JOBS SERIES Issue No. 32 DIAGNOSTIC TOGO R ap h a e l a K ar l e n an d F r i e d e r ik e R o t h e r Confronting Challenges and Creating Opportunities for More Good Quality Jobs for All DIAGNOSTIC TOGO R ap h a e l a K ar l e n an d F r i e d e r ik e R o t h e r Confronting Challenges and Creating Opportunities for More Good Quality Jobs for All © 2023 International Bank for Reconstruction and Development / The World Bank. 1818 H Street NW, Washington, DC 20433, USA. Telephone: 202-473-1000; Internet: www.worldbank.org. Some rights reserved This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Rights and Permissions This work is available under the Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) http://creativecommons.org/licenses/by/3.0/igo. Under the Creative Commons Attribution license, you are free to copy, distribute, transmit, and adapt this work, including for commercial purposes, under the following conditions: Attribution —Please cite the work as follows: Karlen, Raphaela and Friederike Rother (editors), 2023. “Togo Jobs Diagnostic—Confronting Challenges and Creating Opportunities for More Good Quality Jobs for All.” World Bank, Washington, DC. Regional Vice President: Ousmane Diagana Global Director: Michal Rutkowski Regional Director: Dena Ringold Country Director: Coralie Gevers Country Manager: Fily Sissoko Practice Manager: Christian Bodewig Task Team Leaders: Friederike Rother and Raphaela Karlen Translations — If you create a translation of this work, please add the following disclaimer along with the attribution: This translation was not created by The World Bank and should not be considered an official World Bank translation. The World Bank shall not be liable for any content or error in this translation. Adaptations — If you create an adaptation of this work, please add the following disclaimer along with the attribution: This is an adaptation of an original work by The World Bank. Views and opinions expressed in the adaptation are the sole responsibility of the author or authors of the adaptation and are not endorsed by The World Bank. Third-party content — The World Bank does not necessarily own each component of the content contained within the work. The World Bank therefore does not warrant that the use of any third-party-owned individual component or part contained in the work will not infringe on the rights of those third parties. The risk of claims resulting from such infringement rests solely with you. If you wish to re-use a component of the work, it is your responsibility to determine whether permission is needed for that re-use and to obtain permission from the copyright owner. Examples of components can include, but are not limited to, tables, figures, or images. All queries on rights and licenses should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org. ACKNOWLEDGMENTS The Togo Jobs Diagnostic team was led by Raphaela Karlen and Friederike Rother (editors). The editors led Chapter 1: Introduction, Chapter 4: Conclusions and policy recommendations, and the overall report. Lead authors and contributors for the thematic chapters are as follows: • Stephanie Brunelin and Ulrich Zombré led Section 2.1: Growing labor supply faces significant underemploy- ment and important gender and location disparities. • Urbain Thierry Yogo and Marc Stocker led Section 2.2: An economy that creates jobs, but not in sufficient quantity and quality. Dino Merotto, Theresa Osborne, Maho Hatayama and Mario Gronert conducted data analyses that fed into the chapter. The authors were supported by Ernest John Sergenti, Nicholas Woolley and Amevi Rocard Kouwoaye. • Raphaela Karlen led Section 2.3: Formal labor demand remains limited due to low entry rates of new enterprises and the creation of few new jobs. Dino Merotto, Theresa Osborne, Maho Hatayama and Mario Gronert conducted data analyses that fed into the chapter. Frédéric Meunier, Kaliza Karuretwa and Julie Saty Lohi helped contextualize the findings of the chapter. • Clément Joubert, Himanshi Jain and Raphaela Karlen led Section 3.1: Non-poor informal workers can be vulnerable to shocks and represent the “missed middle”—those not covered by social protection. The Deep Dive benefitted from inputs from Melis Guven, Mariam Noelie Hema and Montserrat Pallares-Miralles. • Raphaela Karlen and Gabriel Lawin led Section 3.2: The economy is dominated by informal firms, ranging from subsistence businesses to well-established highly productive businesses. Wendy Cunningham provided valuable guidance in developing and refining the cluster analysis methodology applied in the deep dive. • Paula Cerutti and Guillaume Kroll led Section 3.3: An agricultural sector with a high potential to create good quality jobs on and off the farm. Erick Abiassi and Douti Bessokoh provided comments to refine the deep dive. The editors also wish to acknowledge comments and contributions received by Sara Johansson de Silva, Joachim Boko, Aissatou Ouedraogo, and Felicien Accrombessy. Moreover, the team thanks Cynthia Cindric and Aldo Morri for editorial support, and Alexandra Romanova for the visual design and typesetting of the report. Ulrich Zombré provided valuable support to the team in finalizing the Diagnostic and proofreading the French version of the report. Last but not least, Sébastien Tamegnon, Sharlen Vigan, Nadia Zenia Amoudji-Agnegue and Mariam Denise Brain provided excellent support to the team throughout the preparation of the report and the accompanying dialogue with counterparts. The team is grateful for strategic guidance received by Coralie Gevers, Fily Sissoko, Hawa Cissé Wagué, Christian Bodewig and Jehan Arulpragasam. The authors would also like to thank Wendy Cunningham, Phillippe George Leite and Yoonyoung Cho (concept stage) and Wendy Cunningham, Vincent Palmade and Siv Tokle (decision stage) for their review, comments and guidance. The preparation of the report would not have been possible without the financial and technical support by the Togo office of GIZ (Deutsche Gesellschaft für Internationale Zusammenarbeit). The editors wholeheartedly thank André Roenne and Sika Ekouhoho for their support and strong collaboration in preparation of this report. The Togo Jobs Diagnostic has been shaped by an ongoing dialogue with Togolese authorities and private sector representatives, including through a series of technical meetings with a multisectoral committee led by the Coalition Nationale pour l’Emploi des Jeunes (CNEJ) to discuss preliminary results of the different chapters of the Diagnostic. The team is further grateful to the President’s office and the different sector ministries for their inputs during the consultation process. Also, preparing the Diagnostic would not have been possible without the support of the Institut National de la Statistique et des Etudes Economiques et Démographiques (INSEED) that shared data, which the team greatly acknowledges. v TABLE OF CONTENTS ACKNOWLEDGMENTS v ABBREVIATIONS ix EXECUTIVE SUMMARY 1 1. INTRODUCTION 9 2. MULTISECTORAL JOBS DIAGNOSTIC 15 2.1 GROWING LABOR SUPPLY FACES SIGNIFICANT UNDEREMPLOYMENT AND IMPORTANT GENDER AND LOCATION DISPARITIES 15 2.2 AN ECONOMY THAT CREATES JOBS, BUT NOT IN SUFFICIENT QUANTITY AND QUALITY 32 2.3 FORMAL LABOR DEMAND REMAINS LIMITED DUE TO LOW ENTRY RATES OF NEW ENTERPRISES AND THE CREATION OF FEW NEW JOBS 41 3. DEEP DIVES 61 3.1 NON-POOR INFORMAL WORKERS CAN BE VULNERABLE TO SHOCKS AND REPRESENT THE “MISSED MIDDLE”—THOSE NOT COVERED BY SOCIAL PROTECTION 62 3.2 THE ECONOMY IS DOMINATED BY INFORMAL FIRMS, RANGING FROM SUBSISTENCE BUSINESSES TO WELL-ESTABLISHED HIGHLY PRODUCTIVE BUSINESSES 69 3.3 AN AGRICULTURAL SECTOR WITH A HIGH POTENTIAL TO CREATE GOOD QUALITY JOBS ON AND OFF THE FARM 77 4. CONCLUSIONS AND POLICY RECOMMENDATIONS 87 REFERENCES 95 ANNEX A: CALCULATING EMPLOYMENT ELASTICITIES 97 ANNEX B: METHODOLOGY OF THE CLUSTER ANALYSIS OF INFORMAL BUSINESSES 99 ANNEX C: SUBSIDIZED EMPLOYMENT PROGRAMS 108 vii ABBREVIATIONS AGET Association des Grandes Entreprises du Togo, Association of Large Firms in Togo AMU Assurance Maladie Universelle, Universal Health Insurance ANPE Agence Nationale de Promotion de l’Emploi, National Employment Promotion Agency BCEAO Banque Centrale des Etats de l’Afrique de l’Ouest, Central Bank of West African States CCIT Chambre de Commerce et de l’Industrie du Togo, Chamber of Commerce and Industry of Togo CNEJ Coalition Nationale de l’Emploi des Jeunes, National Coalition for Youth Employment CNSS Caisse Nationale de Sécurité Sociale, National Social Security Fund CPI Corruption Perceptions Index CPSD Country Private Sector Diagnostic CRT Caisse de Retraites du Togo, Pension Fund of Togo DGE Direction Générale de l’Emploi, General Directorate on Employment DJEJ Direction de la Jeunesse et de l’Emploi des Jeunes, Directorate on Youth and Youth Employment EHCVM Enquête Harmonisée sur les Conditions de Vie des Ménages, Harmonized Survey on Household Living Conditions EJV Employment Opportunities for Vulnerable Youth project ERI-ESI Enquête Régionale Intégrée sur l’Emploi et le Secteur Informel, Integrated Regional Employment and Informal Sector Survey FCFA Franc de la Communauté Financière Africaine, Franc of the African Financial Community FSB Filets sociaux et Services de Base, Safety Net and Basic Services GDP Gross Domestic Product GIZ Deutsche Gesellschaft für Internationale Zusammenarbeit GoT Government of Togo ILO International Labor Office INSEED Institut National de la Statistique et des Etudes Economiques et Démographiques, Institute of National Statistics and Economic and Demographic Studies LIC Low-income country MALRD Ministry of Agriculture, Livestock and Rural Development MEF Ministry of Economy and Finance MIP Ministry of Investment Promotion MITLC Ministry of Trade, Industry and Local Consumption MSME Micro-, Small and Medium Enterprises OLS Ordinary Least Squares PURS Programme d’Urgence pour la Région des Savanes, Emergency Program for the Savanes region ix QUIBB Questionnaire des Indicateurs de Base du Bien-être, Core Welfare Indicator Questionnaire SDGs Sustainable Development Goals SEZ Special Economic Zone SME Small and Medium Enterprises SSA Sub-Saharan Africa TVET Technical and Vocational Education and Training WAEMU West African Economic and Monetary Union x EXECUTIVE SUMMARY Togo’s jobs challenges remain important, despite progress made in creating more good quality jobs since 2000. Togo has made significant progress in creating more good quality jobs, with robust growth performance in the past decade, the beginning of a structural transformation process, output shifting slowly out of agriculture and into services, and employment starting to shift into services. However, several jobs-related challenges remain. Togo’s job market is still marked by low productivity, high levels of informality and slow structural transformation, reflected in the large share of underemployment and low-quality jobs. Industry is in the early stages of development; it needs time and the right policy environment to grow. This jobs challenge is compounded by demographic trends, as the working age population is growing with youth entering the labor market with higher education levels at a time of weak labor demand. Furthermore, during the COVID-19 pandemic, reduced demand for Togolese exports has put jobs and incomes in sectors dependent on international trade at risk, including tourism, transport and logistics, manufacturing and agro-processing. A sharp rebound in global trade contributed to a recovery in 2021, but disruptions associated with Russia’s invasion of Ukraine created renewed headwinds in 2022, which adversely impacted key sectors of the economy and dampened households’ purchasing power. Good quality jobs are key to accelerating poverty reduction and enhancing social cohesion in Togo. Following a decade of significant progress in reducing poverty, the COVID-19 pandemic and of Russia’s invasion of Ukraine are likely to have reversed some of these gains in living standards, however. The creation of more good quality jobs plays a key role in any country’s poverty reduction efforts, and will be essential to recover from recent shocks and reinforce earlier gains made in Togo. International research also points to lack of economic opportunities and insufficient social services as key drivers of radicalization of young people. Security threats in the northern region of the country have been growing, with terrorist attacks in Burkina Faso close to the Togolese border increasing in number and severity since 2018, and a first attack reported on Togolese territory in November 2021 in the Savanes region. Access to good quality jobs with a stable income for young Togolese will thus also be part of the solution to the security threats. Jobs outcomes can be evaluated across three dimensions: job creation, quality of jobs and access to jobs. Improving jobs outcomes of the Togolese population encompasses not only the creation of more jobs, but also improving the quality of existing jobs and ensuring those with additional constraints to access jobs are not left out. New jobs can be created through the creation of new firms (this includes those who start a business on their own, without any paid employees), as well as the creation of new jobs in existing businesses. The number of jobs, however, says little about their quality: job quality is a function of their productivity and earnings levels, as well as social protection services to protect workers against income and job losses. Finally, working towards better jobs outcomes for the Togolese economy as a whole also requires a more throughout understanding of additional constraints that different population groups face to access good quality jobs. This notably includes youth, women and rural populations. Government policies and programs are fragmented and do not yet embrace a multisectoral, long-term vision to achieve better jobs outcomes. Addressing Togo’s jobs challenge requires a multisectoral approach. While the policies and programs of different sector ministries touch upon the creation of more jobs and access 1 to these jobs by youth, women and rural populations, coordination is limited, and programs and actors are fragmented. In term of national development objectives and programs, the Feuille de Route Gouvernementale 2020–2025 marks an important shift towards encouraging private sector development and attracting private investments and was developed while Togo was making important progress in improving its business climate. Despite this recent shift towards a private sector-driven development path, further attention to address weak overall economic demand and labor demand-side constraints, as well as stronger leadership in coordinating different government actors and consolidating programs, is necessary to support a more targeted approach to tackle the country’s jobs challenges. To inform a multisectoral Jobs Strategy for Togo, the Jobs Diagnostic applies a data-driven, multisectoral approach to the country’s jobs challenges. The Jobs Diagnostic not only describes trends but also analyzes drivers of economic transformation at the macro level. It looks at sources of job creation and ways to increase the productivity of jobs to boost labor demand and it examines constraints to positive labor market outcomes, including skills (mis-) match, labor market frictions, and constraints on the supply side of the labor market. The Jobs Diagnostic builds on the latest available datasets, which date from before the COVID-19 pandemic, including the Enquête Harmonisée sur les Conditions de Vie des Ménages (EHCVM) 2018–2019; the Questionnaire des Indicateurs de Base du Bien-être (QUIBB) 2006 and 2011; the Enquête Régionale Intégrée sur l’Emploi et le Secteur Informel (ERI-ESI) 2017; the World Bank Enterprise Survey 2011 and 2016; World Development Indicators 2020; data from the Centre de Formalité des Entreprises (CFE); World Bank Ease of Doing Business data points (not using the World Bank’s Ease of Doing Business Index); and Penn World Table data (version 10). Whenever sufficient data is available, Togo’s situation is compared to its structural (Benin, Guinea, and Sierra Leone) and aspirational peers (Ghana, Morocco, and Rwanda). These data provide a rich characterization of labor markets in Togo, but represent pre-COVID-19 times, and thus likely present a more positive picture than the current state. The development of the Diagnostic was accompanied by ongoing consultations with a multi-sectoral technical committee chaired by the Coalition Nationale de l’Emploi des Jeunes (CNEJ). In collaboration with the CNEJ, which works under the responsibility of the Haut Conseil de l’Emploi des Jeunes chaired by the Prime Minister’s Office, the World Bank supported the establishment of a multisectoral technical jobs committee chaired by the CNEJ. Members of the technical committee include the Ministry of Economy and Finance; the National Institute of Statistics and Economic and Demographic Studies; sector ministries and their agencies working on jobs-related issues; private sector representatives; and development partners. Between April 2021 and January 2023, nine technical meetings took place to present and discuss the objectives and the approach applied under the Diagnostic and the preliminary findings from different sections of the Diagnostic. Additional consultations were conducted with the President’s office between November 2022 and January 2023 to finalize this report. The Jobs Diagnostic is part of a two-year analytical program co-financed by the World Bank and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) to support the Government of Togo as it develops a multisectoral strategy on jobs. The analytical program consists of the Jobs Diagnostic, an in-depth value chain study, and technical assistance to the government to develop a multisectoral jobs strategy. The in-depth value chain analysis is a continuation of the previous stream of work between the World Bank and GIZ that allowed identification of high potential value chains for better jobs outcomes for youth and women. It applies an integrated approach to jobs in the agricultural sector by conducting an in-depth analysis of selected value chains, simultaneously assessing constraints on, and opportunities for, better jobs outcomes for youth and women at the macro level, while also exploring constraints and opportunities in the areas of labor supply and demand. The two analytical pieces serve as basis for World Bank technical assistance to the Government of Togo as it develops its first multisectoral Jobs Strategy. 2 10 key facts emerge from the results of the Togo Jobs Diagnostic, as follows: 1 While Togo’s employment rate is high, many are working low productive jobs with meager earnings. The employment rate among Togo’s population is high, yet many jobs entail low productivity and earnings. Togo’s employment rate is high and stands at 76 percent. Between 2006 and 2018, the employment rate remained stable except for those aged 15 to 24, whose employment rate decreased in parallel to an increase in education levels. Indeed, the share of individuals aged 15-24 still in school grew strongly from 26 percent to 38 percent. Women make up the great majority of individuals that are neither in education, nor participating in the labor market: 75 percent of inactive people are women, whose main obstacle to employment is domestic work. Amid the high employment rate, underemployment is a major issue in Togo. Underemployment is often linked to jobs that are insecure, require few qualifications, and provide relatively little remuneration. It reflects inadequate duration of work, that is, less than 35 hours per week. Underemployment is widespread at 61 percent, concerns women and those in rural areas the most, and affects all forms of employment: it stands at 51 percent among salaried workers and 57 percent among the self-employed. Underemployment is more prevalent in the agricultural sector than in other sectors of the economy. 77 percent of people working in agriculture are affected by underemployment, reflecting that agricultural activities are seasonal and only last for a few months, whereas activities related to services and industry can be carried out throughout the year. 2 Unemployment in Togo is low and affects mostly educated youth in urban areas who can afford to wait for the right job opportunity. Unemployment remains low, not least because large parts of the population have no choice but to work to earn a living. The unemployment rate is very low (1.7 percent in 2018) and has halved since 2006 (4 percent). Unemployment has always been higher in urban than in rural areas. It affects mostly young, urban people with high education in the more affluent segments of society (4th and 5th quintile), who can afford to wait for “the right job opportunity” to join the labor market. Togo is no exception: Benin recorded unemployment at 1.4 percent in 2018, Guinea at 5.0 percent in 2019, and Sierra Leone at 4.7 percent in 2014. These figures should not be interpreted as good outcomes; rather, given insufficient social protection, the poor cannot afford not to work. Seeking employment is not without cost and depends on the ability of jobseekers to finance a period of unemployment, either from personal savings or family support. As a result, the majority of job seekers and workers have no choice but to accept any form of occupation, often with low pay and under difficult working conditions. 3 The majority of Togolese, including non-poor, have no access to social protection. Large parts of the population are not covered by any kind of social protection. Social protection instruments help households scope with shocks affecting employment and earnings, and protect them against consequences from sickness and old-age. Togo’s social protection system, like those of many low- and middle- income countries, is designed for formal sector workers (through the contributory CRT and CNSS), and also includes programs directed to the poor (through a non-contributory cash transfer and school canteen programs). However, safety net programs in practice reach very few—13 percent—of the poor and vulnerable. Moreover, there exists no social protection instrument to reach out to non-poor segments of the informal sector, although they account for 44.4 percent of the population, and up to 58.6 percent in Lomé Commune. Referred to as the “missed middle” of social protection, these groups risk falling into poverty if confronted with a shock impacting their economic wellbeing. Since the onset of the COVID-19 pandemic, the Government of Togo has displayed innovation and political will in providing social protection to the “missed middle”: the Novissi program provided 3 digital emergency cash transfers to informal workers most affected by the pandemic, and a 2021 bill established universal health coverage, independent of employment and welfare status. The informal non-poor population displays varying levels of resilience. Some 4.5 percent of Togo’s population lived in a non-poor non-resilient household in 2018: following a shock, they had to resort to negative coping mechanisms harmful to their productivity and human capital. Another 39.9 percent lived in a non-poor resilient household, as they were either not confronted by shocks, or had the capacity to self-insure by using accumulated savings, receiving help from relatives, working more, or getting a loan. The most common types of shocks households reported were health and family events (injuries and deaths of family members), and shocks to farming income and natural disasters. The level of resilience of non-poor households is indicative of their contributory capacity: non-resilient households are unlikely to have income to spare for contributions to protect themselves against shocks. Resilient households, on the other hand, do have a contributory capacity that could be leveraged for innovative savings schemes. The level of resilience in the informal sector is likely to be lower now than in 2018, reflecting the severe negative welfare impact of the pandemic and subsequent global economic instability. 4 Togo’s economy has created jobs but not with sufficient quantity and—especially—quality. Togo’s economy has created jobs in the past, but not in sufficient number and quality to significantly improve the jobs outcomes for its population. While the structure of Togo’s economy has shifted gradually from agriculture to services over the past two decades, employment in low-productivity sectors still dominates the landscape. The primary sector’s share of employment has declined from 48 percent in 2001 to 38 percent in 2019, which has been met with a similar increase in services. Over the same period, total employment increased by 2.8 percent, mainly due to an increase of workers in the industry sector, as well as high and stable employment growth rates in the services sector, and a decline of annual job creation rates in the agricultural sector. Moreover, informality is a dominant feature of Togo’s labor market, with about 90 percent of workers active in the informal sector as of 2017, contributing to an estimated 35 percent of GDP. Economic growth is linked to some job creation, with differences across different periods. Between 2001 and 2019, each percentage point of additional GDP growth was associated with a 0.6 percentage point increase in employment. Employment growth was strongest from 2001 to 2007, which surprisingly is the period with the weakest economic growth. This implies that a significant share of employment growth was due to an increase in labor supply rather than gains in productivity. Also, the analysis shows that a one percentage point increase in GDP growth over the period 2006–2018 increased waged employment by only 0.1 percent, which could imply that a sizeable share of the jobs created are not productive. 5 Togo’s economy needs to grow fast to create more, and more productive jobs, requiring substantive reforms. Amid all the employment challenges that the country is facing, the labor force keeps growing, with large groups of young people with higher education levels entering the labor market every year. This will require the economy to considerably step up the creation of new jobs. The age structure of the Togolese population changed moderately between 2001 and 2019, due to declining fertility combined with increasing life expectancy; the total fertility rate fell from 5.4 births per woman in 2001 to 4.2 in 2019, while life expectancy increased by about 7.5 years. Over the same period, the dependency ratio (the share of children below 15 and old-age population above 65) fell from 85.6 percent to 78.2 percent. The falling dependency ratio could create a demographic dividend that would spur economic growth, but this requires creating jobs fast enough to keep up with the significant increase in the working age population. 4 Given its increasing working-age population, Togo will need to create an additional one million jobs by 2030. To achieve that, growth would need to average at least 4.6 percent per year. Such a growth rate would be enough to absorb new labor market entrants but not to ensure rapid gains in informality and poverty reduction. More fundamental improvements would require significantly faster growth underpinned by reforms supporting structural transformation and job creation. In terms of sectoral distribution and taking into account the effect that services tend to expand more rapidly with economic growth, the service sector’s share of employment could reach 58 percent by 2030, comprising 1.1 million of the 1.4 million new jobs embedded in World Bank projections (with an average of 6 percent GDP growth over the period 2023–2030). 6 The agricultural sector is and will remain a main source of jobs and livelihoods for Togolese in the foreseeable future. The agriculture and food sector has been and will remain the dominant economic and employment sector for the foreseeable future; its performance will be key in improving jobs outcomes for Togo’s population. Agriculture accounts for about 38 percent of total employment, making it the country’s second largest employer after the service sector. Adding agricultural inputs provision and post-harvest activities throughout the value chain, the agriculture and food sectors account for at least two-thirds of all jobs. The sheer number of workers in the agriculture sector is too large for the rest of the economy to productively absorb most of its workers in the decades to come. In fact, the sector created almost a quarter million jobs in 2019, which represented 60 percent of all jobs created that year, underlying the need to focus on improving job quality in agriculture. Agriculture is dominated by very small-scale, low productivity activities. Most agricultural household enterprises represent own account and unpaid family work. It is characterized by underemployment, even during the agricultural seasons. Agricultural labor productivity is very low, standing at 60 percent of the country’s average labor productivity, and has stagnated over time. Low productivity translates into insufficient earnings: in 2018, an agriculture worker earned 29,100 FCFA per month on average, representing only 59 percent of the average income for workers. Moreover, agriculture is the main source of jobs and livelihood for the poorest. Most poor and vulnerable workers are employed in agriculture, perpetuating the vicious cycle of low productivity, poverty, and inequality. Women and youth appear to be particularly vulnerable in agriculture sector employment, with male rural workers leaving agriculture much more rapidly than female workers, and youth more likely than their adult counterparts to participate in agriculture employment. 7 Most informal firms represent low productivity subsistence activities and would not be able to afford the costs associated with operating in the formal economy. Informal businesses, representing the majority of economic activity in Togo, are a heterogeneous group requiring tailored policy options to improve productivity and earnings levels as well as create more jobs. Informality is a dominant feature of Togo’s economy. Informal businesses tend to have a female owner (72 percent); not have any workers beyond the owner (88 percent); and have low monthly profits (revenues less direct costs; approximatively 58,938 FCFA or US$99 per worker on average). Lack of customers (60.5 percent) and difficulties in accessing credit (52.4 percent) are the main challenges informal firms report on. While in the past policies have mostly focused on formalizing firms, notwithstanding their level of productivity and capacity to formalize, it is important to recognize that the informal business landscape is heterogenous, and tailored policies are needed. A cluster analysis of the informal sector results in five main clusters of firms with different profiles and productivity levels. Firms in Cluster 1 (10 percent of informal businesses) are subsistence businesses with a low potential for growth. Businesses in Clusters 2 and 3 (51 percent) are somewhat productive and do have some capacity to grow, although that capacity is limited. Cluster 4 businesses (21 percent) are start-ups with 5 highly educated owners and access to public services; while they currently operate at low levels of productivity, they do have a high growth potential. Cluster 5 businesses (18 percent) are more established firms with a profile similar to that of formal firms. They are connected to public services, and most of them have a fixed location with access to established suppliers and clients. All but Cluster 5 firms likely have too low productivity to benefit from formal status, as the fiscal and other costs associated with being formal risks putting these firms out of business. 8 Formal firm creation has increased as a result from reforms, albeit from a very small base. Togo’s economy is dominated by very small businesses, with few firms of significant size, suggesting constraints to growth. Overall, wage jobs in formal firms tend to have higher earnings and better job security, as well as access to social security, which are important aspects of quality jobs. Yet, Togo’s economy is largely dominated by small businesses, with few firms of significant size. Only around 2,000 firms have an annual turnover above US$100,000 and just 14.5 percent of firms identified in the 2018 Firm Census are formal. The formal private sector has not been creating enough jobs to absorb a growing and gradually better-educated workforce. The elasticity of job-to-sales growth in formal firms fell from 0.95 to 0.82 between 2009 and 2016, indicating that firms have not necessarily hired workers as their sales have grown. In comparison, Cambodia (2016) and Bangladesh (2013) displayed job-to-sales growth elasticities of 3.1 and 5.5, respectively, evidencing labor-intensive growth spurts. Over the past decade, Togo’s (formal) firm entry rate has steadily increased, a positive development which can improve the elasticity of jobs-to-GDP growth over time. An economy creates jobs through the creation of new jobs in existing businesses or the creation of new businesses. Togo’s firm entry rate per 1,000 working age adults went from 0.15 in 2009 to 0.94 in 2020, showing a clear upward trend, especially since 2017, explained by government reforms to simplify the business registration process. Moreover, female business ownership among newly registered firms remains low, with less than one-third of new firms owned by women in 2020. However, since 2014 a steady upward trend has occurred, with female ownership increasing from 22 percent in 2014 to 32 percent in 2020. It should be noted that women are overrepresented in the informal sector and face comprehensive constraints limiting their ability to contribute to the formal private sector. 9 Job creation in the formal sector is held back by high labor costs and low productivity levels. Formal firms do not have the right incentives to create more formal jobs. Given current productivity levels, Togo’s labor costs, composed of the salary, labor income taxes, and social security contributions, appear to be too high to incentivize firms to create more formal jobs. After the 2012 minimum wage increase, wage employment contracted and self-employment increased, a shift that is particularly pronounced among workers with low levels of education. At least 50 percent of urban wage and industry workers earned less in 2011 than what became the new minimum wage in 2012. Togolese firms hire a larger share of workers on a temporary basis than in other countries, which seems to substantiate constraints related to profitably hiring additional workers. Furthermore, Togo’s manufacturing firms use a higher share of semi-skilled and a lower share of unskilled workers than firms in peer countries, possibly the result of relatively high labor costs for unskilled, low productivity jobs. High labor income taxes and social security contributions in Togo might push some workers and firms towards the informal sector. Firms pay a 3 percent tax on gross salaries and 17.5 percent in social security contributions, while employees contribute 4 percent to social security, bringing the total tax wedge to 24.5 percent. Togo’s social security contributions as a share of gross salaries stand at similar levels to those in Benin 6 (16.4 percent), Morocco (15.38 percent), and Guinea (18 percent), but well above Rwanda (with 5.3 percent paid for by the employer and 3.3 percent by the employee). Moreover, labor taxes and contributions as a share of profits are higher in Togo than in its East Asian peers as well as in Rwanda, Sierra Leone, and Ghana, according to 2020 Doing Business data. 10 Access to finance is a major constraint for firms and is especially difficult for women entrepreneurs and sectors where the poor work. Access to finance is a key constraint for productivity growth as well as further job creation in formal and informal businesses alike. Access to finance may be limiting Togolese firm growth, but, overall, this does not seem as big of an obstacle as in some other countries. According to the Enterprise Survey, 51 percent of formal firms identified access to finance as a major constraint for doing business in 2016. This share is higher than in Rwanda (17 percent in 2019), Morocco (28 percent in 2019), Guinea (30 percent in 2016) and Benin (43 percent in 2016). However, it was not perceived as the biggest obstacle: only 24 percent of Togolese formal firms perceive access to finance as the biggest constraint, which is lower than in Rwanda (31 percent in 2019), Benin (33 percent in 2016), Sierra Leone (40 percent in 2017), and Ghana (50 percent in 2013). Moreover, in 2016, 26 percent and 40 percent of formal firms in Togo used banks to finance investments and working capital, respectively, higher than in any structural and East Asian peers. Vulnerable groups are more excluded. Data from the 2016 Enterprise Survey may not capture all business growth constraints related to financing. The banking portfolio in Togo is concentrated in a few sectors. According to BCEAO data, almost 80 percent of bank credit went to the services sector in 2020 and less than 1 percent went to agriculture, which financial operators explain by the lack of credit history and the high level of informality. Digitizing payments represents an opportunity to improve financial inclusion, notably for small enterprises, in a context where access to physical financial services remains low. Moreover, World Bank FINDEX data indicates gender differences in terms of financial inclusion in Togo: in 2021, 29 percent of men compared to 21 percent of women above the age of 15 years had an account at a financial institution; 12 percent of men compared to 11 percent of women above the age of 15 years had saved at a financial institution; and 49 percent of men compared to 39 percent of women above the age of 15 years had made or received a digital payment. Building on the analysis, the Diagnostic allowed to identify the following key recommendations to improve jobs outcomes for all population groups in Togo: (a) Coordination. Combining a multisectoral jobs strategy, the jobs platform anchored at the highest level, and a data exchange platform would ensure a consistent policy approach across the Togolese economy, provide broad direction for the sectoral efforts, ingrain a strong job lens, including through enhanced data for evidence-based decision making, and improve efficiency and effectiveness in economic and social policies. (b) Labor supply. Improved targeting of measures including wage subsidies, training offerings, and cash transfers would allow for a strengthened focus on the most vulnerable groups, including women and youth, in connecting workers to more productive opportunities as wage workers or self-employed. Simultaneously, social protection systems need to be significantly developed and further strengthened for vulnerable households depending on informal work. (c) Overall economic context. Applying a jobs lens in policy decisions would create the space to further develop the potential of agriculture and strengthen urban employment opportunities. From a sectoral perspective, special efforts should be made to develop the large potential of agriculture in Togo, which is critical for employment in Togo. Critical measures include investment in infrastructure and initiatives to promote processing and exports. 7 (d) Labor demand. Promoting the creation of more formal jobs in the private sector, including by reviewing the various elements of total labor costs to improve its competitiveness, enhancing access to finance, and strengthening good governance, would improve conditions to create more formal jobs in the private sector. Policy also needs to strengthen productivity in the informal sector through a more tailored approach to meet the needs of the heterogenous firms in the sector, as this is where most of the jobs are and will remain in the foreseeable future. 8 1. INTRODUCTION 1. Togo has made significant progress in creating more and better jobs, with robust growth performance in the past decade, output shifting slowly out of agriculture and into services, and employment starting to shift into services. Togo, a West African country of 8 million people, 5 administrative regions, and neighboring Ghana, Burkina Faso and Benin, has seen robust growth in the two decades before the COVID-19 pandemic, accompanied by the creation of new jobs. However, several challenges to more and better job creation remain. Togo’s job market is still marked by low productivity and slow structural transformation. Industry is in the early stages of development; it needs time and the right policy environment to grow. This jobs challenge is compounded by demographic trends, as the working age population is growing at a time of weak and inadequate labor demand, mainly outside of the farm sector. FIGURE 1.1 Map of Togo 9 2. But Togo will need to create an additional one million jobs to absorb its growing labor force and, consequently, to sustain a GDP growth rate of at least 4.6 percent per year until 2030. This growth performance would be sufficient to absorb new entrants but not to significantly reduce informality or to ensure rapid gains in average earnings. Given the structure of the Togolese labor market, this report focuses on tapping the immediate potential for productive employment in agriculture and household enterprises, analyzing the potential for productivity and job growth in the informal sector, and assessing the specific challenges and opportunities related to employment on farms, in nonfarm household enterprises, and in (modern) wage jobs with a focus on youth and women. 3. The COVID-19 pandemic and economic disruptions from the war in Ukraine have led to slower gains in GDP per capita and poverty reduction in recent years and have considerable reduced fiscal buffers. During the COVID-19 pandemic, reduced demand for Togolese exports has put jobs and incomes in sectors dependent on international trade at risk. The sectors most affected during the pandemic were tourism, transport and logistics, manufacturing, agriculture, and agribusiness.1 In addition, remittances for Togo declined 3.7 percent in 2020, a decrease of US$17 million from 2019.2 This affected not only consumption, but also productive investments, because in a country such as Togo friends and relatives are the main sources of finance for entrepreneurs.3 A decrease in remittances, in combination with an income drop through other channels, is likely to lower domestic demand for services and products, creating second-order effects on jobs and income. A sharp rebound in global trade contributed to the subsequent recovery in 2021, to 5.3 percent, but disruptions associated with the war in Ukraine created renewed headwinds in 2022, as reflected in decelerating external demand and rising energy, fertilizer, and food prices, which adversely impacted key sectors of the economy and dampened households’ purchasing power. More variable rainfalls also contributed to rising domestic food prices. Both rural poor who rely on agriculture for their own consumption or as a primary source of income and urban poor relying on imported food were adversely impacted. These shocks, combined with growing insecurity in the North, led the government to boost public spending, which contributed to stabilizing growth at an estimated 4.9 percent in 2022, but widened the fiscal deficit to a 3-decade high of 8.4 percent of GDP. 4. Moving forward, significant uncertainty on the economic outlook for the country requires a strategic reevaluation of priorities with constrained fiscal space a significant concern. Uncertainty related to the evolution of global demand, energy, food prices, financing conditions, security risks and climate change imply that the balance of risks to the outlook is largely tilted to the downside in the short term. Deeper disruptions to global trade, commodity, and financial markets could have a severe knock-on effect on a small, open, and relatively indebted country like Togo. Additional downside risks include rising security threats that could weigh on investment, trade, and public finances. Unfavorable weather conditions and the unavailability of inputs could negatively affect agricultural productivity, while the relative weakness of administrative capacity could limit the implementation of reforms and private investment. A policy approach that focuses on job creation, productivity enhancement, movement from informal to formal employment, and support for different profiles of workers and their households will provide both short and long-term economic benefits to Togo’s economic and social stability. A multi-sectoral approach would help address poverty and factors that limit Togo’s economic growth. 5. Good quality jobs are key to accelerating poverty reduction in Togo, where around half of the population still lives in poverty. Between 2011 and 2015, the national poverty rate in Togo decreased from 58.7 percent to 55.1 percent. Poverty is primarily a rural phenomenon, with 69 percent of rural households living below the poverty line in 2015. Female-headed households experience higher rates of poverty than male- headed households (57.5 percent vs. 55 percent).4 In 2018–19, the national poverty rate stood at 45.5 percent, showing an important decrease from 2015.5 The impact of the COVID-19 pandemic and the war in Ukraine 1 World Bank. 2020. Togo Economic Update. 2 World Bank. 2020. Migration and Development Brief 33: Phase II: COVID-19 Crisis through a Migration Lens. 3 World Bank. 2020. Macroeconomic and financial weekly monitoring October 16, 2020. 4 World Bank. 2019. Macro and Poverty Outlook. 5 Core Welfare Indicators Questionnaire (QUIBB, 2006, 2011, 2015) and the West African Economic and Monetary Union (WAEMU) harmonized survey (2018–19). Caution is needed in interpreting the evolution of the poverty share between the two periods, as data are based on two different surveys that are not fully comparable. 10 are likely to have reversed some of these welfare gains. The war, which has limited access to fertilizer and other key agricultural inputs, will likely negatively impact the current agricultural season. In line with this, growth in the agriculture sector is expected to decelerate to 4 percent, down from 6 percent growth in 2021.6 The rural population who disproportionately rely on agriculture (76 percent and 55 percent, respectively, for crop and livestock production)7 as a source of livelihoods are at a greater risk of falling into poverty. Without accelerated growth and targeted large-scale poverty interventions—including the COVID-19 pandemic recovery and social safety net and consumption smoothing programs—Togo’s development progress will continue to be slower than that needed to achieve the Sustainable Development Goals (SDGs) and the World Bank’s Twin Goals of ending extreme poverty by 2030 and boosting shared prosperity. 6. Beyond helping lift people out of poverty, good quality jobs also contribute to stability and social cohesion. Security threats on the country’s eastern and northern borders have been growing, with terrorist attacks in Burkina Faso close to the Togolese border increasing in number and severity since 2018, and a first attack reported on Togolese territory in November 2021 in the Savanes region.8 Recent findings from qualitative surveys in neighboring Benin, as well as international research, point to internal risks associated with radicalization of young people.9 Lack of economic opportunities and insufficient social services fuel a feeling of exclusion and hopelessness for extremist groups to exploit. Access to good quality jobs with a stable income for young Togolese must be part of the solution to the security threats. Despite the importance of jobs for poverty reduction, social cohesion, and stability in Togo, the country’s jobs challenge is tremendous. 7. Over the last decade, a timid structural transformation process has started to take place in Togo, in parallel with growing urbanization. A declining share of the working age population is employed in agriculture, with a growing share employed in manufacturing, transport, commerce, public administration, and other services. Yet whereas industrial employment has only slightly increased since 2006, there have been more pronounced shifts from the agricultural to the service sector. In 2019, 49.4 percent of the work force operated in the services sector, with 12.9 percent in the industry. In parallel, urbanization has increased from 36 to 42 percent between 2006 and 2019.10 Productive urbanization and export led growth are mutually reinforcing drivers of Togo’s structural transformation: export led growth fuels urbanization (with workers moving from rural to urban areas to either take jobs in export industries or to provide services and goods to these workers). At the same time, productive urbanization supports the competitiveness of export industries through economies of agglomeration and specialization. While these factors are mutually reinforcing, Togo’s level of economic development warrants greater attention to supporting the development of export led growth at this stage.11 8. Despite the decreasing share of agriculture in employment, the sector continues to play a predominant role in the Togolese economy and labor market. In 2019, the agricultural sector represented 20.4 percent of Togo’s GDP and 40 percent of its labor force. Cashew nuts, raw cotton, and cocoa beans were among the products with the highest average export values over the last decade. Yet most jobs in agriculture are informal, and productivity growth remains low, leading to low earnings. Mean nominal monthly earnings in 2019 stood at 37,57512 FCFA (US$67) in agriculture, 49,735 FCFA (US$89) in the manufacturing sector, and 91,659 FCFA (US$164) in trade, transportation, accommodation and food, business, and administrative services.13 The country’s agroindustry has not developed sufficiently to increase the level of transformation and value addition that could make this sector a real growth engine that creates jobs on and off the farm. 6 World Bank. 2022. Macroeconomics, Trade & Investment Global Practices. 7 EHCVM 2018/19. 8 In 2022, the Government of Togo put in place the Emergency Program for the Savanes region (Programme d’Urgence pour la Région des Savanes, PURS) to strengthen the resilience of households in the region. 9 World Bank, April 29, 2020. Strengthening Youth Resilience to Radicalization. 10 World Development Indicators, 2020. 11 World Bank, Togo CPSD, forthcoming. 12 Franc de la Communauté Financière Africaine (Franc of the African Financial Community). 13 ILO modeled estimates, 2019. 11 9. Overall, employment in Togo is nearly all informal,14 with youth and women in rural areas facing the biggest challenges to access formal employment. While wage employment in Togo increased from 11 to 19 percent between 2006 and 2011, it decreased to 17 percent in 2019, lower than the Sub-Saharan African (SSA) average (19 percent) and far below aspirational peers such as Morocco (50 percent) and Ghana (26 percent). Moreover, according to the Enquête Harmonisée sur le Conditions de Vie des Ménages (EHCVM), more than half of Togolese workers were engaged in informal subsistence activities that generate low income in 2018. Similarly, underemployment almost doubled for all worker groups, irrespective of educational achievement and residence (urban/rural). 10. Another challenge Togo faces is integrating a growing number of young people with a rising level of education into the labor market. Although the fertility rate in Togo has been decreasing faster than the low-income country (LIC) average, it remains high, at above 4 live births per woman. Due to high fertility rates in the past, Togo’s labor force is expected to grow by 35 percent (1.3 million) by 2030.15 This calls for particular attention to youth and the specific constraints they face in participating in the labor market, as well as to the capacity of the labor market to absorb them into productive jobs (matching between youth arriving on the labor market and available jobs). For youth to productively perform their jobs, their levels of education and skills are key (qualitative matching between youth arriving on the labor market and required qualifications). The share of the working age population with secondary and post-secondary education increased by more than 15 percentage points between 2011 and 2015, according to the QUIBB surveys. Although Togo has a low average unemployment rate, unemployment is higher for youths living in urban areas and for youths with higher education, which is typically associated with youths queuing for formal jobs. To raise both the quantity and the productivity, and thus the quality, of jobs for increasingly educated workers, Togo will have to accelerate creation of private sector wage jobs, which should include increasing the number of enterprises and helping them to grow to a productive scale so that they create more jobs. At the same time, it is key to ensure a business environment that allows existing firms to prosper to keep existing jobs and create new ones. 11. While a large part of the working age population in Togo has at least one job, the quality of those jobs tends to be low. In 2018, Togo’s labor force participation rate stood at 77 percent with an unemployment rate of 1.7 percent. However, underemployment poses a significant challenge: while 61 percent of those with a job were underemployed in terms of time spent performing work, a majority of workers operate at low levels of productivity in the informal sector and are underemployed in terms of earnings, with limited or no access to social protection schemes. Of those with a job, 59 percent were own-account workers, 19 percent contributing family workers, and 16 percent employees. The share of own-account workers, who tend to operate under vulnerable working conditions, is significantly higher in rural (62 percent) than urban areas (55 percent), with little gender difference (58 percent of men vs. 60 percent of women).16 Own-account workers and family workers often operate at low levels of productivity. To counteract the wage and productivity stagnation in these informal and low-quality jobs, improving access to good quality jobs—that is, jobs with higher productivity and earnings—will play a key role in the country’s poverty reduction efforts as well as help strengthen social cohesion and improve security. 12. Addressing Togo’s jobs challenge requires a multi-sectoral approach, yet the Government’s strategies and policies do not yet fully embrace a multi-sectoral, long-term vision to achieve better jobs outcomes for all population groups. Togo’s National Development Plan (2018–2022) touches upon some of the labor supply and demand side issues, yet could benefit from a stronger jobs lens to identify strategic 14 Informality is a multidimensional phenomenon affecting firms and workers. The ILO uses two approaches to define informality: the firm approach and the workforce approach. For the firm approach, the most common definition of informality is based on the criterion of legal and tax registration. In Togo, a firm not registered with the Centre de Formalité des Entreprises (CFE) and which does not pay taxes to the Office Togolais des Recettes (OTR) is defined as informal. For the workforce approach, informality implies that an employee is not registered with the Caisse nationale de sécurité sociale (CNSS). Registering employees with the CNSS generally represents the last administrative step in the firm formalization process. In reality, informality is a continuum. The level of formality of many firms is between these two extremes. 15 Jobs Group Demography tool using World Development Indicators and UNPop Projections. 16 ILO modeled estimates, 2019. 12 priorities that support large-scale job creation and economic transformation. Moreover, the National Strategic Youth Employment Plan (2013–2022) shows a strong focus on issues affecting labor supply: the Plan’s pillars focus on promoting sectoral policies that integrate youth employment, strengthen self-employment capacity, reduce underemployment, and promote youth access to finance. The Feuille de Route Gouvernementale 2020–2025 marks an important shift towards encouraging private sector development and attracting private investments and was developed while Togo was making important progress in improving its business climate. Despite this recent shift towards a private sector-driven development path, further attention to macro-economic constraints, including the external shocks discussed above, as well as labor demand-side constraints and opportunities can support a more targeted approach to the country’s jobs strategy. 13. The Jobs Diagnostic applies a multi-sectoral approach to Togo’s jobs challenges by describing key labor market trends and conducting a data-driven diagnosis of key constraints to improving jobs outcomes at the macro, labor demand, and labor supply levels. The Jobs Diagnostic not only describes trends but also analyzes drivers of economic transformation at the macro level: on the labor demand side, the Diagnostic looks at sources of job creation and ways to increase the productivity of jobs, while on the labor supply side, it looks at labor market outcomes, including any skills (mis-) match, policy-based labor market frictions, labor supply constraints, and ease of access to higher productivity jobs. The Diagnostic also reviews the limited data available to evaluate how the repercussions of the COVID-19 pandemic will affect jobs and incomes for years to come. At the same time, constraints identified from pre-COVID-19 pandemic data are likely to persist in its aftermath. 14. The Togo Jobs Diagnostic has been developed using the latest available datasets. The following data sets have been used to conduct the Togo Jobs Diagnostic: Enquête Harmonisée sur les Conditions de Vie des Ménages (EHCVM) 2018–2019; Questionnaire des Indicateurs de Base du Bien-être (QUIBB) 2006 and 2011; Enquête Régionale Intégrée sur l’Emploi et le Secteur Informel (ERI-ESI) 2017; World Bank Enterprise Survey 2011 and 2016; World Development Indicators 2020; Données sur les nouvelles créations d’entreprises—Centre de Formalité des Entreprises (CFE), September 2021; World Bank Doing Business data points (not using rankings); and Penn World Table data (version 10). Whenever feasible, Togo’s situation has been compared to its structural peers (Benin, Guinea, and Sierra Leone) and aspirational peers (Ghana, Morocco, and Rwanda), aligned with other World Bank analytical work in recent years.17 15. Insights gathered in a series of meetings with a multi-sectoral technical committee chaired by the Coalition Nationale de l’Emploi des Jeunes (CNEJ) complement the quantitative analysis. In collaboration with the CNEJ, which works under the responsibility of the Haut Conseil de l’Emploi des Jeunes chaired by the Prime Minister’s Office, the World Bank has supported the establishment of a multi-sectoral technical committee chaired by the CNEJ. Members of the technical committee are the Institute of National Statistics and Economic and Demographic Studies (Institut National de la Statistique et des Etudes Economiques et Démographiques, INSEED); the National Employment Promotion Agency (Agence Nationale de Promotion de l’Emploi, ANPE); the Ministry of Investment Promotion (MIP); the Association of Large Firms in Togo (Association des Grandes Entreprises du Togo, AGET); the General Directorate on Employment (Direction Générale de l’Emploi, DGE); the Chamber of Commerce and Industry of Togo (Chambre de Commerce et de l’Industrie du Togo, CCIT); the Ministry on Agriculture, Livestock and Rural Development (MALRD); the Directorate on Youth and Youth Employment (Direction de la Jeunesse et de l’Emploi des Jeunes, DJEJ); the Central Bank of West African States (Banque Centrale des Etats de l’Afrique de l’Ouest, BCEAO); and the Ministry of Economy and Finance (Ministère 17 The Diagnostic uses the same peer countries as previous analytical work conducted by the World Bank, including the latest Country Economic Memorandum and the Togo Expenditure Review. It is in the context of the preparation of these analytical reports that the government and the Bank agreed on Benin, Guinea and Sierra Leone as structural peers, and Ghana, Morocco and Rwanda as aspirational peers. Structural peers were defined as countries that have similar economic characteristics as Togo in 2014–2018 focusing on coastal economies with a GDP per capita, population, size of government and human development index within +/– 30 percent range of that of Togo during the benchmarking period 2014–2018. Aspirational peers were defined as countries that possessed similar structural characteristics as Togo in 2005–2010 and have enjoyed faster per capita growth since. Selection criteria included (i) a GDP per capita, population, size of government and human development index within +/– 30 percent range of that of Togo during the benchmarking period 2005–2018; (ii) faster per capita growth than Togo since 2010; and (iii) government suggestions. 13 de l’Economie et des Finances, MEF); as well as development partners. Between April 2021 and November 2022, seven technical meetings took place to present and discuss preliminary findings from the Togo Jobs Diagnostic. Feedback from the meetings has been integrated into the final version of the Diagnostic. 16. The Jobs Diagnostic is part of a two-year analytical program co-financed by the World Bank and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) to support the Government of Togo (GoT) as it develops a multisectoral strategy on jobs. The analytical program consists of the Jobs Diagnostic, an in-depth value chain study, and technical assistance to the GoT to develop a multisectoral jobs strategy. The in-depth value chain analysis is a continuation of the previous stream of work between the World Bank and GIZ that allowed identification of high potential value chains for better jobs outcomes for youth and women;18 it applies an integrated approach to jobs in the agricultural sector by conducting an in-depth analysis of selected value chains, simultaneously assessing constraints on, and opportunities for, better jobs outcomes for youth and women at the macro level, while also exploring constraints and opportunities in the areas of labor supply and demand. Going forward, the World Bank will offer technical assistance to the GoT in developing a multisectoral jobs strategy, aimed to become a reference document on jobs for the GoT and its development partners. 17. This Diagnostic presents the data relevant to a jobs-focused economic development plan, the results of the analysis of the data, and a number of policy recommendations based on the analysis. Given the predominance of the informal and agricultural sectors, two deep dives in these areas are presented. As stated, a data-driven multi-sectoral program that focuses on jobs in the Togolese economy can provide a strong yet dynamic structure on which to develop the stability, sustainability, and growth needed to help Togo achieve its economic and social goals. 18 High potential value chains are those with an assured market and job opportunities that take into account the specific constraints youth and women face in accessing jobs. 14 2. MULTISECTORAL JOBS DIAGNOSTIC 18. In this first part, the Jobs Diagnostic is focusing on a multisectoral core diagnostic, analyzing key labor market trends. In particular, the diagnostic describes labor market outcomes on the labor supply side; analyzes drivers of economic transformation at the macro level; and looks at sources of job creation and ways of increasing the productivity of jobs. The core diagnostic uses the latest available data, at macro-economic, household and firm level. 2.1 GROWING LABOR SUPPLY FACES SIGNIFICANT UNDEREMPLOYMENT AND IMPORTANT GENDER AND LOCATION DISPARITIES Togo has a young and active population with large cohorts of new entrants with increasing education levels joining the labor market every year. 19. Togo’s young workforce is growing, with large cohorts of new entrants joining the labor market every year. Togo’s young population and high demographic growth (2.4 percent per year) is close to the SSA country average (2.6 percent per year). In 2020, individuals aged 0–14 represented 3.4 million people (41 percent of the total population), almost 72 percent of the working-age population in 2020. Such strong demographic growth is a major employment challenge, as the labor market will have to absorb large cohorts of new candidates over the next decades, with an expected 1.3 million additional jobs needed between 2025 and 2035 (616,000 for the period 2025 to 2030 and 707,000 for the period 2030 to 2035). FIGURE 2.1.1 Age pyramid in 2020 90–94 75–79 90–94 Age group 45–49 30–34 15–19 514 510 562 559 0–4 612 608 800 600 400 200 0 200 400 600 800 1,000 people Men Women Source: Authors’ calculations based on World Development Indicators data. 15 20. Between 2006 and 2018, the level of education of the working-age population improved, mainly through an increase in primary school enrollment. This improvement in level of educational achievement has mainly affected the younger generations of workers and future workers, with fewer individuals in these generations without access to education: for example, only 11 percent of those aged 15–24 have never been to school compared with 40 percent among those aged 35–54.19 Although it is difficult to access recent data on returns to education in Togo, international economic literature shows that returns to education for the labor market can be significant, particularly for women.20 Data on formal sector employee incomes in Togo indicate that secondary education is profitable and that returns from secondary education greatly increased between 2011 and 2018. FIGURE 2.1.2 Education over time 50 47 44 44 40 34 30 29 Percentage 30 24 19 19 20 10 3 4 3 0 No education Primary education Secondary education Higher education 2006 2011 2018 Source: Authors’ calculations based on QUIBB and EHCVM data. 21. Large disparities in access to education between urban and rural individuals and between men and women reinforce existing inequalities in access to employment and quality jobs. The increased level of schooling mentioned above has not affected urban and rural households or men and women in the same way. Rural households have less opportunity in terms of education, since 41 percent of rural individuals have not attended school compared to only 15 percent of urban individuals. Further disparity appears between men and women, with 39 percent of women having never gone to school compared to only 17 percent of men. Finally, when overlapping domicile with gender, rural women are the most affected by these disparities in access to education. Half of rural women have never been to school, creating a major impact on the type and quality of jobs to which they can aspire. 22. The completion rate for the first cycle of secondary education21 in Togo has improved but at a slightly slower pace than in other countries. The completion rate has doubled in almost 20 years, from 26 percent in 2001 to 50 percent in 2019. However, the increase has been relatively slower than in Benin where the completion rate for the first cycle of secondary education almost tripled from 2001 to 2016. According to the World Development Indicators (WDI) database, currently one out of two individuals does not finish the first cycle of secondary education in Togo. The ratio is 1 of 9 in Ghana. Increasing this ratio and the level of secondary education in general is important to improve the quality of jobs. 19 QUIBB and EHCVM. 20 Psacharopoulos, George. “Returns to Education: A Further International Update and Implications.” The Journal of Human Resources, vol. 20, no. 4, 1985, pp. 583–604, https://doi.org/10.2307/145686. 21 The first cycle of secondary education in the Togolese schooling system corresponds to years of schooling 7 to 10, or from the 6th class to the 3rd class according to the Togolese education system. 16 FIGURE 2.1.3 Education of Togolese aged 15–64 100 3 0 3 7 1 4 9 3 6 5 6 10 12 6 13 7 9 6 90 19 11 18 13 19 10 80 24 16 17 18 24 20 70 32 28 24 26 25 20 31 60 Percentage 33 32 28 43 29 50 26 40 25 34 29 25 26 30 23 22 53 54 20 41 39 21 40 29 17 27 27 10 17 22 15 11 7 0 Total Urban Rural Male Female Urban Rural Urban Rural 15–24 25–34 35–54 55–64 male male female female None Primary Secondary first cycle Secondary second cycle Post-secondary Source: Authors’ calculations based on EHCVM 2018 data. FIGURE 2.1.4 Completion rate of first cycle of secondary education, total (% of relevant age group) 100 80 78 72 64 Percentage 60 50 50 46 42 40 35 36 35 26 20 20 17 12 0 2001 2019 2001 2016 2001 2019 2011 2019 2001 2019 2001 2019 2001 2014 Togo Benin Morocco Rwanda Sierra Leone Ghana Guinea Source: Authors’ calculations based on World Development Indicator data. The employment rate among Togo’s working age population is high, with low unemployment. 23. Togo’s employment rate is high, but jobs tend to be poor quality. The employment rate varies significantly according to whether individuals work with or without direct remuneration. Between 1982 and 2013, the International Labor Office (ILO) revised its employment ”definition” from a broad concept of employment including unpaid family workers to a more restrictive definition that excludes them. Under the more restrictive definition in Togo, the employment rate fell from 76 percent to 64 percent.22 This indicates a 22 Calculated from 2018 EHCVM data. 17 great many poor-quality jobs in Togo, with slightly more than 400,000 people working within their households without direct remuneration. Three of four such family workers are women, most of them living in rural areas, and their participation in the labor market is impeded by social norms and the lack of qualifications. Family work is one of the most insecure forms of employment because it does not lead to direct remuneration; most of the time it consists in participating in the household’s agricultural and pastoral work with no individual pay. The Jobs Diagnostic will take family workers into account to allow for comparisons of the employment rate over time. BOX 2.1.1: DEFINITIONS OF KEY TERMS: Employment rate: The share of the working-age population in employment. An individual is considered employed if she has worked at least one hour over the previous seven days, or has not worked but was temporarily absent and is expected to go back. Definition 1 (applied in the context of this Jobs Diagnostic)—Broad definition of employment (direct and indirect remuneration): Employment includes all individuals engaged in any activity intended to produce goods and services in exchange for remuneration or profit, even for just one hour over the preceding seven days, and those individuals having worked without remuneration for another member of the household during the preceding seven days. FIGURE B2.1.1.1 Broad definition of employment Working age population (15 to 64 years) 4,064,462 (53% of total population) Inactive population Active population 917,120 (23% of 15 to 64 years) 3,122,390 (77% of 15 to 64 years) In school Inactive Employed Unemployed 497,566 (12%) 419,544 (10.3%) 3,052,677 (76%) 69,722 (1.7%) 18 Definition 2—Restricted definition of employment: Concentrating on individuals receiving direct remuneration for goods and services. FIGURE B2.1.1.2 Restricted definition of employment Working age population (15 to 64 years) 4,064,462 (53% of total population) Inactive population Active population 1,356,856 (34% of 15 to 64 years) 2,682,653 (66% of 15 to 64 years) In school Inactive Employed Unemployed 497,566 (12%) 859,291 (21%) 2,600,128 (64%) 82,525 (2%) Household enterprise: Unincorporated, nonfarm business owned by households (activity does not need to be physically located in the household dwelling unit). This includes self-employed people who run a business that may employ family members without pay, but also self-employed people who run a business that employs a small number of nonfamily workers on a casual basis. Inactivity rate: The share of the working-age population outside the labor force (active population). It includes all those who were not employed or unemployed over the previous seven days, and are not actively looking for work, because they are in school, are retired, or for any other reason. Self-employed workers: People working on their own account, with one or more partners or in a cooperative without employees. Unemployment rate: The share of the labor force that has worked less than one hour over the previous seven days, is actively looking for work, and is available to work in the next 15 days. Wage-employed workers: Individuals who have declared being employed by an employer outside the household and are paid (either with or without a contract, in cash or in-kind). Working-age population: Individuals between ages 15 and 64. 24. Unemployment in Togo is low. About 4 percent of those aged 15–64 in 2006 were unemployed, and the rate fell to 1.7 percent in 2018. Togo is not an exception but rather reflects the inability of unemployment figures to accurately describe the functioning of the labor market in developing countries. For comparison, Benin recorded unemployment at 1.4 percent23 in 2018, Guinea at 5 percent in 2019, and Sierra Leone at 4.7 percent in 2014. These figures should not be interpreted as there being no employment problems in these countries; rather, given insufficient social protection, poor people are compelled to take any work to survive. Seeking employment is not without cost and depends on the ability of jobseekers to finance a period of unemployment, either from personal savings or family support. As a result, jobseekers recorded in official statistics are most likely to belong to the more affluent segments of the population; other workers have no choice but to accept any form of occupation, even though it is badly paid and/or working conditions are difficult. 23 World Bank (World Development Indicators, national estimate) for Benin, Guinea and Sierra Leone. 19 25. Between 2006 and 2018, the employment rate remained stable except for those aged 15 to 24, whose employment rate decreased in parallel to an increase in education levels. Indeed, the share of individuals aged 15-24 still in school grew strongly, from 26 percent to 38 percent. A key question is whether education systems produce high quality and labor market relevant skills, especially in view of changing and hard-to-predict demands of the labor market. However, the rise in school attendance is encouraging because it is likely to teach young people to learn and to develop their capacity to adapt, two indispensable qualities for professional life. FIGURE 2.1.5 Changes in employment rate between 2006 and 2018 100 1 1 9 9 1 12 23 3 1 2 26 80 4 1 38 2 2 4 Percentage 60 2 88 87 88 40 79 79 76 65 62 48 20 0 2006 2011 2018 2006 2011 2018 2006 2011 2018 Total 15–24 25–64 Employed Unemployed Inactive In school Source: Authors’ calculations based on EHCVM 2018 data. 26. Unemployment remains insignificant and mainly affects educated urban youth. The unemployment rate is very low in Togo (1.7 percent in 2018) and has halved since 2006 (4 percent). However, whatever the period, it is higher in urban than in rural areas. Breaking down this indicator for 2018 shows that unemployment mainly concerns men and women in urban areas. The unemployed are essentially young people with high education in the more affluent segments of society (Q4 and Q5), who likely can afford to wait for “the right opportunity” to join the labor market. 27. High employment rates mask significant disparities between gender and areas of residence. Major employment indicators have remained stable over the last decade with the employment rate dropping slightly from 79 percent in 2006 and 2011 to 76 percent in 2018. The main changes occurred in inactivity rates, which grew for men and those living in urban areas, a sign that urban men, who are usually educated, can wait for quality jobs. There has been a notable rise in women’s access to education, with the share of working-age women having attended school doubling between 2011 and 2018. This indicates that girls are beginning to catch up in terms of education, although the level of education for females remains insufficient at present to guarantee women access to good quality jobs, especially in rural areas. 20 FIGURE 2.1.6 Unemployment Rate in 2018 16 14 12 Percentage 10 8 6 4 2 0 co da Pri e ec ycle ar fir ary Po on cle 5 e e 4 4 4 4 1 2 3 4 e e ry al on Q al al –2 –3 –5 –6 Q Q Q Q al al t da To c cy m m m em m 15 25 35 55 st d c N on fe st an an lf an rb rb ra -s nd ry se rb U U Ru U y Se on c Se Source: Authors’ calculations based on EHCVM 2018 data. FIGURE 2.1.7 Changes in employment rate between 2006 and 2018 100 9 9 6 7 7 6 5 9 10 12 11 14 15 12 12 6 6 8 14 9 8 10 6 7 14 0 80 4 12 4 13 2 4 10 11 17 1 2 1 2 1 2 2 7 3 2 Percentage 60 87 88 40 79 79 79 80 78 80 78 82 76 74 72 67 68 20 0 2006 2011 2018 2006 2011 2018 2006 2011 2018 2006 2011 2018 2006 2011 2018 Total Male Female Urban Rural Employed Unemployed Non-labor force In school Source: Authors’ calculations based on EHCVM 2018 data. 28. Without considering students, the great majority of individuals not participating in the labor market are women: 75 percent of inactive people are women, whose main obstacle to employment is domestic work. The rate of non-participation in the labor market is 14 percent among women and only 6 percent among men. It is also higher among individuals who have never attended school and in some more remote areas, such as the Savanes region. Reasons for not participating in the labor market vary depending on gender. For women, both in urban and rural areas, the main reason is linked to domestic work. For men, illness or disability are the main reasons, in addition to the seasonality of rural activities. 21 FIGURE 2.1.8 Rate of non-participation in the labor market in 2018 60 50 50 44 40 Percentage 30 29 28 29 25 26 23 23 20 18 18 20 20 21 20 18 18 17 14 14 14 16 10 12 12 12 10 12 11 9 9 8 9 8 8 10 6 7 7 7 6 0 Po ond le le ec ycle ux s e e an l 4 4 4 4 e ra Pr e ar firs ry ry al e ra ne al al –2 –3 –5 –6 m on un yc ra t a da Ka ea rb Ru To M m iti im va tc 15 25 35 55 c nt N m on U at Fe ar Sa Ce m Pl M co c -s y se é ar st m y nd Lo co nd Se co Se Inactivity rate Inactivity (not including students) rate Source: Authors’ calculations based on EHCVM 2018 data. TABLE 2.1.1 Reasons for non-participation in labor market by gender and location in 2018 Urban Rural Men Women Men Women Too young 5% 2% 5% 3% Too old/retired 18% 7% 7% 7% Domestic work 2% 38% 0% 41% Illness/disability 17% 11% 28% 14% Do not want to work 2% 2% 2% 2% Waiting to start own business 9% 11% 9% 8% Waiting for response to job application 5% 1% 1% 0% Lack of work 9% 4% 7% 2% Do not know how to look for work 3% 1% 0% 1% Seasonal unemployment 6% 2% 17% 7% Other 24% 21% 21% 15% Source: Authors’ calculations based on EHCVM 2018 data. 22 Togo’s labor market is characterized by low job quality, high underemployment, and precarious self-employment. 29. Self-employment is the dominant form of employment in Togo, responsible for almost six of ten jobs. Self-employment is extremely widespread in developing countries. Its importance usually reflects the inability of the labor market to create decent jobs. This means self-employment is often synonymous with lower quality jobs, as it represents the only possibility for workers without necessary qualifications for formal salaried jobs or for those workers with more skills who are waiting for good quality jobs. FIGURE 2.1.9 Principal employment type over the 12 months preceding the survey Family worker Trainees 19% 5% Employer 1% Private wage 12% Employee 16% Self-employed 59% Public wage 3% HH wage 1% Source: Authors’ calculations based on EHCVM 2018 data. 30. The prominence of self-employment is reflected in key sectors of the economy. As a result, activities linked to agriculture24 (59.5 percent), trade (74.5 percent), and craftwork (71.2 percent) are dominated by self-employment, except for other services (42 percent). Wage-employment is more common in sectors such as public administration (98.2 percent), financial services (64.4 percent), and transport (49.5 percent). 31. Self-employment is more common in rural than urban areas. In fact, 62 percent of jobs in rural areas are self-employment jobs, compared to 55 percent in urban areas. Wage employment, which represents less than two of ten jobs, is essentially urban and male. In contrast, three of four family workers are women, the vast majority of whom live in rural areas. Consequently, income insecurity is greater in rural areas and among women. 32. The predominance of self-employment is not specific to Togo, but there has been very little change between employment types over time. Benin, Sierra Leone, and Ghana all have self-employment rates above 50 percent. The most striking finding, however, is how the share of employment types has not changed since 1998. From 1998 to 2018, the self-employment rate in Togo remained stable at 52 percent, whereas it went from 60 percent to 39 percent in Rwanda over the same period. Over the last 20 years, Togo also experienced weaker growth in the number of wage workers than some other countries, such as Morocco, Rwanda, and Ghana. From 1998 to 2018, the number of wage workers went from 19 percent to only 21 percent in Togo, while Rwanda has seen a 23-percentage point and Ghana an 8-percentage point increase. The changes in these three countries—Ghana, Morocco, and Rwanda—are signs of underlying structural change, which does not appear to be the case in Togo. 24 The agricultural sector is the main employer with 42 percent of workers in the Togolese economy, followed by trade (wholesale and retail trade, as well as hotels and restaurants) and other services (teaching, personal services (hairdressers, tailors, etc.) and various other professions). 23 FIGURE 2.1.10 Spread of employment types in key sectors of the economy in 2018 Agriculture (42%) Trade (20%) Other services (16%) Arts and crafts (13%) Construction and mining (4%) Transport Public administration Financial services Water, gas, electricity 0 10 20 30 40 50 60 70 80 90 100 Percentage Self-employed Family worker Employer Wage worker Source: Authors’ calculations based on EHCVM 2018 data. FIGURE 2.1.11 Spread of employment types by gender and location in 2018 100 2 1 2 1 90 80 70 58 60 62 62 60 Percentage 50 40 6 10 9 30 6 27 28 20 25 29 10 5 3 8 7 0 Men Women Urban Rural Wage worker Apprentice/Intern Family worker Self-employed Employer Source: Authors’ calculations based on EHCVM 2018 data. 24 FIGURE 2.1.12 Spread of employment types in comparator countries from 1998–2018 100 90 28 28 80 39 52 52 70 60 82 63 60 75 72 60 21 Percentage 28 80 50 29 40 28 25 9 30 15 49 32 20 16 43 16 31 6 6 10 21 25 19 17 8 11 8 9 11 0 1998 2018 1998 2018 1998 2008 1998 2018 1998 2018 1998 2018 Togo Benin Morocco Rwanda Sierra Leone Ghana Paid employees Self-employed employers Self-employed unpaid family workers Other self-employed Source: Authors’ calculations based on World Development Indicator data. 33. Underemployment is widespread, especially for women and in rural areas. Underemployment reflects inadequate duration of work, that is, less than 35 hours per week, which is too little to ensure sufficient income from most activities in Togo. Including the months worked in the previous 12 months, it appears that 60.5 percent of individuals are underemployed. There is significant underemployment in all forms of employment: it is 51 percent among salaried workers and 57 percent among the self-employed. Therefore, beyond the relatively low unemployment rate, underemployment is one of the main employment problems in Togo, often linked to jobs that are insecure, require few qualifications, and provide relatively little remuneration. FIGURE 2.1.13 Visible underemployment (<35 hours/week)—main job 100 90 84 80 70 72 70 67 60 57 Percentage 60 54 53 52 51 50 44 48 42 40 35 33 32 31 32 30 24 20 11 10 0 Total Male Female Urban Rural Wage Trainees Family Self- Employer worker employed Taking into account the number of months of activity per year Standard month Source: Authors’ calculations based on World Development Indicator data. 25 34. Underemployment is more prevalent in the agricultural sector than in other sectors of the economy. According to the EHCVM survey data, 57 percent of people working in agriculture are affected by underemployment. If we consider the duration of the activities during the year, this rate rises to 77 percent. This is because agricultural activities last for a few months, whereas activities related to services and industry can be carried out throughout the year. FIGURE 2.1.14 Visible underemployment (<35 hours/week)—main job by sector 90 80 77 70 60 57 52 Percentage 50 47 41 40 33 30 20 10 0 Agriculture Industry Services Taking into account the number of months of activity per year Standard month Source: Authors’ calculations based on EHCVM 2018 data. 35. The rate of visible underemployment is higher in Togo than in all selected comparator countries except Sierra Leone. Underemployment in both rural and urban areas also disproportionately affects women in the countries examined, but in lower proportions than in Togo, except Sierra Leone. FIGURE 2.1.15 Rates of visible underemployment across countries 100 90 80 70 93 89 Percentage 60 50 40 40 45 30 38 28 85 94 20 22 20 62 16 57 46 10 39 12 30 26 28 16 0 Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Togo Sierra Leone Rwanda Guinea Ghana 2018 2014 2016 2012 2012 Female Male Source: Authors’ calculations based on Jobs Indicators Benchmarking tool and EHCVM 2018 data. 26 36. Education level is positively correlated to job quality. Individuals with no education are overrepresented among family workers, with almost half of family workers never having attended school. Unsurprisingly, individuals with some secondary or higher education are more likely to find salaried employment; only three of ten self-employed people have more than a primary education. Beyond major employment categories, income data would be needed to better understand return on education in the employment market. Unfortunately, these data are not available from the 2018 ECVMH survey. FIGURE 2.1.16 Distribution of education level by employment type Post-secondary 24 9 8 2 2 Secondary second cycle 23 8 18 5 5 Secondary first cycle 29 44 28 24 26 Primary 16 30 25 31 23 None 8 10 20 38 45 0 10 20 30 40 50 60 70 80 90 100 Percentage Wage worker Trainee Family worker Self-employed Employer Source: Authors’ calculations based on EHCVM 2018 data. School-to-work transition is characterized by gender and location inequalities. 37. The employment rate for youth aged 15 to 35 increases greatly with age, but not all young people are equal in terms of access to employment. The overall employment rate is 36 percent for 15-year- olds, and increases to 94 percent for 34-year-olds. Transition from school into employment is slowest for young urban people because of higher post-primary school enrollment; 76 percent of urban individuals aged 15 are still attending school, compared to only 43 percent of rural 15-year-olds, as people living in rural areas tend to join the labor market earlier. 38. Gender inequalities in access to education also persist in transition to employment, with a significant inactivity rate among women even after age 25. Because women have less access to education, they join the labor market earlier: at age 15, the employment rate of women is 8 percentage points higher than for men (19 percent compared to 11 percent, respectively). However, it reaches a ceiling much faster and stays significantly lower. By age 30, only 78 percent of women have a job compared to 87 percent of men, and the inactivity rate among women is 15 percent at age 30 compared to only 4 percent for men. Women face significant social and cultural barriers, particularly in rural areas, which drive them toward marriage and early pregnancies, creating a significant barrier to women’s access to employment. 39. Youth have low-quality jobs and work in the most vulnerable sectors of the labor market. Young people who join the labor market early mainly work as family workers (90 percent of rural youth and 72 percent of urban youth aged 15). The work situation tends to improve for youth in urban areas as they age, with an increase in the rate of salaried employment, but in rural areas salaried employment is almost nonexistent; only 6 percent of those aged 30 in rural areas have salaried employment, compared to 33 percent in urban areas. 27 FIGURE 2.1.17 Transition from school to employment in urban areas 100% 90% 93 94 94 96 95 90 89 86 80% 82 85 83 84 85 79 78 70% 72 71 74 69 60% 63 62 66 64 66 67 56 50% 50 49 40% 45 40 41 30% 36 30 31 20% 25 25 26 26 28 18 19 10% 10 0 15 17 19 21 23 25 27 29 31 33 35 15 17 19 21 23 25 27 29 31 33 35 Men’s age, years Women’s age, years Employed Unemployed In school Inactive Source: Authors’ calculations based on EHCVM 2018 data. FIGURE 2.1.18 Transition from school to employment in rural areas 100% 98 97 98 90% 95 93 93 92 94 91 90 91 90 92 94 80% 84 87 84 85 84 83 82 85 84 82 70% 77 75 76 75 73 70 71 60% 64 63 50% 56 55 57 49 40% 46 47 46 44 42 30% 20% 10% 0 15 17 19 21 23 25 27 29 31 33 35 15 17 19 21 23 25 27 29 31 33 35 Men’s age, years Women’s age, years Employed Unemployed In school Inactive Source: Authors’ calculations based on EHCVM 2018 data. 28 FIGURE 2.1.19 Type of employment of young people according to age in urban areas 100 4 12 12 12 90 17 21 20 25 80 38 37 44 42 48 49 70 58 57 56 57 61 64 52 72 60 Percentage 46 72 86 70 43 16 50 61 27 12 77 4 40 33 9 9 4 13 3 6 2 30 20 44 44 44 2 36 33 35 36 33 36 32 33 31 28 31 10 24 25 16 18 18 11 5 0 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Age, years Wage worker Family worker Employer Self-employed Source: Authors’ calculations based on EHCVM 2018 data. FIGURE 2.1.20 Type of employment of young people according to age in rural areas 100 7 6 12 14 90 27 28 80 41 41 44 46 50 70 57 59 62 63 64 66 67 65 69 70 60 Percentage 50 92 90 86 79 40 68 64 45 53 46 36 30 48 33 32 26 20 30 29 24 17 26 28 25 10 14 10 13 11 13 3 2 2 6 5 8 5 6 9 8 5 6 5 9 6 6 0 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 Age, years Wage worker Family worker Employer Self-employed Source: Authors’ calculations based on EHCVM 2018 data. 29 Togo’s few wage-employed workers are predominantly male and concentrated in the service sector. 40. Salaried work increased from 11 percent to 16 percent of jobs between 2006 and 2018. Representing less than two of ten jobs in 2018, it remains essentially urban and male. Men occupy eight of ten salaried jobs in the public sector and more than seven of ten in the private sector. Service activities account for the largest number of employees (80 percent of salaried jobs), ahead of industry (15 percent) and the agricultural sector (5 percent). Although salaried work can cover a wide range of situations in terms of remuneration and working conditions, it generally refers to a type of work highly prized because of its stable income. Just as women and rural workers are overrepresented in insecure jobs, based on duration of work, they are also excluded from salaried work. Rural women are the least well represented group with 5.1 percent of salaried jobs. In the public and private sector, urban workers occupy 69 percent and 76 percent of salaried jobs, respectively. Compared with other countries, salaried work remains the privilege of urban men in Ghana, Guinea, and Sierra Leone. Countries like Rwanda and Morocco, meanwhile, have succeeded in significantly increasing the share of salaried work in employment, even in rural areas. FIGURE 2.1.21 Type of employment by gender and domicile for selected countries 100 90 22 26 25 28 30 31 80 38 41 40 46 47 43 54 52 49 51 70 57 59 63 12 72 76 60 80 Percentage 83 29 88 50 6 52 72 65 40 21 47 67 19 34 16 43 44 12 1 63 15 30 47 45 45 5 20 40 26 19 13 35 32 12 7 17 31 22 26 23 10 15 1 16 16 6 3 5 2 8 8 4 3 0 5 Women Men Women Men Women Men Women Men Women Men Women Men Women Men Women Men Women Men Women Men Women Men Women Men Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Rural Urban Ghana Guinea Morocco Rwanda Sierra Leone Togo 2012 2012 2013 2016 2014 2018 Employer Family worker Salaried worker Self-employed Source: World Bank—Jobs Indicators Benchmarking tool (I2D2) and Authors’ calculations based on EHCVM 2018 data. More than 60 percent of Togolese households work in non-agricultural activities, most of which are small and precarious. 41. Non-agricultural family businesses are small and precarious and fairly widespread among Togolese households. Some 60 percent of households practiced a non-agricultural activity in the past 12 months. However, less than 8 percent of these non-agricultural enterprises have employees, and more than 97 percent of the enterprises use family labor. In addition, very few businesses (3 percent) have a tax identification number and the majority of them operate either at home (31 percent) or on the public highway (27 percent). 30 FIGURE 2.1.22 Size of non-farm businesses by type of employee 100 1% 10% 18% 80 Percentage 60 55% 40 81% 20 35% 0 Non-farm businesses Non-farm businesses with employees with family workers 1 employee 2–4 employees 5 employees or more Source: Authors’ calculations based on EHCVM 2018 data. 42. The majority of non-agricultural businesses operate in the service sector. Retail and wholesale trade (32 percent) and food products processing and sale (30 percent) are the main activities households carry out. Other services (20 percent), the third main non-agricultural activity households carry out, includes the manufacture and sale of handicrafts, carpets, jewelry, and hairdressing, among others. In addition, it should be noted that 20 percent of households involved in non-agricultural activities combine at least two different activities. FIGURE 2.1.23 Types of non-farm activities undertaken by households Small business 31.8 Food processing and sales 30.2 Other services 19.8 Construction 12.1 Transport/repair 11.0 Textile manufacturing 9.6 Liberal profession (doctor, translator, etc.) 4.1 Restaurant 4.0 Rental 0.3 0 5 10 15 20 25 30 35 Percentage Source: Authors’ calculations based on EHCVM 2018 data. 43. Non-agricultural family businesses generate income year-round and are mainly run by women (63 percent). However, they are constrained in terms of financing and clients. Indeed, 72 percent of the enterprises are financed by their own funds and their main clients are essentially households. 31 2.2 AN ECONOMY THAT CREATES JOBS, BUT NOT IN SUFFICIENT QUANTITY AND QUALITY 44. Despite some positive economic developments in the country over the past two decades, Togo’s economy is still characterized by untapped potential from its geographic location, demographic opportunities, and relative stability. Following a period of openness and investment in the early part of the century and a period of fiscal consolidation and pro-business measures, the country should be well-placed for strong economic growth. Indeed, the government has engaged in implementing an ambitious strategic roadmap with large public investment goals. However, a lack of productivity growth (especially in agriculture) hampers the development prospects of the country, while prospects for further growth from public investment are constrained by deteriorating fiscal space. Following a period of moderate expansion in the first two decades of this century, recent international events have created significant challenges for the economic prospects of the country. 45. Since the late 2000s, Togo has been in a period of sustained economic expansion supported by structural reforms, infrastructure spending, and private sector investments. Prior to the COVID-19 pandemic, Togo’s economic growth was relatively robust, reaching an average of 5.6 percent over the period 2012–2019. However, a rapid increase in public infrastructure investment up to 2017 led to a significant buildup in domestic debt levels and services costs, which led the government to change course and undertake significant fiscal consolidation measures from 2017 to 2019. At the same time, business climate reforms (including measures easing business creation and transfer of property, receiving loans, construction permits, electricity connection, and trading across borders) supported dynamic private investment and helped maintain real GDP growth at an average of 4.9 percent. Since the outset of the COVID-19 pandemic, some of the regained fiscal space was used to increase public investment, which helped prevent a recession in 2020 (+1.8 percent), while a sharp rebound in global trade contributed to the subsequent recovery in 2021 (+5.3 percent). Disruptions associated with the war in Ukraine created significant headwinds in 2022, as reflected in decelerating external demand and rising energy, fertilizer, and food prices, which adversely impacted key sectors of the economy and dampened households’ purchasing power. Both rural poor who rely on agriculture for their own consumption or as a primary source of income and urban poor relying on imported food were adversely impacted. These shocks, combined with the insecurity in the Savanes region, led the government to undertake emergency spending, which contributed to stabilizing growth at an estimated 4.9 percent in 2022, but increased the budget deficit to 8.4 percent of GDP. 46. The deceleration in economic activity during the COVID-19 pandemic has weighed on jobs and income, including in the formal sector. GDP growth went from 5.5 percent in 2019 to 1.8 percent in 2020, reflecting a decrease in private consumption and private investment due to a drop in household incomes from business interruption. A survey of 160 formal Togolese firms in May 2020, in the early days of the COVID-19 pandemic, found that 13 percent were just partially operating, and 22 percent were temporarily closed, with tourism, hotels, restaurants, transport, logistics, and warehousing most affected by business closures. Small and medium enterprises (SMEs) were relatively more affected by business closures (25 percent) than large enterprises (14 percent). Furthermore, 96 percent of firms declared a reduction in sales over the 30 days preceding the survey, with almost half of surveyed firms declaring a reduction of more than 50 percent over the reporting period. Firms also reported putting 22 percent of employees on leave without pay, reducing wages and/or benefits for 21 percent, and reducing working hours for 61 percent.25 Also, the number of firms created dropped by 46 percent between March 2020, when the first COVID-19 case was recorded, and April 2020, when the state of health emergency was declared.26 While data was collected early in the pandemic, consultations during the World Bank Country Private Sector Diagnostic (CPSD) missions suggested that the situation for firms would likely worsen as the pandemic persisted.27 25 Etude de l’impact du Covid-19 sur le Secteur Privé au Togo : Une évaluation informée par des données (World Bank, June 2020). 26 Centre de Formalité des Entreprises (CFE). https://cfetogo.tg/statistiques-10.html. Accessed on September 6, 2021. 27 World Bank, Togo CPSD, forthcoming. 32 47. Rising inflation has significantly impacted real income gains in 2022, particularly for low-income earners. Consumer Price Inflation peaked at 7.5 percent in 2022, up from 4.5 percent in 2021 and an average of less than 2 percent over the preceding decade. While domestic food prices have been the most important driver of inflationary pressures in 2022, the rising cost of imported goods played an increasing role, with elevated energy prices translating into rising transport, food, and housing costs, as reflected in core inflation remaining above 6 percent. Elevated inflation is having broad-based effects on the domestic economy, affecting the most vulnerable populations through the rising cost of basic goods while further stressing public finances. For agricultural households, the shock to energy and fertilizer prices associated with the war in Ukraine was exacerbated but more variable rainfalls in recent years, further impacting livelihoods. FIGURE 2.2.1 GDP growth and per capita income, 2001–2019 a) Trend in GDP growth, 2001–2019 b) Per capita income, Togo compared to its peers 8 2,000 1,800 Constant 2010 USD per person 6 1,600 4 1,400 2 1,200 1,000 0 800 –2 600 400 –4 200 –6 0 01 03 05 07 09 11 13 15 17 19 21 19 0 19 4 68 19 2 19 6 80 19 4 19 8 92 20 6 20 0 20 4 08 20 2 16 20 20 20 20 20 20 20 20 20 20 20 6 6 7 7 8 8 9 0 0 1 19 19 19 19 20 2001–2007 2008–2016 Togo Benin Guinea 2017–2019 2020–2022 Ghana Rwanda Sub-Saharan Africa Source: Authors’ calculations based on World Development Indicator data. Poverty is decreasing but remains high in many regions. 48. Togo’s performance in raising living standards has fallen short of its potential, and the COVID-19 pandemic nearly halted poverty reduction. Although poverty has declined in recent years, more than two-fifths of the population still lives in poverty. The poverty rate (using the national poverty line) decreased from 58.7 percent in 2011 to 55.1 percent in 2015, and recent estimates suggest that poverty continued to fall to reach 45.5 percent in 2018–19.28 Also, inequality—as measured by the Gini index—decreased by 0.013 percentage points between 2011 and 2015; its contribution to poverty reduction is modest compared to growth (–1.4 versus –2.2). Economic repercussions from the COVID-19 pandemic and the war in Ukraine are likely to deteriorate the welfare of the population. The poverty rate also remains high compared to leading WAEMU economies, while inequality is the second highest in the region after Burkina Faso.29 49. Slow improvement in living standards partly reflects low labor productivity, falling labor income, and low job quality. Labor productivity expanded 3 percent per year between 2008 and 2016, higher than 28 Based on the new WAEMU household survey: Enquête Harmonisée sur les Conditions de Vie des Ménages (EHCVM) but could not be strictly compared with the previous national household surveys. 29 Cross-country comparison of the EHCVM 2018/19. 33 the WAEMU average, allowing a gradual catch-up to WAEMU economies, but represents only 30 percent of the aspirational peer average. Mean wage and hours of work are both falling. Between 2011 and 2018, mean wages across key general economic sectors fell by an average 27.3 percent—34 percent in the agriculture sector,30 15.5 percent in the industry sector, and 32.6 percent in the service sector—while working hours decreased 11 percent (about five hours per week). Deterioration in job quality is reflected in the significant increase in underemployment and the slight decline in waged work since 2011. FIGURE 2.2.2 Poverty dynamic and inequality, Togo versus peers a) Poverty rate b) Poverty rate and Gini index for 70 selected countries 61.7 60 0.4 58.7 60 55.1 0.35 50 Poverty rate (%) 0.3 50 45.5 40 Gini index 0.25 Percentage 40 30 0.2 20 0.15 30 0.1 10 0.05 20 0 0 er so au o n i re l al ga 10 g ni ig oi Fa M ss To ne Be N Iv Bi a Se d’ a- in ne rk te 0 Bu Cô ui 2006 2011 2015 2018–2019 G Poverty rate Gini index Source: Authors’ calculations based on EHCVM 2018–19 data. Some job creation, mainly in the industry and service sectors, has accompanied economic growth. 50. While the structure of the Togolese economy has shifted gradually from agriculture to services over the past two decades, employment in low-productivity sectors still dominates the landscape. The primary sector’s share of employment has declined from 48 percent in 2001 to 38 percent in 2019. This has been met with a similar increase in services from 39 percent of employment in 2001 to 49 percent in 2019. Meanwhile, industry’s share of employment has remained relatively constant, at 13 percent. Moreover, the agricultural sector remains a significant burden on productivity, with two-thirds of rural households and 71 percent of the poor in Togo categorized as agricultural households, and adoption of yield-enhancing technologies is limited, with only 37 percent and 8 percent of agricultural households, respectively, using fertilizer and improved seeds and barely 1 percent using irrigation. Furthermore, expansion of agriculture production since the mid-2000s (3.6 percent per year on average) was mainly driven by increases in cropped area rather than efficiency gains, which contributed to deforestation and soil degradation.31 51. Despite relatively volatile growth, employment has trended up over the last two decades, driven by higher job creation in the industry and service sectors. Total employment increased by 2.8 percent between 2001 and 2019, mainly due to an increase of workers in the industry sector. Employment growth in industry increased from an annual average of 1.6 percent during 2001–2007 to 3.1 percent in 2017–2019, as 30 Agricultural wage data may not be reliable, however, because the number of workers observed with agricultural wages drops precipitously between 2011 and 2018. 31 World Bank, 2022. Togo Country Economic Memorandum. 34 significant investments in road infrastructure boosted labor demand in construction. The service sector registered the highest (4.2 percent) and most stable employment growth rate over the same period. Job creation in the agriculture sector declined significantly from an average annual 2.2 percent between 2001–2007 to 1.2 percent over the period 2017–2019, reflecting a steady decrease in agricultural productivity. 52. Being sourced from capital accumulation, and with a strong focus on large public and private investment projects, growth in Togo did not translate into significant gains in aggregate productivity. Despite some shift from an agriculture-dominated economy to a services-led one over the last 15 years, the majority of the working population still struggles in low productivity, and mostly informal, agriculture and services jobs. The private sector is also highly concentrated, with large firms accounting for 77 percent of the total turnover, and the region of Lomé acting as home to more than 60 percent of firms in the country. The agriculture sector is critical to raising income per capita and reducing poverty in Togo, but productivity has been stagnant in the sector and conditions have deteriorated further in recent years. Total factor productivity has mostly been stagnant over the last 30 years, below that of peer countries, and average agricultural labor productivity has grown at just 0.6 percent per year, also below that in peer countries. Employment dynamics or employment growth rates over time were globally in line with growth in value addition with relatively high heterogeneity across sectors.32 FIGURE 2.2.3 Employment dynamic and growth, 2001–2019 a) Employment growth rate by sector 4.5 4,2 4,1 4,2 4,1 4.0 3.5 3,3 2,9 3,1 3.0 2,8 2,7 2,9 2,6 2.5 2,2 2.0 1,6 1.5 1,4 1,2 1.0 0,9 0.5 0 2001–2019 2001–2007 2008–2016 2017–2019 Total growth Employment growth Employment growth Employment growth employment rate rate, Agriculture rate, Industry rate, Services b) Growth in value addition by sector 9 9,0 8 7,3 7 6,6 6 5,6 5,6 5 4 4,0 3,9 3,8 3 2,9 2,8 2 2,0 1,8 1,6 1,2 0,9 1 0,6 0 2001–2019 2001–2007 2008–2016 2017–2019 Total growth Growth Agriculture Growth Industry Growth Services value added value added value added value added Source: Authors’ calculations based on World Development Indicator data. 53. But high employment rates mask a lack of productive jobs. In 2018, more than half of Togolese workers were engaged in informal subsistence activities that generate low income.33 The share of workers employed in waged jobs increased from 11 percent to 19 percent between 2006 and 2011, but has declined 32 Centre de Formalité des Entreprises (CFE). https://cfetogo.tg/statistiques-10.html. Accessed on September 6, 2021. 33 EHCVM, 2018/2019. 35 FIGURE 2.2.4 Employment dynamic and job quality, 2006–2019 a) Employment type over time 100 27 28.2 26.8 Percentage 80 60 28.9 28.4 33.2 40 32.8 24.3 24.7 20 6.9 14.8 13.4 0 4.3 4.3 1.9 2006 2011 2018 Wage public Wage private Self-employed agriculture Self-employed Other non-agriculture b) Employment type by country 100 20 20 80 69 42 44 64 Percentage 62 76 42 45 60 70 74 32 37 2 22 11 40 3 6 31 24 0 5 20 26 1 46 51 6 1 1 1 2 2 5 36 40 11 23 11 22 26 0 10 7 8 06 19 06 19 06 19 06 19 06 19 06 19 20 20 20 20 20 20 20 20 20 20 20 20 Togo Benin Guinea Morocco Ghana Bangladesh Paid employees Self-employed employers Self-employed unpaid Other self-employed family workers Source: Authors’ calculations based on World Development Indicator data. since. In 2019, the percentage of workers with a wage job stood at 17 percent, lower than the SSA average (19 percent) and far below aspirational peers such as Morocco (50 percent) and Ghana (26 percent). Similarly, underemployment almost doubled for all worker groups and irrespective of educational achievement and residence (urban/rural). The increase in the share of waged workers during the earlier period partly reflects strong annual growth averaging 6 percent between 2008 and 2011 and the significant increase of paid employees in the private sector. The reduction in the share of private sector waged workers since 2011, combined with an increase in self-employment in the non-agriculture sector, has driven the decline in paid employment in the last decade. 54. Informality is a dominant feature of Togo’s labor market and will remain important. In 2017, about 90 percent of workers were in the informal sector, with the sector contributing an estimated 35 percent of Togo’s GDP. While the share of paid employment increased in Togo between 2006 and 2018, it remains low, compared to its peers. The informal sector is characterized by precarious working conditions, with only 30.9 percent of the workforce having an employment contract. This precariousness is more pronounced in rural (29.3 percent) than in urban areas (32.3 percent) and results in a very low hourly wage rate, estimated at 235 FCFA (US$0.40). While job creation, especially in the service sector, has accompanied growth, these dynamics hide a deficit in private sector waged employment. 55. Economic growth is linked to more jobs in Togo, but not necessarily to better jobs. Between 2001 and 2019, every one percentage point of additional GDP growth was associated with a 0.6 percentage point 36 increase in employment. However, the employment intensity of growth from 2001 to 2019 varies across the sub-periods of the analysis. Employment growth was strongest from 2001 to 2007, which is surprisingly the period with the weakest economic growth. This implies that a significant share of employment growth was due to an increase in labor supply that, given high poverty levels, have to find some work, rather than gains in productivity. Also, the analysis shows that a one percentage point increase in GDP growth over the period 2006–2018 increased waged employment by only 0.1 percent, which could imply that a sizeable share of the jobs created are not productive. Along gender lines, male and female employment elasticities are about the same. TABLE 2.2.1 Employment elasticities by sex, 2001–2019 2001–2019 2001–2007 2008–2016 2017–2019 Total 0.6 2.5 0.5 0.5 Female 0.7 2.4 0.5 0.5 Male 0.7 2.6 0.5 0.5 GDP growth 4.0 1.0 5.7 4.8 Source: Authors’ calculations based on World Development Indicator data. 56. While all three general economic sectors experienced employment growth, the service sector exhibits the highest employment elasticity. Table 2.2.2 presents two types of elasticities: (i) GDP elasticity shows the percentage point change in sector-specific employment associated with a one percentage point change in overall GDP, and (ii) value-added elasticity gives the percentage point change in sector employment associated with a one percentage point change in output in the corresponding sector. The figures show that employment has been generated in the service sector at a faster rate than in agriculture and manufacturing. However, the gap is not considerable, suggesting a slow structural change not associated with a net job loss in primary and secondary sectors. Looking at value-added elasticities, it appears that the service sector has the most job-intensive growth, followed by the industrial sector. This result suggests that employment in the agriculture sector responds more to global output than sector-specific output. TABLE 2.2.2 Employment elasticity and value-added growth by sector, 2001–2019 Agriculture Industry Services Sector GDP elasticity 0.7 0.6 0.8 Sector value-added elasticity 0.3 0.6 1.0 Average annual value-added growth 2.0 3.9 2.9 Source: Authors’ calculations based on World Development Indicator data. 37 Togo will need to create an additional 1 million jobs by 2030 to absorb the growing working-age population and will therefore need to maintain a GDP growth rate of at least 4.6 percent per year to ensure the status quo. 57. Decreasing fertility and increasing life expectancy are creating opportunities for Togo from the coming demographic dividends. The age structure of the Togolese population changed moderately between 2001 and 2019, due to declining fertility combined with increasing life expectancy; the total fertility rate fell from 5.4 births per woman in 2001 to 4.2 in 2019, while life expectancy increased by about 7.5 years. Over the same period, the dependency ratio fell from 85.6 percent to 78.2 percent. The falling dependency ratio could create a demographic dividend that spurs economic growth, but this requires creating jobs fast enough to keep up with the significant increase in the working age population. Thus, in order to take advantage of these demographic changes, the employment elasticity should be equal to or higher than the relative change between working age population and growth. Over the period 2001 to 2019, employment growth (2.8 percent) just matched the increase in the working age population (2.9 percent). However, during the period of slow growth (mainly during the years 2001–2007), the jobs created were likely of poor quality, as the productivity growth was negative (–1.7). FIGURE 2.2.5 Demographic change and labor supply a) Fertility and life expectancy b) Working age population Fertility rate, total (births per woman) 6 62 5,000 Life expectancy at birth, total (years) 4,500 5 60 4,000 58 3,500 4 56 3,000 3 2,500 54 2,000 2 52 1,500 1,000 1 50 500 0 48 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Fertility rate, total (births per woman) Working age population, female Life expectancy at birth, total (years) Working age population, male Working age population Source: Authors’ calculations based on World Development Indicator data. 58. Given its increasing working-age population, Togo will need to create an additional one million jobs by 2030, which would require a GDP growth rate of at least 4.6 percent per year to ensure that status quo in labor market conditions. A simple extrapolation of the current labor market trends shows that Togo will need to create one million jobs by 2030 to absorb future labor market entrants. To achieve that, growth would need to average at least 4.6 percent per year based on historical trends, notably the elasticity of jobs-to-GDP growth that stood at 0.6 over the period of 2001 and 2019. Such growth rate would be enough to absorb new labor market entrants but not to ensure rapid gains in informality and poverty reduction, while labor productivity would remain lackluster in this scenario. More fundamental improvements in labor market conditions would require significantly faster growth underpinned by reforms supporting structural transformation and job creation. Under the current baseline projections by the World Bank, growth would average 6 percent over the period 2023–2030, which would help create 1.4 million new jobs by 2030, while the more optimistic 38 FIGURE 2.2.6 Scenarios for job creation a) Projected growth and job creation path b) Projected job creation path by sector 300 250 0.7 Number of jobs (thousands) 0.6 Number of jobs (thousands) 250 200 0.5 200 Sector share 150 0.4 150 100 0.3 100 0.2 50 50 0.1 0 0 0 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 Expected jobs created (Roadmap growth assumptions) New jobs projected, Services Sector share, Services Expected jobs created (WB growth projections) New jobs projected, Industry Sector share, Industry Jobs required to absorb demographic changes New jobs projected, Agriculture Sector share, Agric. Source: Authors’ calculations based on World Development Indicator and EHCVM 2018 data. assumptions of the Feuille de Route Gouvernementale 2020–2025 (7 percent) would be consistent with the creation of 1.6 million new jobs. In terms of sectoral distribution, a continuation of current hiring trends would imply the service sector’s share of total employment increasing from 49 percent to 54 percent by 2030, with agriculture’s share decreasing by an almost identical amount, from 38 percent to 33 percent, and industry remaining broadly unchanged at 12 percent. However, these trends do not account for the effect that services tend to expand more rapidly with economic growth. Taking this into account, the service sector’s share of employment could reach 58 percent by 2030, comprising 1.1 million of the 1.4 million new jobs embedded in World Bank projections. Ensuring high quality services jobs and expanding manufacturing to alleviate some pressure on the services industry should be prioritized to avoid undercutting productivity in the services industry. Structural transformation remains sluggish, with a slow shift from agriculture to services, but almost no productivity growth. 59. Increases in labor productivity can help boost labor demand across sectors. Labor productivity expanded by an average of 1.1 percent between 2001 and 2019 in Togo, mainly driven by higher productivity growth in the industry (1.3 percent) and service sectors (0.8 percent). In the most recent period (2017–2019), productivity gains came from a significant increase in labor productivity in the service sector. Although within-sector reallocation (from less to more productive firms within a specific sector) has accounted for the larger share of productivity growth over the last decade, workers are also gradually shifting away from agriculture to the service sector, which could have supported incremental gains in aggregate productivity. The case of Togo stands in contrast to that of Sub-Saharan countries and in particular Rwanda and Ghana, where, first, overall labor productivity growth has been higher and second, the contribution of structural transformation has been more significant. 39 FIGURE 2.2.7 Structural change and employment dynamic a) Per capita labor productivity b) Growth in per capita value added, 2001–2019 2,500 Togo (inflation-adjusted 2010 dollars) Sub-Saharan 2,000 Africa USD per person Rwanda 1,500 Ghana 1,000 Guinea Benin 500 –2 –1 0 1 2 3 4 5 6 7 0 Within-sector productivity Dynamic reallocation Agriculture Industry Services Participation rate Static reallocation 2001 2019 Employment rate Demographic change c) Employment share by sector, 2001–2019 d) Value added share by sector, 2001–2019 100 100 90 90 80 38.8 41.7 80 47.6 49.4 48.7 48.0 54.0 Percent of total Percent of total 70 70 58.2 employment value added 60 60 13.4 12.4 50 12.9 50 12.9 19.7 19.1 40 40 22.2 19.5 30 30 47.8 45.9 20 39.6 37.7 20 32.9 31.6 10 10 23.8 22.3 0 0 2001 2007 2016 2019 2001 2007 2016 2019 Agriculture Industry Services Agriculture Industry Services Source: Authors’ calculations based on World Development Indicators data TABLE 2.2.3 Growth in labor productivity, 2001–2019 Overall Agriculture Industry Services 2001–2019 1.1 0.5 1.3 0.8 2001–2007 –1.7 –0.4 –1.0 –3.1 2008–2016 2.9 0.6 5.6 2.3 2017–2019 2.6 1.6 0.7 3.1 Source: Authors’ calculations based on World Development Indicators data. 60. When well managed, urbanization has the potential to drive overall productivity growth and improve the quantity and quality of jobs through scale and agglomeration effects and specialization. In East Asia and parts of Latin America, urbanization has been accompanied by growth in the manufacturing sector, faster job creation, and subsequent large rises in GDP per capita, so similar results would be expected in other regions of the world, including West Africa. Togo has experienced high levels of movement from rural 40 to urban areas: in 1950, less than 5 percent of Togolese lived in urban areas compared to about 42 percent today. Yet during the same stretch of time, per capita incomes have barely grown. One reason for that is that the manufacturing sector has struggled to establish itself as urbanization increases, as has been the case in other urbanizing regions (East Asia, Latin America). Urbanization has not been associated with an increase in the relatively productive skilled manufacturing sector in Togo. Togo’s cities, and especially Greater Lomé, have difficulty generating the economies of scale and specialization that high-productivity industries need to generate sufficient employment. 61. The informal sector can create more jobs when formal sector employment shrinks, but this often comes at the expense of job quality. The informal sector in Togo is characterized by lower labor productivity and poor working conditions, including lack of social protection, low rate of paid employment (about 1.7 percent in 2017 compared to 31 percent in the formal sector), and lower wages (235 FCFA/US$0.40 per hour on average compared to 1,770 FCFA/US$3 in the formal sector). Between 2001 and 2019, the share of the informal economy decreased slightly from 35.8 percent to 33.1 percent of GDP,34 while annual growth in total employment stagnated at around 2.8 percent. Also, the correlation between GDP and informal sector growth is weak, with an elasticity of informal sector to growth estimated at –0.11. Achieving better job quality in a context of widespread informality is challenging and will require increasing the productivity of informal activities as well as providing incentives for private informal firms to formalize. FIGURE 2.2.8 Informal sector, employment and growth 10 40 8 35 6 30 Informal sector Growth rates 4 25 2 20 0 15 –2 10 –4 5 –6 0 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 Informal sector (share of GDP) Value-added growth Employment growth Source: Authors’ calculations based on World Development Indicators data 2.3 FORMAL LABOR DEMAND REMAINS LIMITED DUE TO LOW ENTRY RATES OF NEW ENTERPRISES AND THE CREATION OF FEW NEW JOBS 62. The Togolese economy is dominated by very small informal firms, with few firms of significant size. According to the 2018 firm census,35 96.9 percent of Togolese firms are small, and 93 percent of firms are single-owner operated without employees and with average annual turnover of less than US$5,000. Only 34 Melina, L. and Schneider, F. (2019). Shedding Light on the Shadow Economy: A Global Database and Interaction with the Official One. Cesifo Working Papers 7981. 35 The 2018 Firm census covers formal and informal businesses with fixed premises for professional use (i.e. a room built with durable materials and an entrance door). The scope of the census was extended to also cover public hospitals, businesses that operate not within a fixed premise as defined by the census, but next to it (including carpentry, brick making, car and motorcycle repair; etc.), artisanal bakers, and NGOs. 41 a little more than 2,000 firms in Togo have an annual turnover above US$100,000,36 with just 303 large firms with a turnover exceeding US$1.7 million. The latter are mainly present in banking, industry (agro-food and industrial products), services (insurance, hotel industry, transit and logistics, port handling), mining, construction, public works, and commerce. Furthermore, just 14.5 percent, or around 17,000 of the firms identified in the census are formal, representing 41.8 percent of jobs; the majority of firms in the census tend to be located more towards the southern part of the country, with the Lomé region containing more than 60 percent of firms and accounting for 71 percent of employment.37 63. The formal private sector has not been creating enough jobs to absorb a growing and gradually better-educated workforce. The employment-to-sales elasticity for formal firms has fallen somewhat over time and is lower in Togo than in some peer countries. The elasticity of job-to-sales growth in formal firms fell from 0.95 to 0.82 between 2009 and 2016, indicating that firms have not necessarily hired workers as their sales have grown.38 Togo’s job-to-GDP growth elasticity is also lower than for structural peers Benin (1.8 in 2016), Guinea (0.96 in 2016), and Sierra Leone (0.82 in 2017).39 Its East Asian peers display a much higher job creation-to-sales growth elasticity, with 3.1 and 5.5 respectively for Cambodia (2016) and Bangladesh (2013), evidencing a labor-intensive growth spurt in these countries. FIGURE 2.3.1 Elasticity of job growth to sales growth (average) in formal firms, by country 6 5.5 Elasticity of job growth to sales growth (average) 5 4 3.1 3 2 1.8 0.69 0.71 0.82 0.86 0.95 0.96 1 0.45 0 20 cco 20 ana 20 nda 20 go 20 one 20 go 20 nea 20 nin 20 dia 20 esh 19 13 19 16 17 09 16 16 16 13 To To bo o Be d h ui a Le or la G Rw G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. 64. Elasticity of jobs-to-sales growth in formal firms also differs by sector, yet with diverging results from the overall economy. In 2016, jobs-to-GDP elasticity was highest in the construction sector at 0.91, closely followed by the manufacturing sector at 0.9 and the services sector at 0.68. Formal manufacturing and construction firms therefore stand out as more labor intensive compared to formal services firms. In the formal services sector, the elasticity of job creation-to-sales growth fell substantially between 2009 and 2016, from 3.9 to 0.68. In the formal manufacturing sector, Togo had a very low job-to-sales elasticity in 2009 (0.33), which almost tripled to 0.9 by 2016, placing it ahead of most of its structural and aspirational peers, as well as Cambodia. 36 INSEED defines large firms as those with a turnover above 1 billion FCFA, medium firms as those with turnover between 1 billion FCFA and 60 million FCFA, and small firms as those with a turnover less than 60 million FCFA (around US$100,000). 37 World Bank, Togo CPSD, forthcoming. 38 The latest available data in the Enterprise Survey of the World Bank for Togo is from 2016. The analysis took into account data from 2011 and 2016 in preparing the write-up of this chapter. 39 The World Bank Enterprise Survey is answered by business owners and top managers. Sometimes the survey respondent calls company accountants and human resource managers into the interview to answer questions in the sales and labor sections of the survey. The manufacturing and services sectors are the primary business sectors of interest and formal (registered) companies with five or more employees are targeted for interviews. 42 FIGURE 2.3.2 Elasticity of job-to sales growth (average), Togo 1.0 0.91 0.9 0.9 Elasticity of job growth to sales growth (average) 0.8 0.68 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Services Manufacture Construction Source: Authors’ calculations based on World Bank Enterprise Survey. FIGURE 2.3.3 Elasticity of job-to-sales growth (average) in the service sector, by country 5.9 Elasticity of job growth 6 3.9 to sales (average) 4 2 0.68 0.7 0.81 0.99 1.3 0.3 0 –2 –4 –3.1 –6 –8 –7 20 dia 20 esh 20 cco 20 go 20 nda 20 ana 20 one 20 nea 20 go 20 nin 16 13 19 16 19 13 17 16 09 16 To To bo o Be d h ui a Le or la Rw G G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. FIGURE 2.3.4 Elasticity of job-to-sales growth (average) in the manufacturing sector, by country 4 3.7 Elasticity of job-to-sales 3.5 growth (average) 3 2.5 2 1.8 1.5 0.71 0.73 0.74 0.8 0.83 0.9 1 0.57 0.5 0.33 0 20 go 20 cco 20 ana 20 nda 20 one 20 nin 20 dia 20 go 20 nea 20 esh 09 19 13 19 17 16 16 16 16 13 To To bo o Be d h ui a Le or la G Rw G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. 43 The entry rate of formal firms has been rising since 2017 and is expected to improve jobs-to-GDP elasticity. 65. Over the past decade, Togo’s (formal) firm entry rate has steadily increased, a positive development which can improve the elasticity of jobs-to-GDP growth over time. Togo’s firm entry rate per 1,000 working age adults went from 0.15 in 2009 to 0.94 in 2020, showing a clear upward trend, especially since 2017. Formal firms were notably created in the service sector in urban areas, which saw an increase in wage employment. The growing entry rate has the potential to improve the elastic of job growth-to-GDP growth over time, provided that the positive trend continues in the wake of the COVID-19 pandemic recovery. FIGURE 2.3.5 New business entry and new business entry rate between 2009 and 2020 5,000 1.00 4,500 0.90 (new LLCs per 1,000 adults) Liability Companies (LLC) Number of new Limited 4,000 0.80 New business density rate 3,500 0.70 3,000 0.60 2,500 0.50 2,000 0.40 1,500 0.30 1,000 0.20 500 0.10 0 0 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Year Number of new Limited Liability Companies (LLC) New business density rate (new LLCs per 1,000 adults) Source: Authors’ calculations based on World Bank Enterprise Survey. 66. Government reforms to simplify the business registration process explain the increase in new business entry since 2017. Over the past few years, a series of reforms have reduced the time to register a company, including reduction of minimum capital requirements, a decrease in registration fees that can now be paid directly at a one-stop-shop, and waving of a previous requirement to notarize company documents. Between 2004 and 2020, the number of procedures required to register a business were reduced from 13 to 3.40 67. Togo’s business entry rate for 2020 compares well to Benin, Nigeria, and Côte d’Ivoire, yet still has some way to go to achieve entry rates observed in its aspirational peers. While Togo fares relatively well compared to Benin and is at similar levels as Nigeria and Côte d’Ivoire, it still has some way to go to catch up with aspirational peers Rwanda and Morocco, which have an entry rate of 2.17 and 2.24 respectively for 2020. 40 Business reforms in Togo-Doing Business-World Bank Group. 2020. 44 FIGURE 2.3.6 Enterprise entry rate (registrations) per 1,000 working age adults in 2020 Togo 0.94 Rwanda 2.17 Nigeria 0.88 Morocco 2.24 Guinea 0.45 Côte d'Ivoire 0.78 Cambodia 0.54 Benin 0.61 0 0.50 1.00 1.50 2.00 2.50 Source: Authors’ calculations based on World Bank Entrepreneurship data. 68. Female formal business ownership remains limited, albeit with an upward trend. Female business ownership among newly registered firms remains low, with less than one-third of new firms owned by women in 2020. However, since 2014 a steady upward trend has occurred, with female ownership increasing from 22 percent in 2014 to 32 percent in 2020. It should be noted that women tend to work more in the informal sector and face constraints that can limit their ability to contribute to the formal private sector. FIGURE 2.3.7 Number and share of new business owners who are women is steadily growing (2014 to 2020), although at low levels 3,000 35 Number of new female 2,500 30 Share of new female business owners, % business owners 25 2,000 20 1,500 15 1,000 10 500 5 0 0 2014 2015 2016 2017 2018 2019 2020 Number of new female business owners Share of new female business owners Source: Authors’ calculations based on World Bank Entrepreneurship data. 45 Low profitability related to hiring additional employees might partly explain the low elasticity of jobs-to-GDP growth. 69. Low profitability of hiring additional employees is a key factor contributing to low job-to-GDP growth elasticity in Togo. Total labor costs relative to production costs, and wage per worker relative to GDP per capita, are high. Since productivity remains low, the profitability relative to labor costs per worker is low. 70. For formal firms in Togo, the share of (non-capital) production costs going to wages, benefits, and social security contributions is relatively high. At 70 percent, labor costs as a share of production costs were very high in Togo in 2016 compared to East Asian peers (Cambodia at 26 percent and Bangladesh at 33 percent), and remain somewhat high compared to Ghana (54 percent in 2013) and Sierra Leone (55 percent in 2017). The level of labor costs is an important consideration for firms when evaluating the profitability of hiring additional workers, and thus may suppress the economy’s possibility of creating more formal wage jobs. 71. Labor costs per worker (before salary taxes) are high relative to GDP per capita. Togo’s wage per worker as a share of GDP per capita stood at 549 percent in 2016, higher than in any peer countries except Cambodia (724 percent in 2016) and Rwanda (2,446 percent in 2019). Labor costs per worker relative to GDP per capita are lowest in Ghana (102 percent in 2013), Sierra Leone (165 percent in 2017), and Benin (192 percent in 2016). FIGURE 2.3.8 Total labor costs relative to production costs, by country 75 78 80 69 70 (% of production costs) 66 Total labor costs 60 54 55 40 33 26 20 0 20 dia 20 esh 20 ana 20 one 20 nin 20 nda 20 go 20 cco 20 nea 16 13 13 17 16 19 16 19 16 To bo Be o d h ui Le a or la G Rw G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. FIGURE 2.3.9 Wage per worker relative to GDP per capita, by country 3,000 Wage per worker (% 2,500 2,446 of GDP per capita) 2,000 1,500 1,000 724 549 500 336 102 165 192 194 202 0 20 ana 20 one 20 nin 20 esh 20 cco 20 nea 20 go 20 dia 20 nda 13 17 16 13 19 16 16 16 19 To bo Be o d h ui Le a or la G Rw G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. 46 72. Employing additional workers became less profitable for formal firms in Togo between 2009 and 2016. Per capita profitability relative to labor costs per worker fell from 35 percent in 2009 to 13 percent in 2016. Besides Ghana and Togo’s East Asian peers, all aspirational and structural peers have a higher profitability relative to labor costs per worker. FIGURE 2.3.10 Profitability per worker relative to labor costs per worker, by country Profitability per worker 60 relative to labor costs 45 40 35 26 28 per worker 20 20 13 14 6.6 0 –20 –9.8 –40 –60 –48 20 nda 20 ana 20 dia 20 esh 20 go 20 nin 20 one 20 cco 20 nea 20 go 19 13 16 13 16 16 17 19 16 09 To To bo o Be d h ui a Le or la Rw G G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. 73. Higher labor costs may be responsible for declining elasticity of employment-to-sales of formal firms in Togo. High total labor costs relative to production costs and low worker productivity relative to per capita labor costs reduce incentives for firms to hire additional workers, which depresses job creation in Togo’s formal economy. Given productivity and income, the total cost of labor (salary, taxation, and social security) is not competitive and may explain the weak incentive to hire employees. 74. Togo’s 2008 and 2012 minimum wage increases made hiring lower skilled, lower-wage workers suddenly more costly for formal firms relative to value added per worker. The increase in per capita value added in the agricultural and the industry sectors over time was too slow to keep pace with the 2008 and 2012 increase in the formal minimum wage. At least 50 percent of urban wage and industry workers earned less in 2011 than the eventual 2012 minimum wage, and median wages for agricultural workers were well below the minimum wage. Also, median earnings in rural areas remained much lower than the minimum wage in all sectors except services, suggesting that compliance with formal requirements was unlikely to be viable for those firms. 47 FIGURE 2.3.11 Trends in the real minimum wage and value added per worker, agriculture and industry 1.2 450,000 400,000 1.0 350,000 Scaled value added Minimum wage in 0.8 300,000 per worker 2010 LCU 250,000 0.6 200,000 0.4 150,000 100,000 0.2 50,000 0 0 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Year Scaled Real Agricultural GDP per worker Scaled Industry GDP per worker Minimum wage Source: National accounts. TABLE 2.3.1 Median monthly earnings in current CFA All Agriculture Industry Services Urban 2011 35,000 6,462 32,000 40,000 2015 50,000 35,000 50,000 50,400 2018 45,000 30,000 35,000 45,000 Rural 2011 12,681 5,815 21,000 35,000 2015 16,154 13,000 15,000 25,000 2018 33,600 21,000 25,000 35,000 Source: Authors’ calculations based on EHCVM and QUIBB data. 75. Labor market trends over recent years seem to confirm that the cost of labor is too high to allow the economy to create enough jobs. While the share of workers in private wage employment expanded from 6.9 percent to 14.8 percent between 2006 and 2011, it contracted to 13.4 percent after the minimum wage increase in 2012. At the same time, the share of the working-age population that is self-employed has grown from 28.9 percent in 2006 to 33.2 percent in 2018. 48 FIGURE 2.3.12 Employment over years, age 15–64 100 27 28.2 26.8 80 Percentage 60 28.9 28.4 33.2 40 24.3 24.7 32.8 20 14.8 13.4 6.9 0 4.3 4.3 1.9 2006 2011 2018 Wage public Wage private Self-employed agriculture Self-employed Other non-agriculture Source: Authors’ calculations based on EHCVM and QUIBB data. 76. These trends differ according to workers’ level of education. The share of wage employment for Togolese who have not completed primary education increased between 2006 and 2011, but then declined as many Togolese reverted to self-employment. The opposite trend is occurring among Togolese who have completed secondary or some tertiary education, indicating that the rising cost of labor pushed lower-skilled workers out of the formal wage economy. FIGURE 2.3.13 Employment over years by education, age 15–64 100 90 22.3 16.9 21 31.4 25.7 29.6 80 45.2 53.3 4.1 16.4 53 9.4 70 64.7 64.7 60.7 67.5 67.5 10.6 Percentage 60 73.5 12.9 24.1 1.8 50 1.3 40 73.6 33.3 23.8 30.8 66.7 67.8 30 55.6 1.8 58.5 22.3 27 50.2 22.5 20 23.6 29.8 28.2 10 21.5 1.1 21.4 3 15.9 13 12.3 0.7 3.5 0 5.5 9 n e e e y n e e e y n e e e y ar ar ar et et et et et et et et et io io io nd nd nd at pl pl pl at pl pl pl at pl pl pl m m m m m m m m m uc uc uc co co co co co co co co co co co co ed ed ed se se se in in in in in in y y y t- t- t- o o o ar ar ar os os os y y y y y y N N N ar ar ar ar ar ar nd nd nd /p /p /p im nd im nd im nd co co co ry ry ry Pr Pr Pr co co co ia ia ia Se Se Se rt rt rt Se Se Se te te te e e e m m m So So So 2006 2011 2018 Paid employee Employer Non-paid employee Self-employed Source: Authors’ calculations based on EHCVM and QUIBB data. 49 77. A shift back into agriculture work took place between 2011 and 2018, especially for youth. The share of agricultural employment decreased from 53.2 percent in 2006 to 41.0 percent in 2011, with an increase of employment in the industry. However, these trends reversed between 2011 and 2018, bringing the share of agricultural employment back to 43.1 percent. The trend reversal is particularly pronounced for younger people (15–24). FIGURE 2.3.14 Industry over time and by age, age 15–64 a) Over time, age 15–64 b) By age 100 100 90 90 33.3 35.1 32.8 80 39.9 41.8 41.1 80 45.7 40.1 45.9 41.3 46.5 41.4 70 70 6.2 Percentage 60 Percentage 60 6.4 19.8 19.5 7 50 8.7 15.4 14 50 17.2 15.8 19 16.3 40 40 30 60.6 53.5 47.7 30 45.7 45.2 43.3 44.6 53.2 20 35.2 37.2 20 41 43.1 10 10 0 4 4 4 4 4 4 4 4 4 0 –2 –3 –6 –2 –3 –6 –2 –3 –6 15 25 35 15 25 35 15 25 35 2006 2011 2018 2006 2011 2018 Agriculture Industry Services Agriculture Industry Services Source: Authors’ calculations based on EHCVM and QUIBB data. 78. High labor income taxes and social security contributions in Togo might push some workers and firms towards the informal sector. In Togo, the total tax wedge41 is 24.5 percent of the wage bill (without other income taxes). Firms pay a 3 percent tax on gross salaries and 17.5 percent in social security contributions, while employees contribute 4 percent to social security. Social security contributions for wage employees in the formal private sector include 12.5 percent for old age pension, 3 percent for family benefits, and 2 percent for work injury. Togo’s social security contributions as a share of gross salaries stand at similar levels to those in Benin (16.4 percent), Morocco (15.38 percent), and Guinea (18 percent), but well above Rwanda (with 5.3 percent paid for by the employer and 3.3 percent by the employee). Moreover, labor taxes and contributions as a share of profits are higher in Togo than in its East Asian peers as well as in Rwanda, Sierra Leone, and Ghana, according to 2020 Doing Business data. The high share of labor taxation and social security contributions and subsequent high labor costs might push some firms towards the informal sector. 79. Togolese firms hire a larger share of workers on a temporary basis than in other countries, which seems to substantiate constraints related to profitably hiring additional workers. In Togo, the share of temporary workers increased from 11 percent to 12 percent of all workers between 2009 and 2016. This is higher than in any of Togo’s peers, ranging from 1.6 percent in Bangladesh (2013) to 8.3 percent in Rwanda (2019). This might indicate that firms avoid regulations associated with more permanent workers. 41 The tax wedge is defined as the ratio between the amount of taxes paid by an average worker (single person at 100% of average earnings) and the corresponding total labor cost for the employer. 50 FIGURE 2.3.15 Labor taxes and contributions as a share of profits 30 28.6 26.4 as a share of profits 25 23.1 23.3 Labor taxes and contributions 20 14.7 15 11.3 10 6 5 2.3 0 0 sh a da e na o co n a di on ne g ni de oc an ha To bo Be ui Le or la Rw G G m ng M ra Ca er Ba Source: Doing Business data 2020 Si FIGURE 2.3.16 Proportion of temporary workers (out of all workers) by country workers (out of all workers) 14 Proportion of temporary 12 12 11 10 8.3 7.9 8 6.4 6.2 6 5.1 4 3 1.6 1.8 2 0 20 esh 20 dia 20 nea 20 one 20 cco 20 ana 20 nin 20 nda 20 go 20 go 13 16 16 17 19 13 16 19 09 16 To To bo o Be d ui h Le a or la G Rw G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. FIGURE 2.3.17 Proportion of semi-skilled and un-skilled workers (out of all production workers), by country a) Proportion of semi-skilled workers b) Proportion of unskilled workers (out of all production workers), by country (out of all production workers), by country Proportion of semi-skilled production workers) (%) Proportion of un-skilled production workers) (%) 50 44 45 50 workers (out of all workers (out of all 40 40 35 32 27 29 30 30 24 20 21 19 20 20 17 10 10 7.3 0 0 20 nea 20 cco Si 20 da 20 one 20 nin 20 go 20 nea 20 go Si 20 nin 20 one 20 nda 20 cco M 6 19 er 19 17 16 16 16 16 er 16 Rw 7 M 9 19 an To To o Be Be o 1 1 1 ui ui Le Le a or or Rw G G ra ra Source: Authors’ calculations based on World Bank Enterprise Survey. 51 80. Furthermore, Togo’s manufacturing firms use a higher share of semi-skilled and a lower share of unskilled workers than firms in peer countries, possibly the result of relatively high labor costs. In Togo’s manufacturing sector, just 17 percent of workers were un-skilled, 45 percent were semi-skilled, and 38 percent were high-skilled in 2016. The share of un-skilled workers is lower only in Guinea (7.3 percent in 2016), and in none of the peer countries do manufacturing firms have a higher share of semi-skilled workers. These figures seem to indicate that the current level of labor costs significantly restrict job opportunities of un-skilled workers in the formal sector. External financing remains costly and access limited, although it differs by sector. 81. Access to finance may be limiting Togolese firm growth, but, overall, this does not seem as big of an obstacle as in some other countries. While formal firms in Togo perceive access to finance as a major constraint, it is not brought forward in the Enterprise Survey as the most important constraint as often as by firms in some peer countries. According to the survey, 51 percent of firms in Togo in 2016 identified access to finance as a major constraint for doing business. This share is higher than in Rwanda (17 percent in 2019), Morocco (28 percent in 2019), Guinea (30 percent in 2016) and Benin (43 percent in 2016). However, it was not perceived as the biggest obstacle: only 24 percent of Togolese formal firms perceive access to finance as the biggest constraint, which is lower than in Rwanda (31 percent in 2019), Benin (33 percent in 2016), Sierra Leone (40 percent in 2017), and Ghana (50 percent in 2013). FIGURE 2.3.18 Share of firms identifying access to finances as a major constraint, by country 70 65 Percent of firms identifying 62 access to finances as a 60 51 major constraint 50 43 40 28 30 30 23 20 17 17 10 0 20 dia 20 nda 20 esh 20 cco 20 nea 20 nin 20 go 20 ana 20 one 16 19 13 19 16 16 16 13 17 To bo o Be d ui h a Le or la Rw G G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. FIGURE 2.3.19 Share of firms identifying access to finance as biggest constraint, by country finance as biggest obstacle 60 50 identifying access to 50 Percent of firms 40 40 33 31 30 24 20 14 10 6.5 7.6 4.3 0 20 cco 20 nea 20 dia 20 esh 20 go 20 nda 20 nin 20 one 20 ana 19 16 16 13 16 19 16 17 13 To bo o Be d ui h a Le or la Rw G G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. 52 82. Furthermore, although the share of loans requiring collateral in Togo is high, the level of collateral is normal. In Togo, 91 percent of loans require collateral, only surpassed by Rwanda (99 percent in 2019), and Guinea (100 percent in 2016). At the same time, the value of collateral needed for a loan is about as high as in Togo’s peer countries and has not fundamentally changed between 2009 and 2016. FIGURE 2.3.20 Proportion of loans requiring collateral, by country 99 100 requiring collateral (%) 100 91 87 Proportion of loans 80 84 78 79 80 72 60 40 20 0 20 cco 20 dia 20 nin 20 ana 20 esh 20 one 20 go 20 nda 20 nea 19 16 16 13 13 17 16 19 16 To bo o Be d h ui Le a or la G Rw G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. FIGURE 2.3.21 Value of collateral needed for a loan (percent of the loan amount), by country 400 356 (% of the loan amount) Value of collateral needed for a loan 300 271 231 240 231 238 226 200 165 100 63 0 20 esh 20 nin 20 dia 20 ana 20 cco 20 nda 20 one 20 go 20 go 13 16 16 13 19 19 17 09 16 To To bo Be o d h a Le or la G Rw m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. 83. Compared to its peers, a larger share of Togolese firms uses banks to finance investments and working capital. In 2016, 26 percent and 40 percent of formal firms in Togo used banks to finance investments and working capital, respectively, higher than in any structural and East Asian peers. Only in Morocco does a larger share of firms access bank loans to finance investment or working capital, with 44 percent and 45 percent respectively in 2019. As a result, the share of investments financed by internal funds is lower for firms in Togo, except for Morocco: while formal firms finance 65 percent of investments with internal funds in Togo, this share stands at 54 percent for Morocco, but reaches 89 percent for Rwanda (2019) among the aspirational peers, 92 percent in Guinea (2016) among structural peers, and 96 percent in Cambodia (2016) among East Asian peers. 84. However, data from the 2016 Enterprise Survey may not capture some business growth constraints related to financing. The banking portfolio in Togo is concentrated in a few sectors. According 53 FIGURE 2.3.22 Share of firms using banks to finance investments, by country 50 44 Percent of firms using 40 banks to finance investments 30 26 20 21 20 12 12 7 9.2 10 2.5 0 20 dia 20 one 20 nea 20 nin 20 nda 20 esh 20 ana 20 go 20 cco 16 17 16 16 19 13 13 16 19 To bo Be o d ui h Le a or la Rw G G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. FIGURE 2.3.23 Share of firms using banks to finance working capital, by country 50 45 43 Percent of firms using 40 banks to finance 40 working capital 30 30 25 26 20 18 11 8.6 10 0 20 one 20 nea 20 dia 20 ana 20 nin 20 esh 20 go 20 nda 20 cco 17 16 16 13 16 13 16 19 19 To bo Be o d ui h Le a or la G Rw G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. FIGURE 2.3.24 Proportion of investments financed by internal funds, by country 96 investments financed by 100 89 90 91 92 internal funds (%) 80 75 76 Proportion of 65 60 54 40 20 0 co go sh na da n ni 6 e on 17 a ne 6 a di 6 r oc 019 To 016 a de 013 ha 013 an 019 Be 201 e ui 01 bo 201 L 20 M o 2 2 gl 2 G 2 R w 2 ra G 2 m Ba n er Ca Si Source: Authors’ calculations based on World Bank Enterprise Survey. 54 to BCEAO data, almost 80 percent of bank credit went to the services sector in 2020: 28 percent went to trade and hospitality services, 24 percent to various services, 17 percent to transport, and 18 percent to construction. Less than 1 percent went to agriculture, which financial operators explain by the lack of credit history and the high level of informality. Digitizing payments represents an opportunity to improve financial inclusion, notably for small enterprises, in a context where access to physical financial services remains low.42 Moreover, World Bank FINDEX data indicates gender differences in terms of financial inclusion in Togo: in 2021, 29 percent of men compared to 21 percent of women above the age of 15 years had an account at a financial institution; 12 percent of men compared to 11 percent of women above the age of 15 years had saved at a financial institution; and 49 percent of men compared to 39 percent of women above the age of 15 years had made or received a digital payment.43 A series of microeconomic risks and distortions has a negative effect on firm entry and the creation of more good quality jobs. 85. Togolese formal firms consider tax rates as the biggest constraint in the business environment. Tax rates are cited as the most important constraint by 55.8 percent of Togolese survey firms in 2016, more than firms in any peer country. In the 2016 firm survey, 56 percent of firms in Togo identified tax rates a major constraint, while this share ranges in peer countries from 6.5 percent in Cambodia (in 2016) to 52 percent in Ghana (in 2013). Similarly, 26 percent of Togolese firms identified tax rates as the biggest constraint in 2016, which is also higher than in any of its peers. FIGURE 2.3.25 Share of firms identifying the business environment area as a major constraint Tax rates 55.8 Access to finance 51.2 Practices of competitions in the informal sector 50.6 Electricity 50.1 Political instability 46.6 Corruption 44.7 Tax administration 41.5 Customs and trade regulations 35.8 Transportation 35.5 Crime, theft, and disorder 32 Inadequately educated workforce 21.1 Access to land 20.8 Business licensing and permits 15.2 Labor regulation 7.9 0 10 20 30 40 50 60 Source: Authors’ calculations based on World Bank Enterprise Survey. 42 World Bank, Togo CPSD, forthcoming. 43 World Bank FINDEX. https://www.worldbank.org/en/publication/globalfindex/Data. Accessed on December 19, 2022. 55 FIGURE 2.3.26 Share of firms identifying tax rates as a major constraint, by country 60 56 identifying tax rates as 52 50 47 major constraint Percent of firms 38 40 40 36 30 20 11 10 6.5 7.3 0 20 dia 20 esh 20 nda 20 nea 20 one 20 cco 20 nin 20 ana 20 go 16 13 19 16 17 19 16 13 16 To bo o Be d ui h a Le or la Rw G G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. FIGURE 2.3.27 Share of firms identifying tax rates to be the biggest obstacle, by country 30 be the biggest obstacle 26 identifying tax rates to 25 24 Percent of firms 20 15 15 9.3 9.7 10 10 5.3 6.3 5 1.4 0 20 esh 20 ana 20 dia 20 one 20 nea 20 nin 20 cco 20 nda 20 go 13 13 16 17 16 16 19 19 16 To bo Be o d h ui Le a or la G Rw G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. FIGURE 2.3.28 Share of firms identifying corruption as a major constraint, by country 60 Percent of firms identifying 50 corruption as a major 50 44 45 46 46 39 40 constraint 31 30 20 10 10 0.2 0 20 nda 20 dia 20 nea 20 nin 20 ana 20 go 20 one 20 cco 20 esh 19 16 16 16 13 16 17 19 13 To bo Be o d ui h a Le or la Rw G G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. 56 86. Corruption in Togo also negatively affects the general investment climate, thus suppressing firm entry and wage job creation. In the Enterprise Survey, 45 percent of Togolese firms identified corruption as a major constraint, close to most peers with the exception of Rwanda (0.2 percent in 2019), Cambodia (10 percent in 2016), and Guinea (31 percent in 2016). However, 86 percent of firms in Togo in 2016 indicated having paid informal payments, a higher share than only Rwanda (100 percent in 2019). Transparency International’s Corruption Perceptions Index (CPI) confirmed these findings, where Togo scored 30 out of 100 points in 2021 and ranked 128th out of 180 countries. In comparison, Togo scored somewhat the same as structural peers Guinea (score 25, ranking 150) and Sierra Leone (score 34, ranking 115), but well below Benin (score 42, ranking 78) and aspirational peers Morocco (score 39, ranking 87), Ghana (score 43, ranking 73) and Rwanda (score 53, ranking 52). However, it scored better than both Cambodia (score 23, ranking 157) and Bangladesh (score 26, ranking 147), its East Asian peers. FIGURE 2.3.29 Share of firms who paid informal payments, by country 100 100 Percent of firms who paid 82 86 76 informal payments 80 60 43 40 32 20 16 0 0 20 nea 20 one 20 nin 20 cco 20 dia 20 ana 20 go 20 nda 16 17 16 19 16 13 16 19 To bo Be o ui h Le a or G Rw G m M ra Ca er Si Source: Authors’ calculations based on World Bank Enterprise Survey. FIGURE 2.3.30 Corruption Perception Index 2021 (score out of 100, with 0 highly corrupt and 100 clean) 60 53 Corruption Perception 50 42 43 39 40 Index 2021 34 30 30 25 26 23 20 10 0 o a e n co na da a sh ne on di g ni de oc ha an To bo Be ui Le or la G Rw G m ng M ra Ca er Ba Si Source: Transparency International. 57 87. Furthermore, more than half of Togo’s firms identified practices of informal competitors as a major constraint, higher than in any peer countries. While there is a healthy degree of competition in most sectors, having to compete with the practices of informal competitors is perceived as “unfair,” as informal firms tend not to be as burdened by regulation and taxation. In this context, it is important to note that the private sector is heavily fragmented and dominated by the informal sector, with just 14.5 percent or around 17,000 firms in Togo operating in the formal sector. FIGURE 2.3.31 Share of firms identifying practices of competitors in the informal sector as a major constraint, by country 60 Percent of firms identifying practices 50 51 of competitors in the informal 50 sector as a major constraint 42 42 39 40 32 29 30 20 14 9.1 10 0 20 esh 20 nda 20 ana 20 dia 20 nea 20 cco 20 one 20 nin 20 go 13 19 13 16 16 19 17 16 16 To bo o Be d h ui a Le or la Rw G G m ng M ra Ca er Ba Si Source: Authors’ calculations based on World Bank Enterprise Survey. 88. While Togo’s economy has not been creating enough good quality jobs to absorb the growing labor force, the GoT has introduced several reforms to lift constraints related to formal firm entry and job creation: (a) Togo’s new Labor Code has improved labor regulation flexibility. Togo’s 2021 Labor Code introduced several changes, including: (i) reinforced measures to protect workers against age and gender discrimination, (ii) different types of contracts and descriptions of associated conditions, (iii) flexibility on working hour arrangements and of conditions for remote work, (iv) mandatory health insurance for all employees, and (v) clarification on the creation of unions and representation within them. According to international evidence, economies with flexible employment regulations tend to have a smaller informal sector and therefore more formal wage jobs. Formal sector wage jobs tend to be of higher quality than informal jobs, as they come with higher earnings and better worker protection. The share of non- agricultural employment in the informal sector correlates negatively with the employing workers score (a proxy for employment regulation flexibility). In economies with more rigid employment regulations, un-skilled and semi-skilled workers carry this burden rather than firms, as they tend not to find wage jobs corresponding to their skill level and are pushed towards the informal sector. 58 FIGURE 2.3.32 Economies with flexible employment regulation have smaller informal sectors 100 informal sector (% of total employment) Average nonagricultural employment in 80 60 40 20 0 0 20 40 60 80 100 Employing workers score (0–100) Sources: Doing Business database; World Development Indicators database. (http://data.worldbank.org/data-catalog/world-development- indicators), World Bank. Note: The figure shows the employing workers indicator set score and informal employment rate (2003–18 average). The sample comprises 68 economies. The relationship is significant at the 1% level after controlling for income per capita. (b) Reforms have simplified the tax system. Since 2016, the GoT has introduced major reforms to simplify the tax system. Among other changes, the government has eliminated several minor taxes—including on wages, supplementary income taxes, and specific manufacturing and beverage trade taxes—and lowered the corporate income tax rate from 28 to 27 percent. The reforms have sought to improve compliance and facilitate tax payment.44 (c) Reforms have also simplified the business registration process. This has led to an increase in new business entry since 2017. Over the past few years, a series of reforms have reduced the time to register a company, including reduction of minimum capital requirements, decreases in registration fees that can now be paid directly at a one-stop-shop, and waving of document notarization. Between 2004 and 2020, the number of procedures required to register a business decreased from 13 to 3.45 Togo’s business entry rate for 2020 compares well to Benin, Nigeria, and Côte d’Ivoire, but still has some way to go to achieve entry rates observed in its aspirational peers. (d) The development of several zone projects has the ambition and potential to improve industrial infrastructure by attracting new investments. These include the recently inaugurated Plateforme Industrielle d’Adetikopé situated 25 km north of Lomé, a special economic zone (SEZ) planned under the Togo-China Merchant Group partnership signed in 2015, and the development of 10 agro-poles by 2030. Moreover, a series of reforms have made the business environment more conducive to growth, including simplification of the process to create a new business, streamlining requirements for property registration and reduction of related costs, and facilitation of access to credits.46 44 World Bank, Togo CPSD, forthcoming. 45 Business reforms in Togo-Doing Business-World Bank Group. 46 World Bank, Togo CPSD, forthcoming. 59 89. Togo’s reforms, as well as ongoing efforts to attract more investment, show the GoT’s commitment to further promoting private sector development, a policy firmly at the core of the Feuille de Route Gouvernementale 2020–2025. These reforms and efforts are expected to increase the number of good quality jobs. While the 2016 Enterprise Survey (ES) data have not allowed assessment of effects from these reforms on firm entry and formal firms’ hiring, the reforms are expected to improve results in the next ES round. This will provide an opportunity to re-assess where Togo stands on its path to provide good quality jobs to all Togolese, and take stock of additional reforms that might be needed from a jobs perspective. 60 3. DEEP DIVES 90. Building on the analysis of macro, labor supply and labor demand side constraints in the first part, the Jobs Diagnostic is, in a second part, focusing on three deep dives warranting further investigation. The deep dive topics were identified based on key characteristics of Togo’s labor market highlighted in the initial analysis, the current knowledge base on these elements, and consultations with counterparts. Based on this, the second section of the Jobs Diagnostic is focused on the following topics: (a) Assessment of the resilience of non-poor informal workers, or the “missed middle” of social protection (b) Assessment of the productivity of informal businesses (c) Jobs potential in agriculture, both on and off the farm 91. The first two deep dives are motivated by the fact that 9 out of 10 jobs in Togo’s economy are informal. Informality will remain a key characteristic of Togo’s economy in the near future. While informal employment is often associated with low levels of productivity and earnings, it can also provide more independence, with 3 out of 10 businesses owners citing to be their own boss as main motivation to create the business. Moreover, the informal sector is largely female, with 7 out of 10 business owners being women; yet, female entrepreneurs, on average, are less productive than their male counterparts. The first two deep dives therefore aim to gain a better understanding of the heterogeneity of informal sector workers and informal businesses to identify tailored policy options: 92. The first deep dive is dedicated to informal sector workers, with a focus on non-poor households deriving their income from the informal sector: they don’t have access to social insurance schemes for private or public sector workers in the formal sector, nor are they eligible for social programs targeting those below the poverty line. They are commonly considered the “missed middle” of social protection, as there is no policy instrument in place to protect them from shocks that impacts their employment and earnings. Depending on their level of resilience, these households may need to resort to negative coping mechanisms and risk to slide into poverty. The deep dive presents the landscape of current social protection schemes and their respective target populations. It then analyzes the types and incidence of shocks that households are confronted with, before assessing the level of resilience of the “missed middle” as a basis to explore tailored policy options to extend social protection (through contributory and non-contributory schemes) to all population groups. 93. The second deep dive is focused on informal businesses—the enterprises run by informal sector workers that are not register and do not pay taxes. While policy options in the past tended to be focused on formalization efforts, they have not proven to be efficient, unless they are targeted to those informal firms that have a profile closer to formal firms. Togo has made important progress in simplifying firm registration procedures, leading to an important increase of registered firms over the past few years. Yet, formalization is not just about the cost of formalizing, but also the cost of being formal, including paying taxes and complying with labor regulations. Depending on the level of productivity and structure of informal businesses, this might not be achievable, and might drive some firms out of business. The deep dive presents the results of a cluster analysis that aims to shed light on the heterogeneity among informal businesses: some are well structured, relatively more productive and better integrated in the market, while others could be characterized as low productive 61 subsistence activities. The group in-between has some growth potential, and targeted policies could help these firms become more productive, better structured and more integrated into markets, hence increasing the earnings of those affiliated with a path to formalization in the medium to long term. 94. The third deep dive is focused on jobs in the agricultural sector, given its importance for Togo’s economy. Agriculture and connected activities along the value chain, including input provision and post-harvest activities, account for 2 out of 3 jobs in Togo. While productivity levels are low, especially for farming activities, agricultural value chains are a vehicle to offer jobs opportunities on and off the farm to workers across a broad array of skills profiles. The deep dive discusses key employment characteristics in Togo’s agricultural sector. It then presents key conclusions from a recent World Bank study on jobs in value chains, focusing on shea, red pepper and ginger as examples of high potential value chains for better jobs outcomes. The conclusions and recommendations are a result of applying an explicit jobs lens to agricultural value chain development; an approach that warrants more attention to identify and utilize synergies at the intersection of employment and agricultural policies. 3.1 NON-POOR INFORMAL WORKERS CAN BE VULNERABLE TO SHOCKS AND REPRESENT THE “MISSED MIDDLE” —THOSE NOT COVERED BY SOCIAL PROTECTION 95. As an economic concept, informality can be seen as a multidimensional phenomenon affecting both firms and workers. The ILO uses two approaches to define informality: the firm approach and the workforce approach. For the firm approach, the most common definition of informality is based on the criterion of legal and tax registration. In Togo, a firm not registered with the Centre de Formalité des Entreprises (CFE) and which does not pay taxes to the Office Togolais des Recettes (OTR) is defined as informal. For the workforce approach, informality implies that an employee is not registered with the Togo Pension Fund (Caisse de Retraites du Togo, CRT) or the National Social Security Fund (Caisse Nationale de Sécurité Sociale, CNSS). Registering employees with the CNSS generally represents the last administrative step in the firm formalization process. In reality, informality is a continuum, and the level of formality of many businesses is somewhere in-between: some businesses do pay taxes at the local level, but are de facto informal, while others are registered with the CFE and pay taxes, but their employees are not registered with the CNSS. Almost half of Togo’s population lives in a non-poor informal household and is considered the “missed middle” of social protection. 96. Togo’s social protection programs are designed to cover formal sector workers and the poor. Togo has contributory and non-contributory social protection schemes, covering respectively formal sector workers and households below the poverty line. Togo provides social insurance protection—old age, disability, survivors, sickness, and maternity benefits (sick leave)—to all civil servants and to those formally employed in the private sector through two separate, contributory, pension schemes: the CRT scheme is the public sector scheme, which has a total of 19,300 beneficiaries and 34,000 contributors (civil servants and military personnel), and the CNSS is the private sector scheme with a total number of 40,240 beneficiaries, and 129,161 contributors.47 The total number of contributors to both CRT and CNSS represents only around 5 percent of the active working age population, hence more than 95 percent of people are not covered by a contributory scheme. The non- contributory schemes target poor households, with the main programs being the cash transfer program (covering all five administrative regions), under the Safety Net and Basic Services (Filets Sociaux et Services de Base, FSB) Project, and the school canteen program. So far, these programs remain limited in actual coverage, reaching 12.7 percent and 2.4 percent of the poor and vulnerable, respectively. 47 ILO, 2018. Analyse du système de protection sociale à travers le processus SPPOT: Vers un socle national de protection sociale pour le Togo. 62 97. Those households operating in the informal economy that are not poor are the “missed middle” of social protection policy and might be vulnerable to poverty in case of a shock that impacts their economic wellbeing. Until recently, Togo’s social protection strategy has only marginally considered non-poor households depended on earnings from the informal economy: they are excluded from the traditional contributory pension schemes and do not qualify for support under the non-contributory social programs targeting those below the poverty line. The COVID-19 pandemic as well as the war in Ukraine have highlighted the vulnerability of non-poor segments of the informal sector, which account for 44.4 percent of the population, and up to 58.9 percent in the Lomé Commune. FIGURE 3.1.1 The poor, the formal, and social protection’s “missed middle” a) Overall population b) By region 100 100 5.6 5.2 10.2 10.5 5.9 10.1% Non-poor formal 90 90 18.8 80 28.9 80 40.4 70 45.9 33.5 42.4 70 Percentage 44.4% Non-poor informal 60 58.9 Percentage 60 50 50 40 30 65.1 40 54.1 56.0 48.9 47.4 20 30 10 22.3 45.5% Poor 20 0 un é e ux le ra s 10 ne m m m ra Ka e ea m Lo iti va nt at ar Sa Ce 0 Pl M co All households Poor Non-poor informal Non-poor formal Source: Authors’ calculations based on EHCVM 2018 data. Note: A household is deemed “formal” if the head or their spouse is formally employed, in the sense that they receive social protection benefits through their job. 98. Since the onset of the pandemic, however, the GoT has displayed remarkable efficiency, innovation, and political will in providing social protection to the “missed middle.” In April 2020, the GoT put in place the Novissi program, a digital emergency cash transfer scheme targeted at those most affected by pandemic containment measures, i.e. those active in the informal sector. Under the first stage of the Novissi program, around 573,000 beneficiaries received cash transfers, totaling US$19.5 million, to mitigate the economic impacts of lockdowns and curfews. With additional financing by the international NGO GiveDirectly, the number of beneficiaries reached a cumulative 820,000, with transfers totaling US$22.7 million by August 2021.48 The commitment to expand social protection to those not previously covered is also reflected in the 2021 bill establishing universal health coverage (Assurance Maladie Universelle, AMU), which will add a layer of protection for all workers, independent of their employment and welfare status. The AMU has introduced two schemes: the non-contributory medical assistance scheme (Régime d’Assistance Médicale, RAM), targeting the poor and vulnerable, and the contributory mandatory basic health insurance scheme (Régime d’Assurance Maladie Obligatoire de base, RAMO), targeting formal sector workers as well as auto-entrepreneurs and informal workers and business owners with a capacity to contribute. Once operational, the AMU will provide non-poor informal workers and poor households with a mechanism to absorb health-related shocks without resorting to negative coping mechanisms, provided that the health care system works effectively, and improve their contributory capacity for other social protection schemes, absent the need to budget for health contingencies. 48 The World Bank, 2021. Togo: Public Expenditure Policy Note. 63 The most common types of shock for households are health and family events such as injuries and deaths of family members, followed by shocks to farming income and natural disasters. 99. In the 36 months preceding the 2018 household survey, about a third of households reported no shocks, with important differences regarding the incidence and type of shocks between formal, poor, and non-poor informal households. The most common types of shock households reported49 were health and family events such as injuries and deaths of family members, hitting 42 percent of households, followed by shocks to farming income and natural disasters, which jointly hit around 28 percent of households over that period. Households connected to the formal sector or in urban and richer regions reported fewer shocks and were less affected by natural disasters and farming income shocks. Poor households were most affected by shocks, with health and family events affecting almost half of households and farming income and natural disaster shocks affecting one in five, each around 5 percentage point higher for poor than for non-poor informal households. While almost a third of households reported no shocks in the 36 months preceding the 2018 household survey, this is in line with regional analyses that have shown that covariate shocks, whether economic or natural, tend to be lower in coastal countries. Yet, while Togolese households are less likely to report covariate shocks, idiosyncratic shocks tend to be highly prevalent, especially in terms of morbidity.50 FIGURE 3.1.2 Incidence of shocks for different types of households (past 36 months) a) By type of households b) By region 0.5 0.6 0.45 0.5 0.4 0.35 0.4 0.3 0.25 0.3 0.2 0.2 0.15 0.1 0.1 0.05 0 0 un é Poor Non-poor Non-poor e ux le ra s m m ne m e ra Ka ea m Lo iti informal formal va nt at ar Sa Ce Pl M co No shocks Health and family events Farming income shocks Natural disaster shocks Other shocks Price shocks and Labor market shocks financial problems Source: Authors’ calculations based on EHCVM 2018 data. 49 The methodology takes into account the following types of shocks: health and family events, farming income shocks, natural disaster shocks, price shocks and financial problems, labor market shocks, and other shocks. 50 The World Bank. Togo Poverty and Gender Assessment, 2022. 64 While most Togolese households were able to self-insure from shocks before the onset of the pandemic, regional and gender differences persist. 100. Before the COVID-19 pandemic, most Togolese households had the capacity to self-insure from shocks, either by using accumulated savings, by receiving help from relatives, or by getting a loan. The fraction of households that reported a shock and that had to resort to more harmful strategies in the 36 months preceding the 2018 survey was less than 5 percent across all subgroups. One exception is the Savanes region, where more than 10 percent of households resorted to selling livestock to cope with a shock. Female- headed households were less likely to use savings and more likely to receive family help. It is important to note that the data used for this analysis is from 2018, the latest available household level data. The pandemic as well as the food, fuel, and fertilizer crises induced by the war in Ukraine is likely to have deteriorated the welfare of the Togolese population, which will impact how households cope with shocks. FIGURE 3.1.3 Five most common coping strategies employed by households a) All households hit by a shock b) By type of household 0.7 0.7 0.6 0.6 Fraction of households Fraction of households 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0 0 All households hit by a shock Poor Non-poor Non-poor informal formal Draw from savings Help from family and friends Borrow from bank Sell food stock Sell livestock Source: Authors’ calculations based on EHCVM 2018 data. Before the pandemic, a significant share of informal households were non-poor with the capacity to save and participate in risk-pooling schemes. 101. To estimate the number of non-poor informal households having the capacity to save, the analysis reviews the shock resilience of Togolese households before the COVID-19 pandemic. Shock- resilience in the context of this analysis is defined as the capacity of a household to absorb an economic shock without resorting to negative coping strategies. The following coping mechanisms are considered negative, as they diminish the human and physical potential and the overall well-being of the household: reduce food consumption, sell productive assets, take children out of school, and being forced to migrate. On the other hand, households that are able to draw from savings, borrow from a bank, receive support from family, friends, or the government, or increase their labor supply to increase earnings are considered resilient. The Diagnostic works with the assumption that resilient households depending on informal sector income do have the capacity to save to participate in risk-pooling schemes.51 51 In terms of methodology, it is important to note that households that did not report a shock in the previous 36 months in the 2018 household survey were automatically classified as resilient. In practice, this might not always be the case, and might therefore overestimate the share of informal households that are resilient in the face of a shock. 65 FIGURE 3.1.4 Five most common coping strategies employed by households Shocks Non-resilient Resilient Reduce food Sell Take kids Be forced Draw from Borrow from Receive help Increase consumption productive out of to migrate savings bank from family, labor supply assets school friends, etc. Source: Authors, based on Guven, Melis, Himanshi Jain, and Clement Joubert. 2021. Social Protection for the Informal Economy: Operational Lessons for Developing Countries in Africa and Beyond. 102. In 2018, 45.5 percent of Togo’s population lived in a poor household, and 4.5 percent lived in informal non-poor non-resilient households. Poor households live at or near subsistence, with a limited ability for short-term consumption smoothing. Cash transfers and support to improve their economic inclusion help them smooth consumption, sustainably increase their earnings and exit poverty. In light of their inability to contribute, non-contributory social protection programs such as the FSB, the school canteen program, and the RAM are designed to support them realize their potential and insure against (health) risks. Non-resilient households, on the other hand, are above the poverty line but exhibit signs of economic distress, and because of cash constraints, tend to resort to coping strategies that are harmful in the long run. They are unlikely to benefit from social assistance, but also unlikely to have income to spare for contributions to protect themselves against shocks. Poor as well as non-poor non-resilient households lack the capacity to contribute, over an extended time and on a regular basis, to a scheme protecting them from the economic fallout of future shocks. FIGURE 3.1.5 Typology of households by poverty profile 100 10.1% Formal, non-poor 90 Percentage of population, by type 80 70 39.9% Informal, non-poor, resilient 60 50 4.5% Informal, non-poor, non-resilient 40 30 45.53% Poor 20 10 0 All households Source: Authors’ calculations based on EHCVM 2018 data. 66 103. Moreover, 39.9 percent of Togolese live in a non-poor informal household considered resilient, while 10.1 percent live in a household that has access to a private or public social insurance scheme. An estimated 39.9 percent of Togolese live in a household depended on earnings from the informal sector, are not poor, yet have a low exposure to shocks or can self-insure. These households either did not face a shock in the 36 months preceding the 2018 survey, or if they did, were able to resort to coping mechanisms that did not negatively affect their wellbeing and productivity. There are regional differences as to how many Togolese live in a household estimated to be non-poor informal, yet resilient: while this share is estimated to be 21 percent in Savanes, it goes up to 58.1 percent in Lomé commune. Overall, urban areas had more formality (17.7 vs. 4.8 percent in rural areas) and lower poverty (26.5 vs. 58.7 percent in rural areas) and were more resilient to shocks (54.0 vs. 30.1 percent in rural areas) according to the 2018 household survey. However, the COVID-19 pandemic may have changed this pattern, since social distancing, travel restrictions, and economic lockdowns may have affected urban jobs (leisure, hospitality, retail sales) more severely than subsistence farmers.52 As to gender differences, female-headed households are less connected to the formal economy (6.1 vs. 11.2 percent for male-headed households), but are otherwise similar in terms of poverty and resilience profiles. In addition, overall, 10.1 percent of households were already connected to formal social insurance through at least one of their members. FIGURE 3.1.6 Typology of households by region and location a) By region b) By urban vs. rural households 100 5.9 5.6 5.2 100 4.8 10.5 10.2 18.8 17.7 % Population by hh. type % Population by hh. type 21.0 80 34.2 80 30.1 31.2 40.1 35.6 8.0 60 2.3 6.2 60 6.4 5.7 6.8 58.1 54.0 40 65.1 40 56.0 54.1 0.9 1.8 48.9 47.4 58.7 20 22.3 20 26.5 0 0 un é ux le e s ra m m ne m ra e ea Ka Rural Urban m Lo iti va nt at ar Sa Ce Pl M co Poor Non-poor informal non-resilient Non-poor informal resilient Formal Source: Authors’ calculations based on EHCVM 2018 data. Informal workers in non-poor resilient households are more likely to be wage-employed, work in services, and have access to bank accounts, while at the same time make use of savings groups. 104. While non-poor informal households do not currently have access to social protection systems, they would benefit from contributory risk-pooling schemes to better weather shocks. Short-term and longer-term risk pooling schemes would support the “missed middle” of social protection become more resilient and productive in their economic activities. Because informal resilient households are not observably cash-strapped, they could be targeted for new, innovative contributory schemes. Such a scheme could either allow for short-term savings to be tapped into to meet short-term needs such as unemployment, education, and housing, and/or could help households build long-term savings for better old age protection. An in-depth understanding of their employment status and sector of activity, as well as their level of financial inclusion 52 https://blogs.worldbank.org/opendata/labor-market-impacts-covid-19-four-african-countries 67 FIGURE 3.1.7 Employment characteristics by type of household a) Type of employment b) Sector of employment 100 100 3.8 2.4 5.4 90 90 32.4 % Population by hh. type 2.9 % Population by hh. type 59.8 80 51.8 4.5 39.9 80 2.3 70 28.6 70 60 3.8 20.5 60 48.6 50 38.9 50 40 39.8 42.1 40 51.9 5.5 30 7.1 46.5 30 20 64.8 10.8 20 10 25.7 31.2 2.4 10 25.1 0 1.6 Poor Non-poor Non-poor Formal 0 informal informal Poor Non-poor Non-poor Formal non-resilient resilient informal informal non-resilient resilient Agriculture Manufacturing Wage earner Self-employed Unpaid worker Services Construction Source: Authors’ calculations based on EHCVM 2018 data. and literacy, is key to design schemes that effectively respond to the needs of informal households for better protection against shocks and to improve their overall productivity and earnings in the longer term.53 105. The four types of households have distinct employment profiles in terms of the type and sector of employment. Informal workers in resilient households are more likely to be wage earners and less likely to be self-employed relative to those in non-resilient and poor households. They are also much less likely to work in agriculture and more likely to work in construction and services. The fact that informal wage earners, service workers, and construction workers tend to be more resilient than other groups implies that they could more easily participate in schemes that require individuals to contribute. In contrast, employment patterns are similar between the poor and the non-resilient. 106. The levels of financial inclusion also differ by type of household. Only 35 percent of the poor hold any type of bank account versus 54 percent of the non-resilient, 62.3 percent of the resilient, and 87 percent of the formal. Formal Togolese rely overwhelmingly on classic banks while the informal resort to farmers banks (Caisse rurale d’épargne) and mobile banking. The potential for mobile banking is still largely unrealized given the high penetration of mobile phones (70 percent) even among the poor. About one-quarter of households participate in traditional saving groups (Tontines) with no major difference by household type. 53 Guven, Melis, Himanshi Jain, and Clement Joubert. 2021. Social Protection for the Informal Economy: Operational Lessons for Developing Countries in Africa and Beyond. 68 FIGURE 3.1.8 Financial inclusion by type of household a) Bank account ownership b) Use of type of bank accounts 100 70 87.2 60 80 50 62.3 Percentage Percentage 60 53.6 40 40 35.4 30 20 20 10 0 0 Poor Informal, Informal, Formal, non-poor, non-poor, non-poor Classic Postal Farmers Mobile Prepaid non-resilient resilient Bank Bank Bank Bank Card Poor Informal, non-poor, non-resilient Informal, non-poor, resilient Formal, non-poor c) Household participation in tontines d) Mobile phone and mobile banking usage 30 100 95.8 26.5 86.7 25.4 25 80 77.1 21.9 22.8 69.4 20 60 Percentage Percentage 39.6 15 40 29.1 29.4 10 20 17.3 5 0 Poor Informal, Informal, Formal, 0 non-poor, non-poor, non-poor Poor Informal, Informal, Formal, non-resilient resilient non-poor, non-poor, non-poor non-resilient resilient Having a mobile... ...with mobile banking Source: Authors’ calculations based on EHCVM 2018 data. 3.2 THE ECONOMY IS DOMINATED BY INFORMAL FIRMS, RANGING FROM SUBSISTENCE BUSINESSES TO WELL-ESTABLISHED HIGHLY PRODUCTIVE BUSINESSES Informal enterprises are overrepresented in the commerce sector and are mainly run by self-employed women with low education and low monthly profits. 107. The majority of informal businesses in Togo are led by women. Data from the 2018 ERI-ESI in Togo shows that 72 percent of informal business owners are women, with little difference between Lomé (72 percent) and other urban areas (70 percent). The data also shows that 40 percent of informal businesses operate in the commerce sector, followed by industry (34 percent) and services (26 percent). In rural areas, industry is the first sector of operation of informal businesses (38 percent), while in urban settings informal businesses operate more in the commerce sector (48 percent in Lomé and 43 percent in other urban areas) than in industry (26 percent in Lomé and 33 percent in other urban areas). 69 FIGURE 3.2.1 Distribution of informal businesses by sector and location 50 48 45 43 40 40 38 35 34 35 33 Percentage 30 27 27 26 26 24 25 20 15 10 5 0 Rural Lomé Other urban Total Industry Commerce Service Source: Authors’ calculations based on ERI-ESI 2018 data. 108. Own account workers with low education and low monthly profits dominate the informal sector. About nine in ten informal businesses do not have any workers beyond the owner (88 percent). Overall, the average number of workers, including the owner, is 1.24 in rural areas, 1.21 in Lomé, 1.29 in other urban areas, and 1.29 overall. On average, informal business owners have 29 years of work experience54 (similar across location) with 4.40 years of formal education (5.52 years in Lomé, 5.05 years in other urban areas, and 3.57 years in rural areas). The average lifetime of firms is ten years, indicating that the business owners have worked in other firms. Monthly profits (revenues less direct costs)55 are approximatively 58,938 FCFA per worker on average. Important gaps exist in labor productivity between gender, sectors, locations, and firm size. 109. Labor productivity (output per worker)56 is lowest in female-owned informal firms, in the industry sector, and in rural areas. Comparing the distributions of (net) labor productivity in female and male-owned firms shows that average labor productivity (per worker) is lower in female-owned firms. The average net labor productivity per person in male-owned firms is 1.5 times higher than in female-owned firms. However, this is largely driven by the fact that workers in female-owned firms work fewer hours per month: 169 hours com- pared to 282 hours per month in male-owned firms. Hence, female-owned firms actually have slightly higher output by hour worked than male owned firms. This suggests underemployment in female-owned firms, but also that women might view their business as a means to complement household income while taking care of household chores. Moreover, sectoral distribution of productivity shows that average net labor productivity is highest in firms operating in the commerce sector, with service a close second and industry third; average net 54 Year of work experience is proxied by age minus six minus number of years of formal education. 55 Direct costs include salary and benefits, raw materials, equipment and supplies, utilities, rental fees, transportation and maintenance, repairs, taxes and duties, and interest paid. 56 All workers independent of their skills and education level and whether they are paid or not are included in the calculation. 70 FIGURE 3.2.2 Labor productivity by gender, sector, and location a) By gender (per worker) b) By gender (per hour worked) .3 .25 .2 .2 Density Density .15 .1 .1 .05 0 0 0 5 10 15 0 5 10 15 Log (Net labor productivity per person in 2018) Log (Net labor productivity per hour worked in 2018) Male owner Female owner Male owner Female owner c) By location d) By sector .3 .3 .2 .2 Density .1 Density .1 0 0 0 5 10 15 0 5 10 15 Log (Net labor productivity per person in 2018) Log (Net labor productivity per person in 2018) Rural informal firms Industry Lomé informal firms Commerce Other urban informal firms Service Source: Authors’ calculations based on ERI-ESI 2018 data. Note: Net labor productivity is measured as the ratio of net output per person primarily employed per month. FIGURE 3.2.3 Labor productivity by firm size a) By firm size per worker b) By firm size per hour worked .3 .3 .2 .2 Density Density .1 .1 0 0 0 5 10 15 0 5 10 15 Log (Net labor productivity per person in 2018) Log (Net labor productivity per person in 2018) Self-employed Micro-enterprise Source: Authors’ calculations based on ERI-ESI 2018 data. 71 labor productivity per person in commerce and service is 2.7 and 1.6 times higher than in industry, respectively. Furthermore, regional distribution of labor productivity shows some variation: labor productivity of informal businesses in Lomé is highest with firms in other urban areas second; labor productivity per person in informal businesses in Lomé and other urban areas is 1.6 and 1.5 times higher than in rural areas, respectively. The high labor productivity of informal firms in urban areas compared to rural areas may be from agglomeration effects,57 suggesting that they have access to larger markets and production factors. 110. Moreover, there is an inverse relationship between firm size and labor productivity. The data also shows that self-employed workers (auto-entrepreneurs) are more productive than micro-enterprises (firms with at least two workers and up to 19 workers). Their net labor productivity per hour worked is on average 1.4 times higher than that of micro-enterprises. 111. Substantial heterogeneity in labor productivity within sectors suggests scope for intra-sectoral labor productivity gains. Notwithstanding differences between categories, the charts above illustrate even more heterogeneity within categories. Consistent with other countries, the data shows that net labor productivity per person in informal firms operating at the 75th percentile is 13 times larger than the productivity in firm operating at the 25th percentile in the commerce sector, suggesting important opportunities for productivity gains within commerce even under existing business models. Moving firms operating in the industry and service sectors from the 25th to the 75th percentile in the distribution would increase their labor productivity 11 times and 9 times, respectively. Low innovation in informal enterprises results in strong competition and lack of customers; business closures are linked to market constraints and a lack of liquidity. 112. The main challenges informal firms report are market constraints and difficulty accessing equipment, inputs, and credit.58 More than 60 percent of owners complained about lack of customers while 52 percent report access to credit as their main challenge. Informal firm owners in urban areas complained more about the lack of customers (67 percent in Lomé and 64 percent in other urban area) than those in rural areas (56 percent). The results suggest that many informal firms offer the same products and/or services, without introducing much innovation, which leads to a lack of customers. In rural areas and in the industry sector, owners complained disproportionally about technical difficulties in manufacturing, the cost of credit, and difficulties in the supply of raw materials. The magnitude and the ranking of different obstacles is similar across different groups of firms. 113. The three main reasons that firms close are market constraints, bankruptcy, and lack of raw materials.59 Firms report that they closed because of lack of customers (37.3 percent), liquidity (9.9 percent) and raw materials (7.4 percent), and because of too much competition (3.3 percent). Market-related reasons (lack of customers) are more pronounced in Lomé (48.1 percent vs. 36.0 percent in other urban areas and 32.3 percent in rural areas), while firms in other urban areas and rural areas cite lack of liquidity and raw materials more often as reasons for their exit than those in Lomé. Moreover, with 37.9 percent, those that are self-employed report lack of customer more often as a reason for firm exit than micro-enterprises (32.5 percent). 57 Duranton and Puga, 2004. 58 Potential barriers firms identified include the following: problems related to infrastructure (water/phone/internet); insecurity; obsolete equipment; lack of space/suitable premises; technical management difficulties; staff instability; lack of management skills; power outages; too many regulations, taxes, and duties; difficulties in the supply of raw materials; difficulties in recruiting qualified personnel; difficulties in accessing credit; difficulties in accessing equipment; technical difficulties in manufacturing; lack of customers; and finally, too much competition. Each firm could identify multiple barriers, resulting in total percentages higher than 100 percent. 59 The potential reasons for firm exit include the following: lack of customers; lack of liquidity; lack of raw materials; too much competition; problems with machines/equipment; problems with premises/space; political/social instability; organization and management problems; too many regulations/taxes/fees; technical manufacturing problems; lack of qualified personnel; and other reasons. 72 FIGURE 3.2.4 Top five challenges facing informal firms a) All informal firms, % b) By location, % Difficulties in the Difficulties in the 30.7 27.7 16.2 supply of raw materials supply of raw materials 32.2 27.0 Credit is too costly 28.0 Credit is too costly 18.0 33.5 Technical difficulties in Technical difficulties in 31.1 34.1 33.2 manufacturing manufacturing 35.8 Difficulties in Difficulties in 57.4 52.4 56.3 accessing credit accessing credit 48.4 64.4 Lack of customers 60.5 Lack of customers 67.2 55.6 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 80 Other urban Lomé Rural c) By sector, % d) By firm size, % Difficulties in the 23.2 Difficulties in the 23.8 supply of raw materials 29.3 supply of raw materials 29.2 28.2 27.8 25.7 Credit is too costly 27.8 Credit is too costly 28.5 28.4 Technical difficulties in 36.0 Technical difficulties in 35.2 31.2 manufacturing 36.0 manufacturing 33.9 Difficulties in 54.6 Difficulties in 51.4 52.3 accessing credit 50.8 accessing credit 52.5 61.9 57.5 Lack of customers 60.2 Lack of customers 61.0 59.9 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 Service Commerce Industry Micro enterprise Self-employed Source: Authors’ calculations based on ERI-ESI 2018 data. FIGURE 3.2.5 Top five reasons for informal firms exit a) By location, % b) By firm size, % 2.5 2.6 Too much 3.8 Too much competition 3.4 competition 3.4 4.1 6.3 Other reasons 3.1 Other reasons 4.5 3.7 6.8 5.8 Lack of raw 6.1 Lack of raw materials 8.3 materials 7.6 11.0 8.2 Lack of 6.2 Lack of liquidity 11.4 liquidity 10.2 36.0 32.5 Lack of 48.1 Lack of customers 32.3 customers 37.9 0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 Other Urban Lomé Rural Micro enterprise Self-employed Source: Authors’ calculations based on ERI-ESI 2018 data. 73 There is a great deal of heterogeneity among informal businesses, and each segment requires tailored policy support to achieve better employment outcomes. 114. The informal business landscape in Togo is heterogenous. A cluster analysis of these firms has resulted in a profile of informal firms in Togo that is a more complex picture than averages (see Annex B for a description of the technical approach). Five main clusters for firms emerged from the data. Firms in Cluster 1 (10 percent of informal businesses) are subsistence businesses with a low potential for growth. Businesses in Clusters 2 and 3 (51 percent) are somewhat productive and do have some capacity to grow, although that capacity is limited. Cluster 4 businesses (21 percent) are start-ups with highly educated owners and access to public services; while they currently operate at low levels of productivity, they do have a high growth potential. Cluster 5 businesses (18 percent) are more established firms with a profile similar to that of formal firms. They are connected to public services, and most of them have a fixed location with access to established suppliers and clients.60 FIGURE 3.2.6 Typology of informal firms in Togo Cluster Characteristics Older entrepreneurs, mostly women, with low education levels, the smallest Subsistence businesses with low number of workers, and low access to public services, who created their business growth potential (9.58%) either to earn more than in a waged job or out of family tradition (Cluster 1). Female auto-entrepreneurs in urban areas with the greatest access to public Somewhat productive businesses services, but with limited networks, and who created their business to earn more with some growth potential (51.05%) than in a waged job (Clusters 2 and 3). Young entrepreneurs with a high education level, operating in urban areas. While Start-ups operating at low levels of these firms have the lowest productivity, they have some capacity to import and do productivity, but with high growth business with bigger firms, have high levels of access to public services, and were potential (21.31%) created for the entrepreneur to become independent (Cluster 4). Well-established older businesses, with the largest number of workers in the Well-established highly commerce sector, with greater access to public services and established suppliers productive businesses (18.05%) and clients (Cluster 5). Source: Authors’ calculations based on ERI-ESI 2018 data. 115. Cluster 1 firms are subsistence businesses with low potential for growth, representing about 9 percent of informal businesses. Entrepreneurs in this group are the oldest (64 years old), with the lowest level of education (only 22.5 percent of them have formal education), who run slightly older businesses, with an average firm age of 20 years, the second highest among all groups. Most Cluster 1 firms are active in commerce (44 percent) and are located in rural areas (67 percent). They have the smallest average workforce with 1.19 workers, with 87.8 percent in self-employment, and the share of female ownership is highest among all clusters (78 percent versus 65.1 percent in Cluster 4, 75.6 percent in Cluster 2, 76.4 percent in Cluster 3, and 64.2 percent in Cluster 5). Cluster 1 has the second highest productivity among informal firms (95,108 FCFA per worker per month), with gross labor productivity slightly higher than for Cluster 2 and 3 firms, and 1.25 times higher than the start-up firms in Cluster 4. About 12 percent of owners created their business because it is a family tradition, the highest share reporting this across the five clusters. Around 54 percent of businesses in this cluster complain about too much competition. Also, these businesses have the lowest access to public services such as electricity, telephones, and Internet, with 15 percent, 24 percent, and 0 percent, respectively. 60 Annex B provides information on a variety of firm and entrepreneur characteristics in each cluster. The table includes both endogenous and exogenous variables in the clustering. Annex B also provides the list of endogenous variables in the cluster analysis. 74 116. Firms in Clusters 2 and 3 make up about 51 percent of informal businesses and are somewhat productive, with some potential to grow. These clusters are composed of firms that have some access to production factors, have limited networks, and are located mostly in rural areas. The data shows that entrepreneurs in Clusters 2 and 3 have relatively high levels of education, with 66 percent of owners having at least a primary education level. The average age of the firms is lower (6.0 years old for Cluster 2 and 7.0 years old for Cluster 3) than those in Clusters 5 and 1, yet above Cluster 4 firms. A large share of the firms is active in commerce (41 percent for Cluster 2 and 46 percent for Cluster 3) and located in rural areas (48 percent for Cluster 3 and 56 percent for Cluster 2). Also, Cluster 3 firms have a high level of access to public services such as electricity, clean water, and telephones. Around half (50.1 percent) of the firms in Cluster 3 have a permanent work site, with the ability to do business with big and formal firms and some capacity to import. Most firm owners in Clusters 2 and 3 report that they started the business because they could earn more being self-employed than they would in a salaried job (50 percent for Cluster 2 and 54.8 percent for Cluster 3). They complain disproportionately about lack of customers and limited access to credit. 117. Cluster 4 firms are low-productivity start-ups with a high growth potential, and represent about 21 percent of informal businesses. About 89 percent of all firms are auto-entrepreneurs. Compared to the other clusters, firms in Cluster 4 have the lowest level of labor productivity (76,090 FCFA per worker per month), yet have relatively high levels of access to public services, such as electricity, telephones, and Internet. Less than half (48 percent) of the firms in this cluster have a permanent work site. They have some capacity to do business with big and formal firms and to import. At 4.10 years, the average age of the firms is lowest across the clusters. Firm owners in this cluster are young (27 years old on average), with the highest level of education (8 years of schooling, with corresponds to grade 5 in the Togolese schooling system), mostly female (65 percent), and started their business to have higher pay than in a salaried job (39 percent) or to be independent (40 percent), the highest share among all clusters. Firms in this cluster operate less in the commerce sector compared to firms in other clusters (34 percent versus 40 percent in Cluster 2, 46 percent in Cluster 3, 36 percent in Cluster 5, and 44 percent in Cluster 1). They complain disproportionately about a lack of customers, suggesting they are less innovative than firms in other clusters and their networks may be smaller. 118. Businesses in Cluster 5 comprise a small number (18.05 percent) of more established, high- productivity businesses. They tend to be more innovative, with solid access to production factors and established suppliers and clients. Entrepreneurs in this group have relatively low levels of education (only 66.4 percent of them have formal education) and run older firms, averaging 22 years, the highest among the five clusters. Firms in this cluster are overrepresented in the industry sector (40.8 percent) and in urban areas (49.5 percent). They have the largest average workforce with 1.30 workers (the entrepreneur/ owner and 0.3 employees on average); 83.4 percent are auto-entrepreneurs, and the share of female ownership is lowest among all clusters (64.2 percent). Cluster 5 has the highest productivity among informal firms (134,517 FCFA per worker per month), with gross labor productivity 1.78 times higher than for Cluster 4 firms. Owners in this group mostly want to be firm owners because pay is higher than in a salaried job, while 10 percent created their business because it is a family tradition, the second highest share across the five clusters. They complain less than owners in other clusters about lack of customers, limited access to credit, or cost of credit. This suggests that firms in this cluster are introducing some innovation, which leads to less complains about lack of customers. However, they are most likely to not have a fixed location (55.3 percent). Informal business productivity in Togo is determined by the gender of the owner, the sector of activity, the age of the enterprise, market integration, and access to factors of production. 119. There is a large labor productivity gap based on gender of the owner among informal firms in Togo. There is a 27 percent labor productivity gap for female-owned firms. The unconditional gender difference in productivity was 27 percent favoring male-owned firms for all firms in all areas, 33 percent in rural areas, 28 percent in Lomé, 40 percent for micro-firms, and 29 percent for auto-entrepreneurs. Once controlling for owner, firm, and environmental characteristics, the gender gap increases in all specifications (Annex B, Table 3). 75 The conditional gender difference in productivity favors all male-owned firms by 36 percent, 43 percent in rural areas, 29 percent in Lomé, 27 percent in other urban areas, 49 percent for micro-firms, and 34 percent for auto-entrepreneurs. TABLE 3.2.1 Determinants of productivity (Y=Log [Production per worker per month (FCFA/worker)]) Other Auto- Micro- Overall Rural Lomé urban entrep. entreprise Gender of owner (1=female) • • • • • • Commerce + + + + + • Service + + + + Log (Firm age) + + + + + Access to credit (dummy) + + + + + + Log (Firm size (number of employees)) • • • • Degree of personal closeness with clients + + + Capacity of the firm to import or do business + + + + with other large firms Access to clean water/Internet (dummy) + + + Source: Authors’ calculations based on ERI-ESI 2018 data. Note: Dot (“•”) indicates a negative and significant correlation; plus sign (“+”) indicates a positive and significant correlation. 120. The main factors associated with informal firm productivity in Togo are sector of activity, firm age, firm size, access to finance, degree of personal closeness with clientele, capacity of the firm to import or do business with other large firms, and access to factors of production. Overall, operating in the commerce and service sectors compared to industry, running a firm with long life span, having access to credit, enjoying a high degree of personal closeness with clientele, doing business with other large firms, or having access to international markets all positively correlate with productivity. However, owner education level and access to the Internet do not significantly correlate with productivity. For firms in rural areas, access to the Internet at work premises positively correlates with productivity. Firm size negatively correlates with productivity in all specifications. This is consistent with the inverse relationship between productivity and firm size in the informal sector—that is, the smaller the firm the more productive.61 61 Amin et Islam, 2015. Are Large Informal Firms More Productive than the Small Informal Firms? Evidence from Firm-Level Surveys in Africa, World Development, Volume 74, 2015, Pages 374–385, ISSN 0305750X, (https://www.sciencedirect.com/science/article/pii/ S0305750X15001175). 76 The most important factors explaining the gender gap are lower ability of women owners to import or do business with other large companies and the young age of women-owned businesses. 121. Factors contributing to closing the gender gap are firm size, sector of operation, and access to credit. Having a smaller firm is the main factor working to the advantage of female-owned firms, due to the inverse relationship between firm size and productivity. Furthermore, higher involvement of women-owned businesses in the commerce sector and greater access to credit works to their advantage in terms of labor productivity. TABLE 3.2.2 Determinants of gender gaps Level Returns Years of education • Commerce + • Service + Firm age • Access to credit (dummy) + + Log (Firm size (number of employees)) + Degree of personal closeness with clients • Capacity of the firm to import or doing business with other large firms • Access to clean water/Internet (dummy) • Source: Authors’ calculations based on ERI-ESI 2018 data. Note: Dot (“•”) indicates a positive and significant effect (something that increases the gender gap); plus sign (“+”) indicates a negative and significant effect (something that reduces the gender gap). The table refers to the Binder-Oaxaca decomposition. The “explanatory factors” column shows the variables that explain the gap, and the “unexplained factors” column shows the return on investment. For example, for the indicator “Years of education,” all other things being equal, for equal levels of education, women have lower return on education. 122. Factors widening the gender gap are female owners’ lower capacity to import or do business with large firms and younger age of female-owned firms. The most important factors in widening the gender gap are female owners’ lower capacity to import or do business with large firms in Lomé (0.3 of overall gap), degree of its closeness with the clientele in rural areas (0.3 of overall gap), and younger age of female- owned firms (0.17 of overall gap). 3.3 AN AGRICULTURAL SECTOR WITH A HIGH POTENTIAL TO CREATE GOOD QUALITY JOBS ON AND OFF THE FARM 123. Improving agricultural labor productivity, the quality of agriculture employment, and the income of agriculture workers are all critical components of the jobs agenda in Togo. Given the large share of employment in agriculture, the agriculture sector has a central role to play in offering the Togolese population better employment opportunities. No matter how fast urban wage jobs grow, they will not be created quickly 77 enough to absorb all the new entrants to Togo’s labor market in the short and medium term. Moreover, agriculture employment plays to Togo’s comparative advantage. It has the potential to significantly increase the earnings of the poor directly, which could further expand the demand for nonagricultural goods and services, and, thus, contribute to further increasing off-farm employment. 124. This deep dive discusses agricultural employment in Togo and possible policy entry points to maximize agriculture’s contribution. It starts with a brief profile of agriculture workers, agriculture productivity, and poverty and crop patterns, then focuses on some characteristics of agriculture that are important from a jobs perspective. It also provides examples of agricultural subsectors that have not received enough attention but could be promising, given their links to global value chains. This section does not focus on traditional export crops. While expanding the production and productivity of export crops remains vital to both the growth and jobs agendas, this has been extensively discussed recently through many reports, including World Bank Reports.62 The agriculture sector is the main source of jobs and livelihood for most Togolese, especially for the poor; it creates the greatest number of new jobs every year and is a key sector for creating better-paying jobs for all. 125. Togo’s agriculture and food systems are central to the country’s jobs agenda. The agriculture sector accounts for about 38 percent of total employment in 2019. It is the country’s second largest employer, after the service sector. Adding agricultural inputs provision and all post-harvest activities throughout the value chain, agriculture and food account for at least two-thirds of jobs. While strong economic growth in the last few decades has provided new employment for low-skilled workers in the industry and service sectors, providing low-skilled workers with more productive and better paying jobs, agriculture remains the main source of jobs and livelihood for more than a third of the Togolese population. The sheer number of workers in the agriculture sector is too large for the rest of the economy to productively absorb most of its workers in the decades to come, especially when 3.4 million young people will reach working age in the next 15 years, equivalent to 40 percent of the current total population. This fact points to the need to focus on improving employment outcomes within the agriculture sector rather than solely relying on other sectors to create better jobs. In fact, the sector created almost a quarter million jobs in 2019, which represented 60 percent of all jobs FIGURE 3.3.1 Share of employment by sector of activity, 1991–2019 60 50 Percentage 40 30 20 10 0 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 19 19 19 19 19 19 19 19 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 Agriculture Industry Services Source: World Development Indicators, 2022. 62 World Bank, 2022. Togo Country Economic Memorandum. 78 created that year,63 and is expected to continue creating most jobs in the near future, underlying the need to focus on improving job quality in agriculture. Agriculture is a key sector for creating better-paying and better-quality jobs for all, but is not delivering on its potential: labor productivity is low and the sector is dominated by smallholders. 126. Labor productivity in the sector is very low, at 60 percent of the country’s labor productivity. While labor productivity has increased in the industry and services sectors, it has remained relatively constant in the agricultural sector. Average labor productivity in agriculture grew at about 0.5 percent per annum between 1991 and 2019 and has actually shown decreasing rates of productivity growth in recent years, with an average annual productivity growth rate of 0.55 percent from 2002–2006 to 2014–2019 compared to 0.67 percent from 1991–1996 to 2002–2006.64 In fact, expansion in agricultural output over the last three decades (3.1 percent annually) was almost entirely due to an increase in the amount of land being cropped (2.8 percent annually), with little growth coming from use of other inputs (0.3 percent), and zero percent growth due to technical or allocative efficiency or technological progress. Low labor productivity not only impacts earnings of agricultural workers but also overall food security, as well as the ability of the country to increase its participation in regional and global value chains. FIGURE 3.3.2 Productivity and the distribution of labor in Togo, 2019 180 Relative gross value added 160 140 120 100 80 60 40 20 0 0 10 20 30 40 50 60 70 80 90 100 Employment share Agriculture Services Industry Source: World Development Indicators, 2022. Note: Labor productivity is measured as the annual value added (the value of output less the value of intermediate consumption) per employee. 127. Agriculture in Togo is dominated by smallholders, self-employed workers, and family workers and is characterized by underemployment. Togo has a very small number of formal agriculture-related enterprises. Almost 98 percent of agriculture workers are self-employed or family workers. Not all smallholder farmers are equally well positioned to adopt new technologies, commercialize their staples, and/or shift to higher-value crops and products. However, poorer farmers can increase their earnings as wage laborers on more commercially oriented farms or in the expanding off-farm economy, including job expansion in agricultural value chains, which need to be further developed to enable commercially oriented farmers to increase productivity.65 Enhancing agricultural productivity will also require addressing agricultural underemployment. The underutilization 63 ANPE, 2020. 64 World Bank, 2022. Togo Country Economic Memorandum. 65 World Bank, 2020. Agriculture, Jobs, and Value Chains in Africa. 79 of farm labor, due to agriculture’s seasonality and low output per hectare (low yields), keeps smallholder earnings low while also contributing to the heterogeneity in agricultural labor productivity. People primarily employed in agriculture work substantially fewer hours on an annual basis than those primarily engaged in nonagricultural activities. More than half (57 percent) of the agriculture workers are underemployed compared to 40 percent and 33 percent in the industry and services sectors respectively. This partially explains a significant part of the agricultural per person productivity gap. It also indicates that agriculture is not intrinsically less productive, and thus equally worthy of investment as other, perhaps more productive, sectors.66 128. Agriculture income is significantly smaller than the income of workers in the industry and service sectors. Many Togolese workers continue to earn much of their income in agriculture, as self-employed workers on their farm. In 2018, on average, an agriculture worker earned 29,100 FCFA per month compared to 36,500 FCFA for construction workers, 40,200 FCFA for manufacture workers, and 62,250 FCFA for transport workers. In fact, agriculture average income reaches only 59 percent of the average income of a worker in Togo, consistent with the labor productivity gap. Moreover, 20 percent of agriculture workers are involved in a second economic activity to complement their income. 129. Poor people in Togo are more likely to live in rural areas, work primarily in agriculture, and remain relatively isolated from markets and services. Just over three-quarters (77 percent) of poor people live in rural areas, which have a higher incidence of poverty (69 percent) compared to Lomé (35 percent) and other urban areas (40 percent). Poverty generally increases as one moves away from the wealthier coastal Maritime region and north to the regions of Plateaux (49 percent of poverty) and Kara (56 percent of poverty), then (south again) to Centrale (47 percent of poverty), and finally to the driest region, Savanes (65 percent of poverty), which borders Burkina Faso. Agriculture accounts for more than half of the jobs in the poorest regions of the country (Central, Plateaux, Kara, and Savanes regions), employing two thirds of the Savanes region workers. 130. Creating more and better-paying agriculture jobs is key for the poverty reduction agenda. Agriculture is the main source of jobs and livelihood for the poorest. On average, agriculture workers are poorer than industry and services workers. Most poor and vulnerable workers—those in the first and second quintiles of the income distribution, youth, and the less educated—are employed in agriculture, perpetuating the vicious cycle of low productivity, poverty, and inequality. In fact, agriculture work and low skills are strongly connected, decreasing as workers attain higher levels of education. Almost two thirds of the working population with no education are employed in agriculture, compared with only 3.5 percent of those who attained a post-secondary degree.67 131. Agriculture employment replicates and amplifies the gender and age disparities of the Togolese labor market, with specific gender and age dynamics at work within the sector. Women and youth appear to be particularly vulnerable in agriculture sector employment, although male workers currently outnumber females. Additionally, male rural workers have been leaving agriculture much more rapidly than female workers in Togo. In 2006, 59 percent of all male workers in Togo worked in agriculture compared to 48 percent of women workers, while in 2018, 43 percent of all male workers in Togo worked in agriculture compared to 40 percent of women workers. Likewise, youth are more likely than their adult counterparts to participate in agriculture employment, although youth outflows from the rural labor force have been faster than that of prime age workers.68 132. Moreover, women and youth face heightened difficulties to integrate into and reap the benefits from agriculture value chains. Women and youth often lack access to land to grow at significant scale, to capital to invest in tools and machinery, and to technical skills to increase efficiency. Against this backdrop, they are often stuck in activities with low value addition at the production stage, unable to engage in product transformation or commercialization and generate significant revenues from their work. From a jobs perspective, a key question is 66 World Bank, 2020. Agriculture, Jobs, and Value Chains in Africa. 67 EHCVM (2018/2019). 68 EHCVM (2018/2019). 80 FIGURE 3.3.3 Characteristics of agriculture workers in Togo a) Agriculture workers by share b) Characteristics of agriculture of income quintiles, 2018 workers, 2018 80 70 % of total workers 60 70 50 % of total workers 60 40 50 30 20 40 10 30 0 Informal Workers Formal Worker Male Female Young (15–24) Prime Age (25–64) Old Age (65+) No education Primary Secondary Post secondary 20 10 0 t nd ird th st es ie ur co Th or lth Fo Se Po ea W Formality Gender Age Education c) Share of agriculture workers d) Average monthly income per by region, 2018 worker, 2018 80 140,000 Average monthly income 70 120,000 per worker (XOF) 100,000 % of total workers 60 50 80,000 Total Share 40 60,000 National average 30 40000 20 20,000 10 0 re n g ce rt s es g s . in tie ce 0 tio rin in po tu ic er m vi in ili rv uc tu ul m ns ad er M e le ux ra s un é ut se m ic ne ac tr a m m m ls ra Ka ic Tr gr ea e Co ns ic er uf iti m Lo va ia bl nt bl A at Co th ar an nc Sa Pu Ce Pu Pl O M na M co Fi Source: EHCVM, 2018; EHCVM, 2011; EHCVM, 2006 and World Development Indicators, 2022. thus how to ensure that smallholders and micro-entrepreneurs, especially those lead by women and youth, have opportunities to “climb the chain” and engage in productive activities with higher value added. The objectives of the ‘Agriculture for Jobs Agenda’ are to enhance agricultural labor productivity in existing crops, diversify the agricultural output mix, and add value to ultimately provide rural workers, especially youth, with better quality jobs as the slow structural transformation of the country continues. 133. The major crops grown in Togo are primarily consumed domestically. These include corn, sorghum, rice, yams, manioc, peanuts, beans, and soy. Staple food crops, mainly cassava, yams, maize, millet, and sorghum, account for two-thirds of crop production by weight and employ 87 percent of all agriculture workers. Production of these relatively lower value-to-weight items has grown rapidly, as have poultry products and rice, also primarily for the domestic market. At the same time, milk production has declined, and beef has stagnated, even though 81 domestic and regional demand for them have surged. Rapid milk demand growth is largely serviced by imports of cheap milk powder, and cattle are supplied by northern neighbors.69 134. Togo’s major exports crops include cotton, cocoa, coffee, palm oil, and groundnuts, which have all grown at a slower pace than the population. Cash crops make up about 20 percent of the country’s export earnings, while they employ less than 5 percent of the agriculture workers. These are mainly exported in only moderately processed form, such as cotton lint or cocoa beans. Domestic palm oil and groundnut consumption have grown more rapidly than production, limiting growth of net exports.70 135. Another important dynamic for the agriculture jobs agenda is that regional poverty patterns are associated with cropping systems. Many agriculture workers in the poorest regions of Togo grow starchy and bulky staple food crops (97 percent in the Savanes region, 89 percent in the Plateau, and 80 percent in Kara), while only a small share of agriculture workers grow cotton, cocoa, coffee, fruits, or vegetables (less than 5 percent in each region). Starchy and bulky staple food crops are among the lowest paying crops. For example, on average, a cereal crop farmer in the Plateau makes 54,000 FCFA a month, compared to 212,000 FCFA made by a cotton farmer or 67,000 FCFA made by a cocoa and coffee farmer. 136. The available theoretical, empirical and historical evidence suggests that agricultural productivity growth can have an important impact to increase the earnings of the poor and reduce poverty.71 Increasing agricultural labor productivity should be a central aspect of the jobs agenda in Togo. It could improve the quality of jobs and raise the earnings of the poor directly, which would in turn help generate demand for nonagricultural goods and services, and, thus, off-farm employment as well. The disparity in agricultural labor productivity in Togo suggests significant room for improvement in productivity levels. Income growth among smallholder farmers has proven to be an effective means to generate inclusive employment and reduce poverty. The conditions to do so are present right now in Togo. However, policies should focus on developing agricultural production chains with significant employment and export potential, as small labor productivity or wage increases benefiting a large share of the population may generate more and better jobs in the aggregate than large productivity increases. Investing in value chains has high potential to create better jobs and higher revenues in agricultural production and transformation, including for the most vulnerable. 137. Agricultural value chains offer opportunities to create more and better jobs, on and off the farm. Developing agricultural value chains implies the creation or strengthening of commercial linkages among the different actors involved in the process of bringing a commodity from a stage of production to its final consumption, where each step adds value to the product. Strong value chains offer market opportunities for local producers and build on the capacity of local firms to transform locally, instead of exporting directly off the farm. In such cases, multiple steps of the chain take place domestically and the added value being generated can then be shared among various actors within the country. A vibrant ecosystem of firms involved in agricultural transformation and accompanying services is particularly useful from a jobs perspective. Value chain development is thus an approach seeking to create value from agricultural production and transformation and facilitate the integration of labor supply and demand at each step of the chain. 138. Identifying value chains with high market potential and low barriers to entry is thus central to the jobs agenda. A 2019 analysis building on the “Jobs in Value Chain Toolkit” developed by the Jobs Group at the World Bank examined commodities with untapped market demand and high inclusion opportunities for youth and women in Togo. Building on a list of 22 agricultural value chains identified as priority in national strategic documents and consultations with key government stakeholders and the private sector, the analysis ranked 69 World Bank. Togo—Country Economic Memorandum 2022: Toward Sustainable and Included Growth (English). 70 World Bank. Togo—Country Economic Memorandum 2022: Toward Sustainable and Included Growth (English). 71 World Bank. 2020. Agriculture, Jobs, and Value Chains in Africa. 82 these chains on the basis of their weight in Togo’s trade balance and their propensity to generate revenues for local producers. The analysis identified a total of nine chains with highest employment potential especially for youth and women—namely farmed fish, fonio (a local grain), honey, mushroom, pineapple, sesame, shea, soy, and spices such as ginger and red pepper. The results were presented and validated during a national workshop organized in October 2019 and attended by representatives from the government, the private sector, agricultural associations, academic research, and civil society. Understanding jobs constraints and opportunities is a prerequisite to impactful public action in the ‘Employment in Value Chains’ agenda. 139. To this end, the World Bank has conducted a new study analyzing key barriers to more, better, and increasingly inclusive jobs in high-potential value chains in Togo.72 The study seeks to complement the Jobs Diagnostics by providing an in-depth analysis with a job lens for three of the nine value chains previously identified—namely shea, ginger, and red pepper. Specifically, shea was selected due to its strong and growing demand on the global food and cosmetic markets, the underexploited and natural availability of shea trees in the three northern regions of Togo, and the long cultural tradition of women involvement in shea nut collection and shea butter production. Spices were selected as a second case study, with a focus on ginger and red pepper. Ginger is characterized by strong economic potential both domestically and internationally. It can be produced during the lean season and harvested when necessary,73 offering a good opportunity for farmers to diversify outputs and smooth revenues throughout the year. Red pepper benefits from a dynamic domestic market and can be grown almost anywhere in the country, with multiple harvests per year. None of these crops requires large plots of land to be produced nor the acquisition of advanced machinery to be transformed. As a result, they constitute promising choices for vulnerable employment groups like women and youth. 140. In the shea value chain, key challenges to jobs relate to logistics, strenuousness of traditional transformation methods, and deforestation of shea trees. In Togo, like in other countries of the “shea belt,”74 shea trees have not yet been successfully domesticated as a species, and most of the production relies on natural regeneration in forests and savannahs. During the harvest season, nuts that have fallen off the trees are picked up by so-called shea collectors, which have traditionally been women. Collection occurs in the wild outside of plantations. The efficient collection and storage of shea nuts represents a major challenge for aggregators who seek to supply small and larger firms with nuts for transformation into shea butter—the main output of the shea value chain, which is praised for its moisturizing qualities in the cosmetic industry and its capacity to serve as an affordable cocoa butter substitute in the food industry. In the sample of shea value chain actors interviewed for the study, transportation (40 percent) and storage (33 percent) were reported as the two most expensive inputs towards the production of butter, suggesting that investments at this level could alleviate some constraints for firms. In addition, half of the shea butter producers interviewed reported using traditional transformation methods, which require high quantities of firewood and water. These techniques are relatively inefficient and force workers, usually women, to travel long distances and work in difficult conditions under high heat. The felling of shea trees to produce charcoal, a common practice in rural Togo, only worsens these issues. Trees take 15 to 20 years to bear fruit and up to 50 to reach maximum capacity. Deforestation directly impacts the production base of the shea value chain and puts its future in jeopardy. 141. Climate risks, market volatility, contaminations, and limited access to land constitute the key barriers to jobs in the ginger value chain. Ginger is produced almost exclusively by smallholders with virtually 72 World Bank. Togo : Economic inclusion of youth and women into high potential value chains. Forthcoming. 73 The root of the ginger plant (rhizome), from which spices and other ginger-based products are made, can remain in the ground during multiple months. 74 The Shea Belt is the dry savannah region of West Africa stretching from Senegal in the west to Sudan in the east, and onto the foothills of the Ethiopian highlands, covering a total of 21 countries: Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Ethiopia, Eritrea, Ghana, Guinea Bissau, Ivory Coast, Mali, Niger, Nigeria, Senegal, Sierra Leone, South Sudan, Sudan, Togo, Uganda, Democratic Republic of the Congo, Kenya, and Guinea. 83 FIGURE 3.3.4 Most expensive production inputs in the shea value chain in Togo 40% 33% 18% 8% 3% Sowing Tilling Storage Transport Other Source: World Bank. Togo: Analyse approfondie des chaines de valeur à fort potentiel d’emploi pour les jeunes et les femmes. Forthcoming. FIGURE 3.3.5 Ginger import and export values in Togo (US$, 2016–2019) 194,929 192,625 197,681 200,000 155,243 150,000 100,000 50,000 2,329 2,058 2,341 2,029 0 2016 2017 2018 2019 Exports value Imports value Source: FAOStat—Togo Crop and Livestock Products Data: Import and export value, Ginger. no large-scale plantations in operation in Togo. Firms typically purchase fresh ginger through aggregators and transform it into spices (powder) or ginger juice. The demand for ginger is high in Togo, while the local supply is unable to meet it. As a result, Togo presents a large trade deficit in this commodity, which could be explained by several factors. First, erratic climate patterns seem to constitute a major challenge for smallholders; 44 percent of those interviewed for the study reported extended periods of drought and unavailability of water as the biggest challenges to production. Second, and connected to former, the local market price of ginger is very volatile. It fluctuates from an average of 354 FCFA per kg during the low season to more than twice as much, 728 FCFA, during the high season, among the sample of value chain actors interviewed. Exposure to pests appears to be a third key issue. A 2013 study of spices commercialized in Togo showed that ginger was by far the most contaminated of all, pointing to poor practices during storage and transportation and resulting in significant post-harvest losses. Finally, in Togo ginger is typically grown on relatively small plots in the back of dwellings or along the roads, making it complicated to ramp up production in the face of high demand. These four elements combined create bottlenecks that hinder the reliable supply of fresh ginger to firms and vendors down the chain, which appears to be detrimental to business growth. As a result, most firms in the ginger value chain are of relatively small size and unable to provide significant employment opportunities. Of the sample surveyed, only 12 percent had ever hired outside of the family circle. 142. For red pepper, producers are exposed to climate risks and limited access to land, while firms face competition from the informal sector and price fluctuations. Red pepper has the advantageous ability to generate revenues relatively quickly. In optimal conditions, harvest can start as early as four months after plantation and stretch over multiple months. Two harvests per year are possible with irrigation, highlighting the importance of access to water. Both firms and smallholders are involved in production in Togo. The fruit of the plants are dried up and grinded to produce spices (powder), which are sometimes mixed up with other 84 seasonings. Red pepper is often grown alongside other crops and constitutes an additional revenue stream for many households. While this situation is positive from an income generation perspective, it also brings competition to the market. In the sample of firms interviewed for the study, competition from the informal sector was cited as the primary challenge to business growth. Similar to what was observed in the ginger value chain, most firms commercializing red pepper are of relatively small size, with only 13 percent relying on external workers. The red pepper sector shares other similarities with the ginger sector. Red pepper is often grown on small plots, with limited opportunities to expand production. In addition, the price of red pepper fluctuates significantly throughout the year—from an average of 739 FCFA during the low season to 1,543 FCFA during the high season among the sample interviewed. Price volatility is due to climatic patterns, but also depends on the zone of production (some areas are more productive at certain time of the year) and cultural celebrations (where food demand is high). Togo consumes most of the red pepper it produces, being a net importer or exporter depending on the years, although never beyond 70 tons. Cross-cutting constraints to jobs in agricultural value chains center around three key topics—finance, skills, and access to markets. 143. Besides the above-mentioned challenges specific to each value chain, a number of shared constrained were also identified in the study, impacting both the creation of jobs, the quality of jobs, and the inclusiveness of jobs. 144. First, limited access to finance hinders investments in productive assets and prevents firms from scaling up their activities. Lack of financing makes it challenging for producers to acquire equipment to transform raw material and for firms to acquire more efficient machinery and tools. This is particularly true for women and youth entrepreneurs who often lack the collaterals and revenues required to secure loans from financial institutions, which was reported as the primary reason for the lack of credit access across the three value chains analyzed. Due to a limited number of alternatives, personal savings have thus remained the primary source of financing for most smallholders and Micro-, Small and Medium Enterprises (MSMEs) interviewed. The study found that while 35 percent of shea producers in the sample had access to cooperative or community savings, the share was less than 5 percent among ginger and red pepper producers. Among MSMEs, less than 15 percent had ever received a loan from a financial institution. The situation is further complicated by limited levels of financial inclusion. Across the shea, ginger, and red pepper value chains, the share of producers who owned a bank account ranged from between 15 to 36 percent. FIGURE 3.3.6 Import and export quantities in Togo (metric tons, 2000–2019) 70 60 50 Metric tons, 2000–2019 40 30 20 10 0 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 Export quantity Import quantity Source: FAOStat—Togo Crop and Livestock Products Data: Import and export quantity, Dried Chilis and Peppers. 85 145. Second, the prevalence of low-skilled workers in agriculture constitutes a barrier to business growth and to the economic inclusion of vulnerable groups. Women and youth in particular identified the costs of education as well as the lack of information on where or what type of courses to take as two key challenges to accessing jobs or growing their business (rather than the unavailability of training opportunities per se). Trainings in good agricultural practices (including climate smart agriculture) as well as in business creation and management were praised as useful investments to increase productivity, revenues, and profits among those who had recently benefit from courses. Focus group discussions with MSMEs confirmed that this situation makes it hard for firms to find skilled workers, especially youth and women, to join their company. In addition, across the three value chains, firms constantly ranked the lack of qualifications as one of the top two reasons for not getting their supplies from youth or women. 146. Limited access to markets, especially export markets with high earnings potential, was identified as another important issue. At the production and transformation stages, value chain actors complained about poor infrastructure—including limited access to water/irrigation and storage facilities—as well as a lack of cost-effective solutions for packaging and transportation. Together these issues make it hard to meet the quality and quantity requirements of the largest buyers, which pay higher prices. Indeed, in the three value chains, finding producers able to supply in sufficient quality and quantity was ranked the primary pain point for aggregators and transformers. For products with high export potential, such as shea and ginger, one of the key challenges reported by firms was related to securing the certifications required to export and access lucrative niche markets abroad, such as for organic or fair-trade products that carry high value. This situation is detrimental to job outcomes across the chains since local supply is unable to meet the growing demand from domestic and international markets. 86 4. CONCLUSIONS AND POLICY RECOMMENDATIONS 147. The Togo Jobs Diagnostic analyzes the factors that impact the jobs outcomes for the Togolese population through a comprehensive, data driven exercise. It applies an integrated approach to review trends in Togo’s labor market, analyze underlying constraints, and identify opportunities for pathways to accelerated poverty reduction and enhanced social cohesion. It is organized around three main chapters covering the supply and demand sides of the labor equation as well as the domestic and external economic environment, and three deep dives into topics that are critical to improve jobs outcomes for all Togolese. These topics include the level of resilience of informal workers and their households with a focus on the “missed middle” or those not currently covered through social protection, the heterogeneity among informal businesses, and jobs in the agricultural sector, both on and off the farm. The main conclusions of the Diagnostic are summarized in the following paragraphs. 148. Access to employment is high in Togo, but the quality of jobs, in terms of productivity, earnings, or security, is low. On the labor supply side, the Diagnostic found that Togo has a young, active, and growing population, eager to join and participate in the labor market. The level of educational achievement of the working-age population has improved over time, yet large disparities in access to education between urban and rural individuals and between men and women reinforce existing inequalities in access to (quality) jobs. While the overall employment rate is high, self-employment is the dominant form of employment in Togo. Gender and location inequalities in access to good jobs remain pervasive. Unemployment is low and mostly concentrated among more educated urban youth. Few workers are wage employed, with salaried jobs concentrated in the service sector and held by men in urban areas. Moreover, while 60 percent of households engage in some form of non-agricultural work to generate a continuous income stream throughout the year, the majority of these household enterprises remain small and precarious. 149. Despite economic growth, job creation in the private wage sector has remained subdued and needs to significantly expand to offer jobs for the rapidly growing working age population. The Diagnostic noted that despite sustained economic expansion supported by structural reforms, infrastructure spending, and private sector investments, growth in Togo has not translated into significant gains in aggregate productivity since the late 2000s. Also, poverty in many regions remains high and efforts to reduce it have been hampered by a sequence of adverse shocks since the COVID-19 pandemic in 2020. Economic growth has led to some job creation over the past decade, mainly in industry and in the services sector, but there continues to be a deficit in private-sector waged employment. Moreover, to provide the estimated 1 million new jobs needed by 2030 to absorb the new labor market entrants, Togo will need to maintain a GDP growth rate of at least 4.6 percent per year just to maintain the status quo in terms of employment, informality, and earning conditions. Ensuring convergence towards higher living standards and better paying jobs would require significantly higher growth underpinned by faster structural transformation. 150. Important constraints, including costs of labor and capital, reduce demand for labor in the formal sector. On the formal labor demand side, the Diagnostic found that the pace of formal job creation 87 has not kept up with economic growth. Yet the entry rate of formal firms has been rising since 2017, thanks to major reforms that simplify the business registration process. The low job creation to GDP growth reflects in part the lack of incentives for private sector firms to hire additional employees, keeping the creation of formal jobs below its potential. This is partly attributable to the level of total labor cost (salaries, taxes, social security contributions) that are too high considering the current productivity and earnings levels in the economy. Also, access to financing for firms remains limited and costly and availability varies significantly by economic sector, with less than 1 percent of bank credits going to the agricultural sector despite its importance in the economy. Other factors that weigh on firm entry and the creation of more good quality jobs are tax rates, corruption and competition by informal firms. 151. Policies tailored to differences in household vulnerability and contributory capacity are required to extend social protection to households operating in the informal sector. The deep dive on informal worker resilience concluded that the majority of informal sector households are subject to different shocks but are not covered by any social protection policy, whether in theory or in practice. Almost half of Togo’s population lives in non-poor households deriving their incomes from the informal sector; these households are highly vulnerable to shocks. As many of these households are neither covered by social safety nets nor enrolled in the social protection schemes offered by formal employment, they are considered as the “missed middle” of social protection. They generally do not qualify for social safety net programs and hence lack access to support to assist in times of distress. Shocks most often come in the form of health and family events, such as illness or death, and natural disasters. Before the pandemic, most non-poor informal households were able to self-insure from shocks, although there are substantial regional and gender differences in this ability. 152. The informal sector is dominated by low productivity subsistence firms but also harbors highly productive firms. The deep dive on the heterogeneity among informal businesses noted that informal firms are over-represented in the commerce sector and are mainly run by self-employed women with low levels of education and low profitability. More generally, important gaps exist in labor productivity within the larger informal firm community across genders, sectors, locations, and firm size. However, there is also significant spread in labor productivity across firms as well as within each of these categories. A cluster analysis allowed for quantification and differentiation between (i) subsistence businesses, created out of necessity; (ii) somewhat productive businesses with limited growth potential, representing half of informal businesses; (iv) start-ups operating at low levels of productivity but with high growth potential; and (v) well-established, highly productive businesses with a profile similar to the one of formal firms, representing less than one fifth of all informal business. This differentiated landscape of informal businesses suggests that a one-size-fits-all strategy to achieve better employment outcomes does not work. Formalization programs—the focus of current policy—may work for those businesses that have a profile closer to that of formal firms but are not adequate for the majority of firms, whether from the perspective of increasing formalization or promoting productivity. 153. The agriculture sector holds a significant but unleveraged potential for creating more productive, better-paying, and inclusive jobs. The deep dive on jobs in agriculture found that agriculture not only represents the main source of jobs and livelihoods, but also creates the largest number of new jobs every year. The agriculture sector holds the key to better-quality jobs. This is true on the farms but also along the value chains that include processing, marketing, mechanization services, input provision, and transport. However, a number of factors have limited the ability for agriculture to deliver on that potential, including low labor productivity and the domination of the sector by smallholders that face a variety of constraints to accessing formal markets. 154. The challenges presented throughout this Diagnostic, along with the opportunities highlighted, lead to specific policy recommendations tailored to address the issues confronting workers, firms, and the overall economy to promote better jobs outcomes for Togo’s current and future labor force (see text table below). These policy options have been designed to support the large variety of worker profiles and households that together make up the Togolese workforce and are thus a key determinant of the future growth of the country’s economy. To make them more robust, the recommendations take into account global conditions and the potential for external shocks affecting Togo’s economy. At an overall level, the report acknowledges that to successfully 88 develop and implement a comprehensive jobs strategy for more and better jobs in Togo, the government needs to apply a cross-sectoral approach and reach out to the private sector and the civil society to jointly develop and implement solutions. Henceforth, one of the key policy recommendations is to engage key actors of the Togolese economy and labor market throughout the policy cycle. The specific policy recommendations cover a broad range of areas to address the identified challenges, consistent with the Diagnostic: (a) Coordination. Combining a multisectoral jobs strategy, the jobs platform anchored at the highest level, and a data exchange platform would ensure a consistent policy approach across the Togolese economy, provide broad direction for the sectoral efforts, ingrain a strong job lens, including through enhanced data for evidence-based decision making, and improve efficiency and effectiveness in economic and social policies. (b) Labor supply. Improved targeting of measures including wage subsidies, training offerings, and cash transfers would allow for a strengthened focus on the most vulnerable groups, including women and youth, in connecting workers to more productive opportunities as wage workers or self-employed. Simultaneously, social protection systems need to be significantly developed and further strengthened for vulnerable households depending on informal work. (c) Overall economic context. Applying a jobs lens in policy decisions would create the space to further develop the potential of agriculture and strengthen urban employment opportunities. From a sectoral perspective, special efforts should be made to develop the large potential of agriculture in Togo, which is critical for employment in Togo. Critical measures include investment in infrastructure and initiatives to promote processing and exports. (d) Labor demand. Promoting the creation of more formal jobs in the private sector, including by reviewing the various elements of total labor costs to improve its competitiveness, enhancing access to finance, and strengthening good governance, would improve conditions to create more formal jobs in the private sector. Policy also needs to strengthen productivity in the informal sector through a more tailored approach to meet the needs of the heterogenous firms in the sector, as this is where most of the jobs are and will remain in the foreseeable future. 155. The table below prioritizes policy options and summarizes policy recommendations. The outlined policy options will affect everyone, but will make a particular difference for women and young workers. In addition, some barriers in the labor market that have been identified are specific to women and youth. Those are summarized separately, complementing the policy options. 89 TABLE 4.1 Togo Jobs Diagnostic: Policy recommendations Responsible Challenges Policy recommendations Timeframe government unit Opportunity 1—Coordination of a multisectoral jobs agenda: Adopt a multi-sectoral approach to the jobs agenda to transform the economy and create more good quality jobs for all population groups The institutional landscape, Use this Jobs Diagnostic, previous jobs-related analytical work, the At the highest Immediate policies, programs and Feuille de Route Gouvernementale 2020–2025, as well as jobs related government level systems on jobs are frag- policies, strategies, and programs currently in place or under development mented with low labor market as an opportunity to develop a “Multisectoral Jobs Strategy for Togo” outcomes. applying a jobs lens to the supply side of the labor market, the demand side, and the overall economic environment. The implementation of such a multisectoral jobs strategy can be a pathway to create better job opportunities for all population groups by identifying and utilizing synergies across sectors, actors, and programs. Develop and periodically update a systematic mapping of all jobs-related At the highest Short term programs and their impact (if available) that is accessible to key public government level and private sector actors. Absence of a systematic coor- Institutionalize a Jobs Platform anchored at the highest level. A At the highest Immediate dination mechanism led by cross-sectoral technical team that is also mirrored by a cross-minis- government level strong government leadership. terial coordination unit needs to be established and institutionalized (Steering committee). Based on lessons from the dialogue and the technical committee established throughout the 2-year consultation process of this Jobs Diagnostic, the cross-sectoral governance structure should include representatives from sectoral ministries contributing to the jobs agenda, private sector actors and civil society and needs to be anchored at the highest government level to mobilize the different actors on a regular basis and to enhance intersectoral decision-making. Such a committee would be in charge of supporting the coordination of actors contributing to the jobs agenda in Togo, as well as the prepa- ration of the multisectoral Jobs Strategy and its action plans, and their implementation, monitoring and evaluation. Opportunity 2—Labor supply side: Connect vulnerable population groups (youth, women, rural populations and those living with disabilities) to good quality jobs and strengthen the resilience of the working age population Mismatch between job oppor- Develop a national vocational training and a demand-driven skills Ministry of TVET, Short term tunities and qualification. training and certification system with the involvement of firms, training in collaboration Low levels of certification in providers, and workers’ organizations. Certification could be conducted with: Ministry of the formal and the informal according to competency-based standards in key sectors and occupations. higher education, TVET sectors. • Conduct a comprehensive TVET assessment to evaluate the government institutional setup, technical design and operationalization of the institutions and Vocational training support is agencies working current system and whether it allows to form the skills needed by relatively low given the sup- on professional the private sector. Such an assessment will allow to further refine port TVET provides for skills training, building the subsequent recommendations; acquisition, and also low in on a public-private • Improve the vocational training through increase of the budget allocated overall education spending. partnership to vocational training in Togo, which is vital for competitiveness and Coverage of technical and swift out-of-school transition; vocational education is low • Scale-up the strengthened apprenticeship program to urban and and serves large well-ed- rural areas; ucated, urban young men • Deliver literacy, socio-emotional, and business skills trainings to those disproportionally. with low (or no) formal education; • Design and implement an exit/graduation strategy for beneficiaries of public employment programs and accompanying measures. Complement support with skills development programs with empha- sis on micro-entrepreneurship and socio-emotional training; and • With informal apprenticeship being a widespread mode of learning, develop and implement a certification framework for informal workshops and apprentices. 90 Responsible Challenges Policy recommendations Timeframe government unit Youth, women, rural popula- Improve the targeting mechanism within program design to reach the Ministry of Medium tions, and those living with most vulnerable populations. For instance, linking labor market programs’ grassroot term disabilities face particular databases with those of the social registry while ensuring a focus of development, constraints in accessing good labor market programs on vulnerable populations such as women, youth, Ministry of social quality jobs as illustrated by rural populations, and those living with disabilities. affairs, Ministry the age, gender and location of informal disparities across most of the sector, Ministry of employment indicators. labor, Ministry of Self-employment is the dom- agriculture, and inant form of employment in related public Togo, indicating high levels agencies of underemployment, and is Based on international and national experience, tackle constraints Ministry of Medium most often characterized by specific to youth, women, rural populations in accessing good quality grassroot term low levels of productivity and jobs in a more explicit way, including through: development, earnings. • Economic inclusion measures (including coaching, entrepreneurial, Ministry of social Few workers have access to life skills and personal initiative training, the organization of savings affairs, Ministry wage employment, which groups) to strengthen the entrepreneurial skills, increase access to of informal tend to be of higher quality finance, and improve productivity and earnings; sector, Ministry of and are concentrated among • Wage subsidies (see Annex C for more information), internships, labor, Ministry of men in urban areas. and apprenticeship programs for youth to gain work experience agriculture, and and develop their own network that can help them access more related public productive wage jobs; agencies • Cash-for-work programs for unskilled youth to have an income over some time, ideally combined with entrepreneurship support and a matching grant scheme, building on the lessons of the successful Employment Opportunities for Vulnerable Youth (EJV) project; and • Improve the economic inclusion of youth, women, and rural populations into (agricultural) value chains to enable them to seize economic and job opportunities along the value chain, including in transport, mechanization services, input provision, processing, and marketing. Non-poor informal households Establish a social insurance scheme adapted to the specific needs and Ministry of Medium are mostly excluded from the contributory capacity of informal workers and their households, labor, Ministry term the social protection system, based on emerging evidence from other countries, including Rwanda of economy and which makes them particularly and Ghana. Non-poor households are expected to have varying levels finance, Ministry of vulnerable to economic shocks of contributory capacity. Such a tailored scheme could take the form informal sector and risks to push them into of a combined short and long-term voluntary savings scheme to take poverty. advantage of profitable investment opportunities, to smooth consumption when income is uneven and unpredictable, including for the old age, and to insure against emergencies. In fact, it has been observed in other countries that people are more likely to save to pay for education, to face emergencies and unforeseen events in the future, and to invest in a business, than they are to save for old age. The role of the government, in collaboration with the private sector, would be to set incentives through behavioral tools (to overcome the psychological biases that prevent saving), and financial and technological innovations (to make financial products accessible and low cost). Such a scheme could either leverage existing structures, such as the CNSS, or build on a new entity. 91 Responsible Challenges Policy recommendations Timeframe government unit Ensure an effective implementation of the AMU to enable households Ministry of Short to to better protect themselves against idiosyncratic and covariate health health, Ministry medium term shocks, regardless of their work status. AMU’s non-contributory medical of grassroot assistance scheme (RAM) is targeted at the poor and vulnerable and development, and should also cover those informal households that are not poor and not National institute resilient and, hence have limited contributory capacity while exhibiting for health insurance signs of economic distress, especially when confronted with a (health) shock. AMU’s contributory mandatory basic health insurance scheme (RAMO) is among others targeted at auto-entrepreneurs and informal workers and business owners with a capacity to contribute. To provide protection to these population groups, operationalizing and accelerating their enrollment would be key. Opportunity 3 Overall economic performance and jobs: Support structural transformation and the creation of more good quality jobs through strategic investments Economic transformation has Apply a “jobs lens” in policy decisions to prioritize investments that At the highest Short, been slow, productivity growth support economic transformation and job creation. government medium to in the different sectors weak, level, Ministry long term and economic growth not of economy and inclusive enough. finance, and Ministries in charge of economic and social development Improve living standards of the urban population through (i) increasing Ministry of Medium to connectivity within and between cities; (ii) improving access to vital territorial long term infrastructure (energy, water and sanitation, public transportation); and administration, (iii) improving urban planning and financing. Ministry of investment promotion, Ministry of transport, and Ministry of energy Improve trade competitiveness through (i) expanding access to inter- Ministry of Medium to national markets for further diversification of exports; (ii) supporting commerce long term key growth industries to leveraging potential as regional hub; and (iii) and industry, strengthening trade facilitation by simplifying and reducing the cost of and Ministry custom procedures. of investment promotion The development of (agricul- Increase investment in productive assets and accelerate business growth: Ministry of Medium to tural) value chains has not • Establish a guarantee fund targeting high-potential value chains, to economy and long term yet reached its full potential encourage financial institutions to lend to companies ready to invest finance, Ministry and could become a motor in these sectors. Such a fund could partially absorb the first tranche of commerce and for economic growth and job of losses that banks may experience should their investments prove industry, Ministry creation75 unprofitable, thereby reducing risks for private financiers; and of agriculture, • Facilitate the establishment of producer cooperatives, thereby and Ministry offering an opportunity for firms to establish and scale contract of investment farming systems. Moreover, provide grants directly to firms to help promotion them acquire more efficient processing equipment and machinery. Additional investments at various steps of the chains would help both producers and transformers to scale up their activities and meet the growing demand. 75 More and more detailed recommendations can be found in the accompanying report: World Bank. Togo: Economic inclusion of youth and women into high potential value chains. Forthcoming. 92 Responsible Challenges Policy recommendations Timeframe government unit Improve access to (export) markets: Ministry of Medium to • Finance public infrastructure investments targeted to alleviating key commerce and long term constraints reported by value chain actors. Key categories include access industry, Ministry to water and electricity, as well as warehousing and storage facilities of economy and at the community level. Existing initiatives such as the network of finance, Ministry agricultural development zones (ZAAP) that provide access to land of investment and irrigation to rural populations should be supported and scaled; promotion, Ministry • For export products, a dedicated marketplace or trade hub adapted of agriculture, and to international norms could help attract internal buyers (this may Ministry of energy include building on or further supporting private initiates like the Adetikopé Industrial Platform); • Sponsor or commission the creation of a digital platform (e-market) to help connect sellers with buyers; and • Commission the Togolese Normalization Agency (ATN) to develop quality standards for selected value chains. Invest in more and better research to ensure that the adoption of Ministry of Short to technology contributes to improvements in productivity, job quality and agriculture, and medium term earnings. There are several market solutions available, such as machinery agricultural services, to overcome the indivisibility of farming capital and capture development the economies of scale implied in mechanization. agency Jobs and incomes have been Continue to support the private sector through better access to finance Ministry of Short to heavily affected by recent and targeted support for the most affected businesses to help them economy and medium term international shocks. weather the economic consequences of the COVID-19 pandemic and finance, and the growing inflation. Ministry of commerce and industry Expand analytical oversight of spending programs to ensure that Ministry of Short term government support is effective, cost-efficient, and reaches the most economy and vulnerable. finance Develop a transparent and credible fiscal consolidation strategy through Ministry of Short term expansion of the tax base, consolidation of tax expenditures and subsidies, economy and and management of state liabilities. finance Opportunity 4 Labor demand: Facilitate the creation of more formal firms and the creation of more wage jobs in the formal private sector, while at the same time providing tailored support to different profiles of informal businesses to increase productivity and earnings Total cost of labor (package Review the levels of the different elements of the labor cost to assess Ministry of Short term comprised of salaries, taxes, how to improve the competitivity of the labor cost in the formal private labor, Ministry social security contributions) is sector. Given that the minimum wage remains low, and the overall of economy and too high given current produc- tax base remains small, the proposed focus would be on the social finance, and public- tivity and earnings levels in the security contributions paid to the CNSS, which are high compared private consultation economy, pushing lower skilled to those observed in some of the peer countries Togo aspires to. framework worker into the informal sector. The focus should not only be on the level of employer and employee contributions, but should equally take into account the long term viability of the CNSS scheme. 93 Responsible Challenges Policy recommendations Timeframe government unit Access to finance remains lim- Promote access to finance for firms, including micro- and small enterprises Ministry of Medium ited and costly, with availability and those active in the agricultural sector to boost investments, economy and term varying significantly by sector. productivity and employment: finance, SME-SMI • Establishing guarantee funds would be one way of allowing financial agency, and institutions to service those businesses that bear higher risks and take Agricultural out smaller loans, making administrative costs proportionally costly; development • Place specific focus on access to finance for businesses in the agency agricultural sector, along the value chains: currently, only 1 percent of bank loans are allocated to the agricultural sector, despite its importance for the overall economy in Togo and for job creation more specifically; and • Include women and female entrepreneurs, including by enforcing the amended penal code (new article 312 of the penal code) that prohibits any discriminations based on gender in access to credit and economic activities. A well-functioning complaint mechanism for female entrepreneurs subject to potential discriminations would support these efforts. Consider, also, informational, behavioral and normative constraints holding back women from applying for finance. A “one-size-fits-all” approach A tailored approach is needed to support different profiles of informal Ministry of informal Short to in supporting informal businesses to create better economic opportunities and earnings: sector, Ministry medium term businesses is ineffective and • Subsistence businesses operating at low levels of pro- of commerce inefficient. ductivity: support economic inclusion programs that tackle and industry, various constraints simultaneously. Such programs usually offer a and SME-SMI bundle of support activities: life skills training, entrepreneurship development training, facilitation of savings and loan associations, access to agency market facilitation, and business grants. Their main objective is to improve the productivity and the earnings of those associated with these businesses. The economic inclusion component of the Togo Safety Nets and Basic Services projects is currently in a pilot phase and could be scaled up following a review and integration of lessons learned. • Low productivity businesses with untapped growth poten- tial: support their further development and productivity, so that they grow and create more jobs. Policies include entrepreneurship training; consulting services on technical and managerial aspects; the provision of matching grants; and facilitation of connection to higher value markets. In the medium term, and as these businesses become more productive, established and integrated into the market, support to formalizing these businesses would be appropriate. • Well-established, higher productivity businesses with a profile similar to formal firms: focus on accompanying these businesses towards business, tax and social security registration, as they appear to have the capacity to formalize, based on their profile and ability to do business with other firms. Support can take the form of incentives as well as coercive measures, or a combination of both, focusing on the formalized firms’ improved access to services that support firm productivity and strengthen working conditions. 94 REFERENCES Amin and Islam. 2015. Are Large Informal Firms More Productive than the Small Informal Firms? Evidence from Firm- Level Surveys in Africa, World Development, Volume 74, 2015, Pages 374–385. Centre de formalité des entreprises (CFE). https://www.cfetogo.tg/statistiques-10.html. Accessed on September 6, 2021. Cunningham, Wendy and Maloney, William, (2001). Heterogeneity among Mexico’s Microenterprises: An Application of Factor and Cluster Analysis, Economic Development and Cultural Change, 50, issue 1, p. 131-56. Duranton and Puga, 2004. Chapter 48—Micro-Foundations of Urban Agglomeration Economies, Editor(s): J. Vernon Henderson, Jacques-François Thisse, Handbook of Regional and Urban Economics, Elsevier, Volume 4, 2004, Pages 2063–2117. Enquête régionale intégrée sur l’emploi et le secteur informel (ERI-ESI) 2017. Enquête harmonisée sur les conditions de vie des ménages (EHCVM) 2018–2019. Halvorsen, Robert and Palmquist, Raymond, (1980). The Interpretation of Dummy Variables in Semilogarithmic Equations, American Economic Review, 70, issue 3, p. 474–75. ILO, 2018. Analyse du système de protection sociale à travers le processus SPPOT: Vers un socle national de protection sociale pour le Togo. INSEED, Institut National de la Statistique et des Etudes Economiques et Démographiques (Institute of National Statistics and Economic and Demographic Studies). Islam, I. and Nazara, S, 2000. “Estimating employment elasticity for the Indonesian economy”. ILO Technical Note, Jakarta. Islam, R. 2004. “The nexus of economic growth, employment and poverty reduction: an empirical analysis”. Recovery and Reconstruction Department, Geneva, ILO. Kapsos, S. (2005). The Employment Intensity of Growth: Trends and Macroeconomic Determinants. Employment Strategy Papers 12, Geneva: ILO. Lall, Somik & Henderson, J. & Venables, Anthony. (2017). Africa’s Cities: Opening Doors to the World. 10.1596/978-1-4648-1044-2. Melis Guven, Himanshi Jain et Clement Joubert, 2021. Social Protection for the Informal Economy: Operational Lessons for Developing Countries in Africa and Beyond. Melina, L. and Schneider, F. (2019). Shedding Light on the Shadow Economy: A Global Database and Interaction with the Official One. Documents de travail du Cesifo 7981. Nienke M. Beintema and Gert-Jan Stads (2014). Chapter 5 in 2013 Global Food Policy Report, pp 53–62 from International Food Policy Research Institute (IFPRI). Organisation internationale du travail, Indicateurs clés du marché du travail, 2019. Penn World Table data (version 10). https://febpwt.webhosting.rug.nl/ Psacharopoulos, George. Returns to Education: A Further International Update and Implications. The Journal of Human Resources, vol. 20, no 4, 1985, p. 583–604. Questionnaire des indicateurs de base du bien-être (QUIBB) 2006 et 2011. Transparency International, 2021. Indice de perception de la corruption (IPC). World Bank, 2019. Macro and Poverty Outlook. World Bank, 2019. Étude sur l’Inclusion économique des Jeunes dans des chaînes de valeur à fort potentiel. World Bank, 2020. Togo Economic Update. World Bank, 2020. Migration and Development Brief 33: Phase II: COVID-19 Crisis through a Migration Lens. World Bank, 29 April 2020. Strengthening Youth Resilience to Radicalization. Evidence from Tajikistan. 95 World Bank, 2020. World Development Indicators 2020. World Bank, 2020. Macroeconomic and financial weekly monitoring October 16, 2020. World Bank, 2020. Agriculture, emplois et chaînes de valeur en Afrique. World Bank, 2020. Doing Business Databank. World Bank, June 2020. Étude de l’impact du Covid-19 sur le secteur privé au Togo : Une évaluation informée par des données. World Bank, 2020. Investing in People: Social Protection for Indonesia’s 2045 Vision. World Bank, 2021. Togo: Public Expenditure Policy Note. World Bank, 2022. Macroeconomics, Trade & Investment Global Practices. World Bank, Enterprise Survey 2011 et 2016. World Bank, 2022. Évaluation de la pauvreté et du genre au Togo. World Bank, 2022. Mémorandum économique du Togo. World Bank, FINDEX databank. https://www.worldbank.org/en/publication/globalfindex/Data. Accessed on December 19, 2022. World Bank Blog: https://blogs.worldbank.org/opendata/labor-market-impacts-covid-19-four-african-countries World Bank, Country Private Sector Diagnostic, CPSD Togo, forthcoming. World Bank, forthcoming. Analyse approfondie des chaînes de valeur à fort potentiel d’emploi pour les jeunes et les femmes au Togo. World Bank. Togo Poverty and Gender Assessment, forthcoming. 96 ANNEX A: CALCULATING EMPLOYMENT ELASTICITIES CALCULATING EMPLOYMENT ELASTICITIES The methodology used to calculate elasticities is inspired from Kapsos (2005). Employment elasticity is defined as the percentage change in the number of employed persons in an economy or region associated with a percentage change in economic output, measured by gross domestic product. Within this broad definition, two methods are often utilized for calculating elasticities. The first, called arc elasticity, is given by the following equation: ( E1 − E0 ) / E0 ϵi = (1) ( Y1 − Y0 ) / Y0 The numerator simply gives the percentage change in employment between periods 0 and 1, while the denom- inator gives the corresponding percentage change in output. In this chapter, we use the Compound Annual Growth Rate (CAGR) formula to calculate the growth rate of employment and output. ( Y1 − The second method is better known as the multivariate Y0 ) / Y0 regression model. It is obtained by estimating log-linear the following equation by OLS: ln E = α + β1 ln Y + β2 ( ln Y * D ) + ϵ (2) The elasticity of employment with respect to GDP is given by β1 + β2 and represents the change in employment associated with a differential change in output. This study favors the use of the arc elasticity method because of the short dimension of the available time series. INTERPRETING EMPLOYMENT ELASTICITIES The following table presents the different possible ways of interpreting employment elasticities. 97 TABLE A.A.1 Interpreting employment elasticities GDP growth Employment elasticity Positive GDP growth Negative GDP growth (–) employment growth (+) employment growth ϵ < 0 (+) productivity growth (–) productivity growth (+) employment growth (–) employment growth 0 ≤ ϵ ≤ 1 (+) productivity growth (–) productivity growth (+) employment growth (–) employment growth ϵ > 1 (–) productivity growth (+) productivity growth Limitations of the arc elasticities method used As stated by Islam and Nazara (2000) and Islam (2004), employment elasticities calculated using the arc method tend to be very unstable and may therefore be inappropriate for comparison purposes. Also, this methodology only considers information pertaining to historical employment and output growth. Nevertheless, elasticities calculated provide a clear indication of how employment and output have evolved together over time. Thus, the results should be interpreted as correlation rather than causality. 98 ANNEX B: METHODOLOGY OF THE CLUSTER ANALYSIS OF INFORMAL BUSINESSES METHODOLOGY OF THE CLUSTER ANALYSIS OF INFORMAL BUSINESSES In order to profile informal businesses in Togo, a cluster analysis was used, mainly to account for the multiplicity of factors driving informal business heterogeneity. The analysis follows Cunningham and Maloney (2001) and applies their cluster analysis framework to profile informal businesses. A cluster analysis is a descriptive and exploratory technique that attempts to group objects based on their similarity. It operates on the matrix of pairwise distances (or dissimilarities) between the objects to be clustered. As such, the number of clusters is not predetermined but rather depends upon how similar or dissimilar the observations are across n-dimensions. In our empirical approach, we used Stata software to implement the cluster analysis. The variable construction (endogenous variables used in the clustering) followed the Cunningham and Maloney (2001) approach: Characteristics of the business owner: • Education: responses will be categorized as count variables where 0 is no education, 1 is 1 year of education, and so forth. • Years of work experience: this is total work experience in any sector and will be calculated as (age—6- years of education). • Gender: dummy variable 0 if male and 1 if female. Characteristics of the business • Age of the business: number of years in operation. • Activity sector: dummy variables for industry, service, and commerce. • Number of workers: counts of the number of workers including the owner if he/she is an owner-manager. • Permanence of work site (Site): takes a value of 1 if the firm has a permanent locale; 0.5 if the firm can be moved but has a specific work site as recognized by other firm owners and its customers; and 0 if the firm does not have a permanent work site. Entrepreneurial dynamics, proxy for the business owner’s plans and needs for financing and access to services • Clients (Clients): captures the degree of personal closeness with the clientele. The responses will be categorized into four groups: large formal clients (large businesses, large factories), coded as 1; smaller, less formal clients (small businesses, small factories, or workshops), coded as 0.66; informal clients (the public), coded as 0.33; and family, coded as 0. • Suppliers: captures the capacity of the firm to import or do business with other large firms. The responses will be categorized into four groups: large formal clients (large businesses, large factories), coded as 1; smaller, less formal clients (small businesses, small factories, or workshops), coded as 0.66; informal clients (the public), coded as 0.33; and family, coded as 0. • Initial financing for firms (Financing-initial): captures the source of startup capital; ranges from personal savings, friends, and informal markets to formal banking institutions. There are nine possible responses in the survey that will be reclassified into three general categories: formal credit markets (banks 99 or savings banks), coded as 1; semiformal credit markets (credit from clients, credit from suppliers), coded as 0.5; and informal credit markets (loans from friends or relatives, private loans, savings, and severance payments), coded as 0. • Demand for additional financing (Financing-new): whether the firm has sought additional financing from any source. It will take a value of 1 if the firm has applied for credit, 0 otherwise. • Financing problems (No credit problems): whether the owner cites credit availability as a business problem. It will take a value of 1 if credit is not cited as a business problem and a 0 otherwise. • Access to electricity: whether the firm has access to electricity. It will take a value of 1 if yes and 0 otherwise. • Access to clean water: whether the firm has access to clean water. It will take a value of 1 if yes and 0 otherwise. TABLE A.B.1 Owner and firm characteristics, by Cluster with productivity as exogenous clustering variable Cluster 4 2 3 5 1 Total Age in years 26.64 32.39 45.02 43.76 64.18 38.97 Education Owner has no level of education 0.0% 33.6% 44.8% 33.7% 77.5% 33.1% Owner has primary education 27.7% 45.6% 36.5% 37.9% 20.5% 36.0% Owner has secondary education 68.3% 20.3% 18.7% 27.5% 1.8% 29.7% Owner has tertiary education 4.1% 0.5% 0.0% 1.0% 0.1% 1.2% Years of education 8.214 3.752 3.12 4.214 1.047 4.391 Years of work experience 12.43 22.64 35.9 33.54 57.13 28.58 Female 65.1% 75.6% 76.4% 64.2% 78.0% 71.7% Age of the business 4.098 5.791 6.958 21.64 19.61 9.865 Sector of activity Industry 36.3% 29.8% 29.8% 40.8% 34.8% 33.7% Commerce 34.2% 40.6% 45.9% 36.2% 44.4% 39.9% Service 29.5% 29.6% 24.4% 23.0% 20.7% 26.4% Number of workers 1.211 1.244 1.252 1.304 1.198 1.245 Number of hours worked per month 200.2 199.4 212.7 205.4 181.7 201.8 100 Cluster 4 2 3 5 1 Total Number of hours worked per worker per month 148.1 152.1 165.5 157.4 151.6 155.0 Firm’s location Rural Firms 46.1% 56.4% 48.3% 50.5% 66.8% 52.4% Lome Firms 28.8% 24.6% 30.3% 26.8% 18.5% 26.5% Other Urban Firms 25.1% 19.0% 21.4% 22.7% 14.7% 21.1% Gross labor productivity (Output in FCFA/ 76,090.70 87,281.10 91,548.20 134,517.80 95,108.80 95,022.90 person per month) Site Permanent work site 47.8% 46.2% 50.1% 55.3% 57.7% 50.1% Mobile work site 52.2% 53.8% 49.9% 44.7% 42.3% 49.9% Clients Big firms 3.1% 2.1% 2.6% 2.2% 2.1% 2.4% Small firms 3.9% 4.3% 4.9% 5.8% 6.2% 4.8% Individuals 93.0% 93.6% 92.5% 92.1% 91.7% 92.8% Suppliers Suppliers: Big firms 7.4% 6.4% 8.4% 9.3% 4.0% 7.3% Suppliers: Small firms 31.5% 32.2% 28.9% 25.3% 27.9% 29.7% Suppliers: Individuals 61.1% 61.4% 62.8% 65.4% 68.1% 63.0% Financing to start firm Bank and MFI 2.8% 2.9% 3.4% 2.5% 1.8% 2.8% Private Loans 29.6% 23.5% 26.5% 26.2% 19.0% 25.5% Personal savings 60.0% 66.5% 63.5% 66.0% 76.2% 65.3% Credit (Clients and/or suppliers) 7.6% 7.1% 6.7% 5.3% 3.1% 6.4% Additional financing Too costly 14.9% 16.8% 15.8% 17.0% 13.5% 15.9% Do not need 48.4% 41.0% 43.0% 41.7% 54.5% 44.4% Process too complicated 17.3% 22.7% 19.8% 18.9% 12.9% 19.3% Did not meet your needs 14.1% 14.1% 16.6% 17.8% 15.2% 15.4% Did not apply for other reason 5.3% 5.6% 4.8% 4.6% 4.0% 5.0% Applied, did not get 1.5% 2.0% 2.1% 1.4% 0.7% 1.7% Firm size Self-employed 89.0% 90.0% 88.0% 83.4% 87.8% 88.0% 101 Cluster 4 2 3 5 1 Total 2–4 Workers 10.1% 8.2% 9.8% 15.1% 11.8% 10.5% 5+ Workers 0.9% 1.8% 2.2% 1.5% 0.4% 1.5% Why did you start this business Did not find salaried work (large company) 1.6% 1.5% 1.5% 2.7% 0.6% 1.7% Did not find salaried work (small business) 9.6% 6.4% 4.4% 3.1% 3.1% 5.7% Higher pay than salaried 39.2% 50.0% 54.8% 46.5% 57.2% 48.8% To be independent (own boss) 40.4% 30.7% 28.0% 29.0% 23.7% 31.2% Family tradition 3.7% 4.0% 5.4% 10.3% 11.7% 6.1% Other reasons 5.6% 7.4% 6.0% 8.4% 3.6% 6.5% Problems Difficulties in the supply of raw materials 26.1% 26.0% 28.1% 28.5% 33.7% 27.7% Lack of customers 63.3% 61.7% 57.4% 59.5% 59.6% 60.5% Difficulties in accessing credit 53.0% 52.8% 50.6% 52.5% 53.6% 52.4% Credit is too costly 27.4% 26.2% 27.9% 30.5% 30.6% 28.0% Difficulties in recruiting qualified personnel 19.3% 20.0% 21.9% 23.1% 24.3% 21.2% Lack of space / lack of suitable premises 3.8% 3.3% 5.1% 4.4% 4.3% 4.1% Difficulties in accessing equipment 23.9% 24.1% 22.0% 23.4% 17.5% 22.9% Technical difficulties in manufacturing 35.2% 30.9% 35.4% 36.1% 35.0% 34.1% Technical management difficulties 4.5% 4.2% 4.3% 5.9% 5.2% 4.7% Lack of management skills 4.6% 5.4% 4.0% 5.3% 4.1% 4.8% Too much regulation, tax and duties 10.3% 11.6% 12.7% 14.7% 12.6% 12.2% Obsolescence of equipment 3.2% 2.5% 2.4% 3.8% 2.8% 2.9% Problems with power outages 9.1% 8.2% 8.0% 10.6% 10.0% 8.9% Problems related to insecurity 9.1% 11.7% 8.9% 10.4% 8.6% 10.0% Staff instability 2.1% 2.7% 2.2% 3.4% 1.4% 2.5% Problems related to other infrastructure 0.8% 1.3% 1.8% 1.2% 1.3% 1.3% (water/telephone/internet) % of the sample (weighted) 21.31% 29.59% 21.47% 18.05% 9.58% 100% Observations 829 1,234 872 740 401 4,076 102 TABLE A.B.2 OLS Regression: Factors driving informal business productivity in Togo Log (Output per person per month [FCFA/person]) [1] [2] [3] [4] [5] [6] All Rural Lomé Other Self- Micro-firms Locations urban employed Gender of the owner [1=Female] –0.315*** –0.397*** –0.335** –0.084 –0.343*** –0.507*** (0.058) (0.080) (0.137) (0.094) (0.066) (0.152) Constant 10.617*** 10.636*** 10.609*** 10.583*** 10.682*** 10.394*** (0.049) (0.069) (0.116) (0.072) (0.058) (0.089) Adjusted R-squared 0.010 0.016 0.009 –0.000 0.011 0.029 Gap (%) 27% 33% 28% 8% 29% 40% Observations 3,985 2,412 604 969 3,518 467 Note: ***/**/* indicate statistical significance at the 1/5/10 percent level respectively. % gap values are obtained with the exp(b)–1 transformation (Halvorsen and Palmquist [1980]). TABLE A.B.3 OLS Regression: Factors driving informal business with control variables Log (Output per person per month [XOF/person]) [1] [2] [3] [4] [5] [6] All Rural Lomé Other Self- Micro-firms Locations urban employed Female owner –0.454*** –0.568*** –0.337** –0.318*** –0.421*** –0.675*** (0.064) (0.085) (0.158) (0.109) (0.070) (0.161) Years of education 0.013 0.007 0.021 0.006 0.014 –0.002 (0.009) (0.012) (0.019) (0.014) (0.009) (0.026) Log (Manager experience in years) –0.041 –0.052 0.016 0.002 –0.081 0.286 (0.070) (0.109) (0.146) (0.111) (0.075) (0.199) Sector of operation (reference: Industry) Commerce 0.713*** 0.840*** 0.395*** 0.760*** 0.741*** 0.655*** (0.060) (0.077) (0.149) (0.114) (0.063) (0.200) Service 0.205*** 0.157* 0.297* 0.122 0.250*** –0.064 (0.065) (0.081) (0.174) (0.121) (0.071) (0.165) Lome Firms –0.148* –0.195** 0.345 (0.077) (0.082) (0.228) 103 Log (Output per person per month [XOF/person]) [1] [2] [3] [4] [5] [6] All Rural Lomé Other Self- Micro-firms Locations urban employed Other Urban Firms 0.038 0.001 0.304* (0.062) (0.066) (0.169) Log (Firm age) 0.177*** 0.190*** 0.165** 0.148*** 0.199*** –0.015 (0.035) (0.054) (0.071) (0.057) (0.038) (0.091) Access to credit [dummy] 0.381*** 0.385*** 0.327** 0.448*** 0.381*** 0.381** (0.073) (0.110) (0.156) (0.113) (0.080) (0.176) Firm size (reference: Auto-entrepreneur=1 worker) 2–4 Workers –0.284*** –0.476*** 0.001 –0.198 0.538** (0.085) (0.110) (0.217) (0.137) (0.255) 5+ Workers –0.950*** –1.357*** –0.626 –0.399 (0.246) (0.298) (0.533) (0.557) Degree of personal closeness with the clientele 0.682*** 1.191*** 0.118 0.376 0.750*** 0.218 (0.229) (0.447) (0.469) (0.295) (0.250) (0.664) Capacity of the firm to import or doing 0.183** –0.057 0.429** 0.310** 0.128 0.510** business with other large firms (0.078) (0.110) (0.167) (0.136) (0.084) (0.207) Permanent work site –0.062 –0.082 –0.104 0.016 –0.039 –0.238 (0.053) (0.068) (0.144) (0.095) (0.057) (0.150) Access to electricity [dummy] –0.109 –0.071 –0.202 –0.073 –0.107 –0.115 (0.077) (0.118) (0.161) (0.119) (0.085) (0.175) Access to clean water [dummy] 0.219*** 0.255** 0.260 –0.008 0.215** 0.171 (0.084) (0.111) (0.163) (0.151) (0.093) (0.186) Access to telephone at work premise 0.003 –0.008 –0.111 0.178* 0.031 –0.190 [dummy] (0.061) (0.089) (0.131) (0.102) (0.065) (0.166) Access to internet at work premise [dummy] 0.127 0.944* 0.057 0.203 –0.070 0.846*** (0.166) (0.515) (0.211) (0.317) (0.179) (0.320) Constant 9.860*** 9.847*** 9.754*** 9.695*** 9.887*** 8.731*** (0.236) (0.356) (0.516) (0.410) (0.250) (0.745) Adjusted R-squared 0.102 0.145 0.044 0.101 0.095 0.172 Gap (%) 36% 43% 29% 27% 34% 49% Observations 3,937 2,383 592 962 3,477 460 Note: ***/**/* indicate statistical significance at the 1/5/10 percent level respectively. % gap values are obtained with the exp(b)–1 transformation (Halvorsen and Palmquist [1980]). 104 TABLE A.B.4 Oaxaca-Blinder decomposition of the gender differential in urban informal firm productivity Log (Output per person per month [XOF/person]) [1] [2] [3] [4] [5] [6] All Rural Lomé Other Self- Micro-firms Locations urban employed Overall Male owner 10.623*** 10.641*** 10.620*** 10.583*** 10.689*** 10.395*** (0.055) (0.084) (0.117) (0.065) (0.066) (0.092) Female owner 10.307*** 10.247*** 10.267*** 10.507*** 10.344*** 9.874*** (0.037) (0.048) (0.081) (0.067) (0.037) (0.137) difference 0.316*** 0.394*** 0.353*** 0.077 0.345*** 0.521*** (0.062) (0.092) (0.127) (0.088) (0.072) (0.159) explained –0.139*** –0.174*** 0.016 –0.242*** –0.076** –0.154 (0.037) (0.054) (0.079) (0.065) (0.037) (0.110) unexplained 0.454*** 0.568*** 0.337** 0.318*** 0.421*** 0.675*** (0.062) (0.081) (0.148) (0.097) (0.072) (0.156) explained Years of education 0.033 0.019 0.065 0.013 0.035 –0.006 (0.025) (0.034) (0.069) (0.030) (0.025) (0.071) Log (Manager experience in years) 0.005 0.005 –0.003 –0.000 0.009 –0.055 (0.008) (0.011) (0.026) (0.006) (0.008) (0.041) Sector of operation (reference: Industry) Commerce –0.161*** –0.145*** –0.100** –0.248*** –0.170*** –0.020 (0.021) (0.029) (0.040) (0.043) (0.024) (0.030) Service 0.006 0.003 0.007 0.007 0.012* 0.009 (0.005) (0.005) (0.014) (0.008) (0.007) (0.023) Lome Firms 0.000 –0.004 –0.023 (0.003) (0.004) (0.024) Other Urban Firms 0.001 0.000 0.012 (0.001) (0.000) (0.016) Log (Firm age) 0.053*** 0.051*** 0.057* 0.047** 0.062*** –0.001 (0.013) (0.016) (0.032) (0.020) (0.015) (0.004) Access to credit [dummy] –0.027*** –0.021** –0.027 –0.044** –0.031*** –0.049 (0.008) (0.010) (0.017) (0.018) (0.009) (0.030) 105 Log (Output per person per month [XOF/person]) [1] [2] [3] [4] [5] [6] All Rural Lomé Other Self- Micro-firms Locations urban employed Firm size (reference: 1 worker) 2-4 Workers –0.034*** –0.060*** 0.000 –0.034 –0.036 (0.012) (0.017) (0.014) (0.029) (0.025) 5+ Workers –0.027*** –0.041** –0.019 –0.009 (0.010) (0.018) (0.019) (0.012) Degree of personal closeness with the clientele 0.010* 0.020* 0.002 0.001 0.008 0.005 (0.006) (0.012) (0.008) (0.005) (0.006) (0.015) Capacity of the firm to import or doing 0.010* –0.002 0.048* 0.003 0.007 0.000 business with other large firms (0.006) (0.005) (0.029) (0.011) (0.006) (0.021) Permanent work site 0.000 0.002 –0.006 0.000 0.001 0.016 (0.002) (0.004) (0.010) (0.001) (0.002) (0.016) Access to electricity [dummy] –0.003 –0.002 –0.000 –0.003 –0.001 0.002 (0.003) (0.004) (0.010) (0.005) (0.002) (0.007) Access to clean water [dummy] –0.005 –0.008 0.004 0.000 –0.009 0.001 (0.004) (0.005) (0.010) (0.010) (0.005) (0.007) Access to telephone at work premise [dummy] 0.000 –0.002 –0.014 0.020 0.005 –0.017 (0.010) (0.017) (0.018) (0.015) (0.010) (0.018) Access to internet at work premise [dummy] 0.001 0.006 0.001 0.004 –0.001 0.010 (0.002) (0.005) (0.003) (0.006) (0.002) (0.018) unexplained Years of education 0.275*** 0.359*** 0.184 0.102 0.255** 0.548** (0.098) (0.129) (0.252) (0.169) (0.110) (0.267) Log (Manager experience in years) 0.608 1.862** –0.809 –0.081 0.584 –0.105 (0.508) (0.780) (0.903) (0.672) (0.555) (1.348) Sector of operation (reference: Industry) Commerce 0.102** 0.084 0.255* 0.003 0.122** –0.011 (0.045) (0.054) (0.135) (0.067) (0.052) (0.072) Service –0.085** –0.112** –0.023 –0.069 –0.083* –0.062 (0.038) (0.047) (0.111) (0.053) (0.046) (0.092) Lome Firms –0.046 –0.038 –0.198* (0.046) (0.057) (0.102) 106 Log (Output per person per month [XOF/person]) [1] [2] [3] [4] [5] [6] All Rural Lomé Other Self- Micro-firms Locations urban employed Other Urban Firms –0.051* –0.034 –0.251** (0.027) (0.031) (0.099) Log (Firm age) –0.214 –0.694*** 0.538* –0.343* –0.144 0.119 (0.146) (0.225) (0.276) (0.199) (0.170) (0.347) Access to credit [dummy] –0.038* 0.001 –0.063 –0.071* –0.035 –0.043 (0.022) (0.030) (0.049) (0.043) (0.025) (0.088) Firm size (reference: 1 worker) 2-4 Workers 0.016 0.033 –0.016 0.022 –0.198 (0.026) (0.038) (0.047) (0.053) (0.425) 5+ Workers 0.015 0.040*** 0.008 –0.023 (0.010) (0.015) (0.015) (0.020) Degree of personal closeness with the clientele 0.071 –0.079 0.209 0.150 –0.026 0.786* (0.179) (0.329) (0.331) (0.210) (0.219) (0.460) Capacity of the firm to import or doing –0.005 –0.022 0.118 –0.100 0.013 –0.167 business with other large firms (0.048) (0.059) (0.140) (0.077) (0.053) (0.117) Permanent work site –0.037 –0.064 0.061 –0.147* –0.068 0.096 (0.059) (0.082) (0.126) (0.090) (0.063) (0.187) Access to electricity [dummy] –0.032 –0.041 –0.002 –0.072 –0.036 0.045 (0.038) (0.038) (0.109) (0.082) (0.038) (0.145) Access to clean water [dummy] 0.019 0.030* 0.009 0.002 0.010 0.070 (0.019) (0.017) (0.053) (0.038) (0.017) (0.064) Access to telephone at work premise [dummy] –0.015 –0.043 0.072 0.090 –0.021 0.153 (0.059) (0.069) (0.152) (0.096) (0.063) (0.180) Access to internet at work premise [dummy] 0.013 –0.005 0.043 –0.002 0.014 –0.033 (0.011) (0.004) (0.033) (0.019) (0.010) (0.030) Constant –0.142 –0.781 –0.248 0.858 –0.092 –0.074 (0.538) (0.819) (0.988) (0.764) (0.585) (1.442) Observations 3,937 2,383 592 962 3,477 460 Note: ***/**/* indicate statistical significance at the 1/5/10 percent level respectively. % gap values are obtained with the exp(b)–1 transformation (Halvorsen and Palmquist [1980]). 107 ANNEX C: SUBSIDIZED EMPLOYMENT PROGRAMS Subsidized employment programs come operate in many ways. Unlike the situation with adults, where wage subsidies can be used to protect existing employment, subsidized employment programs for youth typically focus on moving young people into jobs that they did not already hold. Wage subsidy programs can operate in many ways, either through a direct transfer to employers, reductions in payroll taxes, or income tax credits. These programs aim at promoting participation in the labor market of the beneficiaries, while compensating employers for screening, orientation, and initial training costs. Wage subsidies may also be given to employers to stimulate the demand for specific workers or to employees to provide incentives for re-employment. In contrast to subsidized employment programs are public works programs, providing the job and (most often) compensation directly to the individual. These programs were an important tool in mitigating the impacts of the COVID-19 pandemic. Public works can be managed directly by the government or contracted out to NGOs or the private sector. The target populations for subsidized employment programs tend to be more vulnerable youth and those who are further away from being able to obtain employment without assistance. For example, Jordan’s New Entrants to Work (NEW) program offers vouchers to unemployed first-time job seekers that cover the cost of social security contributions during an on-the-job training period, with the idea that young workers without prior job experience are perceived as less productive than those with work experience, and will thus need the additional assistance to find a job, particularly one that provides training. Idmaj in Morocco followed successfully a similar approach with a youth wage subsidy to a lower wedge between the total cost of labor and take-home pay in a context of high taxation of formal labor. Liberia’s Emergency Employment Action Programme (LEEP/LEAP)76 was another employment program with a strong public works component, in this case addressed in particular to demilitarized youth. TABLE A.C.1 Subsidized employment programs features and principles Feature Principles for design and implementation of Subsidized Employment Projects/Wage Subsidies Recipient • In developing countries, wage subsidies are almost always paid to the firm (rather than the youth), in part for administrative reasons. Targeting • The target population is often limited to newly-hired workers (previously unemployed) in certain age groups (e.g. youth below 30, older workers above 50) or with another disadvantage in the labor market (e.g. long- term unemployed, disabled, displaced). • Narrow targeting of the subsidy is desirable to reduce the budgetary cost of the program and maximize the benefits to the favored group. Eligibility criteria should be as simple as possible. • Targeting of those with no or little work experience and facing the highest risk of unemployment and inactivity is also desirable to reduce the deadweight loss. 76 http://pdf.usaid.gov/pdf_docs/PNADF305.pdf 108 Feature Principles for design and implementation of Subsidized Employment Projects/Wage Subsidies Duration • Subsidies should usually be limited in time. Common durations for time-limited subsidies are six months to 1–2 years. • Short-term subsidies (below six months) are useful to overcome employers’ initial reluctance to hire but depending on the type of job may be unlikely to achieve skills learning objectives. • Medium-term and longer subsidy periods (six months up to two years) can permit young workers to develop necessary skills, and as a result increase their productivity. • That said, the core skills demanded by a given job are likely to have been acquired by 1 year for most jobs, and longer subsidy periods should thus be the exception. Level • A proportional subsidy (as a proportion of the wage) is usually recommended over fixed subsidies as it aligns incentives between program and employer (the employer contributes). • Setting a ceiling to the subsidy (unless it is fixed) may be required to encourage the integration of disadvantaged youth, since this design naturally predisposes employers towards hiring low-skilled (and hence low-paid), due to the fact that capped subsidies (like fixed amounts) cover a larger proportion of wage costs for low-paid workers. • It can also be advisable that the share/amount of the subsidy decreases over time, reflecting potential increases in productivity. • Higher subsidies may also be used for more disadvantaged groups and smaller employers. Payment vehicle • Wage subsidies are typically paid in one of three ways: through the tax system, through the social security system, or as direct programmatic payments to the employer or worker. Piggybacking on an existing system lowers the administrative costs of the subsidy program. If the payment goes to the employers, they should usually not receive payments ex-ante, but be reimbursed for part of the labor costs they have occurred. Conditionalities • For firms, restrictions on the dismissal of previous workers can be considered as well as stipulations on extending the employment contract after the subsidy expires. • Conditions for beneficiaries can include requiring beneficiaries to participate in job-related training, either before or after being hired. Linkages • To maximize their impact, wage subsidies might also need to be linked to other active labor market programs such as training, counseling, intermediation and job search assistance. Verification • Wage subsidies must be accompanied by a rigorous monitoring system to prevent and/or detect fraud and abuse. The monitoring system must ensure that each employee for whom the subsidy is claimed is not only eligible for the subsidy but is actually working, that the claimed wages are being paid, and that the data used to determine the amount of the subsidy are accurate. 109 Address: 17 76 G S t NW, Washington, DC 20006 Website: ht tp://w w w.worldbank .org/en/topic/jobsanddevelopment Twit ter : @W BG_ Jobs Blog: ht tp://blogs.worldbank .org/jobs