Hostname: page-component-89b8bd64d-46n74 Total loading time: 0 Render date: 2026-05-09T01:11:34.637Z Has data issue: false hasContentIssue false

Endogenous Benchmarking and Government Accountability: Experimental Evidence from the COVID-19 Pandemic

Published online by Cambridge University Press:  23 June 2023

Michael Becher
Affiliation:
School of Politics, Economics & Global Affairs, IE University, Madrid, Spain Institute for Advanced Study in Toulouse, Toulouse, France
Sylvain Brouard
Affiliation:
Sciences Po, Center for Socio-Political Data (CDSP) & Center for Political Research (CEVIPOF), CNRS, Paris, France
Daniel Stegmueller*
Affiliation:
Department of Political Science, Duke University, Durham, NC, US
*
Corresponding author: Daniel Stegmueller; Email: daniel.stegmueller@duke.edu
Rights & Permissions [Opens in a new window]

Abstract

When do cross-national comparisons enable citizens to hold governments accountable? According to recent work in comparative politics, benchmarking across borders is a powerful mechanism for making elections work. However, little attention has been paid to the choice of benchmarks and how they shape democratic accountability. We extend existing theories to account for endogenous benchmarking. Using the COVID-19 pandemic as a test case, we embedded experiments capturing self-selection and exogenous exposure to benchmark information from representative surveys in France, Germany, and the UK. The experiments reveal that when individuals have the choice, they are likely to seek out congruent information in line with their prior view of the government. Moreover, going beyond existing experiments on motivated reasoning and biased information choice, endogenous benchmarking occurs in all three countries despite the absence of partisan labels. Altogether, our results suggest that endogenous benchmarking weakens the democratic benefits of comparisons across borders.

Information

Type
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

Table 1. Experimental groups, treatment headlines

Figure 1

Table 2. Exact Binomial test of non-random benchmark selection

Figure 2

Figure 1. Pre-treatment political orientation and positive benchmark selection.Note: Marginal effects of pre-treatment satisfaction with the head of executive and pre-treatment party identification (indicator variable for identifying with the governing party) on the probability of a respondent choosing a positive cross-national benchmark (for the country). Shown are marginal effects calculated from linear probability models without covariates () and adjusted () for survey-design (pre-treatment) covariates. Satisfaction is scaled by two standard deviations (Gelman 2008). Confidence intervals (with 90 per cent and 95 per cent coverage) are based on heteroscedasticity-consistent standard errors.

Figure 3

Figure 2. Exogenous information and evaluation of government performance.Note: Average treatment effects of exogenous positive versus negative benchmarking information provision. Difference-in-means () and covariate-adjusted () estimates. Confidence intervals (with 90 per cent and 95 per cent coverage) are based on heteroscedasticity-consistent standard errors. Randomization p-values that test the sharp directional null hypothesis are shown on the far right.

Figure 4

Figure 3. Experiment 2: Three-stage design. Respondent choices and randomized benchmarks.Note: Number of observations in parentheses. The complete vignette text and the list of five comparison countries are available in Online Appendix A.4.1.

Figure 5

Figure 4. Pre-treatment political orientation and benchmark selection.Note: Marginal effects of pre-treatment satisfaction with the head of the executive on the probability of a respondent choosing a (i) positive vs neutral or (ii) negative vs neutral benchmark in France. Shown are marginal effects calculated from linear probability models without covariates () and adjusted () for survey-design (pre-treatment) covariates. Confidence intervals (90 per cent and 95 per cent) are based on robust standard errors.

Figure 6

Figure 5. Benchmark choice, exogenous benchmarking information, and evaluation of government performance.Note: Shown are group differences weighted by sample inclusion probability. Confidence intervals (with 90 per cent and 95 per cent coverage) are based on robust standard errors.

Supplementary material: File

Becher et al. supplementary material

Becher et al. supplementary material
Download Becher et al. supplementary material(File)
File 624.9 KB