Policy evaluation assesses the effect of public programs on participants' socio-economic outcomes in the fields of labor, education, health, family and socio-economic policies. To retrieve causal effects, it relies on counterfactual methods that compare treated and untreated groups while accounting for confounding factors.
The department assesses the impact of socio-economic programs across various domains, including labor, education, family, health, mobility, and housing. Through rigorous scientific studies on the causal effects of public policies, it provides evidence-based policy recommendations grounded in cost-benefit analyses. The objective is to assist policymakers in addressing market and policy failures, enhancing societal welfare, and developing effective responses to the complex and evolving challenges of contemporary society.
A key challenge in public policy evaluation is identifying causal effects rather than simple correlations. Public policies often target individuals with specific characteristics, making it problematic to determine policy impact by comparing the outcomes of participants and non-participants, as these groups may differ intrinsically. Likewise, evaluating participants' outcomes before and after an intervention may be misleading, as changes could result from external factors unrelated to the policy.
Randomized control trials (RCTs) are considered the gold standard for establishing causality. In policy pilot programs, participants are randomly assigned to treatment and control groups. Randomization ensures that, with sufficiently large groups, differences in average outcomes can be attributed to the intervention, as the groups are statistically equivalent apart from the treatment.
When RCTs are infeasible, non-experimental methods are employed to approximate randomization. These include natural experiments, quasi-experiments, and observational studies. In such cases, researchers construct comparable treatment and control groups by controlling for other confounding factors that may also influence the outcome. Micro-econometric techniques are applied, including methods based on observable characteristics, such as matching estimators, and methods that address unobserved differences, such as difference-in-differences, regression discontinuity design, instrumental variables, and synthetic control method.
By employing these rigorous methodologies, program evaluation provides policymakers with reliable, evidence-based insights, enabling more informed decision-making and ensuring that public resources are allocated effectively to achieve targeted social and economic outcomes.
