Heterogeneous Links Between Corruption and Innovation in a Global Economy
Bibliographic record
Abstract
This study examines the impact of corruption on business innovation from a comparative perspective and shows that this relationship is inherently heterogeneous across firms and countries. It addresses two main research questions: (i) Does corruption facilitate or hinder innovation in the countries studied? (ii) To what extent is the relationship between corruption and innovation mediated/shaped by countries’ institutional configurations and firm characteristics (foreign and domestic ownership)? We analyze data from the fifth and sixth waves (2012–2016 and 2018–2019) of the EBRD’s World Bank Business Environment and Enterprise Performance Survey (BEEPS), using a balanced panel of 3584 establishments in 22 Eastern European and Central Asian economies. The results provide two key insights into the relationship between corruption and innovation. First, the institutional setting plays a crucial role in shaping both the strength and the direction of this relationship, for example, when comparing EU and non-EU countries. Second, the impact of corruption at the firm level varies depending on the ownership structure: the ‘greasing’ effect is particularly relevant for foreign firms operating in weak institutional environments, but appears to be ineffective—if not ‘sanding’—for foreign firms in contexts with stronger anti-corruption controls.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".