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Record W2550180037 · doi:10.1177/0256090920030305

Disinvestments in India: Needed Change in Mindset

2003· article· en· W2550180037 on OpenAlexaboutno aff
Vipin Malik

Bibliographic record

VenueVikalpa The Journal for Decision Makers · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsDivestmentMindsetChinaGovernment (linguistics)Initial public offeringBlueprintRestructuringDeveloping countryBusinessEconomicsEconomic policyEconomic growthPolitical scienceFinance

Abstract

fetched live from OpenAlex

In the nineties, India's budgeting, fiscal deficits, and balance of payments problems kick started the government's urge to unlock the huge investments chained in the state-owned enterprises (SOEs). The blueprint was the successful global model of privatization/divestment which was initiated by Margaret Thatcher in the eighties in the UK and implemented by other countries including Unified Germany, former USSR, the erstwhile socialist countries, Western Europe, Canada, Japan, and even China. The developed nations attained a high level of success followed by the developing and the least developed countries. While developed and OECD countries opted for Initial Public Offerings (IPOs), Russia adopted a system of vouchers for buying shares of public sector companies at auctions, and smaller states in the former Soviet Union and East European countries opted for trade and negotiated sales. Developing countries like Brazil and Chile made principal divestments of significantly large government stakes with no reservations to pass on control to foreign investors. Greece and Korea opted for convertible bonds. Considering that the debate on the need for disinvestments is very old, the question is: is there anything new and is there a game plan? Also, why is the media hesitant in presenting analysis of the decision-makers' mindset? It leads to the inference that the implementers have perhaps little commitment to disinvestments which is in contrast to what Hungary and China have achieved by their professional approach. Fortunately, the efforts to pursue reforms have not openly been reversed or given up by any government of the day. The Four Ps of disinvestment – Policy, Promise, Prognosis, and Performance – look grim. In the recent past, we have been witnessing a lot of debate on the disinvestments scenario suggesting dynamic movement. In reality, the sale of equity of only 49 companies has so far been accomplished (a few only privatized). In comparison, Hungary identified 1,288 SOEs, transformed them into companies for privatization, and in 2002, only 79 companies were left for privatization. Against a target of Rs 100 billion, the financial year 2000–2001 closed with a collection of Rs 18.70 billion. Against a target of Rs 120 billion, the financial year 2001–2002 closed with a collection of Rs 56 billion inclusive of special dividend of VSNL at Rs 18.87 billion and Rs 11.54 billion of IBP bought by another public sector undertaking (PSU). Against a target of Rs 120 billion, the financial year 2002-2003 closed with a collection of Rs 33 billion. The target for financial year 2003-04 is Rs 132 billion (US$ 2.87 billion). To set things on the recovery path, introspection on what aberrations have entered the system is necessary. In the words of the President to the Joint Session of Parliament in February 2002, “... The prolonged fiscal haemorrhage from the majority of these enterprises cannot be sustained any longer...” How do we ensure that the disinvestment process is on track? The following five-point agenda would be useful for policy-makers: Trust the homegrown expert for implementation. Place administrative control in the hands of the Finance Minister. Hand over companies that are a burden on the government to the employees. Do not involve a PSU/SOE in the bidding process. Manage revivals professionally.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.296
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2003
Admission routes1
Has abstractyes

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