Month: September 2016

In the long-ago days of the 20th century, it was difficult to find out about small or medium companies that were far away: hard to discover they existed, hard to get details about their products and pricing, hard to place an order, and especially if the firm was in another country, hard to pay for an order and track shipping. That\’s all becoming easier, which suggests that small and medium enterprises may be able to draw on wider markets, and not be so focused on local markets. Thus, the World Trade Organization in its World Trade Report 2016 focuses on the theme of \”Leveling the Playing Field for SMEs\”–that is, \”small and medium enterprises.\”

Much of the report focuses on practical issues affecting small and medium enterprises: connectedness, international trade rules, finance and others. Here, I\’ll just point to two patterns that I found intriguing. Here\’s a figure showing the share of exports and imports attributable to small and medium enterprises, which are defined as as employing 10-250 people (the blue bars), and also micro-enterprises (the gray dots), which are defined as employing 0-9 people.

Unsurprisingly, the developed economies where small and medium enterprises have the largest share of their trade tend to be smaller European economies, like Estonia, Cyprus, and Ireland. It\’s interesting that Italy, which is the fourth-largest economy in Europe, gets more than half its exports from micro, small and medium enterprises. It does jump out at me that the US economy is by far the lowest for small and medium enterprises as a share of its trade. Traditionally, small and medium US firms focused on the enormous US market.

The pattern is different if you look at not the share of exports from small and medium firms, but instead at the share of exporting firms that are small and medium firms. In the US, for example, the micro, small, and medium firms make up about 26-28% of the value of trade. But of the total number of US firms involved in international trade, micro, small, and medium firms make up more than 90%. Here\’s the figure:

It will be interesting to see how these patterns evolve over time. It seems as if the division of production across national borders into global value chains and the ability to export and import services, not just goods, should mean a larger role for small and medium enterprises in global trade. Or course, it will cause some of them to shift categories and turn into larger companies, as well.  

\”Shadow banking\” refers to financial organizations that in various ways receive funds from savers and lend money in financial markets–but are not banks. For example, a money market mutual fund receives money from investors, who can be thought of as \”depositors,\” and then invest the money in bonds, which can be thought of as lending the money to whatever government or private entity issued the bonds. But it\’s not a bank! Alex Muscatov and Michael Perez of the Federal Reserve Bank of Dallas offer a nice quick overview of this sector  in \”Shadow Banking Reemerges, Posing Challenges to Banks and Regulators\” (Economic Letter, July 2016).

They offer a helpful table of sic types of nonbank financial entities: retirement funds, mutual funds, broker-dealers, alternative investment funds, financing firms, and insurance companies. Here\’s a chart with a short description of each category and how it can be connected to traditional banks.

Our national conversation about financial regulation is often focused on banks, but when you think back to the big meltdowns of financial firms that stressed the economy back in 2008, there are a lot of non-bank financial companies like Lehman Brothers, Bear Stearns, the insurance company AIG, the mutual fund Reserve Primary Fund, and others. Indeed, the importance of shadow banking is growing. Muscatov and Perez write:

The  NBI’s [non-bank intermediaries] importance has increased over the past four decades (Chart 1). In 1980, it accounted for roughly 40 percent of the domestic financial sector. As mutual funds’ prominence increased and life insurance companies fueled the markets for corporate bonds and commercial real estate, NBI growth greatly outpaced that of banks. By 1990, NBI accounted for two-thirds of the intermediation market and has continued to slowly gain share.

Here\’s the Chart 1 to which they were referring. The red dashed line is the total liabilities of traditional banks. The shaded areas show the shadow banking or \”nonbank intermediation\” firms. Notice that before the financial crisis in 2008, liabilities of banks don\’t soar; after the crisis, they don\’t fall. The financial crisis instead happened in the shadow banking sector, where you can see the sharp rise in liabilities before the crisis circa 2008 and the sharp fall afterwards.

The basic lesson here is that if you still think banks are the core representative institutions in the financial system of high-income economies, you are a few decades out of date. If you are concerned about the dangers of financial sector risks cartwheeling into the real economy, you need to think about the shadow banking sector. Muscatov and Perez point out that while banking regulators do try to think about risks from the shadow banking sector, \”Still, many areas of NBI remain obscured from regulators’ view, and not all NBI is subject to supervision.\” For a specific warning call, the Global Financial Stability Report published by the IMF in April 2016 includes a chapter on \”The Insurance Sector — Trends and Systemic Risk Implications.\”  The report says:

\”The chapter shows that across advanced economies the contribution of life insurers to systemic risk has increased in recent years, although it clearly remains below that of banks. This increase is largely due to growing common exposures to aggregate risk, caused partly by a rise in insurers’ interest rate sensitivity. Thus, in the event of an adverse shock, insurers are unlikely to fulfill their role as financial intermediaries precisely when other parts of the financial system are failing to do so as well.\”  

For those interested in shadow banking, here are a few previous posts on the subject:

• \”A Shadow Banking Schematic\” (May 27, 2014)
• \”Shadow Banking: US Risks Persist\” (October 8, 2014)
• \”Credit Without Banks: Shadow Banking\” (November 6, 2014)

A US presidential election is, as the political science geeks like to point out, really 50 separate state elections, each one in a winner-take-all format. With the US national election day less than two months away, it seemed like a good time to review some (occasionally controversial) differences across how these state elections are conducted, which are compiled by the National Council of State Legislatures and available on its website.

Voter ID

The first five states to require voter ID were South Carolina (in 1950), Hawaii (1970), Texas (1971), Florida (1977) and Alaska (1980).  But during the last 15 years or so, the trend is clearly toward a a rising number of states imposing such requirements.

The NCSL makes a couple of key distinctions between these laws. One is whether the requirement is for a photo ID (like a driver\’s license) or a non-photo ID (like a bank statement). Another distinction is whether the law is \”non-strict\” or \”strict.\” Pretty much all voter ID laws offer some ways in which people without an ID can cast a provisional ballot, but the difference is whether that ballot will then be counted without further action by the voter (\”non-strict\”), or whether the person casting the ballot needs to do something after election day, like return to an election office and show a valid ID, before their ballot will be counted (\”strict\”). A state-by-state list of voter ID rules is available here at the NCSL website.

Absentee and Early Voting

States do offer some options for those who can\’t or don\’t want to vote on election day, but these options vary considerably. The NCSL summarizes in this way:

1. Early Voting: In 37 states (including 3 that mail ballots to all voters) and the District of Columbia, any qualified voter may cast a ballot in person during a designated period prior to Election Day. No excuse or justification is required. … 
2. Absentee Voting: All states will mail an absentee ballot to certain voters who request one. The voter may return the ballot by mail or in person. In 20 states, an excuse is required, while 27 states and the District of Columbia permit any qualified voter to vote absentee without offering an excuse. Some states offer a permanent absentee ballot list: once a voter asks to be added to the list, s/he will automatically receive an absentee ballot for all future elections.
3. Mail Voting: A ballot is automatically mailed to every eligible voter (no request or application is necessary). In-person voting sites may also be available for voters who would like to vote in-person and to provide additional services to voters. Three states mail ballots to all eligible voters for every election. Other states may provide this option for some types of elections.

The specific rules for early and absentee voting vary considerably across states, as well. Fopr example, the average starting time for early voting is 22 days before an election, but it varies across states from as much as 45 days before to just four days before. The average early voting period average is 19 days, but again, this varies across states from four to 45 days. Some states require that you give an approved reason for requesting an absentee ballot, while other will give an abstentee ballot to anyone who requests one. A state-by-state list of these rules is available here.

Same Day or Online Registration

\”Thirteen states plus the District of Columbia presently offer same-day registration (SDR), allowing any qualified resident of the state to go to the polls or an election official\’s office, either before or on Election Day, register to vote, then cast a ballot, all in that day. California, Hawaii and Vermont have enacted same-day registration but have not yet implemented it.  In most other states, voters must register by a deadline prior to Election Day. The deadline varies by state, with most falling between eight and 30 days before the election. …\”

\”As of June 14, 2016 a total of 31 states plus the District of Columbia offer online registration, and another seven states have passed legislation to create online voter registration systems, but have not yet implemented them.\”

Rules about Recounts

\”43 states permit a losing candidate, a voter, a group of voters or other concerned parties to petition for a recount. In a few states, the vote totals for the top two candidates must be within a specified margin in order for the losing candidate to be able to request a recount. For example, in Idaho, a candidate may petition for a recount if the difference between the requesting and winning candidates is less than 0.1 percent of the total votes cast for the office. In at least five states, a political party officer can request a recount, and in at least 17 states, a voter can petition for a recount. … In most of the states that permit a candidate or other interested party to demand a recount, the petitioner is required to pay a deposit toward the cost of conducting the recount. If the recount reverses the result of the election, that person’s deposit is refunded. If the recount does not change election results, the petitioner is required to pay most of the costs associated with the recount. Automatic recounts are paid for by the state or county that conducts the recount.\”

There are many other differences in how states conduct elections: for example, differences in voter registration, how lists of eligible voters are maintained, the rules about primary elections, the number of polling stations on election day and the hours they are open, the qualifications for poll-watchers, the ways in which paper or electronic voting equipment is used, and more.

It\’s useful to consider this wide variation across states, in part because for many of us it tends to challenge our preconceptions about how voting should happen.  For example, it seems to me that voting by mail has the potentially important problem that many voters will find it harder to cast a truly secret ballot. But clearly residents of Colorado, Oregon, and Washington disagree. I\’m not a big fan of early voting rules, because I think there\’s some value to people having a chance to change their minds right up to election day, rather than being pressured to lock in their vote early, but many states clearly disagree with this view. My home state of Minnesota tends to pat itself on the back for not having a voter ID law, but it\’s a useful exercise in humility to remember that most other states disagree on the merits of such a rule.

In some ways, the differences across states in voting are a litmus test for how you feel about a federalist country in which state and local governments have a substantial degree of autonomy on many issued, including administration of elections, or whether you tend to favor greater control by the central government.

Every now and again, I\’ll post a cartoon suitable for tacking up on a bulletin board, or blending into an economics lecture. This one is from back in 2008, from Dale Everett at the Anarchy In Your Head website, but I just saw it for the first time, so I pass it along.

Addendum: A helpful reader points out that a similar comment was attributed to Ronald Coase by David Landes, who said in a 1983 symposium published in the Journal of Law and Economics: \”Ronald said he had gotten tired of antitrust because when the prices went up the judges said it was monopoly, when the prices went down, they said it was predatory pricing, and when they stayed the same, they said it was tacit collusion.\”

The citation is Edmund W. Kitch, \”The Fire of Truth: A Remembrance of Law and Economics at Chicago, 1932-1970,\” Journal of Law & Economics, Vol. 26, No. 1 (Apr., 1983), pp. 163-234. Quotation is on p. 193.

\”More than 40 million people living in the United States were born in other countries, and almost an equal number have at least one foreign-born parent. Together, the first generation (foreign-born) and second generation (children of the foreign-born) comprise almost one in four Americans. It comes as little surprise, then, that many U.S. residents view immigration as a major policy issue facing the nation. Not only does immigration affect the environment in which everyone lives, learns, and works, but it also interacts with nearly every policy area of concern, from jobs and the economy, education, and health care, to federal, state, and local government budgets.\”

That\’s the beginning of the just-released 500+ page report from the National Academy of Sciences on \”The Economic and Fiscal Consequences of Immigration,\” edited by Francine D. Blau and Christopher Mackie. A prepublication copy of the report (essentially, uncorrected proofs) can be downloaded for free here. The conventional approach, followed in this report, is to divide up the effects of immigration into two areas; effects on native jobs and wages, and effects on government budgets and services. But before getting to those issues, I found some of the basic findings about immigration over the last couple of decades to be intriguing. Quoting from the summary:

• The number of immigrants living in the United States increased by more than 70 percent—from 24.5 million (about 9 percent of the population) in 1995 to 42.3 million (about 13 percent of the population) in 2014; the native-born population increased by about 20 percent during the same period.
• Annual flows of lawful permanent residents have increased. During the 1980s, just under 600,000 immigrants were admitted legally (received green cards) each year; after the 1990 Immigration Act took effect, legal admissions increased to just under 800,000 per year; since 2001, legal admissions have averaged just over 1 million per year.
• Estimates of the number of unauthorized immigrants in the United States roughly doubled from about 5.7 million in 1995 to about 11.1 million in 2014. Gross inflows, which had reached more than 800,000 annually by the first 5 years of the 21st century, decreased dramatically after 2007; partly as a result, the unauthorized immigrant population shrank by about 1 million over the next 2 years. Since 2009, the unauthorized immigrant population has remained essentially constant, with 300,000-400,000 new unauthorized immigrants arriving each year and about the same number leaving.
• The foreign-born population has changed from being relatively old to being relatively young. In 1970 the peak concentration of immigrants was in their 60s; in 2012 the peak was in their 40s.
• Educational attainment has increased steadily over recent decades for both recent immigrants and natives, although the former still have about 0.8 years less of schooling on average than do the latter. Such averages, however, obscure that the foreign born are overrepresented both among those with less than a high school education and among those with more than a 4-year college education, particularly among computer, science, and engineering workers with advanced degrees. The foreign and native born populations have roughly the same share of college graduates.
• As time spent in the United States lengthens, immigrants’ wages increase relative to those of natives and the initial wage gap narrows. However, this process of economic integration appears to have slowed somewhat in recent decades; the rate of relative wage growth and English language acquisition among the foreign-born is now slightly slower than it was for earlier immigrant waves. The children of immigrants continue to pick up English language skills very quickly.
• Geographic settlement patterns have changed since the 1990s, with immigrants increasingly moving to states and communities that historically had few immigrants. Nonetheless, the majority of the foreign-born population continues to reside in large metropolitan centers in traditional gateway states.

The conventional wisdom on the economic effects of immigration is that the effect on jobs is minimal. The number of jobs in a developed economy expands over time as the population expands–whether the growth in population is from native-born workers or from immigration.  Unemployment rates rise and fall with recessions and upswings, but there is no long-term trend to higher unemployment rates over time. On how immigrants affect the total number of jobs for native workers, the NAS report puts it this way:

\”The literature on employment impacts finds little evidence that immigration significantly affects the overall employment levels of native-born workers. However, recent research finds that immigration reduces the number of hours worked by native teens (but not their employment rate).\”

However, immigration can potentially have an effect on the distribution of wages, potentially substituting for some kinds of native workers and thus holding down their wages, but also potentially complementing other native workers and leading to higher wages for them. The wage effects of immigration is a tough topic for research. For example, imagine that immigrants tend to go to areas where wages are higher and jobs are more plentiful. If this plausible assumption holds true, then there will be a positive correlation in which areas with more immigrants will also tend to have better jobs and wages, but that correlation certainly doesn\’t prove causality. In addition, low-skilled and high-skilled immigrants will be substitutes and complements for different kinds of workers. In some places and jobs, immigrants may even be competing more with earlier immigrants, rather than with native workers.  Given these complexities, on how immigrants affect wages for native workers, the NAS report is necessarily more equivocal:

\”When measured over a period of 10 years or more, the impact of immigration on the wages of natives overall is very small. However, estimates for subgroups span a comparatively wider range, indicating a revised and somewhat more detailed understanding of the wage impact of immigration since the 1990s. To the extent that negative wage effects are found, prior immigrants—who are often the closest substitutes for new immigrants—are most likely to experience them, followed by native-born high school dropouts, who share job qualifications similar to the large share of low-skilled workers among immigrants to the United States. …

\”Until recently, the impact of high-skilled immigrants on native wages and employment received less attention than that of their low-skilled counterparts. Interest in studying high-skill groups has gained momentum as the H1-B and other visa programs have contributed to a rapid rise in the inflow of professional foreign-born workers (about a quarter of a million persons per year during the last decade). Several studies have found a positive impact of skilled immigration on the wages and employment of both college-educated and noncollege educated natives. Such findings are consistent with the view that skilled immigrants are often complementary to native-born workers, especially those who are skilled; that spillovers of wage-enhancing knowledge and skills occur as a result of interactions among workers; and that skilled immigrants innovate sufficiently to raise overall productivity. However, other studies examining the earnings or productivity prevailing in narrowly defined fields find that high-skill immigration can have adverse effects on the wages or productivity of natives working in those fields.\”

The NAS report also notes some other economic effects of immigration:

\”The contributions of immigrants to the labor force reduce the prices of some goods and services, which benefits consumers in a range of sectors including child care, food preparation, house cleaning and repair, and construction. Moreover, new arrivals and their descendants are a source of demand in key sectors such as housing, which benefits residential real estate markets. … Importantly, immigration is integral to the nation’s economic growth. Immigration supplies workers who have helped the United States avoid the problems facing stagnant economies created by unfavorable demographics—in particular, an aging (and, in the case of Japan, a shrinking) workforce. Moreover, the infusion by high-skill immigration of human capital has boosted the nation’s capacity for innovation, entrepreneurship, and technological change.\”

On the issue of how immigration affects government budgets and services, the research takes a variety of perspectives. For example, a single immigrant with a job, no children and law-abiding, tends to pay more in taxes (including sales taxes and income tax withholding) than consumed in government services. A high-skilled immigrant will earn more income and pay higher taxes than a low-skilled immigrant. An immigrant with children in public schools will consume more services. An low-skilled immigration with a lower income level who works long enough to be eligible for Social Security and Medicare will consume more in services. In thinking about how immigration affects government budgets and services, it makes a difference if one takes a short-run perspective of a year, or the typically accumulation of taxes paid and government services over a lifetime.  These lifetime patterns will vary among first-, second-, and third-generation immigrants. Thinking about the costs of government services means that you need to think of immigrants as consuming a share of publicly provide goods like, say, national defense.

With such complexities duly noted, the NAS report lays out some overall conclusions. For example, a standard finding is that over a lifetime, immigration has a positive fiscal effect for the federal government but a negative effect for state and local government.

\”Viewed over a long time horizon (75 years in our estimates), the fiscal impacts of immigrants are generally positive at the federal level and negative at the state and local levels. State and local governments bear the burden of providing education benefits to young immigrants and to the children of immigrants, but their methods of taxation recoup relatively little of the later contributions from the resulting educated taxpayers. Federal benefits, in contrast, are largely provided to the elderly, so the relative youthfulness of arriving immigrants means that they tend to be beneficial to federal finances in the short term. In addition, federal taxes are more strongly progressive, drawing more contributions from the most highly educated. The panel’s historical analysis indicates that inequality between levels of government in the fiscal gains or losses associated with immigration appears to have widened since 1994. The fact that states bear much of the fiscal burden of immigration may incentivize state-level policies to exclude immigrants and raises questions of equity between the federal government and states. …

\”For the 2011-2013 period, the net cost to state and local budgets of first generation adults (including those generated by their dependent children) is, on average, about $1,600 each. In contrast, second and third-plus generation adults (again, with the costs of their dependents rolled in) create a net positive of about $1,700 and $1,300 each, respectively, to state and local budgets. These estimates imply that the total annual fiscal impact of first generation adults and their dependents, averaged across 2011-13, is a cost of $57.4 billion, while second and third-plus generation adults create a benefit of $30.5 billion and $223.8 billion, respectively. By the second generation, descendants of immigrants are a net positive for the states as a whole, in large part because they have fewer children on average than do first generation adults and contribute more in tax revenues than they cost in terms of program expenditures.\”

A different way to slice this data is to look compare first-, second-, and third-generation immigrants at different ages.

\”Cross-sectional data from 1994-2013 reveal that, at any given age, the net fiscal contribution of adults in the first generation (and not including costs or benefits generated by their dependents) was on average consistently less favorable than that of the second and third-plus generations. Relative to the native-born, the foreign-born contributed less in taxes during working ages because they earned less. However, this pattern reverses at around age 60, beyond which the third-plus generation has
consistently been more expensive to government on a per capita basis than either the first or second generation; this is attributable to the third-plus generation’s greater use of social security benefits.\”

Another finding is that because immigrant over the last few decades have become better-educated, their fiscal effect has also improved.

\”Today’s immigrants have more education than earlier immigrants and, as a result, are more positive contributors to government finances. …  Whether this education trend will continue remains uncertain, but the historical record suggests that the total net fiscal impact of immigrants across all levels of government has become more positive over time.\”

My overall sense is that immigration is overall a positive force for the US economy, and I support a allowing steady stream of legal immigration over time–especially high-skilled migrants who have already spent time in the United States being trained at US colleges and universities, or working in US-based firms. But I would also say that the positive economic effects of immigration are not enormous, and can be negative for certain subgroups. Moreover, I suspect that while our social controversies over immigration may often be phrased in economic terms, a lot of the heat and energy in these controversies arises from perceptions about non-economic consequences of immigration.

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.