Douglas Clement has an \”Interview with David Autor\” in The Region, published by the Federal Reserve Bank of Minneapolis (September 7, 2016). The subheading give a sense of the scope of the conversation: \”MIT economist on tech, trade & job markets, how Chinese imports affect U.S. politics & family structure, and the Janus-faced gig economy.\” The interview is a lively and interesting read throughout, but here is a sampling of the comments that caught my eye.
On using Commuting Zones (CZs) to look at local employment effects of rising Chinese exports:
\”The U.S. Department of Agriculture developed the CZ [commuting zone] concept. Basically, they are contiguous counties where most of the people live and work within the same county cluster and where the centroid of the county is commutable from the edges of the CZs. It was [David] Dorn who discovered them in his doctoral thesis work, in the sense that they were out there, but no one outside the Department of Agriculture had used them for economic research. Following Dorn’s lead, we started using them because we thought they had a lot of attractive properties—they’re a sort of “revealed preference” measure of local labor markets. They cover the entire mainland United States, and their boundaries can be drawn consistently over time (unlike metropolitan statistical areas, which are regularly redefined as populations shift). Now a lot of scholars are using them, which is great. …
The basic idea is simple: We see China’s share of U.S. manufacturing goods consumption rising rapidly in the 1990s and even more so in the 2000s. Is this due to changes in China’s competitive position—lower prices, higher quality—or is it due to shifts in U.S. consumer tastes or even due to declines in U.S. production capacity …
To make progress, we studied China’s exports to eight other high-income countries simultaneously in each of 392 goods categories (covering all of manufacturing). Our idea was that if Chinese exports to the United States are driven in part by falling costs and rising quality, then Chinese import penetration in other rich countries in precisely these same goods categories should rise in parallel. And this hypothesis is strongly confirmed by the data. The bivariate correlation between the rise in China’s market penetration at the product level in the United States and these eight other countries ranges from 0.55 to 0.96.
For our analysis of the impact of the China shock on U.S. labor markets, we use only the component of rising China—U.S. import penetration that is shared with these other countries—that is, we use the component that we can confidently attribute to China’s improved competitive position.
We take these predicted changes in import penetration into the United States by goods category, and then we project that down to these commuting zones, looking at the geographic structure of U.S. manufacturing employment. Manufacturing is always very geographically concentrated. When you’re talking about furniture, for example, you’re talking about Tennessee or the Carolinas; you’re not talking about 50 states making furniture. The same is true if you’re talking about toys or leather goods or textiles—they’re very localized.
So that was the strategy; we thought it was a good idea, and we didn’t have a strong prior about what we would find. We thought we would find contraction of import-competing manufacturing employment, and we did. That was not surprising, but what happens after that was an unknown.
We were quite startled by how slow and incomplete the adjustment process was and the fact that you didn’t see offsetting gains in employment in other sectors. You see people entering unemployment or exiting the labor force, and wages falling modestly, but much adjustment was on the employment margin, not the earnings margin.
That is another thing that differentiated what we were doing. Historically, trade economists have relied upon full employment models, where people may lose jobs but they don’t lose work per se because they are quickly reallocated across sectors. According to such models, you should expect markets to clear by wages falling. But, in fact, what tends to happen is that people lose employment, and wages don’t really change for those who stay in employment.
Similarly, if we were in full general equilibrium all the time, the commuting zone would be irrelevant as an outcome measure because markets would clear nationally. The local shock is geographically dissipated because there’s effectively a law of one price of skill. And if that law did not hold in the very short run, workers would move to areas with higher wages until wages were again equalized. But it turns out that not only are the trade shocks locally concentrated—due to the concentrated geography of manufacturing—they also primarily play out locally. Much of the pain of adjustment is borne at the point of impact. … At the level of commuting zones, looking at workers initially in the impacted industry, you just don’t see the kind of diffusion or reallocation that general equilibrium models suggest. It’s not that it doesn’t happen eventually, but it happens slowly and painfully.
How the polarization of Congress spread to polarization of the public
\”What’s really fascinating—something we were not aware of—is that while the polarization of Congress has been going on for 30 years, basically since Ronald Reagan took office, the polarization of the U.S. electorate is much more recent. It’s really just been in the last 10 years.
By “polarization,” I mean the clustering of beliefs along party lines. So, for example, it’s increasingly the case that your view on global warming is highly predictive of your view on Mexicans, is highly predictive of your view on how high or low taxes should be and whether people should have a right to open-carry weapons.
Those beliefs didn’t used to be as correlated. So people have more strongly held, more divergent views. They have much more negative views of the other party as well. There is rising disapproval of cross-party marriage: The idea that you would be upset if your kid married someone of the other party has risen. Pew has documented this phenomenon. This has coincided with the rise of the Tea Party, and it’s most pronounced after 2006.
We’re seeing this same phenomenon in the data that we’re using to look at the trade exposure. We don’t want to say that all political polarization is due to trade exposure; I’m sure it’s not. But the localized adverse economic impact of the China shock in the 2000s does appear to be a kind of unnoticed contributor. In fact, you can see the antecedents of the current divisive presidential race playing out in the House in the 2000s—and specifically, in the locations where manufacturing was most hard hit.
On the longer-term relationship between technology and jobs
\”It’s a very natural thing to think that if computers do more work, people do less work. But I think the answer is much more nuanced—and, of course, economists have recognized these nuances for centuries.
Computerization changes what type of work people do—that’s very clear; we see the occupational change going on. But the part that people miss is that displacement of a set of tasks or even entire job categories does not augur the end of work. In the last 200 years, technology has totally changed the work that we do. Most of the jobs we have didn’t really exist in any significant number 200 years ago. As a result, work is much better. It’s more interesting, it’s more productive, it’s safer and more rewarding.
My optimism on this topic comes in part from the fact that we’ve already gone through incredibly dramatic adjustments and have been largely made enormously better off for it. It’s not just because we’ve increased aggregate wealth, but more of us work in paid jobs now than 100 years ago. At the turn of the 20th century, most women worked in grueling unpaid employment in the household. Now the majority work in better jobs, for pay, in the formal labor market.
We’ve adjusted to the displacement of human labor by automation along at least three margins. One is that we’ve just created many new and interesting things to do. Think about software development or tourism or all kinds of travel and food and restaurants. We do all kinds of creative and interesting things we didn’t do before.
Two, more of us work, but we work fewer hours. People don’t work until the day they die. They work 40- and 50-hour weeks instead of 80-hour weeks. They work five days a week instead of seven. They take vacations. So they’ve spread the work in a way that’s constructive and leads to a better quality of life.
And the other thing, of course, is that as we get wealthier, our consumption demands rise, so we create a lot of work because we choose to consume rather than just taking it all in leisure. If a worker in 2015 wanted to have a 1915 level of income, he or she could work about 17 weeks a year. But most of us choose not to. We’d rather have a bigger house and a couple of cars and whatever else.
Full disclosure: Autor was the Editor of the Journal of Economic Perspectives from 2009-2014, and thus was my boss for six years.