Fears of Redistribution, Decentralization and Secession: Evidence from Bolivia’s Referendum for Departmental Autonomy
Bibliographic record
Abstract
Recent years have witnessed strong movements toward decentralization and secession. The former Soviet Union, Yugoslavia, Czechoslovaquia and Serbia and Montenegro have disintegrated. Movements for regional autonomy and even independence have gained larger support in Bolivia, Canada, Spain, France and Italy. What is the importance of redistributive politics in explaining decentralization and secession outcomes in a democratic polity? This paper attempts to answer this question building a simple rational choice model in which individuals’ preferences over alternative institutional regimes are derived from their economic and social consequences. The model predicts that, in a national referendum, relatively rich (poor) people in relatively rich regions and relatively poor (rich) people in the relatively poor regions will support (oppose) decentralization and secession. As a consequence, relatively rich (poor) regions will have an absolute majority supporting (opposing) decentralization. The national outcome will depend on the gaps between regional and national median incomes and the sizes of the population in each region. An important quality of the model is that it has sharp quantitative implications. Given information on regional and national median incomes, and the proportion of an electoral pool whose income is below these levels, the model predicts the proportion of the electoral poll that will support or oppose decentralization. I use data from Bolivia’s referendum for departmental autonomy and estimates of per-capita household income indicators to contrast observed vs. predicted voting outcomes. The model accounts for almost 2/3 of the variation in voting behavior. The fit is surprisingly good in light of model’s simplicity. This result suggests that fears of redistribution play an important role in shaping decentralization and secession outcomes, at least in the Bolivian case.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".