Parental Responses to Information About School Quality: Evidence from Linked Survey and Administrative Data

joint with Ellen Greaves (Bristol), Iftikhar Hussain (Sussex) and Birgitta Rabe (Essex)

June 2021

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 (second round), Economic Journal.

Social Structure and the Localized Delivery of Development Interventions

joint with Oriana Bandiera (LSE), Robin Burgess (LSE), Erika Deserranno (Northwestern),

Ricardo Morel (IPA) and Munshi Sulaiman (BRAC)

May 2021

Abstract There has been a dramatic rise in use of the local delivery model for development interventions, where local agents are hired as intermediaries to target interventions to potential beneficiaries. We study this model in the context of an agricultural extension intervention in Uganda, where delivery agents are tasked to target needy farmers in their community. We investigate how the web of vertical ties between the delivery agent and potential beneficiaries, horizontal ties between the actual delivery agent and a counterfactual delivery agent, and community divisions, interact to provide social incentives to delivery agents, thus determining how the intervention unfolds within rural economies. We use a two-stage randomization design, across and within communities, to identify counterfactual delivery agents. We reveal the central role community divisions play: delivery agents exert effort to target farmers only when they are from opposing sides of community divides to the counterfactual agent. However, delivery agents are then also more likely to target non-needy farmers. This goes against the pro-poor intent of the intervention, and leads to a 11% loss in potential surplus. However, welfare overall increases by 6% because the surplus gains from targeting more farmers offsets the effects of targeting the non-needy. Our analysis shows how viewing development policy through the lens of social structure helps explain why intervention effectiveness varies across communities, and how interventions can increase inequality within communities. We conclude by discussing design implications for the local delivery model.

The Anatomy of a Public Health Crisis: Household Responses Over the Course of the Zika Epidemic in Brazil

joint with Ildo Junior (World Bank)

May 2021

Abstract The global frequency and complexity of viral outbreaks is increasing. In 2015, Brazil experienced an epidemic caused by the Zika virus. This represents the first known association between a flavivirus and congenital disease, marking a `new chapter in the history of medicine' [Brito 2015]. We use tens of millions of administrative records to document household responses to a public health alert linking the emerging Zika virus and congenital disease. We study such informational responses in relation to both risk avoidance behaviors (avoiding pregnancy), risk mitigation behaviors during pregnancy, as well as consequent impacts on birth outcomes. On risk avoidance, we find a 7% reduction in pregnancies post-alert, a response triggered immediately after the alert, and driven by higher SES women. On risk mitigation during pregnancy, we document that post-alert all groups -- irrespective of race, marital status, education, age, and socioeconomic status -- were able to reduce the risks of microcephaly during pregnancy, in line with preventative measures such as wearing long and light colored clothing, using mosquito repellent etc. not being costly. We also find an increased use of ultrasounds (9%) in the first trimester of pregnancy, and abortions (5%), especially late term abortions. We find muted impacts on birth outcomes once we account for post-alert selection of mothers into conception and birth. We conclude by discussing the extent to which our findings extend to household responses to public health alerts on other emerging viral threats.

Worker Heterogeneity and Job Search: Evidence from a Six-year Field Experiment in Uganda
joint with Oriana Bandiera (LSE), Vittorio Bassi (USC), Robin Burgess (LSE),
Munshi Sulaiman (BRAC) and Anna Vitali (UCL)

December 2020

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.

Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition
joint with Aureo de Paula (UCL) and Pedro CL Souza (Warwick)

August 2020

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.