrative expenses Taxes Property insurance premium Labor & unemployment insurance premiums Financial costs Interest expenses Operating profits Total profits Total wages payable in the year (credited accumulated amount) Total wages payable with respect to the primary operation Total benefits payable in the year (credited accumulated amount) Total benefits payable with respect to the primary operation Value-added tax payable in the year Total industrial intermediate inputs Direct materials Intermediate inputs in manufacturing expenses Intermediate inputs in administrative expenses Intermediate inputs in operating expenses

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40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

ILT KHM OAL R&D PED NOE GOV SAL WKR VAD EXP WFIO WBIO CFIO CBIO WWD CWD

Long-term investment Capital from Hong Kong & Macao Office expenses R&D expenses Employment education expenses Number of enterprises Gross output value (by prices in the year) Industrial sales revenue (by prices in the year) Annual average number of workers Industrial value added Export value Forward average wage/salary Backward average wage/salary Forward average compensation Backward average compensation Distance-weighted average wage/salary Distance-weighted average compensation

Note: All value indicators are in thousands of yuan; the numbers of persons are in persons.

Table 5-6 Main Variables of Sample Enterprises by Ownership Type, 1998-2006 Average Foreign Average Average Number Ratio of Ratio of Foreign Ratio of per Domestic Capital of Employees per Intermediate Capital to Wage Rate Capital Exports per Person Enterprise Inputs Registered Capital (yuan/person) Person (%) (yuan/person) (yuan/person) (Persons) (%) (%) DIEs 1998 7,021 93 34,764 391 71.20 9.33 2.61 1999 7,711 1,041 41,009 371 70.19 8.92 2.48 2000 8,594 1,160 48,814 353 70.33 9.38 2.32 2001 10,065 1,164 56,050 321 70.28 8.99 2.03 2002 10,768 1,271 59,834 303 70.11 9.27 2.08 2003 12,013 1,175 63,049 283 70.27 9.51 1.83 2004 13,702 702 72,881 222 71.22 8.72 0.95 2005 15,238 1,082 72,699 231 71.09 8.69 1.47 2006 17,344 1,083 77,552 217 70.83 8.92 1.38 Average 11,437 1,062 58,545 285 70.71 9.01 1.78 FIEs 1998 11,192 61,479 33,701 293 76.39 38.78 64.59 1999 11,863 68,375 35,102 293 74.94 37.14 66.08 2000 12,783 71,604 33,343 303 74.62 40.40 68.23 2001 13,398 76,218 32,669 298 74.57 38.95 70.00 2002 14,667 76,918 31,417 305 74.24 40.94 71.00 2003 14,992 73,828 28,411 326 74.48 41.52 72.21 2004 16,119 70,218 25,593 307 75.36 44.87 73.29 2005 17,941 71,932 24,771 337 74.94 42.70 74.38 2006 20,377 75,779 24,455 348 75.39 43.09 75.60 Average 15,889 72,466 28,404 318 75.01 42.03 71.84 All Year

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1998 7,543 8,514 34,630 375 72.44 16.43 19.73 1999 8,263 9,996 40,224 358 71.37 16.00 19.90 2000 9,235 11,937 46,448 344 71.48 17.79 20.45 2001 10,641 14,147 52,005 317 71.50 17.59 21.39 2002 11,513 15,720 54,407 304 71.32 18.65 22.42 2003 12,667 17,132 55,441 292 71.59 19.64 23.61 2004 14,344 19,165 60,322 239 72.57 20.68 24.11 2005 15,984 20,653 59,459 253 72.32 19.63 25.78 2006 18,220 22,670 62,207 243 72.27 19.87 26.71 Average 12,364 15,935 52,267 291 72.01 19.10 23.36 Source: A database containing production and operation data about enterprises above a given size in China’s secondary sector.

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Chapter 6 Empirical Analysis of the Effects of FDI on Employment in Chinese Enterprises There is an “employment-development” correlation between employment/employment quality/employee skills and development. Many developing and transforming economies are baffled by unemployment, a great many low-paid jobs, and poor working conditions. In China, for example, a huge population and heavy pressure to create jobs are among the basic facts. The employment issue has long had remarkable effects on economic and social development in China. The real unemployment rate in Chinese urban areas was 11.45% in 2004, according to estimates from relevant authorities. In the following years, ten million people joined the labor force each year, while fourteen million others lost jobs. The difference between annual labor supply and demand in the urban areas remains about ten million people each year even though the economy has been growing at an annual rate of 8%-10%. In the meantime, there are a great number of rural laborers waiting for jobs in urban areas. Increasing the number of jobs and improving employment quality are therefore important for the policy objectives of developing economies (UNCTAD 1999: 257, 258), especially for fast-transforming, big countries like China. In developing or transforming countries, the most important effect of FDI on residents of the host country, especially the poor, is creating jobs (IFC2000: 16). In most developing countries, jobs that MNCs provide directly in the host country generally produce few effects. In other words, the poor in these countries are mostly in rural areas, where they are jobless or not fully employed, with nearly no possibility of being employed by any FIE (UNCTAD 1994: 185). There are only a handful of exceptions, such as China. This chapter begins with an overview of existing works regarding how FDI affects employment in the host country, before setting and estimating an empirical analysis model and drawing conclusions.

6.1 Overview of the Effects of FDI on Employment in the Host Country Since Chapter 2 already outlines this issue, this chapter will give a deeper overview that focuses on something different. 6.1.1 The Effects of FDI on Employment in the Host Country: a Theoretical Overview FDI is usually one of the main forms of overseas investment by MNCs, which typically set up subsidiaries in the host country to organize production. Accordingly, FIEs’ employment effects in the host country will first be influenced by the behavior of the MNCs that are their parents. With regard to employment and skill improvement, MNCs differ from non-MNCs primarily in that the former distribute jobs among their international branches. Specifically, employment effects of MNCs’ overseas subsidiaries depend on macroscopic and microscopic factors. Microscopic factors refer to corporate innovation and strategy around international production, the industry group that the company belongs to, production activities that it carries out, and corporate sizeķ.Macroscopic ķ MNCs account for only 3% of global jobs, mainly because they focus on capital- and technology-intensive activities. In the meantime, MNCs account for 20% of non-agricultural jobs in developed economies and some

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factors include location advantages of different countries as well as the status of the host and home countries’ labor markets (including the availability and costs of laborers at different levels of skill)ķ (UNCTAD 1999: 259). Next, we will explain these factors one by one. First, the motives of MNCs to carry out international production. Foreign companies that seek resources and efficiencies mostly make direct investment in manufacturing. Labor cost is their primary consideration when they make direct investment in labor-intensive production. But for foreign companies that seek markets, the labor force of the host country is no longer the primary consideration. Second, MNCs’ strategies for international production and corresponding organization (UNCTAD 1994: 166-172; UNCTAD 1999: 258-261). When carrying out international production nowadays, MNCs generally employ one of three strategies, including stand-alone, simple integration, and complex integration. The stand-alone strategy means that all or several neighboring overseas subsidiaries of an MNC primarily aim to serve the host country and neighboring markets and that there are no strong connections among these subsidiaries. Under this strategy, subsidiaries will copy most of their parent’s value-chain activities, with technology development and financial activities generally being carried out at the headquarters. In the service sector, FDI aims to seek markets and FIEs will especially copy the ratios of factors of production in the home country, including their parents’ skills and capital densities, which are all helpful for improving employment quality in the service sector of the host country relative to that of its manufacturing industry. As they become increasingly integrated into the institutional framework of the host country, FIEs have industrial relationships that typically become similar to the pattern in that country. Subsidiaries carry out activities (including employment) in the main market areas since they basically aim to serve the local market. In this case, most jobs created by subsidiaries are in the host country and their number depends on the volume of market demand in that country, the business sizes of subsidiaries, and competition in the local market, whereas labor cost may only be a minor and relative short-term factor. Such jobs are characterized by being relatively stable, safe and diverse (except for the highest capabilities such as R&D and headquarters’ functions). The specialty and improvement rate of job skills depend on market competition and relevant policies of the host country. If the host country encourages the production of alternatives to imports, for example, skill improvement in FIEs will slow down. The simple integration strategy means that an MNC puts one or several parts of its value chain of production in one or several subsidiaries in a particular host country or international region and that these subsidiaries operate in a limited scope and aim to provide the parent with certain inputs or products, which will then be moved to more competitive locations for the next production procedure. These overseas subsidiaries have stronger ties with each other and weaker ones with the other subsidiaries. Under this strategy, the quantity and quality of jobs provided by subsidiaries

developing economies. This suggests that their direct contribution to the manufacturing and service sectors should not be overlooked. Activities carried out by these companies and the technical parameters of such activities dictate the capital, labor and knowledge densities of their production. All these occur under the constraints of technologies available for selection by the companies and the costs of factor combinations. With a given production function, it is corporate size that decides the headcount and the amount of investment in training and skills development. ķ The other differences include: the fact that a lot of MNCs are bigger, more complex in technology, and under heavier competitive pressure; the capability of delivery through FDI; goods and services for non-trade purposes. A lot of MNCs have more employees than non-MNCs in the same industry because of their larger sizes. Also, jobs created by unit output at MNCs are fewer than those at other companies since they are more technology-intensive and, behaviorally, more competitive.

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depend on the characteristics of the host country’s location advantages. The complex integration strategy means that each overseas subsidiary of an MNC specializes in making a particular kind of product, carrying out a particular type of processing activity, or implementing a particular function, and that such products, processing activities, or functions are already organically included in the parent’s integrated regional or global production network. Moreover, there are close business ties among more subsidiaries in larger geographic areas. Places where end products are made and consumed may be farther from each other. International market strategies are more about seeking assets. Under the complex integration strategy, jobs created per unit of output are fewer than those created under the first two strategies since MNCs have realized the specialization of business functions, more reasonable integration of the production chain, and a higher overall operating efficiency. The employment effects of FIEs depend on the roles of their production activities in the MNC system’s realizing the goal of maximizing business performance, so it is more difficult to forecast such effects. To adapt to the deepening global production integration, MNCs organize production activities with the following characteristics. First, MNCs will reorganize activities of their subsidiaries at particular times to continually make the overall system more efficient. Second, they employ new methods of organizing work (e.g., the lean production technique) or apply advanced technologies in traditional manufacturing processes to fuel organizational and technological innovation intended to save labor. Third and lastly, they replace in-house activities with inter-company purchase/distribution/exclusive sales arrangements, which lead to weaker direct employment effects and stronger indirect ones. All three factors will cause weaker employment effects. Third, the employment effects of FDI in the forms of M&A and green field FDI. There are obvious differences between these forms in the short term, whereas there is no sufficient evidence that there are systematic differences between them in the long term (UNCTAD, 2000: 180-188). Today, green field FDI accounts for about 95% of total FDI in China, while M&As account for about 5%. As China further opens to the rest of the world, however, this pattern may gradually change – M&A FDI will have an increasing share. It is necessary therefore to pay sufficient attention to the employment effects of M&A FDI. The employment effects of M&A FDI often vary with the motives of M&As and characteristics of the merged or acquired companies (UNCTAD, 2000: 180). Since market-seeking M&As aim to enter the host country, region, or international marketing network, the current staff of the targeted companies will generally be retained to satisfy the newly acquired market, and new jobs will be provided if the market grows further or the FIE expands its market share. Accordingly, the direct employment effects of such M&As are typically neutral or positive. M&A that seeks strategic assets will also retain valuable skills and the excellent employees of the targeted companies. Corporate operations will expand and jobs be created if M&A promotes in-house collaboration. Efficiency-seeking M&As aim to include the merged or acquired companies into the system of the MNC that has merged with or acquired them. Coordination and integration are made inside the system to increase the overall efficiency of the MNC. And the result depends on the technological levels of the merged or acquired companies, the extent of employee redundancy between them and other subsidiaries, market developments, and the global strategy of the MNC. M&A that aims at short-term financial objectives may take the number of job cuts as an objective for the necessary reorganization, and this will doubtlessly cause short-term unemployment. But if the merged or acquired companies make profits and M&As are part of investment diversification, then more jobs will be created. M&A during privatization will usually lead to job cuts after changes in ownership occur. Job losses that appear shortly after M&As may not cause net losses in the economy, because

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the merged or acquired local enterprises might have already been struggling in competition and been on the verge of bankruptcy before they were merged or acquired. In this case, some jobs have actually been saved at the companies being merged or acquired. Moreover, they may become competitive again and create more jobs after reorganization and making initial adjustments. This is a common phenomenon during privatization (UNCTAD 2000: 183). The indirect employment effects of M&A FDI refer to the fact that after M&As, MNCs that have international production networks will typically establish “crowding-out” and “crowding-in” (i.e., competitive/complementary) industrial correlations with local enterprises and, in addition to the aforementioned direct effects, will produce indirect employment effects on local enterprises (UNCTAD 2000: 180-183). Such correlations may occur whether in the market for intermediate or for end products. Regarding correlations in the local market for intermediate products, there are big differences between M&A and green field FDI in the short and medium terms but not in the long term. In developing countries, the long-term, dynamic employment effects of M&A FDI are still unclear. Whether the follow-on employment effects will become stronger or not than in the early period after FDI entry depends on multiple factors, including productivity, multiplier, and growth effects. There have been few comprehensive quantitative assessments on long-term changes in the employment level caused by different M&A investments (UNCTAD, 1999: 263). Fourth, the labor intensiveness of the sector or industry where FDI is made (UNCTAD 1994: 166). Usually, the employment effects of FDI will be weaker (but the income level will be higher) if investment focuses on capital- or technology-intensive production activities, such as mining, heavy industry, high-tech industry, finance, insurance, telecoms or R&D. Production activities that are more labor intensive, such as manufacturing activities as part of the processing trade, furniture making, hotels, and restaurants, will employ more laborers and, thus, increase their share in income distribution. This will likely narrow the income gap between the group of employees and investors, on the one hand, as well as among employees themselves, on the other. As a result, it is advisable to analyze the effects of FDI inflow on the income gap among residents of the host country on a per-sector basis. The effects of FDI-involved production activities’ preferences in terms of labor skills on the income gap among workers have received more attention (International Labor Office, 2002). Laborers needed by MNCs include absolutely and relatively skillful ones. This will lead to a higher skill bonus and, accordingly, a wider wage/salary gap and be likely to crowd out some local enterprises, unless labor supply in the developing economy is sufficient to satisfy such differentiated needs. Fifth, the competitive/complementary relationships between FDI-involved production activities and those carried out by DIEs (UNCTAD 1994:166). FDI’s growth effects, especially spillover effects, and the resulting distribution effects, largely depend on their local linkage effects, especially backward ones, that is, the linkage effects produced when FIEs purchase goods and services (as production inputs) in the host country’s market (other FIEs or local suppliers). Such linkage effects contain continuous and increasing exchange between DIEs and FIEs in terms of information, technology, skill and other assetsķ. Horizontal linkage effects between FIEs and DIEs of course also reflect the extent to which they are competitive or complementary with each other, and they will also greatly influence the comprehensive employment effects in the host country. ķ The backward linkage effects on the host country depend on how local suppliers compare with other suppliers in terms of costs and earnings, such as relative quality, reliability, flexibility and similarity, and on the cultures of FIEs’ home countries, motives of and strategies for investment, technology and market positioning, the roles of FIEs in the MNCs’ systems, years in service, how they are founded (i.e., M&A or greenfield FDI), business sizes and industry categories (UNCTAD 2001: 127-156).

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Linkage effects between DIEs and FIEs can promote the spread of knowledge and skills to the former from the latter. This will help DIEs improve their technologies and management while increasing their productivity, output and jobs, promote market diversification and exports, make it more likely for DIEs to become global suppliers or MNCs, produce spillover to other sectors of the host country’s economy through demonstrative effects, the flow of trained laborers, spin-offs and competitive effects, and further integrate FIEs into the local market and promote the appearance and improvement of industry clusters comprising FIEs and DIEs (ibid: 129). Nonetheless, not all connections with FIEs will bring equivalent benefits, as some of these connections are even harmful. If they hold dominant positions in the host country’s market, for example, FIEs will often shift adverse effects (e.g., weaker demand, higher wages/salaries, and lower profits) to local enterprises when they negotiate with local suppliers to share benefits. This will be unfair for local suppliers. From the perspective of sectors, linkage effects usually work only in a limited scope in the primary sector. They work in a much larger scope in the manufacturing sector, but with big differences between industries. Linkage effects are very wide in the food industry (the ratio of intermediate inputs to the total output is very high) and narrower in the textile and apparel industries (FIEs produce clothes that are often exported and, given their requirements for cloth and quality, local suppliers need to be rather complex and large); engineering activities are somewhere in between (engineering processes can be broken down, but outsourcing is limited in areas with stringent technical requirements, such as machinery and precision meters). Linkage effects are limited in the tertiary sector: they are more likely to occur in service industries such as the retail and building industries that require tangible intermediate inputs as well as foreign-invested hotels that purchase local foods, furniture, and in-room equipment; services in some service industries, such as the back offices of airlines, banks and retailers, have gradually been outsourced to reduce salary costs. Today, such outsourcing is fundamentally international, but will gradually become domestic as service levels and communications technologies improve in developing economies. Sixth, the host country’s location advantages. If it has remarkable cost advantages, then the host country will attract foreign investment in labor-intensive manufacturing. This will directly create jobs that are large in number but low in skill requirements, which are accordingly less stable or safeķ.The indirect effects on job creation may be insignificant because FIEs rely on imports for intermediate inputs. If it has certain precious resources or strategic assets, then the host country will attract FDI that requires them. The quantity and quality of the created jobs largely depend on the capital intensiveness and technological complexity of mining/separation or agricultural activities as well as the extent to which relevant processing is made in the host countryĸ. Seventh, the openness of the host country’s trade and industrial policies. As the host country adopts freer trade and industrial policies, the ability of FIEs to support employment with respect to tradable goods and service partially depends on how fast they conduct reorganization amid international competition, on the one hand, and the inflow of new FDI, on the other. With a given status of the labor market, this depends on the host economy’s trade and industrial policies. A country with abundant low-cost labor will receive significantly greater (direct and indirect) ķ

This is because an increase in the wage rate will offset its relative location advantages and such FDI is prone to exit. ĸ As a result, the motives and strategies of MNCs and the location advantages of their host countries jointly decide the amounts of FDI flowing into these countries and FDI’s categories and characteristics in terms of industrial distribution before deciding the quantity and quality of jobs and the potential of technological improvement in their host countries.

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job-creation effects from FDI-involved, export-oriented activities. The sustainability of such effects depends on several factors such as whether technologies and skills in FIEs will improve with a rising wage rate and whether the purchase of their intermediate inputs is domestically or internationally correlatedķ. A country that encourages the production of alternatives to imports may also stimulate job creation by attracting FDI, especially when it has a large market. But jobs provided by foreign subsidiaries that seek markets will grow at a lower rate if the host country maintains a high level of market protection. This will cause a lag in technology while reducing the size of the economy. Usually, new entrants into a competitive environment with fewer barriers are inclined to create more sustainable output and job growth, but this largely depends on how much competition is introduced. A sudden change to a competitive structure may cause massive job cuts and it takes time to absorb the resulting unemployment. Eighth, other factors such as the quality of the labor force, the efficiency of the labor market, and the quality of the labor market system. The quality of the labor force refers to the levels and makeup of laborers’ skills, the potential of managers in training, etc. The efficiency of the labor market refers to the labor law, unionization, labor market breakdown (e.g., by race or gender), the transparency of this market, etc. Usually, it is more likely for FDI with higher employment quality and better training practices to flow into the host country if its labor market is more efficient and its laborers have higher skills (UNCTAD, 2000: 181). 6.1.2 Basic Properties of the Employment Effects of FDI Generally, the job-creation effects of FDI have three properties, including: job creation or destruction, direct or indirect employment effects, and static or dynamic employment effects. First, job-creation and job-destruction effects. Yu Yongding (2004) believed that the former refers to jobs provided by FIEs directly (and indirectly through complementary relationships with DIEs) while the latter refers to FIEs competing with DIEs and accordingly destroying jobs provided by the latter. Both effects may occur in the same operating period of different FDI or in different operating periods of the same FDI. Second, direct and indirect employment effects. Direct employment effects refer to effects in which FIEs create jobs directly. Indirect employment effects refer to the employment effects that FIEs have on other enterprises, which are produced through the competitive/complementary correlations between the two types of enterprises in processes such as the income multiplier or accelerator principle. FIEs have indirect employment effects in three categories. Category 1 is vertical effects including: upstream or backward indirect effects, that is, FIEs’ employment effects on local suppliers of raw materials, parts, components, and services; downstream or forward indirect effects, that is, FIEs’ employment effects on local customers (such as distributors and service providers). Category 2 is horizontal effects, including: horizontal effects in the narrow sense, that is, FIEs’ employment effects on the other enterprises in the same industry; horizontal effects in the broad sense, that is, FIEs’ employment effects on enterprises outside their respective industries. Category 3 is macroscopic, indirect effects, that is, employment effects produced by FIE employees and shareholders, who spend their incomes across the host country, or effects in which FIEs cause job cuts in the host country by increasing imports for production (UNCTAD, 1994: 192, Table ķ Under trade liberalization, whether FIEs purchase (or import) intermediate inputs locally or overseas (i.e., from foreign suppliers or other companies in the MNCs’ systems) is first decided by costs and transportation. The host countries’ trade policies are less important. In this sense, the inflow of FDI will also affect the amounts and makeup of imports into the host countries (UNCTAD 2000: 190-191).

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IV.12). MNCs’ indirect employment effects can be reflected by the employment multiplier, which depends on FIE activities and products, supplier capabilities as well as the ratio and size of outsourcing. FIEs will have great indirect employment effects if they have production activities that are highly integrated into the local market and that involve suppliers of massive inputs (e.g., in food processing), distributors/exclusive sales agents, or service providers (e.g., in engineering and electric power). Moreover, such effects are greater if the network of suppliers is more complex (UNCTAD, 2000: 265, 266, 286). Moreover, the indirect job-creation effects of FIEs will be negative if their production activities displace local competitors. In the worst case, local companies that specialize in traditional labor-intensive production are displaced by MNCs that specialize in capital-intensive production and that will employ fewer people than those hired by the former. This is the “labor-saving effect,” which will further reduce the job-creation effects if working with the international demonstrative effect. This is because the international demonstrative effect will stimulate a change of demand for finished products to capital-intensive products from labor-intensive ones (Hemmer et al, 2005). Local laborers crowded out by FIEs’ production activities may of course be reemployed in reorganization, which can increase efficiencies and competitiveness and, thus, will create stable and increasing job opportunities (UNCTAD, 1999: 261). UNCTAD estimates about developing countries indicate that FDI’s indirect employment effects are usually greater than its direct ones, as the former are once to twice as great as the latter (UNCTAD, 1994: 163-164). For the manufacturing industry, FDI’s indirect job-creation effects are usually assumed to be twice as great as its direct ones (UNCTAD 1994: 192). MNCs’ activities in their host countries have relatively significant indirect employment effects, according to relevant research (UNCTAD 1994: Chapter IV, Note 8). Overall, indirect effects are positive and significant. The input-output method was used to estimate jobs created indirectly by subsidiaries of MNCs in Thailand, the Philippines, and the entirety of Southeast Asian countries in the early 1980s. The results show that such jobs are once to twice as many as those they create directlyķ. But this coefficient is only 0.25 when it comes to offshore assembly production or, in export processing zones, production activities that lack forward and backward correlations. Research by Dupuy and Savary (1993) in eight developed and developing countries indicates that this coefficient is 1.6 (the average backward and forward coefficients are 0.9 and 0.7, respectively) (UNCTAD 1994: 192-195). Relevant research also shows that indirect job-creation effects vary with the nature of FDI projects, the industries in which investment is made, the purchase policies of foreign investors, and the status of the host country. The longer an FIE operates in the host country, the greater its indirect employment effectsĸ. ķ

You may refer to Miranda (1994: 19) and Watanabe (1993: 136). For discussions on indirect employment effects, you may refer to Parisotto (1995), Aaron and Andaya (1998) as well as Dupuy and Savary (1993). Employment multipliers by industry with consistent statistical bases can only be available from US data, which is calculated by the US Department of Commerce using input-output matrices. As for the US manufacturing industry, employment multipliers by industry are in the interval of [3, 7] (they will reach 7 in the food industry and similar industries). For the estimation of employment multipliers at the corporate level, you may refer to Bridge (1998: 2). Brimble et al (1998: 12, 29) used the 1980 Thai input-output table to estimate indirect employment multipliers of FDI in that country. He found that the indirect employment multiplier for the entire manufacturing industry was 1.7 and that the indirect employment multipliers by industry were in the interval of [0.5, 7.8].Among all industries, non-electric machinery manufacturing was at 7.8, food/beverage/tobacco industries were at 6.5, and the wood/wooden product industries were at 0.5. In developing host countries, distributors or agents rely on production by individuals or households in informal sectors. Indirect employment effects will also occur if ĸ

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Third, FDI’s employment effects include static and dynamic ones. Overall, FDI’s job-creation effects may be the dominant effects in the short and medium terms and will vary with host countries, industries and operating periods in the long term. This all depends on balancing between FDI’s creation and destruction effects, and it is difficult to determine the overall result (UNCTAD 2000: 265-266). MNCs change their strategies, investment behavior, employment, HR management, and investment in skill development so as to respond to the effects of changes in the national, regional and global status on their competitive positions and profit-making opportunities. The intensified globalization and global competition are making MNCs’ business strategies more complex and international production structures more integrated. All these contribute to great changes in the geographic distribution of MNCs’ activities (UNCTAD 1999: 261). Short-term job losses caused by FDI will be offset by a larger number of new jobs in the long term if FDI adds to the competitiveness, efficiency and export orientation of local enterprises before increasing the productivity of the latter or stimulating the opening of new DIEs or FIEs. By comparison, FDI will reduce jobs if FIEs are closed or relocated, protected activities are liberalized, parent companies change their strategies, M&As occur to parent companies in the home countries, or companies newly acquired in the host countries are reorganized. There are times when the status of the host country’s labor market is changing, FIEs in manufacturing are turning to skilled-labor-intensive products from labor-intensive products with low value added, or FIEs in the service sector are using low-cost, educated, and skilled laborers in the host country. In these cases, activities that are within MNCs’ production systems and that are suitable for outsourcing can be combined, and proper combinations of wages/salaries and skills can also create jobs with slightly higher value added. As is discussed above, the different ways of FDI entering the host country also produce dynamic employment effects. Hemmer et al (2005) believed that the inflow of FDI produces job-creation effects that will be favorable for equality in the long term but that this depends on whether the poor have human capital or not. FDI will push technological progress in the long term to an extent that depends more on how fast FIEs improve technologies during integration into their host countries than on the initial technology transfer. Faster technological progress will lead to higher growth, more jobs and greater positive effects on the incomes of the poor. But this depends on whether the poor in the host country are able to meet higher human capital requirements for faster technological progress (UNCTAD 1999: 263), and human capital depends on whether the host country’s policies regarding education distribution are sufficient. FDI affects human capital and, hence, the income gap (Basu et al, 2003).

MNCs purchase goods or services from these sectors (UNCTAD 1999).

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Table 6-1 Potential Employment Effects of FDI Inflow: Static Effects Direct Effects

Quantity

Positive

Negative

Increasing capital and creating jobs during industry expansion

M&A FDI may cause job losses

Indirect Effects Positive Negative Increasing jobs through the Relying on imports forward/backward or displacing inter-industry linkage & existing companies multiplier effects to cause job losses Competition between FIEs and Experience in good DIEs will destroy the organization operation will latter’ original spill over to DIEs from FIEs wage-rate levels

Some practices (e.g., employment and push) are not Quality welcome in host countries Making already Increasing, possibly Higher crowded cities even Encouraging suppliers to better new jobs in unemployment if more crowded, plus move to regions with Location regions with high FIEs displace DIEs a further imbalance sufficient labor supply employment or rely on imports among regions Note: this table only depicts the static effects of FDI inflow on employment in the host countries, with no explanations for its dynamic and general effects. Source: UNCTAD (1994: 167, Table IV.1). Higher wages/salaries and productivity than DIEs

.

6.1.3 Empirical Issues regarding the Employment Effects of FIEs in their Host Countries The assessment of MNCs’ employment effects is faced with conceptual and empirical issues (UNCTAD 1994: 164, 169). First, developing economies lack data regarding jobs created directly and indirectly by FIEs, including data regarding employment quality in FIEs (UNCTAD 2000: 264, 270). This imposes obvious restrictions on the assessment of employment effects. Second, it is difficult to measure and simulate all the implications of indirect employment. Third, the opportunity cost of jobs caused by the inflow of FDI into the host countries must be taken into account. Fourth, employment effects of FDI inflow must be analyzed on an extensive and dynamic basis. To this end, a time reference for FDI’s employment effects must be found and interactions between FIEs’ activities and the structure and competitive properties of the markets where they are as well as policies of their host countries be taken into account. Fifth, a great deal of research on a single host or home economy is done at the macroeconomic level and relies on the aggregated number of jobs provided by MNCs. This research also analyzes how changes in the structure of international trade caused by MNC activities in their host countries affect employment (Dunning 1993b; UNCTAD 1994: 169)ķ. In short, international production activities of MNCs have direct and indirect dynamic employment effects, positive and negative. Their actual effects depend on a number of factors, and it is difficult, therefore, to reach any general conclusion. Empirical tests are required in this context. Sixth, OECD (1995) found that with regard to how and how much FDI affects employment, no ķ UNCTAD (1994: 175, Table IV.3) found that the number of jobs provided by MNCs in China was 3,200,000 in 1990 and 6,000,000 in 1992; the numbers of jobs provided by subsidiaries of MNCs in developing economies in the two years were 9,000,000 and 12,000,000, respectively. The number of jobs provided by subsidiaries in developed economies in the two years were both 17,000,000, and the number of jobs provided by their parent companies in the home countries in the two years were 70,000,000 and 73,000,000, respectively (Parisotto, 1993).

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general conclusion is available from existing literatureķ. This indeed reflects the complexity of empirical instruments and methodological flaws. Regarding the effects of FDI on employment in the host countries, microscopic methods are generally used in international works to examine the total number of jobs and their structure and distribution. On the side of the quantity, a cost function framework is generally used and the relative wage coefficient between the home and host countries is used as the elasticity of substitution between them in terms of labor force. On the side of the structure, FDI’s demand for relevant laborers is generally divided into demand for skilled laborers and that for unskilled ones. By comparison, there are only a small number of Chinese works regarding the same topic and most of them are written from the macroscopic perspective. They typically deduce the decision function of labor from the production function, so the form of the former depends on the setting of the latter. But there are divergences on “deduction.” Some people deduced the function of demand for labor force directly from the production function, such as Wan Xinrong et al (2005); and some others deduced it from business profit maximization, such as Wang Jian (2005) and Fu et al (2005). Empirical research is also faced with the following details-relevant issues, such as: is it necessary to introduce the variable of human capital into the production function framework? Is it necessary to introduce the variable of actual wages and to introduce the variable of price through the relationship between the actual variable and the nominal ratio? In the production function framework, is it necessary to materialize the TFP in the Solow production function into multiple variables? How should the variable of the exchange rate be introduced, through FDI in a foreign currency or as an independent factor? In addition, FDI’s employment effects should include those that it produces through its spillover effects on domestic investment, TFP, and other factors (such as Wang Jian, 2005).

6.2 An Empirical Model of FDI’s Employment Effects on Chinese Enterprises The producer’s employment issue can also be deduced from its behavior of profit maximization. We can also deduce an equilibrium model of employment from Issus (5.1) through (5.10) in Chapter 5. (6.1)

The logarithmic linear expression of this model is: (6.2) To simplify the formulation, all variables in the following texts are at the original levels but actually represent their respective logarithmic transformations, with the exception of the empirical contents. Like the empirical model regarding the wage rate (the average compensation) described in the previous texts, this theoretical model uses a fully consistent scheme of adding control variables when it is applied to microscopic panel data: (I’) Let all units in dimensions such as region, industry, ownership type and business size be ķ Wang Zhenzhong (2000) found from the perspective of changes in the net increase that FDI’s employment effects were positive. Wang Weimin (2000) found that FDI’s employment effects were negative on the primary and secondary sectors, positive on the tertiary sector, and negative as a whole. Niu Yongping (2001) found that such effects were positive after making quantitative analysis of FDI and the total number of jobs in China, for example.

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independent from each other:

(II’) Let regions not be independent from each other: ,

(III’) Assume that industries are not independent from each other in terms of wage rate (or average compensation) and the extent to which they affect each other depends on how much they are technologically correlated (the stronger the correlation, the greater the effects).Industries with higher wage rates (or average compensation) will cause those with lower wage rates to increase such rates, whereas the latter will cause the former to decrease such rates: ,

(IV’) Let foreign capital’s spillover effects inside and outside the industry be only within the region: ,

(V’) Let foreign capital’s spillover effects inside and outside the industry be inside and outside the region: ,

(VI’) Let wholly foreign-invested enterprises and foreign-invested joint-stock companies inside and outside the region have spillover effects inside and outside the industry: , or

(VII’) Let medium- and large-sized FIEs inside and outside the region have spillover effects inside and outside the industry:

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, or

(VIII’) Let spillover effects be extensive in all industries and regions in China regardless of ownership types and business sizes:

Also for the purpose of simplification, the above-listed expressions of the model do not have the time property (subscript t or t – n, n = 1, 2, …) of the variables. For the calculation of relevant variables, please refer to Table 5-4.

6.3 Main Conclusions Factors that affect employment in enterprises include internal and external factors. Foreign capital’s effects on employment in enterprises are considered more as an external factor. To intuitively display estimation results, this book does not list GMM estimation results separately unless it is necessary to do so. Instead, it visualizes all estimation results together when GMM estimation results are significantly consistent with the other results. 6.3.1 Effects of FIEs’ Wage Rates on Employment (1) An Increase in Wage Rate or Compensation Entails a Small Number of Employees Like in DIEs, every one percentage point increase in the wage rate or compensation in FIEs will cause their number of employees decrease by six to thirty percentage points. All estimation results from Models I’ through VIII’ indicate that the wage rate (F01) is always a significant variable that affects the number of employees (F05), that its coefficient is always negative, and that most of its point estimates are between -0.26 and -0.01. For the same method, estimates of F01’s coefficient in different models are very concentrated. Most point estimates are between -0.15 and -0.13under the GLSFE method, between -0.09 and -0.07 under the GLSRE and MLE methods, and between -0.26 and -0.23 under the XTABond method. In addition, all estimates of the effects of compensation (F02) on F05 in Models I’ through VIII’ are similar to what is mentioned above, except that most point estimates of F02’s coefficient are between -0.30 and -0.06. (2) Effects of Various Wage Rates on Employment in Local Industries The average wage rate or compensation in an enterprise may be affected by external factors from inside or outside the industry and the region and from DIEs or FIEs, including the wage rates in non-local FIEs in other industries (F26), in local DIEs in other industries (F11), in local FIEs in other industries (F23), in local DIEs in the same industry (F20), in non-local DIEs in the same industry (F17), in local FIEs in the same industry (F32), in non-local FIEs in the same industry (F29) and in non-local DIEs in other industries (F14) in a descending order of strength of effects.

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Fig. 6-1 Effects of Wage Rate or Compensation on the Number of Employees: the Distribution of Significant Estimates Note: F01, F02, and F05 are the average wage/salary, the average compensation, and the total number of employees in enterprises, respectively.

Fig. 6-2 Effects of Various Wage Rates in DIEs and FIEs on Employment: the Distribution of Significant Estimates Note: the variables’ names and codes are the wage rates in local DIEs in other industries (F11), in local FIEs in other industries (F23), in local DIEs in the same industry (F20), in local FIEs in the same industry (F32), in non-local DIEs in other industries (F14), in non-local FIEs in other industries (F26), in non-local DIEs in the same industry (F17), and in non-local FIEs in the same industry (F29) respectively.

The upper half of Fig. 6-2 shows that for employment at enterprises in a particular industry in a region:

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(1) The employment effects of the average wage rate in local DIEs in other industries (F11) are at least twice as great as those of the average wage rate in local FIEs in other industries (F23). Thirteen out of twenty estimates of F23’s employment effects in five models are statistically significant, including eleven positive ones, with a simple average of 0.0655.Sixteen out of twenty-eight estimates of F11’s employment effects in seven models are statistically significant and positive, with a simple average of 0.133. (2) The employment effects of the average wage rate in local DIEs in the same industry (F20) are more than 2.3 times as great as those of the average wage rate in local FIEs in the same industry (F32). Eleven out of twenty estimates of F32’s employment effects in five models are statistically significant, with a simple average of 0.0142.Sixteen out of twenty-eight estimates of F20’s employment effects in seven models are statistically significant, with a simple average of 0.033. In a region, regardless of whether it is inside or outside the industry, therefore, the employment effects of the average wage rate of FIEs (F32) are smaller than those of the average wage rate of DIEs (F20) or F23 is smaller than F11. The lower half of Fig. 6-2 shows the effects of the average wage rate of non-local FIEs in the same industry on employment at enterprises in a particular industry in a region. The estimation results indicate that as for the effects of the average wage rate of non-local enterprises on employment at enterprises in a particular industry in a region: DIEs are greater than FIEs in this industry; FIEs are greater than DIEs outside this industry. A descending order of strength of effects is the wage rates in non-local FIEs in other industries (F26), in non-local DIEs in the same industry (F17), in non-local FIEs in the same industry (F29), and in non-local DIEs in other industries (F14). Among them, both F26 and F17 have positive employment effects, while both F29 and F14 have negative ones. The simple averages of the estimate sequences of F26, F17, F29, and F14 are 0.6211, 0.0191, -0.1107 and -0.53, respectively. Regarding the ratio of the statistically significant estimates of the targeted variable to the total estimates of the same variable in relevant models, F26, F17, F29, and F14 are 100%, 50%, 100% and 66.7% respectively. 6.3.2 Effects of Employment-relevant Competition/Complementation on Employment in the Local Industry (1) As for Effects on Employment at Enterprises in a Particular Industry in a Region, There Is more Competition than Complementation from local and non-local DIEs and FIEs inside and outside the Industry Ratios to total employment, if sorted by how much they affect employment in the local industry, are the ratios of local DIEs in the industry (F22), local FIEs in the industry (F34), local FIEs in other industries (F25), local DIEs in other industries (F13), non-local FIEs in the industry (F31), non-local DIEs in the industry (F19), non-local DIEs in other industries (F16), and non-local FIEs in other industries (F28). The simple averages of their estimate sequences in relevant models are 0.1577, -0.00357, -0.17769, -0.24868, -0.88479, -1.05381, -2.64886, and -2.93464, respectively. Seven of the above-listed eight variables – F13, F16, F19, F22, F25, F28 and F31 – each have an estimate sequence with fully consistent signs, which are all negative signs, as is shown in Fig. 6-3. F34 is the only exception. Estimation results of these seven variables under multiple methods exhibit high consistency in terms of signs.

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Fig. 6-3 Effects of Local & Non-local DIEs and FIEs inside & outside the Industry on Employment in the Local Industry: the Distribution of Significant Estimates Note: the variables and their codes are: the ratios of local DIEs in the industry (F22), local FIEs in the industry (F34), local FIEs in other industries (F25), local DIEs in other industries (F13), non-local FIEs in the industry (F31), non-local DIEs in the industry (F19), non-local DIEs in other industries (F16), and non-local FIEs in other industries (F28).

There are nineteen significant estimates of the coefficient of F34 in corresponding models, including twelve negative ones – six from the RE method and the remaining six from the MLE method – and seven positive ones, six from the FE method (there are seven estimates of F34 from this method) and the remaining one, which is the only one, from the ABond method. Under the same model setting, estimates from the FE and RE methods passed the Hausman test, which suggests that there are no systematic differences in estimates between these two methods. In other words, even if there are great differences (e.g., opposite signs) between the estimated coefficients of F34 under the FE and RE methods in each model, there are no systematic differences, as a whole, between all estimates of the coefficient of the variable under these two methods in each model. There are six models with F34 being an explaining variable. F34 is significant only in Model 26 among estimation results under the ABond method in each equation. We may thus infer that estimates of F34 under the ABond method are unlikely to be universal. Also, we may thus infer F34’s effects on employment in the local industry from the simple average of the nineteen coefficients, which equals -0.00357 and indicates that higher ratios of local DIEs and FIEs in the industry to total employment will cause a decrease in the number of jobs in the local industry. a) Effects of Employment at Medium and Large FIEs on Employment in the Local Industry Effects of employment at medium- and large-sized FIEs on that at local enterprises in the same industry, if sorted in a descending order of strength of effects, are the ratios of non-local medium-

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and large-sized FIEs in other industries (F40), local medium- and large-sized FIEs in other industries (F37), local medium- and large-sized FIEs in the industry (F46), and non-local mediumand large-sized FIEs in the industry (F43), whose simple averages of estimate sequences in relevant equations are 0.17945, -0.07210, -0.08982, and -0.71528 respectively.

Fig. 6-4 Effects of Ratios of FIEs to Total Employment on the Number of Jobs in the Local Industry by Size and Capital Structure Note: the variables and their codes are the ratios of local medium- and large-sized FIEs in other industries (F37), non-local medium- and large-sized FIEs in other industries (F40), local medium- and large-sized FIEs in the industry (F46), non-local medium- and large-sized FIEs in the industry (F43),local wholly foreign-invested enterprises and foreign-invested joint-stock companies in the industry (F58),local wholly foreign-invested enterprises and foreign-invested joint-stock companies in other industries (F49), non-local wholly foreign-invested enterprises and foreign-invested joint-stock companies in the industry (F55), and non-local wholly foreign-invested enterprises and foreign-invested joint-stock companies in other industries (F52).

The upper half of Fig. 6-4 shows that variables other than F40 have coefficients whose estimates are all negative under all methods in each relevant equation. This suggests that higher ratios of local medium and large FIEs inside and outside the industry and of non-local medium- and large-sized FIEs in the industry will cause a decrease in the number of jobs at local enterprises in the industry. And this is employment-relevant competition rather than complementation. There are seven estimates of F40’s effects under four methods in two equations pertaining to employment in the local industry. Among them, estimates under the FE and ABond methods are positive, whereas those under the RE and MLE methods are negative. All estimates of the coefficients of the variables under the FE and RE methods in each equation passed the Hausman test, which means that there are no systematic differences between them. It is impossible therefore to determine which of the four methods is more reliable. The simple average of the seven estimates is positive, which means that a higher ratio of non-local medium- and large-sized FIEs to total employment in other

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industries will cause an increase in the number of jobs in the local industry. In other words, there is a complementary relationship between them, and we may thus infer that medium- and large-sized FIEs are more inclined to establish industrial correlations with the local industry. b) Effects of Ratios of Wholly Foreign-invested Enterprises and Foreign-invested Joint-stock Companies to Total Employment on Employment in the Local Industry When it comes to capital structure, wholly foreign-invested enterprises and foreign-invested joint-stock companies are the main forms of FIEs in China’s manufacturing industry. As for the effects of wholly foreign-invested enterprises and foreign-invested joint-stock companies on employment in the local industry, a descending order of the simple averages of estimated coefficients is: local wholly foreign-invested enterprises and foreign-invested joint-stock companies in the industry (F58), local wholly foreign-invested enterprises and foreign-invested joint-stock companies in other industries (F49), non-local wholly foreign-invested enterprises and foreign-invested joint-stock companies in the industry (F55), and non-local wholly foreign-invested enterprises and foreign-invested joint-stock companies in other industries (F52), whose simple averages of coefficients are -0.02311, -0.08325, -0.82918, and -2.08627, respectively. Estimates of the coefficients of variables other than F58 under different estimation methods in relevant models are all negative, which suggests that the corresponding enterprises have competitive/repulsive, rather than complementary/promoting, effects on employment in the local industry. As for the effects of the ratio of wholly foreign-invested enterprises and foreign-invested joint-stock companies to total employment in the local industry (F58) on employment in that industry, three estimates are positive and came from the FE method (two) and the ABond method (one); four others are negative and came from the RE method (two) and the MLE method (two). There are very small differences between positive estimated coefficients and between negative ones, as opposed to very big differences between positive and negative coefficients. Estimates of relevant models have all passed their respective tests, including the Hausman test, so it is impossible to select any estimate from among them. Since the simple average of the above-mentioned seven estimates is negative, we may say that the effects of F58 are consistent with the negative effects of the other three variables mentioned above. In descending order, the effects on employment growth in the local industry are: the ratios of local DIEs to total employment in the industry (F22), local FIEs to total employment in the industry (F34), local FIEs to total employment in other industries (F25), local DIEs to total employment in other industries (F13), non-local FIEs to total employment in the industry (F31), non-local DIEs to total employment in the industry (F19), non-local DIEs to total employment in other industries (F16) and non-local FIEs to total employment in other industries (F28). Variables other than F22 have negative effects, and a variable has a greater effect than those of the variable(s) arranged before it. Next, we will summarize how employment calculated in different ways affects employment at enterprises in the region on the basis of Fig. 6-5.

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Fig. 6-5 Effects of DIEs and FIEs on Employment in the Local Industry in Descending Order Note: the horizontal axis represents a descending order of the employment effects of relevant variables, and the vertical axis represents the simple arithmetic averages of these effects. F22 is the ratio of local DIEs to total employment in the industry; F34,the ratio of local FIEs to total employment in the industry; F25, the ratio of local FIEs to total employment in other industries; F13, the ratio of local DIEs to total employment in other industries; F31, the ratio of non-local FIEs to total employment in the industry; F19, the ratio of non-local DIEs to total employment in the industry; F16, the ratio of non-local DIEs to total employment in other industries; and F28, the ratio of non-local FIEs to total employment in other industries.

(i) There is more competition than complementation in the relationship between local and non-local industries. Employment in a particular industry in a region is affected more by changes in employment in industries (including the same industry) in other regions (F16, F19, F28, and F31) than by such changes in industries (also including the same industry) in the same region (F13, F22, F25 and F34), as is shown in Fig. 6-5. (ii) The expansion of FIEs – whether in the same industry/region or not – will reduce total employment in the host country. The expansion of DIEs in the same industry is the only factor that can increase total employment in China. A decrease in employment in an industry in one region will result from an increase in employment at local FIEs in that industry (F34), local FIEs in other industries (F25), or non-local FIEs inside or outside that industry (F31 or F28), just like an increase in employment at local DIEs in other industries (F13) or non-local DIEs inside or outside the industry (F19 or F16).The expansion of local DIEs in the industry (F22) is the only factor that will promote the overall expansion of local enterprises in that industry, as is shown in Fig. 6-5. (iii) Employment-relevant competition within an industry is weaker than between industries. Total employment in a particular industry in a region is affected more by changes in employment in other local industries (F25 and F13) than by such changes in local FIEs or DIEs in that industry (F22 or F34), that is, |F34| or |f22| < |F13| or |F25|. (iv) Employment changes in FIEs, whether in one industry/region or not, generally have weaker effects on total employment in that industry than do such changes in DIEs. Employment changes in FIEs, whether in a particular local industry, in other local

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industries, or in the same industry in other regions, have weaker absolute effects on total employment in the local industry than do such changes in DIEs of the same type (|F34| < |F22|, |F25| < |F13|, and |F31| < |F19|).Non-local FIEs in other industries are the only type of FIEs whose employment changes have stronger absolute effects on employment in the local industry than do such changes in DIEs of the same type (|F28| < |F16|). 6.3.3 Effects of Other Factors (1) Further Investment, Whether by FIEs or DIEs (F04 or F03), Will Increase Jobs that They Provide, and FIEs Have Greater Employment Effects than DIEs Among estimates under the four methods, only those under the dynamic XTABond method meet the expectations of economic theory. Fig. 6-6 illustrates the distribution of all the estimated coefficients of F03 and F04 under XTABond. Every one percentage point increase in foreign (or domestic) capital will lead to a job increase of 2.5-7 (or 1.5-3.5) percentage points.

Fig. 6-6 Effects of Domestic Capital (F03) and Foreign Capital (F04) on the Number of Employees (F05): the Distribution of Significant Estimates (2) Higher Tendency toward Processing (F06) Makes Foreign Capital’s Employment Effects Weaker than Those of Domestic Capital Fig. 6-7 illustrates the distribution of 64 regression results of F05 with respect to F06. These results consistently indicate that for products made by an enterprise, a higher tendency toward processing (F06) will lead to weaker employment effects. Every one percentage point increase in such tendency will cause a decrease of 10-15 percentage points in the enterprise’s employment effects. FIEs typically have a higher tendency toward processing than do DIEs, so their employment effects are naturally weaker than those of DIEs.

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Fig. 6-7 Effects of Tendency toward Product Processing and Export on Employment: the Distribution of Significant Estimates (3) Higher Tendency toward Product Export (F07) Makes Foreign Capital’s Employment Effects Stronger than Those of Domestic Capital Fig. 6-7 illustrates the distribution of 64 regression results of F05 with respect to F07. These results consistently indicate that every one percentage point increase in tendency toward product export (F07) will cause an increase of 15-45 percentage points in employment. Since FIEs typically have a higher tendency toward product export than do DIEs, employment effects per unit of foreign capital are stronger than those of domestic capital. (4) A Higher Ratio of Foreign Capital to Corporate Capital Stock (F09) Causes a Faster Job Increase Every one percentage point increase in F09 will cause a job increase of 6-23 percentage points, as is shown in Fig. 6-8. Since employment effects per unit of foreign capital (the coefficient of F03 in F05 regressions) are greater than those of domestic capital (the coefficient of F04 in F05 regressions) and the ratio of foreign capital to capital stock (F09) is inversely proportional to that of domestic capital (F08), we can infer that F08 has weaker employment effects than F09.

Fig. 6-8 Employment Effects of the Ratio of Foreign Capital to Registered Capital (F09) and of the Local Distribution of the Same Industry (F10): the Distribution of Significant Estimates

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(5) FIEs Are More Highly Concentrated in Counties (Cities) (F10) than DIEs in the Same Industry and Thus Have Greater Employment Effects than DIEs Every one percentage point increase in F10 will cause a job increase of 13-16 times, as is shown in Fig. 6-8. Foreign capital is mostly distributed in eastern China, especially economically better-developed regions such as the Pearl River Delta, the Yangtze River Delta, and the Bohai Rim, where some developed counties (cities) have set up specific economic/industrial/high-tech zones. By comparison, domestic capital in an industry is geographically much less concentrated than foreign capital in the same industry, and, naturally, has much weaker employment effects than the latter.

6.4 Summary In this chapter, we estimated the behavioral equation of employment at enterprises using the dynamic panel data analysis method and drew the following conclusions. Employment at an enterprise is affected by factors including the wage rate at that enterprise (negative effects), wage-rate and employment spillover effects from other enterprises and so on. Wage-rate spillover effects from other enterprises include those from FIEs and DIEs, whether inside or outside the enterprise’s industry or region. As for the effects of the average wage rate among non-local enterprises on employment at enterprises in a particular industry in a region, DIEs are greater than FIEs in that industry, while FIEs are greater than DIEs outside that industry. With regard to the employment spillover effects of other enterprises, there is more competition than complementation from those enterprises, whether they are DIEs or FIEs and whether they are inside or outside the industry/region. The expansion of FIEs – whether in the same industry/region or not – will reduce total employment in the host country. The expansion of DIEs in the same industry is the only factor that can increase total employment in China. Employment-relevant competition in an industry is weaker than between industries. Employment changes in FIEs, whether in a particular industry/region or not, generally has weaker effects on total employment in that industry than do such changes in DIEs. In addition, further investment, whether by FIEs or DIEs, will increase the number of jobs that they provide, and FIEs have greater employment effects than do DIEs. A higher tendency toward processing makes foreign capital’s employment effects weaker than those of domestic capital. A higher tendency toward product export makes foreign capital’s employment effects stronger than those of domestic capital. A higher ratio of foreign capital to corporate capital stock causes a faster job increase. FIEs have a higher concentration in counties (cities) than do DIEs in the same industry, so they have greater employment effects than DIEs.

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Chapter 7 Estimating a Simultaneous Equation Model of the Effects of FDI on Wage Rates and Employment at Chinese Enterprises The wage rate and size of employment at an enterprise are a pair of variables, or there is a cause-and-effect relationship between them, according to the results of the theoretical model in Chapter 3. This relationship makes the effects of FDI on the wage rate and employment form a small system of simultaneous equations. Accordingly, a method of estimating simultaneous equation models must be used.

7.1 Empirical Setting of the Simultaneous Equation Model of Wage Rates and Employment In Chapter 5, Empirical Equation (11) of wage rates at enterprises is set as follows: (7.1) , , and ; d and f represent domestic and where foreign capital, respectively, k and L represent capital stock per capita and total employment, represents the openness of the real sector (EX and Y represent export and respectively, total output, respectively), and l represents the ratio to total employment. In this equation, L is one of the variables that decides the wage rate, w. In Chapter 6, Empirical Equation (14) of the size of employment at enterprises is set as follows: (7.2) where all variables mean the same as in Equation (7.1). In this equation, the wage rate, w, is one of the variables that decides the size of employment at enterprises. As a result, the wage rate, w, and employment size have a relationship in which they decide each other. The endogeneity issue still exists in this relationship even though numerous identical control variables are taken into account (please refer to Chapters 5 and 6 for relevant information). Sixteen groups of simultaneous-equation, empirical models of the wage rate and employment are built on the basis of empirical model settings regarding both of them in Chapters 5 and 6: (I) Let all units in dimensions such as region, industry, ownership type, and business size be independent of each other:

119

(II) Let regions be not independent of each other: ‫݌‬

݂

݂

‫݌‬

݂

݂

‫ܯ‬ ݅ ݀ ݀ ‫݆݅ݍ݌ݓ‬ ൌ ‫ݓ‬ሺ‫ ݆݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫݆݅ݍ݌‬ ǡ ݇‫ ݆݅ݍ݌‬Ǣ ‫ ݆݅ݍ݌ܮ‬ǡ ݈‫݆݅ݍ݌‬ ǡ ݈‫ ݆݅ݍ݌‬Ǣ ‫ݓ‬ሺ‫݌‬ሻ Ǣ ܽ‫ ݍ‬ǡ ܽ݅ ǡ ݆ܽ ሻ ൝ ݅ ݂ ݂ ‫݌‬ ‫ܯ‬ ݀ ݀ ‫ ݆݅ݍ݌ܮ‬ൌ ݃ሺ‫ ݆݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫ ݆݅ݍ݌‬ǡ ݇‫ ݆݅ݍ݌‬Ǣ ‫ ݆݅ݍ݌ݓ‬ǡ ݈‫ ݆݅ݍ݌‬ǡ ݈‫ ݆݅ݍ݌‬Ǣ ‫ݓ‬ሺ‫݌‬ሻ Ǣ ܽ‫ ݍ‬ǡ ܽ݅ ǡ ݆ܽ ሻ

൝

‫ܯ‬ ݅ ݀ ݀ ൌ ܿሺ‫ ݆݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫݆݅ݍ݌‬ ǡ ݇‫ ݆݅ݍ݌‬Ǣ ‫ ݆݅ݍ݌ܮ‬ǡ ݈‫݆݅ݍ݌‬ ǡ ݈‫ ݆݅ݍ݌‬Ǣ ܿሺ‫݌‬ሻ Ǣ ܽ‫ ݍ‬ǡ ܽ݅ ǡ ݆ܽ ሻ ܿ‫݆݅ݍ݌‬ ‫݌‬

݂

݂

‫ܯ‬ ݀ ݀ ‫ ݆݅ݍ݌݅ܮ‬ൌ ݃Ԣ ሺ‫ ݆݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫݆݅ݍ݌‬ ǡ ݇‫ ݆݅ݍ݌‬Ǣ ܿ‫ ݆݅ݍ݌‬ǡ ݈‫݆݅ݍ݌‬ ǡ ݈‫ ݆݅ݍ݌‬Ǣ ܿሺ‫݌‬ሻ Ǣ ܽ‫ ݍ‬ǡ ܽ݅ ǡ ݆ܽ ሻ

(III) Assume that industries are not independent of each other in terms of wage rate (or average compensation): ‫݌‬

݂

݂

‫݌‬

݂

݂

‫ܮܤ‬ ݅ ݀ ݀ ‫݆݅ݍ݌ݓ‬ ൌ ‫ݓ‬ሺ‫ ݆ ݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫݆݅ݍ݌‬ ǡ ݇‫ ݆݅ݍ݌‬Ǣ ‫ ݆݅ݍ݌ܮ‬ǡ ݈‫݆݅ݍ݌‬ ǡ ݈‫ ݆݅ݍ݌‬Ǣ ‫ݓ‬ሺ‫ݍ‬ሻ Ǣ ܽ‫ ݌‬ǡ ܽ݅ ǡ ݆ܽ ሻ ൝ ݅ ݂ ݂ ‫݌‬ ‫ܮܤ‬ ݀ ݀ ‫ ݆݅ݍ݌ܮ‬ൌ ݃ሺ‫ ݆݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫݆݅ݍ݌‬ ǡ ݇‫ ݆݅ݍ݌‬Ǣ ‫ ݆݅ݍ݌ݓ‬ǡ ݈‫݆݅ݍ݌‬ ǡ ݈‫ ݆݅ݍ݌‬Ǣ ‫ݓ‬ሺ‫ݍ‬ሻ Ǣ ܽ‫ ݌‬ǡ ܽ݅ ǡ ݆ܽ ሻ

൝

‫ܮܤ‬ ݅ ݀ ݀ ൌ ܿሺ‫ ݆݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫݆݅ݍ݌‬ ǡ ݇‫ ݆݅ݍ݌‬Ǣ ‫ ݆݅ݍ݌ܮ‬ǡ ݈‫݆݅ݍ݌‬ ǡ ݈‫ ݆݅ݍ݌‬Ǣ ܿሺ‫ݍ‬ሻ Ǣ ܽ‫ ݌‬ǡ ܽ݅ ǡ ݆ܽ ሻ ܿ‫݆݅ݍ݌‬ ‫݌‬

݂

݂

‫ܮܤ‬ ݀ ݀ ‫ ݆݅ݍ݌݅ܮ‬ൌ ݃Ԣ ሺ‫ ݆݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫݆݅ݍ݌‬ ǡ ݇‫ ݆݅ݍ݌‬Ǣ ܿ‫ ݆݅ݍ݌‬ǡ ݈‫݆݅ݍ݌‬ ǡ ݈‫ ݆݅ݍ݌‬Ǣ ܿሺ‫ݍ‬ሻ Ǣ ܽ‫ ݌‬ǡ ܽ݅ ǡ ݆ܽ ሻ

(IV) Let foreign capital’s spillover effects inside and outside the industry be only within the region: ݂

‫݌‬

൝

݂

݂

݂

݂

݂

‫݌‬

݂

݂

݂

݂

݂

݂

݂

݀ ݀ ݀ ݀ ݀ ݀ ǡ ݇‫ ݆݅ݍ݌‬Ǣ ‫ ݆݅ݍ݌ݓ‬ǡ ݈‫݆݅ݍ݌‬ ǡ ݈‫ ݆݅ݍ݌‬Ǣ ‫ ݍ݌ݓ‬ǡ ݈‫ ݍ݌‬Ǣ ‫݌ݓ‬ሺ‫ݍ‬ሻ ǡ ݈‫݌‬ሺ‫ݍ‬ሻ Ǣ ‫ݍ݌ݓ‬ ǡ ݈‫ݍ݌‬ Ǣ ‫݌ݓ‬ሺ‫ݍ‬ሻ ǡ ݈‫݌‬ሺ‫ݍ‬ሻ ሻ ‫ ݆݅ݍ݌݅ܮ‬ൌ ݃ሺ‫ ݆݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫݆݅ݍ݌‬ ݂

‫݌‬

൝

݂

݀ ݀ ݅ ݀ ݀ ݀ ݀ ൌ ‫ݓ‬ሺ‫ ݆݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫݆݅ݍ݌‬ ǡ ݇‫ ݆݅ݍ݌‬Ǣ ‫ ݆݅ݍ݌ܮ‬ǡ ݈‫݆݅ݍ݌‬ ǡ ݈‫ ݆݅ݍ݌‬Ǣ ‫ ݍ݌ݓ‬ǡ ݈‫ ݍ݌‬Ǣ ‫݌ݓ‬ሺ‫ݍ‬ሻ ǡ ݈‫݌‬ሺ‫ݍ‬ሻ Ǣ ‫ݍ݌ݓ‬ ǡ ݈‫ݍ݌‬ Ǣ ‫݌ݓ‬ሺ‫ݍ‬ሻ ǡ ݈‫݌‬ሺ‫ݍ‬ሻ ሻ ‫݆݅ݍ݌ݓ‬

݂

݂

݂

݀ ݀ ݅ ݀ ݀ ݀ ݀ ൌ ܿሺ‫ ݆݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫݆݅ݍ݌‬ ǡ ݇‫ ݆݅ݍ݌‬Ǣ ‫ ݆݅ݍ݌ܮ‬ǡ ݈‫݆݅ݍ݌‬ ǡ ݈‫ ݆݅ݍ݌‬Ǣ ܿ‫ ݍ݌‬ǡ ݈‫ ݍ݌‬Ǣ ܿ‫݌‬ሺ‫ݍ‬ሻ ǡ ݈‫݌‬ሺ‫ݍ‬ሻ Ǣ ܿ‫ݍ݌‬ ǡ ݈‫ݍ݌‬ Ǣ ܿ‫݌‬ሺ‫ݍ‬ሻ ǡ ݈‫݌‬ሺ‫ݍ‬ሻ ሻ ܿ‫݆݅ݍ݌‬ ݂

‫݌‬

݂

݂

݂

݂

݂

݀ ݀ ݀ ݀ ݀ ݀ ǡ ݇‫ ݆݅ݍ݌‬Ǣ ܿ‫ ݆݅ݍ݌‬ǡ ݈‫݆݅ݍ݌‬ ǡ ݈‫ ݆݅ݍ݌‬Ǣ ܿ‫ ݍ݌‬ǡ ݈‫ ݍ݌‬Ǣ ܿ‫݌‬ǡሺ‫ݍ‬ሻൌ‫ Ͳݍ‬ǡ ݈‫݌‬ሺ‫ݍ‬ሻ Ǣ ܿ‫ݍ݌‬ ǡ ݈‫ݍ݌‬ Ǣ ܿ‫݌‬ሺ‫ݍ‬ሻ ǡ ݈‫݌‬ሺ‫ݍ‬ሻ ሻ ‫ ݆݅ݍ݌݅ܮ‬ൌ ݃Ԣ ሺ‫ ݆݅ݍ݌ݔ‬ǡ ݉‫ ݆݅ݍ݌‬ǡ ݈‫ ݍ‬Ǣ ݇‫݆݅ݍ݌‬

(V) Let foreign capital’s spillover effects inside and outside the industry be inside and outside the region: ’

ǡ Žˆ Ǣ š ǡ  ǡ Ž Ǣ  † ǡ  ˆ Ǣ  ǡ Ž† ǡ Žˆ Ǣ ™ ˆ ǡ Žˆ Ǣ ™ ˆ ǡ Žˆ Ǣ ™ ˆ ‫ ‹ ™ۓ‬ൌ ™ ൥ ’“‹Œ ’“‹Œ “ ’“‹Œ ’“‹Œ ’“‹Œ ’“‹Œ ’“‹Œ ’“ ’“ ’ሺ“ሻ ’ሺ“ሻ ሺ’ሻሺ“ሻ ሺ’ሻሺ“ሻ ൩ † † † † ˆ ˆ † † † † ۖ ’“‹Œ ™ሺ’ሻ“ ǡ Žሺ’ሻ“ Ǣ ™’“ ǡ Ž’“ Ǣ ™ ǡŽ Ǣ ™ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ ™ሺ’ሻ“ ǡ Žሺ’ሻ“ ’ሺ“ሻ ’ሺ“ሻ

’

ˆ ˆ ˆ š’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  †’“‹Œ ǡ  ˆ’“‹Œ Ǣ ™’“‹Œ ǡ Ž†’“‹Œ ǡ Žˆ’“‹Œ Ǣ ™’“ ǡ Žˆ’“ Ǣ ™’ሺ“ሻ ǡ Žˆ’ሺ“ሻ Ǣ ™ሺ’ሻሺ“ሻ ǡ Žˆሺ’ሻሺ“ሻ Ǣ ‫‹ ۔‬ ൩ ۖ ’“‹Œ ൌ ‰ ൥ † † † ˆ ˆ † † † † ™ሺ’ሻ“ ǡ Žሺ’ሻ“ Ǣ ™’“ ǡ Ž’“ Ǣ ™’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ ™ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ ™ሺ’ሻ“ ǡ Žሺ’ሻ“ ‫ە‬ ’

ǡ Žˆ Ǣ š ǡ  ǡ Ž Ǣ  † ǡ  ˆ Ǣ  ǡ Ž † ǡ Žˆ Ǣ … ˆ ǡ Žˆ Ǣ … ˆ ǡ Žˆ Ǣ … ˆ ‫ ‹ … ۓ‬ൌ … ൥ ’“‹Œ ’“‹Œ “ ’“‹Œ ’“‹Œ ’“‹Œ ’“‹Œ ’“‹Œ ’“ ’“ ’ሺ“ሻ ’ሺ“ሻ ሺ’ሻሺ“ሻ ሺ’ሻሺ“ሻ ൩ ’“‹Œ ˆ ˆ † † † † † † † † ۖ …ሺ’ሻ“ ǡ Žሺ’ሻ“ Ǣ …’“ ǡ Ž’“ Ǣ … ǡŽ Ǣ …ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ …ሺ’ሻ“ ǡ Žሺ’ሻ“ ’ሺ“ሻ ’ሺ“ሻ

’



ˆ ˆ ˆ š’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  ’“‹Œ ǡ  ˆ’“‹Œ Ǣ …’“‹Œ ǡ Ž†’“‹Œ ǡ Žˆ’“‹Œ Ǣ …’“ ǡ Žˆ’“ Ǣ …’ሺ“ሻ ǡ Žˆ’ሺ“ሻ Ǣ …ሺ’ሻሺ“ሻ ǡ Žˆሺ’ሻሺ“ሻ Ǣ ‫‹ ۔‬ ൩ ۖ’“‹Œ ൌ ‰′ ൥ † † † † ˆ ˆ † † † † …ሺ’ሻ“ ǡ Žሺ’ሻ“ Ǣ …’“ ǡ Ž’“ Ǣ …’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ …ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ …ሺ’ሻ“ ǡ Žሺ’ሻ“ ‫ە‬

120

(VI) Let wholly foreign-invested enterprises and foreign-invested joint-stock companies inside and outside the region have spillover effects inside and outside the industry: ’

š ǡ  ǡ Ž Ǣ  † ǡ  ˆ Ǣ  ǡ Ž† ǡ Žˆ Ǣ ™ ˆ͵Ͷ ǡ Žˆ͵Ͷ Ǣ ™ ˆ͵Ͷ ǡ Žˆ͵Ͷ Ǣ ™ ˆ͵Ͷ ǡ Žˆ͵Ͷ Ǣ ‫ ‹ ™ۓ‬ൌ ™ ൥ ’“‹Œ ’“‹Œ “ ’“‹Œ ’“‹Œ ’“‹Œ ’“‹Œ ’“‹Œ ’“ ’“ ’ሺ“ሻ ’ሺ“ሻ ሺ’ሻሺ“ሻ ሺ’ሻሺ“ሻ ൩ ’“‹Œ ˆ͵Ͷ ˆ͵Ͷ † † † ۖ ™ሺ’ሻ“ ǡ Žሺ’ሻ“ Ǣ ™’“ ǡ Ž†’“ Ǣ ™ † ǡ Ž† Ǣ ™ሺ’ሻሺ“ሻ ǡ Ž†ሺ’ሻሺ“ሻ Ǣ ™ሺ’ሻ“ ǡ Ž†ሺ’ሻ“ ’ሺ“ሻ ’ሺ“ሻ

’

ˆ͵Ͷ ˆ͵Ͷ ˆ͵Ͷ ˆ͵Ͷ ˆ͵Ͷ ˆ͵Ͷ ǡ Ž’“ Ǣ ™’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ ™ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ š’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  †’“‹Œ ǡ  ˆ’“‹Œ Ǣ ™’“‹Œ ǡ Ž†’“ ‹Œ ǡ Žˆ’“‹Œ Ǣ ™’“ ‫‹ ۔‬ ൩ ۖ ’“‹Œ ൌ ‰ ൥ ˆ͵Ͷ ˆ͵Ͷ † † † † † † † † ™ሺ’ሻ“ ǡ Žሺ’ሻ“ Ǣ ™’“ ǡ Ž’“ Ǣ ™’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ ™ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ ™ሺ’ሻ“ ǡ Žሺ’ሻ“ ‫ە‬ ’

š ǡ  ǡ Ž Ǣ  † ǡ  ˆ Ǣ  ǡ Ž† ǡ Žˆ Ǣ … ˆ͵Ͷ ǡ Žˆ͵Ͷ Ǣ … ˆ͵Ͷ ǡ Žˆ͵Ͷ Ǣ … ˆ͵Ͷ ǡ Žˆ͵Ͷ Ǣ ‫ ‹ … ۓ‬ൌ … ൥ ’“‹Œ ’“‹Œ “ ’“‹Œ ’“‹Œ ’“‹Œ ’“‹Œ ’“‹Œ ’“ ’“ ’ሺ“ሻ ’ሺ“ሻ ሺ’ሻሺ“ሻ ሺ’ሻሺ“ሻ ൩ ’“‹Œ † † † ˆ͵Ͷ † † † ۖ …ሺ’ሻ“ ǡ Žˆ͵Ͷ ǡ Ž† Ǣ …ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ …ሺ’ሻ“ ǡ Ž†ሺ’ሻ“ ሺ’ሻ“ Ǣ …’“ ǡ Ž’“ Ǣ … ’ሺ“ሻ ’ሺ“ሻ



’

ˆ͵Ͷ ˆ͵Ͷ ˆ͵Ͷ ˆ͵Ͷ ˆ͵Ͷ š’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  †’“‹Œ ǡ  ˆ’“‹Œ Ǣ …’“‹Œ ǡ Ž†’“‹Œ ǡ Žˆ’“‹Œ Ǣ …’“ ǡ Ž’“ Ǣ …’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ …ሺ’ሻሺ“ሻ ǡ Žˆ͵Ͷ ‫‹ ۔‬ ሺ’ሻሺ“ሻ Ǣ ൩ ۖ’“‹Œ ൌ ‰′ ൥ ˆ͵Ͷ ˆ͵Ͷ † † † † † † † † …ሺ’ሻ“ ǡ Žሺ’ሻ“ Ǣ …’“ ǡ Ž’“ Ǣ …’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ …ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ …ሺ’ሻ“ ǡ Žሺ’ሻ“ ‫ە‬

(VII) Let medium- and large-sized FIEs inside and outside the region have spillover effects inside and outside the industry: ’

Œͳʹ Œͳʹ

Œͳʹ

Œͳʹ

Œͳʹ

Œͳʹ

† † ˆ ˆ ‫ ‹ ™ ۓ‬ൌ ൥š’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  ’“‹Œ ǡ  ’“‹Œ Ǣ ’“‹Œ ǡ Ž’“‹Œ ǡ Ž’“‹Œ Ǣ ™’“ ǡ Ž’“ Ǣ ™’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ ™ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ൩ Œͳʹ Œͳʹ ۖ ’“‹Œ ™ ǡŽ Ǣ ™ † ǡ Ž† Ǣ ™ † ǡ Ž† Ǣ ™ † ǡ Ž† Ǣ ™ † ǡ Ž† ሺ’ሻ“

ሺ’ሻ“

’“

’“

’ሺ“ሻ ’ሺ“ሻ

ሺ’ሻሺ“ሻ ሺ’ሻሺ“ሻ

ሺ’ሻ“

ሺ’ሻ“

Œͳʹ Œͳʹ ’ Œͳʹ Œͳʹ Œͳʹ Œͳʹ ‫۔‬ š’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  †’“‹Œ ǡ  ˆ’“‹Œ Ǣ ™’“‹Œ ǡ Ž†’“‹Œ ǡ Žˆ’“‹Œ Ǣ ™’“ ǡ Ž’“ Ǣ ™’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ ™ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ ‹ ൩ ۖ’“‹Œ ൌ ‰ ൥ Œͳʹ Œͳʹ † † † † † ™ሺ’ሻ“ ǡ Žሺ’ሻ“ Ǣ ™’“ ǡ Ž†’“ Ǣ ™’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ ™ሺ’ሻሺ“ሻ ǡ Ž†ሺ’ሻሺ“ሻ Ǣ ™ሺ’ሻ“ ǡ Ž†ሺ’ሻ“ ‫ە‬ ’

Œͳʹ Œͳʹ

Œͳʹ

Œͳʹ

Œͳʹ

Œͳʹ

† † ˆ ˆ ‫ ‹ … ۓ‬ൌ … ൥š’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  ’“‹Œ ǡ  ’“‹Œ Ǣ ’“‹Œ ǡ Ž’“‹Œ ǡ Ž’“‹Œ Ǣ …’“ ǡ Ž’“ Ǣ …’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ …ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ൩ ’“‹Œ Œͳʹ Œͳʹ † † † † † ۖ …ሺ’ሻ“ ǡ Žሺ’ሻ“ Ǣ …’“ ǡ Ž†’“ Ǣ …’ሺ“ሻ ǡ Ž†’ሺ“ሻ Ǣ …ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ …ሺ’ሻ“ ǡ Ž†ሺ’ሻ“



’ Œͳʹ Œͳʹ Œͳʹ Œͳʹ Œͳʹ Œͳʹ ‫۔‬ š’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  †’“‹Œ ǡ  ˆ’“‹Œ Ǣ …’“‹Œ ǡ Ž†’“‹Œ ǡ Žˆ’“‹Œ Ǣ …’“ ǡ Ž’“ Ǣ …’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ …ሺ’ሻሺ“ሻ ǡ Žሺ’ሻሺ“ሻ Ǣ ൩ ۖ‹’“‹Œ ൌ ‰′ ൥ Œͳʹ Œͳʹ † † † † † ǡ Ž†’“ Ǣ …’ሺ“ሻ ǡ Ž’ሺ“ሻ Ǣ …ሺ’ሻሺ“ሻ ǡ Ž†ሺ’ሻሺ“ሻ Ǣ …ሺ’ሻ“ ǡ Ž†ሺ’ሻ“ …ሺ’ሻ“ ǡ Žሺ’ሻ“ Ǣ …’“ ‫ە‬

(VIII) Let spillover effects be extensive in all industries and regions in China regardless of ownership types and business sizes: ’

൝

† ˆ ‹ ™’“‹Œ ൌ ™ሾš’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  †’“‹Œ ǡ  ˆ’“‹Œ Ǣ ’“‹Œ ǡ Ž†’“‹Œ ǡ Žˆ’“‹Œ Ǣ ™“† ǡ Ž†“ ǡ ™“ˆ ǡ Žˆ“ ǡ ™ሺ“ሻ ǡ Ž†ሺ“ሻ ǡ ™ሺ“ሻ ǡ Žˆሺ“ሻ ǡ ሿ ’

† ˆ ‹’“‹Œ ൌ ‰ሾš’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  †’“‹Œ ǡ  ˆ’“‹Œ Ǣ ™’“‹Œ ǡ Ž†’“‹Œ ǡ Žˆ’“‹Œ Ǣ ™“† ǡ Ž†“ ǡ ™“ˆ ǡ Žˆ“ ǡ ™ሺ“ሻ ǡ Ž†ሺ“ሻ ǡ ™ሺ“ሻ ǡ Žˆሺ“ሻ ǡ ሿ ’

† ˆ ‹ …’“‹Œ ൌ …ሾš’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  †’“‹Œ ǡ  ˆ’“‹Œ Ǣ ’“‹Œ ǡ Ž†’“‹Œ ǡ Žˆ’“‹Œ Ǣ …“† ǡ …“† ǡ …“ˆ ǡ …“ˆ ǡ …ሺ“ሻ ǡ Ž†ሺ“ሻ ǡ …ሺ“ሻ ǡ Žˆሺ“ሻ ሿ ൝ ‹ ’ † † ˆ ’“‹Œ ൌ ‰ሾš’“‹Œ ǡ ’“‹Œ ǡ Ž“ Ǣ  ’“‹Œ ǡ  ˆ’“‹Œ Ǣ …’“‹Œ ǡ Ž†’“‹Œ ǡ Žˆ’“‹Œ Ǣ …“† ǡ Ž†“ ǡ …“ˆ ǡ Žˆ“ ǡ …ሺ“ሻ ǡ Ž†ሺ“ሻ ǡ …ሺ“ሻ ǡ Žˆሺ“ሻ ሿ



121

Given the endogeneity of the wage rate (or compensation) and employment as variables, these simultaneous-equation empirical models will be estimated using the reg3 command in the STATA software.

7.2 Empirical Conclusions regarding the effects of FDI on Wage Rates at Enterprises This section estimates the empirical models of wage rates at enterprises. For the results, please refer to Table 7-1. Below is the analysis of these results. 7.2.1 FDI Has a Greater Effect on Wage Rates at Enterprises than Does Domestic Capital Regarding investment made by enterprises themselves, foreign capital (F04) and domestic capital (F03) will both increase their wage rates (F01). Both F03 and F04 always have positive coefficients, according to the estimates of the seven models. FDI’s effects in pushing wage rates are 40%-75% greater than those of domestic capital (F03). The estimates of the seven models show that F01’s elasticity coefficient with respect to F04 is about 0.06 or 0.07, that is, every 1% increase in FDI will cause an increase of about 0.06% or 0.07% in wage rates at enterprises. F01’s elasticity coefficient with respect to F03 is about 0.04 or 0.05, which is about 0.02 or 0.03 percentage points smaller than its elasticity coefficient with respect to F04. Table 7-1 Elasticity Coefficients of Wage Rates at Enterprises with Respect to Wage Rates and Employment at Other Enterprises Wage-rate Spillover Effect

FIEs

Nati onwid e

Loc al

Non -local

This Industry Other Industri es This Industry Other Industri es This industry Other industri es

Wholly Foreign-invested Enterprises & Foreign-invested Joint-stock Companies

Employment Spillover Effect

DIEs

FIEs

Wholly Foreign-invested Enterprises & Foreign-invested Joint-stock Companies

DIEs

0.07

N/A

0.13

0.08

N/A

Insignifica nt

-0.14

N/A

1.11

0.40

N/A

Insignifica nt

0.311

0.25

0.34

Insig nificant

Insignificant

-0.03

0.07

0.041

0.10~0.22

-0.02

-0.04 ~ -0.15

-0.03

Insignificant

-0.53

0.54

Insignific ant -0.35

-0.04 -0.06 Insig nificant

-0.04 Insignificant

-0.12 -1.8 ~ 0.86

Note: 1. The elasticity coefficient of wage rate in the previous period was -0.25 for DIEs in other industries across China, -0.14 for local FIEs in the same industry and -0.04 for local wholly foreign-invested enterprises and foreign-invested joint-stock companies in other industries. In addition, “N/A” means that the corresponding value is unavailable since no empirical model was estimated. “Insignificant” is based on a confidence level of 95%.

There is actually such a wide gap between foreign and domestic capital in terms of effects on the wage rate in the same enterprise. This deserves further analysis. Nonetheless, investment made by an enterprise itself has weaker effects on its wage rate. The subsequent analysis will indicate that

122

investment made by an enterprise itself has much weaker effects on its wage rate than those spilled over from the wage rates of other enterprises and competition among them. We discriminated other enterprises that affect the wage rate of an enterprise by whether they are in the same industry/region as that of this enterprise and, then, by whether they are FIEs or DIEs. In addition, we analyzed the effects of wholly foreign-invested enterprises and foreign-invested joint-stock companies. For relevant estimation results, please refer to Supplementary Table 7-1. 7.2.2 Wage-rate Spillover Effects of FIEs in the Same Industry (1)

Overview

a) FIEs’ wage-rate spillover effects on wage rates of enterprises in the same industry across China are almost equivalent to the immediate effects on the wage rates of representative enterprises produced by their receiving FDI. The spillover effects from the average wage rate (F62) and employment at FIEs in the same industry across China are almost equal to the wage-rate increase effects caused by enterprises introducing foreign capital (F04). The elasticity coefficients of F62 and F64 are 0.07 and 0.08, respectively, which are equal to or slightly greater than that of F04. FIEs in the same industry across China can be grouped into local and non-local ones, of which local ones have significantly greater spillover effects on the wage rates of local enterprises in the same industry than such effects caused by their sizes. b) The wage rates of FIEs (F32) have very great spillover effects on those of local enterprises in the same industry, as opposed to the insignificant effects of their sizes. F32 has an elasticity coefficient of about 0.31, which suggests that every 1% increase in the average wage rate among local FIEs in an industry in a year will cause a 0.31% increase in the average wage rate among local enterprises in the same industry in this year. This is much higher than the effect of F04. Such competitive effects of wage rates at local FIEs in the same industry mainly occur in the current year. A 1% increase in F32 in Year t will cause a 0.14% decrease in the average wage rate among the other local enterprises in the same industry in Year t+1 (L. F32 has an elasticity coefficient of -0.14). This may be because an increase in the wage rates of local FIEs in Year t greatly raises non-local laborers’ expectations for a further increase in the wage rates in Year t+1. As a result, they will flock into this region in Year t+1 such that they outnumber jobs available from local FIEs, and this will naturally benefit the other local enterprises in this industry. The average wage rate of wholly foreign-invested enterprises and foreign-invested joint-stock companies (F56), which are a major part of local FIEs in the same industry, has an elasticity coefficient of 0.25, plus a one-year-lagged elasticity coefficient of -0.1. This directly reflects the above-mentioned characteristic. Nonetheless, there seems to be no systematic relationship between the sizes of local FIEs in the same industry (such sizes are reflected by employment ratio F34 or F58) and the wage rate of this enterprise. F34 is the ratio of local FIEs in an industry to total employment in this industry. F58 is the ratio of local wholly foreign-invested enterprises and foreign-invested joint-stock companies to total employment in this industry. Neither of them is significant in empirical estimation. This may be because wage-rate-relevant competition between this enterprise and local FIEs in the same industry aims primarily to attract new laborers. c) An increase in the average wage rate of non-local FIEs in the same industry (F29) or an

123

increase in employment ratios (F31 and F55) tend to cause a decrease in wage rates of local enterprises in the same industry. F29 has an elasticity coefficient of -0.03, but the average wage rate of non-local, wholly foreign-invested enterprises and foreign-invested joint-stock companies in the same industry (F53) is insignificant in the models. Employment at non-local FIEs in the same industry (F31) has an elasticity coefficient of -0.06. Of these, employment at wholly foreign-invested enterprises and foreign-invested joint-stock companies (F55) has an elasticity coefficient of -0.04. (2)

Comparison of the Indirect Wage-rate Spillover Effects of DIEs in the Same Industry The average wage rate of nationwide DIEs in the same industry (F59) has an elasticity coefficient of 0.13, which is greater than that of the wage-rate spillover effects of nationwide FIEs in the same industry (F62 has an elasticity coefficient of 0.07). Nationwide enterprises are grouped into local and non-local ones, of which the average wage rate of local DIEs in the same industry (F20) has an elasticity coefficient of about 0.34, which is statistically significant in Models IV through VII. By comparison, the average wage rate of non-local DIEs in the same industry (F17) is not statistically significant in several models. The employment ratio of nationwide DIEs in the same industry (F61) is insignificant. The employment ratio of local DIEs in the same industry (F22) is significant only in Model IV among the involved models, with an elasticity coefficient of -0.03. The employment ratio of non-local DIEs in the same industry (F19) has an elasticity coefficient of -0.12. We may summarize, therefore, that the wage rate of a representative enterprise of the host country of course receives positive spillover effects from the wage rates of FIEs in the same industry (especially local ones), but that the same effects it receives from DIEs in the same industry (especially local ones) are almost twice as great as the former. By comparison, it seems that effects from the employment ratios of the other enterprises in the same industry (whether DIEs or FIEs) tend to be insignificant. 7.2.3 FIEs’ Wage-rate Spillover Effects between Industries

(1) The wage rates of nationwide FIEs in other industries have negative spillover effects on those of representative enterprises, and these effects partially offset the positive spillover effects from the average wage rate of nationwide FIEs in the same industry The wage rate of a representative enterprise in a particular industry (F01) receives negative spillover effects from the average wage rate of nationwide FIEs in other industries (F68). F68 has an elasticity coefficient of -0.14, plus a one-year-lagged elasticity coefficient of -0.46. As is mentioned above, F01’s elasticity coefficient with respect to F62 is 0.07. (2) The spillover effects of wage rates at nationwide FIEs in other industries on pay slips result mainly from those of the average wage rate among non-local enterprises in other industries The average wage rate of non-local FIEs in other industries (F26) has an elasticity coefficient of -0.53 in Model VI and is insignificant in Model V, but it has a one-year-lagged elasticity coefficient of 0.46. Specifically, the average wage rate of non-local wholly foreign-invested enterprises and foreign-invested joint-stock companies in other industries (F50) has an elasticity coefficient of 0.54 in Model VI. Comparatively, the average wage rate of local FIEs in other industries (F23) has minor spillover effects on F01. F23 has an elasticity coefficient of about 0.07 in Model IV and about 0.08 in Model V. Specifically, the average wage rate of local wholly foreign-invested enterprises and foreign-invested joint-stock companies in other industries (F47) has an elasticity

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coefficient of 0.04 in Model VI, plus a one-year-lagged effect of -0.04. (3) The spillover effects of employment at nationwide FIEs in other industries on pay slips are statistically insignificant in most cases, or have a smaller elasticity coefficient even if they are statistically significant Employment at local FIEs in other industries (F25) has an elasticity coefficient of -0.04 in Model IV and is insignificant in Model V. Specifically, employment at local wholly foreign-invested enterprises and foreign-invested joint-stock companies in other industries (F49) has an elasticity coefficient of -0.02 in the models. Employment at non-local FIEs in other industries (F28) is insignificant in Models V and VI. Specifically, employment at non-local wholly foreign-invested enterprises and foreign-invested joint-stock companies in other industries (F52) is insignificant in Model VI. (4)

In other industries across China, the average wage rate and employment ratio of DIEs have greater spillover effects on the wage rates of representative enterprises than do those of FIEs a) F01 has an elasticity coefficient with respect to the average wage rate of nationwide DIEs in other industries (F65) of1.1, plus a one-year-lagged elasticity coefficient with respect to the latter of -0.25. F01 has an elasticity coefficient with respect to the average wage rate of local DIEs in other industries (F11) of 0.10-0.22, plus an elasticity coefficient with respect to the average wage rate of non-local DIEs in other industries (F14) of -0.35. b) The employment ratio of nationwide DIEs in other industries (F67) is insignificant in the models. Nonetheless, the employment ratio of local DIEs in other industries (F13) has an elasticity coefficient with respect to F01 of -0.14 to -0.15, and that of non-local DIEs in other industries has an elasticity coefficient with respect to F01 of -1.8 to 0.86, which seems to indicate that the interregional distribution of labor has greater spillover effects on the wage rates of representative enterprises than does the intra-regional distribution of labor. In general, the wage rates of representative enterprises (F01) receive the greatest wage-rate spillover effects from other enterprises, whereas size (employment)-relevant competition from other enterprises tends to be statistically insignificant or, even if statistically significant, on the weak side. F01 receives most of the wage-rate spillover effects from DIEs (especially local DIEs in the same industry), which are followed by FIEs (especially local FIEs in the same industry). 7.2.4 Other Empirical Conclusions about Wage Rates of Enterprises (1) The wage rates of enterprises (F01) have typical characteristics of stickiness. Results of all the empirical models indicate that the values of one-year-lagged F01 (L1.F01) and two-year-lagged F01 (L2.F01) both have positive effects on the current value. F01 has an elasticity coefficient with respect to them of 0.4-0.5 and 0.13-0.22 respectively, which suggests that F01 growth is 60%-70% from its own stickiness. (2) A job increase at an enterprise itself in the current period will cause a 0.12%-0.2% wage decrease in this period, but will cause a 0.18%-0.2% increase in its wage rate in the next year. (3) A higher ratio of intermediate inputs (F06) at an enterprise in the current year will cause its wage rate to decrease in this year (the latter has an elasticity coefficient with respect to the former of about 0.18%), but will cause a 0.05%-0.08% increase in its wage rate in the next year. (4) The ratio of products exported by an enterprise in the current year (F07) will contribute to a higher wage rate at this enterprise in this year (the latter has an elasticity coefficient with

125

respect to the former at 0.02-0.05), but will cause a 0.02%-0.04% decrease in its wage rate in the next year. (5) A higher ratio of foreign capital to the registered capital of an enterprise (F09) will cause a decrease in its wage rate, and the latter has an elasticity coefficient with respect to the former of -0.05% to -0.07%. (6) A higher concentration of the same industry in total local employment (F10) will contribute to an increase in the wage rates of enterprises. Every one percentage point increase in the former in the current year will cause a 0.7-3 percentage point increase in the latter in this year, but will cause a 1.1-2.6 percentage point decrease in these enterprises’ wage rates in the next year. (7) The wage rates of enterprises receive significant geographical spillover effects (F71) and inter-industry linkage spillover effects (F73), and the corresponding elasticity coefficients are 0.3 and 0.48 respectively.

7.3 Empirical Conclusions on the Effects of FDI on Employment at Enterprises 7.3.1 FDI Has Weaker Direct Job Creation Effects than Does Domestic Capital For an enterprise that receives FDI (F04), every 1% increase in FDI made in a particular period will contribute to a 0.03%-0.04% increase in its employment (F05) in a future period, but will cause a 0.04%-0.05% decrease in employment in the current period. By comparison, investment made by means of domestic capital (F03) has a greater effect on the increase of employment. Employment (F05) has an elasticity coefficient with respect to F03 in the previous year at 0.04%-0.05% and, in the same period, at -0.02%.The former is 0.01-0.02 percentage points higher than the corresponding coefficient of F03, whereas the latter is 0.02-0.03 percentage points higher than the same coefficient. In general, domestic capital has greater direct job-creation effects than does FDI. 7.3.2 Indirect Employment Effects of FIEs in the Same Industry Employment (F05) at a representative enterprise receives destructive effects from the average wage rate of nationwide FIEs in the same industry (F62). The former has an elasticity coefficient with respect to the latter of -0.06. This means that every one percentage point increase in the average wage rate of such FIEs will cause a 0.06 percentage point decrease in employment at this representative enterprise. More intuitively, every one percentage point increase in employment at such FIEs (F64) will cause a 0.24 percentage point decrease in employment at this enterprise. There is a competitive relationship between such FIEs and this representative enterprise in terms of employment. Even so, however, employment at the representative enterprise receives much greater indirect destructive effects from nationwide DIEs in the same industry, and the corresponding elasticity coefficient (F61) is -0.29. We will look at FIEs in the same industry by grouping them into local and non-local ones. Regarding employment, there is also a competitive relationship between the representative enterprise and local FIEs in the same industry, although it is less competitive than the nationwide average, not to mention the level of competition between this enterprise and local DIEs in the same industry. Employment at the representative enterprise (F01) has an elasticity coefficient with respect to the wage rate of local FIEs in the same industry (F32) and their employment ratio (F34) at -0.07 to -0.05 and -0.14 to -0.09 respectively. The former is comparable to F01’s elasticity

126

coefficient with respect to F62, whereas the latter is higher than F01’s elasticity coefficient with respect to F64. By comparison, the representative enterprise has an elasticity coefficient with respect to the wage rate of local DIEs in the same industry (F20) and their employment ratio (F22) at -0.03 and 0.1 respectively, plus an elasticity coefficient with respect to one-year-lagged F22 of0.16 – -0.11. Employment-relevant competition between the representative enterprise and non-local FIEs in the same industry exhibits itself differently in terms of wage-rate spillover effects and employment-relevant competition effects. The representative enterprise has an elasticity coefficient with respect to the average wage rate of non-local FIEs in the same industry (F29) and their employment of 0.04 and -0.3, respectively. This reflects the following fact: a higher average wage rate among non-local FIEs in the same industry will cause more jobs at the representative enterprise, but an increase in their employment (F31) will cause job losses at the latter. This strongly indicates that there is a very strong competitive relationship between the representative enterprise and non-local FIEs in the same industry in terms of employment. By comparison, employment-relevant competition between it and non-local DIEs in the same industry is even fiercer. The former has an insignificant elasticity coefficient with respect to the average wage rate of the latter (F17), plus an elasticity coefficient with respect to their employment ratio (F19) of - 0.4 to -0.26, which is similar to the corresponding elasticity coefficient with respect to non-local FIEs in the same industry. Table 7-2 Elasticity Coefficients of Employment at Enterprises with Respect to Wage Rates and Employment at Other Enterprises Wage-rate Spillover Effect

FIEs

Nati onwid e

Loc al

Non -local

This Industry Other Industri es This Industry Other Industri es This industry Other Industri es

-0.06

Wholly Foreign-invested Enterprises & Foreign-invested Joint-stock Companies N/A

Employment Spillover Effect Wholly Foreign-invested Enterprises & Foreign-invested Joint-stock Companies

DIEs

FIEs

DIEs

Insignific ant

-0.24

N/A

-0.29

9.23 (-7.0 4)

N/A

(7.17)

0.88 (-2.4)

N/A

2.81 (-0.66)

-0.07 ~ -0.05

-0.04

-0.03

-0.14 ~ -0.09

-0.04

0.1 (-0.16 ~ -0.11)

-0.04

Insignificant

-0.08

-0.23 ~ -0.15 (0.1 ~ 0.16)

-0.07

0.16 (-0.1)

0.04

-0.06

Insignific ant

-0.3

(-0.2)

-0.4 ~ -0.26

-22 ~ -3.4 (-6.35 ~ 8.3)

19.4 (5.84)

-6.5 – -0.18

-46 ~ 14.7 (-20. 8)

36.43

-25 ~ -4.1 (-10.4 ~ 3.79)

Note: Numbers in parentheses represent the representative enterprise’s elasticity coefficients with respect to the one-year-lagged values of the corresponding variables. Numbers in the form of intervals suggest that the estimation results of the same variable are statistically significant in all models, but that their coefficients are different from each other. In addition, “N/A” means that corresponding values are unavailable since the empirical models are not estimated. “Insignificant” is based on a confidence level of 95%.

7.3.3 FIEs’ Indirect Employment Effects between Industries On a nationwide average, FIEs outside the industry where the representative enterprise is located

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have job-creation effects on employment at this enterprise in the current period, but will have destructive effects on its employment in the next year. And comparison shows that the destructive effects are dominant. Employment at the representative enterprise has an elasticity coefficient with respect to the average wage rate of nationwide FIEs in other industries (F68) and their employment ratio (F70) in the current period at 0.88 and 9.23, respectively, and, with respect to their one-year-lagged values, of -2.4 and -7 respectively. Research results show that such destructive effects are mostly from non-local FIEs in other industries. The representative enterprise has an elasticity coefficient with respect to the average wage rate of these FIEs (F26) and their employment (F28) in the current period of -22 to-3.4 and -46 to14.7, respectively, and, with respect to their one-year-lagged values, of -6.35 to8.3 and -20.8, respectively. By comparison, the representative enterprise has an elasticity coefficient with respect to the average wage rate of local FIEs in other industries (F25) and their employment (F27) of -0.04 and -0.07, respectively, the absolute values of which are much smaller than those of the corresponding elasticity coefficients with respect to non-local FIEs in other industries. 7.3.4 Empirical Conclusion on Other Factors Affecting Employment at Enterprises (1) Employment at enterprises (F05) has typical characteristics of stickiness and is stickier than wage rates (F01). Results of all empirical models indicate that the values of one-year-lagged F05 (L1.F05) and two-years-lagged F05 (L2.F05) both have positive effects on the current value. F05 has an elasticity coefficient with respect to them of0.8 and 0.1, respectively, which suggests that F05 growth is 90% from its own stickiness. By comparison, the corresponding values of F01 are 0.4 – 0.5 and 0.13 – 0.22, respectively. (2) A wage-rate increase at an enterprise itself in the current period will cause a 0.08% job decrease in this period (it is statistically significant only in two of the seven models), but will cause a 0.1%-0.15% increase in its jobs in the next year (it is statistically significant in all the models). (3) A higher ratio of intermediate inputs (F06) at an enterprise in the current year will cause its employment (F05) to decrease in that year (the latter has an elasticity coefficient with respect to the former of about 0.05%), but will cause a 0.06%-0.09% increase in its employment in the next year. (4) The ratio of products exported by an enterprise in the current year (F07) will contribute to employment (F05) at that enterprise in that year (the latter has an elasticity coefficient with respect to the former of 0.2-0.23), but will cause a 0.1% decrease in its employment in the next year. (5) A higher ratio of foreign capital to the registered capital of an enterprise (F09) will cause an increase in its employment (F05) in the current period (the latter has an elasticity coefficient with respect to the former of 0.10% – 0.12%), but will cause a decrease of about 0.07% in its employment in the next year. (6) A higher concentration of the same industry in the total local employment (F10) will contribute to an increase in employment at enterprises (F05). Every one percentage point increase in the former in the current year will cause an increase of about 13.5 percentage points in the latter in that year, but will cause a decrease of about 11 percentage points in the latter in the next year. (7) Employment at enterprises receives statistically insignificant geographical spillover effects (F71) but significant inter-industry linkage spillover effects (F73).The elasticity coefficient of employment with respect to their current and one-year-lagged values is 0.5 and

128

-0.61, respectively: certain job-creation effects are followed by greater job-destruction effects.

7.4 Summary Given the endogeneity issue caused by the fact that wage rates and employment are the cause and effect of each other, this chapter constructs a small system of simultaneous equations on the basis of the behavioral equations of wage rates and employment at enterprises, before estimating this system using 3SLS. The following are the relevant conclusions. The immediate force for higher wage rates at enterprises is from further capital investment made in enterprises themselves, including additional FDI and domestic capital, of which the former has effects 40%-75% greater than those of the latter in terms of increasing their own wage rates. Resistance to an increase in the wage rates of enterprises is mostly from the wage-rate spillover effects of other enterprises. Among them, positive wage-rate spillover effects from nationwide FIEs in the same industry are comparable to the effects from an increase in direct investment in enterprises, but they are still about 50% weaker than the same effects from DIEs in the same industry (especially local DIEs). Nonetheless, these effects from DIEs in the same industry are almost fully offset by negative wage-rate spillover effects from nationwide FIEs in other industries, especially non-local ones. Further capital investment in enterprises produces direct job-creation effects, but such effects from FDI are weaker than those from domestic capital. Indirect employment effects from other enterprises are greater than the aforementioned direct effects. Specifically, indirect employment effects from nationwide enterprises in the same industry (including FIEs and DIEs) are usually destructive, whereas employment effects from nationwide FIEs in other industries are creative and then destructive. Moreover, such destructive effects are relatively dominant and mostly from non-local FIEs in other industries. Destructive employment effects from local FIEs in the same industry are weaker than those from non-local DIEs in the same industry and the nationwide average, not to mention those from local DIEs in the same industry, which are greater than the nationwide average. In addition, the wage rates and employment at enterprises are very sticky (especially employment).They are the inverse functions of each other and have an error correction mechanism. A higher ratio of intermediate inputs into products will cause lower wage rates and employment levels at enterprises in the current year, but they will restore in the next year. A higher ratio of products exported by enterprises will contribute to higher wage rates and employment levels in the current year, but will cause both of them to decrease in the next year. A higher ratio of foreign capital to corporate capital will favor an increase in employment, not in wage rates. In a particular region, a higher concentration within an industry will contribute to an increase in wage rates and employment levels (especially the latter) in the current year, but both will risk decreasing in the next year. Geographical proximity effects play a significant role in the increase of wage rates at enterprises, not in the increase of their employment levels. Inter-industry linkage favors the increase of wage rates at enterprises, and it has creative effects on employment at enterprises but will have greater, destructive effects on it afterward. How can we comprehensively assess FDI’s overall direct and indirect effects on wage rates and employment at enterprises? In the next chapter, we will employ the composite index and its breakdown method to research this issue on the basis of this chapter.

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Supplementary Table 7-1 3SLS Estimates of the Effects of FDI, Employment, and Other Variables on the Average Wage Rate of Enterprises (F01) Independent Variable Code Name _cons

Model I 2.49 (64.32) 0.49 (104.61) 0.22 (53.71) 0.05 (21.76) -0.01 (-4.09) 0.0700 (27.94)

Model II 0.05 (0.63) 0.47 (88.31) 0.20 (49.1) 0.05 (31.57)

Model III -0.11 (-1.1) 0.47 (85.7) 0.20 (48.63) 0.05 (31.83)

0.0705 (27.74)

0.0693 (27.44)

13 14

-0.01 (-5.95)

-0.01 (-3.3)

-0.01 (-2.8)

-0.01 (-2.93)

15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

-0.15 (-8.63) 0.14 (9.34) -0.19 (-11.55) 0.08 (4.73) 0.02 (2.34) -0.03 (-4.92) -0.06 (-6.09) 0.69 (6.92) -1.11 (-2.67)

-0.19 (-7.83) 0.18 (8.34) -0.18 (-10.85) 0.07 (4.13) 0.02 (2.34) -0.03 (-3.91) -0.07 (-6.74) 1.85 (4.09) -1.11 (-2.67)

-0.21 (-8.17) 0.20 (8.63) -0.19 (-11.27) 0.07 (4.43) 0.03 (2.83) -0.04 (-4.11) -0.07 (-7.04) 2.23 (4.65) -1.44 (-3.28)

-0.18 (-7.73) 0.18 (8.34) -0.17 (-11.67) 0.05 (3.7) 0.05 (4.83) -0.02 (-2.56) -0.05 (-5.44) 2.67 (6.27) -2.45 (-6.3) 0.10 (9.13) -0.06 (-5.36) -0.05 (-3.74)

No. 1 2 3 4 5 6 7 8 9 10 11 12

57

Model IV 0.16 (2.47) 0.41 (73.26) 0.13 (34.29) 0.04 (18.79) -0.01 (-2.34) 0.0572 (25.63)

Model V 1.58 (3.14) 0.41 (75.59) 0.13 (34.69) 0.04 (29.65)

0.0558 (37.8)

-0.19 (-9.31) 0.19 (9.95) -0.17 (-11.64) 0.05 (3.67) 0.04 (4.43) -0.02 (-2.95) -0.05 (-6.08) 2.95 (7.34) -2.58 (-7.04)

Model VI 0.22 (1.15) 0.40 (76.93) 0.13 (35.51) 0.04 (19.99) -0.01 (-2.73) 0.0603 (27.07)

Model VII 1.04 (12.15) 0.41 (79.07) 0.15 (39.72) 0.04 (30.97)

Model VIII -6.74 (-8.11) 0.45 (84.61) 0.19 (47.66) 0.05 (29.68)

0.0669 (28.97)

-0.01 (-2.73)

-0.01 (-2.85)

-0.12 (-6.11) 0.12 (6.8) -0.17 (-11.63) 0.05 (3.59) 0.02 (2.67) -0.02 (-2.16) -0.06 (-7.34) 1.79 (4.61) -1.63 (-4.55) 0.11 (9.97) -0.08 (-7.12) -0.04 (-4.32)

-0.15 (-7.43) 0.15 (8.02) -0.18 (-11.49) 0.06 (3.72) 0.03 (2.93) -0.02 (-3.14) -0.07 (-7.9) 1.53 (3.85) -1.43 (-3.83) 0.22 (20.53) -0.13 (-12.23) -0.15 (-4.19) 0.08 (2.39)

0.0682 (27.44) -0.01 (-2.52 ) -0.19 (-8.07) 0.19 (8.68) -0.18 (-11.23) 0.08 (4.61) 0.03 (3.17) -0.03 (-3.05) -0.07 (-7.63) 1.87 (4.12) -1.18 (-2.87)

-0.35 (-2.92) -1.80 (-4.05) 0.00

-0.86 (-5.5) 0.41 (2.8)

-0.12 (-5.98)

0.32 (61.69) -0.11 (-21.83) -0.03 (-2.1)

0.34 (66.49) -0.12 (-22.8)

-0.08 (-5.91) 0.33 (62.37) -0.11 (-20.37)

0.37 (68.06) -0.11 (-20.85)

-0.02

130

58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120

(-2.12) 0.07 (6.34) -0.05 (-5.36) -0.04 (-2.73)

0.08 (8.3) -0.06 (-6.63)

-0.53 (-2.01)

0.30 (60.66) -0.14 (-29.31) -0.03 (-1.85)

0.46 (3.35) -1.75 (-2.98) -0.03 (-4.05) -0.06 (-2.86) 0.31 (63.14) -0.14 (-28.7)

0.00 (2.06) 0.00 (2.4) -0.03 (-3.03) -0.05 (-7.56) 0.03 (4.88) 0.01 (7.32) -0.08 (-6.37) 0.05 (3.34) 0.04 (5.24) -0.04 (-4.49) -0.02 (-2.43) 0.54 (2.19) -0.04 (-2.65) 0.25 (57.92) -0.10 (-24.25) 0.13 (12.75) 0.07 (8.49) 0.08 (6.29) 1.10 (7.00) -0.25 (-2.42)

131

121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140

0.28 (2.65) -0.37 (-2.72) 2.56 (4.11) -4.19 (-6.34) 0.30 (35.63)

Obs Parms RMSE R2 Chi2 P-value

44,897 29 0.3210 0.6432 80,802 0

45,562 15 0.3676 0.5480 55,853 0

0.48 (5.89) -0.18 (-1.98) 46,176 26 0.3256 0.6384 83,481 0

46,176 27 0.3248 0.6401 83,768 0

46,581 31 0.3361 0.6175 75,405 0

46,727 16 0.3678 0.5422 56,641 0

46,730 13 0.3681 0.5415 54,968 0

47,730 23 0.3635 0.5528 58,640 0

Note: Coefficients of some variables in this table appear to be zero, but are actually not zero. This is mainly caused by limiting the number of decimal places for tabulation. In addition, numbers in parentheses are t values.

Supplementary Table 7-2 3SLS Estimates of the Effects of FDI, Wage Rate, and Other Variables on Employment (F01) No. 1 2

Independ ent Variable _cons

Model I -0.11 (-1.13)

3 4

0.81 (186.72)

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

0.10 (24.05) -0.10 (-3.62) 0.14 (8.28) -0.02 (-4.64) 0.04 (11.29) -0.01 (-3.33) 0.03 (8.34) -0.05 (-2.05) 0.08 (3.44) 0.23 (18.88) -0.09 (-7.79) 0.10 (4.47) -0.07 (-3.23)

Model II

Model III

Model IV

Model V

Model VI

Model VII

Model VIII

0.47 (4.16) 0.81 (183.72 ) 0.10 (23.7) -0.08 (-2.92) 0.14 (8.06) -0.02 (-5.3) 0.04 (12.05) -0.02 (-3.53) 0.03 (8.18) -0.05 (-2.14) 0.08 (3.09) 0.23 (18.3) -0.09 (-7.68) 0.11 (4.83) -0.07 (-3.18)

0.79 (5.67) 0.81 (184.67 ) 0.10 (23.22) -0.08 (-2.88) 0.14 (8.24) -0.02 (-5.09) 0.04 (11.8) -0.02 (-3.48) 0.03 (8.11) -0.05 (-1.98) 0.08 (3.45) 0.23 (18.39) -0.09 (-7.56) 0.10 (4.46) -0.07 (-3.01)

0.48 (5.92) 0.81 (192.79 ) 0.10 (24.87)

35.61 (3.99) 0.80 (191.28 ) 0.10 (25.18)

18.45 (9.62) 0.80 (187.04 ) 0.10 (24.71)

-12.40 (-9.98) 0.80 (191.82 ) 0.10 (25.19)

0.12 (19.85) -0.02 (-4.92) 0.04 (11.3) -0.02 (-4.68) 0.03 (8.51)

0.13 (21.32) -0.02 (-4.34) 0.05 (12.78) -0.02 (-4.63) 0.04 (10.62)

0.06 (2.77) 0.23 (18.28) -0.10 (-8.37) 0.11 (4.77) -0.06 (-2.63)

0.07 (3.24) 0.20 (15.93) -0.10 (-8.55) 0.12 (5.27) -0.07 (-3.34)

0.12 (20.32) -0.01 (-3.96) 0.05 (11.86) -0.02 (-4.2) 0.04 (9.68) -0.05 (-2.11) 0.09 (3.53) 0.20 (16.09) -0.11 (-8.58) 0.12 (5.17) -0.07 (-3.06)

2.32 (2.44) 0.80 (189.29 ) 0.10 (24.74) -0.08 (-4.77) 0.15 (14.36) -0.02 (-4.27) 0.05 (12.85) -0.01 (-3.44) 0.04 (9.7) -0.05 (-2.26) 0.09 (3.71) 0.21 (16.65) -0.10 (-8.01) 0.12 (5.4) -0.07 (-3.34)

0.10 (18.88) -0.02 (-5.33) 0.05 (13.54) -0.02 (-4.79) 0.04 (10.47)

0.06 (3.08) 0.20 (16.25) -0.10 (-8.4) 0.12 (5.2) -0.07 (-3.22)

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31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92

13.52 (29.8) -11.28 (-24.62) -0.08 (-7.72) -0.16 (-7.21) -0.10 (-6.62) -6.50 (-3.77) 2.73 (3.61) -25.02 (-4.66) -10.36 (-2.67) -0.40 (-13.43) -0.03 (-4.95) 0.10 (4.53) -0.13 (-5.85) -0.04 (-4.26) -0.23 (-5.28) 0.10 (2.5) -3.37 (-3.5) 8.27 (3.67) -46.16 (-3.8) -20.77 (-6.73) -0.04 (-3.83) -0.30 (-9.84) -0.07 (-11.75) -0.14 (-6.05) -0.01 (-6.05) -0.07 (-4.28) 0.01 (6.43) 0.77 (2.89) -0.20 (-8.62) -0.16 (-8.4) 0.09 (4.62)

13.27 (28.93) -11.05 (-23.84)

13.51 (29.59) -11.28 (-24.44)

13.34 (28.88) -11.20 (-24.08)

13.38 (29.09) -11.02 (-23.83)

13.43 (28.68) -11.01 (-23.32) -0.08 (-7.72)

13.34 (29.09) -11.16 (-24.19) -0.07 (-7.21)

-0.10 (-6.62)

-0.10 (-6.39) -0.18 (-2.26)

-0.16 (-7.21)

-6.50 (-3.77) 2.73 (3.61) -25.02 (-4.66) -10.36 (-2.67) -0.40 (-13.43)

-0.26 (-12.49)

-4.08 (-14.98) 3.79 (14.17) -0.30 (-14.17)

-0.03 (-4.95)

-0.13 (-5.85)

-0.23 (-5.28) 0.10 (2.5)

-0.07 (-11.75) -0.14 (-6.05)

0.10 (4.53) -0.16 (-5.81) -0.04 (-4.26) -0.15 (-3.77) 0.16 (3.96) -3.37 (-3.5) 8.27 (3.67) -46.16 (-3.8)

0.10 (4.33) -0.11 (-4.53)

-21.82 (-7.51) -6.35 (-3.57) 14.17 (13.32) -20.77 (-6.73)

-0.04 (-3.83) -0.30 (-9.84) -0.05 (-6.82) -0.09 (-3.91) -0.01 (-6.05) -0.07 (-4.28) 0.01 (6.43) 0.77 (2.89) -0.20 (-8.62) -0.16 (-8.4) 0.09 (4.62)

13.63 (29.97) -11.01 (-23.98)

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93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142

F73

Obs Parms RMSE R-sq Chi2 P-value

-0.07 (-4.3) 19.41 (8.04) 5.84 (3.1) 36.43 (4.31) -0.06 (-5.7) -0.20 (-9.05) -0.04 (-7.29) -0.04 (-2.56) -0.29 (-9.12) -0.06 (-6.63) -0.24 (-7.14) 2.81 (11.96) -0.66 (-4.36) 7.17 (17.54) 0.88 (5.76) -2.40 (-13.15) 9.23 (10.76) -7.04 (-6.37) 0.68 (9.23) -0.76 (-10.17) 0.50 (4.28) -0.61 (-4.91) 44,897 32 0.5300 0.84 240,991 0

-0.07 (-4.3) 19.41 (8.04) 5.84 (3.1) 36.43 (4.31) -0.06 (-5.7) -0.20 (-9.05) -0.04 (-7.29) -0.04 (-2.56) -0.29 (-9.12) -0.06 (-6.63) -0.24 (-7.14) 2.81 (11.96) -0.66 (-4.36) 7.17 (17.54) 0.88 (5.76) -2.40 (-13.15) 9.23 (10.76) -7.04 (-6.37) 0.70 (9.23) -0.80 (-10.17)

45,562 18 0.5332 0.84 242,387 0

0.50 (4.28) -0.61 (-4.91) 46,176 21 0.5335 0.84 244,154 0

46,176 31 0.5289 0.84 249,242 0

46,581 29 0.5270 0.84 252,915 0

46,727 18 0.5327 0.84 247,812 0

46,730 16 0.5327 0.84 247,732 0

46,730 24 0.5303 0.84 250,933 0

Note: Coefficients of some variables in this table appear to be zero, but are actually not zero. This is mainly caused by limiting the number of decimal places for tabulation. In addition, numbers in parentheses are t values.

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Chapter 8 Measuring the Effects of FDI on Income Distribution in China On the basis of the empirical estimation results described in Chapters 5 through 7, this chapter will take sample data to measure the wage gap among Chinese enterprises in the secondary sector above a given size, before identifying FDI’s contribution and analyzing the dynamics of this contribution. In this chapter, we will first discuss principles of measuring the income gap from the methodological perspective as well as the selection of indicators. We will then detail issues such as data used to measure the income gap before presenting the measurement results.

8.1 Methodology for Measuring Income Distribution Chapter 2 already outlines the methodology for measuring the income gap and covers common income-gap measures, assessment criteria, and categorization. This section will further explain part of them. 8.1.1 Common Income-gap Measures In literature regarding the income gap, the most common income-gap measures include the Gini coefficient (e.g., Tsai, 1995; Hemmer et al, 2005), variance, the CV of income, the logarithm of income variance, the variance of logarithms of income, the Atkinson index, the Dalton index, the Theil index, the Herfindahl index, etc. (see Table 8-1). Table 8-1 Formulas of Main Income-gap Measures and the Satisfaction of the Four Assessment Criteria No.

Measure

Formula

Satisfied Criteria*

1

Gini Coefficient

a, b, c

2

Variance

a, c, d

3

Coefficient of Variation

a, b, c, d

4

Log of Variance

c, d

5

Variance of Logarithms

c, d

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6

Atkinson Index

a, b, c, d

7

Dalton Index

a, d

8

Theil Index

a, b, c, d

9

Herfindahl Index

a, b, d

Note: the four assessment criteria include the weak principle of transfers (a), income homogeneity (b), population homogeneity (c), and decomposability (d). In all formulas in this table, y represents income, represents the average income (of the group), and n is the number of groups that the population is divided into under a particular represents the correlation coefficient between income recipients in the ascending order of criteria. In Formula 1, income and the corresponding income sequence. In Formulas 6 and 7, U(·) is the social utility function and is assumed in this book to be a constant relative inequality aversion utility function.

Income gap measures are often divided into relative and absolute gap measures. These two groups measure two fundamentally different concepts. The absolute gap depends on changes in the absolute amounts of real incomes, whereas the relative gap depends on the ratios among different incomes. Relative gap measures are often conceptually relevant, frequently used in practice and contain real relationships, so they are widely used. If we use absolute gap measures, then we will have to reach the conclusion that economic growth always causes a widening income gap while economic recession always causes a narrowing one – because there are differences in book value between the poor’s real income and that of the rich, and income variance often increases during economic growth and decreases during economic recession (Fields, 2001: 16). The income gap can also be divided into permanent and temporary gaps (Durlauf, 1992; Burkhauser and Poupore, 1997). Such categorization actually means that income distribution among income recipients can never be absolutely equal because of the permanent gap, which in turn may result from the exogeneity or unchangeability of particular inequalities among individuals. From the perspective of policy, this means that income inequality to a certain extent is unavoidable but that other inequalities can be controlled. ķ 8.1.2 Criteria of Assessing Income Gap Measuresķ Cowell (1995), Deininger, and Squire (1998), Chakravarty (1999), and Fields (2001) argue that there are generally four criteria for assessing income gap measures, including the weak principle of transfers, population homogeneity, income homogeneity and decomposability (Chakravarty, 1999: 165-169; Fields, 2001: 15-18). These criteria receive extensive support from literature about ķ

Contents of this part are excerpted from Qin et al (2009) unless otherwise stated.

136

inequalities (Chakravarty, 1999: 167)ķ. The weak principle of transfers, also known as Pigou-Dalton Condition, means that when income transfers to the poor from the rich but will not cause any change in the relative gap between them, such a transfer will narrow the income gap. Population homogeneity, also known as population independence, means that when there are two income gap measures for the same population, the income gaps they reflect are equivalent despite that one measure is quantitatively m times greater than the other. Income homogeneity, also known as scale independence or normalization, means that measures of relative income gaps have nothing to do with income scales (measurement units). Decomposability means that the income gaps among different groups of income recipients and among all recipients are measured by the same indicator and that a particular quantitative combination of income gaps among these groups is the income gap within this population. Of the above-listed income-gap measures, CV, the Theil index and the Atkinson index are the only ones that satisfy all the four criteria (see Table 8-1). 8.1.3 Grouping in the Measurement of Income Distribution The granularity of the grouping of income recipients, or the value of n, is one of the factors that affect the accuracy of income-gap measurements. A smaller n means that conclusions drawn from such measurements are less reliable (Wade, 2001). Regions, industries/sections/segments, enterprises, households, and individuals can all be the basic unit for the grouping of income recipients. These basic units are increasingly fine by the number of individuals they contain, that is, categorization by region is the coarsest and that by individual the finest. Categorization by region, industry, enterprise, or household can only roughly reflect the income gap among residents (for relevant discussions, please refer to Husbands and Money, 1970; Kuznets, 1957, 1963; Cutright, 1967; Fiala, 1987). When data is available, therefore, the number of groups, n, should be as large as possible; it reaches the maximum when it equals the total number of residents, N, contained in the population (N/n is the average number of residents per group). Liu Shiguo (2008) took the wage/salary data regarding the entire staff of a Taiwanese-invested processing and trade company in Dongguan City, Guangdong Province in November 2008 and examined the income gaps among groups (see Table 8-2). His research demonstrated that the value of n is critical for the accuracy of income distribution measurements. Table 8-2 Effects of the Granularity of Grouping on Income-gap Measurement Results: a Case Grouping Criterion

Number of Normalized Theil Theil Index (T) Groups (n) Index (T’) Entire staff (incl. Chinese Mainland & Taiwanese employees) Nationality 2 0.0607 0.0589 Chinese Mainland employees Department 17 1.1858 0.6945 Job Title 240 3.1263 0.9561 Base Pay 441 2.1408 0.8824 Wage Value 1,045 2.4658 0.9151 Headcount 22,815 1.0450 0.6483 Source: Liu Shiguo (2008), Table 2.

Gini Coefficient (G) 0.1724 0.7581 0.8994 0.8638 0.4894 0.4199

ķ Cowell (1995) discovered another criterion – the strong principle of transfers – which means that the transfer of income at any amount to a “poor” family from a “rich” one will cause a narrower income gap, but it will become wider once the distance between the amounts of incomes of the two families increases.

137

According to Table 8-2, T’ as a measure of the wage gap in this enterprise equals 0.6483 if the headcount, which equals 22,815, is used as the basic unit. And this value is in contrast to the wage-gap measurements corresponding to the other grouping criteria including nationality, job title, base pay, and wage value, because the corresponding numbers of groups are much smaller than the headcount. The wage gap among departments is the only one close to this value, but it is probably accidental. When basic data are available, therefore, the number of groups of subjects should be as large as possible to make sure that conclusions drawn from income-gap measurements are more reliable.

8.2 Decomposition of the Theil Index The formula of the Theil index in Table 8-1 can be transformed into: (8.1)

The Theil index: The normalized Theil index is: (8.2) The formula of the Gini coefficient will then be used:

(8.3) where n is the headcount, n is the number of employees in Group i, yi is the income level of Group i, is the average income level of all the groups, and rank(.) is the ranking function. Since the values of the Theil index (T) are limitless, it is difficult to understand its meaning. It is therefore normalized into T’, whose values are between -1 and 1. The values of the Gini coefficient (G) are between 0 and 1. Greater (or smaller) values of T, T’ and G mean that the income gap is wider (or narrower). In this book, we will decompose the Theil index on the basis of the works of Conceição et al (2000) and Sala-i-Martin (2002). For what is researched in this book, this index can be expressed as: (8.4) where y is income; is the average income of all the basic units in the panel; P,Q,I and J are the numbers of groups of regions, industries, ownership types, and sizes, respectively (in the samples, since their annual values may vary, they all come with a subscript t in this formula); p, q, I, and j represent Region p, Industry q, Ownership Type I, and Size j; t is year; and N is the total number of basic units in the panel. Put Nt in this equation into Σ and we have:

We can see that this index is a weighted sum, where the weight is

, that is, the ratio of the

income of each basic unit in the panel to the total income of all basic units. And we may decompose

138

this index to distinguish the distribution of the total income within a group from that between the groups. For the purpose of simplification, we will omit the subscript tin the subsequent texts unless it is necessary to include it. We may add the following expression to the right side of this equation and then deduct this expression from it: , or

, or

, or Here we select the first expression. On this basis, we use a property of the logarithmic function –

– and define the weight,

. The Theil index can then be

transformed into:

(8.6)

where Terms 1 and 2 on the right side of this equation equal

and

, respectively. Next, we merge Terms 1 and 4 as well as Terms 2 and 3 into a single term to obtain:

(8.7)

where Terms 1 and 2 on the right side of this equation are the intra-group and inter-group income gaps respectively. In this way, we can further decompose the two terms on the right side of this equation until we can no longer do so. We can then obtain the following expression: (8.8)

Further simplify it into: (8.9) where TP, TQ, TI and TJ represent the Theil indexes of regions, industries, ownership types, and sizes, respectively; , , , and represent the average incomes per region, industry, d is the average income of all basic units. ownership type, and business size, respectively; and And under the logic of Equation (8.9), we can also first decompose the total gap by domestic capital and FDI: (8.10) where Tf and Td are the Theil indexes of FDI’s and domestic capital’s wage effects, respectively;

139

and are the average wages of basic units in the panel involving in the distribution of FDI’s and domestic capital’s wage effects, respectively. On this basis, we further decompose Tf and Td, respectively according to Equation (8.9). For each basic unit in the panel, Equation (8.10) is intended to decompose the total amount of wages into foreign capital’s and domestic capitals’ effects according to foreign capital’s direct (when the enterprise is an FIE) and/or indirect effects (when the enterprise is a DIE) on its wage rate and employment, where: Foreign capital’s distribution effects = “the wage distribution effects produced by the FDI operated by a basic unit in the panel itself” + “the wage distribution effects spilled over from all FIEs;” Domestic capital’s wage distribution effects = “the wage distribution effects produced by the domestic capital operated by a DIE itself” + “the wage distribution effects spilled over from all DIEs.” If expressed by the empirical model terminology set in Chapters 5 through 7, the explaining variables of Model I, for example, are somewhere between F01 and F10. Among them, the first nine variables are all the variables of a basic unit in the panel themselves; wage distribution effects (in terms of wage rates and employment) decided completely by some of these nine variables are effects decided by this basic unit itself. Specifically, the effects of foreign capital (F04) and its ratio to the registered capital (F09) on the wage rate (F01) and employment (F05) belong to “the wage distribution effects produced by the FDI operated by a basic unit in the panel itself” defined above. Obviously, basic units in the panel characterized by F04>0 and F09>0 include non-FIEs whose F09 is greater than zero and smaller than 10%, in addition to FIEs whose F09 is no smaller than 10%. Why is it that not all the wage distribution effects on basic units in the panel are defined as “foreign capital’s distribution effects” even if the units meet all those conditions? The wage rate and employment of each basic unit in the panel are affected by the capital variables (F03 and F04), their relevant variables (F08 and F09), other variables of this unit (those between F01 and F09 and their lagged terms), as well as variables of the other basic units (e.g., all the variables between F10 and F74 and their lagged terms), according to the eight empirical models set in Chapters 5 through 7 and their estimation results. The lagged terms of F01 or F05 and some of the variables between F10 and F74 may contain the indirect effects of F04 and F09, but are all excluded from the definitions of the effects of F04 and F09 given difficulties in identification. And the wage spillover effects of basic units in the panel except for FIEs are also excluded for the same difficulties. In other words, only “the wage distribution effects spilled over from FIEs into DIEs” are taken into account. “The wage distribution effects of domestic capital” are also defined for the same reason. We have no reason to believe that “foreign capital = FIEs” or “domestic capital = DIEs.” And we will further divide FDI’s and domestic capital’s wage distribution effects into self-determined wage distribution effects, as well as wage distribution effects spilled over from DIEs and FIEs, respectively. Self-determined wage distribution effects are the behavior in wage distribution determined by the behavioral variables of an enterprise itself. They include “the wage distribution effects produced by the FDI operated by a basic unit in the panel itself” as part of foreign capital’s distribution effects and “the wage distribution effects produced by the domestic capital operated by a DIE itself” as part of domestic capital’s wage distribution effects. The so-called wage distribution effects spilled over from DIEs refer to “the wage distribution effects spilled over from all DIEs” as part of domestic capital’s wage distribution effects. They include wage distribution effects spilled over to FIEs and those spilled over among DIEs. The so-called

140

wage distribution effects spilled over from FIEs refer to “the wage distribution effects spilled over from all FIEs” as part of foreign capital’s distribution effects. They include wage distribution effects spilled over to DIEs and those spilled over between FIEs. Next, we will divide wage distribution effects into the effects of foreign capital and those of domestic capital before breaking them down into “self-determined” wage distribution effects and wage distribution effects spilled over from DIEs and FIEs, respectively. We define the following formula of FDI’s wage distribution effects on the basis of the estimation results of the empirical models described in Chapters 5 through 7. Note that the empirical estimation results in Chapters 5 and 6 are obtained using the GMM method, and those in Chapter 7, the 3SLS method. The two types of results are consistent with each other in terms of sign, only the former are quantitatively and systematically smaller than the latter. Here we take the simple arithmetic averages of their corresponding estimation results as the foundations for the subsequent measurement of the income gap. In Model I, FDI’s wage distribution effects are:

In Model II, FDI’s wage distribution effects are:

In Model III, FDI’s wage distribution effects are: In Model IV, FDI’s wage distribution effects are: In Model V, FDI’s wage distribution effects are:

In Model VI, FDI’s wage distribution effects are:

In Model VII, FDI’s wage distribution effects are:

In Model VIII, FDI’s wage distribution effects are:

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These formulas include the effects of FDI on wage rates and employment and discriminate the two parts by parentheses. Since most variables in the models are in the form of natural logarithm, which is identical to that of the original variables, all the above-listed expressions use exp(.) to restore the original order of magnitude of these variables so as to facilitate the subsequent measurement of the income gap. Likewise, we will list the empirical estimation results of domestic capital’s wage distribution effects as follows: In Model I, domestic capital’s wage distribution effects are:

In Model II, domestic capital’s wage distribution effects are:

In Model III, domestic capital’s wage distribution effects are:

In Model IV, domestic capital’s wage distribution effects are:

In Model V, domestic capital’s wage distribution effects are:

In Model VI, domestic capital’s wage distribution effects are:

In Model VII, domestic capital’s wage distribution effects are:

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In Model VIII, domestic capital’s wage distribution effects are:

8.3 Measurement Results of the Effects of FDI on the Income Gap Measures for FDI’s wage distribution effects include the Theil index. These wage-gap indices are calculated on the basis of the estimation results of the aforementioned models, with almost completely consistent trends and relative levels among the eight models. 8.3.1 Wage Gap with Respect to FDI Normalized Theil indexes of the overall wage distribution within units in the panel as well as wage distribution with respect to FDI, domestic capital, and total capital are shown in Table 8-3 and Fig. 8-1. In the 1999-2006 period, the Theil index, T, of wage distribution with respect to FDI is between 1.26 and 1.59; the normalized Theil index, T’, is between 0.71 and 0.8; their simple averages in the period are 1.4379 and 0.761 respectively. By comparison, the wage gap, T, with respect to total capital is between 1.11 and 1.22; T’ is between 0.67 and 0.71; and their simple averages in the period are 1.17 and 0.69 respectively. The wage gap, T, with respect to domestic capital is between 0.82 and 0.97; T’ is between 0.57 and 0.63; and their simple averages in the period are 0.9 and 0.59 respectively. Table 8-3 Wage Gap by Capital Type: 1998-2006 Theil Index (T) Normalized Theil Index (T’) Between Between Year Foreign Domestic Total Foreign Domestic Foreign & Total Foreign & Capital Capital Capital Capital Capital Capital Domestic Domestic Capital Capital 1998 1.5818 0.7944 1999 1.3825 0.8823 0.0000145 1.1324 0.7490 0.5862 0.0000145 0.6777 2000 1.2652 0.9663 0.0000109 1.1158 0.7178 0.6195 0.0000109 0.6723 2001 1.3076 0.9455 0.0000102 1.1265 0.7295 0.6115 0.0000102 0.6758 2002 1.5070 0.8509 0.0000061 1.1789 0.7784 0.5729 0.0000061 0.6924 2003 1.5938 0.8233 0.0000062 1.2086 0.7968 0.5610 0.0000062 0.7014 2004 1.3646 0.9696 0.0000056 1.1671 0.7445 0.6208 0.0000056 0.6887 2005 1.5128 0.8780 0.0000025 1.1954 0.7797 0.5844 0.0000025 0.6974 2006 1.5701 0.8622 0.0000023 1.2162 0.7920 0.5778 0.0000023 0.7036 Note: The above-listed calculations are based on the estimation results of the eight models – the Theil index of each model and, then, the simple average of all the models’ Theil indexes are calculated.

Overall, the T and T’ with respect to FDI’s wage distribution effects are 60% and 29% (or 23% and 10%) higher than the corresponding values with respect to domestic capital (or total capital), respectively; and the T and T’ with respect to domestic capital’s wage distribution effects are 23%

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and 14.5% lower than the corresponding values with respect to total capital, respectively. An important fact is that all the Theil indexes between FDI’s and domestic capital’s wage distribution effects are very small. The wage gap with respect to FDI in the sample period exhibits a W-shaped curve. This curve has three peaks at 1998, 2003, and 2006, among which the peaks in 1998 and 2006 are almost equivalent to each other while the one at 2003 is slightly higher than the former. There are troughs at 2000 and 2004, among which the latter is higher than the former. This curve can be divided into two V-shaped parts at 2003, that is, one V in the 1998-2003 period and the other V in the 2004-2006 period. We can see from Fig. 8-1 that the latter has a higher trough than that of the former. There is a difference of 0.33, or 26% of the peak value, between the Theil indices of the peaks and troughs across the W-shaped curve. The wage gap with respect to domestic capital in the 1999-2006 period exhibits an M-shaped curve. The years in which the peaks and troughs of this curve occurred happen to be identical to the years in which the troughs and peaks of the above-mentioned W-shaped curve occurred, although the difference between the peak and trough values across this curve is smaller than the corresponding difference of the latter. The two distribution curves combine to make the curve of the wage gap with respect to total capital exhibit a slow, steady uptrend.

Fig. 8-1 Theil Indices of Wage Distribution Estimated on the Basis of Measurement Models by Capital Type: 1998-2006 Note: FDI’s and domestic capital’s wage-gap curves are drawn on the basis of the estimation results of Model I.

8.3.2 Wage Gap with Respect to FIEs The wage gap between FIEs and DIEs can be calculated using two types of data – estimation results of measurement models and real-world data. (1) Conclusions Based on Estimation Results of Measurement Results Regarding the 2000-2006 period, Table 8-4 shows the estimation results based on measurement models: the Theil index, T, of wage distribution within FIEs is between 0.87 and 1.12, the normalized Theil index, T’, is between 0.58 and 0.68, and their simple arithmetic averages are 0.981 and 0.624,respectively. In the case of DIEs, T is between 0.78 and 1.23, T’ is between 0.54 and 0.71, and their simple arithmetic averages are 0.985 and 0.623, respectively; in the case of all enterprises, T is between 0.92 and 1.14, T’ is between 0.6 and 0.68, and their simple arithmetic

144

averages are 1.01 and 0.64 respectively. Table 8-4 Wage Gap between Corporate Ownership Types Based on Measurement Models: 2000-2006 Theil Index (T) Normalized Theil Index (T’) Between All Between All FIEs DIEs FIEs&DIEs Enterprises FIEs&DIEs Enterprises 2000 0.9065 0.8506 0.0426 0.9212 0.5961 0.5729 0.0417 0.6019 2001 0.9240 0.7852 0.0666 0.9213 0.6031 0.5440 0.0645 0.6020 2002 0.8769 0.9958 0.0095 0.9459 0.5839 0.6306 0.0095 0.6117 2003 1.0239 1.2260 0.0062 1.1312 0.6408 0.7065 0.0062 0.6773 2004 1.0055 1.0402 0.0188 1.0416 0.6341 0.6466 0.0186 0.6471 2005 1.0185 0.9374 0.0382 1.0162 0.6389 0.6083 0.0375 0.6380 2006 1.1090 1.0609 0.0350 1.1200 0.6701 0.6539 0.0344 0.6737 Note: This table lists the simple averages of Theil indices calculated on the basis of estimation results of sixteen models that contain F08 and F09, respectively. Year

FIEs

DIEs

According to the estimation results of measurement models, the simple averages of T and T’ with respect to the wage gap within FIEs are 0.46% smaller and 0.1% greater than the corresponding values of DIEs, respectively, that is, there is nearly no difference between them. The same averages are 3.3% and 1.9% smaller than the corresponding values of all enterprises, respectively, whereas those of DIEs are 2.8% and 2% smaller than the corresponding values of all enterprises, respectively. As for the 2000-2006 period, the Theil indices of wage distribution within FIEs, DIEs and all enterprises exhibit W-shaped trends, as is shown in Fig. 8-2. This suggests that the wage gaps with respect to all the ownership types were widening in this period, when the maximum occurred in 2003. For the sample period, the wage gaps within FIEs, DIEs and all enterprises have almost identical trends, or W-shaped up trends. In other words, the Theil indices are higher in the recent stage than in the early stage, but are much higher in the mid-term (2003). There are two troughs that occurred in the 2001-2002 period and 2005, respectively, but the value of the latter is greater than that of the former.

Fig. 8-2 Theil Indices of Wage Distribution Based on Estimation Results of Measurement Models by Ownership Type: 2000-2006 Source: based on data in Table 8-4.

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(2)

Conclusions Based on Real-world Data

As for the 1998-2006 period, the wage gaps based on real-world data by corporate ownership type are shown in Table 8-5 and Fig. 8-3. Table 8-5 Theil Indices of Wage Distribution Based on Real-world Data by Ownership Type: 1998-2006 Year 1998 1999 2000 2001 2002 2003 2004 2005 2006

FIEs 1.1966 1.1440 1.1723 1.0646 1.1085 1.0938 1.1680 1.2688 1.3269

Theil Index (T) Between FIEs & DIEs DIEs 1.1808 0.0102 1.1749 0.0204 1.2852 0.0140 1.1144 0.0293 1.3340 0.0093 1.2736 0.0143 1.2223 0.0280 1.3238 0.0289 1.3632 0.0304

All Enterprises 1.1989 1.1799 1.2428 1.1188 1.2306 1.1980 1.2232 1.3252 1.3755

FIEs 0.6978 0.6815 0.6904 0.6551 0.6700 0.6651 0.6890 0.7188 0.7347

Normalized Theil Index (T’) Between All FIEs & DIEs Enterprises DIEs 0.6930 0.0101 0.6985 0.6911 0.0202 0.6927 0.7234 0.0139 0.7114 0.6719 0.0288 0.6733 0.7366 0.0093 0.7079 0.7202 0.0142 0.6982 0.7055 0.0277 0.7057 0.7339 0.0285 0.7343 0.7442 0.0299 0.7473

Fig. 8-3 Theil Indices of Wage Distribution Based on Real-world Data by Ownership Type: 1998-2006 The Theil index, T, of wage distribution within FIEs is between 1.06 and 1.33, the normalized Theil index, T’, is between 0.65 and 0.74, and their simple arithmetic averages are 1.17 and 0.69, respectively. The T of wage distribution within DIEs is between 1.11 and 1.37, T’ is between 0.67 and 0.75, and their simple arithmetic averages are 1.25 and 0.71, respectively. The T and T’ of wage distribution between FIEs and DIEs are both between 0.009 and 0.04, and their simple arithmetic averages are both 0.02. The T of wage distribution within all enterprises is between 1.11 and 1.38, T’ is between 0.67 and 0.75, and their simple arithmetic averages are 1.23 and 0.71, respectively. We can see, therefore, that the simple arithmetic averages of the T and T’ of wage distribution within FIEs are 6.4% and 2.8% (or 4.9% and 2.8%) smaller than the corresponding values of DIEs (or all enterprises), respectively. The simple average of the T of wage distribution within DIEs is 1.6% greater than the corresponding value of all enterprises, and they have the same T’. According to Fig. 8-3, the wage gap within FIEs was decreasing in the 1998-2001 period, but

146

then gradually increased, especially after 2003, and reached the maximum in the sample period. The wage gap within DIEs decreased significantly in 1999, 2001, and 2004 compared with the previous year, but then increased rapidly and reached the maximum in 2006. It fluctuated more frequently and widely than did the wage gap within FIEs. Compared with the wage gaps based on measurement results by ownership type, those calculated using real-world data have characteristics including wider gaps and more frequent fluctuations, but both types show an uptrend while fluctuating. (3) Wage Gap with Respect to Non-capital Variables The overall wage gap is from the stickiness of distribution effects themselves, the ratio of intermediate inputs at enterprises, the ratio of exports to total products, industry concentration, spillover effects, individual effects, unexplained factors, and gaps between groups of enterprises, in addition to the distribution effects of FDI and domestic capital. Table 8-6 lists the Theil indices of wage distribution calculated on the basis of wage distribution effects of affecting factors, and such effects in turn are based on the settings and estimations of empirical models described in Chapters 5 through 7. Table 8-6 Decomposition of the Overall Wage Gap (Theil Index) Year Inter-factor Gaps in Distribution Effects: FDI Domestic Capital Stickiness of Wage Distribution Ratio of Intermediate Inputs Ratio of Exports Industry Concentration in Region Spillover Effects Individual Effects Residual Between Groups Overall Gap

1998

1999

2000

2001

2002

2003

2004

2005

2006

1.5858 --

1.3825 0.8823

1.2652 0.9663

1.3076 0.9455

1.5070 0.8509

1.5938 0.8233

1.3646 0.9696

1.5128 0.8780

1.5701 0.8622

0.7784

0.7878

0.7630

0.8555

0.8119

0.8068

0.8827

--

--

--

0.0082

0.0088

0.0074

0.0093

0.0081

0.0061

0.0106

0.0102

--

0.0033

0.0030

0.0030

0.0027

0.0028

0.0030

0.0027

0.0024

--

0.0074

0.0058

0.0029

0.0031

0.0060

0.0162

0.0090

0.0036

--

0.0094

0.0087

0.0086

0.0077

0.0073

0.0066

0.0064

0.0071

0.0021

0.0022

0.0022

0.0023

0.0023

0.0023

0.0017

0.0025

0.0025

--1.1989

--1.1799

0.9853 0.7957 1.2428

1.0843 0.6577 1.1188

1.1952 0.7482 1.2306

1.2670 0.6906 1.1980

1.1471 0.7424 1.2232

1.1860 0.8347 1.3252

1.3131 0.8584 1.3755

This table shows that: (i) capital’s distribution effects, the stickiness of distribution, differences between groups, and unexplained factors all cause wide wage gaps, as the resulting Theil indices of wage distribution are all between 0.65 and 1.6; (ii) the ratio of intermediate inputs, industry concentration, spillover effects, and individual effects all cause low Theil indices of wage distribution, as none of them exceeds 0.02. The wage gap specific to each factor shows that FDI had the widest wage gap in the sample period (see Table 8-6). It is not only higher than domestic capital (see Fig. 8-1), but also higher than all other factors in terms of wage gap.

147

8.3.3 FDI’s Contribution to the Wage Gap We can look at the contribution of each factor to the overall wage gap from static and dynamic perspectives. The static perspective refers to the ratio of the gap caused by each factor to the overall wage gap, while the dynamic perspective refers to the contribution of the same gap to changes in the overall wage gap. (1) FDI’s Contribution to the Wage Gap a) FDI’s Contribution to the Wage Gap with Respect to Capital Regarding the wage gap with respect to total capital, FDI’s contribution is 23 percentage points higher than that of domestic capital. According to the upper part of Fig. 8-4, FDI contributed 58%-66% of the wage gap with respect to total capital in the 1998-2006 period, while domestic capital contributed 36%-44%.Their simple arithmetic averages were 61.5% and 38.5% respectively, or the former is 23 percentage points higher than the latter. From a dynamic perspective, FDI’s contribution in the sample period was roughly a W-shaped curve with a slight uptrend, while the contribution of domestic capital was roughly an M-shaped curve with a slight downtrend. These characteristics were almost completely consistent with their respective wage gaps and the gaps’ trends.

148

Fig. 8-4 Contributions of the Gaps with Respect to Capital Ownership and Corporate Ownership Types to the Total Wage Gap: 1998-2006 b) FDI’s Contribution to the Overall Wage Gap Table 8-7 and Fig. 8-5 show the contributions of the wage gaps caused by various factors to the overall wage gap. The primary source is the between-group gap, which contributed 57%-65% of the overall wage gap in the sample period, with a simple arithmetic average of 61%. Table 8-7 Contributions of Factors to the Overall Wage Gap: 2000-2006 Unit: % FDI Domestic Capital Stickiness of Wage Distribution Ratio of Intermediate Inputs Ratio of Exports Industry Concentration in Region Spillover Effects Individual Effects Residual Between Groups Overall Gap

2000 11.3110 8.6395

2001 12.9865 9.3900

2002 13.6068 7.6824

2003 14.7825 7.6366

2004 12.3953 8.8078

2005 12.6839 7.3617

2006 12.6836 6.9648

6.9595

7.8238

6.8890

7.9348

7.3749

6.7649

7.1306

0.0783 0.0269

0.0731 0.0294

0.0838 0.0245

0.0753 0.0255

0.0556 0.0277

0.0891 0.0227

0.0822 0.0196

0.0515

0.0288

0.0282

0.0553

0.1472

0.0754

0.0288

0.0774 0.0193 8.8094 64.0271 100

0.0857 0.0227 10.7684 58.7915 100

0.0698 0.0205 10.7915 60.8035 100

0.0676 0.0209 11.7513 57.6502 100

0.0599 0.0150 10.4203 60.6964 100

0.0533 0.0206 9.9442 62.9843 100

0.0570 0.0201 10.6069 62.4063 100

149

Fig. 8-5 Contributions of Factors Such as Capital to the Overall Wage Gap: 2000-2006 Table 8-7 also shows that the effects of FDI on the wage gap constitute the second biggest source of the overall wage gap and contributed 11%-15% in the sample period, with a simple arithmetic average of 12.9%. It has a contribution curve that rises rapidly before 2004, falls at 2004 and rises again and gently after that, as is shown in the upper half of Fig. 8-5. Domestic capital contributed 7%-9.4% of the overall wage gap in the sample period, with a simple arithmetic average of 8.1%. It exhibits an M-shaped curve that fluctuates in a downtrend. Its contribution was equivalent to 52%-76% of that of FDI. Stickiness contributed 6.7%-8% of the overall wage gap, with a simple arithmetic average of 7.3%. Its contribution was equivalent to 51%-62% of that of FDI and was slightly lower than that of domestic capital. None of the ratio of intermediate inputs, spillover effects, the ratio of exports, industry concentration, or individual effects contributed more than 0.2% of the overall wage gap, and they combined to contribute 0.2%-0.31%, with a simple arithmetic average of 0.25%. According to the lower half of Fig. 8-5, the contribution of the ratio of intermediate inputs fluctuated around 0.08%.The contribution of local spillover effects decreased year by year to 0.06% from 0.08%.Both the ratio of exports and individual effects had more stable contributions, which were 0.02%-0.03% and 0.015%-0.023%,respectively. The contribution of industry concentration in the region to the overall gap was 0.03%-0.15% and fluctuated widely, as it rose rapidly in the 2002-2004 period before falling rapidly. The contribution of unexplained factors to the overall wage gap was also significant at 8.8%-11.8% in the sample period, with a simple arithmetic average of 10.4%. Their contribution was higher than that of domestic capital and presents an overall uptrend. If the time series of these wage gaps were not too short, we could have employed the regression method and factor-specific wage gaps to analyze sources of the overall wage gap.

150

c) The Contribution of FIEs to the Wage Gap The contributions of FIEs to the wage gaps with respect to different corporate ownership types are fundamentally equal to the corresponding contributions of DIEs. If calculations are based on the estimation results of models, then the overall contribution of FIEs is two percentage points higher than that of DIEs (see the middle part of Fig. 8-4).If calculations are based on real-world data, then the contribution of FIEs is one percentage point lower than that of DIEs (see the lower part of Fig. 8-4). Combine the two types of calculations and we can see that FIEs and DIEs make almost equal contributions. From a dynamic perspective, the contribution of FIEs showed a slight downtrend in the sample period, while that of DIEs showed a slight uptrend. Regarding the contribution of the gap between FIEs and DIEs to the overall wage gap, the simple arithmetic average was 1.56% based on the estimation results of models and 0.88% based on real-world data in the sample period, both of which were very low. For calculations based on real-world data, the contribution of this gap fluctuated widely and exhibited a general downtrend in the sample period, during which the average in the last three years (0.62%) was 0.5 percentage points lower than the average in the first three years (1.11%). (2)

The Contribution of FDI to Changes in the Overall Wage Gap

The contributions of the gaps with respect to the distribution effects of factors to changes in the overall wage gap are shown in the lower half of Table 8-8. How are these contributions calculated? Take the contribution of FDI to changes in the Theil index of the overall wage gap as an example. The formula is: (8.11) (8.12) where is the ratio of the wage gap with respect to FDI’s wage effects to the overall wage gap, g is the growth rate of the wage gap index, and n is the number of variables based on which the wage gap is decomposed. The formulas to calculate the contributions of other factors can be worked out in the same way. After the decomposition, if the time series are long enough, then we can employ the regression method to analyze the contributions of factor-specific wage gaps to the overall wage gap so as to summarize the common characteristics at different time points. Unfortunately, the time series available here are obviously not long enough. The contribution of domestic capital to the overall wage gap was 7%-9.4% in the sample period, with a simple arithmetic average of 8.1%, and it exhibits an M-shaped curve that fluctuates in a downtrend. This contribution was equivalent to 52%-76% of that of FDI. Stickiness contributed 6.7%-8% to the overall wage gap, with a simple arithmetic average of 7.3%. Its contribution was equivalent to 51%-62% of that of FDI and was slightly lower than that of domestic capital. None of the ratio of intermediate inputs, spillover effects, ratio of exports, industry concentration, or individual effects contributed more than 0.2% of the overall wage gap, and they combined to contribute 0.2%-0.31%, with a simple arithmetic average of 0.25%. According to the lower half of Fig. 8-5, the contribution of the ratio of intermediate inputs fluctuated around 0.08%. And

151

according to the upper half of Table 8-8, the overall gap decreased in only three years of the eight-year sample period and increased in the other five years. Specifically, it was increasing in the 2004-2006 period. The proportion of the number of years in which the wage gap with respect to FDI’s distribution effects decreased to the number of years in which it increased was also 3:5, but the correlation coefficient between its fluctuation ratio and that of the overall wage gap was 0.29. This suggests that FDI made certain positive contributions to widening the overall wage gap. Similarly, the ratio of intermediate inputs, individual effects, and the between-group gap also made certain positive contributions in the sample period. Table 8-8 Fluctuation of Wage Gaps and the Contributions of Factors to the Overall Gap: 1999-2006 Year

Ove rall Gap

1999

-1.5 9

2000 2001 2002 2003

5.33 -9.9 8 10.0 0 -2.6 5

Fore ign Capital -12. 60 -8.4 8

--

7.01 -15.99

15.2 5

-10.01

-3.15

26.19

5.76 -14. 38 10.8 6

8.34 3.79

3.79

1

0.29

--

2002

--

2003

--

2004

--

2005

--

2006

--

--

1.20

2006

2001

--

9.52

2005

--

--

Gap with Respect to the Factor’s Distribution Effects Rati Ratio of Industry Spillo o of Intermediat Concentratio ver Export e Inputs n in Region Effects s Year-over-year Growth

-2.16

2.11

2000

Stickin ess

3.36

2004

Correlat ion Coefficien t with the Fluctuatio n of the Overall Gap

Dome stic Capital

-20. 71 -3.8 0 19.8 1 -29. 53 -100.8 9 16.1 4 12.6 7

-3.23

12.13

-12.58

17.77

-5.10

-24.55

-9.45

-0.62

73.44

-1.80

9.40

-4.23

-0.17

-0.32

0.66

14.83

--8.5 2 -1.8 0 -8.0 5 1.18 10.6 8 -11. 05 -10. 44

-0.4 7

1.87

-0.84

0.13

-2.46

0.19

9.36

-31.48

0.40

64.39

-19.20

-0.88

-9.98

-0.55

0.49

-3.50

16.77

-0.10

0.00 -0.0 2 -0.0 1 0.13 -0.0 4 -0.0 6

Resid ual

Interact ion Term

--

--

--

--

4.10

-21.71

-7.42

-2.17

--

--

-49.58

-0.34

5.96

10.04

-17.34

7.57

-10.3 9

-0.85

10.23

13.76

90.83

-5.69

-0.65

6.00

-7.70

171.88

-9.59

-26.59

-9.46

7.50

-44.53

-3.61

48.92

3.39

12.42

-60.30

11.10

1.35

10.71

2.84

-0.07

-0.23

0.23

-0.09

0.97

-0.12

-0.01

--

--

0.26

0.00

-0.01

-8.86

111.26

0.02

-0.09

0.00

11.02

80.93

Contribution to the Fluctuation of the Overall Gap -0.0 -0.10 -0.28 5

-9.40

Individ ual Effects

-24.4 2 -52.7 5

-0.97

0.15

0.01

4.51

-0.31

-0.26

176.50

-0.79

-0.03

0.09

4.24

90.42

-1.20

0.16

0.01

28.08

47.17

205.26

Note: Calculations are based on the data listed in Tables 8-4 and 8-5. When explaining each factor’s contribution to the fluctuation of the overall gap, we should pay extra attention to their respective signs. In 2001, for example, the Theil index of the overall gap decreased by 9.98%, whereas the wage gap with respect to FDI increased by 3.4%. Since they had opposite signs, the latter had a negative contribution to the fluctuation of the former, that is, it widened the overall gap in the year, and the de facto decrease in the overall gap resulted from the synergy of the other factors.

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The gap with respect to domestic capital’s wage distribution effects decreased in five years and increased in the remaining three years. The correlation coefficient between it and the fluctuation of the overall gap was -0.17. This suggests that domestic capital’s distribution effects alleviated or even partly offset the widening of the overall gap. Likewise, stickiness, the ratio of exports, industry concentration in the region, and spillover effects caused wage gaps that fluctuated in a direction opposite that of the overall gap’s fluctuation. In the 2000-2006 period, contribution from the inter-group gap had a bigger absolute value than any other source of the overall gap and was always positive. This suggests that this factor made the greatest contribution to changes in the overall gap and was consistent with the overall gap in terms of the direction toward which the latter was changing. Regarding the contribution of FDI’s distribution effects to the fluctuation of the overall gap, its annual absolute values were smaller only than those of the inter-group gap. Nonetheless, this contribution was in the same direction as that of the aforementioned fluctuation in only three years (i.e., it was favorable for a widening gap) and in a direction opposite to the latter in four other years (it decreased the extents to which the gap widened in two years and narrowed in the other two years). In 2000 and 2004 when the overall gap widened, the gap with respect to FDI’s wage distribution effects narrowed and thus decreased the extent to which the overall gap widened. In 2002, 2005, and 2006 when the overall gap widened, the gap with respect to FDI’s wage distribution effects also widened and thus increased the extent to which the overall gap widened. In2001 and 2003 when the overall gap narrowed, the gap with respect to FDI’s wage distribution effects widened and thus decreased the extent to which the overall gap narrowed. Regarding the contribution of domestic capital’s distribution effects to the fluctuation of the overall gap, its absolute values were the third highest if the residual is not taken into account. This contribution was in the same direction as that of the aforementioned fluctuation in four years – it increased the extent to which the overall gap widened in 2000 and 2004, and decreased together with the latter in 2001 and 2003. This contribution was in a direction opposite that of the fluctuation in three other years – it decreased the extent to which the overall gap widened in 2002, 2005, and 2006. Interestingly, the contribution of FDI to the overall gap was opposite that of domestic capital in seven years and was always greater than the latter in terms of absolute value. We may safely say that their contributions to the overall gap were completely opposite rather than working together to widen or narrow the overall gap. 8.3.4 Reviewing the Relationship between FDI and the Income Gap Let us return to the relationship between FDI and the income gap to take a look at the empirical results. For the purpose of this book, income is represented by wages/salaries. Let us first look at the relationship between the gap with respect to FDI’s wage distribution effects and the variable of FDI. The former comes from the sample data in this book, whereas FDI statistics are collected in different scopes, including the registered capital of foreign companies (i.e. the sum of the registered capital of Hong Kong, Macao, and Taiwanese companies and that of foreign companies) and domestic capital as part of the sample data in this book, as is shown in Fig. 8-6.

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Fig. 8-6 Capital vs. Wage Gap: 1999-2006 Fig. 8-6 indicates that registered capital, whether of foreign companies or local ones, displayed an obvious uptrend in the sample period, but it doesn’t seem that the wage gaps with respect to domestic capital and FDI as well as the overall wage gap all show a particularly significant trend like the one indicated in Fig. 1-1. Now that both the wage gaps and registered capital contain trend variables, can we discover a particularly significant relationship between them after we remove these trend variables? Table 8-9 depicts changes in the wage gap with respect to FDI in the sample industries, year-over-year changes in FDI, and the correlation between them, and also lists changes in the overall wage gap in the sample data, changes in FDI in China’s secondary sector, and changes in the total FDI in China. Table 8-9 Correlation between Changes in the Wage Gap with Respect to Foreign Capital in Sample Industries and Changes in FDI in Different Scopes: 2000-2006 Registered Capital of Foreign FDI in China’s FDI in China Companies in Secondary Sector Sample Industries Year-over-year Growth (%) 2000 -8.5 5.3 14.0 6.8 1.0 2001 3.4 -10.0 18.3 18.1 15.1 2002 15.3 10.0 13.6 15.1 12.5 2003 5.8 -2.7 12.7 0.1 1.4 2004 -14.4 2.1 29.9 15.5 13.3 2005 10.9 8.3 12.1 -0.7 19.4 2006 3.8 3.8 17.0 -5.7 0.4 Correlation Coefficient with the Growth Rate of the Wage Gap with Respect to FDI in the Sample Industries 2000-2006 1 0.219 -0.730 -0.224 0.237 Correlation Coefficient with the Growth Rate of the Wage Gap with Respect to FDI in the Sample Industries 2000-2006 0.219 1 -0.242 -0.281 0.043 Year

Wage Gap with Respect to Foreign Capital in Sample Industries

Overall Wage Gap in Sample Industries

Source: Values of variables relevant to the samples are from the sample collection or calculations based on measurement results. The other variables are from the CEIC.

The correlation coefficients between changes in the wage gap with respect to FDI in the sample industries and changes in the registered capital of foreign companies in these industries, FDI in the entire secondary sector, and FDI in China were -.073, -0.224, and 0.237 respectively, as is shown in Table 8-9. The correlation coefficients between changes in the overall wage gap in the sample industries and changes in the latter three were -0.242, -0.281, and 0.043 respectively. Specifically,

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the correlation coefficient between changes in the wage gap with respect to FDI in the sample industries and changes in the registered capital of foreign companies in these industries is methodologically most reliable: it is completely based on sample data and empirical measurement results and contains no extrapolation. The correlation coefficient between changes in the wage gap with respect to FDI in the sample industries and changes in FDI in the entire secondary sector are also fundamentally reliable: samples for this research are enterprises in the secondary sector above a given size, which constitute the leading part of this sector. And since about 70% of FDI was in the secondary sector during the sample period, the correlation coefficient with changes in FDI in China is still helpful to a certain extent for determining how reliable the conclusions in this book are. If we assume that our samples are sufficiently representative for researching the relationship between the wage gaps and total FDI, then, since 0.237 and 0.043 mean low or nearly no correlation, they won’t prevent us from reaching the conclusions of this research: there is no strong and positive correlation between changes in the wage gaps and the fluctuation of total FDI, and they may even be negatively correlated to each other. In fact, FDI growth can be limitless from a theoretical perspective, but the fluctuations of relevant wage gaps are limited. The normalized Theil indexes vary between -1 and 1, and the Gini coefficient is between 0 and 1, for example. Accordingly, it is impossible for a linear relationship to exist between them in the real world.

8.4 Summary All the so-called wage gaps in the subsequent texts are Theil indices in wage distribution unless otherwise specified. Their values are all simple arithmetic values of annual Theil indexes in the sample period. For the purpose of this book, the Theil index became the preferred measure for wage gaps especially because its decomposability is very suitable for this research. (1) Among all the factor-specific wage gaps, the gap that exists in FDI’s wage distribution effects is the highest. The wage gaps that exist in domestic capital, the stickiness of distribution, the between-group gap, and unexplained factors are slightly lower, but are all much higher than those caused respectively by the ratio of intermediate inputs, industry concentration, spillover effects, and individual effects. The wage gap with respect to FDI was on average60% higher than the gap with respect to domestic capital and 23% higher than the gap with respect to total capital. Overall, it exhibits a W-shaped curve whose troughs are in a slight uptrend. Its peaks and troughs happen to correspond to the troughs and peaks of the wage distribution curve of domestic capital, which is in a slight, M-shaped downtrend. (2) Regarding the wage gaps with respect to capital, the contribution of FDI was in a slight uptrend in the sample period, whereas that of domestic capital was in a slight downtrend. On average, the former was nearly one fourth higher than the latter. In the overall wage gap, the contribution of FDI was 12.9%, which was 4.8 percentage points higher than that of domestic capital at 8.1%, and 5.6 percentage points higher than that of the stickiness of distribution at 7.3%. By comparison, the ratio of intermediate inputs, spillover effects, the ratio of exports, industry concentration, and individual effects combined to contribute only 0.25% to the overall wage gap. The contribution of FDI increased rapidly before 2004, decreased in 2004, and then increased slowly. That of domestic capital fluctuated in an M-shaped downtrend. Those of stickiness, the ratio of intermediate inputs, spillover effects, the ratio of exports, and individual effects were all stable, and that of industry concentration fluctuated widely, as it

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increased sharply before decreasing quickly. (3) Calculated on the basis of estimation results of measurement models and real-world data, respectively, the wage distribution curves of FIEs both exhibit a slight uptrend, but the latter reflects a wider wage gap with more frequent fluctuations. When calculated on the basis of real-world data, the wage gap with respect to FIEs was lower than the one with respect to DIEs. When based on estimation results of measurement models, the curve of wage distribution at FIEs is almost identical to that of DIEs. Regarding wage gaps by corporate ownership type, the contribution of FIEs was in a slight downtrend, whereas that of DIEs was in a slight uptrend, and their averages were almost equal to each other; the gap between FIEs and DIEs made very small contribution to the overall wage gap and fluctuated widely, but was generally in an uptrend. (4) FDI’s wage distribution effects could increase or decrease the extent to which the overall gap widened, or decrease the extent to which the overall gap narrowed. Regarding its contribution to the fluctuation of the overall wage gap, the absolute values of its contribution were smaller than those of the contribution of the inter-group gap in the sample period and greater than those of the contribution of domestic capital. But FDI’s contribution had a sign opposite to that of the contribution of domestic capital, that is, their contributions were opposite to each other to a certain extent and thus were favorable for alleviating the fluctuation of the overall wage gap. (5) There was no significant linear trend between FDI and the wage-gap sequence in the sample period, whereas the variable of FDI had a level-value sequence that exhibits a significant trend of linear growth. Moreover, there was no strong and positive correlation between the fluctuations of correlation-coefficient measurements. Instead, they could be negatively correlated to each other to a certain extent. In fact, it is impossible for a linear relationship to exist between them in the real world. In general, FDI is one of the more important factors, rather than the only one, which affects the overall wage gap. FDI’s wage distribution effects can only explain 11%-15% of the overall wage gap and 10%-20% of its changes (in most cases). The wage distribution effects of other factors, such as the inter-group gap, domestic capital, and the stickiness of wage distribution, are relatively important explaining variables for the overall wage gap.

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Chapter 9 Main Conclusions, Policy Recommendations, and Follow-up Research This chapter will summarize main conclusions of this book, explore policy recommendations from these conclusions, and put forward an agenda of future research.

9.1 Main Conclusions This book focuses on researching how FDI affects the wage gap among employees by deciding wage rates and employment. Regarding the specific research process, we first created a general equilibrium model for an open economy on the basis of the endogenous growth theory, before identifying the condition for general equilibrium after FDI enters the host country: demand for high-skilled labor relative to low-skilled labor equals their relative endowments. The entry of FDI will cause this condition to change and, thus, bring about changes in wage rates as well as the volume and structure of employment, before ultimately causing changes in the income gap in the host country. Second, we took what we inferred from the theoretical model and set empirical models based on different assumptions. We employed the dynamic panel data analysis method as well as the production/operation data of enterprises in China’s secondary sector above a given size to simulate the effects of FDI on wage rates and employment at enterprises. Third, we took the results of panel data analysis to measure the overall wage gap among basic units in the panel and the fitted wage gaps. We then decomposed the wage gaps to identify FDI’s contribution based on its effects on wage rates and employment before comparing it with the wage gap with respect to FIEs. When examining the wage gaps with respect to FDI and FIEs as well as their contributions to the overall wage gap, we always compared them with those of domestic capital and DIEs, thereby gaining an accurate and comprehensive understanding of the relationship between FDI and the income gap in China. On the basis of the overview in Chapter 2, we explored how FDI affects income distribution in the host country using the general equilibrium model. This model assumes that the expansion of the FIS results from technological advances, which in turn are biased toward high-skilled labor. The FIS is more skills biased than the non-agricultural DIS. Accordingly, the expansion of the FIS causes this sector to have stronger demand for high-skilled labor than for low-skilled labor, but it is more difficult for the market to satisfy the former demand than the latter. Is it possible for changes in the relative demand for high-skilled labor caused by the FIS to be balanced with the existing relative endowment between high-skilled and low-skilled labor in the market? The role of the skill-building sector in developing low-skilled labor into high-skilled labor is critical for this issue, especially in the case of full employment in the labor market. In this process, since the wage rate in agricultural sector is lower than that of non-agricultural DIS, which in turn is lower than that of the non-agricultural FIS, low-skilled labor will move from the first sector to the latter two while high-skilled labor will move from the non-agricultural sector to the FIS. This will cause the group of laborers who receive low wage rates to become smaller and the group of laborers who receive high wage rates to become larger, thereby increasing the average wage rate of all laborers. The wage gap between low-skilled and high-skilled labor will widen even if we assume that their respective average wage rates remain unchanged. This is because

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technological advances caused by foreign capital are biased toward high-skilled labor and, accordingly, the number of high-skilled laborers will increase faster. If the expansion of the FIS is driven not by technological advances but by similar projects that are less technologically advanced, then its demand for low-skilled labor will increase faster than its demand for high-skilled labor. As a result, low-skilled laborers who move to the FIS with a higher wage rate from the agricultural sector with a lower one will outnumber high-skilled laborers who move to the FIS with a higher wage rate from the non-agricultural DIS with a lower one. Accordingly, laborers as a whole will receive a higher average wage rate, but it will increase at a lower rate than when technological advances occur in the FIS. On the other hand, the wage gap between high-skilled and low-skilled laborers will narrow. In this situation, the skill-building sector is less significant for general economic equilibrium. The aforementioned conclusion presupposes that there is no obstacle to the inter-sector flow of labor, whether or not technological advances drive the output expansion of the Fisted migration of agricultural labor into non-agricultural sectors is not fast enough to narrow the existing income gap in China since it began reform and opening. This is because of the household registration system and the rapid development of China’s coastal regions. This has caused the coexistence of the flow of labor and a widening income gap between rural and urban areas. The dynamic panel data analysis method is ideal for this research’s objective based on the individual behaviors of enterprises. This method was detailed in Chapter 4. According to correlations between the “characters” of the researched individuals and their observed variables, panel data implicitly contains all the possible combined models of random and fixed effects. Current mainstream panel data analysis methods are mostly developed for “standard panels” with large N and small T. But there is still great potential outside these “standard” panel data analysis methods. The dynamic panel data model is a method with extensive potential applications. The GMM is ideal for dynamic panel data analysis among all statistical inference methods. The effectiveness of GMM estimation depends on the relationship between initial conditions and error terms and is relevant to the selected number of moment conditions. Moreover, GMM estimation results are very sensitive to how dynamic panel data models are set and what moment conditions are selected, but consistent MMSC and the downward testing procedure ensure that the right model and moment conditions are selected. In addition, the common interdependence between individuals in panel data plays an important role in their behavioral mechanism. It can be measured by constructing a covariance matrix among individual units. With regard to the spatial autocorrelation among individual units in panel data, we may set up an SWM and employ the SAR, SMA and non-parametric methods to construct a covariance matrix among individual units. We may select a suitable statistic from among Moran’s I, LMLAG, R-LMLAG, LMERR, R-LMERR, LM, etc., to check spatial autocorrelation while employing the MLE, IVM, LSM, or GMM for estimation. Chapter 4 also presents how Stata statistical software deals with linear dynamic panel techniques. In Chapters 5 and 6, we took the inference results from the theoretical model in Chapter 3 and the overview in Chapter 2, respectively to set behavioral models of wage rates and employment at enterprises. We also made estimations using the dynamic panel data analysis method presented in Chapter 4. Given the endogeneity issue caused by the fact that wage rates and employment are the cause and effect of each other, we constructed a small system of simultaneous equations in Chapter 7 on the basis of the behavioral equations of wage rates and employment. We employed the 3SLS method to estimate this system and made relevant conclusions, and the conclusions that were drawn

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using the dynamic panel data analysis method in Chapters 5 and 6 verify each other. Below is a summary of relevant conclusions. Table 9-1 Qualitative Relations between Wage Rates/Employment at Enterprises and Explaining Variables Explaining Variables Employme nt Wage Rate Employment

Negative Positive*

Wage Rate

Investme nt

Positive* Negative

Positive Positive

Industry Concentration Positive Positive

Ratio of Foreign Capital Negative Positive

Ratio of Intermediate Inputs Negative Negative

Ratio of Exports Negative Positive

Note: * represents the lagged term of a dependent variable, with positive effects on both wage rates and employment. Source: based on the empirical conclusions in Chapter 5 through Chapter 7.

Wage rates and employment are the deciding variable of each other but with negative effects on each other, according to Table 9-1. Both additional investment made by enterprises and higher industry concentration will increase wage rates and jobs at enterprises, whereas a higher tendency of enterprises toward processing will decrease them. Higher ratios of FDI and exports will decrease wage rates at enterprises but will greatly increase jobs they provide. Both wage rates and employment have typical characteristics of stickiness or rigidity, that is, they can both strengthen themselves dynamically. Additional capital investment made by enterprises includes FDI and domestic capital, of which the effects of the former on an increase in wage rates at enterprises are about 40%-75% higher than those of the latter. For the part of an enterprise, the urge to increase the wage rate is mostly from the wage-rate spillover effects of other enterprises. Specifically, the positive wage-rate spillover effects from nationwide FIEs in the same industry are comparable to the effects from an increase in direct investment within the enterprise, but are about 50% lower than the positive wage-rate spillover effects from DIEs in the same industry (especially local ones). Nonetheless, these effects from DIEs in the same industry are almost completely offset by the negative wage-rate spillover effects from nationwide FIEs in other industries. Such negative effects are mostly from other industries in other regions. Additional capital investment made by an enterprise will create jobs directly, but FDI produces weaker effects than domestic capital in this regard. Indirect employment effects from other enterprises are stronger than direct ones from capital expansion within the enterprise itself. Specifically, indirect employment effects from nationwide enterprises in the same industry (including FIEs and DIEs) are usually destructive, whereas employment effects from nationwide FIEs in other industries are creative and then destructive. Moreover, such destructive effects are relatively dominant and mostly from non-local FIEs in other industries. Destructive employment effects from local FIEs in the same industry are weaker than those from non-local DIEs in the same industry and the nationwide average, not to mention those from local DIEs in the same industry, which are greater than the nationwide average. Chapter 8 presents the properties of income-gap measures and relevant assessment criteria. We believe that the Theil index is ideal for this research especially because it is decomposable. It was on the basis of the calculations of this index that we were able to draw relevant conclusions. Among all the factor-specific wage gaps, the gap that exists in FDI’s wage distribution effects is higher than

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the wage gaps caused by domestic capital, the stickiness of distribution, the between-group gap, or unexplained factors. It is also much higher than those caused respectively by the ratio of intermediate inputs, industry concentration, spillover effects, and individual effects. The wage gap with respect to FDI was on average 60% higher than the gap with respect to domestic capital and 23% higher than the gap with respect to total capital. Overall, it exhibits a W-shaped curve whose troughs are in a slight uptrend. Its peaks and troughs happen to correspond to the troughs and peaks of the wage distribution curve of domestic capital, which is in a slight, M-shaped downtrend. Regarding the wage gaps caused by capital, the contribution of FDI was in a slight uptrend in the sample period, whereas that of domestic capital was in a slight downtrend. On average, the former was nearly one fourth higher than the latter. In the overall wage gap, the contribution of FDI was 12.9%, that of domestic capital 8.1%, and that of stickiness 7.3%. By comparison, the ratio of intermediate inputs, spillover effects, the ratio of exports, industry concentration, and individual effects combined to contribute only 0.25% to the overall wage gap. The contribution of FDI increased rapidly before 2004, decreased in 2004, and then increased slowly. That of domestic capital fluctuated in an M-shaped downtrend. Those of stickiness, the ratio of intermediate inputs, spillover effects, the ratio of exports, and individual effects were all stable. That of industry concentration fluctuated widely, as it increased sharply before decreasing quickly. Although the spillover effects of foreign capital/FIEs on domestic capital/DIEs are the focus of discussion and examination for numerous works (including this book), empirical conclusions show that in the 2000-2006 period, the Theil indices of DIEs’ and FIEs’ spillover effects were as low as 0.00917 and 0.00694 respectively. The contribution of FDI to the wage gaps at enterprises increased rapidly before 2004, decreased in 2004, and then increased slowly. That of domestic capital fluctuated in an M-shaped downtrend. Those of stickiness, the ratio of intermediate inputs, spillover effects, the ratio of exports, and individual effects were all stable, and that of industry concentration fluctuated widely, as it increased sharply before decreasing quickly. Calculated on the basis of estimation results of measurement models and real-world data, respectively, the wage distribution curves of FIEs both exhibit a slight uptrend, but the latter reflects a wider wage gap with more frequent fluctuations. When calculated on the basis of real-world data, the wage gap with respect to FIEs was lower than the one with respect to DIEs. When based on estimation results of measurement models, the curve of wage distribution at FIEs is almost identical to that of DIEs. Regarding wage gaps by corporate ownership type, the contribution of FIEs was in a slight downtrend, whereas that of DIEs was in a slight uptrend, and their averages were almost equal to each other; the gap between FIEs and DIEs made very small contribution to the overall wage gap and fluctuated widely, but was generally in an uptrend. FDI’s wage distribution effects could increase or decrease the extent to which the overall gap widened, or decrease the extent to which the overall gap narrowed. Regarding its contribution to the fluctuation of the overall wage gap, the absolute values of its contribution were smaller than those of the contribution of the between-group gap in the same period and greater than those of the contribution of domestic capital. But FDI’s contribution had a sign opposite to that of the contribution of domestic capital, that is, their contributions were opposite to each other to a certain extent and thus were favorable for alleviating the fluctuation of the overall wage gap. There was no significant linear trend between FDI and the wage-gap sequence in the sample period, whereas the variable of FDI had a level-value sequence that exhibited a significant trend of linear growth. Moreover, there was no strong and positive correlation between the fluctuations of correlation-coefficient measurements. Instead, they could be negatively correlated to each other to a

160

certain extent. In fact, it is impossible for a linear relationship to exist between them in the real world. In general, FDI is one of the more important factors, rather than the only one, affecting the overall wage gap. FDI’s wage distribution effects can only explain 11%-15% of the overall wage gap and 10%-20% of its changes (in most cases). The wage distribution effects of other factors, such as the between-group gap, domestic capital and the stickiness of wage distribution, are also relatively important explaining variables for the overall wage gap.

9.2 Policy Implications We are now in a period when the income gap keeps widening across society. Forces that can alleviate this trend are therefore much needed. Is FDI such a force? The empirical conclusions in Chapters 5 through 7 have already verified this. FDI accounted for about 23% of the total paid-up capital in China’s industries above a given size in the sample period, but contributed 13% of the overall wage gap. By comparison, domestic capital accounted for 77% of the total registered capital in the same period, but contributed only 8.1% of the overall wage gap. To address the income gap in China, therefore, we must pay much attention to and rely on the effects of FDI. In most years in the sample period, FDI in China was characterized by a very high ratio of exports since it was mostly involved in manufacturing for the processing trade, according to Table 9-1. FIEs employ a large number of laborers from low-income regions. While creating a lot of jobs for this group, FIEs let these laborers receive much higher incomes than those available in their hometowns. Given relatively weaker job creation effects, however, FIEs have still caused a wide income gap among corporate employees. As more FDI has entered the tertiary sector, especially high-end services (e.g., telecom, financial and IT services), in recent years, Chinese employees working in relevant FIEs have tended to have higher incomes than their counterparts in DIEs. Since FIEs in these fields employ much fewer low-income Chinese workers than the manufacturing industry, they contribute to a widening income gap among Chinese residents. We should say that the shift of FIEs to high-end services from the manufacturing industry is favorable for economic and social development in China and that the resulting income distribution effects are understandable and acceptable to a high extent. Measuring and assessing the role of development in improving welfare is at the core of research on the issue of income distribution. Since development is defined in different ways, there are different views of the relationship between growth and distribution during development and, then, of the relationship between fairness and efficiency. According to traditional Western economics, it is impossible to realize fairness and efficiency at the same time, or there is a trade-off between them: higher fairness must come at the cost of efficiency and vice versa (e.g., Okun, 1975). Since FDI can explain only about 13% of the wage gap in enterprises in China’s secondary sector, we should expand our vision to the entire economy and society so as to narrow the income gap in China. In addition, it is necessary to eliminate monopoly in industries and to regulate how income is distributed by the state-owned sector, especially SOEs. This is also important for narrowing the income gap among Chinese residents. To this end, one of the effective measures is to further open what is traditionally monopolized by the state-owned sector to the private sector and the FIS, thereby narrowing the income gap.

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9.3 Follow-up Research Extend the time series of data and improve data quality. We may update data each year to increase the T of the panel, and use business registration information to correlate information such as sources of foreign capital, years in service, and imports/exports with the production/operation data of FIEs, for example. This requires the unified management of FIEs’ registration information, statistics about their production, operations, and international trade, etc. Make greater efforts to research the influence of the correlation between FIEs and DIEs. We can research this correlation in two respects: the input/output and spatial proximity effects. On the side of the input/output effects, it is necessary to discriminate foreign sources of inputs from domestic sources at the level of raw data so as to analyze how FIEs’ characteristics – processing trade and local procurement – affect the income gap among Chinese residents. Spatial proximity effects involve complex data calculations, which require us to be familiar with Matlab. Take economic census data to further research how FDI affects income distribution in the tertiary sector, which has become one of the major areas for FDI in China. We may use detailed economic census data in combination with simple high-frequency data (including time-series data). This requires us to employ panel analysis techniques when cross-section data is used in combination with time-series data. Make further research on how FDI affects the distribution of capital gains, especially in combination with estimating the size and distribution of round-tripping FDI. In fact, the income gap between laborers and capitalists may be a critical source of the overall income gap. Deepen research on income distribution among enterprises by researching income distribution among employees. We may gain a deeper understanding of this issue if we can properly estimate income distribution among employees within all the sample enterprises on the basis of information about income distribution among employees within individual enterprises. The measurement results of this research can explain only about 50% of the average wage gap among enterprises, whereas the remaining 50% can probably be better explained by the wage gap among employees within enterprises. FDI may affect wage rates and employment in ways that vary with region, industry and corporate ownership type. We may research this issue by trying dynamic panel analysis techniques with time-varying coefficients. Both relevant formulas and empirical results indicate that industry mix, geographical distribution, and business size are also factors that influence FDI’s income distribution effects. How public policies should guide industry plans becomes an important issue. The Chinese government has published an industry catalog to attract foreign capital. This catalog is intended to improve China’s industry mix and ultimately ensure the sustainable growth of its economy. How can the development and revision of this catalog be guided under the goal of relevant policies, that is, narrowing the income gap? After this question is answered, how much and how can public policies influence regional industry plans so as to narrow the income gap? All these issues require further research.

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References 1.

2.

3. 4. 5. 6. 7. 8. 9. 10.

11. 12. 13. 14.

15. 16.

17. 18. 19. 20. 21.

Aaron, C. and Andaya, T. (1998). “The link between foreign direct investment and human poverty alleviation and social development: a framework,” paper prepared for the High-Level Roundtable on Foreign Direct Investment and its Impact on Poverty Alleviation. Singapore, 14-15 Dec., mimeo. Adelman, Irma and Robinson, Sherman (1989). “Income distribution and development,” in: Hollis Chenery†and T.N. Srinivasan (Ed.). Handbook of Development Economics, Elsevier, edition 1, volume 2, Chapter 19, pages 949-1003. Aghion, Philippe and Howitt, Peter (1992). “A Model of Growth through Creative Destruction,” Econometrica, vol.60(2), pages 323-351. Ahn, Seung C. and Schmidt, Peter (1995). “Efficient estimation of models for dynamic panel data,” Journal of Econometrics, Elsevier, vol.68(1), pages 5-27, Jul.. Akerlof, Gorge (1997). “Social Distance and Social Decisions,” The Fisher-Shultz Lecture of the 1995 World Congress of the Econometric Society, Econometrica, Sep.. Alderson, Arthur and Nielsen, François (1999). “Income Inequality, Development, and Dependence: A Reconsideration,” American Sociological Review, vol.64(4), pages 606-631. Alesina, Alberto and Rodrik, Dani (1994). “Distributive Politics and Economic Growth,” The Quarterly Journal of Economics, MIT Press, vol.109(2) , May, pages 465-490. Alfaro, Laura and Andrés Rodríguez-Clare (2004). “Multinationals and Linkages: An Empirical Investigation,” Economia, Spring, pages113-169. Alfaro, Laura; Areendam Chanda; Sebnem Kalemli-Ozcan and Selin Sayek (2002). “FDI and Economic Growth: The Role of Local Financial Markets”. Andrews, Donald W.K. and Biao Lu (1999). “Consistent model and moment selection criteria for GMM estimation with application to dynamic panel data models,” Cowles Foundation Discussion Paper No. 1233, Yale University. Andrews, Donald W.K. and Biao Lu (2001). “Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models,” Journal of Econometrics, vol.101, pages 123-164. Anselin, L. (1988). “A test for spatial autocorrelation in seemingly unrelated regressions,” Economics Letters, Elsevier, vol.28(4), pages 335-341. Anselin, L. (1992). “Space and applied econometrics: Introduction,” Regional Science and Urban Economics, Elsevier, vol.22(3) , Sep., pages 307-316. Anselin, L. (1999). “Interactive techniques and exploratory spatial data analysis,” In P. Longley, M. Goodchild, D. Maguire and D. Rhind (Ed.), Geographical Information Systems: Principles, Techniques, Management and Applications, New York, Wiley, pages 251-264. Anselin, L. (2006). “Spatial Econometrics,” In T.C. Mills and K. Patterson (Ed.). Palgrave Handbook of Econometrics: Vol.1, Econometric Theory, Basingstoke, Palgrave Macmillan, pages 901-969. Anselin, L. and Bera, A.K. (1998). “Spatial dependence in linear regression models with an introduction to spatial econometrics,” in Ultah A. and Giles D.(Ed.) Handbook of Applied Economic Statistics, Marcel Dekker, New York, NY, pages 237-289. Anselin, L. and R. Florax (1995). “Small sample properties of tests for spatial dependence in regression models: Some further results,” Research Paper 9414 Anselin, L. and S Rey (1991). “Properties of tests for spatial dependence in linear regression models,” Geographical Analysis. Arellano, M. and Honore, Bo (2001). “Panel data models: some recent developments,” in: J.J. Heckman and E.E. Leamer (Ed.). Handbook of Econometrics, Elsevier, edition 1, vol.5, Chapter 53, pages 3229-3296. Arellano, M. and O. Bover (1995). “Another look at the instrumental variable estimation of error-components models,” Journal of Econometrics, vol.68, pages 29-51. Arellano, M. and S. Bond (1991). “Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations,” Review of Economic Studies, vol.58, pages 277-297.

163

22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34.

35. 36. 37. 38. 39. 40. 41.

42. 43. 44. 45. 46.

Arthur, Brian (1989). “Increasing returns, competing technologies and lock-in by historical small events: the dynamics of allocation under increasing returns to scale,” Economic Journal, vol.99, pages 116–31. Arthur, Brian (1996). "Increasing Returns and the Two Worlds of Business,” Harvard Business Review, vol.74(4), Jul.-Aug.. Arthur, Lewis (1954). “Economic Development with Unlimited Supplies of Labour,” Manchester School, Vol.XXII, May, pages 139-191. Asiedu, Elizabeth (2002). “On the Determinants of Foreign Direct Investment to Developing Countries: Is Africa Different?” World Development, vol.30(1), Jan., pages 107-119. Aten, Bettina (1996). "Evidence of Spatial Autocorrelation in International Prices," Review of Income and Wealth, Blackwell Publishing, vol.42(2), Jun., pages 149-163. Bagliano, FC and G Bertola (2004). Models for Dynamic Macroeconomics, Oxford Scholarship Online Monographs. Baltagi, B. (1995). The Econometric Analysis of Panel Data. New York, Wiley. Barro, Robert J. and Sala-i-Martin, Xavier (2003). Economic Growth, second edition, The MIT Press, pages 54,151,220ˈ212-214ˈ239-240, 247, 251-271, 285, 368-370. Basu, Parantap and Guraiglia, Alessandra (2003). “Foreign Direct Investment, Inequality, and Growth,” available at the website http://www.ssrn.com. Bellak, Christian (1997). “The measurement of foreign direct investment: a critical review,” The International Trade Journal, vol.XII(2), Summer, pages 227-258. Benabou, R. (1996). “Inequality and growth,” In B. Bernanke and J. Rotemberg (Ed.). NBER Macro-Annual 1996, Cambridge: MIT Press, pages 11–76. Bhandari, Bornali (2005). “Does foreign direct investment affect US income inequality?” Downloaded from the website http://www.ssrn.com. Blommestein, Hans J. (1983). “Specification and estimation of spatial econometric models: A discussion of alternative strategies for spatial economic modelling,” Regional Science and Urban Economics, Elsevier, vol.13(2), May, pages 251-270. Blommestein, Hans J. (1985). “Elimination of circular routes in spatial dynamic regression equations,” Regional Science and Urban Economics, Elsevier, vol.15(1), Feb., pages 121-130. Blomström, Magnus and Kokko, A. (1998). “Multinational Corporations and Spillovers,” Journal of Economic Surveys, vol.12 (3), pages 247–277. Blundell, R. and F. Windmeijer (2000). “Identifying demand for health resources using waiting times information,” Health Economics, John Wiley and Sons, Ltd., vol.9(6), pages 465-474. Blundell, R. and S. Bond (1998). “Initial conditions and moment restrictions in dynamic panel-data models,” Journal of Econometrics, vol.87, pages 115-143. Borensztein, E; Gregorio, J De and Lee, JW. (1998). “How Does Foreign Direct Investment Affect Economic Growth?” Journal of International Economics, vol.45(1), Jun., pages 115-135. Bornschier, Volker and Chase-Dunn, Christopher (1985). Transnational Corporations and Underdevelopment, New York; Praeger, pages120-121 Bornschier, Volker; Chase-Dunn, Christopher and Rubinson, Richard (1978). “Cross-National Evidence of the Effects of Foreign Investment and Aid on Economic Growth and Inequality: A Survey of Findings and Reanalysis,” The American Journal of Sociology, vol.84(3), Nov., pages 651-683. Borraz, F. and E. López-Córdova (2004). “Has Globalization Deepened Income Inequality in Mexico?” Inter-American Development Bank Working Paper, Mimeo. Bourguignon, Francois (1998). “Distribution, redistribution and development: Where Do We Stand?” DELTA Working Papers 98-11, DELTA Bourguignon, Francois and Morrison, Christian (1989). “External trade and income distribution,” Journal of Development Economics, vol.41(1), pages 207-209. Bourguignon, Francois and Morrison, Christian (1995). “Inequality and Development: The Role of Dualism,” DELTA Working Papers 95-32, DELTA. Bridge, J. (1998). “Supply-chain dynamics - A case study of the automotive sector,” paper presented at the International Workshop on Global Production and Local Jobs: New Perspectives on Enterprise Networks, Employment and Local Development Policy, Mar. 9-10 (Geneva: ILO),

164

47. 48. 49. 50.

51. 52. 53. 54.

55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65.

66. 67. 68.

69. 70.

Brimble, P. and A. Sibunruang (1988). “The employment effects of manufacturing multinational enterprises in Thailand,” Multinational Enterprises Programme Working Paper No. 54 (Geneva: ILO). Bruno, M; Ravallion, M and Squire, L (1998). Equity and Growth in Developing Countries: Old and New Perspectives on the Policy Issues. MIT Press: Cambridge, MA. Burkhauser, R.V. and Poupore, J.G. (1997). “A Cross-National Comparison of Permanent Inequality in the United States and Germany,” vol.79(1), pages 10-17. Bussmann, Margi; Indra de Soysa and John R. Oneal (2002). “The Effect of Foreign Investment on Economic Development and Income Inequality,” Discussion Papers 18718, University of Bonn, Center for Development Research (ZEF). Carkovic, Maria and Levine, Ross (2003). “Does Foreign Direct Investment Accelerate Economic Growth?” University of Minnesota, Working Paper. Case, Anne C (1991). “Spatial Patterns in Household Demand,” Econometrica, Econometric Society, vol.59(4), Jul., pages 953-965. Case, Anne C (1992). “Neighborhood influence and technological change,” Regional Science and Urban Economics, Elsevier, vol.22(3), Sep., pages 491-508. Case, Anne C. and Rosen, Harvey S. and Hines, James Jr. (1993). “Budget spillovers and fiscal policy interdependence: Evidence from the states,” Journal of Public Economics, Elsevier, vol.52(3), Oct., pages 285-307. Chakrabarti, A. (2001). “The Determinants of Foreign Direct Investment: Sensitivity Analyses of Cross-Country Regressions,” Kyklos, 54(1), pages 89-113. Chakravarty, S.R. (1999). “Measuring inequality: The axiomatic approach,” In J. Silber (Ed.). Handbook of Income Inequality Measurement, Boston/Dordrecht/London: Kluwer Academic Publishers, pages 163-186. Chandra, Ramesh and Sandilands, Roger J. (2005). “Does modern endogenous growth theory adequately represent Allyn Young?” Cambridge Journal of Economics, vol.29(3), pages 463-473. Chase-dunn, Christopher (1975a). International Economic Dependence in the World-System. A Dissertation to Department of Sociology, Stanford University, Aug.. Chase-Dunn, Christopher (1975b). “The Effects of International Economic Dependence on Development and Inequality: A Cross-National Study,” American Sociology Review, vol.40(6), Dec., pages720-38. Chen Li-Min and Xie Huai-Zhu (2004). “Analysis of How FDI Affects Wages in China,” Shandong Economy, vol. 2004 (6), pages 31-35. Chen Zong-Sheng (1991). Income Distribution in Economic Development, Shanghai Joint Publishing Company. Chen Zong-Sheng (1997). “The status, trends and affecting factors of the income gap among Chinese urban residents,” Economic Research Journal, vol. 1997 (3). Chen Zong-Sheng and Zhou Yun-Bo (2002). Rethinking of Income Distribution in Reform and Development, Economic Science Press. Chen, Chung; Chang, Lawrence and Zhang, Yimin (1995). “The Role of Foreign Direct Investment in China’s Post- 1978 Economic Development,” World Development, vol.23(4), pages 691-703. Chen, Weiyan (2003). Foreign Direct Investment and Wage Inequality Across and Within Nations: An Empirical Analysis. A dissertation of Oregon State University, provided by ProQuest Information and Learning Company, UMI Microform No. 3080141. Cheng Hui-Fang (2002). “Foreign direct investment and open endogenous economic growth,” Economic Research Journal, vol. 2002 (10). Cheng Xin-Zhang (2005). “FDI, migrant workers, manufacturing convergence and regional income distribution,” Shanghai Economic Review, vol. 2005 (7). Cheshire, P. and E Malecki (2004). “Growth, development, and innovation: A look backward and forward,” in Raymond J.G.M. Florax and David A. Plane (Ed.): Fifty Years of Regional Science (Advances in Spatial Science), Springer, pages 249-268. Choi, Changkyu (2006). “Does foreign direct investment affect domestic income inequality?” Applied Economics Letters, Taylor and Francis Journals, vol.13(12), Oct., pages 811-814. Chowdhury, Abdur and Mavrotas, George (2006). “FDI and Growth: What Causes What?” The World Economy, vol.29(1), Jan., pages 9-19.

165

71. 72. 73. 74. 75.

76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96.

97.

Cliff, A.D. and Ord, J.K. (1973). Spatial Autocorrelation (Monographs in spatial and environmental systems analysis), London: Pion (Sep.). Cole, William E and Sanders, Richard D (1985). “Internal Migration and Urban Employment in the Third World,” American Economic Review, American Economic Association, vol.75(3), Jun., pages 481-94. Conceição, Pedro and James K. Galbraith (2000). “Constructing long and dense time-series of inequality using the Theil index,” Eastern Economic Journal, vol.26(1), Jun., pages 61-74. Conley, T. G. (1999). “GMM estimation with cross sectional dependence,” Journal of Econometrics, Elsevier, vol.92(1), Sep., pages 1-45. Conyon, M and S Girma, S Thompson (1999). “The impact of foreign acquisition on wages and productivity in the UK,” Research Paper, 99/8 of Centre for Research on Globalisation and Labour Markets, School of Economics, University of Nottingham. Cooper, Richard N. (2001). Growth and Inequality: the Role of Foreign Trade and Investment. Mimeo. Harvard University. Available at http://cowles.econ. yale.edu /conferences/brainard/. Cowell, F.A. (1995). Measuring inequality, LSE Economics Series. Oxford University Press. Cutright, Phillips (1967). “Inequality: A Cross-National Analysis,” American Sociological Review, vol.32(4), Aug., pages 562-578. Dai Qian and Bie Zhao-Xia (2006). “FDI, accumulation of human capital and economic growth,” Economic Research Journal, vol. 2006 (4). Darity, William Jr and Davis, Lewis S. (2005). “Growth, trade and uneven development,” Cambridge Journal of Economics, vol.29(1), pages 141-170. Darnell, Adrian C. (1994). A Dictionary of Econometrics, Edward Elgar Publishing, Jul.. Davies, James B.; Susanna Sandstrom, Anthony Shorrocks and Edward N. Wolff (2006). “The World Distribution of Household Wealth,” http:// iariw.org. de Mello Jr., Luiz R. (1997). “Foreign Direct Investment in Developing Countries: A Selective Survey,” Journal of Development Studies, vol.34, pages 1-34. de Mello Jr., Luiz R. (1999). “Foreign Direct Investment-led Growth: Evidence from Time Series and Panel Data,” Oxford Economic Papers, vol.51(1), pages 133-151. Deininger, K and Squire, L (1996). “A New Data Set Measuring Income Inequality,” World Bank Economic Review, vol.10(3), pages 565-591. Deininger, K and Squire, L (1998). “New Ways of Looking at Old Issues: Inequality and Growth,” Journal of Development Economics, vol.57, pages 259-287. Delong, JB and LH Summers (1991). “Equipment, investment, relative prices, and economic growth,” Quarterly Journal of Economics, CVI: 2, pages 445-502. Dinopoulos, Elias; Costas Syropoulos and Bin Xu (2002). “Intra-Industry Trade and Wage-Income Inequality,” Mimeo, University of Florida. Doreian, Patrick (1980). “Linear Models with Spatially Distributed Data: Spatial Disturbances or Spatial Effects?” Sociological Methods and Research, vol.9(1), pages 29-60. Driffield, Nigel and Karl Taylor (2002). “Spillovers from FDI and skill structures of host-country firms,” downloaded from the website http://www.ssrn.com. Driscoll, John C. and Aart C. Kraay (1998). “Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data,” The Review of Economics and Statistics, vol.80(4), pages 549-560. Druska, Viliam and Horrace, William C. (2003). “Generalized Moments Estimation for Panel Data,” NBER Working Paper No. T0291. Dubin, Robin A. (1988). “Estimation of Regression Coefficients in the Presence of Spatially Autocorrelated Error Terms,” The Review of Economics and Statistics, vol.70(3), pages 466-474. Dunning, John H. (1970). Studies in International Investment. London, George Allen and Unwin. Dunning, John H. (1993). The Theory of Transnational Corporations. United Nations. Duo Qin, Marie Anne Cagas, Geoffrey DucanesˈXinhua He, Rui Liu and Shiguo Liu Shiguo Liu (2005). “Income Disparity and Economic Growth: Evidence from China,” Working Papers 548, Queen Mary, University of London, Department of Economics. Duo Qin, Marie Anne Cagas, Geoffrey DucanesˈXinhua He, Rui Liu and Shiguo Liu (2009). “Effects of income inequality on China’s economic growth,” Journal of Policy Modeling, vol.31, pages 69-86.

166

98. 99. 100. 101. 102. 103. 104. 105. 106.

107. 108. 109. 110. 111. 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123.

Dupuy, C and J. Savary (1993). “Les effets indirects des entreprises multinationals sur l’emploi des pays d’accueil,” ILO Working Paper No. 72. Durlauf, Steven N. (1992). “A Theory of Persistent Income Inequality,” NBER Working Papers 4056, National Bureau of Economic Research, Inc. Durlauf, Steven N. (1994). “Spillovers, stratification, and inequality,” European Economic Review, vol.38(3-4), pages 836-845. Edwards, Rob (2006). “Statistical issues arising from globalization: the IMF response,” presented at Ad Hoc Seminar on Statistical Issues in a Globalized Economy held in Beijing, China. Fan Yan-Hui and Duan Jun-Shan (2003). “FDI and income distribution among Chinese residents,” Finance& Economics, vol. 2003 (2). Fang Hui (2005). “Theoretical analysis of how FDI affects factor income distribution,” The Journal of Shandong University of Finance, vol. 2005 (6). Fang Hui and Liu Hong-Jie (2005). “The effects of FDI on income distribution in China,” China Opening Journal, vol. 2005 (6). Feenstra, Robert C. and Hanson, Gordon H. (1995). “Foreign Investment, Outsourcing and Relative Wages,” NBER Working Paper No. W5121. Feenstra, Robert C. and Hanson, Gordon H. (1997). “Foreign Direct Investment and Relative Wages: Evidence from Mexico’s Maquiladoras,” Journal of International Economics, Vol.42(3and4), May, pages 371–394. Fei, John. CH and G. Ranis (1964). Development of the Labor Surplus Economy: Theory and Policy. Illinois, USA. Fiala, Robert (1987). “Labor Force Structure and the Size Distribution of Income within Countries, 1960-80,” International Studies Quarterly, vol.31(4), Dec., pages 403-422. Fiebig, Denzil G. (1999). “Seemingly unrelated regression,” In Baltagi (Ed.). Companion in Theoretical Econometrics, Blackwell Publishers. Fields, Gary S. (2001). Distribution and Development: A New Look at the Developing World. New York: Russell Sage Foundation. New York: Cambridge, MA: MIT Press. Findlay, Ronald (1978). “Relative Backwardness, Direct Foreign Investment, and the Transfer of Technology: A Simple Dynamic Model,” Quarterly Journal of Economics, Vol.92(1), Feb., pages 1-16. Firebaugh, Glen (1999). “Empirics of World Income Inequality,” The American Journal of Sociology, Vol.104(6), May, pages 1597-1630. Fishman and Simhon (2001). “The Division of Labor, Inequality and Growth,” Downloaded from the website http://www.ssrn.com. Frees, Edward W. (1995). “Assessing cross-sectional correlation in panel data,” Journal of Econometrics, Elsevier, vol.69(2), Oct., pages 393-414. Fu, Xiaolan (2004). “Limited linkages from growth engines and regional disparities in China,” Journal of Comparative Economics, vol.32(1), Mar., pages 1-19. Fu, Xiaolan and Balasubramanyam, V. N. (2005). “Exports, Foreign Direct Investment and Employment: The Case of China,” FED working paper (FE 20050035). Http://www.fed.org.cn. Fujita, Masahisa and Dapeng Hu (2001). “Regional disparity in China 1985-1994: The effects of globalization and economic liberalization,” The Annals of Regional Science, vol.35(1), pages 3-37. Galor, Oded and Zeira, Joseph (1993). “Income Distribution and Macroeconomics,” Review of Economic Studies, Blackwell Publishing, vol.60(1), Jan., pages 35-52. Gao Min-Xue (2008). Report on the Development Status of Foreign Economy – The Cross-sectional Analysis Based on the First Economic Census, Chapter 1, Economic Science Press. Girling, R. (1973). “Dependency and persistent income inequality,” in Structures of Dependency (Ed.) by F. Bonilla and R. Girling, Institute of Political Studies, Stanford, CA, pages 83–101. Glaeser, E. and Kohlhase, J. (2004). “Cities, regions and the decline of transport costs,” papers in Regional Science, vol.83(1), pages 197-228. Glaeser, Edward L and Sacerdote, Bruce and Scheinkman, Jose A (1996). “Crime and Social Interactions,” The Quarterly Journal of Economics, MIT Press, vol.111(2), May, pages 507-48. Goesling, Brian (2001). “Changing Income Inequalities within and between Nations: New Evidence,” American Sociological Review, Vol.66(5), Oct., pages 745-761.

167

124. Goldberger, Arthur S. (1972). “Structural Equation Methods in the Social Sciences,” Econometrica, Vol.40(6), Nov., pages 979-1001. 125. Goodchild, MF (1986). Spatial autocorrelation, Geo Books. 126. Görg, Holger and David Greenaway (2001). “Foreign Direct Investment and Intra-Industry Spillovers: A Review of the Literature,” Research Paper 2001/37, Globalisation and Labour Markets Programme, Nottingham, Leverhulme Centre for Research on Glabalisation and Economic Policy. 127. Görg, Holger and David Greenaway (2004). “Much Ado about nothing? Do domestic firms really benefit from FDI,” World Bank Research Observer, vol.19(2). Sep., pages 171-198. 128. Graham, Edward M. and Paul R. Krugman (1995). Foreign Direct Investment in the United States. Third Edition, Institute for International Economics, Washington, DC Jan.. 129. Gustafsson, Bjorn, and Mats Johansson (1999). “In Search of Smoking Guns: What Makes Income Inequality Vary over Time in Different Countries?” American Sociological Review, vol.64, pages 585–605. 130. Hall, P.; N.I. Fisher and B. Hoffmann (1994). “On the nonparametric estimation of covariance functions,” The Annals of Statistics, vol.22, pages 2115–2134. 131. Haltiwanger, John C. and Julia I. Lane and James R. Spletzer (1999). “Productivity Differences across Employers: The Roles of Employer Size, Age, and Human Capital,” American Economic Review, American Economic Association, vol.89(2), May, pages 94-98. 132. Hansen, Henrik and John Rand (2006). “On the Causal Links between FDI and Growth in Developing Countries,” The World Economy, Blackwell Publishing, vol.29(1), pages 21-41. 133. Hansen, Lars Peter (1982). “Large Sample Properties of Generalized Method of Moments Estimators,” Econometrica, Econometric Society, vol.50(4), Jul., pages 1029-1054. 134. Hanson, Gordon H. (2001). “Should Countries Promote Foreign Direct Investment?” G-24 Discussion Papers 9, United Nations Conference on Trade and Development. 135. Haskel, Jonathan E. and Matthew J. Slaughter (2002). “Does The Sector Bias Of Skill-biased Technical Change Explain Changing Skill Premia?” European Economic Review, vol.46(10), Dec., pages 1757-1783. 136. Hemmer, Hans-Rimbert; Ralf Krüger and Jennifer Seith (2005). “Foreign Direct Investment and Income Inequality revisited,” available at http://opus.zbw -kiel .de/ volltexte/2005/3732/. 137. Herkenrath, M and V Bornschier (2003). “Transnational Corporations in World Development–Still the Same Harmful Effects in an Increasingly Globalized World Economy?” Journal of World-Systems Research, vol.ix(1), Winterˈ pages 105–139. 138. Hicks, JR (1932). The Theory of Wages. London, Macmillan. 139. Hiemenz, U. (1989). “Foreign Direct Investment and Capital Formation in China since 1979: Implications for Economic Development,” China’s Contemporary Economic Reforms as a Development Strategy. Proceedings of an International Symposium Held at the University of Duisburg, Federal Republic of Germany. D. Cassel and G. Heiduk, eds. Nomos Verlagsgesellschaft, Baden-Baden, pages 85-108. 140. Holtz-Eakin, Douglas and Newey, Whitney and Rosen, Harvey S (1988). “Estimating Vector Autoregressions with Panel Data,” Econometrica, Econometric Society, vol.56(6), Nov., pages 1371-95. 141. Hsiao, Cheng (2003). Analysis of Panel Data, Cambridge University Press; 2 edition (Feb.). 142. Hsiao, Cheng and Li, Qi (2001). “A Consistent Test for Conditional Heteroskedasticity in Time-Series Regression Models,” Econometric Theory, Cambridge University Press, vol.17(01), Feb., pages 188-221. 143. Huang Dan et al (1999). “Theory of decreasing marginal propensity to consume,” The Journal of Quantitative & Technical Economics, vol. 1999 (5). 144. Huang Wei-Min (2000). “Empirical analysis of how FDI affects China’s Macro-economy,” Economic Review, vol. 2000 (6). 145. Huang, J. S. (1984). “The autoregressive moving average model for spatial analysis,” Australian Journal of Statistics, vol.26, pages 169–178. 146. Huang, Yasheng (2002). Selling China: Foreign Direct Investment during the Reform Era (Cambridge Modern China Series). Cambridge University Press. 147. Husbands, C.T. and Rov W Money (1970). “The Cross-National Study of Inequality: A Research Note,” American Sociological Review, vol.35(2), Apr., pages 319-325. 148. Ikemoto, Yukio, and Mine Uehara (2000). “Income Inequality and Kuznets' Hypothesis in Thailand,” Asian Economic Journal, vol.14, Dec., pages 421-443.

168

149. International Finance Corporation (IFC) (2000). Annual Report 1999 (Washington, D.C.: IFC). Also available on the internet at http:// www.ifc.org/publications /pubs/ar/ar.html. 150. International Labor Office (2002). “Investment in the global economy and decent work,” Geneva: Nov., GB.285/WP/SDG/2. 151. International Monetary Fund (2008). Balance of Payment and International Investment Position Manual, sixth Edition (BPM6, draft). Washington D.C.. 152. Ioannides, Y. M. (1990). “Trading uncertainty and market form,” International Economic Review, vol.31, pages 619-638. 153. Ioannides, Y. M. (1997). “Evolution of trading structures,” In W. Brian Arthur, Steven N. Durlauf and David A. Lane (Ed.). The economy as an evolving complex system II, Reading, MA: Addison-Wesley, pages 129-167. 154. Isard, Walter and C Smith (1990). Location Analysis and General Theory, Location Analysis and General Theory. Macmillan Houndmills, Basingstoke, Hampshire. 155. Jiang Xiao-Juan and Li Hui (2005). “The actual interregional income gap is smaller than the nominal one in China – a research added with interregional price differences,” Economic Research Journal, vol. 2005 (9). 156. Jiang, Xiaojuan (2003). FDI in China: Contributions to Growth, Restructuring and Competitiveness in China in the 21st Century. New York: Nova Science Publishers, Inc. 157. JonesˈRonald W. (1965). “The Structure of Simple General Equilibrium Models,” Journal of Political Economy, University of Chicago Press, vol.73. 158. Kanbur, Ravin and Xiaobo Zhang (2004). “Fifty years of Regional Inequality in China: A Journey through Central Planning, Reform and Openness,” World Institute for Development Economics Research, Research Paper No. 2004/50. 159. Kar, Saibal and Guha-Khasnobis, Basudeb (2006). “Economic Reform, Skill Formation and Foreign Capital,” The World Economy, vol.29(1), Jan., pages 79-94. 160. Kelejian, HH and DP Robinson (1999). “The Influence of Spatially Correlated Heteroskedasticity on Tests for Spatial Correlation”, in Advances in Spatial Econometrics, Heidelberg: Springer. 161. Kindleberger, Charles P (1969). “Measuring Equilibrium in the Balance of Payments,” Journal of Political Economy, University of Chicago Press, vol.77(6). Nov./Dec., pages 873-891. 162. King, L. P. and B. Váradi (2002). “Beyond Manichean economics: foreign direct investment and growth in the transition from socialism,” Communist and Post-Communist Studies, vol.35(1), Mar., pages 1-21. 163. Kong Jing-Yuan (2005). Report on Income Distribution among Chinese Residents (2005), Table 1-1, Economic Science Press. 164. Krongkaew, Medhi and Ragayah Haji Mat Zin (2003). “Income distribution and sustainable economic development in East Asia: a comparative analysis”. 165. Krugman, Paul (1991a). “Cities in Space: Three Simple Models,” NBER Working Papers 3607, National Bureau of Economic Research, Inc. 166. Krugman, Paul (1991b). “First Nature, Second Nature, and Metropolitan Location,” NBER Working Papers 3740, National Bureau of Economic Research, Inc. 167. Krugman, Paul (1995). “Technology, Trade, and Factor Prices,” NBER Working Papers 5355, National Bureau of Economic Research, Inc. 168. Krugman, Paul (1998). “What's New about the New Economic Geography?” Oxford Review of Economic Policy, Oxford University Press, vol.14(2), Summer, pages 7-17. 169. Kuznets, Simon (1957). “Quantitative aspects of the economic growth of nations: II, Industrial distribution of national product and labor forces,” Economic Development and Cultural Change, Supplement to 5, Jul., pages 1-45. 170. Kuznets, Simon (1963). “Quantitative aspects of the economic growth of nations: III, Distribution of income by size,” Economic Development and Cultural Change, 11, (Jan., Part 2), pages 1-80. 171. Lai Ming-Yong, Bao Qun, Peng Shui-Jun and Zhang Xin (2005). “Foreign direct investment and technology spillover: a theoretical explanation of absorptive capability,” Economic Research Journal, vol. 2005 (8). 172. Lensink, R and O Morrissey (2001). “Foreign Direct Investment: Flows, Volatility and Growth in Developing Countries,” Presented at the Development Economics Study Group Conference 2001, University of Nottingham, 5-7 Apr.. 173. Lenski, Gerhad (1966). Power and Privilege. New York: McGraw-Hill.

169

174. Li Pei-Lin, Chen Jin-Guang, Zhang Yi and Li Wei (2008). Social Harmony and Stability in China Today, Social Sciences Academic Press (China). 175. Li Shi-Guang (2004). “International trade, FDI, technological advances and income gap – a comprehensive analysis model,” The Journal of International Trade, vol. 2004 (6). 176. Li Xue-Hui and Xu Luo-Dan (2002). “Empirical analysis of how FDI affects wages in foreign capital-rich regions,” Nankai Economic Studies, vol. 2002 (2). 177. Li Zhi-Jie and Hu Feng (2007). “On graduate unemployment and its institutional causes,” China Economic Times, section 5, March 2, 2007. 178. Lin, Justin Y and Wang, Gewei and Zhao, Yaohui (2004). “Regional Inequality and Labor Transfers in China,” Economic Development and Cultural Change, University of Chicago Press, vol.52(3), Apr., pages 587-603. 179. Lipsey, Robert (2001). “Foreign Direct Investment and the Operations of Multinational Firms: Concepts, History, and Data,” NBER Working Papers 8665, National Bureau of Economic Research, Inc. 180. Lipsey, Robert (2002). “Home and host country effect of FDI,” NBER Working Papers 9293, National Bureau of Economic Research, Inc. 181. Lipsey, Robert (2006). “Measuring the Impacts of FDI in Central and Eastern Europe,” NBER Working Papers 12808, National Bureau of Economic Research, Inc. 182. Lipsey, Robert (2007). “Defining and Measuring the Location of FDI Output,” NBER Working Papers 12996, National Bureau of Economic Research, Inc. 183. Lipsey, Robert and Fredrik Sjöholm (2001). “Foreign Direct Investment and Wages in Indonesian Manufacturing,” NBER Working Paper 8299, National Bureau of Economic Research. 184. Lipsey, Robert and Fredrik Sjöholm (2004). “Host Country Impacts Of Inward FDI: Why Such Different Answers?” EIJS Working Paper Series 192, The European Institute of Japanese Studies. 185. Liu Shi-Guo (2007). “The effects of FDI on the income gap in a developing host country: an overview,” Forward-looking Issues of Global Economic Development, Social Sciences Academic Press (China). 186. Liu Shi-Guo (2008). Report on Income Distribution in Foreign-invested Enterprises in the Pearl River Delta (National Condition Report), the Bureau of Scientific Research Management of Chinese Academy of Sciences, 2008. 187. Liu Shi-Guo (2009). The Latest Global FDI Statistics, The Latest Global Economic & Social Statistics 2009, China Statistics Press. 188. Liu Xiao-Dong and Lu Qing (1991). “Empirical analysis of the level of income equality in China,” the Journal of Peking University (Philosophy and Social Sciences), vol. 1991 (6). 189. London, Bruce and Thomas Robinson (1989). “The Effect of International Dependence on Income Inequality and Political Violence,” American Sociology Review, vol.54(2), Apr., pages 305-308. 190. Lou Hai-Miao, Jiang Dong-Sheng and Han Lin (2004).“FDI and the average wage of employees – empirical analysis of interregional differences,” Statistics & Information Forum, vol. 2004 (6). 191. Lou Ji-Wei, Yu Yun-Yao, Yuan Shui-Chun and Cui Zheng-Hua (2006). “On correlations among efficiency, fairness and justice,” Study Times, vol. 340. 192. Luo Peng (2005). “On how FDI affects wages in the host country,” Modern Management Science, vol. 2005 (2). 193. Manski, Charles F. (1993). “Dynamic choice in social settings: Learning from the experiences of others,” Journal of Econometrics, Elsevier, vol.58(1-2), Jul., pages 121-136. 194. Marshall, A (1920). The Principles of Economics. London: English Language Book Society. 195. Matyas, L and P Sevestre (1996). The Econometrics of Panel Data: A Handbook of the Theory with Applications. Kluwer Academic Publishers, Dordrecht. 196. McCann, P and D Shefer (2004). “Agglomeration, economic geography and regional growth,” papers in Regional Science. 197. Milanovic, Branko (2002). “True World Income Distribution, 1988 and 1993: First Calculation Based on Household Surveys Alone,” Economic Journal, Royal Economic Society, vol.112(476), Jan., pages 51-92. 198. Milanovic, Branko (2003). “Can We Discern the Effect of Globalization on Income Distribution? Evidence from Household Budget Surveys,” International Trade, 0303004, EconWPA. 199. Mody, Ashoka and Murshid, Antu Panini (2005). “Growing up with capital flows,” Journal of International Economics, Elsevier, vol.65(1), Jan., pages 249-266.

170

200. Moller, S., Alderson, A. S. and Nielsen, F. (2005). “Income Inequality in the United States”. Paper presented at the annual meeting of the American Sociological Association, Marriott Hotel, Loews Philadelphia Hotel, Philadelphia, PA Online. 201. Mundell, Robert (1957). “International Trade and Factor Mobility,” American Economic Review, vol.47(3), pages 321-335. 202. NBS (National Bureau of Statistics, China). “Conference on China’s macroeconomic situation in 2012 by MA Jiantang, Director of NBS”, Jan 18, 2013. 203. Newey, Whitney K and West, Kenneth D (1987). “A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix,” Econometrica, Econometric Society, vol.55(3) , May, pages 703-708. 204. Niu Yong-Ping (2001). “On the relationship between FDI and the number of jobs in China,” Economic Perspectives, vol. 2001 (11). 205. Nunnenkamp, Peter and Spatz, Julius (2003). “Foreign Direct Investment and Economic Growth in Developing Countries: How Relevant Are Host-Country and Industry Characteristics?” (Jul.). Kiel Working Paper No. 1176. 206. OECD (1995). Benchmark Definition of Foreign Direct Investment, Paris, http://www.oecd.org /dataoecd/10/16/2090148.pdf. 207. OECD (2002). OECD Global Forum on International Investment: Attracting International Investment for Development. Paris: OECD Publications Service. 208. OECD (2008). Benchmark Definition of Foreign Direct Investment, 4th edition, Investment Division, Directorate for Financial and Enterprise Affairs, Organisation for Economic Co-operation and Development, 2 rue André-Pascal, Paris 75116, France, www.oecd.org/daf/investment/statistics. 209. Oi, WY and TL Idson (1999). “Firm size and wages,” in: O. Ashenfelter and D. Card (Ed.) Handbook of Labor Economics, Chapter 33, pages 2165-2214. 210. Okun, AM (1975). “Equality and efficiency,” The Brookings Institution 1775 Massachusetts Avenue, NW, Washington. 211. Ord, K. (1975). “Estimation methods for models of spatial interaction,” Journal of the American Statistical Association, vol.70, Mar., pages 120-126. 212. Ord, K. and Getis (2001). “Testing for local spatial autocorrelation in the presence of global autocorrelation,” Journal of Regional Sciences, vol.41, pages 411-432. 213. Pan Yi-Xing (2003). “Empirical analysis of FDI and the average wage of employees in state-owned enterprises in Shenzhen City – the interactions between the reform of state-owned enterprises and FDI,” Finance & Economics, vol. S1. 214. Parisotto, A. (1993). “Direct Employment in multinational enterprises in industrialized and developing countries in the 1980s: Main characteristics and recent trends,” in: P. Bailey, A. Parisotto and G. Renshaw, eds., Multinationals and Employment. The Global Economy of the 1990s (Geneva: ILO), pages 33-68. 215. Parisotto, A. and G. Renshaw (1995). “Recent trends in employment in transnational corporations,” in OECD: Foreign Direct Investment, Trade and Employment (Paris: OECD), pages 67-78. 216. Parka, Byeong U.; Robin C. Sicklesb and Le′opold Simar (2007). “Semiparametric efficient estimation of dynamic panel data models,” Journal of Econometrics, vol.136, pages 281–301. 217. Patterson, Neil; Marie Montanjees, John Motala, Colleen Cardillo (2004). “Foreign direct investment: Trends, Data Availability, Concepts, and Recording Practices,” Washington, D.C. : International Monetary Fund. 218. Perotti, Roberto (1996). “Growth, income distribution, and democracy: What the data say?” Journal of Economic Growth, vol.1(2), pages 149-187. 219. Pesaran, M. Hashem and Tosetti, Elisa (2007). “Large Panels with Common Factors and Spatial Correlations,” IZA Discussion Paper No. 3032. 220. Qiu Xiao-Hua et al (2000). Report on Major Research Projects of the National Bureau of Statistics of China 2000. 221. QuahˈD. (1992). International Patterns of Growth: I. Persistence in Cross-Country Disparities, unpublished manuscript, London School of Economics. 222. Quan Heng (2004). Analysis of Income Distribution vs. Economic Growth: Experience and Theories in a Transforming China, Shanghai Academy of Social Sciences Press.

171

223. Rama, M. (2003), “Globalization and The Labor Market,” The World Bank Research Observer, vol.18(2), Fall, pages 159-186. 224. Robinson, Richard (1976). “The World Economy and Income Distribution within States: A Cross-National Study,” American Sociological Review, vol.41(4), Aug., pages 638-659. 225. Rodreiguez-Clare, Andres (1996). “Multinationals, Linkages, and Economic Development,” The American Economic Review, vol.86(4), Sep., pages 852-873. 226. Rodrik, Dani (1994). “Do Low-Income Countries Have a High-Wage Option?” CEPR Discussion Papers 862. 227. Rodrik, Dani (1997). Has Globalization Gone too Far? Washington, D.C.: Institute for International Economics. 228. Romer, Paul (1986). “Increasing Returns and Long-Run Growth,” The Journal of Political Economy, vol.94, Oct., pages 1002-37. 229. Romer, Paul (1987). “Growth Based on Increasing Returns Due to Specialization,” The American Economic Review, vol. 77(2), pages56-62. 230. Romer, Paul (1990). “Endogenous Technological Change,” Journal of Political Economy, Vol.98(5), Part 2: The Problem of Development: A Conference at the Institute for the Study of Free Enterprise System, Oct., pages S71-S102. 231. Romer, Paul. (1993). “Idea gaps and object gaps in economic development,” Journal of Monetary Economics, vol.32(3), Dec., pages 543-573. 232. Roodman, David (2009). “How to do xtabond2: An introduction to difference and system GMM in Stata?” The Stata Journal, vol.2009 (9), Number 1, pages 86–136. 233. Ru Xin, Lu Xue-Yi and Li Pei-Lin (2005). Society of China Analysis and Forecast (2006), Social Sciences Academic Press (China). 234. Ru Xin, Lu Xue-Yi and Li Pei-Lin (2008). Society of China Analysis and Forecast (2008), Social Sciences Academic Press (China). 235. Sala-i-Martin, Xavier (2002). “The World Distribution of Income (Estimated from Individual Country Distributions),” Economics Working Papers 615, Department of Economics and Business, Universitat Pompeu Fabra, revised in May.. 236. Sargan, John D (1958). “The Estimation of Economic Relationships Using Instrumental Variables,” Econometrica, vol.263, pages 393-415. 237. Sargan, John D (1988). “Testing for Misspecification after Estimating Using Instrumental Variables,” in Maasoumi, E. (Ed.): Contributions to Econometrics: John Denis Sargan, Vol.1, Cambridge: Cambridge University Press. 238. Shen Kun-Rong and Geng Qiang (2001). “FDI, technology spillover and endogenous economic growth: measurement, checking and empirical analysis of data in China,” Social Sciences in China, vol. 2001 (5). 239. Sylwester, Kevin (2005). “Foreign direct investment, growth and income inequality in less developed countries,” International Review of Applied Economics, vol.19(3), pages 289-300. 240. Tang, S and S Selvanathan (2005). Foreign Direct Investment and Regional Income Inequality in China, Griffith University, Australia. 241. The Income Distribution Research Team, Nankai University (1990). “The status and root causes of and countermeasures for income inequality in China,” Nankai Journal, vol. 1990 (2). 242. The Project Team of the Research Office of the State Council (1997). “Analysis of and Recommendations on the income gap among urban residents,” Economic Research Journal, vol. 1997 (8). 243. Topa, Giorgio (1996). “Social interactions, local spillovers and unemployment,” Ph.D. Dissertation, Department of Economics, University of Chicago. 244. Tsai, Pan-Long (1995). “Foreign Direct Investment and Income Inequality: Further Evidence,” World Development, vol.23, pages 469-483. 245. UNCTAD (1994). World Investment Report: Transnational Corporations, Employment and the Workplace. New York and Genewa: United Stations, pages 166-172, 175,185. 246. UNCTAD (1999). World Investment Report: FDI and the Challenge of Development. New York and Genewa: United Stations, pages 257, 258-261, 263. 247. UNCTAD (2000). World Investment Report: Cross-border M and A and Development. New York and Genewa: United Stations, pages 127-156, 180-181, 183, 188, 190-191. 248. UNCTAD (2001): World Investment Report: Promoting Linkages. New York and Genewa: United Stations.

172

249. UNCTAD (2006). World Investment Report: FDI from Developing and Transition Economies: Implications for Development. New York and Genewa: United Stations. 250. UNDP (2006). Human Development Report 2006: Beyound Scarcity: Power, Poverty and the Global Water Crisis, Palgrave Macmillan: Houndmills, Basingstoke, Hampshire RG21 6XS and 175 Fifth Avenue, New York, NY 10010. 251. United Nations, IMF, Eurostat, OECD and World Bank (1993). System of National Accounts (SNA 1993). Washington, DC. 252. Velde, te Dirk Willem (2003). “Foreign Direct Investment and Income Inequality in Latin America, Experiences and policy implications,” Overseas Development Institute (ODI). 111 Westminster Bridge Road, London SE1 7JD, UK. 253. Velde, te Dirk Willem, and O. Morrissey (2002). “Foreign Direct Investment, Skills and Wage Inequality in East Asia,” paper presented at DESG Nottingham 2002, see also http://www.odi.org.uk/iedg/meetconf.html. 254. Voitchovsky, S. (2005). “Does the Profile of Income Inequality Matter for Economic Growth? Distinguishing Between the Effects of Inequality in Different Parts of the Income Distribution”. Journal of Economic Growth, vol.10(3), pages 273-296. 255. Wade, Robert Hunter (2001). “The Rising Inequality of World Income Distribution,” IMF Finance and Development, vol.38(4), Dec.. 256. Wan Guang-Hua (2006). Economic Development and Income Inequality: Methods and Evidence, Shanghai: Shanghai Joint Publishing Company and Shanghai People’s Publishing House, pages 128-149. 257. Wan Guang-Hua (2006). Globalization vs. Interregional Income Gaps: Evidence from China, Economic Development and Income Inequality: Methods and Evidence, Shanghai Joint Publishing Company and Shanghai People’s Publishing House. 258. Wan Xin-Rong, Shi Wei and Fang Xiao-Jun (2005). “Empirical analysis of how FDI affects employment – in the case of Guangdong Province,” Nankai Business Review, vol. 8, issue 2. 259. Wan, Guanghua; Ming Lu and Zhao Chen (2004). “Globalization and Regional Income Inequality: Evidence from within China,” World Institute for Economic Research of United Nation University, Discussion Paper No. 2004/10. 260. Wang Jian (2005). “Measuring the effects of employment creation of FDI in China,” Statistical Research, vol. 2005 (3). 261. Wang Xiao-Lu (2007). “Gray incomes and the income gap among residents in China,” Comparative Studies, vol. 31, CITIC Press Corporation. 262. Wang Xiao-Lu (2010). “Gray income and national income distribution,” Comparative Studies, vol. 48, CITIC Press Corporation. 263. Wang Xiao-Lu, Li Shi, Ding Ning-Ning and Zhao Xiao (2010). “An insight into ‘gray incomes’,” China Economic Times, section 5, September 17, 2010. 264. Wang Zhen-Zhong (2000). Theoretical Analysis of Foreign Capital in China, Economy & Management Publishing House. 265. Watanable, S. (1993). “Growth and Structural Change of Japanese overseas direct investment: Impications for labor and management in host countries,” in Bailey, A. Parisotto and G. Renshaw, eds., Multinationals and Employment: The Global Economy of the 1990s (Geneva: ILO). 266. Wei Zhong (2006). “Changes in the income gap among Chinese residents,” published at the Sino-Japan economic dialog held by Chinese Academy of Social Sciences and Japan Association of Corporate Executives in Beijing on October 16, 2006. 267. Wei, Shang-Jin and Yi Wu (2001). “Globalization and Inequality: Evidence from Within China,” NBER Working Papers 8611, National Bureau of Economic Research, Inc. 268. Windmeijer, F. (2000). “Efficiency comparisons for a system GMM estimator in dynamic panel data model,” in Heijmans, R.D.H. D.S.G. Pollock and A Satorra (Ed.), Innovations in Multivariate Statistical Analysis. A Fistchirift for Heinz Neudecker. Kluwer Aacademic Publishers, pages 175-184. 269. Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. 1st ed, Cambridge, MA: MIT Press. 270. World Bank (1995). China: Regional Disparities. Washington DC: World Bank. 271. World Bank (2005). Economic Growth in the 1990s: Learning from a Decade of Reform. Washington DC, The World Bank.

173

272. World Bank (2006).Report on the Investment Environment in 120 Chinese Cities, Hangzhou News Website, November 11, 2006, http://www.hangzhou.com.cn/20060801/ca1228129.htm. 273. Wu Jian (2002). Savings, Investment & Economic Growth, Sustainability of China’s Economic Growth, Economic Science Press. 274. Wu Yu-Ming (2005). Spatial Econometric Analysis of Economic Growth and Income Gaps in China (A Collection of Books Written by Young and Middle-aged Economists), Economic Science Press. 275. Xiang Shu-Jian (1998). “Measurement and regression analysis of the Gini coefficient of income inequality among residents in China,” The Theory and Practice of Finance and Economics, vol. 1998 (1). 276. Xie Xiao-Qing (2005). “The effects of FDI on employment and wages in China,” Statistics and Decision, vol. 2005 (6). 277. Xing, Y. and Zhang, H. (2004). “FDI and regional income disparity in host countries: evidence from China,” Economia Internazionale/International Economics, vol.57 (3), pages 363-379. 278. Xu, Bin (2000). “Multinational Enterprises, Technology Diffusion and Host Country Productivity Growth,” Journal of Development Economics, vol.62, pages 477-493. 279. Xuan Ye and Zhao Shu-Dong (2005). “Analysis of how FDI affects wages – empirical research on Jiangsu Province,” Nankai Economic Studies, vol. 2005 (1). 280. Yang Yi-Yong (2010). “The income distribution reform program is expected to come out in the year,”21st Century Business Herald, http://www.21cbh.com/HTML/2010-3-8/167634_2.html. 281. Yeung, Bernard (1999). “Foreign Investment and Emerging Markets: Highlights of a Conference,” organized by the William Davidson Institute, http://www.worldbank.org/html/ prddr/trans/ julaug99/pgs26-28.htm 282. Yu Yong-Ding (2004). “The effects of FDI on the Chinese economy,” International Economic Review, vol. (2004) 3 and 4. 283. Yuan Cheng and Lu Ting (2005). “Foreign direct investment and managerial knowledge spillover – evidence from entrepreneurs in China's private sector,” Economic Research Journal, vol. 2005 (3). 284. Zhang Hao-Guang and Jiang Xiu-Lan (2004). “We must prevent foreign capital from widening the income gap among Chinese residents,” Finance & Economics, vol. 2004 (2). 285. Zhang, X. and Zhang, K. (2003). “How does globalization affect regional inequality within a developing country? Evidence from China,” The Journal of Development Studies, vol.39, pages 47-67. 286. Zhao Ling-Di and Liu Xiu-Peng (2007). “An overview of research on how FDI affects income distribution,” Economic Perspectives, vol. 2007 (4). 287. Zhao Ren-Wei, Li Shi and Li Si-Qin (1999). Further Research on Income Distribution among Chinese Residents, China Financial & Economic Publishing House. 288. Zhao Ying (2003). “Openness and income inequality in China,” World Economic Papers, vol. 2003 (3). 289. Zhou Hua (2006). “The long-term effects of FDI on income distribution in the host country: China as an example,” Nankai Economic Studies, vol. 2006 (5). 290. Zhu Ting-Xing (2006). “A literature review on FDI and processing trade benefits assignment,” the Journal of Lanzhou Commercial College, vol. 2006 (2).

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Postscript This book was the culmination of The Effects of FDI on the Income Gap among Chinese Residents (No. YZDN/510214), a major project of the Chinese Academy of Social Sciences (CASS) in 2007. It was finished more than one year later than planned as a result of continual changes in the research ideas and of the processing of massive data. This project was research made on the basis of my doctoral dissertation written at the School of Statistics, Renmin University of China (RUC). I’m therefore very grateful to Professor PENG Fei as my advisor. It is his academic expectations for me that let me focus on my study. It is his encouragement that empowers me to move forward through hardship. It is his handling my academic queries with patience that enables me to make breakthroughs. It is his academic wisdom that makes me always have the greatest admiration for scholarship. And it is his academic insight and horizons that allow me to feel the beauty of academic breadth and depth. And I must thank relevant experts for their great support. They are: YU Yongding, ZHANG Yuyan, HE Fan, LI Xiangyang, HE Xinhua, SONG Hong, SHI Xiaoyu, YAO Zhizhong, LU Tong and TU Qin, senior research fellows at the Institute of World Economics and Politics (IWEP), CASS; WANG Xin, Director at the IWEP, CASS; WANG Ling and WU Haiying, associate researchers at the IWEP, CASS; Dr. CAO Yongfu and Dr. SONG Zhigang at the IWEP, CASS; Dr. FAN Maoyong, Assistant Professor of Department Economics at Ball State University, Indiana, USA; ZHAO Renwei and WEI Zhong, senior research fellows at the Institute of Economics (IE), CASS; LI Xuesong, senior researcher fellow at the Institute of Quantitative & Technical Economics (IQTE), CASS; Professors YI Danhui, GU Lan, ZHAO Yanyun, GAO Minxue, HE Xiaoqun, JIN Yongjin, MENG Shengwang and WANG Xiaojun at the School of Statistics, RUC; Prof. ZHAO Guoqing at the School of Economics, RUC; Prof. LI Shi at the School of Economics and Business Administration, Beijing Normal University (BNU); Prof. HAO Xuguang at the Business School, the University of International Business and Economics (UIBE); and QI Shaocheng, a senior statistician and former deputy director at the Department of National Economic Accounting, National Bureau of Statistics (NBS). They encouraged me to visit overseas, assisted me in my work, or gave a lot of good advices to help me explore academic details. All these allowed me to increase knowledge of economics, econometrics and other disciplines and, thus, to finish this project smoothly. My special gratitude goes to YU Jianxun, ZOU Zhaohui, SHI Yujun, CAO Lin, LIU Rong, LI Falin, and other persons for their professional data support. And I am also grateful to Dr. HUANG Guohua, Dr. LIU Chao, Dr. WEI Chuanhua, and others for their academic discussions with me, which broadened my horizons and answered some of my questions. I must sincerely thank my family, especially my parents and my wife Ms. Shi Yujun. Since I was so busy with this project that I had little or even no time for the family, they did most of the household duties with ample understanding, encouragement, and support. Without their contribution, I’m afraid that I could not have been able to finish this research. Unfortunately, my dear mother Madam Lee Weihua accidently left this world leaving me in great regrets, deep pains and countless loving thoughts. I would like to devote this book to her and wish her safe, easy, happy and eternal in heaven. The publishing of this book has been financed by the World Economic Statistics, a Key

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Construction Discipline at the CASS. I’m therefore very grateful to YU Yongding and ZHANG Yuyan, the former and current Head of this discipline and Director of the IWEP, respectively, and HE Xinhua, Director of the Department of World Economic Statistics, IWEP. Although I have done my best, there are still flaws in this book that result from an imperfect initial design or lack of knowledge. I hope to fix these flaws as soon as possible and continually push this research forward.