Bibliographic record
Abstract
The phenomenon of zombie firms is gaining the attention of economists across different countries of the world; the increased interest is particularly evident after periods of economic crises. In our study, we focus on the development of zombie firms in the period before and after the 2008 crisis within two different economies, i.e., Germany and the Czech Republic, to provide insight into how different conditions and the overall economic context affect the fact that companies are more prone to becoming zombie firms. We implemented a difference-in-differences regression model to estimate the treatment effect by comparing the change (difference) in the differences in observed outcomes between these two countries. The data were obtained from two databases—the database Albertina by Bisnode a.s. providing financial statements of enterprises in the Czech Republic, and the database provided by Creditreform AG, which includes annual report data for a large sample of German companies. The dataset of German enterprises included 1,444,698 observations, i.e., 338,923 firms, and the dataset of Czech enterprises included 2,139,462 observations, i.e., 523,542 firms, both across the years 2000–2016, i.e., the data sample covered the period before and after the 2008 crisis. The different development of the share of zombie firms after the great financial crisis between Germany and the Czech Republic was proven as statistically significant. The findings confirm Germany is a country with a more stable economy and with a significantly lower risk of zombie firms’ persistence, while the Czech Republic is at the level of the European average in terms of zombie share. The results also suggest an influence of post-crisis monetary policy on companies and the possible link between low interest rates and a growing share of zombies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".