The Economic Cost of Gender Gaps in Effective Labor: Africa's Missing Growth Reserve

ABSTRACT

This study analyzes the impact of the gender gap in effective labor – defined as the combined effect of the gender gaps in labor force participation and education – on economic output per worker. The results indicate that the gender gap in effective labor has a negative effect on the economic output per worker in African countries. A 1 percent increase in the gender gap in effective labor leads to a reduction in output per worker by 0.43–0.49 percent in Africa overall, 0.29–0.50 percent in Sub-Saharan Africa, and 0.26–0.32 percent in a wider group of countries from Africa and Asia. The total annual economic losses due to gender gaps in effective labor could be as high as US$255 billion for the African region. Results confirm that Africa is missing its full growth potential because a sizeable portion of its growth reserve – women – is not fully utilized.

KEYWORDS:

• Gender
• labor
• growth

JEL Codes:

• F43
• J01
• J16

NOTES ON CONTRIBUTORS

Amarakoon Bandara is Economic Advisor at the Regional Bureau for Africa of the United Nations Development Programme based in Zimbabwe. He holds a PhD in economics from Boston University. His main research interests are in macroeconomics, international finance, inclusive growth, poverty reduction, and gender issues.

ACKNOWLEDGMENTS

The author wishes to thank the Associate Editor; four anonymous reviewers; participants at the Centre for the Study of African Economics (CSAE) 2012 Conference on Economic Development in Africa held at Catherine's College, Oxford University on March 18–20, 2012; participants at the International Association of Feminist Economics (IAFFE) Annual Conference held at Universitat de Barcelona, Barcelona on June 27–29, 2012; participants at the 18th International Panel Data Conference held at Banque de France, Paris on July 5–6, 2012; and Heri Marco for his assistance in compilation of data. The views expressed in the paper are those of the author and do not necessarily reflect those of the United Nations Development Programme (UNDP). Any remaining errors and omissions are the responsibility of the author.

Notes

1 Together, these gaps imply an even higher gender gap in labor force participation in North Africa, partly explained by cultural norms and biases.

2 Skilled workers may also attract higher wages. See Marcel Fafchamps (2009).

3 See Anne Mikkola and Carrie A. Miles (2007) and Elissa Braunstein (2007) for a review of economic literature on the relationship between gender inequality and economic development. Naila Kabeer and Luisa Natali (2013) give a summary of the literature on the growth-gender relationship.

4 According to Statistics Canada (2006), on average, women spend about an hour a day more on basic household chores than their male counterparts. The gap may be even higher in African countries due to gender bias.

5 Several studies analyze the implications of gender inequality on development. While Janet G. Stotsky (2006) analyzes the implications of gender inequality on macroeconomic policy, Matthias Doepke and Michèle Tertilt (2011) investigate how women's empowerment promotes economic development.

6 Michael Bleaney, Norman Gemmell, and Richard Kneller (2001) apply the Anderson–Hsiao IV estimator for annual panel dataset with T>N.

7 Please note the high standard deviation in the inflation rate. This is due to large inflation rates in some countries during certain time periods. I cannot treat them as outliers, as several countries have been experiencing high inflation episodes from time to time. As such, I keep those high-inflation countries in the data.

8 The use of labor participation assumes full employment, which may not necessarily be the case. Similarly, average years of schooling is taken as a proxy for skills of the labor force. Given the reservations on the quality of education in most African countries, average years of schooling may not be the most appropriate variable to capture labor skills. Nonetheless, I rely on these due to data limitations.

9 The newly formed South Sudan is not included.

10 For all countries in the sample, the data range is 1970–2010, except for Eritrea (1980–2010) and Libya (1990–2010). However, it should be noted that there are still missing data within these respective data ranges.

11 Results for model in equation (13)(13) are not reported, but are available on request. Openness of the economy positively but weakly impacts output per worker, while women's effective labor has a positive and significant effect. Labor with no education has a negative effect on output regardless of gender considerations. The impact of men's effective labor is negative and weak. When inflation is excluded as an explanatory variable earlier, results become stronger. The Sargan test results for all estimated models indicate that the instrument sets used in them are valid.

12 Knowles, Lorgelly, and Owen (2002) interpret the coefficient on the gap differently: it reflects the output elasticity with respect to men's education.

13 Estimation results of the model in equation (13)(13) are not reported, but are available. Estimation results indicate that women's labor with no education has a negative impact on output while women's effective labor has a positive effect. Men's effective labor has a negative but weakly insignificant effect on output. When inflation is excluded, most regressors become stronger and significant, except labor variables. Both openness and physical capital variables become significant, having positive effects. When gender disaggregated labor and education enter the model as separate explanatory variables, education or labor alone does not seem to have an impact on output.

14 Estimation results of model in equation (13)(13) are not reported, but are available in the Supplemental Online Tables on the publisher's website. Results indicate that women's raw labor has a negative effect on growth, while women's effective labor has a positive effect. Men's effective labor has a negative impact, but it is an insignificant impact. Women's education has a positive impact on growth.

15 The economic costs for regions are estimated by multiplying real GDP by the gender gap in effective labor and the estimated coefficient from the model in equation (14)(14) . Regional real GDP for the regions are the sum of respective country real GDPs. A similar approach is used for computing the effective labor. The formula for calculating the cost (for all cross-sections) is given by: . Results are not reported but are available in the Supplemental Online Tables on the publisher's website.