Wages and the sectoral composition of the labor market

A fact often thrown around these days is that wages for the average worker have stagnated, but a really interesting short paper from the Chicago Fed, What Does the Changing Sectoral Composition of the Economy Mean For Workers?, by business economist Isaac Sorkin, reminds us that looking at a single number isn’t fully informative. The  basic idea here is that  we should take into account the changes in the types of jobs available as well as their pay and nonpay aspects when we look at the labor market.

To demonstrate why this is, imagine an economy with two types of jobs: Job 1 pays $50/hr and Job 2 pays $10/hr. At time t=0, Job 1 is 40% of the labor market and decreasing slowly over time via a random normal process with trend.


If wages remain constant, the average wage in the economy will decrease due solely to compositional effects.


However, even if we allow for wages in both jobs to rise via a random normal process, the average wage in the economy can still decrease thanks to the sectoral shift.

Here’s the path of wages:


And here’s the average wage with the same change in the job share path as before:


So it’s possible to tell a story with across the board increases in wages declining/stagnant average wages.

Another aspect to focus on is the nonpay part of the job: this is things like schedules, working conditions, benefits, etc. If we focused solely on wages, and assumed Job 1 and Job 2 were the same in all other aspects, then the previous example would look unequivocally bad for workers. But imagine that the reason Job 1 pays so much more than Job 2 is that it is very dangerous – the high pay is to offset the low nonpay aspects. Could we still then easily say that the ending state of lower wages and safer jobs is is a worse situation than the starting state of higher wages and dangerous jobs?

This is the difficult question Mr. Sorkin begins to address in this paper. In previous work he has created measures of nonpay job aspects by sector and made an attempt to quantify them. By combining with wage data, he comes up with this graphic:


The diagonal line is where the pay and non-pay aspects cancel out. Jobs above the line are more desirable, and those below are less desirable. So for example, we can see that the sector with the lowest nonpay dimension, mining, is more than compensated by the highest pay dimension. With this information, we can start to see how simply looking at wages is not telling the whole story as the economy evolves.


The second graphic shows how this has played out with the sectoral shifts in the labor market over the last 25 years. As the sectoral composition of the economy has changed, shifting away from manufacturing and toward more service related jobs (though not as much as we might have expected), pay has clearly decreased but nonpay value has increased. Overall though, the net trend is still negative.

It’s interesting to note that the trend in nonpay seems to be jumping during recessions and remaining pretty stable otherwise, whereas pay has been on a steady decline. My hunch is that the last two recoveries have been led mostly by those less desirable jobs, rather than a broad based increase in all jobs back to their previous level.

Now obviously quantifying nonpay aspects of work is difficult, and Mr. Sorkin doesn’t pretend to have a full grasp on the issue, but as a starting point this is a very interesting story and I hope to see more research along these lines in the future.


Work and Dignity

Listening to last week’s iteration of the Ezra Klein show, with Labor Secretary Tom Perez, gave rise to some thoughts about the minimum wage and related topics. I haven’t gelled everything into a cogent argument as yet, but want to get some stuff down anyway.

So during the show, Ezra attempts to delve into the idea of the dignity of work, which is a topic very near and dear to my heart. You don’t often hear it discussed in much depth in discussions about labor policy, though, probably because most people working in and around the relevant fields that study and communicate such things have pretty dignified jobs. But so to back up, there are as I understand it two ways to think about this dignity of work idea. One is that there is an intrinsic dignity to work: the very act of working itself, regardless of what it may be, is a good for people and the best way to enable human flourishing. This is a view I’ve heard from people like AEI president Arthur Brooks in books like The Conservative Heart. The other view is that the dignity of work comes from the outcome: the ability to support yourself and/or your family is the source of dignity. This is the view held by Secretary Perez during the conversation.

Understood in these terms, we can see why issues like the minimum wage can lead to such sharp disagreement. For those more inclined to the former view, anything that leads to unemployment should be avoided, whereas in the latter view tend to be more skeptical of the disemployment effects and focus on the living wage issue. Let’s expand on this for a moment.

To reiterate, support for an increase in the minimum wage up to $15 is rooted in the idea that people who work full time should not live in poverty. Why are people working full time living in poverty? It seems like a simple question, but actually many goods are pretty much the cheapest they have ever been. Spending on non durable goods, like food and clothing, as total share of household expenditures has dropped almost 20 percentage points since 1960:


Think also of other luxuries like entertainment: for about $200/year you can get access to huge libraries of music and movies.  If we can buy most of the things we want for cheap, how can people be living in poverty?

(As an aside, it should be clear that poverty in the US is almost never the same as the extreme poverty we think of in developing countries. Generally, the issue is more that poverty inhibits human flourishing. To go back to the entertainment example, sure, you can stream tons of films for less than the price of a movie ticket, but you’re doing that alone or with a couple people at most. Humans are social creatures, and we like to be experiencing things with other people – movies, concerts, sports games, etc. And actually, the prices of these things have been increasing more than inflation. I’ll have to find the source, but I recall reading something once about how being poor in the US means you probably have access to the necessities of life, but are isolated from wider society because the cost of participating in most social activities is too expensive.)

The problem is the cost of the things we consider essential have risen. Housing, health care, and education are taking up increasing shares of expenditures.  When it comes to housing, as I’ve mentioned before, there are a lot of regulatory issues driving up the cost of housing in the areas of the country with the highest productivity. For a striking graphic, a recent Pew Charitable Trusts report shows a sharp increased in the share of income taken up by housing among the poor.



Health care is something we’re still trying to get a handle on, and here the solution to lower costs may end up being what many other countries do: the government simply decides how much procedures cost.  (There’s a strong case to be made that health care is not subject to the same incentives as other markets, and so we can’t rely on them to provide the outcomes we want). I haven’t read enough into the rising cost of education, but I get the sense that the necessity of college for a good paying job leads to increased and inelastic demand for education, and the incentive for colleges to compete on amenities leading to the country club-ification of the undergraduate experience in which universities keep adding expenses and administrative staff.

So it’s possible we’re thinking about this the wrong way; maybe we should be thinking about ways to make these things cheaper rather than distorting the labor market. In economics terms, we should focus on the supply side, rather than the demand side.

Let’s imagine thought that it’s not possible to lower the cost of these things, for whatever reason, and we decide we want to focus on getting people more money. What’s the best way to do this? The most popular suggestion is of course raising the minimum wage. I’m generally skeptical of federally mandated minimum wage increases because I think it’s a rather blunt tool, though this is certainly a contentious issue and one where there isn’t much slam dunk evidence on either side (though the recent spate of minimum wage increases in some states and localities will hopefully provide some good evidence). There are other ways, though, two of which are wage subsidies and a universal basic income (UBI).

Wage subsidies are a system where the government picks up some of the tab for paying employees, rather than the employer. So if we wanted a minimum wage of $15/hr, the government would pay the difference between what people were already getting and $15. The advantage of this system is that it isn’t distortionary on the employer side, and could plausibly increase employment if businesses were allowed to price labor where (in econ 101 terms) the marginal benefit of hiring an employee equaled the marginal cost. One problem is that these businesses would also have an incentive to game the system, and pay as little as possible to have government pick up most of the tab. There are ways to deal with this, but I’m not sure how well they would play out in reality.

The other way, UBI, has been getting a lot of press lately. Under this scheme, everybody in the country is guaranteed some income, say $10,000/year. The advantage is that this is pretty simple; really it’s just a scaled up Social Security. Obviously it would be expensive, but not prohibitively so; it also depends on which flavor of UBI is implemented, whether it augments or replaces the welfare state. Again, this is also less distortionary than a minimum wage.

Let’s go back to the whole reason I started discussing this: the dignity of work. The real worry for a lot of people is the employment disincentive effects of a UBI: why work if the government will pay me anyway? And if work is a moral good, won’t society be worse off? Those who believe in the outcome based dignity of work probably aren’t bothered too much by this, though they may be worried by the disincentive effects as leading to the unsustainability of the program.

So what do I think? Something I haven’t seen much in the UBI pieces that have been floating around is the aspect I actually think is most important – the establishment of an alternative to the labor market should force bad jobs to be better.

Based on my experience in the lower end of the labor market, jobs like retail salesperson and cashier (which are the most common professions in the US! ) are pretty lacking in dignity.


Part of that is the work itself isn’t very inspiring, but also that the big companies employing you don’t particularly care about you or investing in you or making your life pleasant. There’s no bargaining power at the lower end, nor do I think there really could be via traditional means like unionization. There’s no incentive for employers to make these jobs better; people work them typically because they don’t have any other options. What a UBI does is give people that other option. The only choices of jobs available to you are shitty jobs? So don’t take them! Employers then have to think of ways to entice people to take these kind of jobs.

While there’s been a lot of talk about the fear of automation taking away these jobs, there’s a pretty strong case that the current wave is over for now. Anecdotally, the company I work for has stopped putting in automated checkouts in stores, and actually takes them out when remodeling stores because people just don’t like them. So I’m not too worried that providing a labor market alternative will lead to companies just automating those jobs.

Furthermore, it’s my belief (maybe hope) that most people do like to work, they want to be engaged with society in some capacity, and so we wouldn’t see masses of people giving up and living on the government dime.

To recap: A UBI may be the best way to satisfy both views of the dignity of work, whether through the act itself or through the outcomes. By adding to people’s income, you give them expanded means to support themselves and their families. And by giving people an alternative to the labor market, bad jobs will get better and give people even more dignity in their work. The ultimate goal of maximizing human flourishing is accomplished in either case.

Wages, Standards, and Robust Labor Markets

This week’s EconTalk was a very interesting episode with James Besson discussing his new book, Learning By Doing: The Real Connection Between Innovation, Wages, and Wealth.

Let’s start with a common view of how the Industrial Revolution proceeded. The conventional view, or at least the mental model I have, is that throughout the period wages steadily rose commensurate with technological advances. But Besson argues that when you take a detailed look at the data from the US and UK, you don’t see this pattern. For instance, if you look at US textiles, one of the posterboard examples of early industrialization, what you see are several decades of stagnant wages despite increasing size and output of the market. It’s not until after the Civil War that you start to see increases in wages. What can account for this?

Besson claims that it wasn’t until this time the textile market became standardized enough to create a robust labor market in textile workers. How can this be, if the sector had been growing for decades before?

The key lies in the idea of human capital, the idea that through training and education workers build up internal means of production. Often the term is used rather loosely, which can mislead. Think of the new textile worker, coming in from the rural farm. She doesn’t have any training or experience, and so can’t command a high wage. She is hired and trains at the closest textile mill, learning the complicated movements of the loom. After a few years she’s mastered the equipment and become the most productive worker in the mill. We can say she’s build up substantial human capital, far more than her coworkers. But still, she hasn’t gotten a raise, and if she asks for one, she is routinely denied.

Why? Well, her human capital is firm specific. There were no standards for looms or other textile technology at this time, so her knowledge ties her to the mill she works at. The mill across the street uses totally different machinery, so she’s no better than a fresh hire off the street.

It wasn’t until historical forces led to standards in the textile that a robust labor market developed, because now workers’ human capital was transferable across firms. The threat of leaving for competitors now had bite, and wages rose to match what the workers were actually worth.

This idea is of course very relevant today, as we think about decades of stagnant wage growth for many workers in the economy. If the labor market is too fragmented, if workers’ human capital is too tied up in firm specific technologies, then firms have the upper hand in setting wages.

A modern day example of barriers to workers in the labor market are non-compete agreements. These are agreements workers have to sign when joining a firm that relinquish the workers’ ability to work at a competitor firm for some period of time after leaving their company. The idea is to protect trade secrets, so you see them often in the technology sector. However, there is an obvious side effect in that they tend to keep workers settled where they are, since leaving will keep you shut out of your industry for some years.

Differential enforcement of these non-compete agreements across states has led to researchers estimating the effects they have on innovation. While today Silicon Valley is obviously the king of the technology world, this wasn’t pre-ordained. Boston’s Route 128 was a high tech sector rival to Silicon Valley in the 80s, but Massachusetts enforces non-compete agreements whereas California does not. Without the cross pollination of ideas and incentives for workers to innovate, Route 128 stagnated while Silicon Valley raced ahead.

Unfortunately, these agreements have spread beyond the world of technology; famously, the sandwich chain Jimmy John’s forces its workers to sign a non-compete agreement. That’s right, if you work a minimum wage sandwich job you may be shut out of the sandwich labor market for several years if you leave, which is of course patently ridiculous.

What about occupational licensing? On the one hand, if having clear standards leads to robust labor markets, then having a licensing process should be a good thing right? Well, there a big difference between a certification from a standards body that a worker meets certain specifications versus a legal requirement that a worker must meet certain specifications (which almost always require some years of schooling from particular institutions). Furthermore, since these licenses are different from state to state, they inhibit labor mobility since the human capital is tied to a particular state.

All in all this was a very good episode with a lot of food for thought, and I look forward to reading the book.

Why Is Infant Mortality Higher in the United States than in Europe?

It’s common knowledge among the Aaron Sorkin segment of the population that the United States has a high infant mortality rate compared to other developed nations. But if you were to ask why, you probably wouldn’t get a good answer; even for researchers the reason has been a bit mysterious. A new paper in the American Economic Journal of Economic Policy provides some answers, but like any good research leads to more questions as well.

Depending on who you ask, as of 2015 the US infant mortality rate (IMR, defined as the number of deaths of infants under one year old per 1000 live births) is around 5.6~5.9. In the EU, the rate is closer to 3.7. In the grand scheme of things we’re actually doing pretty decent; below is a plot of  IMR vs GDP per capita (PPP) for most countries in 2014.



We can see that there is a pretty clear correlation (on the log scale) between the two. Above the trend line is higher IMR than expected for a given per capita GDP, and below is lower. Most rich OECD countries are well below the line; we are slightly above. However, our neighborhood north of the line is mostly rich oil countries like United Arab Emirates, Saudi Arabia, Kuwait, Qatar, etc.; countries that were it nor for oil wealth would be far poorer and shifted to the left. In other words, for an advanced economy we seem to be pretty lonely above the trend line. So what’s going on?

There are three channels that can explain this difference. As is the case with crimes, countries have differing reporting standards for infant mortality, making cross country comparison difficult. For example, an extremely premature birth that doesn’t survive may be counted as a live birth in some countries or as a miscarriage in others, which alters the deaths/live births ratio. A second confounding problem is the incidence of low birth weight (LBW), which is associated with lower survival rates. The US has higher rates of low birth weight than other developed countries, so it could be possible that a higher IMR is a compositional effect, which has its own policy implications. Finally, the timing of deaths is often not available in data. A concentration of higher IMR early on or later in the first year again has very different policy implications. Early on could suggest substandard medical infrastructure, while a random or later distribution indicates some deeper issues.

The authors of ‘Why Is Infant Mortality Higher in the United States Than Europe?’, Alice Chen, Emily Oster, and Heidi Williams, were able to obtain microdata on the US, Austria, and Finland that allowed them to investigate all three of these problems. Particularly, the novel thing was getting access to detailed timing information, which allow distinction between neonatal (within the first month) and post neonatal (months 2-12) deaths.

With regards to reporting differences, some of the higher US IMR is indeed due to differences in reporting. After controlling for this, the average excess US IMR compared with these countries is reduced by about 40%. They also found that higher rates of low birth weight could account for another significant fraction of the US disadvantage, but not all.

The rest of the excess deaths were located in the post neonatal time period, and the graphs in the paper are really quite striking. In LBW children, the three countries perform similarly. In the first month of life with normal birth weight children the US performs comparably to Austria and Finland. But thereafter the probability of death in the US pulls away significantly (all subsequent images are from the paper).




This suggests the high US IMR is not due to any deficiencies in US medical care or facilities; if that were the case, we should expect elevated rates throughout the first year as well as with LBW births. Instead, these deaths are occurring at home.

A  natural follow up is to ask whether this phenomenon is randomly distributed throughout the population or is concentrated in space and/or socioeconomic status. Using the US Census Divisions, they find that the Northeast region performs best, whereas the East South Central (Alabama, Kentucky, Mississippi and Tennessee) performs significantly worse than the rest of the country.




As you may suspect, these areas have very different levels of income, and indeed it seems the main source of the effect is from socioeconomic status.



This research could find no ‘smoking gun’ as to the cause of increased mortality in the first year of life. Again, issues with reporting and coding prevent any clear conclusions. SIDS is hard to correctly diagnose, and the other major causes are the nebulous ‘Accidents’ and ‘Other’.

In the United States the focus has historically been on reducing instances of LBW,  but in light of this paper we can conclude that policies aimed at reducing premature births and/or low birth weight will only go so far. The authors suggest that supporting home nurse visits for new mothers, as is policy in several European countries (including the two analyzed here), could be effective in reducing the infant mortality rate through the mechanism of increased parental knowledge. Clearly this is an understudied topic and more research is needed, particularly into the cause of these deaths.


What’s the Big Idea, Again? (tl;dr)

The United States has a higher infant mortality rate than many other comparably rich countries. This is partly due to differences in reporting and higher incidence of low birth weight, but also due to higher rates of infant mortality in the 2-12th months of life. This phenomenon is concentrated both geographically and economically, particularly among the disadvantaged in areas like the Deep South. Areas like New England, with their higher share of the economically advantaged, see essentially no difference in IMR compared with other rich European countries.


What Do We Do?

I know many people are uncomfortable putting human life in monetary terms, and understandably so. But in certain situations it can be illuminating. The authors use the value of a statistical life, most commonly quoted at around $7 million, to put this in context. Given approximately 4 million births a year, our IMR is ‘costing’ us about $84 billion each year. Which on the flip side, suggests it would be worth spending at least $7,000 per child to reach Austrian levels of infant mortality, and even more if we just focused on disadvantaged births. If that’s the calculus, supporting home nurse visits is a no brainer. And if you think a human life is worth far more than $7 million, then home nurse visits are even more of a no brainer.

It has been argued in the past that in the United States we care deeply about the life of a child until the moment they are born. After that, they’re on their own. And I think this research bears that out to some degree. It’s also another symptom of the increasing levels of inequality here. The advantaged among us, with jobs with generous parental leave, with leisure time to research the dos and don’ts of pregnancy and early life, with the means to afford outside caregivers, are doing fine. Yes, tragedies still happen, but no more than anywhere else in the world. The disadvantaged, the ones who work until they can’t stand anymore, who can’t even take full use of their 12 weeks of unpaid leave because they have to get back to work to pay the bills, are struggling. And in too many instances, failing.

As a society we should be doing more to support disadvantaged mothers and their children. We can argue over what the best course is, whether it is in the form of easy access to family planning services, professional health care in the home, or through more fundamental issues like education or family stability. But I don’t think we can argue that we do enough already. If life begins at conception, then we must also remember it continues after birth.

Labor Markets, Trade, and the China Shock


During this election season, trade protectionism has reared its head across the political spectrum; no candidate seems willing to believe (or admit to believing) that free trade is good for the country. The closest thing to a defense anyone can seem to muster is to claim that our current trade deals are in fact bad deals, which at least leaves the implication that good trade deals exist. While public polling suggests free trade is more popular than you might think given the political rhetoric, a substantial fraction of Americans are suspicious of trade, despite overwhelming support of free trade among economists.  A National Bureau of Economic Research paper from earlier this year sheds light on why this difference of opinion exists.


The China Shock: Learning from Labor Market Adjustment to Large Changes in Trade’, by David Autor, David Gorn, and Gordon Hanson, looks at the economic impact of trade with China on US manufacturing. To simplify immensely, they find that the costs of trade are higher than previously thought (but importantly, still a net benefit to both nations). To fully appreciate their findings we will need to set the stage with a simple model of trade impacts, but first I’d like to define what is meant by ‘the China shock’.


The example of China is basically without historical precedent in having a very large, cheap pool of labor enter the manufacturing sector in a very short period of time. After the death of Mao in 1976, China embarked on a series of reforms throughout the 80s and 90s that liberalized portions of the economy and which culminated in the country joining the World Trade Organization in 2001. This ensured the Most Favored Nation status China had enjoyed from the US and Europe throughout the 90s was made permanent, as well as opening other global markets to Chinese exports.  From about 1990 to 2000, China’s share of world manufacturing exports more than doubled from 1.9% to 5%, and in the decade and a half since joining the WTO has more than tripled again to almost 20%.  This veritable explosion of manufacturing capacity in just over two decades, coupled with low tariff access to most developed countries’ markets, is the China shock.


According to the authors, on the eve of China’s accession to the WTO, there was a rough consensus among economists on the impacts of trade: (1) Trade had not been a major contributor to the decline in manufacturing employment or rise in wage inequality in developed countries; these trends predated the era of trade liberalization and major deals like NAFTA seemed to have had little additional impact. (2) Workers in regions that specialized in hard hit areas of manufacturing (Pennsylvania steel, Detroit auto, etc.) could move to other regions without much trouble if trade eliminated jobs in their area. (3) Labor market impacts of trade would not be confined solely to trade exposed industries; due to the ‘law of one price’ for skill, these impacts would be spread across all similarly skilled workers.  


Under this set of assumptions (more or less the neoclassical model of trade), the concentrated impact of a particular trade shock is absorbed by the national labor market. Think of heating a pot of water with a flame; while in the instant the water closest to the flame is hotter than the rest, soon the heat is spread throughout the entire vessel evenly. In this simple model, labor of a particular skill level is like the water molecules: highly mobile and totally identical.


With this in mind, the impact of a trade shock at the regional level should be as follows (after allowing for some time for adjustment):





Manufacturing employment should decrease because the local industry is no longer competitive, leading to a pool of labor seeking work. They have two options: they can leave the area to find similar work elsewhere in the country and/or or reallocate to the local non-manufacturing sector in jobs with a similar skill level. As such, we would not expect any differences in the regional unemployment rate or labor force participation. Finally, the change in local wages (for all workers, not only manufacturing) relative to any changes in national wages should be zero (this follows from the assumption of one price for skill).


Now, as you might imagine, actually estimating the real world impacts of a trade shock on a region is very tricky. I’ll leave the complicated bits to the actual paper, but in a nutshell what the authors did was exploit the variation in regional manufacturing and the variation in exposure to Chinese manufacturing to make these estimates. For example, Tennessee has a large concentration of furniture producers, which is a sector where the Chinese had substantial exports. In contrast, much of Alabama has lots of heavy industry which did not experience much competition from China. Even within industry sectors, there a substantial amount of variation in exposure to the China shock, which gives the authors an additional level of control to make more precise estimates. (In econometrics, having such variation is crucial. Imagine trying to estimate the impact of an additional year of education on income with data that only specifies that an individual has completed high school or college). To compare with the table from earlier, qualitatively what the authors found was this (all non-zero results were highly statistically significant):





It should not be surprising that the employment in trade exposed manufacturing decreased; this jibes with the earlier prediction.  What is surprising, though, is every other factor did the opposite of what was expected. Workers did not leave the region or shift to the non-manufacturing sector. They remained in the area and joined the ranks of the unemployed or left the labor force altogether. And the average change in wages (again, for all workers) in trade exposed regions was negative, not zero. To return to the analogy of heating a pot of liquid, it’s as if instead of water the pot were filled with peanut butter.


What can account for this? One explanation could be that the because size of the China shock was orders of magnitude greater than any other previous trade shocks, we are still in an adjustment period. Under this view, the neoclassical model of trade is essentially correct and the findings here are due to an exceptionally long adjustment time required for the labor market to return to equilibrium.


Another possibility for long adjustment time is that the rise in the US trade deficit has reduced the demand for sectoral reallocation among US tradables. Implicit in the neoclassical trade model is the assumption that countries are engaging in balanced trade. Simply put, countries trade goods and services for currency, which is then used to buy back other goods and services. Money is neutral, desirable only as a medium of exchange. So if manufacturing sectors A, B, and C in the US shrink because China has a competitive advantage, we should expect sectors X,Y, and Z in which the US has an advantage to expand. But what has actually happened is that the US has consistently run a very large trade deficit that ballooned during the 90s.




On a yearly basis we import billions of dollars more than we export. And China is particularly responsible for this growth, as the figure below demonstrates.


We can think of the trade deficit as reallocating demand across time. For a variety of reasons, China is willing to give us lots of stuff without us giving them anything but dollars in return. Future US consumption has been pulled into the present, and eventually we will have to run a trade surplus with China to make up for it. In the meantime, the depressed demand for US goods has slowed the reallocation within the tradables sector that neoclassical theory would predict.

While this is an interesting idea, and probably plays some part in what’s been happening, I consider the most likely explanation (and the larger story here) to be that US labor markets are not as flexible as we once thought. The neoclassical assumption of ease of labor mobility seems to be particularly under fire in recent research. As with the China shock,, research regarding the aftermath of the Great Recession also suggests that people find it hard to leave economically depressed areas.  Furthermore, income convergence among US states has declined due to rising housing costs in the highest productivity areas. For the hundred years from 1880 to 1980, people in the US migrated from poor areas to rich areas. Nowadays, if people move at all, they tend to go to areas where housing costs are the lowest (which tend to be poorer). In this vein, I highly recommend Ryan Avent’s The Gated City and/or Matt Yglesias’ The Rent is Too Damn High. Other research regarding the aftermath of the Great Recession also suggests that people find it hard to leave economically depressed areas.


The transferability of human capital across industries is probably lower than assumed in the neoclassical model as well. Frictions like occupational licensing, in addition to reducing interstate mobility, make it harder for people to switch to other kinds of relatively low skilled labor without spending significant time and money on certification (jobs requiring a license in some states include florist, barber, and scrap metal recycler).  


To sum up, while the costs of trade are more concentrated and more intense than was previously thought, it is still unambiguously the case that trade with China has been a net good for humanity. Hundreds of millions of Chinese have been lifted out of poverty, and Americans have been able to enjoy substantially cheaper goods. The real issue here is how to deal with the distributional impact of trade to ensure everyone can benefit, which I hope to discuss in a later post.


What makes a soda tax regressive?

I’ve been running in to articles about soda taxes, after Philadelphia’s three-cents-per-ounce soda tax became an issue in the Pennsylvania Democratic primary. A strange omission I’ve been noticing is that some pundits can’t quite seem to name exactly what makes a soda tax regressive.

Jonathan Chait mostly evades the issue, until claiming that Bernie Sanders’ opposition to a soda tax on the grounds that it is regressive is inconsistent with Bernie Sanders’ own tax plan:

Indeed, it is very hard to understand how Sanders’s opposition to it can be reconciled with his own platform, which — both in its specifics and its general theme — rests upon higher taxes on the non-rich. To oppose a new tax-and-transfer plan based solely on the regressive character of the financing source, without any consideration of the benefits of the spending, would rule out his own plans as well.

Chait seems to try to muddy the argument a little by arguing that a regressive tax isn’t regressive if it has progressive effects, and it is by this virtue that Sanders’ tax platform and the Philadelphia soda tax are progressive, but nestled in this argument is the implication that the Sanders tax plan is overall a regressive one. It’s hard to see how it is. A regressive tax is one that taxes a greater percentage of a poor person’s income than a rich person’s income, but the Sanders tax plan increases the tax burden of the lowest quintile of Americans by 1.2% while increasing it on the highest quintile by 12.8%.

This New York Times article gets closer to being able to explain why a soda taxes are regressive:

It can be seen as achieving an admirable public health goal of less sugar consumption or as a very regressive tax that falls more on the poor than the rich, since the poor tend to drink more soda.

It’s certainly true that the fact that poor Americans drink more soda makes a soda tax more regressive, but it glosses over the fact that consumption taxes are by nature regressive. If every American drank a single can of soda every day, they’d all pay the same dollar amount in tax, which means people with less income pay a higher portion of their income in soda tax. Rich people could drink several times more soda than poor people and a tax on soda could still be regressive, as long as the portion of their income they spent on soda (and the soda tax) was smaller.

This might warrant a separate post, but I do want to revisit the idea that a tax isn’t really regressive if it is specifically levied to fund a program that helps poor people more than they are taxed. It seems to me that revenue sources are fungible: any money going from a soda tax to a universal preschool program could be replaced by the same amount of money coming from, say, property taxes, while the soda tax revenue goes toward maintaining historic buildings in wealthy neighborhoods. Even if Philadelphia were to specifically tie the revenue from taxing soda to funding its preschool program (as I suspect it has), what if the soda tax works as intended and people drink significantly less soda? There’s probably some limit to how much you can tax soda before you reach the peak of the sin-tax Laffer Curve and people start smuggling their Mountain Dew in from Delaware. When that happens, do you find a new source of revenue (proving that the revenue was fungible all along) or leave the preschool program underfunded? I’m sure there are practical political reasons for tying a certain source of revenue to a certain program, but it’s hard to see how a progressive program can make a regressive tax into a progressive one by association.

A quick look at US trade

I’m working on a post about the Autor et al paper from earlier this year regarding the impact of trade with China on US manufacturing, and in doing so I’ve been playing around with some trade data from the US Census Bureau. Here I’d just like to put up some of what I’ve found.

First, just the basics:


The country specific data that I’m using only go back to 1987, unfortunately. Looking at trade overall we see that while the US has consistently run a deficit, it began to balloon  around 1997 until 2006 or so. After the global recession (which really had a big impact on trade!) the deficit has remained stable at a level slightly less than the previous peak.

Looking at the evolution of our trade partners over time, here’s what happened to our top five trade partners plus China from 1987 until today.



Here we can see the remarkable rise of China in context, and it really is remarkable. Note the acceleration after China joined the WTO at the end of 2001. It’s hard to be sure yet, but it also looks like the curve reached another inflection point around 2006 or 07, and since then Chinese imports have been increasing at a decreasing rate. Also apparent is the steady rise of trade with Mexico, which also looks to have taken off after NAFTA in 1994. Taiwan’s imports have basically remained steady the past 30 years, as have Japan’s.


Looking in terms of percent of total imports, the patterns of Chinese and Japanese trade are almost exact inverses of one another. Just before the beginning of Japan’s first lost decade, they provided nearly the same share of US imports as China does today. I was just a toddler during the 80s, but from what I understand there was a real fear that Japan was going to overtake our economy, much as people fear China’s rise today. I tend not to worry too much about China overtaking us economically for a number of reasons, and seeing Japan stall out and stagnate for essentially 20 years is one of them. On the other hand, China does have an order of magnitude greater population, and they’ve got quite a ways to go before getting to our level of per capita GDP. I’m sure Mike has a word or two about that.

Finally, I wanted to see if the nature of trade has changed over time. In industrial organization, often something called the Herfindahl Index, or HHI, is used in measuring the level of concentration in an industry. It ranges from 0 to 1:  values closer to 0 indicates a large number of small firms, and values closer to 1 indicate a small number of large firms. So higher levels suggest a decrease in competition. I constructed an HHI type index for all of the countries that trade with the US using their ‘market share’ of total US imports and exports for each year in the dataset.


Again, higher values indicate higher concentration: more trade with fewer countries. Now, overall the range of values is pretty low and pretty similar across imports and exports. As a final quick check, I wanted to see if these variations were simply due to the value of the dollar relative to other currencies. When the dollar is strong, our exports are less competitive and so the trade HHI should be higher in those times due to reduced trade, and vice versa for imports.

As quick regression I ran

hhi = year + twdi + nafta + wto

where hhi is the trade HHI *100^2 (basically to make things easier to see), twdi is a Trade Weighted Dollar Index, and nafta and wto are dummy variables for the years in which NAFTA and China’s membership in the WTO are in effect.



lm(formula = im ~ Year + twdi + nafta + wto, data = twdi)

 Min 1Q Median 3Q Max 
-61.324 -22.092 -9.477 22.190 98.867 

             Estimate Std. Error t value Pr(>|t|) 
(Intercept) -7356.091   5114.772  -1.438 0.16329 
Year            4.212      2.550   1.652 0.11161 
twdi           -1.473      1.159  -1.271 0.21579 
nafta         -89.175     30.399  -2.933 0.00726 **
wto           -70.701     32.236  -2.193 0.03821 * 
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 40.82 on 24 degrees of freedom
Multiple R-squared: 0.5255, Adjusted R-squared: 0.4464 
F-statistic: 6.644 on 4 and 24 DF, p-value: 0.0009525



lm(formula = ex ~ Year + twdi + nafta + wto, data = twdi)
 Min 1Q Median 3Q Max 
-77.265 -20.362 -3.355 15.429 53.024
              Estimate Std. Error t value Pr(>|t|) 
(Intercept) 10419.4969  4071.2267   2.559 0.0172 * 
Year           -5.1497     2.0296  -2.537 0.0181 * 
twdi            6.7052     0.9224   7.269 1.65e-07 ***
nafta          63.1336    24.1972   2.609 0.0154 * 
wto            21.9839    25.6586   0.857 0.4000 
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 32.49 on 24 degrees of freedom
Multiple R-squared: 0.8132, Adjusted R-squared: 0.7821 
F-statistic: 26.13 on 4 and 24 DF, p-value: 1.936e-08

As expected, exports are highly correlated to the TWDI, and NAFTA seems to significant to both imports and exports, but since this is time series data for a relatively short period of time, I’d take that with a grain of salt. It’s interesting that the TWDI doesn’t seem to affect the import index; I’ll have to think of why that would be.

That’s all for now, though I could spend days poring over this. What I really wish we had was more historical trade data, as pretty much the entire time period covered by this Census Bureau data is from the era of liberalized trade. To even go back another couple decades would be really interesting.