Does the China Shock Explain the Trump Swing?

‘It was in the counties where the highest number of jobs were lost because of the China shock,’ Adam Tooze writes in the current edition of the London Review of Books, ‘that Trump scored best in the 2016 election.’ What Tooze seems to have thrown his immense intellectual weight behind is a theory of US politics that traces the white working class revolt that put Trump in the White House to the China shock. ‘Thanks to the painstaking work of labour economists we can trace, county by county,’ he writes, ‘the impact of Chinese imports and the loss of factory jobs across the US.’

Actually, the economists concerned, Autor, Hanson and Dorn, did not work at the county level. Instead, they develop their measure of exposure to Chinese competition at the level of multi-county commuting zones. The logic of their argument is sound. Extraordinary productivity growth in China — the result of Stalinism with Chinese characteristics — had a dramatic impact on many American industries. Regional economies reliant on these industries suffered job losses in manufacturing and wage declines in non-manufacturing; as they have shown.

The Toozian hypothesis that the China shock put Trump in the White House is testable. Merely testing it against noise is not persuasive however. We must test it against an alternate hypothesis. We have previously argued that what put Trump in the White House is the class-partisan realignment — a logic endogenous to US politics. The realignment can be read off the following graph.

Class-Partisan Realignment in US Presidential Elections, 2000-2016

Here we test these hypotheses against each other. We obtain the Autor-Hanson-Dorn measure of exposure to import competition from China from Dorn’s website. We obtain overdose deaths from the USDA and electoral data from MIT election lab by county. We then aggregate the data by commuting zones. Our main explanandum, the response variable, is the swing to Trump — the difference in GOP vote share between 2012 and 2016. We also look at overdose deaths as the response. Throughout, we estimate robust linear regression by IRLS using the Andrew Wave norm. We standardize both the response and the features using sklearn’s RobustScaler function. The slope coefficients can therefore be thought of as elasticities or betas.

We begin by replicating the main Autor-Hanson-Dorn result that exposure to China explains the spatial variation in the decline in manufacturing employment across the 722 US commuter zones. We find a large and significant elasticity of -0.50 meaning that a one standard deviation change in exposure to Chinese imports predicts half a standard deviation decline in manufacturing employment. This is in line with the results presented in their AER paper.

Table 1. Can exposure to China explain the decline of US manufacturing employment?
coefstd errP
China-0.5040.0180.000
Response is change in US manufacturing employment.

We then jump straight into the simple linear models. The simple linear models allow us to identify the total effect of the features on the Trump swing. We find modest but statistically significant total fixed effects for the China shock (b=0.11) and change in manufacturing employment (b=-0.13). We also find that the total fixed effects of the class-partisan realignment (b=-0.38) and overdose death rates (b=0.40) are four times as large. Straight off the bat, then, we can see that our features give us a much stronger handle over the Trump swing than the features of the Toozian hypothesis.

Table 2. Simple linear models.
Can the China shock explain Trump?
coefstd errP
China0.1110.0240.000
Can the decline in mfg emp explain Trump? 
coefstd errP
MfgEmp-0.1310.0370.000
Can class-partisan realignment explain Trump?
coefstd errP
College-0.3830.0300.000
Can overdose deaths explain Trump?
coefstd errP
Overdose0.4000.0350.000
Response is Trump Swing.

Another way to examine the same one-on-one relationships is to examine the means of the features by Trump swing quintiles. The steeper and more monotonic the gradient, the more confidence we can have in the feature.

The top and bottom quintiles of Trump 722 commuter zones are off in import competition from China.
The lowest quintile by Trump swing is an outlier in college graduation rates — affluent antiracist cities.
Trump swing is a linear function of overdose death rates.
The top three quintiles of Trump swing are low but flat in change in mfg emp.

We can see that the gradient is much more robust for college graduation rate and overdose death rates compared to decline in manufacturing employment or the China shock.

The strength of the one-on-one relationships only take us so far, however. In order to identify the underlying causal diagram, we must control for class-partisan realignment. Table 3 reports the estimates. We can see that the fixed effect of the China shock, already pretty small, is attenuated even more once we control for college graduation rate. Same for decline in manufacturing employment. While both remain significant at the 5 percent level, the elasticities fall to around 0.06. The Toozian hypothesis is thus revealed as a second order correction to our model.

Table 3. Controlling for class-partisan realignment.
Can the China shock explain Trump after controlling for class-partisan realignment?
coefstd errP
College-0.3730.0300.000
China0.0630.0220.004
Can the decline in mfg emp explain Trump after controlling for class-partisan realignment?
coefstd errP
College-0.3730.0300.000
MfgEmp-0.0670.0340.045
Response is Trump Swing.

Our second feature, overdose deaths, is potentially causally downstream from the China shock. That is, overdose deaths may be a mediator between the China shock/manufacturing decline and the Trump swing. Is it? Table 4 shows our regression estimates with overdose death rates as the response and the China shock and change in manufacturing employment as the features. The pattern is consistent with the interpretation that the effect of the China shock on overdose deaths is significant (b=0.23) and mediated by decline in manufacturing employment (since it falls into insignificance once we control for the latter).

Table 4. Explaining overdose deaths.
Can the China shock explain overdose deaths?
coefstd errP
China0.2340.0220.000
Can the decline in mfg emp explain overdose deaths?
coefstd errP
MfgEmp-0.3850.0320.000
Is the China effect in overdose deaths mediated by mfg emp?
coefstd errP
China0.0410.0230.078
MfgEmp-0.3560.0360.000
Response is overdose death rates. 

Since overdose deaths are a strong predictor of the Trump swing and the China shock propagated to overdose deaths, we have a clear causal channel for the Toozian hypothesis. Table 5 documents that overdose is an effective mediator between the China shock/decline in manufacturing and the Trump swing (since it “kills” their coefficients).

Table 5. Overdose as a mediator between China shock and Trump.
Is overdose a mediator between China and Trump?
coefstd errP
Overdose0.3850.0360.000
China0.0440.0230.052
Is overdose a mediating variable between mfg emp and Trump?
coefstd errP
Overdose0.4020.0380.000
MfgEmp0.0040.0370.925
Response is Trump Swing.

We have thus shown that Toozian causal vector between the China shock and the Trump swing is a second order correction to the class-partisan realignment. Moreover, we have seen that decline in manufacturing is a mediator between China and Trump, and overdose deaths (and likely deaths of despair more generally) is a mediator between decline in manufacturing employment and the swing to Trump. These facts are also clear from the kitchen sink regression.

Table 6. Trump’s Kitchen Sink.
coefstd errP
College-0.3170.0290.000
Overdose0.3010.0360.000
MfgEmp0.0560.0370.135
China0.0440.0230.055
Response is Trump Swing.

What put Trump in the White House was thus not the exogenous shock of the rise of China as a global manufacturing superpower but a logic endogenous to American politics.


Postscript. Turns out, we were not class-reductionist enough. If we restrict attention to blue collar manufacturing employment — not the change but the level — we get another way to identify the working-class. The change in manufacturing employment, whether due to China, Mexico or automation, still doesn’t give us a good handle on the Trump swing. But the size of the manufacturing working-class turns out to be the most important predictor of the Trump swing — even bigger than college graduation rate and overdose death rates. Note that all these variables contain a very strong class signal: recall that deaths of despair, overdose deaths in particular, are entirely a blue collar phenomena in the sense that it is confined to whites without college degrees.

The American industrial working-class resides in commuter zones exposed to China.

Exposure to China is strongly associated with the level of blue-collar mfg employment.
coefstd errP
China0.3470.0210.000
Response is employment in blue-collar mfg jobs. 
Blue-collar mfg employment is weakly associated with overdose death rates.
coefstd errP
MfgEmpNoBA0.2140.0370.000
Response is overdose death rates. 
Blue-collar mfg employment is strongly associated with the Trump swing.
coefstd errP
MfgEmpNoBA0.4200.0350.000
Response is Trump swing. 
… even after controlling for college graduation rate.
coefstd errP
College-0.3240.0270.000
MfgEmpNoBA0.3500.0320.000
Response is Trump swing. 
Primacy of the working-class in Trump’s election.
coefstd errP
College-0.2820.0270.000
Overdose0.2380.0320.000
MfgEmpNoBA0.3300.0330.000
China-0.0340.0210.100
Response is Trump swing. 
Class-Reductionist model of the Trump swing. 
coefstd errP
College-0.2810.0270.000
Overdose0.2300.0320.000
MfgEmpNoBA0.3100.0310.000
Response is Trump swing. 

Our selected model suggests that 35 percent of the explained variation is explained by college graduation rate, 23 percent by overdose deaths, 42 percent by the size of the manufacturing working-class. Of course, 100 percent of the explained variation is explained by the class-party sorting documented in the very first graph of this essay; reproduced below.

Class-Party Realignment in US Presidential Elections, 2000-2016

6 thoughts on “Does the China Shock Explain the Trump Swing?

  1. The antipathy of the elites to classical liberal values runs quite deep, as they embrace the (engineered, IMHO) COVID crisis to introduce soul-destroying and depopulating measures such as Agenda 21 and ChiCom-style surveillance totalitarianism.

  2. Thank you for the interesting analysis; I would only say, your model predicting the Trump swing from the China shock and the College variable seems consistent with a causal structure in which College is a mediator. I could imagine that the China shock played a causal role in the shift of white working class voters to the Republican party over time, which in turn helped cause Trump’s election.

    1. Doesn’t work. China is a weak predictor of College (b = -0.096, P<0.001) so College cannot serve as the mediator between China and Trump. What the evidence suggests is that Trump is the culmination of a twenty year long class-party sort (see first graph of the article). This is entirely a class realignment. See my postscript.

  3. Interesting article, lots of food for thought here. Thanks for this series of posts.

    Re: realignment of party vs college attendance (class)… I wonder if it has to do with the year in which the voter attended college, roughly = [ election_year – voter_age + 20 ].

    The thinking here is that maybe pre-2000, the bulk of voters who attended college would’ve done so prior to the vietnam war and the cultural changes associated with that time. By 2016, bulk of voters who attended college would have done so post-vietnam. It sortof implies that maybe some funamental political alignments are formed at a young age, a big leap…

      1. Were both shifts were boomers tho?

        The 1990s shift was of the standard teachings in educated culture, right? its success or failure depended on adoption by the mid-career professionals. Mid-career in the 90’s meant boomers, and the phenomenon of “awokening” was specific to the college/grad subset of them. (How were beliefs of working class culture evolving in the 90’s?)

        The 2010s shift in election results (data in this article) is driven by voter demographics (centered around retirement age) rather than professional culture, that I think centers around the mid-career. Hence still boomers, with the time delay. But here, both “halves” of the generational cohort are brought into play. I’m not totally confident of this, should look at the age breakdown of changes in voting trends in the 2010s.

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