Club Convergence among the Major Indian States During 1982–2014: Does Investment in Human Capital Matter?

Hembram, Sulekha; Maji, Souparna; Haldar, Sushil Kr.

doi:10.1177/1391561419850300

Cited by 10 publications

(9 citation statements)

References 44 publications

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“…In the Indian context, the imbalanced economic performance among the regions has also attracted substantial interest among researchers, policymakers and planners. As a result, several empirical studies on the convergence among Indian states/regions have been conducted (see e.g., Cashin and Sahay, 1996; Marjit and Mitra, 1996; Bajpai and Sachs, 1996; Ghosh et al , 1998; Rao et al , 1999; Nagaraj et al , 2000; Dasgupta et al , 2000; Aiyar, 2001; Sachs et al , 2002; Singh et al , 2003; Adabar, 2004; Bhattacharya and Sakthivel, 2004; Kar, and Sakthivel, 2006; Kar and Sakthivel, 2007; Nayyar, 2008; Ghosh, 2008; Das et al , 2010; Chikte, 2011; Sahoo, 2012; Kumar and Subramanian, 2012; Ghosh et al , 2013; Mallick, 2014; Cherodian and Thirlwall, 2015; Sofi and Durai, 2016; Sanga and Shaban, 2017; Chakraborty and Chakraborty, 2018; Mishra and Mishra, 2018; Hembram et al , 2019; Lolayekar and Mukhopadhyay, 2019; Lolayekar and Mukhopadhyay, 2020). Based on different samples of the states/regions over different periods, these studies give mixed results on the convergence of the Indian states/regions.…”

Section: Review Of Literaturementioning

confidence: 99%

“…According to the endogenous growth theory, forces like human capital and research and development put a stop to the reduction in the marginal product of capital. Eventually, the works of Barro and Sala-i-Martin (1992) and Sala-i-Martin (1996) explore the concept of absolute β-convergence, which refers to that economies, irrespective of their initial conditions, will converge with each other (Hembram et al , 2019). There are two ways to measure the existence of convergence, i.e.…”

Section: Framework Of Analysismentioning

confidence: 99%

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Regional economic growth in India: convergence or divergence?

Nayak

Sahoo

2021

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Purpose This paper aims to examine the convergence in per-capita income (measured as per-capita net state domestic product) of regions in India during the period 1990–1991 to 2017–2018. Two separate analyses have also been done for the sub-periods, i.e., 1990–1991 to 2003–2004 and 2004–2005 to 2017–2018, to find out the effect of the second phase of economic liberalization in India. Design/methodology/approach In a panel data study, the estimation of absolute and conditional beta (β)-convergence and sigma (σ)-convergence across 17 Indian regions have been done. To measure the dispersion of per-capita income across the regions in India, the standard deviation of logs, Gini coefficient, Mehran measure, Piesch measure, Kakwani measure and Theil index have been estimated. In addition to this, these indices have been regressed over time. Findings This study finds the presence of absolute and conditional β-convergence; the regions with low initial per-capita income have grown faster than the regions with high initial per-capita income. Further, this study finds that foreign direct investment (FDI) inflow and the availability of power enhance growth across regions. However, this study finds the presence of σ-divergence, which indicates that the economic inequality among the regions in India has widened over the periods, calling for policy interventions to promote growth in the backward regions through the promotion of FDI inflow and the availability of power. Originality/value This study highlights the rising economic inequality among the regions in India by analyzing the latest available data through appropriate econometric techniques.

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Section: Review Of Literaturementioning

confidence: 99%

Section: Framework Of Analysismentioning

confidence: 99%

Regional economic growth in India: convergence or divergence?

Nayak

Sahoo

2021

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“…The kernel density estimator is a non‐parametric and common method to consider external shape of the distribution dynamics (Hembram, Maji, & Haldar, 2019). Equation () presents the formula of the kernel density function:

{\overset{false^}{G}}_{B} ((), x) = \frac{1}{n} \sum_{i = 1}^{n} Z_{B} ((), C - C_{i}) = \frac{1}{nB} \sum_{i = 1}^{n} Z ((), \frac{C - C_{i}}{B}),

where n is the number of observations,

Z_{B} = \frac{1}{B} L ((), \frac{x}{B})

presents scaled kernel, B is the bandwidth C i is i th observation and C shows a particular point (Conlen, 2019).…”

Section: Methodsmentioning

confidence: 99%

“…The kernel density estimator is a non-parametric and common method to consider external shape of the distribution dynamics (Hembram, Maji, & Haldar, 2019). Equation 1presents the formula of the kernel density function:…”

Section: Kernel Densitymentioning

confidence: 99%

Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach

Shabani

Shahnazi

2020

Regional Science Policy & Practice

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Coronavirus disease 2019 (COVID-19), as a contagious disease, has negative externality and public policies are essential to control it. To provide control solutions, identifying the factors affecting the spread of COVID-19 and its distribution dynamics are very important for policy-makers. Although there have been many studies examining various factors affecting the spread of COVID-19, there are research gaps on the distribution dynamics of COVID-19, its future trend prediction with the current policies, and the effects of neighbours on the distribution dynamics of COVID-19. Hence, this paper used the data published on the confirmed COVID-19 cases (C-COVID-19) from 9 February 2020, to 27 July 2020, to investigate the spatial distribution dynamics of COVID-19 and its prediction in 40 Asian countries. The Markov chain and the spatial Markov chain were used in this study. The results show that the COVID-19 in Asia did not tend to zero with the current policies, and the neighbours had effects on the spread of COVID-19. Therefore, policy-makers should use cooperative policies between countries instead of domestic monopoly policies.

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“…Notwithstanding of existence of different tests, the above studies are only based on two clubs, which might not be the case of Indian states/UTs as they vary in PCO. Further, Ghosh et al (2013) and Hembram et al (2019) have tried to relax this assumption by implementing the clustering algorithms and Markov chain tests, respectively to identify the club convergence in per capita income across the Indian states/ UTs. The findings of these studies are not again consistent, which appeals for future research on this issue at aggregate PCO and sectoral level across the Indian states/UTs.…”

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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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Club Convergence among the Major Indian States During 1982–2014: Does Investment in Human Capital Matter?

Cited by 10 publications

References 44 publications

Regional economic growth in India: convergence or divergence?

Regional economic growth in India: convergence or divergence?

Spatial distribution dynamics and prediction of COVID‐19 in Asian countries: spatial Markov chain approach

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

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