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Understanding Income Inequality. Special Considerations. Key Takeaways Income inequality studies help to show the disparity of incomes among different population segments. When analyzing income inequality, researchers commonly study distributions based on gender, ethnicity, geographic location, and occupation. Case studies and analyses of income inequality, income disparity, and income distributions are provided regularly by a variety of top sources.
The Gini Index is a popular way to compare income inequalities universally across the globe. An income gap refers to the difference in income earned between demographic segments. Article Sources. Investopedia requires writers to use primary sources to support their work. These include white papers, government data, original reporting, and interviews with industry experts.
We also reference original research from other reputable publishers where appropriate. You can learn more about the standards we follow in producing accurate, unbiased content in our editorial policy. Compare Accounts. The offers that appear in this table are from partnerships from which Investopedia receives compensation. Families in all strata experienced a loss in income in this decade, with those in the poorer strata experiencing more pronounced losses.
The pattern in income growth from to is more balanced than the previous three decades, with gains more broadly shared across poorer and better-off families. Other than income, the wealth of a family is a key indicator of its financial security. Wealth, or net worth, is the value of assets owned by a family, such as a home or a savings account, minus outstanding debt, such as a mortgage or student loan.
Accumulated over time, wealth is a source of retirement income, protects against short-term economic shocks, and provides security and social status for future generations. The period from the mids to the mids was beneficial for the wealth portfolios of American families overall. Housing prices more than doubled in this period, and stock values tripled. But the run up in housing prices proved to be a bubble that burst in Home prices plunged starting in , triggering the Great Recession in and dragging stock prices into a steep fall as well.
The wealth gap among upper-income families and middle- and lower-income families is sharper than the income gap and is growing more rapidly. The period from to was relatively prosperous for families in all income tiers, but one of rising inequality. Figures are expressed in dollars. The wealth gap between upper-income and lower- and middle-income families has grown wider this century. As of , upper-income families had 7. These ratios are up from 3.
The reason for this is that middle-income families are more dependent on home equity as a source of wealth than upper-income families, and the bursting of the housing bubble in had more of an impact on their net worth. Upper-income families, who derive a larger share of their wealth from financial market assets and business equity, were in a better position to benefit from a relatively quick recovery in the stock market once the recession ended.
As with the distribution of aggregate income, the share of U. The richest families in the U. The tilt to the top was most acute in the period from to The wealthiest families are also the only ones to have experienced gains in wealth in the years after the start of the Great Recession in By , this ratio had increased to , a much sharper rise than the widening gap in income.
S has increased since and is greater than in peer countries Income inequality may be measured in a number of ways , but no matter the measure , economic inequality in the U. The ratio increased in every decade since , reaching Comparisons of income inequality across countries are often based on the Gini coefficient , another commonly used measure of inequality. More globally, the Gini coefficient of inequality ranges from lows of about 0.
Say "Alexa, enable the Pew Research Center flash briefing". It organizes the public into nine distinct groups, based on an analysis of their attitudes and values.
Even in a polarized era, the survey reveals deep divisions in both partisan coalitions. Use this tool to compare the groups on some key topics and their demographics. Pew Research Center now uses as the last birth year for Millennials in our work. The Statistical Office of South Korea began to collect information about household income, debt, wealth, and welfare to know the dynamics of finance after the global financial crisis in — The sample size was 10, cases until , and after that, it increased to 20, households since Linking survey data with registered administrative data provides a new research possibility on inequality research with more accurate information on personal and household income.
Administrative registered data in the SHFLC included the various data source to measure household income: the annual income and tax from the National Tax Office, the transfer income and pension from the Social Insurance Office, the health insurance and health service information from the National Health Insurance, educational allowance from the Ministry of Education, and child allowance from the Ministry of Health and Welfare.
Table 1 reports different measures of inequality of income and wealth in the survey data and the linked data in As we expected, the linked data displays a higher maximum income than the survey data, revealing that the survey data underreport the income of the top income. It also shows that the survey data do not accurately identify the income of the low-income groups since the median shifts from to As a result, Gini coefficients measured by the linked data, 0.
Footnote 2 The linked data that uses income from the administrative record shows the fact that the survey data collected by the Statistical Office in South Korea tend to underestimate the level of the top income and the degree of inequality of the household income. On average, the popular perception of the rising inequality has been derived by not only income inequality but also wealth inequality.
Wealthy persons consume luxury goods and cars and live expensive housings in gated communities. Also, the increasing housing price contributes to the perception of wealth inequality as well as wealth inequality itself. In South Korea, the rising housing price in recent years has contributed to the fear of the youth of the middle class and the working class, as housing ownership was very low among young adults see Fig. The Korean youth has shown the lowering marriage rate and birth rate of the married couple mainly due to the housing shortage and housing bubbles over the decades as well as the high rate of unemployment Kim, Distribution of earnings and housing ownership, Notes: Earnings ratio refers to the ratio of earnings relative to average earnings, and the ratio of housing ownership indicates the proportion of housing owners among each age group.
In this paper, income is measured by annual household income containing the market income and public transfer income. The market income includes earnings from work, profits from business, property income such as interests or rents, and the private transfer. Pre-tax income will be used in the following to focus on the impacts of contributors to before-tax income inequality. Wealth is measured by the price of a variety of possession, including properties such as houses, lands, buildings, and cars.
Linking the survey data with the administrative data provides more accurate data on income and wealth than any other data set in South Korea. The joint distribution of income and wealth shows highly polarized distribution, characterized by the two poles around income poor-wealth poor and income rich-wealth rich.
Figure 2 displays a topographic picture of the joint distribution of income and wealth in However, in the middle of income and wealth distribution in Fig.
It shows that there is a low level of association between income and wealth in the middle of income group and in the middle wealth group. High income and high wealth clusters are formed in the poles.
Table 2 reports the summary of the description of major independent variables. Those are mostly the characteristics of the head of households except family size. The majority of the household head is male The measurement of education is done by the calculation of years of education rather than categories based on the level of education. Family size refers to the number of family members. In the subsequent analysis, family size is categorized from one to six since there is a non-linear relationship between family size and income.
Occupation includes non-working people as well as the working population since it deals with the entire household. Employment status also includes the category of non-working people. Soldiers in the occupational category are excluded in the following analysis because the market does not determine their incomes.
To identify the extent to which some factors contribute to inequality of income and wealth, we use the regression-based inequality decomposition developed by Gary Fields How can we explain the high level of household income inequality and wealth inequality in South Korea?
Which factors contribute to inequality of income and wealth? Do demographic factors and family change affect the distribution of household income?
How is income inequality associated with wealth inequality? The regression-based inequality decomposition method might be helpful for getting answers to some of those questions. Footnote 3. We take two steps to decompose inequality of household income and wealth by each contributing factor. The first step is to specify income functions and wealth functions, respectively, and estimate coefficients of variables in those functions by linear regression analyses.
The second step is to estimate the variance of household income explained by the covariance of each independent variable and household income. It is a decomposition of the total explained variance R 2 into contributions by each independent variable based on Shapley value. Table 3 reports the results of the regression-based inequality decomposition based on the model that specifies some income functions.
The best-fitting models, that is, the model with the highest R 2 and significant coefficients, are used to estimate the contribution of each variable to the variance of household income and wealth. The residuals refer to the unexplained variance of inequality of household income by the model, 1- R 2. The number indicates the proportion of the variance of household income explained by each variable.
For example, the sex of household head explains 2. Those are relatively low because household dynamics are different from individual earnings in the labor market. In Table 3 , the occupation of the household head is the largest contributor to the variance of household income.
Footnote 4 It explains Individual characteristics such as sex and age do not contribute to the variance of household income that much, while education exerts modest influence on it by 7.
Household wealth is the second largest contributor to the variance of household income by Family size plays a significant role in household income inequality.
It explains Model 2 in Table 3 provides a more parsimonious model in which occupation and industry of household head are excluded, and only the employment status of the household head is included. Employment status consists of regular employment, non-regular employment, employing other employees, self-employment without employees and atypical workers, and non-working person. Model 2 fits the data as good as the model 1 with the almost same explained variance, R 2 0.
The impact of employment status on household income inequality comes from inequality between working persons and non-working persons. Both occupation and employment status include non-working persons, who increased household income inequality substantially. Table 4 displays the contribution of each variable to the total variance of wealth, following the same equation for household income and a modified equation dropping occupation and industry and adding loans. Model 1 in Table 4 includes household income in the independent variables to explain wealth.
Unlike the decomposition of income inequality, individual characteristics and work-related variables do not display large impact on wealth inequality.
Model 2 introduces loans and drops occupation and industry in the wealth equation to explain wealth inequality. While debt might be a survival resource for the poor, it can be a resource for the middle class and the rich for new investment. Thus, loans can be a leverage for enhancing wealth for the rich. As Fig. The high-income groups have more chance to eleviate their wealth and promote their life chances through the financial markets.
We observe the polarization of the financial markets in favor of the wealthy class. Significant contributors to wealth inequality are quite different from those of income inequality. When we apply the same variables in the equations, we find out very different impacts on income and wealth. First of all, human capital variables are very weak in their impact on wealth inequality. Therefore, we include only selected variables that were significantly affecting wealth inequality.
Loans alone contribute to wealth inequality by Although the accumulation of income for a period could contribute to the growth of wealth, loans seem to be more directly associated with wealth. As housing price has continued to increase in the twenty-first century, the deregulation of financial markets and severe competition among financial institution has promoted the mortgage loans to the middle class.
As the real estate bubble grows for a while, wealth inequality will continue to increase as well. The above results display two things. First, the dynamics of income and wealth are different from each other. While income inequality and wealth inequality are significant dimensions of economic inequality, the mechanisms of the formation of income and wealth are very different.
Income is an outcome of economic activities as well as profits from the capital, whereas loans are a financial resource based on income and wealth. Second, the credit system as a core institution in the financialized economy functions for the social group, which already enjoys the advantage. That exacerbates income distribution and economic inequality. Analyzing the SHFLC data that linked the survey data with administrative data in South Korea, this paper attempts to explore income inequality and wealth inequality and their reciprocal relationship mediated by financial behavior.
While income inequality and wealth inequality are the two major dimensions of social inequality, sociologists did not pay much attention to income inequality and wealth inequality. Rather, individual social mobility based on occupation has been a core research topic among sociologists in social stratification. Wage inequality has been a focal arena among sociologists in the research on the labor market and gender gap.
The analysis of inequality of income and wealth in this paper reveals at least five findings. First, the linked data combining the survey data with administrative data shows higher income inequality than the survey data.
The SHFLC data provide more accurate information about household income, which was registered information around different governmental agencies. It provides a new possibility for social scientists to investigate income inequality.
Second, wealth inequality is much more severe than income inequality in South Korea. While ordinary people perceive wealth inequality as a core of economic inequality, sociologists did not pay much attention to it for various reasons.
Fourth, the dynamics of income and income inequality are different from the dynamics of wealth and wealth inequality. Household income has been affected by occupation and family size as well as work-related variables, such as employment status and occupation. Unlike the expectation of researchers on occupation, the effect of occupation on income inequality is not based on the technical division of labor but the division between working and non-working population.
Thus, employment status rather than occupation provides a better fitting model with fewer variables. Wealth has been most affected by household income and loans in South Korea. It indicates that income directly affects wealth, and loans mediate the formation of wealth. As the housing market bubble has grown for the last decade, loans and credit systems play a significant role in aggravating wealth inequality.
Fifth, the positive feedback loops are working between income and wealth through capital income and credit systems. As wealth is the most significant factor of income inequality and income is the second most important factor of wealth inequality, income inequality tends to increase further with the rise of the real estate price in recent years in South Korea.
There are some limitations to this paper. One is the problem of the representativeness of the survey data. Although the size of the Survey of the Household Finance and Living Conditions is much larger than the previous survey data, the wealthy families reported in the media are not sufficiently included in the data. Furthermore, the extremely poor are also excluded due to the limitation of accessibility to them. Thus, the underestimation of income and wealth inequality may be possible.
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