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Campaign communication and legislative leadership

Published online by Cambridge University Press:  04 April 2024

Stefan Müller*
Affiliation:
School of Politics and International Relations, University College Dublin, Dublin, Ireland
Naofumi Fujimura
Affiliation:
Graduate School of Law, Kobe University, Kobe, Japan
*
Corresponding author: Stefan Müller; Email: stefan.mueller@ucd.ie
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Abstract

Do policy priorities that candidates emphasize during election campaigns predict their subsequent legislative activities? We study this question by assembling novel data on legislative leadership posts held by Japanese politicians and using a fine-tuned transformer-based machine learning model to classify policy areas in over 46,900 statements from 1270 candidate manifestos across five elections. We find that a higher emphasis on a policy issue increases the probability of securing a legislative post in the same area. This relationship remains consistent across multiple elections and persists even when accounting for candidates' previous legislative leadership roles. We also discover greater congruence in distributive policy areas. Our findings indicate that campaigns provide meaningful signals of policy priorities.

Information

Type
Original 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
© The Author(s), 2024. Published by Cambridge University Press on behalf of EPS Academic Ltd
Figure 0

Figure 1. Comparing the correspondence between the frequencies of policy areas in the test set based on the BERT predictions and human coding of the same set of statements.

Figure 1

Figure 2. Issue salience in candidate manifestos. Horizontal bars represent 95 percent bootstrap confidence intervals for each average. The dashed vertical lines and the numbers in the top-right corner of each box show the average issue salience across the five elections.

Figure 2

Table 1. Predicting legislative leadership posts in a policy area

Figure 3

Figure 3. Predicted probabilities of obtaining legislative leadership posts conditional on the salience of the same policy area in candidate manifestos. Plot shows predicted probabilities based on Models 1–5 in Table 1. The remaining variables are held constant at their respective mean or modal values. Gray areas indicate 95 percent confidence intervals. The small vertical lines display the observed values of Manifesto Salience.

Figure 4

Figure 4. Predicted probabilities of obtaining a legislative leadership post, conditionally on the interaction effect between manifesto salience and the three broad issue areas, based on Model 1 in Table A7. The remaining variables are held constant at their respective mean or modal value. Gray areas indicate 95 percent confidence intervals. The small vertical lines display the observed values of Manifesto Salience.

Figure 5

Figure 5. Coefficient estimates and 95 percent confidence intervals of Manifesto Salience based on separate logistic regression models for each policy area. Standard errors are clustered on the manifesto level.

Figure 6

Figure 6. Predicted probability of obtaining a post (Combined Measure) conditional on the interaction effect between portfolio importance and varying levels of manifesto salience. The remaining variables are held constant at their respective mean or modal value. Predicted probabilities are based on Model 1 of Table 2. The small vertical lines display the observed values of Portfolio Importance.

Figure 7

Table 2. Predicting legislative leadership posts in a policy area conditional on the interaction effect between manifesto salience and perceived portfolio importance

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