PhD thesis defence by Harvey Stafford
Chapter 1, co-authored with Eugenia Vella, examines the macroeconomic and distributional effects of immigration in the presence of endogenous automation. We develop a dynamic stochastic general equilibrium model with search-and-matching frictions, four worker types distinguished by skill and nativity, and automation capital that substitutes for low-skilled labor. Calibrated to Germany using microdata from the German Socio-Economic Panel (SOEP), the model highlights how the skill composition of immigration shapes technological adoption and aggregate outcomes. High-skilled immigration generates substantially larger output gains than low-skilled immigration by stimulating investment in automation capital through complementarity between skilled labor and automated production. In contrast, low-skilled immigration reduces incentives to automate, dampening productivity growth and output per capita. The model also uncovers important distributional effects: high-skilled immigration compresses the skill premium, whereas low-skilled immigration widens it, while both shocks increase the native–immigrant wage gap. These findings underscore the importance of considering automation when evaluating immigration policy.
Chapter 2 studies how immigration inflows propagate across sectors via the production network. Using the American Community Survey to derive a commuting zone (CZ)-sector-year panel and identifying immigration inflows via a shift- share instrument we find immigration shocks propagate upstream through derived demand with a one standard deviation increase in a sector’s upstream exposure— meaning its customers are receiving more immigrant inflows— is associated with a 7.6% increase in native employment. This crucially depends on network centrality with the propagation result is concentrated in sectors supply to central sectors. Therefore the network position determines the spillovers of immigration inflows across production linkages within a local labor market.
Chapter 3, co-authored with Alexander Monge-Naranjo, quantifies the economic consequences of large-scale deportations by integrating occupational choice with production network analysis. Using 2023 American Community Survey data, we document that undocumented workers concentrate heavily in specific occupations and sectors—nearly 20% of construction workers and two-thirds of elementary occupation workers are estimated undocumented. we develop a general equilibrium model combining occupational sorting with input-output linkages to capture how deportations affect the economy through worker reallocation and network propagation. A 50% reduction in undocumented workers generates an 18.6% GDP decline, with production networks amplifying the direct shock by 5.1 percentage points. The composition of deportations critically matters: removing primary-educated workers causes a 10.8% GDP decline—more than double the non-targeted effect—while tertiary-educated deportations exhibit the highest network amplification (2.20×). Non-citizen workers experience smaller wage declines than citizens, with primary-educated non-citizens enjoying a 6.7 percentage point scarcity premium due to their high deportation exposure. These findings demonstrate that immigration policy has first-order macroeconomic consequences shaped by both occupational comparative advantage and sectoral interdependencies.
The event will take place online.