Ownership transformation and firm \nperformance in the successor states of the \nformer Yugoslavia
Bibliographic record
Abstract
Privatization became a major component of economic policy around the world since \nthe mid-1980s despite the conflicting theoretical arguments and empirical evidence \nfor this policy. The inconclusive evidence, apart from reflecting genuine differences \namong countries and industries under investigation, is also the result of \nmethodological and empirical problems this literature is beset with. This provides the \nmotivation for this research project which intends to contribute to the literature by \naddressing some of these problems and also by applying it to a particular set of \ncountries, the successor states of the former Yugoslavia, that have been either not \nstudied at all or not studied as a group despite the fact that they share the same history \nand the same economic, political and social background which are distinct from other \ntransition economies. As with the established empirical literature in the field, the \nresearch focuses on the impact of privatization on the performance of firms in the \nbroad context of the neoclassical theory and its extensions. \nThe thesis aims at investigating the impact of privatization on companies’ \nperformance in Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, \nSerbia and Slovenia, independent countries that emerged from the disintegration of \nthe former Yugoslavia. In doing so, this thesis initially embarks on a critical review of \ntheoretical and empirical literature, identifying their theoretical predictions and \nassessing their empirical validity, highlighting a variety of methodological problems \nfrom which the previous studies have suffered. The empirical investigation of this \nthesis uses Stochastic Frontier Analysis to estimate the efficiency of companies with \ndifferent ownership structures. It also addressed the issue of missing data by \nemploying a multiple imputation procedure. In addition, policy evaluation \neconometrics using matched difference-in-difference estimators is employed for \nestimating the causal relationship between ownership transformation and companies’ \nperformance. Special attention is paid to addressing the issue of selection bias which \nis the main challenge in evaluating the effect of privatization. \nThe empirical results suggest that privatization is associated with improvement in \ncompanies’ performance in terms of technical efficiency and sales levels, while it is \nassociated with a significant drop in employment levels. Also, privatization is \nassociated with improvement in performance over time. The results suggest that there \nis some heterogeneity across countries, industries and ownership types. In particular, \nthey show that the average efficiency scores of companies in the successor states vary \nsystematically across these countries with Slovenian companies being the most \nefficient, followed by those in Croatia, Montenegro, Bosnia, Serbia and Macedonia i.e., \nin some order of institutional and economic development in the region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".