What exactly is “gentrification”? How should we deal with it?

Nov 26, JDN 2458083

“Gentrification” is a word that is used in a variety of mutually-inconsistent ways. If you compare the way social scientists use it to the way journalists use it, for example, they are almost completely orthogonal.

The word “gentrification” is meant to invoke the concept of a feudal gentry—a hereditary landed class that extracts rents from the rest of the population while contributing little or nothing themselves.

If indeed that is what we are talking about, then obviously this is bad. Moreover, it’s not an entirely unfounded fear; there are some remarkably strong vestiges of feudalism in the developed world, even in the United States where we never formally had a tradition of feudal titles. There really is a significant portion of the world’s wealth held by a handful of billionaire landowner families.

But usually when people say “gentrification” they mean something much broader. Almost any kind of increase in urban real estate prices gets characterized as “gentrification” by at least somebody, and herein lies the problem.

In fact, the kind of change that is most likely to get characterized as “gentrification” isn’t even the rising real estate prices we should be most worried about. People aren’t concerned when the prices of suburban homes double in 20 years. You might think that things that are already too expensive getting more expensive would be the main concern, but on the contrary, people are most likely to cry “gentrification” when housing prices rise in poor areas where housing is cheap.

One of the most common fears about gentrification is that it will displace local residents. In fact, the best quasi-experimental studies show little or no displacement effect. It’s actually mainly middle-class urbanites who get displaced by rising rents. Poor people typically own their homes, and actually benefit from rising housing prices. Young upwardly-mobile middle-class people move to cities to rent apartments near where they work, and tend to assume that’s how everyone lives, but it’s not. Rising rents in a city are far more likely to push out its grad students than they are poor families that have lived there for generations. Part of why displacement does not occur may be because of policies specifically implemented to fight it, such as subsidized housing and rent control. If that’s so, let’s keep on subsidizing housing (though rent control will always be a bad idea).

Nor is gentrification actually a very widespread phenomenon. The majority of poor neighborhoods remain poor indefinitely. In most studies, only about 30% of neighborhoods classified as “gentrifiable” actually end up “gentrifying”. Less than 10% of the neighborhoods that had high poverty rates in 1970 had low poverty rates in 2010.

Most people think gentrification reduces crime, but in the short run the opposite is the case. Robbery and larceny are higher in gentrifying neighborhoods. Criminals are already there, and suddenly they get much more valuable targets to steal from, so they do.

There is also a general perception that gentrification involves White people pushing Black people out, but this is also an overly simplistic view. First of all, a lot of gentrification is led by upwardly-mobile Black and Latino people. Black people who live in gentrified neighborhoods seem to be better off than Black people who live in non-gentrified neighborhoods; though selection bias may contribute to this effect, it can’t be all that strong, or we’d observe a much stronger displacement effect. Moreover, some studies have found that gentrification actually tends to increase the racial diversity of neighborhoods, and may actually help fight urban self-segregation, though it does also tend to increase racial polarization by forcing racial mixing.

What should we conclude from all this? I think the right conclusion is we are asking the wrong question.

Rising housing prices in poor areas aren’t inherently good or inherently bad, and policies designed specifically to increase or decrease housing prices are likely to have harmful side effects. What we need to be focusing on is not houses or neighborhoods but people. Poverty is definitely a problem, for sure. Therefore we should be fighting poverty, not “gentrification”. Directly transfer wealth from the rich to the poor, and then let the housing market fall where it may.

There is still some role for government in urban planning more generally, regarding things like disaster preparedness, infrastructure development, and transit systems. It may even be worthwhile to design regulations or incentives that directly combat racial segregation at the neighborhood level, for, as the Schelling Segregation Model shows, it doesn’t take a large amount of discriminatory preference to have a large impact on socioeconomic outcomes. But don’t waste effort fighting “gentrification”; directly design policies that will incentivize desegregation.

Rising rent as a proportion of housing prices is still bad, and the fundamental distortions in our mortgage system that prevent people from buying houses are a huge problem. But rising housing prices are most likely to be harmful in rich neighborhoods, where housing is already overpriced; in poor neighborhoods where housing is cheap, rising prices might well be a good thing.
In fact, I have a proposal to rapidly raise homeownership across the United States, which is almost guaranteed to work, directly corrects an enormous distortion in financial markets, and would cost about as much as the mortgage interest deduction (which should probably be eliminated, as most economists agree). Give each US adult a one-time grant voucher which gives them $40,000 that can only be spent as a down payment on purchasing a home. Each time someone turns 18, they get a voucher. You only get one over your lifetime, so use it wisely (otherwise the policy could become extremely expensive); but this is an immediate direct transfer of wealth that also reduces your credit constraint. I know I for one would be house-hunting right now if I were offered such a voucher. The mortgage interest deduction means nothing to me, because I can’t afford a down payment. Where the mortgage interest deduction is regressive, benefiting the rich more than the poor, this policy gives everyone the same amount, like a basic income.

In the short run, this policy would probably be expensive, as we’d have to pay out a large number of vouchers at once; but with our current long-run demographic trends, the amortized cost is basically the same as the mortgage interest deduction. And the US government especially should care about the long-run amortized cost, as it is an institution that has lasted over 200 years without ever missing a payment and can currently borrow at negative real interest rates.

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.

When are we going to get serious about climate change?

Oct 8, JDN 24578035

Those two storms weren’t simply natural phenomena. We had a hand in creating them.

The EPA doesn’t want to talk about the connection, and we don’t have enough statistical power to really be certain, but there is by now an overwhelming scientific consensus that global climate change will increase hurricane intensity. The only real question left is whether it is already doing so.

The good news is that global carbon emissions are no longer rising. They have been essentially static for the last few years. The bad news is that this is almost certainly too little, too late.

The US is not on track to hit our 2025 emission target; we will probably exceed it by at least 20%.

But the real problem is that the targets themselves are much too high. Most countries have pledged to drop emissions only about 8-10% below their 1990s levels.

Even with the progress we have made, we are on track to exceed the global carbon budget needed to keep warming below 2 C by the year 2040. We have been reducing emission intensity by about 0.8% per year—we need to be reducing it by at least 3% per year and preferably faster. Highly-developed nations should be switching to nuclear energy as quickly as possible; an equitable global emission target requires us to reduce our emissions by 80% by 2050.

At the current rate of improvement, we will overshoot the 2 C warming target and very likely the 3C target as well.

Why aren’t we doing better? There is of course the Tragedy of the Commons to consider: Each individual country acting in its own self-interest will continue to pollute more, as this is the cheapest and easiest way to maintain industrial development. But then if all countries do so, the result is a disaster for us all.
But this explanation is too simple. We have managed to achieve some international cooperation on this issue. The Kyoto protocol has worked; emissions among Kyoto member nations have been reduced by more than 20% below 1990 levels, far more than originally promised. The EU in particular has taken a leadership role in reducing emissions, and has a serious shot at hitting their target of 40% reduction by 2030.

That is a truly astonishing scale of cooperation; the EU has a population of over 500 million people and spans 28 nations. It would seem like doing that should get us halfway to cooperating across all nations and all the world’s people.

But there is a vital difference between the EU and the world as a whole: The tribal paradigm. Europeans certainly have their differences: The UK and France still don’t really get along, everyone’s bitter with Germany about that whole Hitler business, and as the acronym PIIGS emphasizes, the peripheral countries have never quite felt as European as the core Schengen members. But despite all this, there has been a basic sense of trans-national (meta-national?) unity among Europeans for a long time.
For one thing, today Europeans see each other as the same race. That wasn’t always the case. In Medieval times, ethnic categories were as fine as “Cornish” and “Liverpudlian”. (To be fair, there do still exist a handful of Cornish nationalists.) Starting around the 18th cenutry, Europeans began to unite under the heading of “White people”, a classification that took on particular significance during the trans-Atlantic slave trade. But even in the 19th century, “Irish” and “Sicilian” were seen as racial categories. It wasn’t until the 20th century that Europeans really began to think of themselves as one “kind of people”, and not coincidentally it was at the end of the 20th century that the European Union finally took hold.

There is another region that has had a similar sense of unification: Latin America. Again, there are conflicts: There are a lot of nasty stereotypes about Puerto Ricans among Cubans and vice-versa. But Latinos, by and large, think of each other as the same “kind of people”, distinct from both Europeans and the indigenous population of the Americas.

I don’t think it is coincidental that the lowest carbon emission intensity (carbon emissions / GDP PPP) in the world is in Latin America, followed closely by Europe.
And if you had to name right now the most ethnically divided region in the world, what would you say? The Middle East, of course. And sure enough, they have the worst carbon emission intensity. (Of course, oil is an obvious confounding variable here, likely contributing to both.)

Indeed, the countries with the lowest ethnic fractionalization ratings tend to be in Europe and Latin America, and the highest tend to be in the Middle East and Africa.

Even within the United States, political polarization seems to come with higher carbon emissions. When we think of Democrats and Republicans as different “kinds of people”, we become less willing to cooperate on finding climate policy solutions.

This is not a complete explanation, of course. China has a low fractionalization rating but a high carbon intensity, and extremely high overall carbon emissions due to their enormous population. Africa’s carbon intensity isn’t as high as you’d think just from their terrible fractionalization, especially if you exclude Nigeria which is a major oil producer.

But I think there is nonetheless a vital truth here: One of the central barriers to serious long-term solutions to climate change is the entrenchment of racial and national identity. Solving the Tragedy of the Commons requires cooperation, we will only cooperate with those we trust, and we will only trust those we consider to be the same “kind of people”.

You can even hear it in the rhetoric: If “we” (Americans) give up our carbon emissions, then “they” (China) will take advantage of us. No one seems to worry about Alabama exploiting California—certainly no Republican would—despite the fact that in real economic terms they basically do. But people in Alabama are Americans; in other words, they count as actual people. People in China don’t count. If anything, people in California are supposed to be considered less American than people in Alabama, despite the fact that vastly more Americans live in California than Alabama. This mirrors the same pattern where we urban residents are somehow “less authentic” even though we outnumber the rural by four to one.
I don’t know how to mend this tribal division; I very much wish I did. But I do know that simply ignoring it isn’t going to work. We can talk all we want about carbon taxes and cap-and-trade, but as long as most of the world’s people are divided into racial, ethnic, and national identities that they consider to be in zero-sum conflict with one another, we are never going to achieve the level of cooperation necessary for a real permanent solution to climate change.

The temperatures and the oceans rise. United we must stand, or divided we shall fall.

A tale of two storms

Sep 24, JDN 2458021

There were two severe storm events this past week; one you probably heard a great deal about, the other, probably not. The first was Hurricane Irma, which hit the United States and did most of its damage in Florida; the second was Typhoon Doksuri, which hit Southeast Asia and did most of its damage in Vietnam.

You might expect that this post is going to give you more bad news. Well, I have a surprise for you: The news is actually mostly good.

The death tolls from both storms were astonishingly small. The hurricane is estimated to have killed at least 84 people, while the typhoon has killed at least 26. This result is nothing less than heroism. The valiant efforts of thousands of meteorologists and emergency responders around the world has saved thousands of lives, and did so both in the wealthy United States and in impoverished Vietnam.

When I started this post, I had expected to see that the emergency response in Vietnam would be much worse, and fatalities would be far higher; I am delighted to report that nothing of the sort was the case, and Vietnam, despite their per-capita GDP PPP of under $6,000, has made emergency response a sufficiently high priority that they saved their people just about as well as Florida did.

To get a sense of what might have happened without them, consider that 1.5 million homes in Florida were leveled by the hurricane, and over 100,000 homes were damaged by the typhoon. Vietnam is a country of 94 million people. Florida has a population of 20 million. (The reason Florida determines so many elections is that it is by far the most populous swing state.) Without weather forecasting and emergency response, these death figures would have been in the tens of thousands, not the dozens.

Indeed, if you know statistics and demographics well, these figures become even more astonishing: These death rates were almost indistinguishable from statistical noise.

Vietnam’s baseline death rate is about 5.9 per 1,000, meaning that they experience about 560,000 deaths in any given year. This means that over 1500 people die in Vietnam on a normal day.

Florida’s baseline death rate is about 6.6 per 1,000, actually a bit higher than Vietnam’s, because Florida’s population skews so much toward the elderly. Therefore Florida experiences about 130,000 deaths per year, or 360 deaths on a normal day.

In both Vietnam and Florida, this makes the daily death probability for any given person about 0.0017%. A random process with a fixed probability of 0.0017% over a population of n people will result in an average of 0.0017n events, but with some variation around that number. The standard deviation is actually sqrt(p(1-p)n) = 0.004 sqrt(n). When n = 20,000,000 (Florida), this results in a standard deviation of 18. When n = 94,000,000 (Vietnam), this results in a standard deviation of 40.

This means that the 26 additional deaths in Vietnam were within one standard deviation of average! They basically are indistinguishable from statistical noise. There have been over a hundred days in Vietnam where an extra 26 people happened to die, just in the past year. Weather forecasting took what could have been a historic disaster and turned it into just another bad day.

The 84 additional deaths in Florida are over four standard deviations away from average, so they are definitely distinguishable from statistical noise—but this still means that Florida’s total death rate for the year will only tick up by 0.6%.

It is common in such tragedies to point out in grave tones that “one death is too many”, but I maintain that this is not actually moral wisdom but empty platitude. No conceivable policy is ever going to reduce death rates to zero, and the people who died of heart attacks or brain aneurysms are every bit as dead as the people who died from hurricanes or terrorist attacks. Instead of focusing on the handful of people who died because they didn’t heed warnings or simply got extraordinarily unlucky, I think we should be focusing on the thousands of people who survived because our weather forecasters and emergency responders did their jobs so exceptionally well. Of course if we can reduce the numbers even further, we should; but from where I’m sitting, our emergency response system has a lot to be proud of.

Of course, the economic damage of the storms was substantially greater. The losses in destroyed housing and infrastructure in Florida are projected at over $80 billion. Vietnam is much poorer, so there simply isn’t as much infrastructure to destroy; total damage is unlikely to exceed $10 billion. Florida’s GDP is $926 billion, so they are losing 8.6%; while Vietnam’s GDP is $220 billion, so they are still losing 4.5%. And of course the damage isn’t evenly spread across everyone; those hardest hit will lose perhaps their entire net wealth, while others will feel absolutely nothing.

But economic damage is fleeting. Indeed, if we spend the government money we should be, and take the opportunity to rebuild this infrastructure better than it was before, the long-run economic impact could be positive. Even 8.6% of GDP is less than five years of normal economic growth—and there were years in the 1950s where we did it in a single year. The 4.6% that Vietnam lost, they should make back within a year of their current economic growth.

Thank goodness.

Think of this as a moral recession

August 27, JDN 2457993

The Great Depression was, without doubt, the worst macroeconomic event of the last 200 years. Over 30 million people became unemployed. Unemployment exceeded 20%. Standard of living fell by as much as a third in the United States. Political unrest spread across the world, and the collapsing government of Germany ultimately became the Third Reich and triggered the Second World War If we ignore the world war, however, the effect on mortality rates was surprisingly small. (“Other than that, Mrs. Lincoln, how was the play?”)

And yet, how long do you suppose it took for economic growth to repair the damage? 80 years? 50 years? 30 years? 20 years? Try ten to fifteen. By 1940, the US, US, Germany, and Japan all had a per-capita GDP at least as high as in 1930. By 1945, every country in Europe had a per-capita GDP at least as high as they did before the Great Depression.

The moral of this story is this: Recessions are bad, and can have far-reaching consequences; but ultimately what really matters in the long run is growth.

Assuming the same growth otherwise, a country that had a recession as large as the Great Depression would be about 70% as rich as one that didn’t.

But over 100 years, a country that experienced 3% growth instead of 2% growth would be over two and a half times richer.

Therefore, in terms of standard of living only, if you were given the choice between having a Great Depression but otherwise growing at 3%, and having no recessions but growing at 2%, your grandchildren will be better off if you chose the former. (Of course, given the possibility of political unrest or even war, the depression could very well end up worse.)

With that in mind, I want you to think of the last few years—and especially the last few months—as a moral recession. Donald Trump being President of the United States is clearly a step backward for human civilization, and it seems to have breathed new life into some of the worst ideologies our society has ever harbored, from extreme misogyny, homophobia, right-wing nationalism, and White supremacism to outright Neo-Nazism. When one of the central debates in our public discourse is what level of violence is justifiable against Nazis under what circumstances, something has gone terribly, terribly wrong.

But much as recessions are overwhelmed in the long run by economic growth, there is reason to be confident that this moral backslide is temporary and will be similarly overwhelmed by humanity’s long-run moral progress.

What moral progress, you ask? Let’s remind ourselves.

Just 100 years ago, women could not vote in the United States.

160 years ago, slavery was legal in 15 US states.

Just 3 years ago, same-sex marriage was illegal in 14 US states. Yes, you read that number correctly. I said three. There are gay couples graduating high school and getting married now who as freshmen didn’t think they would be allowed to get married.

That’s just the United States. What about the rest of the world?

100 years ago, almost all of the world’s countries were dictatorships. Today, half of the world’s countries are democracies. Indeed, thanks to India, the majority of the world’s population now lives under democracy.

35 years ago, the Soviet Union still ruled most of Eastern Europe and Northern Asia with an iron fist (or should I say “curtain”?).

30 years ago, the number of human beings in extreme poverty—note I said number, not just rate; the world population was two-thirds what it is today—was twice as large as it is today.

Over the last 65 years, the global death rate due to war has fallen from 250 per million to just 10 per million.

The global literacy rate has risen from 40% to 80% in just 50 years.

World life expectancy has increased by 6 years in just the last 20 years.

We are living in a golden age. Do not forget that.

Indeed, if there is anything that could destroy all these astonishing achievements, I think it would be our failure to appreciate them.

If you listen to what these Neo-Nazi White supremacists say about their grievances, they sound like the spoiled children of millionaires (I mean, they elected one President, after all). They are outraged because they only get 90% of what they want instead of 100%—or even outraged not because they didn’t get what they wanted but because someone else they don’t know also did.

If you listen to the far left, their complaints don’t make much more sense. If you didn’t actually know any statistics, you’d think that life is just as bad for Black people in America today as it was under Jim Crow or even slavery. Well, it’s not even close. I’m not saying racism is gone; it’s definitely still here. But the civil rights movement has made absolutely enormous strides, from banning school segregation and housing redlining to reforming prison sentences and instituting affirmative action programs. Simply the fact that “racist” is now widely considered a terrible thing to be is a major accomplishment in itself. A typical Black person today, despite having only about 60% of the income of a typical White person, is still richer than a typical White person was just 50 years ago. While the 71% high school completion rate Black people currently have may not sound great, it’s much higher than the 50% rate that the whole US population had as recently as 1950.

Yes, there are some things that aren’t going very well right now. The two that I think are most important are climate change and income inequality. As both the global mean temperature anomaly and the world top 1% income share continue to rise, millions of people will suffer and die needlessly from diseases of poverty and natural disasters.

And of course if Neo-Nazis manage to take hold of the US government and try to repeat the Third Reich, that could be literally the worst thing that ever happened. If it triggered a nuclear war, it unquestionably would be literally the worst thing that ever happened. Both these events are unlikely—but not nearly as unlikely as they should be. (Five Thirty Eight interviewed several nuclear experts who estimated a probability of imminent nuclear war at a horrifying five percent.) So I certainly don’t want to make anyone complacent about these very grave problems.

But I worry also that we go too far the other direction, and fail to celebrate the truly amazing progress humanity has made thus far. We hear so often that we are treading water, getting nowhere, or even falling backward, that we begin to feel as though the fight for moral progress is utterly hopeless. If all these centuries of fighting for justice really had gotten us nowhere, the only sensible thing to do at this point would be to give up. But on the contrary, we have made enormous progress in an incredibly short period of time. We are on the verge of finally winning this fight. The last thing we want to do now is give up.

Will China’s growth continue forever?

July 23, JDN 2457958

It’s easy to make the figures sound alarming, especially if you are a xenophobic American:

Annual GDP growth in the US is currently 2.1%, while annual GDP growth in China is 6.9%. At markte exchange rates, US GDP is currently $18.6 trillion, while China’s GDP is $11.2 trillion. If these growth rates continue, that means that China’s GDP will surpass ours in just 12 years.

Looking instead at per-capita GDP (and now using purchasing-power-parity, which is a much better measure for standard of living), the US is currently at $53,200 per person per year while China is at $14,400 per person per year. Since 2010 US per-capita GDP PPP has been growing at about 1.2%, while China’s has been growing at 7.1%. At that rate, China will surpass the US in standard of living in only 24 years.

And then if you really want to get scared, you start thinking about what happens if this growth continues for 20, or 30, or 50 years. At 50 years of these growth rates, US GDP will just about triple; but China’s GDP would increase by almost a factor of thirty. US per-capita GDP will increase to about $150,000, while China’s per-capita GDP will increase all the way to $444,000.

But while China probably will surpass the US in total nominal GDP within say 15 years, the longer-horizon predictions are totally unfounded. In fact, there is reason to believe that China will never surpass the US in standard of living, at least within the foreseeable future. Sure, some sort of global catastrophe could realign the world’s fortunes (climate change being a plausible candidate) and over very long time horizons all sorts of things can happen; but barring catastrophe and looking within the next few generations, there’s little reason to think that the average person in China will actually be better off than the average person in the United States. Indeed, while that $150,000 figure is actually remarkably plausible, that $444,000 figure is totally nonsensical. I project that in 2065, per-capita GDP in the US will indeed be about $150,000, but per-capita GDP in China will be more like $100,000.

That’s still a dramatic improvement over today for both countries, and something worth celebrating; but the panic that the US must be doing something wrong and China must be doing something right, that China is “eating our lunch” in Trump’s terminology, is simply unfounded.

Why am I so confident of this? Because, for all the proud proclamations of Chinese officials and panicked reports of American pundits, China’s rapid growth rates are not unprecedented. We have seen this before.

Look at South Korea. As I like to say, the discipline of development economics is basically the attempt to determine what happened in South Korea 1950-2000 and how to make it happen everywhere.

In 1960, South Korea’s nominal per-capita GDP was only $944. In 2016, it was $25,500. That takes them from solidly Third World underdeveloped status into very nearly First World highly-developed status in just two generations. This was an average rate of growth of 6.0%. But South Korea didn’t grow steadily at 6.0% for that entire period. Their growth fluctuated wildly (small countries tend to do that; they are effectively undiversified assets), but also overall trended downward.

The highest annual growth rate in South Korea over that time period was an astonishing 20.8%. Over twenty percent per year. Now that is growth you would feel. Imagine going from an income of $10,000 to an income of $12,000, in just one year. Imagine your entire country doing this. In its best years, South Korea was achieving annual growth rates in income comparable to the astronomical investment returns of none other than Warren Buffett (For once, we definitely had r < g). Even if you smooth out over the boom-and-bust volatility South Korea went through during that period, they were still averaging growth rates over 7.5% in the 1970s.

I wasn’t alive then, but I wouldn’t be surprised if Americans back then were panicking about South Korea’s growth too. Maybe not, since South Korea was and remains a close US ally, and their success displayed the superiority of capitalism over Communism (boy did it ever: North Korea’s per capita GDP also started at about $900 in 1960, and is still today… only about $1000!); but you could have made the same pie-in-the-sky forecasts of Korea taking over the world if you’d extrapolated their growth rates forward.

South Korea’s current growth rate, on the other hand? 2.9%. Not so shocking now!

Moreover, this is a process we understand theoretically as well as empirically. The Solow model is now well-established as the mainstream neoclassical model of economic growth, and it directly and explicitly predicts this sort of growth pattern, where a country that starts very poor will initially grow extremely fast as they build a capital base and reverse-engineer technology from more advanced countries, but then over a couple of generations their growth will slow down and eventually level off once they reach a high level of economic development.

Indeed, the basic reason is quite simple: A given proportional growth is easier to do when you start small. (There’s more to it than that, involving capital degradation and diminishing marginal returns, but at its core, that’s the basic idea.)

I think I can best instill this realization in you by making another comparison between the US and China: How much income are we adding in absolute terms?

US per-capita GDP of $53,200 is growing at 1.2% per year; that means we’re adding $640 per person per year. China per-capita GDP of $14,400 is growing at 7.1% per year; that means they’re adding $1,020 per year. So while it sounds like they are growing almost six times faster, they’re actually only adding about 40% more real income per person each year than we are. It’s just a larger proportion to them.

Indeed, China is actually doing relatively well on this scale. Many developing countries that are growing “fast” are actually adding less income per person in absolute terms than many highly-developed countries. India’s per capita GDP is growing at 5.8% per year, but adding only $340 per person per year. Ethiopia’s income per person is growing by 4.9%—which is only $75 per person per year. Compare this to the “slow” growth of the UK, where 1.0% annual growth is still $392 per person per year, or France, where “stagnant” growth of 0.8% is still $293 per person per year.

Back when South Korea was growing at 20%, that was still on the order of $200 per person per year. Their current 2.9%, on the other hand, is actually $740 per person per year. We often forget just how poor many poor countries truly are; what sounds like a spectacular growth rate still may not be all that much in absolute terms.

Here’s a graph (on a log scale) of GDP per capita in the US, Japan, China, and Korea, from World Bank data since 1960. I’d prefer to use GDP PPP, but the World Bank data doesn’t go back far enough.

As you can see, there is a general pattern of growth at a decreasing rate; it’s harder to see in China because they are earlier in the process; but there’s good reason to think that they will follow the same pattern.

If anything, I think the panic about Japan in the 1990s may have been more justifiable (not that it was terribly justified either). As you can see on the graph, in terms of nominal GDP per capita, Japan actually did briefly surpass the United States in the 1990s. Of course, the outcome of that was not a global war or Japan ruling the world or something; it was… the Nintendo Wii and the Toyota Prius.

Of course, that doesn’t stop people from writing news articles and even publishing economic papers about how this time is different, not like all the other times we saw the exact same pattern. Many Chinese officials appear to believe that China is special, that they can continue to grow at extremely high rates indefinitely without the constraints that other countries would face. But for once economic theory and economic data are actually in very good agreement: These high growth rates will not last forever. They will slow down, and that’s not such a bad thing. By the time they do, China will have greatly raised their standard of living to something very close to our own. Hundreds of millions of people have already been lifted out of abject poverty; continued growth could benefit hundreds of millions more.

The far bigger problem would be if the government refuses to accept that growth must slow down, and begins trying to force impossible levels of growth or altering the economic data to make it appear as though growth has occurred that hasn’t. We already know that the People’s Republic of China has a track record of doing this sort of thing: we know they have manipulated some data, though we think only in small ways, and the worst example of an attempt at forcing economic growth in human history was in China, the so-called “Great Leap Forward” that killed 20 million people. The danger is not that China will grow this fast forever, nor that they will slow down soon enough, but that they will slow down and their government will refuse to admit it.

Why “marginal productivity” is no excuse for inequality

May 28, JDN 2457902

In most neoclassical models, workers are paid according to their marginal productivity—the additional (market) value of goods that a firm is able to produce by hiring that worker. This is often used as an excuse for inequality: If someone can produce more, why shouldn’t they be paid more?

The most extreme example of this is people like Maura Pennington writing for Forbes about how poor people just need to get off their butts and “do something”; but there is a whole literature in mainstream economics, particularly “optimal tax theory”, arguing based on marginal productivity that we should tax the very richest people the least and never tax capital income. The Chamley-Judd Theorem famously “shows” (by making heroic assumptions) that taxing capital just makes everyone worse off because it reduces everyone’s productivity.

The biggest reason this is wrong is that there are many, many reasons why someone would have a higher income without being any more productive. They could inherit wealth from their ancestors and get a return on that wealth; they could have a monopoly or some other form of market power; they could use bribery and corruption to tilt government policy in their favor. Indeed, most of the top 0.01% do literally all of these things.

But even if you assume that pay is related to productivity in competitive markets, the argument is not nearly as strong as it may at first appear. Here I have a simple little model to illustrate this.

Suppose there are 10 firms and 10 workers. Suppose that firm 1 has 1 unit of effective capital (capital adjusted for productivity), firm 2 has 2 units, and so on up to firm 10 which has 10 units. And suppose that worker 1 has 1 unit of so-called “human capital”, representing their overall level of skills and education, worker 2 has 2 units, and so on up to worker 10 with 10 units. Suppose each firm only needs one worker, so this is a matching problem.

Furthermore, suppose that productivity is equal to capital times human capital: That is, if firm 2 hired worker 7, they would make 2*7 = $14 of output.

What will happen in this market if it converges to equilibrium?

Well, first of all, the most productive firm is going to hire the most productive worker—so firm 10 will hire worker 10 and produce $100 of output. What wage will they pay? Well, they need a wage that is high enough to keep worker 10 from trying to go elsewhere. They should therefore pay a wage of $90—the next-highest firm productivity times the worker’s productivity. That’s the highest wage any other firm could credibly offer; so if they pay this wage, worker 10 will not have any reason to leave.

Now the problem has been reduced to matching 9 firms to 9 workers. Firm 9 will hire worker 9, making $81 of output, and paying $72 in wages.

And so on, until worker 1 at firm 1 produces $1 and receives… $0. Because there is no way for worker 1 to threaten to leave, in this model they actually get nothing. If I assume there’s some sort of social welfare system providing say $0.50, then at least worker 1 can get that $0.50 by threatening to leave and go on welfare. (This, by the way, is probably the real reason firms hate social welfare spending; it gives their workers more bargaining power and raises wages.) Or maybe they have to pay that $0.50 just to keep the worker from starving to death.

What does inequality look like in this society?
Well, the most-productive firm only has 10 times as much capital as the least-productive firm, and the most-educated worker only has 10 times as much skill as the least-educated worker, so we might think that incomes would vary only by a factor of 10.

But in fact they vary by a factor of over 100.

The richest worker makes $90, while the poorest worker makes $0.50. That’s a ratio of 180. (Still lower than the ratio of the average CEO to their average employee in the US, by the way.) The worker is 10 times as productive, but they receive 180 times as much income.

The firm profits vary along a more reasonable scale in this case; firm 1 makes a profit of $0.50 while firm 10 makes a profit of $10. Indeed, except for firm 1, firm n always makes a profit of $n. So that’s very nearly a linear scaling in productivity.

Where did this result come from? Why is it so different from the usual assumptions? All I did was change one thing: I allowed for increasing returns to scale.

If you make the usual assumption of constant returns to scale, this result can’t happen. Multiplying all the inputs by 10 should just multiply the output by 10, by assumption—since that is the definition of constant returns to scale.

But if you look at the structure of real-world incomes, it’s pretty obvious that we don’t have constant returns to scale.

If we had constant returns to scale, we should expect that wages for the same person should only vary slightly if that person were to work in different places. In particular, to have a 2-fold increase in wage for the same worker you’d need more than a 2-fold increase in capital.

This is a bit counter-intuitive, so let me explain a bit further. If a 2-fold increase in capital results in a 2-fold increase in wage for a given worker, that’s increasing returns to scale—indeed, it’s precisely the production function I assumed above.
If you had constant returns to scale, a 2-fold increase in wage would require something like an 8-fold increase in capital. This is because you should get a 2-fold increase in total production by doubling everything—capital, labor, human capital, whatever else. So doubling capital by itself should produce a much weaker effect. For technical reasons I’d rather not get into at the moment, usually it’s assumed that production is approximately proportional to capital to the one-third power—so to double production you need to multiply capital by 2^3 = 8.

I wasn’t able to quickly find really good data on wages for the same workers across different countries, but this should at least give a rough idea. In Mumbai, the minimum monthly wage for a full-time worker is about $80. In Shanghai, it is about $250. If you multiply out the US federal minimum wage of $7.25 per hour by 40 hours by 4 weeks, that comes to $1160 per month.

Of course, these are not the same workers. Even an “unskilled” worker in the US has a lot more education and training than a minimum-wage worker in India or China. But it’s not that much more. Maybe if we normalize India to 1, China is 3 and the US is 10.

Likewise, these are not the same jobs. Even a minimum wage job in the US is much more capital-intensive and uses much higher technology than most jobs in India or China. But it’s not that much more. Again let’s say India is 1, China is 3 and the US is 10.

If we had constant returns to scale, what should the wages be? Well, for India at productivity 1, the wage is $80. So for China at productivity 3, the wage should be $240—it’s actually $250, close enough for this rough approximation. But the US wage should be $800—and it is in fact $1160, 45% larger than we would expect by constant returns to scale.

Let’s try comparing within a particular industry, where the differences in skill and technology should be far smaller. The median salary for a software engineer in India is about 430,000 INR, which comes to about $6,700. If that sounds rather low for a software engineer, you’re probably more accustomed to the figure for US software engineers, which is $74,000. That is a factor of 11 to 1. For the same job. Maybe US software engineers are better than Indian software engineers—but are they that much better? Yes, you can adjust for purchasing power and shrink the gap: Prices in the US are about 4 times as high as those in India, so the real gap might be 3 to 1. But these huge price differences themselves need to be explained somehow, and even 3 to 1 for the same job in the same industry is still probably too large to explain by differences in either capital or education, unless you allow for increasing returns to scale.

In most industries, we probably don’t have quite as much increasing returns to scale as I assumed in my simple model. Workers in the US don’t make 100 times as much as workers in India, despite plausibly having both 10 times as much physical capital and 10 times as much human capital.

But in some industries, this model might not even be enough! The most successful authors and filmmakers, for example, make literally thousands of times as much money as the average author or filmmaker in their own country. J.K. Rowling has almost $1 billion from writing the Harry Potter series; this is despite having literally the same amount of physical capital and probably not much more human capital than the average author in the UK who makes only about 11,000 GBP—which is about $14,000. Harry Potter and the Philosopher’s Stone is now almost exactly 20 years old, which means that Rowling made an average of $50 million per year, some 3500 times as much as the average British author. Is she better than the average British author? Sure. Is she three thousand times better? I don’t think so. And we can’t even make the argument that she has more capital and technology to work with, because she doesn’t! They’re typing on the same laptops and using the same printing presses. Either the return on human capital for British authors is astronomical, or something other than marginal productivity is at work here—and either way, we don’t have anything close to constant returns to scale.

What can we take away from this? Well, if we don’t have constant returns to scale, then even if wage rates are proportional to marginal productivity, they aren’t proportional to the component of marginal productivity that you yourself bring. The same software developer makes more at Microsoft than at some Indian software company, the same doctor makes more at a US hospital than a hospital in China, the same college professor makes more at Harvard than at a community college, and J.K. Rowling makes three thousand times as much as the average British author—therefore we can’t speak of marginal productivity as inhering in you as an individual. It is an emergent property of a production process that includes you as a part. So even if you’re entirely being paid according to “your” productivity, it’s not really your productivity—it’s the productivity of the production process you’re involved in. A myriad of other factors had to snap into place to make your productivity what it is, most of which you had no control over. So in what sense, then, can we say you earned your higher pay?

Moreover, this problem becomes most acute precisely when incomes diverge the most. The differential in wages between two welders at the same auto plant may well be largely due to their relative skill at welding. But there’s absolutely no way that the top athletes, authors, filmmakers, CEOs, or hedge fund managers could possibly make the incomes they do by being individually that much more productive.