Publications and working papers

Why Don’t Poor Families Move? A Spatial Equilibrium Analysis of Parental Decisions with Social Learning

02/2024

Abstract

In the United States, less-educated parents tend to allocate little time to parent-child activities, reside in disadvantaged neighborhoods, and underestimate the relevance of parental inputs for later outcomes. This paper proposes a social learning mechanism that can lead to socioeconomic differences in parental beliefs and decisions. The key elements are young adults learning through the observations of older people within their neighborhood but being prone to erroneous inferences by imperfectly correcting for selection induced by residential segregation. I incorporate the social learning mechanism in a quantitative spatial and overlapping generations model of human capital accumulation and parental decisions. Once calibrated to the United States, the model accurately captures both targeted and non-targeted parental behavior across socioeconomic groups. It displays relatively modest levels of erroneous beliefs, contributing to a 3% increase in income inequality (measured by the income Gini index) and a 14% reduction in social mobility (measured by the income rank-rank coefficient). A housing voucher policy improves the neighborhood quality of eligible families, raising children’s future earnings. When the policy is scaled up, long-run and general equilibrium responses in parental beliefs amplify the effects of the policy, reducing inequality and improving social mobility.

Macroeconomic Consequences of Early Education Tracking

with Lukas Mahler. 01/2024

Abstract

We study the long-run macroeconomic, distributional, and intergenerational effects of school tracking—the allocation of students to different types of schools—by incorporating school track decisions into a general-equilibrium heterogeneous-agent overlapping-generations model. The key ingredient in the model is the child skill production technology, where a child’s skill development depends on her peers and the instruction pace in her school track. We show analytically that this technology can rationalize reduced-form evidence on the effects of school tracking on the distribution of child skills. We then calibrate the model using representative data from Germany, a country with a very early school tracking policy. Our calibrated model predicts that an education reform that postpones the tracking age from ten to fourteen generates sizable improvements in intergenerational mobility but comes at the cost of modest losses in aggregate human capital and economic output, reducing aggregate welfare. This efficiency-mobility trade-off is rooted in the effects of longer comprehensive schooling on child learning and depends crucially on the presence of general equilibrium effects in the labor market. Finally, our calibrated model predicts that policies reducing the parental influence in the school track choice increase both social mobility and aggregate economic output, improving aggregate welfare.

Estimating Impact with Surveys versus Digital Traces: Evidence from Randomized Cash Transfers in Togo

with Emily Aiken, Joshua Blumenstock, Dean Karlan and Chris Udry. 09/2023

Abstract

Do non-traditional digital trace data and traditional survey data yield similar estimates of the impact of a cash transfer program? In a randomized controlled trial of Togo’s COVID-19 Novissi program, endline survey data indicate positive treatment effects on beneficiary food security, mental health, and self-perceived economic status. However, impact estimates based on mobile phone data —processed with machine learning to predict beneficiary welfare— do not yield similar results, even though related data and methods do accurately predict wealth and consumption in prior cross-sectional analysis in Togo. This limitation likely arises from the underlying difficulty of using mobile phone data to predict short-term changes in wellbeing within a rural population with fairly homogeneous baseline levels of poverty. We discuss the implications of these results for using new digital data sources in impact evaluation.

When Effective Teacher Training Falls Short in the Classroom: Evidence from an Experiment in Primary Schools

with Adrien Bouguen, Marc Gurgand, Valerie Munier and André Tricot. 08/2023

J-PAL
Abstract

Although in-service teacher training programs are designed to enhance the performance of several cohorts of students, there is little evidence on the persistence of their effects. We present the two-year results of a large-scale randomized study of an intensive in-service teacher training program conducted in France during and after the training program’s implementation year. Our results highlight the short-run effectiveness of the training program: it successfully improves students’ performance but only during the implementation year. A detailed analysis of teachers’ outcomes indicates that teachers changed their pedagogical vision and practices but struggled to apply skills to contents not directly covered during training.

Machine Learning and Mobile Phone Data Can Improve the Targeting of Humanitarian Assistance

with Emily Aiken, Dean Karlan, Chris Udry and Joshua Blumenstock. Nature (2022).

CEGA
Abstract

Targeting is a central challenge in the administration of anti-poverty programs: given available data, how does one rapidly identify the individuals and families with the greatest need? Here we show that non-traditional “big” data from satellites and mobile phone networks can improve the targeting of anti-poverty programs. Our analysis compares outcomes – including exclusion errors, total social welfare, and measures of fairness – under different targeting regimes. Relative to other feasible targeting options, the machine learning approach reduces errors of exclusion by 4-21%. These results highlight the potential for new data sources to contribute to humanitarian response efforts, particularly in crisis settings when traditional data are missing or out of date.

Work in progress

Employment Transitions: Mechanisms For Gender Earnings Gap

with Valentina Melentyeva

Abstract

We use German administrative data to compare women’s and men’s career choices and labor income profiles over their working lives. We find a significant and robust gender gap in job-to-job transitions. When working full-time, women are less likely than men to transition from one employer to another. The first finding is that while commuting preferences and sector selection do not explain this gap, the birth event largely explains it. Second, we want to quantify the consequences of the gender gap in job-to-job transitions for the gender earnings gap. If full-time job-to-job transitions increase wage growth, this phenomenon most likely contributes to the well-documented child penalty.

Impacts of a Multi-component Reading Intervention for 6th-grade Students: Results from a Clustered Randomized Controlled Trial

with Marina Tual, Maryse Bianco, Pascal Bressoux and Marc Gurgand

Abstract

This study aimed to design, implement and analyze the effectiveness of a multicomponent reading comprehension intervention for sixth-grade students with reading difficulties. We conducted a clustered randomized study with 21 French middle schools. In ten of them, teachers have implemented fluency workshops and explicit comprehension instruction. We measure the impact on 568 target students, and show no statistically significant effect of the intervention. The lack of statistical effects contrasts with observations indicating that, overall, the school leaders and the teachers declared to be satisfied with the proposed device and the actions conducted. However, our results align with a growing research base showing that setting up additional support and disseminating research tools is not enough to observe real progress in students’ performance. Indeed, our field observations revealed significant implementation difficulties.