The Labor Productivity Gap between Formal Businesses Run by Women and Men

ABSTRACT

This study analyzes gender differences in labor productivity in the formal private sector, using data from 126 mostly developing economies. The results reveal a sizable unconditional gap, with labor productivity being approximately 11 percent lower among women- than men-managed firms. The analyses are based on women’s management, which is more strongly associated with labor productivity than women’s participation in ownership, which has been the focus of most previous studies. Decomposition techniques reveal several factors that contribute to lower labor productivity of women-managed firms relative to firms managed by men: Fewer women-managed firms protect themselves from crime and power outages, have their own websites, and are (co-)owned by foreigners. In addition, in the manufacturing sector, women-managed firms are less capitalized and have lower labor costs than firms managed by men.

KEYWORDS:

• Firms
• women entrepreneurs
• decomposition
• manufacturing industry
• productivity
• services

JEL CODES:

• J16
• L26
• D21

ACKNOWLEDGMENTS

We would like to thank five anonymous reviewers for their feedback. We would also like to express our gratitude to Jorge Rodriguez Meza for feedback.

SUPPLEMENTAL DATA

Supplemental data for this article can be accessed at https://doi.org/10.1080/13545701.2020.1797139https://doi.org/10.1080/13545701.2020.1797139.

Notes

1 The difficulty in accounting for time periods is one of the limitations of cross-sectional datasets.

2 Details of the methodology can be accessed through the following link: http://www.enterprisesurveys.org/methodology.

3 The survey question is “Is the Top Manager female?” and an affirmative response classifies the firm as women-managed.

4 For the full derivation and the assumptions please see first section in the Online Appendix.

5 An alternative to labor productivity would be to use a TFP measure. However, TFP measures are problematic and are not properly defined for services sector, which is important for women (World Bank 2011; International Labor Organization 2012). The data requirements to produce accurate TFP estimates are extensive. High response rates would be needed in key variables capturing measurements of capital. Typically census or administrative data may have higher response rates for such financial information, but such datasets are rare in developing economies and tend to not have information on gender or the business environment. Furthermore, a panel dataset of at least two periods would be needed to accurately measure TFP estimates (Olley and Pakes 1996; Levinsohn and Petrin 2003; Ackerberg, Caves, and Frazer 2015). We instead chose the route of using labor productivity as it is a relatively cleaner estimate, and it has commonly been used in the literature that uses the Enterprise Surveys data (Hallward-Driemeier 2013; Clarke, Qiang, and Xu 2015; Gui-Diby, Pasali, and Rodriguez-Wong 2017; Islam, Palacios López, and Amin 2018).

6 The coefficient estimate is -0.143. Since the dependent variable is log-transformed, we follow Halvorsen and Palmquist (1980) in calculating the percentage difference in labor productivity as (exp(-0.143)-1)*100. For control variables (which are not the focus of the study) we report the approximate effect (that is, the coefficient estimate).

7 The contribution calculations are obtained by taking the ratio of the coefficient of the variable concerned in the decomposition results (in columns 2 or 3) over the gender gap or difference indicated column 1. For instance, the younger age of women-managed firms’ contribution to an 8 percent narrowing of the gap is calculated as 0.010/0.117.

8 While gender gaps in school enrollment have closed or reversed in most developing countries over the past decades, adult women still often have lower levels of educational attainment than adult men.

9 In the detailed decomposition, the identification of the relative contributions of the regression intercept versus categorical variables with 3+ categories to the total structural effect depends on the choice of omitted category (Fortin, Lemieux, and Firpo 2011). We omit the sector with the lowest labor productivity (manufacturing) to mimic the case of a continuous variable.

Additional information

Notes on contributors

Asif M. Islam

Asif M. Islam is Senior Economist for the Middle East and North Africa Region of the World Bank Group. His research focuses on private sector development. He has published in peer-reviewed journals on several dimensions of the private sector including entrepreneurship, technology, crime, informality, and gender. He has also published on fiscal policy, environment, and agriculture. He co authored several reports including the World Development Report (2019)- The Changing Nature of Work, What's Holding Back the Private Sector in MENA? Lessons from the Enterprise Survey, and Uncharted Waters: The New Economics of Water Scarcity and Variability. He holds a PhD in Applied Economics from the University of Maryland-College Park.

Isis Gaddis

Isis Gaddis is Senior Economist with the World Bank’s Gender Group and Research Fellow with the Institute of Labor Economics (IZA). She was previously based in Dar es Salaam, Tanzania. Isis was a member of the core team for the Word Bank's 2018 Poverty and Shared Prosperity report and coauthored the 2016 regional study Poverty in a Rising Africa. Her main research interest is empirical microeconomics, with a focus on the measurement and analysis of poverty and inequality, gender, labor, and public service delivery. She holds a PhD in economics from the University of Göttingen, where she was a member of the development economics research group from 2006 to 2012.

Amparo Palacios López

Amparo Palacios-López is Senior Economist in the Development Data Group of the World Bank. Her area of research is agricultural and rural development, with a focus on gender, labor, and welfare. As a member of the LSMS-ISA team, she supports projects in Nigeria, Malawi, Tanzania, and Uganda. Palacios-López has a PhD in Agriculture and Resource Economics from the University of Maryland, College Park, and holds both an MA in Economics from the Pontificia Universidad Católica de Chile and an MS in Agriculture and Resource Economics from the University of Maryland, College Park.

Mohammad Amin

Mohammad Amin is Senior Economist in the Enterprise Analysis Unit with the World Bank. He joined the team in October 2006. Prior to joining the Enterprise Analysis Unit he served as a consultant with the Development Economics Research Group of The World Bank. His research areas include gender, international trade, international migration, institutional economics, and regulation. His most recent research focuses on the impact of regulation and competition on the performance of retail stores in India. He holds a PhD in Economics from Columbia University.