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Статті в журналах з теми "Spatial income inequality"

Автор: Grafiati

Опубліковано: 11 березня 2023

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1

Rey, Sergio J. "Bells in Space." International Regional Science Review 41, no. 2 (July 27, 2016): 152–82. http://dx.doi.org/10.1177/0160017615614899.

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Social and interregional inequality patterns across US states from 1929–2012 are analyzed using exploratory space–time methods. The results suggest complex spatial dynamics for both inequality series that were not captured by the stylized model of Alonso. Interpersonal income inequalities of states displayed a U-shaped pattern ending the period at levels that exceeded the alarmingly high patterns that existed in the 1920s. Social inequality is characterized by greater mobility than that found for state per capita incomes. Spatial dependence is also distinct between the two series, with per capita incomes exhibiting strong global spatial autocorrelation, while state interpersonal income inequality does not. Local hot and cold spots are found for the per capita income series, while local spatial outliers are found for state interpersonal inequality. Mobility in both inequality series is found to be influenced by the local spatial context of a state.

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2

Panzera, Domenica, and Paolo Postiglione. "Measuring the Spatial Dimension of Regional Inequality: An Approach Based on the Gini Correlation Measure." Social Indicators Research 148, no. 2 (October 19, 2019): 379–94. http://dx.doi.org/10.1007/s11205-019-02208-7.

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Abstract Traditional inequality measures fail to capture the geographical distribution of income. The failure to consider such distribution implies that, holding income constant, different spatial patterns provide the same inequality measure. This property is referred to as anonymity and presents an interesting question about the relationship between inequality and space. Particularly, spatial dependence could play an important role in shaping the geographical distribution of income and could be usefully incorporated into inequality measures. Following this idea, this paper introduces a new measure that facilitates the assessment of the relative contribution of spatial patterns to overall inequality. The proposed index is based on the Gini correlation measure and accounts for both inequality and spatial autocorrelation. Unlike most of the spatially based income inequality measures proposed in the literature, our index introduces regional importance weighting in the analysis, thereby differentiating the regional contributions to overall inequality. Starting with the proposed measure, a spatial decomposition of the Gini index of inequality for weighted data is also derived. This decomposition permits the identification of the actual extent of regional disparities and the understanding of the interdependences among regional economies. The proposed measure is illustrated by an empirical analysis focused on Italian provinces.

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3

Khan, Muhammad Salar, and Abu Bakkar Siddique. "Spatial Analysis of Regional and Income Inequality in the United States." Economies 9, no. 4 (October 22, 2021): 159. http://dx.doi.org/10.3390/economies9040159.

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Understanding the spatial or geographical dependence of income inequality and regional inequality is crucial in the study of inequality. This paper employs a multi-scale, multi-mechanism framework to map and analyze historical patterns of regional and income inequality in the United States (US) by using state and regional panel data spanning over a century. To explore the patterns systematically and see the role of spatial partitioning, we organize the data around several established geographical partitions before conducting various geographical information system (GIS) analyses and statistical techniques. We also investigate the spatial dependence of income inequality and regional inequality. We find that spatial autocorrelation exists for both types of inequality in the US. However, the magnitude of spatial dependence for regional inequality is declining whereas it is volatile for income inequality over time. While income inequality has been at its peak in the most recent decades, we also notice that regional inequality is at its lowest point. As for the choice of partitioning, we observe that within inequality dominates for Census Divisions and Bureau of Economic Analysis (BEA) regions. Conversely, we see that between inequality overall contributes the most to the inequality among Census Regions.

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4

Reis, Eustáquio. "Spatial income inequality in Brazil, 1872–2000." EconomiA 15, no. 2 (May 2014): 119–40. http://dx.doi.org/10.1016/j.econ.2014.06.006.

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5

Paul, Satya, Prem Thapa, and Giovanna Prennushi. "Spatial Dimensions of Income Inequality in Nepal." Journal of Developing Areas 46, no. 1 (2012): 241–63. http://dx.doi.org/10.1353/jda.2012.0010.

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6

Ragoubi, Hanen, and Sana El Harbi. "Entrepreneurship and Income Inequality: A Dynamic Spatial Panel Data Analysis." Tékhne 17, no. 1 (December 31, 2019): 10–30. http://dx.doi.org/10.2478/tekhne-2019-0012.

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Abstract This paper extends the empirical debate of Ragoubi and El Harbi (2018) on the dynamic relationship between entrepreneurship and income inequality. Using a dynamic spatial panel data analysis for both 33 high-income countries and 39 middle-income and low-income countries over the period 2004–2014, the main empirical findings are summarised as follows. First, the results indicate that entrepreneurship is a spatial and persistent phenomenon. Second, there is strong support for the existence of an inverted U-shaped relationship between entrepreneurship and income inequality espoused by the Kuznets Curve hypothesis for middle-income and low-income countries. Third, the interaction between income inequality and income per capita has a significant negative effect on the entrepreneurial activity for middle-income and low-income countries. Fourth, a significant positive association is found between the interaction variable and entrepreneurship for high-income countries. Fifth, the findings show evidence of significant positive and negative short-run direct effects of income inequality on the entrepreneurial activity for middle-income and low-income countries. Finally, there are significant negative short-run spillover effects of income inequality on the entrepreneurial activity for middle-income and low-income countries.

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7

Gaubert, Cecile, Patrick Kline, Damián Vergara, and Danny Yagan. "Trends in US Spatial Inequality: Concentrating Affluence and a Democratization of Poverty." AEA Papers and Proceedings 111 (May 1, 2021): 520–25. http://dx.doi.org/10.1257/pandp.20211075.

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We use Bureau of Economic Analysis, census, and Current Population Survey data to study trends in income inequality across US states and counties from 1960-2019. Both states and counties have diverged in terms of per capita pretax incomes since the late1990s, with transfers serving to dampen this divergence. County incomes have been diverging since the late 1970s. These trends in mean income mask opposing patterns among top-and bottom-income quantiles. Top incomes have diverged markedly across states since the late 1970s. In contrast, bottom-income quantiles and poverty rates have converged across areas in recent decades.

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8

Li, Xinyu, Sunghwan Kim, and Yongshang Liu. "The Spillover Effects of Privatization on Efficiency and Income Inequality in China." International Academy of Global Business and Trade 19, no. 1 (February 28, 2023): 173–91. http://dx.doi.org/10.20294/jgbt.2023.19.1.173.

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Purpose - This study examines the spatial and inter-temporal spillover effects of privatization on the corporate efficiency and regional income inequality of Chinese state-owned enterprises (SOEs). Design/Methodology/Approach - The spatial Durbin model (SDM) is used in regressions to examine the spatial and inter-temporal spillover effects of the privatization of SOEs on improving the efficiency and income inequality of Chinese firms across regions. A panel dataset of Chinese-listed firms from 2008 to 2018 is used. The stochastic frontier analysis method is applied in estimating corporate efficiency. Findings - First, the privatization of Chinese SOEs increased their efficiency, but exacerbated their income inequality. Second, the globalization activities after the privatization of Chinese SOEs increased their efficiency, but exacerbated their income inequality. Specifically, exports decrease income inequality, while outward foreign direct investment or OFDI has an inverse U-shaped effect on income inequality. Third, the privatization improved overall corporate efficiency within the province and that of neighboring provinces. Fourth, the Chinese SOE firms after privatization aggravated income equality within the province and that of neighboring provinces. Research Implications - In general, the results of this study indicate that the privatization of SOEs and the globalization activities after the privatization have improved the efficiency of Chinese firms, but worsened income equality within the province and that of neighboring provinces. Therefore, there is a strong need for governmental policies to cure income equality in provinces around the location of privarized firms.

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9

Zeng, Zhixin, and Xiaojun Wang. "Spatial Effects of Domestic Tourism on Urban-Rural Income Inequality." Sustainability 13, no. 16 (August 21, 2021): 9394. http://dx.doi.org/10.3390/su13169394.

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Although much of the recent research has explored the relationship between domestic tourism and income inequality among regions, provinces, and cities, few studies have examined the impact of domestic tourism on income inequality between urban and rural areas within a region. This paper uses a panel dataset covering China’s 31 provinces for 21 years to investigate the spatial spillover effect of domestic tourism on urban-rural income inequality. An increase in domestic tourism revenue in neighboring provinces leads to a reduction in the local province’s urban-rural income inequality. Innovatively, we decompose domestic tourism revenue and consider the circumstances in different provinces. An increase in the number of neighboring provinces’ domestic tourists’ arrival decreases the local province’s urban-rural income inequality in western provinces but increases the inequality in eastern provinces; the effect is insignificant in central provinces. In order to improve urban-rural income inequality by attracting domestic tourists, this study suggests a collaborative strategy for the western region, a low-priority strategy for the central region, and a mitigation strategy for the eastern region.

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10

Gladkiy, A. S. "A multi-scale approach to the study of spatial inequality of population income in Brazil." Regional nye issledovaniya 72, no. 2 (2021): 111–23. http://dx.doi.org/10.5922/1994-5280-2021-2-10.

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The aim of the research is to identify the key features of spatial inequality of income distribution in Brazil and its representation on different spatial scales: on the regional, state or municipal level, as well as special statistical grids (mesoregions and microregions). The economic development of Brazil in the beginning of the XXI century is characterized by reducing of the level of income inequality, as well as a certain decrease of the level of spatial inequality between the southern and northeastern regions. The common rule is that the heterogeneity of income distribution gradually increases from top to the lower levels of spatial division. The analysis of inequality measures has proven that despite of the general decrease of regional inequality in 2000–2010, the lower levels of territorial division have shown the lowest progress in reducing regional inequality. The paper also proposes the ways to illustrate spatial inequality when applying polyscale method, based on mapping the variation of average population income in Brazil on different scale levels.

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11

Ahmed, Sofia. "Does Economic Geography Matter for Pakistan? A Spatial Exploratory Analysis of Income and Education Inequalities." Pakistan Development Review 50, no. 4II (December 1, 2011): 929–53. http://dx.doi.org/10.30541/v50i4iipp.929-953.

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Generally, econometric studies on socio-economic inequalities consider regions as independent entities, ignoring the likely possibility of spatial interaction between them. This interaction may cause spatial dependency or clustering, which is referred to as spatial autocorrelation. This paper analyses for the first time, the spatial clustering of income, income inequality, education, human development, and growth by employing spatial exploratory data analysis (ESDA) techniques to data on 98 Pakistani districts. By detecting outliers and clusters, ESDA allows policy makers to focus on the geography of socio-economic regional characteristics. Global and local measures of spatial autocorrelation have been computed using the Moran‘s I and the Geary‘s C index to obtain estimates of the spatial autocorrelation of spatial disparities across districts. The overall finding is that the distribution of district wise income inequality, income, education attainment, growth, and development levels, exhibits a significant tendency for socio-economic inequalities and human development levels to cluster in Pakistan (i.e. the presence of spatial autocorrelation is confirmed). Keywords: Pakistan, Spatial Effects, Spatial Exploratory Analysis, Spatial Disparities, Income Inequality, Education Inequality, Spatial Autocorrelation

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12

Kim, Minhye, Suzin You, Jong-sung You, Seung-Yun Kim, and Jong Heon Park. "Income-Related Mortality Inequalities and Its Social Factors among Middle-Aged and Older Adults at the District Level in Aging Seoul: An Ecological Study Using Administrative Big Data." International Journal of Environmental Research and Public Health 19, no. 1 (December 30, 2021): 383. http://dx.doi.org/10.3390/ijerph19010383.

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This study investigated income-related health inequality at sub-national level, focusing on mortality inequality among middle-aged and older adults (MOAs). Specifically, we examined income-related mortality inequality and its social factors among MOAs across 25 districts in Seoul using administrative big data from the National Health Insurance Service (NHIS). We obtained access to the NHIS’s full-population micro-data on both incomes and demographic variables for the entire residents of Seoul. Slope Index of Inequality (SII) and Relative Index of Inequality (RII) were calculated. The effects of social attributes of districts on SIIs and RIIs were examined through ordinary least squares and spatial regressions. There were clear income-related mortality gradients. Cross-district variance of mortality rates was greater among the lowest income group. SIIs were smaller in wealthier districts. Weak spatial correlation was found in SIIs among men. Lower RIIs were linked to lower Gini coefficients of income for both genders. SIIs (men) were associated with higher proportions of special occupational pensioners and working population. Lower SIIs and RIIs (women) were associated with higher proportions of female household heads. The results suggest that increasing economic activities, targeting households with female heads, reforming public pensions, and reducing income inequality among MOAs can be good policy directions.

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13

Fawaz, Fadi A. "Spatial Dependence of Income Inequality among Trading Partners." Middle East Development Journal 3, no. 2 (January 2011): 215–32. http://dx.doi.org/10.1142/s1793812011000375.

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14

Paredes, Dusan, Victor Iturra, and Marcelo Lufin. "A Spatial Decomposition of Income Inequality in Chile." Regional Studies 50, no. 5 (September 2, 2014): 771–89. http://dx.doi.org/10.1080/00343404.2014.933798.

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15

Ibragimova, Zulfiya, and Marina Frants. "Inequality of Opportunities: The Role of the Spatial Factor." Spatial Economics 16, no. 4 (2020): 44–67. http://dx.doi.org/10.14530/se.2020.4.044-067.

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The concept of equal opportunities was shaped on the back of western social philosophy at the end of the XX century as a result of the development of egalitarian theory of social justice. According to it, the determinants of individual achievements should be divided into two categories: ‘circumstances’, which individual has no control on, and ‘efforts’, which individuals should be responsible for. Our research deals with measuring opportunity inequality in the Russian Federation and its regions. The regions of the Russian Federation are well known to be very heterogeneous, consequently, the variation of their social and economic indicators is significant. As a result, the level of opportunity inequality may fluctuate significantly among the regions. Our research is designed to test the hypothesis. The analysis is based on the data from the survey ‘Survey of Income and Participation in Social Programs’ conducted by the Federal state statistics service of the Russian Federation. The estimation technique is parametric and based on the ex-ante definition of equality of opportunity. The mean logarithmic deviation is used as inequality index. According to our calculations, the contribution of opportunity inequality to the labor income inequality in Russia is approximately 30%. Spatial factors (region of residence and type of settlement) are responsible for nearly 70% of opportunity inequality. Absolute level of opportunity inequality varies widely among regions – from 0,0117 to 0,0547 in 2017. The contribution of opportunity inequality to inequality of labor income ranges from 7,24 to 27,35% in 2017 across regions. The growth of opportunity inequality is shown to be correlated with the decrease of the economic development rate and the increase of labor income inequality

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16

Labron Carter, Perry, Jason M. Post, and Cynthia L. Sorrensen. "Spatial Environmental Inequality in Lubbock, Texas." Current Research Journal of Social Sciences and Humanities 1, no. 1 (June 25, 2018): 01–12. http://dx.doi.org/10.12944/crjssh.1.1.01.

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Environmental inequality assumes a near proximity of environmental health hazards, hazardous waste processing and releasing facilities to minority and low-income communities. Research in environmental inequality and environment justice over the past twenty years suggests that hazardous waste facilities are often located near minority and low-income neighborhoods. We conducted a study evaluating and quantifying environmental inequality in Lubbock County, Texas. Our study analyzed both spatial and statistical relationships between population demographics and spatial proximity to hazardous waste releasing facilities. Hazardous waste facility data used in the study were collected from the Environmental Protection Agency’s Toxic Release Inventory (TRI). Population statistics from the U.S. Census comprise the demographic data for this analysis. Spatial regression models were estimated to evaluate the relationship between distance from TRI sites and neighborhood / census block group demographics. A statistically significant relationship with proximity to hazardous waste facilities was found in communities having significant minority populations.

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17

Mastronardi, Luigi, and Aurora Cavallo. "The Spatial Dimension of Income Inequality: An Analysis at Municipal Level." Sustainability 12, no. 4 (February 21, 2020): 1622. http://dx.doi.org/10.3390/su12041622.

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This paper focuses on the analysis on income inequality in Italy at the municipal level of the areas defined by the National Strategy for Inner Areas. We discuss an analysis of the economic and spatial dynamics of the phenomenon through the construction of the Gini’s coefficient and the estimation of the regression model for the evaluation of the determinants of inequality. We highlight the influence of the spatial dimension on income inequality in Italy. Inequality appears to be greater in densely populated urban centers with a strong incidence of tertiary activities and young population. Conversely, in the inner areas, the distribution of income is more balanced due probably to the weakness of the social and economic structure that determines low levels of income and job opportunities mainly in the agricultural sector.

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18

Souza, Helson Gomes de, Francisco José Silva Tabosa, Jair Andrade de Araújo, and Pablo Urano de Carvalho Castelar. "A spatial analysis of how growth and inequality affect poverty in Brazil." Revista de Administração Pública 55, no. 2 (March 2021): 459–82. http://dx.doi.org/10.1590/0034-761220190349.

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Abstract This research seeks to analyze the impacts of economic growth and income inequality on Brazilian states’ urban and rural poverty, considering the effects of the initial levels of development and inequality. The elasticities of income and inequality of poverty were calculated through a dynamic spatial panel, using an adaptation of the approach developed by Kalwij and Verschoor (2004), and data from 2004 to 2014. Incorporating the spatial factor allows us to capture the effects of the geographical location on local poverty. The results suggest that a poverty reduction occurs more intensely when associated with reductions in the inequality levels. Income elasticities were greater (in absolute terms) in rural areas, while the inequality elasticities were greater in the urban area estimates. The growing trend of the inequality elasticity and the decreasing trend of the income elasticity suggest a positive trend of economic growth and a negative trend of poverty. Likewise, if the reduction in inequality shows a negative trend, the absolute value of poverty will decrease. Thus, a public policy to combat poverty through economic growth or reducing inequalities applied to the urban or rural environment would obtain more efficient results if applied in the long term.

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19

Evellin Dewi Lusiana, Evellin Dewi Lusiana, Henny Pramoedyo Henny Pramoedyo, and Barianto Nurasri Sudarmawan. "Spatial Quantile Autoregressive Model: Case Study of Income Inequality in Indonesia." Sains Malaysiana 51, no. 11 (November 30, 2021): 3795–806. http://dx.doi.org/10.17576/jsm-2022-5111-23.

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Substantial economic development in Indonesia has dramatically increased inequality in the last decade. This issue will hinder the country’s long-term economic development as well as creating socioeconomic instability and violence. This study analysed the effects of macroeconomic factors such as gross regional domestic product, investment, unemployment rate, and labour-force participation, on Indonesian provinces’ inequality. Since the economic development in Indonesia is mostly concentrated on Java Island, a spatial based analysis was appropriate. In addition, we also considered a method that enabled a specific level of inequality modelling, since previous studies used a mean-based analysis. Therefore, we proposed a spatial quantile autoregressive (SQAR) technique. The results showed that the Gini index of Indonesian provinces had a significant positive spatial autocorrelation (SA). Regions with similar Gini index values tended to cluster together. In addition, local analysis of the SA showed Java Island as a region that was characterized by high inequality, while Sumatra and Kalimantan Island were not. By contrast, the SQAR model suggested that there were various effects of macroeconomic factors on inequality at different levels of quantile. As a consequence, distinct approaches to handling inequality should be taken for provinces with low, medium, and high Gini index values.

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20

Knight, John, and Lina Song. "The spatial contribution to income inequality in rural China." Cambridge Journal of Economics 17, no. 2 (June 1993): 195–213. http://dx.doi.org/10.1093/oxfordjournals.cje.a035230.

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21

Ragoubi, Hanen, and Sana El Harbi. "Entrepreneurship and income inequality: a spatial panel data analysis." International Review of Applied Economics 32, no. 3 (July 5, 2017): 374–422. http://dx.doi.org/10.1080/02692171.2017.1342776.

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22

Shin, Hyuseok. "Analysis of Domestic Regional Income Disparaty and Spatial Inequality." Geographical Journal of Kore 55, no. 4 (December 31, 2021): 447–57. http://dx.doi.org/10.22905/kaopqj.2021.55.4.6.

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23

ROSALES, FRANCISCO, ADOLFO POSADAS, and ROBERTO QUIROZ. "MULTIFRACTAL CHARACTERIZATION OF SPATIAL INCOME CURDLING: THEORY AND APPLICATIONS." Advances in Complex Systems 11, no. 06 (December 2008): 861–74. http://dx.doi.org/10.1142/s0219525908001891.

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A spatial curdling index [Formula: see text] is presented for studying income concentration in two-dimensional fields originated by a cascade process, resembling resource allocation maps. Computational simulations of spatial distributions show that the relation between [Formula: see text] and measures of income inequality such as the Gini coefficient [Formula: see text] forms a concave area. Acknowledgment of this fact brings additional information for formulating geopolitical strategies for income inequality reduction, and for comparison purposes in a set of countries or across time within a country. With the aim of showing the importance of index [Formula: see text], a prototype of the empirical application is shown based on the Peruvian and Swedish income maps.

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24

Novotný, Josef. "On the measurement of regional inequality: does spatial dimension of income inequality matter?" Annals of Regional Science 41, no. 3 (March 7, 2007): 563–80. http://dx.doi.org/10.1007/s00168-007-0113-y.

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25

Polbin, Andrey, and Tatiana Ivakhnenko. "Convergence of Income Inequality in Russia’s Regions." Spatial Economics 18, no. 4 (2022): 68–92. http://dx.doi.org/10.14530/se.2022.4.068-092.

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In this paper we test convergence of income inequality in Russia’s regions for the period 1995–2020. To do this, conditional and unconditional beta convergence models for the regional Gini index are evaluated on cross-sectional and panel data using time and spatial effects. Estimates of the models show that both conditional and unconditional convergence of income inequality takes place in Russia’s regions. It is shown that the rate of convergence varies significantly within the considered period: the levels of income inequality in the regions converged most strongly at the beginning of the period with a gradual slowdown in the rate of convergence in subsequent periods. This result may be related to the recovery growth and redistribution policy in the 2000s, as well as the consequences of the 2014 crisis. The use of the same initial characteristics, such as GRP per capita, level of education and population, accelerates convergence. Spatial effects are statistically significant for models of unconditional, but not conditional convergence, but do not affect the estimates obtained. When considering a panel data structure with the inclusion of fixed time effects, convergence estimates increase for both unconditional and conditional convergence

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26

Ponce, Pablo, José Álvarez-García, Mary Cumbicus, and María de la Cruz del Río-Rama. "Spatial Externalities of Income Inequality on Security in Latin America." Mathematics 9, no. 3 (January 27, 2021): 245. http://dx.doi.org/10.3390/math9030245.

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The aim of this research is to analyse the effect of income inequality on the homicide rate. The study is carried out in 18 Latin American countries for the period 2005–2018. The methodology used is the Generalized Least Squares (GLS) model and the data were obtained from World Development Indicators, the World Health Organization and the Inter-American Development Bank. Thus, the dependent variable is the homicide rate and the independent variable is income inequality. In addition, some control variables are included, such as: poverty, urban population rate, unemployment, schooling rate, spending on security and GDP per capita, which improve the consistency of the model. The results obtained through GLS model determine that inequality has a negative and significant effect on the homicide rate for high-income countries (HIC) and lower-middle-income countries (LMIC), whereas it is positive and significant for upper-middle-income countries (UMIC). On the other hand, the control variables show different results by group of countries. In the case of unemployment, it is not significant in any group of countries. Negative spatial dependence was found regarding spatial models such as: the spatial lag (SAR) and spatial error (SEM) method. In the spatial Durbin model (SDM), positive spatial dependence between the variables was corroborated. However, spatial auto-regressive moving average (SARMA) identified no spatial dependence. Under these results it is proposed: to improve productivity, education and improve the efficiency of security-oriented resources.

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27

Colas, Mark, and Kevin Hutchinson. "Heterogeneous Workers and Federal Income Taxes in a Spatial Equilibrium." American Economic Journal: Economic Policy 13, no. 2 (May 1, 2021): 100–134. http://dx.doi.org/10.1257/pol.20180529.

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We study the geographic incidence and efficiency of an income tax by estimating a spatial equilibrium model with heterogeneous workers. The US income tax shifts households out of high-productivity cities, leading to locational inefficiency of 0.25 percent of output. Removing spatial tax distortions increases inequality because more educated households are more mobile and own larger shares of land. Flattening the tax schedule, or introducing cost-of-living adjustments or local wage adjustments leads to efficiency gains but causes substantial increases in inequality. Differences in mobility and land ownership across skill groups create an equity-efficiency trade-off that is unique to spatial settings. (JEL H24, H22, D31, J31, J24, R23)

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28

Mirza, M. Usman, Chi Xu, Bas van Bavel, Egbert H. van Nes, and Marten Scheffer. "Global inequality remotely sensed." Proceedings of the National Academy of Sciences 118, no. 18 (April 26, 2021): e1919913118. http://dx.doi.org/10.1073/pnas.1919913118.

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Economic inequality is notoriously difficult to quantify as reliable data on household incomes are missing for most of the world. Here, we show that a proxy for inequality based on remotely sensed nighttime light data may help fill this gap. Individual households cannot be remotely sensed. However, as households tend to segregate into richer and poorer neighborhoods, the correlation between light emission and economic thriving shown in earlier studies suggests that spatial variance of remotely sensed light per person might carry a signal of economic inequality. To test this hypothesis, we quantified Gini coefficients of the spatial variation in average nighttime light emitted per person. We found a significant relationship between the resulting light-based inequality indicator and existing estimates of net income inequality. This correlation between light-based Gini coefficients and traditional estimates exists not only across countries, but also on a smaller spatial scale comparing the 50 states within the United States. The remotely sensed character makes it possible to produce high-resolution global maps of estimated inequality. The inequality proxy is entirely independent from traditional estimates as it is based on observed light emission rather than self-reported household incomes. Both are imperfect estimates of true inequality. However, their independent nature implies that the light-based proxy could be used to constrain uncertainty in traditional estimates. More importantly, the light-based Gini maps may provide an estimate of inequality where previously no data were available at all.

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Owens, Ann. "Income Segregation between School Districts and Inequality in Students’ Achievement." Sociology of Education 91, no. 1 (November 9, 2017): 1–27. http://dx.doi.org/10.1177/0038040717741180.

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Large achievement gaps exist between high- and low-income students and between black and white students. This article explores one explanation for such gaps: income segregation between school districts, which creates inequality in the economic and social resources available in advantaged and disadvantaged students’ school contexts. Drawing on national data, I find that the income achievement gap is larger in highly segregated metropolitan areas. This is due mainly to high-income students performing better, rather than low-income children performing worse, in more-segregated places. Income segregation between districts also contributes to the racial achievement gap, largely because white students perform better in more economically segregated places. Descriptive portraits of the school districts of high- and low-income students show that income segregation creates affluent districts for high-income students while changing the contexts of low-income students negligibly. Considering income and race jointly, I find that only high-income white families live in the affluent districts created by income segregation; black families with identically high incomes live in districts more similar to those of low-income white families. My results demonstrate that the spatial inequalities created by income segregation between school districts contribute to achievement gaps between advantaged and disadvantaged students, with implications for future research and policy.

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Amelia, Amelia, and Tri Diana. "Spatial Analysis of Fiscal Balance Fund on Income Inequality in West Kalimantan." Jurnal Economia 17, no. 1 (April 29, 2021): 1–19. http://dx.doi.org/10.21831/economia.v17i1.31731.

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Abstract: The research aims to analyze the effect of fiscal balance fund on income inequality in West Kalimantan by considering spatial inter-relationships between existing districts/cities. The study showed that the Spatial Durbin Model with fixed effect was empirically suitable. A variant of spatial autoregression model using Gini Ratio during the period of 2010 – 2018 in 14 districts/cities of West Kalimantan. The study concludes that income disparities between districts/cities were low and constant or the income was relatively distributed per capita. Spatial interactions between districts/cities and their neighbors are also relatively low. Spatial aspect, fiscal balance fund and regional minimum wage have a significant negative effect. On the contrary, the industrial workforce, educated workforce and medical personnel do not affect income inequality in West Kalimantan. This study provides academics with the understanding of the importance of spatial dependence in income inequality model because the economic activity is always related to the neighbor.Keywords: fiscal balance fund, income inequality, spatial aspect Analisis Spasial Dana Perimbangan Terhadap Disparitas Pendapatan Kalimantan BaratAbstrak: Penelitian ini bertujuan menganalisis pengaruh dana perimbangan terhadap disparitas pendapatan di Kalimantan Barat dengan mempertimbangkan keterkaitan spasial antar kabupaten/kota yang ada. Studi ini menghasilkan pemilihan model spasial durbin dengan efek tetap secara empiris sudah tepat. Variansi dari model autoregresif spasial menggunakan Indeks Gini kurun waktu 2010–2018 silang tempat dari 14 kabupaten/kota di Kalimantan Barat. Hasil penelitian menyimpulkan disparitas pendapatan antar kabupaten/kota rendah dan konstan atau relatif merata dalam pendapatan per kapita. Interaksi spasial antar kabupaten/kota dengan tetangganya juga relatif rendah. Aspek spasial, dana perimbangan dan UMR secara negatif signifikan mempengaruhi disparitas pendapatan. Sedangkan tenaga kerja industri, tenaga kerja terdidik dan tenaga medis tidak mempengaruhi disparitas pendapatan di Kalimantan Barat. Penelitian ini memberikan wawasan bagi kalangan akademisi tentang pentingnya memasukkan spatial dependence kedalam model ketimpangan pendapatan karena proses kegiatan ekonomi selalu berkaitan dengan wilayah tetangga.Kata kunci: dana perimbangan, disparitas pendapatan, aspek spatial

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31

Süß, Philipp. "Regional Market Income Inequality and its Impact on Crime in Germany: A Spatial Panel Data Approach with Local Spillovers." Jahrbücher für Nationalökonomie und Statistik 240, no. 4 (March 26, 2020): 387–415. http://dx.doi.org/10.1515/jbnst-2018-0052.

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AbstractEconomic theory predicts a positive effect of an increase in income inequality on the prevalence of crime, but the international empirical evidence is mixed. For Germany, research on this topic is virtually non-existent. Therefore, I used fixed effect regressions to estimate the effect of a market income inequality proxy on property damages, thefts from motor vehicles, domestic burglaries and assaults in Germany. The models without spatial lags suggest economically small to moderate own-district elasticities between 0.13 and 0.95. The models with spatial lags generally show insignificant own-district estimates, but significant spatial spillovers.

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Ezcurra, Roberto, Pedro Pascual, and Manuel Rapún. "The Spatial Distribution of Income Inequality in the European Union." Environment and Planning A: Economy and Space 39, no. 4 (April 2007): 869–90. http://dx.doi.org/10.1068/a3893.

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Hoffmeister, Onno. "THE SPATIAL STRUCTURE OF INCOME INEQUALITY IN THE ENLARGED EU." Review of Income and Wealth 55, no. 1 (March 2009): 101–27. http://dx.doi.org/10.1111/j.1475-4991.2008.00308.x.

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34

Silveira Neto, Raul Da M., and Carlos R. Azzoni. "Non-Spatial Government Policies and Regional Income Inequality in Brazil." Regional Studies 45, no. 4 (February 25, 2010): 453–61. http://dx.doi.org/10.1080/00343400903241485.

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NISSAN, EDWARD, and GEORGE CARTER. "Spatial and Temporal Metropolitan and Nonmetropolitan Trends in Income Inequality." Growth and Change 30, no. 3 (January 3, 2006): 407–29. http://dx.doi.org/10.1111/j.1468-2257.1999.tb00037.x.

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36

Yao, Shujie. "Industrialization and spatial income inequality in rural China, 1986-92." Economics of Transition 5, no. 1 (May 1997): 97–112. http://dx.doi.org/10.1111/j.1468-0351.1997.tb00005.x.

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37

Chakravorty, Sanjoy. "A Measurement of Spatial Disparity: The Case of Income Inequality." Urban Studies 33, no. 9 (November 1996): 1671–86. http://dx.doi.org/10.1080/0042098966556.

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38

Azam, Mehtabul, and Vipul Bhatt. "Spatial Income Inequality in India, 1993–2011: A Decomposition Analysis." Social Indicators Research 138, no. 2 (July 11, 2017): 505–22. http://dx.doi.org/10.1007/s11205-017-1683-4.

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39

Percoco, Marco. "Spatial Health Inequality and Regional Disparities." REGION 8, no. 1 (January 24, 2021): 53–73. http://dx.doi.org/10.18335/region.v8i1.325.

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Geography and the quality of the environment may have long lasting effects on the living standards of individuals and this, in its turn, may affect even substantially the distribution of income and regional disparities. In this paper I consider malaria as a measure of “bad geography” and propose some evidence showing that it was a major determinant of the health of individuals (as measured by the height of conscripts) and its disparities between individuals and regions in Italy. In particular, to estimate the relationship between malaria exposure and height, I rely on the “fetal origins hypothesis”, that is I hypothesize that exposure to malaria in utero or during childhood has persistent effects on health. Periods under scrutiny in this paper are the last two decades of the XIX century, a period without major public health interventions, and the years around the eradication era in the 1950s. My results support the hypothesis that geographically targeted policies may reduce health inequality between regions and within regions.

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40

Jestl, Stefan, Mathias Moser, and Anna Katharina Raggl. "Cannot keep up with the Joneses: how relative deprivation pushes internal migration in Austria." International Journal of Social Economics 49, no. 2 (November 10, 2021): 210–31. http://dx.doi.org/10.1108/ijse-03-2021-0181.

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PurposeUsing aggregated data at the municipality level allows the authors to assess the role of relative deprivation (RD) – a measure of income inequality – on top of absolute income in shaping internal migration in Austria.Design/methodology/approachIn this study, the authors analyse the effect of regional income inequality on emigration rates of Austrian municipalities using a unique spatial dataset that is constructed based on Austrian administrative register data. The register-based data contain information on the municipality of residence of all individuals aged 16 and over that have their main residency in Austria, as well as their income and socio-demographic characteristics.FindingsThe authors find that increases in relative deprivation in a municipality are related to higher emigration from the municipality. Allowing for heterogeneous effects across income, education and age groups reveals that the effect is stronger among those with comparably low levels of income and among low-skilled and young individuals.Originality/valueThe unique spatially disaggregated perspective is based on novel data from Austrian administrative registers, which comprehensively capture the economic situation and geographic movements of the whole Austrian population. Most importantly, this approach allows for measuring income inequality within local communities and for a direct identification of social groups that are more sensitive to inequality.

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41

Couillard, Benjamin K., Christopher L. Foote, Kavish Gandhi, Ellen Meara, and Jonathan Skinner. "Rising Geographic Disparities in US Mortality." Journal of Economic Perspectives 35, no. 4 (November 1, 2021): 123–46. http://dx.doi.org/10.1257/jep.35.4.123.

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The twenty-first century has been a period of rising inequality in both income and health. In this paper, we find that geographic inequality in mortality for midlife Americans increased by about 70 percent between 1992 and 2016. This was not simply because states like New York or California benefited from having a high fraction of college-educated residents who enjoyed the largest health gains during the last several decades. Nor was higher dispersion in mortality caused entirely by the increasing importance of “deaths of despair,” or by rising spatial income inequality during the same period. Instead, over time, state-level mortality has become increasingly correlated with state-level income; in 1992, income explained only 3 percent of mortality inequality, but by 2016, state-level income explained 58 percent. These mortality patterns are consistent with the view that high-income states in 1992 were better able to enact public health strategies and adopt behaviors that, over the next quarter-century, resulted in pronounced relative declines in mortality. The substantial longevity gains in high-income states led to greater cross-state inequality in mortality.

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42

Annim, Samuel Kobina, and William Gabriel Brafu-Insaidoo. "Poverty and Inequality in Ghana: Analysis of the Dimensions, Trends and Spatial Perspectives." Oguaa Journal of Social Sciences 7, no. 3 (December 1, 2015): 5–25. http://dx.doi.org/10.47963/joss.v7i3.297.

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This study assesses the poverty and inequality situation in Ghana using the last four rounds of the Ghana Living Standards Survey (1991–2013). The FGT poverty incidence, Gini and Generalized Entropy inequality measures and regression analysis are used to examine trends, spatial distribution and correlation between poverty inequality and poverty. e ndings suggest that the proportion of population dened as income-poor but non-poor in consumption have increased overtime. Also, a decline in wealth inequality is observed, but rural inequality overtime has increased to outpace urban inequality. Minimizing wealth inequality especially, in rural areas, has the potential of accelerating poverty reduction in Ghana.

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Kuo, Chun-Tung, and Duan-Rung Chen. "Double disadvantage: income inequality, spatial polarization and mortality rates in Taiwan." Journal of Public Health 40, no. 3 (December 27, 2017): e228-e234. http://dx.doi.org/10.1093/pubmed/fdx179.

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44

Fawaz, Fadi, and Masha Rahnama-Moghadamm. "Spatial dependence of global income inequality: The role of economic complexity." International Trade Journal 33, no. 6 (October 29, 2018): 542–54. http://dx.doi.org/10.1080/08853908.2018.1535336.

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45

Yildirim, Jülide, Nadir Öcal, and Süheyla Özyildirim. "Income Inequality and Economic Convergence in Turkey: A Spatial Effect Analysis." International Regional Science Review 32, no. 2 (November 24, 2008): 221–54. http://dx.doi.org/10.1177/0160017608331250.

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46

Jung, Samuel Moon, and Chu-Ping C. Vijverberg. "Financial development and income inequality in China – A spatial data analysis." North American Journal of Economics and Finance 48 (April 2019): 295–320. http://dx.doi.org/10.1016/j.najef.2019.03.001.

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47

DeVerteuil, Geoffrey. "Fragments of Inequality: Social, Spatial, and Evolutionary Analyses of Income Distribution." Annals of the Association of American Geographers 97, no. 1 (March 2007): 219–20. http://dx.doi.org/10.1111/j.1467-8306.2007.00532_4.x.

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48

Lin, Chun-Hung A., Suchandra Lahiri, and Ching-Po Hsu. "Population Aging and Regional Income Inequality in Taiwan: A Spatial Dimension." Social Indicators Research 122, no. 3 (August 19, 2014): 757–77. http://dx.doi.org/10.1007/s11205-014-0713-8.

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49

Hing, Leanne S. Son, Anne E. Wilson, Peter Gourevitch, Jaslyn English, and Parco Sin. "Failure to Respond to Rising Income Inequality: Processes That Legitimize Growing Disparities." Daedalus 148, no. 3 (July 2019): 105–35. http://dx.doi.org/10.1162/daed_a_01752.

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Why is there not more public outcry in the face of rising income inequality? Although public choice models predict that rising inequality will spur public demand for redistribution, evidence often fails to support this view. We explain this lack of outcry by considering social-psychological processes contextualized within the spatial, institutional, and political context that combine to dampen dissent. We contend that rising inequality can activate the very psychological processes that stifle outcry, causing people to be blind to the true extent of inequality, to legitimize rising disparities, and to reject redistribution as an effective solution. As a result, these psychological processes reproduce and exacerbate inequality and legitimize the institutions that produce it. Finally, we explore ways to disrupt the processes perpetuating this cycle.

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50

Bobkov, Vyacheslav N., Yelena V. Odintsova, and Nikolay V. Bobkov. "Relevance of Developing a National Programme to Increase Income, Reduce Poverty and Inequality." Level of Life of the Population of the Regions of Russia 16, no. 2 (2020): 7–24. http://dx.doi.org/10.19181/lsprr/2020.16.2.1.

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The Object of the Study. Russian citizens. The Subject of the Study. Socioeconomic relations that lead to absolute monetary and non-monetary poverty and high socioeconomic inequality. The Purpose of the Study. Justification of the relevance and approaches to the development of a national Programme to increase real monetary income, reduce poverty and excessive inequality. The Main Theoretical and Empirical Provisions of the Article include a description of the following areas of poverty and socioeconomic inequality in Russia: 1) in monetary income and capital (wealth); 2) in current consumption of goods; 3) in the sectoral aspect; 4) in the provision of comfortable housing and access to social services; 5) in the spatial aspect; 6) in the level and quality of life of urban and rural populations; 7) in age groups; 8) in the use of digital technologies. The estimation of real monetary income, poverty and inequality was carried out based on official social standards and indicators and those developed by the authors. The excessiveness of inequality in Russia is argued. The tools for increasing real monetary incomes, reducing poverty and excessive inequality are substantiated in the framework of the author's proposals to the development of the draft national Programme "Increasing the Population's Income, Reducing Poverty and Inequality". Proposals, stages and deadlines for the implementation of this Programme have been developed, as well as its possible indicators, resource and regulatory support. In the new situation the sensitivity of society to the problems caused by poverty and high inequality is significantly aggravated, which, according to the authors, makes the need to develop a national Programme non-alternative.

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