Year: 2019

KAHNEMAN: First of all, let me explain what I mean by noise. I mean, just randomness. And it’s true within individuals, but it’s especially true among individuals who are supposed to be interchangeable in, say, organizations. …

I’ll tell you where the experiment from which my current fascination with noise arose. I was working with an insurance company, and we did a very standard experiment. They constructed cases, very routine, standard cases. Expensive cases — we’re not talking of insuring cars. We’re talking of insuring financial firms for risk of fraud.

So you have people who are specialists in this. This is what they do. Cases were constructed completely realistically, the kind of thing that people encounter every day. You have 50 people reading a case and putting a dollar value on it.

I could ask you, and I asked the executives in the firm, and it’s a number that just about everybody agrees. Suppose you take two people at random, two underwriters at random. You average the premium they set, you take the difference between them, and you divide the difference by the average.

By what percentage do people differ? Well, would you expect people to differ? And there is a common answer that you find, when I just talk to people and ask them, or the executives had the same answer. It’s somewhere around 10 percent. That’s what people expect to see in a well-run firm.

Now, what we found was 50 percent, 5–0, which, by the way, means that those underwriters were absolutely wasting their time, in the sense of assessing risk. So that’s noise, and you find variability across individuals, which is not supposed to exist.
And you find variability within individuals, depending morning, afternoon, hot, cold. A lot of things influence the way that people make judgments: whether they are full, or whether they’ve had lunch or haven’t had lunch affects the judges, and things like that.
Now, it’s hard to say what there is more of, noise or bias. But one thing is very certain — that bias has been overestimated at the expense of noise. Virtually all the literature and a lot of public conversation is about biases. But in fact, noise is, I think, extremely important, very prevalent.

There is an interesting fact — that noise and bias are independent sources of error, so that reducing either of them improves overall accuracy. There is room for . . . and the procedures by which you would reduce bias and reduce noise are not the same. So that’s what I’m fascinated by these days.

Now, it’s hard to say what there is more of, noise or bias. But one thing is very certain — that bias has been overestimated at the expense of noise. Virtually all the literature and a lot of public conversation is about biases. But in fact, noise is, I think, extremely important, very prevalent. …

COWEN: Do you see the wisdom of crowds as a way of addressing noise in business firms? So you take all the auditors, and you somehow construct a weighted average? …

KAHNEMAN: With respect to the underwriters, I would expect, certainly, that if you took 12 underwriters assessing the same risk, you would eliminate the noise. You would be left with bias, but you would eliminate one source of error, and the question is just price. Google, for example, when it hires people, they have a minimum of four individuals making independent assessments of each candidate. And that reduces the standard deviation of error at least by a factor of two.

COWEN: So is the business world, in general, adjusting for noise right now? Or only some highly successful firms?

KAHNEMAN: I don’t know enough about that. All I do know is that, when we pointed out the results, the bewildering results of the experiment on underwriters, and there was another unit — people who assess the size of claims. Again, actually, it’s more than 50 percent. Like 58 percent. The thing that was the most striking was that nobody in the organization had any idea that this was going on. It took people completely by surprise.
My guess now, that wherever people exercise judgment, there is noise. And, as a first rule, there is more noise than people expect, and there’s more noise than they can imagine because it’s very difficult to imagine that people have a very different opinion from yours when your opinion is right, which it is. …

COWEN: If you’re called in by a CEO to give advice — and I think sometimes you are — how can I reduce the noise in my decisions, the decisions of the CEO, when there’s not a simple way to average? The firm doesn’t have a dozen CEOs. What’s your advice? …
KAHNEMAN: [T]here is one thing that we know that improves the quality of judgment, I think. And this is to delay intuition. … Delaying intuition until the facts are in, at hand, and looking at dimensions of the problem separately and independently is a better use of information.

The problem with intuition is that it forms very quickly, so that you need to have special procedures in place to control it except in those rare cases …  where you have intuitive expertise. That’s true for athletes — they respond intuitively. It’s true for chess masters. It’s true for firefighters … I don’t think CEOs encounter many problems where they have intuitive expertise. They haven’t had the opportunity to acquire it, so they better slow down. … It’s not so much a matter of time because you don’t want people to get paralyzed by analysis. But it’s a matter of planning how you’re going to make the decision, and making it in stages, and not acting without an intuitive certainty that you are doing the right thing. But just delay it until all the information is available.

COWEN: And does noise play any useful roles, either in businesses or in broader society? Or is it just a cost we would like to minimize?

KAHNEMAN: There is one condition under which noise is very useful. If there is a selection process, evolution works on noise. You have random variation and then selection. But when there is no selection, noise is just a cost. … Bias and noise do not cover the universe. There are other categories.

LIBOR stands for \”London Interbank Offered Rate.\” For a long time, it was probably most common  benchmark interest rate in the world–that is, it was the built into trillions of dollars worth of loans and financial contracts that if the LIBOR interest rate went up or down, the contract would adjust accordingly.

However, a huge scandal erupted back in 2010. Turns out that the LIBOR was not based on actual market transactions; instead, LIBOR was based on a survey in which someone at a bank gave a guess on what interest rate their bank would be charged if the bank wanted to borrow short-term from another bank on a given morning, in a particular currency. A few of the people responding to the survey were intentionally giving answers that pulled LIBOR up just a tiny bit one day, or pulled it down a tiny bit another day. Given that the LIBOR was linked to trillions of dollars in financial contracts, market traders who knew in advance about these shifts could and did reap fraudulent profits.

LIBOR tightened up its survey methods. But it clearly made sense to shift away from using a benchmark interest rate based on a survey, and instead to use one based on an actual market for short-term low-risk borrowing. Various committees formed to consider options. As I noted here about six weeks ago, the US is switching from LIBOR to SOFR–the Secured Overnight Financing Rate. I wrote: \”It refers to the cost of borrowing which is extremely safe, because the borrowing is only overnight, and there are Treasury securities used as collateral for the borrowing. The SOFR rate is based on a market with about $800 billion in daily transactions, and this kind of overnight borrowing doesn\’t just include banks, but covers a wider range of financial institutions. The New York Fed publishes the SOFR rate every morning at 8 eastern time.\”

But what about the switch away from LIBOR in the rest of the world? Andreas Schrimpf and Vladyslav Sushko describe what\’s happening in \”Beyond LIBOR: a primer on the new benchmark rates,\” which appears in the March 2019 issue of the BIS Quarterly Review (pp. 29-52). Here\’s  table showing the alternative risk-free rate (RFR) benchmarks being used with other currencies.

There are several big issues ahead in this area. One is that the LIBOR is actually going to be discontinued in 2021, so any loan or financial contract with a benchmark rate will have to migrate to something else. There will be literally trillions of dollars of contracts that need to shift in this way. Moreover, the LIBOR debacle has made a lot of financial industry participants think more carefully about exactly what benchmark interest rate may be appropriate in any given contract–for example, an appropriate benchmark might include not only an overnight risk-free rate, but also some built-in adjustment for other kinds of risks, including risks over different periods of time or risks at the firm or industry level.

For most of us, discussions of benchmark interest rates have a high MEGO (My Eyes Glaze Over) factor. But when I think in terms of trillions of dollars of loans and financial contracts around the world, all being adjusted in ways that are thoughtful but untested, I find it easier to pay attention to the subject.

Every few years, the OECD puts out an its Society at a Glance report. The later chapters offer  comparisons across high-income countries on a variety of economic, demographic, health, education, and social variables. For the 2019 edition, the first chapter is on the more specific topic,  \”The LGBT challenge: How to better include sexual and gender minorities?\” Here, I\’ll focus on labor market issues, but there is more in the chapter on other issues.

This figure summarizes the results of 46 studies of differences in employment and wages for LBGT people across the OECD countries. The usual method in these studies is to adjust for lots of observable factors: age, education, race/ethnicity, children in the household, hours worked, occupation/industry, location (like urban or rural), and so on.  The horizontal axis shows various groups. The figure then shows the gap that remains after adjusting for these factors, in employment rates, labor earnings, and the extent to which this group is found in high managerial roles.

A common pattern in these studies is that gaps look worse for men than for women. Indeed, LGB women or just Lesbians as a group have positive employment and labor earnings gaps compared to the rest of the population.

As social scientists have long recognized, this kind of \”gap\” study suggests the presence of discrimination, but it doesn\’t prove either that discrimination exists or, just as important, it doesn\’t point to the main locus of discrimination. The \”gap\” doesn\’t measure anything directly: it\’s just what is left over after accounting for the other factors on which data existed.

Even if the gap does result from discrimination, a \”gap\” study is uninformative about whether the main force of that discrimination hits early in life, perhaps in treatment in schools and families, or whether it\’s mainly because of discrimination by employers later in life.  The OECD study offers a useful example: \”For instance, the fact that lesbians and gay men in Sweden display lower employment rates in regions with more hostile attitudes toward homosexuals may simply reflect that more productive lesbians and gay men are more likely to move out of regions showing low acceptance of homosexuality.\”

Thus, it\’s common to complement \”gap\” studies with other methods that provide more direct evidence of discriminatory behavior. For example, in a \”correspondence\” study, the researcher sends out job applications to real job ads. The applications are functionally identical, except that some of the applications include evidence some information that could  lead an employer to infer sexual orientation or gender identity–say, listing a membership in a certain volunteer organization, or giving the name of a job candidate\’s partner in a way that is likely to lead to inferences about the sex of that partner.  The OECD describes the results of 13 studies across 10 countries taking this approach:

Homosexual female and male applicants are 1.5 times less likely to be invited to a job interview when sexual orientation is conveyed through their volunteer engagement or work experience in a gay and/or lesbian organisation. By contrast, insisting on the family prospects of female fictitious candidates by signalling homosexuality through the sex of the candidate’s partner leads to the virtual disappearance of hiring discrimination against lesbians. This pattern could reflect that employers attach a lower risk of maternity to lesbians relative to heterosexual women and are therefore less inclined to discriminate against them …

Correspondence studies have also been done in the market for rental housing–that is, applying for apartments rather than jobs.

In the rental housing market, correspondence studies show that homosexual couples get fewer responses and invitations to showings from the landlords than heterosexual couples, a result mainly driven by male same-sex partners – see Ahmed, Andersson, & Hammarstedt (2008[38]) and Ahmed & Hammarstedt (2009[39]) in Sweden; Lauster & Easterbrook (2011[40]) in Canada; U.S. Department of Housing and Urban Development (2013[41]) in the United States and Koehler, Harley, & Menzies (2018[24]) in Serbia. In Serbia, for instance, almost one in five (18%) of same-sex couples were refused rental of an apartment by the landlord, while none of the opposite-sex couples were. This average result masks strong disparities by gender: 29% of male same-sex couples wererejected, as opposed to only 8% of female same-sex couples. The absence (or lower magnitude) of discrimination against female same-sex couples could flow from landlords’ well documented preference for female rather than male tenants (Ahmed, Andersson and Hammarstedt, 2008[38]). In this setting, the benefit of having two women as tenants could counterbalance the perceived cost of renting to a lesbian couple.

Other experiments create situations in which people who need help in some way: asking for money, or a \”wrong number\” or \”lost letter\” approach.

\”In the United Kingdom, various experiments have also involved actors wearing a T-shirt with either a pro-gay slogan or without any slogan. These actors approach passers-by asking them to provide change. The findings point to less help provided to the ostensibly pro-gay person.\”

In the \”wrong-number\” approach, households get a call from someone who says their car has broken down, they are at a payphone, they are out of change, and now they have called a wrong number. They ask the person receiving the call to make a call to their boyfriend or girlfriend. Those who ask for a call to placed to someone of the opposite sex are more likely to get help than someone who asks for a call to be placed to someone of the same sex.

In the  “lost-letter technique,” a number of unmailed letters, with addresses and stamps are dropped in city streets. Some of the letters are addressed to LGBT organizations; some are not. Those that are not are more likely to be dropped in the mail by whoever finds them.

For most noneconomists, discrimination is just morally wrong, and that\’s enough. Economics can add that discrimination also leads to underuse and misallocation of society\’s human resources, which imposes costs on the economy as a whole.

About 40 years ago, when I was a junior on the high school debate team, we argued for the abolition of the money bail system. Like many positions taken by high school juniors in debate tournaments, our arguments were sweeping and simplistic. But we were correct in recognizing that there are real problems with money bail.

As one example, 14 elected prosecutors wrote a joint letter to New York state lawmakers on March 6, 2019.  The prosecutors  wrote:

We support ending money bail because safety, not wealth, should be the defining feature of the pretrial justice system. Three out of every four people in New York cannot afford to pay the bail amount that the judge sets at their arraignment. That means many people are jailed simply because they are too poor to purchase their freedom. … The only people who should be detained pretrial are those who a judge finds pose a specific, clear and credible threat to the physical safety of the community, or who are a risk of intentionally evading their court dates. Jails across New York frequently are over-capacity, and they are filled with people who do not need to be there. … Research shows that people who spend even a short period in jail, as opposed to being released pretrial, are more likely to commit a future crime. This makes sense. Jail is traumatizing. Jobs are lost. Families can’t pay rent. For reasons big and small, people who are away from their family, their job, and their community become more vulnerable and less stable.

Patrick Liu, Ryan Nunn, and Jay Shambaugh provide a useful backgrounder on this subject in \”The Economics of Bail and Pretrial Detention,\” written for the Hamilton Project at the Brookings Institution (December 2018). Will Dobbie and  Crystal Yang have now offered \”Proposals for Improving the U.S. Pretrial System,\” written as a Hamilton Project Policy (March 2019).  Here\’s an overview comment from the conclusion of the Liu, Nunn, and Shambaugh paper:

\”Bail has been a growing part of the criminal justice system. Nonfinancial release has been shrinking, and more and more defendants are using commercial bonds as a way to secure their release while awaiting trial. Bail can make it more likely that defendants will reappear in court, and as such reduce costs for the criminal justice system. There are, however, extensive costs. Beyond the direct costs of posting the bail, either from paying a fee or having to liquidate assets, widespread use of bail has meant that many people are incarcerated because they are unable to post bail.

\”Nearly half a million people are in jail at any given time without having been convicted of a crime. The overwhelming majority of these people are eligible to be released—that is, a judge has deemed that they are safe to be released—but are unable to raise the funds for their release. The impact of monetary bail falls disproportionately on those who are low-income, cannot post bail out of liquid assets, and thus often remain in jail for extended periods. Furthermore, as a growing body of literature has shown, the assignment of financial bail increases the likelihood of conviction due to guilty pleas, and the costs—to both individuals and society— from extra convictions can be quite high.\”

Let\’s spell some of this out more explicitly.

Nearly half a million people are incarcerated on any given day without having been convicted of a crime. Add it all up, and over 10 million people during a given year year are locked up without being convicted of anything. Roughly one-quarter of all inmates in state and local jails have not been convicted. Here\’s a figure from Liu, Nunn, and Shambaugh:

In the last few decades, the use of money bail has been rising. As Dobbie and Yang write (figures and references omitted):

The high rate of pretrial detention in the United States in recent years is largely due to the increasing use of monetary or cash bail—release conditional on a financial payment—and the corresponding decreasing use of release on recognizance (ROR), a form of release conditional only on one’s promise to return to the court. The share of defendants assigned monetary bail exceeded 40 percent in 2009 in the set of 40 populous U.S. counties where detailed data are available, an 11 percentage point–increase from 1990. The fraction of defendants released on their own recognizance decreased by about 13 percentage points over the same period in these counties, with only 14 percent of defendants being released with no conditions in 2009. The widespread use of monetary bail directly leads to high pretrial detention rates in most jurisdictions because many defendants are unable or unwilling to pay even relatively small monetary bail amounts. In New York City, for example, an estimated 46 percent of all misdemeanor defendants and 30 percent of all felony defendants were detained prior to trial in 2013 because they were unable or unwilling to post bail set at $500 or less.

The time that accused people spend in pretrial detention can be significant. Liu, Nunn, and Shambaugh write:

[T]the amount of time that a person is detained if they are unable to afford bail is substantial, ranging from 50 to 200 days, depending on the felony offense. The pretrial detention period is also growing …  From 1990 to 2009, the median duration of pretrial detention increased for every offense, ranging from an increase of 34 percent for burglary to 104 percent for rape.  … Even for durations that are relatively short—for example, 54 days for those accused of a driving-related felony—pretrial detention represents a nearly two-month period during which individuals are separated from their families and financial hardships are exacerbated. Moreover, the typical wait until trial is much longer in some places than others (e.g., 200 days in one sample of Pennsylvania counties).

Dobbie and Yang also point out that when it comes to international comparisons, the US locks accused people at a much higher rate before trial than other countries. This figure shows the number of people detained pre-trial per 100,000 population. The US is the tallest bar on the far right.

Sorting out the costs and benefits of different levels of pretrial detention isn\’t easy. The direct costs of holding people in jails and prisons is straightforward. But how many of those accused people would not have appeared before the court? If they did not appear for court, how would the cost of finding them have compared to the cost of locking them up for days–and in some cases for weeks or even months? What are the additional costs of being locked up in terms of loss of employment opportunities, or stresses on families? How many of those would have committed crimes if not detained? (And how comfortable are we as a society with locking people up not because they have been convicted of a crime, but because we suspect they might commit a crime in the future?) When thinking about conditions of pretrial release, judges are supposed to take all of this account: for example, the presumption that an accused person is innocent, the risk of the person not showing up for trial and the costs of finding them, the risk of the person committing another crime if they are not detained, the person\’s social ties to the community, the person\’s economic ability to put up a monetary bond.

To figure out the effects of different methods of pretrial detention, a social scientist might ideally like to take a large pool of people accused of crimes and conduct a randomized experiment, in which some randomly get offered differing levels of money bail, some are released on their own recognizance, and we see what happens. While it would be grossly inappropriate for the justice system to plan to operate in this way, it turns out that this randomized experiment is being conducted by reality.

Decisions about whether to offer bail, or at what level, are not made consistently across the judicial system. When there are multiple judges in a given court, some will tend to be tougher in granting bail and some will be easier, so whether defendants like it or not, they are living in a randomized experiment depending on the judge to whom they are randomly assigned. In addition, the evidence show that even the same judge will treat accused people with seemingly identical characteristics in the same way, which adds another element of randomness. Thus, research in this area can start by setting aside those who are essentially always granted bail or essentially never granted bail, and instead focus on those with seemingly identical characteristics who are more-or-less randomly granted bail in some cases but not in others.

Dobbie and Yang have been among the leading researchers in this area, and they describe the results of this research in their paper.  For example:

Those who are detailed pretrial are more likely to be found guilty, mainly because those who are detailed pretrial are more likely to take a plea bargain–which may include credit for time already served. Pretrial detention clearly reduces the risk of pretrial flight and pretrial crime, but at least in some studies, greater exposure to jail time before the trial is associated with a rise in posttrial crime. Defendants who because of the randomness ins the system are released pretrial, rather than being held pretrial, are more likely to have income and to be employed 2-4 years later.  In some jurisdictions, the randomness in the process of granting bail takes the form of racial disparities.

For those of us who aren\’t ready to take the plunge and eliminate the money bail system altogether, what might we do to move in the direction of reducing the use of money bail and rationalizing the system? Dobbie and Yang offer some proposals based on the existing research.

Some are pretty simple. When defendants are released on their own recognizance before trial, set up a system of text message or emails to remind them of their court date. For low-risk crimes, make greater use of writing citations, rather than arresting people, and when people are arrested, lean toward releasing them on their own recognizance. For those defendants where a higher degree of monitoring seems appropriate, make greater use of electronic or personal monitoring.

Some more complex proposals involve machine learning. It\’s now possible to plug the data on the characteristics of those who get bail, or are released on their recognizance, into a computer algorithm, which can look for patterns in those who are more or less likely to flee before trial, or more or less likely to commit crimes. The feedback based on studies can then be turned over to judges, so they have a systematic sense of  how they ruled in past similar cases, and how they compare with how other judges have ruled in similar cases. It\’s easy to feel queasy about this approach. Are we going to let the result of computer number-crunching play a substantial role in whether people are granted bail? But computer number-crunching may have greater clarity and consistency in its decisions than at least some judges, and could help produce results that both let more people out before trial while also leading to lower pretrial flight risks and crime. Pilot tests along these lines in jurisdictions willing to give it a try seem warranted.

\”The Federal Government is the Nation’s largest employer, and its footprint is global. The total workforce comprises approximately 2.1 million non-postal civilian workers and 1.4 million active duty military, as well as approximately one million military reserve personnel serving throughout the country and the world. The postal workforce includes an additional 500,000 employees. Approximately 85 percent of the Federal workforce, or 1.7 million people, live outside of the Washington, D.C., metropolitan area. Notably, an even larger “indirect” workforce carries out much of the work paid for by Federal funds. This includes Federal contractors and State, local, and nonprofit employees whose jobs are funded by Federal contracts, grants and transfer payments.\”

Teachers of intro economics, as well as industrial organization classes, are often on the lookout for recent examples of market shares that can be used for talking about the extent to which certain markets are concentrated or competitive. The W3 Counter offers a monthly breakdown of market shares for browsers and platforms. For February 2019, here\’s a figure for internet browser market share:

Here\’s a figure showing patterns over time, and thus showing the substantial rise of Chrome and the mild rise of Firefox, and the  corresponding falls of Internet Explorer/Edge  and Safari. Depending on whether you are a glass half-full or half-empty person, you will have a tendency to see this either as proof that Google\’s Chrome has a worrisome level of market dominance, or as proof that even seemingly dominant browser market shares can fall in a fairly short time.

Finally, here\’s a table with the market share of various platforms in February 2019, but it needs to be read with care, since it lists multiple versions of Windows, Android, and ioS/Mac.

In the latest installment of its \”Global Perspectives\” series of conversations, Robert S. Kaplan of the Dallas Fed, discussed national and global economic issues with Greg Mankiw on March 7, 2019. The full 50 minutes of video is available here.

For a quick sample, here\’s what Mankiw had do say on what economists don’t understand about politicians, and vice versa:

I don’t think economists fully understand the set of constraints that politicians operate under, probably because we have tenure, so we can say whatever we want. The politicians don’t. They constantly have to get approval by the voters, and the voters have different views of economic issues than economists do. So the politicians are sort of stuck between the voters they have to appeal to and the economists who are giving them advice. I think understanding the difficult constraints that politicians operate under would be useful.

In terms of what politicians don’t understand about economists, I think they often turn to (economists) for the wrong set of questions. My mentor, [Princeton University economist] Alan Blinder, coined what he calls Murphy’s Law of economic policy, which says that economists have the most influence where they know the least, and they have the least influence where they know the most.

Politicians are constantly asking us, ‘What’s going to happen next year?’ But we are really bad at forecasting. I understand why people need forecasting, as part of the policy process, but we’re really bad at it, and we’re probably not going to be good any time soon. On the other the hand, there are certain problems where we kind of understand the answer. We understand that rent control is not a particularly good way to run a housing market. We understand that if you want to deal with climate change, you probably want to put a price on carbon. If you have a city that suffers from congestion, we can solve that with congestion pricing. 

A common goal for principles of economics courses is to teach students to \”think like economists.\” I\’ve always been a little skeptical of that  high-sounding goal. It seems like a lot to accomplish in a semester or two. I\’m reminded of an essay written by Deirdre McCloskey back in 1992, which argued that while undergraduates can be taught about economics, thinking like an economist is a much larger step that will only in very rare cases happen in the principles class. Here\’s McCloskey (\”Other Things Equal: The Natural,\” Eastern Economic Journal, Spring 1992):

\”Bower thinks that we can teach economics to undergraduates. I disagree. I have concluded reluctantly, after ruminating on it for a long me, that we can\’t. We can teach about economics, which is a good thing. The undergraduate program in English literature teaches about literature, not how to do it. No one complains, or should. The undergraduate program in art history teaches about painting, not how to do it. I claim the case of economics is similar. Majoring in economics can teach about economics, but not how to do it….

As an empirical scientist I have to conclude from this and other experiences that thinking like an economist is too difficult to be a realistic goal for teaching. I have taught economics, man and boy, for nearly a century, and I tell you that it is the rare, gifted graduate student who learns to think like an economist while still in one of our courses, and it takes a genius undergraduate (Sandy Grossman, say, who was an undergraduate when I came to Chicago in 1968). Most of the economists who catch on  do so long after graduate school, while teaching classes or advising governments: that\’s when I learned to think like an economist, and I wonder if your experience is not the same. 

\”Let me sharpen the thought. I think economics, like philosophy, cannot be taught to nineteen-year olds. It is an old man\’s field. Nineteen-year olds are, most of them, romantics, capable of memorizing and emoting, but not capable of thinking coldly in the cost-and-benefit way. Look for example at how irrational they are a few years later when getting advice on post-graduate study. A nineteen-year old has intimations of immortality, comes directly from a socialized economy (called a family), and has no feel on his pulse for those tragedies of adult life that economists call scarcity and choice. You can teach a nineteen-year old all the math he can grasp, all the history he can read, all the Latin he can stand. But you cannot teach him a philosophical subject. For that he has to be, say twenty-five, or better, forty-five. …

In practical terms, the standard principles of economics course is a long march through a bunch of conceptual ideas: opportunity cost, supply and demand, perfect and imperfect competitions, comparative advantage and international trade, externalities and public goods, unemployment and inflation, monetary and fiscal policy, and more. The immediate concern of most students is to master those immediate tools–what McCloskey calls learning \”about\” economics. But I do think that in the process of learning \”about,\” many principles students get a meaningful feeling for a the broader subject and mindset. In the introduction to my own principles textbook, I write:

There’s an old joke that economics is the science of taking what is obvious about human behavior and making it incomprehensible. Actually, in my experience, the process works in the other direction. Many students spend the opening weeks of an introductory economics course feeling as if the material is difficult, even impossible, but by the middle and the end of the class, what seemed so difficult early in the term has become obvious and straightforward. As a course in introductory economics focuses on one lesson after another and one chapter after another, it’s easy to get tunnel vision. But when you raise your eyes at the end of class, it can be quite astonishing to look back and see how far you have come. As students apply the terms and models they have learned to a series of real and hypothetical examples, they often find to their surprise that they have also imbibed a considerable amount about economic thinking and the real-world economy. Learning always has an aspect of the miraculous.

Thus, I agree with McCloskey that truly \”thinking like an economist\” is a very rare outcome in a principles course, and unless you are comfortable as a teacher with setting a goal that involves near-universal failure, it\’s not a useful goal for instructors. But it also seems true to me that the series of topics in a conventional principles of economics course, and how they build on each other, does for many students combine to form a comprehensible narrative by the end of the class. The students are not thinking like economists. But they have some respect and understanding for how economist think.

During the 1990s, a social and legal expectation arose in the United States that single mothers would usually be in the workforce, even when their children were young. In turn, this immediately raised a question of how child care would be provided. The 2019 Economic Report of the President. from the White House Council of Economic Advisers,  offers some useful graphs and analysis of this subject.

Here\’s are some patterns in the labor force for \”prime-age\” women between the ages of 25 and 54, broken down by single and married, and children or not. Back in the early 1980s, for example, single women with no children (dark blue line) were far more likely to be in the labor force than other women in this age group, and less than half of the married women with children under the age of six (green line) were in the labor force.

But by about 2000, the share of single prime-age women with no children in the labor force has declined, and had roughly converged with labor force participation rates of the other groups shown–except for the labor force participation rates of married women with children under six, which rose but remained noticeably lower. The report notes: \”These married mothers of young children who are out of the labor force are evenly distributed across the educational spectrum, although on average they have somewhat less education than married mothers of young children as a whole.\”

The two especially big jumps in the figure are for labor force participation of single women with children, with the red line referring to single women with children under the age of six and the gray line referring to single women with children over the age of six. Back around 1990, single and married women with children over the age of six were in the labor force at about the same rate, and single and married women with children under the age of six were in the labor force at about the same rate. But after President Clinton signed the welfare reform legislation in 1996 (formally, the
Personal Responsibility and Work Opportunity Reconciliation Act of 1996), work requirements increased for single mothers receiving government assistance.

If single mothers with lower levels of incomes are expected to work–especially mothers with children of pre-school age–then child care becomes of obvious importance. But as the next figure shows, the cost of child care is often a sizeable percentage of the median hourly wage in a given state. And of course, by definition, half of those paid with an hourly wage earn less than the median.

Mothers who are mainly working to cover child care costs face some obvious disincentives. The report cites various pieces of research that lower child care costs tend to increase the labor force participation of women. For example, a 2017 \”review of the literature on the effects of child care costs on maternal labor supply … concludes that a 10 percent decrease in costs increases employment among mothers by about 0.5 to 2.5 percent.\”

So what steps might government take to make child care more accessible to households with low incomes? Logically, the two possibilities are finding ways to reduce the costs, or providing additional buying power to those households.

When it comes to reducing costs, one place to look is at the variation in state-level requirements for child care facilities. It\’s often politically easy to ramp up the strictness of such requirements; after all, passing requirements for child care facilities doesn\’t make the state spend any money, and who can object to keeping children safe? But when state regulations raise the costs of providing a service, the buyers of that service end up paying the higher costs. The report points out some variations across states by staffing requirements.

\”For 11-month-old children, minimum staff-to-child ratios ranged from 1:3 in Kansas to 1:6 in Arkansas, Georgia, Louisiana, Nevada, and New Mexico in 2014. For 35-month-old children, they ranged from 1:4 in the District of Columbia to 1:12 in Louisiana. For 59-month-old children, they ranged from 1:7 in New York and North Dakota to 1:15 in Florida, Georgia, North Carolina, and Texas. Assuming an average hourly wage of $15 for staff members (inclusive of benefits and payroll taxes paid by the employer), the minimum cost for staff per child per hour would range from $2.50 in the most lenient State to $5 in the most stringent State for 11-month-old children, from $1.25 to $3.75 for 35-month old-children, and from $1.00 to $2.14 for 59 month-old children.\”

Here\’s a figure illustrating the theme.