Dimensions of Resilience in Developing Countries: Informality, Solidarities and Carework [1st ed.] 978-3-030-04075-8, 978-3-030-04076-5

Table of contents :
Front Matter ....Pages i-xxiii
Introduction (Jacques Charmes)....Pages 1-9
Front Matter ....Pages 11-11
A Brief History of 50 Years of Conceptualisation and Measurement of the Informal Economy (Jacques Charmes)....Pages 13-36
Trends and Characteristics of the Informal Economy and Its Components (Jacques Charmes)....Pages 37-92
Policies and Actions Addressing Populations Depending on the Informal Economy (Jacques Charmes)....Pages 93-119
Front Matter ....Pages 121-121
Community, Individualism and Social Capital, the Political Economy of Transfers (Jacques Charmes)....Pages 123-140
Front Matter ....Pages 141-141
Definition and Measurement of Work and Unpaid Care Work (Jacques Charmes)....Pages 143-157
Unpaid Care Work Across the World as Measured by Time-Use Surveys (Jacques Charmes)....Pages 159-185
What Women Are Worth? Valuation of the Care Economy in Various Regions of the World (Jacques Charmes)....Pages 187-216
Back Matter ....Pages 217-224

Citation preview

Demographic Transformation and Socio-Economic Development 10

Jacques Charmes

Dimensions of Resilience in Developing Countries Informality, Solidarities and Carework

Demographic Transformation and Socio-­Economic Development Volume 10

Editors-in-chief: Yves Charbit and Dharmalingam Arunachalam

This dynamic series builds on the population and development paradigms of recent decades and provides an authoritative platform for the analysis of empirical results that map new territory in this highly active field. Its constituent volumes are set in the context of unprecedented demographic changes in both the developed—and developing—world, changes that include startling urbanization and rapidly aging populations. Offering unprecedented detail on leading-edge methodologies, as well as the theory underpinning them, the collection will benefit the wider scholarly community with a full reckoning of emerging topics and the creative interplay between them. The series focuses on key contemporary issues that evince a sea-change in the nexus of demographics and economics, eschewing standard ‘populationist’ theories centered on numerical growth in favor of more complex assessments that factor in additional data, for example on epidemiology or the shifting nature of the labor force. It aims to explore the obstacles to economic development that originate in high-growth populations and the disjunction of population change and food security. Where other studies have defined the ‘economy’ more narrowly, this series recognizes the potency of social and cultural influences in shaping development and acknowledges demographic change as a cause, as well as an effect, of broader shifts in society. It is also intended as a forum for methodological and conceptual innovation in analyzing the links between population and development, from finely tuned anthropological studies to global, systemic phenomena such as the ‘demographic dividend’. Reflecting the boundary-blurring rapidity of developing nations’ socio-economic rise, the editors are actively seeking studies relating to this sector, and also to Russia and the former Soviet states. At the same time as addressing their underrepresentation in the literature, the series also recognizes the critical significance of globalization, and will feature material on the developed world and on global migration. It provides everyone from geographers to economists and policy makers with a state-of-the-art appraisal of our understanding of demographics and development. More information about this series at http://www.springer.com/series/8813

Jacques Charmes

Dimensions of Resilience in Developing Countries Informality, Solidarities and Carework

Jacques Charmes IRD University of Paris Descartes Paris, France

Demographic Transformation and Socio-Economic Development ISBN 978-3-030-04075-8    ISBN 978-3-030-04076-5 (eBook) https://doi.org/10.1007/978-3-030-04076-5 Library of Congress Control Number: 2019930078 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1 Introduction..............................................................................................    1 Informality.................................................................................................    2 Solidarities.................................................................................................    4 Unpaid Care Work.....................................................................................    6 References.................................................................................................    8 Part I Informality 2 A Brief History of 50 Years of Conceptualisation and Measurement of the Informal Economy.........................................  13 Theories and Concepts..............................................................................   13 Statistical Definitions.................................................................................   17 The Underground, Shadow, Illegal, Parallel, Non-observed Economy and How It Is Captured or Estimated Through Modelling....................................................................................   21 Surveys and Data Collection in a Historical Perspective..........................   23 Indirect Methods....................................................................................   23 Direct Survey Methods..........................................................................   25 Sources of Data..........................................................................................   27 Conclusion.................................................................................................   30 Annex........................................................................................................   30 References.................................................................................................   34 3 Trends and Characteristics of the Informal Economy and Its Components.................................................................................   37 Introduction...............................................................................................   37 Trends in Employment..............................................................................   37 Characteristics of the Informal Economy..................................................   51 Pluri-Activity, Multiple Jobs, Secondary Activities and Employment in the Informal Economy..............................................   57

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vi

Contents

Contribution of the Informal Economy to GDP........................................   70 Conclusion.................................................................................................   84 Annex........................................................................................................   85 References.................................................................................................   91 4 Policies and Actions Addressing Populations Depending on the Informal Economy....................................................  93 Introduction...............................................................................................   93 Taxing the Informal Activities...................................................................   94 Upgrading the Informal Activities Within the Value Chain.......................   95 Organising the Populations Dependent on the Informal Economy...........   99 Main Pillars of Policies Designed to Tackle the Informal Economy: Social Protection, Skills Enhancement and Financing.............   103 Social Protection....................................................................................   103 Technical and Vocational Skills Enhancement.......................................   110 Micro-finance.........................................................................................   113 Conclusion.................................................................................................   117 References.................................................................................................   118 Part II Solidarities 5 Community, Individualism and Social Capital, the Political Economy of Transfers.........................................................   123 Introduction...............................................................................................   123 Measurement, Sources and Methods.........................................................   124 Vulnerability and Shocks.......................................................................   127 Traditional Solidarities...........................................................................   128 Magnitude, Trends and Characteristics of Transfers from Household to Household in Sub-Saharan Africa.......................................   128 The Relative Share of Transfers in Households’ Income......................   128 Trends in the Share of Transfers in Total Household Income................   130 Salient Features of Transfers in Sub-Saharan Africa.............................   132 Conclusion.................................................................................................   138 References.................................................................................................   139 Part III Unpaid Care Work 6 Definition and Measurement of Work and Unpaid Care Work........... 143 Introduction...............................................................................................   143 Definition of Work and Current Debates on the Concept..........................   144 The Restricted and the Extensive Definition of Work and Economic Activity in the 1993 and 2008 System of National Accounts..............................................................................   144 Reasons for Exclusion of Domestic and Personal Services for Own Consumption and Discussion of the Arguments.....................   150

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The Concept of Care Work or Care Economy...........................................   152 Conclusion: Services for Own Final Use and Volunteer Work as Components of the Care Economy..............................................   154 References.................................................................................................   156 7 Unpaid Care Work Across the World as Measured by Time-Use Surveys................................................................................ 159 Introduction...............................................................................................   159 Sources and Indicators...............................................................................   160 Sources...................................................................................................   160 Indicators................................................................................................   164 Paid and Unpaid Care Work Across the World..........................................   165 Changes over Time in Time Spent in Unpaid Care Work as Markers of Resilience...........................................................................   170 Conclusion: Changes Across the Life Cycle.............................................   179 References.................................................................................................   185 8 What Women Are Worth? Valuation of the Care Economy in Various Regions of the World............................................................. 187 Introduction...............................................................................................   187 Why Valuating the Care Economy?...........................................................   188 Methods of Valuation of Care Work..........................................................   190 Size and Contribution of the Care Economy.............................................   194 Review of Time-Use Surveys in 26 Selected Countries and Methods of Valuation of Unpaid Care Work.......................................   196 Review of Time-Use Surveys.................................................................   196 Unpaid Care Work and Paid Work in the 26 Countries.........................   197 Comparison of Estimates of the Care Economy in 26 Countries..............   202 Conclusion.................................................................................................   209 Annex........................................................................................................   210 References.................................................................................................   215 General Conclusion.......................................................................................... 217 Annex................................................................................................................ 219 References of Time-Use Surveys....................................................................... 219 Middle East North Africa.............................................................................. 219 Central Asia................................................................................................... 220 Eastern Europe.............................................................................................. 221 Sub-Saharan Africa....................................................................................... 222 Western Europe............................................................................................. 223

Acronyms

ANPE ANSD ATUS BRAC CAUTAL CCT CFA CME CODI CSF CSO ECOSIT EESI ENEMPSI EPAM EPSF EU FCFA ICATUS ICLS ICT ILC ILO INSEE FDI GLSS

Agence Nationale de Promotion de l’Emploi (Mali) Agence Nationale de Statistique et de la Démographie (Senegal) American Time Use Survey Building Resources Across Communities (initially Bangladesh Rehabilitation Assistance Committee) Clasificación de Actividades de Uso del Tiempo para América Latina y el Caribe Conditional cash transfers Communauté Francophone d’Afrique Contribution sur les Micro Entreprises (Burkina Faso) Core Diagnostic Instrument Community social funds (Ghana) Civil Society Organisation Enquête sur la Consommation des ménages et le Secteur Informel au Tchad (Chad) Enquête sur l’Emploi et le Secteur Informel Enquête Nationale sur l’Emploi et le Secteur Informel (Madagascar) Enquête Périodique Auprès des Ménages (Madagascar) Enquête Pauvreté et Structures Familiales (Senegal) European Union Franc CFA International Classification of Activities for Time Use Statistics International Conference of Labour Statisticians Information and communication technologies International Labour Conference International Labour Organisation Institut National de la Statistique et des Etudes Economiques (France) Foreign direct investment Ghana Living Standard Survey ix

x

GDP GEP GHP GNI GNP GVA HCP HETUS IDWF ILFS INEGI INS INSD INSEED INSTAT ISTAT JASPA LFCLS LIM LSMS MBO MENA MFIs MGNREGA MIMIC MLM MSE NBS NGO NHIS ODA ODHD OECD OEF ONS OTUP PAS PPP PREALC SDGs SEWA

Acronyms

Gross domestic product Gross economic product Gross household production Gross national income Gross national product Gross value added Haut Commissariat au Plan (Morocco) Harmonised European Time Use Surveys International Domestic Workers Federation Integrated Labour Force Survey (Tanzania) Instituto Nacional de Estadistica y Geografia (Mexico) Institut National de Statistique (Cameroon, Democratic Republic of Congo) Institut National de Statistique et de Démographie (Burkina Faso) Institut National de la Statistique, des Etudes Economiques et Démographiques (Chad) Institut National de Statistique (Madagascar) Istituto Nazionale di Statistica (Italy) Jobs and Skills Programme for Africa Labour Force and Child Labour Survey (Zimbabwe) Labour inputs matrices Living Standards Measurement Study Membership-based organization Middle East North Africa Micro finance institutions Mahatma Gandhi National Rural Employment Guarantee Act (India) Multiple Indicators Multiple Causes Micro Loan Management Micro and small enterprise National Bureau of Statistics (Tanzania) Non-governmental organisation National Health Insurance System (Ghana) Official Development Assistance Observatoire du Développement Humain Durable (Mali) Organisation for Economic Cooperation and Development Observatoire de l’Emploi et de la Formation (Mali) Office National des Statistiques (Algeria) Other Targeted Ultra-Poor Proficient Acquired Skills (Uganda) Purchasing power parity Regional Programme on Employment for Latin America and the Caribbean Sustainable Development Goals Self-Employed Women Association (India)

Acronyms

xi

SILC Statistics on Income and Living Conditions (European Union) SNA System of National Accounts SPIREWORK Social Protection Plan for the Informal Economy and Rural Workers (African Union) SRCV Suivi des Ressources et Conditions de Vie (Survey on Resources and Living Conditions) (France) SSL Star Shea Limited SSN Star Shea Network STUP Specially Targeted Ultra-Poor SUT Supply and Use Tables ToT Training of Trainers TUP Targeting the Ultra-Poor TUS Time Use Survey TVET Technical and Vocational Education and Training UCT Unconditional cash transfers UGAPRIVI Ugandan Association of Private Training Providers UHABA Ugandan Hair and Beauty Alliance UN United Nations UNDP United Nations Development Programme UNSD United Nations Statistics Division USSIA Ugandan Small-Scale Industries Association VAT Value added tax WAEMU Western Africa Economic and Monetary Union WIEGO Women in Informal Employment: Globalizing and Organizing ZIMSTAT Zimbabwe Statistical Office

List of Charts

Chart 2.1 Components of the informal sector and of informal employment in the labour force ..................................................... 19 Chart 2.2 Components of informal sector, informal employment and employment in the informal economy by institutional sectors in the System of National Accounts Source: Charmes (2013) Note: the “imputed rents” are the rents that the owners-occupiers are supposed to pay to themselves. They do not correspond to any employment and are not part of the informal economy......................................................... 20 Chart 3.1 Trends in employment in the informal economy by 5-year period (until 2009) and by 2-year period (since 2010) and by region Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years. Some figures have been interpolated between 1995–1999 and 2010–2011 in Southern and South Eastern Asia, for example Notes: 5-year period 2010–2014 of Table 3.1 has been replaced by three 2-year periods in this chart The thin black curve is a 2-year mobile average. The periodicity is shorter for the last period compared with Table 3.1................... 40 Chart 3.2 Trends in employment in the informal economy by 5-year period (until 2009) and by year (since 2010) in Northern Africa Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: The thin black curve is a 2-year mobile average.................. 42

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List of Charts

Chart 3.3 Trends in employment in the informal economy by 5-year period (until 2009) and by 2-year period (since 2010) in sub-Saharan Africa Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: The thin black curve is a 2-year mobile average.................. 44 Chart 3.4 Trends in employment in the informal economy by 5-year period (until 2009) and by year (since 2010) in Latin America Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: The thin black curve is a 2-year mobile average.................. 45 Chart 3.5 Trends in employment in the informal economy by 5-year period (until 2009) and by 2-year period (since 2010) in Southern and South Eastern Asia Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: The thin black curve is a 2-year mobile average.................. 47 Chart 3.6 Trends in employment in the informal economy by 5-year period (until 2009) and by year (since 2010) in transition countries Sources: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: The thin black curve is a 2-year mobile average.................. 49 Chart 3.7 World estimates of employment in the informal economy as a share of nonagricultural employment (92 countries, most recent year) Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: In yellow, Africa; in red, Asia; in green, Middle East-North Africa; in blue, Latin America; in white, transition countries......................................................................... 50 Chart 3.8 Main components and characteristics of nonagricultural employment in the informal economy by region in 2005–2010 Source: Charmes (2011). A worldwide overview of trends and characteristics of employment in the informal economy and informal sector in a gender perspective. Contribution to the update of the ILO-WIEGO Women and Men in the Informal Economy................................................................ 52

List of Charts

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Chart 3.9 Employment in the informal economy is negatively related to GDP per capita Sources: Database used for previous tables and Human Development Report for GDP per capita (PPP) Note: See country codes by region and by alphabetical order in annex Tables 3.32 and 3.33............................................... 54 Chart 3.10 Employment in the informal economy is positively related to poverty Sources: Database used for previous tables and Human Development Report 2015 for the proportion of population living under poverty line Note: See country codes by region and by alphabetical order in annex Tables 3.32 and 3.33............................................... 56 Chart 3.11 Employment in the informal economy in Burkina Faso 1985 (Main jobs) Source: Author’s calculations based on Population Census 1985, see: Charmes (1998b)........................................................... 60 Chart 3.12 Employment in the informal economy in Burkina Faso 1985 (Total number of jobs) Source: Author’s calculations based on Population Census 1985, see: Charmes (1998b)........................................................... 60 Chart 3.13 Pluri-activity rates (most recent year) Source: Table 3.25 supra Note: *Informal employment or informal sector only................... 69 Chart 3.14 Annual growth rate of components of the informal economy (including agriculture) in Mexico 2004–2016 Source: INEGI http://www.inegi.org.mx/est/contenidos/ proyectos/cn/informal/default.aspx based on Table 3.28............... 79 Chart 3.15 Annual growth rate of components of the informal economy (excluding agriculture) in Mexico 2004–2016 Source: Table 3.30.......................................................................... 82 Chart 3.16 Contribution of the informal sector to GDP and share of employment in the informal economy (Years 2010s) (26 countries) Sources: Database used for previous tables Note: See country codes by region and by alphabetical order in annex Tables 3.32 and 3.33............................................... 84 Chart 6.1 Forms of work and the System of National Accounts 2008 Source: Resolution 1 concerning statistics of work, employment and labour underutilization, 19th ICLS, ILO Geneva, 2013.......... 148 Chart 6.2 SNA and non-SNA production in the System of National Accounts Note: SNA production is in grey.................................................... 155

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List of Charts

Chart 7.1 Gender distribution of paid work, unpaid care work and total work: world average, 74 countries Source: Author’s database.............................................................. 165 Chart 7.2 Relative shares of women and men in paid work, unpaid care work and total work in 74 countries Source: Author’s database.............................................................. 166 Chart 7.3 Women’s paid work and unpaid care work across the world Source: Author based on database of 76 countries......................... 167 Chart 7.4 Men’s paid work and unpaid care work across the world Source: Author based on database of 76 countries......................... 168 Chart 7.5 Share of women and men in total unpaid care work Source: Author based on database of 76 countries......................... 169 Chart 7.6 Women’s share of unpaid work in total women’s work Source: Author based on database of 76 countries......................... 171 Chart 7.7 Men’s share of unpaid work in total men’s work Source: Author based on database of 76 countries......................... 172 Chart 7.8 Trends in unpaid care work and paid work in the USA Source: Charmes (2018)................................................................. 173 Chart 7.9 Trends in unpaid care work and paid work in Canada Source: Charmes (2018)................................................................. 174 Chart 7.10 Trends in women’s unpaid care work in Northern, Western and Southern Europe, 10 countries Source: Charmes (2018)................................................................. 175 Chart 7.11 Trends in men’s unpaid care work in Northern, Western and Southern Europe, 10 countries Source: Charmes (2018)................................................................. 175 Chart 7.12 Trends in women’s paid work in Northern, Western and Southern Europe, 10 countries Source: Charmes (2018)................................................................. 176 Chart 7.13 Trends in men’s paid work in Northern, Western and Southern Europe, 10 countries Source: Charmes (2018)................................................................. 176 Chart 7.14 Trends in women’s and men’s unpaid care work in other countries of other regions: 12 countries Source: Author’s database.............................................................. 177 Chart 7.15 Trends in women’s and men’s paid work in other countries of other regions: 12 countries Source: Author’s database.............................................................. 178 Chart 7.16 Patterns of change in time spent by women in unpaid care work by age group: 32 countries Source: Author’s database (see Charmes 2018)............................. 180 Chart 7.17 Patterns of change in time spent by men in unpaid care work by age group: 32 countries Source: Author’s database (see Charmes 2018)............................. 181

List of Charts

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Chart 7.18 Caring for and helping household members by age groups in the USA, average 2006–2016 Source: Own compilations from ATUS 2006–2016, Charmes (2017).............................................................................. 182 Chart 7.19 Caring for and helping non-household members by age groups in the USA, average 2006–2016 Source: Own compilations from ATUS 2006–2016, Charmes (2017).............................................................................. 183 Chart 7.20 Time spent in caring for household members and informal help to other households in Greece, 2013–14 Source: Own compilations from Greece TUS 2013–14 (Charmes 2017).............................................................................. 184 Chart 8.1 Various methods of estimation of unpaid care work...................... 192 Chart 8.2 Women’s unpaid care work exceeds paid work in all countries (except Ghana) (in minutes per day) Source: Database Charmes (2018) Note: in each subregion, countries are ranked according to the increasing order of women’s unpaid care work.................... 197 Chart 8.3 Men’s paid work exceeds unpaid care work in all countries (in minutes per day) Source: Database Charmes (2018) Note: in each subregion, countries are ranked according to the increasing order of men’s unpaid care work........................ 198 Chart 8.4 Women’s paid work never exceeds 228 min per day; men’s paid work is never less than 149 min per day................................ 199 Chart 8.5 Women’s unpaid work is never less than 211 min per day; men’s unpaid work generally does not exceed 166 min per day Source: Database Charmes (2018) Note: In each subregion, countries are ranked according to the increasing order of women’s indicator................................. 200 Chart 8.6 Women’s burden in unpaid care work ranges from 1.5 to 6.9 times men’s..................................................................... 200 Chart 8.7 Women’s paid work ranges from 1/8 to 6/7 that of men’s............. 201 Chart 8.8 Women’s burden in total work always exceeds men’s Source: Database Charmes (2018)................................................. 201 Chart 8.9 Share of the care economy in % of extended GDP Source: Table 8.5 Note: The three countries on the left hand side are not strictly comparable, because their estimates are based on the legal minimum salary (or median salary)............................ 204 Chart 8.10 Distribution of the burden of the care economy between women and men Source: Table 8.5............................................................................ 205

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List of Charts

Chart 8.11 The share of the care economy is negatively related to female labour force participation rate Sources: Table 8.5 and for Female labour force participation rate: national data extracted from ILOSTAT http://www.ilo.org/ global/statistics-and-databases/lang%2D%2Den/index.htm Note: See Country codes by region and by alphabetical order in annex to Chap. 3: Tables 3.32 and 3.33...................................... 205 Chart 8.12 Discrepancies between official and own estimates of the share of the care economy in the extended GDP Source: Table 8.6 and own calculations Note: Countries are ranked by increasing order of the gap between estimates........................................................................... 208

List of Tables

Table 2.1 List of countries covered in three databases and sources of data for the 3rd edition of the ILO Women and Men in the Informal Economy......................................................................................... 30 Table 3.1 Employment in the informal economy in % of nonagricultural employment by 5-year periods in various regions and subregions................................................................................ 38 Table 3.2 Share of employment in the informal economy in total nonagricultural employment by 5-year period and by year since 2010 in Northern Africa........................................................ 41 Table 3.3 Share of employment in the informal economy in total nonagricultural employment by decade in sub-Saharan Africa (34 countries)...................................................................... 43 Table 3.4 Share of employment in the informal economy in total nonagricultural employment by 5-year period and by year since 2010 in Latin America (19 countries)........................... 45 Table 3.5 Share of employment in the informal economy in total nonagricultural employment by 5-year period in Asia (20 countries).................................................................................. 46 Table 3.6 Share of employment in the informal economy in total nonagricultural employment by 5-year period in transition countries (12 countries).................................................................. 48 Table 3.7 Main components and characteristics of nonagricultural employment in the informal economy by region in 2005–2010.................................................................................. 51 Table 3.8 Multiple jobs in Italy: 1981–1997.................................................. 59 Table 3.9 Impact of pluri-activity on size and characteristics of employment in the informal economy: Burkina Faso 1985.......................................................................... 59 Table 3.10 Trends in pluri-activity rates in Burkina Faso between 1985 and 1998................................................................................ 60 xix

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Table 3.11 Table 3.12 Table 3.13 Table 3.14 Table 3.15 Table 3.16 Table 3.17 Table 3.18 Table 3.19 Table 3.20 Table 3.21 Table 3.22 Table 3.23 Table 3.24 Table 3.25 Table 3.26 Table 3.27 Table 3.28 Table 3.29 Table 3.30 Table 3.31 Table 3.32 Table 3.33

List of Tables

Trends in pluri-activity rates in Mali between 2004 and 2013....... 62 Trends in pluri-activity rates in Chad in 2003–2004 and 2011...... 63 Trends in pluri-activity rates in Cameroon 2005, 2010.................. 63 Trends in pluri-activity rates in Tanzania (2001–2006–2014)........ 64 Trends in pluri-activity rates in Morocco, 2010–2013................... 65 Trends in pluri-activity rates in Algeria, 2010–2014...................... 65 Pluri-activity in Senegal (2010–2011)............................................ 65 Pluri-activity rates in Zimbabwe 2014 and Gambia 2012.............. 65 Pluri-activity rates in Madagascar (2012)...................................... 66 Pluri-activity rates in Mali, by institutional sectors, industries and socio-professional categories in the main activity, 2010.................................................................................. 66 Pluri-activity by main and secondary activities in Madagascar 2012........................................................................ 67 Pluri-activity rates in the Democratic Republic of Congo (RDC) in 2004–2005 and 2012...................................................... 68 Pluri-activity rates in Mexico 2012................................................ 68 Pluri-activity rate in Sri Lanka 2014.............................................. 68 Pluri-activity rates (most recent year)............................................ 69 Contribution of informal sector to GDP in various developing countries: years 2000s (33 countries).......................... 72 Contribution of informal sector to GDP in various developing countries: years 2010s (32 countries).......................... 74 Trends in components of the informal economy (including agriculture) as shares of GDP in Mexico 2003–2016..................................................................... 77 Annual growth rate of components of the informal economy (including agriculture) in Mexico 2004–2016................ 78 Trends in components of the informal economy (excluding agriculture) as shares of GDP in Mexico 2003–2016.................... 80 Annual growth rate of components of the informal economy (excluding agriculture) in Mexico 2004–2016............................... 81 Country codes and country names by region................................. 85 Country codes by alphabetical order.............................................. 87

Table 5.1 Transfers in the household income and expenditures survey of Ethiopia 2015–2016........................................................ 126 Table 5.2 Proportion of households reporting a particular event or shock that affected their assets or their wellbeing during the previous 4 years: Ethiopia 2006.................................................................... 127 Table 5.3 Structure of household income in nine sub-Saharan African countries (end of 1990s to beginning of 2000s)............................. 129 Table 5.4 Comparisons of income sources in five African countries in the 1990s and the 2000s............................................................. 131 Table 5.5 Sources of household income in Ethiopia, 1999–2016.................. 132

List of Tables

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Table 5.6 Structure of household income in seven sub-Saharan African countries (end of 1990s to beginning of 2000s) by socio-economic category of the household head....................... 133 Table 5.7 Structure of household income in seven sub-Saharan African countries (end of 1990s to beginning of 2000s) by quintile of income per head....................................................... 134 Table 5.8 Share of transfers in household income per quintile of the household expenditure, Ethiopia 2004–2016....................... 135 Table 5.9 Annual amounts of transfers in Senegal 2010–2011...................... 136 Table 5.10 Transfers received and sent by purpose in Senegal 2010–2011.................................................................... 137 Table 5.11 Share of transfers in household’s income by number of children aged less than 18 in the household, Mali 2006............ 138 Table 5.12 Share of transfers in household’s income in poor and nonpoor households, Mali 2006.............................................. 138 Table 7.1 Time-use surveys: 76 countries and 133 surveys (years) by region......................................................................................... 162 Table 8.1 Size of unpaid labour compared to GDP in various countries (Years 1990s).................................................................................. 193 Table 8.2 Adjusted GDP inclusive of household production using various compensation types for non-market labour, USA, 2004 and 2014................................................................................ 195 Table 8.3 The home production in the UK in 2014........................................ 195 Table 8.4 Contribution to home production by function in the UK in 2014........................................................................... 195 Table 8.5 Size of the care economy in the 26 countries................................. 203 Table 8.6 Comparisons of estimates of the care economy in 13 countries................................................................................ 206 Table 8.7 Overview of TUS main characteristics in the 26 countries under study..................................................................................... 210 Table 8.8 Parameters for the valuation of unpaid work in 12 MENA, Central Asia and Eastern Europe countries (GDP and salaries in national currencies).................................................................... 213 Table 8.9 Parameters for the valuation of unpaid work in 13 sub-Saharan, Western Europe and Latin American countries (GDP and salaries in national currencies)...................................... 214

List of Boxes

Box 2.1 Tentative Typology by Keith Hart (1971)......................................... 14 Box 2.2 The Multicriteria Definition of the ILO Report for Kenya (1972)............................................................................... 15 Box 2.3 “Jua Kali”: Origins of a Local Concept for Designating the Informal Sector........................................................................... 17 Box 2.4 Summary of Current Definitions...................................................... 21 Box 2.5 Variety of Sources, Complementary of Findings.............................. 28 Box 4.1 An Interesting Good Practice in Value Chain by a EU-Funded Project “Market Access Through Cooperative Action” Implemented by Ghana-PlanetFinance............................................. 98 Box 4.2 Other Social Protection Related Targets Goals................................. 104 Box 4.3 The Social Protection Floors............................................................ 105 Box 4.4 An Interesting Good Practice in Expanding Social Protection by a EU-Funded Project “Market Access Through Cooperative Action” Implemented by Ghana-PlanetFinance............................... 107 Box 4.5 Supporting Micro-finance Institutions for Enhancing the Livelihoods of Vulnerable People Dependent on the Informal Economy: Good Practice by a EU-Funded Project “Market Access Through Cooperative Action” Implemented by Ghana-PlanetFinance........... 114 Box 6.1 Important Note About the Concept of Unpaid Care Work Used in this Book.............................................................................. 147 Box 6.2 Definitions of the Components of “Unpaid Care Work” by the Resolution Concerning Statistics of Work, Employment and Labour Utilization Adopted by the 19th ICLS in 2013.............. 148

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Chapter 1

Introduction

Earnings of the poor populations have three major sources: informal activities, cash or in-kind transfers from other households and from the communities (that replace the lack of public social transfers) and unpaid care work (not taken into account by GDPs but accounting for the living standards). The political economy of development cannot be understood without taking into account the informal activities of the majority of the people, the role of social capital in societies where public social transfers are lacking and the hidden contribution of women to the household well-­ being through their unpaid care work. Recognising and assessing the role of these three forms of resilience is of major importance in order to understand how large and poor populations can make a living and survive under such conditions characterised by high unemployment and underemployment rates and more generally huge demographic challenges, as well as food insecurity, economic and social instability and uncertainty and globalised competition. The concept of informality comes from the very beginning of development economics when analysts had to describe economies tightly embedded in societies and confronted with modernity and markets. This is why national accountants were the first to look at the phenomenon in their attempt to measure GDP. Then came the concerns for unemployment and underemployment in the early 1970s when labour economists seized the issue. Until now, informality has remained a confusing concept for most observers and policymakers, who commonly mix underground, parallel and criminal activities with traditional, spontaneous and non-registered – but legal – activities, which play a major role for poor populations to earn a living. In the same vein, social capital, which encompasses solidarities in traditional societies and can be measured by the amount of transfers (received and given) from household to household, can be envisaged as an obstacle to individualism, private initiative and economic development. Still, during crises, it is a powerful shock absorber and can play the role of missing public social protection systems in economies where paid employment remains the exception.

© Springer Nature Switzerland AG 2019 J. Charmes, Dimensions of Resilience in Developing Countries, Demographic Transformation and Socio-Economic Development 10, https://doi.org/10.1007/978-3-030-04076-5_1

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1 Introduction

Finally, unpaid care work and the time budget devoted by women to the care economy, which often more than double their workload, can be analysed as one of the major causes of gender inequality and the root of the so-called feminisation of poverty. Paradoxically, it harbours a large potential for growth, if only it is put at the centre of “social capital” and if policymakers worry about it at all and have the willingness to design and enforce appropriate gender and empowerment policies. This book aims at providing empirical evidence based on available – though not always clearly highlighted – descriptive statistics recently collected on informalities, inter-household transfers and time use.

Informality Informality is a matter of resilience, seeking to understand how poor populations make a living or survive with less than 1.90 US$ per person and per day, which is the level of poverty line as defined by the World Bank. Transcribed in terms of active persons, this threshold would mean that a household with a single worker would have to earn between 5$ and 10$ per day in order to feed a family of 3–5 persons. Informality is also a matter of relationship with the State. What the State fails to regulate is informal. This is why the term is negatively connoted: it is a failure of the state. The greater or lesser magnitude of informality conveys the image of the state’s powerlessness to enforce the regulations it edicts: hence the tendency to equate informality and illegality. However, the history of the apparition of informality is somewhat different. From whatever point of view we take, informality characterises the populations that are not recognised by the dominant power. For the states or governments, informality covers the populations who do not pay taxes and/ or do not comply with the regulations. For employers, informality characterises the unfair competition of economic operators who do not respect the rules of the regulated free market. And for trade unions and civil society organisations, the informal workers are those whose rights are not recognised and therefore cannot benefit from any protection or rights. “In the beginning, all workers were informal” as Dan Gallin (2011) puts it in his historical overview of organising informal workers. That describes the situation of all workers when workers first appear in the contemporary meaning of the term, at the dawn of the industrial capitalist age, in 18th century Europe, later elsewhere in the world. Informal workers are workers whose rights are not recognized and who are therefore unable to exercise those rights.

The Labour Laws resulted from the struggles by organised workers, and Commercial Laws resulted from the will of entrepreneurs to formalise relationships between them. The early workers of nascent capitalism were the first informal workers benefiting neither from any protection nor any rights, be they working for capitalist firms or as petty producers in the survivalist economy providing goods and services at low prices to the entire labour surplus issued from rural-urban

Informality

3

migrations thus permitting low levels of wages. In many ways, the same process has been at play in developing countries. What is called “development” can be seen as the transfer of labour to a capitalist sphere formalised in Commercial and Labour Laws and now generalised at world level. All workers who are not part of this sphere are reputed “informal”. Here again all workers were informal in the beginning, once the capitalist sphere began to penetrate societies and economies that were totally formal in their own rights. In traditional societies, each person has his/her place and role, there is no unemployed nor inactive, and paid and unpaid work are all the same and not differentiated. And somehow at the beginning, all members of traditional societies (if not all workers) are formal. Then came globalisation and the rule of the so-called free market imposed by colonialism that made the formal traditional societies informal. Suddenly the traditional way of living of the population became informal because these populations were not paying taxes to an external power and had to grow production for the market in order to be able to pay taxes (remember that in the early times of colonisation farmers or communities were obliged to plant fields especially dedicated to the market in order to be able to pay taxes). What has been called “development”, “progress” and “modernisation” is just the imposition of external rules to societies that had – and still have – their own rules and norms. And so what was formal becomes informal all of a sudden by the strength and the will of imperialism and colonialism and then globalisation. At this new start, all is informal and development is a process by which the informal economy must necessarily become formal. Recently (in 2015) the tripartite (i.e. government, workers and employers) International Labour Conference’s (ILC) recommendation 204 made of formalisation the acme of progress. Even today, in some nomadic societies or other traditional societies, each member has his/her place and role in the society, there is no unemployed or inactive, and everybody is formal, whereas in Gallin’s statement, all workers are informal at the beginning of the industrial revolution and the development of capitalism that inspired the theoreticians of the free market, the State is inexistent, and all workers are informal depending on the goodwill of their employers who have no rule to apply and no protection to provide. In fact, “the whole economy is informal, an economy where no social rules apply, where the strong prevail by virtue of their sole strength, because they do not meet with organized opposition. That was the economy of early capitalism” (Gallin, ibid.). Another way of describing these situations of transitions is by reference to the social contract that founds the cohesion of a society. To be part of the social contract that ensures the cohesion of a society, it is expected that citizens fulfil their duties to benefit from their rights, but for populations or people who do not benefit from public services (lack of access to school or to health centres, absence of infrastructure, lack of access to credit, etc., because these services are not present in the regions where they live or because they are not adapted to their ways of life), what is the incentive to pay taxes or to register? Why should you register, pay taxes and respect the rules if you only rely on yourself to earn your own living, create your job and support your family without any support from the formal sphere? In a recent interview and in reaction to the scandal of the “Paradise papers” and fiscal optimisation, Pascal Saint-Amans, director of the Fiscal Policy and Administration Centre at

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OECD (Le Monde 8 November 2017), notes “multinational firms push back the borders between what is legal and what is illegal”. Similarly, the transformation of wageworkers into self-entrepreneurs is also a means of pushing back the limits between formality and informality for formal firms in search of costs cutting. Finally, the question of informality must be addressed by both ends: the transition from informal to formal but also the regression from formal to informal when formal enterprises decide not to apply the norms and regulations enacted by the state. One can conclude that informal workers are “excluded” by the State, which enacts norms and regulations not adapted to specific populations, on the one hand, and also by formal firms that decide not to apply these norms and regulations to certain populations, on the other hand. Keith Hart, one of the inventors of the concept of “informality”, is not far to consider that today all has become informal: Everyone ignores the rules, especially the people at the top – the politicians and bureaucrats, the corporations, the banks – and they routinely escape being held responsible for their illegal actions. Privatization of public interests is probably universal, but the alliance between money and power used to be covert, whereas now it is celebrated as a virtue. The informal economy has taken over the world, while cloaking itself in liberal rhetoric (Hart 2015, 2016).

He adds that he could not anticipate what happened next (i.e. after he coined the concept of informality in the early 1970s): under a neoliberal imperative to reduce the state’s grip on ‘the free market’, manifested in Africa as ‘structural adjustment’, national economies and the world economy itself became radically informal (Hart 2015). Not only did the management of money go offshore, but corporations outsourced, downsized and casualized their labour forces, public functions were privatized, often corruptly, the drugs and illicit arms trades took off, the global war over ‘intellectual property’ assumed central place in capitalism’s contradictions, and whole countries (…) abandoned any pretence of formality in their economic affairs. (…) The market frenzy led to the ‘commanding heights’ of the informal economy taking over the bureaucracy (Hart 2016).

The first part of this book is comprised of three chapters. Chapter 2 provides a brief history of the concepts of the informal sector, informal employment and informal economy. It discusses the prevailing definitions of the informal economy, their related methods of measurement and sources of data. Chapter 3 is an assessment of trends in size of the informal economy, including the contribution of the informal economy to GDP. Chapter 4 addresses the policies and actions designed to support the populations dependent on the informal economy or to include them in the global economy from the viewpoint of the State as well as of the actors on the field.

Solidarities The second part of this book is dedicated to the size and the role of transfers from households to households.

Solidarities

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Marcel Mauss’ essay on gift (1925) has inspired reflections on economic behaviour based on reciprocity: transfers are not one way but reciprocal and are the foundation of social cohesion. A more modern vision of these kinds of transfers would today look at them as the basis of social capital. In the 1970s, a number of authors drew attention to the macroeconomic magnitude of the economy of grants or gifts. Boulding (1972) conceptualised households not only as the main driving force for the market economy, with household purchases covering about 60% of GNP, but also as the most important agent in the grants economy. This is the economy of oneway transfers, grants or gifts, given mainly within households from those earning money incomes to other members, children, spouses and dependants not earning a money income. Research on inter-household transfers by Morgan and Baerwaldt (1971) showed that transfers within households in the United States were over $300 billion, three times the transfers of $90 billion from governments and private charity. In Europe, the solidarity within families is regularly measured through the survey on living conditions. In countries where the extension of social security systems based on social contributions paid by the employees and their employers is limited because paid employment is tiny and the informal economy large, the poor and the population in general continue to resort on traditional forms of solidarity or social capital. Especially in Africa, the role of extended families and of geographical or ethnic or simply local-neighbourhood communities is still of major importance: echoing the theories of gift and reciprocity elaborated by Marcel Mauss (1925), it results into a set of rights and obligations. Social capital (Coleman 1988; Putnam 2000; Dasgupta and Serageldin 1999) can be defined as a set of social relationships and networks, norms and values, which lead to social cohesion, cooperation and the realisation of shared objectives and interests. Interestingly, the World Development Report 2000/2001 (World Bank 2001) distinguishes three dimensions of social capital and of networks upon which it is built: family relationships (“bonding”), relationships based on common interests or common conditions (“linking”) and strategic relationships established with dominant and powerful people (“bridging”). Expenditures spent in costly ceremonies for weddings or funerals – often conducing to inflationist processes such as ceremonies for placing the dead in Madagascar – are a means for demonstrating and expanding social power, which combines “bonding” and “bridging”. Depending on the point of view adopted, these rights and obligations may be considered and analysed as obstacles to development, to private initiative and to capital accumulation (Charmes 1977, 1978) or as forms of corruption or “complexity”: In her global encyclopaedia of informality, Alena Ledeneva presents an impressive list of informal behaviours, especially in former socialist countries, illustrative of “bridging” (Ledeneva 2018). Or they can also be viewed as a form of social protection, which is particularly efficient in times of economic crises or health risks, providing the youth, the unemployed, the disabled, the sick and the elderly with food, shelter and care and with powerful networks for seeking jobs or income-generating activities. Some authors (see, in particular, Vuarin 2000, for Bamako) have described such systems that they present as real systems of social protection, showing how they can

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1 Introduction

be mobilised for covering the costs of illness (Vuarin 1993). Oduro (2010) describes the interplay between formal and informal systems of social protection and shows that the scaling up of formal social protection would not necessarily displace informal social protection systems and would not have negative effects on the welfare of the poor. Devereux and Sabates-Wheeler (2004), citing Ouma (1995) and Davies (1996) about Uganda, oppose the popular and optimistic view according which “acts of reciprocity, altruism, social cohesion and personal intimacies were sufficient to guarantee social protection in both good and bad times to all members of any ethnic nationality by ensuring equity and social justice” to the “dark side” of social capital “that often engenders relations of subservience and dependence” or stressing that the burden of caring for relatives (especially the ill and infirm) falls mainly on women. Finally, there would be an “over-estimate of the capacity of these mechanisms to deal with shocks, especially at the community level”. Vuarin (2000) distinguishes between the various forms of mutual assistance that prevail in the traditional society (such as “grins” – elective local neighbourhood groups for men – or “tontines” which are rotating savings and micro-credit groups for women, also called “merry-go-round” in Eastern Africa), on the one hand, and the request for support addressed to powerful, influential, higher status individuals in a clientelist, neo-patrimonial logic, through a network of intermediaries that constitutes the social capital of the applicant. Mutual assistance groups are widespread in traditional societies, and we will see (Chap. 4, Part I of this book) that these groups are often mobilised by social actors who attempt to promote social protection or economic development by using such groups as efficient means towards this aim. Whereas mutual assistance groups are characterised by a permanent negotiation and adjustment of their positions by the individual members who activate or reactivate their social relationships and their reciprocal rights and duties ensuring that the exchange remains equal and excluding those who fail, the demand for support from higher status individuals remains an unequal exchange based on hierarchy and negotiation. This dual system of mutual assistance and hierarchical demand for support neither intends to operate redistribution and transfers from the wealthy to the poor nor to contribute alleviating social inequalities. As such it does not fit the redistributive requirements of modern social protection systems, but only their requirement for universality. This second part of the book is comprised of one chapter (Chap. 5) addressing the role of communities, sources of social capital undermined by the progress of individualism, but still capable of ensuring the minimum social protection that the State has failed until now to provide on a universal basis.

Unpaid Care Work Household production and unpaid care work are matters of resilience, as it is observed that they increase during economic crises (and decrease with economic booms) when they provide household members with services that they are no longer

Unpaid Care Work

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able to get on the market, due to lack of money. The resilience of people and societies can therefore be observed – among other manifestations – in work and its various forms. But what is work? To answer this question, it is necessary to come back to the definition of production because the labour force concepts are founded on the definition of production. The definition of production is a subject of debate since a long time among the economists. For long the concept of production as defined by the economists did not include the services. Adam Smith (1723–1790), influenced by the Physiocrats (late 1750s), circumscribed the definition of production to the sole goods. This conception continued with Marx (1818–1883) and the material balances (equivalent to the national accounts) in the former socialist countries. Nancy Folbre (1991) recalls that the 1875 Census of Massachusetts listed housewife as a productive occupation as opposed to “not engaged wives merely ornamental”. But the definition of production, and of work, extended to services will be introduced by Alfred Marshall (1842–1924) who, in its Economics of Industry (a work from 1879 written with Mary Paley Marshall, but it is in its 4th edition in 1909 that the question at stake was raised), laid the foundations of the modern conception of the national production (GDP): Everything that is produced in the course of a year, every service rendered, every fresh utility brought about is a part of the national income. Thus it includes the benefit derived from the advice of a physician, the pleasure got from hearing a professional singer, and the enjoyment of all other services which one person may be hired to perform for another.

Later on, in 1920, in The Economics of Welfare, his student, Arthur C.  Pigou (1877–1959), drew the consequences of the limitative interpretation of Marshall’s ideas, by those who were interested in the measurement of production, to the sole services transiting to the market. He pointed out the paradox of the gentleman who lowers the national welfare when marrying his maid (Part I, Chap. 4 of his Economics of Welfare). The extension of the notion of production (and subsequently of the notion of work) to the whole category of services was taken over by feminist economists such as Margaret Reid in her Economics of Household Production in 1934 where she states that “if an activity is of such character that it might be delegated to a paid worker, then that activity shall be deemed productive”. Some of the economists who founded the system of national accounts addressed the issue of housework valuation (Kuznets 1941; Clark 1958), but it was up to Gary Becker’s (1965, 1981) theoretical works to root them into the framework of economic theory. Marilyn Waring (1988) is the author of an influential book (Counting for Nothing: What Men Value and What Women Are Worth) that summarises the situation: women’s work is neglected by the National Accountants (“what men value”) who do not take into account the entire domestic and care work mainly done by women (“what women are worth”). But how to measure domestic and care work as there is no price fixed by the markets? Marshall’s vision, however, opened the door to such a valuation provided that these services are subjects of transactions on the market. The quantities of services or at least the number of hours and days dedicated to these

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activities still had to be measured. Time-use surveys that the Beijing Conference (1995) contributed to put back on the front of the stage have become today the indispensable tool that allows enabling accurate assessment of non-recognised and non-recorded work of women, and the past recent years have seen the rapid development of this type of surveys. There are more than 75 countries in the world that have carried out time-use surveys, among which only 10 in sub-Saharan Africa and 6 among lower-income developing countries. An even smaller number of countries went up to estimate the value of domestic unpaid work in comparison with the GDP. This third part of the book is comprised of three chapters. Chapter 6 comes back on the definition of work and productive activities in the labour force concepts and in national accounts and delineates the frontiers of unpaid care work. Chapter 7 presents a compilation of magnitude and trends on unpaid care work across 75 countries and Chap. 7 attempts to valuate the care economy in comparison with GDP in 26 countries from various regions of the world.

References Becker, G. (1965). A theory of the allocation of time. The Economic Journal, 493–517. Becker, G. (1981). A treatise on the family. Harvard University Press, Cambridge, MA. Boulding, K. (1972). The household as Achilles’Heel. Journal of Consumer Affairs, 6(2), 110–119. Charmes, J.  (1977). De l’ostentation à l’accumulation. Production et reproduction des rapports marchands dans les sociétés traditionnelles à partir de l’analyse du surplus, in Ouvrage collectif: “Essais sur la reproduction des formations sociales dominées”. Travaux et Documents de l’ORSTOM, n° 64, 192p., pp. 105–137. Charmes, J. (1978). Les blocages socio-culturels au développement en tant que manifestations de rapports de domination. Mondes en développement, 24, 877–908. Clark, C. (1958). The economics of housework. Quarterly Bulletin of the Oxford University Institute of Statistics, 20, 205–211. Coleman, J.  S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94(Suppl), 95–120. Dasgupta, P., & Serageldin, I. (Eds.). (1999). Social capital: A multifaceted capital. Washington, DC: World Bank. Davies, S. (1996). Adaptable livelihoods: Coping with food insecurity in the Malian Sahel. London: Macmillan. Devereux, S., & Sabates, W.  R. (2004). Transformative social protection (IDS Working Paper 232). Brighton: Institute of Development Studies. Folbre, N. (1991). The unproductive housewife: Her evolution in 19th century economics thought. Signs: Journal of Women in Culture and Society, 16, 463–484. Gallin, D. (2011). Organizing informal workers: Historical overview. Presentation given at WIEGO Workshop Organising Informal Workers: Building and Strengthening Membership-­ Based Organisations, Bangkok, Thailand. Available at http://wiego.org/sites/wiego.org/files/ reports/files/Organizing_informal_workers_historical_overview_Gallin.pdf Hart, K. (2015). How the informal economy took over the world, In P.  Moertenboeck, H. Mooshammer, T. Cruz, & F. Forman (Eds.), Informal market worlds reader: The architecture of economic pressure (pp. 33–44). NAI010 Publishers.

References

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Hart, K. (2016). The real economy: the challenge of dialectical method. University of Pretoria. Paper presented to a conference “Real economy: Ethnographic inquiries into the reality and the realization of economic life”, Rio de Janeiro, June 16–18, 2016. Kuznets, S. (1941). National income and its composition, 1919–1938. New York: National Bureau of Economic Research. Ledeneva, A. (Ed.). (2018). The global encyclopaedia of informality: Understanding social and cultural complexity (Vol. 1). London: UCL Press. Mauss, M. (1925). Essai sur le don. Forme et raison de l’échange dans les sociétés archaïques. Published in l’Année Sociologique, Quadrige/Presses universitaires de France, 2007. Morgan, J.  N., & Baerwaldt, N.  A. (1971). Trends in inter-family transfers. Paper presented at a joint session of the Association for the Study of the Grants Economy and the American Economic Association, New Orleans, December 1971. Oduro, A. D. (2010). Formal and informal social protection in Sub-Saharan Africa. Paper prepared for the Workshop “Promoting resilience through social protection in Sub-Saharan Africa” organised by the European Report on Development in Dakar, 28–30 June 2010. Ouma, S. O. A. (1995). The role of social protection in the socioeconomic development of Uganda. Journal of Social Development in Africa, 10(2), 5–12. Putnam, R. D. (2000). Bowling alone. The collapse and revival of American Community. New York: Simon & Schuster. Reid, M. (1934). Economics of household production. New York: Wiley. Vuarin, R. (1993). Quelles solidarités sociales peut-on mobiliser pour faire face au coût de la maladie ? In J. Brunet-Jailly (Ed.), Se soigner au Mali. Paris: Karthala. Vuarin, R. (2000). Un système africain de protection sociale au temps de la mondialisation. Paris: L’Harmattan. Waring, M. (1988). Counting for nothing: What men value and what women are worth. University of Toronto Press. World Bank. (2001). World development report 2000/01. Attacking poverty. Washington: World Bank, Oxford University Press.

Part I

Informality

Chapter 2

A Brief History of 50 Years of Conceptualisation and Measurement of the Informal Economy

Theories and Concepts It is now almost 50 years that the first attempts of definition and data collection on informal sector and informal employment on a large scale were launched in the early 1970s. Long before, however, works by Boeke on Indonesia (1953), Arthur Lewis on “Economic Development with Unlimited Supplies of Labour” (1954) and Clifford Geertz on “Peddlers and Princes in Indonesia: Social development and Economic Change in Two Indonesian Towns” (1963) – who later on invented the concept of bazaar economy (Geertz 1978) – paved the way of dualistic approaches which, before being disputed, offered an extraordinary space for expansion to the new theories of economic development. It must also be noted that close behind these precursors, it was up to national accountants to be the first to propose procedures for overall estimates of the traditional sector, agricultural and nonagricultural, monetary and nonmonetary in their attempts to measure GDP (OECD 1965, 2002; Blades 1975; Charmes 1989) within the central framework of the System of National Accounts (SNA, first established in 1953 and further revised in 1960, 1964, 1968, 1993 and 2008). It is in 1971 that the concept of “informality” was born, quasi-simultaneously, at the two extremes of the African continent: in Ghana with the notion of “informal income opportunities” by Keith Hart (1971) and in Kenya with the multicriteria definition of the informal sector by the International Labour Organisation (ILO) report of the World Employment Programme (1972, with Richard Jolly and Hans Singer as main editors) (Boxes 2.1 and 2.2).

© Springer Nature Switzerland AG 2019 J. Charmes, Dimensions of Resilience in Developing Countries, Demographic Transformation and Socio-Economic Development 10, https://doi.org/10.1007/978-3-030-04076-5_2

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Box 2.1: Tentative Typology by Keith Hart (1971) The tentative typology of Keith Hart (1971), based on his fieldwork in Nima (Accra low-income neighbourhood) for a PhD in Anthropology at Cambridge, was presented in a paper delivered at the conference on urban unemployment in Africa, at the Institute of Development Studies of the University of Sussex, 12–16 September 1971: (1) Formal income opportunities (a) Public sector wages (b) Private sector wages (c) Transfer payments – pensions, unemployment benefits (if any), etc. (2) Informal income opportunities (legitimate) (a) Primary and secondary activities  – farming, market gardening, building contractors and associated activities, self-employed artisans, shoemakers, tailors, etc. and manufacturers of beers and spirits (b) Tertiary enterprises with relatively large capital inputs  – housing, transport, utilities, commodity speculation, rentier activities, etc. (c) Small-scale distribution – market operatives, petty trade, streethawkers, caterers in food and drink, bars, carriers (kayakaya), commission agents and dealers (d) Other services – musicians, launderers, shoeshiners, barbers, night soil removers, photographers, etc., brokerage and middlemanship (the maigada system in markets, law courts, etc.) and ritual services, magic and medicine (e) Private transfer payments  – gifts and similar flows of money and goods between persons, borrowing, begging (3) Informal income opportunities (illegitimate) (a) Services – “spivvery” in general; receiving stolen goods, usury and pawnbroking (at illegal interest rates), drug-pushing, prostitution, poncing (“pilot boy”), smuggling, bribery, political corruption Tammany Hall style, protection rackets (b) Transfers  – petty theft (pickpockets, etc.), larceny (burglary and armed robbery), peculation and embezzlement, confidence tricksters (money doubling, etc.), gambling

Theories and Concepts

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Box 2.2: The Multicriteria Definition of the ILO Report for Kenya (1972) The ILO report on Kenya is one of the several reports of the World Employment Programme conducted by the ILO in the 1970s. The Kenya mission was headed by Hans Singer, with Richard Jolly, Dharam Gaï and John Weeks (from IDS), Ajit Bhalla and Louis Emmerij (from ILO), among the most well-­ known. The authors note that their thinking in these matters has been “greatly influenced and helped by a number of sociologists, economists and other social scientists in the Institute of Development Studies at the university of Nairobi” and they add: “One begins to sense that a new school of analysis may be emerging, drawing on work in East and West Africa and using the formal-informal distinction to gain insights into a wide variety of situations” (p.6, footnote 1). The definition lies in the introduction of the report (p.6): Informal activities are the ways of doing things, characterised by: (a) (b) (c) (d) (e) (f) (g)

Ease of entry Reliance on indigenous resources Family ownership of enterprises Small scale of operation Labour-intensive and adapted technology Skills acquired outside the formal school system Unregulated and competitive markets

(…) The characteristics of formal sector activities are the obverse of these, namely: (a) (b) (c) (d) (e) (f) (g)

Difficult entry Frequent reliance on overseas resources Corporate ownership Large scale of operation Capital-intensive and often imported technology Formally acquired skills, often expatriate Protected markets (through tariff quotas and trade licenses)

The first notion, introduced by Hart, was individual-based and inspired many sociological and anthropological studies in Africa and elsewhere (Bromley and Gerry 1979); in Latin America, in particular, it made the regular labour force surveys getting started the measurement of the so-called marginalisation of workers on the basis of a level of earnings under the minimum wage and in connection with poverty. The second conception (ILO) was establishment- or enterprise-based and was at the origin of numerous studies and surveys by the ILO in Africa (Nihan et  al. 1978; Maldonado 1987) and through its Jobs and Skills Programme for Africa (JASPA), in

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Latin America (Tokman 1987), through its Regional Programme on Employment for Latin America and the Caribbean (PREALC) and in Asia (Sethuraman 1981) generally at capital city levels. Both approaches (individual-based and enterprise-based) put the state as the central cause of emergence of these petty activities, either by the intrinsic nature of an emerging capitalism supported by the new independent states and in need of such a labour reserve/surplus (Lebrun and Gerry 1975; Gerry 1979) or by the barriers that prevent private initiative to blossom out. The first approach was inspired by the Marxist theory of the labour reserve/surplus (Hart mentions “the reserve army of underemployed and unemployed”, as will do Lebrun and Gerry) and will focus on the lower tier of the working poor. The second approach will focus on the higher tier, “the modern informal sector” as Georges Nihan – not afraid by a contradiction in terms – put it, surveying the most visible part of the informal sector, in fixed establishment and the most likely to develop, grow and modernise, a conception and theory that will culminate with Hernando de Soto (1986) who quotes that it can take several years in Peru for a start-up to be in compliance with the laws, whereas a few days, if not less, are sufficient in the USA. The two-tier conception of informal sector was forged by Gary Fields (1990) identifying “the voluntary participation in upper-tier informal activities but not easy entry ones” echoing the survivalist “involutive” subsector and the evolving microenterprise subsector of Philippe Hugon (1980), not to mention the intermediate or “missing middle” sector coined by John Page et William Steel (1986). These conceptions have remained deeply rooted in the World Bank research works on the sector until the recent book by Perry et al. (2007) revisiting Albert Hirschman’s “Exit, Voice and Loyalty” (1970) and applying it to the informal sector operators by distinguishing informality driven by exclusion from informality driven by voluntary exit. Such conceptions of a dichotomy within the informal sector, which itself is the result of a dichotomy or a dualistic approach, prelude to the vision of the informal sector as a continuum as expressed by Guha-Khasnobis et al. (2006) in the introduction “Beyond Formality and Informality” to their book. Chen (2012) summarises the approaches of the informal economy by distinguishing: (1) the dualist approach, as illustrated by the ILO report on Kenya that looks at the informal economy as a separate economy, delinked from the formal economy; (2) the structuralist approach (in fact the Marxian approach), illustrated by Castells and Portes (1989) that treats the informal economy as a segment subordinated to the formal economy; and (3) the legalist approach (illustrated by De Soto) that sees the informal economy as a rational response by economic units to overregulation and bureaucracy. Non-compliance with the official regulations is far from meaning that these activities are illegal. Charmes (1990) notes that the inability of the state to make the operators comply with the laws it edicts is rather a matter of inadequacy, powerlessness and even unwillingness with regard to those jobs spontaneously created in a context of high unemployment and underemployment. An example sheds light on these variations for the understanding of the concept. In 1987, during the 14th International Conference of Labour Statisticians (ICLS), a preliminary discussion took place about the informal sector. As the discussion was going on and was mainly focussing on “moonlighting”, a term widely used to characterise the underground economy, the representative of Kenya – the country

Statistical Definitions

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where the concept of informal sector was coined at the beginning of the 1970s – asked for the floor and exposed to the audience that in his country, the informal sector was not comprised of these persons who operate in the moonlight, but rather of those working in the open sun. As a matter of fact, in Kenya, the term “Jua Kali”, which means in Swahili language “under the burning sun”, is used to circumscribe the operators of the informal sector (Box 2.3).

Box 2.3: “Jua Kali”: Origins of a Local Concept for Designating the Informal Sector Kenneth King, whose first works on the informal sector in Kenya date from the first half of the 1970s and book on the The African Artisan dates from 1977, wrote in 1996: “Jua Kali in Swahili means ‘hot sun’. But over the course of the 1980s, and perhaps a little earlier, it came to be used of the informal sector artisans, such as car mechanics and metal workers who were particularly noticeable for working under the hot sun because of the absence of premises. People began to talk of taking their car to jua kali mechanics. Gradually, the term was extended to refer to anyone in self-employment, whether in the open air or in permanent premises. On 28 May 1988, The Standard reported that the Minister of Technical Training and Applied Technology wished to encourage the use of the term jua kali rather than informal sector and had therefore announced that the small-scale industry which had come to be known as the informal sector would henceforth assume the name Jua Kali Development Programme” (King 1996).

The 15th ICLS resolution adopted in 1993 hence stipulated that “activities performed by production units of the informal sector are not necessarily performed with the deliberate intention of evading the payment of taxes or social security contributions, or infringing labour or other legislations or administrative provisions. Accordingly, the concept of informal sector activities should be distinguished from the concept of activities of the hidden or underground economy”. An implication on the definition and on the related methods of data collection is that non-registration of the individual (in the labour or social security registers) or non-registration of the enterprise (in the fiscal or commercial registers) is a basic criterion for the definition of the concept of informality.

Statistical Definitions International definitions are applied – with national variations and adaptations – in the statistical surveys. It is a two-pronged definition which is currently used: the establishment-based definition of the informal sector adopted in 1993 followed the footsteps of the ILO Kenya report (1972) and was based on the subsequent research

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on the “modern” informal sector of microenterprises in sub-Saharan Africa. It was completed a decade later by a job-based definition of informal employment, returning to the original idea of Hart (1971), but based on the rapid increase of the externalisation process of labour and the development of outworkers, home-based workers and precarious jobs correlative with globalisation: both definitions overlap in some way and require an explanation about their scope in the labour force and among the institutional sectors of the System of National Accounts. The informal sector was defined by the 15th International Conference of Labour Statisticians (ILO 1993a, b), as comprised of enterprises of own-account workers and enterprises of informal employers (a dichotomisation that could remind the two tiers or two subsectors identified by analysts), referring to the characteristics of the economic units in which the persons work: legal status (individual unincorporated enterprises of the household sector), non-registration of the economic unit or non-­ registration of its employees or size under five permanent paid employees, at least some production for the market. The conference recommended the mixed (household-establishment) surveys in order to capture the informal sector: in this approach, all economic units operated by households’ members are enumerated in the sampled households and then surveyed in a second stage through an establishment questionnaire. Later on in 1997, the Delhi Group on informal sector statistics was set up by the UN Statistical Commission in order to improve and develop the definition and data collection on this sector: since then the group has met regularly, and the reports and contributions are available on the website of the Ministry of Statistics and Programme Implementation of India (www.mospi.nic.in). The 17th ICLS (ILO 2003) has adopted guidelines for defining informal employment as comprising all jobs carried out in informal enterprises as well as in formal enterprises by workers and especially employees “whose employment relationship is, in law or in practice, not subject to national labour legislation, income taxation, social protection or entitlement to certain employment benefits (advance notice of dismissal, severance pay, paid annual or sick leave…) because of non-declaration of the jobs or the employees, casual or short duration jobs, jobs with hours or wages below a specified threshold, (…), place of work outside premises of employer’s enterprise (outworkers), jobs for which labour regulations are not applied, not enforced, or not complied with for any other reason”. Informal employment is therefore usually defined by the absence of social protection or non-payment of social contribution (mainly health coverage) or the absence of written contract (but this criterion can only be applied to paid employees and is consequently narrower than social protection). Nevertheless, individuals may benefit of social protection through the contribution of another member of the family. Consequently, the appropriate definition should be related to the payment of social contributions by the workers concerned rather than to the entitlement of the workers to social benefits. This new extended definition of informality is interesting in that it meets a usual practise in various parts of the developing world (in Latin America and in some countries of Asia) where labour force surveys are often used to collect data on social protection coverage. As a consequence, the absence of social protection preferably

Statistical Definitions

19

to the absence of written contract (which applies to wage employees only) has become the prevalent criterion for the measurement of informal employment. The introduction of questions in order to capture social protection (especially health protection) has then rapidly disseminated in countries where household surveys are less regular or did not include such questions. Nevertheless, practices continue to be diverse across regions and countries: the ideal consists in data collection through labour force surveys or other household surveys capturing both informal employment and informal sector employment, but this later practice still remains rare. Chart 2.1 below simplifies the complexity of both concept and shows that they are not mutually exclusive as components of the labour force, and Chart 2.2 tries to shed light on the position of informal sector and informal employment among the institutional sectors of the System of National Accounts (SNA). Cell (2) means that in the informal sector, some individuals may have a formal job (it may happen where the criteria of non-registration of the unit or non-­ registration of the employees – which are optional – are not used in the national definition: this is why informal employment is not inclusive of informal sector in total). It may also occur due to the fact that some workers in the informal sector benefit from social security as beneficiaries of parents or spouses who are registered. Such a category is assumed to be small. But the main category is cell (3), which represents informal jobs in the formal sector. This category is assumed to be huge and growing up. Finally, cells (5) and (7) are components of the households themselves: the households are the employers of paid domestic workers, and the production of goods for own final use refers to subsistence agriculture or subsistence activities in general, which do not go to the market. In order to avoid inconsistencies between the definitions of the two concepts, it can be useful and practical to consider that the informal sector is a component of the informal economy, and it is this definition which we adopt for measurement of size and contribution of the informal economy in the next chapters: employment in the informal economy comprises all persons (whatever their employment status) working in informal enterprises, plus all persons working informally in other sectors of

Chart 2.1  Components of the informal sector and of informal employment in the labour force The two cells in grey cover the “informal sector”, while the four cells in double line cover “informal employment”:   – Employment in the informal sector = (1) + (2)   – Informal employment = (1) + (3) + (5) + (7)   – Employment in the informal economy = ((1) + (2)) + ((3) + (5) + (7))

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Enterprises/economic units/institutional sectors

Institutional sectors General Government Nonfinancial corporations Financial corporations Non-profit institutions serving households Households: unincorporated enterprises Households: Others

Subsectors

Formal Unincorporated Enterprises: Informal sector Production of goods for own final use Paid domestic services Imputed rents

Jobs Formal Informal 1 2

3 5

4 6

–

7

8 –

9 –

Employment in the informal sector = (5) + (6) Informal employment = (2) + (4) + (6) + (7) + (9) Employment in the informal economy = ((5) + (6)) + ((2) + (4) + (7) + (9)) Chart 2.2  Components of informal sector, informal employment and employment in the informal economy by institutional sectors in the System of National Accounts Source: Charmes (2013) Note: the “imputed rents” are the rents that the owners-occupiers are supposed to pay to themselves. They do not correspond to any employment and are not part of the informal economy

the economy, i.e. formal enterprises, households with paid employees (domestic workers) or own-account workers producing goods (primary goods or manufactured goods) for the household’s own final use. By definition, all contributing (unpaid) family workers are classified in the informal employment. Consequently, formal paid employees working in the informal sector (a category which may exist where the definition of informal sector does not use the criterion of registration of the employees) and unpaid family workers working in the formal sector are equally classified in informal employment. As a consequence, such a definition slightly diverge from the ILO definition of informal employment, and in order to avoid miscomprehension between the two approaches, it has been convened to refer to the concept of informal economy, which is broader than the concept of informal employment. Measuring the contribution of informal sector and informal employment to the GDP requires an understanding of where these activities and jobs are positioned in the various institutional sectors of the SNA. Chart 2.2 hereafter attempts to make such an understanding easier: the informal sector is a subsector of the household institutional sector – it is only a part of it (and not necessarily the most important part), and it does not belong to any of the other institutional sectors. Informal employment, on the contrary, cuts across all institutional sectors, including the government sector, and it cannot be defined according to the fundamental unit of the SNA, i.e. the economic units. Informal employment needs to be measured within the labour input matrix, an instrument ensuring that all jobs and all hours of work

The Underground, Shadow, Illegal, Parallel, Non-observed Economy and How It Is…

21

are considered in the measurement of the contribution of each institutional sector to the value added of all industries that compose the GDP (see Chap. 2 infra). In practice and because it is difficult to impute to a particular firm or economic unit, the contribution of informal employment to GDP is often allocated as a residual to the household institutional sector (Box 2.4). Box 2.4: Summary of Current Definitions In summary, the informal economy is comprised of microenterprises operated on a small scale by individual entrepreneurs, as well as of producers for own-­ account and paid employees who are not covered or not contributing to social security. It should not be confounded with the so-called “shadow” or “illegal” economy. Statistically speaking, employment in the informal economy is comprised of: (i) Employment in the informal sector of microenterprises (operating under a certain size threshold in terms of number of paid employees or number of workers, and registered or not, depending on national definitions) (ii) Informal employment outside the informal sector, itself comprised of: (a) Informal employment in the formal sector, i.e. paid employees not covered by social security (b) Domestic workers not covered by social security (c) Employment in production activities for own final use In national accounts (i.e. GDP), the informal sector is a subsector of the household institutional sector, which also includes paid domestic workers as well as production activities for own final use: these components are generally clearly identified in the national accounts of countries that compile the detailed accounts of the household sector. Informal employment in the formal sector contributes to all other institutional sectors but is rarely identified in national accounts and is often included in the household sector.

 he Underground, Shadow, Illegal, Parallel, Non-observed T Economy and How It Is Captured or Estimated Through Modelling Estimates of the underground or shadow economy have been attempted since the 1980s by Tanzi (1980), Feige (1989) and Frey and Werner (1984), among others, and more recently by Schneider et al. (2000). Such macromodel methods are based on monetary methods, global indicator methods or latent variable methods (OECD 2002). Examples of monetary methods are the transaction method, described by Feige, where the total stock of money (currency + demand deposits) multiplied by

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the velocity of circulation equals the total number of transactions paid by this amount of money multiplied by the price of transactions (M*V = P*T), or the cash/ deposit ratio method (which is assumed to be affected by the changes in taxation and other government regulations), or also the cash demand method (Tanzi) which assumes that other factors affect the demand for cash money. An example of the global indicator method is the approach by electricity consumption as a physical indicator of the overall economic activity (Kaufman and Kaliberda 1996), or even by the light data (Harati 2014). Finally the latent variable method, recently popularised by Schneider et  al. (2000, 2005, 2010), draws on a wide range of explanatory variables applied to more than 160 countries. The shadow economy is defined as including all market-based legal production of goods and services that are deliberately concealed from public authorities for any of the following reasons: 1 . To avoid payment of income, value added or other taxes 2. To avoid payment of social security contributions 3. To avoid having to meet certain legal labour market standards, such as minimum wages, maximum working hours, safety standards, etc. 4. To avoid complying with certain administrative procedures, such as completing statistical questionnaires or other administrative forms The empirical method is based on the statistical theory of unobserved variables, which considers multiple causes and indicators of the phenomenon to be measured, i.e. it explicitly considers multiple causes leading to the existence and growth of the shadow economy, as well as the multiple effects of the shadow economy over time. A Multiple Indicators Multiple Causes (MIMIC) model is used, where the shadow economy is the unobserved variable and is analysed with respect to its relationship to the observed variables using the covariance matrix of the latter. For this purpose, the unobserved variable is first linked to the observed indicator variables in a factor analytical model, (measurement model). Second, the relationships between the unobserved variable and the observed explanatory (causal) variables are specified through a structural model. The MIMIC model is the simultaneous specification of a factor model and a structural model (Schneider et al. 2010). The model is based on several indicators defined as causal variables and indicator variables (some of them can be moved from one category to the other): Causal variables: –– Business freedom (time to open a business, financial costs to start a business, minimum capital stock to start a business and costs for obtaining a licence) –– Economic freedom (state, corruption) –– Fiscal freedom (fiscal burden) –– Economic activity rate –– GDP per capita PPP –– Size of government (public expenditure in % of GDP) –– Share of direct taxes –– Quality of regulation (price control, weakness of bank control) –– Government effectiveness (perception of the quality of public services)

Surveys and Data Collection in a Historical Perspective

23

Indicator variables: –– –– –– –– –– –– –– ––

GDP growth rate Unemployment rate Inflation rate Currency (M0/M1) Openness: share of foreign trade in GDP Population aged 15–64 Total population Total active population

Despite their ability to providing estimates of the shadow economy for a lot of countries and across long periods of time, the macromodel methods are of little use in national accounts because they are unable to disaggregate the estimates across the various industries and they cannot be matched with the actual part of the shadow economy which is already included in the current GDP estimates by the very nature of the methods for compiling national accounts. Furthermore, these models require an estimate for a base year or for a country of reference and therefore cannot avoid the reference to the previous definitions and methods of data collection.

Surveys and Data Collection in a Historical Perspective The main sources of data are the most recent national labour force surveys and/or the mixed (household-establishment) surveys. However, many of the published reports are not always available, and where they are available, they may not contain the required classifications and tabulations; in some countries, the reference to the concepts of informal employment or informal sector is not even mentioned. In these cases, the main source of data has been the ILO questionnaires sent during the year 2011 by the Bureau of Statistics of the ILO to all statistical offices of the member countries (developing countries and transition countries), requesting from the national offices to fill detailed tables on statistics on employment in the informal sector and informal employment, with a special table on metadata allowing the knowledge of coverage of surveys and definitions of concepts. The detailed sources and specificities of definitions according to national circumstances can be found in Charmes (2011).

Indirect Methods Historically, the first methods used for estimating the size and contribution of the informal economy were indirect. In terms of employment, a residual balance can be obtained through the comparison of total employment by economic activities (given by a population census or a labour force survey or any other household survey capturing employment) and registered employment (given by official registers and large and medium establishment surveys). This tool is currently used by national

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accountants who systematically build labour input matrices (LIM) based on these principles, the idea being to be able to impute employment to production. The availability of ad hoc informal sector surveys in the more recent periods has allowed a refinement of the labour input matrix, nonregistered employment being distributed between microenterprises, home-based workers and other residual workers (not declared by formal firms). The ISTAT conceived, in 1987, a comparative method of sources resulting in the measurement of multiple jobs. Such a method was actually already used for indirect measurement of the informal sector in developing countries and led to the so-called residual method. But the Italians improved it, and since then, it is known as the “Italian” measurement method of labour input for achieving the exhaustiveness of GDP (ISTAT 1993, 1999; Calzaroni 2000, Calzaroni et  al. 2000; OECD 2002). Aiming at comparing employment data from the household side (supply side: number of persons employed) with employment data from the enterprise and administration side (demand side: number of jobs), the method first attempts to harmonise the data by adjusting for: –– Reference period: household data are dated during the year, for instance, in the population census, or they are an average for the year, for instance, in a labour force survey based on a continuous rotating sample, while enterprise data are dated from 1st of January or 31st of December and refer to permanent workers. –– Territorial coverage: the labour force survey covers the households within the geographical boundaries, and some household members may be commuting workers to neighbouring countries; the enterprise surveys cover the workers in the establishments within the country, but some workers may be commuting foreigners. –– Conceptual discrepancies: for instance, the classification according to industrial sectors is more reliable on the demand side than on the supply side. Comparisons of the number of jobs are made at a detailed level of industrial sectors, regions and statuses in employment. Three cases are distinguished: –– Regulars, for which the number of employed persons equals the number of jobs –– Full-time irregulars, for which the number of employed persons exceeds the number of jobs –– Regular with multiple jobs, for which the number of jobs exceeds the number of employed persons The re-evaluation of the Italian GDP was of nearly 16% for the year 1981, and multiple jobs accounted for more than 41% in this re-evaluation (Charmes 1991). Since then, the method was applied annually. On the production side, national accountants also use the technique of residual balance in the construction of the supply and use tables (SUTs) attempting to equate the supply of a commodity (production + imports) and its use (final and intermediate consumption, investment – if the commodity can be used for capital formation – and exports). Imbalances can be imputed to the production by informal firms or informal workers, with the support of information contained in the labour input matrix. LIMs and SUTs are current usual tools of national accountants.

Surveys and Data Collection in a Historical Perspective

25

Direct Survey Methods Whereas household labour force surveys have not always collected data for the measurement of the informal economy, establishment censuses and surveys have collected such data by their very conception: size and type of employment are basic questions, as well as legal status and type of bookkeeping (ILO 2013a). Economic censuses (based on registers) or door-to-door establishment censuses followed by sample surveys of establishments have for long accompanied national accountants in their works and still continue to do so. However, the method presents a disadvantage in that it fails capturing the activities that are not performed in premises: home-based, mobile, on construction sites, etc., even if in some cases (for instance, Egypt, a country with the longest experience of censuses), the census is designed to capture the activities taking place in homes. Another drawback of the method is that sample surveys based on lists of establishments produced by censuses must be implemented immediately after the census, because the lists are rapidly outdated. Further to the adoption of the definition of the informal sector in 1993, the socalled mixed surveys have been recommended. These two-stage surveys consist in a sample household survey (preferably a labour force survey, but not systematically) conducted in a first stage and used  – among other objectives  – to identify those households’ members who are operating informal activities, whether at home, or in the streets or in premises, as main or secondary activities. In a second stage that can be immediate, an establishment survey is conducted to capture these informal activities identified in the first stage. Consequently, the mixed surveys can provide data on the informal economy and its components in the first stage and detailed data on the informal sector in the second stage. Where mixed surveys were not conducted, labour force surveys started to collect information on the criteria defining the informal sector and later on informal employment with the advantage of providing data for assessing trends, provided that this type of survey is generally regular with short periodicities (annual, biannual, quarterly and even monthly). In countries with a strong basis of micro-small and medium enterprises (for instance, India, Kenya, Nigeria), “combined surveys” have been privileged. They consist in conducting in parallel (simultaneously) a sample household survey dedicated to the measurement of home-based and mobile activities (not establishment-based) and an area-sampling establishment/enterprise survey (with the establishment census used as a sampling frame, not for its list of establishments but for the spatial distribution of the establishments). The two decades 1970s and 1980s have been standing out for the priority given to the enterprise-based approach, and this is not so surprising if we consider that the building of national accounts and the reign of GDP made data collection on production and earnings a necessity. Economic censuses and even door-to-door censuses of establishments regularly followed by sample surveys of establishments were the rule. It is also the period when adapted and sophisticated designs of questionnaires were tested for the measurement of production, showing, for instance, in

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Tunisia (1976–1982) that direct declaration was often underestimated by half compared with other controlled methods. But even where extended to mobile (nonsedentary) vendors, the census approach of activities failed to capture the bulk of home-based workers or rather outworkers – that is all these workers who do not perform their activities in the premises of an enterprise and who are not enterprise-based. This is why from the very end of the 1980s and especially further to the 1993 International Conference of Labour Statisticians, which defined the concept of informal sector, a change of methodological paradigm intervened: the first mixed household-establishment surveys were conducted in Mali (1989) and in Mexico (1991), just before the 1993 ICLS recommendation proposed this type of survey as the most appropriate for capturing all the diversity of informal sector activities. Many countries conducted such surveys at national level (India, 1999–00; Tanzania, 1991; South Africa, 2002; Cameroon, 2005; Morocco, 2007; among others) or at capital city or urban levels (the series of 1-2-3 surveys in the 8 francophone countries of West Africa as well as in Cameroon and Madagascar) during the 1990s and the early 2000s. Asia followed in the second half of the 2000s (with mixed surveys in Bangladesh, the Philippines and Indonesia and also Cambodia, Mongolia and Armenia). The decades of the 1990s and the 2000s have thus been the decades of mixed surveys. At the same time, efforts started to include adapted questions or even short sections in the questionnaires of regular household surveys (labour force surveys or living conditions surveys) in Latin America and in Asia (Pakistan, Thailand), while the LSMS (Living Standard Measurement Study) questionnaires (and the surveys of the same type, for instance the GLSS in Ghana), as well as the “integrated” or “priority” surveys on living conditions of households, introduced a section for capturing the activities of own-­ account and employers’ enterprises. With the 2002 International Labour Conference (ILO 2002a, b) and the 2003 ICLS (ILO 2003), the pendulum comes back to emphasise the individual-based definitions. Efforts are made in order to capture information on the type of contracts and social protection for the paid employees and the benefit of some kind of social protection for all the workers and more generally for the whole population through household surveys and even population censuses. To sum up, one can say that the first two decades (1970s and 1980s) were those of the implementation of censuses and surveys, a concern that still goes on for national accounts purposes. This period allowed reaching a better knowledge of the upper tier of the informal sector (the micro and small enterprises or MSEs). The following decade (1990s) until the beginning of the 2000s has been that of mixed surveys, achieving the requirement of accumulating knowledge on the characteristics of the various components of the informal sector including the lower tiers, for policy purposes, especially employment creation. Finally, the 2000s saw the rise of the household surveys as the main vehicle of data collection on informality, firstly because they had been conveniently selected to be the first stage of the mixed surveys, secondly because they have often become regular – if not permanent (annual or even quarterly) – and thirdly because they can accommodate a special section or module to informality in its broad sense (informal employment and informal sector).

Sources of Data

27

It seems that the 2010s will know the repetition of mixed surveys at national level, especially in francophone Africa (Madagascar, Niger, Cameroon, DR Congo and a current ongoing programme of mixed surveys in the eight countries of the Western Africa Economic and Monetary Union (WAEMU)), whereas Anglophone Africa seems to privilege combined surveys (Kenya, Ghana, Nigeria).

Sources of Data Today estimates of informal employment and informal sector employment exist in many countries, sometimes for long periods. But systematic and comprehensive comparisons worldwide remain difficult for at least two reasons: (1) firstly harmonisation of concepts at international level is far from being reached; (2) secondly – and especially – the two concepts of informal sector and informal employment are neither mutually exclusive (and as such not additive) nor the latter totally inclusive of the former; informal employment does not include the informal sector in totality. This is why statistics of informal employment and informal sector employment are generally presented separately. We deliberately opt for a definition of employment in the informal economy as comprising employment in the informal sector and informal employment outside the informal sector (i.e. the unprotected workers in the formal sector and the domestic workers in the households, not to mention the persons working in the production of goods for own final use by the households). Macro-economic pictures of the informal economy, as a share of labour force or production (GDP), have for long been estimated by economists and statisticians and used for policy purposes. Indirect estimation requires detailed national data that are rarely accessible except on site. Since the adoption of international definitions, national surveys have been carried out in many countries. The main sources of data on informal employment and informal sector employment are the ad hoc national surveys conducted by national statistical offices and available on their official websites. Estimates exist at national level since the late 1970s to early 1980s, but it was in 1990 that Charmes presented a first tentative international comparison at world level (for 18 countries) in the OECD “Informal sector revisited” (Turnham, Salomé and Swartz 1990). This first work was updated in 2002 for the ILO-WIEGO Women and Men in the Informal Economy (for 25 countries) prepared for consideration by the 90th International Labour Conference (ILO 2002b; ILO-WIEGO 2002), in 2008 for the OECD publication “Is Informal normal?” (Jütting and de Laiglesia 2009). Tables presented in this book have been prepared for the updating 2012 (47 countries) of the ILO-WIEGO publication (ILO 2013b; ILO-WIEGO 2013) and updated since then. They are based on the author’s database (containing 85 countries at the date of this publication) built on: –– A systematic compilation of data and surveys available on the websites of national statistical offices

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–– A compilation of the ILO database on informal employment on http://www.ilo. org/ilostat/ Box 2.5 explains the differences between the three databases, and Table 2.1 in the Annex of this chapter lists the countries included in each of these three major databases. Regarding data on contribution to GDP, they are based on information provided by national accounts, specifically on the household institutional sector. Many countries provide details for value added by institutional sector and by economic activity. At least total value added by households (not disaggregated by economic activity) is available for an even greater number of countries. Again, data are accessible on the official websites of national statistical offices, and an exhaustive compilation (United Nations Statistics Division 2004, 2015, 2017) is regularly

Box 2.5: Variety of Sources, Complementary of Findings Several compilations of employment in the informal economy have been made available recently (see, for instance, ILO 2018; OECD 2018). Statistics on employment in the informal economy presented in this book may differ from these recent compilations for various reasons (objectives, methods of extrapolation, coverage, sources, definitions). Objectives: One of the objectives of the ILO-OECD compilations is to reach a global estimate of informal employment at world and regional levels: Two billion of the world’s employed population aged 15 and over would work informally (61.2% of total employment and 50.5% if agriculture is excluded) (ILO 2018). Method of extrapolation: These global results are obtained by extrapolating the figures obtained for the set of countries gathered, to all countries by region. In this book, we are not pursuing such an objective, and we prefer to work on unweighted averages by region and subregion to obtain profiles by region rather than global estimates. Another objective of the ILO/OECD publications has been to work from microdata in order to be able to conduct crossanalyses of informal employment and poverty, for example. This can lead to macro-­estimates of the informal economy that do not fit with the results already published. It is true however that the use of microdata has made possible the cross-classification of many variables with the estimates of informal employment. Coverage: The second edition of the ILO-WIEGO compilation in 2013 did not go so far as to extend the definitions of the informal economy to developed countries of Europe, Northern America, Japan and Australia. A chapter was devoted to non-standard employment (temporary employment, part-time employment, own-account self-employment), but those figures were not (continued)

Sources of Data

Box 2.5 (continued) merged with the figures compiled for developing countries and transition economies. One of the reasons for that was that the application of the criteria of the definitions is not possible because the criteria were not coined for these economies, the legal and institutional framework is totally different and surveys do not collect such criteria. Sources: The third update of Women and Men in the Informal Economy (ILO 2018) has expanded its sources of data to non-official surveys such as the Labour Market Panel Surveys (LMPS) in the MENA countries or to competing national surveys (such as Living Conditions Surveys or LSMS, preferred to Labour Force Surveys or mixed surveys on the informal sector). Whereas the former have the advantage of facilitating access to microdata, they can enter in contradiction with the latter (for instance, in Morocco or Tunisia), and the quality of data collection is questionable: non-response rates may be high in such heavy, and overburdening surveys and replacements for second rounds (in the case of panel surveys) may not fit with best statistical practices. In this book, these types of surveys have only been used in the absence of other national surveys. A second reason why our database is not strictly comparable is that we have considered that the concepts of informality as internationally defined are not strictly applicable in developed countries. The 117 countries covered by the third edition of the ILO statistical update include 22 developed countries as well as 11 countries also covered by the EU statistics on income and living conditions (EU-SILC) of 2012 (it must be noted that EU-SILC is an ex post harmonisation of data rather than a common and unique survey applied to this huge set of countries). Our database of 99 countries only includes developing and transition countries. Table  2.1 in Annex of this chapter provides the list of countries in the three databases: Own database for this book, ILOSTAT and the 2018 ILO compilation on women and men in the informal economy. Definitions: The 2018 ILO publication presents an algorithm merging the two definitions of informal sector and informal employment. Though very detailed and precise, this algorithm transforms the original definitions that wanted to remain “umbrella definitions” with flexibilities and alternatives (x or y depending on availability or national context) into strict characterisation (x and y). It thus pretends reaching full harmonisation between countries and surveys, but it casts doubts on the real application of such a detailed definition to current surveys. Also the algorithm allows both definitions of informal sector and informal employment to be strictly compatible, the latter being totally inclusive of the former.

29

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published by the UN Department for Economic and Social Affairs, Statistics Division: https://unstats.un.org/unsd/nationalaccount/pubsDB.asp?pType=3.

Conclusion In conclusion it can be said that the definitions of the concepts of informality have progressed since nearly half a century: they now shape data collection in most developing and transition countries in the world so that their magnitude, trends, structures and determinants can be more easily assessed in a comparative perspective across countries and regions as well as over time. Though some scholars and institutions continue to ignore these progresses and throw confusion with their own ad hoc definitions or the assimilation of the concept to the shadow economy as measured through modelling for the sake of convenience, it is important to recognise and highlight the clarifications achieved by international definitions and their convergence towards an extended notion of work and a future notion of productive activities (a question that will be treated in Part III of this book). Employment in the informal economy is consequently comprised of employment in the microbusinesses of the informal sector, unprotected employment in the formal sector and unprotected employment in the households (domestic workers, as well as producers of goods for own final use by the households), and all these components of the labour force have their counterparts in the national accounts.

Annex Table 2.1  List of countries covered in three databases and sources of data for the 3rd edition of the ILO Women and Men in the Informal Economy

Annex

31

Table 2.1 (continued)

(continued)

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Table 2.1 (continued)

(continued)

Annex Table 2.1 (continued)

In red: countries in own data base and not in ILO 3rd edition In green: countries in ILO 3rd edition and not in own database In yellow: countries in both ILO and own database but with different sources of survey

33

34

2  A Brief History of 50 Years of Conceptualisation and Measurement of the Informal…

References Blades, D. (1975). Non-monetary (Subsistence) activities in the national accounts of developing countries. Paris: OECD. Boeke, J. H. (1953). Economics and economic policy of dual societies as exemplified by Indonesia. New York: Institute of Pacific Relations. Bromley, R., & Gerry, C. (Eds.). (1979). Casual work and poverty in third world cities. Chichester: Wiley. Calzaroni, M. (2000). L’occupazione come strumento per la stima esaustiva del PIL e la misura del sommerso. Seminario “La Nuova Contabilita Nazionale”, 12–13 June 2000, ISTAT, Roma, 70p. Calzaroni, M., Pascarella, C., & Pisani, S. (2000). Il sommerso. Aspetti metodologici e quantificazioni per una estima esaustiva dell’input di lavoro e del PIL [The underground. Methodological aspects and quantification of an exhaustive estimation of labour input and GDP]. Seminar ‘La Nuova Contabilita Nazionale, 12–13 January, ISTAT, Roma. Castells, M., & Portes, A. (1989). World underneath: The origins, dynamics and effects of the informal economy. In A. Portes, M. Castells, & A. Benton, L. (Eds.), The informal economy. Studies in advanced and less developed countries (327, pp. 11–37). Baltimore: Johns Hopkins University Press. Charmes, J.  (1989). Trente cinq ans de comptabilité nationale du secteur informel au Burkina Faso (1954–89). Leçons d’une expérience et perspectives d’amélioration [35 years of national accounts of the informal sector in Burkina Faso (1954–89). Lessons of an experience and perspectives of improvement] (108p). Ouagadougou: Ministère du Plan et de la Coopération, PNUD-DTCD. Charmes, J. (1990). A critical review of concepts, definitions and research on informal sector. In Turnham, D. Salome, B. & Schwarz, A. (Eds.), The informal sector revisited (pp. 11–51). Paris: OECD Development Centre Seminars. Charmes, J. (1991). Pluri-activité des salariés et pluri-activité des agriculteurs. Deux exemples de mesure et d’estimation: Italie et Burkina-Faso. In S. Montagné-Villette (Ed.), Espaces et travail clandestins (pp. 25–32). Paris: Masson. 157 p. Charmes, J.  (2011). A worldwide overview of trends and characteristics of employment in the informal economy and informal sector in a gender perspective. Contribution to the update of the ILO-WIEGO Women and Men in the Informal Economy. Charmes, J.  (2012). The informal economy worldwide: Trends and characteristics. Margin-The Journal of Applied Economic Research, 6(2), 103–132. Charmes, J. (2013). Informal sector, informal employment and national accounts. African Group on Employment and Informal Sector (AGEIS), Yaoundé, Cameroon, April 29–May 3, 2013. Chen, M. A. (2012). The informal economy: Definitions, theories and policies. WIEGO Working Paper N°1. Cambridge, MA. De Soto, H. (1986). El otro sendero. Lima: El Baranco. Feige, E. (1989). The underground economies: Tax evasion and information distortion. Cambridge/ New York: Cambridge University Press. Fields, G. (1990). Labour market modelling and the urban informal sector: Theory and evidence. In D. Turnham, S. Bernard, & S. Antoine (Eds.), The informal sector revisited (pp. 49–69). Paris: OECD Development Centre Seminars. Frey, B. S., & Pommerehne, W. W. (1984). The hidden economy: State and prospect for measurement. The Review of Income and Wealth, 30(1), 1–23. Geertz, C. (1963). Peddlers and princes: Social development and economic change in two Indonesian towns. Chicago/London: Comparative Studies of New Nations, University of Chicago Press. Geertz, C. (1978, May). The Bazaar economy. The American Economic Review, 68 (2) (Suppl.: Papers and proceedings of the ninetieth annual meeting ), 28–32.

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Gerry, C. (1979). Petty production and capitalist production in Dakar: The crisis of the self-­ employed. In R. Bromley (Ed.), The urban informal sector: Critical perspectives on employment and housing policies (Vol. 6, N°9/10). Pergamon, Oxford, published as a special issue of World Development. Guha-Khasnobis, B., Kanbur, R., & Ostrom, E. (Eds). (2006). Linking the formal and informal economy: Concepts and policies. Unu-Wider Studies in Development Economics. Harati, R. (2014). Four essays on informality in the MENA region and a new measure of the shadow economy using light data. PhD dissertation, PSE, University of Paris1. Hart, K. (1971). Informal income opportunities and urban employment in Ghana. The Journal of Modern African Studies, II, 61–89. Hirschman, A. O. (1970). Exit, voice and loyalty. Responses to decline in firms, organizations and states. Cambridge, MA: Harvard University Press. Hugon, P. (1980). Dualisme sectoriel ou soumission des formes de production au capital. Peut-on dépasser le débat? Revue Tiers Monde, 21(82), 235–259. ILO. (1972). Employment, incomes and equality. A strategy for increasing productive employment in Kenya. Geneva: ILO. ILO. (1993a). Statistics of employment in the informal sector. Report for the 15th international conference of labour statisticians – Geneva, 19–28 January 1993. ILO. (1993b). Report of the conference. Report of the 15th international conference of labour statisticians – Geneva, 19–28 January 1993. ILO. (2002a). Decent work and the informal economy, report VI, 90th session of the international labour conference, Geneva. ILO. (2002b). Women and men in the informal economy. A statistical picture. Geneva: ILO, Employment sector. ILO. (2003). Report 1, General report, 17th international conference of labour statisticians, Geneva, 24 November – 3 December 2003. ILO. (2011). Statistical update on employment in the informal economy. Geneva: ILO Department of Statistics. ILO. (2013a). Measuring informality: A statistical manual on the informal sector and informal employment. Geneva: ILO Department of Statistics. ILO. (2013b). Women and men in the informal economy, A statistical picture (2nd ed.). Geneva: ILO, Employment Sector. ILO. (2018).Women and men in the informal economy. A statistical picture (3rd ed., 156p). Geneva: ILO, Employment sector. ILO-WIEGO. (2002). Women and men in the informal economy. A statistical picture (64p). Geneva: International Labour Office. ILO-WIEGO. (2013). Women and men in the informal economy. A statistical picture (2nd ed., 205p). Geneva: International Labour Office. ISTAT. (1993). The underground economy in Italian economic accounts (Annali di Statistica, series X, Vol. 2). Roma: ISTAT. ISTAT. (1999). L’occupazione non regolare nelle nuovo stime di contabilita nazionale, Anni 1992– 1997. Roma: ISTAT. Jütting, J. P., & de Laiglesia, J. R. (Eds.). (2009). Is informal normal? Towards more and better jobs in developing countries. Paris: An OECD Development Centre Perspective. Kaufmann, D., & Kaliberda, A. (1996). Integrating the unofficial economy into the dynamics of post-socialist economies: A framework of analysis and evidence. Policy Research Working Paper. N° WPS 1691. Washington, DC: World Bank. King, K. (1977). The African Artisan (p. 226). London: Heinemann. King, K. (1996). Jua Kali Kenya, change and development in an informal economy, 1970–95. London: James Currey, Eastern African Studies. Lebrun, O., & Gerry, C. (1975). Petty producers and capitalism. Review of African Political Economy, 3, 20–32.

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Lewis, W.  A. (1954, May). Economic development with unlimited supplies of labour. The Manchester School, 22(2), 139–191. Maldonado, C. (1987). Petits producteurs urbains d’Afrique francophone. Genève: BIT. Nihan, G., et  al. (1978). Le secteur non structuré “moderne” de Nouakchott, Lomé, Bamako, Yaoundé, Kigali. World Employment Programme, Various documents, BIT, Genève. OECD. (1965). Methods of national accounting for Kenya. Paris: Nairobi. OECD. (2002). Measuring the non-observed economy. A handbook. Paris: OECD-IMF-ILO-CIS Stat. OECD. (2018). Tackling vulnerability in the informal economy. Insights from the analysis of micro and macro data. Paris: OECD Development Centre. Page, J., & Steel, W. (1986). Le développement des petites entreprises questions économiques tirées du context africain. Washington, DC: World Bank. Perry, G., Maloney, W., et al. (2007). Informality, exit and exclusion. World Bank Latin American and Caribbean Studies, Washington, The World Bank. Schneider, F. (2005). Shadow economies of 145 countries all over the world: Estimation results over the period 1999 to 2003. Johannes Kepler University of Linz, Austria. Schneider, F., & Enste, D. (2000, March). Shadow economies: Size, causes, and consequences. Journal of Economic Literature, XXXVIII, 77–114. Schneider, F., Buehn, A., & Montenegro, C. (2010). Shadow economies all over the world: New estimates for 162 countries from 1999 to 2007. Background paper for In from the shadow: Integrating Europe’s informal labor, a World Bank regional report on the informal sector in Central, Southern Europe and the Baltic countries. Sethuraman, S. V. (1981). The urban informal sector in developing countries. Geneva: ILO. SNA. (1993). System of national accounts. New York: Commission of the European Communities, IMF, OECD, UN, WB. SNA. (2008). System of national accounts (662p). New  York: Commission of the European Communities, IMF, OECD, UN, WB. Tanzi, V. (1980). The underground economy in the United States: Estimates and implications. Banca Nazionale di Lavoro Quarterly Review, 135, 427–453. Tokman, V. (1987). El sector informal: quince anos despues. Santiago: PREALC. Turnham, D., Salomé, B., & Schwartz, A. (1990). The informal sector revisited. Paris: OECD Development Centre Seminars. United Nations Statistics Division. (2004). National accounts statistics: Main aggregates and detailed tables: 2002–2003. New York: Department of Economic and Social Affairs, Statistics Division. United Nations Statistics Division. (2015). National accounts statistics: Main aggregates and detailed tables: 2014. Part 1, 2, 3 and 4, New  York: Department of Economic and Social Affairs, Statistics Division. United Nations Statistics Division. (2017). National accounts statistics: Main aggregates and detailed tables: 2016. Part 1, 2, 3 and 4, New  York: Department of Economic and Social Affairs, Statistics Division.

Chapter 3

Trends and Characteristics of the Informal Economy and Its Components

Introduction While the criteria for the measurement of informal sector and informal employment were introduced in the national surveys, policymakers sometimes showed some reluctance to use these terms: as already mentioned, Kenya preferred referring to “Jua Kali” and Tunisia designed policies addressing crafts and small businesses. For many countries and governments, the informal economy remains assimilated to the illegal economy with its negative image. However, year after year, indicators on informality in its more positive sense have been compiled, the size and significance of which depend on the countries’ political and social structures, national and local economic policies and governments’ willingness to enforcing their own fiscal or labour legislation. Despite such difficulties, macro-economic pictures of the informal economy, as a share of labour force or production (GDP), are available, for many countries and since quite a long time if we include indirect measures for the initial estimates or for some countries.

Trends in Employment Depending on the availability by region and by period, trends in employment in the informal economy may be presented by different periods (decades, 5-year, 2-year or year) in the following tables and charts. Table 3.1 hereafter attempts to assess the trends of employment in the informal economy by 5-year periods over the past 4 decades and by region and subregion. The interpretation of this table (as well as the following tables) requires four preliminary remarks. Firstly, the indicator is based on nonagricultural employment, whereas the definitions of the informal sector, informal employment and the informal economy are © Springer Nature Switzerland AG 2019 J. Charmes, Dimensions of Resilience in Developing Countries, Demographic Transformation and Socio-Economic Development 10, https://doi.org/10.1007/978-3-030-04076-5_3

37

38

3  Trends and Characteristics of the Informal Economy and Its Components

Table 3.1  Employment in the informal economy in % of nonagricultural employment by 5-year periods in various regions and subregions Regions Northern Africa Sub-Saharan Africa Western Africa Middle Africa Eastern Africa Southern Africa Latin America Southern and South Eastern Asia Western Asia Transition countries

1975– 1979 39.6 63

1980– 1984 67.3

1985– 1989 34.1 72.5

1990– 1994 76.0

1995– 1999 47.5 78.1

2000– 2004 47.3 70.0

52.9

54.2 69.9

2010– 2014 48.8 74.6

75.6 80.5

55.9 69.8

62.7 59.5 70.5

81.1 78.3 72.2 42.7 57.0 65.5

43.2 18.0

22.1

20.5

66.4

52.5 65.2

2005– 2009 53.0 72.9

Source: Charmes Jacques (2012) updated with new countries and more recent years Note: Figures in italics are based on a too small number of countries to be representative

inclusive of agricultural activities. There are two reasons why an indicator based on nonagricultural employment has been preferred: in countries where agriculture is predominant and occupies the bulk of the labour force (e.g. most sub-Saharan African, Southern and Eastern Asian countries), the share of employment in the informal economy including agriculture is often above 90%, and changes over time may not be visible because of the volume of the labour force but also because the importance of change may remain hidden behind the dramatic flows of ruralurban migrations. An indicator based on nonagricultural employment makes these changes more visible and its greater variability is a better tracer of change. Secondly, the table is based on estimates prepared according to various procedures, which have changed over time depending on the availability of sources and data. Therefore, it is far from being homogeneous in definitions and methods of compilation. Sources for this table and type of estimate used for a specific country and a specific year or period have been given in details in Charmes (2009). From the mid of the 1970s and until the end of the 1980s–early 1990s, the figures for the first three 5-year periods (in Northern Africa, sub-Saharan Africa and Asia) are mainly resulting from an application of the residual method, which consists in comparing total employment (in population censuses or labour force surveys) and registered employment (in economic or establishment censuses or administrative records); censuses of establishments  – where they exist  – allow identifying the informal sector on the one hand and informal employment outside the informal sector on the other hand. From the beginning of the 1990s, the results mainly come from the first mixed surveys and focus on the informal sector, while in the 2000s the labour force surveys become the main source of data and provide figures on informal employment and employment in the informal economy at large.

Trends in Employment

39

Thirdly, another limitation comes from the fact that it is not exactly the same set of countries for which estimates are available from one period to another: consequently the average can be non-significant except if there are at least a minimum number of countries which are present all over the periods. Fourthly, it must be kept in mind that the averages by region and subregion are not weighted: each country has the same weight in the average, whatever the size of its population. The reason for such a choice is that we want to highlight regional profiles rather than real estimates of the overall magnitude of the phenomenon in the region. This method avoids that the regional average is pulled by the most populated country (India for South Asia, for instance, or Nigeria for Western Africa). These are clarifications rather than limitations. Several observations and conclusions can be drawn from the data compiled for 85 countries. Until the end of the years 2000s, in all regions, the informal economy is on the rise: at 53% of nonagricultural employment in Northern Africa, 72.9% in sub-­ Saharan Africa, 59.5% in Latin America, 70.5% in Southern and South-Eastern Asia and 22.1% in transition countries. But since the beginning of the 2010s, a reversal in trend seems to be observed in all regions except in sub-Saharan Africa where the informal economy culminates at the highest level: 74.6% in 2010–2014, a trend due to the sharp increase in Western Africa (81.1%). Chart 3.1 below, based on the same sources and on 2-year periods since 2010, summarises and confirms the trends by region: employment in the informal economy is capping at more than 74% in sub-Saharan Africa, while increasing up to 27.4% in transition countries; in all other regions, the informal economy is on the decline (61.7% in Southern and South Eastern Asia, 52.8% in Latin America and 48.5% in Northern Africa). Sub-Saharan Africa culminated at 78.1% at the end of the 1990s and Latin America and Northern Africa at 59.5% and 53%, respectively, at the end of the 2000s. But the most striking evidence arising from Chart 3.1 is the peak that all regions experienced during the period 2005–2009: given the mode of construct of the database (the most recent year is given preference in the period), it means that employment in the informal economy has dramatically increased during and after the financial crisis (2007–2008) highlighting the contracyclical role of the informal economy. Northern Africa (Table 3.2), which is the region where estimates are the most numerous all over the 4 decades, can be taken as an illustration of the countercyclical behaviour of employment in the informal economy: it increases when the rate of economic growth is decelerating and contracts when the rate of growth increases. Tunisia is a good example: starting from a relatively high level (38.4% of total nonagricultural employment), employment in the informal economy drops (down to 35%) in the mid of the 1980s when the implementation of structural adjustment programmes induces its rapid growth until the end of the 1980s (39.3%) and even until the end of the 1990s (47.1%). Then the informal economy drops dramatically (35%) in the mid of the 2000s with the rapid growth of the Tunisian economy and starts growing again until 2007 (36.8%). Surprisingly it drops down again to 33.9% in 2012, after the revolution of 2011, thanks to the hiring of the unemployed in the civil service by the new authorities, a policy that cannot be held long, and after this

40

3  Trends and Characteristics of the Informal Economy and Its Components 78.1

Share of informal economy in non-agricultural employment

76 Sub-Saharan Africa 67.3 63.0

72.5

69.9 65.2

Southern and South Eastern Asia 54.2 52.5 53.0 Latin America 47.5 39.6 Northern Africa 36.9 34.1

69.8 67.6

72.3 70.5

69.6 64.6

59.5 55.9

74.8 74.5

53

56.7 50.8

47.3

61.7 53.5 52.8 48.5

40.8

27.4

Transition countries 22.1 18.0

23.3 18.0

Chart 3.1  Trends in employment in the informal economy by 5-year period (until 2009) and by 2-year period (since 2010) and by region Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years. Some figures have been interpolated between 1995–1999 and 2010–2011 in Southern and South Eastern Asia, for example Notes: 5-year period 2010–2014 of Table 3.1 has been replaced by three 2-year periods in this chart The thin black curve is a 2-year mobile average. The periodicity is shorter for the last period compared with Table 3.1

short remission, the informal economy seems to initiate a lasting increase. In Algeria, getting out of an administered and centralised economy, the informal economy has continuously grown up from 21.8% in the mid of the 1970s up to 45.6% at the end of the 2000s, with a small and short decrease (41.3%) at the beginning of the 2000s. After a new increase up to 45.6% at the end of the years 2000s, the authorities launched strong policies of employment creation for the youth that explain the long and lasting drop observed since then (37.3% in 2013). Morocco is also characterised by a continuous increase in the informal economy, from 56.9% at the beginning of the 1980s up to 78.5% at the end of the years 2000s, initiating a decrease at the turn of 2010 (69.2% in 2013). Egypt is also experiencing countercyclical behaviours in the growth of the informal economy since the end of the 1990s. In average for the region, the most recent period is characterised by a huge increase of employment in the informal economy, growing from 47.3% at the beginning of the 2000s up to 53.0% at the end of the decade. Then starting again to decrease since the beginning of the 2010s.

38.4 58.7

1975– 1979 39.6 21.8

56.9 35.0

1980– 1984

39.3 37.3

1985– 1989 34.1 25.6

1990– 1994

1995– 1999 47.5 42.7 44.8 47.1 55.2

2000– 2004 47.3 41.3 67.1 35.0 45.9

Source: Charmes Jacques (2012) updated with new countries and more recent years Note: Non-weighted averages. Figures in italics refer to employment in the informal sector only

Regions/countries/ years Northern Africa Algeria Morocco Tunisia Egypt

2005– 2009 53.0 45.6 78.5 36.8 51.2

2010– 2014 48.8 37.3 69.2 38.8 49.6

2011 2012 48.2 40.7 37.7 70.9 71.5 33.9 49.6

2013 2014 2015 48.5 37.3 37.7 69.2 37.8 38.8 40.8 49.7

Table 3.2  Share of employment in the informal economy in total nonagricultural employment by 5-year period and by year since 2010 in Northern Africa

Trends in Employment 41

42

3  Trends and Characteristics of the Informal Economy and Its Components

Chart 3.2  Trends in employment in the informal economy by 5-year period (until 2009) and by year (since 2010) in Northern Africa Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: The thin black curve is a 2-year mobile average

Chart 3.2 illustrates the convergence in 1995–1999 of the somewhat erratic prior trends for the four countries of the region, then the strong divergence between Morocco and Tunisia until the peak of 2005–2009 (underestimated for Tunisia where the estimate dates from 2007) and the convergence since then. Table 3.3 groups sub-Saharan Africa countries by decades in order to have more observations for each period (22 countries for the years 2000s, 7 for the years 1980s and 11 for the years 1990s). The last decade is characterised by a numerous set of countries (22), but only 5 of them provided estimates for the previous periods, making it difficult to assess the trend for the region. Finally, 26 countries have collected data for the last 5-year period. The figures for the region give an image of a continuously growing informal economy (from more than 60% in the 1970s to more than 70% during the three following decades), until the years 2010s, which seem to be characterised by a sharp increase. Even if the share of employment in the informal economy at 74.6% is not representative for all countries of the region, if we compare the 16 countries for which data are available in the 2000s and for the most recent period, then the rate of employment in the informal economy has increased from 70.4% up to 75.1% between the two periods, or nearly 5 percentage points. In the most recent period, employment in the informal economy ranges from 41.4% in South Africa (a country with a large base of wage workers) to 96% in

Trends in Employment

43

Table 3.3  Share of employment in the informal economy in total nonagricultural employment by decade in sub-Saharan Africa (34 countries) Regions/countries/years 1975–1979 Sub-Saharan Africa 63.0 Benin Burkina Faso Burundi Cameroon Chad Congo Rep. Cote d’Ivoire Dem. Rep. Congo (ex Zaire) Gambia Ghana Guinea Kenya Lesotho Liberia Madagascar Malawi Mali 63.1 Mauritania Mauritius Mozambique Namibia Niger 62.9 Nigeria Rwanda Senegal Seychelles Sierra Leone South Africa Sudan Tanzania Togo Uganda Zambia Zimbabwe

1980–1989 70.0 70.0

1990–1999 71.7 92.9 77.0

95.2

59.6

64.4 61.4

78.6 80.0

86.7 71.6

94.1

73.5

42.9

2000–2009 2010–2014 72.5 74.6 96.3 96.2 90.5 90.7 83.1* 79.5 67.3 90.7 85.8 73.8 81.9 77.0 71.7 83.7 65.3 77.7 76.8 70.7 56.4 73.7 82.7 56.9 87.2 43.8 88.8 78.6

60.5 90.7 75.4 81.3 96.0

43.9 88.5 71.5 56.2

76.0

39.1

32.7

57.7

76.7

58.3

73.5 76.3 51.6

55.8 79.4 41.4 31.9 73.1* 86.3 93.5 76.4 85.6

Source: Charmes Jacques (2012) updated with new countries and more recent years Note: Non-weighted averages. Figures in italics refer to employment in the informal sector only. Figures in italics and with * refer to employment in the informal sector and secondary activities. Figures in bold and italics mean that the average is based on a too small set of countries to be representative

44

3  Trends and Characteristics of the Informal Economy and Its Components

Chart 3.3  Trends in employment in the informal economy by 5-year period (until 2009) and by 2-year period (since 2010) in sub-Saharan Africa Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: The thin black curve is a 2-year mobile average

Benin and in Mauritania. In three more countries, the share of employment in the informal economy is higher than 90% (in Uganda, Chad and Madagascar). Generally, the share of employment in the informal economy seems higher in Western and Middle Africa than in Eastern and Southern Africa (Table 3.1). Chart 3.3 shows the trends for sub-Saharan Africa and for a set of countries in the region by 5-year period until 2009 and by 2-year period since then: Benin, Chad, Mauritania, Niger and Burkina Faso are capping at the top with shares of informal economy higher than 90% or just below, recently joined by Uganda, Ghana and Côte d’Ivoire  – a country where the regular and dramatic rise of the informal economy has preceded, accompanied and followed the political crisis and turmoil as well as the recent return to growth. At the bottom, South Africa, Namibia and Zimbabwe are characterised by shares of informal economy below or just above 50%. In Latin America, employment in the informal economy, which has peaked at more than 59% in the late 2000s, drops from 59.5% at the end of the years 2000s down to 52.8% in 2015. Shares in nonagricultural employment range from 31.1% in Ecuador, 33.2% in Uruguay and 33% in Brazil to 66.8% in Guatemala, 73.3% in Peru and 74.9% in Honduras (Table 3.4). Chart 3.4 shows the trends by 5-year period until 2009 and by year since 2010. Haiti is at the top with a share of informal economy higher than 90% (but no recent data is available for this country). Interestingly, the Dominican Republic has reached a comparable peak in 2011 (with more than 83%) and remained at this level until 2014, as a probable consequence of the earthquake that devastated its neighbour

Trends in Employment

45

Table 3.4  Share of employment in the informal economy in total nonagricultural employment by 5-year period and by year since 2010 in Latin America (19 countries) Regions/ countries/years Latin America Argentina Bolivia Brazil Chile Colombia Costa Rica Dominican Republic Ecuador El Salvador Guatemala Haiti Honduras Mexico Nicaragua Panama Paraguay Peru Uruguay Venezuela

1990– 1994 52.5 47.5 56.9 60.0

1995– 1999 54.2 53.3 63.5 60.0 35.8 38.4 44.3 47.6 53.5 56.6

2000– 2004 55.9 60.8 51.1

2005– 2009 59.5 50.0 75.1 42.2 57.4 48.2 48.8

74.9

53.5 68.2

56.1

55.5

92.6 58.2 59.4 37.6 65.5

38.8

46.9

50.1 49.4 67.9 43.4 49.4

91.4 75.2 54.3 69.4 44.0 70.7 71.3 43.8 48.1

2010 2011 2012 2013 57.5 56.7 55.4 53.5 48.5 47.7 47.8 47.4 56.6 54.9 49 38.4 38.1 36.9 24.9 59.6 64.5 64.6 63.7 33.8 33.6 32.2 30.7 47.9 83.8 83.1 80.7

2014 2015 57.7 52.8 46.8 55.3 55.9 33 55.5 42.9 43.2 76.1 49.5

56.4 52.0 49.8 49.3 31.1 65.8 65.7 66.1 65.4 63.2 75.1 74.6 76.8 74.4 64.5 66.8 76.4 54.2 75.0 42.8 70.3 70.3 37.7

70.7 72.8 73.4 74.9 54.2 54.6 53.9 53.4 69.9 39.3 39.5 40.4 45.2 65.8 66.5 64.5 69.1 67.5 67.1 68.8 73.3 35.5 34.1 33.2 38

Source: Charmes Jacques (2012) updated with new countries and more recent years Note: Non-weighted averages

Chart 3.4  Trends in employment in the informal economy by 5-year period (until 2009) and by year (since 2010) in Latin America Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: The thin black curve is a 2-year mobile average

46

3  Trends and Characteristics of the Informal Economy and Its Components

Table 3.5  Share of employment in the informal economy in total nonagricultural employment by 5-year period in Asia (20 countries) Regions/countries/years Southern and South Eastern Asia Bangladesh Cambodia India Indonesia Laos Malaysia Mongolia Nepal Pakistan Philippines Sri Lanka Thailand Timor-Leste Vietnam Western Asia Iran Lebanon Palestine Syria Turkey Yemen

1985– 1989 53.0

76.2 39.2

1990– 1994 65.2

73.7

1995– 1999 69.9

2000– 2004

83.4 77.9

2005– 2009 71.5a

2010– 2014 66.6

76.9

90.8 67 84.7

84.2 77.0

89.5

39.0

57.4

70.5

64.6 72.0

51.4

51.5

43.5

41.7 57.1

42.9 30.9

70.0

43.2 48.8 51.8 43.4 30.7 33.2 51.1

26.3 86.4 73.0 73.3/ 84.0b 62.1 41.1 62.0 68.5

57.2 31.4 30.6

24.5 73.6 49.3 49.1 42.8 52.2

51.5 27.2 75.1

Source: Charmes Jacques (2012) updated with new countries and more recent years Note: Non-weighted averages. Figures in italics refer to informal sector employment only a Without Mongolia. bincluding secondary activities

country Haiti. While Mexico experiences a flat trend around the average for the region since the end of the 2000s (similarly to Argentina at a lower level), other countries such as Colombia, Ecuador, Brazil or Uruguay know a downward trend over the past recent years. And in Honduras, Peru, Paraguay, Panama, Bolivia and Costa Rica, this same trend has recently turned upward. In Southern and South Eastern Asia (Table 3.5), employment in the informal economy that stabilised around 70% of nonagricultural employment in the mid2000s (if the average does not include Mongolia, a country that could be more appropriately classified among the transition economies) has shown a tendency to decline since then, with an average of 66.6% during the period 2010–2014, with shares ranging from 41.1% in Thailand to 84.2% in India and 86.4% in Nepal. It is however the reverse trend that stands out from weighted averages: the informal economy seems to be on the rise in such populous countries as India, Pakistan, Bangladesh and also Indonesia (with a share of 86% in 2016 – not included in the table).

Trends in Employment

47

Share of informal economy in non-agricultural employment

90

80

Bangladesh

Laos Nepal

Pakistan

Indonesia

India

India

Indonesia Pakistan

70

Philippines

Vietnam Pakistan

60

Timor Leste

Cambodia Southern and South Eastern Asia Myanmar

Indonesia

Thailand

50

Southern and South Eastern Asia

40

Pakistan Indonesia

Sri Lanka

Timor Leste Philippines

Sri Lanka Thailand

30 Mongolia

20

Mongolia Malaysia

10 1985-89

1990-94

1995-99

2000-04

2005-09

2010-11

2012-13

2014-15

Chart 3.5  Trends in employment in the informal economy by 5-year period (until 2009) and by 2-year period (since 2010) in Southern and South Eastern Asia Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: The thin black curve is a 2-year mobile average

Countries of Western Asia (which can be classified with Northern Africa in the Middle East-North Africa (MENA) region, as they are presenting many similar characteristics, in particular low female activity rates) are characterised by a moderate share of employment in the informal economy: their average share of employment in the informal economy is around 40–50% (43.2% in 2000–2004). Chart 3.5 highlights the trends by 5-year period until 2009 and by 2-year period since then for the Southern and South Eastern Asia region. It clearly shows that the regional declining trend that started at the end of the years 1990s has begun to reverse between 2012–2013 and 2014–2015 (a tendency that could have been even more visible if we had taken into account the share of informal economy in Indonesia for the year 2016). As regards Western Asia, it is not possible to assess the trends for the region, except for Palestine and Turkey where regular (yearly) estimates are made available. Lastly, transition countries (Table 3.6) are making their way out of their former administered-­centralised-­waged economies, and they see their share of employment in the informal economy (still often measured through the concept of informal sector as in Russia and Ukraine) increasing little by little from 18% at the beginning of the years 2000s up to 22.1% at the end of the decade, with maxima in Kyrgyzstan (59.2% for the informal sector), Moldova (41.8%) and Azerbaijan (41%) and min-

48

3  Trends and Characteristics of the Informal Economy and Its Components

Table 3.6  Share of employment in the informal economy in total nonagricultural employment by 5-year period in transition countries (12 countries) Regions/countries/years Transition countries Albania Armenia Azerbaijan Bulgaria Kyrgyzstan Macedonia Moldova Romania Russia Serbia Slovakia Ukraine

1995–1999

2000–2004 18

2005–2009 22.1 19.8 41

5.4

44.4

59.2

21.5 22.0 8.6

22.1

4.7 7.0

12.1 6.6 6.4 9.4

2010–2014 20.5 43.0 21.5 43 15.3 12.8 13.6 12.4 8.9 17.6

Source: Charmes Jacques (2012) updated with new countries and more recent years Note: Non-weighted averages. Figures in italics refer to informal sector employment only

ima in Ukraine and Russia (9.4% and 12.1%, respectively, for the informal sector) and Serbia and Slovakia (6.6% and 6.4%, respectively). The most recent 5-year period (2010–2014) shows a slight decline of the regional average (20.5%). However, except for Moldova, all countries have experienced a regular increase of their informal economy during this 5-year period. The details of yearly trends as shown on Chart 3.6 hereafter provide a better understanding of the situation. The regional average alternates peaks (in 2005–2009, as in most regions, 2011, 2013, 2015 lasting in 2016) and hollows. The trend for Ukraine is outstanding with the political crisis and the war in 2014 that are reflected in the dramatic increase of the share of informal economy from 17.6% in 2014 to 61.4% in 2015. Besides the regular increase of the informal economy in Serbia (at the bottom of the Chart) and in Azerbaijan (at the top) and the regular decline in Moldova, it can be noted that the informal economy is on the rise in Russia where it is measured by the informal sector employment. It is interesting to note however that there is an estimate of informal employment in Russia for the year 2014, at 35.4% (Russia Longitudinal Monitoring Survey of the Higher School of Economics). Except in this latter region, which is at its starting point, employment in the informal economy generally represents more than 50% of total nonagricultural employment in all developing regions (Chart 3.1 supra). With upward trends in sub-­Saharan Africa (and a tendency to stabilise) and in transition countries and slowly declining trends in Asia, Latin America and Northern Africa, it seems that there is a kind of

Trends in Employment

49

65 Ukraine Kyrgyzstan

Share of informal economy in non-agricultural employment

55 Transition countries

45

Albania A lbania

Azeerbaij Az aijaan Azerbaijan

Armenia

Azerbaijan Bulgaria

35

Albania

TTransition ranssition ran n ccountries ountries

Transition countries

25 Romania

Macedonia

Moldova

Moldova Armenia A Arm Ar rmenia

Armenia

Transition countries

15

Russia

Romania maania

Armenia

Russia

Russia Moldova M oldova

Macedonia

Serbia

Russia

5

Moldova Moldo Mol dovva

Ukraine

Bulgaria Bulga Bulg aria

Ukraine Uk U raine Ukraine

Russia

SSerbia erbia

Slovakia Sl Slovak l kia i

1995-99

2000-04

2005-09

2010

2011

2012

2013

2014

2015

2016

-5

Chart 3.6  Trends in employment in the informal economy by 5-year period (until 2009) and by year (since 2010) in transition countries Sources: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: The thin black curve is a 2-year mobile average

convergence between the various regions at world level. However, in all regions there are two categories of countries: with high or low share of informal economy and with increasing or declining trend. Chart 3.7 below shows the share of employment in the informal economy in the 92 countries for which data are ­available. Countries are ranked by decreasing share of employment in the informal economy. The regional averages may differ from those presented in the preceding paragraphs because they are based on the most recent year and therefore may include older estimates for countries where no recent data are available. Sub-­Saharan African countries concentrate on the left-hand side of Chart 3.7, whereas transition countries concentrate on the right-hand side. However, countries from all regions are scattered across all size categories.

0

20

40

60

80

100

120

Sub Saharan Africa 75,3 ASIA 61,2

Most recent year

La n America 53,2 MENA 49,1

Transi on countries 26,6

Chart 3.7  World estimates of employment in the informal economy as a share of nonagricultural employment (92 countries, most recent year) Source: Author’s calculations based on Charmes Jacques (2012) updated with new countries and more recent years Note: In yellow, Africa; in red, Asia; in green, Middle East-North Africa; in blue, Latin America; in white, transition countries

Share of informal economy in non-agricultural employmentemployment Benin 2011 Mauritania 2012 Ghana 2015 Uganda 2012 Hai 2007 Burkina Faso 2014 Chad 2011 Madagascar 2012 Cote d’Ivoire 2012 Laos 2008 Niger 2012 Mozambique 2009 Guinea 1998 Senegal 2011 Nepal 2008 Togo 2013 Indonesia 2016 Congo (Rep) 2008 Zimbabwe 2014 India 2012 Rwanda 2016 Gambia 2013 Pakistan 2015 Sierra Leone 2014 Kenya 1999 Burundi 2014 Malawi 2013 Zambia 2014 SUB-SAHARAN AFRICA Bangladesh 2012 Yemen 2014 Honduras 2014 Peru 2015 Mali 2015 Democra c. Rep. Congo… Nigeria 2013 Lesotho 2009 Nicaragua 2014 Morocco 2013 Paraguay 2014 Vietnam 2008 Cameroon 2014 Cambodia 2012 Guatemala 2015 Tanzania 2014 El Salvador 2014 Myanmar 2015 Ukraine 2015 ASIA (South-South East) Liberia 2010 Kyrgyzstan 2008 Pales ne 2016 Mauri us 2009 Bolivia 2015 Seychelles 2012 Botswana 2012 Mexico 2015 LATIN AMERICA Timor Leste 2012 Lebanon 2002 Egypt 2013 Dominican Republic 2015 Philippines 2013 Colombia 2016 MENA Iran 2002 Sri Lanka 2014 Angola 2011 Argen na 2016 Namibia 2016 Azerbaijan 2016 Panama 2014 Costa Rica 2015 Tunisia 2015 South Africa 2016 Venezuela 2012 Algeria 2014 Thailand 2016 Uruguay 2013 Brazil 2014 Albania 2016 Sudan 2011 Syria 2007 Ecuador 2015 Turkey 2014 TRANSITION COUNTRIES Chile 2013 Romania 2004 Mongolia 2016 Armenia 2015 Russia 2016 Serbia 2016 Bulgaria 2012 Malaysia 2013 Macedonia 2010 Moldova 2015 Slovakia 2008

50

3  Trends and Characteristics of the Informal Economy and Its Components

Characteristics of the Informal Economy

51

Characteristics of the Informal Economy As it is defined, employment in the informal economy is a broad concept, and it is interesting to go beyond the macro-picture and understand the variety of its components. Table 3.7 and Chart 3.8 hereafter summarise some of the main characteristics of employment in the informal economy. Microbusinesses of the informal sector prevail in all regions but especially in sub-Saharan Africa and in Asia, whereas informal employment outside the informal sector is particularly widespread in MENA and transition countries. Employment in the informal sector accounts for more than 80% of total employment in the informal economy in sub-Saharan Africa and a little bit less than 80% in Asia, two regions where microbusinesses are largely predominant. It also means that in these two regions, informal employment outside the informal sector absorbs only 20% of the workers in the informal economy, against nearly 50% in transition economies, 41% in Middle East-North Africa and 35% in Latin America. Women are more numerous than men in the informal economy in sub-Saharan Africa; everywhere else men predominate in the informal economy. On the other hand, the informal economy is prevalent in women’s employment both in sub-­ Saharan Africa and in Latin America. Contrary to popular belief, it is only in sub-­ Saharan Africa that women outweigh men (51.1%) in the informal economy; in other regions they work less often than men in the informal economy (from 46.5% in Latin America to 35.8% in Asia, 33.2% in transition economies and down to 16.4% in Middle East-North Africa (MENA)). The incidence of informal economy Table 3.7  Main components and characteristics of nonagricultural employment in the informal economy by region in 2005–2010

Regions/ countries Middle East-North Africa Sub-­ Saharan Africa Asia Latin America Transition

% of informal sector in employment in the informal economy 58.7%

% of informal workers outside informal sector 41.3%

% of women in the informal economy 16.4%

% of self-­ employed in the informal economy 39.9%

% of employment in industries in the informal economy 41.4%

80.4%

19.6%

51.1%

64.9%

24.2%

79.4% 64.6%

20.6% 35.4%

35.8% 46.5%

53.3% 52.1%

41.7% 26.8%

50.5%

49.5%

33.2%

32.7%

18.0%

Source: Charmes (2011). A worldwide overview of trends and characteristics of employment in the informal economy and informal sector in a gender perspective. Contribution to the update of the ILO-WIEGO Women and Men in the Informal Economy

52

3  Trends and Characteristics of the Informal Economy and Its Components

Chart 3.8  Main components and characteristics of nonagricultural employment in the informal economy by region in 2005–2010 Source: Charmes (2011). A worldwide overview of trends and characteristics of employment in the informal economy and informal sector in a gender perspective. Contribution to the update of the ILO-WIEGO Women and Men in the Informal Economy

Characteristics of the Informal Economy

53

Chart 3.8 (continued)

in female nonagricultural employment is much higher: 60.9% in sub-Saharan Africa, 54.3% in Latin America and 44.8% in Asia and only 30.5% in the MENA region and 10.8% in transition countries. In these two latter regions, women are better positioned in wage work. In all regions, women are generally relatively more numerous in informal employment outside the informal sector: this is because many are employed as domestic workers and also as home-based workers for the manufacturing industries (particularly in the textile industry). Self-employment represents between 1/3 (transition economies followed by MENA), half (Latin America and Asia) and 2/3 (sub-Saharan Africa) of ­employment in the informal economy. And finally, industries (manufacturing but also construction) account for less than ¼ of total employment in the informal economy in transition countries, sub-Saharan Africa and Latin America but more than 40% in MENA countries and Asia. Employment in the informal economy is generally assimilated to low productivity, low income and poverty and the fact is that it is negatively related to GDP per capita and to poverty rate, as illustrated on Charts 3.9 and 3.10 hereafter. Chart 3.10 shows that very high proportions of employment in the informal economy are associated with very high shares of population living under poverty

54

3  Trends and Characteristics of the Informal Economy and Its Components

Chart 3.9  Employment in the informal economy is negatively related to GDP per capita Sources: Database used for previous tables and Human Development Report for GDP per capita (PPP) Note: See country codes by region and by alphabetical order in annex Tables 3.32 and 3.33

Characteristics of the Informal Economy

55

Chart 3.9 (continued)

line in Madagascar and Zimbabwe, for example, or with moderate rate of poverty such as in Benin or Mauritania or also with low poverty rates (Indonesia, Morocco). And relatively low proportions of employment in the informal economy can be associated with low poverty rates in Brazil, Thailand or Tunisia, as well as with high poverty rates as in South Africa.

56

3  Trends and Characteristics of the Informal Economy and Its Components

Chart 3.9 (continued)

Chart 3.10  Employment in the informal economy is positively related to poverty Sources: Database used for previous tables and Human Development Report 2015 for the proportion of population living under poverty line Note: See country codes by region and by alphabetical order in annex Tables 3.32 and 3.33

Pluri-Activity, Multiple Jobs, Secondary Activities and Employment in the Informal…

57

 luri-Activity, Multiple Jobs, Secondary Activities P and Employment in the Informal Economy A very often forgotten and underestimated dimension of informality – and undoubtedly a vigorous form of resilience – is the pluri-activity, that is, the multiple jobs in which people dependent on the informal economy are engaged. Not only informal employment has been rising in all countries during the recent period, but also the image of a standard job that is permanent, full-time and secured has shifted towards an expanding non-standard, temporary, part-time and unsecured informal job associated with the flexibilisation of labour markets (Standing 1999). To earn a living, it is often necessary to have two or several jobs simultaneously or alternatively within a given reference period. Seasonality is a main cause for pluri-activity of farmers and agricultural workers who carry out nonfarm activities during the dry or the cold season. But due to increasing rural-urban migrations, seasonal secondary activities may become permanent so that farming become secondary. Secondary jobs of farmers are also often associated with the processing of agricultural products (which may be non-market activities) and their commercialisation. Pluri-activity of farmers is the form that has usually been the best captured in censuses and surveys. Time-related underemployment and inadequate employment situations (ILO 1998a, b, 2000) are another cause of the expansion of multiple jobs: casualisation results in multiple paid jobs with several or many employers; two part-time jobs may be equivalent to one full-time job: the difficulty then lies in the measurement. Last, but not least, full-time permanent paid jobs may be associated with other permanent jobs for own-account. Where the main official job (often a public job in civil service or in public enterprises) is characterised by low wages but secure tenure, soft hierarchy and lack of control, secondary activities mushroom in the informal sector, so that these main official activities may be sought for their security and the opportunity they provide to work for own-account. The new forms of work organisation (work through lunch, 4-day week, work-time reduction) favour the development of this type of pluri-activity. In such cases, the normal number of hours in the main job is not entirely worked so that it results in a productivity decrease, which takes its roots in the low level of wages. Women often cumulate several of these various situations at the same time, and this is a reason for their higher level of invisibility in the participation to the labour force and in the contribution to production. In many societies women’s rights to own a land are not recognised by law or by customs; as a consequence, they are captured as unpaid family workers in labour force surveys. More often than men, they are involved in secondary activities for the processing of agricultural and food products, and in many cases, these secondary activities remain non-market and furthermore home-based. All these characteristics lead to the invisibility of women in agricultural and related activities: they don’t own the land, they work as unpaid family workers, and their secondary activities are home-based, non-market and not captured. Emphasised early since the adoption of the definition of the informal sector in 1993, pluri-activity was tentatively captured  – although rarely analysed or pub-

58

3  Trends and Characteristics of the Informal Economy and Its Components

lished – in many surveys. The magnitude of employment in the informal economy presented in tables of previous sections of this chapter is somewhat underestimated in the sense that it only captures the main activities of the persons occupied. The 1993 definition clearly stated “the informal sector is defined irrespective of the kind of workplace where the productive activities are carried out, (…), and its operation as a main or secondary activity of the owner” (ILO 1993a, b). Capturing the secondary activities and more generally the multiple jobs of the labour force is also a challenge of data collection. Most of the mixed surveys on the informal sector have dedicated a set of questions, if not an entire module, to secondary activities; furthermore, the secondary activities feed the list of economic units to be surveyed during the second phase (establishment) of the mixed surveys. Pluri-activity on the one hand is associated with time and seasonality in the case of farmers for whom the calculation of full-time equivalent requires to know the number and the duration of off-farm activities. Multiple jobs holding on the other hand is associated with time-related underemployment for those situations where it is necessary to have several jobs with several employers to earn a living, either simultaneously (during a same day or week) or consecutively (from 1 day or week, or month, to another). And finally, it is associated with inadequate forms of employment in these situations where a permanent full-time job does not provide sufficient earnings to the employees as it may be the case in the civil service or public sector in some countries, so that the worker must get a second job as an own-account worker, for example. For simplification purpose, we consider here that the generic term of “pluri-activity” covers all of these situations. Depending on the period of reference used, surveys may capture one, several or all forms of pluri-activity. It is too often taken for granted that data on pluri-activity do not exist or are not reliable. The quality and reliability of the data collected are proportional to the care taken to the design of the questions. Examples below show not only the magnitude of the phenomenon but also the extent to which it can change the vision that one can have of the structure of employment in a country, in terms of activities, gender or urban/ rural divides. Not only data collection on secondary activities should be systematised, but also analyses of the findings should be deepened and above all more oriented towards the needs of national accountants: as a matter of fact, pluri-activity is mainly analysed to characterise the individuals (Who are the multiple jobs holders? What are their main activities, their employment statuses, etc.), and little is said of what are these secondary activities (in terms of industries, statuses, etc.). National accountants need to know more about the characteristics of the secondary activities, especially to build labour inputs matrices. Despite recent efforts towards a better capture of secondary activities, data remain rare. Indisputably Italy developed the most sophisticated measurement of multiple jobs, as already explained (see Chap. 2). Table 3.8 below presents the estimates and trends of pluri-activity in this country since the first attempt to measure the phenomenon in 1982. There were approximately a little bit less than 1/3 of multiple jobs holders among the occupied and a little bit less than 1/4 of multiple jobs in the total number of jobs (“work positions” in the Italian terminology). There is not much variation in the shares of these two indicators between 1981 and 1997, and it seems to be due to the increase in unemployment rates. Finally, the conver-

Pluri-Activity, Multiple Jobs, Secondary Activities and Employment in the Informal…

59

Table 3.8  Multiple jobs in Italy: 1981–1997 In thousands and % Total employment (1) Number of multiple jobs holders (2) Total number of jobs (3) Total number of jobs in permanent equivalent Rate of pluri-activity (4) = (2)/(1) Share of multiple jobs in total number of jobs (5) = (2)/(3)

1981 21,349 6436 27,785 22,340 30.1% 23.2%

1985 22,618 6909 28,548

1990 23,327 7182 29,421

1995 21,993 6843 28,836 22,528 30.5% 30.8% 31.1% 24.2% 24.4% 23.7%

1997 22,107 6780 28,887 22,558 30.7% 23.5%

Sources: Calzaroni (2000), Charmes (1991), and ISTAT (1999) Table 3.9  Impact of pluri-activity on size and characteristics of employment in the informal economy: Burkina Faso 1985 In % of total number of nonagricultural jobs Women Men Urban Rural Production activities Trade and services activities % of total number of nonagricultural jobs

Main jobsa 40.9 59.1 54.5 45.5 25.0 75.0 70.0

Secondary jobsb 78.2 21.8 2.0 98.0 60.9 39.1

Total number of jobs 68.9 31.1 15.0 85.0 52.3 47.7 90.8

Source: Author’s calculations based on Population Census 1895, see: Charmes (1991, 1996, 1998) Notes: aMain job of the person occupied in the informal economy. bnumber of secondary jobs or additional jobs in the nonagricultural informal economy, performed by persons occupied in the formal, informal and agricultural sectors. Women count for 40.9% of the total number of main jobs in the informal economy, but they count for 78.2% of secondary jobs in the informal economy; in total they finally count for 68.9% of the total number of jobs (main+secondary) in the informal economy.

sion of the total number of jobs (main as well as secondary) in permanent equivalent shows that in full-time equivalent, the numbers are slightly higher than the numbers of occupied persons (from 105% in 1981 to 102% in 1997). Figures presented in Table 3.9 and Charts 3.11 and 3.12 hereafter for Burkina Faso are not recent (and it has now become difficult to compile such data for this country), but they are illustrative of the phenomenon of pluri-activity. In 1985, employment in the informal economy accounted for 70% of nonagricultural employment in Burkina Faso. Employment in the informal economy was predominantly male (59.1%), urban (54.5%) and tertiary (75%). However, a huge number of women were engaged in secondary activities (78.2% of nonagricultural secondary activities were female), mainly in rural areas (98% of secondary activities) and in production (manufacturing) activities (60.9% of secondary activities), so that the informal economy, when including secondary activities, becomes predominantly female (68.9% of total jobs), rural (85%) and manufacturing (52.3%). As a whole (Table 3.10 hereafter), secondary activities (including agriculture) represented an important share of the total number of jobs in Burkina Faso, with a

60

3  Trends and Characteristics of the Informal Economy and Its Components

Chart 3.11  Employment in the informal economy in Burkina Faso 1985 (Main jobs) Source: Author’s calculations based on Population Census 1985, see: Charmes (1991, 1996, 1998)

Chart 3.12  Employment in the informal economy in Burkina Faso 1985 (Total number of jobs) Source: Author’s calculations based on Population Census 1985, see: Charmes (1998) Table 3.10  Trends in pluri-activity rates in Burkina Faso between 1985 and 1998 In % of total occupied Urban Rural Total

1985 Women 7.0 25.2 24.1

Men 7.9 27.2 26.4

Total 7.6 26.5 25.1

1991 Women 12.1 24.3 23.4

Men 15.4 27.7 26.4

1994–1995 Total Women Men 14.3 26.0 24.9 30.2 25.8

Total 18.3 31.6 34.5

1998 Total 18.0 41.1 38

Sources: Charmes (1996); INSD (1996, 2000), Results of the Priority Survey 1994 and 1998

Pluri-Activity, Multiple Jobs, Secondary Activities and Employment in the Informal…

61

tendency to increase between 1985 (25.1%) and 1998 (38%). Further surveys unfortunately did not continue to collect and analyse this indicator, the importance of which was yet demonstrated (and taken into account in the National Accounts). Despite the fact that these figures are pretty old, this example should invite national accountants (as well as labour statisticians) to use recent surveys – which measure pluri-activity – in such a perspective. More recent surveys on employment and informal sector in Mali (2004, 2007 and 2010), Cameroon (2005 and 2010), DR Congo (2005 and 2012), Chad (2003 and 2011), Senegal (2010–2011), Tanzania (2001, 2007 and 2014) and Madagascar (2012) have, for instance, shown pluri-activity rates often higher than 20%. Very high and increasing pluri-activity rates were observed in Mali, rising from 15% in 2004 up to 27% in 2007 and 32% in 2010 (Table 3.11 below), as well as in Chad (Table 3.12): In Cameroon and in Tanzania (Tables 3.13 and 3.14 below), the trends do not show an increase, but the levels remain very high. In Northern Africa, pluri-activity rates are much lower but increasing (from 2.2% to 2.6% in Morocco between 2010 and 2013 and from 3% to 4.2% in Algeria during the same period) (Tables 3.15 and 3.16). Whereas Senegal (2010–2011) provides the example of a country where male pluri-activity rates are twice those experienced by females (Tables 3.17 and 3.18), Madagascar (2012) probably shows the highest pluri-activity rates ever measured (Table 3.19): with more than seven active out of ten running secondary activities (and three active women out of four), Madagascar offers the picture of a survival economy where the informal operators are constrained to seek as many opportunities as they can find in order to attempt earning a living. In most countries, rural areas are characterised by the highest pluri-activity rates; urban areas are lower and the main city even lower. This is related on the one hand to the seasonality of activities in rural areas, which implies to perform other activities during the dry season, and on the other hand, it is also related to the processing of agricultural products. Mali is a good example of the detailed analyses undertaken to characterise secondary activities from the point of view of the main activity: Table  3.20 below shows in which institutional sectors, industries and socio-­ professional categories the persons involved in pluri-activity work in their main activity, but not what are these secondary activities in terms of industries and socio-­ professional categories. The results reflect the seasonality of activities (the more seasonal the main activity, the higher the pluri-activity rate) and the hierarchy of income (the lower the income in the main activity, the higher the pluri-activity rate, with the exception of NGOs). Table 3.21 identifies for Madagascar (2012) in which industries the second jobs are performed. As expected, more than ¾ (77.5%) of the farmers have a secondary job (a high figure that can be mitigated by the fact that 38.7% of these secondary activities are within the primary sector). And more than one-third (38.7%) of the population has a second activity in the primary sector. Farmers operate secondary activities in other services (46.2%). Civil servants, health and education workers are between 40 to 50% to have their secondary activities in agriculture: in other words, in rural areas most workers are also farmers.

Men

18

2004 Women

10

15

Total

Men

32

2007 Women

23

Source: Based on Bourdet et al. (2012) and ANPE/OEF (2013)

In % of total occupied Bamako Other urban Rural Total

Table 3.11  Trends in pluri-activity rates in Mali between 2004 and 2013

27

Total

2010 Women 9 23 33 30 Men 5 26 38 33

Total 6 25 36 32

2013 Women 1 32 35 31

Men 2 28 33 28

Total 2 30 34 29

62 3  Trends and Characteristics of the Informal Economy and Its Components

Pluri-Activity, Multiple Jobs, Secondary Activities and Employment in the Informal…

63

Table 3.12  Trends in pluri-activity rates in Chad in 2003–2004 and 2011 In % of total occupied Urban  N’Djaména  Other urban Rural Total

2003–2004 Women

Men

Total

2011 Women

Men

Total

2.8 11.4 12.2 11.2

3.3 11 12.8 10.3

3.2 11.2 12.5 10.7

7.1 17.2 26.6 25

6.2 21.5 25.5 23.5

6.5 19.9 26 24.1

Sources: INSEED, ECOSIT 2 and 3 Table 3.13  Trends in pluri-activity rates in Cameroon 2005, 2010 In % of total occupied Urban  Douala  Yaoundé  Other urban Rural Total

2005 Total 17.3 14.0 11.6 44.7 37.0

2010 Women 13.2 7.6 7.6 21.9 30.5 25.1

Men 15.1 13.5 12.2 18.6 35.1 27.7

Total 14.3 11.0 10.1 20.0 32.8 26.5

Sources: INS (2006 and 2011): EESI 2005 and EESI 2010

Table 3.22 points out another interesting distinction: pluri-activity may refer to alternate (and complementary) activities during a reference period of a year or to simultaneous activities, multiple jobs performed during a short reference period of a day or a week; the first type of pluri-activity is logically more widespread than the second one, because it includes the latter. But in both cases, pluri-activity remains higher in rural areas than in urban areas. It is difficult to find data on pluri-activity in other regions, probably because the low figures derived from data collection prevent statistical institutions to publish their results. Mexico (Table 3.23) is an exception with an overall pluri-activity rate of 2.6% in 2012 (and 2.8% in 2005): Here again, agriculture is the main provider of a workforce available for multiple jobs (5.7%), followed by the informal workers of the formal sector (5.6%). Compared to the total occupied population, workers in informal employment (without health coverage) display a pluri-activity rate of 3.6% (against 2.6%). In Asia, Sri Lanka published some data on secondary activities (Table 3.24), and Nepal estimated the pluri-activity rate at 46.8% in 2008. Finally Table 3.25 and Chart 3.13 summarise what we know on the importance of pluri-activity for the most recent period. Such high levels mean that the size of employment in the informal economy is even more important than the picture that has been presented earlier. Some countries are taking pluri-activity into account in the compilation of their GDP, but they are not many. Mexico and Cameroon are ­exceptions in this regard.

2000–2001 Women 26.5 16.2 17.7 Men 17.4 17.3 17.3

Total 21.7 16.8 17.5

2006 Women 46.0 55.7 53.6 Men 30.8 44.9 41.6

Total 38.2 50.5 47.7

2014 Women 19.7 20.3 20.1

Men 23.8 32.5 29.4

Total 24.4 25.0 24.8

a

Sources: National Bureau of Statistics, ILFS 2006 and 2014 To what extent such huge changes between two rounds of the ILFS can be explained? Tanzania is one of the rare countries (if not the only one) to have strictly applied a definition of employment that follows the definition of production as measured in the GDP. Consequently, activities such as “fetching water” or “fetching firewood” are counted in the measure of employment. The 2013 revision of the concepts of labour force and employment has attempted to harmonise these definitions (see Part III of this book). According to the 2006 ILFS report (p. 51), this “large increase may be partly explained by an increase in access to loans due to liberalisation of financial institutions, as well as an increasing tendency for Tanzanians to engage in secondary activities to supplement their income. Among employed females with secondary activities, 35.0% are involved in collection of fuel and/or water. Here the increase in secondary activities reflects the fact that more women are now engaged in other economic activities, with collection of fuel and/or water thus becoming a secondary activity”

In % of total occupied Urban Rural Total

Table 3.14  Trends in pluri-activity rates in Tanzania (2001–2006–2014)a

64 3  Trends and Characteristics of the Informal Economy and Its Components

Table 3.15  Trends in pluri-activity rates in Morocco, 2010–2013 2010 Females 0.5 0.9 0.8 Males 0.9 4.9 2.7 Both sexes 0.8 3.6 2.2

Urban Rural National Urban Rural National Urban Rural National

2011

2012

2013

0.5 1.1 0.9

0.3 1.2 0.9

0.4 1.0 0.8

0.6 6.0 3.0

0.7 6.2 3.2

0.6 6.3 3.2

0.6 4.4 2.5

0.6 4.6 2.6

0.6 4.5 2.6

Sources: HCP, Labour Force Surveys Table 3.16  Trends in pluri-activity rates in Algeria, 2010–2014 2010 Females

2011

National

2012

2013

2014

2.2

2.8

1.5

4.2

4.6

3.4

3.8

4.2

3.1

Males National National

Both sexes 3.0

2.3

Sources: ONS, Annual Labour force survey Table 3.17  Pluri-activity in Senegal (2010–2011) In % of total occupied Dakar Other urban Rural Total

2010–2011 Women 3.6 7.7 13.2 10

Men 6.2 12.2 29.4 20.4

Total 5.1 10.4 22.2 16

Source: ANSD 2015, EPSF II 2011 Table 3.18  Pluri-activity rates in Zimbabwe 2014 and Gambia 2012 In % of total occupied Urban Rural Total Urban Rural Total

Women Zimbabwe 2014 6.9 9.9 9.2 Gambia 2012 2.4 15.3 9.3

Men

Total

7.4 13.8 12.0

7.1 11.8 10.6

3.6 20.1 11.2

3.1 17.8 10.4

Source: ZIMSTAT, LFCLS 2014; Gambia Bureau of Statistics, GLFS 2012

66

3  Trends and Characteristics of the Informal Economy and Its Components

Table 3.19  Pluri-activity rates in Madagascar (2012) In % of total occupied Urban Rural Total

2012 Women

Men

72.2

70.2

Total 53.9 75.5 71.1

Source: INSTAT, ENEMPSI 2012 Table 3.20  Pluri-activity rates in Mali, by institutional sectors, industries and socio-professional categories in the main activity, 2010 In % of total occupied Institutional sectors Public sector Formal private Informal private NGOs, international Domestic workers Industries Agriculture Fishing Extractive industries Manufacturing industries Construction Trade Hotels, restaurants Transport Health Collective or personal activities Household activities Education Socio-professional categories Senior executives Middle management White collars Blue collars Unskilled Employers Own account workers Associates Apprentices Contributing family workers Total

Women

Men

Total

17 17 29 45 26

28 19 33 42 30

25 18 31 44 33

34 – – 17 80 26 37 – 11 14 28 20

39 50 44 23 20 19 46 10 33 25 19 33

37 50 44 20 23 24 38 10 23 20 27 31

2 19 30 19 28 27 30 23 20 34 30

15 19 18 23 25 25 33 43 23 32 33

12 19 20 22 25 26 31 38 22 33 32

Source: ANPE/OEF: EPAM 2010, based on Bourdet et al. (2012)

1.0 0.8 0.0 0.0 1.5 2.7

1.5

3.7

25.3

38.7

1.7

1.1

0.8

0.9 1.4 2.2 1.4 0.1 0.4

1.7

1.4 1.5 0.4 0.0 1.1 1.7

5.1 1.4

3.2

0.9

0.3 6.3 1.6 0.8 0.3 2.4

2.1 2.0

4.4

8.3

4.5 7.6 11.5 5.0 5.8 4.2

4.1 6.2

0.6

1.5

0.9 2.9 1.3 1.0 0.3 0.2

0.7 0.4

38.0 34.0 42.6 40.6 50.9 13.0

0.5 1.0

0.4

1.7

0.2 0.0 1.1 0.0 2.1 0.0

0.0 0.1

0.6 0.0

0.1 0.6

3.6 5.2

53.6 44.5

0.5 4.5

0.4 0.6

0.3 1.5

0.9 5.1

50.0 23.8

0.4 0.5

Civil Transport service 0.5 0.4

Primary Food Other sector Clothing industries Construction industries Trade 38.7 4.2 0.7 1.0 3.6 4.2

Source: INSTAT, ENEMPSI 2012

Secondary main Primary sector Clothing Food industries Construction Other industries Trade Transport Civil service Health Education Services to households Other services Total

Table 3.21  Pluri-activity by main and secondary activities in Madagascar 2012

0.2

0.0

0.1 0.2 1.6 4.9 0.0 0.5

0.0 0.7

0.3 0.0

Health 0.2

0.1

1.0

0.0 0.4 0.8 0.0 3.6 0.0

0.0 0.3

0.1 0.0

0.6

1.7

0.6 1.3 2.9 1.7 0.0 5.7

2.1 1.1

0.7 1.0

46.4

54.7

52.2 43.6 34.1 44.7 34.2 69.1

31.8 41.7

42.2 57.7

100.0

100.0

100.0 100.0 100.0 100.0 100.0 100.0

100.0 100.0

100.0 100.0

Services to Other Education households services Total 0.0 0.4 46.2 100.0

71.1

41.4

50.9 37.1 34.2 42.1 63.2 45.1

53.5 62.7

65.2 47.6

Pluri-­ activity rates 77.5

68

3  Trends and Characteristics of the Informal Economy and Its Components

Table 3.22  Pluri-activity rates in the Democratic Republic of Congo (RDC) in 2004–2005 and 2012 In % of total occupied Urban Kinshasa Rural Total

2004–2005 12 past months 13.0 9.1 24.4 21.9

2012 12 past months 15.9 8.5 21.4 19.2

Past week 9.1 6.5 17.8 15.8

Past week 10.4a 4.6 13.1 11.9

Source: INS, Enquête 1–2–3 Nationale RDC, Phase 1 (2004–2005) et INS (2014) Note: aWithout Kinshasa

Table 3.23  Pluri-activity rates in Mexico 2012 Main jobs Informal sector 12,639,914 Formal sector 27,059,339 Agriculture 6,441,160 Domestic workers 2,222,708 Total occupied 48,363,120 Without health coverage Informal sector 12,536,961 Formal sector 9,010,127 Agriculture 6,071,406 Domestic workers 2,129,119 Total occupied 29,747,613

Secondary jobs 158,541 701,403 366,901 39,260 1,266,104

Main+secondary 12,798,455 27,760,742 6,808,060 2,261,968 49,629,224

% secondary/main jobs 1.3% 2.6% 5.7% 1.8% 2.6%

157,570 500,902 364,096 39,011 1,061,578

12,694,530 9,511,029 6,435,502 2,168,130 30,809,191

1.3% 5.6% 6.0% 1.8% 3.6%

Source: INEGI, Matrices anuales puestos ingressos 05–13

Table 3.24  Pluri-activity rate in Sri Lanka 2014 In % of total occupied Urban Rural Total

2014 Women

Men

6.1

9.9

Source: Sri Lanka Labour Force survey 2014

Total 2.6 9.6 8.6

Pluri-Activity, Multiple Jobs, Secondary Activities and Employment in the Informal… Table 3.25  Pluri-activity rates (most recent year) Countries Algeria Algeriaa Morocco Burkina Faso Cameroon Chad DR Congo Gambia Liberia Madagascar Mali Senegal Tanzania Tanzaniaa Zimbabwe Nepal Sri Lanka Mexico

Year 2013 2013 2013 1998 2010 2011 2012 2013 2010 2012 2010 2010–2011 2014 2014 2014 2008 2014 2012

Women 2.8

Men 4.6

0.8

3.2

25.1 25

27.7 23.5

9.3 13.5 72.2 30 10 20.1 41.5 9.0

11.2 18.6 70.2 33 20.4 29.4 46.7 12.0

6.1

9.9

Note: aInformal employment or informal sector only

Chart 3.13  Pluri-activity rates (most recent year) Source: Table 3.25 supra Note: *Informal employment or informal sector only

Both sexes 4.2 6.1 2.6 38 26.5 24.1 19.2 10.4 16.0 71.1 32 16 24.8 44.0 10.6 46.8 8.6 2.6

69

70

3  Trends and Characteristics of the Informal Economy and Its Components

Contribution of the Informal Economy to GDP As explained in Chap. 2 (section “Statistical definitions”), the informal sector can clearly be identified as a subsector of the unincorporated enterprises of the household institutional sector in the System of National Accounts (SNA), and its contribution to the GDP can be measured relatively easily. This does not hold true for informal employment outside the informal sector, which cuts across all institutional sectors of the SNA and is comprised of (1) the informal workers of the formal sector, (2) the domestic workers and (3) the subsistence producers in the primary sector (and in the secondary sector). Whereas paid domestic services and subsistence production for own final use are also components of the household sector and can be identified in the SNA, informal employment in the formal sector is never (or at least exceptionally) identified in the SNA. Countries that prepare labour input matrices may estimate this component of total labour inputs but rarely indicate its contribution to GDP. India is an exception (Kolli and Sinharay 2011a, b). Mexico is another one and quite interesting because it is the only country to compile and to publish the shares and the trends of the informal economy and its components in the GDP (INEGI 2015 and 2017). This is why it will be interesting to come back in details on the Mexican experience below. Indeed, in the fourth revision of the System of National Accounts (SNA 1993) and in the fifth revision (SNA 2008 which dedicates an entire chapter to the informal aspects of the economy: chapter 25), the informal sector was defined as a subsector of the household institutional sector: as such, its contribution to GDP can be measured. Informal employment in the formal sector, on the contrary, is a hidden or non-observed part of the economic units constituting the other institutional sectors of the system of national accounts. Therefore it cannot be easily distinguished from the formal units and estimates are rarely available. Very often though, indirect estimates of the informal economy are imputed to the household sector that plays a role of residual balance. The estimation of the underground economy through econometric modelling (Chap. 2, section “The underground, shadow, illegal, parallel, non-observed economy and how it is captured or estimated through modelling”: see for instance, Schneider 2005; Schneider and Enste 2000; Schneider et al. 2010) is interesting, but the comparison of the results with the current GDP is particularly difficult to interpret because the national accounts already include a part of the underground and illegal economy. The fact that private incorporated and public enterprises employ informal workers does not mean that the contribution of these workers is not taken into account in the output of the firms (unless the goods or services produced are illegal by nature) although it has an impact on the value added. Supply and use tables by products are the instrument by which national accountants attempt balancing production and its uses (consumption, investment), as well as the reconciliation of the three GDP estimates on the production side, the expenditure side and the income side; a part of the hidden economy – supposedly the major part – which does not show up on one side, may show up in one or two of the other sides and justifies adjustments in the volume or the value of output.

Contribution of the Informal Economy to GDP

71

A tentative estimate of the informal sector contribution can be made for those countries, which prepare the household sector accounts. But the availability of the household accounts is not sufficient: the distribution of gross value added by industry is also required, because production for own final use (not transiting through the market) must be excluded as it is not part of the international definition of the informal sector: this can be dealt with by excluding the agricultural and related activities. Other exclusions are the imputed rents and paid domestic services (which rarely go beyond 1–5 percentage points in total GDP), without forgetting that – depending on national definitions – some unincorporated firms may belong to the formal sector within the household sector, but the necessary data are rarely available; consequently the results presented in Tables 3.26 and 3.27 remain proxies, but these proxies are acceptable. It is therefore necessary to isolate the informal sector by using the table of national accounts cross-classifying the gross value added by industries and institutional sectors. If all countries distinguish the various institutional sectors in their national accounts, not all of them present the accounts of the institutional sectors in details, especially by industries. The compilation of the UN statistics division (United Nations 2004, 2015 and 2017) and its regular updating allows identifying the countries with detailed accounts of the household institutional sector. Table 3.26 hereafter is based on these compilations, as well as on national sources and a special report by Afristat (1999) on the national accounts of the West Africa Economic and Monetary Union countries. As far as it has been possible, “imputed rents” and “private households employing persons” have been subtracted. It must be stressed that, unlike the employment estimates, the data on contribution of the informal economy to GDP can hardly be assessed as regard their trends. The reasons are that major changes in these estimates intervene on the occasion of the preparation of a new base year that updates the information available for the compilation of national accounts: changes between 2 base years are mainly methodological. Furthermore, in the absence of regular (annual) data on employment in the informal economy, most countries assume that the informal economy declines from year to year, between 2 base years. Here again Mexico is an exception, where data on informal employment and its components are collected on a yearly basis. For the years 2000s (Table  3.26) in sub-Saharan Africa, the informal sector including the agricultural household sector contributes to nearly 2/3 of the GDP (65.0% in arithmetical non-weighted mean), with a maximum in Burundi (73.7%) and a minimum in Senegal (51.5%). Excluding agriculture, the share of informal sector in GDP represents approximately 1/3 of total GDP (31.6%) with a maximum in Cameroon (36.0%) and a minimum in Burkina Faso (21.7%). Moreover, the nonagricultural informal sector is higher than 50% of the nonagricultural gross value added (52.5%) with a maximum in Burundi (66.6%) and a minimum in Burkina Faso (36.2%). In India, which is the only Asian country where data are available, the informal sector (including agriculture) contributes to 54.2% of total GDP (2008) and still to 38.4% if agriculture is excluded. With 46.3% of total nonagricultural gross value added, the informal sector stricto sensu is among the highest contributors to nonagricultural GVA in all regions. In Northern Africa, these three indicators amounted to 35.1%, 23.9% and 27.1%, respectively. With 29.7% of total GDP (with

72

3  Trends and Characteristics of the Informal Economy and Its Components

Table 3.26  Contribution of informal sector to GDP in various developing countries: years 2000s (33 countries) Countries (years) Northern Africa Algeria (2003) Egypt (2008) Morocco (2007) Tunisia (2004) Sub-Saharan Africa Benin (2000) Burkina Faso (2000) Burundi (2005) Cameroon (2003) Niger (2009) Senegal (2000) Togo (2000) India (2008) Latin America Brazil (2006) Colombia (2006) Guatemala (2006) Honduras (2006) Mexico (2009) Venezuela (2006) Transition countries Armenia (2008) Azerbaijan (2008)

Informal sector (including agriculture) in % of total GDP 35.1%

Informal sector (excluding agriculture) in % of nonagricultural GVA 27.1%

Informal sector (excluding agriculture) in % of total GDP 23.9%

37.9%

30.4%

27.1%

27.8% 32.8%

16.9%

14.7%

41.8%

34.1%

29.8%

65,0%

52,5%

31,6%

71.6% 55.8%

61.8% 36.2%

33.6% 21.7%

73.7%

66.6%

33.9%

57.6%

46.3%

36.0%

72.6% 51.5%

51.5% 48.8%

29.0% 35.1%

72.5% 54.2% 29.7%

56.4% 46.3% 25.2%

32.2% 38.4% 24.0%

21.6% 37.5%

32.3%

29.4%

36.9%

34.0%

30.2%

31.5%

18.1%

20.8%

25.7%

24.5%

22.7%

17.0%

16.3%

15.7%

19.5%

13.9%

10.7%

27.5%

19.5%

15.5%

17.8%

13.1%

12.4% (continued)

Contribution of the Informal Economy to GDP

73

Table 3.26 (continued) Countries (years) Belarus (2008) Bulgaria (2006) Estonia (2008) Kazakhstan (2009) Kyrgyzstan (2008) Latvia (2007) Lithuania (2008) Macedonia (2008) Moldova (2008) Russia (2009) Serbia (2008) Slovenia (2005) Ukraine (2008)

Informal sector (including agriculture) in % of total GDP 6.7%

Informal sector (excluding agriculture) in % of nonagricultural GVA 3.7%

Informal sector (excluding agriculture) in % of total GDP 3.4%

21.6%

16.5%

15.1%

10.7%

10.1%

9.8%

23.0%

20.0%

18.7%

45.2%

27.5%

20.3%

11.3% 14.1%

10.2% 11.8%

9.9% 11.8%

22.5%

14.0%

12.4%

20.0%

12.3%

11.0%

10.6% 25.0% 19.5%

8.6%

8.2%

16.4%

12.9%

11.9%

Source: Charmes (2012) Note: Non-weighted averages by region

agriculture) and 24% (without agriculture), Latin America stands at a rather low level, with Venezuela at the bottom (in 2006) and Colombia at the top. Transition countries of Eastern Europe and Central Asia stand even lower at 19.5% of total GDP (with agriculture) and 10.7% without agriculture, with Kyrgyzstan at the top (45.2% and 20.3%, respectively) and Belarus at the bottom (6.7% and 3.4%, respectively). During the years 2010s (Table 3.27), the share of informal sector (including agriculture) dropped to 55.6% in sub-­Saharan Africa (as compared with the years 2000s) and its share excluding agriculture in total GDP to 24.8% and to 42.1% of nonagricultural GVA.  The maximum is in Mali (with 64.4% and 28.4%, respectively), but without agriculture, it is Benin that stands at the top (with 35.5%), and the minimum in Togo (48.1% and 15.5%). As a share of nonagricultural value added, the maximum of informal sector’s contribution is observed in Niger (with 55.1%). In the Middle East-North Africa (MENA) region and for the same period (years 2010s), the contribution of the informal sector including agriculture is equivalent to a little bit more than 1/3 of total GDP (34.1%) and a little bit less than ¼ (24.1%) if the agricultural household sector is excluded. Finally the nonagricultural

74

3  Trends and Characteristics of the Informal Economy and Its Components

Table 3.27  Contribution of informal sector to GDP in various developing countries: years 2010s (32 countries) Countries (years) Northern Africa Algeria (2013) Egypt (2012) Morocco (2014) Tunisia (2012) Sub-Saharan Africa Benin (2012) Burkina Faso (2012) Burundi (2013) Cameroon (2009) Mali (2013) Niger (2013) Togo (2011) India (2013) Latin America Brazil (2013) Colombia (2013) Guatemala (2012) Honduras (2011) Mexico (2013) Nicaragua (2011) Transition countries Armenia (2012) Azerbaijan (2012)

Informal sector (including agriculture) in % of total GDP 34.1%

Informal sector (excluding agriculture) in % of nonagricultural GVA 24.1%

Informal sector (excluding agriculture) in % of total GDP 20.6%

43.5%

39.4%

33.8%

21.1% 29.9%

9.2%

7.5%

41.7%

23.8%

20.5%

55.6%

42.1%

24.8%

57.8% 49.1%

53.5% 29.5%

35.5% 17.6%

59.6%

44.2%

22.8%

52.4%

44.2%

31.4%

64.4% 58.0% 48.1% 41.6% 29.2%

44.9% 55.1% 23.0% 34.1% 26.7%

28.4% 22.2% 15.5% 25.9% 22.6%

18.1% 31.7%

19.5% 31.1%

15.8% 26.8%

35.4%

32.6%

29.1%

26.3%

22.0%

17.4%

23.9%

22.7%

21.1%

40.0%

32.2%

25.3%

18.0%

15.6%

12.7%

29.3%

15.6%

11.3%

22.3%

19.7%

17.5% (continued)

Contribution of the Informal Economy to GDP

75

Table 3.27 (continued) Countries (years) Belarus (2013) Bulgaria (2011) Estonia (2014) Kazakhstan (2013) Kyrgyzstan (2013) Latvia (2012) Lithuania (2013) Macedonia (2011) Moldova (2013) Romania (2013) Russia (2013) Ukraine (2013)

Informal sector (including agriculture) in % of total GDP 8.3%

Informal sector (excluding agriculture) in % of nonagricultural GVA 7.5%

Informal sector (excluding agriculture) in % of total GDP 6.2%

15.8%

16.3%

13.4%

7.9%

8.8%

7.4%

23.6%

23.6%

20.9%

33.5%

26.3%

19.4%

12.0% 10.8%

13.3% 10.1%

11.2% 8.8%

19.2%

13.9%

10.7%

15.6%

11.5%

8.3%

24.8%

25.0%

20.7%

8.4% 19.8%

7.9% 19.3%

6.5% 15.2%

Source: Our compilations of UN (2015) and national sources for Africa and Mexico Note: Non-­weighted averages by region

informal sector represents 29.2% of total nonagricultural gross value added. The minima are observed in Egypt for the three indicators (27.8%, 14.7% and 16.9%, respectively) and the maxima in Algeria (respectively, 43.5%, 39.4% and 33.8%). In India, still the only country of Asia to publish estimates of the contribution of informal sector to GDP, the share of total GDP stands at 41.6% (with agriculture) and at 25.9% without agriculture. The informal sector represents 34.1% of nonagricultural GVA. In Latin America and for the years 2010s, six estimates are available. The estimates here prepared approximate and assimilate the informal sector to the household sector (minus subsistence agriculture, households with employed persons and imputed rents), but national methodologies and official definitions may be more complex. In the case of Mexico and emerging economies, the informal sector is only a segment of unincorporated enterprises of the household sector, and it also includes other modalities of informality. The informal sector (including agriculture) represents in average 29.2% of total GDP with a maximum in Nicaragua (40%) and a minimum in Brazil (18.0%). Excluding agriculture, the informal sector contributes in average to 22.6% of total GDP (maximum in Guatemala with 29.1% and minimum in Brazil with 15.8%), and it contributes to 26.7% of nonagricultural gross value added (32.6% in Guatemala and 19.5% in Brazil).

76

3  Trends and Characteristics of the Informal Economy and Its Components

Finally it is for the transition economies that the number of estimates is the greatest with 15 countries. This is not surprising, given that the system of national accounts has been implemented recently in these former socialist countries, which were used to apply a specific system of material balances: applying a new system, national accountants have tended to strictly follow the rules of the central framework of the SNA 1993. The private sector recently emerged in the transition countries where paid employment in public enterprises was the rule, and it is expected to grow more and more, especially the microenterprises of the informal sector, hence the importance of the efforts towards their measurement. With a contribution of 18% to total GDP in average, the informal sector (including agriculture) is at a maximum in Kyrgyzstan (33.5%) and a minimum in Estonia (7.9%). When excluding agriculture, the contribution of informal sector drops down to 12.7% in average (20.9% in Kazakhstan and 6.2% in Belarus) and to 15.6% of nonagricultural GVA (26.3% in Kyrgyzstan and 7.5% in Belarus). During the years 2000s, sub-Saharan Africa was the region with the largest estimates for the contribution of informal sector to GDP: nearly 2/3 including agriculture, 1/3 excluding agriculture and ½ of nonagricultural gross value added. It was followed by India with around 50% of total GDP (including agriculture) and 38% excluding it and 46% of nonagricultural GVA. Then came the Northern African countries with, respectively, 37%, 24% and 27%, Latin America (with 30%, 24% and 25%) and lastly transition countries (with 19%, 11% and 14%). In the 2010s all regions have the same ranking, though at a lower level. However the informal economy increases its share of nonagricultural GVA in Latin America and its share of total GDP in transition economies (without agriculture). The informal sector (in its broad sense, including agriculture, as well as in its strict sense, excluding agriculture) is the largest contributor to GDP in the regions where agriculture is predominant (sub-Saharan Africa and Asia). Assessing trends in the contribution of the informal sector to GDP is more difficult because changes in values are only due to assumptions of national accountants and the only noticeable changes are structural, when a new base year allows radical changes based on updated sources and new surveys. This is the case for data available for the years 2010s that have been compiled for new base years. Generally speaking these new data seem to show a slight decrease of the share of informal sector in GDP. This result is consistent with the observed drop in employment in the informal economy in most regions, except sub-Saharan Africa, but the differences in the sets of countries cannot allow deeper analyses. Data for Mexico allows assessing the trends of the informal economy during the most recent period. Tables 3.28 and 3.29 and Chart 3.14 are directly extracted from INEGI, highlighting the trends of the informal economy (including agriculture) during the period 2004–2015. They clearly show that the informal sector and the other modalities of informality follow contradictory trends: the annual growth rate of the informal sector accelerates when other modalities of informality slow down, and conversely the former slows down when the latter accelerates. Interestingly one can observe the impact of the 2008 financial crisis, with a rapid deceleration of the informal economy and of the other forms of informality, whereas the informal sector does not seem affected by the crisis (showing a slight increase of the growth rate (at 1.5%)). Tables 3.30 and 3.31 and Chart 3.15 highlight the same phenomenon, but for the nonagricultural informal

2004 23.3 11.1 12.1

2005 23.8 11.4 12.4

2006 23.3 11.1 12.2

2007 23.5 10.9 12.6

2008 22.9 10.2 12.7

2009 24.4 12.4 12.0

2010 23.5 11.5 12.0

Source: INEGI http://www.inegi.org.mx/est/contenidos/proyectos/cn/informal/default.aspx Notes: r revised, p preliminary

Informal economy Informal sector Other informal

2003 23.6 11.3 12.3

2011 23.1 11.4 11.7

2012 23.4 11.1 12.3

2013 23.6 11.3 12.3

Table 3.28  Trends in components of the informal economy (including agriculture) as shares of GDP in Mexico 2003–2016 2014 23.1 11.3 11.8

2015r 22.9 11.4 11.5

2016p 22.6 11.2 11.4

Contribution of the Informal Economy to GDP 77

2005 3.5 6.5 0.7

2006 4.0 2.2 5.8

2007 2.5 1.2 3.7

2008 1.2 1.2 1.1

2009 −2.7 1.5 −6.7

2010 2.8 0.2 5.3

Source: INEGI http://www.inegi.org.mx/est/contenidos/proyectos/cn/informal/default.aspx Notes: r revised, p preliminary

Informal economy Informal sector Other informal

2004 3.8 3.9 3.7

2011 2.3 2.2 2.4

2012 4.8 2.9 6.6

2013 0.3 0.2 0.4

Table 3.29  Annual growth rate of components of the informal economy (including agriculture) in Mexico 2004–2016 2014 −0.9 0.9 −2.5

2015r 0.8 0.1 1.4

2016p 1.2 0.8 1.6

78 3  Trends and Characteristics of the Informal Economy and Its Components

Chart 3.14  Annual growth rate of components of the informal economy (including agriculture) in Mexico 2004–2016 Source: INEGI http://www.inegi.org.mx/est/contenidos/proyectos/cn/informal/default.aspx based on Table 3.28

Contribution of the Informal Economy to GDP 79

2003 24.7 12.2 12.5

2004 24.1 11.7 12.4

2005 24.1 11.7 12.4

2006 23.4 11.1 12.3

2007 23.2 10.7 12.5

2008 23.0 9.8 13.2

2009 24.5 12.7 11.8

2010 23.9 12.1 11.8

2011 23.5 12.1 11.4

2012 23.1 11.5 11.5

2013 22.7 11.7 11.1

2014 22.0 11.6 10.4

Source: Author’s calculations based on detailed tables from INEGI http://www.inegi.org.mx/est/contenidos/proyectos/cn/bs/tabulados_1.aspx#

Informal economy Informal sector Other informal

Table 3.30  Trends in components of the informal economy (excluding agriculture) as shares of GDP in Mexico 2003–2016 2015r 21.7 11.7 10.1

80 3  Trends and Characteristics of the Informal Economy and Its Components

2005 9.3 8.9 9.8

2006 8.7 6.1 11.2

Source: Author’s calculations based on Table 3.29 Notes: r revised, p preliminary

Informal economy Informal sector Other informal

2004 10.7 9.1 12.3

2007 7.3 5.0 9.4

2008 7.8 −0.5 15.0

2009 3.5 25.9 −13.1

2010 7.2 4.2 10.4

2011 8.3 10.2 6.3

2012 5.8 3.1 8.7

Table 3.31  Annual growth rate of components of the informal economy (excluding agriculture) in Mexico 2004–2016 2013 0.8 3.2 −1.6

2014 2.2 4.7 −0.4

2015r 3.6 5.9 1.1

Contribution of the Informal Economy to GDP 81

Chart 3.15  Annual growth rate of components of the informal economy (excluding agriculture) in Mexico 2004–2016 Source: Table 3.30

82 3  Trends and Characteristics of the Informal Economy and Its Components

Contribution of the Informal Economy to GDP

83

economy, the trends are sharp: during the crisis, the informal sector has dramatically increased (+25.9%), whereas the other forms of informality have dramatically dropped (−13.1%). Globally the informal economy increased at a pace of 3.5% (that is more slowly than in previous years), as a consequence of the impact of the crisis on its two diverging components. In other words, during the crisis many microbusinesses turned to informal, whereas the formal firms dismissed their informal workers in a first attempt to absorb the shock, and it is probable that many of these dismissed workers entered the informal sector as own-account workers. Chart 3.15 highlights the diverging tendencies of the two components of the informal economy in almost all years of the period except during the 3 last years when they seem to follow the same trends. Chart 3.16 shows the relation between the share of employment in the informal economy and the contribution of the informal sector to GDP. As expected, Niger and Benin are located in the upper right corner, whereas transition countries stand at the lower left corner. In general, there is a strong relation between the two indicators: the share of the nonagricultural informal economy in GDP (GVA) is increasing with the share of informal economy in nonagricultural employment. Almost all countries are close to the linear trend except Algeria (where the methodology clearly overestimates the household sector) and Egypt (where it underestimates the household sector). To a certain extent, those figures are underestimated because the informal economy in general and the informal sector in particular are usually characterised by weak statistics, despite the recent progress of which the present compilation is an illustration. Furthermore, the contribution of informal sector to GDP does not always nor systematically take into account informal employment outside the informal sector, which is scattered all over the various institutional sectors. The volume of this subcomponent of the informal economy can be now estimated in terms of jobs: the question is then to know what value added can be imputed to these jobs. India attempted such an exercise with its labour input matrix (Kolli and Sinharay 2011a, b), and it estimated at 43.9% informal employment in the public and private corporate sector in 2004–2005 and at 21.6% its contribution to the gross value added of these sectors (12.1% in 1990–2000) and 34.7% of nonagricultural activities (including the informal sector). As already seen above, Mexico is also a country that succeeded in measuring the two components of its informal economy, both in terms of employment and in terms of value added. But at the same time, these same figures of the contribution of informal sector to GDP may be found overestimated because they are based on the assumption that the household sector can be assimilated to the informal sector. If this can be considered as approximately true in regions with large traditional subsistence agriculture and small formal sector, it is not so justified for emerging economies.

Informal sector (excluding agriculture) in % of non agricultural GVA

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0.6 NIG

BEN

0.5 MLI

CAM

0.4

COL

0.3 KGZ

ROU

0.2

TUN

BRA UKR ARM BGR Transition countries MKD MDA

0.1

0

BDI IND

ALG

RUS

10

20

30

Northern Africa AZE

MEX

NIC

GTM

Sub Saharan Africa BFA

Latin America TGO

HND

EGY

40 50 60 70 Informal economy in % of non-agricultural employment

80

90

100

Chart 3.16  Contribution of the informal sector to GDP and share of employment in the informal economy (Years 2010s) (26 countries) Sources: Database used for previous tables Note: See country codes by region and by alphabetical order in annex Tables 3.32 and 3.33

Conclusion In conclusion, and despite the lack or weakness of data, there is now much empirical evidence on the role of the informal economy as a factor of resilience for the poor and also middle classes in times of crisis, constrained growth or intense restructuring. In many countries the informal economy constitutes the main entry point to the labour market, especially for the youth and rural migrants. And for those who, in their main jobs, do not earn as much as they need (be they farmers, civil servants, employees or own-account workers), holding multiple jobs in the informal economy represents an opportunity. It is not then surprising that governments, between temptation to intervene or to let go, finally came to accompany the dynamics at stake in the informal economy, a tendency that sublimated in the recent unanimous adoption of the recommendation 204 on the transition from the informal to the formal economy by the tripartite International Labour Conference that succeeded in 2015 in making converging governments, workers and employers.

Annex

85

Annex Table 3.32  Country codes and country names by region

MENA Algeria 2014 Morocco 2013 Tunisia 2015 Egypt 2013 Iran 2002 Lebanon 2002 Palestine 2016 Syria 2007 Turkey 2014 Yemen 2014 Sub-Saharan Africa Angola 2011 Benin 2011 Botswana 2012 Burkina Faso 2014 Burundi 2014 Cameroon 2014 Chad 2011 Congo (Rep) 2008 Cote d’Ivoire 2012 Democratic. Rep. Congo 2012 Gambia 2013 Ghana 2015 Guinea 1998 Kenya 1999 Lesotho 2009 Liberia 2010 Madagascar 2012 Malawi 2013 Mali 2015 Mauritania 2012 Mauritius 2009 Mozambique 2009 Namibia 2016 Niger 2012 Nigeria 2013 Rwanda 2016 Senegal 2011 Seychelles 2012 Sierra Leone 2014 South Africa 2016 Sudan 2011

ALG MAR TUN EGY IRN LBN PSE SYR TUR YEM AGO BEN BWA BFA BDI CMR TCD COG CIV COD GMB GHA GIN KEN LSO LBR MDG MWI MLI MRT MUS MOZ NAM NER NGA RWA SEN SYC SLE ZAF SDN (continued)

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Table 3.32 (continued) Tanzania 2014 Togo 2013 Uganda 2012 Zambia 2014 Zimbabwe 2014 Latin America Argentina 2016 Bolivia 2015 Brazil 2014 Chile 2013 Colombia 2016 Costa Rica 2015 Dominican Republic 2015 Ecuador 2015 El Salvador 2014 Guatemala 2015 Haiti 2007 Honduras 2014 Mexico 2015 Nicaragua 2014 Panama 2014 Paraguay 2014 Peru 2015 Uruguay 2013 Venezuela 2012 Asia (South-South East) Bangladesh 2012 Cambodia 2012 India 2012 Indonesia 2016 Laos 2008 Malaysia 2013 Mongolia 2016 Myanmar 2015 Nepal 2008 Pakistan 2015 Philippines 2013 Sri Lanka 2014 Thailand 2016 Timor-Leste 2012 Vietnam 2008 Transition countries Albania 2016

TZA TGO UGA ZMB ZWE ARG BOL BRA CHL COL CRI DOM ECU SLV GTM HTI HND MEX NIC PAN PRY PER URY VEN BGD KHM IND IDN LAO MYS MNG MMR NPL PAK PHL LKA THA TLS VNM ALB (continued)

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87

Table 3.32 (continued) Armenia 2015 Azerbaijan 2016 Bulgaria 2012 Kyrgyzstan 2008 Macedonia 2010 Moldova 2015 Romania 2004 Russia 2016 Serbia 2016 Slovakia 2008 Ukraine 2015

Table 3.33  Country codes by alphabetical order

Country code AGO ALB ARE ARG ARM AUS AUT AZE BDI BEL BEN BFA BGD BGR BIH BLR BOL BRA BWA CAF CAN CHE CHL CHN CIV CMR COD COG COL COM

ARM AZE BGR KGZ MKD MDA ROU RUS SRB SVK UKR

Country name Angola Albania United Arab Emirates Argentina Armenia Australia Austria Azerbaijan Burundi Belgium Benin Burkina Faso Bangladesh Bulgaria Bosnia and Herzegovina Belarus Bolivia Brazil Botswana Central African Republic Canada Switzerland Chile China Cote d’Ivoire Cameroon Congo, Dem. Rep. Congo, Rep. Colombia Comoros (continued)

Table 3.33 (continued) Country code CPV CRI CUB CZE DEU DJI DNK DOM DZA ECU EGY ERI ESP EST ETH FIN FRA GAB GBR GEO GHA GIN GMB GNB GNQ GRC GTM GUY HKG HND HRV HTI HUN IDN IND IRL IRN IRQ ISL ISR ITA JAM JOR JPN KAZ KEN

Country name Cape Verde Costa Rica Cuba Czech Republic Germany Djibouti Denmark Dominican Republic Algeria Ecuador Egypt, Arab Rep. Eritrea Spain Estonia Ethiopia Finland France Gabon United Kingdom Georgia Ghana Guinea Gambia, The Guinea-Bissau Equatorial Guinea Greece Guatemala Guyana Hong Kong SAR, Honduras Croatia Haiti Hungary Indonesia India Ireland Iran, Islamic Rep. Iraq Iceland Israel Italy Jamaica Jordan Japan Kazakhstan Kenya (continued)

Table 3.33 (continued) Country code KGZ KHM KOR KSV KWT LAO LBN LBR LBY LKA LSO LTU LUX LVA MAR MDA MDG MEX MKD MLI MMR MNE MNG MOZ MRT MUS MWI MYS NAM NER NGA NIC NLD NOR NPL NZL OMN PAK PAN PER PHL PNG POL PRK PRT

Country name Kyrgyz Republic Cambodia Korea, Rep. Kosovo Kuwait Lao PDR Lebanon Liberia Libya Sri Lanka Lesotho Lithuania Luxembourg Latvia Morocco Moldova Madagascar Mexico Macedonia, FYR Mali Myanmar Montenegro Mongolia Mozambique Mauritania Mauritius Malawi Malaysia Namibia Niger Nigeria Nicaragua Netherlands Norway Nepal New Zealand Oman Pakistan Panama Peru Philippines Papua New Guinea Poland Korea, Dem. Rep. Portugal (continued)

Table 3.33 (continued) Country code PRY PSE QAT ROU RUS RWA SAU SDN SEN SGP SLE SLV SOM SRB SSD STP SUR SVK SVN SWE SWZ SYC SYR TCD TGO THA TJK TKM TLS TTO TUN TUR TZA UGA UKR URY USA UZB VEN VNM VUT WSM YEM ZAF ZMB ZWE

Country name Paraguay West Bank and Gaza Qatar Romania Russian Federation Rwanda Saudi Arabia Sudan Senegal Singapore Sierra Leone El Salvador Somalia Serbia South Sudan Sao Tome & Principe Suriname Slovak Republic Slovenia Sweden Swaziland Seychelles Syrian Arab Republic Chad Togo Thailand Tajikistan Turkmenistan Timor-Leste Trinidad and Tobago Tunisia Turkey Tanzania Uganda Ukraine Uruguay United States Uzbekistan Venezuela, RB Vietnam Vanuatu Samoa Yemen, Rep. South Africa Zambia Zimbabwe

References

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INEGI. (2017). Sistema de Cuentas Nacionales de México. Medición de la economía informal. 2003 - 2015, Base 2008. Valor agregado bruto del sector informal, serie 2003-2015, por sector de actividad. Mexico. INS. (2005). Enquête sur l’emploi et le secteur informel au Cameroun en 2005 (EESI). Yaoundé. 82p. INS. (2007). L’Emploi, le Chômage et les Conditions d’Activité en République Démocratique du Congo : Principaux résultats de la phase 1 de l’Enquête 1-2-3 2004-­2005. Kinshasa.58p. INS. (2011). Deuxième enquête sur l’emploi et le secteur informel au Cameroun en 2010 (EESI 2). Phase 1. Enquête sur l’Emploi. Yaoundé. 131p. INS. (2014). Enquête 1-2-3. Résultats de l’enquête sur l’emploi, le secteur informel et sur la consommation des ménages 2012. Kinshasa.163p. INSD. (2000). Analyse des résultats de l’enquête prioritaire sur les conditions de vie des ménages 1998. Ouagadougou. 282p. INSD. (1996). Analyse des résultats de l’enquête prioritaire sur les conditions de vie des ménages, Direction des Statistiques Générales, Projet d’Appui Institutionnel aux Dimensions Sociales de l’Ajustement, Ouagadougou, 278p. INSEED. (2006). Deuxième Enquête sur la Consommation et le Secteur Informel au Tchad 2003– 04 (ECOSIT2). Rapport final. N’Djaména. 100p. INSEED. (2013). L’Emploi au Tchad en 2011. Troisième Enquête sur la Consommation et le Secteur Informel au Tchad (ECOSIT3). Rapport final. N’Djaména. 102p. INSTAT. (2013). Enquête Nationale sur l’Emploi et le Secteur informel ENEMPSI 2012. Vols. 1 and 2. Antananarivo. 85+91p. ISTAT. (1999). L’occupazione non regolare nelle nuovo stime di contabilita nazionale, Anni 1992– 1997, ISTAT, Roma, 15 + 5p. Kolli, R., & Sinharay, A. (2011a, July–December). Share of Informal sector and Informal Employment in GDP and Employment, The Journal of Income and Wealth, Indian Association for Research in National Income and Wealth, New Delhi, 33(2). Kolli, R., & Sinharay, A. (2011b, July–December). Informal Employment by Institutional Sectors and Activities in India. The Journal of Income and Wealth, Indian Association for Research in National Income and Wealth, New Delhi, 33(2). National Bureau of Statistics. (2007). Analytical Report of the Integrated Labour Force Survey ILFS 2006, Dar Es Salam, 124p. National Bureau of Statistics. (2015). Analytical Report of the Integrated Labour Force Survey ILFS 2014, Dar Es Salam, 152p.  ONS. (various years). Enquête Emploi auprès des Ménages. Alger. Schneider, F. (2005). Shadow economies of 145 countries all over the world: Estimation results over the period 1999 to 2003. Linz: Johannes Kepler University of Linz. Schneider, F., Buehn, A., & Montenegro, C. (2010). Shadow Economies all over the World: New Estimates for 162 Countries from 1999 to 2007. Background paper for In from the Shadow: Integrating Europe’s Informal Labor. Southern Europe/Baltic countries: A World Bank regional report on the informal sector in Central. Schneider, F., & Enste, D. (2000). Shadow Economies: Size, Causes, and Consequences. Journal of Economic Literature, XXXVIII, 77–114. SNA. (1993). System of national accounts. New York: Commission of the European Communities/ IMF/OECD/UN/WB. SNA. (2008). System of national accounts. New York: Commission of the European Communities/ IMF/OECD/UN/WB. Standing, G. (1999). Global Labour Flexibility: Seeking distributive justice. Basingstoke: Macmillan, 441p. ZIMSTAT. (2015). Labour Force and Child Labour Survey LFCLS 2014. Harare. 295p.

Chapter 4

Policies and Actions Addressing Populations Depending on the Informal Economy

Introduction The faculty of resilience of the informal economy early caught the attention of governments hesitating between intervention and laisser-faire. Why intervene given that jobs are created? But how let it go given these jobs are not decent? Today official initiatives towards the informal economy take place within the conceptual and empirical framework of the transition from the informal to the formal economy (ILC recommendation 204). Since its inception as a concept and the first attempts of its measurement, and also depending from which end of the telescope it is observed, the informal economy has inspired two main types of policies from the governments or the international institutions: the first one is a basic component of poverty alleviation strategies, that is, the provision of income-generating activities or of a minimum of workdays per period of time; the second one is the support to the creation or the promotion of microenterprises. Through their main components and variants, these policies can be analysed around three main pillars that are social protection, skills enhancement through Technical and Vocational Education and Training (TVET), and finance (especially micro-finance), as well as two specific approaches of upgrading the informal activities within the value chain and organising the populations dependent on the informal economy. Skills enhancement and finance deal with the supply side of the informal production, seeking to improve or develop the means of production, increasing the manpower’s skills, or a more capital-intensive organisation of the micro-firms. Social protection on the other hand is a different approach that could be seen as intervening further to the supply side approach. As a matter of fact, it has often been undertaken independently and even prior to policies addressing the improvement of the means of production. By many aspects, a more universal social protection system can be considered as an efficient means of increasing the

© Springer Nature Switzerland AG 2019 J. Charmes, Dimensions of Resilience in Developing Countries, Demographic Transformation and Socio-Economic Development 10, https://doi.org/10.1007/978-3-030-04076-5_4

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productivity of the workforce, making it healthier and more confident in the future and in a soothed relationship with the employers. However, at the macroeconomic level, the very first concern of the States and of the financial institutions providing aid and counselling to the governments of developing countries was about taxation: if it was true that the informal economy did represent such a share of GDP, then it should be transformed into governments’ resources and revenues, all the more so as the provision of support to these firms transform them into formal activities that become liable to the payment of taxes. In this chapter, we will address successively the issue of taxation of informal activities, then the policies aiming at upgrading the informal activities within the value chain and at organising the populations dependent on the informal economy and finally the three pillars of policies designed to tackle the informal economy: social protection, skills enhancement and finance.

Taxing the Informal Activities Although the willingness to tax the poor may be found paradoxical and counter-­ productive in countries where tax evasion is likely to be more important among formal actors, the huge numbers of informal operators allow considering the broadening of a very narrow tax base. Doing so, governments are focussing their action towards the upper tier of the informal economy  – that is, the informal microenterprises  – rather than the lower tier or the income-generating activities. Many governments and international institutions, such as the World Bank, often continue to apprehend the question of the informal economy through the only glasses of tax evasion and the shadow economy: the share of GDP or the amount of loss in taxes is deducted through macromodelling (Schneider’s estimates are very often taken as references for the size of the informal economy, what they are not; see Chap. 2). The shortfall for VAT is stressed. Early research by ILO in Africa and Latin America have shown that if they were compelled to pay the various taxes to which the formal sector is submitted, many of the informal activities would not be able to survive (Maldonado 1999). On the other hand, informal entrepreneurs pay for the value added tax (VAT) on their purchases as if they were final consumers even if they don’t claim for VAT from their clients. Moreover, they are often submitted to the payment of briberies that surveys have shown to be at least equivalent and often higher than the payment of the taxes that they should have paid. Further, it is far from sure that the payment of official taxes would protect the informal operators from paying undue briberies (for a recent assessment of such informal taxes, see, for instance, Nkuku and Titeca 2018, about the Democratic Republic of Congo). And finally informal operators may not consider themselves as beneficiaries of the public services that taxes are supposed to provide to the population (electricity, water, sanitation, security, education, health, etc.), given their poor accessibility. In other words, they are not part of the social contract.

Upgrading the Informal Activities Within the Value Chain

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As a precondition of formalisation, the design of a tax that would fit with the realities experienced by the informal operators has been attempted in several countries in Africa and Latin America. In Burkina Faso, the contribution of the informal sector (become contribution of microenterprises: CME, in 2014) is a unique tax (called synthetic or presumptive tax in other countries) – paid quarterly – by the operators whose annual turnover is less than 15 million FCFA, the amount of which is fixed according to several criteria (geographical location, category of activity). However the impact is low, and the cost of collection is high. Some good practices can be quoted in Argentina and Brazil where fiscal taxes were combined with social contributions (Van Elk and de Kok 2014). In Argentina, the “Monotax” system was implemented in 1998 to replace the income tax, the VAT and the social contributions (including for pensions) by a unique monthly lump tax, the amount of which is fixed according to the level of annual turnover, the consumption of electricity or the size in square meters of the workshop (the one of the three criteria that determines the highest payment is applied). Globally, the total amount of taxes due is reduced as compared to what the micro-entrepreneur would have had to pay otherwise. Seventy percent of the receipts go to the social security and 30% to the local governments. The SIMPLES programme in Brazil consists in the simplification of the fiscal system by replacing the whole set of taxes and social contributions with a unique monthly contribution. Furthermore social contributions are no longer based upon salaries, but on a fixed share of total receipts (or turnover), hence an incentive for the entrepreneur to hire employees and/or register his (her) informal employees (Fajnzylber et al. 2011). These programmes generally exempt the beneficiaries from all taxation that would be due for prior activities. Another efficient method that was introduced in Sao Paulo in 2007 and proved to be successful consisted in taking the consumers’ viewpoint into account by organising lotteries on purchases’ receipts: it encourages clients to ask for proof of their purchase, and it puts pressure on businesses to register. Although sometimes accused of “crowding out of tax morale” and having negative long-term welfare effects (Fabbri and Wilks 2016), such programs have also been used in Argentina, Chile, Costa Rica, as well as in China (‘fapiao’), Greece, Slovakia and Portugal.

Upgrading the Informal Activities Within the Value Chain A value chain “describes the full range of activities that are required to bring a product or service from conception, through the intermediary phases of production (involving a combination of physical transformation and the input of various producer services), to delivery to final consumers, and final disposal after use” (Kaplinsky and Morris 2002). Value chain analysis is “the assessment of a portion of an economic system where upstream agents in production and distribution processes are linked to downstream partners by technical, economic, territorial,

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institutional and social relationships. The effects of policies targeting specific production processes extend their primary impacts in the economic system according to the same path as the main inputs and outputs. Analysing impacts of policy options through value chains provides decision makers and other stakeholders with anticipated evidence on likely changes directly induced by policies” (Bellù 2013). For development projects and their implementing partners aiming to the enhancement of the livelihoods for people dependent on the informal economy, it has become common to conduct value chain analyses if only for ensuring that the income-generating activities or the informal activities they try to support among the populations of beneficiaries are not condemned to fade away due to the lack of commercial opportunities. How to be sure that the skills training provided to the youth fit to the markets’ needs? How to be confident that the production for which inputs are purchased will meet customers beyond the tight local markets? Preliminary value chain analyses are a preamble accompanying baseline surveys that capture the situation of the targeted populations at the beginning of the project implementation. The conceptual framework of value chains for the informal economy is traditionally oriented towards home-based workers subcontracted – through various intermediaries  – by large outsourcing firms seeking cuts in their labour costs, maximisation of their profits and flexibility (see, e.g. McCormick and Schmitz 2002); that is what we have called “informal employment outside the informal sector” (see Chap. 2 supra). The informal workers within the formal sector are subcontracted directly by large companies, or indirectly through various intermediary enterprises on behalf of large companies. More broadly speaking “outworkers” are not generally working within the premises of large companies, but outside: in their own homes or in unsecure premises or also in secure premises but under harsh working conditions. The challenge of policies addressing the situation of these subcontracted outworkers in the value chain and their transition to the formal economy strive to make them benefit of more decent work conditions (see, for instance, Lin Lean Lim 2015 on the Ikea supply chain and the role of codes of conduct). Such situations are particularly vulnerable and likely to favour child labour as the home-based workers paid by the task will take advantage of being seconded by their children. The hard-working conditions and low pay that prevail in a system with subcontracting in cascade require actions in terms of obtaining decent work conditions through organisation, bargaining and social dialogue and on the other hand in terms of sensitisation towards enforcement and reinforcement of more efficient corporate social responsibility, making large companies accountable for the working conditions of the workers they hire, even indirectly through subcontracting, giving voice to consumers and their representative organisations. In Asia and Latin America, subcontracting of informal workers is widespread. Although these forms of labour relationships also exist in Africa, especially in Northern, Southern and Eastern Africa, value chains in these regions are above all relating to agriculture and primary products, including export crops where informal smallholders are equally concerned. And in such cases, the value chain is also a matter of contracting.

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Similarities can indeed be found between outworkers at the bottom of industrial value chains and farmers at the bottom of agricultural or agri-food value chains. The outworker in the garment industry may own his/her sewing machine and be provided with raw materials by the contractor. In the same way, dairy-processing companies may provide farmers owners of land with cows and various inputs. “The contract farmer is the most popular one (…). During the contract, the farmers receive the small cow, feed, medical treatment from the milk processor. They nurture cows in their own land and sell the raw milk to the same milk processor. The contract farmer plays an assembling role and gains the assembling profit, which is usually low. This type of farmer appears in Ho Chi Minh City” (Nguyen Viet Khoi and Tran Van Dung 2014). It could be said more clearly: farmers become quasi-wage earners, exactly like the garment outworkers, and they come to consider themselves as such, rather than working for their own account, all the more so as they are often closely tied up by debts. In Ethiopia or Zambia, or more generally in sub-Saharan Africa, the milk and dairy products value chain is a challenge (Abdulsamad and Gereffi 2016) raising concerns about collection of milk from a huge number of breeders, about preservation and cold maintenance all along the chain, guarantee of hygiene and unfair competition with multinational firms that benefit from production subsidies in developed countries. But it can also show a very high potential. As to the informal sector segment of the informal economy it is by no means an isolated set of activities that would operate autonomously. Many informal activities operate in connection with the global markets, for example, waste pickers, whose recycling work and recycled materials may find a second life on international markets. Informal workers are also diverse. In most part of the developing world, the informal self-employed predominate over the informal paid employees, but the home-based workers paid by the task are also likely to be part of value chains, as well as the very small producers or gatherers or collectors, such as the shea nuts producers in Ghana (see infra) or the milk and dairy products producers. Value chains policies or strategies are not per se policies or strategies fostering the transition to the formal economy: they can be instruments that aggravate the working conditions of the poor if they are not accompanied or implemented by institutions dedicated to this transition and to the enhancement of the livelihoods of people dependent on the informal economy. The insertion of informal workers into value chains may be synonymous of long hours and hard conditions of work for low wages or low rewards, whereas the inclusion into international markets may also provide new opportunities for small own-account producers and more favourable environment for the development of new businesses, innovative processes and better working conditions. Climbing up the value chain or expanding share in the value chain is a common strategy for enhancing the livelihoods of vulnerable populations dependent on the informal economy, especially when it is about agricultural or other primary products, or waste management. Contract farming is the equivalent of subcontracting in industries (Box 4.1).

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Box 4.1: An Interesting Good Practice in Value Chain by a EU-Funded Project “Market Access Through Cooperative Action” Implemented by Ghana-PlanetFinance The example of shea nut and butter producers is interesting in this regard. Shea nut is the fruit of a tree that grows in the bush of the Sudano-Sahelian region in sub-Saharan Africa. Shea nut gathering and processing is an exclusively female activity. At the top end of the value chain are very expensive cosmetics. Giving more space and value to the bottom end of the chain has been the objective of various development projects in Western Africa. These projects build on a better dissemination of price information (through mobile phones), bulking of the quantities gathered and processed and improvement in quality. So the intervention on the value chain is, on one hand, a matter of organising, coordinating, skills training and upgrading and raising awareness. On the other hand, it is a matter of infrastructures, equipment and change in obsolete production processes and access to credit. In Ghana, PlanetFinance (a French organisation of the social and solidarity economy) supported poor rural women shea producers who take their margins from collecting shea nuts, removing pulp and drying them, to perform additional activities in the value chain. Such activities included trade, gathering the products in bulk and increasing efforts to meet the quality and the quantity demands of large buyers. The objective was to capture a more important additional value in the chain through (1) an increase of the quantities produced (improved productivity, increased storage, sales at appropriate times when prices increase), (2) bundling volumes and (3) an improvement in quality. This type of strategy usually requires multipronged actions such as (1) organising, (2) sensitisation, (3) education and training, (4) grant of small loans through microcredit, (5) use of ICTs to access market information and manage operations and transactions at the bottom of the value chain and (6) building contractual relationships with international buyers or upper actors in the chain. Production prefinancing and warehousing services have an important impact on the quantities produced. Collective selling undermines the inability of producers to commit to future price levels. This is why, in the course of the project, a creative approach to improving chain governance was implemented. A social private company (the Shea Star Ltd.: SSL) was set up in which the women have shares (through Star Shea Network, SSN). SSL offers marketing services to the numerous member groups, searches international markets for nuts and butter buyers and takes charge of the commercialisation of the shea products that the women sell in bulk. This approach enables progress on increased savings and investments. SSL also managed to process refined shea butter through a tolling arrangement in Europe before sale to final clients. This significantly increased the volume of the unrefined shea butter that (continued)

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Box 4.1 (continued) women were able to sell. SSL plays a major role in supporting and assisting women to fulfil the protocols for fair trade, organic and traceable shea products, as well as in providing them with some key inputs such as packaging and prefinancing. Transparency in the distribution of value-added shares between SSL and women producers is ensured during the associations meetings. Community association members participate in the negotiation and distribution within their network. They also supervise the quality of nuts and butter through a quality assurance system and participate in the aggregation of products at approved warehouses. Occasionally, groups declined to process particular butter orders due to less motivating market prices. In other cases, they bargained to receive higher prices thus proving their empowerment in analysing market prices. Women producers can still sell their production to local markets or other buyers but are committed to the arrangements with SSL. This is because bulk selling enables them to put their earnings to good use such as for the payment of school fees, the purchase of household assets, working and farming tools. Despite its holistic approach, the women’s associations and the project missed however making arrangements with some support services such as transporters, owners of donkey carts, tricycles, “loading boys” who could have strengthened their place in the value chain. Regarding the question of whether the social enterprise model is working well, one could state that women producers are not always able to meet all orders from buyers. But when this happens, they must buy nuts or butter from other women outside their community groups. This has resulted in interesting cascading effects because the required quality from the outside women pushed the beneficiaries to share with them their improved practices.

 rganising the Populations Dependent on the Informal O Economy Organising is at the core of the actions and policies designed to enhancing the livelihoods of populations dependent on the informal economy. Whatever the angle under which the question is apprehended, organising is the way and means to address it. Organising aims at obtaining the recognition of informal workers’ rights and supporting the communities of working poor. Sooner or later, all projects intervening in the field come to the organisation of the populations they support, because organising is key for financing, for extending social protection, for increasing its

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share in the value chain and more generally to gain visibility, voice and self-esteem and confidence.

Organising Is Key for Financing Many community-based organisations are created and implemented in order to initiate saving and lending groups. Pooling resources helps increasing access to financing. They are a manifestation of the strength resulting from organising. These grassroots organisations are typically the background upon which public actors and/ or civil society organisations can build broader policies towards delineating more ambitious strategies for supporting microbusinesses or cooperatives, or achieving universal health or social protection coverage, or more generally transitioning from the informal to the formal economy. Organising is key for extending social protection. In the informal economy where most workers are self-employed, community-based organisations gather into larger saving and lending groups, which are a first step towards regular and adapted contribution to mutual funds ensuring health coverage and other risks that fit with the needs of populations. Governments as well as other actors in the field can help promoting these kinds of organisations. Organising is key for being taken into account as a player in the value chains. Seizing opportunities in value chains requires from producers at the bottom of the chains they become organised. Community-based organisations eventually supported by government actions or NGOs are generally the starting point towards increasing quantities collected and quality. These are the required conditions to be in a better position to negotiate with intermediaries or multinational firms in order to receive better prices and gain more room in the value chains. Organising is key for gaining visibility and voice and having their rights recognised.  “We Are Poor But So Many” is the title of a famous book written by Ela Bhatt  (2006), the founder of the Self-Employed Women Association (SEWA) in India. Collective action is the origin and purpose of trade unions and employers’ associations in the formal economy. The informal economy operators and workers need to follow the footprints of their predecessors in the formal sector, who can selves help in this regard. Organising is key for gaining self-esteem and confidence when facin public authorities. Not only numbers but also self-confidence is necessary to gain voice and visibility. One among recurrent difficulties encountered by populations dependent on the informal economy, especially women (but not only), is their shame when posing their problems or requests to the administration. Organising is a means towards acquiring self-esteem and confidence, with life skills attached to behavioural experiences in public when organising collectively around the main issues faced by the community of belonging.

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Formal workers have their trade unions and employers their own organisations. Each on their side, these organisations have opened their eyes and increased awareness about the informal economy, and both have attempted to open to the self-­ employed as well as to the informal workers (for the trade unions), with mitigated success. Early since the first discussions that paved the way to the adoption of the ILO Recommendation 204 on the transition from the informal to the formal economy, the trade unions engaged into the support and coaching of workers’ cooperatives and other types of associations of informal workers. As a matter of fact, it would be too much a top-down approach to pretend that the workers of the informal economy have not their own types or kinds of organisations. Many self-help groups long existed among vulnerable populations through which they exchange workforce (for instance, for cropping in peasant societies) or they save their meagre daily receipts for further access to credit (for instance, in these rotating systems of credit called “tontines” in Western and Central Africa or “merry-­ go-­rounds” in Eastern and Southern Africa (see Chap. 5 infra). Similar forms of associations also exist in Asia. In other contexts, some kinds of cooperatives continue to exist that have been implemented, for instance, in rural areas in order to facilitate the delivery of fertilisers or other agricultural inputs and the outflow of crops. Of course, some particular categories of workers, such as the domestic workers or the home-based workers, are especially deprived of any kind of grassroots organisations, due to their individualistic and isolated modes of production. Except in such situations where the creation of grassroots organisations is a fundamental starting point, in many other situations, the action regarding organisation will therefore be a support to pre-existing forms of organisations in order to strengthen them and to disembed them from their possible blockages or from their lack of resources. Two types of actions can be distinguished towards the organisation of vulnerable populations: a first and important one, which is illustrated by organisations such as SEWA (the Self-Employed Women Association) or WIEGO (Women in Informal Employment: Globalizing and Organizing), is the attempt to intervene at global (regional, national and international) and political levels in support of the recognition of informal workers’ rights. Examples of such actions refer to workers such as waste pickers, domestic workers, street vendors and transport workers, in other words informal workers who are not geographically located in places where they can meet together and who are especially vulnerable. Another type of action towards organising informal workers refers to local or sectoral development projects  – donor- or government-funded – that try to rely on or revitalise pre-existing self-help groups in order to help communities in self-financing or contributing to social protection schemes or more generally giving visibility and voice, confidence and self-esteem. The lack of legal protection as well as of social protection is a characteristic of workers in the informal economy because of the absence of recognition by policymakers and more generally by political authorities and also because of the

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absence of suitability of their situation for unionisation and collective bargaining (Chen et al. 2015; Spooner 2013; Schurman and Eaton 2012). In their paper for the Human Development Report 2015, Chen et  al. (2015) emphasise the need for informal workers to organise in order to overcome such structural disadvantages, and they note that they “are increasingly self-organizing or getting organized into unions, cooperatives, or associations” and that such organisations “have engaged in collective action of different forms: bargaining, negotiating and advocacy, mobilization and campaigns, production and marketing, and mutual aid and self-­ help”. The objective is to increase voice collectively through organising and representation in policymaking, rule-setting, collective bargaining or negotiating processes. Two major actors in organising the informal workers: SEWA and WIEGO. Two institutions have been particularly active in the organisation of informal workers in the recent past and are still very active, the Self-Employed Women Association (SEWA) and the Global Network WIEGO (Women in Informal Employment: Globalizing and Organizing). Although dedicated to the cause of poor women, these two institutions have gone far beyond the support of poor women workers. Funded in the 1970s by Ela Bhat in Ahmedabad (India) as a set of cooperatives, the Self-Employed Women Association was recognised as a trade union in 1983, having gain affiliation to the International Union of Food, Agriculture, Hotel, Restaurant, Catering, Tobacco and Allied Workers. This was an important landmark as it meant for the first time that informal self-employed workers were recognised within the trade union movement as workers and as such with a right to form their own trade unions. With today more than 2 million members, SEWA is the largest trade union of informal workers in the world and pursues a joint strategy of struggle (collective bargaining, negotiations, campaigns and advocacy) and development in financial services (it is a major micro-finance institution), social services, housing and basic infrastructure services and training and capacity building. Not only SEWA organises its members in trade unions, but it also helps them to form cooperatives or other kinds of associations at local level as well as State or national federations (Chen et al. 2015). By many aspects, SEWA has “pioneered creative approaches to unionism, challenging the conception of what a union should be and do” (Bonner 2006). As a trade union, SEWA struggled towards the adoption of the Convention on Home-Based Workers in 2008 and the Convention on Domestic Workers in 2011, and as a cooperative, it is organised around four sources of security/insecurity: work, income, food and social security. WIEGO was founded in 1997 with SEWA as one of its founding members. It is an international network of membership-based organisations, activists, practitioners from development agencies, researchers and statisticians that focus on securing livelihoods for the working poor, especially women, in the informal economy. It aims at creating change by building capacity among informal worker organisations, expanding the knowledge base and influencing local, national and international policies. One of its main objectives has been to support the development of membership-based organisations (MBOs) – trade unions, cooperatives and worker associations  – that are democratic and representative, as well as national and

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international alliances and networks among which the main are the International Domestic Workers Federation (IDWF), HomeNet (for the home-based workers) and StreetNet International (for the street vendors).

 ain Pillars of Policies Designed to Tackle the Informal M Economy: Social Protection, Skills Enhancement and Financing Beyond interventions at the macro-level through legislation, regulatory systems and creation of enabling environment, the tools and instruments used by the donors in their interventions aimed at the informal economy mainly consist in the provision of training, micro-finance, access to markets and services, advocacy, representation and voice. Through their main components and variants, the policies tackling the informal economy can be analysed around three main pillars that are social protection, technical and vocational skills’ enhancement and finance (especially micro-finance). Skills’ enhancement and provision of finance deal with the supply side of the informal production, seeking to improve or develop the means of production, increasing the manpower’s skills, or a more capital-intensive organisation of the micro-firms. Social protection on the other hand is a different approach that could be seen as intervening further to the supply side approach. As a matter of fact, it has often been undertaken independently and even prior to policies addressing the improvement of the means of production. By many aspects, a more universal social protection system can be considered as an efficient means of increasing the productivity of the workforce, making it healthier, more confident in the future and in a smoothed relationship with the employers.

Social Protection The expansion of social protection is key for policies aiming at encouraging the transition from the informal to the formal economy. As early as 2011, the African Union adopted a Social Protection Plan for the Informal Economy and Rural Workers (SPIREWORK), “in recognition of significant contribution of the informal economy to GDP, jobs creation, poverty alleviation, social cohesion and political stability in Africa”. The African Union acknowledges that “social protection (…) has the potential to be the backbone of any strategy towards the modernization or (…) formalisation of the informal economy” (African Union 2011). Universal social protection is goal 1.3 of the SDGs: “Implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and vulnerable” and there are many other related targets as shown in Box 4.2.

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Box 4.2: Other Social Protection Related Targets Goals Target 1.5: “Build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disasters” Target 3.8: “Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all” Target 5.4: “Recognize and value unpaid care and domestic work through the provision of public services, infrastructure and social protection policies and the promotion of shared responsibility within the household and the family as nationally appropriate” Target 8.8: “Protect labour rights and promote safe and secure working environments for all workers, including migrant workers, in particular women migrants, and those in precarious employment” Target 10.4: “Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equality” Target 13.1: “Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries” Source: https://unstats.un.org/sdgs/indicators/indicators-list/

Various coalitions, partnerships and initiatives have emerged to deal with these issues: the Inter-agency Social Protection Assessments (https://ispatools.org/) that defines social protection as a set of policies and programs aimed at preventing or protecting all people against poverty, vulnerability and social exclusion, and designed a Core Diagnostic Instrument (CODI), in partnership with the major international institutions and bilateral donors, the EU Social Protection Systems Programme (http://www.oecd.org/dev/inclusivesocietiesanddevelopment/eu-socialprotection-systems-programme.htm), in partnership with OECD and Finland or also Socialprotection.org (http://socialprotection.org/). The World Bank and the ILO also decided to gather their efforts in a Universal Social Protection Initiative towards the achievement of SDGs goal 1.3 (World Bank and ILO 2015). “Achieving universality would facilitate the delivery of the World Bank’s corporate goals of reducing poverty and increasing shared prosperity and the ILO’s mandate of promoting decent work and social protection for all” (and could we add now, the transition from the informal to the formal economy) (Box 4.3).

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Box 4.3: The Social Protection Floors In 2012, the International Labour Conference adopted recommendation 202 on social protection floors: Social protection floors are nationally defined sets of basic social security guarantees that should ensure, as a minimum that, over the life cycle, all in need have access to essential healthcare and to basic income security which together secure effective access to goods and services defined as necessary at the national level.

Recommendation 202 applies to all, including workers in the informal economy. National social protection floors should comprise at least the following four social security guarantees, as defined at the national level: 1. Access to essential healthcare, including maternity care 2. Basic income security for children, providing access to nutrition, education, care and any other necessary goods and services 3. Basic income security for persons in active age who are unable to earn sufficient income, in particular in cases of sickness, unemployment, maternity and disability 4. Basic income security for older persons

As anyone can easily imagine, such systems have difficulties to become universal because in many countries, paid employment represents a small share of the active population: this is particularly the case in countries where agriculture and/or informal self-employment is widespread. Although the contributory systems have been extended to self-employment and to many if not all kinds of occupations, the coverage of social security fails to be universal. This is why social assistance programmes (“social safety nets”) have been generalised. They provide targeted populations with coverage for some of the risks at the costs of the taxpayer (redistributive systems). Moreover, in the absence of State support, vulnerable populations may quasi-­ exclusively rely on self-help groups that can provide assistance to their members in need. Extended families provide support to cover health expenditures, unemployment, and other benefits and allowances. It will be shown (Chap. 5 infra) that in sub-­Saharan Africa in the 1980–1990s, for instance, monetary and in-kind transfers between households – including remittances – could represent as much as 25% of the average household income that is a share equivalent to social expenditures or public transfers in European countries.

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Social protection systems thus have three dimensions: –– An insurance dimension that relates to the formal sector –– An assistance dimension that relates to the most vulnerable populations uncovered by social security –– A community-based dimension Safety nets are diverse: in 2015, the World Bank assessed the state of social safety nets at world level (World Bank 2015). From price subsidies on basic goods or cash transfers that have sometimes become conditional to infants’ vaccination and children’s schooling (behavioural conditions) or subject to several criteria (such as income levels or demographic or socio-economic characteristics) to the provision of a number of workdays through labour-intensive public works, there is a huge variety of safety nets. The most well-known and successful of these conditional cash transfers programmes is the Brazilian “Bolsa Familia” that dramatically lowered the national poverty rate. Such schemes are, however, often criticised for their deficient targeting, especially when non-conditional. The World Social Protection Report 2017–2019 (ILO 2017) recalls that according to ILO estimates, in 2015 “only 29% of the working-age population and their families across the globe had access to comprehensive social security systems. In other words, almost three-quarters, or 71 per cent, of the world’s population, about 5.2  billion people, do not enjoy access to comprehensive social protection”: A global figure that is close to our estimates for employment in the informal economy. This is why the reflexion on social protection is at the core of the policies addressing the informal economy, especially the policies for the transition to the formal economy. In this regard, civil society organisations and NGOs implementing donor-­ funded projects have an important role to play in strengthening the capacities of public institutions as well as in reinvigorating traditional self-help groups towards ensuring a more effective social protection. Capacity strengthening is on the agenda of most development policies and projects. It applies to public institutions, private and community institutions as well as individuals. Capacity strengthening of public institutions can help meeting the goal of formalising the informal economy by expanding its benefits to the most remote and vulnerable populations. Projects aiming at enhancing the livelihoods of vulnerable populations dependent on the informal economy have nonetheless found original ways and means of achieving the objective of expansion of coverage. This includes examples of national health insurance systems that combine organising small informal operators with providing outreach. It also includes incentivising local and central administrations in charge of the national health insurance system. The basic principle is that public service providers should go to the population to offer the said services and collect corresponding taxes or premiums instead of waiting for populations to come forward on their own. Conducting outreach and incentivising local or central administrations (social security, health insurance services) to search and convince informal economy operators to register rather than wait for them to come and register on their own is a good practice that has been recently implemented in various contexts. In Ghana, for

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instance, the National Health Insurance System (NHIS) was established under the Act 650 in 2003 in order to “provide basic health services to persons resident in the country through mutual and private health insurance schemes”. Members receive a card, which enables them to go to hospital and benefit from general outpatient services, inpatient services, oral health, eye care, emergencies and maternity care, including prenatal care, normal delivery and some complicated deliveries, without direct payments. In 2008, nearly 54% of the population were covered, but vulnerable groups failed to benefit from the scheme (Abebrese 2012) (Box 4.4).

Box 4.4: An Interesting Good Practice in Expanding Social Protection by the EU-Funded Project “Market Access Through Cooperative Action” Implemented by Ghana-PlanetFinance The NHIS was also successfully involved in facilitating the registration and membership card renewal of the EU supported project’s beneficiaries (already mentioned in this chapter under the Box on value chain). The project focused, among other aspects, on market access of women shea producers through cooperative action. Using a combination of sensitisation, logistics support and techniques for facilitating registration and organising women producers, it was possible to reach the goal of improved access to these public health services. Meetings with the staff of regional offices of NHIS were used to convince them to participate in sensitising clients on the importance of the NHIS and help them understand that the project valued their role as service providers. Logistics support, such as vehicle and a public address system, was also provided to facilitate their travel to the sites for mass registration.  NHIS staff gave presentations of the role of their institution and the benefits for the population in the project field sites. The NHIS staff was asked numerous questions since many women were unaware of the existence of such health insurance schemes. They also had to listen to complaints about non/late delivery of cards, difficulties in renewing cards as well as about the frustrations that card bearers encountered at various delivery points. This exposure helped make the NHIS staff more aware of the harsh conditions of remote and vulnerable populations. Techniques for facilitating registration included getting women to make contributions in instalments towards registration and renewals. It also included scheduling bulk registrations in the communities thus extending registration operations beyond the project’s beneficiaries through a cascading effect. Project officers also contributed by picking up the expired cards of women and bringing them to the NHIS offices. Women producers have been organised in community social funds (CSF) that were connected to micro-finance institutions to obtain financial support for their production activities but also for NHIS registration. This allowed the CSF members to share their health (continued)

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Box 4.4 (continued) risks as they can pool resources together through the CSF to access healthcare. An increased attendance to health facilities has been observed with earlier treatment of sicknesses/diseases. NHIS officials now travel to renew expired cards and register new clients as a result of the collaboration between the NHIS and the shea associations. The good practice described is an illustration of the role that civil society organisations can play in complementing government institutions to fulfil development agendas such as the achievement of universal healthcare services. The logistical support provided may raise the question of the sustainability of the good practice. It is up to the NHIS administration to incentivise its staff through the achievement of quantitative goals for new registrations and renewals.

For WIEGO (Women in Informal Employment: Globalizing and Organizing), the international network dedicated since 1997 to empowering informal workers and securing informal livelihoods, social protection is a high priority for informal workers who list, after the increase and sustainability of their income, access to health services, child care and savings/security for the old age, among their highest priorities. Based on its wide experience, WIEGO considers that informal workers should be integrated, wherever possible, into formal schemes, rather than being covered through small schemes especially designed for them. Short-term safety nets are definitely not enough, and social protection for informal workers must be mainstreamed and be a long-term commitment (http://wiego.org/wiego/social-protection-informal-workers). Although not fitting with such requirements, safety nets are still, in many countries, the only policies meant to provide the poor with a source of income. At world level, more than 1.9 billion people are covered by social safety nets (and among them, more than 526 million people are enrolled in the 5 major social safety nets). Globally, only one-third of the poor are yet covered. In average, a developing country runs about 20 different safety nets. Total spending in social safety nets totalled twice the amount needed to provide every person living in extreme poverty with an income of 1.25 US$ a day. However it represents much less than the amount of subsidies on fuel, which are possibly assumed to have crowded out public spending on social safety nets and pro-poor policies. The assessment established by the World Bank shows that in 2015, school feeding and unconditional cash transfers were the most widespread programs with 131 and 130 countries, respectively (out of 157), followed by public works (94 countries), unconditional in-kind transfers (UCTs, 92), then conditional cash transfers (CCTs, 63) and lastly fee waivers (reduced medical or educational or housing/utilities fees, 49). By the number of beneficiaries, Bolsa Familia is the most important of the CCTs (45 million representing 24% of the population) and Di-Bao (China)

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for the UCTs (75  million), whereas India comes first for the public works with MGNREGA (Mahatma Gandhi National Rural Employment Guarantee Act, 58 million beneficiaries). It is admitted that the typical cash transfer programs in lowerincome countries do not provide adequate income support as they cover only 10% of the average consumption of the poor. Especially the beneficiaries of public works programs that provide workdays at special periods of the year cannot be considered as having decent jobs and being covered by social protection. These types of programs are not therefore a means of transitioning from the informal to the formal economy. The BRAC’s graduation approach targeting the ultra-poor is one of such examples and presents the advantage of a strong and reliable empirical evaluation (BRAC 2016). BRAC stands for Building Resources Across Communities (initially Bangladesh Rehabilitation Assistance Committee). It is the largest non-governmental organisation for international development in the world and is said to reach more than 126 million people in 14 countries. In 2002, BRAC developed the Targeting the Ultra-Poor (TUP) Programme through a graduation approach after noticing that the social safety nets failed to reach the extremely poor. The approach addresses the social, economic and health needs of poor families simultaneously by combining the satisfaction of immediate needs with longer-term interventions in life and technical skills training, asset transfers, enterprise development and savings towards more sustainable livelihoods. Selected through a participative process, the beneficiaries are given a productive asset chosen from a list, and the corresponding training, as well as life skills training and consumption support during a given period. They have access to savings schemes and health services. All these supports are supposed to provide the households with a “big push” for a self-employment activity. The program is costly, reaching an amount per household equivalent to the household consumption as measured by the baseline survey. In fact, BRAC implements two approaches, one for the Specially Targeted Ultra-Poor (STUP) who receive the productive asset and the second for the Other Targeted Ultra-Poor (OTUP), marginally less deprived, who receive a soft loan for acquiring the asset. In Bangladesh, the program reached more than 600,000 households, and its replication was decided in 20 countries. In 2006, the approach was adapted and tested in eight countries. A randomised evaluation through control trials was conducted in six of them (India, Pakistan, Ethiopia, Ghana, Honduras, Peru), the results of which were published in Science (Banerjee et al. 2015) looking at the progress at the end of the program and 1 year later. Measuring the impact of the program on ten key outcomes (consumption, food security, productive and household assets, financial inclusion, time use, income and revenues, physical health, mental health, political involvement and women’s empowerment), the study found significant impact on all of them, 1 year after the end of the project or 3 years after the transfer of the productive assets. And in five out of six countries, the extra earnings exceeded the program cost. The study concludes that the multifaceted approach is sustainable and cost-effective. These remarks bring us back to the issue of taxation of informal activities (section “Taxing the informal activities” supra). Until recently and still often, social

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protection policies and fiscal policies were treated separately, and it is now widely admitted that they should be treated jointly. Observing that in some countries such as Ethiopia, almost 70% of the poor pay tax and that subsidies mainly benefit the rich, researchers have developed a new concept of “net social protection” (Lustig 2016a, b). According to them, analysing the impact of spending on social protection without the impact of financing on the poor is useless: When financing is added in the picture, the net effect of social spending on social protection can increase poverty. In Tunisia and Ethiopia, the first two deciles were found to be net receivers, whereas the others are net payers and in Ghana and Tanzania, all deciles were found to be net payers. The discussion on transition from the informal to the formal economy is therefore inseparable from the discussion on social contract, social inclusion, citizenship and the payment of taxes. For some policymakers and observers, presumptive taxes are the means to bring as many people as possible into the tax system, while for others citizenship is not linked to the payment of taxes. Social protection for informal workers must be mainstreamed and be a long-term commitment, but good practices and lessons learned from projects have still a role to play in the implementation of such national policies. In this respect, national policies could play a role of coordination. Furthermore, inefficient bureaucracies, immersed in clientelist practices, cronyism and devoid of resources, need to be sensitised, incentivised and accompanied by grassroots organisations in order to operate their mutation, not to say their revolution: serving the people rather than having the people at their service.

Technical and Vocational Skills Enhancement Originally, the informal sector was defined, among other criteria, by skills acquired on-the-job, outside the formal school system, most of its workers having no school level, even primary (ILO 1972). Since the early times of the development of the informal economy, when the acquisition of skills outside of the formal education system was a defining characteristic of informality, much water has passed under the bridge and in particular the proportion of population having completed primary and even secondary school has dramatically increased, including in the informal sector and in the informal economy at large. Today many informal workers have by now a primary or even secondary level of education, without even mentioning the young unemployed graduates temporarily (if not permanently) earning a living in the informal economy. Nevertheless, the fact remains that informal workers generally lack the theoretical background and the skills that they could have acquired in the Technical and Vocational Education and Training (TVET) formal system. On the other hand, youth trained in the TVET formal systems lack practical experience acquired on the job, while people having worked in the informal economy struggle to access to formal recognition of the skills they have acquired. In some cases, the large proportion of the youth that learn a craft on the job is older that they were formerly, and, above all, they are more

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educated than their masters or the owners of the workshops. Consequently, the status of apprentice has changed and trainees rather than apprentices are demanding more than learning by the eyes (Charmes 1982); they need that their practice be strengthened by more theoretical knowledge. Technical and vocational skills enhancement is therefore one of the major issues that should be taken into account in addressing the informal economy, for at least two reasons: on the one hand, on-the-job training remains the main provider of skills in the informal economy, and on the other hand, all the youth enrolled in TVET official programmes will not be absorbed in the formal labour market, and many of them will have to find their way within the informal economy. This highlights the strong interconnection between TVET provision and the informal economy on the following fields: –– Recognition and upgrading of skills developed in the informal economy, training of informal trainers and upgrading of informal apprenticeship schemes –– Access to higher-level skills training for improving the informal workers’ performance –– Formal and informal TVET schemes enabling learners to develop entrepreneurship and start-up businesses to come out from poor wages and insecure jobs TVET thus stands among the major domains of State intervention for improving the performances of informal operators, in terms of productivity and quality. The challenges lay in the place and role given to apprenticeship and the ways and means by which formal TVET systems strive to upscale the traditional-informal systems. Recognition of skills informally acquired is also a challenge as well as the adaptation of formal systems to match the new needs of the global economy. Two implications result from these observations: firstly, it is required from formal TVET systems that they ensure the complementation and recognition of skills acquired on the job in order that those skills can match with the formal sector’s needs so that the informal apprentices are not locked in the informal economy. And secondly, the young trainees of the formal TVET systems should be provided with entrepreneurship skills that would allow them creating jobs of their own and finding their way through private initiative, rather than counting on wage jobs that are lacking on the formal labour market. Today one of the most important issues that transition policies from the informal to the formal economy are facing is the absence of recognition of skills informally acquired, a precondition that is nevertheless necessary if a start is to be given to mobility from the informal to the formal enterprises. Furthermore, a recurrent criticism regarding TVET systems is their lack of adaptability and flexibility (especially as regard updating and ability to respond to the most sought needs). In many countries, the main problem with the official TVET systems is that they are often accused of producing types of qualifications that are not adapted – or no longer adapted – to market needs. That is, the types of training do not match the employers’ demand for different types of skilled workers. As a result, graduates of formal TVET institutions often have no other recourse than finding or creating a job in the informal economy. Employment in the informal economy is thus fed by the outputs

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of the formal TVET system that do not fit with the qualifications required in the labour market. At the same time, individuals who have completed informal systems of apprenticeship or skills training are prevented from finding jobs in the formal sector due to the absence of formal recognition of their training. Thus TVET formal systems should not be designed only to supply the formal sector with the trainees that it requires but also to improve the productive capacities of the larger number of informal workers. The paradox that TVET systems are facing is that informal on-the-job trainees often lack the theoretical background they deliver and should have access to their teachings, whereas at the same time, these formal systems lack opportunities of actual on-the-job practices and experiences in entrepreneurship for their trainees in the real conditions of labour markets dominated by the informal economy: practical experiences offered to formal trainees are often limited to large and medium enterprises rather than micro and small enterprises where they could be confronted to the real problems of entrepreneurship. Apprenticeship Traditional apprenticeship has played a major role in Africa: in Northern Africa with craftsman guilds and their influence on all industries and in sub-Saharan Africa on ethnic or cast basis. With the modernisation process, the system of traditional apprenticeship has lost weight but remains important in absolute numbers. Paradoxically, the importance of apprenticeship in the total labour force is less well-­ known than it was some decades ago. The reason is that, more and more often, labour force surveys have applied the legal minimum age for defining the labour force, whereas the measurement of child labour was sent back to dedicated modules or surveys, which did not systematically collect information on status in employment. Still the number of apprentices is admittedly high in Africa, and apprenticeship remains a major entry to the labour market and a major provider for self-employment. One of the advantages of work-based learning that characterises apprenticeship is to facilitate the transition from learning to work by ensuring a better understanding of the workplace culture and the acquisition of good work habits or in other words a good proficiency in all dimensions of the craft, not only technical but also in entrepreneurship skills. The drawback is that apprenticeship often hides poor wages and work conditions, as well as lack of basic occupational health and safety conditions. The official recognition of diplomas and training certificates is a long and bureaucratic process in most countries. It can take years with a low probability of success and with considerable costs for individuals and institutions. An interesting good practice for projects on TVET consists in negotiating the certification of acquired skills with employers’ associations. Prioritising the development and implementation of systems of recognition of informally acquired skills since it facilitates mobility from the informal to the formal economy is a good practice that was implemented in Uganda by SwissContact Germany in a EU-funded project “Validation of Nonformal and Informal Training”. The “Proficient Acquired Skills” (PAS) document was developed and implemented in partnership with the Ugandan Association of

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Private Training Providers (UGAPRIVI). The PAS certifies the skills and competencies of an individual in a particular trade and assesses the strengths of the holder as well as his skills gaps. A large-scale and low-cost non-formal educational and training approach of “Local Skills Development” and DACUM was developed. Through a participatory process and method (DACUM for “Developing A CUrriculuM”) for describing any occupation in terms of duties, tasks, knowledge, skills and traits, in relation to eight trades, hairdresser, tailor, motorcycle mechanic, motor vehicle mechanic, welder, metal fabricator, plumber and carpenter and joiner, the project aimed at providing appropriate training opportunities to the unemployed and potential or actual workers in the informal economy. The method developed private sector driven and innovative non-formal modes of skills training and their accreditation, in partnership with several employers’ associations such as the Ugandan Small Scale Industries Association (USSIA) or the Ugandan Hair and Beauty Alliance (UHABA). It included interventions in capacity building, institutional development, coaching and technical assistance of training providers, stimulation of collective action of workers and employers for linkages and network development, career guidance, pre-­ vocational skills training and advocacy. Once the assessments for the selected trades are completed and the qualification process is in place to deliver the PAS document, it is no longer depending on human and financial resources. The provision of PAS documents is then a service that can be delivered without permanent costs: the costs of the qualification and certification processes are charged to the beneficiaries. The online Worker’s PAS database captures the personal data with corresponding assessment results of each trainee to be promoted among potential employers. The PAS progressively gained recognition through approval of the Directorate of Industrial Training and became part of the Ugandan Vocational Qualification Framework. Among the main benefits and uses of the PAS, the facilitation of placements of trainees among USSIA member enterprises should be noted. Likewise the matching of identified skills with the needs of the labour market and the enhancement of employability of PAS’ holders through the demonstration of skills to clients are important factors. The access of small businesses to tendering is facilitated. In the psychosocial sphere, there is increased personal confidence of the holders, increased support from relatives and recognition in the community as well as better interaction with local/central authorities.

Micro-finance The positive role of micro-finance in enhancing the livelihoods of people dependent on the informal economy is generally recognised although some voices have begun to be heard about the risks of over-indebtedness. But the simple financing (not only micro-finance) of the informal micro-entrepreneurs by the banking system also requires to be tackled seriously if the transition to the formal economy is at stake. As a matter of fact, one of the main characteristics of the informal sector has always been not to rely on external sources of funding. Most of the microenterprises of the

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informal sector continue to start their activities with limited initial capital drawn from own or family resources. The small amount of initial capital that allows the start-up of an informal microenterprise mainly originates from own or family savings. Informal operators rarely seek credit at the initial stage. They do, however, regularly buy on credit from their immediate and daily/weekly providers of the goods they sell or transform. Nevertheless, the combination of tight needs with large numbers of operators constitutes a vibrant market and has been the basis for the development of micro-finance. Micro-finance is defined as a set of financial services such as savings, microcredit, insurance and money transfer, adapted to the needs of low-income and poor persons (especially those who do not have bank accounts). Microcredit is not a usual loan; it is most often combined with others elements: the borrower benefits from tips that will help her/him use the borrowed money in the best way, among others, how to keep accounts, calculate a cost, comply with regulations and choose a particular approach or a project. For the World Bank, the amount considered as defining microcredit corresponds to 30% of the GDP per capita in a country (from 250 US$ in Madagascar to 400 US$ in Cameroon and 1300 US$ in Tunisia). Because they have no collaterals, the poor have no access to the official banking system (see De Soto 2003). In this sense, financial inclusion – the objective that micro-finance aims at achieving – may be seen as a dimension of social inclusion. It is currently estimated at a value of 60–100 billion US$. Micro-finance has earned a major place in funding income-generating activities over the recent years. This is primarily due its financialisation  – a process that consists in raising funds on the international capital market. This was, however, to the detriment of its value with respect to solidarity and it still far from satisfies the needs of the global poor (Box 4.5).

Box 4.5: Supporting Micro-finance Institutions for Enhancing the Livelihoods of Vulnerable People Dependent on the Informal Economy: Good Practice by the EU-Funded Project “Market Access Through Cooperative Action” Implemented by Ghana-PlanetFinance It is generally taken for granted that micro-finance institutions are by nature dedicated to the support of the poorest and the most remote populations. This is not quite true in practice, however. Micro-finance institutions are usually not (or no longer) non-profit institutions. They are institutions like others and consequently often do not address the concerns of the poorest, particularly those of poor rural women engaged in gathering and agro-processing activities. Like other institutions, MFIs need motivation, support and training. They need to be sensitised and incentivised to reach the poorest population. They need to be strengthened to do so and develop new credit products. The project supported not only women producers in the shea nut and butter value chain but also MFIs as one of the actors of its holistic approach to development. (continued)

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Box 4.5 (continued) Building on earlier initiatives of MFIs and the experiences of beneficiary communities, the project helped MFIs to target poor rural women engaged in agro-processing and whom traditional financial institutions normally avoid. One of the actions carried out was to provide technical support to partner MFIs to refine and develop new credit products for clients. Such new products enabled clients to acquire appropriate business inputs, pay the premium of the NHIS (health insurance) and stabilise their incomes throughout the year. Several lessons were learnt from previous experiences: (1) MFIs providing group loans need to develop specific internal competences in order to effectively provide financial services in a value chain framework; (2) MFIs sustainability also depends on their capacity to offer better timely loans in relation to the shea value chain financial needs; and (3) MFIs can feel harmed by some actors in the value chain (e.g. bulk buyers) who have the potential to offer more competitive prefinancing services. Also, the burdening of MFIs loan officers limits the effectiveness of their actions.   The participation of MFIs in the formation of production groups was decided in order to increase sense of ownership and willingness to provide financial services. It was also decided to improve their efficiency through the provision of training in specific software for the management and monitoring of the loan process. A total of four loan products were developed and used: nuts working capital loan, butter working capital loan, roaster loan and grinding mill loan. The use of roasters has led to an increase in quality and productivity in butter processing. The project organised Training of Trainers (ToT) workshop on group dynamics and business management for nine staff of the two MFIs. The project supported the activities of the MFIs involving meetings, workshops and experience sharing. The MFIs indicated that this has been very useful in carrying out their planned activities. With PlanetFinance support, the MFIs have been able to provide their staff with motorbikes, laptops and micro loan management (MLM). Staff of the MFIs described this assistance as highly beneficial because it provided them with an enabling environment to operate. The MFI staff explained that their capacities in key areas such as loan management were improved. The MFIs took the initiative to conduct needs assessments after which loans have been disbursed to women. Feedback from staff of the MFIs further shows that they used knowledge gained from the training to design and carry out follow-up monitoring. They indicated that they have been able to raise some additional funds from other sources to support product refinement and new products development for clients. The MFI staff also provided support to the project through business management training for some communities and monitored the development of women’s groups through their field visits. The training was facilitated through interactive lectures, group discussions and presentations as well as individual assignments focussing on group by-laws, leadership roles, conflict resolution and identifying shea as a business, increasing income through shea and selling to large-scale buyers.

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Micro-finance has long existed in traditional societies under the form of rotating savings/credit schemes or clubs (known as “tontines” in Western and Central Africa or “merry-go-round” in Eastern Africa, see Chap. 5 infra) to benefit their members. Important actors gradually emerged between the mid-1970s and the mid-1980s, including the Grameen bank that Nobel Peace Prize winner Mohamed Yunus founded in 1983. Others include the Self-Employed Women Association (SEWA)’s microcredit Bank based in Ahmedabad, Gujarat India. SEWA was founded in 1974 and has more than 2,000,000 members today. Such institutions are based on trust and solidarity and provide support to poor households, helping them in their income-­ generating activities. As of February 2018, the Grameen Bank, for example, present in 60  countries, has 8,951,000 members (96.1% women) and has cumulatively disbursed 17  billion US$ since its inception. Its outstanding loans are currently valued at 1694 million US$ for 11,039,705 borrowers as micro-entrepreneurs and 77,402 beggar members (benefiting from loans with zero interest rate), for an overall rate of recovery of more than 98%. Among the main features of microcredit is the high proportion of women beneficiaries. This is probably because many actors in the micro-finance sector target women but also because women are less likely to default on their loans than men. They borrow lower amounts than men and have a high rate of recovery for not so low interest rates (compared to formal loans). New Actors and Financialisation of Micro-finance In addition to these major and early actors in the field, Islamic micro-finance institutions have started to play an important role (especially in East Asia and in Middle East North Africa). Governments have become more and more involved in the financing of initial capital for the micro and small entrepreneurs, and it is a major dimension of their active policies of employment creation mostly targeting the young unemployed graduates. Governments may also engage in the provision of small assets and working capital for income-generating activities through programmes dedicated to poverty alleviation. Revealing itself profitable, microcredit has attracted international investors. A major change during the past decade has been the entry of micro-finance institutions into the capital market through partnerships with international banks, investors and investment funds. These have started to invest in the micro-finance market in search of profit and changed the overall landscape of micro-finance institutions.  he Search for Profit Has Progressively Taken Precedence over the T Solidarity Motivation There are many observers now who think that financialisation of micro-finance institutions (MFIs) is not compatible with their basic values. All the more so because of stories of indebted farmers in India and elsewhere who committed suicide because they were not able to repay their debts. These stories have made the headlines and

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suggested that trust and solidarity have lost ground to these newcomers in search of profit so that it is difficult to say if it actually creates wealth or, on the contrary, if it encourages indebtedness (see, e.g. Guérin 2015). However these micro-finance institutions in search of profit can also play a role in supporting the poor and vulnerable people dependent on the informal economy, even if they firstly seek after the most rewarding loans. Extension of Micro-finance and Potential for Growth The number of users (borrowers, members) of the services of micro-finance institutions is estimated at 200  million at global level. This large number is still to be compared to the three billion poor. The fact is that the majority of informal sector enterprises and income-generating activities that mainly mobilise the family network remain dependent on usurers. These figures also indicate that ways and means for the transition from the informal to the formal economy have to be found and that a larger access to funding is still an issue to be solved. Micro-finance can be a solution for such a transition.

Conclusion The size and growth of the informal economy as currently defined in terms of employment and contribution to GDP are too important to remain left to their spontaneous development all the more so as the characteristics of the related jobs are far from what could be expected from a rights-based approach. Moreover the potential of these economic activities could be enhanced as well as the living conditions for the informal workers provided that an enabling environment is created. The transition from the informal to the formal economy is henceforth on the agenda of many international agencies. The various domains open for States’ and civil society organisations’ intervention have been explored since a long time and the review of policies benefit from many experiences in the field at macro (national), meso (regional) and micro (local, households/enterprises) levels. Taxation is the only one of these domains that is in the hands of the State. In all other cases, the micro-level experiences tested and implemented by CSOs (funded by international or bilateral donors) can be capitalised, adapted to local conditions, generalised and converted into national policies. Similarly, national policies can be supported, enhanced and improved at local levels, thanks to CSOs, particularly in contexts of scarce budgetary resources. By expanding the informal markets to new customers, the value chain policy framework is an efficient tool for increasing quantities, quality and productivity, which also mobilises actions of financing, training and organising. For all actions designed to support operators and workers in the informal economy, organising is key for raising awareness, giving voice and empowering people

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usually kept aside by national policies. It is the necessary counterpart of fair competition with the actors of the formal sector who otherwise could be tempted to transform the crowd of informal self-employed into informal employees for the sole benefit of the formal firms. The poor are many and their number gives them the power of being considered as a powerful vibrant market for micro-finance institutions. These institutions can promote savings among the poor, not only for enabling them to access microcredit in view of satisfying their consumption needs but also for investing in productive equipment and furthermore for contributing to health insurance schemes. Technical and vocational training as well as life skills training are also essential tools for consolidating the gains acquired in other domains such as financing or value chain. Furthermore the recognition of skills acquired on the job in the microenterprises of the informal sector is indispensable whether one wants the transition from the informal to the formal economy to be made smoother. Finally the universalisation of health coverage and of social protection in general (especially health coverage and old-age pensions) is a sustainable development goal adopted by the international community, and experiences in the field have shown that progress could be achieved in the expansion of coverage through micro-finance schemes.

References Abdulsamad, A., & Gereffi, G. (2016). East Africa dairy value chains: Firm capabilities to expand regional trade. Center on Globalization, Governance & Competitiveness, Duke University; International Growth Centre, London School of Economic and Political Science. Abebrese, J. (2012). Social protection in Ghana, An overview of existing programmes and their prospects and challenges. Friedrich Ebert Foundation. African Union. (2011). Social protection plan for the informal economy and rural workers 2011– 2015 (SPIREWORK). Addis Ababa. Bellù, L.  G. (2013). Value Chain Analysis for Policy Making, Methodological Guidelines and country cases for a Quantitative Approach. FAO, EasyPol series 129, Roma, 172p. Bhatt, E. (2006). We are poor but so many: The story of self-employed women in India. Oxford: Oxford University Press. Bonner, C. (2006). Organizing informal transport workers: Global research project. Overview report. London: International Transport Workers Federation. BRAC. (2016). BRAC’s Ultra-Poor Graduation Programme: An end to extreme poverty in our lifetime. http://www.brac.net/images/index/tup/brac_TUP-briefNote-Jun17.pdf Charmes, J.  (1982). L’apprentissage sur le tas dans le secteur non structuré en Tunisie. In La politique de l’emploi-formation au Maghreb. 1970–1980. CRESM-CNRS, 472 p. Collection “Etudes de l’Annuaire de l’Afrique du Nord”. Annuaire de l’Afrique du Nord, année 1980, cf. pp. 357–396. Also published in Cahiers ORSTOM, série Sciences Humaines, 1985, XXI(2– 3): 305–328. http://horizon.documentation.ird.fr/exl-doc/pleins_textes/pleins_textes_4/sci_ hum/36878.pdf Chen, M., Bonner, C., & Françoise, C, WIEGO Network. (2015). Organizing informal workers: Benefits, challenges & successes (Background Paper for HDR 2015). http://www.hdr.undp.org/ en/search-papers?page=1 de Soto, H. (2003). The mystery of capital: Why capitalism triumphs in the west and fails everywhere else. New York: Basic Books.

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Fabbri, M., & Wilks, D. C. (2016). Tax Lotteries: the Crowding-out of Tax Morale and Long-run Welfare Effects. Erasmus School of Law, Rotterdam University. Fajnzylber, P., Maloney, W.  F., & Montes-Rojas, G.  V. (2011). Does formality improve micro-­ firm performance? Evidence from the Brazilian SIMPLES program. Journal of Development Economics, 94(2). Guérin, I. (2015). La microfinance et ses dérives. Emanciper, discipliner ou exploiter? [Microfinance and its drifts. Empowering, disciplining or exploiting?]. Demopolis. 291p. ILO. (1972). Employment, incomes and equality. In A strategy for increasing productive employment in Kenya. Geneva: ILO. ILO. (2017). World social protection report, Universal social protection to achieve the sustainable development goals 2017–19. Geneva, 431p. http://www.ilo.org/wcmsp5/groups/ public/%2D%2D-dgreports/%2D%2D-dcomm/documents/publication/wcms_245201.pdf Kaplinsky, R., & Morris, M. (2002). A handbook for value chain research. Paper prepared for the International Development Research Centre (IDRC). Institute of Development Studies. 109p. Lin Lean Lim. (2015). Employment relationships and working conditions in an IKEA rattan supply chain. International Labour Office – Jakarta: ILO. Lustig, N.. (2016a). Fiscal policy, inequality, and the poor in the developing world (Economics Working Paper 1612). Tulane University. Lustig, N. (2016b). Fiscal redistribution in low and middle income countries. Tulane University CGD and IAD, Presented at DevTalks, Development Centre OECD, Paris. Maldonado, C. (1999). Le secteur informel en Afrique face aux contraintes légales et administratives. Genève: BIT. McCormick, D., & Schmitz, H. (2002). Research manual on homeworkers in the garment industry. Brighton: IDS. Nguyen Viet Khoi, & Tran Van Dung. (2014, September). The dairy industry in Viet Nam: A value chain approach. International Journal of Managing Value And Supply Chains (IJMVSC) 5, 3. Nkuku, A. M., & Titeca, K. (2018). Market governance in Kinshasa: The competition for informal revenue through ‘connections’ (branchement) (Working Paper 2018-03). IOB, Institute of Development Policies, University of Antwerp. Schurman, S.  J., & Eaton, A.  E. (2012). Trade Union Organizing in the informal economy: A review of the literature on organizing in Africa, Asia, Latin America, North America and Western, Central and Eastern Europe. Report to the American Center for International Labour Solidarity, New Brunswick: Rutgers University. Spooner, D. (2013). Challenges and experiences in organizing home-based workers in Bulgaria (WIEGO Organizing Brief N°7). Van Elk, K., & de Kok, J. (2014). Enterprise formalization: Fact or fiction? A quest for case studies. Eschborn: ILO, GIZ. World Bank. (2015). The state of social safety nets 2015. Washington, DC. World Bank and ILO. (2015). A shared mission for universal social protection, Concept note, 5p.

Part II

Solidarities

Chapter 5

Community, Individualism and Social Capital, the Political Economy of Transfers

Introduction Social capital and household-to-household transfers have played a major role in maintaining the living standards and providing the livelihood to those members of extended families who are in need in sub-Saharan African societies. The deepening of the globalisation process with the extension of individualisation as its correlate, the start of long delayed demographic transitions with the hope to benefit from the demographic dividend but also the entry into ageing societies, has characterised the recent period; in addition, successive crises occurred during the past two decades: all are factors explaining why the embeddedness of traditional societies in sound social interrelations – in social capital – may be seen as vanishing. The challenges of poverty, gender equality and more generally most of those borne by the SDGs raise the question of the possibility and opportunity of a social protection floor for all in Africa. Examples from other regions of the developing world are convincing, but can it work in sub-Saharan Africa where informal employment predominates? According to Ouma (1995) and Davies (1996), “the erosion of these informal support systems is often attributed to the disruptive impacts of colonialism and commodification. Economically, the commercialisation of labour and the increasing cash orientation of economic activity (to pay taxes or purchase goods and services) undermined individual acts of altruism or reciprocity (e.g. neighbours assisting each other with farming chores) or collective efforts (e.g. building or maintaining community infrastructure)”. In the same vein and as early as the 1970s, some authors had highlighted the dissolution process of social structures and the transformation of reciprocity at stake with the commodification of the economies (Charmes 1977, 1978). Some other authors also note for a more recent period (Marie 2000; Marie 2008a, b; Courade 2006) that various structural or cyclical factors, such as the ­structural adjustment programmes in the 1980s; the devaluation of the CFA Franc in the mid of the 1990s; the food, financial and economic crises more recently; and © Springer Nature Switzerland AG 2019 J. Charmes, Dimensions of Resilience in Developing Countries, Demographic Transformation and Socio-Economic Development 10, https://doi.org/10.1007/978-3-030-04076-5_5

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more generally the globalisation process, seem to have got the better of such solidarity within the traditional communities. The most recent statistical data on income transfers do not however illustrate a decrease in their share of total household income, and with the continuous growth of informal employment these are indicators for the longing capacity (though evolving) of these two forms of resilience in sub-Saharan African societies to maintain their role.

Measurement, Sources and Methods How can social capital be measured? As a measure of the intensity of family and friend relationships, of social networks, and of belonging to various associations and networks of political or social power, social capital is intangible; it does not depreciate with its use, but all the reverse: it depreciates if non-used. It is inalienable and cannot result in ownership. It is cumulative. And as all forms of capital, it needs to be maintained. For Coleman (1988) it is a public good that can generate externalities (all returns are not for the holder). If the measure of social capital cannot be directly realised through the efforts invested in social relationships and if only the returns on such assets can be evaluated, then it is possible to approximate social capital with the return on investment (advanced capital). Therefore transfers inter vivos, in cash and in kind, can represent this “social income”. Transfers can be envisaged either as an investment (transfers sent to other households or entities) or as a return on investment (transfers received from other households or entities). The period of capitalisation is important and consequently the interest rate associated to this capital and the marginal value of the transferred unit are equally important (Ballet and Mahieu 2003). Social capital can then be measured by the extent and intensity of networks: among the various methods of measurement, the amounts and shares of transfers (in kind or in cash, including remittances from abroad) in total household income (inflows) and in total household expenditure (outflows), as well as the time devoted to unpaid care work provided to other households and to volunteering activities in total women’s and men’s time budget, are the most easy to collect and the most widely available. In sub-Saharan Africa, income-expenditures surveys or budget-consumption surveys are regularly conducted at national levels. Time-use surveys have also been recently carried out in many developing countries and allow comparing time budget for unpaid care work and for paid work. In this Part II of the book, we will particularly focus on transfers, whereas time budgets will be analysed in Part III infra. Transfers are far from representing the entirety of ways and means by which solidarity is expressed. The number of persons welcomed from outside (e.g. rural youth coming to towns for studying of for seeking jobs) and the number of meals served in supplement to the usual number of family members are other forms of transfers that could be measured. These forms of solidarity are not however systematically measured in household surveys, except in specific contexts, for instance,

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c­ onsecutively to the political crisis in Togo in 1993, the neighbouring countries – Benin and Ghana – welcomed refugees, in camps in Ghana, within households in Benin. During several months the Beninese households hosted their relatives from across the border, and the importance of the burden was measured (Charmes 1993). Similarly Burkina Faso measured the impact of the political crisis in Côte d’Ivoire in 2002 (in terms of remittances and returns from migration) on the living conditions of households (INSD 2006). Transfers as part of the income and expenditures of the households have been more systematically captured in households’ surveys. This is why they hold our attention in what follows all the more so as  income-­ expenditure surveys are regularly carried out in sub-Saharan Africa. They often limit data collection or analysis to expenditures that are easier to capture than income, and expenditures are often taken as a more reliable proxy to income than a direct measure of income. However, the relative shares of the various sources of income are generally relatively consistent and reliable. Transfers, which are an expression or demonstration of social capital, are comprised of remittances from migration but also gifts in kind or in cash made at the occasion of weddings, funerals or other social events. Reciprocity is required in the system of traditional solidarity and must be taken into account: it means that in other periods of the year or in the years to come, the beneficiaries of transfers will have to spend amounts, which can be nonnegligible, and as a matter of fact, transfers can also be measured on the expenditures side (and not only on the income side). In other words, these types of transfers play, on the expenditures side, the same role as contributions to social security funds, and, on the income side, the same role as allowances. Transfers can therefore be measured as a share of household’s income or as a share of household’s expenditures: in other words the households can receive transfers from other households (or other institutions), or they can send transfers to other households. On the income side, transfers include public transfers (various allowances) as well as private transfers. On the expenditures side, transfers only include gifts sent to other households. The balance between transfers received and transfers sent indicates at household level whether the household is creditor or debtor, but at national level, it shows that the balance is in general positive between inflows and outflows. A reason for that is the importance of remittances from abroad that have not immediate counterparts. It also provides evidence of the existence of some redistribution. Table 5.1 identifies the various components of what is called “transfers” in the households’ income and expenditures survey of Ethiopia. It shows that the official security system of the formal public and private sectors only represents 5.0% of total household expenditure (i.e. income). Social assistance provided by the government or the NGOs accounts for 10.9%. Remittances from households to households are the main source of transfers with 66.2% supplemented by remittances from abroad (8.0%) and gifts offered for various ceremonies (8.6%). Traditional forms of solidarities on the other hand only weigh for 0.3%. Remittances from abroad are of course an important component of households’ transfers. It has been shown that they exceed by far the flows of Official Development Assistance (ODA) in many countries, especially in sub-Saharan Africa and that they are more stable than the flows of Private Capital or Foreign Direct Investment (FDI) (World

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Table 5.1  Transfers in the household income and expenditures survey of Ethiopia 2015–2016 In % of total transfers Components of transfers “Equb” is an association established by a small group of people in order to provide 0.3% substantial rotating funding for members in order to improve their lives and living conditions “Edir” is a traditional community organisation whose members assist each other during the mourning process. Members make monthly financial contributions forming the Edir’s fund. They are entitled to receive a certain sum of money from this fund when needed Social security 5.0% Social assistance from government or NGOs Consumption or use of 10.9% donation items from government/NGOs Sale of donation items from government/NGO’s Donation in cash from government/NGO Consumption of use of donation items from government Sale of donation items from government Donation in cash from government Remittances from local households and persons 66.2% Remittances from abroad 8.0% Gifts (wedding and other sources) 8.6% Other (alms, begging, dowry, etc.) 1.1% Total (10.2% of total households’ expenditures) 100.0% Source: Based on Central Statistical Agency (2018), Household Consumption Expenditure Survey (HCE), 2015–2016

Bank and KNOMAD 2017). However, within the entire amount of transfers looked at in this chapter, remittances from migrants abroad are only a small part of the total amount of flows in cash or in kind received by the households (8% in Ethiopia, for instance, in 2015–16 but up to 25.6% in Senegal and even 30.8% in Dakar in 1994–95). In sub-Saharan Africa, the share of private transfers in total household income is nearly equivalent to the share of public social transfers in Europe. Household-to-­ household transfers play an important role in the redistribution of income between urban and rural areas, rich and poor, youth and elderly and migrants and their families that stayed at home. They compensate for the absence or the weakness of public social protection, which is lacking due to the low extent of wage employment in most low-income countries. Trends in transfers received or sent allow assessing their role during economic crises. Similarly, care of the children, the sick, the elderly

Measurement, Sources and Methods

127

and the disabled is generally a women’s duty: in modern societies, the market supplies a great deal of these activities. These various forms of solidarities are meant to shrink with economic development, but they should be supported or finally supplanted by the state in order to consolidate new and sound forms of social protection, more in phase with contemporary realities. In what follows, we mainly focus on sub-Saharan Africa where household-to-household transfers have for long been observed through national surveys.

Vulnerability and Shocks In many African countries, the vulnerability of households to climate shocks or life events remains determinant, and the risk is permanent for large shares of the population to fall into poverty or extreme poverty: in 2006 in Ethiopia (Table 5.2), 86% of rural households and 67% of urban households had undergone a shock during the past 4 years, challenging the fragile balance of population living at the border of the poverty line. In such contexts, informal activities and solidarities constitute two pillars of survival strategies and social capital and are, with women’s unpaid work, important factors of resilience. We have seen in Part I of this book what have been the role and place of the informal activities in the recent period. We intend now to show how effective have been solidarities over the past decades.

Table 5.2  Proportion of households reporting a particular event or shock that affected their assets or their wellbeing during the previous 4 years: Ethiopia 2006

Urban Undergone a shock 67 Illness in family 22 Price shock 21 Lost job 18 Death in family 15 Theft/crime 13 Cattle death 6 Land expulsion 6 Crop disease 6 Drought 5 Rain/flood 3 Frost 1

Rural 86 31 38 6 14 14 36 3 40 44 22 12

Source: Young Lives: Ethiopia, Round 2 Survey report, cited in: The 2010 European Report on Development

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Traditional Solidarities In a context where more than 80% of the employed nonagricultural population (and more than 90% of the total employed population) are not covered by any social protection, where income and wages in the informal jobs are far from reaching levels compatible with fair living standards and cannot serve as a basis for contributing to public social security systems, how can households face risks and shocks in their everyday life? Given the generally low quality of health services and of most basic services, it is clear that mandatory social contributions are looked at as a taxation rather than an insurance premium, and even salaries in casual jobs in the formal sector are often negotiated on the basis of the level of the legal minimum salary and complemented by an amount in cash from hand to hand in order to avoid the payment of social contributions. Voluntary social contributions are also difficult to raise, all the more so as traditional solidarities in the extended families or village or ethnic communities continue to play. In traditional societies, it is a matter of honour and shame to perform his statutory precedence, at any level, by filling his obligations with respect to his dependents, his protected, the youth of his community. To this aim it is required to provide his people with minimum care in terms of accommodation, food, health and education (Vuarin 2000). Social capital that was firstly defined as “the sum of the resources, actual or virtual, that accrue to an individual or a group by virtue of possessing a durable network of more or less institutionalized relationships of mutual acquaintance and recognition” (Bourdieu 1980; Bourdieu and Wacquant 1992) consequently remains strong cement in sub-Saharan societies.

 agnitude, Trends and Characteristics of Transfers M from Household to Household in Sub-Saharan Africa The Relative Share of Transfers in Households’ Income Table 5.3 hereafter is explicit in this regard: it does not only show the importance of the informal sector in the formation of total household income even in rural areas (25.7% at national level, 40.5% in urban areas and 20.6% in rural areas), it also demonstrates the role of transfers from household to household, in cash or in kind, in guaranteeing minimum living standards. With a share of 12.6% of the average household income in the 1990s (15.8% in urban areas) and even nearly 20% in countries like Chad or Senegal, transfers – as measured in income-expenditures or living standards household surveys – reach a level that is quite considerable. As a matter of fact, the measurement of transfers may vary from country to country. However, the category generally includes – as in Ethiopia (see Table 5.1 infra) or in

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Table 5.3  Structure of household income in nine sub-Saharan African countries (end of 1990s to beginning of 2000s)

Countries National Burkina Faso (1998) Chad (1995) Ethiopia (1999–2000) Ghana (1998–1999) Guinea (1994–1995) Madagascar (1999) Mali (1994) Senegal (1994–1995) Tanzania (2000–2001) Urban Burkina Faso Chad Ghana Ethiopia Guinea Mali Senegal Rural Burkina Faso Chad Ghana Guinea Ethiopia Mali Senegal

Agricul ture 37.6 41.2 21.4 60.7 39.8 49.4 45.1 26.5 14.6 39.7 6.4 8.4 3.5 10.6 5.3 5.2 1.9 10.1 55.1 64.7 34.6 57.5 78.9 82.3 44.8 23.1

Share of total household income Other Informal Wages Transfers income sector 12.6 7.1 25.7 15.9 18.7 6.6 9.8 8.4 28.3 10.5 19.6 20.2 13.9 10.4 11.2 3.9 35.0 16.9 4.6* 3.7 22.2 15.7 6.1 6.7 16.9 25.1 9.6 1.3 38.8 21.5 10.8 2.3 27.9 24.4 18.9 14.2 30.0 12.2 14.7 3.4 15.8 9.8 40.5 29.3 42.3 19.0 21.3 9.1 36.7 23.6 24.8 11.4 46.7 30.0 7.1* 5.7 31.9 34.2 14.6 14.0 42.9 36.0 6.0 9.9 53.0 32.5 8.7 3.8 29.8 29.9 19.2 14.9 11.1 8.6 20.6 5.8 14.1 4.2 7.6 9.3 22.2 0.9 15.7 26.6 27.9 9.0 3.0* 2.6 8.4 2.1 6.1 4.6 4.0 3.1 6.7 4.0 28.3 13.4 12.4 1.2 39.2 8.0 18.0 11.7

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: Charmes J. (2003) updated with Tanzania, Ethiopia and Madagascar. Compilations of the author based on national sources (living standards or income-expenditure surveys) Note: Averages for the nine countries (national) or seven countries (urban and rural) are not weighted For Burkina Faso, the category “other income” included nonmonetary income mainly from agriculture, and these sources of income have been added to agricultural income by imputing the average value for other income. For the other countries, the category “other income” is mainly comprised of income from property. And for Ghana (*), transfers are net (after subtraction of the expenditures side)

Tanzania  – public transfers (such as employers’ sickness benefits, family allowances, pensions and insurance annuities) but also gifts as well as remittances (which account for more than 93.5% of total transfers in Tanzania). Ghana only published the value of net transfers (received less spent).

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Trends in the Share of Transfers in Total Household Income To what extent the share of transfers could be affected in times of crises under the impact of individualism as suggested by authors such as Marie (1988a and b) and Courade (2006) or could it to the contrary increase in order to help the poor coping with crises? The most recent statistical data on income transfers do not illustrate a decrease in their share of total household income, and the continuous growth of informal employment are indicators of the dynamics of these two forms of resilience in sub-Saharan African societies. Table 5.4 attempts to identify the trends along the two decades: 1990s and 2000s. It is based on a different set of countries, which explains why the figures are slightly different from Table 5.3. Clearly and paradoxically, the share of transfers in total household income has increased and not decreased as would have suggested observers for whom the individualisation process is strongly impacting the communities and their traditional solidarities. For the five countries, the share of transfers (not taking Ghana into account because it was only net transfers that were recorded in this country – that is, transfers received minus transfers sent) amounted to 10.8% of total household income in the 1990s, and it increased up to 13.3% in the years 2000s. This increase has particularly affected the rural areas. Between the two periods, it is Mali that experienced the most dramatic changes with a huge increase in the share of transfers (from 10.8% up to 18.2% in 12 years, from 1994 to 2006), especially in urban areas where this share doubled (from 8.7% to 17.5%); in rural areas, where the share of agriculture in total household income also dramatically increased, the share of transfers increased from 12.4% up to 18.5% during the same period. A major feature for the period is also the huge drop in urban transfers in Burkina Faso (from 21.3% down to 15.6%), concomitantly with an important increase in rural areas (from 7.6% up to 10.3%). In the meantime, however, the crisis in Côte d’Ivoire had affected the volume of transfers incoming to Burkina Faso, and the 2003 survey added a module on this topic. Table 5.5 below shows the trends of transfers in Ethiopia over the last 15 years. It is interesting to note the regular increase of salaries as an important source of income for households at national level as well as in urban and rural areas. The decline in the share of transfers in 2010–2011 corresponds to a severe decline in the GDP per capita in 2010, whereas the sharp increase in 2015–2016 is concomitant with the acceleration of GDP per capita since 2011. It is important to note that in 2015–2016, 67.4% of these transfers were in kind (85.3% in rural areas against 42.4% in urban areas). In other countries, transfers in kind seem to be relatively less important: 22.5% in Senegal (and 34.1% in rural areas) in 2010–2011.

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Table 5.4  Comparisons of income sources in five African countries in the 1990s and the 2000s Countries

National Burkina Faso (1998) Ethiopia (1999–2000) Ghana (1998–1999) Mali (1994) Tanzania (2000–2001) Urban Burkina Faso (1998) Ethiopia (1999–2000) Ghana (1998–1999) Mali (1994) Tanzania (2000–2001) Rural Burkina Faso (1998) Ethiopia (1999–2000) Ghana (1998–1999) Mali (1994) Tanzania (2000–2001)

Agriculture 45.6 41.2 69.2 39.8 26.5 51.4 5.6 8.4 5.3 10.6 1.9 1.9 61.9 64.7 82.3 57.5 44.8 60.4

National 43.0 Burkina Faso (2003) 44.7 Ethiopia (2004–2005) 52.4 Ghana (2008) 34.9 Mali (2006) 43.4 Tanzania (2007) 39.7 Urban 7.5 Burkina Faso (2003) 7.0 Ethiopia (2004–05) 4.6 Ghana (2008) 12.3 Mali (2006) 11.1 Tanzania (2007) 2.4 Rural 58.6 Burkina Faso (2003) 62.6 Ethiopia (2004–2005) 65.3 Ghana (2008) 57.7 Mali (2006) 57.7 Tanzania (2007) 49.6 Sources and notes: ibid. Table 5.3

Share in total household income Informal Other Salaries Transfers sector incomes Years 1990 24.5 13.2 10.8 4.1 18.7 6.6 9.8 8.4 8.8 8.4 8.0 5.6 35.0 16.9 4.6 * 3.7 38.8 21.5 10.8 2.3 21.0 12.5 14.7 0.6 40.7 31.5 17.3 6.9 42.3 19.0 21.3 9.1 31.9 34.2 14.6 14.0 46.7 30.0 7.1* 5.7 53.0 32.5 8.7 3.8 29.7 41.7 24.7 2.0 18.5 7.6 9.9 3.5 14.1 4.2 7.6 9.3 4.0 3.1 6.7 4.0 27.9 9.0 3.0* 2.6 28.3 13.4 12.4 1.2 18.1 8.3 12.8 0.3 Years 2000 24.6 16.7 13.3 4.5 29.0 14.4 11.9 11.1 14.0 10.4 7.1 5.5 24.8 28.8 8.4* 3.1 35.0 18.2 2.0 30.4 13.2 16.1 0.6 36.3 38.0 16.0 4.6 41.0 36.4 15.6 1.1 35.7 37.0 8.7 14.0 30.7 42.7 10.7* 3.6 66.9 17.5 2.9 37.9 36.0 22.1 1.5 19.5 7.6 12.4 4.3 23.2 3.9 10.3 0.4 8.2 3.3 6.7 16.5 18.8 14.8 6.1* 2.6 20.9 18.5 1.6 27.8 8.3 14.1 0.3

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

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5  Community, Individualism and Social Capital, the Political Economy of Transfers

Table 5.5  Sources of household income in Ethiopia, 1999–2016 Share in total household income Informal Other Agriculture Transfers Salaries Total sector incomes Ethiopia National (1999–2000) 69.2 8.8 8.4 8.0 5.6 100.0 (2004–2005) 52.4 14.0 10.4 7.1 5.5 100.0 (2010–2011) 56 13 14 6 11 100.0 (2015–2016) 54.4 13.4 15.9 10.2 6.2 100.0 Urban (1999–2000) 5.3 31.9 34.2 14.6 14.0 100.0 (2004–2005) 4.6 35.7 37.0 8.7 14.0 100.0 (2010–2011) 7 28 37 9 18 100.0 (2015–2016) 6.9 29.4 40.2 13.1 10.5 100.0 Rural (1999–2000) 82.3 4.0 3.1 6.7 4.0 100.0 (2004–2005) 65.3 8.2 3.3 6.7 16.5 100.0 (2010–2011) 76 7 4 5 8 100.0 (2015–2016) 77.2 5.7 4.2 8.7 4.1 100.0 Sources: Based on Central Statistical Agency (2018), Household Consumption Expenditure Survey (HCE), various years Years

Salient Features of Transfers in Sub-Saharan Africa Transfers in cash and in kind are only the visible part of the iceberg of solidarity in traditional sub-Saharan societies. Today, co-development policies and projects conducted by public (states, regions, cities) or private actors (NGOs, associations) can offer to wider segments of the population a better access to public goods such as health centres, schools, roads, water, etc. These are new ways and means by which remittances come to support the migrants’ families in their villages of origin. Solidarities express themselves also by the provision of shelter and food to the children and young adults who are members of the extended families and are migrating from rural villages to the cities for schooling or for seeking jobs. Besides the urban/ rural location of the households, the living standards surveys carried out from the mid of the 1990s to the beginning of the 2000s provide even more information on the characteristics of the households concerned by transfers, for example, the socio-­ economic category of the household head (Table 5.6), the quintile of income per head (Table 5.7) or the sex of the household head. However the absence or weakness of harmonisation for these types of surveys, especially in the definition of concepts or classifications used for tabulations, implies some complexities in the following tables. Table 5.6 clearly shows that it is the households whose heads are inactive, that is, the elderly or women (widows, divorced or also spouses of polygamous) who are the main beneficiaries of transfers: for these households, transfers represent 38.6% of their income, as compared with an average of 13.2% for all households. The phenomenon is particularly important in the countries of Middle Africa (Chad: 72%, Gabon 46.8% or the Central African Republic). In other words, transfers

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133

Table 5.6  Structure of household income in seven sub-Saharan African countries (end of 1990s to beginning of 2000s) by socio-economic category of the household head Socioeconomic category of the household head Subsistence farmers Burkina Faso (1998) Chad (1995) Gambia (1993) Guinea (1994 –1995) Mali (1994) Non-farm own-account workers Burkina Faso (1998) Central African Rep. (1994 –1995) Chad (1995) Gabon (1994) Guinea (1994–1995) Mali (1994) Public paid employees Burkina Faso (1998) Central African Republic Chad (1995) Gabon (1994) Guinea (1994 –1995) Mali (1994) Private paid employees Burkina Faso (1998) Central African Rep.(1994 –1995) Chad (1995) Gabon (1994) Guinea (1994 –1995) Mali (1994) Other inactive Burkina Faso (1998) Central African Rep.(1994 –1995) Chad (1995) Gabon (1994) Gambia (1993) Guinea (1994 –1995) Mali (1994)* All categories Burkina Faso (1998) Central African Rep.(1994 –1995) Chad (1995) Gabon (1994) Gambia (1993) Guinea (1994 –1995) Mali (1994)

Agricul ture 43.3 15.0 23.6 39.0 55.2 55.4 4.3 2.7 6.7 1.6 9.3 1.0 1.2 1.7 1.4 1.3 0.8 0.6 1.0 0.9 1.0 1.8 0.6 0.6 9.2 9.9 2.2 2.8 12.0 8.3 20.0 17.8 15.3 3.7 21.4 27.0 12.6 26.5

Share of total household income Other Informal Transfers Wages income sector 25.9 3.8 14.1 7.2 10.7 1.7 7.3 65.4 26.1 5.4 23.6 21.4 36.0 7.0 15.0 4.0 29.5 1.3 12.6 1.4 27.3 3.4 11.9 2.0 68.2 9.2 10.6 10.7 66.1 10.3 5.5 15.4 17.2 54.9 21.2 63.6 2.7 23.6 8.5 69.7 14.8 20.0 69.9 6.0 4.9 9.9 84.9 5.7 5.3 3.1 13.2 63.1 13.4 14.6 19.9 23.2 19.8 35.3 15.1 73.8 9.7 5.8 64.3 16.1 12.6 65.2 14.8 20.0 7.2 1.4 15.0 75.6 10.4 76.5 9.0 3.5 15.9 64.6 13.1 10.5 7.0 53.9 16.2 22.0 18.7 70.9 9.4 7.6 59.7 20.9 9.8 68.4 17.1 14.4 24.8 64.6 7.6 2.4 21.3 70.3 3.7 4.1 23.9 15.4 38.6 17.0 10.8 9.0 32.2 38.1 35.7 10.2 51.9 4.8 3.3 72.0 17.0 27.3 46.8 25.9 26.0 22.0 31.0 9.0 29.5 17.1 35.8 9.3 32.9 31.0 13.5 2.6 38.1 21.6 13.2 16.2 18.7 6.6 9.8 49.6 37.4 36.0 22.9 28.3 10.5 19.6 20.2 66.7 17.6 18.3 36.0 24.0 9.0 4.0 41.0 31.2 12.4 2.8 38.8 21.5 10.8 2.4

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: Charmes J. (2003). Compilations of the author based on national sources (living standards or income-expenditure surveys) Note: Multi-country averages are not weighted. For Burkina Faso, the category “other income” included nonmonetary income mainly from agriculture: figures in italics are not counted in the average. For Ghana (*), transfers are net (after subtraction of the expenditures side) and for Gabon, the indicator is inclusive of all sources of income from work. Due to the mode of calculation of averages, totals by categories do not exactly equal 100%

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Table 5.7  Structure of household income in seven sub-Saharan African countries (end of 1990s to beginning of 2000s) by quintile of income per head

Quintile of income per head First quintile Burkina Faso (1998) Gabon (1994) Ghana (1998–1999) Guinea (1994–1995) Mali (1994) Second quintile Burkina Faso (1998) Gabon (1994) Ghana (1998–1999) Guinea (1994–1995) Mali (1994) Third quintile Burkina Faso (1998) Gabon (1994) Ghana (1998–1999) Guinea (1994–1995) Mali (1994) Fourth quintile Burkina Faso (1998) Gabon (1994) Ghana (1998–1999) Guinea (1994–1995) Mali (1994) Fifth quintile Burkina Faso (1998) Gabon (1994) Ghana (1998–1999) Guinea (1994–1995) Mali (1994) All categories Burkina Faso (1998) Gabon (1994) Ghana (1998–1999) Guinea (1994–1995) Mali (1994)

Agricul ture 48.7 55.0 53.7 46.8 39.3 50.1 59.5 46.2

Share of total household income Informal Other Wages Transfers sector income 30.2 6.0 14.2 2.9 26.8 1.8 11.0 5.4 33.0 17.4 30.7 10.0 3.3* 2.3 20.7 4.3 28.1 42.4 7.8 9.6 0.9 32.8 7.0 11.7 2.5 27.8 2.4 7.3 3.0 23.8 10.5 34.7 12.2 4.0* 3.0

44.7 43.2 50.6

35.8 35.9 31.5

6.3 10.3 4.9 14.6

11.6 9.3 7.4 16.2 4.4

1.6 3.7 5.5 12.6 3.0

40.2

37.9

38.8 32.5 36.6

38.3 36.8 35.2

11.3 16.0 8.7

9.0 9.4 8.9

2.6 5.4 10.7

36.6

37.4

18.1

4.1*

3.9

24.2 19.5 15.9

37.8 37.9 42.0

21.3 25.0 14.2

28.6

33.0

26.3

15.2 9.5 8.1 14.1 6.4*

1.5 9.5 19.6 12.5 5.7

14.0 27.3 30.4

38.8 38.0 37.3

34.6 19.9 10.2

39.8

35.0 41.0 38.8

16.9 31.2 21.2

9.4 10.3 8.3 17.6 4.6* 12.4 10.8

3.2 7.1 13.9 12.9 3.7 2.8 2.3

26.5

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: Charmes J. (2003). Compilations of the author based on national sources (living standards or income-expenditure surveys) Note: Multi-country averages are not weighted. For Ghana (*), transfers are net (after subtraction of the expenditures side). For Gabon, results are published by quartiles (the fourth quartile is imputed to the fifth quintile for the calculation of the average), and for Guinea first quintile, the figure for transfers includes other income. Averages are calculated without Guinea. Due to the mode of calculation of averages, totals by categories do not exactly equal 100%

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135

would play the role of old-age pensions or/and family allowances. Households whose heads are subsistence farmers are a little bit above this average (with 14.1% of their income originating in transfers) and households whose heads are nonfarm own-account workers (informal micro-entrepreneurs) are well below the average (with 10.3% of their income originating in transfers). These two figures could be the two faces of a same reality: many informal operators are rural/urban migrants who send remittances to their village communities once they have well established their activities. The gap between the two categories may be even larger as the own-­ account workers host youth coming to town for studying or learning a job (apprenticeship for boys or domestic work for girls). The invisible counterpart is then the free labour force that they gain in the exchange (note that the phenomenon of “placed children” is widespread in Western Africa). Table 5.7 classifies households by quintiles of income per head. In average (and particularly in Gabon), the poorest households (first and second quintiles) are characterised by the highest shares of transfers in total income, respectively, 14.2% and 11.7% as compared with 10.3% for all households. For the third, fourth and fifth quintiles, however, the share of transfers are not negligible (with, respectively, 9.3%, 9.4% and 9.5%). The same relationship is observed in Ethiopia in 2004–2005 and 2010–2011, but surprisingly the relationship is strictly inversed in 2015–2016, a period that corresponds to a decelerating growth rate, whereas the previous period was marked by its acceleration (Table 5.8). Table 5.8  Share of transfers in household income per quintile of the household expenditure, Ethiopia 2004–2016

2004–2005 2010–2011 2015–2016 Remittances from local households 2010 Remittances from abroad 2010 Total expenditures 2010 Remittances from local households 2015 Remittances from abroad 2015 Total expenditures 2015

First Second Third quintile quintile quintile In % of total household expenditure 15.0 9.4 7.3 10 8 7 3.6 4.7 5.5 In Birr 922.61 1063.99 1195.59

Fourth quintile

Fifth quintile

All

6.4 6 8.4

4.9 6 10.7

7.1 7 7.5

1386.76

2051.65

1324.12

7.88

33.86

59.66

89.30

517.29

141.60

8496.14

13592.92

18135.61

24087.07

42000.30

21262.53

783.71

1508.94

2287.22

3758.92

6552.84

2979.79

40.34

93.16

142.72

333.85

1189.14

360.20

22767.44

34006.79

44463.32

48600.77

72069.37

44392.28

Sources: Central Statistical Agency (2018), Household Income Expenditure Surveys, various years Note: The definition of transfers is here restricted to remittances from local households and from abroad, and not to all the categories of transfers (as in previous tables)

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As regards the sex of the household head, the 1994–1995 survey of living standards in Burkina Faso revealed that transfers represented 32.4% of total household income in female-headed households against 8.4% in male-headed households, confirming that widows, divorcees and spouses of polygamous heavily rely on such sources of income. In Mali (2006), these proportions were, respectively, 25.9% and 15.9% (ODHD 2008). Another remarkable finding of a survey carried out in Antananarivo in 1997 (INSTAT 1997) is that the probability of receiving a transfer is higher for those households who have themselves sent transfers, which illustrates the central role of reciprocity. These analyses should be reinforced by more empirical data, and the fact is that more and more living standards surveys dedicate a module of their questionnaires and a section of their report to transfers, including the provision of shelter and food, though they have not systematically pursued the collection of data on income. This clearly indicates the recent interest for the role of transfers in alleviating the impact of shocks and crises (in Senegal, DPS 2004 and ANSD 2015; Gambia, 2010; Mali, ODHD 2008; and Burkina Faso, INSD 2006). Some interesting observations can be made for several countries: Senegal, Gambia, Burkina Faso and Mali. Senegal is an interesting case for which it is possible to evaluate the balance between transfers received and transfers sent (Table 5.9 below). In 2010–2011 the transfers received amounted 696 billion FCFA or 17.9% of the annual household expenditure (income were not captured), whereas the transfers sent to other households or institutions amounted 273.8 billion FCFA or 7.1% of total household expenditure. The balance amounted 422.25 billion FCFA or 10.9% of the household expenditure. Interestingly these same shares were lower in Dakar where wealth is located and higher in rural areas but above all in other towns (with 23.6% for transfers received and 16% for the balance). Looking now at the reasons for which these amounts are transferred (Table 5.10), we can see that education and health receive 4.2% of the total amount (7% in Dakar), ceremonies 3.5% and other gifts 21.9%, but the most important reasons for receiving transfers are “financial problems” (35.5% especially in other towns (44%)), showing clearly that such transfers correspond to explicit requests for support. Unfortunately, the category “other transfers” is also important (33.7%) but does not Table 5.9  Annual amounts of transfers in Senegal 2010–2011 In billion FCFA Household expenditure Transfers sent Transfers received Balance received – sent In % of household expenditure Transfers sent Transfers received Balance received – sent

National 3880 273.8 696 422.25

Dakar 1670 141 229 88

Other towns 842 64.2 199 135

Rural 1370 68.7 268 199

7.1% 17.9% 10.9%

8.4% 13.7% 5.3%

7.6% 23.6% 16.0%

5.0% 19.6% 14.5%

Source: Based on ANSD (2015), EPSF II. 2010–11

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Table 5.10  Transfers received and sent by purpose in Senegal 2010–2011 In % Education Financial problems Health Weddings, funerals, dowries, other ceremonies Other gifts Other transfers Other, undeclared Total Education Financial problems Health Weddings, funerals, dowries, other ceremonies Other gifts Other transfers Other, undeclared Total

National Received 2.5 35.5 1.7 3.5 21.9 33.7 1.2 100.0 Sent 4.4 37.4 1.4 11.4 24.1 15.9 5.4 100.0

Dakar

Other towns

Rural

5.6 25.8 1.4 3.8 34.8 27.9 0.7 100.0

1.6 44.0 0.8 2.6 19.2 29.6 2.2 100.0

0.6 37.5 2.8 4.1 12.8 41.6 0.6 100.0

5.3 40.6 1.2 7.0 19.8 20.4 5.7 100.0

3.8 39.7 2.1 13.0 23.6 11.9 5.9 100.0

3.2 28.8 1.1 17.9 33.2 10.4 5.4 100.0

Source: Based on ANSD (2015), EPSF II. 2010–11

allow knowing what is the exact reason. The distribution of transfers sent follows the same patterns, except for ceremonies for which the amounts sent represent a higher share than the amounts received, a sign that dependents – even when they are poor – try to manifest their solidarity in such occasions. The survey also reveals that 63.5% of the beneficiaries of transfers are women. 28% of the beneficiaries are spouses; 15.5% are sons and daughters, 19% fathers and mothers and 11.4% brothers and sisters. 10.2% of the beneficiaries have no family relationship with the sender. The Gambia 2010 integrated household survey on income and expenditure (Gambia Bureau of Statistics 2011) is more difficult to interpret because it has not clearly distinguished transfers within the sources of incomes and the expenditures (for instance, a transfer sent for a wedding is different from an expenditure made for a wedding), but an interesting observation made by the survey is that a popular traditional microfinance institution (“Osusu”) accounting for 5.3% of the household expenditure and for 4.8% of the household income highlighted the importance of such an institution in the household budget and a rather balanced amount on the expenditure side (5858 thousands GMD) and on the income side (6231 thousands GMB). The 2006 integrated household survey in Mali also showed that the share of transfers received increases with the number of children in the household, whereas the share of transfers sent decreases (Table 5.11 below). For poor households, transfers represent a higher share of their income than for nonpoor, but again public transfers contribute less to the income of the poor households (0.7%) than to the nonpoor households (2.6%), and it is only private transfers that re-equilibrate the

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Table 5.11  Share of transfers in household’s income by number of children aged less than 18 in the household, Mali 2006 Number of children in the household/ source of income Public transfers received Private transfers received Transfers sent

Less than five children 2.4 14.6 2.1

5–7 1.9 16.8 1.7

8–12 1.2 17.4 0.9

13– 17 0.2 18.8 0.6

18 and more 0.7 21.7 0.9

All 1.7 16.5 1.5

Source: ODHD (2008) Table 5.12  Share of transfers in household’s income in poor and nonpoor households, Mali 2006 Public transfers received Private transfers received Transfers sent

Poor 0.7 17.7 1.2

Nonpoor 2.6 15.4 1.8

All 1.7 16.5 1.5

Source: ODHD (2008)

situation: transfers received account for 18.4% of the poor’s income against 18% for the nonpoor (Table 5.12). The report concludes that without transfers, the number of poor households would increase by more than 16 percentage points.

Conclusion Of course household-to-household transfers and gifts exist in all societies, including in the most developed countries. In France the survey on Resources and Living Conditions (SRCV), which is part of the EU system of Statistics on Income and Living Conditions (SILC), revealed that in 2014 (INSEE 2017), 10% of the surveyed population received financial aid from their families (25% for students including apprentices, 22% for the unemployed and 18% for the first quintile). But in European countries, it is public transfers that have the most remarkable impact on income: up to 35.2% of the median disposable income in Luxemburg, for instance, when including pensions, and 9.4% when excluding pensions (Eurostat 2014). In most developing countries, only a minority of the labour force, and a minority of the population, benefit from social protection, and even then, benefits are often limited to healthcare, more rarely to benefits such as sick leave, maternity leave, pensions or unemployment benefits. The success of programmes such as “Bolsa Familia” in Brazil raises the question of adequacy and efficiency of the implementation of a minimum social protection for all as a means for alleviating poverty. Faced with persistent poverty and rising inequality, a worldwide consensus evolved on the need to extend social protection. The 2012 International Labour Conference adopted the Recommendation 202 on Social Protection Floors, which was later endorsed by the G20 and the United Nations and reaffirms the critical role of social protection

References

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for progress, recovery, greater equity and social justice for a fait globalisation. They are nationally defined sets of at least four basic social security guarantees that ensure basic income security for all: access to universal essential health care, social protection for children, social protection for people of working-age and pensions and care for older persons. Two lessons can be drawn from the sustainability and adaptability of traditional solidarities: voluntary systems of social protection should take inspiration from the ways and means of functioning of community solidarities; and social protection and social assistance should complement them where and when external shocks are too strong for them to cope with.

References ANSD. (2015). Enquête Pauvreté et Structure Familiale 2010–11, Rapport de synthèse des résultats, Dakar, 422p. Ballet, J. & Mahieu, F. R. (2003). Le capital social, mesure et incertitude du rendement. In: Ballet, J., & Guillon, R. (eds.), Regards croisés sur le capital social. Paris: L’Harmattan. Bourdieu Pierre (1980, janvier), Le capital social, notes provisoires, Actes de la recherche en sciences sociales Vol. 31, pp. 1–3. Bourdieu, P., & Wacquant, L. (1992). Réponses. Pour une anthropologie réflexive. Paris: Le Seuil. Central Statistical Agency. (2018). Ethiopian Household Consumption Expenditure (HCE) survey 2015–16. Statitical Bulletin. CSA. Addis Ababa. Charmes, J.  (1977). De l’ostentation à l’accumulation. Production et reproduction des rapports marchands dans les sociétés traditionnelles à partir de l’analyse du surplus, in Ouvrage collectif: “Essais sur la reproduction des formations sociales dominées” (pp. 105–137). Travaux et Documents de l’ORSTOM, n° 64, 192p. Charmes, J. (1978). Les blocages socio-culturels au développement en tant que manifestations de rapports de domination. Mondes en développement, n°24, pp. 877–908. Charmes, J. (1993). Les conséquences de l’afflux des réfugiés du Togo sur l’économie et la société béninoise: Méthodes et mesures. PNUD-HCR. Charmes, J. (2003). ‘Le capital social: quelques conceptions et données empiriques tirées du contexte africain’. In J. Ballet & R. Guillon (Eds.), Regards croisés sur le capital social. Paris: L’Harmattan. Coleman, J.  S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94(Supplement), 95–120. Courade, G. (Ed.). (2006). L’Afrique des idées reçues. Paris: Belin. Davies, S. (1996). Adaptable livelihoods: Coping with food insecurity in the Malian Sahel. London: Macmillan. Direction de la Prévision et de la Statistique (DPS). (2004). Rapport de synthèse de la deuxième enquête sénégalaise auprès des ménages (ESAM II), 260p. European Report on Development (ERD). (2010). Social protection for inclusive development. A new perspective on EU Cooperation with Africa. San Domenico di Fiesole: Robert Schuman Centre for Advanced Studies, European University Institute. Eurostat. (2014). Living conditions in Europe, 2014 edition. Luxemburg. Gambia Bureau of Statistics. (2011). Integrated household survey income and expenditure, poverty assessment 2010 (80p). Kanifing: Gambia Bureau of Statistics. ILO. (2012). Social protection floors for social justice and a fair globalization. Report IV(1) International Labour Conference, 101st session 2012, Geneva. INSD. (2006). Analyse des résultats de l’enquête Burkinabe sur les conditions de vie des ménages 2003, Rapport final, 223p.+46p.

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INSEE. (2017). France, Portrait social, Paris, 271p. INSTAT. (1997). Transferts entre les ménages et réseaux de solidarité dans l’agglomération d’Antananarivo en 1997. Premiers résultats de l’Enquête SET 1997, Projet Madio. Marie, A. (2000). Individualization strategies among city Dwellers in Contemporary Africa: Balancing the shortcoming of community solidarity and the individualism of the struggle for survival. International Review of Social History, 45, 137–157. Marie, A. (Ed.). (2008a). L’Afrique des individus, Itinéraires citadins dans l’Afrique contemporaine (Abidjan, Bamako, Dakar, Niamey). Paris: Karthala. Marie, A. (2008b). Du sujet communautaire au sujet individuel, Une lecture anthropologique de la réalité africaine contemporaine. In Marie, A. (Ed.), 2008, L’Afrique des individus. Paris: Karthala. Observatoire du Développment Humain Durable ed la Lutte contre la Pauvreté (ODHD). (2008). Transferts de revenus et réduction de la pauvreté au Mali. Bamako: ODHD-UNICEF. Ouma, S. O. A. (1995). The role of social protection in the socioeconomic development of Uganda. Journal of Social Development in Africa, 10(2), 5–12. Vuarin, R. (2000). Un système africain de protection sociale au temps de la mondialisation (252p). Paris: L’Harmattan. World Bank and KNOMAD. (2017). Migration and remittances: Recent development and outlook. Migration and Development Brief, 28, 40p.

Part III

Unpaid Care Work

Chapter 6

Definition and Measurement of Work and Unpaid Care Work

Introduction Even before the Beijing Conference on women in 1995 and until now, both the System of National Accounts (SNA) and the International Conference of Labour Statisticians (ICLS hosted by the ILO and in charge of defining the concepts of labour force) have attempted to pave the way towards a better account of all forms of work – formal and informal and paid and unpaid. The 4th revision of the SNA in 1993 set the 15th ICLS definition of the informal sector in its prescriptions, as well as the unpaid domestic and care work in an extension of the definition of work, recommending for both concepts specific measurement through satellite accounts. And the new definition of work and employment adopted in 2013 by the 19th ICLS is an important landmark towards the recognition of the importance of unpaid care work. Today, with the progress in the conduct of time-use surveys in more and more countries, it can be reasonably expected that in a near future unpaid care work will be included in the calculation of GDP through the SNA central framework. The SNA defines the economic production (and GDP) as the production of all goods destined to the market or to own final use, and the production of all services destined to the market. The definition of work is derived from the definition of production: all labour destined to economic production as defined by the SNA.  However the SNA provides an extended notion of work including the care economy, which is comprised of all services produced for own final consumption by the households. Time-use surveys help measuring the time devoted by women (and men) to the household chores and the care economy. Estimation of the care economy sometimes leads to nearly double the value of GDP and often to increase it by more than 50%, therefore increasing the contribution of women to the well-being of societies. The chapter relates the steps and progress made in the definition of work and unpaid care work and reviews the definitions, of work and of the care economy, contributing to highlight the hidden, ignored and unrecognised contribution of women to the wealth © Springer Nature Switzerland AG 2019 J. Charmes, Dimensions of Resilience in Developing Countries, Demographic Transformation and Socio-Economic Development 10, https://doi.org/10.1007/978-3-030-04076-5_6

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of nations. A first section presents the definition of work, its restrictive and extensive definitions and the current debates on the concept, in particular the reasons for excluding the unpaid care services from the central framework of the SNA. In a second section, the concept of care economy is analysed through the development of feminist economic thinking, and a conclusive section synthesises the content of the concept in an operational perspective.

Definition of Work and Current Debates on the Concept  he Restricted and the Extensive Definition of Work T and Economic Activity in the 1993 and 2008 System of National Accounts The labour force concepts are based on the definition of productive activities by the SNA. The 1982 International Conference of Labour Statisticians (ICLS) resolution “concerning the economically active population, employment, unemployment and underemployment” unambiguously recalls that “the economically active population comprises all persons (…) who furnish the supply of labour for the production of economic goods and services as defined by the United Nations systems of national accounts…”. The underestimation of women’s work in the labour force statistics and national income has been discussed repeatedly since the 1970s. This underestimation has been categorised as occurring in four general areas of activity: subsistence production, informal paid work, domestic production and volunteer work (Beneria 1992). While the first two problems are thought to be surmountable through designing more comprehensive and accurate methods of data collection (conceptual issues being minor), the last two require clarification at the conceptual and definitional level itself. The measurement of work hinges on the issue of the definition of work. In the traditional system of national accounts, the main distinctions are made by the concept of the production boundary. Three kinds of work are distinguished: (1) paid work or work for the production of goods and services that can be marketed is defined as SNA activity, that is, activity included within the production boundary of the system of national accounts; (2) unpaid work or work that is not produced for the market is defined as extended SNA activity; and (3) activity that are not accounted for within the SNA are defined as non-SNA activities. There has been a lot of debate of what constitutes each of these activities, and the borderline between many of them is quite fuzzy and often differs in economies with differing degrees of market penetration. These differences have been recognised in the SNA over time as indicated below. The 13th ICLS, 1982 (ILO 1982), recognised three of the problematic categories identified earlier, the contribution of persons engaged in household duties (domestic work), doing community and social services to the welfare of the society (volunteer work) and subsistence production. Commenting on such type of work it stated, “while it seems that the extension of the concept of work for the measurement of the economically active population beyond the present production boundary (as defined

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by the SNA) may tend to dilute the concept, certain categories of persons not economically active but contributing to output and welfare should be recognised in a system of employment and related statistics and accounted for by separate statistics. Three such categories are homemakers, persons doing community and volunteer services and persons engaged in certain borderline subsistence activities”. Thus, while the ICLS accepts the notion of the “production boundary” in determining the concept of “economic activity” or “work”, it suggests the need to measure specific categories from those “outside the labour force”. The SNA production boundary is more restrictive and includes “all production actually destined for the market, whether for sale or barter. It also includes all goods or services provided free to individual households or collectively to the community by government units or Non-Profit Institutions serving households. (…). The SNA therefore includes all production of goods for own use within its production boundary, as the decision whether goods are to be sold or retained for own use can be made even after they have been produced, but it excludes all production of services for own final consumption within households (except for the services produced by employing paid domestic staff and the own-account production of housing services by owner-occupiers)” (SNA 2008, §§ 1.40–1.42). Reference to the SNA plays a major role in time-use statistics because one of the objectives of such data collection is to build satellite accounts of household production that come and complement the central framework of the national accounts. Reference to the labour force concepts is not less important as time-use surveys collect data on time spent in employment. Since their conception, there has been a close link between the concepts of labour force and employment on one hand, and the scope of production activities as measured by the National Accounts on the other. From 1982 to 2013, the “employed” were defined by international statistics standards adopted by the International Conference of Labour Statisticians (ICLS) as comprising all persons above a specified age who during a specified brief period were either in paid employment or in self-employment. Prior to 2013 there was a one-to-one correspondence between the productive activities of the employed and the production boundary as defined by the United Nations System of National Accounts (SNA). Currently, employment is a much narrower concept capturing work for pay or profit only, as per Resolution 1 of 19th ICLS in 2013 (ILO 2013). Yet, it should be noted that the definitions adopted in the TUS this part of the book is based on in all cases utilise the pre-19th ICLS definition of employment. As a matter of fact, before the adoption of the new standards in 2013, measurement of employment was intended to include work for pay or profit as well as some forms of unpaid work (included in SNA work activities). However, the unpaid forms of work that were included as part of employment prior to 2013 such as own-use production of goods, where the production was intended for own use (e.g. subsistence farming), could be excluded from measurement if they were not deemed to represent an important contribution to household consumption. As a result, these activities were poorly captured or not at all measured to estimate employment in labour force surveys. This meant that workers engaged in subsistence farming were not well-­ identified or monitored for policy purposes. Attempts to use the results of time-use

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surveys for a better capture of women’s SNA activities in countries where female labour force participation rates are low have turned short. Similarly, until 2013 there were no international statistical standards to define work in own-use provision of services or volunteer work, so that work such as unpaid care work in one’s own household or volunteer care work for other households even when captured was not measured in any consistent way. And as the SNA (2008) puts it: “The location of the production boundary in the SNA is a compromise, but a deliberate one that takes account of the needs of most users. In this context it may be noted that in labour force statistics economically active persons are defined as those engaged in productive activities as defined in the SNA. If the production boundary were extended to include the production of personal and domestic services by members of households for their own final consumption, all persons engaged in such activities would become self-employed, making unemployment virtually impossible by definition. This illustrates the need to confine the production boundary in the SNA and other related statistical systems to market activities or fairly close substitutes for market activities”. Although water and wood fetching have long been considered as production of goods by the System of National Accounts and thus an economic activity, most labour force surveys did not count them as part of the activities to identify the employed. The 2006 Integrated Labour Force Survey of Tanzania was one exception.1 More common has been for countries to measure these activities separately from employment or to not measure them at all. In this report these activities – where measured – have been included in “paid work” as part of SNA productive activities. However some countries include these activities in their valuation of household production (see Chap. 8 infra). In an attempt to reconcile these various conceptions and definitions, the 19th International Conference of Labour Statisticians (ILO 2013) recognised “the need to revise and broaden the existing standards in order to enable better statistical measurement of participation of all persons in all forms of work and in all sectors of the economy (…) in particular (…) to estimate volume of work or labour input for national production accounts, including existing ‘satellite’ accounts, and the contribution of all forms of work to economic development, to household livelihoods and to the well-being of individuals and society”. Resolution I adopted by the Conference identifies – for separate measurement – “five mutually exclusive forms of work: (a) Own-use production work comprising production of goods and services for own final use (b) Employment work comprising work performed for others in exchange for pay or profit 1  See section 5.7 of the national report of ILFS 2006, p. 39: “As explained above, employment in the private sector was divided into four sub-sectors namely; agriculture, informal sector, household-related economic work and other private. Other household chores were excluded, but fetching water and collecting firewood activities were included in the category household-related economic work in line with the SNA” (National Bureau of Statistics 2007).

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(c) Unpaid trainee work comprising work performed for others without pay to acquire workplace experience or skills (d) Volunteer work comprising non-compulsory work performed for others without pay (e) Other work activities (not defined in this resolution)” Chart 6.1 below presents the position of these various forms of work in relation with the production boundaries of the System of National Accounts “Unpaid care work” which is referred to in this part of the book corresponds to the own-use production work of services and to the volunteer work in households producing services (in light grey on the chart), all activities inside the general production boundary of the SNA, but outside the strict SNA production boundary (Box 6.1).

Box 6.1: Important Note About the Concept of Unpaid Care Work Used in this Book In this book unpaid care work is limited to the unpaid services that are not taken into account in the compilation of GDP. It is neither comprised of the work undertaken by contributing family workers nor of activities such as fetching water and firewood that are not considered as services but as primary activities (in the sense of extractive or picking industries) by the System of National Accounts. Although these activities are part of the problem of women’s work invisibility, there are several reasons why this book does not include them within unpaid care work. Firstly, the compilation of time spent by contributing family workers would require to have the data disaggregated by employment status and furthermore by detailed employment status (whereas many countries only disaggregate their data between paid employment and self-employment). Secondly, subsistence agriculture and other production of goods for own final use can hardly be considered as being care work. And thirdly in most developing countries, agricultural production is measured by crop areas and yield per acre rather than by the output of the farming economic units, and therefore, data on agricultural employment are only used for the distribution between market and non-market agriculture, or subsistence and market agriculture, as well as for the distribution of value added between compensation of employees and mixed income. As a wage is imputed to “contributing” family workers in national accounts, time measurement of unpaid work extended to family workers would introduce an obstacle to the comparison between total GDP and domestic production valued on the basis of time spent in unpaid care work. Despite these difficulties, it is clear that the estimation of total unpaid work including the production of goods and services for the market by unpaid “contributing” family workers is an important indicator that could be calculated and compared to unpaid care work in a selected set of developing countries.

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-

-

Chart 6.1  Forms of work and the System of National Accounts 2008 Source: Resolution 1 concerning statistics of work, employment and labour underutilization, 19th ICLS, ILO Geneva, 2013

Box below is an extract from the resolution of the 19th ICLS highlighting the definitions of the components of “unpaid care work”. Although these new definitions have been adopted, and because of the extensive and numerous works that have referred to the concept, we will continue to use in this book the notion of “unpaid care work” for convenience and clarity (Box 6.2). Box 6.2: Definitions of the Components of “Unpaid Care Work” by the Resolution Concerning Statistics of Work, Employment and Labour Utilization Adopted by the 19th ICLS in 2013 Persons in own-use production work are defined as all those of working age who, during a short reference period, performed any activity to produce goods or provide services for own final use, where: (a) “Any activity” refers to work performed in the various activities under paragraph 22(b) and (c) for a cumulative total of at least 1 h. (b) Production of “goods” (within the 2008 SNA production boundary) covers: (i) Producing and/or processing for storage agricultural, fishing, hunting and gathering products (ii) Collecting and/or processing for storage mining and forestry products, including firewood and other fuels (iii) Fetching water from natural and other sources (iv) Manufacturing household goods (such as furniture, textiles, clothing, footwear, pottery or other durables, including boats and canoes) (v) Building, or effecting major repairs to, one’s own dwelling, farm buildings, etc. (c) Provision of “services” (beyond the 2008 SNA production boundary but inside the general production boundary) covers: (continued)

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Box 6.2 (continued) (i) Household accounting and management, purchasing and/or transporting goods (ii) Preparing and/or serving meals, household waste disposal and recycling (iii) Cleaning, decorating and maintaining one’s own dwelling or premises, durables and other goods and gardening (iv) Childcare and instruction, transporting and caring for elderly, dependent or other household members, etc. (d) “For own final use” is interpreted as production where the intended destination of the output is mainly for final use by the producer in the form of capital formation or final consumption by household members or by family members living in other households: (i) The intended destination of the output is established in reference to the specific goods produced or services provided, as self-declared (i.e. mainly for own final use). (ii) In the case of agricultural, fishing, hunting or gathering goods intended mainly for own consumption, a part or surplus may nevertheless be sold or bartered. Persons in volunteer work are defined as all those of working age who, during a short reference period, performed any unpaid, non-compulsory activity to produce goods or provide services for others, where: (a) “Any activity” refers to work for at least 1 h. (b) “Unpaid” is interpreted as the absence of remuneration in cash or in kind for work done or hours worked; nevertheless, volunteer workers may receive some small form of support or stipend in cash, when below one third of local market wages (e.g. for out-of-pocket expenses or to cover living expenses incurred for the activity), or in kind (e.g. meals, transportation, symbolic gifts). (c) “Non-compulsory” is interpreted as work carried out without civil, legal or administrative requirement, which are different from the fulfilment of social responsibilities of a communal, cultural or religious nature. (d) Production “for others” refers to work performed: (i) Through or for organisations comprising market and non-market units (i.e. organisation-based volunteering) including through or for self-help, mutual aid or community-based groups of which the volunteer is a member (ii) For households other than the household of the volunteer worker or of related family members (i.e. direct volunteering)

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 easons for Exclusion of Domestic and Personal Services R for Own Consumption and Discussion of the Arguments The SNA gives several reasons to justify the non-inclusion of domestic and personal services for own final use. Recognising that a huge amount of labour is devoted to these activities and that they highly contribute to economic welfare, the SNA reminds that the GDP does not intend to measure welfare but that its main aims are analytical and policy purposes. Although not put first, a preliminary reason for non-­inclusion of domestic and personal services for own consumption is that it can be difficult to estimate monetary values for imputing prices to these services, given that there may be not suitable market prices for such outputs, incomes and expenditures. Precisely, the issue is not only of imputing monetary values to outputs but also to decide of the values for imputing to income generated through the production of these services, and to the consumption of the output. In this respect, the three flows of the National Accounts (production, income and expenditure) have not the same significance as for other goods or services: for domestic and personal services for own consumption, the decision to produce is simultaneous to the decision to consume, and the imputed income is an artefact which does not give to the producer the same opportunities for expenditures and consumption as the cash income, or even the imputed income generated by the production of goods for own consumption (either by postponing consumption or consuming goods and services other than the ones produced). According to the SNA, these “self-contained” activities have therefore limited repercussions on the rest of the economy, and the inclusion of too large non-­ monetary flows of this kind would obscure what is happening on markets and would reduce the analytical usefulness of the SNA framework (SNA 1993, §6.19–6.22). Furthermore, such an imputed income cannot be taxed, and it cannot result into other expenditures than the services that generated it, contrary to goods produced for own consumption that can be stored and eventually sold later so that they can be switched between market and non-market use, even after they have been produced. Imputed values are not equivalent to monetary values for analytic and policy purposes, and their relevance is small with regard to the understanding of major economic disequilibria such as inflation, deflation or unemployment. Last but not least, it is said that if these activities were to be included in an extended SNA production, then “all persons engaged in these activities would become self-­ employed, making unemployment virtually impossible by definition” (SNA 1993, § 1.22). Accordingly, the SNA insists on the fact that the location of the production boundary is a deliberate compromise taking account of the needs of most users, and it suggests (§ 21.18) that the change in the production boundary is a typical issue to be tackled within a satellite account. All the above arguments can be debated: The absence of suitable market prices for these services is not true in developed countries where both prices for the corresponding services and wages for the qualified workers do exist at local level, urban and rural. But it is more difficult in developing countries, especially in rural areas where these services are supposed to

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be the most extended: There is indeed no price for the services because there is no market for them, but wages for agricultural workers can be used, provided that time use is known. Time budgets become strategic information that is still lacking in most cases, but the situation is changing rapidly in this field (see Chap. 7 infra). The second argument is more difficult to debate: in most cases, the value of output, income and expenditure (and also value added) will be the same for these domestic and personal services. It is an artefact, and because this artefact deals with huge values, the fear of the SNA editors that the flows used for the analysis of market behaviour and disequilibria be “swamped” by huge flows of non-monetary values must be addressed seriously. Will the inclusion improve the instrument of the national accounts or won’t it make it less efficient? If the objective of the whole exercise is to take the measure of the invisible production, can’t it be more properly approached in a satellite account? Here two issues can be addressed. The first one refers to this stage of development when domestic activities come more and more to the market, because women enter the labour market and cannot perform these activities as much as they used to do. The potential market that these domestic activities represent and the monetary activities that they are becoming could have been better anticipated, accompanied and followed up if they had previously been included as non-monetary activities in the production boundary. To the above argument, it has been retorted that “measured growth rates are biased upwards as more and more women move into the labour market while decreasing their input in household production” (Weinrobe 1974). Ironmonger (1989a, b) also argued that market disequilibria have their counterparts in household production: the market sector draws resources from the non-market sector in period of expansion and releases them in periods of decline. The second issue is a gender one: all these domestic and personal services are not likely to become monetary: for many, if not all women, entering the labour market means having a twofold work-time budget which usually exceeds men’s by more than one third (see Chap. 7 infra): implications on productivity, health and poverty are many. Finally, the “swamping” issue could be solved by the compilation of two series of national accounts and later on by the retropolation of the new series. A satellite account conceived in order to stick as much as possible to the central framework is a step towards this objective, and a few countries have already attempted to build it (Varjonen et  al. 1999). Chapter 8 will present estimates of household production in a set of countries by region. Finally the argument addressing the issue of labour force statistics may be the easiest to counter: if the actual definition of SNA production was strictly applied in labour force surveys (including, for instance, firewood and water fetching and the processing of agricultural products), very few persons of active age would fall outside in rural areas of developing countries. Why also should housing services be excluded? Here again, time-use surveys may be devastating for our beliefs. The fact is that there is an inevitable gap between the concepts and their application and interpretation in data collection systems. The traditional view has thus been restrictive in defining work but has recognised the need for an extended concept of work. It further records the problems related to

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measuring work with the extended concept and even suggests some alternatives for accounting for such work. The traditional view has, however, centred around the notion of the market and attempted to quantify the contribution of work in terms of time and output. For the concepts to be more inclusive of women’s work, the contribution of each activity to welfare would need to be considered. It has been argued that “the welfare criteria are likely to become more prevalent if economic change and economic activities are evaluated through their contribution to social development rather than through strictly economistic indicators” (Beneria 1992).

The Concept of Care Work or Care Economy For Becker (1965, 1981), households are not only consumers but also producers: according to the traditional consumer’s theory of choice, households seek to maximise utility through the consumption of goods and services, but, following Becker, these goods and services are not ready to consume; they have to be transformed into commodities, and commodities are produced by combining time (labour) and goods. Such a definition of household production provided the bases for further recommendations on estimating the significance of unpaid work in household production by the UN Report on the Decade for Women (1985), the World Summit for Social Development (Copenhagen, 1995) and the 4th World Conference on Women (Beijing, 1995) which paved the way for more elaborated research and wide data collection by appropriate means. Time-use surveys are one of these means. Feminist economists have called attention to the serious neglect of the non-­ market sector of the economy. They point to the fact that the dominant economic theory views labour as a non-produced input and thus disregard the role of unpaid labour in social reproduction, and in household and community work (Cagatay et al. 1995). Further the neglect of the care economy was reflected in the dominance of the male breadwinner model, which has shaped much of social policy in industrialised and developing countries. Recent thinking on the issue of care work, however, shows a subtle change in the perception with a blurring of the dichotomy of paid and unpaid work. The Feminist thinking on the issue revolves around the idea of the sexual division of labour. A formulation traces the history of feminist representation of this idea into three stages. In the first stage, “gender polarisation”, the sexual division of labour was rigid, assigning men to paid work in the economy and women to unpaid care work in the household. Masculine qualities involved earning a living for the family, or the male breadwinner model was in full force even in social policy decisions. In stage two, “gender freedom”, women increasingly entered the paid labour force and into the traditional male jobs. Women took on men’s qualities associated with this work such as economic independence and competitive careerism. In this stage success for women came to mean earning as much money as possible in the economy. In stage three, what the author calls “integrative feminism”, individuals of both sexes

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actively begin to strive to integrate paid and unpaid care work and feminine and masculine qualities (Matthaei 2001). The stage two processes created problematic developments: the entry of women into the paid workforce has allowed men to feel less responsible for financially supporting their spouses and children. It may have contributed to the higher divorce rates, low child support and alimony. Further, devaluation of unpaid care work contributed to the loss of public support for welfare payments to single mothers and campaigns for workfare and elimination of welfare. A second problem is the phenomenon of “double day” or the double burden of women. Women’s entry into the paid workforce without giving up unpaid care work or without alternative arrangements for the domestic work has created the problem of long working hours and conflict within the households. Thirdly, it has accentuated the race and class tension among women. Working-class women who are forced into low-paid, dead-­ end jobs are not able to see what is liberating about paid work. A final problem is the devaluation of femininity and the unpaid work of caring (Matthaei 2001), hence the call for the third stage of “integrative feminism”. One of the early moves in the late 1960s, in what has been called first-stage feminist economics, was an attempt to incorporate women’s domestic labour into the domain of economics and to analyse it as a form of work. The aim was to gain recognition of women’s work within the home, to show that it was not a personal choice and to show the significance for reproductive work to the economy as a whole. In characterising women’s domestic work as work, a particular notion of work was implicit which has the following characteristics: a conception of the alternative use of time, such as the “opportunity cost” of time, some form of division of labour and sufficient separation of the worker from the work that it did not matter who performed it (Himmelweit 1995). Himmelweit also felt that by insisting that domestic contributions are valued as work, much of the caring and self-fulfilling activity was excluded. The personal and relational aspects of caring work remain unrecognised by the economics of work and by the society that operates within it. She argues that the development of feminist economics needs to transcend the polarisation of life into “work” and “nonwork” in order that social and economic policy can address ways to mitigate its deleterious effects. Among others, Nancy Fraser (1997) advocates a “universal caregiver model” in which economic institutions are reconstructed for a typical worker who is also actively involved in unpaid caring work, contrasting it with the “universal bread winner” model. Nancy Folbre (2001) describes the key role played by feminine “caring” work in the economy and the problems with its undervaluation not only for women and children but also for the society at large. This new line of thought on care work, in some ways, reflects the growth of non-standard forms of work where working part-time and at home are increasing the chances of integrating paid market work and unpaid care work. Some attempts to integrate market and care work through social policy have been made in some of the Western European countries (see Daly 2001).

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The care economy is at the centre of the debate between the partisans of, and the opponents to, the extension of the SNA production boundary to the general production boundary. It deals with activities and transactions within the household, which are mostly performed by women and are part of their invisible contribution to economic welfare: this is why it has been discussed since the first international conferences addressing the advancement of women. It was a reason explaining the extension of the production boundary to all economic goods in the 4th revision of the SNA in 1993, when the battle for including domestic and personal services was lost. But the Beijing Conference in 1995 furbished the weapon of time-use surveys by recommending it as a means for a more reliable measurement of women’s activities (economic as well as domestic). The issue which could have been considered as unimportant for industrialised countries takes another dimension when it comes from domestic work to care work, because the number of the elderly and the disabled and the corresponding number of establishments, institutions and workers engaged in the care economy has been increasing dramatically. It is beginning to become an issue in developing countries as well. It means that market prices are available and that there is an increasing demand for these services. Similarly, the increased female participation rates create a demand for domestic services and care of children. There is an urgent need for understanding and measuring the care economy in industrialised societies. Developing countries where the care economy represents a huge part of the production may benefit of such an evolution of ideas. Moreover, industrialised economies have also experienced a dramatic increase in “volunteer work” so that national accountants have given more attention to the valuation of production and labour involved in non-profit institutions: The Johns Hopkins University has carried out a comparative non-profit sector project, and a Handbook on Non-profit Institutions in the System of National Accounts was prepared by the UN (2003) where the treatment of imputed volunteer labour inputs is examined.

Conclusion: Services for Own Final Use and Volunteer Work as Components of the Care Economy The measurement of employment in its official and restricted definition is entangled in several difficulties which would disappear if the question to respond was simply “What are you doing?”, but as soon as the question becomes more abstractive: “Have you performed an economic activity?”, then the ambiguities and subtleties of the underlying definition arise, which cannot be summarised in a few words. Several questions (with at least one of them enumerating a never-complete list of activities) are often necessary to make the concept clearer in the mind of the respondent, assuming it is already clear in the mind of the interviewer. One of the subtleties  – and not the least  – is that a same activity can be economic or not economic, depending on who performed it and for what purpose. Given the

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complexity of the concept and of its understanding, the ideal method would be to ask neutral, objective and concrete questions to the respondents and to proceed to the classification of the population surveyed afterwards, by applying the precise standards of the definition. This is precisely the methodology of time-use surveys. The 1993 SNA made it clear that all production of goods was to be included in the production, and it left the cutoff line dividing economic and noneconomic activities in the services sector where the division principle is the devolution of the services to the households’ own final use (and not the destination to the market or not, because the production of government and non-profit institutions services are exceptions). The following chart attempts to summarise and synthesise the rules organising the compilation of GDP (Chart 6.2): Services for own final use fit with the “third-party” criterion, but volunteer work may not: a person engaged in volunteer work may do it for the satisfaction of an ideal, and it cannot be taken for granted that the time devoted to these activities could be done by a third person in order to satisfy the need (the ideal) of the first person. It remains however that the time devoted to volunteer work does result in a quantity of services that are not valued in the GDP because non-market services are estimated by the amount of salaries paid to the workers involved (and intermediate consumption). Volunteer work is even broader as it also includes informal help to other households: if domestic and care services are to be measured, it automatically results that volunteer work in domestic and care services should also be estimated. It can also be considered that in household economics and at the life scale of a generation and of a community, these transfers in kind become equal as time given will be compensated by time received in a logic of gift and counter-gift that makes the reality of “social capital” (see Chap. 5 supra): Marcel Mauss’s essay on gift (1925) has inspired reflections on economic behaviour based on reciprocity. Volunteer work in inter-household relationships would thus be one of the flows resulting in social capital: it can be noted that not only services are concerned but also goods. Finally, recent research (Varjonen et al. 1999: 56–57, see also Eurostat 2003) divides household production into five main functions: (1) provision of housing, (2) provision of nutrition, (3) provision of clothing, (4) provision of care and education and (5) volunteer work, including ancillary activities such as related transportation, cleaning, shopping, gardening and management.

-

Chart 6.2  SNA and non-SNA production in the System of National Accounts Note: SNA production is in grey

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Therefore the unpaid care work includes all non-SNA productive activities falling within the general production boundary. Following the Guide to Producing Statistics on Time Use: Measuring Paid and Unpaid Work (United Nations Statistics Division 2004)2 and the International Classification of Activities for Time-Use Statistics (ICATUS),3 unpaid care work and paid work can be defined as follows: Unpaid care work (or non-SNA work activities) consists in the three categories of the classification: –– Providing unpaid domestic services for own final use within households (06) –– Providing unpaid caregiving services to household members (07) –– Providing community services and help to other households (08) Paid work (or SNA work activities) is defined as comprising: –– Work for corporations/quasi-corporations, non-profit institutions and government (formal sector work) (01) –– Work for household in primary production activities (02) –– Work for household in non-primary production activities (03) –– Work for household in construction activities (04) –– Work for household providing services for income (05).

References Becker, G. (1965, September). A theory of the allocation of time. The Economic Journal, 75, 493–517. Becker, G. (1981). A treatise on the family. Cambridge, MA: Harvard University Press. Beneria, L. (1992). Accounting for Women’s work: The Progress of two decades. World Development, 20(11), 1547–1560. Cagatay, N., Elson, D., & Grown, C. (1995). Gender, adjustment and macroeconomics: Introduction. World Development, 23(11), 1827–1836. Daly, M. (2001). Care work: The quest for security. Geneva: International Labour Office. Eurostat. (2003). Household production and consumption. Proposal for a methodology of household satellite accounts. Task force report for Eurostat, Unit E1, Luxemburg. Folbre, N. (2001). The invisible heart: Economics and family values. New York: The New Press. Fraser, N. (1997). After the family wage. A post-industrial thought experiment. In N.  Fraser (Ed.), Justice interruptus. Critical reflections on the “Postsocialist” condition (pp.  41–66). New York/London: Routledge. Himmelweit, S. (1995). The discovery of “unpaid work”: The social consequences of the expansion of “work”. Feminist Economics, 1(2), 1–19.

 https://unstats.un.org/unsd/publication/SeriesF/SeriesF_93E.pdf  The new ICATUS 2016 (United Nations Statistics Division 2016:  https://unstats.un.org/unsd/ demographic-social/time-use/icatus-2016/) now distinguishes, in accordance with the 19th ICLS: unpaid domestic services for household and family members (3); unpaid caregiving services for household and family members (4); and unpaid volunteer, trainee and other unpaid work (5) and for SNA work: employment and related activities (1); production of goods for own final use (2). However this new classification has not been applied yet.

2 3

References

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ILO. (1982). Resolution concerning statistics of the economically active population, employment, unemployment and underemployment. Adopted by the thirteenth international conference of Labour Statisticians, October 1982, Geneva. ILO. (2013). Resolution concerning statistics of work, employment and labour underutilization. Adopted by the 19th international conference of Labour Statisticians, 11th October 2013, Geneva. Ironmonger, D. (Ed.). (1989a). Households work. Sydney: Allen and Unwin. Ironmonger, D. (1989b). Research on the household economy. In D. Ironmonger (Ed.), Households work. Sydney: Allen & Unwin. Matthaei, J.  (2001). Healing ourselves, healing our economy: Paid work, unpaid work and the next stage of feminist economic transformation. Review of Radical Political Economics, 33(4), 461–494. Mauss, M. (1925). Essai sur le don. Forme et raison de l’échange dans les sociétés archaïques, published in l’Année Sociologique, Quadrige/Presses universitaires de France, 2007. National Bureau of Statistics. (2007). Analytical report of the integrated labour force survey ILFS 2006, Dar Es Salam, 124p. SNA. (1993). System of national accounts. New York: Commission of the European Communities, IMF, OECD, UN, WB. SNA. (2008). System of national accounts. New York: Commission of the European Communities, IMF, OECD, UN, WB. United Nations Statistics Division. (2003). Handbook on non-profit institutions in the system of national accounts. Department of Economic and Social Affairs, Statistics Division, Studies in Methods, Series F., No. 91, New York. United Nations Statistics Division. (2004). Guide to producing statistics on time use: Measuring paid and unpaid work. Department of Economic and Social Affairs, Statistics Division, Studies in Methods, Series F., No. 93, New York. United Nations Statistics Division. (2016). International classification of activities for time use statistics. https://unstats.un.org/unsd/demographic-social/time-use/icatus-2016/ Varjonen, J., Niemi, I., Hamunen, E., Sandström, T., & Pääkkönen, H. (1999). Proposal for a satellite account of household production. Eurostat working papers 9/1999/A4/11, Luxemburg, Eurostat. http://www.stat.fi/tup/kantilinpito/satel98.pdf Weinrobe, M. (1974). Household production and national production, an improvement of the record. Review of Income and Wealth. 20, 89–102.

Chapter 7

Unpaid Care Work Across the World as Measured by Time-Use Surveys

Introduction Informal paid work is a formidable reserve of resilience and so is unpaid care work as well as time spent in social activities: socialising or leisure. In this chapter we use data from time-use surveys for measuring the potential of resilience contained in unpaid care work including volunteering care work, which, in a certain manner, is also part of “socialising”. Time-use surveys are less well-known than other household surveys and their results and findings are less widely disseminated. They are also more complex to understand and to use for common users. This is why a preliminary presentation of sources (time-use surveys), methodologies, principles and indicators that they generate is developed in a first section of this chapter. Then the importance of unpaid care work at world level and across the world is examined in a second section, emphasising in particular the huge gender inequalities resulting from our patriarchal societies where the bulk of unpaid care work is assigned to women. A third section scrutinises trends over time and highlights the general tendency towards a more balanced distribution of the unpaid care burden between men and women over time. A final section concludes with observations and evidences about the gender distribution of unpaid care work across the life cycle, which can provide some insights on its role in resilience.

© Springer Nature Switzerland AG 2019 J. Charmes, Dimensions of Resilience in Developing Countries, Demographic Transformation and Socio-Economic Development 10, https://doi.org/10.1007/978-3-030-04076-5_7

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Sources and Indicators Sources Time-use surveys are the main sources of data for the measurement of “unpaid care work”, understood as own-use production work and volunteer work as defined in Chap. 6. Data collection on time spent in paid and unpaid work is still a challenge although the experience of time-use surveys is now rather long, especially in Europe. But the harmonisation of the methods of data collection is far from being achieved. Today, the most reliable and robust data on time use are based on diaries (i.e. the complete enumeration of activities during a 24-hour lapse time) and international classifications of time-use activities (ICATUS, HETUS or CAUTAL or their variants)1 rather than on methodologies based on a set of various stylised questions on a reference period of a week. Recently, many household surveys have added short sections or modules on time use that follow synthetic methodologies (short tasks surveys, stylised diaries) that are not 24-hour diaries. The data set used in this chapter relies on time-­ use surveys that were either stand-alone or full-fledged diaries as modules of regular household surveys. The classifications of time-use activities detail the various human activities around 11/12 major groups of activities: Paid work Unpaid domestic services for the household Unpaid care services for the household Unpaid care services for other households and volunteering Learning Socialising and community participation Leisure: culture Leisure: hobbies, past time Leisure: sports Mass media Personal care and maintenance Travel/transport A specific remark must be made about transport: the ICATUS, for example, recommends to collect information on transport for each of the other groups of activities: travel related to work, to unpaid domestic services, to childcare, to learning, etc. So that time spent in commuting for these activities become part of the time dedicated to each of these activities. As a matter of fact, statistics presented in this 1  ICATUS, International Classification of Activities for Time-Use Surveys https://unstats.un.org/ unsd/demographic-social/time-use/icatus-2016/ HETUS: Harmonised European Time-Use Surveys CAUTAL: Clasificación de Actividades de Uso del Tiempo para América Latina y el Caribe.

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chapter include travel in each of the activities whenever it is possible. Of course where transport/travel is captured separately, it is not possible to impute travel time to specific activities. In order to facilitate cross-country comparisons and ensure a certain degree of quality and relevance, we have been dealing only with: –– Surveys conducted at national level, or at least on large samples of different regions in the country (India, China) or at urban level (Iran, Panama), have been taken into account, and not surveys covering only the capital city (Argentina is one exception) or small areas. –– Surveys based on a diary, i.e. a questionnaire designed for the collection of time spent in the various activities, per time slots of – at most – 1 h (and more usually 10, 15 or 30 min slots) along the 24 h of a day. There is however an exception to this rule, for Latin America: only a few of the 12 Latin American countries included in the database have used a diary for the data collection of time use (and only two published official results)  – the other countries have developed a detailed questionnaire for each eligible household member asking for the number of hours weekly spent (on weekdays, on the one hand, on weekend days, on the other hand) according to a detailed list of activities (from 16 broad categories for the least detailed, to more than 80 sub-categories for the most detailed). And as a matter of fact, the national reports published their results in number of hours and minutes per week rather than in hours and minutes per day. –– Surveys using a detailed classification of time-use activities, i.e. one of the international classifications (ICATUS, HETUS or CAUTAL or their variants), or national classifications based on a systematic classification of the activities. Data collections based on a too short list of activities (less than 10 items) have not been taken into account. Therefore living standards surveys collecting data for a limited list of activities (usually less than 10) and for a reference period of a week or a month have not been taken into account. In other words, the time-use surveys gathered and analysed here are nationwide and based on diaries, and they provide data that allow distinguishing between various components of paid work (formal/informal/subsistence) and unpaid care work (unpaid domestic services/care work/volunteering), as well as various components of leisure and cultural activities (sports/hobbies/culture/mass media), and finally time spent for satisfying physiological needs (sleeping, eating, self-care, etc.). The database that has been built for this work contains neither surveys restricted to a town (capital or not), to a small region, or pilot surveys limited to small, non-representative samples, nor the surveys using a methodology which is not based on diary questionnaires and a detailed classification of activities. More specifically, this chapter is based on the compilation of 133 time-use surveys carried out in 76 countries through diaries, and at national level (Table  7.1 hereafter). The compilation excludes the household surveys collecting time-use data through a short list of stylised questions (for instance, the Living Standards Measurement Study LSMS-type of surveys). As previously explained the only exception to this rule is for Latin America where, in the absence of time-use surveys

Korea (1999) (2004) (2009) Mauritius (2003) Kyrgyzstan (2010) South Africa Mongolia (2000) (2010) (2007) (2011)

Kazakhstan (2012)

Madagascar (2001)

Palestine (1999–2000) (2012–2013) Qatar (2012–2013) Tunisia (2005–2006) Turkey (2006) (2014–2015)

Mali (2008)

India (1998–1999)

Ghana (2009)

Oman (2007–2008)

Morocco (2011–2012)

Iraq (2007)

Cameroon (2014) Azerbaijan (2008) (2012) Cape Verde Cambodia (2012) (2004) Ethiopia (2013) China (2008)

Iran (2009)

Asia (12 countries/18 surveys) Armenia (2004)

Sub-Saharan Africa (10 countries /13 surveys) Benin (1998) (2015)

Middle East-­ North Africa (9 countries/11 surveys) Algeria (2012)

El Salvador (2010) Mexico (2002) (2009) (2014) Panama (2011)

Ecuador (2012)

Cuba (2001)

Colombia (2012–2013) Costa Rica (2004)

Latin America (12 countries /15 surveys) Argentina (Buenos Aires) (2005) Chile (2015)

Italy (1988–1989) (2002–2003) (2008–2009) (2013–2014)

Greece (2013–2014) Ireland (2005)

Germany (2001– 2002) (2012)

Finland (1979) (1987) (1999) (2009) France (1986) (1999) (2010)

Belgium (1999) (2005) (2013) Denmark (2001)

Europe (15 countries/34 surveys) Austria (2008–2009)

Table 7.1  Time-use surveys: 76 countries and 133 surveys (years) by region

Lithuania (2003) Macedonia (2014–2015) Moldova (2011–2012)

Hungary (1999–2000) (2009–2010) Latvia (2003)

Belarus (2014–2015) Bulgaria (2009–2010) Estonia (2009–2010)

Transition (13 countries/14 surveys) Albania (2010–2011) USA (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016)

North America (2 countries 19 surveys) Canada (1992) (1998) (2005) (2010) (2015)

New Zealand (1998–1999) (2009–2010)

Other Developed (3 countries/9 surveys) Australia (1992) (1997) (2006) Japan (2001) (2006) (2011) (2016)

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Tanzania (2006) (2014)

Thailand (2004) Uruguay (2007) (2013) (2009) (2014–2015)

Pakistan (2007) Paraguay (2016) Taiwan (2004) Peru (2010)

Serbia (2010–2011)

Poland (2003–2004) Romania (2011–2012)

Spain (2002–2003) Slovenia (2009–2010) (2000–2001) Sweden (2000– 2001) (2010–2011) UK (2000) (2005) (2015)

Netherlands (2005–2006) Norway (1970) (1980) (1990) (2000) (2010) Portugal (1999)

Sources and Indicators 163

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based on diaries, the method used was a long and detailed set of questions on a week reference period. In this respect, the comparison of the findings for Latin America with the other regions is illuminating and must be interpreted with caution in the sense that it shows the tendency of stylised questions to overestimate the time dedicated to unpaid activities, one of the reasons for that being that it mixes simultaneous activities. Cape Verde in Africa has also used the same method. Several surveys used the technique of diaries in Latin America: Cuba in 2001 (but the results were only published by region), Argentina (for the city of Buenos Aires that was added to the compilation), Chile for Gran Santiago in 2008 (not added to this review because a national survey was carried out more recently), Brazil in 2010 and Venezuela in 2012 (but no publications followed for these two countries; this is why they are not mentioned in this review). The database upon which the following results originate was established in view of the preparation of the Human Development Report 2015, (UNDP 2015; Charmes 2015) then the ILO Report on “Care Work and Care Jobs for the Future of Decent Work” for the ILO Centenary Initiative on Women at Work (ILO 2018; Charmes 2018) and is continuously updated. It consists in the gathering of as many time-use surveys (TUS) data and metadata as possible and the building of synthetic indicators on time use across the world, based on this data set.

Indicators There are three basic indicators for time use: time use for participants, participation rate and time use for total population (also called social time in some surveys). Except for physiological needs (sleeping, eating), not all the population is involved in the various activities: time use for participants is an approach of the reality experienced by the population; for instance, a workday is approximately of 8 h for a paid employee, and a care workday is of more than 7  h for a young mother, but the whole population is not at work during the reference period, and not all women are young mothers entirely dedicating their time to care. People may be in vacation or ill or temporarily inactive. Furthermore, the statistics are yearly and weekly averages, including summer holidays or weekends, etc. Participation rates indicate the proportion of population, of workers, of mothers, etc., who, during the period of reference, participate in a precise activity. And time use for total population in a given activity is the ratio of the total time recorded in the survey by the total population or also the multiplication of time use for participants by participation rates. Time use for total population is an indicator that allows reconstituting a complete 24 h day (or 1440 min). It synthesises the actual time spent in a given activity and participation rates. It allows international comparisons as well as comparisons over time. The average time spent in the activity by the total covered population is the indicator used here as well as in Chap. 8 infra: in order to compute an annual value, the simple multiplication by 365 is enough, provided that the survey methodologies take the weekly and the seasonal variations into account.

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Chart 7.1  Gender distribution of paid work, unpaid care work and total work: world average, 74 countries Source: Author’s database

Paid and Unpaid Care Work Across the World On average, at world level, in a 24 h-day, 4 h and 11 min are dedicated to paid work (17.5%), nearly 3 h to unpaid care work (12.4%) and the remaining hours to learning, socialising, leisure and personal care and maintenance (70.1%). For women (Chart 7.1), these numbers and proportions are, respectively, 3 h (181 min or 12.6%) for paid work, 4 h and 32 min (272 min or 18.9%) for unpaid care work and finally 7 h and 32 min (31.5%) for total work, against 5 h and 21 min (321 min or 22.4%), 1 h and 24 min (84 min or 5.8%) and finally 6 h ad 46 min (28.2%) for men. These figures are based on the latest time-use data from 74 countries representing more than 67.7% of the total world population in 2015. Converted into man/woman 8 h workdays, it would mean that, at world level (for the population aged 15+), unpaid care work is equivalent to more than 738 million workdays per year,2 to be compared with 1,040 billion workdays in paid work, that is, 41.5% of the total workload. Because gender inequalities are intense in unpaid care work (women contribute to more than three quarters (76.4%) of total  unpaid care work, whereas they contribute to hardly more than one third (36.1%) of total paid work), the faculty of resilience through unpaid care work, which can be beneficial for the household, can also occur to the detriment of women (Chart 7.2 hereafter). When looking at resilience in this domain, it must be kept in mind that it is also a matter of sharing responsibilities and burdens between men and women. Globally, women contribute for 52.9% to total work, against 47.1% for men. 2  Total population aged 15+ in 2015 = 5,452,476,000. Example of calculation for unpaid care work: (178  min per day*365  days per year* total population aged 15+)/(60  min per hour* 8  h per workday) = 738,015,340.

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Chart 7.2  Relative shares of women and men in paid work, unpaid care work and total work in 74 countries Source: Author’s database

Charts 7.3 and 7.4 rank countries by decreasing order of time spent in total work (paid and unpaid), for women and men, respectively. As already explained, we find on top of the chart countries that have used stylised questions rather than diaries (Latin American countries and Cape Verde). The world average stands at approximately 7 h and a half and the median at 7 h (420 min) for women’s total work whereas for men’s total work, the world average is 6 h and 46 min (406 min) and the median 6 h and 9 min (369 min). Indentations (between unpaid and paid work) to the right indicate imbalances to the detriment of paid work, whereas indentations to the left indicate a more balanced distribution between paid work and unpaid care work. China, Madagascar, Cambodia Thailand, Taiwan and Ghana are the only countries where time dedicated by women to paid work exceeds time spent in unpaid care work (Chart 7.3), whereas time dedicated by men to paid work exceeds time spent in unpaid care work in all countries (Chart 7.4) Chart 7.5 summarises the results: in all countries women’s unpaid care work exceeds men’s. At the bottom of the chart, four countries (Mali, Cambodia, Pakistan and India) are characterised by a very low participation of men to unpaid care work (less than 10%). Then come Arab countries, as well as Albania and El Salvador with male participation at less than 16%. The world average stands at 28.4% of male participation, influenced by the weight of China (28.4% also). The median stands at 28.6%. More in-depth analyses show that it is in emerging countries that women’s contribution to unpaid care work is the highest (80.1%), followed by (low income) developing countries (74.9%) and developed countries (65.6%) (Charmes 2018). On top of the chart, Northern European countries are outstanding with male

Paid and Unpaid Care Work Across the World

Chart 7.3  Women’s paid work and unpaid care work across the world Source: Author based on database of 76 countries

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Chart 7.4  Men’s paid work and unpaid care work across the world Source: Author based on database of 76 countries

Paid and Unpaid Care Work Across the World

Chart 7.5  Share of women and men in total unpaid care work Source: Author based on database of 76 countries

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participation exceeding 40% (Sweden is approaching parity with 44.7%, Norway and Denmark), followed by Finland, Canada and Estonia (more than 39%). Charts 7.6 and 7.7 finally show the profiles of countries regarding the composition of female and male work: At world level, unpaid care work accounts for 54.1% of female total work. Only six countries are below this average: Cameroon, Ghana, Madagascar, China, Taiwan and Thailand. The median stands at 64%. As to men (Chart 7.7), the world average is 29.8% and the median 31.4%.

 hanges over Time in Time Spent in Unpaid Care Work C as Markers of Resilience Time-use surveys are not generally annual surveys. The American time-use survey is the only one to have been carried out on an annual basis since 2003, and the Canadian survey is now implemented every 5 years. In other regions, the periodicity – if any – is rather every 10 years. Consequently, the impact of crises is only visible in the USA and Canada, whereas the trends visible in the other regions are rather illustrative of long-term structural changes. Chart 7.8 below clearly indicates that for the USA, trends in unpaid care work are downward oriented for women as well as for men, with a more rapid decline for women, so that in the country where the gender gap in unpaid care work is the least extended, it is still declining. Similarly trends in paid work are also downward oriented, less rapidly for women than for men. In terms of proportion of total work, the share of unpaid care work remained remarkably stable around 59 % for women, against 38 % for men. The trends in time spent in paid work clearly illustrate the impact of the financial crisis, showing a dramatic drop between 2008 and 2010 among men (from 271 min per day or 4  h and 31  min down to 245  min or 4  h and 5  min), a drop that is interestingly preceded by a similar pattern among women 1 year before from 2007 to 2009 (from 188 min or 3 h and 8 min down to 171 min or 2 h and 51 min). In other words women were the first to be impacted by the crisis, and they were also the first to recover in 2010. A similar shifted drop is observed between 2012 and 2013 for women and between 2014 and 2015 for men. It is also interesting to note that similar drops – though less marked and ahead of paid work – are observed for unpaid care work, especially among men, as if just before the crisis men (and women) tended to reduce their unpaid care work (due to more intensive paid work?). In Canada (Chart 7.9), women and men’s unpaid care work is also downward oriented (more rapidly for women, so that the gap is reducing) and dropped below the levels of the USA. Regarding paid work the trends are downward oriented, more rapidly for men compared with women. The impact of the 2008 financial crisis is again clearly visible: time spent by men in paid work is reduced by 27 min between 2005 and 2010 and by 60 min between 2005 and 2015. For women, the drop is less marked but still reduced by 6 min between 2005 and 2010, and by 24 min between

Changes over Time in Time Spent in Unpaid Care Work as Markers of Resilience

Chart 7.6  Women’s share of unpaid work in total women’s work Source: Author based on database of 76 countries

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Chart 7.7  Men’s share of unpaid work in total men’s work Source: Author based on database of 76 countries

Changes over Time in Time Spent in Unpaid Care Work as Markers of Resilience

173

Chart 7.8  Trends in unpaid care work and paid work in the USA Source: Charmes (2018)

2005 and 2015. Interestingly an increase in unpaid care work is observed among women (+ 5 min) and especially among men (+ 14 min) between 2005 and 2010. Then after 2010, the downward trend in unpaid care work resumes even more strongly, especially among women. Charts 7.10 and 7.11 highlight the trends in women and men’s unpaid care work in Northern, Western and Southern Europe. Time spent by women in unpaid work has rapidly declined since the 1970s, in most countries, especially where unpaid care work was taking longer hours. In the recent period six countries have come down from 6 or 5 h per day under the limit of 4 h per day (240 min). In three other countries (Italy, Spain and Estonia), unpaid care work is still above 4 h, but they are

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Chart 7.9  Trends in unpaid care work and paid work in Canada Source: Charmes (2018)

rapidly re-joining the other group. Regarding men, the trends are unclear. While Norway and Sweden are characterised by an increased contribution of men to unpaid work (up to more than 3 h per day), as well as Finland and Belgium (above 2 h) but at a lower level, and also a very rapid increase in Spain (starting from a very low point), all the other countries have seen a recent decline of men’s contribution to unpaid work: the UK, Italy (after a very strong increase), Estonia and, more slowly, France. Simultaneously, Charts 7.12 and 7.13 show that the contribution of men to paid work has been on the decline in all countries (except Belgium) of Northern, Western and Southern Europe, whereas the contribution of women was rather on the increase (except in Estonia, Finland and Italy for the most recent period)

Changes over Time in Time Spent in Unpaid Care Work as Markers of Resilience 360

Norway

Italy

340

France

320 Minutes per day

175

Estonia

300 280

Spain Germany

UK

260 240

Sweden

Finland

220 200

Belgium 1970

1979-80

1987-90

1999-2000

2005

2009-2015

Chart 7.10  Trends in women’s unpaid care work in Northern, Western and Southern Europe, 10 countries Source: Charmes (2018) 210

Sweden

190

Norway

Mintes per day

170

Estonia Germany

150

France Finland UK Belgium

130 110

Spain

90 70

1970-74

1979

1987

1999

2005

Italy

2009-2015

Chart 7.11  Trends in men’s unpaid care work in Northern, Western and Southern Europe, 10 countries Source: Charmes (2018)

In other countries in other regions, the picture is more complex to interpret (Charts 7.14 and 7.15 hereafter) For some of the 12 countries, there are only two points for the whole period, so that a straight line only means that the two extremes have been interpolated.

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Sweden Finland

170

Estonia Minutes per day

150

France

130

UK

Norway Belgium

Italy

90

70

Spain

Germany

110

1970-74

1979

1987

1999

2005

2009-2015

Chart 7.12  Trends in women’s paid work in Northern, Western and Southern Europe, 10 countries Source: Charmes (2018)

350

300

Norway

France

Italy

250 Estonia UK

Finland

Sweden Spain

Germany

200

Belgium

150

100

1970-74

1979-80

1987-90

1999-2002

2005

2009-2015

Chart 7.13  Trends in men’s paid work in Northern, Western and Southern Europe, 10 countries Source: Charmes (2018)

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Chart 7.14  Trends in women’s and men’s unpaid care work in other countries of other regions: 12 countries Source: Author’s database

Moreover it should be noted that the scale of the vertical axis is different for women and men (men’s levels are far below for unpaid work and so are women for paid work). Three categories of countries can be distinguished. A first category follows the trends observed in European countries (decline in women’s unpaid work and increase in men’s): it is the case for Japan and Thailand with a particular sign of

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Chart 7.15  Trends in women’s and men’s paid work in other countries of other regions: 12 countries Source: Author’s database

resilience in Japan during the financial crisis of the years 2008–2009 when women and men increased much of their time dedicated to unpaid work although their time spent in paid work did not seem to be much impacted (the stability of paid work is remarkable enough). In Thailand after the crisis, women reduced their time spend in paid work whereas men increased a lot their time in paid work. A second category

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of countries such as South Africa is characterised by a slow increase in women’s unpaid care work and a sharper increase in men’s, whereas both women and men increase their contribution to paid work. A third category of countries stands just at the opposite, Benin, for example, where women increased their burden of unpaid care work and reduced their contribution to paid work between 1998 and 2015, whereas men reduced their contribution to unpaid care work as well as to paid work (but less dramatically than women). Tanzania is in the same category but men increased their contribution to paid work between 2006 and 2014. Finally in Mexico, women and men sharply increased their contribution to unpaid care work (more rapidly for men), while men reduced their participation to paid work (women increasing their participation, then stabilising it in the recent period).

Conclusion: Changes Across the Life Cycle The resilience potential of unpaid work is unequally distributed between men and women, but also between generations. In the previous sections we have seen the enormous burden of unpaid work that lies on women’s shoulders and a general tendency in developed countries towards a better balanced sharing that is slowly taking place. Of course adult women bear the bulk of the childcare burden. Charts 7.16 and 7.17 hereafter show the general pattern emerging from the data available for 32 countries. Women’s chart is clearly downward oriented from adulthood to old age, whereas men’s chart is upward oriented from adulthood to old age. Women increase their time spent in unpaid care work from youth to adulthood then reduce it from adulthood to old age in most countries (with four exceptions: Italy, Japan, Uruguay, Belgium), whereas  – at a much lower level  – men also increase their time spent in unpaid care work from youth to adulthood, but they continue to increase it from adulthood to old age in most countries (with seven exceptions: USA, Cape Verde, Chile, Colombia, Ethiopia, Argentina, Benin) As already indicated, unpaid care work is a broad concept that includes household chores such as the preparation of meals, washing dishes, the cleaning of house and clothes, shopping and other activities related to home maintenance on the one hand, and childcare and adult care either within the household or for other households on the other hand. During their adults’ life, women and men spend time caring for their own children (and their old parents), and then, becoming older, they spend time caring for their grandchildren who generally do not belong to their own household. This is why it is interesting to enter into the details of the broad category in order to understand the provision of unpaid care services between generations within the household or between households and to compare the respective times dedicated to care of children within the household and caring children for other households. In Part II of this book, we have seen the importance of inter-household transfers in developing countries. Time-use surveys allow us to document interhousehold exchange of unpaid childcare and adult care services.

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Chart 7.16  Patterns of change in time spent by women in unpaid care work by age group: 32 countries Source: Author’s database (see Charmes 2018)

Data available for the USA are the most illustrative in this regard. Chart 7.18 are based on averages for the years 2006–2016, as we are not interested in the annual variations that have no particular significance. It is in the age groups with young children 25–34 and 35–44 that women (and men at a lower level) devote the longest hours to care of household members and mainly childcare (93 and 85 min per day, respectively, for women, and 34 and 46 min for men or approximately about one

Conclusion: Changes Across the Life Cycle

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Chart 7.17  Patterns of change in time spent by men in unpaid care work by age group: 32 countries Source: Author’s database (see Charmes 2018)

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Chart 7.18  Caring for and helping household members by age groups in the USA, average 2006–2016 Source: Own compilations from ATUS 2006–2016, Charmes (2017)

third of women’s burden). Chart 7.19 (at a much smaller scale) shows that on the contrary it is in the age groups with grandchildren that both women and men aged 55–64 and 65–74 are dedicating more time to caring for other households’ members. Interestingly the average for the male population aged 15+ (21 min) is exactly half the time spent by women (42 min). These findings clearly highlight the role of grandparents in unpaid caring (see also: Samuels et al. 2018). It also shows that such a role can be measured only if the distinction between household and non-household members is clearly identified in the classifications for time-use activities, which is not the case in many countries. In Europe, data are not systematically available at the required detailed level because the category of the classification “Voluntary work and meetings” captures too heterogeneous activities that are not often distinguished. Chart 7.20 presents findings for the most recent time-use survey in Greece (2013–2014). The “informal help to other households” overtakes “care for household members” after age 45 for women and age 65 for men.

Conclusion: Changes Across the Life Cycle

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Chart 7.19  Caring for and helping non-household members by age groups in the USA, average 2006–2016 Source: Own compilations from ATUS 2006–2016, Charmes (2017)

Within the unpaid care work, childcare and adult care are components which are frequently underestimated because they are often performed simultaneously with other types of unpaid work or because they are mixed with other forms of unpaid work. Care for children or for the elderly can consist in being present in case of need and not only in time especially dedicated to care. However a few minutes per day can end with high figures when multiplied by the whole population, 365 days per year, and valuated by the average wage or even by the minimum wage. Compared to the current GDP, unpaid work can represent from 50 to more than 100% of current GDP, and unpaid care work can be compared to the expenditures that households devote to the equivalent services on the market: nurseries, kindergarten, retirement homes, etc. In the next chapter, we propose monetary valuations of unpaid care work as a whole.

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Chart 7.20  Time spent in caring for household members and informal help to other households in Greece, 2013–14 Source: Own compilations from Greece TUS 2013–14 (Charmes 2017)

References

185

References3 Charmes, J. (2015). Time use across the world. Findings of a world compilation of time-use surveys (Working paper for the Human Development report 2015). New York: UNDP-HDRO. Charmes, J.  (2017). Unpaid care delivered by older people. A tentative estimate of the role of grandparents in caring for their grandchildren (21p). London: ODI. Charmes, J. (2018). The unpaid care work and the labour market. An analysis of time use data based on the latest world compilation of time-use surveys. Geneva: ILO. ILO. (2018). Care work and care jobs for the future of decent work (ILO Centenary Initiative on Women at Work). Geneva: ILO. Samuels, F., Samman, E., Hunt, A., Rost, L., & Plank, G. (2018). Between work and care. Older women’s economic empowerment. London: ODI. UNDP. (2015). Human development report 2015. Work for human development. New York.

 Most results are drawn from author’s database.

3

Chapter 8

What Women Are Worth? Valuation of the Care Economy in Various Regions of the World

Introduction Attempts to building satellite accounts of household production are not new. Since the seminal works by Margaret Reid (1934), then Harry Becker (1965, 1981) and the striking firebrand by Marilyn Waring on “what men value and what women are worth” (1988), and thanks to the early time-use surveys conducted in Europe and other developed countries, scholars have proposed methods (Goldschmidt-Clermont 1982; Ironmonger 1996) for valuating housework and care work and compare their value with the current GDPs. But it is with the 4th revision of the System of National Accounts in 1993 (SNA 1993) that it was suggested to build satellite accounts of household production, and a detailed methodology was proposed by Varjonen et al. (1999, 2014). More recently, the Stiglitz, Sen and Fitoussi report on the Measurement of Economic Performance and Social Progress (2009) made of such a valuation one of the orientations towards more comprehensive GDPs. In the most recent period, countries such as Tunisia (2006), Ecuador (2007–2010), Peru (2010), Morocco (2014), Benin (2015) and Hungary (2016) have conducted time-use surveys in order to progress towards such evaluations, and a country like Mexico computed a satellite account of unpaid work in the households for several years (INEGI 2014). Section “Introduction” will present the objectives of the exercise and section “Why valuating the care economy?” the status of time-use surveys in these regions as well as the methods of valuation of care work. Section “Methods of valuation of care work” will assess unpaid and paid work in a set of 26 countries that conducted at least some valuation works on household production since the mid of the years 2000s, whereas section “Size and contribution of the care economy” will compare the estimates of the care economy in these 26 countries.

© Springer Nature Switzerland AG 2019 J. Charmes, Dimensions of Resilience in Developing Countries, Demographic Transformation and Socio-Economic Development 10, https://doi.org/10.1007/978-3-030-04076-5_8

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Why Valuating the Care Economy? Among the obstacles towards achieving gender equality on the labour market and breaking the glass ceiling that prevents women to progress as rapidly as men in their job and career, the burden of homework and care work is one of the most insidious because it remains widely invisible, is still not well informed and is not actually taken into account by policies. Recent research by ODI (Samman et al. 2016) has emphasised the global childcare crisis, and the ILO flagship report on “Care work and care jobs for the future of decent work” (IL0 2018) is the most recent attempt of such an assessment at global level. Ironmonger (2000) defines the concept of household production as “the production of goods and services by the members of a household, for their own consumption, using their own capital and their own unpaid labour. Goods and services produced by households for their own use include accommodation, meals, clean clothes and childcare. The process of household production involves the transformation of purchased intermediate commodities (for example, supermarket groceries and power-utility electricity) into final consumption commodities (meals and clean clothes). Households use their own capital (kitchen equipment, tables and chairs, kitchen and dining room space) and their own labour (hours spent in shopping, cooking, laundry and ironing)”. It must however be kept in mind that for national accountants the concept of unpaid work mainly refers to the household chores (meals preparation and maintenance of the household as well as child care and care of other members of the household), that is, all activities of services that household members perform for the own final use by the household. Since the production of goods for own final use has been included into the compilation of GDP (ever since the 4th revision of the System of National Accounts in 1993, and even since the 3rd revision for activities such as water or firewood fetching in 1968), these activities should be considered as paid work. More recently, community services and help to other households (volunteering activities) have become a concern and have been added as a component of unpaid work. Therefore, unpaid work is mainly comprised of three sets of activities: –– Domestic services for own final use within the households –– Unpaid caregiving services –– Community services and help to other households These three categories of activities are clearly identified in the last revision of the International Classification of Activities for Time-Use Statistics (ICATUS) (UNSD 2016). In all regions of the world and all countries, women’s contribution to “unpaid work” – that is, these activities provided by household members for own use by the household and not being taken into account for the compilation of GDP – surpasses men’s by a factor that ranges from 2 to 8. Consequently, women’s total work (including paid work) exceeds men’s by far, illustrating what is commonly qualified as “time poverty”: because of their home duties, women have less time to dedicate

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to paid work so that they earn less income than their male counterparts and are individually poorer. As early as 1987, Goldschmidt-Clermont identified, among others, four main uses for household economy measurement: (1) to ensure that government policies help non-market household production to be allocated an amount of productive resources commensurate with its economic significance; (2) to help formulate labour market policies and labour market planning; (3) to establish household income comparisons, to measure standards of living and to formulate welfare policies; and (4) to help ensure that unpaid household workers are granted the social status and social benefits enjoyed by other workers (Goldschmidt-­ Clermont and Pagnossin-Aligisakis 1995). And the 4th revision of the System of National Accounts (SNA 1993) called for the compilation of satellite accounts of household production. Recent concern has shed light on the consequences of such invisibility. One of the targets of SDGs’ Goal 5 “Achieve gender equality and empower all women and girls” focuses on the necessity to “Recognize and value unpaid care and domestic work through the provision of public services, infrastructure and social protection policies and the promotion of shared responsibility within the household and the family as nationally appropriate”, and the recent UN Secretary General’s High-­ Level Panel on Women’s Economic Empowerment has put emphasis on tackling women’s unpaid care and work (UN 2016), recommending to “provide adequate support to enable women to work productively, including by investing in quality public care services and decent care jobs, social protection for all, and infrastructure that supports women’s safe access to economic opportunities”. Since 1993, the SNA has recommended to measure the household production in a satellite account. The idea is not to include household production in GDP but to measure it as a separate magnitude, gross household production (GHP), and then obtain a better understanding how the two economies evolve, develop and interact with each other. It is likely that, once estimates of GHP become available, they will be seen to have highly significant value for analytic and policy purposes. There is a broad misunderstanding among economists about the relative growth rates of the market economy and the household economy through time. More than 30 years ago Nordhaus and Tobin showed that economic growth rates have been over stated. They observed that “measured growth rates are considered biased upwards, as more and more women move into the labour market while decreasing their input in household production” (Nordhaus and Tobin 1973). However, their analysis ignored (for lack of data) what growth in household production of services was taking place simultaneously with the growth of market production. Ironmonger (2004) states that the present SNA-based measures are not only under estimates of total economic production and income; they are also under estimates of the rates of growth of total economic production and income. Ironmonger (1989) also argued that market business cycles have their counterparts in household production: the market economy draws resources from the household economy in period of expansion and releases them in periods of decline. The household uses these resources for production of services in a counter-cyclical way to maintain aggregate services production and consumption. Consequently, the actual cyclical variability of total

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economic production as measured by the gross economic product (GEP) – which is the sum of gross household product (GHP) and GDP  – is less than the cyclical variability at present observed through the incomplete SNA-based measures of GNP, GDP and GNI. Another consequence of these results is that the present SNA-based measures of income per head of population could show less disparity or inequality between countries than would be shown by the more complete GEP-based measures of income. The GEP measures in general would show poor countries to be relatively poorer and rich countries relatively richer, because there would be relatively more household production in rich countries than in poor countries. This still remains to be proved. It can be assumed that cutbacks in national budgets and especially in social services increase unpaid care work and impact more women, thus restricting their access to labour market. Policies seeking for more efficiency in the public sector and the market economy may well be in fact a simple shift of costs from the market economy to the household economy. Finally, a full account of the unpaid care economy and of the household production at large would surely enhance the economic status of women by recognising their essential economic role in building and maintaining the human capital of the household members (children and adults).

Methods of Valuation of Care Work Three methods can be used to valuate unpaid work in household production: –– The output-based method –– The input-based method using the opportunity costs –– The input-based method using the market replacement cost In general, production is valued on the basis of the output, and from the output are derived the value added and the other significant aggregates of the national accounts. The output-based method is preferred in national accounts. It means that a market price has to be found for each of the services constituting the unpaid work but also that a volume or a quantity of the services produced (and consumed) is available. The number of prepared meals can be calculated, and the value of similar meals in a restaurant will be imputed, but the number of shirts washed or ironed is more difficult to calculate, and the computation of each detailed unpaid service may lead to resort on too many assumptions and complications: the number of square meters of floor to be cleaned, for instance. The argument is even stronger for developing countries where the equivalent goods and services may not at all exist on the market. Then estimates of intermediate consumption and consumption of fixed capital have to be calculated in order to generate an estimate of value added. Another way of using the output method is to calculate the components of the output and add them up, that is value added, intermediate consumption and consumption of fixed capital, a methodology currently used in the UK (ONS 2002, 2016; Holloway et al.

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2002; Short 2000). In other words, the output-based method has also to solve the same methodological issues that the input-based methods. The input-based methods have taken the lead due to the greater number of timeuse surveys carried out in the recent period. The input-based methods consist in valuing household production by the costs of production, similarly to the methods used for the valuation of non-market government services or non-profit institutions services, i.e. by summing up costs of labour inputs, net taxes on production and intermediate consumption: in these cases, value added is equal to the costs of labour inputs. One can easily understand that such a valuation can be based on time spent in the various activities multiplied by the corresponding salaries. Time and salaries are supposed to be easier to obtain than volumes and prices. It is true for countries that have conducted time-use surveys. But the issue of wages is more complex because it raises several questions: –– The first question deals with the choice of the recipient of wages: is it the wage to which the person engaged in household production could pretend if employed on the labour market in a job corresponding to her/his skills (opportunity cost), or is it the wage of the person who could perform the household duties at the corresponding wage rate on the labour market (replacement cost)? –– The second question deals with the mode of calculation of wages: net wages, gross wages or compensation of employees? In other words, does the estimation have to be based on net wages, or does it have to include social contributions paid by employees (gross) or even the social contribution paid by the employers (compensation of employees)? Other questions arise depending on the choice between opportunity cost and replacement cost: –– If the opportunity cost is chosen: should the wage rate be the rate offered on the labour market (average wage for the type of job and corresponding skills) or the reservation wage rate (which is subjective and can be either lower or higher than the market rate)? –– If the replacement cost is chosen: should it be the wage of a generalist worker (a domestic worker) or of specialist workers (for each detailed activity: a cook, a nurse, a gardener, etc.)? And a final question is: should we use the female wage rate or the average wage rate, given the existence of a huge wage gender gap? The following Chart 8.1 summarises the alternatives. As shown in the diagram, the number of possible estimates may be great and provide with a large scale of variations. The literature on the subject extensively debates on the advantages and disadvantages of the various methods. Goldschmidt-Clermont (1982) is a good starting point for an extensive review prior to more recent reflections and Sousa-­ Poza et al. (1999) for the latest. The main argument against the opportunity cost method is that very different values can be imputed to the same activity depending on who performs it, within the

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Output based Input based

Quantities * prices Labour costs: Time * wage (average or gendered) Replacement cost Generalist

Specialist

Market

In the home

Opportunity cost

Market

Reservation

Net wage Gross wage Compensation Net wage Gross wage Compensation Net wage Gross wage Compensation Net wage Gross wage Compensation Net wage Gross wage Compensation

Chart 8.1  Various methods of estimation of unpaid care work

same household or in different households: painting a house will be valued more if done by an engineer than if done by a house painter. Moreover, the reservation wage can be higher than the “offered” wage to explain why the house worker prefers to stay at home: a disputable issue, actually. This is why the replacement cost method is generally preferred. Three options are possible: –– Use the wage of specialists in market enterprises, for instance, a cook in a restaurant or a nurse at a day care centre, but the conditions of work are very different from home and have an impact on productivity. –– Use the wage of specialised workers in the home: a nurse, a window cleaner, a gardener, etc. –– Use the wage of a generalist worker: a domestic employee, but then some of the domestic activities cannot be performed by such a worker, for instance, car repair. For satellite account purposes, this third option is to be preferred (Varjonen et al. 1999). It can be noted that values obtained through the opportunity cost method are generally twice as high as the values obtained by the replacement cost method using the wage of generalist workers. As to the question of net or gross wages, it is not of minor importance as taxes and social contributions can represent more than half of the total wage bill. It is generally recognised that statistics on net wages (after payment of income tax and social contribution) are not available, while those on gross wages (including or not social contribution of employers) are available: compensation of employees is the overall total corresponding to the actual cost of labour and should be preferred for this reason. The recommendations made by Varjonen et al. (1999) in the perspective of the implementation of systematic and

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193

complete satellite account for the household production are clearly for the replacement cost method using the generalist workers’ gross wage. Moreover, Sousa-Poza et al. (1999) distinguish between paid working hours and actual hours worked, which lead to other variations in the estimates (see Table 8.1 hereafter). And is it necessary to recall that whatever the wage rate finally chosen, it will re-introduce within the household production the gender gap which exists in the market economy. When replaced in the context of developing countries, these debates usually disappear in view of the weaknesses of available statistics but also because of market limitations. In rural areas, it is clear that the only generalist or specialised worker’ wage is the agricultural worker’s wage, which is usually very low. And in urban areas, the domestic worker is very often low paid, if even paid, many receiving their Table 8.1  Size of unpaid labour compared to GDP in various countries (Years 1990s) Value as % of extended GDP Input-based methods

Countries Australia Austria Canada Denmark Finland France Germany Netherlands New Zealand Norway Sweden Switzerland (1) Switzerland (2) UK (3) UK (4) USA

Years 1992 1992 1992 1987 1990 1975 1991 1990 1990

Market replacement Opportunity cost method cost method Average Average wage Offered Reservation Generalist Specialist wage gross wage wage method method net 52 69 54 58 58 31 46 34 43 35 37 40 59 45 44 44 46 50 68 72 54 82 68 43 52

1992 1991 1997

39 58.3

1997 38.2 1999 1999 1976

60

63.3

54.0

38 45 48.4

37.6

34.3

35.4

41.9

50 44 32

51 (43) 45 (37) 44

Output-­ based method

57

37 62.5

Source: Based on Sousa-Poza et  al. (1999), and Fouquet and Chadeau (1981) for France, and Short S. (2000) for UK Notes: Figures between brackets for UK are obtained by differentiating wages by gender     (1) Using gross actual salaries     (2) Using net paid salaries     (3) Compensation of employees    (4) Wages and salaries

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payment in kind (food and shelter). In practice, three options are commonly used: (1) the use of the legal minimum salary or a multiplier of it where statistics on salaries are not available, (2) the use of the wage rate for domestic workers (replacement cost for generalist) and (3) the average wage rate as it appears to be a good compromise between the opportunity cost and the replacement cost.

Size and Contribution of the Care Economy Depending on the methods used for the valuation, it is generally agreed that the care economy, including the production of goods (i.e. including activities which are, or should be, already in the GDP), would have represented between 35% and 55% of the GDP in various developed countries in the 1990s. Higher estimates do exist as shown on Table  8.1, but they must be taken as academic exercises rather than operational and reliable figures. For the 12 selected OECD countries where time-use surveys have been carried out, the average number of hours and minutes of unpaid labour per week was 16 h and 4 min for men and 31 h and 52 min for women (1,98 times more). These time budgets resulted into the following estimation of the unpaid labour contributing to the extended GDP, based on the general production boundary. It should be noted that an estimate at 50% (or 30%), for instance, means that the current official GDP would have to be multiplied by a factor 1.5 (or 1.3) if the general production boundary was to be adopted. More recent estimates exist for several developed countries in Europe or other OECD countries such as Finland in 2006, the USA in 2005 (Landefeld et al. 2009) and 2014 (Bridgman 2016), France in 2010 (Roy 2012a, b)  and the UK for 2005 and 2014 (ONS 2016). In Finland (Varjonen et  al. 2014), the gross value added of household production totalled 75  billion Euros in 2006 and would have increased the current GDP by 39%. In France, the household production in its extended definition was estimated at 26.3% (and 27.7% including voluntary activities) of the GDP in 2010 using the legal minimum salary and at 39.5% (41.6% including voluntary activities) with the minimum salary including the social contributions paid by the employer (Roy 2012a, b). When using the replacement wage for the specialist (including social contribution by the employers), the proportion reaches 70.5% of the GDP. For the USA, Landefeld et al. (2009) arrive at an estimate of 21% of the extended GDP in 2004 or an increase of 29% of current GDP. Interestingly, their estimates show the diverse impact of the type of compensation used for valuating the household production (Table 8.2): from 16.7% of the extended GDP with the minimum wage and 21% with the housekeeper’s wage, up to 22% with the quality-adjusted specialist wage (adjusted for the lower quality of the service compared with the service obtained from a specialist), 24.3% with the specialist wage and 41% with the opportunity wage. Updating these estimates for 2014, Bridgman (2016) obtains

Size and Contribution of the Care Economy Table 8.2  Adjusted GDP inclusive of household production using various compensation types for non-market labour, USA, 2004 and 2014

195 In billions dollars Current GDP Housekeeper wage Specialist Quality-adjusted specialist Opportunity Minimum wage

2004 11,734 14,855 15,505 15,043 19,909 14,080

2014 17,348.1 21,345

Source: Landefeld et  al. (2009) and Bridgman (2016)

Table 8.3  The home production in the UK in 2014

Home production GVA In % GDP Growth rate since 2005 Annual growth rate since 2005 GDP annual growth rate since 2005 Extended GDP Annual growth rate since 2005

2014 £1018.9bn 56.1% 46.8 4.4% 3.5% £2836.2bn 3.8%

Source: ONS (2016)

Table 8.4  Contribution to home production by function in the UK in 2014

Childcare Transport Household housing services Nutrition Clothing and laundry Adult care Voluntary activity

31% 23% 15% 14% 9% 6% 2%

Source: ONS (2016)

18.7% of the extended GDP, highlighting the decrease of the share of household production in the GDP. Some interesting indicators have been produced by ONS for the UK (Tables 8.3 and 8.4 hereafter). From 2005 (when it represented 52.2% of the current GDP) to 2014 (when it comes up to 56.1%), home production (the term used by ONS) has increased at an annual growth rate of 4.4% (to be compared with 3.5% for the current GDP), so that the extended GDP increased at an annual rate of 3.8%. Childcare is the most important contributor to home production (31%) and also the most important driver of growth, followed by transport for contribution (23%) and growth as well; then come household housing services and nutrition (respectively,

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15% and 14%), clothing and laundry representing only 9%, adult care 6% and voluntary services 2%.

 eview of Time-Use Surveys in 26 Selected Countries R and Methods of Valuation of Unpaid Care Work Review of Time-Use Surveys The analysis covers 26 countries: Algeria, Morocco, Tunisia and Turkey in the MENA region; Azerbaijan, Mongolia, Kazakhstan, Kyrgyzstan in Central Asia; Bulgaria, Hungary, Macedonia, Romania and Serbia in Eastern Europe; Finland, France, Germany and the UK in Western Europe; Benin, Cameroon, Ethiopia, Ghana, South Africa and Tanzania in sub-Saharan Africa; and Ecuador, Mexico and Peru in Latin America. All these countries have been selected because they have recently conducted a time-use survey with data collection based on diaries (except for Latin America), a necessary condition for reliable data, and most of them attempted to estimate the household production in comparison with their current GDP. Moreover in two of these countries estimates have been made available for two different years (Mongolia in 2007 and 2011 and Turkey in 2006 and 2014). Annex Table 8.7 summarises the main characteristics of these time-use surveys. In the 26 countries, the time-use surveys have been carried out recently, between 2005–2006 for the earliest (Tunisia) and 2014–2015 for the latest (Turkey, UK), as stand-alone surveys (except in Kazakhstan where it was a module of a living standards household survey). All surveys use the technique of diaries, which is the most reliable one and ensures the completion of activities within a 24-h duration, and rotating sampling across the whole year (except in Mongolia where it is quarterly, in Algeria and in sub-Saharan Africa and Latin America where the surveys covered only 2 months or a quarter), which allows taking seasonal variations into account. In all countries all eligible members are surveyed (except in Morocco, Cameroon and South Africa where there was a random selection of adults and children). Most surveys have used two diaries, one for weekdays and one for weekend days. The minimum age for data collection ranges from 5, 6, 7 or 8 (Tanzania, Benin, Morocco, UK, respectively) to 10 (Turkey 2014–2015, Kazakhstan, Bulgaria, Macedonia, Romania, Cameroon, Ethiopia, Ghana, South Africa, Finland, Germany), 12 (Algeria, Kyrgyzstan, Mongolia, Ecuador, Mexico, Peru) and 15 (Azerbaijan, Tunisia, Turkey 2006, Serbia, France, Hungary). And finally in most countries, the diaries have been filled by interviewers or with the support of interviewers (Morocco, Tunisia, Turkey and transition countries of Eastern Europe), using either the International Classification for Time-Use Surveys (ICATUS) in Central Asian countries or the Harmonised European Time-Use Surveys (HETUS) classification in MENA and transition countries of Eastern Europe and CAUTAL in Latin America.

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Unpaid Care Work and Paid Work in the 26 Countries Unpaid work refers to all services that household members perform for the own final consumption of the household or other households (in the case of volunteer work), excluding the production of goods for own final consumption that are already measured by the national accounts. However some satellite accounts include in the unpaid work some activities such as fetching water or fetching firewood (as it was done in Tunisia and in Morocco). Paid work on the other hand refers to all economic activities that are performed for the market of for self-subsistence (in the case of agriculture and manufactured products) either by paid employees and own-account workers, or contributing family members. Depending on the classification used and the groupings and subgroupings of the activities, paid work includes time spent in looking for work or setting up a business and related travel. Time spent in unpaid care work may also include related travel. In some countries, all time spent in travels has been put under a unique subgrouping without possibility for distributing the related time to paid/unpaid work. In the 26 countries, women’s unpaid care work exceeds men’s (Chart 8.2). The maxima are observed in the MENA region where time spent by women in unpaid work is above 300 min per day (5 h), with a maximum of 327 min in Tunisia, in two Latin American countries (but in these countries the use of stylised questions and of a week reference period tend to overestimate the results) and in Azerbaijan. Interestingly enough, between 2006 and 2014, this duration has decreased from 317 to 257 min in Turkey: rather than a better sharing of household duties by men whose time spent in unpaid work remained the same – 51 min – during the period (see Chart 8.3), this trend illustrates an increased involvement of women in the labour

Chart 8.2  Women’s unpaid care work exceeds paid work in all countries (except Ghana)  (in minutes per day) Source: Database Charmes (2018) Note: in each subregion, countries are ranked according to the increasing order of women’s unpaid care work

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Chart 8.3  Men’s paid work exceeds unpaid care work in all countries (in minutes per day) Source: Database Charmes (2018) Note: in each subregion, countries are ranked according to the increasing order of men’s unpaid care work

force that is hardly translated in an increase of only 1 min per day (from 68 to 69) in time spent by women in paid work. In Eastern Europe, time spent in unpaid work is also long (close to 300  min in Serbia and Bulgaria and to a lesser extent in Romania: 287 min), except in Macedonia where the minimum is observed among all ten transition countries, at only 224 min per day (less than 4 h). In Central Asia, the indicator is clearly under 300 min per day, especially in Kazakhstan where the minimum is observed for the region (246 min). Azerbaijan is an exception with an exceptionally high level of time spent by women in unpaid care work (349 min in 2012). In Europe and sub-Saharan Africa, the women’s burden of unpaid care work is lower (with maxima in Ethiopia, 291 min, and in Germany, 253 min). Time spent by men in unpaid work is everywhere under 166 min per day (2.8 h), with minima in the MENA region, especially in Morocco (43  min) and in sub-Saharan Africa (where it barely accounts for more than an hour, especially in Benin with 42 min and with the exception of Ethiopia). Romania is also characterised by a low level of men’s involvement in unpaid care work (100 min). In all other countries, time spent by men in unpaid work is approximately a little bit more than 2 h per day that is hardly half the time spent by women. As regards women’s time spent in paid work, sub-Saharan Africa is the region where the highest levels are observed (with South Africa as an exception), which is consistent with their high economic participation rates in this region. Latin American levels are also high for the reasons already indicated. Women’s lowest levels of time spent in paid work are observed in the Middle East North Africa region with a minimum of 30  min for Algeria and a maximum of 105 min for Tunisia.

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Men’s time spent in paid work (Chart 8.3) is especially high in sub-Saharan Africa and in MENA, as well as in Latin America (for the same specified reasons): maxima are observed in Mexico and Peru, Tanzania and Morocco. The minimum is in Mongolia with 149 min in 2007. Charts 8.4 and 8.5 show the predominance of men over women in paid work and of women over men in unpaid work in all countries and all regions, with maxima in MENA countries (and Latin America) and minima in Central Asian countries (especially in Mongolia where women are close to parity for paid work) and Europe. Women never exceed 228 min per day in paid work (Ghana 2009), whereas men’s time in paid work is never less than 149 min per day (Mongolia 2007). Interestingly it is in Mongolia that men’s paid work is close to 150 min per day, a country where women’s paid work is also close to (but below) this limit. Women’s unpaid work (Chart 8.5) is never less than 211 min per day (Finland 2009), whereas men’s unpaid work rarely exceeds 150 min per day (164 min in Bulgaria and 166 min in Germany). Finally among the 26 countries, women’s burden in unpaid care work ranges from 1.5 to 6.9 times men’s: the excess of women’s unpaid work over men’s (Chart 8.6) ranges from 1.5 to 1.6 in Western Europe up to more than 5 in MENA countries and in Benin with a maximum at 6.9 in Morocco. Over the year, the indicator drops by 0.3% point in Mongolia (between 2007 and 2011) and by 3% points in Turkey (between 2006 and 2014–15) showing some progress in the sharing of household duties between women and men. Regarding paid work (Chart 8.7), women’s paid work ranges from 1/8 to 6/7 that of men’s. Disparities between women and men measured by the gap of women to men range from 0.2 to 0.3 in MENA countries and Azerbaijan, 0.5 to 0.7 in Central Asia and Eastern Europe, with Mongolia outstanding at more than 0.8 and progressing between 2007 and 2011. As for total paid and unpaid work, that is, the

Chart 8.4  Women’s paid work never exceeds 228 min per day; men’s paid work is never less than 149 min per day

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Chart 8.5  Women’s unpaid work is never less than 211 min per day; men’s unpaid work generally does not exceed 166 min per day Source: Database Charmes (2018) Note: In each subregion, countries are ranked according to the increasing order of women’s indicator

Chart 8.6  Women’s burden in unpaid care work ranges from 1.5 to 6.9 times men’s

total burden of work (Chart 8.8), the indicator (women to men) ranges from a little bit more than 1  in Germany and Morocco to 1.5  in Mongolia. Therefore it is simultaneously in countries where the gender gap in unpaid work is the strongest (or the lowest) that the gap in the burden of total work is the least (respectively, Morocco and Germany), illustrating a clear sexual division of labour between paid and unpaid

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Chart 8.7  Women’s paid work ranges from 1/8 to 6/7 that of men’s

Chart 8.8  Women’s burden in total work always exceeds men’s Source: Database Charmes (2018)

tasks (in Morocco) or on the contrary an equal sharing of these tasks (in Germany). And at the other extreme, in Mongolia where the parity is nearly reached for paid work, the gender gap in the burden of total work is the widest (1.5 but dropping to 1.4 over time), which means that greater equity on the labour market has not been accompanied with better policies designed to alleviate unpaid work for women at work.

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 omparison of Estimates of the Care Economy in 26 C Countries Most countries among the selected 26 have applied their own methodologies. In this section we compare the methods adopted by the countries with a simplified method using the average wage and/or the legal minimum wage for the replacement cost. The average wage in the economy is often available and seems to be a good compromise between the opportunity cost and the replacement cost. The method used for valuating the care economy consists in the calculation of the number of hours per year spent in unpaid care work, transformed in number of months (on the basis of 40-h per  week, or 173  h per month), valuated with the average monthly wage rate and multiplied by the number of women (men) in the age group for which time use has been collected. We have not used a different wage rate for women and for men. The total value obtained is compared with the GDP at current prices for the same year, as well as with the extended GDP (i.e. the current GDP adjusted for unpaid care work). The formula consists in estimating the total number of hours worked in a year, converted in months, then multiplying by the monthly wage rate and by the population of reference and dividing by the current GDP (at current prices) or by the extended GDP (current GDP + value added by the care economy).



{((( Number of minutes per day × 365 / 60 ) / ( 40 hours × 52 weeks / 12 months)) × monthly salary × Number of women in the age group) + (((Number of minutes per day × 365 / 60 ) / ( 40 hours × 52 weeks / 12 months)) × monthly salary × Number of men in the age group )} / GDP or extended GDP at current prices



The parameters for these calculations are provided in Annex Tables 8.8 and 8.9. Table 8.5 below summarises the findings. Several assumptions must be kept in mind that lay behind this valuation exercise. Firstly the variable used for the wage rate is not the same for the 26 countries. In general, it is the average gross salary (or gross earnings) that includes the social contributions paid by the workers. In Turkey, the non-agricultural wage rate has been used. In one case (Morocco), we refer to the compensation of employees that comprises not only the social contribution of the employees, but also the contributions of the employers. The selected salary is then imputed to the entire population in the age group (7+; 10+; 12+; 15+): for the age groups 7+ and 12+, it has been necessary to distribute proportionally the age groups 5–9 or 10–14 in order to obtain the intermediate age group. All information was extracted from national statistical yearbooks or other national statistical reports mentioned in the references. The calculations have been made separately for women and men, but the same wage rate has been used for both. Compared to usual similar exercises, a different wage rate has not been applied for each activity (for instance, for cleaning, preparing meals, nursing, teaching chil-

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Table 8.5  Size of the care economy in the 26 countries

Countries

Year

Algeria Morocco Tunisia Turkey Turkey Benin Cameroon Ethiopia Ghana South Africa Tanzania Azerbaijan Kazakhstan Kyrgyzstan Mongolia Bulgaria Hungary Macedonia Romania Serbia Finland France Germany UK Ecuador Mexico Peru

2012 2011–2012 2005–2006 2006 2014–2015 2015 2014 2013 2009 2010 2014 2012 2012 2010 2011 2009–2010 2009–2010 2014–2015 2011–2012 2010–2011 2009 2010 2012 2014 2010 2014 2010

In % of the GDP at current prices 63.0 140.4 113.6 123.9 82.4 60.4 21.3 57.9 23.7 24.8 119.4 89.6 40.8 142.4 86.7 82.0 52.7 77.2 86.8 119.2 66.3 64.4 105.1 57.0 59.4 149.8 83.8

Size of the care economy In % of the Share of extended GDP women 38.7 97.0 58.4 98.0 53.2 97.4 55.3 97.5 45.2 96.2 37.6 96.6 17.5 89.4 36.7 85.4 19.2 91.9 19.9 85.4 54.4 93.4 47.3 90.1 29.0 84.4 58.8 88.8 46.4 82.5 45.0 75.3 34.5 84.2 43.6 87.5 46.5 82.6 54.4 81.6 39.9 70.8 39.2 73.2 51.2 70.9 36.3 75.4 37.3 92.7 60.0 90.0 45.6 86.4

Share of men 3.0 2.0 2.6 2.5 3.8 3.4 10.6 14.6 8.1 14.6 6.6 9.9 15.6 11.2 17.5 24.7 15.8 12.5 17.4 18.4 29.2 26.8 29.1 24.6 7.3 10.0 13.6

Source: Calculations based on Tables 8.8 and 8.9 in annex Note: In Italics estimates based on legal minimum salary (Cameroon, Ghana) or median salary (South Africa)

dren, etc.) as requested by the replacement cost at specialist wage rate method or by the opportunity cost method, because the detailed wage rates or the detailed time uses were not always available. Moreover for the exercises carried out directly by the National Statistical Offices, the wage rates used are imputed to each individual in the sample (with the individual’s weight in the sample), whereas in the present exercise, the wage rate is imputed to the whole population. These are rough estimates, but they can be considered as good proxies: comparisons show some discrepancies with existing official estimates but these discrepancies remain small and can be explained by other factors such as the age group (Morocco), the reference wage rate (Tunisia) or the gendered net wage (Turkey) or also the inclusion of unpaid production of goods.

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In 23 of the 26 countries (Table 8.5 and Chart 8.9), the share of the care economy in the extended GDP ranges from 29.0% in Kazakhstan and 34.5% in Hungary to 58.4% in Morocco and 60.0% in Mexico (see previous remark about Latin American countries). The three other countries are not strictly comparable and are characterised by lower shares because in the absence of statistics on wages, the legal minimum wage was used to value the unpaid care work in Cameroon and Ghana, and the median wage in South Africa. It should also be noted that Kazakhstan is a country where the oil industry accounts for more than ¼ of total GDP. The care economy would account from 1/3 to 3/5 of the extended GDP. Current GDPs would therefore be multiplied by a factor equal to 1.5–2.1. Table 8.5 and Chart 8.10 also show that women contribute to the care economy from a minimum of 70.8% (in Finland) up to a maximum of 98% in Morocco. In all MENA countries and in Benin, women bear more than 96% of the care burden. In 17 countries among the 26, they bear more than 85% of the total burden, and in 3 countries only (in Western Europe) they bear less than 75%. The size of the care economy does not seem to be related to GDP per capita at purchasing power parity (PPP). Chart 8.11 shows that the size of the care economy is negatively related to female labour force participation rate, which is obvious provided that women who stay at home are devoting more time to unpaid care work than women who are employed on the labour market. In countries where female labour force participation rates are high, an important proportion of childcare, adult care and other household chores is purchased on the market. Several of the 26 countries have attempted such valuation exercises: Finland, France, Germany and the UK in Western Europe and in Eastern Europe, Hungary. Tunisia, Morocco, Turkey (2006), Ecuador, Mexico and Peru also did. In Western Europe, the UK recently published a compendium of household satellite accounts 2004–2014 (ONS 2016, see also supra Tables 8.3 and 8.4) in which the methodol-

Chart 8.9  Share of the care economy in % of extended GDP Source: Table 8.5 Note: The three countries on the left hand side are not strictly comparable, because their estimates are based on the legal minimum salary (or median salary)

Comparison of Estimates of the Care Economy in 26 Countries

205

Chart 8.10  Distribution of the burden of the care economy between women and men Source: Table 8.5

Chart 8.11  The share of the care economy is negatively related to female labour force participation rate Sources: Table  8.5 and for Female labour force participation rate: national data extracted from ILOSTAT http://www.ilo.org/global/statistics-and-databases/lang%2D%2Den/index.htm Note: See Country codes by region and by alphabetical order in annex to Chap. 3: Tables 3.32 and 3.33

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ogy used for each detailed activity is provided: using the replacement cost salary, the home production defined as measuring “the value of adult and child care, household housing services, nutrition, clothing and laundry, transport and volunteering” is equivalent to 56.1% of the current GDP in 2014 and 35.9% of the extended GDP: two estimates which are very close to those we obtained with our own methodology (see Table 8.6). Interestingly, it is found that childcare accounts for 31.5% of total home production. France distinguishes three perimeters or circles of household production (Roy 2012a, b): household’s chores constitute the first “restricted” perimeter; the “median” perimeter includes activities that can hardly be distinguished from hobbies or leisure such as sewing, gardening, fishing, hunting or shopping or interacting with children. It should be noted here that the real character of these activities strongly depends on the context: in developing countries most of these activities are clearly not hobbies. Finally, the “extensive” perimeter includes travels and walking the dog: here again should be noted the possible mix-up between caring domestic animals in developing countries (such as feeding and milking the cow) and pets’ care in developed countries. For the extensive perimeter and using the specialist’s replacement cost, the household production in 2010 would represent 50.1% of the current GDP and 32.7% of the extended GDP. Estimates for Finland and Germany are available for the year 2001 (Rüger and Varjonen 2008): in Finland the household production (non SNA) was estimated at 40.1% of current GDP and Table 8.6  Comparisons of estimates of the care economy in 13 countries

Countries Year Morocco 2011– 2012 Tunisia 2005– 2006 Turkey 2006 Benin 2015 Cameroon 2014 Hungary 2009– 2010 Finland 2009 France 2010 Germany 2012 UK 2014 Ecuador 2010 Mexico 2014 Peru 2010

Own estimates In % of GDP at current prices 140.4

In % of extended GDP 58.4

Official estimates In % of In % of current GDP extended GDP 62 49.3

113.6

53.2

43.4

30.1

123.9 60.4 21.3 52.7

55.3 37.6 17.5 34.5

45a 38.3 17.8 47b

30.9a 27.7 15.1 31.2b

66.3 64.4 105.1 57.0 59.4 149.8 83.8

39.9 39.2 51.2 36.3 37.3 60.0 45.6

40.1c 50.1 34.4c 56.1 18.2 19.7d 24.4

28.7c 32.7 25.6c 35.9 15.4 16.5d 19.6

Source: Calculations based on Tables 8.8 and 8.9 in annex Note: In Italics estimates based on legal minimum salary (Cameroon, Ghana) or median salary (South Africa) a Estimate made by scholars (not official) b Opportunity cost, compensation of employees c Year 2001 d Year 2012

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207

28.7% of extended GDP, whereas it was estimated at, respectively, 34.4% and 25.6% in Germany for this same year. In Eastern Europe, Hungary compiled its time-use data for building a satellite account of household production for the year 2009–2010 that can be used as benchmarking. The share of the care economy ranged from a minimum of 22% with the generalist net wage rate, up to a maximum of 47% with the method of opportunity cost for compensation of employees (Central Statistical Office of Hungary 2016). Using multipliers of the legal minimum salary in an attempt of applying the specialist approach for replacement wage, Tunisia obtained an estimate of 47.4% for the share of the care economy in comparison with the current GDP (République Tunisienne 2011): a multiplier of 3 was used for caring or for monitoring children’s schoolwork, for example, of 2 for preparing meals or gardening, of 1.5 for cleaning and washing and of 1 for fetching water and firewood. One can note that in 2006 the average salary that we used for our own estimation represented 2.34 times the legal minimum salary. Morocco (Royaume du Maroc 2014) computed an estimate of 62% of the current GDP (and 49.3% of the extended GDP) by using an average salary derived from the compensation of employees (and, respectively, 34.5% and 39.7% by using the legal minimum salary) and limiting the exercise to the population aged 15 and over. For Turkey in 2006, estimates by two scholars, Ipek Ilkkaracan Ajas and Umut Gündüz (2009), are available. They value the care economy at 30% of GDP by the minimum wage, 45% by the opportunity cost wage and 26% by the generalist wage rate. It should be noted that except for the minimum wage, they used the net wages differentiated by sex and by activity, which partly explains the gap with our own estimates. In sub-Saharan Africa, two countries attempted estimates of the household production: Benin (INSAE 2017) and Cameroon (INS 2017). While the latter used the wage of the domestic worker in order to proceed to the valuation and ended up with a share of 17.8% of current GDP (and 15.1% of extended GDP), the former established a correspondence table between professions and time-use activities in order to apply a wage rate to each activity based on the average professional income in the 2013 Population Census and reached a share of 38.3% of current GDP (and 27.7% of extended GDP) in 2015. Ecuador prepared estimates by detailed industries (INEC 2014) based on the specialist’s replacement cost and obtained a share of 15.4% of current GDP in 2010 or 18.2% of the extended GDP. Peru (INEI 2016) valued unpaid care work by using an hybrid method between the specialist and the generalist replacement cost and obtained 20.4% of the current GDP and 16.9% of the extended GDP in 2010, whereas the pure specialist method ended with, respectively, 24.4% of current GDP and 19.6% of extended GDP. For the year 2012, Mexico estimated the household production at 19.7% of current GDP, which makes 16.5% of extended GDP (INEGI 2014). Table 8.6 summarises and compares our own estimates with the official (or academic) estimates, and Chart 8.12 visualises the discrepancies between official or academic estimates and our own estimates. In the UK the two estimates fit the most closely, and such a result is all the more interesting that the published methodology is the most detailed. It proves that the use of the average salary (or the average earning) for valuating the total number hours spent in unpaid care work globally is suitable for obtaining an estimate of the magnitude of the household production without entering into the details and the

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difficulties of building a complete satellite account. Hungary’s estimates are also quite close, as well as Cameroon’s estimates, but in this latter case, the coincidence comes from the use of the domestic worker’s wage by the official estimate, which is lower than the legal minimum wage. The gaps remain acceptable for France, Morocco and Benin where discrepancies can be explained by differences in age groups or unpaid work categories. More surprising are the gaps in Latin American countries (Finland, Germany and Turkey compare different years for own estimates and official estimates). One could have expected much higher sizes of the care economy in these countries provided that the time-use survey methodology ends up with longer hours in unpaid care work. For these countries, official estimates are even lower than estimates based on the legal minimum salary, which means that globally the imputed salary for unpaid care work is too much underestimated. It should be noted that the enormous gap for Mexico also takes its source in the difference in years of estimates (2014 for our own estimates and 2012 for official estimates). Finally Chart 8.12 clearly shows that official estimates tend to assume valuation of unpaid care work at wage rates under the legal minimum wage, a position that indicates a voluntary underestimation of the care economy. The GDP growth recorded between 2006 and 2014 in Turkey partly originates from the 7-percentage points increase in female labour force participation rate. Similarly, Tunisia has made of an increase of women’s economic participation rate from 25% to 35% a condition or a hypothesis – if not a goal – of its new development plan (2016–2020). Although the plan does not suggest any policy or measure and this hypothesis is rather taken as a contextual condition of the realisation of the plan, it could be interesting to model the impact of the realisation of such a condition.

Chart 8.12  Discrepancies between official and own estimates of the share of the care economy in the extended GDP Source: Table 8.6 and own calculations Note: Countries are ranked by increasing order of the gap between estimates

Conclusion

209

Conclusion In conclusion the estimates of the care economy presented here confirm the importance of these activities included in the extended notion of production. It can be interpreted as the amount of value added that would accrue from the marketisation of the unpaid care further to a massive entry of women on the labour market, for example. Unpaid care work being defined as “all (unpaid) services which one person may be hired to perform for another” (to use Alfred Marshall’s expression), the withdrawal of women from unpaid care work at home would automatically impact the labour market and the GDP through the hiring of new care workers as well as through the new jobs performed by women who earlier performed these tasks for free within their households. But what could be the impact of such changes in countries where the female labour force participation rate is already very high? It can be assumed that, depending on the context, an increase in rural-urban migrations and a transition of the workforce from agriculture to services or international migration flows would provide the requested labour for the care economy. Work is definitely a concept whose components and their variations reflect how people and more specifically women and men respond to crises and obstacles encountered across lifetime. Whereas in Part I of this book we have examined how vulnerable people earn their living through undertaking informal activities voluntarily or by constraint, Part III has included the time variable which is key to understand gender inequalities. As a matter of fact, we have seen that across the world and with no exception, women spend more time than men at work. Across age groups, rural/urban location, activity and matrimonial statuses, and also over the year (weekends, vacations, seasons), a woman’s workday reaches 7 h and 33 min in average and a man’s workday 6 h and 44 min. That makes a total amount of 2,756 h of work or 344.5 8-h days per year for a woman, against 2,458 h or 307 8-h days for a man. But, in an average workday, women dedicate 3.2 times more time to unpaid care work than men and 1.8 times less time than men in paid work. It is in these figures that the concept of “time poverty” originates. Because of their burden of unpaid care work, women have less time to devote to paid work and income-­ generating activities. When employment on labour markets shrinks, households’ resilience manifests itself in a prompt return of women to homework where their work for free maintains a minimum of well-being. The valuation of household production highlights the importance of women’s contribution to the well-being of households: in 26 countries where the valuation exercise was undertaken, officially or not, it represents between 0.4 and 1.4 of the current GDP and between 29% and 60% of the extended GDP (i.e. including household production). Such a magnitude implies recognition of this hidden amount of work whose 70% to 98% rest on women’s shoulders.

Kyrgyzstan 2010 Mongolia 2011

Kazakhstan 2012

Central Asia Azerbaijan 2008 2012

Year Quarterly

Year

2014– Year 2015

Turkey

Year

2006

Turkey

Year Period Middle East-North Africa Algeria 2012 2 months Morocco 2011– Year 2012 Tunisia 2005– Year 2006

Module of the household budget survey Module of a living standard survey Stand alone Stand alone

Stand alone

4,929 households 4,000 households

12,000 households, 33,830 individuals

10+

12+ 12+

3,910 households, 9,633 individuals

15+

10+

5,070 households, 11,815 individuals 11,440 households

11,594 individuals

15+

Stand alone, subsample of household survey Stand alone 15+

All eligible Random selection

22,138 individuals 9,200 households

12+ 7+

Stand alone Stand alone

All eligible All eligible who were at home

All eligible

All eligible

All eligible

All eligible

All eligible

Type of sample

Minimum Type of survey age Sample size

Table 8.7  Overview of TUS main characteristics in the 26 countries under study

Annex

Interview Mixed

Mode of data collection

One diary One diary

Interview Interview

Two diaries Interview

Two diaries Interview

Two diaries Mixed

Two diaries Interview

Two diaries Mixed

One diary One diary

Survey instrument

ICATUS

ICATUS

HETUS

HETUS

HETUS

HETUS

HETUS HETUS

Classification used

210 8  What Women Are Worth? Valuation of the Care Economy in Various Regions…

Ethiopia Ghana South Africa Tanzania

4 quarters

2014

Module of household survey

1 month Stand alone 2 months Stand alone 4th quarter Stand alone

2013 2009 2010

52,262 individuals 9,297 individuals 30,897 individuals 10,553 individuals

10+ 10+ 10+ 5+

Minimum Year Period Type of survey age Sample size Transition countries of Eastern Europe Bulgaria 2009– Year Stand alone 10+ 5,503 households 2010 Hungary 2009– Year Stand alone 15–84 2010 Macedonia 2014– Year Stand alone 10+ 2,080 households 2015 Romania 2011– Year Stand alone 10+ 18,720 households 2012 Serbia 2010– Year Stand alone 15+ 2,340 households 2011 Sub-Saharan Africa 6+ 4,920 households, Benin 2015 1 month Module of 13,026 individuals household survey 10+ 4,988 households Cameroon 2014 1 month Module of household survey

All eligible

Interview

One diary

Household head, spouse, random man/women among others aged 10–14 and 15+ All eligible All eligible Random selection

One diary

Interview

Interview Interview Interview

Interview

One diary

All eligible

One diary One diary One diary

Two diaries Mixed

All eligible

(continued)

ICATUS

ICATUS ICATUS ICATUS

ICATUS

Ad hoc detailed

HETUS

HETUS

Two diaries Mixed

All eligible

HETUS

HETUS

HETUS

All eligible

Classification used

All eligible

Two diaries Mixed

Type of sample

Mode of data collection

Survey instrument Annex 211

2010

Peru

12+

12+

12+

8+

4,580 households

18,996 households

20,767 households

9,388 individuals in 4,238 households provided 16,553 diary days

15+ 15,441 individuals 11+ (15+) 17,383 individuals 10+ 5,000 households, 11,000 individuals

All eligible

All eligible

Weekday/ weekend day Weekday/ weekend day Weekday/ weekend day

Weekday/ weekend day

All eligible

CAUTAL

CAUTAL

Ad hoc detailed

Interview

Interview

Ad hoc detailed

Mixed

Interview

Ad hoc detailed

Ad hoc detailed Ad hoc detailed

Classification used

Mixed

Two diaries Mixed Three diaries

All eligible

Mode of data collection

Two diaries Mixed

All eligible

All eligible

All eligible

Type of sample

Survey instrument

Notes: Blanks mean that no information was found in the methodological documents or that the survey results were obtained from an international database (e.g. OECD) The mixed mode of data collection means that diaries are self-recorded by the interviewees and individual/household questionnaires are filled by interviewers

Stand alone

4th quarter Stand alone

2014

Mexico

Stand alone

2 months

Latin America Ecuador 2012

UK

Stand alone August 2012–July 2013 Stand alone 2014– April 2015 2014–Dec. 2015

Germany

2012

Year

2010

France

Stand alone

Year

3,795 individuals

Stand alone

Period

Year Western Europe Finland 2009 10+

Minimum Type of survey age Sample size

Table 8.7 (continued)

212 8  What Women Are Worth? Valuation of the Care Economy in Various Regions…

468*

12,860,000

14,377,458

28,535,571

16,208,698,4 802,607

29,507 (2011) 22/h = 3813***** 14254 (domestic workers)

Population Men

Population Total

GDP at current prices (millions)

Average salary

12,24/h = 2121,6

200

7,569,000

3,747,200

3,821,800

119

32,574,699 3,735,600

51

349

531

1278**

758,390,8

1273.5

2642**

93,5

398,5

1,748,167,8 54,743,7

58,355,523 65,085,743 7,269,400

2,008,544

2,103,269

100

275

17,439

101,263

500

7189

31,015,186 220,369,3

13,635,264 4,111,813

6,480,991

7,154,273

111

246

Azerbaijan Kazakhstan Kyrgyzstan 2012 2012 2010

29,094,232 32,511,044 3,533,800

(2007) 29261291

51

257

Turkey 2014

6,791,230

3,528,277

3,262,953

164

298

140,000

424,200

240

645

73,500

202,525

27,224,599

8,325,000 (2011)

3,932,000

4,393,000

108

236

Bulgaria Hungary 2009–2010 2009–2010

13,173,800 73,780

2,084,800

1,000,700

1,084,100

139

290

Mongolia 2011

565,097,2

19,236,472

9,303,987

9,932,485

136

287

Romania 2011–2012

3,407,563

6,161,584

2,971,868

3,189,716

148

301

Serbia 2010– 2011

10,080 700 (thousands)

3800

31,590 *** 2032 **** 61,426 in (thousands) (average on 2014 12 months) 47,061 in 2010

527,631

1,840,975

918,453

922,522

85

224

Macedonia 2014–2015

* CNSS (private sector): monthly net wages; ** monthly gross wage (nonagricultural); *** gross earnings October 2014; **** average gross earnings on 12 months; ***** compensation of employees Formula: (((Number of minutes per day × 365 / 60) / (40 hours × 52 weeks / 12 months)) × monthly salary × number of women in the age group)/GDP (or extended GDP) at current prices (((Number of minutes per day × 365 / 60) / (40 hours × 52 weeks / 12 months)) × monthly salary × number of men in the age group)/GDP (or extended GDP) at current prices

Legal minimum 18,000 salary

41,871

13,208,000

14,158,113

Population Women

26,068,000

43

54

Time spent in unpaid work Men

54

326

300

312

Time spent in unpaid work Women

317

Tunisia Turkey 2005–2006 2006

Morocco Algeria 2012 2011–2012

Table 8.8  Parameters for the valuation of unpaid work in 12 MENA, Central Asia and Eastern Europe countries (GDP and salaries in national currencies)

7,583

7,404

14,987

4,405

4,340

8,745

31,870

15,325

16,545

125

Ethiopia 2013 291

40,000

52,640

36,270

1,255.9 (2012)

4,734,154 15,846,400 866,920.7 (2014)

74

42

*Income from work

Time spent in unpaid work Women Time spent in unpaid work Men Population Women (thousands) Population Men Population total GDP at current prices (millions) Average salary Monthly legal minimum salary

Cameroon 2014 212

Benin 2015 221

31,366

15,113

16,253

98

41,223

20,306

20,917

64

South Tanzania Africa 2010 2014 229 238

71.69

2,800 (median) 2,084

308,075

36,597.6 2,748,008.3 79,718.4

18,124

8,703

9,421 (2010)

68

Ghana 2009 220

3,218.8

181,029

4,745

2,313

2,432

139

Finland 2009 211

1,343.8

2,567

1,998,481

52,652

25,158

27,494

148

France 2010 234

57,223

28,166

29,057

133

10,915

5,352

5,563

78

92,171

44,381

47,790

146

21,931

10,849

11,082

136

Peru 2010 339

3,462

1,379

2,155.5

240

308*

1,995

8,596

580

971.9*

2,754,860 1,864,640 56,481.1 13,279,400 416,784

74,252

36,233

38,019

166

Germany Ecuador Mexico 2012 UK 2015 2010 2014 253 229 273 423

Table 8.9  Parameters for the valuation of unpaid work in 13 sub-Saharan, Western Europe and Latin American countries (GDP and salaries in national currencies)

References

215

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General Conclusion

It has not escaped the reader that the concept of resilience, which gives its title to this book, was not initially and properly defined in-depth. Resilience can be briefly defined as the ability to recover from setbacks, adapt to change and keep going in the face of adversity and external shocks. The challenge of the exercise was to track its offprints through readily available statistics. All along the chapters of this book, we have tracked signs of resilience hidden behind the aridity of statistical figures. In this sense, we have privileged tables and charts presenting demonstrative descriptive statistics related to structures and trends at national and global levels. By doing so, we highlighted the shades that such data give to see and suggest or hide to our sight, by unveiling a landscape of the real economy that numbers of usual economic works dedicated to the study of determinants and too often limited in their scope and coverage  – leave invisible. Another characteristic of our work is that most of the statistical data that we used are easily available to the common user provided that they arise from official publications posted on official websites of National Statistical Institutions. And finally the three domains of knowledge that have been investigated in this book (informality, inter-­ household solidarity transfers and time use)  – though remaining understudied  – have experienced, with highs and lows in the recent period, renewed interest from the scientific community as well as from policymakers. Informality  – if we leave aside the assimilation to illegality  – is a form of resilience in that it grows independently and sometimes despite of what measures and policies designed by governments may pursue. Where economies and policymakers are unable to provide paid jobs or economic opportunities to the population and the youth in particular, people have no other solution than to get by and create their own activities without wondering whether they have to obtain prior permission to do it. Despite recent concerns about organising the smooth transitioning from the informal to the formal economy (the new doxa of international institutions), the informal economy continues to follow its path of growth, with slowdowns and speedups and representing the real economic life of the bulk of the © Springer Nature Switzerland AG 2019 J. Charmes, Dimensions of Resilience in Developing Countries, Demographic Transformation and Socio-Economic Development 10, https://doi.org/10.1007/978-3-030-04076-5

217

218 

General Conclusion

populations in developing countries. Informality has even contaminated the formal sector which itself tends to evade too restrictive regulations while the payment of insufficient salaries pushes formal employees to undertake informal activities as multiple jobs contributing to make of pluri-activity a major aspect of resilience. Solidarity could be considered as a pre-existing form of resilience embedded in the traditional social structures. It is a form of social capital that is the expression of kinship, ethnic or territorial relationships or brotherhood that pre-exist and substitute to failing states. Although it can take various forms, among which the fostering of boys and girls or youth during periods of schooling or job search, or the provision of unpaid care services to other households, it is the inter-household transfers in cash or in kind that has been privileged in this study. The importance of inter-­ household transfers has shown their role as a factor of resilience in sub-Saharan Africa. Contrary to informality and time use, the sources of income are not systematically collected in household income and expenditures surveys, and this kind of analysis could not unfortunately be expanded to other regions of the developing world where transfers are mainly captured as remittances from migration. Lastly unpaid care work (approached through time use and monetary valuation) was analysed as a foundation of the inceptive household economy embedded in the patriarchal structures and gendered division of labour: the functioning of the labour markets cannot be understood without taking into consideration the provision of unpaid care services necessary to the physical and social reproduction of the labour force. Since the mid-1990s, time-use surveys are implemented and repeated in more and more countries, and the corpus of data allows proceeding to interesting global analyses regarding the heavy burden of care borne by women, which prevents them to access to paid work in due proportion compared with men and locks them up in time poverty, a situation that conduces to the “feminisation of poverty”. Beyond the fundamental gender inequality encapsulated in unpaid care work that inspires policies towards more equal sharing of responsibilities and duties within the household, the care economy is a huge reservoir of resilience in periods of crisis or external shocks as it ensures the provision of services that cannot be any more purchased on the market when cash is lacking. The purpose and ambition of this book was to highlight the importance and the role of informality, inter-household transfers and unpaid care work in every day’s economic life of our contemporaries. It also aimed at tightly interlinking the significance of the data with the ways and methods by which they have been collected. The three domains investigated in this book remain largely unrecognised, misunderstood and unexplored, and methods of data collection are still open to innovation. This is why it is so important to unveil these realities together with a better understanding of the tools that are forged to capture them. It is hoped that the reader has got a better sense of the scope, extension and topicality of these phenomena and a better understanding of what issues they raise for development economics as well as for the imperfect statistical tools which provide the materials for such reflexions.

Annex

References of Time-Use Surveys Middle East North Africa Algeria Office National des Statistiques (2013), Enquête Nationale Emploi du Temps ENET 2012, Alger, 90p. Morocco Royaume du Maroc, Haut Commissariat au Plan (2014), Le Budget temps ou l’Enquête Nationale sur l’Emploi du Temps au Maroc 2011–2012, Principaux résultats, Rabat, 44p. Royaume du Maroc, Haut Commissariat au Plan (2014), Le Budget temps ou l’Enquête Nationale sur l’Emploi du Temps au Maroc 2011–2012, Présentation des premiers résultats par Monsieur Ahmed Lahlimi Alami, Haut-Commissaire au Plan, Rabat, 10p. Tunisia République Tunisienne, Ministère des Affaires de la Femme (2011), Budget temps des femmes et des hommes en Tunisie, 2005–2006, Tunis, 189p.

© Springer Nature Switzerland AG 2019 J. Charmes, Dimensions of Resilience in Developing Countries, Demographic Transformation and Socio-Economic Development 10, https://doi.org/10.1007/978-3-030-04076-5

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Turkey Turkstat (2007), Results of the Time Use Survey 2006, Press Release N°119, July 25, 2007, Ankara, 3p. Tables available on http://www.turkstat.gov.tr/PreTablo. do?alt_id=1068 Turkstat (2016), Time Use Survey 2014–2015, Press Release N°18,627, December 4, 2015, Ankara, 2p. Tables available on http://www.turkstat.gov.tr/PreTablo. do?alt_id=1009 Ipek Ilkkaracan Ajas, Umut Gündüz (2009), Time-use, the Value of Non-Market Production and its Interactions with the Market Sector: The Case of Turkey, Paper presented at International Conference on Inequalities and Development in the Mediterranean Countries, Mimeo, Istanbul, 29p.

Central Asia Azerbaijan State Statistical Committee (2017), Women and Men in Azerbaijan, Statistical Yearbook 2016, Bakou, 191p. State Statistical Committee (2011), Main Results of Time use survey of Azerbaijan, 2007–2008, Statistical Bulletin, Bakou, 62p. Kazakhstan Ainur Dossanova (2014), The experience of Kazakhstan in conducting time use surveys, Agency of Statistics of the Republic of Kazakhstan Committee of Statistics of the Ministry of national economy of the Republic of Kazakhstan (2014), The experience of Kazakhstan in conducting time use surveys, presented at the « Time Use Survey data analysis workshop » 11–15 October 2014, Bangkok, 18p. Committee on Statistics of the Ministry of National Economy of the Republic of Kazakhstan (2014), Women and Men of Kazakhstan 2009–2013, Statistical Yearbook. (In Russian: Комитет по статистикеМинистерства национальной экономики Республики Казахстан (2014), Женщины И Мужчины Казахстана 2009–2013, Статистический сборник. Kyrgyzstan National Statistics Committee (2014), Results of the time use survey in Kyrgyzstan, 2010, presented at the « Time Use Survey data analysis workshop » 11–15 October 2014, Bangkok, 5p.

Annex

221

National Statistics Committee (2015), Femmes et homes en République de Kyrgyzie 2010–2014, Bichkek, 142p. In Russian: Национальный Статистический Комитет Кыргызской Республики (2015), Женщины И Мужчины Кыргызской Республики 2010–2014, Бишкек. Mongolia National Statistical Office of Mongolia (2009), Report of the time-use survey 2007, NSO, UNDP, Ulanbaatar, 96p. National Statistical Office (2014), Time use survey Mongolia, presented at the « Time Use Survey data analysis workshop » 11–15 October 2014, Bangkok, 12p.

Eastern Europe Bulgaria National Statistical Institute (2011), 2009–2010 Time Use Survey, Basic Results, Sofia, 13p. Tables available at: http://www.nsi.bg/census2011/pageen2. php?p2=167&sp2=168 Hungary Központi Statisztikai Hivatal (2012), Időmérleg 2009/2010, Összefoglaló adattár, Budapest, 180p. Hungarian Central Statistical Office (2016), Value of domestic work and household satellite account in Hungary, Statistical reflexions, Budapest, 6p. Macedonia State Statistical Office of FYR of Macedonia (2015), Time use survey, 2014/2015, Skopje, Statistical review, State statistical office, Population and social statistics, 118p. (In Russian): Државен завод за статистика – АНКЕТА за користење на времето, 2014/2015 година,– Скопје, Статистички преглед / Државен завод за статистика. Население и социјални статистики −118 стр, Romania National Institute of Statistics (2013), Time use in Romania, Press Release No. 307 of December 20, 2013, Bucharest, 7p.

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National Institute of Statistics (2013), Time use in Romania, Bucharest, 522p. In Romanian: Institutul National de Statistica (2013), Utilizarea Timpului in Romania, Bucurest, 522p. Serbia Statistical Office of the Republic of Serbia (2012), Time Use in the Republic of Serbia 2010–2011, Beograd, 86p.

Sub-Saharan Africa Benin INSAE/PNUD (1998), Enquête emploi du temps au Bénin, Méthodologie et résultats, Cotonou, 32p. + 156 p. annexes. INSAE (2017), Enquête Modulaire Intégrée sur les Conditions de Vie des Ménages 2ème edition (EMICoV-2015), Rapport d’analyse du volet emploi du temps, INSAE-GIZ. Cameroon INS (2017), Enquête sur l’emploi du temps au Cameroun en 2014, Rapport d’analyse, Yaoundé. Ethiopia Central Statistical Agency (2014), How Women and Men Spend Their Time, Ethiopian Time Use Survey 2013, Main Report, Addis Ababa, 104p. Ghana Ghana Statistical Service (2012), How Ghanaian women and men spend their time, Ghana Time-Use Survey 2009, Main Report, Accra, GSS-UNECA, 121p. South Africa Statistics South Africa (2001), How South African Women and Men spend their time, A survey of time use, Pretoria, 118p.

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Statistics South Africa (2013), A survey of time use 2010, Pretoria, 67p. + 43p. Tanzania Fontana Marzia and Luisa Natali (2008), Gendered Patterns of Time Use in Tanzania: Public Investment in Infrastructure Can Help, Paper prepared for the IFPRI Project on ‘Evaluating the Long-Term Impact of Gender-focussed Policy Interventions’, December 2008, 53p. United Republic of Tanzania, National Bureau of Statistics, (2007), Analytical Report for Integrated Labour Force Survey ILFS 2006, Dar es Salam, 124p. United Republic of Tanzania, National Bureau of Statistics, (2015), Analytical Report for Integrated Labour Force Survey 2014, Tanzania mainland, Dar es Salam, 152p.

Western Europe Finland Tables available eli__akay/

at:

http://pxnet2.stat.fi/PXWeb/pxweb/en/StatFin/StatFin__

France Ricroch Layla et Benoît Roumier (2011), Depuis 11 ans, moins de tâches ménagères, plus d’Internet, INSEE Première, N°1377, 4p. Tables available at: https://www. insee.fr/fr/statistiques/2118047?sommaire=2118074 Germany Destatis (2015), Zeitverwendungserhebung Aktivitäten in Stunden und Minuten für ausgewählte Personengruppen 2012/2013, Statistisches Bundesamt, Wiesbaden, 162p. Destatis (2016), Wie die Zeit vergeht. Analysen zur Zeitverwendung in Deutschland, Beiträge zur Ergebniskonferenz der Zeitverwendungserhebung 2012/2013 am 5/6 Oktober 2016 in Wiesbaden, 400p. Tables available at:https://www.destatis. de/DE/ZahlenFakten/GesellschaftStaat/EinkommenKonsumLebens bedingungen/Zeitverwendung/Zeitverwendung.html#Tabellen

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United Kingdom Centre for Time Use Research (2016),- United Kingdom Time Use Survey, 2014– 2015, UK Data Archive Study Number 8128, University of Oxford, Oxford, 22p. www.timeuse.org Sarah Morris, Alun Humphrey, Pablo Cabrera Alvarez, Olivia D’Lima (2016), The UK Time Diary Study 2014–2015, Technical report, UK Data Archive Study Number 8128 – United Kingdom Time Use Survey, 2014–2015, University of Oxford, NatCen Social Research, London, 226p. Latin America Ecuador INEC (2014), Encuesta de Uso del Tiempo, Quito, 37p. INEC (2013), Metodologia de la Encuesta Especefica de Uso del Tiempo 2013, Quito, 53p. INEC (2013), Plan de tabulados EUT, available at: http://www.ecuadorencifras.gob. ec/uso-del-tiempo-2/ Mexico INEGI (2010), Encuesta Nacional sobre el Uso del Tiempo ENUT 2009, Metodología y tabulados básicos, Aguascalientes, 264p. INEGI (2010), Clasificación mexicana de actividades de uso del tiempo CMAUT, Aguascalientes, 151p. INEGI (2005), Encuesta Nacional sobre el Uso del Tiempo ENUT 2002, Tabulados básicos definitivos, Aguascalientes, 71p. Tables available at: http://www3.inegi. org.mx/sistemas/tabuladosbasicos/tabgeneral.aspx?s=est&c=27602 Peru INEI (2011), Encuesta Nacional De Uso Del Tiempo 2010, Principales Resultados, Lima, 242p.