Macroeconomic impacts of female labour productivity shock in agriculture: evidence from a CGE model applied to a Sub-Saharan African country

ABSTRACT

The agricultural sector is generally recognized as the engine of economic growth, poverty reduction and food security in countries with a high share of poor people employed in that sector. However, in most of Sub-Saharan African countries, the sector underperforms partly because women, who represent a crucial resource in the rural economy as farmers, face more severe constraints than men in accessing productive resources. In this paper, we use a gendered CGE model for Burkina Faso to simulate a greater access of women to productive resources. The results show that real GDP increases by 2.31%, and household real income and consumption improve significantly. We also found that the same productivity shock applied to the male labour displays positive but lower impacts. These results underline the gains that can be achieved at the macroeconomic, sectoral and household levels through better access of women to productive resources in the agricultural sector.

KEYWORDS:

• Female labour productivity
• productive resources
• Burkina Faso

JEL CLASSIFICATION:

• J01
• J16
• J24
• Q18

Disclosure statement

No potential conflict of interest was reported by the author.

Notes

¹ The poverty headcount at $1.25 a day in 2008 PPP is estimated at 47.5%, compared to an average of 25.2% for the developing countries as a whole (Chen and Ravallion 2013).

² In its 2012 world development report, the World Bank argues that ‘gender differences in agricultural productivity disappear when access to and use of productive inputs are taken into account’ (World Bank 2012, 46). In other words, promoting women’s access – in equitable way – to productive resources would increase women’s productivity in agriculture by 20 to 30%.

³ For reasons of space constraints, the reader interested in the details will be able to consult this reference.

⁴ The original SAM consists of skilled and unskilled labour; a capital factor; and a land factor. We disaggregated each production factor by gender, relying on two databases: the 2014 Continuous Multisectoral Survey (CME) and the 2013 Permanent Agricultural Survey (PAS). The GSAM also distinguishes 17 activities, including 5 agricultural sectors, that produce 19 goods and services. Nine household groups, based on the main activity of the household head, are distinguished. The gendered SAM and tables in excel format (households’ sources of income; sectoral distribution of production factors) are provided in supplemental material.