Economic Growth and Income Inequality in China, India and Singapore

Mukhopadhaya, Pundarik; Shantakumar, G; Rao, Bhanoji

doi:10.4324/9780203809266

Cited by 6 publications

(4 citation statements)

References 95 publications

(115 reference statements)

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“…An example can best illustrate the severe limitations of combining observations calculated on different bases using only global fixed adjustments. Consider the question of whether income inequality is higher in China or India posed by Mukhopadhaya, Shantakumar, and Rao (:102). The left panel in Figure shows unadjusted data for the two countries since 1985 from the Giniall series of Milanovic's () All the Ginis data set.…”

Section: The Problem Of Comparabilitymentioning

confidence: 99%

The Standardized World Income Inequality Database*

Solt

2016

Social Science Quarterly

829

650

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Objective Since 2008, the Standardized World Income Inequality Database (SWIID) has provided income inequality data that seek to maximize comparability while providing the broadest possible coverage of countries and years. This article describes the current SWIID's construction, highlighting differences from its original version, and reevaluates the SWIID's utility to cross‐national income inequality research in light of recently available alternatives. Methods Coverage of inequality data sets is assessed across country‐years; comparability is evaluated in terms of success in predicting the Luxembourg Income Study (LIS), recognized in the field as the gold standard in comparability, before those data are released. Results The SWIID offers coverage double that of the next largest income inequality data set, and its record of comparability is three to eight times better than those of alternate data sets. Conclusions As its coverage and comparability far exceed those of the alternatives, the SWIID remains better suited for broadly cross‐national research on income inequality than other available sources.

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Section: The Problem Of Comparabilitymentioning

confidence: 99%

The Standardized World Income Inequality Database*

Solt

2016

Social Science Quarterly

829

650

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“…City growth in India is associated directly with the economic policy at both state and central level. With opening up of global markets, implementation of foreign direct investment (FDI), schemes boosting agriculture, engineering, infrastructure and industry sectors such as digital India, make in India, dedicated freight corridors and industrial corridor (Mukhopadhaya, 2017;Paul & Mas, 2016) have picked up pace in the last decade. This has led the country to attain top rank positions in ease of doing business index (63rd rank as of May 2019), global competitiveness index stabilising the economy and thus projecting Indian cities as potential world cities (World Bank, 2020).…”

Section: 2mentioning

confidence: 99%

Impact assessment of Corridor Oriented development

Chandrashekar

Aithal

2021

IRSPSD International

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Rapid urbanisation has been a factor affecting cities negatively and irreversibly in developing countries like India, adversely leading to depleting natural resources and promoting unbalanced and uneven urbanism. To handle the influx of population into core urban regions and to promote holistic, sustainable development, government and planning agencies are now looking upon regional development. Developing countries like India has laid plans for future urban corridor-oriented development. This study aims to understand the urban growth of two major developing cities influenced by transport corridor through a methodological approach using multi-temporal satellite data and its position in India's network of cities. Land use analysis was validated with the aid of measures such as overall accuracy and kappa statistics, with good values of more than 85% and 0.75 respectively were achieved. The hierarchical network analysis indicated five different clusters based on the urban growth rate. Among these clusters, Bangalore, Ahmedabad and Pune cluster was further shortlisted for analysis based on the urban transport corridor affecting the growth of these cities. Cellular automata-based SLEUTH model was adopted in this work to carefully observe sub-division level details of the region under the influence of the corridor. Exhaustive calibration, with three phases of coarse, fine and final, validation procedure along with statistical fit measures reveal urban expansion for Ahmedabad region has witnessed an increase from 497.50 km 2 (2017) to 826.24 km 2 (2025) while Pune region has experienced tremendous urban area transformation of 901.11 km 2 in the year 2025 against 497.27 km 2 in 2017. Results of this analysis would help policymakers and planners to inculcate decisions concerning future urban trends accommodating safer, healthier, sustainable and liveable urban ecosystem.

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“…The official poverty line is used for the rural measurement, while for the urban the Minimum Income Guarantee line is applied. 10 See alsoMukhopadhaya et al (2011) andLi et al (2014).…”

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confidence: 99%

Is the ADB’s Conjecture on Upward Trend in Poverty for China Right? An Analysis of Income and Multidimensional Poverty in China

Yang

Mukhopadhaya

2018

Soc Indic Res

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In order to investigate the Asian Development Bank's (ADB) finding of increasing trend in income poverty in China since 2000, this paper studies income and multidimensional poverty in China between 2000 and 2011 using China Health and Nutrition Survey data. It is observed the ADB proposed approach, adjusted for vulnerability, demonstrates an upward trend in income poverty. Income poverty is decreasing, however, for the World Bank's poverty cut-offs ($1.25 or $1.90). To measure multidimensional poverty, along with income (with ADB's adjusted Asian poverty line and World Bank's poverty lines), other indicators such as health, education and living standards are considered in this paper. The evident disparities and diversities in rural and urban multidimensional poverty are further examined. Per capita net income, highest level of education and flush toilet are found to be major contributors to both rural and urban poverty. The rural-urban disparity in terms of mild and moderate poverty appears to have decreased in the period before 2009, however, there have been increases since then, and the gap in terms of severe poverty remained quite high in this decade. We find that food insecurity does not play a major role in the rural-urban disparity in poverty. In the recent period, health insurance has become more prominent in explaining urban destitution, while the rural population is found to be more vulnerable to income fluctuations. Our results also show long-term poverty to be highly influenced by health. Our findings raise questions about the adequacies in the provision of health insurance and the quality of education, particularly in rural China.

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Economic Growth and Income Inequality in China, India and Singapore

Cited by 6 publications

References 95 publications

The Standardized World Income Inequality Database*

The Standardized World Income Inequality Database*

Impact assessment of Corridor Oriented development

Is the ADB’s Conjecture on Upward Trend in Poverty for China Right? An Analysis of Income and Multidimensional Poverty in China

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