The Threat Of Automation-- A New Tool For Political Targeting

There was a fascinating report from Brookings last week about the political implications of automation. The authors begin with a warning: "If economic anxiety is a factor in the nation’s backlash politics, the spread of automation is likely an important context for understanding it." Short version: low-skilled Trump voters in backward states are the ones who have the most to lose. It's all about "routine jobs most at risk of automation, something suitable for someone stupid enough to support Trump two years in. The study attempts to "estimate occupational vulnerability and, essentially, worker precariousness or anxiety about the future-- hence their political relevance."

For example, at the state level, all but one of the ten states most heavily exposed to future job market changes cast its electoral votes for President Trump in 2016.

Specifically, Heartland states like Indiana and Kentucky, with heavy manufacturing histories and low educational attainment, contain not only the nation’s highest employment-weighted automation risks (48.7 and 48.2 percent of tasks vulnerable to replacement, respectively), but also registered some of the widest Trump victory margins. By contrast, all but one of the states with the least exposure to automation, and possessing the highest levels of educational attainment, voted for Hillary Clinton, perhaps reflecting greater comfort with tech trends that have most benefited these same states. The strong association of 2016 Electoral College outcomes and state automation exposure-- leaving aside questions of deeper causality-- very much suggests that the spread of workplace automation and associated worker anxiety about the future may have played some role in the Trump backlash and Republican appeals.

Turn now to the 2018 midterm election, and it’s clear that the finer-grained evidence of congressional district voting reinforces the impression of automation-driven job precariousness and Republican voting. To be sure, no single factor such as tech-driven worker anxiety determines local political behavior. But there’s no mistaking that districts that voted Republican in the 2018 election are subject to higher levels of automation exposure, reflecting GOP voters’ lower education levels and higher involvement with factory, transportation, and lower-skill service-sector jobs.

The average automation potential of Republican districts is now 47.5 percent of all tasks compared to the 44.7 percent figure for Democratic areas-- revealing a modest but noticeable gap that also reflects Republican dependence on higher-exposed smaller towns and rural communities in the Heartland.

Such places in general tend to have lower education levels and greater relative exposure to manufacturing, transportation, or other “routine” activities.

With that said, the party contrast on automation exposure becomes much more dramatic when we look at the range of individual congressional districts’ levels of susceptibility. Now the differences look much larger than they did across states or in aggregate. Specifically, only 4 of the 50 most automation-exposed congressional districts are represented by Democrats, while every one of the 50 least-exposed districts is represented by Democrats.

The members who represent these districts and the percentage of Trump's winning vote in 2016:

• AL-04- Rob Aderholt (80.4%)
• GA-14- Tom Graves (75.0%)
• IN-02- Jackie Walorski (59.3%)
• OH-05- Bob Latta (59.7%)
• OH-04- Gym Jordan (64.3%)
• KY-01- James Comer (72.4%)
• IN-03- Jim Banks (65.1%)
• IN-04- Jim Baird (64.3%)
• WI-07- Sean Duffy (57.7%)
• OH-07- Bob Gibbs (62.5%)

The members who represent these districts and the percentage of Hillary's winning vote in 2016:

• CA-19- Zoe Lofgren (72.9%)
• VA-11- Gerry Connolly (66.9%)
• NY-07- Nydia Velazquez (86.9%)
• NY-12- Carolyn Maloney (83.3%)
• NY-08- Hakeem Jeffries (84.6%)
• NY-13- Adriano Espaillat (92.3%)
• NY-10- Jerry Nadler (78.3%)
• NY-09- Yvette Clarke (83.5%)
• NY-15- Jose Serrano (93.8%)
• VA-08- Don Beyer (73.5%)

What’s more, the differences of automation risk across geography and community type are revealing. The 10 least-exposed congressional districts to automation driven task displacement include big, Democratic high-tech and professional-services oriented districts as Virginia’s 10th and 11th districts (covering Arlington and Fairfax counties in the northern-Virginia tech hub); New York’s 8th, 9th, 10th, 12th, 13th, and 15th districts around New York City; and California’s 19th district encompassing much of Silicon Valley. Other low-exposure districts around the country include Massachusetts’s 7th reaching into downtown Boston, Colorado’s 6th in suburban Denver, and Pennsylvania’s 3rd in central Philadelphia.

By contrast, the most automation-exposed districts in the country include rural or small-town districts such as Alabama’s 4th district; Georgia’s 14th; industrial and nearby rural locations like Indiana’s 2nd and 4th districts or northern Ohio’s 4th, 5th, and 7th districts; and the agricultural area of Wisconsin’s 7th district. Other high-exposure Republican-voting districts include political backlash examples such as Iowa’s 2nd in the southeastern part of that state and Minnesota’s 1st, south of the Twin Cities.

The story told by congressional-district voting very much confirms that jurisdictions exposed to the most automation-based dislocation are some of the most likely to vote Republican. To be sure, as Jed Kolko has noted, it’s nearly impossible to fully disentangle automation from other economic and demographic factors, because “demographic characteristics and economic conditions are themselves related.” But even so, it is clear that to the extent that places experiencing high automation threats are experiencing greater economic stress, that stress is a factor in their voting behavior.

Which suggests two takeaways. One is that automation, and the worker anxieties associated with it, appears to be a subtle, real, and far-reaching factor in voting behavior that may be triggering even more anxiety in red America than blue America, with more stress to come. Such trends underscore the importance of problem-solving to help mitigate the transitions ahead and suggest that it would behoove the presidential candidates to begin describing their responses.

The second takeaway is that while the two Americas are experiencing somewhat different trends, automation is impacting both realms, with large pools of lower-skilled production, transportation, service, and clerical workers at risk across red-blue lines. That means there may be room and reason for cross-party cooperation on efforts to facilitate smoother transitions and reducing hardships for displaced workers and communities affected by automation.

There are only 15 districts with Republican congressmembers that have average automation potentials of 45.2% or less. They should be looked at as red to blue targets. Lowest risk to highest risk:

• CO-05- Doug Lamborn
• NJ-04- Chris Smith
• VA-01- Rob Wittman
• GA-07- Rob Woodall
• TX-03- Van Taylor
• AZ-08- Debbie Lesko
• AZ-06- David Schweikert
• AZ-05- Andy Biggs
• NY-02- Peter King
• NC-02- George Holding
• AL-05- Mo Brooks
• UT-03- John Curtis
• MO-02- Ann Wagner
• NY-01- Lee Zeldin
• CA-50- Duncan Hunter

Similarly, there are only 13 districts with Democratic congressmembers that have average automation potentials of 48.0% or higher. They should be looked at as potentially vulnerable seats. Lowest risk to highest risk:

• MI-05- Dan Kildee
• IA-01- Abby Finkenauer
• OH-13- Tim Ryan
• NV-04- Stephen Horsford
• NV-03- Susie Lee
• NV-01- Dina Titus
• MN-07- Collin Peterson
• OR-04- Pete DeFazio
• CA-16- Jim Costa
• WI-03- Ron Kind
• CA-10- Josh Harder
• IN-01- Pete Visclosky
• IA-02- David Loebsack