Via Mark Thoma, there’s been a round of debate about the winners and losers from free trade, particularly trade with China. See William Polley for a roundup on the question and his summary of the liberal position: “There are winners and losers [from free trade]. But the winners gain more than the losers lose. So effect a transfer from the winners to the losers that still allows the winners to gain but compensates the losers for what they lost. Only then can you really say that free trade (with the compensating side payment) benefits everyone.”
The following statement I’m about to make might be gobbledygook if you don’t speak economics but, trust me, it’s the most “shots fired!” statement that hasn’t been said in this conversation: According to the latest cutting-edge econometrics on the topic, the deadweight losses of how we currently compensate the losers from trade with China, a compensation that probably doesn’t balance out the losses, outweigh the gains from trade with China.
David Autor, David Dorn and Gordon H. Hanson have a new paper out, The China Syndrome: Local Labor Market Effects of Import Competition in the United States, and I think it is a blockbuster. It certainly needs to move to the front of the debate over the implications and consequences of trade with China. David Autor is behind much of the discussion in labor markets recently, particularly the topic of job polarization. He’s a serious intellect in this area, and this is a paper that deserves the econoblogosphere’s attention.
There are two big parts to the paper. The first, and main, conclusion is that areas most impacted by Chinese imports have lower labor force participation and reduced wages in both manufacturing and non-manufacturing firms. I’m going to reprint at length from the paper:
We explore the effect of import competition on U.S. local labor markets that were differentially exposed to the rise of China trade between 1990 through 2007 due to differences in their initial patterns of industry specialization. The focus on local labor markets rather than industries as the unit of analysis allows us to analyze a broad set of economic impacts, both within the manufacturing sector and, critically, in the surrounding labor market. Instrumenting Chinese imports to the U.S. using contemporaneous, industry-level Chinese import growth in other high-income countries, we find that increased exposure of local labor markets to Chinese imports leads to higher unemployment, lower labor force participation, and reduced wages. While the employment reduction is concentrated in manufacturing, wage declines occur in the broader local labor market, and are most pronounced outside of manufacturing….
Figure 2b plots the same bivariate relationship for a trimmed sample that suppresses the 15 CZs
whose variable values differ from the sample medians by more than 5 standard deviations. In
the trimmed sample, which covers 99.1% of U.S. mainland population, the negative relationship
between changes in Chinese import exposure and changes in local manufacturing employment is
larger and clearly visible in the figure, indicating that a rise of $1,000 per worker in a commuting
zones’ exposure to Chinese imports is associated with a decline in manufacturing employment of approximately one fifth of a percentage point of working age population. The mean increase in
Chinese import exposure during 1990-2007 was about $3,300 per worker…..
The sum of the first two coefficients in panel A implies that a $1,000 per worker increase in a CZ’s Chinese import exposure reduces its employment to population rate by 0.58 percentage points. About two-thirds of that decline is due to the loss in manufacturing employment, but there also appears to be a small, though not quite statistically significant reduction in non-manufacturing employment. Columns 3 and 4 of panel A shows that about one-quarter of the reduction in the employment to population ratio is accounted for by a rise in the unemployment to population rate (0.16 percentage points) while the remaining three-quarters accrue to labor force non-participation (0.42 percentage points)….
A $1,000 import exposure shock results in a fairly uniform reduction in the employment-to-population ratio among all three age brackets considered in Table 5 (ages 16-34, 35-49, and 50-64), though the employment losses are more concentrated in manufacturing among the young and relatively more concentrated in non-manufacturing among the old…. A $1,000 per worker increase in a CZ’s exposure to Chinese imports during a decade is estimated to reduce mean weekly earnings by -0.44 log points (about 0.6 percent). Point estimates for wage impacts are largely comparable across gender and education groups, though they are somewhat larger overall for males than for females, with the largest declines found among college males and non-college female
The impact on wages and employment-to-population ratios spreads across both non-manufacturing and manufacturing sectors. There are real costs, real losers to international trade, and the frictions that prevent these workers from being reabsorbed into the economy are also real. The costs are, at the very least, more medium-term than is more commonly assumed.
But the benefits are real too, right? One of the interesting things that the paper makes clear is that the normal compensation for losses from trade mechanisms – especially the Trade Adjustment Assistance Act – are rounding errors. The real mechanisms for redistribution from trade are workers going on disability and health insurance compensation. Food stamps play a major role, and the growth of food stamps over the past two decades should be thought of as as part of trade policy in addition to welfare policy.
Now this is just redistribution of course, no economic gains or losses. However if you assume redistribution is a leaky bucket, the deadweight losses are about equal to the gains. I’m going to reprint this section at length:
What do our results imply about overall U.S. gains from trade with China? In theory, such gains are positive. Trade may lower incomes for workers in industries or regions exposed to import competition, but gains to consumers from increased product variety (Broda and Weinstein, 2006) and income growth in industries and regions with expanding exports should ensure that aggregate gains from trade are greater than zero. Our finding that increased exposure to import competition is associated with lower manufacturing employment and lower wages in exposed local labor markets does not contradict this logic. It just highlights that trade has distributional consequences. To establish a benchmark for the gains from trade with China, we utilize the framework in Arkolakis, Costinot, and Rodriguez-Clare (2010), which yields a simple formula for the gains from trade that holds under a variety of trade models.
Consider an increase in U.S. trade barriers that drives U.S. imports from China to zero. The log change in income that would be needed to keep income constant given the resulting reduction in trade is…[interesting equation work, won’t copy/paste, look it up in the paper]….the log change in income needed to offset the loss of gains from trade would be 0.0007 to 0.0013 (depending on the value of ), equivalent to a change in income of $32 to $61 per capita [footnote: In 2007, U.S. income per capita was $46,700.] Of course, some of the lost imports from China would be offset by an increase in imports from other countries, meaning that the $32-$61 range represents an upper bound on the range of gains from trade with China.
One manner in which adjustment to import competition may eat into these gains from trade is through the deadweight loss associated with individual take-up of government transfers. Such a loss is not a distributional consequence of trade but a loss in economic efficiency associated with U.S. benefit programs. The coefficient estimate on exposure to import competition in the regression for the change in transfers per capita in column 1 of Table 8 implies that the increase in imports per worker from China over the period 1991 to 2007 is associated with an increase in annual per capita transfers of $129.
Using Gruberís (2010) estimate that the deadweight loss from transfers is equal approximately to 40% of their value, the increase in transfers resulting from import exposure implies an increase in deadweight loss over 1991 to 2007 of $52 per capita, a value very close to the gains from trade with China in 2007 that we computed above based on Arkolakis, Costinot, and Rodriguez-Clare (2010). Of course, the deadweight loss from transfers is not permanent, whereas the gains from trade are. As affected workers retire or expire, the loss in economic efficiency from transfers they receive as a consequence of trade with China would dissipate. Nevertheless, in the medium run it appears that losses in economic efficiency from increased usage of public benefits may be of the same order of magnitude as gains from trade from China.
The gains from trade with China are between $32 and $61 per person. (Don’t spend it all in one place.) The deadweight losses are estimated at $52 from the transfer mechanisms in place. This is a very provocative framing and numerical analysis. One conclusion is that since the consequences of trade are very real, and the frictions involved in adjusting in the short and medium term are serious, the government needs to back a free trade regime with serious employment subsidies and mechanisms for coping with the consequences of trade. Another avenue of research that is very important is the argument that the reduced employment situation being temporary and the gains from trade being permanent. Are those, particularly the first, reasonable assumptions? Under what conditions might they break?
There’s also a revolution in how we think of the losses from unemployment. Instead of being a vacation where you coach little league, there are serious consequences to health and happiness that come from a prolonged unemployment spell. How much worse does this look when that is taken into account?
I’m still debating on what to make of it, and where to go from this research. What’s your take?