Formal-informal supply chain linkages and firm productivity in Sub-Saharan Africa: The role of human capital

Djidonou, Robert; Foster-McGregor, Neil; Mathew, Nanditha Working Paper Formal-informal supply chain linkages and firm productivity in Sub-Saharan Africa: The role of human capital UNU-MERIT Working Papers, No. 2025-006 Provided in Cooperation with: Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), United Nations University (UNU) Suggested Citation: Djidonou, Robert; Foster-McGregor, Neil; Mathew, Nanditha (2025) : Formalinformal supply chain linkages and firm productivity in Sub-Saharan Africa: The role of human capital, UNU-MERIT Working Papers, No. 2025-006, United Nations University (UNU), Maastricht Economic and Social Research Institute on Innovation and Technology (UNU-MERIT), Maastricht, https://doi.org/10.53330/JNER2108 This Version is available at: https://hdl.handle.net/10419/326935 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-sa/4.0/ #2025-006 Formal-Informal Supply Chain Linkages and Firm Productivity in Sub-Saharan Africa: The Role of Human Capital Gbenoukpo Robert Djidonou, Neil Foster-McGregor and Nanditha Mathew Published 13 February 2025 DOI: https://www.doi.org/10.53330/JNER2108 Maastricht Economic and social Research institute on Innovation and Technology (UNU-MERIT) email: [email protected] | website: http://www.merit.unu.edu Boschstraat 24, 6211 AX Maastricht, The Netherlands Tel: (31) (43) 388 44 00 UNU-MERIT Working Papers ISSN 1871-9872 Maastricht Economic and social Research Institute on Innovation and Technology UNU-MERIT UNU-MERIT Working Papers intend to disseminate preliminary results of research carried out at UNU-MERIT to stimulate discussion on the issues raised. https://creativecommons.org/licenses/by-nc-sa/4.0/ Formal-Informal Supply Chain Linkages and Firm Productivity in Sub-Saharan Africa: The Role of Human Capital Gbenoukpo Robert Djidonou1,2,3,∗, Neil Foster-McGregor4, and Nanditha Mathew1,5,6 1UNU-MERIT, United Nations University, Maastricht, Netherlands 2IFC, International Finance Corporation, Washington DC, US 3GPI, Global Policy Incubator, Berlin, Germany 4ADB, Asian Development Bank, Manila, Philippines 5UNU-CRIS, United Nations University, Bruges, Belgium 6CREF, Centro Ricerche Enrico Fermi, Roma, Italy ∗ ∗Corresponding author: Gbenoukpo Robert Djidonou, Email: [email protected] 1 Abstract Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in reducing poverty and inequality by generating the majority of jobs, income, and pathways to better employment opportunities. However, informal enterprises are often characterized by low productivity and significant decent work deficits. In Sub-Saharan Africa, where a large share of the workforce is engaged in informal enterprises, transitioning to formality is essential for enhancing productivity, fostering economic growth, and ensuring decent work for all. A critical pathway for informal firms to formalize is through production and worker linkages with formal firms. Using a sample of 13,626 informal firms from three Sub-Saharan African countries, this study examines the performance effects of informal firms with formal linkages and explores the mediating role of human capital. We find that formal backward linkages—where informal firms source inputs from formal firms—are significantly more common than other types of formal-informal linkages. Employing heteroskedasticity-based identification, our findings reveal that the productivity gains from these linkages are not automatic - higher human capital is essential for firms to benefit from knowledge and technology transfers. This highlights the critical role of absorptive capacity in enabling informal firms to leverage knowledge and technology transferred through formal backward linkages, thereby emphasizing the importance of targeted capacity-building interventions in fostering inclusive economic growth. Keywords: Formal-informal linkages, supply chains, informal firm, firm productivity, technology transfer, knowledge transfer, absorptive capacity, human capital, Sub-Saharan Africa. JEL Codes: J46 ; L14 ; L25 ; O12 ; O17 ; O33. 2 1 Introduction Informality is a significant cause and symptom of productivity stagnation in modern sectors in developing countries and a major concern for policymakers (Djidonou and FosterMcGregor (2022)). The formalization of informal firms is seen as a means of improving overall productivity, prompting several countries to enact policies aimed at incentivizing informal firms to register. For instance, the West African Monetary Union (WAEMU) has adopted a single electronic enterprise formalization window to reduce the costs and complexity for firms formalizing their activities. However, the results of such policy initiatives have been mixed, partly because informality is often a deliberate economic choice (Meagher (1995, 2013); Bruhn (2013)). This highlights the importance of examining firm dynamics within the informal sector and understanding the factors driving productivity improvements in this sector. Micro, Small, and Medium Enterprises (MSMEs) in both the formal and informal sectors are one of the main drivers in reducing poverty and inequality by generating the majority of jobs, incomes, and pathways to better employment opportunities. As key drivers of longterm economic growth and development, their role is particularly significant in Africa, where eight out of ten workers are engaged in informal employment (Kanbur (2021)). However, the severe decent work deficits characterizing the informal economy have led to a growing consensus that transitioning to formality is essential for realizing decent work for all, making this transition a priority on policy agendas across Africa. Informality is also potentially a substantial drag on aggregate productivity across SubSaharan Africa (SSA) since informal firms are typically smaller and less productive than their formal counterparts (Ulyssea (2020); Djidonou and Foster-McGregor (2022)). They often lack the managerial and technological capabilities required to expand their activities. One way of alleviating capability gaps and enhancing productivity for informal firms is by 3 creating links with formal firms that have such capabilities. To access such capabilities, however, informal firms may need to have a certain level of absorptive capacity. In this paper, supply chain linkages are identified as a main mechanism through which productivity spillovers may occur from formal to informal firms. The primary focus is on the backward linkages that exist from formal to informal firms. These linkages are particularly noteworthy as they can create a positive feedback loop that benefits both types of firms. By providing informal firms with access to new markets, knowledge, and resources, formal firms can help informal firms increase their productivity and competitiveness. Collaboration with formal firms may further improve product quality and compliance with higher standards. Informal firms may also acquire skills and financial support from formal partners, upgrading their operations and expanding capabilities. Participation in the supply chain provides valuable market intelligence, aiding informed decision-making and market adaptation. However, formal firms also benefit from the flexibility, lower costs, local market knowledge, extensive networks, and innovation of informal firms (Ulyssea (2020)). Productivity spillovers and knowledge transfer from formal to informal firms are not straightforward or automatic. Informal firms with internal competencies are better positioned to benefit from linkages with formal firms. For example, informal firms may need higher levels of absorptive capacity to learn and adopt new techniques and help access the resources required to upgrade their production processes. The concept of absorptive capacity has been used by researchers to explain various organizational phenomena (Cohen et al. (1990)) and is a critical discussion in the Global Value Chain (GVC) literature (Khan et al. (2019); Farole and Winkler (2015)). Absorptive capacity can be understood as the ability to recognize, assimilate, transform, and apply the value of external resources to local economic development (Cohen et al. (1990); Todorova and Durisin (2007)). Previous literature has shown that the level of absorptive capacity is driven by the education and work experience of workers (Spithoven and Knockaert (2011)). Education-based human capital improvement 4 helps firms enlarge their technological boundaries and absorb new technologies (Faems and Subramanian (2013); Brekke (2021)). In the case of formal-informal supply chains, the absorptive capacities of workers can help informal firms benefit effectively from the linkages. The extent to which linkages with formal firms help in the productivity enhancement of informal firms and the moderating role of complementary assets like human capital in this relationship is under-explored, especially in the context of SSA. The current study addresses this gap in the existing literature by examining the impact of firm linkages in supply chains on the transfer of practices, technologies, workers, and knowledge from more productive formal firms to less productive informal firms in developing countries and how education mediates this. Recent evidence collected by the International Labour Organization (ILO) in Latin America finds that supplying to and buying from large domestic and foreign-owned firms acts as a vehicle for transmitting managerial practices to small firms, including informal firms embedded in their supply chains, with these effects incentivizing formalization. The main explanation for such results is that purchasing inputs from formal firms can be a source of productivity spillovers. Through these linkages, informal firms may access new and improved product varieties and adopt more recent technologies and practices that improve their productivity. In this paper, we evaluate the implications of formal linkages for the productivity of informal firms in SSA. Moreover, we study how human capital mediates the impact of formalinformal supply chain linkages on firms’ productivity. To achieve this goal, we use a dataset, consisting of phases 1 and 2 of the ”Enquˆetes 1-2-3” survey (henceforth Survey 1–2–3), which provides detailed information on the linkages between formal and informal firms in SubSaharan African countries. The Survey 1-2-3 adopts the definitions of informality suggested by the ILO, defining informal firms as small production units that do not have written formal accounts and/or are not registered with the tax administration1. 1Following the International Labour Organization (ILO) definition of informal firms, firms are classified 5 Our results indicate that formal backward linkages matter for productivity enhancement in informal firms. However, the effects become insignificant when we control for sector and country dummies. We further find that human capital mediates the impact of formal backward linkages on informal firms’ productivity in all scenarios considered in our analysis. In other words, informal firms with skilled employees benefit to a greater extent in terms of productivity from linkages with formal firms. Our approach has several advantages over past studies. Firstly, the results are based on a survey that is nationally representative and comparable across countries. Secondly, our analysis features a critical methodological advance over previous studies by addressing the endogeneity of informal backward linkages, which may arise due to reverse causality and omitted variables. Thirdly, this is the first study we are aware of to examine the drivers of possible linkages and the role of complementary factors like human capital in the effect of linkages on informal firms’ productivity. This focus is central to understanding the process of capability accumulation, productivity improvement, and dynamics within informal firms, with relevant policy implications. The paper is organized as follows: Section 2 outlines the theoretical framework and reviews relevant literature. Section 3 details the dataset used for the empirical analysis. Section 4 offers descriptive evidence on formal and informal linkages. Section 5 explains the econometric methodology. Section 6 presents the results, focusing on the impact of formal backward linkages on informal firms’ productivity and the mediating role of human capital. Finally, Section 7 summarizes the key findings and provides conclusions. as informal firms if they do not comply with all regulations that apply to their activities. For example, informal firms do not officially register their activities, avoid tax payments, do not hold a formal account, or do not respect employment and operating licenses. 6 practices, such as better inventory management and optimized production processes. These changes result in operational improvements that drive productivity growth. Our first research question seeks to empirically assess whether informal firms with formal linkages exhibit higher productivity or profitability compared to those with only informal linkages. However, the extent to which informal firms benefit from formal backward linkages depends on their human capital and absorptive capacity. Research shows that firms with more skilled labor or higher absorptive capacity—defined as the ability to understand and utilize external knowledge—are better equipped to capitalize on formal linkages. For example, Van Biesebroeck (2005) finds that informal firms with skilled labor are more likely to experience productivity gains from formal backward linkages. Similarly, G¨org and Strobl (2005) demonstrate that firms with greater absorptive capacity are more effective at utilizing the knowledge transferred from formal firms. Studies document the role of human capital in enhancing productivity through formal linkages highlighting the critical function of education and training (Schultz, 1961) and (Becker, 2009). Informal firms with a more educated workforce are better positioned to adopt new technologies and practices, thereby improving productivity. Sonobe and Otsuka (2006) shows that informal firms in developing countries with better-trained employees benefit more from formal linkages. Absorptive capacity then plays a moderating role in the effectiveness of technology transfer through formal-informal linkages. Firms with higher absorptive capacity can effectively assimilate and apply knowledge gained from formal firms, resulting in stronger innovation and performance outcomes. Conversely, firms with low absorptive capacity may struggle to fully benefit from formal linkages, leading to incomplete technology transfer. To date, empirical research has yet to fully explore how absorptive capacity moderates the impact of formal-informal linkages on the performance of informal firms. Therefore, our second research question aims to investigate whether absorptive capacity, as reflected by human capital, mediates the relationship between firm linkages and the productivity of 13 informal firms. 3 DATA We use data from the Survey 1–2–3 collected in 2015 for this study. Survey 1–2–3 is the first nationally representative household and informal firm survey conducted in seven WAEMU countries and some other selected African countries, including Senegal, Togo, Burkina Faso, Mali, Benin, Niger, Cˆote d’Ivoire, Cameroon, the Democratic Republic of Congo, and Morocco. Survey 1–2–3 adopts the definitions suggested by ILO and Alter Chen (2005), defining informal firms as small production units that do not have written formal accounts and/or are not registered with the tax administration. The Survey 1–2–3 employs a standardized questionnaire and was conducted simultaneously in the WAEMU countries, ensuring comparability across datasets. The surveys are reliable due to technical support from the Observatoire Economique et Statistique d’Afrique Subsaharienne (AFRISTAT)2and D´eveloppement, Institutions et Mondialisation (DIAL)3, and financial backing from the World Bank, the European Commission, the French Ministry of Foreign Affairs, the African Development Bank (AfDB), the United Nations Development Program (UNDP), the Department for International Development (DFID), and the governments of each participating country. The surveys were classified as official data (B¨ohme and Thiele (2014)). The Survey 1–2–3 extends the principle of mixed surveys on the informal sector to better understand its role in each economy. It comprises three nested surveys involving different statistical populations: individuals, production units, and households, and covered all provinces, communes, and cities, making the sample nationally representative. The first phase of this survey consists of employment, unemployment, and household activity con2https://www.afristat.org/ 3https://dial.ird.fr/recherche/enquetes/enquetes-1-2-3/ 14 ditions (phase 1: employment survey). The second phase consists of surveying heads of production units on their conditions of activity, economic performance, mode of integration into the production structure, and prospects (phase 2: informal production units survey). Finally, the third phase is a household consumption survey, which aims to estimate household welfare, measure the weight of the formal and informal sectors in their consumption, and analyze the determinants of the choice of different places of purchase (phase 3: investigation of consumption and poverty). The employment survey fulfills a dual objective: to provide the main indicators to describe the situation of individuals and households in the labor market and to serve as a filter survey for phase 2 on informal production units. It provides the information necessary to identify all informal units surveyed during the first phase. In 2015, 21,454 were surveyed in the DRC, 38,599 in Cameroon, and 32,393 in Burkina Faso. During the second phase, 13,626 firms were randomly selected from the informal firms identified in the first phase (Brilleau et al. (2005)). Heads of informal production units were interviewed and were asked questions about their conditions of activity, their economic performance, the mode of insertion into the production and supply chains, and their prospects. The second phase allows us to have specific information on variables such as turnover, production costs, profits, and product quality information, the size of the entity, the human capital of workers, the intensity of inputs, the characteristics of entrepreneurs of informal firms, and their linkages with other firms. The 1–2–3 surveys define informal enterprises as small production units that do not have written formal accounts and/or are not registered with the tax administration. Moreover, the 1–2–3 surveys allow us to define the linkages between formal and informal firms. For example, formal backward linkages pertain to the supply of raw materials, equipment/machinery, finance, and consumer goods from formal to informal firms. In the Survey 1–2–3, informal firms are asked the sector of activity of their main suppliers and clients. 15 In our dataset, formal backward linkages occur when informal firms purchase intermediate inputs from the public and para-public sector, large commercial private enterprises, or large non-commercial private enterprises. Similarly, informal backward linkages occur when informal firms buy from small production units that do not have written formal accounts and/or are not registered with the tax administration. This paper mainly relies on the second phase of the survey, with the sample covering 4,504 firms in the DRC, 4,598 firms in Cameroon, and 4,524 firms in Burkina Faso. The random selection of firms from the first phase of the survey is nationally representative. Several studies have utilized data from the Survey 1–2–3 to analyze various aspects of informal economies in Sub-Saharan Africa. For example, Grimm et al. (2012) used the Survey 1–2–3 data to investigate the performance of informal enterprises in West Africa, highlighting the heterogeneity within the informal sector. Nordman and Vaillant (2014) employed the survey data to study gender disparities in earnings within the informal sector. Additionally, Benjamin and Mbaye (2012) utilized the Survey 1–2–3 to explore the impact of informality on economic development in Sub-Saharan Africa. 4 Descriptive Statistics on Formal and Backward Linkages In the realm of the firm linkages literature, two primary types are often discussed: consumption and production linkages, as elucidated by Hirschman (1985). Consumption linkages pertain solely to sales directed towards final demand, while production linkages can be further categorized into forward and backward linkages. In Survey 1-2-3, forward linkages occur when informal firms sell their goods and services to other firms, whether formal or informal. Specifically, a forward linkage is formal when an informal firm sells its goods and services 16 to a formal firm. Conversely, a forward linkage is informal when informal firms sell their goods and services to other informal firms. Backward linkages occur when informal firms buy their goods and services from other firms. In this context, a backward linkage is informal when informal firms buy their goods and services from other informal firms. Conversely, a backward linkage is formal when informal firms buy their goods and services from formal firms. The assumption that firms cannot simultaneously have both backward and forward linkages, or both formal and informal backward linkages, stems from the design of the survey, which is structured to capture only the primary suppliers or buyers of each firm. The survey focuses on the most significant transactional relationships, prioritizing the main suppliers or clients rather than detailing the full spectrum of interactions a firm may have. The survey’s coding system categorizes a firm’s primary buyers or suppliers, based on the entity that represents the highest value of transactions. Each code corresponds to a different type of buyer or supplier, helping to classify the firm’s relationships within its supply chain4. Consequently, firms are categorized based on their dominant supply chain relationships, either as having formal backward linkages (sourcing from formal firms) or informal backward linkages (sourcing from other informal firms), without room for overlap between the two categories. This approach presents a limitation, as it overlooks secondary or less prominent rela4DESTINATION / ORIGIN CODES OF PRODUCTS: For a firm, we seek the main destination/source (in terms of Sales), meaning the one that represents the highest value. 1 = Public or Parastatal Sector: government, public or parastatal enterprises; 2 = Large Private Commercial Enterprise: private commercial establishment, registered, employing more than 5 people; 3 = Small Commercial Enterprise: private commercial establishment employing up to 5 people; 4 = Large Private Non-Commercial Enterprise: private production or service establishment, registered, employing more than 5 people; 5 = Small Private Non-Commercial Enterprise: private production or service establishment employing up to 5 people; 6 = Household/Individual: This category applies to individuals purchasing for their final consumption. For example, in the case of a business making clothes and delivering them to neighbors to wear, the destination is coded as ”Household” (code 6). However, if the customer is an individual purchasing for resale, it should be coded as ”Small Commercial Enterprise” (code 3). 7 = Direct Export: sales to a foreign partner; 8 = SelfConsumption: This code captures the amount of production from the informal production unit (UPI) that is consumed by the household of its promoter. For a UPI producing and selling donuts, this code indicates the amount of donuts consumed by all members of the promoter’s household. 9 = Association/NGO. 17 tionships that could provide a more nuanced understanding of a firm’s position within the broader economic network. For example, an informal firm might source most of its inputs from other informal firms (classified as having informal backward linkages) but could also occasionally source specialized inputs from formal firms. These secondary formal linkages, though not captured in the survey, may still influence the firm’s performance and development. Similarly, while the survey assumes that firms cannot engage in both backward and forward linkages at the same time, in practice, informal firms may operate in both directions, acting as suppliers to other firms while simultaneously sourcing inputs. By focusing only on the primary suppliers or buyers, the survey limits the scope of the analysis, potentially underrepresenting the complexity of the interactions between formal and informal firms. This restricted view of firm linkages can lead to an incomplete understanding of how firms integrate into larger supply chains, and how these interactions affect their productivity and growth. Despite this limitation, our analysis remains relevant by shedding light on the core dynamics of formal-informal backward linkages and their influence on informal firm performance. Future research could benefit from more comprehensive data collection that includes multiple tiers of suppliers and buyers to better capture the multifaceted nature of firm relationships. Descriptive statistics reveal that households are the predominant consumers of informal goods, with 11,759 households reporting demand for products from informal firms. Moreover, informal firms often rely on households not only as consumers but also as suppliers of intermediary goods within their production chains. Specifically, 2,764 informal firms integrate inputs sourced directly from households. This study, however, deliberately excludes any analysis of linkages between households and informal firms. Instead, it aims to elucidate the dynamics of formal and informal backward linkages among firms only. To provide insights into the dataset, Table 1 presents detailed information on the number of informal firms involved in both formal and informal backward linkages. It highlights that 18 approximately 85 percent of the total linkages observed are backward linkages, underscoring their significance for informal firms in Sub-Saharan Africa. Additionally, the table presents comparative details on the number of informal firms participating in formal and informal forward linkages, highlighting the role of informal firms as suppliers to other entities. The findings demonstrate that only a minority of informal firms are involved in forward linkages, constituting 15 percent of total linkages. Only, 3.03 percent of total linkages are formal forward linkages, while 11.87 percent are informal forward linkages. This paper excludes the analysis of both formal and informal forward linkages from its scope, as its primary focus is to juxtapose formal backward linkages against informal ones. Given this, we define the backward linkage variable as a binary outcome, with the formal backward linkage variable taking the value 1 if the suppliers to informal firms are formal firms and 0 if the suppliers are informal firms. As such, the analysis focuses only on informal firms with a backward linkage to either formal or informal firms. The data indicates that around 16 percent of total linkages are formal backward linkages, while the majority, over 69 percent, are informal backward linkages, indicating that a substantial proportion of informal firms source their inputs and final goods from other informal firms. This trend can be attributed to the preference of informal firms for affordable input sources (Ranis and Stewart (1999)). Despite informal backward linkages being prevalent, the current paper focuses on the potential for interactions with formal firms through backward linkages to impact performance through spillover effects. In this sense and given the focus on informal firms with backward linkages only, the informal firms with backward linkages to other informal firms serve as the control group. To provide an initial comparison between informal firms with formal and informal backward linkages, Table 2 provides information on the average values (and the standard deviation) of a set of key firm performance indicators for firms with formal backward and with informal backward linkages (see Table 3 for a full definition of the variables). The findings 19 indicate that informal firms with formal backward linkages tend to be, on average, more capital-intensive and to employ a higher-skilled workforce relative to informal firms with informal backward linkages. Additionally, these firms tend to be larger in terms of value-added, enjoy better access to electricity and credit facilities, and exhibit higher profits. To examine whether there are differences in productivity (defined as sales per worker) and profitability (defined as profit per worker) between informal firms with formal and informal backward linkages, Figure 1 compares the productivity distribution of these two types of informal firms. The figure demonstrates that informal firms with formal backward linkages tend to exhibit higher productivity levels than those with informal backward linkages. Figure 2 reports similar information, but uses data on profitability rather than productivity, with a similar outcome. While such results are suggestive of the role of formal backward linkages in enhancing the productivity of informal firms, it could also be that formal firms display a preference for engaging in supply chains with informal firms that possess stronger capabilities. This raises the issue of reverse causality, which will be addressed subsequently. Table 1: Frequency and Share of Informal Firms with Different Linkage Types Backward Forward Total Formal 1795 (15.93%) 342 (3.03%) 2137 (18.97%) Informal 7792 (69.16%) 1337 (11.87%) 9129 (81.03%) Total 9587 (85.09%) 1679 (14.90%) 11266 (100%) Note: The figures represent the number of informal firms with backward and forward linkages. The figures in parentheses represent the share of the linkages in total linkages. Source: Authors’ calculation based on 1-2-3 survey. 20 Table 2: Comparison of performance measures between informal firms with formal and informal backward linkages Formal Backward Informal Backward Obs Mean Std. dev. Min Max Obs Mean Std. dev. Min Max Productivity 1759 11.8 1.40 6.5 16.6 7648 11.088 1.33 1.79 19.0 Profitability 1759 11.4 1.69 3.7 16.6 7645 10.766 1.50 3.26 19.0 Education 1795 6.73 4.85 0 18 7792 5.243 4.44 0 20 Capital 1560 10.5 2.17 3.3 17.7 7010 9.687 1.9 2.67 16.5 Value Added 1790 10.7 1.49 4.4 15.9 7758 10.055 1.46 0.996 16.7 Number of workers 1795 1.49 1.10 1 12 7792 1.311 0.893 1 17 Access to Credit 1795 0.065 0.246 0 1 7792 0.037 0.19 0 1 Age of workers 1794 19.1 27.5 0 94 7786 20.361 28.3 0 94 Firm Age 1795 36.5 11.1 12 77 7792 36.734 12.5 7 89 Gender of Workers 1792 0.084 0.277 0 1 7782 0.047 0.212 0 1 Belong to Association 1795 0.607 0.488 0 1 7792 0.336 0.472 0 1 Access to Support 1236 0.453 0.498 0 1 5175 0.449 0.497 0 1 Experience 1794 18.2 25.9 0 89 7787 19.433 26.9 0 89 Access to Water 1795 0.059 0.236 0 1 7792 0.025 0.156 0 1 Source: Authors’ calculation based on 1-2-3 survey. Table 3: Definition of Variables Variable Definition Backward Linkage A binary variable that equals 1 if the firm has a formal backward linkage and 0 if the firm has an informal backward linkage. Productivity Log of total sales per worker Profitability Log of total profit per worker Education Average number of years of schooling of workers Capital Log of capital stock of firms Value Added Log of total value added Number of workers Total number of workers Access to Credit A binary variable equal to 1 if firm has access to finance and 0 otherwise Age of workers Average age of workers in years Firm Age Number of years of firm existence Gender of Workers The share of workers that are male. Belonging to Association A binary variable equal to 1 if the firm belongs to an association and 0 otherwise. Firms were asked whether they were members of professional associations or received help from professional associations. Access to Support A binary variable equal to 1 if the firm has access to professional support from associations and 0 otherwise. Experience Average number of years spent by workers in a firm Access to Water A binary variable equal to 1 if firm has access to water and 0 otherwise. Figure 1: Productivity distribution of informal firms with formal and informal backward linkages Source: Authors’ calculation based on 1-2-3 survey. Figure 2: Profitability distribution of informal firms with formal and informal backward linkages Source: Authors’ calculation based on 1-2-3 survey. 21 5 Econometric Methodology In order to investigate our first research question of whether informal firms with formal linkages have higher productivity (or profitability) than those with informal linkages, we begin by estimating a linear regression model. The econometric model takes the following form: Yic =β1+β2BLic +Xλ +αj+αc+ϵic (1) where Yic is the indicator measuring informal firm productivity or profitability, BLic is the binary variable capturing the presence of formal backward linkages for firm iin country c, and Xis a vector of additional control variables that are discussed below. The regression specification further includes both country (αc) and sector (αj) dummies to control for unobserved heterogeneity across sectors and countries. The measure of productivity that we adopt is sales per worker, which is an estimate of labour productivity. Sales per worker is a commonly used measure in economic and business research to assess the efficiency and output generated by each worker within a firm (Syverson (2011)). Higher sales per worker indicate that a firm is generating more revenue relative to the number of workers employed, suggesting a higher level of output efficiency and productivity (Hsieh and Klenow (2009)). However, it is important to acknowledge that sales alone may not capture the full picture. One limitation of sales per worker is its inability to account for changes in inventories, which can affect the accuracy of productivity estimates. Changes in inventories, such as fluctuations in stock levels, can influence the overall efficiency and productivity of a firm (Olley and Pakes (1992)). As a robustness check, we use profit per worker to measure firm profitability. The inclusion of profit per worker as a measure of firm performance provides valuable insights into the potential cost-saving effects and output enhancements resulting from spillovers received 22 Table 4: Effects of Formal Backward Linkages on Informal Firms’ Productivity: OLS and Sector and Country Dummies (1) (2) (3) (4) (5) (6) Productivity Profitability Productivity Profitability Productivity Profitability Formal Backward 0.343*** 0.133*** 0.357*** 0.146*** 0.228*** 0.145*** (0.036) (0.033) (0.036) (0.031) (0.035) (0.028) log (Value Added) 0.768*** 0.899*** 0.769*** 0.894*** 0.745*** 0.839*** (0.011) (0.010) (0.011) (0.010) (0.015) (0.011) log (Capital Stock) 0.066*** 0.155*** 0.067*** 0.135*** 0.075*** 0.067*** (0.009) (0.008) (0.009) (0.008) (0.009) (0.008) Number of workers -0.478*** -0.463*** -0.476*** -0.471*** -0.438*** -0.459*** (0.021) (0.019) (0.021) (0.018) (0.030) (0.028) Access to Credit 0.193*** -0.018 0.191*** 0.003 0.218*** 0.106*** (0.054) (0.049) (0.054) (0.047) (0.050) (0.037) Firm Age -0.002*** -0.001** -0.002 -0.002 -0.000 -0.000 (0.001) (0.000) (0.002) (0.002) (0.001) (0.001) Access to Support -0.061** 0.048* -0.067** 0.028 -0.049* -0.031 (0.031) (0.028) (0.031) (0.026) (0.028) (0.022) Association membership 0.042 -0.020 0.061 0.012 0.041 0.017 (0.055) (0.050) (0.055) (0.048) (0.046) (0.039) Access to Electricity 0.083* 0.231*** 0.081* 0.182*** -0.017 -0.074* (0.043) (0.039) (0.043) (0.037) (0.042) (0.038) Access to Water -0.081 -0.176*** -0.086 -0.092* 0.081 0.082* (0.065) (0.058) (0.065) (0.056) (0.056) (0.047) Education of workers 0.015*** 0.050*** 0.001 0.003 (0.003) (0.003) (0.004) (0.002) Age of workers 0.002 -0.000 0.002 0.002** (0.001) (0.001) (0.001) (0.001) Sex of Workers -0.100*** 0.104*** -0.083*** 0.063*** (0.033) (0.028) (0.030) (0.024) Experience 0.001 0.000 0.001 0.001 (0.002) (0.002) (0.001) (0.001) Exports 0.216 0.347** 0.264 0.387* (0.181) (0.156) (0.252) (0.219) Country and Sector Dummies YES YES Constant 3.472*** 0.608*** 3.509*** 1.112*** 3.727*** 2.741*** (0.111) (0.100) (0.123) (0.107) (0.360) (0.179) Observations 3,028 3,027 3,024 3,023 3,023 3,022 R-squared 0.689 0.808 0.692 0.826 0.748 0.878 Notes: The first 2 columns control only for firm characteristics, columns 3 and 4 control for both firm and worker characteristics, and the last 2 columns present the results, including firm and country dummies alongside firm and worker characteristics. Figures in parenthesis are clustered robust standard errors; ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗p < 0.1 establishing causality requires further investigation, considering the potential for reverse causality in these relationships. 6.2 Identification through Heteroskedasticity In this section, we report the results of the impact of formal backward linkages on the performance of informal firms using the identification approach known as identification through 29 heteroskedasticity, following the methodology proposed by (Lewbel (2012)). As discussed above, we might face the issue of reverse causality when estimating the impact of formal backward linkages on firm performance. For example, it could be that more productive and profitable informal firms are more likely to establish formal backward linkages, rather than these linkages driving the performance improvements. To address this endogeneity issue, we use the identification through heteroskedasticity method developed by Lewbel (2012), which utilizes the heteroskedasticity present in the data to generate a set of instruments (Baum et al. (2012)). Table 5 presents the results, with columns 1 and 2 showing the outcomes when controlling for firm characteristics, columns 3 and 4 when controlling for both firm and worker characteristics, and columns 5 and 6 when further including country and sector dummies. The outcomes of our variable of interest remain consistent with those obtained using OLS without country and sector dummies, thus confirming our previous findings that formal backward linkages are positively and significantly associated with higher productivity and profitability of informal firms. However, when controlling for country and sector dummies, the impact of formal backward linkages on the performance of informal firms is no longer significant, although the coefficients remain positive. The results indicate that informal firms with formal backward linkages exhibit productivity and profitability levels that are 31% and 49% higher, respectively, compared to firms with informal backward linkages when only firm characteristics are controlled. Similar results are found when controlling for both firm characteristics and worker characteristics, with significance at the 1% level. However, when additionally controlling for country and sector dummies, the impacts become lower and insignificant, suggesting that formal backward linkages alone are not sufficient for firms to achieve higher productivity and profitability. The Hansen J test’s p-value is statistically insignificant at all conventional significance levels, indicating that the internally generated instruments used in the models are uncor30 related with the error term. This result supports the validity of our instruments. Formal backward linkages can provide informal firms with access to better managerial practices, higher quality inputs, and more stable demand, which in turn can boost their productivity and profitability (McKenzie and Woodruff (2014)). These linkages facilitate knowledge transfer and can lead to the adoption of more efficient production techniques and improved business strategies. However, the reduction in significance when controlling for country and sector dummies suggests that contextual factors play a critical role. This finding highlights the necessity of considering the broader economic and institutional environment in which firms operate. For instance, countries with stronger regulatory frameworks and better infrastructure might enhance the effectiveness of formal backward linkages (Bennett and Estrin (2007)). Conversely, in environments with weak institutions and poor infrastructure, the benefits of these linkages may be less pronounced. Additionally, the positive but non-significant results after controlling for country and sector dummies may imply that informal firms need more than just formal linkages to thrive. Access to, skilled labour for example, as well as supportive government policies, are crucial for sustaining growth and improving performance (Beck et al. (2005)). Therefore, policy interventions aimed at strengthening formal-informal firm linkages should be complemented by broader efforts to improve the business environment and provide direct support to informal firms. The following section examines the role of human capital in comparing formal backward linkages with informal backward linkages. 6.3 Backward Linkages, Firm Performance, and Human Capital Improvements in human capital are essential for generating a workforce capable of creativity and innovation, which significantly impacts business development in Sub-Saharan Africa. A low level of human capital hinders firms from performing effectively and efficiently. Or31 Table 5: Effects of Formal Backward Linkages on Informal Firms’ Productivity: Identification using the Lewbel (2012) method (1) (2) (3) (4) (5) (6) Productivity Profitability Productivity Profitability Productivity Profitability Formal Backward 0.308** 0.489*** 0.292** 0.375*** 0.007 0.029 (0.136) (0.125) (0.123) (0.107) (0.085) (0.068) log (Value Added) 0.769*** 0.889*** 0.771*** 0.889*** 0.750*** 0.843*** (0.012) (0.011) (0.012) (0.010) (0.011) (0.009) log (Level Capital) 0.066*** 0.149*** 0.068*** 0.132*** 0.081*** 0.071*** (0.009) (0.008) (0.009) (0.008) (0.009) (0.007) Number of workers -0.477*** -0.468*** -0.475*** -0.474*** -0.433*** -0.456*** (0.021) (0.020) (0.021) (0.018) (0.020) (0.016) Access to Credit 0.195*** -0.039 0.195*** -0.010 0.229*** 0.114*** (0.054) (0.050) (0.054) (0.047) (0.049) (0.040) Firm Age -0.002*** -0.001* -0.001 -0.002 -0.000 -0.001 (0.001) (0.000) (0.002) (0.001) (0.002) (0.001) Access to Support -0.061** 0.055* -0.068** 0.032 -0.050* -0.031 (0.031) (0.028) (0.031) (0.027) (0.028) (0.022) Association membership 0.044 -0.045 0.064 -0.002 0.052 0.026 (0.056) (0.051) (0.055) (0.048) (0.050) (0.040) Access to Electricity 0.085* 0.204*** 0.086* 0.166*** 0.000 -0.060* (0.044) (0.041) (0.044) (0.038) (0.041) (0.033) Access to Water -0.079 -0.197*** -0.082 -0.108* 0.099* 0.096** (0.065) (0.060) (0.065) (0.057) (0.060) (0.048) Level of Education -0.015*** -0.050*** 0.001 0.003 (0.003) (0.003) (0.004) (0.003) Age of workers 0.002 -0.000 0.002 0.002* (0.001) (0.001) (0.001) (0.001) Sex of Workers -0.095*** 0.088*** -0.065** 0.077*** (0.034) (0.029) (0.031) (0.025) Experience -0.001 0.000 -0.001 -0.000 (0.002) (0.001) (0.001) (0.001) Exports 0.228 0.306* 0.304* 0.418*** (0.182) (0.158) (0.165) (0.132) Country and Sector Dummies No No No No Yes Yes Constant 3.464*** 0.688*** 3.495*** 1.161*** 2.956*** 1.385*** (0.114) (0.106) (0.126) (0.110) (0.125) (0.101) Observations 3,028 3,027 3,024 3,023 3,023 3,022 R-squared 0.688 0.800 0.692 0.823 0.744 0.876 Hansen J Statistic 0.292 0.538 0.171 0.131 0.106 4.140 Hansen J Statistic p-value 0.5888 0.4633 0.6794 0.7173 0.7444 0.0419 Notes: The first 2 columns control only for firm characteristics, columns 3 and 4 control for both firm and worker characteristics, and the last 2 columns present the results obtained by adding firm and country dummies. Figures in parenthesis are clustered robust standard errors; ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗p < 0.1. ganizational value creation, fostered by knowledge, skills, competency, and innovation, is critical for both entrepreneurs and workers. The literature argues that a minimum level of education, technology, infrastructure, and health is necessary for informal firms to benefit from potential spillovers (Thoumrungroje and Racela (2022)). Many informal firms with low levels of education suffer from this knowledge gap, which may explain their relatively poor 32 performance compared to productive formal firms. The results in the previous section show that backward linkages alone are insufficient for firms to be productive and generate more profit while controlling for sector and country dummies. This suggests that while formal backward linkages provide potential channels for knowledge and technology transfer, these channels need to be coupled with adequate human capital to realize their benefits. Without a skilled and educated workforce, informal firms may not fully capitalize on the opportunities presented by their linkages with formal firms. For instance, an informal firm might have access to high-quality inputs from formal firms, but without the necessary skills and knowledge, it cannot utilize these inputs efficiently to enhance productivity and profitability. In this section, we examine the role played by education in the learning process generated by linkages between formal and informal firms and its impact on the performance of informal firms in Sub-Saharan Africa. Table 6 summarizes the results from OLS regression with an interaction between formal backward linkages and the average level of education of workers. Columns 1 and 2 of Table 6 show the results of this interaction by considering both firm and worker characteristics, while columns 3 and 4 add sector and country dummies to the control variables. The regression results demonstrate that education mediates the learning process from formal backward linkages and significantly affects the performance of informal firms in SubSaharan Africa. For firms with formal backward linkages, each additional year of education is associated with a 10.7% increase in productivity and a 7% increase in profitability while controlling for both firm and worker characteristics. These findings indicate a similar positive effect of an additional year of worker education on firm performance when they maintain formal backward linkages. Table 7 presents the results of the mediated role of education on firm performance through formal backward linkages using an identification strategy via heteroskedasticity, following 33 the approach proposed by (Lewbel (2012)). We apply Lewbel (2012) method to both the backward linkage variable and its interaction with education. This method aims to enhance the reliability of coefficient estimates by addressing reverse causality problems. Columns 1 and 2 show results using identification through heteroskedasticity, considering both firm and worker characteristics, while columns 3 and 4 add sector and country dummies. The impact of formal backward linkages on informal firm performance, mediated by education, is consistent with previous regressions. The results show that if workers in informal firms have an additional year of education, having backward linkages with formal firms increases their productivity by 3.2% and profitability by 7%. These results remain consistent when controlling for sector and country dummies. The Hansen J p-value, reported in the last row of both columns, is statistically insignificant at all conventional significance levels, indicating that the internally generated instruments used in these models are uncorrelated with the error term. This confirms the validity of our internally generated instruments. Similarly, Lund Vinding (2006) found that the share of highly educated employees is positively correlated with a firm’s ability to produce efficiently. This finding supports the idea that informal firms with highly educated workers can experience productivity spillovers by purchasing inputs from formal firms. Through these linkages, informal firms might access new and improved product varieties, adopt more recent technologies and practices, and enhance their productivity (Newman et al. (2015)). Furthermore, Ratten et al. (2017) emphasize that innovation and entrepreneurial capabilities, which are often higher in more educated workforces, are crucial for firm performance and competitiveness. Our findings suggest that education plays a crucial role in mediating the learning process from formal backward linkages and significantly impacts the performance of informal firms in Sub-Saharan Africa. The positive relationship between education and both productivity and profitability underscores the importance of investing in human capital and fostering linkages between formal and informal sectors for economic development. Enhancing the educational 34 Table 6: Formal Backward Linkages and Informal Firms’ Productivity Using OLS: Mediating Role of Human Capital (1) (2) (3) (4) Productivity Profitability Productivity Profitability Formal Backward 0.528*** 0.326*** 0.324*** 0.325*** (0.063) (0.066) (0.056) (0.052) Formal Backward ×Education 0.027*** 0.028*** 0.016** 0.028*** (0.007) (0.007) (0.006) (0.005) Education (in years) 0.008** 0.042*** 0.005 0.010*** (0.004) (0.003) (0.004) (0.003) log (Value Added) 0.769*** 0.891*** 0.745*** 0.837*** (0.015) (0.012) (0.014) (0.011) log (Level Capital) 0.063*** 0.133*** 0.072*** 0.065*** (0.009) (0.008) (0.009) (0.008) Number of workers -0.396*** -0.393*** -0.366*** -0.372*** (0.025) (0.023) (0.023) (0.022) Access to Credit (1=yes) 0.189*** 0.001 0.217*** 0.105*** (0.053) (0.043) (0.050) (0.037) Firm Age -0.002 -0.002** -0.001 -0.000 (0.001) (0.001) (0.001) (0.001) Access to Support -0.076** 0.020 -0.056** -0.037 (0.031) (0.027) (0.028) (0.023) Association membership 0.055 0.001 0.036 0.004 (0.052) (0.043) (0.046) (0.039) Access to Electricity 0.063 0.162*** -0.022 -0.088** (0.045) (0.044) (0.042) (0.038) Access to Water -0.066 -0.065 0.094* 0.109** (0.059) (0.056) (0.056) (0.048) Age of workers 0.002 -0.000 0.002* 0.002** (0.001) (0.001) (0.001) (0.001) Sex of Workers -0.101*** 0.102*** -0.080*** 0.064*** (0.033) (0.029) (0.030) (0.024) Experience -0.000 0.000 -0.001 -0.001 (0.002) (0.001) (0.001) (0.001) Exports 0.221 0.337 0.275 0.385* (0.269) (0.242) (0.246) (0.202) Country and Sector Dummies Yes Yes Constant 3.409*** 1.021*** 3.617*** 2.585*** (0.135) (0.109) (0.362) (0.176) Observations 3,063 3,062 3,062 3,061 R-squared 0.693 0.826 0.747 0.876 Notes: Included country and sector dummies are not reported. Figures in parenthesis are cluster robust standard error; ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗p < 0.1. attainment of the workforce can enable informal firms to better absorb and implement the knowledge and technologies accessed through formal backward linkages, thereby driving improvements in firm performance and contributing to broader economic growth. 35 Table 7: Formal Backward Linkages and Informal Firms’ Productivity Using Lewbel (2012): Mediating Role of Human Capital (1) (2) (3) (4) Productivity Profitability Productivity Profitability Formal Backward 0.535*** 0.372*** 0.267*** 0.278*** (0.068) (0.059) (0.062) (0.050) Formal Backward ×Education 0.023*** 0.028*** 0.009 0.021*** (0.007) (0.006) (0.007) (0.005) Education (in years) 0.009** 0.042*** 0.003 0.008* (0.004) (0.003) (0.004) (0.003) log (Value Added) 0.768*** 0.890*** 0.746*** 0.837*** (0.011) (0.010) (0.011) (0.009) log (Level Capital) 0.063*** 0.132*** 0.072*** 0.065*** (0.009) (0.008) (0.009) (0.007) Number of workers -0.396*** -0.393*** -0.365*** -0.371*** (0.015) (0.013) (0.013) (0.011) Access to Credit (1=yes) 0.187*** -0.001 0.216*** 0.104*** (0.053) (0.046) (0.049) (0.039) Firm Age -0.002 -0.002 -0.000 -0.000 (0.002) (0.001) (0.002) (0.001) Access to Support -0.075** 0.021 -0.055** -0.036 (0.030) (0.026) (0.028) (0.022) Association membership 0.053 -0.002 0.037 0.005 (0.055) (0.047) (0.050) (0.040) Access to Electricity 0.063 0.158*** -0.019 -0.085*** (0.043) (0.037) (0.041) (0.033) Access to Water -0.071 -0.067 0.090 0.105** (0.064) (0.056) (0.059) (0.047) Age of workers 0.002 -0.000 0.002 0.002** (0.001) (0.001) (0.001) (0.001) Sex of Workers -0.103*** 0.098*** -0.079*** 0.065*** (0.033) (0.028) (0.030) (0.024) Experience -0.001 0.000 -0.001 -0.000 (0.002) (0.001) (0.001) (0.001) Exports 0.216 0.330** 0.278* 0.388*** (0.181) (0.156) (0.164) (0.132) Country and Sector Dummies No No Yes Yes Constant 3.424*** 1.030*** 2.963*** 1.359*** (0.123) (0.106) (0.121) (0.098) Observations 3,063 3,062 3,062 3,061 R-squared 0.693 0.826 0.747 0.876 Hansen J Statistic 7.976 11.969 40.549 44.004 Hansen J Statistic p-value 0.018 0.002 0.359 0.387 Notes: Included country and sector dummies are not reported. Figures in parenthesis are cluster robust standard error; ∗∗∗ p < 0.01, ∗∗ p < 0.05, ∗p < 0.1. 7 Conclusion This paper examines the effects of formal-informal supply chain linkages on the performance of informal firms and investigates how human capital mediates the impact of these linkages on the performance of informal firms. Our study first explores the presence of both formal 36 and informal forward and backward linkages. We find that formal backward linkages, - where informal firms use inputs produced by formal firms - are significantly more prevalent than formal forward linkages, where formal firms source inputs from informal firms. We then assess the impact of formal backward linkages on informal firms’ performance, measured both by productivity and profitability. Our findings indicate that informal firms participating in supply chains with formal firms exhibit significantly higher productivity. This paper is among the first in the informal economy literature to address the endogeneity of formal backward linkages, which can arise due to omitted variables and reverse causality. Using identification through heteroskedasticity, we demonstrate that informal firms with backward linkages to formal firms experience enhanced productivity. However, the impacts turn insignificant when we account for sector and country dummies. Furthermore, we examine how human capital mediates the impact of these backward linkages on the productivity of informal firms in Africa. Our findings underscore that informal firms with workers possessing higher absorptive capacities benefit more from backward linkages in terms of productivity gains. This underscores the crucial role of human capital in maximizing the benefits derived from external linkages with formal firms. Our findings have significant implications both at the organizational level and for policy formulation. We highlight the importance of complementary assets, such as an educated workforce, in leveraging external linkages with formal firms to enhance productivity. However, merely having an educated workforce is not sufficient; firms must actively engage in knowledge transfer and expansion strategies. Building linkages with firms closer to the efficiency and knowledge frontier is essential for achieving sustained productivity improvements. At the firm level, the results highlight the importance of complementary assets, particularly human capital, in maximizing the benefits of formal-informal linkages. However, simply having an educated workforce is not sufficient; firms must also actively engage in knowledge acquisition, and process upgrading to sustain productivity improvements. Building stronger 37 linkages with firms at the efficiency and knowledge frontier is essential for long-term capability development. At the policy level, our study suggests that interventions aimed at enhancing skills development and absorptive capacities among informal workers can significantly amplify the productivity gains from formal-informal linkages. This calls for targeted vocational training programs, business development services, and incentives for formal-informal collaboration to foster inclusive economic growth. More broadly, policy efforts should not only focus on formalization but also on creating enabling conditions for productivity enhancement within the informal sector. By shedding light on the interaction between formal and informal firms and the pivotal role of human capital, this study contributes to a deeper understanding of how supply chain linkages can be leveraged to drive productivity growth in the informal economy. 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Fan (2022): “Cross-national knowledge transfer, absorptive capacity, and total factor productivity: The intermediary effect test of international technology spillover,” Technology Analysis & Strategic Management, 34, 625–640. 46 The UNU-MERIT WORKING Paper Series 2025-01 Development strategies for the green hydrogen economy in emerging economies by Fabianna Bacil, Anthony Black, Marina Domingues, Jun Jin, Rasmus Lema, Glen Robbins and Sören Scholvin 2025-02 Do global value chains and local capabilities matter for economic complexity in EU regions? by R. Boschma, E. Hernández-Rodríguez, A. Morrison and C. 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