Here’s the abstract of a new paper by Jonathan Rothwell (emphasis added): (HT Bryan Riley)
In any dynamic economy, there is a risk of job loss. Job loss resulting from foreign rather than domestic competition has come under intense scrutiny recently with Britain’s exit from the European Union and the election of Donald Trump as president of the United States. While economists generally conclude that trade is broadly enriching, recent works have brought attention to the costs of trade to workers and communities. At the individual level, I find that the risk of layoff and unemployment to workers in trade-exposed sectors is comparable — or even lower — than the risk to workers in non-traded sectors and that these risks have not increased during the period of more intense competition with Chinese imports. At the community level, Autor, Dorn and Hanson (2013) find that local areas have experienced slower job and wage growth and higher unemployment because of import competition with China. Upon analyzing their data, I conclude that their results are biased by the weaker macroeconomic performance of 2000-2007 relative to the 1990s. When I analyze inter-local area economic changes — rather analyzing changes within and across areas — I fail to reject the null hypotheses that import competition has no effect on wage or employment growth, except within the manufacturing sector during the most recent period, or that it has no effect on many other outcomes, including labor force participation, intergenerational mobility, and mortality. During each period, import competition actually predicts an increase in average wages for manufacturing workers, as well as non-manufacturing during the 1990s period, and import competition predicts a shift toward college educated non-manufacturing jobs in the second period. I conclude that foreign competition does not appear to elevate the risk of job loss to a greater extent than domestic competition, and people living in the communities most exposed to foreign competition are no worse off on average.
