Offshore Foreign Direct Investment, Capital Round‐Tripping, and Corruption: Empirical Analysis of Russian Regions
Why this work is in the frame
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Bibliographic record
Abstract
Recent economic geography research has identified the round‐tripping of capital from emerging economies to offshore financial centers (OFCs) and back as foreign direct investment (FDI) as a central element of the global offshore FDI network. However, the factors behind this phenomenon are not yet fully understood. Our study develops a general framework that conceptualizes the phenomenon of round‐trip investment. In particular, we argue that secrecy arbitrage, defined as interplay of onshore corruption and offshore secrecy, largely explains round‐trip investment between onshore jurisdictions and OFCs. First, we argue that part of the round‐trip FDI consists of proceeds from corruption, which is laundered in OFCs and reinvested back to the location of origin. Second, we maintain that the secrecy dimension of the OFC also motivates the round‐tripping of licit capital, as businesses use the secrecy provided by OFCs to hide their true identities from corrupt authorities in the home location. To test the validity of our argument about onshore corruption as a driver for round‐trip investment, we empirically analyze firm‐level data on the distribution of offshore FDI (which, we argue, is largely round‐trip) across Russian regions. Our empirical findings confirm that FDI from OFCs is positively associated with host region corruption, and this relationship is stronger for OFCs with a higher secrecy score. Hence, we conclude that round‐trip FDI is strongly motivated by the interplay between onshore corruption and offshore secrecy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it