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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".