The Making of Miracles in Indian States: Andhra Pradesh, Bihar, and Gujarat
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Résumé
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.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle