joint with Ellen Greaves (Bristol), Iftikhar Hussain (Sussex) and Birgitta Rabe (Essex)
Abstract Multiple inputs determine children's academic achievement. We study the interaction between family and school inputs by identifying the causal impact of information about school quality on parental time investment into children. Our setting is England, where credible information on school quality is provided by a nationwide school inspection regime. Schools are inspected at short notice, with school ratings using hard and soft information. As such soft information is not necessarily known to parents ex ante, inspection ratings provide news to parents that shifts parental beliefs about school quality, and hence their investment into their children. We study this using household panel data linked to administrative records on school performance and inspection ratings. Within the same academic year, we observe some households being interviewed pre school inspection, and others being interviewed post inspection. Treatment assignment is determined by a household's survey date relative to the school inspection date, and shown to be as good as random. We find that parents receiving good news over school quality significantly decrease time investment into their children (relative to parents that will later receive such good news). Our data and design allow us to provide insights on the distributional and test score impacts of the nationwide inspections regime, through multiple margins of endogenous response of parents and children. Our findings highlight the importance of accounting for interlinked private responses by families to new public information on school quality.
Revised and resubmitted, Economic Journal.
Parental Responses to Information About School Quality: Evidence from Linked Survey and Administrative Data
joint with Oriana Bandiera (LSE), Vittorio Bassi (USC), Robin Burgess (LSE),
Munshi Sulaiman (BRAC) and Anna Vitali (UCL)
Abstract Developing countries face the challenge of aiding large cohorts of labor market entrants find good jobs. How to do so is complicated by job seekers differing in their skills, information and traits. We present results from a six-year field experiment studying job search behavior among youth in urban labor markets in Uganda, who at baseline, are unskilled yet optimistic over their job prospects. We engineer heterogeneity across workers through the offer of vocational training, and job assistance to meet with potential employers. Vocational training leads to measurable improvements in skills, while job assistance alters information workers have on their prospects, as call back rates from employers are low. Search behavior varies across the skills distribution: relative to controls, skilled youth become even more optimistic, search more intensively, and direct search towards better firms. The additional provision of job assistance to skilled youth causes them to revise down their beliefs, search less intensively and over lower quality firms. These differential search strategies impact long run outcomes: skilled workers without job assistance have higher employment rates and spell durations, and match to higher quality jobs and firms. Fixed traits across workers such as their cognitive ability and self-evaluation determine search strategies and outcomes because they interlink with how youth respond to the low call back rates from job assistance. Overall, our study provides insights on sources of worker heterogeneity driving labor market inequalities and inefficiencies, and on the design and targeting of labor market programs.
Worker Heterogeneity and Job Search:
Evidence from a Six-Year Experiment in Uganda
joint with Oriana Bandiera (LSE), Niklas Buehren (World Bank),
Markus Goldstein (World Bank) and Andrea Smurra (UCL)
Abstract School closures are a common short run policy response to viral epidemics. We study the persistent post-epidemic impacts of this on the economic lives of young women in Sierra Leone, a context where women frequently experience sexual violence and face multiple economic disadvantages. We do so by evaluating an intervention targeting young women that was implemented during the 2014/15 Ebola epidemic in Sierra Leone. This provided them a protective space where they can find support, and receive information on health/reproductive issues. We document the impacts of the intervention on 4,700 young girls and women tracked from May 2014 on the eve of the Ebola crisis, to the post-epidemic period in 2016. In control villages, school closures led young girls to spend significantly more time with men, teen pregnancies rose sharply, and school enrolment among young girls dropped by 17pp post-epidemic, long after schools had re-opened. These adverse effects on enrolment are halved in treated villages because the intervention breaks this causal chain: it enables girls to allocate time away from men, reduces out-of-wedlock pregnancies by 7pp, and so increases re-enrolment rates post-epidemic. A long term follow up in 2019/20 shows persistent impacts of the intervention on the human capital accumulation of young girls, time they spend with men, and quality of partners matched with. Our analysis has important implications for school closures in response to the current COVID-19 pandemic in contexts where young women face sexual violence, highlighting the protective and lasting role safe spaces can provide in such times.
Do School Closures During an Epidemic have Persistent Effects? Evidence from Sierra Leone in the Time of Ebola
joint with Aureo de Paula (UCL) and Pedro CL Souza (Warwick)
Abstract Social interactions determine many economic behaviors, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are all globally identified. While this result is relevant across different estimation strategies, we then describe how high-dimensional estimation techniques can be used to estimate the interactions model based on the Adaptive Elastic Net GMM method. We employ the method to study tax competition across US states. We find the identified social interactions matrix implies tax competition differs markedly from the common assumption of competition between geographically neighboring states, providing further insights for the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our identification and application show the analysis of social interactions can be extended to economic realms where no network data exists.
Revisions requested, Review of Economic Studies.
Identifying Network Ties from Panel Data:
Theory and an Application to Tax Competition