Does trade with China cost jobs?

Trade with China undoubtedly costs jobs in specific industries. However there is no evidence that it has any impact on the overall number of jobs in the US. Last year I did a number posts criticizing a study by Autor, Dorn and Hanson, for drawing aggregate conclusions from cross-sectional data. Later Paul Krugman made the same criticism:

OK, what about the effect on overall employment? In general, you can’t answer that with a similar computation, because it all depends on offsetting policies. If monetary and fiscal policy are used to achieve a target level of employment – as they generally were prior to the 2008 crisis – then a first cut at the impact on overall employment is zero. That is, trade deficits meant 2 million fewer manufacturing jobs and 2 million more in the service sector.
. . .

Up through 2007 we basically had a Fed which raised rates whenever it thought the economy was overheating; in the absence of the China shock it would have raised rates sooner and faster, so you just can’t use the results of the cross-section regression – which doesn’t reflect monetary policy, which was the same for everyone – to predict how things would have turned out.

Since then a number of papers have provided support for the Sumner/Krugman critique. First there was one by Jonathan Rothwell, and more recently by Ildikó Magyari. Notice how Magyari distinguishes between microeconomic and macroeconomic effects:

What is the impact of Chinese imports on employment of US manufacturing firms? Previous papers have found a negative effect of Chinese imports on employment in US manufacturing establishments, industries, and regions. However, I show theoretically and empirically that the impact of offshoring on firms, which can be thought of as collections of establishments – differs from the impact on individual establishments – because offshoring reduces costs at the firm level. These cost reductions can result in firms expanding their total manufacturing employment in industries in which the US has a comparative advantage relative to China, even as specific establishments within the firm shrink. Using novel data on firms from the US Census Bureau, I show that the data support this view: US firms expanded manufacturing employment as reorganization toward less exposed industries in response to increased Chinese imports in US output and input markets allowed them to reduce the cost of production. More exposed firms expanded employment by 2 percent more per year as they hired more (i) production workers in manufacturing, whom they paid higher wages, and (ii) in services complementary to high-skilled and high-tech manufacturing, such as R&D, design, engineering, and headquarters services. In other words, although Chinese imports may have reduced employment within some establishments, these losses were more than offset by gains in employment within the same firms. Contrary to conventional wisdom, firms exposed to greater Chinese imports created more manufacturing and nonmanufacturing jobs than non-exposed firms.

But the media loves a good story, and the “China stealing American jobs” meme just won’t go away. Here’s a recent article from The Economist:

Since relatively few industrial robots are in use in the American economy, the total job loss from robotisation has been modest: between 360,000 and 670,000. By comparison, analysis published in 2016 found that trade with China between 1999 and 2011 may have left America with 2m fewer jobs than it would otherwise have had. Yet, if the China trade shock has largely run its course, the robot era is dawning.

That’s very misleading. It’s possible that there are 2 million specific workers who lost jobs because of Chinese trade. But there is no evidence that the net number of US jobs was reduced at all.

Fortunately, the public doesn’t seem to be buying all this gloom and doom, as support for trade is soaring dramatically higher. And Trump seems to have abandoned his proposal for 45% tariffs on Chinese goods.

PS. I am on vacation, and my comments will be few and far between.