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Tuesday, January 16, 2018

TimTaylor:Some Economics for Martin Luther King Day

Some Economics for Martin Luther King Day

On November 2, 1983, President Ronald Reagan signed a law establishing a federal holiday for the birthday of Martin Luther King Jr., to be celebrated each year on the third Monday in January. As the legislation that passed Congress said: "such holiday should serve as a time for Americans to reflect on the principles of racial equality and nonviolent social change espoused by Martin Luther King, Jr.." Of course, the case for racial equality stands fundamentally upon principles of justice, not economics. But here are a few economics-related thoughts for the day from the archives:

1) Inequalities of race and gender impose large economic costs on society as a whole, because one consequence of discrimination is that it hinders people in developing and using their talents. In "Equal Opportunity and Economic Growth" (August 20, 2012), I wrote:

A half-century ago, white men dominated the high-skilled occupations in the U.S. economy, while women and minority groups were often barely seen. Unless one holds the antediluvian belief that, say, 95% of all the people who are well-suited to become doctors or lawyers are white men, this situation was an obvious misallocation of social talents. Thus, one might predict that as other groups had more equal opportunities to participate, it would provide a boost to economic growth. Pete Klenow reports the results of some calculations about these connections in "The Allocation of Talent and U.S. Economic Growth," a Policy Brief for the Stanford Institute for Economic Policy Research.

Here's a table that illustrates some of the movement to greater equality of opportunity in the U.S. economy. White men are no longer 85% and more of the managers, doctors, and lawyers, as they were back in 1960. High skill occupation is defined in the table as "lawyers, doctors, engineers, scientists, architects, mathematicians and executives/managers." The share of white men working in these fields is up by about one-fourth. But the share of white women working in these occupations has more than tripled; of black men, more than quadrupled; of black women, more than octupled.

Moreover, wage gaps for those working in the same occupations have diminished as well. "Over the same time frame, wage gaps within occupations narrowed. Whereas working white women earned 58% less on average than white men in the same occupations in 1960, by 2008 they earned 26% less. Black men earned 38% less than white men in the typical occupation in 1960, but had closed the gap to 15% by 2008. For black women the gap fell from 88% in 1960 to 31% in 2008."

Much can be said about the causes behind these changes, but here, I want to focus on the effect on economic growth. For the purposes of developing a back-of-the-envelope estimate, Klenow builds up a model with some of these assumptions: "Each person possesses general ability (common to
all occupations) and ability specific to each occupation (and independent across occupations). All groups (men, women, blacks, whites) have the same distribution of abilities. Each young person knows how much discrimination they would face in any occupation, and the resulting wage they would get in each occupation. When young, people choose an occupation and decide how
much to augment their natural ability by investing in human capital specific to their chosen

With this framework, Klenow can then estimate how much of U.S. growth over the last 50 years or so can be traced to greater equality of opportunity, which encouraged many in women and minority groups who had the underlying ability to view it as worthwhile to make a greater investment in human capital.

"How much of overall growth in income per worker between 1960 and 2008 in the U.S. can be explained by women and African Americans investing more in human capital and working more in high-skill occupations? Our answer is 15% to 20% ... White men arguably lost around 5% of their earnings, as a result, because they moved into lower skilled occupations than they otherwise would have. But their losses were swamped by the income gains reaped by women and blacks."

At least to me, it is remarkable to consider that 1/6 or 1/5 of total U.S. growth in income per worker may be due to greater economic opportunity. In short, reducing discriminatory barriers isn't just about justice and fairness to individuals; it's also about a stronger U.S. economy that makes better use of the underlying talents of all its members.

2) The black-white wage gap--and the share of the gap that is "unexplained"-- is rising, not falling. Here's part of what I wrote about in "Breaking Down the Black-White Wage Gap (September 6, 2017):

Mary C. Daly, Bart Hobijn, and Joseph H. Pedtke set the stage for a more insightful discussion in their short essay, "Disappointing Facts about the Black-White Wage Gap," written as an "Economic Letter" for the Federal Reserve Bank of San Francisco (September 5, 2017, 2017-26). Here are a couple of figures showing the black-white wage gap, and then seeking to explain what share of that gap is associated with differences in state of residence, education, part-time work, industry/occupation, and age. The first figure shows the wage gap for black and white men; the second for black and white women.

Here are some thoughts on these patterns:

1) The black-white wage gap is considerably larger for men (about 25%) than for women (about 15%). Also, the wage gaps seem to have risen since the 1980s.

2) The three biggest factors associated with the wage gap seem to be education level, industry/occupation, and "unexplained."

3) The "unexplained" share is rising over time time. As the authors explain: "Perhaps more troubling is the fact that the growth in this unexplained portion accounts for almost all of the growth in the gaps over time. For example, in 1979 about 8 percentage points of the earnings gap for men was unexplained by readily measurable factors, accounting for over a third of the gap. By 2016, this portion had risen to almost 13 percentage points, just under half of the total earnings gap. A similar pattern holds for black women, who saw the gaps between their wages and those of their white counterparts more than triple over this time to 18 percentage points in 2016, largely due to factors outside of our model. This implies that factors that are harder to measure—such as discrimination, differences in school quality, or differences in career opportunities—are likely to be playing a role in the persistence and widening of these gaps over time." The authors also cite this more detailed research paper with similar findings.

4) In looking at the black-white wage gap for women, it's quite striking that this gap was relatively small back in the 1980s, at only about 5%, and that observable factors like education and industry/occupation explained more than 100% of the wage gap at the time. But as the black-white wage gap for women increased starting in the 1990s, an "unexplained" gap opens up.

5) It is tempting to treat the "unexplained" category as an imperfect but meaningful measure of racial discrimination, but it's wise to be quite cautious about such an interpretation. On one side, the "unexplained" category may overstate discrimination, because it doesn't include other possible variables that affect wages (for example, one could include previous years of lifetime work experience, or length of tenure at a current job, scores on standardized tests, or many other variables). In addition, the variables that are included like level of education are being measured in broad terms, and so it is possible that, say, a blacks and whites with a college education are not the same in their skills and background. On the other side, the "unexplained" category could easily understate the level of discrimination. After all, education levels and industry/occupation outcomes don't happen in a vacuum, but are a result of the income, education, and jobs of family members. For this reason, noting that a wage gap is associated with some different in education or industry/occupation may reflect aspects of social discrimination. The kinds of calculations presented here are useful, but they don't offer final answers.

In short, the black-white wage gap is rising, not falling. The wage gap is also less associated with basic measures like level of education or industry/occupation than it was before. I can hypothesize a number of explanations for this pattern, but none of my hypotheses are cheerful ones.


3) The patterns in which speeding tickets are given for those just a little over the speed limit can  reveal discrimination. I discuss some evidence on this point in "Leniency in Speeding Tickets: Bunching Evidence of Police Bias" (April 5, 2017):

Imagine for a moment the distribution of speed for drivers who are breaking the speed limit. One would expect that a fairly large number of drivers break the speed limit by a small amount, and then a decreasing number of drivers break the speed limit by larger amounts.

But here's the actual distribution of amount over the speed limit on the roughly 1 million tickets given by about 1,300 officers of the Florida Highway Patrol between 2005 and 2015. The graph is fromFelipe Goncalves and Steven Mello, "A Few Bad Apples? Racial Bias in Policing," Princeton University Industrial Relations Section Working Paper #608, March 6, 2017. The left-hand picture shows the distribution of the amount over the speed limit on the speeding ticket given to whites; the right-hand picture shows the distribution the amount over the speed limit on the speeding tickets given to blacks and Hispanics.

Some observations:

1) Very few tickets are given to those driving only a few miles per hour over the speed limit. Then there is an enormous spike in those given tickets for being about 9 mph over the speed limit. There are also smaller spikes at some higher levels. In Florida, the fine for being 10 mph over the limit is substantially higher (at least $50, depending on the county) compared to the fine for being 9 mph over the limit.

2) The jump at 9 mph is sometimes called a "bunching indicator" and it can be a revealing approach in a number of contexts. For example, if being above or below a certain test score makes you eligible for a certain program or job, and one observes bunching at the relevant test score, it's evidence that the test scores are being manipulated. If being above or below a certain income level affects your eligibility for a certain program, or whether you owe a certain tax, and there is bunching at that income level, it's a sign that income is being manipulated. Real-world data is never completely smooth, and always has some bumps. But the spikes in the figure above are telling you something.

3) Goncalves and Mello note that the spike at 9 mph is higher for whites than for blacks and Hispanics. This suggests the likelihood that whites are more likely to catch a break from an officer and get the 9 mph ticket. The research in the paper investigates this hypothesis in some detail ...

In the big picture, one of the reminders from this research is that bias and discrimination doesn't always involve doing something negative. In the modern United States, my suspicion is that some of the most prevalent and hardest-to-spot biases just involve not cutting someone an equal break, or not being quite as willing to offer an opportunity that would otherwise have been offered.


4) Many of the communities that suffer the most from crime are also the communities where the law-abiding and the law-breakers both experience a heavy law enforcement presence, and where large numbers of young men end up being incarcerated. Here are some slices of my discussion from "Inequalities of Crime Victimization and Criminal Justice" (May 20, 2016):

And law-abiding people in some communities, many of them predominantly low-income and African-American, can end up facing an emotionally crucifying choice. One one side, crime rates in their community are high, which is a terrible and sometimes tragic and fatal burden on everyday life. On the other side, they are watching a large share of their community, mainly men, becoming involved with the criminal justice system through fines, probation, fines, or incarceration. Although those who are convicted of crimes are the ones who officially bear the costs, in fact the costs when someone needs to pay fines, or can't earn much or any income, or can only be visited by making a trip to a correctional facility are also shared with families, mothers, and children. Magnus Lofstrom and Steven Raphael explore these questions of "Crime, the Criminal Justice System, and Socioeconomic Inequality" in the Spring 2016 issue of the Journal of Economic Perspectives. ...

It's well-known that rates of violent and property crime have fallen substantially in the US in the last 25 years or so. What is less well-recognized is that the biggest reductions in crime have happened in the often predominantly low-income and African-American communities that were most plagued by crime. Loftrom and Raphael look at crime rates across cities with lower and higher rates of poverty in 1990 and 2008:
"However, the inequality between cities with the highest and lower poverty rates narrows considerably over this 18-year period. Here we observe a narrowing of both the ratio of crime rates as well as the absolute difference. Expressed as a ratio, the 1990 violent crime rate among the cities in the top poverty decile was 15.8 times the rate for the cities in the lowest poverty decile. By 2008, the ratio falls to 11.9. When expressed in levels, in 1990 the violent crime rate in the cities in the upper decile for poverty rates exceeds the violent crime rate in cities in the lowest decile for poverty rates by 1,860 incidents per 100,000. By 2008, the absolute difference in violent crime rates shrinks to 941 per 100,000. We see comparable narrowing in the differences between poorer and less-poor cities in property crime rates. ... "
It remains true that one of the common penalties for being poor in the United States is that you are more likely to live in a neighborhood with a much higher crime rate. But as overall rates of crime have fallen, the inequality of greater vulnerability to crime has diminished.

On the other side of the crime-and-punishment ledger, low-income and African-American men are more likely to end up in the criminal justice system. Lofstrom and Raphael give sources and studies for the statistics: "[N]nearly one-third of black males born in 2001 will serve prison time at some point in their lives. The comparable figure for Hispanic men is 17 percent ... [F]or African-American men born between 1965 and 1969, 20.5 percent had been to prison by 1999. The comparable figures were 30.2 percent for black men without a college degree and approximately 59 percent for black men without a high school degree."

I'm not someone who sympathizes with or romanticizes those who commit crimes. But economics is about tradeoffs, and imposing costs on those who commit crimes has tradeoffs for the rest of society, too. For example, the cost to taxpayers is on the order of $350 billion per year, which in 2010 broke down as "$113 billion on police, $81 billion on corrections, $76 billion in expenditure by various federal agencies, and $84 billion devoted to combating drug trafficking." The question of whether those costs should be higher or lower, or reallocated between these categories, is a worthy one for economists. ... Lofstrom and Raphael conclude:
"Many of the same low-income predominantly African American communities have disproportionately experienced both the welcome reduction in inequality for crime victims and the less-welcome rise in inequality due to changes in criminal justice sanctioning. While it is tempting to consider whether these two changes in inequality can be weighed and balanced against each other, it seems to us that this temptation should be resisted on both theoretical and practical grounds. On theoretical grounds, the case for reducing inequality of any type is always rooted in claims about fairness and justice. In some situations, several different claims about inequality can be combined into a single scale—for example, when such claims can be monetized or measured in terms of income. But the inequality of the suffering of crime victims is fundamentally different from the inequality of disproportionate criminal justice sanctioning, and cannot be compared on the same scale. In practical terms, while higher rates of incarceration and other criminal justice sanctions may have had some effect in reducing crime back in the 1970s and through the 1980s, there is little evidence to believe that the higher rates have caused the reduction in crime in the last two decades. Thus, it is reasonable to pursue multiple policy goals, both seeking additional reductions in crime and in the continuing inequality of crime victimization and simultaneously seeking to reduce inequality of criminal justice sanctioning. If such policies are carried out sensibly, both kinds of inequality can be reduced without a meaningful tradeoff arising between them."

5) An "audit study" of housing discrimination involves finding pairs of people, giving them similar characteristics (job history, income, married/unmarried, parents/not parents) and sending them off to buy or rent a place to live. In "Audit Studies and Housing Discrimination" (September 21, 2016), I wrote in part:

Cityscape magazine, published by the US Department of Housing and Urban Development three times per year, has a nine-paper symposium on "Housing Discrimination Today" in the third issue of 2015. The lead article by Sun Jung Oh and John Yinger asks: "What Have We Learned From Paired Testing in Housing Markets?" (17: 3, pp. 15-59). ...

There have been four large national-level paired testing studies of housing discrimination in the US in the last 40 years. "The largest paired-testing studies in the United States are the Housing Market Practices Survey (HMPS) in 1977 and the three Housing Discrimination Studies (HDS1989, HDS2000, and HDS2012) sponsored by the U.S. Department of Housing and Urban Development (HUD)." Each of the studies were spread over several dozen cities. The first three involved about 3,000-4,000 tests; the 2012 study involved more than 8,000 tests. The appendix also lists another 21 studies done in recent decades.

Overall, the findings from the 2012 study find ongoing discrimination against blacks in rental and sales markets for housing. For Hispanics, there appears to be discrimination in rental markets, but not in sales markets. Here's a chart summarizing a number of findings, which also gives a sense of the kind of information collected in these studies.

However, the extent of housing discrimination in 2012 has diminished from previous national-level studies. Oh and Yinger write (citations omitted): "In 1977, Black homeseekers were frequently denied access to advertised units that were available to equally qualified White homeseekers. For instance, one in three Black renters and one in every five Black homebuyers were told that there were no homes available in 1977. In 2012, however, minority renters or homebuyers who called to inquire about advertised homes or apartments were rarely denied appointments that their White counterparts were able to make

John Case
Harpers Ferry, WV

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