Author Archives: Arnold Kling

The U.S. is banker to the world

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Timothy Taylor writes,

Alexander Monge-Naranjo, in “The United States as a Global Financial Intermediary and Insurer” (Economic Synopses: Federal Reserve Bank of St. Louis, 2020, No. 2) delves into the return on these international investments. He calculates that from 1952-2015, the average annual return on assets that US investors was 5.2%, while the average annual return on assets held by foreign investors in the US economy was 2.5%.

Why does this difference exist, and how can it persist? As Monge-Naranjo argues, the typical pattern is that US investors in other economies are relatively more likely to invest in higher-risk asset–like investments in companies. Conversely, foreign investors in the US economy are relatively more likely to put their money into a safer asset, like US Treasury debt. In this sense, the patterns of international investment in and out of the US economy look like an insurance arrangement for the rest of the world–that is, investors in the rest of the world are trading off lower returns when times are good for safer and steadier returns when times are bad.

I often say that households and nonfinancial businesses seek to issue risky, long-term liabilities and to hold riskless, short-term assets. Banks and other financial intermediaries allow this by doing the opposite. So a bank will make business loans funded by deposits.

The rest of the world treats us link a bank. They issue risky, long-term liabilities (investments in companies) and hold riskless, short-term assets (our Treasury securities), while we do the reverse. Woe to us if there ever is a run on our bank.

General update, April 14

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1. Deirdre McCloskey writes,

Socialism should therefore be called “coercionism.”

. . .another good name for the system that the non-conservatives and the non-socialists among us favor would be “adultism.”

2. I am still cranky, for the same reasons as before. We still do not have results of random-sample testing to have an idea of how many people have the virus in various regions. We still do not know how viral load affects outcomes. We still do not know whether people get it from touching surfaces and then touching their faces, or whether they have to breathe near an infected person. We still are not conducting any scientific experiments. Instead, we are contemplating mass “experiments” with changing regimes for social distancing, arguing over putting at risk either more economic activity or more human lives. Worse yet, we really won’t learn anything from these “experiments.”

We still talk about political leaders “re-opening the economy,” as if the economy is theirs to re-open and individual choices will not be affected by the virus. People still expect that any and every household and business can be saved from the consequences of the virus, because government has the know-how and skill to undertake this. People still conceive of government as an infinite storehouse of riches that is disconnected from any need to obtain the wealth that it purports to distribute.

3. Ben Thompson writes,

what has been increasingly whitewashed in the story of California and Washington’s success in battling the coronavirus1 is the role tech companies played: the first work-from-home orders started around March 1st, and within a week nearly all tech companies had closed their doors; local governments followed another week later.

So, the approximate order of events was: private sector response, then local government response in the west, then response in the east and by the Federal government. But as Thompson points out, the east coast media have missed this aspect of the story.

4. Ali Hortaçsu, Jiarui Liu, and Timothy Schwieg attempt to do some epidemoiology.

we find that 4% to 14% of cases were reported across the U.S. up to March 16

The paper describes their methods. As of March 16, according to this site there were 4300 reported cases. But if the authors are correct, there were between 3000 and 100,000 actual cases.

I suspect that the ratio of unreported cases to reported cases has gone down since then, but I have no idea by how much. We now have almost 600K reported cases, and if we have between 6 and 25 times as many unreported cases, then that would mean between 3.6 million and 15 million people have had the virus.

Economists are probably building better models than epidemiologists. But random-sample testing would be much better.

General update, April 13

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1. Tyler Cowen continues to question the caliber of the practitioners of epidemiology. I would add that

–John Ioannidis in his classic work around 2005-2007 debunking published papers was particularly hard on epidemiological research.

–Robin Hanson was quickly able to point out the problem with treating R0 as a fixed parameter.

–I am frustrated that epidemiology is determined to rely on observational data rather than experiments

–I am frustrated at the eagerness to embrace computer models regardless of the faulty data and parametrizations.

Having said all that, I am also dismayed that laymen put such pressure on epidemiologists to make forecasts. Imagine if it was your job when you see a leaf falling from a tree 40 feet overhead to place your foot on the exact spot where you “forecast” that the leaf will land. As I pointed out when Dr. Fauci forecast between 100K and 240K deaths for the U.S. that this would require the number of deaths to double no fewer than 5 times and no more than 6 times. And this was when the 3DDRR was 2.0, which meant there was no way that such a narrow forecasting range was defensible.

2. A reader recommends that I re-link to a classic essay, lenders and spenders. It clarifies the problem with deficit spending.

3. Do you know about the phenomenon at the shore known as an undertow? On rare occasions at a section of a beach, a strong current will carry people out to sea. If this happens to you, the guidance I have read says that you need to swim parallel to the shore until you no longer encounter the current. Then you can make it back to land.

Think of the economic losses from the virus as an undertow. But instead of swimming, everyone is looking at a boat, the USS Government, where sailors from Congress and the Fed are busily tying ropes, with life preservers on one end and the railing of the boat on the other. The people in the water are each waving their arms and screaming “I need a life preserver. Throw one to me.” They are so frantic that the sailors think that the only way to keep order is to keep tossing more and more life preservers.

But there is a problem. The USS Government has a big hole in the bottom, called the deficit. It is likely to sink. And at that point a school of sharks, called inflation, will arrive on the scene.

We would have been better off swimming. There are some people who really could not swim to safety, and the better swimmers should try to pitch in to help them. But we should not be attributing so much capability to the USS Government.

Alex Tabarrok on how people learn

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Alex Tabarrok writes,

Now let’s apply these issues to another one close to your life. Savings and retirement. Savings also follow an exponential process, albeit one neither as rapid nor as certain as those involving viruses. The same principles apply, however. But in this case instead of wanting to avoid the gains at the end you want to start saving early in order to capture the big gains in your 50s and 60s as you approach retirement. You don’t get many attempts at retirement so you need to use theory rather than experience. And because you don’t get many attempts you need to learn from other people, including other people’s mistakes, to guide your savings decisions today.

Economists usually look at saving for retirement as a decision that will be based on “rational, forward-looking behavior.” I think that Alex is much more realistic. He points out that people learn best from repeated personal experience. If you mess up the decision to save for retirement, you don’t find out the consequences until it is too late. To make good decisions, you have to learn from theory and from the experiences of others.

A related issue is deficit spending by the government. We have no personal experience of deficits leading to bad outcomes. People like me point out that in theory the government has to stop piling on debt at some point. But if people need to learn that lesson from experience, it will be too late.

General update, April 12

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1. Tyler Cowen offers some unsolicited advice from an economist to epidemiologists. More here.

I applaud epidemiologists for thinking exponentially rather than linearly. I boo them for going with computer models without seeming to appreciate how flawed data and parametric simplification make them unreliable.

I would add that the political process selects economists as advisers based on criteria that are uncorrelated, or perhaps negatively correlated, with wisdom. I could imagine the same thing happening when epidemiology mixes with politics.

2. Reader Aaron Lindsey has posted a 3DDRR spreadsheet that makes it easy to see how that indicator has behaved.

Do you want to understand how difficult it is to make epidemiological forecasts? Squint at the chart from April 1st on. Imagine a trend line from April 1 to April 6 extended to the present. The difference between that trend line and the actual behavior may seem small. But because of that difference, we may have twice as many deaths from the virus compared with what would have happened had the trend from April 1 through April 6 continued.

3. Another reader, John Alcorn, found this article.

We identified 103 possible work-related cases (14.9%) among a total of 690 local transmissions. The five occupation groups with the most cases were healthcare workers (HCWs) (22%), drivers and transport workers (18%), services and sales workers (18%), cleaning and domestic workers (9%) and public safety workers (7%). Possible work-related transmission played a substantial role in early outbreak (47.7% of early cases). Occupations at risk varied from early outbreak (predominantly services and sales workers, drivers, construction laborers, and religious professionals) to late outbreak (predominantly HCWs, drivers, cleaning and domestic workers, police officers, and religious professionals).

Their investigation looked at Asian countries other than China.

4. As economists start to think about how all this new government spending is going to be financed, you might want to re-read this post from 2010.

As of 1946, the ratio of debt to GDP was 108.67 percent. From 1947 to 1970, it fell to 27.96 percent. A substantial amount of the drop was due to the fact that the government ran a primary surplus in all but four of those years (the exceptions were 1953, 1959, 1962, and 1968), for a cumulative primary surplus of 43 percent of the 1946 debt.

I was surprised by this. I had not remembered these surpluses. One reason is that the primary surplus excludes interest payments. Including interest payments, the government mostly ran deficits, particularly in the 1960’s. Another reason may be that the U.S. only began to include Social Security surpluses in the overall Budget late in President Johnson’ second term. Had we used the “unified” budget from the beginning, the deficits would have seemed much smaller and we would have counted more surpluses.

Thanks in part to Social Security, which was running surpluses that were not included in the budgets as reported at the time, we were very fiscally responsible in the 1950s and 1960s, so we paid most of the World War II debt.

In contrast, today mainstream thinking is that “interest rates are low, so why bother worrying about the debt?” Olivier Blanchard, one of the leaders of the economics profession, takes this stance. I have never had much regard for him. Over the next decade, we’ll see who turns out to be right.

5. Tyler asks why Belgium is doing so poorly. I immediately looked for a sociological variable. It turns out that Antwerp and Brussels are two of the European cities with very high Muslim populations. The Netherlands, which has several cities that have large Muslim populations, also has a per capita death rate on the high side.

I am using Muslim population as a crude proxy for ghetto-ized living conditions, equivalent to Hispanics in New York City. I am not trying cast any ethnic aspersions on Muslims or Hispanics. What I picture is a lot of people who do not have the privilege of a social-distancing option. They cannot work in home offices or avoid living in crowded conditions. As the Wikipedia list says, “In Western Europe, Muslims generally live in major urban areas, often concentrated in poor neighborhoods of large cities.”

But Germany, which also has major cities with large Muslim populations, is doing relatively well. Berlin also has a high reliance on mass transit, which deepens the mystery of how Germany is containing the virus. This story credits early implementation of test, track and trace.

6. Some of you have expressed an interest in betting on the fate of colleges and universities. You will want to read this.

“Financially, many colleges have been struggling, facing a perfect storm which is going to be even more difficult now,” Robert Franek, editor-in-chief of the Princeton Review, told Campus Reform. “Their costs are up, but their tuition dollars are down as enrollments trend[s] down. The value of their endowments is also down, and with the economy in [a] downturn, alumni and corporate donations are likely to be less.”

The coronavirus relief bill signed by President Donald Trump on March 27 includes $14.3 billion for higher education, with $12.4 billion split between emergency grants to students and money to colleges “to address needs directly related to coronavirus” and to “defray expenses” from lost revenue, reimbursement, technology for distance learning, and payroll.

Tyler’s dire prediction for colleges is based on what he foresees as their inability to sustain the foreign enrollments that have been a crucial source of revenue. But note the huge bailout voted by Congress, even though it requires Republicans voting to give money to Democratic bastions. The higher education lobby has quietly become one of the most powerful and effective political operations in the United States.

The sociological variables

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As we seek to understand different outcomes in different regions or countries, the temptation will be to look for explanations in terms of government policies. But I wonder how much of the differences were caused by sociological variables.

One variable might be ghetto-ization of immigrants. Some European countries have acquired large Muslim populations that have not been assimilated into the native culture. France, Sweden, and the UK come to mind, but perhaps there are others. Here in the U.S., New York has ghettos of Hispanic immigrants, and they account for a significant portion of deaths, perhaps in part because they are likely to work in occupations that require them to leave their homes. At the other end of the spectrum, Japan is notorious for not having a large immigrant population.

Another variable might be herding of the elderly. In the United States, we herd many of the elderly into nursing homes, and at least 10 percent of the deaths in this country have occurred among residents of such facilities. Perhaps living arrangements in Northern Italy produced an elderly herding effect. Japan has a very elderly population, but perhaps they live in more isolated conditions.

Are these or other sociological variables helpful in explaining the severity in Spain? What about the relatively low death rates in California and Texas?