Capital mobility and spatial inequalities in income and industrial location

Zeng, Dao‐Zhi

doi:10.1007/s10888-015-9318-y

Cited by 6 publications

(3 citation statements)

References 18 publications

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“…Kumar (2013) in the Indian context showed branch network having a positive impact on financial inclusion. Similarly, Zeng (2016) examined the impact of capital immobility on regional incomes and inequality. Data (from New World Wealth 2018) on world's top 15 wealthiest cities (including New York, London, Paris and Mumbai) also show that almost all of them are financial centres indicating that wealth creation and financial services may be spatially associated (due to agglomeration economies).…”

Section: Literature Reviewmentioning

confidence: 99%

Regional financial disparity in India: can it be measured?

Arora

Anand

2021

Journal of Institutional Economics

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In this study, we examine disparities in financial development at the regional level in India. The major research questions of the study are: how do we measure the level of financial development at the sub-national level? How unequal is financial development across the states? Does it vary by ownership of financial institutions? To explore these research questions, our study develops a composite banking development index at the sub-national level for three different bank groups – public, private and foreign for 25 Indian states covering 1996–2015. Our findings suggest that despite reforms, banking development is significantly higher in the leading high income and more developed regions compared to lagging ones. Furthermore, we find that all bank groups including public banks are concentrated more in the developed regions. Overall, over the years the position of top three and bottom three states in the aggregate banking index have remained unchanged reflecting lop-sidedness of regional development. We also note improvement in the ranking of some north-eastern states during the period 2009–15.

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Section: Literature Reviewmentioning

confidence: 99%

Regional financial disparity in India: can it be measured?

Arora

Anand

2021

Journal of Institutional Economics

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“…Leveling of regional differences may be hindered by the so-called Matthew effect (the concentration of wealth and poverty) that prevails in the world today [18]. An increase in capital mobility under conditions of globalization can also lead to an increase in interregional inequality [21], although mobility is constrained by high market monopolization. Researchers refer to long distances, poor infrastructure, and unfavorable institutional environment in Russia as barriers to labor and capital mobility [17].…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Relationship between normal and excessive personal income differentiation and regional economic performance indicators

Малкина

2017

Reg. Res. Russ.

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The paper uses A.Yu. Shevyakov's approach to the decomposition of the Gini coefficient into normal and excessive inequalities, the full deflation method for determining real income and real labor productivity, correlation and regression analysis, and the construction of Cobb-Douglas production functions. Three differentiation levels of normal and excessive inequalities (poverty line, social minimum line, and social well-being line) are proposed. For Russian regions in 2013, the study has found an inverse relationship between real per capita income and the Gini coefficient of excessive inequality, as well as a direct relationship between real per capita income and the Gini coefficient of normal inequality, except for the three most affluent regions, which show a reverse trend of decline in normal inequality. Correlations of normal and excessive inequalities with general development indicators, composition and dynamics of the population, the structure of income and gross regional product, sectoral structure of the economy, and the population's wealth are revealed. A positive correlation for normal inequality and a negative correlation for excessive inequality with real output in Russian regions are confirmed based on the inclusion of Gini coefficients in the five-factor interregional Cobb-Douglas production function. It is concluded that excessive inequality, on the one hand, is a consequence of the low development level and, on the other hand, suppresses production possibilities and incentives. Normal inequality promotes economic development, which is first accompanied by a growth in normal inequality, followed by a decline.

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“…Baldwin et al, 2003, ch. 12; Morita et al, 2015; Yamamoto, 2008; Zeng, 2016). However, tariffs are not considered in their models.…”

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confidence: 99%

Mobile capital, optimal tariff, and tariff war

Takatsuka

Zeng

2021

Rev International Economics

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INTRODUCTION"Trade war" has become a hot topic in the media recently. Challenging conventional wisdom, we revisit the characteristics of optimal tariffs and tariff wars by introducing capital as a production factor. We find that, when capital is freely mobile, smaller countries apply higher tariff rates and can be better off under tariff wars than under zero tariffs-small countries may win tariff wars. 1 Even if capital import taxes are available, small countries may win tariff wars, although larger countries can apply higher tariff rates.Since Bickerdike (1906), the optimal tariff theory with strategic interdependence has been developed and extended in various ways. Starting with the seminal paper by Johnson (1953Johnson ( -1954, a number of researchers have examined the relationship among country size, optimal tariffs, and the welfare consequences of tariff wars (e.g.

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Capital mobility and spatial inequalities in income and industrial location

Cited by 6 publications

References 18 publications

Regional financial disparity in India: can it be measured?

Regional financial disparity in India: can it be measured?

Relationship between normal and excessive personal income differentiation and regional economic performance indicators

Mobile capital, optimal tariff, and tariff war

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