Peter Bergman
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peterbergman.bsky.social
Peter Bergman
@peterbergman.bsky.social
4.9K followers 880 following 330 posts

Associate Prof. of Economics @utaustin. Director http://learningcollider.org. @NBERpubs affiliate. Co-Chair @mit @jpal edtech. Schmidt Futures Innovation Fellow. www.peter-bergman.com.

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Peter Bergman @peterbergman.bsky.social · Jun 12
New NBER working paper. We meta-analyze 82 RCTs of low-cost parent programs in 20+ countries. We use data on unwritten RCTs from funder records, RCT registries, author queries, etc. to estimate a model that adjusts for publication bias and characterizes the effect distribution for unwritten studies.
NBER working paper. Paper title: Characterizing the File Drawer: Evidence from a Meta-Analysis of Parent-Interventions Around the World Abstract: We conduct a meta-analysis of 82 randomized controlled trials across more than 20 countries to estimate the effects of low-cost, remote parental engagement interventions delivered through text messages, phone calls, and apps. We estimate a joint likelihood function that incorporates both written studies and unwritten studies identified through trial registries, funder records, research labs, evidence clearinghouses, and other sources. By also recording sample sizes for unwritten studies, the model estimates the distribution of standard errors, identifies write-up probabilities conditional on significance, and characterizes the file drawer by estimating effect distributions for written \textit{and} unwritten studies. Bias-corrected effects are 0.05 SD for test scores, 0.07 SD for grades, 0.05 SD for attendance, and 0.03 SD for enrollment. In the best-identified domain, test scores, statistically insignificant results are still written up at high rates. We also find that larger studies tend to estimate smaller latent effects, which could indicate that true effects are correlated with study precision, violating a common meta-analysis assumption. In smaller-sample domains, our approach helps identify selection probabilities by anchoring the absolute write-up rates. Finally, we estimate the value of additional RCTs to inform adoption decisions. Any single study estimate is unlikely to dissuade adoption because parent interventions have high marginal value of public funds. Instead, future research is most valuable when it can explain heterogeneity across settings.

Reposted by Christian E. Weller, Dean Eckles, Peter Bergman

rmetcalfe.bsky.social
Robert Metcalfe @rmetcalfe.bsky.social · 5h
I'm hiring an economics predoc at @columbiasipa.bsky.social, generally on applied micro projects, but with a large focus on the impacts of AI on economic outcomes using large-scale field experiments with many companies.

Please share widely: apply.interfolio.com/194121
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