An unusual recession, a rapid recovery

Jul 11 JDN 2459407

It seems like an egregious understatement to say that the last couple of years have been unusual. The COVID-19 pandemic was historic, comparable in threat—though not in outcome—to the 1918 influenza pandemic.

At this point it looks like we may not be able to fully eradicate COVID. And there are still many places around the world where variants of the virus continue to spread. I personally am a bit worried about the recent surge in the UK; it might add some obstacles (as if I needed any more) to my move to Edinburgh. Yet even in hard-hit places like India and Brazil things are starting to get better. Overall, it seems like the worst is over.

This pandemic disrupted our society in so many ways, great and small, and we are still figuring out what the long-term consequences will be.

But as an economist, one of the things I found most unusual is that this recession fit Real Business Cycle theory.

Real Business Cycle theory (henceforth RBC) posits that recessions are caused by negative technology shocks which result in a sudden drop in labor supply, reducing employment and output. This is generally combined with sophisticated mathematical modeling (DSGE or GTFO), and it typically leads to the conclusion that the recession is optimal and we should do nothing to correct it (which was after all the original motivation of the entire theory—they didn’t like the interventionist policy conclusions of Keynesian models). Alternatively it could suggest that, if we can, we should try to intervene to produce a positive technology shock (but nobody’s really sure how to do that).

For a typical recession, this is utter nonsense. It is obvious to anyone who cares to look that major recessions like the Great Depression and the Great Recession were caused by a lack of labor demand, not supply. There is no apparent technology shock to cause either recession. Instead, they seem to be preciptiated by a financial crisis, which then causes a crisis of liquidity which leads to a downward spiral of layoffs reducing spending and causing more layoffs. Millions of people lose their jobs and become desperate to find new ones, with hundreds of people applying to each opening. RBC predicts a shortage of labor where there is instead a glut. RBC predicts that wages should go up in recessions—but they almost always go down.

But for the COVID-19 recession, RBC actually had some truth to it. We had something very much like a negative technology shock—namely the pandemic. COVID-19 greatly increased the cost of working and the cost of shopping. This led to a reduction in labor demand as usual, but also a reduction in labor supply for once. And while we did go through a phase in which hundreds of people applied to each new opening, we then followed it up with a labor shortage and rising wages. A fall in labor supply should create inflation, and we now have the highest inflation we’ve had in decades—but there’s good reason to think it’s just a transitory spike that will soon settle back to normal.

The recovery from this recession was also much more rapid: Once vaccines started rolling out, the economy began to recover almost immediately. We recovered most of the employment losses in just the first six months, and we’re on track to recover completely in half the time it took after the Great Recession.

This makes it the exception that proves the rule: Now that you’ve seen a recession that actually resembles RBC, you can see just how radically different it was from a typical recession.

Moreover, even in this weird recession the usual policy conclusions from RBC are off-base. It would have been disastrous to withhold the economic relief payments—which I’m happy to say even most Republicans realized. The one thing that RBC got right as far as policy is that a positive technology shock was our salvation—vaccines.

Indeed, while the cause of this recession was very strange and not what Keynesian models were designed to handle, our government largely followed Keynesian policy advice—and it worked. We ran massive government deficits—over $3 trillion in 2020—and the result was rapid recovery in consumer spending and then employment. I honestly wouldn’t have thought our government had the political will to run a deficit like that, even when the economic models told them they should; but I’m very glad to be wrong. We ran the huge deficit just as the models said we should—and it worked. I wonder how the 2010s might have gone differently had we done the same after 2008.

Perhaps we’ve learned from some of our mistakes.

How rich are we, really?

Oct 29, JDN 2458056

The most commonly-used measure of a nation’s wealth is its per-capita GDP, which is simply a total of all spending in a country divided by its population. More recently we adjust for purchasing power, giving us GDP per capita at purchasing power parity (PPP).

By this measure, the United States always does well. At most a dozen countries are above us, most of them by a small amount, and all of them are quite small countries. (For fundamental statistical reasons, we should expect both the highest and lowest average incomes to be in the smallest countries.)

But this is only half the story: It tells us how much income a country has, but not how that income is distributed. We should adjust for inequality.

How can we do this? I have devised a method that uses the marginal utility of wealth plus a measure of inequality called the Gini coefficient to work out an estimate of the average utility, instead of the average income.

I then convert back into a dollar figure. This figure is the income everyone would need to have under perfect equality, in order to give the same real welfare as the current system. That is, if we could redistribute wealth in such a way to raise everyone above this value up to it, and lower everyone above this value down to it, the total welfare of the country would not change. This provides a well-founded ranking of which country’s people are actually better off overall, accounting for both overall income and the distribution of that income.

The estimate is sensitive to the precise form I use for marginal utility, so I’ll show you comparisons for three different cases.

The “conservative” estimate uses a risk aversion parameter of 1, which means that utility is logarithmic in income. The real value of a dollar is inversely proportional to the number of dollars you already have.

The medium estimate uses a risk aversion parameter of 2, which means that the real value of a dollar is inversely proportional to the square of the number of dollars you already have.

And then the “liberal” estimate uses a risk aversion parameter of 3, which means that the real value of a dollar is inversely proportional to the cube of the number of dollars you already have.

I’ll compare ten countries, which I think are broadly representative of classes of countries in the world today.

The United States, the world hegemon which needs no introduction.

China, rising world superpower and world’s most populous country.

India, world’s largest democracy and developing economy with a long way to go.

Norway, as representative of the Scandinavian social democracies.

Germany, as representative of continental Europe.

Russia, as representative of the Soviet Union and the Second World bloc.

Saudi Arabia, as representative of the Middle East petrostates.

Botswana, as representative of African developing economies.

Zimbabwe, as representative of failed Sub-Saharan African states.

Brazil, as representative of Latin American developing economies.
The ordering of these countries by GDP per-capita PPP is probably not too surprising:

  1. Norway 69,249
  2. United States 57,436
  3. Saudi Arabia 55,158
  4. Germany 48,111
  5. Russia 26,490
  6. Botswana 17,042
  7. China 15,399
  8. Brazil 15,242
  9. India 6,616
  10. Zimbabwe 1,970

Norway is clearly the richest, the US, Saudi Arabia, and Germany are quite close, Russia is toward the upper end, Botswana, China, and Brazil are close together in the middle, and then India and especially Zimbabwe are extremely poor.

But now let’s take a look at the inequality in each country, as measured by the Gini coefficient (which ranges from 0, perfect equality, to 1, total inequality).

  1. Botswana 0.605
  2. Zimbabwe 0.501
  3. Brazil 0.484
  4. United States 0.461
  5. Saudi Arabia 0.459
  6. China 0.422
  7. Russia 0.416
  8. India 0.351
  9. Germany 0.301
  10. Norway 0.259

The US remains (alarmingly) close to Saudi Arabia by this measure. Most of the countries are between 40 and 50. But Botswana is astonishingly unequal, while Germany and Norway are much more equal.

With that in mind, let’s take a look at the inequality-adjusted per-capita GDP. First, the conservative estimate, with a parameter of 1:

  1. Norway 58700
  2. United States 42246
  3. Saudi Arabia 40632
  4. Germany 39653
  5. Russia 20488
  6. China 11660
  7. Botswana 11138
  8. Brazil 11015
  9. India 5269
  10. Zimbabwe 1405

So far, ordering of nations is almost the same compared to what we got with just per-capita GDP. But notice how Germany has moved up closer to the US and Botswana actually fallen behind China.

Now let’s try a parameter of 2, which I think is the closest to the truth:

  1. Norway 49758
  2. Germany 32683
  3. United States 31073
  4. Saudi Arabia 29931
  5. Russia 15581
  6. China 8829
  7. Brazil 7961
  8. Botswana 7280
  9. India 4197
  10. Zimbabwe 1002

Now we have seen some movement. Norway remains solidly on top, but Germany has overtaken the United States and Botswana has fallen behind not only China, but also Brazil. Russia remains in the middle, and India and Zimbawbe remain on the bottom.

Finally, let’s try a parameter of 3.

  1. Norway 42179
  2. Germany 26937
  3. United States 22855
  4. Saudi Arabia 22049
  5. Russia 11849
  6. China 6685
  7. Brazil 5753
  8. Botswana 4758
  9. India 3343
  10. Zimbabwe 715

Norway has now pulled far and away ahead of everyone else. Germany is substantially above the United States. China has pulled away from Brazil, and Botswana has fallen almost all the way to the level of India. Zimbabwe, as always, is at the very bottom.

Let’s compare this to another measure of national well-being, the Inequality-Adjusted Human Development Index (which goes from 0, the worst, to 1 the best). This index combines education, public health, and income, and adjusts for inequality. It seems to be a fairly good measure of well-being, but it’s very difficult to compile data for, so a lot of countries are missing (including Saudi Arabia); plus the precise weightings on everything are very ad hoc.

  1. Norway 0.898
  2. Germany 0.859
  3. United States 0.796
  4. Russia 0.725
  5. China 0.543
  6. Brazil 0.531
  7. India 0.435
  8. Botswana 0.433
  9. Zimbabwe 0.371

Other than putting India above Botswana, this ordering is the same as what we get from my (much easier to calculate and theoretically more well-founded) index with either a parameter of 2 or 3.

What’s more, my index can be directly interpreted: The average standard of living in the US is as if everyone were making $31,073 per year. What exactly is an IHDI index of 0.796 supposed to mean? We’re… 79.6% of the way to the best possible country?

In any case, there’s a straightforward (if not terribly surprising) policy implication here: Inequality is a big problem.

In particular, inequality in the US is clearly too high. Despite an overall income that is very high, almost 18 log points higher than Germany, our overall standard of living is actually about 5 log points lower due to our higher level of inequality. While our average income is only 19 log points lower than Norway, our actual standard of living is 47 log points lower.

Inequality in Botswana also means that their recent astonishing economic growth is not quite as impressive as it at first appeared. Many people are being left behind. While in raw income they appear to be 10 log points ahead of China and only 121 log points behind the US, once you adjust for their very high inequality they are 19 log points behind China, and 145 log points behind the US.

Of course, some things don’t change. Norway is still on top, and Zimbabwe is still on the bottom.