Conversable Economist

The Patient Protection and Affordable Care Act of 2010 added two provisions to the individual income tax: a tax credit for those with low income levels who are purchasing health insurance, and a penalty for those who have not purchased health insurance. How many tax returns are actually including these provisions? The mid-year report of the Office of the Taxpayer Advocate, released in July, offers some information in Chapter II, \”Review of the 2016 Filing Season,\” as well

The Premium Tax Credit is the carrot for buying health insurance. As the report notes: \”The PTC is a refundable tax credit paid either in advance or at return filing to help taxpayers with low to moderate incomes purchase health insurance through the Marketplace.\” For the 2015 tax year returns filed in 2016, 4.8 million returns claimed the Premium Tax Credit, and for this group the total value of the ax credit was $14.3 billion. Over 90% of those returns also asked for the Advanced PTC, as pre-payment for the similar costs expected in 2016.

The Individual Shared Responsibility Payment is the stick. As the report writes: \”Taxpayers are required to report that they have “minimum essential coverage” or were exempt from the responsibility to have the required coverage. If the taxpayer did not have coverage and was not exempt, he or she was required to make an ISRP when filing a return.\” A total of 5.6 million returns includes the ISRP provision, and those returns paid an average ISRP of $442, which works out to about $2.5 billion in total.

The report also evaluates how well the IRS has implemented these provisions, with the overall tone reflected in Chapter III, Area of Focus #9, \”As the IRS Has Gained Experience in Administering theIndividual Provisions of the Affordable Care Act, It HasAddressed Some Previous Concerns But a Few Still Remain.\”

If someone who is selling or renting homes faces two people who are similar except for their race or ethnicity–for example, broadly similar types of jobs, education, income, marital situation, and the like–do they show that person the same number of residences, at similar prices, in the same neighborhoods? 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). They describe paired testing studies as involving six steps:

In-person paired-testing research involves six main steps. First, auditors are selected. Each auditor must be capable of playing the role of a typical homeseeker and not have unusual traits that might influence his or her treatment in the housing market relative to the auditor with whom he or she is paired. Second, auditors are trained about the role they should play during an audit. In most cases, they are instructed to inquire about an advertised unit and then to ask for additional suggestions from the housing provider. … Third, a sample of available housing units is randomly drawn, usually from the major local newspaper. In some audit studies, some neighborhoods are oversampled or the sample from the major newspaper is supplemented with other sources, such as community newspapers. …  Fourth, auditors are matched for each test with one member from a historically disadvantaged group. Paired testers are assigned income and other household traits that make them equally qualified for the sampled advertised unit about which they are inquiring. … Teammates are assigned similar incomes and other traits for a given audit so that differences in these traits do not lead to differences in treatment. … Because membership in a historically disadvantaged group cannot be randomly assigned, this approach cannot fully rule out the possibility that some unassigned trait
influences treatment, thereby biasing estimates of discrimination up or down; however, good management makes this outcome unlikely. … Fifth, audit teammates separately contact the housing agent associated with one of the selected advertisements and attempt to schedule a visit. The initial contacts are completed during a short period, but not so short as to be suspicious to the agent. … Sixth, and finally, after an audit is complete, each audit teammate is asked to record what he or she was told and how he or she was treated. These audit forms provide information on the number of houses or apartments shown to each auditor and also on many other aspects of housing agent behavior. Audit teammates have no contact with each other during an audit and they fill out their
audit survey forms independently. Most audit studies then schedule debriefing sessions in which an audit manager reviews these forms with each auditor to ensure that all information on the forms is accurate.

Similar \”correspondence studies\” can be done by email, in which the pairs of people are distinguished by choosing names that are likely to imply race or ethnicity, but otherwise have broadly similar traits. As Oh and Yinger point out, these kinds of studies can be useful both for measuring discrimination, and also as a law enforcement tool.

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.

Another type of housing discrimination involves \”steering,\” which Oh and Yinger define like this:

\”Steering occurs when the characteristics of the neighborhoods in which a homeseeker is shown houses depend on the homeseeker’s race or ethnicity. Black homeseekers, for example, may be steered away from affluent, predominantly White neighborhoods and instead offered housing in neighborhoods where the residents are largely Black, integrated, relatively poor, or a combination of the three, and White homeseekers may be steered away from neighborhoods where a significant number of Black families reside. …

\”Racial steering is defined to exist if, compared to the White auditor in the same audit, the minority auditor is recommended or shown houses in neighborhoods where the percentage of the population that is White is lower. As exhibit 7 illustrates, each HDS found evidence of steering. The gross estimates of steering in this exhibit range from 4 to 26 percent, and the net measures for both houses recommended and houses inspected are statistically significant for Black homeseekers in 2000 and 2012. The net measure for houses inspected is also significant for Hispanic homeseekers in 2000. … [T]he incidence of steering has become larger over time.\”

 Notice that the paired testing method rules out the possibility that the homebuyers of different racial or ethnic groups are actively seeking out housing in different neighborhoods.

Oh and Yinger discuss how this evidence fits with various hypotheses about discriminatory behavior. For example, are these outcomes a matter of prejudice from the real estate agent, whether consciously or not? For example, several studies find that older agents are more likely to be involved in discriminatory behavior. Or do the outcomes result from a belief by agents, acting without animus, that treating customers of different races and ethnicities in certain ways is more likely to lead to a completed transaction? Documenting patterns is relatively easy; disentangling motives is hard.

But whatever the underlying reason, housing discrimination tends to promote segregation and is illegal. Paired testing studies are a useful tool for demonstrating the existence of such discrimination. The earlier studies in the 1970s and 1980s did seem to have a powerful effect on raising consciousness and enforcement efforts related to housing discrimination. Oh and Yinger report: \”As of 2011, 98 private nonprofit agencies were engaged in fair housing enforcement.\”  Moreover, the US Department of Justice has since 1992 been carrying out a Fair Housing Testing Program, which typically involves about four investigations per year. They cite recent cases in New York, Alabama, Arkansas, Pennsylvania, and Wisconsin, among others.

Some studies of discrimination, like many of the studies looking at wage gaps between different groups, are looking for the extent to which individual attributes (education, experience, and so on) can explain wage gaps. Such studies are looking at overall data about individuals, and so they have little to say about the behavior of specific employers. In contrast, paired testing studies can be revealing for broad patterns in doing social science research, and also can point toward specific discriminatory behavior.

Economists tend to like competition between firms. Competition between firms is good for consumers. It helps keep prices low, and it also encourages creation of new products, new varieties of existing products, and building a reputation for quality. Competition is good for workers, too. It\’s a lot nicer to be a worker in a job market with a bunch of different potential employers, rather than just one or two. When situations arise where it\’s hard for competing firms to function, like providing water or electricity to homes, economists often try to find ways to mimic the incentives that competition would provide.

Thus, it\’s troublesome to see a range of evidence–not fully conclusive but certainly suggestive–that competition is declining in many US markets.  Some of this evidence is summarized in a Council of Economic Advisers report from April 2016, called Benefits of Competition and Indicators of Market Power.  The chair of the CEA, Jason Furman, discusses that report and provides some additional context in his September 16 lecture \”Beyond Antitrust: The Role of Competition Policy in Promoting Inclusive Growth,\” delivered at the Searle Center Conference on Antitrust Economics and Competition Policy at the Northwestern University Law School.

One basic way to measure the extent of competition is the share of total sales being made by the largest four or eight or 50 firms in an industry. Another basic way is to measure the Herfindahl-Hirschman Index, which involves taking the market share of the firms in an industry, squaring them, and adding them up. Thus, an industry with a giant firm that had 50% of the market, four firms that each had 10% of the market, and 10 firms that each had 1% of the market, would have an HHI of 50² + 4(10²) + 10(1²) = 2910. A monopoly firm with 100% of the market would have an HHI of 10,000, while a firm with thousands of very small firms might have an HHI of 100 or less.

The US Census Bureau does an Economic Census of all US firms once every five years. The results for the 2012 Economic Census are now becoming available. Here\’s one comparison from the CEA report showing the share of sales by the top 50 firms in various industries, comparing 1997 and 2012.

A number of studies of individual industries also show a drop in competition. Furman summarizes some of this evidence in his recent talk. Furman says (footnotes omitted):

Along similar lines, The Economist (2016) found that in 42 percent of the roughly 900 industries examined, the top four firms controlled more than a third of the market in 2012, up from 28 percent of industries in 1997. … These broad trends are consistent with a number of industry-specific studies tracking concentration over longer periods of time:

• In financial services, a study found that the loan market share of the top ten banks increased from about 30 percent in 1980 to about 50 percent in 2010 (Corbae and D’Erasmo 2013).
• The share of revenues held by the top four firms increased between 1972 and 2002 in eight of nine agricultural industries tracked in a Congressional Research Service study (Shields 2010).
• According to Gaynor, Ho, and Town (2015), hospital market concentration increased from the early 1990s to 2006. The authors found that the average Herfindahl-Hirschman Index (HHI), a commonly used measure of market concentration, increased by about 50 percent to about 3,200, the level associated with just three equal-sized competitors in a market.
• Wireless providers saw increased concentration, with the FCC (2015) finding that the average HHI in the markets they examined increased from under 2,500 in 2004 to over 3,000 in 2014.
• Railroad market concentration increases between 1985 and 2007 have been documented by Prater et al. (2012).

The CEA report and Furman\’s talk both offer a number of possible reasons for the fall in competition, but there\’s one reason they don\’t emphasize that seems to me worth mentioning. In some sectors, including finance and health care, dramatic changes in regulations have tended to increase the size of firms, because larger firms typically find it easier to bear the costs of in-depth regulations. Indeed, there are a number of cases in which large firms don\’t fight too hard against regulation, because they know that extensive regulation can tend to hinder or block the entry of new firms.

I mentioned at the start that this kind of evidence about less competition isn\’t conclusive. One main reason for that caveat is that competition is really about the choices available to consumers, not the number or size of firms.  To understand this distinction, imagine a situation in which the US economy has thousands of small banks, but each one operates only in a single city or town. In contrast, imagine a situation in which the US economy has only five large banks, but they are all available online to everyone in the US economy. Based on the number of banks, the the situation with many small banks might appear to have more competition. But if you are living in a given small or medium-sized town, you might have more choices with five big banks available online, rather than just one small bank that is operating in your town.

Moreover, firms can a variety of pricing and information strategies so that once you have signed up with one of them, the costs of switching become quite high, and the functional amount of day-to-day competition between them is diminished. Thus, in-depth studies of competition need to look not just at number of firms or share of total sales, but at the ways in which firms are actually competing for customers–and the realistic choices that customers actually have.

With such concerns duly notes, the US economy does seem to be going through a period of diminished competition in many markets. Consumers, beware.

Some job applications have a question which ask if you have a criminal history; if so, you are supposed to put a checkmark in a certain box. Given that African-Americans are statistically more likely to have a criminal history, it might seem obvious that this question tends to reduce job opportunities for African-Americans. But some evidence suggests that this intuition may be wrong; indeed, banning the box might actually reduce job opportunities for African-Americans. The study is called \”Ban the Box, Criminal Records, and Statistical Discrimination: A Field Experiment,\” by Amanda Agan and Sonja Starr (Princeton University International Relations Section, Working Paper #5998, July 2016).  [I learned about a couple of additional studies from responses to this original post, which are now discussed briefly at the end.] are As Agan and Starr write at the start of the paper (citations omitted):

In an effort to reduce barriers to employment for people with criminal records, more than
100 jurisdictions and 23 states have passed “Ban-the-Box” (BTB) policies. Although the details vary, these policies all prohibit employers from asking about criminal history on the initial job application and in job interviews; employers may still conduct criminal
background checks, but only at or near the end of the employment process. Most BTB policies apply to public employers only, but seven states (including New Jersey) and a number of cities (including New York City) have now also extended these restrictions to private employers. These laws seek to increase employment opportunities for people with criminal records. They are often also presented as a strategy for reducing unemployment among black men, who in recent years have faced unemployment rates approximately double the national average … Thus, a policy that increases the employment of people with records should disproportionately help minority men.

Agan and Starr carried out an experiment. They sent out about 15,000 fictitious online job applications to entry-level positions in New Jersey and New York city, both before and after the \”ban-the-box\” policy went into effect. The resumes were set up in pairs, so that they were largely the same resume except for a difference in race; in particular, out of each pair, one job applicant could be identified as  white and one as black. In addition, some of the pairs of hypothetical applicants checked \”the box\” early on, while others did not; some had a high school diploma, or a GED high-equivalency, or neither; some had a gap in their job  history, while others did not.

 The study found that whites with the same credentials are more likely to get a call-back than blacks: as they write, \”white applicants overall received about 23% more callbacks compared to similar black applicants.\” Before \”ban-the-box\” went into effect, admitting to a criminal record definitely made it harder to be hired: that is, \”among employers that asked about criminal convictions in the pre-period, the effect of having a felony conviction is also significant and large: applicants without a felony
conviction are 62% … more likely to be called back than those with a conviction, averaged across races …\”

However, when ban-the-box (BTB) was enacted, the black-white gap in the chances of being called back got larger, not smaller. \”Our estimates of BTB’s effects on callback rates imply that BTB substantially increases racial disparities in employer callbacks. We find that BTB expands the black-white gap by about 4 percentage points, multiplying the gap at affected businesses by a factor of about six. In our main specification, before BTB, white applicants to BTB-affected employers received 7% more callbacks than similar black applicants, but after BTB this gap grew to 45% …\”

The authors suggest that what economists call \”statistical discrimination\” is a possible explanation for these findings. The idea of statistical discrimination is that people might make decisions that have a discriminatory effect not out of animus against a particular group, but because they are using group membership as a marker for a higher probability of certain outcomes. Thus, it is a statistical fact that more blacks have a criminal history than whites. Consider an employer who is both mildly biased against blacks, but also would strongly prefer not to hire someone who has a criminal record. If that employer has information on whether someone has a criminal record, they will continue to be biased against blacks. But if this employer is banned from collecting information on criminal record, they will tend to act on the statistical knowledge that blacks are more likely to have a criminal record than whites. As a result, blacks without a criminal record will have a lower chance of a job callback, and whites with a criminal record will have a higher chance of a job callback.

Of course, one study with fictional resumes isn\’t the final word on any subject. One can concoct scenarios where even if ban-the-box means that blacks got fewer call-backs, perhaps this doesn\’t translate into fewer actual jobs.  But the evidence does suggest that advocates of ban-the-box should open their minds to the possibility that their good intentions about improving employment prospects for low-skilled black workers might in this case be leading to counterproductive results.

Addendum #1: Thanks to Catherine Rampell for pointing out to me that there\’s another recent empirical study of ban-the-box, different methods, but similar results. The study is \”Does \”Ban the Box\” Help or Hurt Low-Skilled Workers? Statistical Discrimination and Employment Outcomes When Criminal Histories are Hidden,\” by Jennifer L. Doleac and Benjamin Hansen, published as NBER Working Paper No. 22469 (July 2016). (These working papers are not freely available online, but many readers will have access through a library subscription.) Instead of using fictional resumes, this study looks at variations in the details and timing of ban-the-box policies. They conclude:

We find that BTB policies decrease the probability of being employed by 3.4 percentage points (5.1%) for young, low-skilled black men, and by 2.3 percentage points (2.9%) for young, low-skilled Hispanic men. These findings support the hypothesis that when an applicant\’s criminal history is unavailable, employers statistically discriminate against demographic groups that are likely to have a criminal record.

Addendum #2: Thanks to Stan Veuger for pointing out yet another recent working paper on this subject, which uses a different approach and emphasizes a different set of tradeoffs. In \”No Woman No Crime: Ban the Box, Employment, and Upskilling,\” Daniel Shoag and Stan Veuger look at employment with a focus on the outcome of ban-the-box an employment rates of those living in high-crime neighborhoods. They study the effects by looking at variations in employment rates that arise from the differences in timing of when cities, counties, and states adopt ban-the-box policies. They find:

\”Using LEHD Origin-Destination Employment, a novel dataset on millions of job postings, and American Community Survey data, we show that these bans increased employment of residents in high-crime neighborhoods by as much as 4%. This effect can be seen both across and within Census tracts, in employment levels as well as in commuting patterns. The increases are particularly large in the public sector and in lower-wage jobs. At the same time, we establish that employers respond to Ban the Box measures by raising experience requirements. While black men benefit on net from these changes, a perhaps unintended consequence of them is that women, who are less likely to be convicted of crimes, see their employment opportunities reduced.\”

The US poverty line is admittedly arbitrary. The level is based on a calculation from back in the 1960s on what it cost a family at that time to buy a bare-bones nutritionally adequate diet, and then updated for inflation over time. The poverty measure is based on money income before taxes, which doesn\’t include government benefits the value of noncash government benefits like Medicaid or food stamps, and also doesn\’t include the earned income tax credit, because that program is counted as income after taxes. Even the US Census Bureau, which calculates the number of Americans below the poverty line, started a couple of years ago to produce an alternative Supplemental Poverty Measure that makes a number of adjustments to the standard measure

But the poverty line reported each year by the Census Bureau does have the merit that, for all its faults, it has been calculated in pretty much the same way for a long time now. The Census Bureau just released its estimates for the poverty rate in 2015 in its report, Income and Poverty in the United States: 2015, co-authored by Bernadette D. Proctor, Jessica L. Semega, and Melissa A. Kollar (September 2016, P60-256). As the report notes, \”Real median household income increased 5.2 percent between 2014 and 2015. This is the first annual increase in median household income since 2007. … The number of full-time, year-round workers increased by 2.4 million in 2015.\” Given those changes, it\’s not a big surprise that \”The official poverty rate decreased by 1.2 percentage points between 2014 and 2015.  … he number of people in poverty fell by 3.5 million between 2014
and 2015.\”

Here are a few illustrative figures. The first shows the poverty rate, with all it warts and flaws, over time. The slightly different color of the lines after 2013 is because the wording of the underlying survey was slightly altered in that year.

Back in the 1960s, the poverty rate was higher for the elderly than for children or adults aged 18-64. But with the expansion of Social Security and Medicare over time, and a larger share of children being born into single-parent families below or near the poverty line, the poverty rate for children has been higher than for adults since the late 1970s.

Poverty rates are also lower for married for married-couple families and especially high for families with a \”female household, no husband present.\”

But while poverty is conventionally measured by income, or income plus access to government benefits, a fuller understanding of what it means to be poor needs to reach beyond income and consumption. Frank Schilbach, Heather Schofield, and Sendhil Mullainathan offer an overview of some social science research in this area in \”The Psychological Lives of the Poor,\” which appeared in the American Economic Review: Papers & Proceedings (May 2016, 106:5,  435–440).  (The volume is not freely available online, but many readers will have access through a library subscription.) The authors argue that those in poverty suffer in a number of ways from less mental \”bandwidth.\” They write (citations and footnotes omitted):

First, a large body of work points toward a two- system model of the brain. System 1 thinks fast: it is intuitive, automatic, and effortless, and as a result, prone to biases and errors. System 2 is slow, effortful, deliberate, and costly, but typically produces more unbiased and accurate results. Second, when mentally taxed, people are less likely to engage their System 2 processes. Put simply, one might think of having a (mental) reserve or capacity for the kind of effortful thought required to use System 2. When burdened, there is less of this resource available for use in other judgments and decisions. Though there is no commonly accepted name for this capacity, we will refer to it in this article as “bandwidth”.

Psychologists often study this underlying resource by imposing “cognitive load” to tax bandwidth and measure the impact on judgments and decisions. The many ways to induce load produce similar results on various bandwidth measures and consequences from reduced System 2 thinking. This insight is particularly useful because it implies that bandwidth is both malleable and measurable. It also suggests a unified approach of studying the psychology of poverty. We can understand factors in the lives of the poor, such as malnutrition, alcohol consumption, or sleep deprivation, by how
they affect bandwidth. And we can understand important decisions made by the poor, such as technology adoption or savings, through the lens of how they are affected by bandwidth. Clearly, bandwidth is not the only important aspect of the psychological lives of the poor; no single metric can take on this role. However, it provides a way to at least partly understand a great many of the thought processes that drive decision-making by the poor. ….

[T]here are reasons to believe that the effects of diminished bandwidth are larger for the poor. Individuals in poverty are more likely to be exposed to many of these factors (e.g., malnutrition, pain, heat) and to experience them more extensively. Further, the poor are less likely to have coping mechanisms, such as direct deposits or automatic enrollments, available to reduce the negative effects of limited bandwidth. Not only is their exposure greater, but the “same mistake” is likely to be more costly for the poor than for the rich. Finally, money is a potential substitute for bandwidth. It is often possible to buy yourself the extra slack you need—hiring someone to cook and clean—or to reduce the factors which lead to lower bandwidth—purchasing a comfortable bed in a quiet neighborhood.

In short, the poor do not only suffer from lack of income. They also suffer from limits on bandwidth that affect the ability to remember decisions that need to be made in the future, or \”executive control\” functions that affect self-control about consumption or saving, or the ability to evaluate risks and benefits accurately. There is also some limited evidence that people under cognitive stress in one area (like hunger or finances) may gain less pleasure from other activities as well–a sort of extra tax on happiness that is imposed by limited bandwidth and poverty. Understanding what policies might help the poor to  help themselves, whether in high-income or low-income countries, means coming to grips with how people act when stress is high and bandwidth is low.

A couple of years ago when the poverty rate was released, I wrote a post on \”Empathy for the Poor: A Meditation\” (September 17, 2014).  In that post, I quoted George Orwell\’s discussion in his 1937 book, The Road to Wigan Pier,  where he makes the case that the poor have adapted to a world to a world of cheap luxuries, including fish-and-chips and the electronic connections that allow them to focus on celebrity culture and sports betting. To me, it has an uncomfortably modern ring in describing a set of potential psychological adaptations for people who find themselves with limited bandwidth.  Here\’s a quotation from Orwell, sliced from that earlier post:

\”What we have lost in food we have gained in electricity. Whole sections of the working class who have been plundered of all they really need are being compensated, in part, by cheap luxuries which mitigate the surface of life.

\”Do you consider all this desirable? No, I don\’t. But it may be that the psychological adjustment which the working class are visibly making is the best they could make in the circumstances. They have neither turned revolutionary nor lost their self-respect; merely they have kept their tempers and settled down to make the best of things on a fish-and-chip standard. . . . Of course the post-war development of cheap luxuries has been a very fortunate thing for our rulers. It is quite likely that fish-and-chips, art-silk stockings, tinned salmon, cut-price chocolate (five two-ounce bars for sixpence), the movies, the radio, strong tea, and the Football Pools have between them averted revolution. Therefore we are sometimes told that the whole thing is an astute manoeuvre by the governing class–a sort of \’bread and circuses\’ business–to hold the unemployed down. What I have seen of our governing class does not convince me that they have that much intelligence. The thing has happened, but by an unconscious process–the quite natural interaction between the manufacturer\’s need for a market and the need of half-starved people for cheap palliatives.\”

When discussing the poverty rate, someone is sure to point out that even most people below the poverty line in the modern United States have access to sufficient calories, television, and cellphones. It\’s of course true that poverty in the modern United States isn\’t like poverty in 19th century Dickensian England. But it remains much harder for people in poverty, and their children, to flourish.

One of the most dispiriting patterns of socioeconomic inequality is that an academic achievement gap is already apparent for children entering kindergarten. Thus, I was intrigued by the evidence collected by Sean F. Reardon and Ximena A. Portilla that the white-black and white-Hispanic gaps in academic abilities of kindergartners appear to have lessened between 1998 and 2010. Their evidence appears in
\”Recent Trends in Income, Racial, and Ethnic School Readiness Gaps at Kindergarten Entry,\” published in the July-September issue of AERA Open (that is, a journal of the American Educational Research Association, 2:3, pp. 1-18).

Here\’s how Reardon and Portilla summarize the evidence:

\”Data from three large, nationally representative samples of kindergarten students indicate that on standardized tests, income and, to some extent, racial/ethnic gaps in school readiness have narrowed over the last dozen years (see Figure 3 for summary of these trends). The declines in income gaps and White-Hispanic gaps in academic skills at kindergarten entry are moderately large and statistically significant; the estimated declines in White-Black math and reading gaps are somewhat smaller, are not statistically significant in reading, and are only marginally significant in math. The evidence regarding trends in gaps in other measures of school readiness are less clear. Racial/ethnic gaps in teacher-reported measures of self-control and approaches to learning declined by 25% to 50%, while the income gap in teacher-reported externalizing behavior increased by 25%.\”

For those not familiar with the terminology, the 90/10 gap refers to the gap between families at the 90th percentile of the income distribution and families at the 10th percentile of the income distribution. The authors also address some obvious questions. For example:

\”How meaningful are these changes? The income achievement gaps in kindergarten entry math and reading declined at the rate of 0.008 and 0.014 standard deviations per year, respectively, over the 1998–2010 period. To put this into context, Reardon (2011) found that the 90/10 income achievement gap grew by roughly 0.020 standard deviations per year among cohorts born in the mid-1970s to those born in the early 1990s. So the rate of decline in the kindergarten readiness 90/10 income gaps appears to be somewhere between 40% and 70% as rapid as the rate of increase in the gap in the prior two decades. Looked at this way, the rate of decline from 1998 to 2010 is not trivial. Nonetheless, the gaps were roughly 1.25 standard deviations in 1998; at the rates that the gaps declined in the last 12 years, it will take another 60 to 110 years for them to be completely eliminated. The rates of decline in the White-Hispanic and White-Black math gaps are similar in magnitude.\”

The authors are providing evidence here, not analyzing potential causes. But they do offer some informed speculation on causes.

\”The most obvious candidate explanation for this decline is perhaps the changes in preschool enrollment patterns over this period. Both the income gap and the White-Hispanic gap in preschool enrollment rates declined since the early 1990s; the White-Black gap in preschool enrollment was unchanged over the same period. These trends are consistent with our finding here that the income and White-Hispanic school readiness gaps declined significantly, while the White-Black gap declined less (and at a rate not distinguishable from zero at conventional levels of significance). Of course, the correlation of preschool enrollment gap trends and school readiness gap trends does not prove that the first caused the second, but it does suggest that further investigation of preschool enrollment trends as a possible primary cause of the narrowing readiness gaps would be informative. We also suggest that increases in child health insurance rates among the near poor may have played a role in these improvements. Another category of explanation might be cultural changes in parenting practices that have increased low-income children’s exposure to cognitively stimulating activities at home. An investigation of these possible causes is beyond the scope of this article, however.

Kindergarten readiness is quite important. In another part of the article, the authors point out that educational attainment gaps appear to have fallen for fourth-graders–but the gains are mostly because of a lower gap at kindergarten, not because schools are doing a better job at reducing the pre-existing gaps at kindergarten.

\”It is also useful to compare the trends in the income and racial/ethnic gaps at kindergarten entry with the trends in the same gaps as the children progress through school. Our analyses show that the trends persist through kindergarten. Moreover, the NAEP data [National Assessment of Educational Progress] … suggest that the racial/ethnic achievement gaps trends that we observe at kindergarten entry persist through fourth grade. … White-Black and White-Hispanic math and reading fourth-grade (or age 9) gaps declined by roughly 0.15 standard deviations between the cohorts born in 1993 and 2005, corresponding to a rate of decline of about 0.012 standard deviations per year, similar to the rate of change of the White-Hispanic kindergarten entry gap and 50% larger than the rate of change of the White-Black kindergarten entry gap. That is, the achievement gaps in fourth grade declined at roughly the same rate as, or moderately faster than, the kindergarten entry gaps. This suggests that the primary source of the reduction in racial/ethnic achievement gaps in fourth grade …  is a reduction in school readiness gaps, not a reduction in the rate at which gaps change between kindergarten and fourth grade.\”

As I\’ve noted in posts over the years, the evidence on how preschool improves later academic performance is weaker than I would like it to be (\”Head Start is Failing Its Test,\” January 29, 2013; \”Preschool for At-Risk Children, Yes; Universal Preschool, Maybe Not,\” May 23, 2013). I would like to see some additional effort put into starting and evaluating programs to affect \”The Parenting Gap for Pre-Preschool\” (September 17, 2013).

Foreign direct investment refers to a situation when a foreign firm invests in an affiliate located in the United States in a substantial enough way that it gains some voice in the management of the enterprise. This is often defined in terms of having ownership of at least 10 percent of the voting stock of the company.  After an inflow of $348 billion in foreign direct investment to the US economy in 2015, the total stock of foreign direct investment in the US economy is $2.9 trillion. Rudy Telles Jr. lays out these facts and other evidence in \”Foreign Direct Investment in the United States: Update to 2013 Report,\” written by  for the Economics and Statistics Administration of the U.S. Department of Commerce (ESA Issue Brief #02-16, June 20, 2016).

Here\’s a figure showing the pattern of FDI into the U.S. economy on a year-to-year basis.

Here are some bullet points from Telles about key patterns (footnotes omitted):

• The United States is the largest recipient of global FDI with an inward FDI stock of $2.9 trillion on a historical-cost basis in 2014. On a current-cost basis, the United States’ FDI stock was more than three times larger than that of the next largest destination country in 2014.
• Investment in the United States remains strong; total stock of FDI in the United States grew at an average annualized rate of 6 percent per year from 2009-2014. … 
•  Advanced economies, led by the United Kingdom, Japan and Germany, hold the largest FDI positions in the United States.
• Majority-owned U.S. affiliates of foreign entities affiliates produced $360.0 billion in goods exports in 2013, and are a catalyst for research and development in America, spending $53.0 billion in R&D and accounting for a record high 16.4 percent of the U.S. total expenditure on R&D by businesses.
• Majority-owned U.S. affiliates of foreign entities employed 6.1 million U.S. workers in 2013, up from 5.8 million in 2011, and generally provide compensation at higher levels than the U.S. average, at nearly $80,000 per U.S. employee in 2013 as compared to average earnings of $60,000 for workers in the economy as a whole.
• The U.S. manufacturing sector continues to benefit greatly from inbound FDI flows, as nearly 70 percent of FDI flows in 2015 and over one-third of jobs at U.S. majority-owned affiliates of foreign entities were in manufacturing in 2013.

I would add that the growth of foreign direct investment fits with other emerging patterns in international trade. For example, companies that use global supply chains will often find it useful to have a substantial ownership share in many of the links of that chain–which means more foreign direct investment. When exporting services, it will often be useful to have a physical presence in the country where the buyers of those services are located, to improve both communication and marketing. Clearly, it is  increasingly unwise to assume that because a good or services is produced in the geographical area of a certain country, it is also being produced by a company that is owned by investors from that same country.

Can China maintain its rapid pace of economic growth in the decades ahead? Jacob Funk Kirkegaard
suggests that one substantial hindrance may be China\’s education system is not keeping up. He lays out the case in \”China’s Surprisingly Poor Educational Track Record,\” which appears as Chapter 3 in
China’s New Economic Frontier: Overcoming Obstacles to Continued Growth, published by the Peterson Institute for International Economics (PIEE Briefing 16-5, September 2016, edited by Sean Miner).

As a starting point, compare countries by per capita GDP and what share of the adult population has at least an upper secondary education. As shown in the figure, the education level of China\’s adult population ranks well below other countries with a roughly similar level of per capita GDP.

China has made dramatic gains in its education level in the last few decades. One standard measure of gains over time is to compare the education level of a younger age group to an older age group, like the average education level of adults age 25-34 with adults age 55-64. The red bars–with China shown in yellow–shows how much the education level of the younger group exceeds that of the older age group. Clearly, China has made substantial gains. But just as clearly, the gains in China\’s education attainment are below those for France, Spain, Brazil, Korea, and others. Moreover, China was starting at a much lower level of educational attainment (the hollow box showing educational attainment for the 55-64 age group is lower for China than for the comparison countries shown here) and so middling gains for China in educational attainment aren\’t helping it to catch up.

Kirkegaard sums up the situation this way:

\”In some ways, China may have been a victim of its own success. The pull effects of its sustained economic boom and rapidly rising wage levels appear to have led too many young people to leave education too early to acquire the skills needed to sustain them (and Chinese economic growth rates) throughout their lifetimes. As Chinese economic expansion shifts toward more skill-intensive growth, those without a secondary education will be less able to find jobs. …  The Chinese government and society appear to have failed to keep enough of the country’s young people in school during the recent decades of economic growth. This is likely to have long-term scarring effects, as public underinvestment in human capital and individual acquisition of needed skills are difficult
to undo. People’s “lower than otherwise would have been the case” skill levels cannot easily be restructured. Skill shortages at the upper secondary level will make it harder for China to move into the production of higher value added goods and services, lead to increased income inequality and geographic wealth diversity, and complicate the transition to a widespread consumption-based economy.\”

Aaron Steelman interviews Erik Hurst in Econ Focus, published by the Federal Reserve Bank of Richmond (First Quarter 2016, pp. 22-26). The brief overview preceding the actual interview describes some of the subjects covered:

\”Hurst has used regional data in a series of papers to look at other macroeconomic phenomena that would be hard to examine using national data alone. He also has done important work on household financial behavior — including consumption and time use over people\’s life cycles — and on labor markets. Business startups have been another interest of his: Much has been written about the importance of entrepreneurship to the U.S. economy, but what, he has asked, actually motivates people to open their own businesses? In addition, in a recent paper, he and co-authors have attempted to quantify how much the decline in barriers to employment of women and minorities has contributed to economic growth. Among his current research interests is explaining the decline in labor force participation among prime working age males.\”

Here are a few of Hurst\’s comments from the interview that caught my eye:

On who the entrepreneurs really are …

\”Most small businesses are plumbers and dry cleaners and local shopkeepers and house painters. These are great and important occupations, but empirically essentially none of them grow. They start small and stay small well into their life cycle. A plumber often starts out by himself and then hires just one or two people. And when you ask them if they want to be big over time, they say no. That\’s not their ambition. This is important because a lot of our models assume businesses want to grow. Thinking most small businesses are like Google is not even close to being accurate. They are a tiny fraction.

\”My work with Ben Pugsley has been emphasizing the importance of nonpecuniary benefits to small-business formation. Because when you ask small-business people what their favorite part of their job is, it\’s not making a lot of money. They do earn an income and they\’re very happy with it, but they get even more satisfaction from being their own boss and having flexibility and all of those other nonpecuniary benefits that come with being the median entrepreneur in the United States.\”

Wages may be flexible at the local/regional level, even if they appear stickier using national-level data. 

The facts are real wages moved very strongly with employment across regions. Nevada was hit very hard by the recession, for example, while Texas was hit much less hard. Wage growth, both nominal and real, was about 5 percent higher in Texas than it was in Nevada during the Great Recession. So if you\’re going to just correlate employment movements and wage movements, both real and nominal, at the local level, you see a pretty strong reduced form correlation.In the aggregate time series, you don\’t. Wages hardly moved at all despite employment falling pretty sharply. So there are some differences in the correlations between wages and employment at the local level and the aggregate level during this recession … When people say the reason we haven\’t seen real robust wage growth in the recovery is because wages were so sticky in the beginning period, I just don\’t think that holds water with the flexibility of wages that we see at the local level.\”

The Great Recession is over–for skilled labor.

\”There is a structural problem for prime-age, lower-skilled workers in the economy. If you take a look at people with a four-year college degree, you can barely see the effects of the recession any longer. There’s been no lasting effect on their employment rate. Almost all of the effect is concentrated among people with less than four-year college degrees.\”

Do the agglomeration benefits of urban areas come from production or from consumption?

\”Many urban models historically assumed that agglomeration benefits usually came from the firm side. Someone might want to be close to the center city, for instance, because most firms are located in the center city. So the spillover for the household was the commuting time to where the firms were, and the firms chose to locate near each other because of agglomeration benefits.

\”I have always been interested in it from another angle. When we all come together as individuals, we may create agglomeration forces that produce positive or negative consumption amenities. Thinking about it this way, when a lot of high-income people live together, maybe there are better schools because of peer effects or higher taxes. Or maybe there are more restaurants because restaurants are generally a luxury good. Or maybe there’s less crime because there is an inverse relationships between neighborhood income and crime, which empirically seems to hold. So, while we value proximity to firms, that’s not the only thing we value. How important are these consumption amenities? And more importantly, how do these consumption amenities evolve over time …\”

The interview also discuss the findings of several paper that appeared in the Journal of Economic Perspectives, where I work as Managing Editor, and which together give a sense of the breadth and depth of Hurst\’s work. (All papers in JEP, from the current issue back to the first issue in 1987, are freely available online compliments of the publisher, the American Economic Association.\”

In the Summer 2008 issue of JEP, Hurst co-authored a paper with Jonathan Guryan and Melissa Kearney on the subject, \”Parental Education and Parental Time with Children.\” Here\’s a comment from Hurst about this work in the interview:

\”[I]f you look at the income gradient of how we spend our time, the richer you are, the less home production you do. But the richer you are, the more childcare you do. So that income gradient between home production and childcare has opposite signs, which tells me it’s not exactly the same good. Whether that’s coming from the utility you get from being with your kids or whether it’s from investing in their human capital, that’s hard to say. We know people from high-income families go to school more, go to the doctor more, and spend more time with their families. So how much of it is investment, how much of it is home production, how much is leisure, I don’t know.

\”I have always advocated that you should have four uses of time — market work, home production, taking care of kids, and leisure — and then treat kids as somewhere between leisure and home production.\”

In the Spring 2016 issue of JEP, Hurst co-authored a paper with Kerwin Kofi Charles and Matthew J. Notowidigdo on the subject \”The Masking of the Decline in Manufacturing Employment by the Housing Bubble.\” Here\’s Hurst describing the paper:

\”The way I usually describe the paper, which I wrote with Kerwin Charles and Matt Notowidigdo, is to take two regions, Detroit and Las Vegas. Las Vegas has very little manufacturing relative to Detroit. Detroit didn’t have a big housing boom but Las Vegas did. … When you look at this early 2000s period, if you focus only on Detroit, you see employment rates going down, particularly among prime-age workers. It looks like there was a structural decline in employment well before the recession ever started. When you look at Las Vegas during the boom, the employment rate was well above long-run local averages. Normally, most people in their 20s and 30s work, but some of them don’t. During this period in Las Vegas, among lower-skilled workers in their 20s and 30s, nearly everybody was working. So when you put aggregate statistics together, when you sum together Detroit and Las Vegas, it looks like employment rates were relatively constant over this time period. But one was really low compared to historical levels, and one was really high relative to historical levels.

\”In this paper, we show that the decline in manufacturing that occurred during this period nationally — when you add in the Detroits, the Worcesters, and the Youngstowns — was masked by the aggregate housing boom in places like Las Vegas, Phoenix, south Florida, and some places in California that were growing well above average. Now one of these was temporary and one isn’t. The housing boom we know busted and then employment in Las Vegas plummeted. If you look at 2010 or 2011, the employment rate in Las Vegas is roughly the same as it was in 2000, meaning it increased and went back to trend, where the old manufacturing centers just continued declining relative to their 2000 level. You have a very temporary boom-bust cycle overlaid with a structural decline, and what you get is kind of a hockey stick pattern for the aggregate.

\”So for macroeconomists looking at the Great Recession, this is important for understanding why the employment rate hasn’t bounced back to its 2007 level. It shouldn’t have because 2007 wasn’t a steady state. In terms of policy implications, this means that monetary policy arguably is not an especially effective tool for strengthening the labor market. Instead, I believe you need to focus on retraining workers or investing in skills in some form. You might also want to look at disability and some other government programs that might act as a drag on unemployment.\”

Union membership varies wildly across high-income countries. In addition, there is a phenomenon of \”collective bargaining coverage,\” often not familiar to American readers, which measures the share of workers who are covered by collective bargaining agreement, even though they are not union members. In the US, union density is almost the same as collective bargaining coverage. But in France, only 7.7% of workers are actual union members while 98% of workers are covered by collective bargaining agreements. Here are some facts on these patterns across high-income countries from the OECD publication called Economic Policy Reforms 2016: Going for Growth.

As a starter, here are figures showing the variation in the share of workers who are covered by a collective bargaining agreement (Panel A) and the share actually belonging to a union (Panel B). Just glancing at the figure should offer two lessons: 1) There\’s a lot of variation across countries; 2) Many of the coverage rate percentages are substantially higher than the union membership percentages; that is, in a lot of countries a large share of workers will find that their compensation is determined by collective bargaining, even though they are not a union member.

Here are some specific examples of the differences between union density and collective bargaining coverage, drawing from the OECD data: