Identity, Economy, and Secessionism in Comparative Perspective
Bibliographic record
Abstract
What motivates the emergence of secessionist movements, and why do individuals support secession once it becomes a salient issue? Scholars have argued that identitarian and economic factors motivate the emergence of secessionism and shape individuals’ preferences on secession, but they have examined only social groups among which secessionist movements have emerged and have not identified the specific ways in which identitarian and economic motivations shape secessionism in different types of regions. My project pioneers a novel approach to secessionism by identifying the universe of potentially secessionist social groups and introducing a theory of why the causal processes motivating secessionism might be different for different regions or groups. Using large-N cross-national analyses, case studies, interviews, and surveys, I examine how cultural identity and economic dynamics shape secessionism across a variety of contexts.I test traditional theories of secessionism using an original dataset containing the universe of potentially secessionist social groups in Europe and the former Soviet Union. To reduce bias from selecting cases on the outcome of interest, my dataset includes a set of groups with potential but unrealized secessionism alongside groups that mobilized for secession. I show that theories that emphasize the role of natural resources and relative deprivation in motivating secessionism lose their explanatory power when tested over this improved sample. I supplement this quantitative analysis by investigating the systematic reasons why economic grievances motivate secessionists in some places while identity-based grievances motivate secessionism in others. Through interviews, ethnographic research, and process tracing, I examine secessionism in Catalonia, Montenegro, Quebec, and Scotland. I show that in Scotland, economic factors have primacy in motivating secessionism, whereas identity-based factors drive secessionism in Quebec. In Catalonia, identitarian and materialist concerns interact to motivate secessionism, while anti-regime pressures encouraged secessionism in Montenegro.I then examine survey data from Catalonia, Northern Ireland, Scotland, and Wales to explore how cultural identity, socioeconomic status, and partisanship predict support for secession. I show that while regional identity is generally a strong predictor of support for secession, socioeconomic status and partisanship are weaker indicators, which problematizes traditional theories of public opinion and suggests that secession is fundamentally distinct from other political issues.This project advances our identification of the causal mechanisms behind secessionism at both the cross-national and individual levels. My cross-national analysis shows that identity and economic factors are indeed important drivers of secessionism, while disaggregating secessionist regions shows how these factors shape secessionism in different contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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; both teacher heads agree on what is shown here.
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".