Impact evaluation assesses the extent to which an intervention (such as a project, program or policy) is causally responsible for observed outcomes, intended and unintended.World Bank Poverty Group on Impact Evaluation, accessed on January 6, 2008 In contrast to outcome monitoring, which examines whether targets have been achieved, impact evaluation is structured to answer the question: how would outcomes such as participants' well-being have changed if the intervention had not been undertaken? This involves counterfactual analysis, that is, "a comparison between what actually happened and what would have happened in the absence of the intervention." Impact evaluations seek to answer cause-and-effect questions. In other words, they look for the changes in outcome that are directly attributable to a program.
Impact evaluation helps people answer key questions for evidence-based policy making: what works, what doesn't, where, why and for how much? It has received increasing attention in policy making in recent years in the context of both developed and developing countries. It is an important component of the armory of evaluation tools and approaches and integral to global efforts to improve the effectiveness of aid delivery and public spending more generally in improving living standards. Originally more oriented towards evaluation of social sector programs in developing countries, notably conditional cash transfers, impact evaluation is now being increasingly applied in other areas such as agriculture, energy and transport.
Counterfactual evaluation designs
Counterfactual analysis enables evaluators to attribute cause and effect between interventions and outcomes. The 'counterfactual' measures what would have happened to beneficiaries in the absence of the intervention, and impact is estimated by comparing counterfactual outcomes to those observed under the intervention. The key challenge in impact evaluation is that the counterfactual cannot be directly observed and must be approximated with reference to a comparison group. There are a range of accepted approaches to determining an appropriate comparison group for counterfactual analysis, using either prospective (ex ante) or retrospective (ex post) evaluation design. Prospective evaluations begin during the design phase of the intervention, involving collection of baseline and end-line data from intervention beneficiaries (the 'treatment group') and non-beneficiaries (the 'comparison group'); they may involve selection of individuals or communities into treatment and comparison groups. Retrospective evaluations are usually conducted after the implementation phase and may exploit existing survey data, although the best evaluations will collect data as close to baseline as possible, to ensure comparability of intervention and comparison groups.
There are five key principles relating to internal validity (study design) and external validity (generalizability) which rigorous impact evaluations should address: confounding factors, selection bias, spillover effects, contamination, and impact heterogeneity.
Confounding occurs where certain factors, typically relating to socioeconomic status, are correlated with exposure to the intervention and, independent of exposure, are causally related to the outcome of interest. Confounding factors are therefore alternate explanations for an observed (possibly spurious) relationship between intervention and outcome. Selection bias, a special case of confounding, occurs where intervention participants are non-randomly drawn from the beneficiary population, and the criteria determining selection are correlated with outcomes. Unobserved factors, which are associated with access to or participation in the intervention, and are causally related to the outcome of interest, may lead to a spurious relationship between intervention and outcome if unaccounted for. Self-selection occurs where, for example, more able or organized individuals or communities, who are more likely to have better outcomes of interest, are also more likely to participate in the intervention. Endogenous program selection occurs where individuals or communities are chosen to participate because they are seen to be more likely to benefit from the intervention. Ignoring confounding factors can lead to a problem of omitted variable bias. In the special case of selection bias, the endogeneity of the selection variables can cause simultaneity bias. Spillover (referred to as contagion in the case of experimental evaluations) occurs when members of the comparison (control) group are affected by the intervention. Contamination occurs when members of treatment and/or comparison groups have access to another intervention which also affects the outcome of interest. Impact heterogeneity refers to differences in impact due by beneficiary type and context. High quality impact evaluations will assess the extent to which different groups (e.g., the disadvantaged) benefit from an intervention as well as the potential effect of context on impact. The degree that results are generalizable will determine the applicability of lessons learned for interventions in other contexts.
Impact evaluation designs are identified by the type of methods used to generate the counterfactual and can be broadly classified into three categories – experimental, quasi-experimental and non-experimental designs – that vary in feasibility, cost, involvement during design or after implementation phase of the intervention, and degree of selection bias. White (2006) and Ravallion (2008)Ravallion, M. (2008) Evaluating Anti-Poverty Programs discuss alternate Impact Evaluation approaches.
Experimental approaches
Under experimental evaluations the treatment and comparison groups are selected randomly and isolated both from the intervention, as well as any interventions which may affect the outcome of interest. These evaluation designs are referred to as randomized control trials (RCTs). In experimental evaluations the comparison group is called a control group. When randomization is implemented over a sufficiently large sample with no contagion by the intervention, the only difference between treatment and control groups on average is that the latter does not receive the intervention. Random sample surveys, in which the sample for the evaluation is chosen randomly, should not be confused with experimental evaluation designs, which require the random assignment of the treatment.
The experimental approach is often held up as the 'gold standard' of evaluation. It is the only evaluation design which can conclusively account for selection bias in demonstrating a causal relationship between intervention and outcomes. Randomization and isolation from interventions might not be practicable in the realm of social policy and may be ethically difficult to defend,Note that it has been argued that “Randomistas is a slang term used by critics to describe proponents of the RCT methodology. It is almost certainly a gendered, derogatory term intended to flippantly dismiss experimental economists and their success, particularly Esther Duflo, one of the most successful experts on randomization.” See Webber, S., & Prouse, C. (2018). The New Gold Standard: The Rise of Randomized Control Trials and Experimental Development. Economic Geography, 94(2), 166–187. although there may be opportunities to use natural experiments. Bamberger and White (2007)Bamberger, M. and White, H. (2007) Using Strong Evaluation Designs in Developing Countries: Experience and Challenges, Journal of MultiDisciplinary Evaluation, Volume 4, Number 8, 58-73 highlight some of the limitations to applying RCTs to development interventions. Methodological critiques have been made by Scriven (2008)Scriven (2008) A Summative Evaluation of RCT Methodology: & An Alternative Approach to Causal Research, Journal of MultiDisciplinary Evaluation, Volume 5, Number 9, 11-24 on account of the biases introduced since social interventions cannot be fully blinded, and Deaton (2009) has pointed out that in practice analysis of RCTs falls back on the regression-based approaches they seek to avoid and so are subject to the same potential biases. Other problems include the often heterogeneous and changing contexts of interventions, logistical and practical challenges, difficulties with monitoring service delivery, access to the intervention by the comparison group and changes in selection criteria and/or intervention over time. Thus, it is estimated that RCTs are only applicable to 5 percent of development finance.
Randomised control trials (RCTs)
RCTs are studies used to measure the effectiveness of a new intervention. They are unlikely to prove causality on their own, however randomisation reduces bias while providing a tool for examining cause-effect relationships. RCTs rely on random assignment, meaning that that evaluation almost always has to be designed ex ante, as it is rare that the natural assignment of a project would be on a random basis. When designing an RCT, there are five key questions that need to be asked: What treatment is being tested, how many treatment arms will there be, what will be the unit of assignment, how large of a sample is needed, how will the test be randomised. A well conducted RCT will yield a credible estimate regarding the average treatment effect within one specific population or unit of assignment. A drawback of RCTs is 'the transportation problem', outlining that what works within one population does not necessarily work within another population, meaning that the average treatment effect is not applicable across differing units of assignment.
Natural experiments Natural experiments are used because these methods relax the inherent tension uncontrolled field and controlled laboratory data collection approaches. Natural experiments leverage events outside the researchers' and subjects' control to address several threats to internal validity, minimising the chance of confounding elements, while sacrificing a few of the features of field data, such as more natural ranges of treatment effects and the presence of organically formed context. A main problem with natural experiments is the issue of replicability. Laboratory work, when properly described and repeated, should be able to produce similar results. Due to the uniqueness of natural experiments, replication is often limited to analysis of alternate data from a similar event.
Non-experimental approaches
Quasi-experimental design
Quasi-experimental approaches can remove bias arising from selection on observables and, where panel data are available, time invariant unobservables. Quasi-experimental methods include matching, differencing, instrumental variables and the pipeline approach; they are usually carried out by multivariate regression analysis.
If selection characteristics are known and observed, they can be controlled for to remove the bias. Matching involves comparing program participants with non-participants based on observed selection characteristics. Propensity score matching (PSM) uses a statistical model to calculate the probability of participating on the basis of a set of observable characteristics and matches participants and non-participants with similar probability scores. Regression discontinuity design exploits a decision rule as to who does and does not get the intervention to compare outcomes for those just either side of this cut-off.
Difference in differences or double differences, which use data collected at baseline and end-line for intervention and comparison groups, can be used to account for selection bias under the assumption that unobservable factors determining selection are fixed over time (time invariant). Difference in differences can also be applied to multiple time points and when an intervention is incrementally introduced in phases.