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IFAD RESEARCH SERIES 7 - Measuring IFAD’s impact: background paper to the IFAD9 Impact Assessment Initiative

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  • Garbero, A.

Abstract

In recent years, the International Fund for Agricultural Development (IFAD) has increasingly strengthened its focus on achieving and measuring results. In 2011-2012, resources were invested in the IFAD9 Impact Assessment Initiative (IFAD9 IAI) in order to: (i) explore methodologies to assess impact; (ii) measure – to the degree possible – the results and impacts of IFAD-financed activities; and (iii) summarize lessons learned and advise on rigorous and cost-effective approaches to attributing impact to IFAD interventions. The initiative reflects a recognition of IFAD’s responsibility to generate evidence of the success of IFAD-supported projects so as to learn lessons for the benefit of future projects. This paper describes the IFAD9 IAI and the range of methods that have been identified to broaden the evidence base for the estimation of IFAD impacts, and presents the results from the aggregation and projection methodology used to compute the Fund’s aggregate impact on key outcomes, while also highlighting what has been learned. The results show that there are many areas in which IFAD‑supported project beneficiaries have had, on average, better outcomes in percentage terms as compared to comparison farmers who were not project beneficiaries. Specifically, IFAD-supported projects are effectively poverty-reducing: when choosing durable asset indexes as the preferred poverty proxies on the grounds that they better approximate long-run wealth, findings point to statistically significant gains. Overall, the analyses strongly imply that IFAD is effectively improving the well-being of rural people in terms of asset accumulation, and higher revenue and income. The IFAD9 IAI represents a pioneering research effort, which has tried to overcome the clear challenges of designing data collection and conducting ex post impact assessments in a context where data were scarce, with a view to measuring progress towards a global accountability goal over a very short period of time. Therefore, an important recommendation is that future impact assessments should be selected and designed ex ante, and structured to facilitate and maximize learning, rather than used solely as an instrument to prove accountability.

Suggested Citation

  • Garbero, A., 2016. "IFAD RESEARCH SERIES 7 - Measuring IFAD’s impact: background paper to the IFAD9 Impact Assessment Initiative," IFAD Research Series 280045, International Fund for Agricultural Development (IFAD).
    • Handle: RePEc:ags:unadrs:280045
    DOI: 10.22004/ag.econ.280045
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    References listed on IDEAS

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    1. Howard White, 2009. "Theory-based impact evaluation: principles and practice," Journal of Development Effectiveness, Taylor & Francis Journals, vol. 1(3), pages 271-284.
    2. James J. Heckman & Hidehiko Ichimura & Petra E. Todd, 1997. "Matching As An Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 605-654.
    3. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute of Labor Economics (IZA).
    4. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    5. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, February.
    6. Markus Frlich, 2004. "Finite-Sample Properties of Propensity-Score Matching and Weighting Estimators," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 77-90, February.
    7. White, Howard, 2009. "Theory-Based Impact Evaluation," 3ie Publications 2009-3, International Initiative for Impact Evaluation (3ie).
    8. Van Kerm, Philippe, 2006. "Comparisons of income mobility profiles," ISER Working Paper Series 2006-36, Institute for Social and Economic Research.
    9. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    10. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    11. Pradhan, Menno & Ravallion, Martin, 1998. "Measuring poverty using qualitative perceptions of welfare," Policy Research Working Paper Series 2011, The World Bank.
    12. Sahn, David E. & Stifel, David C., 2000. "Poverty Comparisons Over Time and Across Countries in Africa," World Development, Elsevier, vol. 28(12), pages 2123-2155, December.
    13. Deon Filmer & Kinnon Scott, 2012. "Assessing Asset Indices," Demography, Springer;Population Association of America (PAA), vol. 49(1), pages 359-392, February.
    14. Guido W. Imbens & Whitney Newey & Geert Ridder, 2005. "Mean-square-error Calculations for Average Treatment Effects," IEPR Working Papers 05.34, Institute of Economic Policy Research (IEPR).
    15. Kosuke Imai & Marc Ratkovic, 2014. "Covariate balancing propensity score," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 243-263, January.
    16. Deon Filmer & Lant Pritchett, 2001. "Estimating Wealth Effects Without Expenditure Data—Or Tears: An Application To Educational Enrollments In States Of India," Demography, Springer;Population Association of America (PAA), vol. 38(1), pages 115-132, February.
    17. Mendola, Mariapia, 2007. "Agricultural technology adoption and poverty reduction: A propensity-score matching analysis for rural Bangladesh," Food Policy, Elsevier, vol. 32(3), pages 372-393, June.
    18. Ravallion, M., 1992. "Poverty Comparisons - A Guide to Concepts and Methods," Papers 88, World Bank - Living Standards Measurement.
    19. Martin Wall & Deborah Johnston, 2008. "Counting Heads or Counting Televisions: Can Asset-based Measures of Welfare Assist Policy-makers in Russia?," Journal of Human Development and Capabilities, Taylor & Francis Journals, vol. 9(1), pages 131-147.
    20. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    21. James Heckman, 1997. "Instrumental Variables: A Study of Implicit Behavioral Assumptions Used in Making Program Evaluations," Journal of Human Resources, University of Wisconsin Press, vol. 32(3), pages 441-462.
    22. Alessandra Garbero, 2014. "Estimating poverty dynamics using synthetic panels for IFAD-supported projects: a case study from Vietnam," Journal of Development Effectiveness, Taylor & Francis Journals, vol. 6(4), pages 490-510, December.
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