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Young candidates: why don’t they get elected?

Published online by Cambridge University Press:  05 May 2026

Daniel Stockemer*
Affiliation:
School of Political Studies, University of Ottawa, Canada
Anna Lopatina
Affiliation:
University of Mainz, Mainz, Germany
*
Corresponding author: Daniel Stockemer; Email: dstockem@uottawa.ca
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Abstract

Parliaments at all levels of government do not necessarily resemble the society they represent. Such parliaments are generally more masculine, less diverse in terms of ethnicity and region, and older than the population. Focusing on youth’s underrepresentation, we are interested in why young people do not get elected. Using the German local elections as a case and looking at a random sample of 100 German municipal elections comprising more than 6400 candidates, we find that youth’s underrepresentation stems to a large degree from their underrepresentation in the candidate pool. In addition, we also discover that youth face some disadvantages in the electoral process. However, these disadvantages are subtle, in that parties are less likely to put young candidates on the top and most electorally advantaged list positions. Voters, in turn, do not generally vote less for young candidates, but they vote less for them if youth occupy a top list position.

Information

Type
Research Note
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 (https://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), 2026. Published by Cambridge University Press on behalf of European Consortium for Political Research
Figure 0

Figure 1. The ladder of political recruitment.

Figure 1

Table 1. Age statistics across candidates and elected candidates

Figure 2

Table 2. Multiple logistic regression models measuring the influence of age on someone’s election chances

Figure 3

Table 3. Age statistics across top-ranked candidates (i.e., candidates listed at list position four or higher)

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Table 4. Multiple logistic regression models measuring the influence of age on someone’s election chances for candidates at the top four list positions

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Table 5. List placement before and after the election

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Table 6. Multiple regression models (OLS) measuring the influence of age on someone’s change in list placement

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Table 7. List placement before and after the election for the top four list placements

Figure 8

Table 8. Multiple regression models (OLS) measuring the influence of age on someone’s change in list placement for candidates at top four list positions

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Table 9. Election prospects based on list placement for different age cohorts

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Table A1. Socio-economic data of the municipalities included

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Table A2. Detailed descriptive statistics of variables included in the study and models