Economic inequality and its effects on income growth is a topic that has been researched extensively. Previous studies with the aim to determine the impact of income inequality on economic growth have functioned more as pointers than solid facts. This study contributes to the continued discussion within the growth economy by exploring the relationship between the growth rate of average income and income inequality using panel data of Swedish municipalities from 2000-2020. Produced independently, this study uses the Gini coefficient as a tool to measure the income inequality among all 290 municipalities in Sweden. The result from the panel estimation shows no clear effect on the impact of income inequalities on income growth. However, when endogeneity is considered a 2SLS regression with the age structure as an instrument variable is used, the estimation shows that income inequality at local level has a positive association to growth of average income. Coherent with earlier studies of income inequality among Swedish municipalities, the result implies that as income inequality increases between income groups, average municipality experiences increase in growth of average income. Furthermore, this disposition compares the estimated results to a system GMM estimation.

Global warming continues to have a negative effect on the environment and, by extension, the economy. Incidence of natural disasters are increasing because of climate change, with flooding being one of the most common and costly types of disaster. Studies on the effect of floods on the economy have revealed that flooding increases the amount of income inequality. However, there is not a unified, qualitative analysis of the reasons for the post-flood increase in inequality. This thesis examines multiple different flood events in several developing countries to answer how flooding affects inequality in developing countries. It produces a synthesized analysis on the underlying causes of flood induced income inequality. These causes were found to be the increased vulnerability of low-income households to flooding and their lower capacity to cope with flood damages, relative to middle- or high-income households. Vulnerability has spatial and structural dimensions, and capacity to cope is influenced by a household’s asset portfolio and post-flood adaptation ability; it is also interconnected with coping capacity and contributes to a cycle of poverty escalation.

This paper examines the effect of governance on inequality, specifically if improvements in the World Bank’s Worldwide Governance Indicators affect inequality as measured by two Gini coefficients: Market Gini, before taxes and redistribution, and Net Gini, after taxes and redistribution. The data for the Gini measurements was taken from the Standardized World Income Inequality Database (SWIID) and the data for the Worldwide Governance Indicators was taken from the World Bank. Data for fifteen (15) years, from the start of the Worldwide Governance Indicators until 2013, was combined with data from SWIID for the same years. In all, data from one hundred fifty-six (156) countries with a full set of six (6) indicators for the years that had at least one corresponding Gini measurements were used in this study: in total one thousand seven hundred and forty-seven (1747) observations. In a pooled OLS regression, controlling for growth with the variable GDP per Capita expressed as a per cent (%) change on an annual basis, the individual indicators gave the following results, where a positive sign indicates increased inequality and vice versa: Control of Corruption and Regulatory Quality showed a positive sign for both Gini measurements. Rule of Law, Government Effectiveness, Political Stability and the Absence of Violence/Terrorism, gave a negative sign for both Gini measurements. Voice and Accountability showed a positive sign for Market Gini and a negative sign for Net Gini. The fact that an improvement in Control of Corruption increased inequality both before and after taxes and redistribution was unexpected and should be further researched.

The aim of this thesis is to investigate possible improvement of scoring models prediction power in retail credit segment by using structural models estimating the future development of behavioral score. These models contain the informa- tion about past development of the behavioral score by parameters which take into account the sensitivity of clients' probability of default on individual market and life changes. These parameters are estimated by Markov Chain Monte Carlo methods based on score history. Eight different types of structural models were applied to real data. The diversification measure of individual models is compared using the Gini coefficient. These structural models were compared with each other and also with the existing scoring model of the credit institution which provided the underlying data. 1

Obtaining variances for the plug-in estimator of the Gini coefficient for inequality has preoccupied researchers for decades with the proposed analytic formulae often being regarded as being too cumbersome to apply, as well as usually based on the assumption of an iid structure. We examine several variance estimation techniques for a Gini coefficient estimator obtained from a complex survey, a sampling design often used to obtain sample data in inequality studies. In the first part of the dissertation, we prove that Bhattacharya’s (2007) asymptotic variance estimator when data arise from a complex survey is equivalent to an asymptotic variance estimator derived by Binder and Kovačević (1995) nearly twenty years earlier. In addition, to aid applied researchers, we also show how auxiliary regressions can be used to generate the plug-in Gini estimator and its asymptotic variance, irrespective of the sampling design. In the second part of the dissertation, using Monte Carlo (MC) simulations with 36 data generating processes under the beta, lognormal, chi-square, and the Pareto distributional assumptions with sample data obtained under various complex survey designs, we explore two finite sample properties of the Gini coefficient estimator: bias of the estimator and empirical coverage probabilities of interval estimators for the Gini coefficient. We find high sensitivity to the number of strata and the underlying distribution of the population data. We compare the performance of two standard normal (SN) approximation interval estimators using the asymptotic variance estimators of Binder and Kovačević (1995) and Bhattacharya (2007), another SN approximation interval estimator using a traditional bootstrap variance estimator, and a standard MC bootstrap percentile interval estimator under a complex survey design. With few exceptions, namely with small samples and/or highly skewed distributions of the underlying population data where the bootstrap methods work relatively better, the SN approximation interval estimators using asymptotic variances perform quite well. Finally, health data on the body mass index and hemoglobin levels for Bangladeshi women and children, respectively, are used as illustrations. Inequality analysis of these two important indicators provides a better understanding about the health status of women and children. Our empirical results show that statistical inferences regarding inequality in these well-being variables, measured by the Gini coefficients, based on Binder and Kovačević’s and Bhattacharya’s asymptotic variance estimators, give equivalent outcomes. Although the bootstrap approach often generates slightly smaller variance estimates in small samples, the hypotheses test results or widths of interval estimates using this method are practically similar to those using the asymptotic variance estimators. Our results are useful, both theoretically and practically, as the asymptotic variance estimators are simpler and require less time to calculate compared to those generated by bootstrap methods, as often previously advocated by researchers. These findings suggest that applied researchers can often be comfortable in undertaking inferences about the inequality of a well-being variable using the Gini coefficient employing asymptotic variance estimators that are not difficult to calculate, irrespective of whether the sample data are obtained under a complex survey or a simple random sample design. / Graduate / 0534 / 0501 / 0463 / aahoque@gmail.com

The role of inequality in development has been the subject of long-standing debates in academic and policy circles. Notwithstanding disagreements about exactly how the two are linked, conventional wisdom agrees that inequality is an objective 'fact' that can be measured free from ideological considerations. New data detect trends towards higher inequality, weaker economic positions for those at the bottom, and a concentration of wealth at the very top of the distribution in most regions. Inequality studies as currently practiced are ill-equipped to accommodate the empirical changes and the resulting theoretical implications. Putting an end to over half a century of mainstream consensus assuming that inequality would automatically recede with developmental progress, the discipline needs rethinking. My thesis proposes a new research agenda for studying inequality that is not only able to integrate these empirical developments, but which also challenges what has been taken for granted: that inequality just is, independently of context, time and observer. Instead, it proposes that along with its objective existence, inequality is a relational phenomenon subjectively experienced relative to a particular context. In five interconnected Sections, my dissertation challenges conventional views of how inequality looks, how it is seen, and what can be done about it, especially in developing countries. The study focuses on the ways in which inequality is perceived, and how it is perpetuated. After an introduction to the subject in Section I, Section II investigates how inadequate measurement perpetuates inequality, proposing a new indicator that shows that inequality is largely defined in the extreme ends of the income distribution. Section III examines the reproduction of inequality at the bottom, contrasting minimum wage policies over recent decades in Mexico with those of other countries in Latin America. In light of a political economy resistant to change, Section IV scrutinizes Mexican elites, exploring how inequality is perceived from the very top of the income distribution, how this affects policy-making and, subsequently, measured inequality levels. Section V concludes by outlining the theoretical and practical implications of my findings.

The aim of the presented work is to investigate possible improvement of scor- ing models prediction power in retail credit segment by using structural models estimating the future development of behavioral score. These models contain the information about past development of the behavioral score by parameters which take into account the sensitivity of clients' probability of default on in- dividual market and life changes. These parameters are estimated with Markov Chain Monte Carlo methods based on score history. Eight different types of struc- tural models were applied on the real data. The diversification measure of indivi- dual models is compared using the Gini coefficient. These structural models were compared with each other and also with the existing scoring model of the credit institution which provided the underlying data. 1

Income inequality and its relationship to long-term GDP per capita growth has been researched for decades since the development of the Kuznet’s Curve. Theoretical and empirical research has shown mixed results including positive, negative, non-existent, or statistically insignificant relationships. Empirical research on income inequality and economic growth in the United States has also shown mixed results. In addition to using existing data, this paper uses originally-constructed Gini Coefficients from 2005 to 2009. A statistically significant negative correlation between income inequality, and both short-term growth and long-term growth is found in the analysis of this data. Finally, this paper attempts to justify a causal relationship between income inequality and long-term growth.

This diploma thesis deals with the problematics of redistribution of public finance. The factors interfering in the process of redistribution are described here. The attention is paid to equality of redistribution of incomes within the group of monitored households. Since the level of redistribution is influenced by different factors, their analyses are carried out here. The levels of incomes redistribution inequality here are measured in monitored Czech hoseholds in the years 2005 until 2007. Calculations comprise analyses of individual types of incomes and items which influence equality of redistribution. A part of conclusion consists of comparison of the results from the mentioned statistics with the data published by OECD. The main aim of the thesis is confirmation or negation of the following hypothesis: "Tax system and social security system markedly influences redistribution of incomes in Czech households.".

The goal of the master thesis titled "Trade openness and income inequality in Eastern Europe" is to analyze the effects on income inequality changes in the population in the period of transformation from centrally planned economies to market economies in the last decade of the twentieth century. The first part of the thesis focuses on the development before the individual countries started to join the European Union. The subsequent liberalization in the early 21st century is evaluated in the second part of the thesis. The multiple regression analysis is used to estimate the effects. The data were provided by the World Bank for the period of from 1989 to 2014. The objective of the thesis is to enlighten the factors which are influencing the changes in income inequality.