Null Hypothesis Watch

“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

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.

Marginal vs. Average Debt to Equity in Housing

Alejandro Justiniano and others write,

if the relaxation of collateral constraints had been widespread, it should have resulted in a surge of mortgage debt relative to the value of real estate.

In the data, however, household debt and real estate values rose in tandem, leaving their ratio roughly unchanged over the first half of the 2000s, as shown in Figure 3. In fact, this ratio only spiked when home prices tumbled starting in 2006.

Pointer from Mark Thoma.

Suppose that back when lenders asked for 20 percent down, three families bought houses for $100,000 each and put $20,000 down each. Total mortgage debt is $240,000 and total home values are $300,000. The ratio of household to real estate debt is 80 percent.

Next, lenders allow someone to buy a house with no money down. As a result, home prices rise to $130,000. Adding $130,000 debt to the debt of the other three households (ignoring any equity they may have built up through paying down mortgage principal), we have total mortgage debt of $370,000. But total home values are $520,000, so that the average ratio of debt to equity has actually fallen, to just over 70 percent.

As long as home prices are rising, the last thing you should expect is for the average debt to equity ratio to rise. The fact that it did not fall is an indication of how powerful the boom in credit was. Only if you use a silly representative-agent model, in which there is no difference between average and marginal borrowers, would you predict something different. I have not read the paper, but I suspect that is what the authors did.

Four Forces Watch: Gentrification

Luke Juday and others report,

  • Since 1990, downtowns and central neighborhoods in cities across the country have attracted significantly more educated and higher-income residents.
  • Young adults (22-34 years old) have increased as a proportion of residents in the center of nearly every city in the country, while falling as a proportion across all other areas.
  • Older residents (ages 60 and up) form a smaller proportion of the inner-city population than they did in 1990.
  • In most cities, a decrease in income and education levels from 1990 to 2012 is evident several miles outside the core. How far outside depends on the city, with the sharpest drop being anywhere from 4 to 15 miles from the center.
  • Households below the federal poverty line are migrating outwards from city centers. The poverty rate has increased significantly several miles outside the core in many cities.

Pointer from WaPo.

This is gentrification. I view it as largely resulting from the New Commanding Heights. Universities and hospitals locate in cities, providing employment opportunities for affluent professionals. At the margin, this drives some poor people out of cities and into close-in suburbs. Meanwhile, close-in suburbs are affected by the decline in employment opportunities for low-skilled workers, which comes in part from factor-price equalization and Moore’s Law.

Kevin Erdmann Revisits the Housing Boom

He writes,

the commonly repeated anecdotes of janitors and checkout clerks being
handed $300,000 mortgages on a hope and a prayer do not appear to be representative. On net, all the new mortgages went to families with incomes around $45,000 and higher.

And elsewhere he writes,

growth in homeownership came from high income households and that households didn’t increase their debt payment/income ratios or their relative consumption of housing during the boom. The evidence against the standard narrative is even more stark when we look at dollar levels, because, despite frequent implications to the contrary, low income households don’t tend to take on nearly as much debt as high income households.

Consider two ratios:

1. Debt-to-income.

2. Debt-to-equity.

Many narratives of the financial crisis focus on debt-to-income ratios. The oppressor-oppressed narrative is that greedy lenders imposed inappropriately high debt-to-income ratios on innocent borrowers, who then could not meet their mortgage payments. The civilization-barbarism narratives stress the use of houses as ATMs and the forced expansion of lending toward irresponsible borrowers.

It seems to me that Erdmann is suggesting that debt-to-income ratios did not got out of line, or perhaps they only got out of line for high-income borrowers. He may be right, although I would suggest looking at the Home Mortgage Disclosure Act (HMDA) data and not just the survey of consumer finances.

Ever since Bob Van Order explained the mortgage default option to me almost 30 years ago, I have viewed debt-to-equity as more important than debt-to-income. If we define sup-prime lending in terms of debt-to-income, then I am inclined not to attach much significance to the proportion of sub-prime loans. To me, the dangerous loans are the ones with low down payments. There is some overlap between those and loans with high debt-to-income ratios, but not enough overlap to equate the two.

Let’s take Erdmann’s analysis of debt and income as accurate. I see no reason to change my preferred narrative of mortgage lending and the housing boom. That is, there was a surge in lending with low down payments. I am pretty confident that the HMDA data support this. In addition, there was a surge of lending for non-owner-occupied homes (speculators).

Think of owner-occupants with low down payments and non-owner-occupants as the speculative component in the housing market. My narrative is that the speculative component soared during the housing boom. These speculators did very well until house prices started to level off late in 2006. Then what had been a virtuous cycle on the way up turned into a vicious cycle on the way down, and the speculative buyers got hammered.

Getting from that to a recession is the more difficult part for me, because I do not allow myself to use the words “aggregate demand.” Instead, to explain the recession I have to make a case that many patterns of specialization and trade became unsustainable, or were finally perceived as unsustainable, while new sustainable patterns were difficult to discover. I might argue that the surge in government economic intervention exacerbated the difficulty of discovering new patterns of sustainable specialization and trade. TARP and the stimulus were largely efforts at redistribution, and that gave people a bigger incentive to focus on grabbing some of the loot than on developing a sustainable new business. Of course, Keynesians will tell you that the problem is that the surge in government intervention should have been bigger and lasted longer.

Piketty and Mort Sahl

Timothy Taylor quotes from a recent journal article by Piketty, and then summarizes,

In case you didn’t catch all that, Piketty is noting that r>g is not useful for discussing income inequality, and does not necessarily lead to wealth inequality, and that the future of wealth inequality is highly uncertain. Instead, Piketty argues in JEP that when the difference between r and g is relatively large, it will tend to exaggerate the effect of other changes that make wealth more unequal. As he writes: “To summarize: the effect of r − g on inequality follows from its dynamic cumulative effects in wealth accumulation models with random shocks, and the quantitative magnitude of this impact seems to be sufficiently large to account for very important variations in wealth inequality.”

It was the humorist Mort Sahl who would say, “I am prepared not only to retract anything I said but to deny under oath that I ever said it.”

Reluctant Heroes Austan Goolsbee and Alan Krueger

They write,

It is fair to say that no one involved in the decision to rescue and restructure GM and Chrysler ever wanted to be in the position of bailing out failed companies or having the government own a majority stake in a major private company. We are both thrilled and relieved with the result: the automakers got back on their feet, which helped the recovery of the U.S. economy. Indeed, the auto industry’s outsized contribution to the economic recovery has been one of the unexpected consequences of the government intervention.

Pointer from Tyler Cowen.

I guess there is no such thing as the seen and the unseen. For those of you who do not know, Goolsbee and Krueger were officials in the Obama Administration as the bailout was being executed. Here, if their arms do not break from patting themselves on the back, it won’t be for lack of trying.

Timothy Taylor, I question the editorial decision to publish this piece, even if you also include an article that challenges the auto bailouts. Could you not find a neutral party to tell the pro-bailout side? If not, then what does that tell us?

Human Capital and Chainsaw Arms

Noah Smith offers this:

So how should we think about human capital? Here’s an analogy that I think works well. You agree that a chainsaw is capital, right? OK, now imagine a chainsaw that you graft permanently onto someone’s arm, like Bruce Campbell in the movie Evil Dead 2. It’s so thoroughly grafted on that you can’t remove it without making it permanently useless.

This chainsaw is very very much like human capital.

My prediction is that this metaphor will become increasingly apt, as implants, drugs, and genetic enhancements become a larger share of human capital. Today’s mouse capital is a preview.

New Commanding Heights Watch

From the NYT.

Ms. Waugh, like many other hard-working and often overlooked Americans, has secured a spot in a profoundly transformed middle class. While the group continues to include large numbers of people sitting at desks, far fewer middle-income workers of the 21st century are donning overalls. Instead, reflecting the biggest change in recent years, millions more are in scrubs.

Pointer from Tyler Cowen. The New Commanding Heights are health care and education. As they increase employment at the margin while manufacturing production work decreases at the margin, male participation in the labor force continues to decline. Note, however, that female labor force participation has been trending down in this century, also.