Abstract
A number of authors have argued that a worker's occupation of employment is at least as important as the worker's industry of employment in determining whether the worker will be hurt or helped by international trade. We investigate the role of occupational mobility on the effects of trade shocks on wage inequality in a dynamic, structural econometric model of worker adjustment. Each worker in our specification can switch either industry, occupation, or both, paying a time-varying cost to do so in a rational-expectations optimizing environment. We also specify a novel model of offshoring based on task-by-task comparative advantage that collapses to a very simple form for simulation. We find that the costs of switching industry and occupation are both high, and of similar magnitude. In simulations we find that a worker's industry of employment is much more important than either the worker's occupation or skill class in determining whether or not she is harmed by a trade shock, but occupation is crucial in determining who is harmed by an offshoring shock.
Highlights
A number of authors have argued that a worker's occupation of employment is at least as important as the worker's industry of employment in determining whether the worker will be hurt or helped by international trade
More recent approaches have divided up workers based on their industry of employment (Revenga (1992), Pavcnik, Attanasio and Goldberg (2004), Artuc, Chaudhuri and McLaren (2010)); region of residence (Topalova (2007), Kovak (2010), Hakobyan and McLaren (2010)); and age (Artuc (2012a)), in each case attempting to quantify how trade shocks affect people in the different groups differently
Several studies have focussed on a division of workers by occupations, often making use of a data set explored by Autor, Levy, and Murnane (2003) that breaks down the ‘task’ composition of a wide range of occupations in US labor data. Authors who exploit these distinctions to look at the differential effects of trade shocks on workers with different types of occupations include Peri and Sparber (2009), Ebenstein, Harrison, McMillan, and Phillips (2009), and Liu and Trefler (2011)
Summary
The value Ct1,j,s is the ‘entry cost’ mentioned above for switching sectors, and the cost indicated in line (7) applies when the worker switches sectors (i = j) but not occupations (k = l). The value Ct2,l,s is the corresponding ‘entry cost’ for switching occupations, and the cost indicated in line (8) applies when the worker switches occupation (k = l) but not sector (i = j). If a worker is switching sectors, that may raise the cost of switching occupations, since there is a rising marginal cost of additional complexity in decision making; or it may lower the cost of switching occupations, since switching sectors already creates as much disruption in the worker’s life as it is possible to create. The highest average wages are found in White-collar occupations, followed by the Tech/Sales category
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