Human Capital HCEO - CEHD

@hceconomics.bsky.social

Human Capital and Economic Opportunity Global Working Group at the @UChicago Center for the Economics of Human Development. We generate & share research on how people achieve their fullest potential. https://hceconomics.uchicago.edu https://cehd.uchicago.

The new Illinois Department of Early Childhood opened July 1, consolidating early childhood care resources under one roof. Our executive director, Alison Baulos, tells WBEZ’s In the Loop podcast that this improves equity and access to quality care for families. www.wbez.org/in-the-loop-...

A look back at the Illinois Department of Early Childhood’s first month

How will the new Illinois Department of Early Childhood simplify accessing early childhood services in the state.

wbez.org

On Friday, @heckmanequation.bsky.social shared research at the Conference of the Society for the Advancement of Economic Theory exploring how personality traits influence economic preferences and behaviors, like risk aversion and decision-making. #IMPA #SAET impa.br/notices/nobe...

Heckman Links Psychology and Economics in Plenary Session - IMPA - Institute for Pure and Applied Mathematics

The study presented here examines how personality traits can influence decisions involving risk

impa.br

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.