Monday, July 22, 2013

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.


  1. Excellent post! I completely agree that most commentators miss the inner heterogeneity of China. By the way, the post more or less covers the case of India also - with the obvious caveat that both the 'rich' and the poor areas of India are much less wealthy than China's.

  2. you can do the same thing between Connecticut and Mississippi in the US, and there has not been convergence in 150 years, and has not sparked growth and the disparities are large and growing.
    In fact I am sure this can be applied for Brazil as well.

  3. Sorry to amend, the disparity has shrunk in the States with the aid of massive fiscal transfers from rich to poor but has held steady since the 70s.
    see pasted article below
    1.8: it’s the ratio of average personal income in the richest state to that in the poorest state in 2012.
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    The number may not be all that surprising, nor are the rankings of the states: Connecticut is richest, with an income of $59,000 per capita, followed by Massachusetts, New Jersey and New York; Mississippi is poorest, at $33,000. What is striking is a much older number: 5.1, the ratio in 1930.

    This means that the disparity between the richest and poorest states has shrunk by almost two-thirds in 80 years. And actually, it happened much faster than that, thanks to an array of powerful social and economic trends.

    Government spending in the Great Depression and World War II hastened the transfer of income from North to South. Growing prosperity, migration and mobility had the same effect. By 1976 the ratio of average income in richest and poorest states had declined to 1.7 (excluding the anomaly of Alaska, with its huge resources and tiny population in its early years of statehood), and it has held fairly steady since then.

    Though the disparity has shrunk sharply, the rankings of rich and poor states have changed little. In 1929 the richest state was New York, at $16,000 per capita (in 2013 dollars), followed by Delaware and Connecticut. In last place was South Carolina, at $4,000, and Mississippi was next to last.

    Yet while income inequality among states has been falling, the pattern among individuals and families tells a different story. The economists Thomas Piketty and Emmanuel Saez report that in 1930, the top 1 percent of Americans received 17 percent of the nation’s total income. Their share declined steadily until 1975 — to a mere 8 percent. But since then the number has been creeping back up, and it now stands again at 17 percent.

  4. Tamer,

    I think what's less important than convergence in income levels is convergence in growth rates. In the Solow steady state, you grow according to n+g, where n is population growth and g is technological progress. However, it may be the case that your savings rate is different so your final level is different.

    To test this, looking at the 3rd quartile to 1st quartile ratios can be instructive. In China, from 2001 to 2011, the 3rd quartile growth province had its GDP increase by a factor of 3.25, whereas the 1st quartile province "only" had an increase by a factor of 2.51. This is a ratio of about 1.3 and a difference of about 1.

    On the other hand, in Mexico, (I only got data for 2003 and 2010, so I exponentiated growth rates accordingly to get a 10 year growth rate), the analogous numbers were only 1.15 and 1.00, for a ratio of 1.15 and a difference of 0.15.

    Both the ratios and raw differences are important here. The ratio because it gives a sense of scale. The difference because it gives a sense of speed.

    So when China converges in growth rates, I'll be thinking that a slowdown is more likely. But for the time being, I'm optimistic that there's still plenty of growth to be won by convergence.