Tag
policy_evaluation
5 findings
Methods & evidenceCaseworker-Level Randomization in Policy EvaluationCaseworker-level randomization successfully eliminated cross-client informational spillovers while maintaining balanced treatment and control groups across observed jobseeker demographic characteristics (passing balance tests in all regions except Geneva).RCTLabour & employmentEndogenous Stratification Equity GainsFor the lowest-predicted-earnings stratum, the job application workshop increased 4-year earnings by 50% over the control mean (p = 0.0696 for equality with the high-predicted-earnings group). This intervention reduced the earnings gap between high- and low-predicted earners from 142% down to 54%.RCTEconomics (general)Expert Forecast OveroptimismLocal field staff exhibited substantial overoptimism, predicting median 6-week employment rates of 20% for cash, 10% for information, and 9% for nudge (compared to actual control/treatment rates under 5%). Senior international staff had more accurate median forecasts (7% for cash, 5% for info, 4% for nudge). Both groups correctly anticipated that cash would be the most effective intervention.RCTLabour & employmentRandomized Controlled Evaluation of Workforce ProgramsRandomized sorting ensured baseline equivalence between groups, establishing that sectoral programs causally increased 2-year earnings by $4,500 in SEIS (18% increase, p < 0.01), 5-year earnings by $2,716 in WorkAdvance (11.5% increase, p < 0.01), and 11-year earnings by $4,616 in Project QUEST (15% increase, p < 0.01).RCTLabour & employmentWelfare Reversal in Large-Scale Policy RolloutWhile a naive microeconometric evaluation calculates a false positive net return of 4,094 DKK (~550 EUR) per participant, structural simulation proves social welfare is maximized at only 20% treatment intensity and drops by 0.13% under full (100%) rollout, while government UI program expenditures are minimized at 30% intensity.Expert Theory