Bibliographic record
Abstract
In India, the term ‘sick units’ refers to economically unviable firms which are kept alive ‘in the public interest’ by means of subsidies of various kinds. Since this practice is common, and large parts of the industrial sector are affected, this phenomenon is referred to as industrial sickness. As of March 2001, the Reserve Bank of India counted over a quarter of a million of sick units with outstanding credit worth more than a quarter of a trillion of Indian Rupees, i.e. about 1.2 percent of Indian GDP. Recognizing that scarce resources are wasted on a great scale, the Government of India enacted special legislation to tackle the problem, namely, the Sick Industrial Companies Act. Based on a rich panel data set of some 4,400 manufacturing companies covering the 1988-1999 period, this thesis investigates the causes of sickness and the responses of firms to the policies that are supposed to remedy it. Chapter 1 motivates the topic, briefly summarizes existing contributions and introduces the dataset. Chapter 2 deals with Indian industrial policy, much of which is still a legacy of the attempt at planning a so-called mixed economy, with the state supposedly ‘occupying the commanding heights’. Until the deregulation process of the early 1990s dismantled discrete barriers to entry, the economic environment involved a labyrinthine system of industrial licensing, the promotion of priority sectors, the regulation of foreign trade, capital flows and investment, protection of jobs in the regulated sector, and directed credit provided by public financial institutions. Chapter 3 first discusses various concepts of sickness. In the following several descriptive statistics from the given firm-level dataset are presented to provide a picture of the dimensions of sickness and its patterns over industries, time, location (state), form of ownership, firm-size and firm-age. The ‘stylized facts’ section is rounded out with an investigation of the relation between a firm’s health status and measures of its profitability, single-factor productivity and liabilities, with firms classified by ten manufacturing sectors. Decreasing sickness rates in the early days of reforms stand vis-à-vis erratic rises in industrial sickness from the mid 1990s onwards. This finding raises two questions: (i) have the reforms ultimately failed to foster productive efficiency? or (ii) is increased sickness in the mid and late 90s just a reflection of the New Economic Policy (NEP) and its attempt to harden budgets? Chapter 4 draws on the former by analyzing productivity and efficiency in 10 separate Indian manufacturing industries over 3 important sub-periods, viz. pre-reform (1989-‘91), transition phase (1992-‘96), and post-reform (1997-‘99). The results corroborate previous results on the general downturn of aggregate manufacturing performance after 1991. Moreover it is shown that sectoral downturns in productivity and mean efficiency went with greater variation in firm performance. Diverging firm-wise efficiency scores combined with increasing failure rates lead to the supposition that NEP reforms have not been generally unsuccessful, but that economically viable firms have considerably benefited from the changes in policy. The key hypothesis of chapter 5 is that prior to the 1991 reforms preferential treatment irrespective of economic viability established systematic disincentives to perform well, and once these were withdrawn, then firms fell into sickness. This hypothesis is tested by running a panel probit model, wherein observed health status in the late nineties is regressed against (i) dummy variables that capture the effect of the policy shock on formerly protected types of firms, (ii) pre-reform measures of budget softness, and (iii) pre-reform measures of economic distress. In a second part, it is investigated whether measures that lowered barriers to entry, and so sharpened competition, affected the dispersion of efficiency levels among firms and the incidence of sickness. While chapter 5 concentrates on reductions in barriers to entry, chapter 6 is more concerned with the remaining barriers to exit (of labor and firms). The starting point is the notion that the status of sickness entails great advantages to the incumbent management and the shareholders (exemptions from debt repayment or other obligations and generous financial assistance) as well as to politicians: once a company has fallen sick, the old management is replaced by a government appointed director. An agency model is tested, the core of which states that the politician-manager rescues the firm, because employment guarantees increase his popularity and his chances of getting re-elected. Inference from single-equation estimation mostly supports the model, but a simultaneous systems approach (a particular choice of the capital structure and the sickness status) would reject it.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".