Taming a Paper Tiger? A Political Economy of Support for the Repression of the Business Elite
Bibliographic record
Abstract
Why do autocrats financially repress some allies but not others during an economic crisis? When too many foxes are in the henhouse, an autocrat may purge his allies in bad times and shrink the winning coalition. However, it is a risky enterprise. If he targets the wrong elite, it may backfire and trigger coups and dissent. Despite the high risk, we still do not know much about precisely who autocrats are likely to sideline from the ruling coalition. I suggest that the autocrat’s business allies are a politically expedient target during economic crises because the people perceive the co-opted business elite as corrupt. Given their low public popularity, the autocrat may justifiably blame the greedy business elite for the country’s economic woes. To develop a framework for public support for repression, I focus on Turkey as a case – a highly polarized country that has experienced a protracted financial crisis. One novel contribution of this study is the use of visual conjoints. I created fake LinkedIn profiles of hypothetical businesspeople with AI-generated profile pictures and business logos. I measured people’s support for their financial repression, depending on their firm’s characteristics, sectoral affiliations, and partisan attitudes toward the government’s economic policies. The results suggest that public support for financial coercion depends on the regime’s political economy. People are more likely to condone the extra-taxation of the business elite, who owe their success to the regime and are perceived as responsible for the economic crisis. This paper contributes to a growing scholarship on micro-level determinants of autocratic purges, and its findings have broad implications for our understanding of elite defection and autocratic power-sharing arrangements.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".