Government Corruption and Foreign Direct Investment Under the Threat of Expropriation
Bibliographic record
Abstract
Foreign investment is often constrained by two forms of political risk: expropriation and corruption. We examine the role of government corruption in foreign direct investment (FDI) when contracts are not fully transparent and investors face the threat of expropriation. Using a novel dataset on worldwide expropriations of FDI over the 1990–2014 period, we find a positive relationship between the extent of foreign investor protections and the likelihood of expropriation when a country’s government is perceived to be highly corrupt, but not otherwise. We then develop a theory of dynamic FDI contracts under imperfect enforcement and contract opacity in which expropriation is a result of illicit deals made with previous governments. In the model, a host-country government manages the FDI contract on behalf of the public, which does not directly observe government type (honest or corrupt). A corrupt type is able to extract rents by encouraging hidden investments in return for bribes. Opportunities for corrupt deals arise from the distortions in the optimal contract when the threat of expropriation is binding. Moreover, a higher likelihood of the government being corrupt increases the public’s temptation to expropriate FDI, magnifying investor risk. The model predicts that expropriation is more likely to occur when the share of government take is low and following allegations of bribes to public officials, and it suggests an alternative channel through which corruption reduces optimal foreign capital flows.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".