The Search for Good Jobs: 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)
Abstract One third of the 420 million young people in Africa are unemployed. Understanding how youth search for jobs and what affects their ability to find good jobs is of paramount importance. We do so using a field experiment tracking young job seekers for six years in Uganda's main cities. We examine how two standard labor market interventions impact their search for good jobs: vocational training, vocational training combined with matching youth to firms, and matching only. Training is offered in sectors with high quality firms. The matching intervention assigns workers for interviews with such firms. At baseline, unskilled youth are optimistic about their job prospects, especially over the job offer arrival rate from high quality firms. Relative to controls, those offered vocational training become even more optimistic, search more intensively and direct search towards high quality firms. However, youth additionally offered matching become discouraged because call back rates from firm owners are far lower than their prior. As a result, they search less intensively and direct their search towards lower quality firms. These divergent expectations and search behaviors have persistent impacts: vocational trainees without match offers achieve greater labor market success, largely because they end up employed at higher quality firms than youth additionally offered matching. Our analysis highlights the foundational but separate roles of skills and expectations in job search, how interventions cause youth to become optimistic or discouraged, and how this matters for long run sorting in the labor market.
Social Incentives, Delivery Agents and the Effectiveness of Development Interventions
joint with Oriana Bandiera (LSE), Robin Burgess (LSE), Erika Deserranno (Northwestern),
Ricardo Morel (IPA) and Munshi Sulaiman (BRAC)
Abstract There has been a dramatic rise in the use of the local delivery model for development interventions, where local agents are hired as intermediaries to target benefits to potential beneficiaries. We study this model in the context of a standard agricultural extension intervention in Uganda using a novel two-stage experimental design. In the first stage, we randomize the delivery of the intervention across communities. In the second, in each community we identify two potential delivery agents and then randomly select one of them. This stage yields exogenous variation in social ties to the actual delivery agent as well as to their counterfactual. We use this to identify how social incentives shape the behavior of delivery agents through them having social ties to farmers in communities from which they are recruited and serve. We document a trade-off between coverage and targeting: delivery agents treat more farmers when they have a greater number of social ties, but they are significantly more likely to target their non-poor ties -- counter to the pro-poor intent of the intervention. We explore reasons why delivery agents target their non-poor ties, and conclude by discussing the implications of our findings for the design of the local delivery model.
Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition
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.