DOCSLIB.ORG

Measuring Income Inequality

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

IJA TRAPEZNIKOVA Royal Holloway University of London, UK Measuring income inequality Summary measures of inequality differ from one another and give different pictures of the evolution of economic inequality over time Keywords: inequality, Gini coefficient, interdecile ratios ELEVATOR PITCH Lorenz curves show income inequality is higher in Brazil than the US and Norway Economists use various metrics for measuring income 1.0 inequality. Here, the most commonly used measures— Norway (2015) US (2016) 0.8 the Lorenz curve, the Gini coefficient, decile ratios, the Brazil (2015) Perfect equality Palma ratio, and the Theil index—are discussed in relation 0.6 to their benefits and limitations. Equally important is the 0.4 choice of what to measure: pre-tax and after-tax income, Share of income consumption, and wealth are useful indicators; and 0.2 different sources of income such as wages, capital gains, 0 taxes, and benefits can be examined. Understanding 0 0.2 0.4 0.6 0.8 1.0 the dimensions of economic inequality is a key first step Share of population toward choosing the right policies to address it. Source: Author's own compilation based on data from PovcalNet. Online at: http://iresearch.worldbank.org/PovcalNet/home.aspx KEY FINDINGS Pros Cons The Lorenz curve is a commonly used metric that If Lorenz curves cross they cannot provide a allows for the quick and visual comparison of conclusive ranking between distributions. inequality across countries. The Gini coefficient values change depending on The Gini coefficient uses information from the what is measured—wages, before- or after-tax entire income distribution and is independent of income, wealth, or consumption. the size of a country’s economy and population. Percentile ratios fail to use all information since Percentile ratios are easy to calculate and focus on they ignore incomes between percentiles. a specific region of the distribution. The Theil index is less intuitive and not directly The Theil index can decompose inequality into comparable across populations with different within- and between-group inequality. sizes or group structures. These commonly used measures are generally The evolution of inequality within a country can in agreement when comparing inequality across appear different depending on the metric used. countries. AUTHOR’S MAIN MESSAGE Despite the relative strengths and weaknesses of the available measures, empirical studies show that they are mainly in agreement when comparing inequality differences across countries. However, the evolution of inequality within a country or the effectiveness of a specific policy can be perceived differently depending on the specific metric under consideration, as well as what variable is being measured. For instance, if policymakers care more about what happens to the poor they should use the Palma ratio instead of the Gini coefficient as their inequality measure and focus on consumption instead of income data. Measuring income inequality. IZA World of Labor 2019: 462 11 doi: 10.15185/izawol.462 | Ija Trapeznikova © | July 2019 | wol.iza.org IJA TRAPEZNIKOVA | Measuring income inequality MOTIVATION There are many reasons why policymakers and researchers alike are concerned with a country’s degree of economic inequality. Recent studies show that persistent income disparities among individuals are associated with poverty and deprivation, mental illness, social unrest, and crime, as well as lower levels of education, employment, and life expectancy [1]. Many public policies such as taxes, welfare benefits, provision of education and health services, price, and competition regulations have distributional implications for income. How equally is income distributed across individuals in countries with different social institutions, education systems, capital, and labor markets? How has inequality evolved over time? How have income distributions changed after tax reforms or financial crises? To be able to answer these important questions, a consistent measure of inequality is needed. DISCUSSION OF PROS AND CONS Dimensions of economic inequality Before choosing a particular metric, a decision must be made about the dimension of economic inequality to be measured. This choice is important, not only from a conceptual point of view, but also because it determines what instruments are available to policymakers trying to correct a given distribution. The discussion in this article focuses on inequality in outcomes, as opposed to inequality of opportunities (such as access to education, skills, and other limitations imposed by parental circumstances or belonging to a low socio-economic class). While the latter is undoubtedly important for understanding the factors behind economic inequality, especially in the context of social mobility, this article relies on more readily observable indicators of living standards, such as income and wealth. Many of the measuring techniques described below, however, can be applied to inequality in education, health, happiness, and life satisfaction in general. When looking at disparities in income among individuals, it is necessary to distinguish between income, wealth, and pay inequality. “Pay inequality” refers to differences in wages paid to different people. This inequality can reflect differences in productivity of workers (e.g. between low- or high-skilled workers), labor market discrimination between groups, or differences in the nature of the job (e.g. miners and sales assistants). “Income inequality” is the extent to which income is distributed unevenly across people or across households. Income encompasses labor earnings (such as wages, salaries, and bonuses), capital income derived from dividends, interest on savings accounts, rent from real estate, as well as welfare benefits, state pensions, and other government transfers. In addition, it is possible to distinguish between individual versus family income, pre-tax versus after-tax (disposable) income, and labor earnings versus capital income. The third dimension of economic inequality is differences in “wealth” distribution. While income refers to the flow of money over a given period (say, a year or a month), an individual’s wealth represents the stock of all assets that a person holds. These include financial assets, such as bonds and stocks, property, and savings. Income and wealth are not perfectly correlated. For instance, a high earner might be in the upper end of the income distribution while being relatively poor in terms of assets; likewise, an elderly IZA World of Labor | June 2019 | wol.iza.org 2 IJA TRAPEZNIKOVA | Measuring income inequality worker might have accumulated substantial wealth (e.g. owning a house) but have low labor earnings. Different policies ranging from income and inheritance taxes to government schemes for first-time home buyers will affect different groups of individuals and might have opposing effects on wealth and income distributions. Consumption versus income inequality Some economists argue that “consumption” is a more appropriate indicator of persistent inequality as it is closely related to permanent income [2]. Consumption inequality is typically lower than income inequality because individuals can smooth temporary shocks to income (such as a temporary layoff or the end of a seasonal job) through saving and borrowing. Disparities in consumption better reflect differences across individuals in the accumulation of assets, access to credit, or the social safety net. Recent studies use the link between income and consumption variation to quantify the role of assets, taxes, transfers, and family labor supply in insuring against permanent income shocks [3], [4]. In practice, the choice between income or consumption measures is often determined by data availability. Advanced economies tend to collect high-quality income data, especially those drawn from administrative tax records. Consumption data are particularly relevant for developing countries, where it is difficult to obtain reliable estimates of income because a large part of the population is self-employed, producing for their own consumption (especially in agriculture), or being paid in kind. For this reason, World Bank Development Indicators on inequality for the least developed countries are typically estimated on household consumption expenditures. Studies that look at the joint analysis of consumption and income data suggest that, while the level of inequality in consumption is typically lower than in disposable income, the overall ranking of countries is similar under both measures [5]. At the same time, the question of whether consumption inequality tracks income inequality over time has been a subject of debate, with mixed empirical results that differ depending on the data sources and methodology used (see [2] for a literature survey based on US data). Unit of measurement Another important issue for inequality statistics is the unit of measurement—whether it is an individual, a tax-paying unit, a family, or a household. Given that income is often shared within a family or a household, it might be more appropriate to look at disparities in household income, as opposed to individual income. Typically, earnings dispersion among households is lower than among individuals because of income pooling within the household, as well as the fact that families can provide insurance against individual risks (e.g. a worker increasing their labor supply in response to the job loss of their partner). The role of this family insurance mechanism has increased
Recommended publications
  • The Lorenz Curve

    The Lorenz Curve

    Charting Income Inequality The Lorenz Curve Resources for policy making Module 000 Charting Income Inequality The Lorenz Curve Resources for policy making Charting Income Inequality The Lorenz Curve by Lorenzo Giovanni Bellù, Agricultural Policy Support Service, Policy Assistance Division, FAO, Rome, Italy Paolo Liberati, University of Urbino, "Carlo Bo", Institute of Economics, Urbino, Italy for the Food and Agriculture Organization of the United Nations, FAO About EASYPol The EASYPol home page is available at: www.fao.org/easypol EASYPol is a multilingual repository of freely downloadable resources for policy making in agriculture, rural development and food security. The resources are the results of research and field work by policy experts at FAO. The site is maintained by FAO’s Policy Assistance Support Service, Policy and Programme Development Support Division, FAO. This modules is part of the resource package Analysis and monitoring of socio-economic impacts of policies. The designations employed and the presentation of the material in this information product do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. © FAO November 2005: All rights reserved. Reproduction and dissemination of material contained on FAO's Web site for educational or other non-commercial purposes are authorized without any prior written permission from the copyright holders provided the source is fully acknowledged. Reproduction of material for resale or other commercial purposes is prohibited without the written permission of the copyright holders.
  • Inequality Measurement Development Issues No

    Inequality Measurement Development Issues No

    Development Strategy and Policy Analysis Unit w Development Policy and Analysis Division Department of Economic and Social Affairs Inequality Measurement Development Issues No. 2 21 October 2015 Comprehending the impact of policy changes on the distribu- tion of income first requires a good portrayal of that distribution. Summary There are various ways to accomplish this, including graphical and mathematical approaches that range from simplistic to more There are many measures of inequality that, when intricate methods. All of these can be used to provide a complete combined, provide nuance and depth to our understanding picture of the concentration of income, to compare and rank of how income is distributed. Choosing which measure to different income distributions, and to examine the implications use requires understanding the strengths and weaknesses of alternative policy options. of each, and how they can complement each other to An inequality measure is often a function that ascribes a value provide a complete picture. to a specific distribution of income in a way that allows direct and objective comparisons across different distributions. To do this, inequality measures should have certain properties and behave in a certain way given certain events. For example, moving $1 from the ratio of the area between the two curves (Lorenz curve and a richer person to a poorer person should lead to a lower level of 45-degree line) to the area beneath the 45-degree line. In the inequality. No single measure can satisfy all properties though, so figure above, it is equal to A/(A+B). A higher Gini coefficient the choice of one measure over others involves trade-offs.
  • Insights from Income Distribution and Poverty in OECD Regions

    Insights from Income Distribution and Poverty in OECD Regions

    How’s life in your region? Insights from income distribution and poverty in OECD regions July 2014 New OECD data show that differences in household income distribution and poverty are large not only among OECD countries but also across regions within a country. The availability of information on household income distribution and poverty at regional level, as well as on other aspects of quality of life, helps policy makers to focus their efforts and enhance the effectiveness of public interventions in a period of tight resources. The forthcoming OECD report How’s life in your region? documents the importance of regional inequalities in many well-being dimensions in OECD countries, provides evidence on the determinants of income inequalities within and between regions, and discusses the links between income inequality and economic growth at regional level. Regional data on the distribution of household income come with various warnings. Household surveys are rarely designed to be representative at the regional level; comparing regions in different countries is tricky, because their size varies; and these income numbers ignore the fact that the cost of living is usually lower in rural areas than in cities, which may have the effect of exaggerating inequality in a country. The new set of indicators of regional income inequality and poverty produced for 28 OECD countries extends the OECD Income Distribution Database (IDD) to NUTS2 regions in Europe and to large administrative regions (e.g. states in Mexico and Unites States) for non-European countries for the year 2010 and providing measures of the statistical reliability of these data.
  • Reducing Income Inequality While Boosting Economic Growth: Can It Be Done?

    Reducing Income Inequality While Boosting Economic Growth: Can It Be Done?

    Economic Policy Reforms 2012 Going for Growth © OECD 2012 PART II Chapter 5 Reducing income inequality while boosting economic growth: Can it be done? This chapter identifies inequality patterns across OECD countries and provides new analysis of their policy and non-policy drivers. One key finding is that education and anti-discrimination policies, well-designed labour market institutions and large and/or progressive tax and transfer systems can all reduce income inequality. On this basis, the chapter identifies several policy reforms that could yield a double dividend in terms of boosting GDP per capita and reducing income inequality, and also flags other policy areas where reforms would entail a trade-off between both objectives. 181 II.5. REDUCING INCOME INEQUALITY WHILE BOOSTING ECONOMIC GROWTH: CAN IT BE DONE? Summary and conclusions In many OECD countries, income inequality has increased in past decades. In some countries, top earners have captured a large share of the overall income gains, while for others income has risen only a little. There is growing consensus that assessments of economic performance should not focus solely on overall income growth, but also take into account income distribution. Some see poverty as the relevant concern while others are concerned with income inequality more generally. A key question is whether the type of growth-enhancing policy reforms advocated for each OECD country and the BRIICS in Going for Growth might have positive or negative side effects on income inequality. More broadly, in pursuing growth and redistribution strategies simultaneously, policy makers need to be aware of possible complementarities or trade-offs between the two objectives.
  • The College Wealth Divide: Education and Inequality in America, 1956-2016

    The College Wealth Divide: Education and Inequality in America, 1956-2016

    The College Wealth Divide: Education and Inequality in America, 1956-2016 Alina K. Bartscher, Moritz Kuhn, and Moritz Schularick Using new long-run microdata, this article studies wealth and income trends of households with a college degree (college households) and without a college degree (noncollege households) in the United States since 1956. We document the emergence of a substantial college wealth premium since the 1980s, which is considerably larger than the college income premium. Over the past four decades, the wealth of college households has tripled. By contrast, the wealth of noncollege households has barely grown in real terms over the same period. Part of the rising wealth gap can be traced back to systematic portfolio differences between college and noncollege households that give rise to different exposures to asset price changes. Noncollege households have lower exposure to the equity market and have profited much less from the recent surge in the stock market. We also discuss the importance of financial literacy and business ownership for the increase in wealth inequality between college and noncollege households. (JEL I24, E21, D31) Federal Reserve Bank of St. Louis Review, First Quarter 2020, 102(1), pp. 19-49. https://doi.org/10.20955/r.102.19-49 1 INTRODUCTION It is a well-documented fact that the college wage premium has increased substantially since the 1980s (see, e.g., Levy and Murnane,1992; Katz and Autor, 1999; and Goldin and Katz 2007). This trend can be traced back to differences in the growth of the demand for and the supply of college-educated workers that are driven by skill-biased technical change, socio- demographic factors, and institutional features (Card and Lemieux, 2001, and Fortin, 2006).
  • Inequality of Opportunity: a Parametric Approach

    Inequality of Opportunity: a Parametric Approach

    23rd Public Economics Meeting, University of Vigo February 4 and 5, 2016 Inequality of Opportunity: a Parametric Approach Jos´eMar´ıaSarabia1, Vanesa Jord´a,Faustino Prieto Department of Economics, University of Cantabria, Avda de los Castros s/n, 39005-Santander, Spain Abstract In this paper we measure the extent of inequality of opportunity us- ing a new parametric approach. We rely on the idea that differences in earning can be ethically fair if they come from choices that indi- viduals are completely responsible for, called efforts. Unacceptable disparities are those come from external factors called circumstances. We use a flexible parametric form to model the joint distribution of circumstances and efforts. Our model avoids the need to break con- tinuous variables of circumstances into groups, while also allowing for the use of categorical variables such as gender or race. Using data from Living Conditions Survey, we estimate the level of inequality of opportunity in Spain in 2005. Our results are robust to different parametric functional forms and indicators of parental education. JEL Classification: D31, D63, J62 Key Words: Multivariate distributions; Inequality of opportunity; Theil in- dices; Decompositions 1 Introduction Income inequality has been of great interest in the economic literature, be- ing also a sort of concern as regards redistribution policies. It is however argued that disparities driven by different levels of efforts of individuals are 1E-mail addresses: [email protected] (J.M. Sarabia). 1 less objectionable than those arise from differences in the characteristics of these individuals which are exogenous to them. Among the last kind of fea- tures we can highlight race, gender, familiar background or region, among others.
  • User Manual for Stata Package DASP

    User Manual for Stata Package DASP

    USER MANUAL DASP version 2.1 DASP: Distributive Analysis Stata Package By Abdelkrim Araar, Jean­Yves Duclos Université Laval PEP, CIRPÉE and World Bank November 2009 Table of contents Table of contents............................................................................................................................. 2 List of Figures .................................................................................................................................. 5 1 Introduction ............................................................................................................................ 7 2 DASP and Stata versions ......................................................................................................... 7 3 Installing and updating the DASP package ........................................................................... 8 3.1 installing DASP modules. ............................................................................................... 8 3.2 Adding the DASP submenu to Stata’s main menu. ....................................................... 9 4 DASP and data files ................................................................................................................. 9 5 Main variables for distributive analysis ............................................................................. 10 6 How can DASP commands be invoked? .............................................................................. 10 7 How can help be accessed for a given DASP module? ......................................................
  • Measuring Inequality LECTURE 13

    Measuring Inequality LECTURE 13

    Measuring inequality LECTURE 13 Training 1 1 Outline for final lectures § Once datasets have been finalized, it is time to produce results, with the aim of representing the patterns emerging from the data. § In practice? § Inequality this lecture § Poverty next lecture § Basic summary statistics on household demographics, education, access to services, etc. § Average expenditures and incomes final lecture Training 2 2 Inequality and poverty measurement 1) a measure of living standards persons 2) high-quality data on households’ living standards 3) a distribution of living standards (inequality) 4) a critical level (a poverty line) below which individuals are classified as “poor” living standard 5) one or more poverty measures Training 3 3 1 Cowell (2011) 99.9% of this lecture is explained with better words in Cowell’s work: this book and other (countless) journal articles Training 4 Warning § During the course we stressed the distinction between the concepts of living standard, income, expenditure, consumption, etc. § In this lecture we make an exception, and use these terms interchangeably § Similarly, I will not make a distinction between income per household, per capita, or per adult equivalent § For once, and for today only, we will be (occasionally) inconsistent Training 5 5 Focus on the term 'inequality' § “When we say income inequality, we mean simply differences in income, without regard to their desirability as a system of reward or undesirability as a scheme running counter to some ideal of equality” (Kuznets 1953: xxvii) § In practice, how can we appraise the inequality of a given income distribution? Three main options: ① Tables ② Graphs ③ Summary statistics Training 8 2 Tables: an assessment § In general, tables are not recommended when the focus is inequality § Difficult to get a clue of the extent of inequality in the distribution by looking at a table.
  • Globalization and Income Distribution: Evidence from Pakistan

    Globalization and Income Distribution: Evidence from Pakistan

    Globalization and Income Distribution: Evidence from Pakistan Shahzad Hussain, Dr. Imran Sharif Chaudhry and Mahmood-ul-Hasan* Abstract It is widely believed that income inequality can be reduced through opening up of the economies of developing countries into the world market. But changes in country’s trade exposure and world market can affect negatively the distribution of resources within the country. This paper empirically explores the impact of globalization on income distribution using econometric time series approach for the period 1972- 2005 in Pakistan. The results are in favor of the conventional wisdom that opening up of the economy into the international market has good effects on the distribution of income. Income inequality can be reduced through foreign capital penetration. Huge trade volume with negative trade balance must be controlled. Key Words: Globalization, Income Distribution, Trade Openness, Foreign Direct Investment, Remittances JEL Classification: D31, F10, F21, F24 I. Introduction Nations across the globe have established progressively closer contacts with the passage of time, but recently the pace has dramatically increased. These closer contacts of different forms are welcomed by the politicians, academics, journalists and economists throughout the world. But many also believe that business-driven globalization is uprooting the old ways of life and threatening the cultures and livelihoods of the poor people. Nevertheless globalization is a multi-dimensional phenomenon. Economic globalization can comprehensively defined as a process of rapid economic integration among countries driven by liberalization of trade, investment and capital flows as well as technological change (Torres, 2001).Globalization is also movement across international borders of goods and factors of production.
  • UNIVERSITY of CALIFORNIA SAN DIEGO Essays on Non-Parametric

    UNIVERSITY of CALIFORNIA SAN DIEGO Essays on Non-Parametric

    UNIVERSITY OF CALIFORNIA SAN DIEGO Essays on Non-parametric and High-dimensional Econometrics A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Economics by Zhenting Sun Committee in charge: Professor Brendan K. Beare, Co-Chair Professor Yixiao Sun, Co-Chair Professor Jelena Bradic Professor Dimitris Politis Professor Andres Santos 2018 Copyright Zhenting Sun, 2018 All rights reserved. The Dissertation of Zhenting Sun is approved and is acceptable in quality and form for publication on microfilm and electronically: Co-Chair Co-Chair University of California San Diego 2018 iii TABLE OF CONTENTS Signature Page . iii Table of Contents . iv List of Figures . vi List of Tables . vii Acknowledgements . viii Vita........................................................................ ix Abstract of the Dissertation . x Chapter 1 Instrument Validity for Local Average Treatment Effects . 1 1.1 Introduction . 2 1.2 Setup and Testable Implication . 5 1.3 Binary Treatment and Instrument . 10 1.3.1 Hypothesis Formulation . 10 1.3.2 Test Statistic and Asymptotic Distribution . 12 1.3.3 Bootstrap-Based Inference . 15 1.4 Multivalued Treatment and Instrument . 18 1.4.1 Bootstrap-Based Inference . 21 1.4.2 Continuous Instrument. 23 1.5 Conditional on Discrete Covariates . 26 1.6 Tuning Parameter Selection . 28 1.7 Simulation Evidence . 29 1.7.1 Data-Generating Processes . 30 1.7.2 Simulation Results . 30 1.8 Empirical Applications . 33 1.9 Conclusion . 34 Chapter 2 Improved Nonparametric Bootstrap Tests of Lorenz Dominance . 36 2.1 Introduction . 36 2.2 Hypothesis Tests of Lorenz Dominance . 39 2.2.1 Hypothesis Formulation .
  • The Racial Wealth Divide in Chicago

    The Racial Wealth Divide in Chicago

    The Racial Wealth Divide in Chicago Optimized for Screen Readers Contents Director’s Letter ...................................................................................................................................... 3 The Racial Wealth Divide in Chicago .................................................................................................... 4 Infographic Highlights ........................................................................................................................ 4 Population ....................................................................................................................................... 4 Business Value ................................................................................................................................ 5 Unemployment Rate ....................................................................................................................... 5 Cost-Burdened Owners ................................................................................................................... 5 Immigrants & Assimilation in Chicago .................................................................................................. 5 Households of Color in Liquid Asset Poverty .................................................................................... 5 Population ........................................................................................................................................... 5 Liquid Asset Poverty by Race ............................................................................................................
  • Income Inequality and the Persistence of Racial Economic Disparities Robert Manduca

    Income Inequality and the Persistence of Racial Economic Disparities Robert Manduca

    Income Inequality and the Persistence of Racial Economic Disparities Robert Manduca Harvard University Abstract: More than 50 years after the Civil Rights Act, black–white family income disparities in the United States remain almost exactly the same as what they were in 1968. This article argues that a key and underappreciated driver of the racial income gap has been the national trend of rising income inequality. From 1968 to 2016, black–white disparities in family income rank narrowed by almost one-third. But this relative gain was negated by changes to the national income distribution that resulted in rapid income growth for the richest—and most disproportionately white—few percentiles of the country combined with income stagnation for the poor and middle class. But for the rise in income inequality, the median black–white family income gap would have decreased by about 30 percent. Conversely, without the partial closing of the rank gap, growing inequality alone would have increased the racial income gap by 30 percent. Keywords: income inequality; race; stratification; disparities HE stubborn persistence of racial income disparities has been a core frustration T of American social policy for the past 50 years (Bloome 2014; Bound and Freeman 1992; Wilson and Rodgers 2016). In 1968, shortly after the passage of the Civil Rights Act, the median family income of African Americans was 57 percent that of whites. In 2016, after almost 50 years of anti-discrimination legislation, attempts to equalize access to education, and cultural change, it was 56 percent. The persistence of the racial income gap is puzzling in light of efforts to equalize employment opportunity and progress toward racial equality in other areas.