Changing Trade Pattern, ICT, and Employment: Evidence Across Countries [1 ed.] 9780691222998

Table of contents :
Changing Trade Pattern, ICT, and Employment: Evidence Across Countries
1. Introduction
2. Model Specification, List of Variables, Sample, and Data Sources
2.1. Model specification
2.2. List of variables
2.3. Sample and data source
3. Methodology
4. Estimation Results
5. Conclusion
Acknowledgments
Appendix A.
References

Citation preview

Journal of International Commerce, Economics and Policy Vol. 14, No. 2 (2023) 2350009 (20 pages) © World Scientific Publishing Company DOI: 10.1142/S1793993323500096

Changing Trade Pattern, ICT, and Employment: Evidence Across Countries

Manish Kumar Sharma* and Anwesha Aditya† Department of Humanities and Social Sciences Indian Institute of Technology Kharagpur West Bengal 721302, India *[email protected] † [email protected] Published 31 March 2023

This paper examines the impact of export diversification and ICT on aggregate and skill-level employment for a sample of 45 and 33 countries from 1990 to 2019 and 1995 to 2019 for OECD & G20 country groups. GMM dynamic panel estimation results suggest that more product-wise concentrated exports lead to new employment opportunities overall, but not geographically diversified exports. Internet has substitution effects on overall employment whereas mobile is insignificant. A greater product-wise diversified export structure expands low-skill-intensive jobs, but greater geographical diversification expands high-skill-intensive jobs. Internet use promotes high-skill-intensive jobs but displaces low-skilled workers. Mobile is found to expand job opportunities for low-skilled workers. Keywords: Export diversification; ICT; employment; skill composition. JEL Classifications: F10, F16, F66, O33

1. Introduction During the recovery of the world economy from the COVID-19 pandemic, it is time to rethink the comparative advantage and specialization theories, as the countries can no longer depend on a concentrated trade structure. The need for economic resilience, stable export earnings, and generating enough employment opportunities has been one of the key policy concerns of the developing countries. Shocks like the COVID-19 pandemic and the 2008 global recession have made these issues more important and urgent to be addressed. Besides the changing pattern of world trade, another important breakthrough over the last few decades has been the technological advancement in Information and Communication Technologies (henceforth, referred to as ICT). ICT enables fast exchange of data and information with almost negligible cost. Though the pandemic profoundly impacted employment, ICT became a saving grace. Cheap communication and digital exchange of data and information enabled the companies to work remotely and round the clock. 2350009-1

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The developing countries trying to achieve maximum gains from globalization should be careful about the shocks and uncertainties arising in the global economy and aim to formulate policies to leverage the shocks. One way to achieve this would be by diversifying their trade basket both in terms of product and destination. However, the impact of trade structure on employment for the domestic economies is not explored enough. Creating stable employment needs faster and stable economic growth, which again depends on export diversification (ED). The empirical evidence reveals that diversification of exports in high-value addition products leads to faster economic growth, as in Aditya and Acharyya (2012, 2013), Agosin (2009), and Hesse (2008). ED allows a country to spread its risks across countries and commodities and absorb adverse terms of trade shocks as observed by Acemoglu and Zilibotti (1997). The impact of ED on employment has largely remained neglected in the existing literature, apart from the studies by Fosu et al. (2018) and Guneri and Erünlü (2020). The two exceptions of which the first study is by Fosu et al. (2018), investigated the issue with a focus on Africa and LDCs and compared it with the advanced countries from 1991 to 2010. Its System GMM estimation results indicated that higher ED led to higher employment, especially industrial employment, but decreased vulnerable employment. The second study by Guneri and Erünlü (2020) investigated the impacts of trade liberalization and ED on the unemployment rate for OECD countries from 1991 to 2014. Their results show that freer trade and more diversification of export of countries decreases unemployment. Whereas the studies on the impact of diversification of exports on employment are scanty, there are substantial studies available on the impact of ICT on demand for labor and have mixed views. One important strand of the literature concludes that ICT increases the demand for skilled workers and hence finds evidence for a skill-biased technical change (SBTC) by replacing unskilled labor. Chun (2003), Bresnahan et al. (1999), Berman et al. (1994), and Autor et al. (1998) found evidence of the same in the US economy and argue that this SBTC has caused wage inequality there. Falk and Seim (2001) found evidence of this phenomenon for the firms in Germany. The SBTC is not only limited to developed countries. Berman et al. (1998) found evidence for the same in some developing countries as well. Goaied and Sassi (2019) assess the ICT–labor relationship for a sample of 167 developed and developing countries from 1990 to 2015. They found the aggregate impact of ICT adoption as saving labor in the short term, and the same continued in the long run as well, resulting in a higher induced structural unemployment. Counter evidence is also available in the literature. Pantea et al. (2014) examined firm-level data for services, manufacturing, and ICT-producing sectors for seven EU countries during 2007–2010 and found that there was no proof of a negative relationship between employment growth and the intensity of ICT use. They pointed out that ICT may benefit labor with ICT complementing skills and create more employment in such sectors, while conversely, some tasks may be automated due to ICT use and may cause machine-labor substitutions. Hence, the combined effects are 2350009-2

Changing Trade Pattern, ICT, and Employment

statistically insignificant. Autor and Salomons (2018) analyzed 19 EU countries from EU KLEMS data for 28 market industries from 1970 to 2007. They found that although automation displaces labor in the industries in which it is created (termed as a direct effect), these self-industry losses are changed into a reverse effect by indirect gains in consumer industries and supported the increases in induced aggregate demand for labor. Hence, they concluded that employment opportunities can expand at the aggregate level due to technological progress. Nevertheless, the interrelationships among these issues have been largely unexplored in the existing literature. In this context, this study seeks to answer the following questions: how does ED (product- and destination-wise) affect employment, overall, and for skilled and unskilled workers? Second, is ICT use labor replacing or augmenting? Furthermore, how is the effect different for skilled and unskilled workers, if at all? To the best of our knowledge, ours is the first attempt to discuss these two issues together: ED and employment, and ICT and employment, after controlling for a wide set of explanatory variables. Note that we have considered two dimensions of ED — commodity-wise and destination-wise. We seek to answer the above questions across countries belonging to different development strata. In particular, we employ a sample of countries belonging to the G20 and OECD. It allows us to see if there is any systematic difference in the association between ED, employment, and ICT across country groups. Furthermore, we examine these issues for skilled and unskilled labor, separately. The main contribution of this paper is that we aim to bring out the importance of setting trade policies for countries keeping in mind their differently skilled labor and thus use the trade pattern accordingly in their favor to create more employment opportunities. Secondly, to the best of our knowledge, it is the first study to emphasize the impact of trade pattern in terms of ED on the skilled and unskilled workers. Third, we also try to examine the importance of scale of production, fixed capital formation, R&D, and liberalization of any country on overall employment and skilled and unskilled workers. Fourth, we aim to find out the impact of ICT in terms of voice communication and internet use on skilled and unskilled employment, giving important insights for both. Thus, the paper has touched upon an unexplored part of international trade and employment. The findings of the paper would prove to be very helpful in designing appropriate policies for creating employment opportunities which is the need of the hour during the post-pandemic recovery. We carry out difference GMM dynamic panel estimation (Arellano and Bond, 1991) for a sample of 45 countries from 1990 to 2019 after controlling for the impacts of output, wage, physical capital formation, FDI, R&D, and trade liberalization. The results suggest that specialization according to comparative advantage can lead to new employment opportunities, whereas geographical diversification of exports does not impact overall employment. Internet has substitution effects on overall employment whereas mobile is insignificant. 2350009-3

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We further distinguish the impacts of ED and ICT for skilled and unskilled workers for a sample of 33 countries from 1995 to 2019. Here, the reason for the reduction in sample size is due to the availability of data on education-wise employment. Interestingly, two types of ED (product- and destination-wise) and the ICT indicators (mobile and internet) have opposite impacts on high-skilled and low-skilled workers. It is found that a more product-wise diversified export structure creates more employment opportunities for unskilled workers. For high-skilled workers, the results are polar opposite. Here, product-wise ED has an insignificant impact. That means specialization according to comparative advantage can be beneficial for the high-skilled workers. In contrast, higher geographical diversification of exports can strongly expand high-skill-intensive jobs. This can imply that if developing countries focus on transitioning unskilled and semi-skilled workers to highly skilled workers, more geographical diversification will make them more employable. Further, we find support in favor of skill-biased technological progress. Greater use of mobile is complimentary for low-skill intensive jobs and insignificant for highskilled workers. On the other hand, better internet connectivity promotes high-skillintensive jobs but displaces low-skilled workers. The paper is structured as follows: Section 2 outlines the model by describing the variables, followed by the sample and data sources. Section 3 discusses the econometric methodology. Section 4 presents the discussion and interpretation of the estimation results. Section 5 concludes the study along with the major findings summarized and mentioning the policy implications that emerge from this study. 2. Model Specification, List of Variables, Sample, and Data Sources 2.1. Model specification To examine the effects of diversification of exports and ICT on the labor market, we follow the existing empirical literature on the determinants of labor demand (Hamermesh, 1993); Pantea et al. (2014). One caveat here is that we can derive the labor demand function from the production function. In contrast, employment data is the equilibrium level value, as we cannot obtain data for labor demand. Due to the presence of natural and involuntary unemployment in most of the economies, we can estimate the augmented labor demand function, as actual employment is constrained by labor demand. We assume that profit-maximizing firms employ labor to minimize the cost of production. Labor demand depends on the amount of output produced, capital input and wage, and other factors like ICT use and ED. Hence, we estimate the following standard conditional labor demand function on output, wage, capital (both domestic capital formation and inflow of foreign capital), R&D, ICT and ED, and tariff. Further, following Bresson et al. (1992) and Goaied and Sassi (2019), we adopt a dynamic framework by including the lagged dependent variable as one of the explanatory variables to capture adjustment to the equilibrium of labor demand. 2350009-4

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Therefore, the labor demand follows the dynamic specification as follows: Lit ¼ α0 þ β0 Wit þ β1 EDit þ β2 ICTit þ β3 Xit þ δi þ "it ,

ð1Þ

where “i” denotes a country, “t” denotes the time period, and “L” is the employment. Note that we have two types of dependent variables — employment and skill composition (in terms of education-wise employment). “ED” represents the measures of ED (product- and market-wise) to be defined in the following section. “ICT” denotes the use of ICT infrastructure in terms of mobile phone and internet use. “W” is the wage or price of labor. “X” is the vector of country-year-specific other control variables like output, R&D expenditure, FDI, GFCF, compensation (used as a proxy for wages) and tariff. The term “δi ” is an unobserved error which is country-specific and an effect invariant with time. For instance, the geographical effects and the institutions’ role remain more or less the same over time, but change across countries. “"it ” is the random error term that changes across both countries and time periods. It is assumed to be not correlated over time. 2.2. List of variables In this section, we discuss the main variables used in our study along with their definitions. Dependent Variables (1) Employment to population ratio (EMP) — Here, we use the ratio of employment to the population available for work. We normalize employment with population size as changes in the population are expected to affect changes in labor supply and demand (Blancheton and Chhorn, 2019). (2) Skill indicator: We consider the ratio of education-wise (both basic and advanced education levels) employment to the population available for work. These two extremes give unskilled and skilled employment shares. According to ILO,1 this is the best available indicator of labor force skill levels. Explanatory Variables (1) Measures of ED: (i) Commodity Concentration Index (CCI) — ED of a product basket can be obtained through the (Hirschman, 1945) commodity concentration index (CCI). CCI is defined as " # 1=2 X 2 CCIk ¼ (αjk ) ð2Þ j αjk ¼ Xjk =Xtk , 1 https://ilostat.ilo.org/resources/concepts-and-definitions/description-employment-by-education/

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where αjk ¼ Share of export of commodity-j to country-k’s total exports to rest of the world. It is calculated as the square root of the sum of squared product shares in a country’s exports and is normalized between 0 and 1. The higher value of CCI represents the less diversified is the export basket. (ii) Geographical Concentration Index (GCI) — This Hirschman–Herfindahl index indicates the dispersion of exports of a country according to destination. It is calculated as the square root of the sum of squared destination shares in a country’s exports and is normalized between 0 and 1. Hirschman Index of Geographical or regional concentration can be calculated as " # 1=2 X 2 GCIs ¼ (βsd ) ð3Þ d βsd ¼ Xsd =Xsw , where βsd ¼ Share of export of source country ‘s’ to destination country-d to total exports of country “s” to the rest of the world “w”. (2) ICT — Information technology enables the exchange of ideas, innovation, and experimentation. ICT infrastructure has played a key role during the COVID-19 pandemic by enabling people to be connected at professional and personal levels even during lockdown and travel restrictions-cum closure of public places. For measuring ICT use, we have considered mobile and internet subscribers. (i) Internet users (% of the population): We have considered the percentage of population which have used internet (from any location) in the last three months. It can be used via computer, mobile phone, personal digital assistant, games machine, digital TV and so on. Internet is a worldwide public computer network providing access to a number of communication services including the World Wide Web and carries email, news, entertainment, and data files, irrespective of the device used. Access can be via a fixed or mobile network (ITU, 2022). According to Kraut et al. (1999), Internet has been characterized as a superhighway to information and as a high-tech extension of the home telephone. However, internet still remains a luxury, and an estimated 37% of the world’s population has still never used internet. Of the 2.9 billion still offline, an estimated 96% live in developing countries (International Telecommunication Union, 2021). (ii) Mobile (Cellular) Subscriptions (Per 100 people): This includes the subscriptions to mobile telephone service (public) using cellular technology, which gives access to the public switched telephone network. It also includes all mobile cellular subscriptions that offer voice communications. The indicator includes and is divided into, the number of post-paid subscriptions, and the number of active prepaid accounts (i.e., that have been used during the last 2350009-6

Changing Trade Pattern, ICT, and Employment

three months). It should be noted that it excludes mobile broadband subscriptions via data cards or USB modems. The mobile cellular subscriptions (per 100 people) indicator is derived by all mobile subscriptions divided by the country’s population and multiplied by 100. In this era of digitization, mobile communications have important impact in rural areas. The mobility, ease of use, relatively low, and declining rollout costs of wireless technologies enable them to reach rural populations with low levels of income and literacy. ITU (2022) mentions that the next billion mobile subscribers will consist mainly of the rural poor. Thus, the main difference between both is that mobile represents the voice communication part of ICT infrastructure while internet represents the access of internet data through any device. In addition, we have controlled for the impacts of other potential determinants of employment as used in the existing literature. These include physical capital formation, output, wage, R&D, FDI, and tariff. The definition and data sources of the respective variables are given in Table A.6 in the Appendix. 2.3. Sample and data source Our sample consists of 45 countries belonging to the G20 and OECD groups (refer to Table A.4 in the Appendix) for the period 1990–2019. The data on the ratio of employment to population available for work, GVA, wage or compensation, R&D, FDI, and Gross Fixed capital formation are obtained from the World Development Indicators database of the World Bank. The measures of the ED — CCI and GCI of the countries are calculated with respect to the world market using the data from WITS (World Integrated Trade Solution) from World Bank (SITC-1, four-digit classification level) from 1990 to 2019. However, due to constraints on data availability on education-wise employment, the sample size for the skill level analysis falls to 33 countries over the period 1995–2019 (see Table A.5 in Appendix for country list). 3. Methodology To examine the impacts of ED on employment, following labor economics and international trade literature, we have used a dynamic framework by including lagged dependent variable as one of the explanatory variables. This dynamic framework helps avoid the specification bias, which may have resulted in the absence of the lag of the dependent variable and gives a consistent estimator of other parameters. It also helps in obtaining the persistent effect of employment. Hence, we use the following dynamic specification of Eq. (1): Yit ¼ α0 þ β0 Yitk þ β1 Xit þ δi þ "it , 2350009-7

ð4Þ

M. K. Sharma & A. Aditya

where Yit ¼ Employment to population ratio in country “i” at time “t”, Yitk ¼ k years of lag of Yit , Xit ¼ Set of independent variables as discussed earlier. “δi ” is a countryspecific, unobserved time-invariant effect and “"it ” is the random error term. Note that using the lag of the dependent variable as one of the independent variables, affects the consistency of OLS and fixed-effect estimators. In the dynamic specification [Eq. (1)], it is found that the unobserved effect correlated with the explanatory variables. Thus, the cross-section estimator will become inconsistent. Secondly, the fixed-effect estimators and cross-section regression cannot address the problem of the endogeneity of the independent variables. To avoid these problems, we have estimated the Generalized Method of Moments (GMM) dynamic panel model given by Arellano and Bond (1991) and Arellano and Bover (1995). In the estimation process, the first difference of the regression equation is taken. It helps to eliminate the unobserved country-specific and time-invariant effects, like geography, political regime, and the rule of law. Therefore, the omitted variable bias is not there and essentially, the dynamic specification of the labor demand in the first difference is estimated. The endogeneity of independent variables due to the problem of inconsistency is tackled using the benefits of the GMM dynamic panel method by using the lag of the independent variables as valid instruments. To further tackle this issue, some specification tests are taken into account, as advised by Arellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond (1998). The first one, the Sargan test, is for over-identifying restrictions. It tests the aggregate validity of the instruments used in the estimation. The second one is the AR(2) test, which tells if serial correlation in the error term is absent. An inherent mechanism possibly finds the first-order serial correlation with regressions in first differences, and the proper specification test would be to go for a second-order serial correlation test. 4. Estimation Results The difference GMM dynamic panel (Arellano and Bond, 1991) estimation results for the sample countries, as reported in Tables A.1–A.3, reflect the effect of variations in various explanatory variables like GCI, CCI, mobile, internet, and control variables GFCF, LGVA, wage, R&D, FDI, and tariff on variations in employment. It may be noted that these results do not bring out the impact of many omitted time-invariant variables such as economic geography or institutional quality. First, we discuss our main variables of interest — ICT (Information and Communication Technologies) and ED. The impact of ICT infrastructure differs for both the overall employment and skill-wise employment. While mobile is insignificant, Internet has substitution effects on overall employment. The impacts of these two ICT indicators are just polar opposite. Greater use of mobile is found to be positively significant for low-skill intensive jobs, whereas better internet connection is required to perform high-skill-intensive tasks. Greater Internet use is found to displace low-skilled 2350009-8

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workers. Mobile use is insignificant for high-skilled workers. The possible explanation for this can be that workers with basic education may lack the access as well as skill to use the Internet (broadband) and thus use mobile more. For example, workers with basic education like Cab drivers and food delivery persons use mobile more, while ICT-based tasks usually have some specific skill requirements like English proficiency, knowledge of computers, and IT skills. Further, workers with advanced education have better access and skills to use broadband internet connection. In its initial era of discovery, ICT use was limited to labor and cost savings. However, it has gradually grown to have wider applications in innovation, quality assurance, research and development, new product building, brand building, and marketing and many more advanced stages. Our findings are consistent with the literature on SBTC. Chun (2003) found that educated workers had a comparative advantage in adopting ICT for 56 industries from 1960 to 1996. Autor et al. (1998) find that the relative demand for college students in the US grew faster from 1970 to 1995 as compared to 1940 to 1995. Moreover, we analyze the impact of export structure on employment — overall and skill-wise. A more commodity-wise concentrated export basket indicated by the higher value of CCI can lead to new employment opportunities overall, whereas geographical diversification of export does not have any impact on overall employment. It indicates that specialization according to comparative advantage can create overall employment opportunities. A plausible explanation for this is that when the scale of production expands for the sectors in which a country has comparative advantage, requirement of labor (especially semi-skilled) increases. However, the effect is completely different when we consider low- and high-skilled workers. A more product-wise diversified export structure creates more employment opportunities for unskilled workers. That means commodity ED leads to an expansion of low-skill-intensive jobs. This can happen as horizontal differentiation may not require skilled labor. Since products are horizontally differentiated, unskilled workers can produce more varieties. This result has strong policy implications for developing countries endowed with unskilled and semi-skilled workers. In the post-pandemic revival period, one of the immediate objectives of the countries is to create sustainable employment opportunities. Our result suggests that ED can be one such policy measure for the sample of OECD and G20 countries. A diversified export basket will not only ensure faster economic growth (Aditya and Acharyya, 2013) but also more employment opportunities. However, geographical diversification does not have any impact on low-skill employment. In contrast, for high-skilled workers, the results are polar opposite. Here, productwise ED has an insignificant impact. That means specialization according to comparative advantage can be beneficial for the medium- and semi-skilled workers. In contrast, higher geographical diversification of exports can strongly expand high-skillintensive jobs. This can imply that if developing countries focus on transitioning unskilled and semi-skilled workers to highly skilled workers, more geographical 2350009-9

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diversification will make them more employable. As the producers and exporters need to cater to market specific requirements to capture new export markets, more skilled labor is required to diversify export basket destination-wise. After discussing our variables of interest, we now discuss the impact of other potential determinants of employment. We find that past employment (overall and skill-wise) has significant positive impact implying the persistence effect. It is very surprising to note that the scale effect measured by the expansion of the size of the economy (in terms of value-added) is positive for overall employment and low-skilled workers but negative for high-skilled workers (refer to columns 3 and 6 of Table A.3, when only FDI is taken as capital) by shrinking job opportunities. In particular, we see that LGVA is positively significant in the aggregate employment and basic educationwise employment model. Therefore, we can say that there is a positive scale effect which implies that increase in output or size of a sector will increase the demand for labor, especially unskilled/semi-skilled. However, mere expansion of the size of a sector by producing more output may not require more skilled labor. Developing new products or innovations can generate job opportunities for skilled labor. That means high-skilled workers are required to develop new product varieties, designs, blueprints, and innovations. Once a variety is developed, production of a larger amount can be carried out by using only unskilled workers. This can be a plausible reason behind the negative relationship between the expansion of the size of the economy and the high-skilled worker’s employment. Following Krugman (1979), Aditya and Acharyya (2015) developed a theoretical model with a fixed amount of capital and variable input, labor. In that framework, the fixed factor (capital) can alternatively be interpreted as human capital (or skilled labor), required to develop new blueprint or design new variety. Once the blueprint or design is developed, production of such a new variety will require only labor and the amount of labor use increases proportionately with production. Next, we find that demand for labor at the aggregate level falls as wage increases. However, for the disaggregated skill level-wise analysis, it has an insignificant impact on the demand for unskilled workers. This makes sense because, in many countries, especially the developing ones, an abundant supply of unskilled labor is available at the ongoing wage rate. For skilled labor, only for the model when FDI is considered, wage rate positively impacts demand for high-skill labor. It is interesting to note that the impact of domestic physical capital accumulation (captured in terms of GFCF) also differs in the three models. GFCF has complementarity effect for overall employment, is insignificant for low-skilled workers, and strongly replaces high-skilled workers. That means investment in domestic fixed asset formation, like the creation of physical infrastructure, creates more employment opportunities overall. This can be especially very important for the medium and semiskilled workers employed in the manufacturing industries, which are not considered in our study in the disaggregated skill-level-wise analysis. However, domestic capital formation does not impact the unskilled workers engaged in agriculture and other 2350009-10

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primary sector activities. In contrast, more capital-intensive automated production technology in the manufacturing and service industries can displace skilled workers to a large extent. As far as net inflow of FDI is considered, it does not have any impact on overall employment and low-skilled workers but is found to be labor displacing for highskilled workers. That means foreign investments go to the sectors or activities which fail to create employment opportunities for unskilled and semi-skilled workers. FDI is an essential source of superior technology from advanced countries to developing ones. FDI often brings state-of-the-art technology, which can displace high-skilled workers. Next, we explore the impact of R&D. A country’s research and development (R&D) investment represents its part of the capital dedicated to developing new innovations, products, services, and solutions. In this paper, we find that greater R&D expenditure, bringing technological improvements, may in turn lead to contraction in employment skill-wise, but is insignificant overall. Better technology of production can displace workers. In fact, the increase in investment in R&D may cause an increase in the use of plant and machinery and thus possibly decrease the demand for both skilled and unskilled workers. Also increase in R&D results in improved products, processes and higher productivity thus requiring less manpower. If we see in Table A.3, R&D results in the contraction of high-skilled labor for FDI-only model, which suggests that R&D investment coming from outside countries may result in lower demand for local highskilled labor, and the high-skilled labor supply is possibly coming from the outside countries or outsourced to these countries. This may be generally true as in developed nations the high R&D cost is reduced by outsourcing the high skill jobs to other cheaper countries in place of using the local labor. Finally, we examine the impact of trade liberalization in terms of tariff reduction on employment. A country’s trade regime can have essential implications on its domestic employment pattern. Theoretically, trade liberalization can create new employment opportunities for unutilized resources by expanding market size. The output depends on the amount of labor used, which in turn depends on the price of labor or wage rate and therefore wage rate is used as one of the control variables. In our case, we find that the protection of domestic industries by the imposition of tariff is found to create more employment opportunities overall (see Table A.1, column 6 when only FDI is considered). Increased domestic production due to the imposition of tariffs absorbs the domestic labor force. However, for high- and low-skilled workers, tariff has an insignificant impact. 5. Conclusion We have examined the impacts of ED and ICT on employment for 45 OECD and G20 countries from 1990 to 2019 after controlling for the effects of value-added, wage, physical capital formation, FDI, R&D expenditure, and trade liberalization. We have 2350009-11

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further explored whether ED and ICT have any differential impacts on high and lowskilled workers for 33 OECD and G20 countries from 1995 to 2019. GMM dynamic panel estimation results suggest that a more product-wise concentrated export basket indicated by the higher value of CCI can lead to new employment opportunities. In contrast, a more product-wise diversified export structure creates more employment opportunities for unskilled workers, which means it expands low-skill-intensive jobs. Geographical diversification of export does not have any impact on overall employment and low-skill employment but can strongly expand high-skill-intensive jobs. Mobile is insignificant, while Internet has substitution effects on overall employment. However, greater use of mobile expands job opportunities for low-skill workers but has a substitution effect for high-skill workers. Better internet complements high-skillintensive jobs but displaces low-skilled workers. These results have strong policy implications for developing countries endowed with unskilled and semi-skilled workers. In the post-pandemic revival period, one of the immediate objectives of the countries is to create sustainable employment opportunities. Our result suggests that ED can be one such policy measure for the set of OECD and G20 countries. Results also suggest that if developing countries focus on transitioning unskilled and semi-skilled workers to highly skilled workers, more geographical diversification will make them more employable. A more nuanced analysis for semi-skilled workers with medium level of education can be worthwhile to analyze. Acknowledgments The authors express their gratitude to the anonymous reviewer as well as editor for their helpful comments and suggestions on the earlier version of the paper. Appendix A Table A.1. Results of Dynamic Panel estimation of the impact of export diversification and ICT on aggregate Employment. Independent Variables Lt1 LGVA LW

(1)

0.37*** (0.05) 9.84*** (0.61) 0.65** (0.28)

(2)

0.39*** (0.05) 9.35*** (0.54) 0.73*** (0.27)

(3)

0.37*** (0.05) 9.92*** (0.60) 0.65** (0.27)

2350009-12

(4)

0.48*** (0.04) 15.46*** (0.77) 1.67*** (0.32)

(5)

0.39*** (0.05) 9.41*** (0.53) 0.73*** (0.27)

(6)

0.48*** (0.04) 15.45*** (0.79) 1.66*** (0.32)

Changing Trade Pattern, ICT, and Employment

Table A.1. (Continued ) Independent Variables GFCF FDI Mobile Internet R&D Tariff CCI GCI Constant Sargan test p-value AR(2) test p-value

(1)

0.24*** (0.02) 0.001 (0.001) 0.002 (0.002) 0.01*** (0.005) 0.06 (0.20) 0.01 (0.03) — 2.44 (1.94) 218.90*** (14.26) 0.2287 0.9349

(2)

0.24*** (0.02) 0.001 (0.001) 0.001 (0.002) 0.01*** (0.005) 0.08 (0.19) 0.03 (0.03) 1.59*** (0.51)

204.39*** (14.01) 0.2765 0.8512

(3)

0.24*** (0.02) —

(4)

—

0.002 (0.002) 0.01*** (0.005) 0.05 (0.20) 0.01 (0.03) —

0.001 (0.002) 0.003 (0.003) 0.04*** (0.01) 0.12 (0.29) 0.03 (0.02) —

2.53 (1.94) 221.00*** (13.71) 0.2256

1.06 (2.63) 343.93*** (17.81) 0.1426

0.9777

0.7245

(5)

0.24*** (0.02) —

(6)

—

0.002 (0.002) 0.01*** (0.005) 0.07 (0.19) 0.0004 (0.03) 1.63*** (0.51) —

0.001 (0.001) 0.003 (0.003) 0.04*** (0.01) 0.12 (0.29) 0.04* (0.02) 0:25 (0.53) —

205.76*** (13.71) 0.2726

343.31*** (18.44) 0.1473

0.8766

0.7452

Notes: Standard errors in parentheses; Note 2. ***p < 0:01, ** p < 0:05, *p < 0:1. Variables description: Dependent Variable Lt ¼ Employment to population ratio Explanatory Variables: LGVA ¼ Log of Gross Value added, LW ¼ Log of Compensation of employees (divided by PPP conversion factor), GFCF ¼ Gross fixed capital formation (% of GDP), FDI ¼ Foreign direct investment, net inflows (% of GDP), Mobile ¼ Mobile cellular subscriptions (per 100 people), Internet ¼ Individuals using internet (% of population), R&D ¼ Research and development expenditure (% of GDP), Tariff ¼ Tariff rate, applied, weighted mean, all products (%), CCI ¼ Commodity concentration index, GCI ¼ Geographical concentration index.

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Table A.2. Results of Dynamic Panel estimation of the impact of export diversification and ICT on labor quality (Basic Education). Independent Variables LQt1 LGVA LW GFCF FDI Mobile Internet R&D Tariff CCI GCI Constant Sargan test p-value AR(2) test p-value

(1)

(2)

0.87*** (0.03) 4.94*** (1.08) 0.05 (0.27) 0.04 (0.03) 0.001 (0.001) 0.01* (0.003) 0.04*** (0.01) 1.56*** (0.24) 0.03 (0.03) —

0.01* (0.003) 0.04*** (0.01) 1.54*** (0.24) 0.03 (0.03) —

0.001 (0.001) 0.01 (0.004) 0.03*** (0.01) 1.56*** (0.25) 0.03 (0.03) —

0.84 (1.93) 125.39*** (24.86) 0.2541

1.35 (1.88) 125.42*** (24.47) 0.2462

0.94 (1.47) 94.79*** (16.88) 0.2605

0.9056

0.87*** (0.03) 5.00*** (1.07) 0.02 (0.28) 0.05* (0.02) —

(3)

0.8971

(4)

0.85*** (0.02) 3.74*** (0.67) 0.06 (0.26) —

(5)

0.86*** (0.03) 5.27*** (1.12) 0.14 (0.24) 0:04 (0.03) 0:0004 (0.001) 0.005* (0.003) 0.04*** (0.01) 1.49*** (0.25) 0.01 (0.04) 2.29*** (0.26) —

0.004* (0.003) 0.04*** (0.01) 1.47*** (0.25) 0.02 (0.04) 2.24*** (0.26) —

0:001 (0.001) 0.004 (0.003) 0.04*** (0.01) 1.52*** (0.24) 0.004 (0.04) 2.36*** (0.34) —

136.03*** (27.07) 0.2608

130.95*** (24.68) 0.2575

107.61*** (16.43) 0.2660

0.9717

0.9458

0.86*** (0.03) 5.11*** (1.04) 0.11 (0.26) 0:04 (0.03) —

(6)

0.9423

0.85*** (0.02) 4.14*** (0.65) 0.17 (0.23) —

0.9824

Notes: Standard errors in parentheses; Note 2. ***p < 0:01, **p < 0:05, *p < 0:1. Variables description: Dependent Variable LQt ¼ Employment to population ratio by Basic Education Explanatory Variables: LGVA ¼ Log of Gross Value added, LW ¼ Log of Compensation of employees (divided by PPP conversion factor), GFCF ¼ Gross fixed capital formation (% of GDP), FDI ¼ Foreign direct investment, net inflows (% of GDP), Mobile ¼ Mobile cellular subscriptions (per 100 people), Internet ¼ Individuals using internet (% of population), R&D ¼ Research and development expenditure (% of GDP), Tariff ¼ Tariff rate, applied, weighted mean, all products (%), CCI ¼ Commodity concentration index, GCI ¼ Geographical concentration index.

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Changing Trade Pattern, ICT, and Employment

Table A.3. Results of Dynamic Panel estimation of the impact of export diversification and ICT on labor quality (Advanced Education-wise). Independent Variables HQt1 LGVA LW GFCF FDI Mobile Internet R&D Tariff CCI GCI Constant Sargan test p-value AR(2) test p-value

(1)

(2)

(3)

(4)

(5)

(6)

0.83*** (0.03) 2.71*** (0.87) 0.19 (0.32) 0.19*** (0.02) 0.01*** (0.00) 0.01*** (0.003) 0.04*** (0.01) 0.47* (0.24) 0.05 (0.06) —

0.82*** (0.03) 2.90*** (0.87) 0.26 (0.33) 0.19*** (0.02) —

0.92*** (0.03) 2.71*** (0.56) 0.41 (0.28) —

0.82*** (0.02) 2.64*** (0.88) 0.43 (0.37) 0.20*** (0.02) —

0.92*** (0.03) 3.22*** (0.54) 0.64** (0.29) —

0.02*** (0.003) 0.04*** (0.01) 0.41 (0.26) 0.05 (0.06) —

0.01*** (0.001) 0.01** (0.004) 0.05*** (0.01) 0.72** (0.28) 0.03 (0.07) —

14.11*** (1.92) 62.04** (24.86) 0.1846 0.8117

14.04*** (1.87) 68.52*** (25.28) 0.1847 0.7065

9.74*** (2.29) 67.89*** (19.49) 0.2825 0.8592

0.83*** (0.03) 2.54*** (0.91) 0.36 (0.35) 0.19*** (0.02) 0.01*** (0.001) 0.01*** (0.004) 0.04*** (0.01) 0.46* (0.25) 0.03 (0.08) 0.70 (0.82) —

0.01*** (0.004) 0.04*** (0.01) 0.41 (0.25) 0.03 (0.08) 0.98 (0.81) —

0.01*** (0.001) 0.01 (0.004) 0.05*** (0.01) 0.80*** (0.27) 0.07 (0.08) 0.88 (0.81) —

66.60*** (23.81) 0.1885 0.9033

70.90*** (23.14) 0.1917 0.8220

72.47*** (19.15) 0.2259 0.7736

Notes: Standard errors in parentheses; Note 2. ***p < 0:01, **p < 0:05, *p < 0:1. Variable description: Dependent Variable HQt ¼ Employment to population ratio by Advanced Education Explanatory Variables: LGVA ¼ Log of Gross Value added, LW ¼ Log of Compensation of employees (divided by PPP conversion factor), GFCF ¼ Gross fixed capital formation (% of GDP), FDI ¼ Foreign direct investment, net inflows (% of GDP), Mobile ¼ Mobile cellular subscriptions (per 100 people), Internet ¼ Individuals using internet (% of population), R&D ¼ Research and development expenditure (% of GDP), Tariff ¼ Tariff rate, applied, weighted mean, all products (%), CCI ¼ Commodity concentration index, GCI ¼ Geographical concentration index.

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Table A.4. Country list. (For overall employment data analysis) Sl. No. 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.

Country Name (Developed)

Sl. No.

Country Name (Developing)

Australia Austria Belgium Canada Czech Republic Denmark Estonia Finland France Germany Greece Hungary Iceland Ireland Italy Japan Latvia Lithuania Luxembourg New Zealand Netherlands Norway Poland Portugal Slovak Republic Slovenia Spain Sweden Switzerland United Kingdom United States

32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45.

Argentina Brazil Chile China Colombia India Indonesia Israel Mexico Russia Saudi Arabia South Africa South Korea Turkey

Table A.5. Country list (For Skill level employment data analysis) Sl. No. 1. 2. 3. 4. 5. 6. 7.

Country Name (Developed)

Sl. No.

Austria Belgium Canada Czech Republic Denmark Estonia Finland 2350009-16

Country Name (Developing)

Changing Trade Pattern, ICT, and Employment

Table A.5. (Continued ) Sl. No.

Country Name (Developed)

Sl. No.

Country Name (Developing)

France Germany Greece Hungary Iceland Ireland Italy Latvia Lithuania Luxembourg Netherlands Norway Poland Portugal Slovak Republic Slovenia Spain Sweden Switzerland United Kingdom United States

29. 30. 31. 32. 33.

Colombia Indonesia Mexico South Africa South Korea

8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28.

Table A.6. List of variables and data sources. S. No.

Variable

Definition

Dependent Variables (1) Employment Rate

(2)

The ratio of Employment to the population available for work. EPR (%) ¼ (Persons employed/Working-age population)100 These two extremes give unskilled and skilled emSkill Indicator: ployment shares, and it is the ratio of educationEmployment to wise Employment to the population available for population ratio work. Represents the Employment rate by Ad1. Basic Educavanced and Basic education levels tion-wise 2. Advance Edu- 1. Includes Primary and Lower secondary education 2. Includes Short-cycle tertiary education, Bachecation-wise lor’s or equivalent level, Master’s or equivalent level, Doctoral or equivalent level education

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Data Sources

WDI

ILO stat

M. K. Sharma & A. Aditya

Table A.6. (Continued ) S. No.

Variable

Definition

Data Sources

Independent Variables (1) Commodity ConThis Hirschman Herfindahl index measures the WITS, United centration Index dispersion of trade value across an exporter’s Nations Com(CCI) products. modity Trade database (UNIt measures the concentration, or diversification, of a COMTRADE) country’s trade in terms of products. It is calculated as the sum of squared product shares in a country’s exports and then normalized between 0 and 1. (2) Geographical Con- This Hirschman–Herfindahl index measures the dis- WITS, UN-COMTRADE persion of trade value across an exporter’s partcentration Index ners. (GCI) It measures the concentration, or diversification, of a country’s trade in terms of trading partners. It is calculated as the sum of squared destination shares in a country’s exports and then normalized between 0 and 1. WDI (3) Gross Value Added It is the value of output less the value of intermediate consumption; it is a measure of the contribution to (Constant GDP made by an individual producer, industry, or Prices, USD) sector. Gross value added at factor cost (formerly GDP at factor cost) is derived as the sum of the valueadded in the agriculture, industry, and services sectors. WDI (4) Wage (USD) Compensation of employees consists of all payments in cash, as well as in kind (such as food and housing), to employees in return for services rendered and government contributions to social insurance schemes such as social security and pensions that provide benefits to employees. WDI (5) R&D Expenditure The gross domestic expenditure on R&D indicator (RND) (% of consists of the total expenditure (current and GDP) capital) on R&D by all resident companies, research institutes, universities, and government laboratories. It is taken as a percent of GDP. 1. Individuals as a Percentage of the population who International Tele(6) ICT Use (ICT): communication have used internet (from any location) in the last 1. Internet users Union (ITU) three months. (% of the popuInternet is a worldwide public computer network. lation) It provides access to a number of communication 2. Mobile Cellular services, including the World Wide Web and Subscriptions carries email, news, entertainment, and data files, (Per 100 people) irrespective of the device and network used.

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Table A.6. (Continued ) S. No.

Variable

Definition

Data Sources

2. Subscriptions to a public mobile telephone service using cellular technology, which provide access to the public switched telephone network. Includes all mobile cellular subscriptions that offer voice communications. Data is derived using administrative data from countries (authority or Ministry) regularly. (7)

(8)

WDI Gross Fixed Capital GFCF (formerly gross domestic fixed investment) includes land improvements (fences, ditches, Formation drains, and so on); plant, machinery, and equip(GFCF) (% of ment purchases; and the construction of roads, GDP) railways, and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. According to the 1993 SNA, net acquisitions of valuables are also considered capital formation. Weighted Average Effectively Applied Weighted Average (%) tariff; The WITS - UNCTAD TRAINS Tariff (%) average of tariffs weighted by their corresponding trade value is used.

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