Author: Aanand Wagh
Globally:
WU, W. et al. (2022) Emphasize that This study looks at how risks, not uncertainty, affect income inequality. We examine how income disparity is impacted by objective uncertainty rather than subjective uncertainty. We must ascribe probabilities to potential outcomes in order to calculate the risk. So that we may address this issue, we use a number of variables, including economic, financial, geopolitical, and political threats. Despite the fact that “geopolitics” is derived from politics, the terms refer to two distinct ideas. Geopolitics is different from politics in that it examines how geography affects a given issue. Politics, on the other hand, is the management of a government or a movement. Geopolitical risk must therefore be taken into account independently. Investigating its connection to income disparity independently from the connection between income inequality and political risk and other dangers is necessary. The connection between income disparity and country dangers is a major topic of literature. Income inequality may be impacted by several risks. The primary elements of the country’s risk include economic, financial, and political risks. In determining income disparity, they all have a significant impact demonstrate how varied effects on income inequality result from particular combinations of the elements of these three risks.
Poverty & Income Inequality:
KIBRIYA, S. et al Says that India experienced widespread poverty and inequality following its independence in 1947, which led to famine (Srinivasan and Sen 1983). The Indian policy makers, however, viewed an agricultural economy and dependence on imports of manufactured goods as somewhat concerning. Even in years with good harvests, India’s poverty rates continuously increased by more than 62%. In order to improve the situation, policymakers chose to promote an industrialized and urban-based economy by starting an import substitution growth that encouraged both private and public investment in the urban sector, made cities easier to access, improved higher education facilities in metropolitan areas, improved city-based public distribution systems, and improved healthcare systems in cities. It is clear that policymakers hypothesized that reducing urban poverty and inequality would spread to the rural sector and reduce poverty and inequality there as well. Although import substitution was stopped and some of these policies were altered by India’s 1992 policy reform, the dynamic interplay between urban and rural poverty and inequality still raises interesting issues, particularly for growing rural economies. In order to understand the relationships between poverty and inequality in the urban and rural sectors, this article looks into urban-rural poverty and Gini indices from India. In order to comprehend the potential short- and long-term dynamics of poverty and inequality between urban and rural settings, we use time series approaches and machine learning algorithms.
Inflation:
ALTUNBAŞ, et al. (2022) Says that Since the beginning of the 1980s, income inequality has increased in many nations, which experts have linked to a number of negative consequences. There are less possibilities for the poor to invest in education and entrepreneurship, political unrest and protectionist pressures, poorer GDP growth over the medium term, and more family debt that can lead to asset market bubbles and financial instability. The rise in income inequality has been attributed to a number of factors, including technological progress that is skill-biased, trade and financial globalization, capital account liberalization, the expansion and liberalization of the financial sector, and the deterioration of labour market institutions. One factor that has received little attention up until recently is monetary policy. This study focuses on the effects of a certain monetary policy regime—inflation targeting (IT)—on income distribution. We specifically evaluate the effect of IT on income distribution throughout the period of 1971–2015 in 121 advanced and developing economies, including 27 IT adopters. Three reasons led to the motivation for our study. First, despite the fact that the relationship between monetary policy and income inequality is a hot topic, the literature has paid relatively little attention to the potential role that an IT regime might play in this.
Agriculture Sector:
Chakravarty al (2019) emphasize that the article contributes to the knowledge of inequality and income generation in India’s agriculture sector. We estimate income inequality in the agricultural sector at the level of the country and its 17 major states by analyzing the National Sample Surveys of Agriculture conducted in 2003 and 2013 using descriptive and regression-based approaches. We demonstrate that (a) there are notable state-level differences in the structures/patterns of income generation from agriculture, (b) the amount of land owned by the household and the share of wages in total income are negatively correlated, (c) income inequality in India’s agricultural sector is extremely high (Gini Coefficient of around 0.6 during the period), and (d) approximately half of the income inequality is explained by the household-level variance in income.
Human capital & income inequality:
SEHRAWAT, M. et al (2019) Says that Researchers and academics have become interested in the causes of the increase in income disparity. Researchers have shown that one of the key elements influencing how much money is distributed among people is human capital. A group of characteristics that are embodied in an individual, such as health, education, and skill development, together define human capital. Growth in each of these areas results in the building of human capital. According to economic literature, a person’s earnings are significantly influenced by their human capital, which is determined by the level of education achieved. Additionally, it has been found that persons with lower levels of education have fewer opportunity to earn more money than those with greater levels of education. For the following reasons, studying how human capital affects income inequality in the Indian economy is an intriguing case study. India’s economy has grown quickly since implementing liberalization reforms in 1991, with real GDP growing at an average annual pace of 6.6% between 1992 and 2011. However, because of the unequal distribution of this rapid growth throughout society, concerns over rising income inequality in the Indian economy have grown. Over the past 25 years, India’s wealth inequality has nearly doubled, especially between 2002 and 2012.
Panel Discussion of: COVID and Income Inequality:
Ruben Durante responded explaining that in the data it is not possible to distinguish between different types of transfers, as these are all paid by the same entity. In particular, he explained that the reason why the authors focused on 2019 was to control for seasonality, which is substantial in Spain, but that it may possible and useful to go back further in time. She then asked whether it is possible to understand if there are differences in the type of benefits which may help explaining part of the heterogeneity. She also asked whether it is possible that there is an underestimation of inequality due to the presence of cash-in-hand payments and whether the authors are able to measure the presence of credit supplied by banks, which may raise some issues with respect to inequality. Ruben Durante first said that the heterogeneity is likely to be due to the presence of a two-tier job market in Spain, which was also highlighted by the two discussants. He then noted that richer people may be more likely to own multiple bank accounts so that it may be possible that the data miss transfers to some of the richer individuals and that this may bias the results. She then asked whether it is possible to look at this in the data and see if these workers have moved to different jobs in less affected sectors.
Aspects of income inequality in a creative region:
BATABYAL, A. A. Says There is a proportional income tax rate and all tax revenues are redistributed to the creative class members by the regional authority (RA) with a uniform, lump-sum transfer. Second, we show that income equality requires the tax rate to equal unity and that the poorest creative class member is better off with a lower tax rate and hence more inequality. Third, given the itch creative class member’s preference over the tax rate and the transfer, we ascertain the tax rate that will be chosen by majority voting. From the vantage point of urban and more generally regional economic growth and development, these people are noteworthy because they possess creative capital which is the “intrinsically human ability to create new ideas, new technologies, new business models, new cultural forms, and whole new industries that really [matter]” (Florida [ 9], p. The creative class is significant, says Florida, because this group of people gives rise to ideas, information, and technology, outputs that are important for the growth and development of cities and regions. Therefore, in this era of globalization, cities and regions that want to prosper need to do all they can to attract and retain members of this creative class because this class is the primary driver of economic growth. Specifically, several researchers have pointed to the existence of one or more kinds of inequality in regions where the creative class is a significant part of the overall workforce. Consider the economy of a stylized region that is creative in the sense of Richard Florida.
Income Inequality and Health Self-Assessment in Russia:
KANEVA, M. A. emphasis In particular, it studies the effect of income on health self-assessments by describing the interactions between these factors, analyzing them, and modeling them econometrically. Based on panel data from the Russian Longitudinal Monitoring Survey (RLMS) of public health and economic status, a pooled generalized ordered logit regression was constructed for the categorical variable “deteriorating self-assessment of health.” It calculates the marginal effects of the model’s independent variables (different income groups, the Gini coefficient, and others) for three different self-assessments of health (good, average, and poor). Econometric modeling showed that Russia satisfies the absolute income and income inequality hypotheses and that there is a statistically significant correlation between income, its inequality (Gini coefficient of income differentiation), and self-assessment of health. In the authors’ opinion, the study’s results, which confirmed the statistically significant correlation between health and income inequality, can (and must!) be taken into account when developing measures for the Russian government’s social policy. The absolute income hypothesis (AIH) is based on the health production function, which maintains that economic resources improve health with a diminishing marginal effect. Wilkinson, notes that in developed countries, health differences between individuals (both objective health and self-assessments of health) are a consequence of the difference between the incomes of the rich and poor and the degree of income inequality between them [Wilkinson]. He could not confirm that income had a significant effect on health but did discover that in the study period, health self-assessments depended on sex, age, education, and participation in the labor market.
The Inherent Conflict Between Progressive Tax Rates and Income Inequality: Lessons from COVID-19 Restrictions:
GOLDMAN, N. C. et al says COVID-19 restrictions, which varied by state and arguably exogenously increased income inequality, provide us with a good setting to understand better whether progressive tax rates during the pandemic contributed to an unexpected increase in tax collections. First, since the COVID-19 pandemic increased economic inequality across the nation, we predict that states with more progressive tax rates will have greater individual income tax collections than states with less progressive tax rates. We then posit that under scenarios of higher income inequality (i.e., greater COVID-19 restrictions), states with more progressive tax rates will have greater tax collections (or a smaller decline in tax collections) than states with less progressive tax rates. In our first set of tests, we provide supporting evidence on the relation between progressive tax rates and income tax collections. Consistent with expectations, we find that both measures of income tax progressivity are positively associated with state income tax collections. In our main tests, we consider the differential effects that high versus low COVID-19restrictions have on the relation between per capita state individual income tax collections and the progressivity of states’ individual income tax structures. We also find that the increase in tax collections from 2019 to2020 can at least be partially explained by states with more progressive tax rate structures, but only when they have increased inequality due to low mobility or high restrictions. We do not attempt to argue that states with more progressive income tax rates are more inclined to institute stricter COVID-19 restrictions because these states are attempting to increase both public health benefits and tax collection benefits.
Income Inequality and Redistribution in Lithuania: The Role of Policy, Labor Market, Income, and Demographics:
ČERNIAUSKAS, N. et al emphasizes We contribute to this literature with a systematic analysis that seeks to understand the trends in income inequality and the redistributive effects of the tax–benefit system in Lithuania by disentangling the role played by changes in policy design from changes in market income distributions (and their driving forces: labor market structure, returns, and demographics). According to Eurostat, the Gini index of household equivalized disposable income in Lithuania grew by 5 points over the period 2011–2015, the highest growth rate of income inequality observed in the European Union (EU) (which saw an average increase in the Gini index of only 0.2 points over the same period). The goal of this paper is to quantify what factors drove large changes in Lithuanian income distributions over the period 2007–2015, which is a central issue for economic research and policy analysis. This is used to generate counterfactual distributions of household disposable incomes obtained via transformations of the income generation process, by “swapping” the characteristics between different periods along four dimensions: (i) labor market structure (e.g., employment, occupation, industry, and sector), (ii) returns structure (e.g., labor income and capital incomes), (iii) demographic composition of the population, and (iv) tax–benefit rules. This table does not allow us to identify the extent to which changes in the tax structure (such as changing social insurance basic monthly pension or prolonging parental leave benefits) and market forces (such as dynamics of earnings) affected these payouts.
11. Summary of income inequality :
The uneven distribution of income among a population is referred to as income inequality. It is a serious problem that has an international impact . There are several causes of income disparity, including variations in labour markets, access to opportunities, social and economic policies, and differences in education, skills, and experience. The effects of income disparity can be profound and multifaceted. Lack of social mobility, poverty, and slower economic growth are possible outcomes. The effects of inequality can be detrimental to one’s health, education, and social cohesiveness.
References
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BATABYAL, A. A. (2021) Aspects of income inequality in a creative region. Annals of Regional Science, [s. l.], v. 67, n. 3, p. 727–735, 2021. DOI 10.1007/s00168-021-01063-0. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=edab19d0-6a2a-3759-8284-aeb23742af5b. Acesso em: 13 maio. 2023.
ČERNIAUSKAS, N. et al (2022). Income Inequality and Redistribution in Lithuania: The Role of Policy, Labor Market, Income, and Demographics. Review of Income & Wealth, [s. l.], v. 68, p. S131–S166, 2022. DOI 10.1111/roiw.12546. Disponível em: https://discovery.ebsco.com/linkprocessor/plink?id=08d2dad4-b964-3da4-a65b-feac506a0605. Acesso em: 13 maio. 2023.
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