2015年-世界发展银行全球_How_Much_of_the_Labor_in_African_Agriculture_Is_Provided_by_Women__36页_1mb

Summary of "How Much of the Labor in African Agriculture Is Provided by Women?"

Core Content

This working paper investigates the extent to which women contribute to agricultural labor in Sub-Saharan Africa (SSA), challenging the commonly cited range of 60 to 80 percent. Using nationally representative household survey data from six SSA countries—Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda—the study provides a more accurate and detailed estimation of women's labor share in crop production.

Main Findings

• Average Female Labor Share: The population-weighted average female share of labor in crop production across the six countries is 40%.
• Country-Level Variations:
  • Highest: Uganda (56%), Tanzania (52%), and Malawi (52%).
  • Lowest: Niger (24%), Ethiopia (29%), and Nigeria (37%).
  • Nigeria Sub-regions:
    • North: 32%
    • South: 51%
• No Systematic Differences Across Crops: Female labor shares are not significantly different across crops or agricultural activities.
• Key Determinants:
  • Land Ownership: Higher female land ownership correlates with higher female labor shares.
  • Education Level: More educated women tend to contribute more to agricultural labor.
  • Household Composition: The gender composition of the household is the primary factor influencing women's labor input.
• Robustness of Findings:
  • The estimates are robust to potential gender and knowledge biases in reporting.
  • The study controls for household-level covariates, including the presence of children, chronic diseases among adults, and socio-economic factors like access to off-farm opportunities and travel time to population centers.

Methodology and Data

• Data Source: The study uses LSMS-ISA (Living Standards Measurement Study – Integrated Surveys on Agriculture) data from 2009–2011, with follow-up surveys.
• Labor Input Measurement:
  • Data is collected at the plot-level for each household member.
  • Surveys in Malawi, Nigeria, and Ethiopia record the number of weeks of work per activity, while Tanzania and Niger record the number of days worked.
  • In Uganda, the total number of days worked is recorded first, and then distributed equally among all working members.
• Outlier Handling:
  • Outliers in labor input data are identified and replaced with missing values.
  • Imputation is performed using single regression-based methods.
• Bias Assessment:
  • The study examines the effect of proxy vs. self-reporting on labor estimates.
  • It uses a regression model to assess the impact of respondent gender and knowledge on the reported labor shares.

Key Correlates of Female Labor Share

• Household Labor Substitutes:
  • The presence of hired or exchange labor and the number of agricultural implements owned or accessed.
• Cultural Gender Roles:
  • The influence of domestic responsibilities on women's ability to allocate time to productive activities.
  • The number of children under 5 and between 6 and 14 and the percentage of adults with chronic diseases.
• Socio-Economic Factors:
  • Education levels of men and women.
  • Access to off-farm employment and travel time to population centers.
  • Livestock ownership (measured in tropical livestock units).
  • Land ownership by women.

Implications for Policy

• The study questions the assumption that increasing female agricultural productivity will significantly boost aggregate crop output.
• It challenges the widespread belief that women account for the majority of agricultural labor in SSA.
• The findings emphasize the importance of household-level factors, such as land ownership and education, in determining women's labor contribution.
• The paper advocates for more systematic and accurate data on women's labor in agriculture to inform policy discussions on gender and development.

Conclusion

The paper provides a critical empirical analysis of women's labor contributions in African agriculture, highlighting the heterogeneity across countries and the limited role of gender in explaining variations in labor shares. It underscores the need for nuanced understanding of labor dynamics and better data collection to support evidence-based policy decisions.