Betting the House, not the Economy

Òscar Jordà, Moritz Schularick and Alan Taylor recently put out an interesting paper on the relationship between interest rates, the housing market and financial crises. It’s been reviewed very positively, for example here and here. They take advantage of the macroeconomic trilemma – countries with fixed exchange rates and open capital markets ‘import’ the monetary policy of other countries –  by treating these imported interest rates as exogenous, providing them with an independent random variable.

People seem to be interpreting it as saying that loose monetary policy causes financial instability through the transmission mechanism of a housing bubble.

To be fair, the opening line of the abstract does give that impression:

Is there a link between loose monetary conditions, credit growth, house price booms, and financial instability?

as does the first paragraph’s use of ‘thus’:

Do low interest rates cause households to lever up on mortgages and bid up house prices, thus increasing the risk of financial crisis?

So one could be forgiven for thinking their conclusion was basically

  • Loose Monetary Policy -> Mortgage & Housing Bubble -> Financial Instability

… Well, one could be forgiven for thinking that unless one was going to write about the article. If you were going to do that, you should actually read the article. Then you would realize they in fact show two separate things:

  • Loose Monetary Policy -> Lower Long-term interest rates + Mortgage & Housing Increases
    • (with reasonable p-values in general)

InterestRatesAndHousingMarket

and

  • Mortgage & Housing Market Bubble -> Financial Instability
    • (with ok p-values, and some suspicion about how canonical their independent variable was)

HousingMarketAndFinancialCrisis

Despite having a clever way of treating monetary policy as an independent variable, they never directly test

  • Loose Monetary Policy -> Financial Instability

even though this would be a major victory for the ‘low interest rates caused the bubble and crisis’ crowd.

Why not test this directly? The authors don’t say, but I suspect it’s because the test would fail to yield significant results. Absence of evidence of such a connection is evidence of its absence. And looking at the significance levels of the two results they did provide, I suspect that combining them would cease to be significant (unless their is another, parallel causal mechanism).

Which is a shame! Their independent variable looked really cool, as did their data set.

I think there’s an underlying theoretical reason to not expect it to work, however (quite apart from nothing ever working in macroeconomics. They rightly make much of their finding that exogenous low interest rates cause increases in housing prices. But this is not necessarily caused by increased demand ‘bidding up’ the value of housing in a bubble.

Rather, consider what sort of an asset housing is. Houses allow you to avoid paying rent in the future; their value is the capitalized value of avoided future rent. When interest rates are low, those future rent payments are discounted at a lower rate, so are more valuable: low interest rates increase the inherent value of housing. House prices rising when rates are low isn’t a bubble unless the interest rates themselves are a bubble; it’s rational cross-asset pricing. So we should expect exogenous falls in interest rates to increase house prices.

But wait there’s more!

Exogenous falls in interest rates probably mean rates are now too low (from a Taylor Rule perspective, or similar), or at least not-as-excessively-high as before. This will tend to increase inflation. And as home ownership represents a hedge against rent inflation, higher inflation yet again increases the value of home-ownership. So once again we have a non-bubble based reason to expect exogenous falls in interest rates to increase house prices.

So we have two reasons to think that low interest rates should cause non-bubble increases in house prices, and journal article that is mildly supportive of this thesis.

What Diversity can do, Design can do better.

People sometime argue that diversity in an organization is good because it improves the quality of ideas generated. Different backgrounds bring different perspectives, which provide different insights, whereas having many people with the same background causes partial redundancy.

This argument seems to be mainly made as a rationalization rather than as a true reason. It’s typically employed to support hiring more blacks, or less frequently more women and hispanics. But rarely do advocates explain exactly what new perspectives these people are meant to bring. Does one’s race give one a unique insight into how to write good code? If not, this argument seems pretty poor as a justification for discriminating in favor of blacks for programming jobs. Do women have special, vagina-based insights into maths or physics? If not, it doesn’t seem to work as a justification for discriminating in favor of women for STEM positions.

Indeed, if you actually wanted a diversity of opinions, you would probably just seek to hire that directly. Maybe your investment team should have majored in Economics, Physics, History and Statistics rather than Economics, Economics, Economics and Economics. Perhaps you should hire some social conservatives to your sociology department rather than actively and openly discriminating against them. Sometimes this strategy is employed – Corporate Boards do try to have people from a wide variety of backgrounds, both inside the company, different companies and even different industries. But I’ve never seen the pro-diversity crowd realize this purported benefit of racial diversity could be much more directly achieved.

Indeed, suppose different races did have different insights into programming. Then you would probably benefit from seeing each race represented. But while you might want some people from each race, there’s no reason to think you’d want them in the same fractions as appear in the overall population. At the moment having a racial breakdown significantly different from the US is enough to have you branded as un-diverse, but is there any reason to think the overall US has the optimal racial make-up for your company? Probably not. Indeed, as the racial make-up of the US is changing over time, even if your organization’s optimal make-up was fashionably diverse at the moment, it won’t be in the future, as the hispanic share increases and the white share decreases.

And if you were actually looking to take advantage of different racial perspectives and advantages, you wouldn’t have a corporate-wide quota or such. Instead, individual job openings would come with desired races attached. We would see a return to “No Blacks or Irish” notices on job postings, brought back at the auspices of political correctness.

Economies of Scale in Individual Labor Supply

Here are two stylized facts about labor economics:

  1. Utility is roughly logarithmic in income
  2. People who earn more per hour also work longer hours.

Together they present a puzzle – those higher income people are higher income primarily because they earn more dollars/hour, not because they work more hours. Yet if utility is logarithmic, there are diminishing returns to income, so we should expect people with higher hourly rates to work fewer hours.

Essentially, the first stylized fact suggests the income effect dominates, while the second suggests the substitution effect dominates.

One solution to this conundrum would be if the hourly rate changed with the number of hours worked. Maybe there are some jobs that simply cannot be done unless you put a huge amount of effort into them: you can’t be a part-time investment banker or corporate lawyer. If so, your productivity would increase dramatically with hours worked, so the demand curve for your labor would be upwards sloping. It’s a bit like a Giffen Good, except the causation goes

  • Higher Quantity -> More Valuable -> Higher Demand -> Higher Price

rather than

  • Higher Price -> More Valuable -> Higher Demand -> Higher Quantity

At the same time, every extra dollar is worth less and less to you, and each hour of leisure lost hurts more than the previous one, so you demand a higher hourly wage the more hours you work. So your supply curve is upwards sloping, roughly exponentially (to offset the logarithmic dollars->utility conversion)

When both supply and demand curves and upwards sloping, it is not clear there is a unique equilibrium – there could be multiple equilibria.

This could explain why we see such a difference between the incomes of

  1. The increasing number of people who do not work at all
  2. People who do ordinary jobs for around 40 hours a week
  3. Extremely high earning extremely hard working people

each group occupies a different one of these equilibria.

 

RCT as I say, not as I do

Randomized Controlled Trials (RCTs) are the gold standard in policy evaluation.

Say you’re investigating a third world development policy, like building schools, or installing water pumps, or distributing malaria-resistant bednets. A random sample of the villages in an area are selected to receive the policy. The other villages form the control group, and receive no special treatment. Metrics on various desiderata are recorded for each village, like income, lifespan and school attendance. By comparing these outcomes between villages with and without the intervention, we can judge whether it made a statistically significant difference.

RCTs give us strong evidence of a causal link between the intervention and the result – we assume there were no other systematic differences between the treatment and control villages, so we have good grounds for thinking the differences in outcome were due to the intervention.

This is a marked improvement over typical methods of evaluation. One such method is simply to not investigate results at all, because it seems obvious that the intervention is beneficial. But people’s intuitions are not very good at judging which interventions work. When Michael Kremer and Rachel Glennerster did a series of education RCTs in Kenya, all their best ideas turned out to be totally ineffective – plausible ideas like providing textbooks or teachers to schools had little impact. The one thing that did make a difference – deworming the children of intestinal worms – was not something you’d necessarily have expected to have the biggest impact on education. Our intuitions are not magic – there’s no clear reason to expect our to have evolved good intuitions into the effectiveness of developmental policies.

A common alternative is to give everyone the intervention, and see if outcomes improve. This doesn’t work either – outcomes might have improved for other reasons. Or, if outcomes deteriorated, maybe they would have been even worse without the intervention. Without RCTs, it’s very difficult to tell. Another alternative to RCTs is to compare outcomes for villages which had schools in the first place to those which didn’t, before you intervene at all, and see if the former have better outcomes. But then you can’t tell if there was a third factor that causes both schools and outcomes – maybe the richer villages could afford to build more schools.

The other main use of RCTs is in pharmaceuticals – companies that develop a new drug have to go through years of testing where they randomly assign the drug to some patients but not others, so we can be reasonably confident that the drug both achieves its aims and doesn’t cause harmful side effects.

One of the major criticisms of RCTs is that they are unfair, because you’re denying the benefits of the intervention to those in the control group. You could have given vaccinations to everyone, but instead you only gave them to half the people, thereby depriving the second half of the benefits. That’s horrible, so you should give everyone the treatment instead. This is a reasonably intelligent discussion of the issue.

But this is probably a mistake. Leaving aside the issue that it’s more expensive to give everyone the treatment than a subset (though RCTs do cost money to run), it’s a very static analysis. Perhaps in the short term giving everyone the best we have might produce the best expected results. But in the long term, we need to experiment to learn more about what works best. It is far better to apply the scientific method now and invest in knowledge that will be useful later than to cease progress on the issue.

Indeed, without doing so we could have little confidence that our actions were actually doing any good at all! Many interventions received huge amounts of funding, only for us to realize, years later, that they weren’t really achieving much. For example, for a while PlayPumps – children’s roundabouts that pumped drinking water – were all the rage, and millions of dollars raised, before people realized that they were expensive and inefficient. Worse, they didn’t even work as roundabouts, as the energy taken out of the system to pump the water meant they were no fun to play with.

Another excellent example of the importance of RCTs is Diacidem. Founded in 1965 by Lyndon Diacidem, it now spends $415 million a year, largely funded by the US government, on a variety of healthcare projects in the third world, where it deliberately targets the very poorest people. Given that total US foreign aid spending on healthcare is around $1,318 million, this is a very substantial program.

Diacidem have done RCTs. They did one with 3,958 people from 1974 to 1982, where they randomly treated some people but not others. The long time horizon and large sample size makes this an especially good study.

Unfortunately, they failed to find any improvement on nearly all of the metrics they used, and as they used a 5% confidence interval, you’d expect one to appear significant just by chance.

 “for the average participant, any true differences would be clinically and socially negligible… for the five general health measures, we could detect no significant positive effect… among participants who were judged to be at elevated risk [the intervention] has no detectable effect.

Even for those with low income and initial ill health, surely the easiest to help, they didn’t find any improvements in physical functioning, mental health, or their other metrics.

They did a second study in 2008, with 12,229 people, and the results were similar. People in the treatment groups got diagnosed and treated a lot more, but their actual health outcomes didn’t seem to improve at all. Perhaps most damningly,

“We did not detect a significant difference in the quality of life related to physical health or in self-reported levels of pain or happiness.”

Given that these two studies gave such negative results, you would expect there to be a lot more research on the effectiveness of Diacidem – if not simply closing it down. When there are highly cost-effective charities than can save lives with more funding, it is wrong to waste money on charities that don’t seem to really achieve anything instead. But there seems to be no will at all to do any further study. People like to feel like they’re doing good, and don’t like to have their charity criticized. Diacidem is political popular, so it’s probably here to stay.

Sound bad?

Unfortunately, things are far worse than that. Diacidem does not actually cost $415 million a year – in 2012, they spent over $415 billion, over 300 times as much as the US spends on healthcare aid. It wasn’t founded by Lyndon Diacidem, but by Lyndon Johnson (among others) Nor does it target the very poorest people in the third world – it targets people who are much better off than the average person in the third world.

The RCTs mentioned above are the RAND healthcare experiment and the Oregon healthcare experiment, with some good discussion here and here.

Oh, and it’s not actually called Diacidem – it’s called Medicaid.