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Evaluation of Development Programs: Using Regressions to assess the Impact of Complex Interventions

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  • Chris Elbers

    (VU University Amsterdam)

  • Jan Willem Gunning

    (VU University Amsterdam)

Abstract

There is a growing interest in extending project evaluation methods to the evaluation of programs: complex interventions involving multiple activities. In general a program evaluation cannot be based on separate evaluations of its components since interactions between the activities are likely to be important. We propose a measure of program impact, the total program effect (TPE), which is an extension of the average treatment effect on the treated (ATET). Regression techniques can be applied to observational data from a representative sample to estimate the TPE for complex interventions in the presence of selection effects and treatment heterogeneity. As an example we present an estimate of the TPE for a rural water supply and sanitation program in Mozambique. Estimating the TPE from randomized controlled trials would appear to be an alternative; however, the scope for using RCTs in this context is limited. See also 'Evaluation of Development Programs: Randomized Controlled Trials or Regressions?' in 'The World Bank Economic Review' (2014), 28(3), 432-445.

Suggested Citation

  • Chris Elbers & Jan Willem Gunning, 2012. "Evaluation of Development Programs: Using Regressions to assess the Impact of Complex Interventions," Tinbergen Institute Discussion Papers 12-081/2, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20120081
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    References listed on IDEAS

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    1. Angus Deaton, 2010. "Instruments, Randomization, and Learning about Development," Journal of Economic Literature, American Economic Association, vol. 48(2), pages 424-455, June.
    2. Chen, Shaohua & Mu, Ren & Ravallion, Martin, 2009. "Are there lasting impacts of aid to poor areas?," Journal of Public Economics, Elsevier, vol. 93(3-4), pages 512-528, April.
    3. Elbers, Chris & Gunning, Jan Willem & de Hoop, Kobus, 2009. "Assessing Sector-wide Programs with Statistical Impact Evaluation: A Methodological Proposal," World Development, Elsevier, vol. 37(2), pages 513-520, February.
    4. Chris Elbers & Samuel Godfrey & Jan Willem Gunning & Matteus van der Velden & Melinda Vigh, 2012. "Effectiveness of Large Scale Water and Sanitation Interventions: the One Million Initiative in Mozambique," Tinbergen Institute Discussion Papers 12-069/2, Tinbergen Institute.
    5. Martin Ravallion, 2012. "Fighting Poverty One Experiment at a Time: Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty : Review Essay," Journal of Economic Literature, American Economic Association, vol. 50(1), pages 103-114, March.
    6. repec:pri:rpdevs:deaton_instruments_randomization_learning_all_04april_2010 is not listed on IDEAS
    7. Martin Ravallion, 2009. "Evaluation in the Practice of Development," The World Bank Research Observer, World Bank, vol. 24(1), pages 29-53, March.
    8. Jishnu Das & Stefan Dercon & James Habyarimana & Pramila Krishnan, 2007. "Teacher Shocks and Student Learning: Evidence from Zambia," Journal of Human Resources, University of Wisconsin Press, vol. 42(4).
    9. Abhijit V. Banerjee & Esther Duflo, 2009. "The Experimental Approach to Development Economics," Annual Review of Economics, Annual Reviews, vol. 1(1), pages 151-178, May.
    10. Rodrik, Dani, 2008. "The New Development Economics: We Shall Experiment, but How Shall We Learn?," Working Paper Series rwp08-055, Harvard University, John F. Kennedy School of Government.
    11. 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.
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    Cited by:

    1. Jan Willem GUNNING, 2012. "How Can Development NGOs Be Evaluated ?," Working Papers P51, FERDI.

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    More about this item

    Keywords

    program evaluation; randomized controlled trials; policy evaluation; treatment heterogeneity; budget support; sector-wide programs; aid effectiveness;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • O22 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - Project Analysis

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