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Women constitute half of the global population, yet their participation in economic activities is far from equal to men’s. Across the globe, women lag men in labor force participation, entrepreneurship, top management positions, and firm productivity. Despite the growing evidence on gender-based gaps and their relevance, a key issue that is largely ignored is how such gaps play out in the informal sectors of developing countries. Understanding gender gaps in the informal sector is crucial because of the large size of the informal sector in developing countries (see Loayza 2016) and because large number of women work in the sector (see Hyland and Islam 2021).
The informal sector is characterized by lower entry barriers, lower capital and skills requirements, and greater flexibility of working hours and location. These features can be amenable to those who face discrimination, have lower levels of education and experience, and often combine their work with providing household care, which is the case of many women across the world at a disproportionate level than men. Does this mean that gender gaps are insignificant or non-existent in the informal sector? What are the factors that increase or decrease gender gaps in the informal sector?
Exploring Gender Gaps in Informal Sectors: Similarities with Formal Sectors and Underlying Causes
A recent study by Islam and Amin (2023) attempts to answer these questions. The study examines survey data on informal businesses in 42 major cities across 14 developing countries in Africa, Asia, and Latin America, gathered by the World Bank’s Enterprise Surveys. It employs both the traditional Kitagawa-Oaxaca-Blinder (KOB) mean and advanced quantile decomposition methodologies to study the differences in labor productivity between informal businesses owned (and run) by women and men.
Key findings from the study include:
First, informal women-owned businesses are found to have about 15.6 percent lower mean labor productivity than men-owned businesses, mirroring similar gaps observed in the formal sector. This underscores the need for targeted policy interventions in the informal sector.
Second, the productivity gap is driven by both the “endowment” and “structural” effects. The “endowment” effect refers to how differences in the level of productivity-enhancing factors like education, experience, and resources, impact the productivity gap. The study finds that the productivity gap is significantly widened (favoring men over women owners) because women owners tend to have lower levels of education, managerial experience, vehicles, electricity, and spending on security. However, the smaller size of women-owned businesses narrows the gap, since larger informal businesses are found to be less productive. Also, women-owned businesses benefit from being disproportionately present in more productive cities than men-owned businesses. Figure 1 provides more details.
As for the “structural” effects, these arise because women and men owned firms benefit differently from a given level of resources. The study finds that the productivity gap is widened because women-owned businesses benefit less from city-specific factors that impact labor productivity than men-owned businesses, suggesting that overall city development does not necessarily reduce gender gaps. The study also highlights the security issue. Spending on security is linked to higher labor productivity for men-owned firms, while the impact on women-owned businesses is much smaller and statistically insignificant; this difference widens the productivity gap. Contract-based production and sales are associated with higher labor productivity of women-owned businesses, while the impact on those men-owned is almost nil. The indication is that strengthening contracting institutions can be an effective way to close part of the gender-based productivity gap in the informal sector. Figure 2 provides more details.
Third, the productivity gap is large at all points of the labor productivity distribution. However, the magnitude varies. The gap is largest at lower levels or quantiles of labor productivity, and it narrows as we move up the productivity ladder (Figure 3). This constitutes a scenario in which “sticky floors” are the primary issue, with “glass ceilings” taking on a secondary role in explaining the gap. “Sticky floors” occurs when relative to men, women find it difficult to take off from low productivity levels. However, having achieved a certain threshold level of labor productivity, it becomes easier for women to compete with men. In contrast, “glass ceilings” occur when women’s prospects are limited beyond a certain level of labor productivity relative to men. The contribution of the individual factors to the gap also varies along the productivity distribution, implying that policy effectiveness can be significantly improved by directing policies to the relevant informal businesses or owners who can benefit most from it.
Figure 3: Gender labor productivity gap at different productivity quantiles
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Source: Islam and Amin (2023)
Last, the study makes an in-depth analysis of the differences and similarities between groups of countries in low-income Africa, middle-income Africa, and Latin America. Several interesting findings emerge. For example, in all groups there is a robust and statistically significant productivity gap, and there are significant individual labor productivity determinants for that gap such as firm size, owner education, electricity use, producing or selling under contract, use of vehicles, use of bank accounts, having a loan, and security payments. Regarding differences, the size of the mean productivity gap varies; there is evidence of “sticky floors” in middle-income Africa and Latin America and the “glass ceiling” effect in low-income Africa; and some individual factors contribute significantly to the productivity gap in one group but not the others. Thus, the study recommends an eclectic approach that incorporates general findings from the literature while appropriately accounting for the prevailing local conditions.
This research highlights the importance of understanding gender gaps in the informal sector and suggests that policies addressing these gaps need to be nuanced and tailored to specific regional contexts. Future research can explore whether gender gaps exist in this sector in wages, profits, growth, and employment. The impact of care provision within the household, culture, laws, and political empowerment on the gender gaps in the sector can also be explored. Finally, identifying the causal impact of the individual factors on the gender gap in the informal sector can provide valuable insights for policymaking.
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