Along with many other controversial issues in 2025, Americans are at odds over the merits of tariffs. Underlying this debate is a more specific one—the impact of increased trade with China over the past 25 years on American manufacturing employment. Advocates of tariffs hope they will bring back blue-collar jobs, to which they ascribe special status in improving the individual, family, and community lives of Americans.
Trade with China began accelerating in the 1990s due to liberal reforms to the Chinese economy. However, anti-trade critics emphasize decisions at the turn of the century made or led by US policymakers: the granting to China of permanent normal trade relations status in 2000 and the concurrent acceptance of China into the World Trade Organization (WTO), which it joined at the end of 2001. Populists have blamed the subsequent wave of imports—the “China Shock”— for destroying manufacturing jobs, reducing employment generally, hurting marriage, and increasing receipt of safety net benefits and deaths of despair.
In this regard, no research has been more influential in providing fuel for the populist bonfire than the work of economists David Autor, David Dorn, and Gordon Hanson (ADH).[i] However, their research has been widely misunderstood, in part due to questionable claims made in their papers. Those papers are primarily about how the China Shock affected some geographic areas relative to others in the US, not the overall impact of increased trade with China. Their findings have rarely been contextualized in a way to inform policymaking. Moreover, other researchers have contested them.
Many observers and experts believe the diffuse benefits from trade with China, such as lower prices, outweigh more-concentrated costs in the form of reduced manufacturing employment. What is perhaps not appreciated enough is that the China Shock may have hurt manufacturing employment much less than conventional wisdom suggests—or even boosted it.
The ADH Estimates of the China Shock’s Effect on Manufacturing Employment Are, Arguably, Small
ADH first assessed what they called the “China Syndrome” in a paper published in 2013.[ii] They leveraged the fact that different geographic areas (“commuting zones,” or CZs) had more or less susceptibility to import competition from China depending on their pre-Shock mix of industries. ADH assigned Chinese import growth in different industries to CZs based on the areas’ initial share of national employment in each industry. In other words, they assumed a CZ that initially had 4 percent of US employment in some manufacturing industry was hit twice as hard by increased Chinese imports within that industry as a CZ that initially had 2 percent of employment in the industry.
ADH summed these amounts across all manufacturing industries to get a measure of each CZ’s overall exposure to Chinese import growth. Finally, they scaled the growth in imports for each CZ by the area’s initial employment level. (Absorbing $1 million in imports is a bigger deal in a CZ with 50,000 workers than in one with 500,000 workers.) They found that stronger growth in Chinese imports in some CZs than in others caused those CZs to have worse manufacturing employment trajectories relative to the others than would have been the case absent the China Shock. If this seems like a very particular way to word the conclusion, the reason will become clearer below.
Assume for now that ADH’s estimates are unassailable. Do they suggest effects large enough to cause the economic, social, and political outcomes that are often blamed on the China Shock? To put them into context, imagine two large commuting zones each with 200,000 working-age people and 20,000 manufacturing workers in 2000.[iii] Imagine one of them was at the 10th percentile of exposure to the China Shock—meaning that it was relatively unexposed to rising Chinese imports—and the other was on the other end, at the 90th percentile. For simplicity, imagine further that they experience no population growth, and that in the absence of the China Shock, they would both have continued to have 20,000 manufacturing workers.
The 2013 paper implies that the CZ with the greater import exposure would have had about 2,700 fewer manufacturing workers than the other in 2007. [iv] On the one hand, that means that one out of every seven manufacturing workers would have lost their job in the one place but wouldn’t have in the other. On the other hand, it would mean a relative decline in the manufacturing employment rate of 1.4 percentage points—14 would-have-been manufacturing workers for every 1,000 working-age people.
This hypothetical example compares a very heavily hit CZ to a little-affected area. Put the less exposed CZ at the median level for import competition exposure instead of the 90th percentile and the job loss in the more exposed place would have been just 900 workers who would have remained manufacturing employees in the other CZ. That’s one in 22 manufacturing workers, or 4-5 employees per 1,000 working-age people.
To be clear, we absolutely should care about manufacturing workers who lose their job because of import competition. That’s true whether they number 2,700, 2.7 million, or 27 workers. And we can almost certainly do a better job crafting policies to help them than our current safety net offers. But effects of this size hardly seem large enough to inspire a populist backlash against trade on their own.
Other studies by ADH have found smaller or larger effects. In a paper published two years after their initial China Shock study, ADH reported an effect on manufacturing employment that was 15 percent lower than their earlier estimate.[v] It accounted for the distinct impact of automation on manufacturing employment differently than did the 2013 paper.
In another follow-up paper (Acemoglu et al., 2016), ADH and two coauthors used an import competition measure defined somewhat differently than in the earlier papers.[vi] The new measure produced much larger cross-CZ effects—more negative by nearly a factor of three.[vii] It’s not obvious, however, that the more recent measure is an improvement, and ADH have offered little justification for the change.[viii]
More to the point, a number of subsequent studies have identified issues with the ADH analyses. When addressed, the magnitude of the China Shock effect tends to decline by over 50 percent.
One relatively minor issue relates to combining pre- and post-China Shock evidence. Rothwell (2017) separated out the 2000-2007 data used by Autor, Dorn, and Hanson (2013) from the 1990-2000 (pre-China Shock) data that they combined it with in their analyses. He found an effect of import exposure for the China Shock era that was smaller by 21 percent than the effect they reported.[ix] The effect for the pre-Shock era was 63 percent smaller. That is to say, simply separating the two periods out produced smaller effects for each period than reported by ADH, whose statistical model assumed the effect was the same for both periods.
Jakubik and Stolzenburg independently affirmed the Rothwell result for 2000-2007 in a 2018 working paper. The published paper (2020), using somewhat different data than ADH, found an effect one-third larger than the estimate reported in Autor, Dorn, and Hanson (2013). However, the variation in import exposure across CZs was also smaller than in ADH’s paper, so the hypothetical exercises above produce smaller job loss totals using their estimates.
As an example, we can compare two CZs with import exposure differing by a standard deviation, and otherwise plug in the same numbers from the earlier exercise. The implied difference in manufacturing employment in 2007 is 1,677 using the Autor, Dorn, and Hanson results but 1,477 using the Jakubik and Stolzenburg results.[x]
The Jakubik and Stolzenburg paper’s main contribution was in identifying the importance of an issue largely neglected in the research literature. They noted that the China Shock papers have generally measured imports in such a way as to assign imports entirely to the industry of the final good (for instance, smart phones) rather than recognizing that other goods and services went into the final good (glass, product design). Moreover, some “Chinese” imports include components made in America; counting the entire value of such an import as coming from the China Shock is perverse. In addition, imports get double-counted if, for instance, a US firm imports some good from China, uses it as in input to production, and sends the resulting intermediate good to a factory in China, which then produces a final good that becomes a US import. The value of the original good imported by the US also gets counted in the value of the final good imported.
When Jakubik and Stolzenburg focused on the “value added” of imports from China (after subtracting out the US value added), the negative impact of Chinese import competition on manufacturing employment across geographic areas increased by 50 percent compared with their estimate using “gross trade”. However, the variation across CZs in exposure to the value-added part of imports was lower by two-thirds than the variation in exposure to imports using gross trade.[xi] Therefore, the hypothetical examples above comparing two CZs would show much smaller differences in manufacturing employment trajectories using the value-added of imports. Instead of the difference in 2007 manufacturing employment being 1,477, it would be 762 jobs.[xii] That’s 55 percent lower than the corresponding estimate from Autor, Dorn, and Hansen.
In another paper, Borusyak, Hull, and Jaravel (2022) made two improvements to the analyses in Autor, Dorn, and Hanson (2013) and found that the effect of the China Shock on manufacturing employment was 55 percent smaller than the latter had estimated.[xiii]
A paper by Feenstra, Ma, and Xu (2019) included cross-CZ methods that seemingly replicated those in Acemoglu et al. but obtained very different results. They estimated a China Shock effect on manufacturing employment that, for reasons that aren’t clear, were about 35 percent smaller than the cross-CZ estimate from Acemoglu et al.[xiv] Consistent with the Borusyak, Hull, and Jaravel results, when they implemented that paper’s improvements, their estimated effect was 50 percent smaller than in Acemoglu et al.[xv]
Bloom et al. (2024) improved ADH’s industry coding and found the effect of the China Shock on manufacturing employment fell by 23 percent.[xvi] Another change produced an even larger impact on the estimate. As noted, ADH looked at changes from 1990 to 2000 and from 2000 to 2007. Bloom and his colleagues pointed out that the underlying establishment data used by ADH is better during years when the economic census is conducted (every five years). When they looked at changes from 1992-1997, 1997-2002, and 2002-2007 (all census years), the China Shock effect on manufacturing employment fell by 38 percent. When they looked only at changes from 1997 to 2007, which they argued is the cleanest comparison, the effect was lower by 58 percent than when looking at 1990-2000 and 2000-2007.
A paper by Chaisemartin and Lei (2023) addressed other assumptions embedded in the ADH methods. Their estimates of the China Shock’s effect on American manufacturing employment were imprecise, but two out of three were positive rather than negative.[xvii] That is, the evidence was consistent with more exposure to Chinese imports being better for a CZ’s manufacturing employment.
Magyari (2017) used the same methodology of Autor, Dorn, and Hanson (2013), except that she looked across manufacturing firms rather than across geographic areas. That is, she leveraged the fact that different firms (potentially with multiple establishments) had more or less susceptibility to import competition from China depending on their establishments’ pre-Shock mix of industries.
Magyari assigned Chinese imports in different industries to firms based on firms’ initial share of national employment in each industry. She found that between 1997 and 2007, manufacturing firms with greater exposure to the China Shock saw bigger increases in manufacturing employment (or smaller declines). Cheaper imports reduced firms’ costs, and firms reallocated employment to establishments in industries less impacted by import competition. Magyari’s results don’t invalidate ADH’s commuting zone results—firms may have reallocated manufacturing jobs across commuting zones—but they cast doubt on whether the China Shock, on net, was bad for American manufacturing employment.
Note that the critiques in these papers have generally not been taken up by the other papers. Presumably, addressing multiple issues treated separately by the studies would have a bigger impact than any of the individual modifications.
Other Studies Have Found the China Shock Had a Negative Impact on Exposed CZs’ Manufacturing Employment but a Positive Impact on Their Total Employment
The ADH papers have found not only that greater exposure to the China Shock was worse for CZs’ manufacturing employment, but for their employment generally. Any employment gains outside of manufacturing were not enough to prevent overall employment growth from worsening. But other studies have contradicted this conclusion too.
We’ve already seen that Chaisemartin and Lei (2023) and Magyari (2017) found evidence that the China Shock wasn’t even necessarily bad for manufacturing employment. Other research using methods similar to ADH has found negative effects for manufacturing employment but more-than-offsetting positive effects on employment outside manufacturing.
Wang et al. (2018), using a cross-CZ approach and import concentration measure based on Acemoglu et al., found that while the China Shock had a negative effect on manufacturing employment, it increased total employment in the exposed CZs. It did so primarily by making intermediate goods in non-manufacturing firms cheaper (computers and office equipment, for example). The Autor research missed this result by failing to distinguish between imported inputs used by American firms and imported final goods.
Bloom et al. (2024) found that after accounting for the varying quality of the establishment data in different years (depending on if the data came from an economic census year), the negative effects of the China Shock on manufacturing employment were more than offset within CZs by positive effects on non-manufacturing employment.
ADH’s Estimates of the China Shock’s Impact on National Employment Dubiously Apply Relative Effects Based on Comparing Geographic Areas
The ADH findings that have garnered the most attention are not the somewhat subtle estimates of relative job declines in more-trade-exposed areas compared to less-exposed ones. Rather, the headline results have been ADH’s estimates of national job loss due to the China Shock. However, these estimates come from dubious calculations using the cross-CZ estimates (or similar cross-industry estimates).
What the original ADH paper found, precisely, was that experiencing stronger Chinese import competition caused greater declines in manufacturing employment in some places or smaller increases relative to places less exposed to import competition. The research design of the paper can’t tell us whether, in the aggregate, Chinese import competition reduced total American manufacturing employment or by how much; it only provides information about how the resulting change differed across geographic areas.
We know that American manufacturing employment has declined for decades (starting well before the China Shock). But imagine that increased imports from China slowed this decline in a small number of very large CZs not strongly exposed to competition from the specific imports China sent our way. Maybe cheap imported inputs reduced business costs and allowed many manufacturers to expand. In that case, we would say that the China Shock increased manufacturing employment in these CZs (relative to a counterfactual in which no Shock occurred). The hypothetical increase caused by the China Shock wouldn’t necessarily have been enough to actually raise manufacturing employment in these CZs, but it would have partly countered the other factors leading those jobs to become rarer.
At the same time, in this hypothetical, it might have been that many smaller CZs were more exposed to import competition and saw relatively large declines in manufacturing employment. Consistent with ADH, we would find that greater exposure to Chinese import competition was associated with worse manufacturing employment trends. But it could very well have been that the “increased” employment in the less-strongly exposed CZs exceeded the reduced employment in the more-strongly exposed ones. That could have been true even as the long-term manufacturing employment trend continued to decline.
Alternatively, perhaps the China Shock raised manufacturing employment in all CZs (relative to there not being a China Shock)—just not as much in the ones with more exposure to imports. That might have occurred if imports universally increased some kinds of manufacturing employment through cheaper inputs, leading to lower business costs. At the same time, import growth might have put workers in other kinds of manufacturing out of a job in more-exposed CZs. Such a universal effect would be netted out in estimating a cross-CZ effect and leave the negative effect of greater import exposure in some CZs relative to others.
Or perhaps Chinese imports lowered consumer prices enough to increase demand for domestic manufacturing (and thereby boost manufacturing employment) by increasing purchasing power. If we just assume that effect, if it occurred, was constant across CZs, it gets netted out in estimating the cross-CZ effect. But if we want to know the national effect of the China Shock, we need to incorporate that universal-to-CZs demand-boosting effect.
Of course, it’s possible the China Shock hurt all CZs, but the magnitude is nonetheless overstated because the relative effect comparing more- and less-exposed CZs nets out universal-to-CZs positive effects.
We can put the issue in terms of the first ADH paper’s findings. It is one thing to conclude that a CZ experiencing $1,000 per worker more in Chinese imports than another area saw its manufacturing employment rate fall 0.6 percentage points more than the other (or grow 0.6 points less). It is quite another to say that because the US saw an increase in Chinese imports of $1,839 per worker from 2000 to 2007, that -0.6 effect implies the China Shock reduced the national manufacturing employment rate by 1.1 percentage points.[xviii] The second conclusion does not necessarily follow from the first.
Nevertheless, ADH applied that estimate to argue that the China Shock reduced national manufacturing employment by 2 million workers. Their headline estimate of 982,000 is a downward adjustment to account for the fact that not all the increase in Chinese imports was due to changes in the supply of, rather demand for, imports. (The cross-CZ effects are all estimates that focus on the China Shock as a supply-side phenomenon.)
In their follow-up papers, ADH similarly estimated an absolute national China Shock effect from their relative cross-CZ estimates. Acemoglu et al. found that the China Shock reduced employment in manufacturing by 2.35 million workers between 1999 and 2011 (or 36 percent). Autor, Dorn, and Hanson (2021) found that 59 percent of the drop in the manufacturing employment rate from 2001 to 2019 was due to the China Shock.[xix]
In fact, Acemoglu et al. argue that the China Shock’s true impact on national manufacturing employment was larger than the 2.35 million suggested by applying their cross-CZ estimates. In a second set of analyses, ADH rely on a cross-industry estimate of the China Shock’s impact on manufacturing employment and (again, questionably) use that to obtain a national estimate. This total comes to 985,000 workers. Because neither of the two approaches in the paper accounts for all the ways that the China Shock might have reduced manufacturing employment, Acemoglu et al. assert that the true effect is larger than either implies.
Even if we were to accept the validity of using cross-CZ or cross-industry China Shock effects to estimate a national effect on manufacturing employment, there are reasons to think the ADH guesses are too negative. First, the smaller cross-CZ effects other studies have found imply that putting improved estimates through the ADH formulas would yield national totals much lower theirs. That’s to say nothing of the studies finding positive employment effects.
Second, expanded trade with China may have increased American manufacturing jobs through greater exports.[xx] All the research discussed above has focused on the impact of increased Chinese imports. Feenstra, Ma, and Xu (2019) used cross-industry methods intentionally comparable to Acemoglu et al. to look not just at the employment effects of increased imports from China but of increased exports to China. Their results are imprecise but suggest that to a great extent, the two effects balanced out—perhaps fully so. (And they estimate an effect of imports that is very similar to that of Acemoglu et al.)[xxi]
Finally, all the research estimating “effects” of the China Shock suffers from an important real-world weakness. They all envision a counterfactual world in which not only do Chinese imports stay frozen at, say, 1990 or 1999 levels, but imports from other developing countries fail to expand to meet the American demand that the China Shock filled. Imagine if China had never liberalized its economy or if it had been kept out of the World Trade Organization. Heck, imagine that we had levied insane tariffs against China in 2000. What would have happened?
If you think that the China Shock reduced employment, do you think all those jobs would have been saved if it could have been prevented? Or do you think that trade with Vietnam and other countries would have grown at a faster rate in the absence of greater trade with China? Implicitly, all this research has in mind that trade with other countries would not have ramped up in the absence of Chinese import competition. But that seems highly dubious. If at least some ramp-up would have happened, then all the research discussed above overstates any negative impact of the China Shock.
A similar argument could be made about automation. Suppress the China Shock and perhaps manufacturer reliance on automation would have accelerated to keep costs and prices down. If that would have been the counterfactual history, and automation would have reduced manufacturing employment more than it did in the presence of the China Shock, then the studies above overstate the effect of the Shock.
Indeed, when we remember that the cross-CZ studies are estimating relative effects (how some places were affected by the China Shock relative to others), we must also take seriously a different sort of counterfactual if the China Shock had been suppressed. If places vulnerable to import competition had avoided those rising imports, they still might have lost manufacturing employment to places elsewhere in the US to which manufacturing had already been migrating for decades. Some places would have lost out relative to others even in the absence of the China Shock, and if the reallocation had accelerated in a world without the Shock, the research discussed above gives the China Shock too much blame. If the cross-CZ estimates of the China Shock are too large in this sense, then applying them to get a national estimate will carry forward the problem.
In the 30 years from 1969 to 1999, the share of the working-age population employed in manufacturing fell at a rate of 1.9 percent per year. Over the 20 years from 1999 to 2019, the drop was 2.0 percent per year; the decline in manufacturing employment has proceeded steadily for decades, with no obvious break created by the China Shock.[xxii]
Conclusion
The Autor, Dorn, and Hanson research has been influential, and for a reason. It uses sophisticated analyses to answer one of the most-discussed policy questions of the day. I have only discussed their findings on employment, but their papers also look at a range of other outcomes potentially related to the China Shock. The critique offered here largely applies to those results too.
Even regarding employment, I have stuck to papers that are in conversation with the ADH research, ignoring studies that have relied on, for instance, macroeconomic modeling.[xxiii] I do not claim to have summarized the entire relevant body of research on the China Shock. Moreover, there are important distributional questions discussed in the papers cited here that I have ignored for sake of length. These include questions about how costs and benefits are distributed geographically and demographically. Similarly, I have avoided discussion of other effects of trade, such as on prices or welfare.
I do not mean to suggest that this critique nor any of the research I have cited refutes the ADH research. Their findings should absolutely inform policymakers’ and citizens’ thinking about the costs and benefits of increased trade with China. But we should not oversimplify, misinterpret, or fail to contextualize those results. Nor should we necessarily elevate them over equally sophisticated studies that point to other conclusions.
The message from this review is that the debate around the effect on workers of increased trade with China since it joined the WTO and was granted permanent normal trade relations status has been unduly alarmist given the ambiguity of the evidence we have at hand. Economic theory offers a variety of reasons to think that voluntary exchange benefits both parties in a trade and that freer trade tends to benefit a country even in the face of trade barriers erected by other countries. If the evidence clearly contradicted the theory, we would want to strongly question the theory. But even confining ourselves narrowly to the evidence on manufacturing employment and Chinese imports—ignoring all the other channels whereby trade affects Americans—we see no such clarity.
A clear-eyed look at the existing evidence should inform both our policy debates around trade and tariffs and our political debates about how increased trade has affected voter preferences. Too often, we grant anti-trade sentiment a greater role in fueling populism than may be warranted. Or we are too quick to assume that sentiment corresponds with a widespread lived experience instead of simply reflecting commitment to ideological priors.
We neglect the importance of great cultural divisions and of social breakdown in assessing what ails our nation to the extent that we focus unduly on trade and economics during a time in which Americans are better off in material terms than humanity has ever known.
[i] While the line of work is associated primarily with Autor, that is by virtue of the authors being listed in alphabetical order. As we’ll see, one widely cited paper they coauthored with two other economists is correspondingly cited as Acemoglu et al.
[ii] Interestingly, the phrase “China Shock” does not feature in their work. It appears to have been coined in 2009 by economists Nicholas Bloom, Mirko Draca, and John van Reenen.
[iii] According to Opportunity Insights data, the 75th percentile of commuting zones had a population (not working-age population) of 290,000 in 2000. The working age population of the US was 65 percent of the overall population in 2000, suggesting the 75th percentile of commuting zones had around 189,000 people. According to Autor, Dorn, and Hanson (2013), Appendix Table 2, the average share of the working-age population in manufacturing across commuting zones was 10.5 percent in 2000.
[iv] This calculation follows the exercise in Autor, Dorn, and Hanson (2013), on p. 2136, but using the estimate in column 6 of Table 3 that they emphasize for their national estimate of manufacturing job loss and the 10th and 90th percentiles of Chinese import exposure from Appendix Table 1 rather than the 25th and 75th percentiles. The calculation is .7*(4.3-1.03)*(-.596)*.01*200000, where the 0.7 is to convert the displayed import exposure growth estimates from 10-year-equivalent ones to the seven-year period from 2000 to 2007.
[v] Compare column 1 of Table 4 to column 6 of the 2013 paper’s Table 3.
[vi] Another way to think about the construction of the import competition measure in the 2013 paper is as follows: starting with the national increase in Chinese imports in each industry, scale it by initial US employment in the industry and then weight this amount for each commuting zone depending on the industry’s initial share of the area’s total employment. Then sum across industries for each commuting zone. In the 2016 paper, the national increase in Chinese imports in each industry is scaled not by initial US employment in an industry, but by initial US domestic purchases of the industry’s output. ADH also used this alternate measure in Autor, Dorn, and Hanson (2021).
[vii] Compare the sum of the two tradeable sector coefficients in column 8 of Table 7 to the column 6 of the 2013 paper’s Table 3. Alternatively, compare the sum of the two tradeable sector estimates in the last column of Table 8 to the 982,000 estimate given on p. 2140 of the 2013 paper.
[viii] To the extent that Acemoglu et al. justified the switch, it was “for consistency with [the] industry-level analysis” earlier in the paper (to which we’ll return). The Acemoglu et al. paper does not try to make a case that the new measure is superior to the old one, and it explicitly continues to endorse the approach from the earlier paper. (On page S175, footnote 42, they write, “This discussion also makes it clear that empirically it is appropriate to combine the shocks of all of the local industries using weights related to their local employment shares, which is the strategy employed here and in Autor et al. (2013).”)
In their earlier approach, the increase in Chinese imports to the US is exactly apportioned across American CZs within each industry, so that the sum of each CZ’s allocated import growth necessarily equals total import growth within each industry. This follows from the construction of the import exposure measure, which apportions industry import growth to CZs depending on each CZ’s share of national employment in the industry. Imagine a simple scenario, where there are only two CZs, the first with 70 percent of employment in some industry, the second with 30 percent. Imagine Chinese import growth for the industry is $10 billion. The first CZ would get 70 percent of the import growth ($7 billion) and the second would get 30 percent ($3 billion). The full $10 billion in import growth for the industry will have been apportioned across CZs. (The increase in imports is then divided by a CZ’s employment in the industry, since the impact of a given increase in imports will depend on the initial size of the industrial workforce. But this is subsequent to the complete apportioning of import growth.)
Contrast this approach with the Acemoglu et al. measure, which does not exactly apportion Chinese import growth across CZs. The formula first scales overall Chinese imports to the US for an industry by the national domestic purchases of industry output. (The impact of a given increase in imports will depend on the initial magnitude of domestic purchases of industrial output.) It then apportions this scaled increase in imports within an industry across CZs depending on the industry’s share of the CZ’s employment. To see that this does not necessarily completely apportion import growth, return to our two-CZ scenario. Assume that the industry in question accounts for 3 percent of employment in both CZs, and for simplicity, temporarily ignore the scaling by national domestic purchases. The first CZ would get ($10B * 0.03=) $300 million of the increased imports, while the second CZ would also get $300 million. That would leave $9.4 billion of import growth unallocated.
The scaling by initial national domestic purchases before apportioning across CZs can mitigate this problem. If we assume, for instance, that domestic purchases were $16.67 million and scale $10 billion by this amount, we then have $600 million to apportion across CZs, and the formula would fully apportion the scaled amount. But that’s just because by construction in this example, the scaled amount is 6 percent of the increase in imports, and the sum of the two CZs’ industrial shares of employment is also 6 percent. In practice, there’s no reason these two amounts must be equal.
The idea in Acemoglu et al. is that if national imports within some industry double relative to initial national domestic purchases of output in the industry, that’s also true in every CZ. The import competition exposure measure for a CZ is then a weighted average of these proportional changes across industries, with the weights corresponding to the share of working-age people in the CZ initially employed in an industry. Different CZs experience different import competition exposure only insofar as their mix of industrial employment differs. In contrast, the idea in the 2013 paper is that if national imports increase by $10 billion, that gets allocated across CZs depending on their share of national employment in the industry. Then these amounts are summed across industries within each CZ. The total is then expressed as a proportion of the CZ’s initial overall employment level. But an alternative way to express the measure in the 2013 paper is to repeat the description of the Acemoglu et al. measure, but replacing “domestic purchases of output” with “total employment” in the first sentence: If national imports within some industry double relative to initial national total employment in the industry, that’s also assumed to be true in every CZ. The import competition exposure measure for a CZ is then a weighted average of these proportional changes across industries, with the weights corresponding to the share of working-age people in the CZ initially employed in an industry. Different CZs experience different import competition exposure only insofar as their mix of industrial employment differs.
[ix] See his Appendix Table 1, row 1.
[x] See Table 3 and Appendix Table 2 of ADH and Tables 1 and 2 of Jakubik and Stolzenburg. A standard deviation is a measure of a typical difference between some CZ and the average CZ. The calculation is .7*(SD)*(EFFECT)*.01*200000, where SD is 2.01 in ADH and 1.32 in Jakubik and Stolzenburg, and EFFECT is -0.596 in ADH and -0.799 in Jakubik and Stolzenburg.
[xi] Compare the standard deviations in rows 2 and 4 of Table 1.
[xii] The calculation is .7*(.453)*(-1.202)*.01*200000.
[xiii] Compare columns 1 and 3 of their Table 4.
[xiv] Compare their Table 6, column 1 to column 3 of Acemoglu et al.’s Table 7 and the eighth row of their Table 8.
[xv] Compare their Table 6, column 4 to column 3 of Acemoglu et al.’s Table 7 and the eighth row of their Table 8. Feenstra, Ma, and Xu cite an earlier draft of the Borusyak, Hull, and Jaravel paper.
[xvi] Compare rows 2 and 3 of their Table 8.
[xvii] See the first three rows of Table 6.
[xviii] The calculation is 1,839/1,000*.596.
[xix] Indeed, ADH go beyond just looking at manufacturing jobs and report national estimates of the total employment decline caused by the China Shock. In Autor, Dorn, and Hanson (2013), the total employment loss comes out to 1.27 million workers from 2000 to 2007. In the cross-CZ analyses in Acemogclu et al. (2016), the total loss from 1999 to 2007 is 2.29 million.
The result for the 2013 paper comes from a calculation similar to the authors in footnote 31, but restricting to 2000 and 2007 and replacing the coefficient in the equation with the sum of the coefficients in Table 5, Panel B, columns 1 and 2. The result for the 2016 paper comes from subtracting the 1991-99 employment loss estimate from the 1991-2007 estimate (-3,031-(-743)).
[xx] The issue of exports is one reason that even ADH’s worker-level evidence of the China Shock’s effects is flawed. Autor, Dorn, Hanson, and Song (2014) found that people in industries exposed to greater Chinese import competition subsequently saw worse employment outcomes than their counterparts in less-exposed industries. But their study did not look at the impact of exports. Nor did it distinguish between imported inputs and imports of final goods. Pierce, Schott, and Tello-Trillo (2024) made the latter distinction in a recent paper that otherwise used similar methods to Autor, Dorn, Hanson, and Song (2014). They found that the China Shock reduced manufacturing employment in more-exposed industries relative to less-exposed industries. But what initially looked like a negative effect on total employment (including jobs outside manufacturing) became positive once the role of inputs was distinguished.
[xxi] Compare the results in columns 2, 3, 6, and 7 of Table 3 to the first row in Acemoglu et al.’s Table 8. See also the Feenstra, Ma, and Xu results for 1999-2007 in Table 4.
[xxii] These are my analyses of the Current Population Survey Annual Social and Economic Supplement, using the IPUMS Online Data Analysis System at https://sda.cps.ipums.org/sdaweb/analysis/?dataset=all_march_samples. I restrict to people ages 16 to 64.
[xxiii] Caliendo, Dvorkin, and Parro (2019), for instance, found that the China Shock reduced manufacturing employment but increased overall employment. Lyon and Waugh (2019) found it increased total employment.