Understanding the Complexities Surrounding Gender Differences in              Agricultural Productivity in Nigeria and Uga...
Outline of presentation          Framing the issue          Methods          Findings          Policy implicationsPage 2
This paper: Provides new estimates of gender differences in agricultural  productivity using IFPRI household survey data ...
Methods: Data         Nigeria 2005                                  Uganda 2003 Collected to evaluate Fadama II, a       ...
Methods: Empirics, tobit model ln Yi = α0 + α1ln Li + α2 ln Ti + ß ln Ei + γ EXTi + δ Genderi + ε        Yi         ith ...
Methods: More on empirics  Allow for mass point at zero using tobit       Treatment of zero as either fallow or no outpu...
Findings: Plotting productivityPage 9
Findings: Summary of tobit estimation results  Variable  Nigeria    Full       Maize    Rice      Cassava   Tomato     Lea...
Findings Productivity significantly lower on plots owned or managed by females;  results hold taking into account farm an...
Policy implications—part 1  Headship as a stratifying     variable underestimates     productivity differences =>     nee...
Policy implications--2                             Variation by region, crop,                              biophysical ch...
Questions, Comments?      Paper funded by the FAO as a background paper for the State of Food and      Agriculture (2010) ...
Upcoming SlideShare
Loading in …5
×

Peterman et al understanding gender complexities jan 17 2011

1,406 views

Published on

  • Be the first to comment

  • Be the first to like this

Peterman et al understanding gender complexities jan 17 2011

  1. 1. Understanding the Complexities Surrounding Gender Differences in Agricultural Productivity in Nigeria and Uganda AMBER PETERMAN, AGNES QUISUMBING, JULIA BEHRMAN, AND EPHRAIM NKONYA IFPRI AFRICA GROWTH FORUM JANUARY 19-20, 2011Harvesting in Nigeria, Credit: Yosef Hadar
  2. 2. Outline of presentation  Framing the issue  Methods  Findings  Policy implicationsPage 2
  3. 3. This paper: Provides new estimates of gender differences in agricultural productivity using IFPRI household survey data from Nigeria (2005) and Uganda (2003) Address some complexities by looking at:  Crop choice  Sensitivity of productivity estimates to choice of stratifying ‘gender’ variable (sex of hh head, sex of plot owner, mixed ownership)  Heterogeneity within agro-ecological zones  Controlling (where possible) for hh-level unobservables  Controlling (where possible) for biophysical characteristics of plotPage 5
  4. 4. Methods: Data Nigeria 2005 Uganda 2003 Collected to evaluate Fadama II, a  Collected to study natural resource national agricultural welfare management and poverty program  Plot level data: 3,625 plots in 851 Household level data: 3,750 hhs hhs Gender variable: Sex of hh head  Gender variable: Sex of crop ownership for plot, also allows for mixed ownership; sex of hh head also collected  Biophysical plot characteristicsBoth countries: Large agricultural sectors, diversity in agro- ecological zones, crop choice, ethnic variation and low women’s status and property rights.Page 6
  5. 5. Methods: Empirics, tobit model ln Yi = α0 + α1ln Li + α2 ln Ti + ß ln Ei + γ EXTi + δ Genderi + ε  Yi ith hh or plot value of crop yield per unit area  Li labor input (hired or family)  Ti vector of land, capital, and other conventional inputs  Ei educational attainment  EXT i index of extension services  Gender i dummy variable for the sex or gender of the farm manager or household head  ε error termPage 7
  6. 6. Methods: More on empirics  Allow for mass point at zero using tobit  Treatment of zero as either fallow or no output  Crop choice modeled using probit and Cragg’s two-tiered unconditional tobit  Uganda: explore robustness to inclusion of fixed effects using Honoré’s fixed effects tobit estimator  All regressions control for age, education of head, hh size; land, irrigation, fertilizer and seeds, extension, labor (previous season inputs);  All full sample regressions control for primary crop indicators (results are robust to inclusion of secondary crop indicators).Page 8
  7. 7. Findings: Plotting productivityPage 9
  8. 8. Findings: Summary of tobit estimation results Variable Nigeria Full Maize Rice Cassava Tomato Leafy Cowpea veg FHH=1 -0.32*** -0.25 -0.03 -0.49 -2.08** -0.34 -0.06 Uganda Full Banana Beans & Maize Sweet Cassava Sorghum peas potato Female -0.27** 0.23 0.07 -0.06 -0.80* -0.27 -0.93** crop owner=1 Mixed -0.29* 0.21 -0.82** -0.65 -0.98*** -0.66 -0.38 owners=1Page 10
  9. 9. Findings Productivity significantly lower on plots owned or managed by females; results hold taking into account farm and hh characteristics and crop choice Results vary across crops, agro-ecological zones, and with inclusion of biophysical characteristics Type of gender indicator matters: extent of productivity differential diluted when headship is used as stratifying variable Productivity lowest on mixed ownership plots, but not robust to inclusion of hh fixed effects. Indicates bargaining difficulty with mixed ownership/decision making?Page 11
  10. 10. Policy implications—part 1  Headship as a stratifying variable underestimates productivity differences => need to pay attention to level of aggregation in collecting sex-disaggregated data  Productivity lowest on female- owned plots =>pay attention to gender differences in control of resources in research and program implementationPage 12
  11. 11. Policy implications--2  Variation by region, crop, biophysical characteristics => address gender in context of regional ecological and biophysical needs, cultural context  Avoid extrapolation of policy findings from very localized studies; increase geographicalCredit: ILRI representativeness of data collection and analytical efforts
  12. 12. Questions, Comments? Paper funded by the FAO as a background paper for the State of Food and Agriculture (2010) and we gratefully acknowledge funding. Thanks to Edward Kato for assistance with data and understanding of local context and to Andre Croppenstedt and two anonymous reviewers for helpful comments on an earlier draft. Paper is forthcoming in the Journal of Development Studies (2011)Page 14

×