Unequal Representation. Are Politicians' Perceptions of Public Opinion Biased in Favour of Wealthier Voters?
Bibliographic record
Abstract
The political science literature has extensively shown that the preferences of wealthier voters tend to be better represented. Specifically, the opinions of politicians align more closely with those of economically privileged voters, and policy decisions are more responsive to the views of these voters. Surprisingly, this literature has not explored whether and why there are similar inequalities in the way politicians perceive public opinion. However, the understanding that politicians have of voter preferences is a crucial element of policy responsiveness, especially between elections.This dissertation addresses this gap by analysing whether and why politicians' perceptions of public opinion are biased in favour of wealthier voters. The empirical analysis relies on survey data collected in four countries: Belgium, Germany, Switzerland, and Canada. Citizens provided their preferences on specific policy statements, while politicians were asked to estimate voter preferences for these statements. The empirical analysis examines the disparities between politicians' estimates of public opinion and the actual levels of support among various income groups.The results show that politicians' perceptions are, on average, closer to the preferences of wealthier voters. However, these inequalities in politicians' perceptions are not as systematic as expected. Specifically, while the views of richer voters are better heard by politicians in economic issues, the views of poorer voters receive better representation in MPs' perceptions in socio-cultural issues. The analysis also explores the factors contributing to economic perceptual biases and reveals that politicians from right-wing political parties, with a higher-status social background, and whose opinions align with those of the wealthy are more likely to have perceptual biases in favour of richer voters.The findings of this dissertation have significant implications for democratic representation. First, they suggest that representation is a multifaceted phenomenon and that poorer voters are not consistently underrepresented in all policy domains. There is no one group that is consistently better represented: different groups might see their views represented, albeit on different issues. Second, significant inequalities still exist in economic issues, with the views of less privileged voters being systematically disregarded by political elites. This finding is concerning for democratic representation and could exacerbate economic inequalities in modern societies. Lastly, this dissertation provides insights for mitigating economic biases in politicians' perceptions, particularly regarding economic matters. Promoting the presence of politicians from less affluent backgrounds in parliament is proposed as a means to improve the overall quality of political representation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".