M. Di Maio, V. Leone Sciabolazza (2020), Conflict exposure and labour market outcomes: Evidence from longitudinal data from the Gaza Strip

This paper documents the effect of variations in the individual-level intensity of conflict exposure on various labour market outcomes for Palestinians living in the Gaza Strip. Combining individual-level longitudinal employment data and geolocalised information on conflict-related events, we show that an increase in conflict exposure of the individual, while it does not affect the employment status on average, it has a heterogeneous impact on job transitions depending on the worker being employed in the private or the public sector. We also find that, for those in the private sector, higher conflict exposure reduces the labour income and the number of hours worked. For those in the public sector, the effect of conflict is instead null on both the labour income and the number of hours worked and it is positive on wages. Finally, we provide suggestive evidence that these results are explained by the combination of two mechanisms, namely the conflict-induced change in the health conditions of the workers (which affects the labour supply) and in the level of the local economic activity (which affects the labour demand).


D. Del Prete, L. Forastiere, V. Leone Sciabolazza, Causal Inference on Networks under Continuous Treatment Interference: an application to trade distortions in agricultural markets, F.R.E.I.T. Working Paper, 1532.

Causal inference often neglects the presence of interference. This takes place when treatment exposure of one unit also affects other units connected through physical, social or economic interactions in a network structure. Extensive work has been done to assess the role played by spillover effects in policy evaluations, but most of the literature focuses on randomized experiments under cluster interference.
This paper presents a methodology to draw causal inference in a non-experimental setting subject to network interference. Specifically, we develop a generalized propensity score-based estimator to estimate both direct and spillover effects of a continuous treatment.
Spillover effects are defined by the exposure to the network treatment, that is, a summary of the treatment received by connected units.
Our estimator also allows to consider asymmetric network connections characterized by heterogeneous intensities. To showcase this methodology, we investigate whether and how spillover effects shape the optimal level of policy interventions in agricultural markets. Our results show that, in this context, neglecting interference may underestimates the degree of policy effectiveness.

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