| Field | Before | After |
|---|---|---|
| Field Trial Status | Before on_going | After completed |
| Field Last Published | Before July 20, 2020 10:18 AM | After January 19, 2023 02:16 PM |
| Field Study Withdrawn | Before | After No |
| Field Intervention Completion Date | Before | After December 15, 2019 |
| Field Data Collection Complete | Before | After Yes |
| Field Final Sample Size: Number of Clusters (Unit of Randomization) | Before | After 200 firms |
| Field Was attrition correlated with treatment status? | Before | After No |
| Field Final Sample Size: Total Number of Observations | Before | After 200 firms (admin data on exports); 172 firms (management practices survey) |
| Field Final Sample Size (or Number of Clusters) by Treatment Arms | Before | After 100 treatment, 100 control |
| Field Data Collection Completion Date | Before | After February 15, 2021 |
| Field Keyword(s) | Before Firms And Productivity | After Firms And Productivity |
| Field Public analysis plan | Before No | After Yes |
| Field Building on Existing Work | Before | After No |
| Field | Before | After |
|---|---|---|
| Field Public | Before No | After Yes |
| Field | Before | After |
|---|---|---|
| Field Public | Before No | After Yes |
| Field | Before | After |
|---|---|---|
| Field Public | Before No | After Yes |
| Field | Before | After |
|---|---|---|
| Field Paper Abstract | Before | After Policymakers often test expensive new programs on relatively small samples. Formally incorporating informative Bayesian priors into impact evaluation offers the promise to learn more from these experiments. We evaluate a Colombian program for 200 firms which aimed to increase exporting. Priors were elicited from academics, policymakers, and firms. Contrary to these priors, frequentist estimation can not reject null effects in 2019, and finds some negative impacts in 2020. For binary outcomes like whether firms export, frequentist estimates are relatively precise, and Bayesian credible posterior intervals update to overlap almost completely with standard confidence intervals. For outcomes like increasing export variety, where the priors align with the data, the value of these priors is seen in posterior intervals that are considerably narrower than frequentist confidence intervals. Finally, for noisy outcomes like export value, posterior intervals show almost no updating from the priors, highlighting how uninformative the data are about such outcomes. |
| Field Paper Citation | Before | After Iacovone,Leonardo; Mckenzie,David J.; Meager,Rachael. Bayesian Impact Evaluation with Informative Priors : An Application to a Colombian Management and Export Improvement Program (English). Policy Research working paper ; no. WPS 10274; Impact Evaluation series Washington, D.C. : World Bank Group. |
| Field Paper URL | Before | After http://documents.worldbank.org/curated/en/099807301092338354/IDU08080a50008e3c047f40a8de0e4e6b95c2d2c |