IDEAS home Printed from https://ideas.repec.org/p/ags/unadrs/280045.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/280045/files/Research%20Series%207.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.280045?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. White, Howard, 2009. "Theory-Based Impact Evaluation," 3ie Publications 2009-3, International Initiative for Impact Evaluation (3ie).
    4. 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.
    5. 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.
    6. Howard White, 2009. "Theory-based impact evaluation: principles and practice," Journal of Development Effectiveness, Taylor & Francis Journals, vol. 1(3), pages 271-284.
    7. VAN KERM Philippe, 2006. "Comparisons of income mobility profiles," IRISS Working Paper Series 2006-03, IRISS at CEPS/INSTEAD.
    8. 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.
    9. 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.
    10. Ravallion, M., 1992. "Poverty Comparisons - A Guide to Concepts and Methods," Papers 88, World Bank - Living Standards Measurement.
    11. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
    12. 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.
    13. 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.
    14. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, April.
    15. Pradhan, Menno & Ravallion, Martin, 1998. "Measuring poverty using qualitative perceptions of welfare," Policy Research Working Paper Series 2011, The World Bank.
    16. 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).
    17. 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.
    18. Deon Filmer & Kinnon Scott, 2012. "Assessing Asset Indices," Demography, Springer;Population Association of America (PAA), vol. 49(1), pages 359-392, February.
    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. 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.
    21. 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.
    22. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    2. Chabé-Ferret, Sylvain, 2015. "Analysis of the bias of Matching and Difference-in-Difference under alternative earnings and selection processes," Journal of Econometrics, Elsevier, vol. 185(1), pages 110-123.
    3. Carlos A. Flores & Oscar A. Mitnik, 2009. "Evaluating Nonexperimental Estimators for Multiple Treatments: Evidence from Experimental Data," Working Papers 2010-10, University of Miami, Department of Economics.
    4. Farrell, Max H., 2015. "Robust inference on average treatment effects with possibly more covariates than observations," Journal of Econometrics, Elsevier, vol. 189(1), pages 1-23.
    5. Mausumi Mahapatro & Deborah Johnston, 2020. "Imperfection Measures and the Production of Poverty: A Case Study of the Use of the Asset Index in Bangladesh," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(2), pages 513-531, November.
    6. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    7. Zeqin Liu & Zongwu Cai & Ying Fang & Ming Lin, 2019. "Statistical Analysis and Evaluation of Macroeconomic Policies: A Selective Review," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201904, University of Kansas, Department of Economics, revised Mar 2019.
    8. Zhao, Zhong, 2008. "Sensitivity of propensity score methods to the specifications," Economics Letters, Elsevier, vol. 98(3), pages 309-319, March.
    9. 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).
    10. Cunguara, Benedito & Darnhofer, Ika, 2011. "Assessing the impact of improved agricultural technologies on household income in rural Mozambique," Food Policy, Elsevier, vol. 36(3), pages 378-390, June.
    11. Dolton, Peter & Smith, Jeffrey A., 2011. "The Impact of the UK New Deal for Lone Parents on Benefit Receipt," IZA Discussion Papers 5491, Institute of Labor Economics (IZA).
    12. Gabriel V. Montes-Rojas, 2011. "Nonparametric Estimation of ATE and QTE: An Application of Fractile Graphical Analysis," Journal of Probability and Statistics, Hindawi, vol. 2011, pages 1-23, October.
    13. John C. Ham & Xianghong Li & Patricia B. Reagan, 2004. "Propensity Score Matching, a Distance-Based Measure of Migration, and the Wage Growth of Young Men," IEPR Working Papers 05.13, Institute of Economic Policy Research (IEPR).
    14. Burt S. Barnow & Jeffrey Smith, 2015. "Employment and Training Programs," NBER Chapters, in: Economics of Means-Tested Transfer Programs in the United States, Volume 2, pages 127-234, National Bureau of Economic Research, Inc.
    15. Chunrong Ai & Lukang Huang & Zheng Zhang, 2018. "A Simple and Efficient Estimation of the Average Treatment Effect in the Presence of Unmeasured Confounders," Papers 1807.05678, arXiv.org.
    16. Gustavo Canavire-Bacarreza & Luis Castro Peñarrieta & Darwin Ugarte Ontiveros, 2021. "Outliers in Semi-Parametric Estimation of Treatment Effects," Econometrics, MDPI, vol. 9(2), pages 1-32, April.
    17. Arraiz, Irani & Calero, Carla & Jon, Songqing & Peralta, Alexandra, 2015. "Planting the seeds: The impact of training on mando producers in Haiti," 2015 Conference, August 9-14, 2015, Milan, Italy 212622, International Association of Agricultural Economists.
    18. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    19. Fredrik Andersson & Harry J. Holzer & Julia I. Lane & David Rosenblum & Jeffrey Smith, 2024. "Does Federally Funded Job Training Work? Nonexperimental Estimates of WIA Training Impacts Using Longitudinal Data on Workers and Firms," Journal of Human Resources, University of Wisconsin Press, vol. 59(4), pages 1244-1283.
    20. Nicolas CARAYOL & Marianne LANOË, 2017. "The Impact of Project-Based Funding in Science: \r\nLessons from the ANR Experience," Cahiers du GREThA (2007-2019) 2017-04, Groupe de Recherche en Economie Théorique et Appliquée (GREThA).

    More about this item

    Keywords

    Agricultural and Food Policy;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:unadrs:280045. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/ifaunit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.