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Threats and the Public Constraint on Military Spending

Published online by Cambridge University Press:  08 November 2023

Matthew DiGiuseppe*
Affiliation:
Faculty of Social and Behavioural Sciences, Institute of Political Science, Leiden, The Netherlands
Alessia Aspide
Affiliation:
Faculty of Social and Behavioural Sciences, Institute of Political Science, Leiden, The Netherlands
Jordan Becker
Affiliation:
Department of Social Sciences, United States Military Academy at West Point, USA Brussels School of Governance, Brussels, Belgium Chaire Économie de défense - Institut des hautes études de défense nationale, Paris, France
*
Corresponding author: Matthew DiGiuseppe; Email: mdigiuseppe@gmail.com
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Abstract

The public places an important constraint on funding security in Europe, and austerity risks making the constraint tighter. Several recent studies show that curtailing military spending is a popular way to reduce debt in Europe. Yet it remains unclear if military spending aversion persists when threats are salient. We fielded an original survey experiment in Italy weeks before the Russian invasion of Ukraine to examine how information about security threats influences military spending preferences and fiscal trade-offs. We found that information about threats increases support for military spending. To validate the survey experiment, we recontacted and remeasured our respondent's preferences three weeks after Russia's invasion and find evidence consistent with our initial experiment. Our findings suggest that, while public opposition to military spending remains high in Italy, external threats dampen the public's opposition to military spending, even under high debt burdens.

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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, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press
Figure 0

Figure 1. Threat treatment.

Figure 1

Figure 2. Effect of treatments on spending preferences.Note: The thick and thin lines indicate the 95 per cent and 90 per cent confidence intervals, respectively, around the coefficients from three separate OLS models estimated with robust standard errors. Each model estimates the effect of each treatment on three separate spending outcomes (N = 1,544). We present the full regression results in the Supplementary Appendix.

Figure 2

Table 1. Multinomial logit models of budgetary trade-offs

Figure 3

Figure 3. Changes in discrete outcomes.Note: The top panels (a) show the percent support for each consolidation strategy in the treatment (Threat) and control (No Threat) conditions. The bottom panels (b) present the estimated change in average predicted probabilities by moving the treatment variable from zero to one based on two multinomial logit models. The dot indicates the change in predicted probability for each of the three outcomes estimated in each model. The lines indicate the 95 per cent confidence intervals around the estimate.

Figure 4

Figure 4. Pre- and post-invasion on spending priorities. (a) (b). Panel 1: The lines indicate the 90 per cent (thick) and 95 per cent (thin) confidence intervals around the coefficients from three separate OLS models estimated with standard errors clustered by respondent. Each model estimates the difference in the DVs from the pre-invasion of Ukraine to the post-invasion of Ukraine (N = 1,154). The confidence intervals are derived from standard errors clustered by respondent. Panel 2: These panels plot the average difference in predicted probabilities pre-invasion to post-invasion estimated from two multinomial logit models. The dot indicates the change in predicted probability for each of the three outcomes estimated in each model. The lines indicate the 90 per cent (wide) and 95 per cent (thin) confidence intervals around the estimate. The confidence intervals are derived from standard errors clustered by respondent.

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