Given the recent events in Ferguson - where white policeman was not indicted for shooting a black young man - that lead to protests around the US to try to stop racial discrimination, I thought it was a (unfortunately) perfect moment to see what economics has to say about this. What is the status of inequality between black and white? Using research from the Urban Institute, Figure 1 suggests that white people have 6 times more wealth than blacks, having this gap increased almost threefold since 1983. So it seems the situation has not got better over time.
Figure 1: The wealth gap in the last three decades
Source: The Urban Institute.
Moreover, whites accumulate more wealth over their lives than black (or Hispanics) do. Focusing on those born between 1943 and 1950, Figure 2 shows that this wealth gap increases over the life cycle. In 1983, whites between 32 and 40 have an average family wealth of $184,000, rising to over a million by age 59 to 67. However, blacks wealth goes from $54,000 to only $161,000 between the same ages. So whites have three and a half more wealth than blacks when they are young, but over seven times more when they are old.
Figure 2: The life cycle of wealth by race
Source: The Urban Institute.
On top of this wealth gap, an over-simplistic look at the data suggests that blacks receive worse sentences and are more likely to be suspended in school. Finally, Figure 3 shows are twice more likely to be unemployed.
Figure 3: Unemployment rate by race.
Source: Bureau of Labor Statistics.
What is behind such big gaps? Econ 101 teaches us that a properly working market system should hire and pay people according to their value. Discrimination makes no sense in competitive environment. Suppose every employer is discriminating against blacks, hence providing them with a lower wage even though they are as productive as whites. This would allow any unbiased person to take over the market. She would be able to hire these discriminated people at a wage level in between the gap (i.e. between the black and white ones) and get a better profit than everyone else, possibly kicking all racist businessmen out of the market. This Econ 101 logic is definitely too simplistic, but should let us frame our thoughts to see what it is missing out.
A possible issue is that blacks and white differ in their characteristics, beyond their color. For example, white people could be more educated. Focusing on unemployment, the question then is: When faced with observably equivalent (i.e. education, experience, etc) black and white job applicants, do employers favor the white one? Evidence goes both ways. Some suggest they do not, claiming the black-white gap stems from supply factors: African-Americans lack many skills when entering the labor market, so they perform worse. Others suggest that employers do discriminate, either by prejudice ("Taste-biased" in economics jargon) or, more usually, by what economists call "statistical discrimination": race is used as a signal for unobservable characteristics. For example, if blacks tend to be raised in worse environments (which could lead to worse productivity), employers who care about productivity (but do not about race) and cannot observe it perfectly, would use race (or ZIP codes) as a signal for it. Hence, black people would be discriminated but not (directly) because of their color.
Data limitations make it difficult to test these views. Researchers posses far less data than employers do, so even if applicants appear similar to researchers they may not be to employers. Employers can observe social skills during interviews and assess the quality behind what is stated in the typical resume information. And any racial difference in labor outcomes could easily be attributed to that. That would be a highly unsatisfactory open ending to this post.
Fortunately, Bertrand and Mullainathan designed a field experiment to try to circumvent this problem. They sent close to 5,000 resumes to more than 1,300 help-wanted ads and measured the call-back for interview for each resume. Since race cannot be explicitly written in the resume, they manipulated the perception of race by (randomly) assigning names to those resumes. Half the names used are white-sounding (e.g. Emily Walsh) while the other half is black-sounding (e.g. Lakisha Washington). A side experiment showed that the names used are associated with their respective races by more than 90% of the people. They also experimented on changing the quality of the resumes, in order to see if call-backs for black are more responsive to quality than for white (like statistical discrimination might suggest). Approximately four resumes are sent to each ad: Black-High (quality), White-High, Black-Low, and White-Low. Even though this does not go further than the call-back stage (i.e. it does not go all the way to employment), this methodology guarantees that the information the researcher and employer have is the same.
Table 1 shows the callback rate for both groups: Whites have 50% more chances of being called back. A white person would need to send 10 resumes to receive one callback, while a black one would have to send 15. Using the data on quality of the resumes (Table 5 in the paper), the return to a white name is equivalent to as much as eight additional years of experience. Moreover, there seems to be no difference depending on the industry or occupation category of the job itself. They all show differences of this sort.
Table 1: Callback rates by age.
A possible issue with this strategy is that when employers read a name like Lakisha, they may assume more than just skin color. They could interpret that the applicant comes from a disadvantaged background. In such a case, signals of quality like experience or special skills should be more important for black applicants. Similarly, ZIP codes could be used to get an idea of the social background of the applicants. If we expected statistical discrimination to be behind the gap, we should expect black applicants callbacks to respond more to either of this. However, the study suggests they don't. Higher quality of resumes improves the callbacks for white applicants but not so much for black ones. And ZIP codes don't seem to matter much either. Finally, a way of looking at this directly is by examining the average social background (proxied by mother's education) for each name used. Table 2 shows the first names used, together with their callback rate and average mother high-school completion rate. The social background hypothesis would suggest higher callback rate for higher mother education. However, no such evidence is found.
Table 2: Social background and callback rate for each name used.
If statistical discrimination is not behind this, what is then? "Taste-based" discrimination where people consciously think worse about blacks seems contradictory to other studies in the literature. In a second paper the same authors (together with Chugh) suggest that a possible explanation is that we might be unintentionally discriminating. Using a tool popular in neuroscience and sociology, the Implicit Association Test (IAT), they suggest that people have unconsciously more difficulties in associating black persons with positive words. And this is found to be harder to control in environments with time-pressure or considerable ambiguity (like looking at job applicants).
What is the best way to improve on unconscious discrimination? Is making differences between skin colors, that go as far as avoiding any topic that refers to colors which are as obviously there as any other parts of our bodies, the correct way to improve our unconscious mind? If we are raised with these concerns of what is politically correct to say, we might be doomed to unconsciously make such an unfair and damaging difference between people's skin colors.
Figure 1: Composition of US Income Inequality.
In the last few years, substantial research from Piketty, Saez, Atkinson and others has brought the topic of inequality back to the front page of economics. They use extensive data, including tax records in some cases, to analyze the evolution of (mainly top) income inequality for a long period of time. Charles Jones has updated and summarized some these studies, which is the basis of this article. The starting question is then: How much inequality is there?
Figure 1 shows the share (and composition) of income held by the top 0.1% of the population. The first striking finding is that there is a long U-shaped pattern: (Top) Inequality was very high before the Great Depression (with the top 0.1% holding as much as 10% of the total income); Lower and steady inequality after WWII; Rising inequality since the 1970s (reaching pre-1930 levels).
Taking into account that GDP can be theoretically split into labor income (e.g. wages, salaries and business income) and capital income (capital income and gains), we can divide the analysis of inequality in a similar fashion. This shows that most of the initial decline is due to a reduction in capital income, while most of the sequential increase is due to labor income (and capital gains possibly). The returns on capital seem to have become relatively smaller for the top 0.1% of the population, while wages and business income have become more important. (A big driver of of this might be the importance of land rents in the income of this part of the population)
If you have read about Piketty's book, you may have heard about the magnitude of wealth inequality. Wealth inequality is much greater than income inequality. While the top 1% of the population hold about 17% of income, the share of wealth held by them in the US is estimated to be above 40%. The cutoff to be in the top 1% of income is 330 thousand dollars a year, while 4 million dollars are needed to be among the wealthiest 1%. Figure 2 shows the path of wealth inequality for the France, the US and the UK. It is seen that wealth inequality was a lot higher before WWI than it is today. However, this hides the fact that wealth inequality has started to increase in the 1960s. On the positive side, (at least for UK and France) it still remains smaller than in the 19th century.
Figure 2: Wealth Inequality.
So far we have discussed how inequality has behaved within labor income and within wealth. Given the importance of inequality within wealth, the remaining question is how has the share of income taken by capital evolved over time. Since most of the capital income is captured by a small number of people, a tiny change in the share taken by capital (instead of labor) can lead to substantial effects on general levels of inequality. While most of the previous plots focused on the top 1%, this is now more about the top 10% (which holds 3/4 of the wealth in the US) versus the bottom 90% (which holds the other quarter, most of which is actually held within the 50-90% range). Figure 3 shows that the share of income taken by capital had either decreased or remained stable until the 1980s. However, since then, the share of income (think of this as the share of the revenues taken by capital and property owners) taken by capital has increased in all three countries.
Figure 3: Capital share of payments.
Inequality is a big concern. However, its causes and consequences remain a puzzle. On the causation side, much research remains to be done. On the consequences side, many views are possible. Regarding the individual level, inequality might affect the chances some people have of making progress, for example through access to education. If children lack basic needs (like food), they most likely won't attend school. Regarding the aggregate level, inequality might also hinder general economic growth. For example, through reduced access to education, innovation might be damaged. However, it has also been claimed that inequality might be necessary for growth. For example, in a very poor country, if wealth is split equally no one might be able to invest. However, higher inequality might allow the richest people to be able to use their extra resources to invest and generate growth. Later, opportunities for the poor ones might flourish, leading to lower inequality. This is known as the Kuznets curve. Whatever your hypothesis is, careful thinking and proper research are probably necessary.
Based on a working paper by Charles Jones.
How do individual labor earnings evolve over the course of a person's life? If you have ever asked yourself "Should I expect my income to increase this year?" and "By how much?" this post might interest you. In a very elegant study, Guvenen, Karahan, Ozkan and Song have tried to answer these questions and more, using over 200 million tax data observations from Social Security Administration (between 1978 and 2010). If you ever thought tax data was not public, this (and my last post) might suggest that they are not. Don't worry. Only a few people are allowed to use this information, and even then they are not allowed to actually see the name of the person behind each income observation.
Looking at employed people between the ages of 25 and 60, they focus on how much earnings grow every year in average. A first look at the data is provided in Figure 1. Taking the average among all the population, yearly income peaks around 50 years old, with an increase as high as 127% from age 25.
Figure 1: Average (Log) Earnings by Age.
If you are past age 50 and have not seen such an increase, you might be wondering what's wrong with yourself. Before entering into such a depressing state of mind, please read a few more lines. This average income path hides a lot of variation across different people. More importantly, it is strongly influenced by the very top earners. Figure 2 shows that the median worker only shows a 38% increase in his earnings between age 25 and 55. It is the very top earners who influence the 127% number before. For example, the Top 1% shows 1500% increase in their earnings in that same period. More than 300 times the median increase in earnings...
Figure 2: Earnings Growth (25 to 55) by Lifetime Earnings.
Another interesting finding is that income does not peak at the same age for everyone. Even though the average person's income peaks around the age of 50, this is not the case for most people. Figure 3 shows that the median worker has almost no income growth between 35 and 45, and only the top 2% actually experience earnings growth after 45.* I hope these depressing findings for the median worker might help your self-confidence. The average numbers shown in Figure 1 are not the appropriate ones to question your life. (Figures 2 and 3 might be...)
Figure 3: Earnings Growth by Decade of Life.
Some other interesting findings in these article are that the dispersion of income growth (i.e. how much income growth differs across individuals) has a U-shape, decreasing with age up to when people are 50 years old where it spikes up again. Top earners are the exception once again, since their income dispersion grows every year of their lives.
How about the asymmetries? Is it more likely to be below or above the average increase in income? The data suggests that as people get older or richer, it is more likely to get negative shocks to income. And these seems to be due to there being more room to fall down (not less room to move up). The higher your income, the more you can lose (remember most people are not willing to pay to work, so you cannot have negative wages).
Finally, let me end with a happy note. Suppose you just saw your income go down. You might be worried that it will remain like this for a long time. The data suggests otherwise. If the decrease was very strong, it is most likely that the persistence will be very short (unless you were a very high earner). In less than a year you should see your income recover most of its previous value.
(Very Small Print Note: this does not mean you should just lay down and wait for this fact to bring your salary back to normal. No complaints are accepted if incomes do not go up.)
* Remember that if a distribution is such that there are a few outliers with extremely high income growth, we will observe an average growth much higher than the one the median worker has. Hence, focusing on the median worker might be more illustrative in these cases.
Based on an article by Guvenen, Karahan, Ozkan and Song.
How much do children's social and economic opportunities depend on parents' income and social status? This is a politically correct way of asking: How doomed are children from poor parents?. The answer is essential to analyze policies that try to make every kids chances more equal. As always, a first step is to analyze what the data has to say about this. Fortunately, Chetty, Hendren, Kline and Saez (economists at Harvard and Berkley) are currently doing some beautiful analysis on this matter. Since opportunities are hard to measure, they focus primarily on income (although they also study education, crime or pregnancy) differences.
Using tax-income data on 40 million children born between 1980 and 1982 and their parents, they are going to rank people according to their income level. Parents are going to be ranked in groups from 1 to 100, according to how they do income-wise relative to other parents. Similarly, children are going to be ranked according to their incomes when they are 30-32 years old relative to the other children. Then, they are going to focus on two measures of intergenerational mobility:
1) Relative Mobility: What are the outcomes of children from low-income families relative to those from high-income ones?
Example: If my parents income increases by one ranking point, how much is my income rank expected to increase?
(The problem with this measure is that higher mobility may be due to richer people doing worse, not poor ones doing better. Hence, the second measure might be more useful.)
2) Absolute Mobility: What are the outcomes of children from families of a given income level in absolute terms?
For example, what is the mean income of a child born from parents in the 25th percentile?
The chart below shows the national statistics of the rank-rank (relative mobility) relationship in 3 countries: Canada, Denmark and US. The slope in the US is 0.341, while the other two are half that much. This suggests that increasing one percentage point in parent rank, increases child mean rank by 0.341 percentage points. The fact that Canadian and Danish data suggest higher relative mobility should be taken with caution since this could be due to worse outcomes from the rich, rather than better ones from the poor. Interestingly, this strong correlation with parents income rank is also observed in children's college (attendance and quality) and teenage pregnancy, suggesting differences emerge well beyond the labor market. This is consistent with evidence from my previous post.
The previous chart suggests that the rank-rank relationship is highly linear. Hence, the authors are going to take advantage of this when analyzing the intergenerational mobility across different areas in the US. The question now is: Is mobility the same across the US? Or are some regions better for children to make the jump forward? Given the issues with relative mobility, we can now focus on absolute mobility: What is the mean income of a child born from parents in the 25th percentile? The heat map below shows that the Southeast shows the lowest mobility in the country, while the Great Plains, West Coast and Northeast display much higher mobility levels (the map should be read the map as darker is worse mobility). While in some regions children of parents in the 25th percentile tend to remain in the same percentile when they grow up, in other areas similar children do twice as better (in income rank terms). This pattern seems robust to controlling for children moving to other areas and cost of living or demographic reasons like marriage differences.
The obvious next question is why are regions' mobility so different from each other? Why children in some areas seem to be born with more opportunities than those in other ones? This question is not directly addressed by the authors, but they provide some correlations with local characteristics. Given econometric issues like selection and endogeneity (also explained in a previous post!), the following should NOT be interpreted as causes.* However, they show interesting descriptive information.
1) Race and Segregation: The higher the share of African-Americans, the lower the mobility observed. However, the data suggests that this holds true for the white people in those areas as well. Hence, it is not that black people tend to remain stagnant. Segregation in the area seems to be correlated with everyone's mobility. Particularly, segregation of poverty seems to be the strongest reason (isolation of rich people does not seem to be behind). Some potential reasons could be: successful role models are not present for the poorest children; worse public goods provision; or access to jobs might be harder in such areas.
2) Income: The average income level is not correlated with mobility (i.e. it is not that richer areas do better or worse). However, areas with higher income inequality show lower degrees of mobility. Interestingly, the inequality in the upper tail is not correlated with mobility. Hence, it is not about the existence of some extremely rich people. It is more about the size of the middle class. The bigger the middle class, the higher the mobility.
3) School Quality: Better schools are associated with higher mobility.
4) Social Capital: Social participation in elections, census or even religious events is positively correlated with mobility.
5) Family Structure/Stability: The higher the number of single parents, the lower the mobility. Once again, this effect extends to children who are born from parents who remained together, suggesting that the effect is not at the individual level but at the social environment one. Regions with more divorce somehow have lower mobility.
To summarize, parents income seems to be very important on children opportunities. However, there is substantial variation across different areas in the US. Some areas seem to fit much better than others the concept of "Land of Opportunity." A child raised in the Great Plains has much better chances of making a leap forward than one born in the Southeast. Segregation, inequality and family structure are highly correlated with mobility. Unfortunately, why remains a mystery.
* Families choose where they live and what institutions they support. So we can imagine that families that prefer to live in areas with better education systems or less income inequality are intrinsically different than those that prefer to live in the more segregated South of the US.
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