August 24, 2016 Brian Kelsey

Trumped

Well if you’re going to get scooped on a story, at least let it be by somebody you read and respect, with better data.

Jonathan Rothwell, formerly at Brookings and now a senior economist at Gallup, has published a working paper, Explaining Nationalist Political Views: The Case of Donald Trump, which sorts through many of the questions I raised in my first piece about the 2016 Election. The Washington Post published a summary of Rothwell’s findings, but I recommend reading the paper itself, as qualifiers usually don’t come through clearly enough in mainstream media coverage of academic research.

Rothwell used Gallup Daily Tracking survey microdata from approximately 87,000 interviews conducted between July 2015 and July 2016, in which American adults were asked how favorably they viewed Trump, as well as a series of identifiers, such as political views and party affiliation, race/ethnicity, educational attainment, occupation, and more. Rothwell then cleverly linked the responses to sub-county level geographies to compare with Raj Chetty’s economic mobility data.

Rothwell’s use of Gallup survey data gets around many of the primary vote data limitations I discussed in my piece. For example, Rothwell was able to directly examine the relationship between a respondent’s view of Trump and his or her socioeconomic and demographic characteristics, rather than having to infer connections, as I did, based on what a county, as a whole, looks like and how residents of that county voted, in the aggregate. Rothwell’s analysis provides a degree of precision that is not possible using county-level vote totals and Census data. Further, Gallup data made it possible for Rothwell to apply weights to the sample to make it nationally representative.

There are many interesting aspects of Rothwell’s analysis, but the key passage is on p. 11 of the paper:

“These results do not present a clear picture between social and economic hardship and support for Trump. The standard economic measures of income and employment status show that, if anything, more affluent Americans favor Trump, even among white non-Hispanics. Surprisingly, there appears to be no link whatsoever between exposure to trade competition and support for nationalist policies in America, as embodied by the Trump campaign.”

The reference to trade competition is in response to a popular argument that Trump’s support is fueled by workers–specifically, white, male workers–in economically distressed communities tied to long-term stagnation or decline in manufacturing and other “blue-collar” employment, a perceived impact of trade liberalization. Rothwell’s analysis found no such evidence, when controlling for demographic characteristics, party affiliation, etc.

And later (p. 12):

“. . . this analysis provides clear evidence that those who view Trump favorably are disproportionately living in racially and culturally isolated zip codes and commuting zones. Holding other factors constant, support for Trump is highly elevated in areas with few college graduates, far from the Mexican border, and in neighborhoods that standout within the commuting zone for being white, segregated enclaves, with little exposure to Blacks, Asians, and Hispanics.”

There’s plenty to debate in the paper–check out twitter for scholarly disagreement about model specification, robustness, and multicollinearity–but I don’t find much that’s debatable in Rothwell’s conclusions, based on my analysis of the county-level presidential primary returns, flawed as they may be as a data set. In fact:

  • White Alone, Not Hispanic or Latino share of total population and educational attainment among White Alone, Not Hispanic or Latino males age 25 or older are statistically significant predictors of Trump’s share of the total primary vote at the county level, consistent with Rothwell’s findings.

Here is the urban-rural split of Trump’s share of the total primary vote compared to Clinton and Sanders for counties included in my data set where White Alone, Not Hispanic or Latino residents make up 50% or more of total population and 50% or more of White Alone, Not Hispanic or Latino males age 25+ have no college:

table_WANH50_WANHMNC50

And, to Rothwell’s point about contact theory (pgs. 8-9, 12), here is the same table as above, but this time showing counties where White Alone, Not Hispanic or Latino residents make up 90% or more of total population and 65% (mean + 1 SD among majority white counties) or more of White Alone, Not Hispanic or Latino males age 25+ have no college:

table_WANH90_WANHMNC65

Only 200 counties, but go back and compare these tables to the one in my last piece and you’ll find that my analysis is generally consistent with Rothwell’s findings.

  • Using data from EMSI, which provides estimates of total employment by industry for small counties where QCEW data is suppressed, I can find no statistically significant relationship between Trump’s share of the total primary vote and any measure of manufacturing employment I could think of testing, including the industry’s current and past shares of total employment in the county, change in industry employment over various time periods, or change in number of male workers in the labor force relative to manufacturing jobs available.

Again, generally consistent with Rothwell’s findings.

There are some interesting exceptions and regional differences, especially when comparing Trump to the other two main candidates. I’ll get into that next time.

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