Income Convergence in Indian Districts: New Evidence From Panel Stationarity Test With Finite Time Dimension

Misra, Biswa Swarup; Kar, Muhsin; Nazlıoğlu, Şaban; Karul, Çağın

doi:10.1002/jid.3502

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…There is a vast literature on the convergence of income for Indian states using different methodologies (among others, Chakraborty & Chakraborty, 2018; Cherodian & Thirlwall, 2015; Das, 2012; Mishra & Mishra, 2018; Misra et al, 2020). Recent empirical evidence suggests that states in India exhibit divergence in income (Das, 2012; Mallick, 2014; Misra et al, 2020). The literature, however, has neglected the analysis of convergence dynamics in the main sources of output (Hernández Salmerón & Romero‐Ávila, 2015).…”

Section: Introductionmentioning

confidence: 99%

Sources of divergence in income in Indian states, 2001–2015

Misra

Nazlıoğlu

Küçükkaplan

2022

Development Policy Review

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Motivation: Empirical studies on income convergence for India indicate that Indian states exhibit divergence in income. The literature, however, has neglected the convergence dynamics of the main sources of output. A pertinent question is whether divergence in income is due to divergence in factor inputs (capital and labour) and/or in total factor productivity (TFP) in India, a low middle-income country with high dispersion of per capita income across its states. Purpose: This article examines the sources of income divergence in Indian states by analysing convergence in TFP, labour, and capital. Methods and approach: We test for stochastic convergence hypothesis by employing state-level data for 19 Indian states during the period 2001-2015. Given the small sample dimension of the Indian statelevel income data, we benefit from the recent developments in nonstationary panel data literature; and conduct novel panel stationarity and unit root tests with a fixed time dimension. We further carry out a robustness analysis to account for cross-section dependency across the states. Findings: TFP as well as the factor inputs (labour and capital) exhibit divergence, implying persistence in income inequality across Indian states. Divergence in labour across the states reflects the fact that migration is primarily an intra-district phenomenon. Poor investment climate in low-income states acts as a barrier for convergence in capital. TFP and capital stock are found to be correlated, and thereby lower investment in poorer states may be responsible for divergence in TFP across the states. Policy implications: Migration in India is primarily an intra-district phenomenon. There is a need to study the reasons which are discouraging inter-district and inter-state migration for better utilization of the labour resource. Government intervention has not been adequate to improve the infrastructure position and encourage capital inflows to low-income states. Low-income states should improve their business climate and create support infrastructure to earn the confidence of investors. Increasing investment in low-income states would help to increase their TFP and catch up with the high-income states.

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Section: Introductionmentioning

confidence: 99%

Sources of divergence in income in Indian states, 2001–2015

Misra

Nazlıoğlu

Küçükkaplan

2022

Development Policy Review

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“…Some studies have also been conducted in the Indian context at the national and sub‐national levels, which show dissimilarities in the findings of output/income convergence. For instance, a group of studies finds evidence in favour of divergence in PCO (Baddeley et al, 2006; Bandyopadhyay, 2011, 2012; Cashin & Sahay, 1996a, 1996b; Ghosh et al, 2013; Misra et al, 2020; Nagaraj et al, 2000; Sachs et al, 2002; Trivedi, 2002). These studies are mostly based on the single steady‐state (or neoclassical notion of convergence).…”

Section: Introductionmentioning

confidence: 99%

Output convergence at sector level across Indian states: Evidence from weak sigma and club convergence analysis

Akram

Ali

2021

J of Intl Development

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The study assesses the per capita output (PCO) convergence hypothesis across 33 Indian states/Union-Territories at the aggregate and sectoral level by using weak-sigma and clustering-algorithm convergence tests for the period of 2011-2012 to 2018-2019. Results from the weak-sigma convergence test show mixed evidence. This suggests that Indian states are not converging to single steady-state.Further, results revealed the existence of multiple clubconvergence at aggregate and sectoral level after applying the clustering-algorithm test. Results also show that the service sector is converging faster than the industry and agriculture sectors. Our findings recommend that sectorspecific policies need to be adopted to boost the aggregate PCO at club level.

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Spatial variability of agricultural development in West Bengal, India: a multivariate statistical approach

Halder¹

2021

GeoJournal

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Income Convergence in Indian Districts: New Evidence From Panel Stationarity Test With Finite Time Dimension

Cited by 4 publications

References 16 publications

Sources of divergence in income in Indian states, 2001–2015

Sources of divergence in income in Indian states, 2001–2015

Output convergence at sector level across Indian states: Evidence from weak sigma and club convergence analysis

Spatial variability of agricultural development in West Bengal, India: a multivariate statistical approach

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