With the recent constant appearance of Alibaba on the news, the increasing relevance of Chinese exports to the world is extremely clear. Low-income countries accounted for just 9% of US manufacturing imports in 1990. But by 2007, they had more than tripled its share. And who do you think was behind this? China accounted for as much as 89% of this increase.
In this period, China's transition to an open economy included a massive 150 million people migrating from rural to urban areas. Imagine reallocating around half of the United States population geographically, with a particular focus on manufacturing production. Add to this formula novel access to foreign technologies as well as capital and Chinese exports growth to seem reasonable. However, did this come to the expense of anyone? This is the main objective of this post. One group being threatened by Chinese takeover of manufactures is obviously the manufacturing workers in the rest of the world. As these goods are easily tradable, we could expect job losses in these sectors. The figure below shows that as Chinese increased its relevance in US imports, the share of the population working in the manufacturing sector in the US decreased by one third.
However, many things could explain this decline. For example, it could be that Americans themselves were getting more educated and moving to other sectors. Alternatively, the service sector could be becoming more productive in the US, offering higher wages and hence draining employees from the manufacturing industries. These (and many other alternatives) do not involve China's exports growth. Moreover, they could be causing the increase in Chinese exports themselves. (Imagine US decides to get out of the production of manufactures, leaving a lot of unsatisfied demand which leads the Chinese to produce more). Hence, in order to make sure we are capturing the correct effect, modern econometric techniques come to the rescue! Autor and Dorn (AER, 2013) basically exploit the differences in the exposure to import competition across cities in the US. For example, it would be expected that - if the leading cause comes from the Chinese side - an area where manufacturing employed 25% of the people to be more affected by Chinese exports than an area that only employs 10% in manufacturing. Particularly, they will differentiate areas by how specialized they are in each division within manufacturing, and how imports from each of these changed over time. And these differences will give us the information we are after.
Looking at wages, the effect found of imports from China is negative. An increase in the imports per worker of around three thousand dollars (which was the average change from 1990 to 2007), would explain a decline of around 2.25% (0.76 times 3). More interestingly, this effect is stronger among men and people without college education. It is important to remark that this can only be calculated for the employed. Hence, if we expected workers with lower ability and earnings to be more likely to lose their jobs after the Chinese expansion, the effect on wages above would be understated. And so wages would have fallen even more for the whole sector, it's just that the effect could be hidden by the increasing number of people losing their jobs.
And what if we divide the effect between sectors? I would have expected the wage effect to be stronger in the manufacturing sector itself. But well, the data seems to suggest the opposite: wages seems to have been unaffected in this sector. However, the manufacturing sector was particularly affected by a major reduction in employment (predicting a decline of 12% due to China's increase in exports).
So most of the effect on wages mentioned for the whole economy seems to come from the non-manufacturing sectors. How can this be possible? Well, (adaptive) story telling is a prerequisite for any upstanding economist. And here is the one that seems most appropriate given the results: the increase in imports from China led to firing of the lower skilled workers in the manufacturing sector but had no effect on their average wages (note this could still involve a decrease in the wages of the ones that remained employed). Having no new paychecks, these newly unemployed decided to reduce spending and so decreased their purchases of services that have to be provided locally (like a haircut or a dentist). This reduced this local sector's revenue. Moreover, the newly unemployed also fled to other sectors looking for jobs. Having lower revenues and seeing lots of people of willing to work for less, other sectors reduced their wages.
Based on an article by David Autor and David Dorn (AER, 2013).
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