More on Growth and Convergence Within Countries

In my last post on China, I touched on the issue of Chinese growth by showing a graph with the distribution of Chinese per capita incomes by province, and arguing that there is a strong convergence story pushing China towards more growth. In the comments, Tamar makes a note that many countries do not converge. For example, per capita incomes in Mississippi and Connecticut differ by a factor of about 2, even though the United States is a relatively developed Country. He also suggested that I take a look at Brazil. And so I did. I took a look at the distribution of  province per capita income divided by country per capita income for three emerging market economies: Brazil, Mexico, and China, and found that indeed, they were quite close!

I wondered if it was because I didn't weight for populations, so I downloaded some Mexican population data from their government's website. I didn't have time to do Brazil, but even comparing China and Mexico I found that the distributions were quite similar.

On first pass, this bodes poorly for a convergence hypothesis.

But let's think back to the Solow model. We should only observe convergence in income levels if technologies and savings rates are all identical. But it's entirely plausible that these can differ across provinces, and that they differ for extended periods of time. Therefore a better metric to evaluate convergence is not whether they converge in levels, but rather if they converge in growth rates. In the Solow model, at the steady state, all countries grow at a rate equal to the rate of population growth plus the rate of technological change. If they're all bound together (eg if they're all large counties in one country), then g should be similar across them, and demographic trends typically do not differ hugely among provinces in the long run.

So if we look at growth rates, now we see convergence at work. As a technical note, I only had data for Mexico from 2003 to 2010. So I got the ratio by exponentiating the 7 year ratio by 10/7. 

So even though Mexico and China have similar distributions in terms of their with country income levels, they have widely different distributions for growth. Therefore I stand by my original belief that China still has a lot of long run growth potential to go as the poor provinces catch up to the rich.

China's Provinces and why National Data can Mislead

Close your eyes and think of China. What do you see?

If you were like me, you saw a large metropolis filled with high rise apartment buildings, inked with chronic air pollution, humming along to the sounds of millions of residents getting through their days.

I believe this is also the image many economic commentators have in their minds when they talk about an upcoming "Chinese" slowdown. But what I want to do in this short little post is to demonstrate why thinking this way neglects one of China's most important quality: its size.

China has a total of 1.34 billion people spread over 23 provinces, 4 municipalities, and 5 autonomous regions. Individual provinces in China can have as many people as entire countries. The coastal province of Guangdong has a population of 105 million -- just shy of Mexico's 112 million and far exceeding every country in the European Union. Sichuan, an inland province (known for its spicy food), has a total of 80 million inhabitants -- larger than the entire Western Untied States combined. In this sense, it's better to think of China as a collection of smaller countries united under a currency union called China, and not as a uniform economic entity.

For example, consider the following map from Wikipedia showing per capita income by province.

As can be seen, there are vast disparities in income. Whereas the coastal provinces are quite rich, the inland ones are quite poor. However, the chart understates these differences because it uses a log color scale. Below is a histogram of the 2012 per capita income and population statistics pulled from the China Data Center associated with the University of Michigan.

GDP per capita in Shanghai was 85,000元 whereas GDP per capita in neighboring Anhui was only  28,792元. Translated into market exchange rates this means an average GDP per capita of $13,848 in Shanghai and only $4690 in Anhui. If we take the Solow model seriously, what this suggests is that there is a massive potential for convergence within China. Even if the inland provinces do not face as favorable conditions as the coastal provinces did when they got rich, do you really expect the 80 million residents of inland Sichuan to stay at 60% of coastal Guangdong's income forever? Especially since China does do so much manufacturing, Dani Rodrik's work on unconditional manufacturing convergence suggests that these poorer provinces will inevitably partially catch up with the richer provinces. There's just not enough income for them to get caught in a middle income trap.

There is also no systematic relationship between population and income. No matter the combination of big or small, rich or poor, there is a Chinese province that fits the description.

Recognizing this heterogeneity also provides a good reason for why looking at China's GDP per capita statistics provide an overly rosy picture of China's wealth and an overly dour prospects of China's future growth. Because there are a few provinces that are now somewhat rich while most provinces are still very poor, mean GDP per capita for the nation does not accurately represent the plight of most provinces. You can see this by the fact that most provinces in the above scatter plot are below the regression line that approximates the mean level of GDP per capita. As a result, we underestimate the role convergence has to play in bringing more Chinese economies out of poverty and therefore underestimate the true growth potential that China has.

Bottom line is that "turning point" arguments that fail to consider the subtleties of individual provinces will lead us astray. Too often, we associate China with middle income images of massive apartment complexes, where in reality much of China is still very poor. Any serious evaluation of where China is going requires careful consideration of how we think growth in individual provinces will evolve. And based on the provincial data, I am quite optimistic.

For Sussing Out Whether Debt Affects Future Growth, the Key is Carefully Taking into Account Past Growth

On Miles' website we have a companion post to the previous post on an instrumental variables analysis of the RR dataset. In the companion post, we walk through more of the regressions and illustrate how controlling for past growth can erase almost any effect of debt on future growth. The core conclusion?

The two of us could not find even a shred of evidence in the Reinhart and Rogoff data for a negative effect of government debt on growth for either growth either in the short run (the next five years) or in the long run (as indicated by growth from five to ten years later).

Even though the estimated slopes are still small, we also discuss why this difference -- between small negative and small positive numbers -- matters for policy. For more, be sure to read the full post here.

Data Release

The data and code used in the Quartz column with Professor Kimball can be found on the data page here.

Innovations in Data in Economics

Imagine you are tasked with investigating the effect that household income changes have on a certain variable, such as the risk of war. But unfortunately, war can affect growth, so how can you disentangle the twoway causality? Check the weather.

In fact, the above approach is precisely the approach used in one of the most influential papers on the relationship between economic growth and civil violence. As Collier notes in The Bottom Billion, because many developing countries depend on agriculture, getting too little or too much rain can severely affect growth. But fortunately for us economists, "prospective rebels do not say, 'it's raining, let's call off the rebellion'". As such, rain functions as an instrumental variable that allows us to proxy for the effect of growth on war, but avoids the effect that war has on growth. Besides in the study of civil conflict, rainfall shocks have long been used to investigate a diverse range of issues, which can range from the role of remittances as insurance, human capital accumulation, and sex-selection. While rainfall shocks seem like quite an obvious tool after the fact, I cannot help but smile at the thought of using them as such a, pardon the pun, instrumental part of research on development.

It also makes me smile because it excites me about what other data sources economists will have to leverage in the future. For example, an important part of Mian and Sufi's work on the effects of subprime mortgages was Saiz' house price elasticity data. Saiz calculated house price elasticities in metropolitan areas based on very specific geographic properties such as the percentage of area covered by water or the presence of steep terrain. He was able to generate such a thorough dataset by using satellite and topological data. Such computations, while impossible a few decades ago, are now much simpler. From the comfort of my apartment, I can easily pull up a street level map of New Delhi* and customize it using open source R. And if even I can manipulate such powerful tools from the comfort of my laptop, just imagine the new opportunities that could open up as the result of concerted research.

Other writers have commented on this "new generation" of economic data, but I think the studies discussed above add a little color on what more data really provides us.

It's tempting to say that more data will give us more correlations to work with and better predictive power. This is not necessarily the case as the number of spurious and uninformative correlations necessarily increase as the amount of data analyzed rises. However, something Big Data does give us is a better way to organize all the "natural" data sitting out there in the world. When Watson was introduced, attention shifted to the possibilities that a "personal Watson" could have on tasks that involved large database searches, such as medical care or legal research. There is no reason for economists to not share in these benefits. Many clever studies pivot on a very clever design, whether rainfall shocks or regression discontinuities because of geography. Thus Big Data may become less of a tool for direct prediction, and instead become an indispensable tool for economists to identify and deploy increasingly clever instruments and natural experiment designs.

This kind of "data mining" would not be so much as for finding correlations but to enrich the datasets that we have available. As I found out this year working on a housing finance project working with the AHS, privacy is a big deal in surveys. But with the possiblity of estimating non-economic public variables such as weather or geography, we have ever more powerful tools for estimating parameters for large groups while preserving the privacy of individual people. And even if merging individual entries is always difficult when comparing multiple datasets, such common public variables would allow us to create a base set of variables to enrich any dataset and analysis.

This change in data capabilities also has implications for the intellectual tools needed by economists to understand the data. While rigorous econometrics, especially spatial econometrics, will stay very important, it may become more important than ever to have a solid foundation in economic history. In the wake of the financial crisis, it has been fashionable to talk about how economic history would have given us a better idea of how to respond to the crash. Yet even beyond these policy implications, a better understanding of economic history could motivate the mining of old data sources, such as newspapers.

A Google scholar search for "rainfall shocks" or "rainfall shock" yields about 1400 results. What will be the future analogous tool for other fields of economics?

*On google maps, go to New Delhi and start scrolling to the west. While you are amazed by the ability to discern individual streets and apartment buildings, observe the dramatic change to a checkerboard of individual farm plots. In fact, the first time I saw this I thought the graphics resolution on my computer messed up the rendering.

What China Could Be Building

Scott recently expressed his optimism in the Chinese growth story, and sees no reason why the recent trouble with housing markets should jeopardize its growth. As a fellow traveler in China, I have to express reservations about his outlook.

One of Scott's central arguments is that Chinese people want housing. No doubt, people want somewhere to live, and recent waves of urbanization have moved more and more of demand to the cities. However, I'm not sure why this translates into an argument that the current housing situation can't be a bubble. Just because there's a need for housing does not mean that there is enough quantity demanded at current prices. There's no physical overstock, but there's a massive market overhang at current levels. If I were a homeless person who just got a job making thousands of dollars, my first priority would certainly not be moving into a sleek urban apartment whose rent would take up almost the entirety of my income. There are other, cheaper options that are not the drivers behind the current real estate rally.

This is likely because the price of houses represents more than the discounted stream of housing services, it rather represents expectations of future growth. Financial repression and low bank deposit rates force wealthy Chinese to try to grow their wealth by investing into assets such as gold, jade, or housing. Shanghai families view housing as critical to "preserving value" in a household, and the purchase of a house may reflect excessive optimism about the future price path due to other people's purchasing, instead of expectations of the value of future housing services. Printing money wouldn't solve the issue because the real cost of those items are too high, so monetary expansion would only worsen the balance sheets of the savers with bank deposits and strengthen those who had the resources to invest in housing.

The crux of the matter is inequality. Who is buying all those consumption goods you see on TV? Who is buying houses to preserve value? Yogi Berra's quote "nobody goes there, it's too crowded" does not fully apply. Nobody goes there because it's too expensive to live for the "millions of of Chinese living in tiny ramshackle homes." But the houses give just enough return for wealthy Chinese investors, who represent a small, but incredibly influential minority.

The real risk is not that the housing won't be used, but that the crash would have secondary effects. Local governments are dependent upon land sales for revenues, meaning a housing crash could have serious implications for government. In Guangdong province, some local governments are actually tearing down mountains to make new land in the ocean, all to sell the land. This, along with the recent reversal of capital flows and possible insolvency of private wealth management firms, represents a serious liquidity risk that can have disastrous consequences.

In terms of sources, I would recommend looking at Patrick Chovanec's articles on the Chinese housing market and financial system. I don't have much time to provide the direct link for each of my claims, but if there seems like something that doesn't jive right I would be happy to explain further.

So let's answer Scott's fundamental question:

So here’s my question for all of you China skeptics that insist they are building way too much housing, infrastructure, heavy industry, etc.  What precisely do you want them to build more of?  And what are the 100s of millions of Chinese living in tiny ramshackle homes to do?  Sit tight for a few more decades while resources pour into nice urban services for the pampered elite?

I want them to start building leaf blowers, so we don't have so many Chinese people in the low productivity position of sweeping streets. I want them to start building farm equipment, so we don't have so many Chinese farmers tending the fields. I want them to build more laundry machines, to free the rural Chinese from scrubbing clothes on washboards. I want them to build electric stoves, so my Grandpa can put away the coal fired outside oven. I want them to build computers that can deliver cheaper education to the masses.

Instead of just focusing on "building," I want them to invest in human capital, so productivity can be at a level that we don't need "make work" jobs. I want them to build more schools and hire better teachers, so classes aren't as large and you're not damned if you can't make it in a top elementary school. I want productivity to be high enough that high end stores don't need more clerks than actual customers.

I want these things among many others that will only be more obvious in a freer market.

That Scott can get a haircut for $4 or an ice cream cone for 50 cents shows how low productivity and wages are in China. Yet they will not grow any faster with more housing or more state directed investments. Cheap subway rides are nice, but are they not just another sign that transportation infrastructure has been built too quickly? I'm not saying China is hitting a ceiling for growth, or that vast swaths of China are condemned to poverty. But what I am saying is that we need to worry about the systemic fragility that underpins the Chinese system, and be very, very concerned about the unknown magnitude of the downside risk.