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Impact Evaluations in Good Times and Bad

Impact Evaluations in Good Times and Bad. Forum Kajian Pembangunan March 22, 2011 Firman Witoelar , DECHD, Discussant. Issues. Selection biases Spillovers and hidden/unintended outcomes Timing of impacts Data requirements. Selection biases. - Non-random program placement

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Impact Evaluations in Good Times and Bad

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  1. Impact Evaluations in Good Times and Bad Forum Kajian Pembangunan March 22, 2011 FirmanWitoelar, DECHD, Discussant

  2. Issues • Selection biases • Spillovers and hidden/unintended outcomes • Timing of impacts • Data requirements

  3. Selection biases - Non-random program placement - Selection bias: reasons to participate in a program are correlate with outcomes of interest • Researchers may not know ‘reasons’ but have data on observables • Is there selection on observables? • What is the direction of the bias? • Choose comparison group carefully based on observables • Can also be selection based on unobservables… • DID may help if unobservables are time invariant • The two biases can work in different directions

  4. Spillovers and “hidden outcomes” • Spillovers • may underestimate program impacts if comparison group is contaminated • hard to deal with due to • market responses • government responses (e.g . local government)

  5. Spillovers and unintended outcomes • Unintended outcomes: • Examples • Employment Guarantee Scheme (Maharashtra, India) • Work is guaranteed at low wage rate: thought to be self-targeted • However, likely to spill to private labor market • No one want to work below EGS wage: wages will be the same between participants and non-participants • Social insurance (e.g. Jamkesmas) • Outcome of interest: program take-up /coverage • But…w ill a universal social insurance lower the take up of employee-provided insurance?

  6. Timing of impacts • When are the programs expected to have impacts? • Short-term or long term impact? • Lasting or dissipating impacts? • Exit strategies: • When programs are phased out, will behavior change?

  7. Data requirements • Data collection should be built-in in the project design and evaluation design (e.g. PKH/CCT) • Same survey instruments administered for program participants and non-participants • Collect well defined outcome measures: self-reported, official records, physical measures • Collect enough information (individuals, household, communities) to deal with heterogeneity • Cover the time period over which the projects are expected to have impacts

  8. Data requirements (continued) • Detailed information about the programs: • institutional background • timing of the programs • program eligibility • other programs that are operating in the communities • Panel data may be desired: • Comparability of survey instruments • Attrition is important: absence of patterns in observables no guarantee (Witoelaret al, forthcoming)

  9. Other examples • Frankenberg , Suriastini, Thomas (2005) – BidanDesa program • 1989 , placement of 50,000 “BidanDesa” • non-random placement • Study exploits: • timing of placement (similar to the Posyandu paper) • anthropometric measures • rich socio-economic panel data • Giles, Satriawan (2011) - post-crisis food supplementation program (PMT) • Study exploits: • communities’ exposure to the program • variation in child age and program eligibility • anthropometric measures • rich socio-economic panel data

  10. On RCT: …also check out current edition Boston Review (March/April 2011) “Small Changes, Big Results” - Glennerster and Kremer (JPAL) arguing for applying experiments and behavior economics in global development) PranabBardhan, Jishnu Das, and others discuss http://www.bostonreview.net/

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