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Page 1 of 16 May 19th 2015 See what is happening at every point in earning distribution For both men and women, large growth on top earnings Usegul for seeing the time frame of increased inequality between 1979 – 1981, inequality starts to skyrocket ECON 139 SP ‘15 Antonovics 8 5-19-15 1Page 2 of 16 If there is no inequality, then it will be the blue straight line with points (0.2, 0.2), (0.4, 0.4) etc. The further away to the 45 degree (slope) line the Lorenz Curve is, the more is the inequality. The Gini Coefficient Gini Coeeficient = Area of the shaded region / (1/2) It’s a comprehensive measure of inequality. • The Gini Coefficient = 0 if there is no inequality. • The Gini Coefficient = 1 if there is perfect inequality (all of the income goes to the top quintile). • In the U.S. the Gini Coefficient is about .43.Page 3 of 16 Latest Numbers: l In 2012, top 1% of households earned 19.3% of the total household income. l In 2012, t op 10% of households earned 48.2% of the total household income. l 95% of the income since 2009 have gone to gains go to the top 1% l In 2012, n top 1% is > $393,000 n top 10% is > $114,000 n Pre-tax household income including realized capital gains. Inequality There’s one good thing about inequality is that it give people incentive to work harder • We’ve seen that inequality has been increasing in the U.S. • How does the U.S. compare to other countries? • Has inequality increased because of increased mobility? • Has there been an increase in equality for other labor market outcomes (besides wages)?Page 4 of 16 Is the Increase in Inequality Explained by an Increase in Earnings Mobility? • Earnings Mobility: reflects the covariance of earnings across years. – If the covariance in income across years is low (how much is my earnings over the years), then earnings mobility is high. • Even if there has been an increase dispersion of annual earnings, it doesn’t mean that there has been an increase in the dispersion of lifetime income—maybe people’s incomes are just bouncing around more from year to year. • Thus, is increased inequality in the United States simply due to an increase in mobility? • First, what does mobility look like in the United States? The likelihood of people stay below (decrease), they have more mobility.Page 5 of 16 Earnings Mobility, Continued • One way to examine whether the increase in inequality can be explained by an increase in mobility is to average income over multiple years to get a measure of permanent income. • Then you can assess the extent to which there has been an increase in inequality for permanent income . . . Little evidence that the rise in inequality can be explained by an increase in mobility The Gini coefficients are higher in annual earnings curve. Has There Been an Increase in Inequality for Other Labor Market Outcomes? • Employment rates for male high school dropouts with 10 years of experience declined from 78.5 to 67.4 percent between 1975 and 1994. • Employment rates for male college graduates with 10 years of experience increased slightly from 91.3 to 95.1 percent between 1975 and 1994. • Economic conditions facing skilled and unskilled workers diverged along many dimensions – Wages – Employment rates What Explains the Increase in Wage Inequality Two possible sources: – Increase in the returns to skills. – Increase in the dispersion of skills.Page 6 of 16 – Note: Some skills are observable to researchers (education, experience), others are unobservable to researchers (motivation, charisma). We will start by looking at changes in the returns to observable skill . . . Massive increase in return to scale, decrease in high school graduates, increase in college graduates What about skills you cannot observe? • What if the returns to unobserved skills have changed? • How do we measure unobserved skills if we can’t see them? • Idea: look at residual wages. A Five Minute Informal Discussion of Regression AnalysisPage 7 of 16 • Suppose you believe the log wage for an individual is given by logWi = α +βXi + ui • Xi captures the education level of individual i. • ui captures factors that affect an individual’s wage that cannot be observed by researchers. • β tells you the change in the log wage associated with a one-year change in schooling. • α tells you the average wage for someone with no years of schooling. What I expect that individual’s earning, given the schooling level Increasing Residual Wage Dispersion Increasing return to unobservable skillsPage 8 of 16 The Residual Wage Distribution • For every person in our data, we can calculate a residual wage. • This will give us a residual wage distribution. • We can ask whether the distribution of residual wages has become more unequal. • For example, in each year we can calculate the difference between the 90th and the 10th percentile of the residual wage distribution. Residual Wage: wage after controlling for age, education, experience and region of residence.Page 9 of 16 Why Have the Returns to Skill Increased? • Supply-side factors – Increases in college enrollment – Increases in female labor force participation – Cohort size—baby boomers – International immigration • Demand-side factors – Skill-biased technological change – Globalization and trade • Institutional factors – Industry wage differentials – Decline in unionization – Decline in the real value of the minimum wagePage 10 of 16 May 21th, 2015 ECON 139 SP ‘15 Antonovics 8 5-21-15 1Page 11 of 16 Still Some Role for Supply-Side Factors 1. The baby boomers enter the labor market in 1970s-- may have depressed the return to college in that decade. 2. International Immigration • From 1979 to 1995, immigration increased the supply of high school dropouts by 20.7 percent and the supply of those with at least a high school degree by 4.1 percent. • Borjas estimates that a third of the decline in the wages of high school dropouts between 1980 and 1995 can be traced to immigration. Institutional Factors: Industry Wage Differentials • Fact: Workers in some industries earn more than workers

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