Legacies of Conflict: Experiences, Self-efficacy and the Formation of Conditional Trust
joint with Niklas Buehren (World Bank), Markus Goldstein (World Bank) and Andrea Smurra (UCL)
September 2022

Abstract An established literature finds those exposed to conflict are more pro-social later in life. We build on this work in two directions using a sample of 4,200 women born into the Sierra Leonian civil war and surveyed 14 years later. First, we introduce the notion of conditional trust, whereby individuals neither outright distrust or outright trust others, but can use their perceived self-efficacy to raise the cooperativeness of others. This takes ideas from the psychology literature documenting survivors of trauma can go through a process of post-traumatic growth generating perceived self-efficacy. We develop a framework to make precise how conditional trust depends on beliefs over others, gains from cooperation, risk aversion, and the key mediating role of self-efficacy in linking conflict and trust. Second, we construct a granular typology of experiences of conflict combining information on a geo-coded measure of exposure to conflict, self-reported memories/recall of victimization, and ages of exposure to conflict. This distinguishes individuals who are traumatized, those with direct first-hand accounts of conflict, and those with second-hand narratives. Empirically, we find exposure to conflict -- either by being in the vicinity of conflict or through specific experiences of conflict -- leads respondents to be significantly more likely to conditionally trust others. We establish perceived self-efficacy is higher among those exposed to conflict and this mediates the impact of conflict on trust preferences. By considering the role of memories, narratives/socialization in shaping experiences of conflict, generating self-efficacy and thus driving trust preferences, we provide new avenues for research on how psychological legacies of trauma early in life shape the long run formation of economic preferences.

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)
July 2022

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.

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.