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Acute Financial Hardship and Voter Turnout: Theory and Evidence from the Sequence of Bank Working Days

Published online by Cambridge University Press:  08 July 2021

MAX SCHAUB*
Affiliation:
WZB Berlin Social Science Center, Germany
*
Max Schaub, Research Fellow, WZB Berlin Social Science Center, Germany, max.schaub@wzb.eu.
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Abstract

How does poverty influence political participation? This question has interested political scientists since the early days of the discipline, but providing a definitive answer has proved difficult. This article focuses on one central aspect of poverty—the experience of acute financial hardship, lasting a few days at a time. Drawing on classic models of political engagement and novel theoretical insights, I argue that by inducing stress, social isolation, and feelings of alienation, acute financial hardship has immediate negative effects on political participation. Inference relies on a natural experiment afforded by the sequence of bank working days that causes short-term financial difficulties for the poor. Using data from three million individuals, personal interviews, and 1,100 elections in Germany, I demonstrate that acute financial hardship reduces both turnout intentions and actual turnout. The results imply that the financial status of the poor on election day can have important consequences for their political representation.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association
Figure 0

Figure 1. Correlation of Measures of Political Participation with PovertyNote: Coefficient plot from regressions of indicated outcomes on the indicator for poverty, defined as earning less than 60% of the means-adjusted median income. All outcomes are standardized to range from 0 to 1. OLS regression controlling for age, sex, education, and parents’ education and including month, year, and state fixed effects. Markers are point estimates, horizontal lines, 95% confidence intervals.

Figure 1

Figure 2. Long Months after Short (LMAS)Note: Figure 2a plots the number of months that lie between one long-month-after-short (LMAS) and the next for the years 2010 to 2020. Figure 2b shows the percentage share of respondents indicating financial difficulties on the given day against the day of the week. The lines are kernel density plots (Epanechnikov kernel with optimal bandwidth) of financial difficulty for respondents interviewed during LMAS (dashed line) or non-LMAS (solid line). Markers are day-of-month averages of financial difficulties. The shaded areas are 95% confidence intervals. ALLBUS 1984–2016 data, n = 56,907.

Figure 2

Figure 3. Causal Effect of LMAS-Induced Financial Hardship on Turnout Intentions (Individual Level)Note: Figure 3 plots the coefficients for individual-level regressions of turnout intentions on the indicator for LMAS. OLS regressions controlling for age, sex, education, and parents’ education and including month, year, and state fixed effects. Markers are point estimates, vertical lines, 95% confidence intervals. ALLBUS 1984–2016, FORSA 1993–2015, and Deutschland Trend 2008–2018 data.

Figure 3

Figure 4. Subgroup Effects of Acute Financial Hardship on Turnout IntentionsNote: Figure 4 plots the coefficients for individual-level regressions of turnout intentions on the indicator for LMAS, separately for the indicated groups. OLS regressions controlling for age, sex, education, and parents’ education and including month, year, and state fixed effects. Markers are point estimates, horizontal lines, 95% confidence intervals. Standard errors for the analysis of inequality clustered at the county level. FORSA data, years of coverage indicated. Data on union membership available only as of 1996.

Figure 4

Figure 5. Causal Effect of LMAS-Induced Financial Hardship on Observed TurnoutNote: Figure 5 plots the coefficients from a multilevel regression of turnout on the indicator for LMAS, with intercepts allowed to vary by the election date and the level at which the election was held (European, national, state, local), and controlling for state and month fixed effects, an indicator for the length of the month, and the turnout in the previous election. Figure 5a shows regressions for all elections (n = 1,089), those held at the end of the month (n = 238), and major elections (excluding municipal elections held at the end of month (n = 51). Figures 5b and 5c show marginal effects of LMAS on elections held during the last week of the month since 1960/1970, separately for elections taking place in contexts with below- and above-average long-term unemployment rates and ratios of incomes to GDP, respectively (n = 201/162). German electoral turnout dataset (compiled by author). Markers are point estimates, vertical lines 95% confidence intervals.

Figure 5

Figure 6. Causal Effect of LMAS-Induced Financial Hardship on MechanismsNote: Figure 6 plots the coefficients for regressions of the indices for potential mechanisms connecting financial hardship with lower turnout intentions on the indicator for LMAS. Markers are point estimates, horizontal lines 95% confidence intervals. Results for the individual components of the indices plus further results are shown in Figure 8A in the Appendix. ALLBUS 1984–2016 and ESS 2002–2017 data.

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