Labor Policy and Implicit Bias

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Commenting on a book about behavioral considerations in public policy, Jason Collins writes,

The opening substantive chapter by Curtis Hardin and Mahzarin Banaji is on bias – and particularly implicit bias. Implicit biases are unconscious negative (or positive) attitudes towards a person or group. Most people who claim (and believe) they are not biased because they don’t show explicit bias will nevertheless have implicit bias that affects their actions.

I think that thinking in terms of the oppressor-oppressed axis is an example of implicit bias. For example, labor policies, such as the minimum wage, are based on an implicit bias that workers are oppressed. I was reminded of this by a recent Tyler Cowen post.

I am often struck by the conflict between one supposition and one fact. First, employers are supposed to be reaping some big surplus from hiring unskilled labor. Second, when a downturn comes, it is unskilled labor who are laid off.

The three-axes model would explain the supposition as a form of implicit bias.

Should the CBO Use Dynamic Scoring?

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John Cochrane writes,

Greg Mankiw has a nice op-ed on dynamic scoring

The issue: When the congressional budget office “scores” legislation, figuring out how much it will raise or lower tax revenue and spending, it has been using “static” scoring. For example, it assumes that a tax cut has no effect on GDP, even if the whole point of the tax cut is to raise GDP.

My thoughts.

1. I am against dynamic scoring. Dynamic scoring means using an economic model. I think that politicians and the press give too much credence to economic models as it is. Even static scoring requires some modeling, but the modeling has more to do with spreadsheet arithmetic as opposed to claiming to be able to predict economic behavior.

2. To the extent that the CBO has to predict economic behavior, I think it should present several scenarios, as opposed to a point estimate or a range. Cochrane says it well:

It’s a fact, we don’t know the elasticities, multipliers, and mechanisms that well. So stop pretending. Stop producing only a single number, accurate to three decimals. Instead, present a range of scenarios spanning the range of reasonable uncertainty about responses.

Responding to another point from Cochrane, Mankiw writes,

you need to specify how the government is going to satisfy its present-value budget constraint. You might be tempted to ask the model what happens if the government cuts taxes and never does anything else. But you won’t get very far. The model will tell you that the government has to do something else eventually, and it won’t tell you what will happen if the government tries to do something impossible.

What I hear Greg saying is that to properly do dynamic scoring, you would need to include a model of future policy responses. That is a point well taken, but I am not sure that I would restrict those policy responses to be only doing things that are possible. Policy makers are doing impossible (that is, unsustainable) things now. The challenge is to predict the outcome of undertaking unsustainable policies until you cannot do so any more.

Of course, the traditional “static” scoring does not solve the problem of how to predict the outcome of unsustainable policies, either.

How to Become a Better Student

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As a teacher, I believe in triage. At the top, there are students who pick up the material with minimal effort. At the bottom, there are no-hopers who cannot seem to learn. In the middle are students where you think that some effort can make a difference.

In college, taking statistics or economics, I was one of the students who picked it up with minimal effort. On the other hand, as a folk dancer, I am a middle student. I am better than the no-hopers who never go beyond beginners’ sessions. But I am not as good as the dancers who can pick up a new dance right away.

Based on my experience with folk dancing, here is my advice to middle students.

1. YouTube is your friend! I encounter many dances that I wish I knew. Before YouTube, I had to muddle through and make mistakes, or give up altogether. Now I have been able to add some of these dances to my repertoire. Similarly, for statistics and economics, just about any concept you would want to learn has a YouTube video.

2. Give yourself more practice than you get from the teacher. Sometimes, a dance session leader will teach a dance for a couple of weeks, then forget about it for a couple months, then put it on again and expect students to remember it. This will leave me totally frustrated if I have been passive. But I can do something about it by practicing the dance on my own, in order to make up for the inadequate practice at the session. As a teacher, after I finish a unit, I often stop giving practice questions on that topic. When I do this, if students want to remember the concepts, they will have to practice on their own.

3. Identify your weak spots and work on them. You can keep doing a dance wrong week after week. Instead, make a mental note of the parts that give you trouble, then later go to YouTube until you have them ironed out. Similarly, if you got a problem wrong, come back to it and do it correctly several times.

4. When you watch someone doing something, articulate what they are doing. If you trying to learn a dance by following, try to say to yourself the steps that the person is doing. Saying “right, left, cha-cha-cha” helps you learn more thoroughly than if you simply follow along. Similarly, if a teacher is doing an exercise in statistics or economics, try to articulate the steps that the teacher is doing. (“Deciding whether this is a shift in demand or a shift in supply” or “using the binomial distribution” or somesuch.)

5. Make the subject seem really important to you. Think of someone you have had a crush on, and pretend that the way to get them to notice you is to become good at the subject.

Attention St. Louis

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I will be here on March 11.

The Discussion Club meets at the Racquet Club Ladue. Doors will open at 5:30 and the formal speaker introduction will begin at 6:00 sharp. The presentation plus one or two questions from the audience will finish around 6:45. Afterwards, members are invited to complementary hors d’oeuvres and drinks with the speaker from 7 to 8.

Talking about the four forces.

I don’t use slides, but if I did I would include this comic strip from Frank and Ernest.

Mixing Markets and Politics

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Randall G. Holcombe writes,

As Kolko (1963) describes it, private sector actors are not merely acting within the framework given by government constraints, the “major economic interests” are designing the constraints under which they act, so that they can retain their dominant positions. . .they sought government regulation and oversight to preserve the status quo

I would say that the forgotten history is that the regulatory state began as an attempt to rein in competition. The idea of pro-consumer regulation only emerged in the latter part of the New Deal. From the start of the Progressive era through Roosevelt’s first term, the goal of progressives was to rationalize the economy by managing it. Alan Brinkley describes how regulation in the name of consumer improvement came to replace regulation in order to rationalize production. (Of course, one can question whether regulation actually achieves either goal.)

Later in the essay, Holcombe writes,

By combining Buchanan and Tullock’s (1962) analysis of logrolling for the benefit of those who are able to engage in political exchange with Coase’s (1960) notion of transaction costs, a theory can be developed in which the elite are in a low-transaction-cost group so they can engage in political exchange for their benefit, at the expense of the masses who are in the high-transaction-cost group. This happens in politics but not in markets because government is able to force people to pay for their programs regardless of whether they want to participate, whereas in markets even those with substantial economic power can obtain resources from the masses only if they voluntarily agree to participate in transactions.

In other words, there is a reason that in politics insiders are insiders and outsiders are outsiders. Housing policy reflects the real estate lobby because the transaction costs of assembling real estate agents, home builders, and mortgage lenders into a pressure group are low. The transaction costs of assembling ordinary taxpayers into a pressure group are high. (Although if 1 percent of taxpayers pay half the taxes, those transaction cost might not be quite so high. Something to watch, perhaps.)

Thus, contrary to conventional wisdom, exit is better than voice at giving power to the little guy.

The New Demographics

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Nicholas Eberstadt writes,

Europe’s most rapidly growing family type is the one-person household: the home not only child-free, but partner- and relative-free as well. In Western Europe, nearly one home in three (32%) is already a one-person unit, while in autonomy-prizing Denmark the number exceeds 45%. The rise of the one-person home coincides with population aging. But it is not primarily driven by the graying of European society, at least thus far: Over twice as many Danes under 65 are living alone as those over 65.

Pointer from Tyler Cowen. The entire essay is recommended.

My impression of the United States is that we have two marital cultures. One culture, more prevalent among the affluent, is traditional marriage, delayed and with fewer children, with reasonably low divorce rates. The other culture, more prevalent among the less-than-affluent, is child-bearing outside of marriage, with a low proportion of long-lasting marriages.

Eberstadt’s global tour makes it difficult to claim that a single factor, such as affluence or local culture or the welfare state, is causing the decline in traditional marriage.

One thought I have is that the traditional family is highly congruent with an agricultural society. Perhaps we are seeing a sort of delayed response to industrialization and urbanization.

Michael Mandel’s Question About Health Care Innovation

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At this event, he asked why we do not see any of the signs of an innovation boom in health care that we saw with personal computers and the Internet. No spectacular new companies. No surge in demand for life sciences knowhow comparable to the surge in demand for computer programming skills. As he wrote last year, he believes that the FDA’s requirement that new treatments be more efficacious than old ones has the effect of stifling disruptive innovation, in which new products first gain traction on the basis of lower price rather than better quality.

Others at the panel pointed out that it may not be the FDA that dictates the innovation pattern. It may be the fact that third party payments dominate American health care. Patients who are not paying for their own health care are not going to provide a market for radically cheaper treatments. And insurance companies are not going to want to pay for radically better treatments that cost a lot. So the only innovations that survive are incremental ones.

However, I want to go back to the original question of why we do not see an innovation boom. My thoughts:

1. My guess is that we have not yet reached a point where all the pieces are in place to produce an innovation boom. Remember that it took several decades to go from the invention of the transistor to the appearance of the personal computer.

2. We do not have an institutional breeding ground for biotech innovation. No equivalent of Bell Labs, or Xerox PARC or the Homebrew Computer Club.

3. Someone in the audience asked a provocative question about whether some other country provides a role model, which country provides a role model for health care innovation? If you thought that the only roadblocks were American customs and regulations, health care innovation would take place in other countries.

Null Hypothesis Watch

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“Scott Alexander” writes,

When they caught up with these kids at age 25, the intervention group was found to have an odds ratio of around 0.6 to 0.7 of having developed various psychiatric disorders the study was testing for, including antisocial personality disorder, ADHD, depression, or anxiety. They had odds ratios around 0.7 of developing drug and alcohol abuse problems by various measures. They reported less risky sexual behavior, less domestic abuse, and fewer violent crimes. All of this was significant at the p < 0.05 level, and some of it was significant at much higher levels like p = 0.001 or below. Subgroup analysis found the data were very similar when you restricted the analysis to various subgroups like boys, girls, whites, blacks, highest-risk, lowest-risk, and by study site (it was a multi-site study)

This was a randomized, controlled study of a group of many interventions. “Scott” goes on to point out a number of caveats. The group of interventions was expensive. A lot of other indicators, including employment rates, did not improve. We do not know whether the results came from one or two of the interventions, or from the combination of all of them.

Still, it looks as though something managed to defeat the null hypothesis. As a controlled trial, it gets over the hurdle of confusing correlation with causality. As a study of long-term outcomes, it gets over the hurdle of fade-out. The results are numerically significant, not just statistically significant. The only remaining hurdle is replicability. My guess is, given the complexity of all those interventions, that the replicability hurdle will be a challenge.

Paul Krugman Sentences I Might Have Written

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I certainly agree with this:

the professional economists who either play important roles in making policy or appear to have influence on the discussion got their Ph.Ds from MIT in the second half of the 1970s. An incomplete list, with dates of degree:

Ben Bernanke 1979
Olivier Blanchard 1977
Mario Draghi 1976
Paul Krugman 1977
Maurice Obstfeld 1979
Kenneth Rogoff 1980

Larry Summers was at Harvard during the same period, but he was an MIT undergrad and very much part of that intellectual circle. Also, just about everyone on the list studied with Stan Fischer, who remains very much in the middle of policy-making.

Note that we are talking about macroeconomic policy. But some important microeconomic policy makers came out of that period as well. Carl Shapiro comes quickly to mind.

Of course, Krugman has other sentences that I could not have written.

Analytically, empirically, the MIT style has had an astonishing triumph.

As you know, I think that macroeconomic data can be twisted to “prove” any theory. You can look at reasonable, credible blog posts by Scott Sumner or Tyler Cowen pointing out many discrepancies between recent macroeconomic performance and the Krugman-style Keynesian analysis. Empirical macroeconomics seems to me to boil down to a pure exercise in confirmation bias.

As you also know, I have a less exalted view of MIT’s approach to economics and of Stan Fischer’s role as the Genghis Khan of macro. See, my recent post on academic hiring networks, my memoirs of a would-be macroeconomist, or my recent essay on camping-trip economics. Read that essay next to Krugman’s post.