The Making of Miracles in Indian States: Andhra Pradesh, Bihar, and Gujarat
Bibliographic record
Abstract
Growth miracles typically have been studied at the country level. The Making of Miracles in Indian States breaks from that tradition and studies three growth miracles in India at the level of the state: Andhra Pradesh, Bihar, and Gujarat. These are three of the largest and most diverse states in India. Andhra Pradesh is situated in the south of India, Bihar in the east, and Gujarat in the west. Bihar is the poorest among all states in India, Gujarat the third richest among the largest eighteen states, and Andhra Pradesh in the middle. Andhra Pradesh and Gujarat have long coastal lines while Bihar is landlocked. Yet, all of these states have grown at rates exceeding 8% for an entire decade in the 21st century. Despite many differences in the initial conditions, several common threads tie the high-growth experiences of the three states. First, accelerated growth has permitted acceleration in the growth of development expenditures in all three states, which has helped improve connectivity to markets. Alongside this growth, poverty has seen accelerated decline. Second, the composition of growth matters. Growth in high-value commodities such as fruits and vegetables, commercial crops, dairy, and animal husbandry in Andhra Pradesh and Gujarat has led to accelerated reduction in rural poverty. However, the failure of labor-intensive industry has stunted the migration of workers out of agriculture into industry. Third, the quality of leadership that brings improved governance with it is central to improved outcomes in the states. Visionary leaders---Chandrababu Naidu in Andhra Pradesh, Nitish Kumar in Bihar, and Narendra Modi in Gujarat---played critical roles in the making of all three miracles. Fourth, the three studies also bring out the importance of pro-market reforms and the adoption of technology in development. Finally, the studies show that good economics is also good politics: voters reward the chief ministers who bring about significant improvement to the people's lives. Available in OSO: Contributors to this volume - Rahul Ahluwalia, University of British Columbia, Vancouver. Archana Dholakia, Gujarat Institute of Development Research, Ahmedabad, Gujarat Ravindra Dholakia, Indian Institute of Management, Ahmedabad, Gujarat. Mudit Kapoor, Indian School of Business, Hyderabad, Andhra Pradesh. Arnab Mukherji, Indian Institute of Management, Bangalore, Karnataka. Anjan Mukherji, National Institute of Public Finance and Policy, New Delhi. Arvind Panagariya, Columbia University, New York, New York. M. Govinda Rao, Member, Fourteenth Finance Commission, New Delhi.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".