16-3 Annual Earnings Distribution, 2007 The distribution of personal earnings among full-time workers is highly unequal and skewed right. The distribution is characterized by (1) much bunching around the leftward mode, (2) an extended rightward tail, (3) a mean, $46,179, that exceeds the median, $35,000, (one-half above, one-half below).
16-4 Distribution of Annual Earnings, 2007 The distribution of personal earnings among full-time workers is highly unequal as the highest 20% of earners received 46.0% of all earnings.
16-5 2. Explaining the Distribution of Earnings Factors include differences in oFormal Education oOn the Job Training oAbility oFamily Background oDiscrimination oRisk Taking & Luck
16-6 Human Capital Theory oFormal education Higher quality and quantity of formal education leads to greater earnings. Differences in ability, discrimination, cost of funds, individuals differ in the amount education people acquire. ∞This leads to differences in earnings. oOn-the-job training More on-the-job training increases earnings. On-the-job training helps explain why older persons have higher earnings.
16-7 Human Capital Theory Persons with more formal education get on- the-job training and thus expand their earnings differentials with less-educated workers. Workers with on-the-job training tend to work more hours per year and thus the variance in annual earnings.
16-8 Modified Human Capital Theory oAbility Direct effect ∞Persons with greater ability will have greater earnings due to their greater productivity. Complementary effect ∞If elements of ability are complementary (e.g., IQ and motivation), then they will have a multiplicative rather than an additive effect on earnings. Human capital effect ∞Persons with more ability will get more education and thus increase earnings inequality.
16-9 Modified Human Capital Theory oFamily background Direct effect ∞A child born into a family with a family-owned business is likely to employed in the family business and have higher earnings later in life. ∞Families with “good connections” may be able to help their children get high-paying jobs with friends and associates. Human capital effect ∞High income families can more readily provide formal education for their children.
16-10 Modified Human Capital Theory oDiscrimination Discrimination increases earnings inequality in several ways: ∞Lowers the earnings of women and minorities ∞Occupational segregation raises the earnings of males and whites as well as lowering the earnings of women and minorities. ∞Poorer black children tend to attend worse schools and are less likely to go to college.
16-11 Modified Human Capital Theory oChance and risk taking Luck and risk taking play a role in the unequal distribution of earnings. ∞A few high paying positions such as a professional athlete, rock star, best-selling authors, CEO, etc. ~This leads to an unequal distribution of earnings since only a few people who try succeed, many fail, and even more don’t try. ∞Random luck determines who gets a high wage offer in the distribution of earnings for a given occupation.
16-12 Question for Thought 1. Speculate as to how successful attempts by the government to tighten the distribution of family income through transfers might inadvertently make the distribution of annual earnings more unequal.
16-13 Question for Thought 1. Critically evaluate this statement: “Lifetime earnings are less equally distributed than annual earnings.”
16-15 Wage Inequality, 90-10 Ratio The 90-10 ratio is earnings at the 90 th percentile divided by earnings at the 10 th percentile. Earnings inequality has risen for both men and women. Earnings inequality grew most rapidly between 1979 and 1989.
16-20 Why the Increase in Earnings Inequality? oDeindustrialization Employment has shifted to the low wage and high variance service sector. The employment shift to services can only explain a small part of the rise in inequality.
16-21 Why the Increase in Earnings Inequality? oImport competition and the decline of unions Increased import competition and the associated decline in unionism has lowered the wages of less-educated workers and thus raised inequality.
16-22 Why the Increase in Earnings Inequality? oIncreased demand for skilled workers The demand for skilled workers has risen relative to less-skilled workers, which has increased inequality. ∞The demand for skilled workers has risen within industries as firms have adopted new technologies. ∞Product demand has shifted across industries towards high-tech industries that employ more skilled workers.
16-23 Why the Increase in Earnings Inequality? oDemographic changes The entrance of less-skilled baby boomers and female workers during the 1970s and 1980s may have increased inequality between new and experienced workers. ∞The increased labor supply may have increased the share of low-wage workers in all industries. ∞The increased labor supply may have decreased the wages of workers in low-wage labor markets. It is likely that labor supply shifts played a small role since most of the increase in inequality has been within each age group.
16-24 Trends in US Upward Mobility oSource: Rana Foroohar, Time, Vol. 178, 11/14/2011, pp. 26-35 oUS no longer the land of upward mobility More mobility in Europe now ∞ In US, if you were born in 1970 into a bottom 20% family, there’s only a 17% chance of making it to the top 40% ∞ 42% of US men with fathers in bottom 20% incomes stay there. ∞ Scandinavians, Canada and Germans have > 2X more upward mobility. Even French 1.5X greater
16-25 Trends in US Upward Mobility oGrowing US inequality has been the primary reason upward mobility has diminished High US inequality makes it harder to jump rungs. Top 1% earn 21% of all income and have 35% wealth. More equality in Europe makes it easier to move to the next rung up. The dominance of financial services led to rapid increase in CEO pay across industries. ∞1970 average US CEO pay 40X lowest company worker (like in Europe today) ∞Today it’s 400X
16-26 Trends in US Upward Mobility oTwo megatrend causes rising inequality Rise of Emerging Economies ∞Since 1980, US companies that make products or services that could be traded with other countries have created zero jobs, i.e., manufacturing ∞Only job growth is in retail and health care which are low wage sectors Tech driven economy ∞ Computerization has taken jobs that used to be done by workers (robots) and reduced the number of workers needed (software) ∞Share of middle income jobs has declined from 52% in 1980 to 42% in 2010.
16-27 Trends in US Upward Mobility oCure: Reform Education & Training oUS education quality is poor 26 th worldwide in overall quality, worse in math & science New teachers are of poor quality ∞23% in top 1/3 of SATs, 30% in 2 nd 1/3 and 47% in the bottom 1/3. US school year is short ∞Asian children get 2 extra years of class time before they graduate high school.
16-28 oReform important because US labor force is relatively high wage and poor quality compared to trading partners. oProspects for reform are dim Conflicts with public teacher’s union and politician desires at the local level Conflicts with tight money budget realities Americans choose to avoid math & science in favor of soft subjects which don’t improve our competitiveness
16-30 Lorenz Curve The Lorenz curve provides a summary of the earnings distribution. The straight red line represents perfect equality in the earnings distribution. Twenty percent of all earners get 20 percent of all earnings, 40 percent of the workers would get 40 percent, etc. The curved blue line illustrates a Lorenz curve. The curve shows that lower income workers do not receive a proportionate share of all earnings. The greater the area between the line of perfect equality and the Lorenz curve, the more unequal the distribution of earnings. Percent of Full-timeEarnings Percent of Full-time Workers Perfect Equality 0 100 Lorenz Curve
16-31 Gini Coefficient oThe Gini coefficient provides a way to quantify the Lorenz Curve. Gini Coefficient = area between Lorenz curve and diagonal total area below diagonal The Gini coefficient ranges between 0 (perfect equality) and 1 (perfect inequality). The Gini coefficient in 2007 was.40
16-32 Cautions oFull-time versus part-time workers The previous data only includes full-time workers. If part-time workers were included income variability would increase. oFringe benefits The previous data did not include fringe benefits. If fringe benefits were included income inequality would increase since those with high incomes also have high fringe benefits.
16-33 Cautions oIndividual versus family income The previous data is based on individual income. The mean and median of the family income distribution is higher. The family income distribution is more equal since the wives of high-income men are less likely to work due to the income effect. oStatic portrayal This income distribution don’t give information about changes in the distribution.
16-34 Cautions oOther income sources High wage income persons tend to have higher rental, interest, and dividend income. ∞Including these sources increases inequality. Low wage income persons are more likely to get income transfers such as welfare. ∞Including these sources decreases inequality. If all other income sources are included, then the income distribution becomes more equal. ∞The income transfer effect is stronger.
16-35 3. Mobility Within the Earnings Distribution
16-36 Life-Cycle Mobility oEarnings rise with age and then fall near retirement. oEven if everyone had the same earnings stream over their career, we would still observe annual earnings inequality since the work force consists of workers of different ages. oThe inequality in annual earnings overstates the inequality in life-time earnings.
16-37 Churning oThere is a lot of churning or year-to-year movement across earnings categories independent of life cycle effects. oThe earnings mobility rates are lower for blacks than whites and less in the lowest and highest earnings categories.