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.
Most people (and particularly my mother) think that economics is only about money. That we economists are basically counting money and telling people where to invest. This post will be the first evidence that there is no boundary to economics. Today I will go as far as to fertility. Yes, we economists have a lot to say about fertility.
Fertility has interested economists for a long time. At least since Malthus' theory on population growth in 1798 economists have been interested in fertility. Malthus basically thought that as people became richer they would have more kids, which would mean less resources for everyone (he expected technological growth to be quite small), leading distress to knock on everyone's doors (though louder on the poorer doors). Fortunately, this is also an example of economists failure at predicting the future.
On top of human capacity to increase resources, which Malthus undervalued, the other assumption that does not hold in Malthus idea is that the higher your income, the more kids you have. But we will get to that later. Let's see the broad picture first. How has fertility evolved in the US in the last 200 years? Here is a plot of number of kids for (married) women born in different years (cohorts in the figure):
The dark blue line shows that the overall average of children born has decreased from a high 5.5. to an average of just below 2. More impressive is the fact that initial differences between groups (black vs. white, urban vs. rural) have narrowed substantially. Differences that used to be as high as 1.5 kids are now smaller than one-fifth of a child. Notice that this data is for married women, so hypothesis that are based on the reduction of marriage as a cause for the reduction in the number of kids are in trouble. It is also interesting to note that most of this compression is coming from the reduction of women having lots of children.
The number of women having either none or one kid has been quite stable around 10-20%, but the number of women having 4 or more children has diminished from almost 70% to below 10%. What seems to be behind this? There are many theories out there (see the paper cited below if interested), but I will stick here to the one of the most popular among economists: money, money, money...Here is a plot of number of kids for different levels of income for all the different cohorts.
The shocking thing about this picture is that all the women born from 1828 to 1958 seem to be gravitating around a constant relation between (real) income and number of kids had. In other words, in either century, women with incomes of around X apples (real income) would have in average a very similar number of kids. More impressively, there seems to be no difference in this relationship for either urban or rural areas. (Caveats aside) This suggests that the main difference between the average number of kids women have in the 1800s versus the 1900s (or in rural compared to urban areas) is mainly income. People are richer today and, for some reason, richer people tend to have less kids. The question is then why?
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