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Record W2890500951 · doi:10.3386/w22174

Social Mobility and Stability of Democracy: Re-evaluating De Tocqueville

2016· preprint· en· W2890500951 on OpenAlexfundno aff
Daron Acemoğlu, Georgy Egorov, Konstantin Sonin

Bibliographic record

VenueNational Bureau of Economic Research · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicCulture, Economy, and Development Studies
Canadian institutionsnot available
FundersCanadian Institute for Advanced Research
KeywordsDemocracyStability (learning theory)Social mobilityPolitical scienceSociologySocial scienceComputer scienceLawPolitics

Abstract

fetched live from OpenAlex

An influential thesis often associated with De Tocqueville views social mobility as a bulwark of democracy: when members of a social group expect to join the ranks of other social groups in the near future, they should have less reason to exclude these other groups from the political process. In this paper, we investigate this hypothesis using a dynamic model of political economy. As well as formalizing this argument, our model demonstrates its limits, elucidating a robust theoretical force making democracy less stable in societies with high social mobility: when the median voter expects to move up (respectively down), she would prefer to give less voice to poorer (respectively richer) social groups. Our theoretical analysis shows that in the presence of social mobility, the political preferences of an individual depend on the potentially conflicting preferences of her "future selves," and that the evolution of institutions is determined through the implicit interaction between occupants of the same social niche at different points in time. When social mobility is endogenized, our model identifies new political economic forces limiting the amount of mobility in society -because the middle class will lose out from mobility at the bottom and because a peripheral coalition between the rich and the poor may oppose mobility at the top.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.529
GPT teacher head0.579
Teacher spread0.051 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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