The Mystery of the Vanishing Benefits : An Introduction to Impact Evaluation

By M. Ravallion. 2001

362 citations recorded by the source corpus.

This article provides an introduction to the concepts and methods of impact evaluation. The author provides an intuitive explanation in the context of a concrete application. The article takes the form of a short story about a fictional character's on-the-job training in evaluation. Ms. Speedy Analyst is an economist in the Ministry of Finance in the fictional country of Labas. In the process of figuring out how to evaluate a human resource program targeted to the poor, Ms. Analyst learns the strengths and weaknesses of the main methods of ex post impact evaluation.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8d1b3af326816efc1ab4bd87b57c13c2617bccc8

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Citations in the corpus

Systematic Review. What is the evidence of the impact of microfinance on the well-being of poor people? (2011, background): “Galasso E, Ravallion M (2004) Social protection in a crisis: Argentina's plan Jefes y Jefas.”
High Noon for Microfinance Impact Evaluations: Re-investigating the Evidence from Bangladesh (2012, methodology): “…to approximate the research design of a RCT by matching participants to non-participants drawn from a suitable population on the basis of a set of common covariates by a predicted probability of programme participation, or the ‘propensity score’ (Ravallion, 2001; Caliendo and Kopeinig, 2005, 2008).”
Microfinance and Poverty : Evidence using Panel Data from Bangladesh (2005, methodology): “The quasi-experimental survey design used by Pitt and Khandker (1998) is one of many methods evaluators use to assess program effects (for a review, see Moffitt 1991 and Ravallion 2001).”
Timing and Duration of Exposure in Evaluations of Social Programs (2008, neutral): “See, for example, Grossman (1994), Heckman and Smith (1995), Ravallion (2001), Cobb-Clark and Crossley (2003), and Duﬂo (2004).”
Assessing the Impact of the National Agricultural Advisory Services (NAADS) in the Uganda Rural Livelihoods (2007, background): “changes in responses and outcomes (rather than levels in a particular year), which reduces the influence of many confounding factors (Ravallion 2001).”
The Impact of Fair Trade Certification for Coffee Farmers in Peru (2012, neutral): “The main challenge for impact evaluation resides in the ability to answer the question: ‘What would have happened to a participating household if they would not have joined in the FT scheme?’ This hypothetical situation—known as the counterfactual—cannot be observed empirically, whereas just taking ”
Which covariates should be controlled in propensity score matching? Evidence from a simulation study (2013, background): “Most studies argue that only variables which affect both the program participation and outcomes should be included in the estimation of the propensity score (e.g. Heckman et al., 1998; Augurzky and Schmidt, 2001; Ravallion, 2001; Bryson et al., 2002; Lechner, 2002; Caliendo and Kopeinig, 2008).”
Does contract farming improve smallholder income? The case of avocado farmers in Kenya (2016, background): “…interventions on specific outcomes face two major challenges related to the impossibility to observe a counterfactual and selection bias due to placement of the programme or selection of the participants into the programme (Ravallion, 2001; Cameron and Trivedi, 2005; Imbens and Wooldridge, 2009).”
Does microfinance reduce rural poverty? Evidence based on household panel data from northern Ethiopia (2009, neutral): “The authors thank participants at various seminars, the editor, and two anonymous reviewers for their insightful comments. and nonparticipants are subject to biases from two sources (Pitt and Khandker 1998; Ravallion 2001).”
The Impact of Cash Transfers on Child Labor and School Attendance in Brazil (2004, background): “On this point, see, for instance Rosenbaum and Rubin (1983) and Ravallion (2001). Four matched samples are obtained. The first two columns of table A.2 in the appendix shows the logit coefficients for the "all children" sample matching and the last two columns of the same table shows the coefficient”