Small Dollars & Globalization: How Layoffs Translate to Costly Political Action

• In Event: Globalization and Labor Market Disruptions

Abstract

The correlation between free trade's negative consequences and political outcomes in the United States has received a lot of scholarly attention over the last decade. A persistent empirical challenge across this work has been the issue of ecological inference, in which empirical evidence aggregated to geographic units is used to infer behaviors of interest theorized at the individual level. Efforts to overcome this limitation that use survey self-reports of public opinion are marred by the preponderant influence of partisanship on these cheap talk responses.

In this project, we exploit a rich new dataset of individual-level small dollar donations to Act Blue to causally identify the true effect of free trade's negative labor market outcomes on costly political behavior. We implement recent methodological innovations in generalized difference-in-differences estimation to compare donor behavior before and after highly salient mass layoffs occur, relative to the change in donation behavior of otherwise similar donors living in otherwise similar areas who were not exposed to these layoffs. We show that trade-related layoffs stimulate political participation through small dollar donations, and that the main beneficiaries are committees pertaining to labor.

In detail, our project uses over half a billion observations of small dollar donations to ActBlue and Win Red, the two main political action committees for the Democrat and Republican parties, respectively. Each donation includes three pieces of information useful to our analysis: the address of where the donor lives, their self-reported occupation, and their self-reported employer. We use each of these pieces of information to thoroughly describe the donor's exposure to free trade's negative consequences, which we operationalize in three associated ways.

First, we use the geographic information to link each donor with trade-related layoffs, also geolocated to the physical address at which the layoffs occurred, obtained from applications to the Trade Adjustment Authority (TAA), part of the Department of Labor. The assumption is that donors living closer to where these layoffs occurred are ``exposed'' in a politically-relevant way, either through directly experiencing the pain of economic dislocation, through associated market contractions due to the layoffs, or know people who were laid off. We link donors with layoffs on the basis of sharing the same county, but test our results' robustness to a variety of different measures, including alternative geographic units as well using the latitude and longitude to define exposure on the basis of arbitrarily drawn units.

Second, we use the individual's self-reported occupation to link to a range of measures of occupational risk, including task routineness and offshorability. Based on existing work, we argue that these measures serve as moderators that influence how individuals perceive trade-related layoffs, with those whose occupations are more precarious being those more sensitive to the negative labor market outcomes.

Third, we use the individual's self-reported employer to determine their industry of employment. This, in turn, allows us to measure the degree to which donors are exposed to import competition, measured as the change in Chinese imports that compete with their industry between 2000 (prior to China's accession to the WTO) and the year prior to when the donation was made.

All three measures provide an unprecedented degree of precision over connecting individuals with free trade's negative consequences. With these measures in hand, we then implement a generalized diff-in-diff estimation strategy in which we compare donor behavior before and after the advent of layoffs, relative to similar changes in donor behavior among those who are insulated from the shock via either geographic location, occupation, or industry of employment. In addition, we restrict the ``insulated'' control group to donors living in the same state, and reweight them to more closely match the pre-layoff donation behavior (Hazlett and Xu, 2018). To bolster our causal interpretation of these estimates even further, we implement the counterfactual estimator proposed by (Liu, Wang and Xu, 2023) which predicts the donation behavior of the treated group based on a model trained on the control group.

Our research contributes a careful analysis of a previously neglected aspect of costly political behavior, overcoming many of the challenges associated with existing research on the anti-globalist wave and its connections with free trade.

Authors

• James Hodgdon Bisbee, Vanderbilt University

• Patrick Y. Wu, American University

• Maggie Macdonald, University of Kentucky

• Megan Brown, University of Michigan

• Rachel Porter, University of Notre Dame