| dc.description.abstract | This thesis consists of three chapters on the effects of immigration on labor markets. The first chapter studies the effects of an informal labor supply shock on the host regions, the second chapter investigates the spillover effects on non-host regions through the production network, and the third chapter provides a method that the first two chapters rely on.
The first chapter studies the effects of Syrian refugees, who are denied work permits and thus can only work informally, on Turkish firms and workers. Using travel distance as an instrument for refugee location, I show that low-skill natives lose both informal and formal salaried jobs. I document two mechanisms: formal firms reduce their formal labor demand and new firms do not enter the formal economy. Estimates imply an elasticity of substitution of 10 between formal and informal workers. Counterfactual exercises predict that granting refugees work permits would have created up to 120,000 formal jobs in the economy through higher informal wages.
The second chapter, co-written with Tishara Garg, investigates how immigration-induced wage shocks can propagate beyond the regions receiving immigrants through the production network. Using the Syrian refugee crisis in Turkey as a quasi-experiment and the near universe of domestic firm-to-firm transaction data from VAT records, we show that the immigration shock propagates both forward and backward along the supply chain. Firms in non-host regions who directly or indirectly buy from host regions demand more labor. Firms who sell to host regions weakly increase their sales. Estimates imply an elasticity of substitution between labor and intermediate goods of 0.76 and an elasticity of substitution of nearly 1 between intermediates. Counterfactual analyses show that the spillover effects on non-host regions are economically meaningful when the host regions are central nodes of the domestic trade network. For example, a 1% increase in labor supply in Istanbul decreases real wages in Istanbul by 0.56% and increases real wages in the average non-host city by 0.38%.
The third chapter, co-written with Jaume Vives-i-Bastida, proposes a Synthetic Instrumental Variables (SIV) estimator for panel data that combines the strengths of instrumental variables and synthetic controls to address unmeasured confounding. We derive conditions under which SIV is consistent and asymptotically normal, even when the standard IV estimator is not. Motivated by the finite sample properties of our estimator, we introduce an ensemble estimator that simultaneously addresses multiple sources of bias and provide a permutation-based inference procedure. We demonstrate the effectiveness of our methods through a calibrated simulation exercise, two shift-share empirical applications, and an application in digital economics that includes both observational data and data from a randomized control trial. In our primary empirical application, we examine the impact of the Syrian refugee crisis on Turkish labor markets. Here, the SIV estimator reveals significant effects that the standard IV does not capture. Similarly, in our digital economics application, the SIV estimator successfully recovers the experimental estimates, whereas the standard IV does not. | |
