The Ambrosini Critique

A few weeks ago, Mike was asking me about the long term unemployed. I’m running some regressions using CPS data so I have the data he asked about just hanging out on my desktop. Public service:

The unemployed during this recession¹ are different than those that don’t become unemployed. They’re younger, less educated, more likely to be male and less likely to be married.

Now, the long-term unemployed (greater than 25 weeks unemployed) are different from the short-term unemployed. They are even less educated, less likely to be white, less likely to be married and slightly older.

Data:

The never unemployed average 40.1 years old, the short-term unemployed 35.5 and the long-term unemployed are 36. Contrary to Tyler Cowen, unemployed workers above 40 with a college degree are not much more likely to be long-term unemployed (they’re 24% of the 40+ long-term sample compared to 21.9% of the 40+ short-term unemployed sample). Relative to all college educated workers, college educated workers older than 40 are just more likely to be unemployed.

1. I looked at 2008 and 2009 data for folks that worked one week or more [↩]

Categorize this chart in the “must see the data to believe” bucket.

Last time, I said that nobody really cares about the direct or partial effect of immigration on natives. This isn’t strictly true. If immigration policy were changed to only allow immigrants of a particular skill-type to come into the country, this would probably upset the people of that skill-type. Each individual immigrant hurts the employment opportunities of natives with the same skills as the immigrant. Immigrants, in total though, can have a positive impact on natives because immigrants have different types of skills. People, immigrant or not, that have different skills tend to compliment each other in the production process. They make each other more efficient.

Perhaps this is the source of the assumption of substitution I asked about before: we tend to imagine groups of people as being homogeneous masses, not individuals with a variety of skills. If immigrants were all the same, with the same skills, and natives all the same, with the same skills, and if those skills perfectly overlapped, then immigrants would compete directly with natives and hurt their employment opportunities. In reality, though, a random immigrant has different skills than a random native. Because they have different sets of skills, this random pair do not compete with each other in the labor market. In fact, on average, a random pairing of natives and immigrants would be more productive than the sum of its parts.

Even in the extreme case where all immigrants had exactly the same skills, this would only hurt the group of natives that had those skills. Everyone else would benefit from having co-workers with complimentary skill sets. Its a common belief that all recent immigrates have been very low educated. This, it is imagined, would hurt very low educated natives. The distribution of education among recent immigrants, however, has been U-shaped. There have been many very low educated immigrants and many very highly educated immigrants.

Even among natives with no high school degree, the pain of immigration has not been felt. There are two reasons for this. First, because it turns out that workers with no high school degree are strong competitors with those with a high school degree in the labor market, very low educated immigrants compete with a much bigger pool of natives, spreading out the pain of direct competition. Second, the pool of low educated natives benefit from the large group of highly educated immigrants as highly educated workers make the low educated much more productive. In other words, the negative effects of low educated immigrants on low educated natives are small and spread out, but the gains from high educated immigrants are acute. On net, the gains swamp the losses and even low educated natives benefit from immigration.

Capital accumulation also plays a key role. Like the Econ 101 growth theory would tell us, immigration, by increasing the labor supply, increases the return to capital. This will give investors an incentive to invest more and the capital stock will grow. Because capital makes labor more efficient, it would increase wages until they returned to their long-run value¹. This undoes some of the negative direct effects of immigration, too.

All of these empirical claims were verified in a paper by Ottaviano and Peri just a couple of years ago². A random immigrant makes a random native more productive because the native is unlikely to have the same skills as the immigrant and because natives and immigrants, even if they have the same education and experience, are not perfect substitutes. Next time we’ll see that this is because immigrants specialize in different tasks than natives and because they induce innovations that complement their skills.

1. This means when capital fully adjusts, the average wage effect of immigration is zero. Different groups might be affected differently, but its a wash on net. It turns out all groups of natives benefit even if slightly from immigration. The only group that is hurt by new immigration is old immigrants. [↩]
2. A good discussion of that paper can be found here. The author reproduces many of Ottaviano and Peri’s findings using a slightly different data set. [↩]

It has taken me too long to introduce the hero of this story. Borjas has had a series of papers, books and editorials since the mid-80’s that have each challenged the consensus economist’s view on immigration in support of the popular view. His work in the late 80’s uncovered the pattern of a secular decline in the quality of immigrants (in terms of educational attainment, work experience and unobserved skills) since WWII, a trend that is common sense today. He also introduced the idea of self-selection of immigrants and gave conditions for when we’d expect immigrants to be higher or lower quality. In the nineties, while setting the ground work for the work I’ll describe below, he started the conversation on the costs and benefits of immigration, sitting on a panel of the National Academy of Sciences evaluating the economic impact of immigration. He wrote papers encouraging researchers and policy makers to balance the fiscal costs of immigrants (welfare, schooling, etc) against any potential production gains.

…

In his 2003 paper called “The labor demand curve is downward sloping,” Borjas challenged the consensus that immigration has a zero effect on native employment outcomes. He worried that studies comparing regions did not properly account for spillover effects from trade, capital movements and labor mobility. The problem with goods, capital and labor movements is that they all tend to equalize wages across regions. This means that when you compare regions that have had more or less immigration, you won’t be able to find a difference in wages even as everyone’s wages have gone down.

He suggested, instead, to compare groups that do not admit mobility. Specifically, he looked at the effect of immigration across skill groups where skill is defined by a combination of education and years of work experience. Intranational trade in goods doesn’t operate in this context. Add to that the fact that international trade is probably not important here and the trade channel for spillovers is shut down. Also, workers can only increase work experience one year at a time (thus they can’t decrease it or move to a level of experience more than one year ahead of their own), so its extremely hard for them to change it to respond to incentives and, in the short run, workers can’t change their education. These things suggest the labor mobility channel is shut down, too. Finally, if you assume, as Borjas does, that capital is not skill specific (or at least to the degree it is, it has a small spillover effect) then there’s no role for capital movement to cause spillover effects.

Below I reproduce his suggestion. On the x-axis is the change in the proportion of immigrants from 1990 to 2000 and the y-axis is the change in native wages. Each dot is a education-experience group¹.

The slope here is negative (-0.35) and quarter standard deviation more negative than the mean found by Longhi, Nijkamp and Poot. This result, which controls for spillover effects, confirms what Borjas says: “immigration lowers the wage of competing workers”.

There are three ways to critique these findings. First, attack the premise. If spillover effects are creating the zero correlation at the local level, we should be able to detect them. We should see native workers fleeing areas of high immigration or we should see industries in high immigration areas reshuffle to take advantage of the skills of immigrants, as trade theory would predict. David Card took up both of these challenges. In 2001, he wrote a paper finding no native flight and in 2007 he wrote a paper finding that least one type of trade spillover doesn’t seem to be emperically important². If spillovers are a problem, they don’t appear to work through the trade and labor mobility channels.

Second, attack the assumption that capital isn’t skill specific or that international trade doesn’t matter. To be honest, I haven’t found any work that does either of these things…

So moving right along. Third, point out that there are much more important indirect effects of immigration than spillover effects. This will be the focus of my next post.

…

For those of us that believe immigration has a net positive benefit on society, Borjas is the perfect enemy³. He has applied honest, fair and constant pressure on our sure beliefs about immigration. His challenges have inspired reams of rebuttals; reams that could not otherwise have been used in support of immigration.

1. Actually, I cheated and used age instead of work experience which gives the same results but is much easier to calculate. [↩]
2. That said, there is an opportunity for us to look for other ways trade affects native workers. For example, perhaps immigrants produce new varieties of goods. Much attention in trade theory these days — someone got a Noble for his work on this — is on the trade effects of new varieties. This wouldn’t be a spillover channel, I don’t think there’s factor price equalization in New Trade models, as much as a possible explanation for native/immigrant complementarity, which I haven’t talked about yet. I’m no trade theorist but perhaps the production based models used by Ottaviano and Peri, e.g., are isomorphic to the love of variety trade models. So where those authors see complementarity in production, trade models would see love of new immigrant produced varieties. We’d need micro evidence on immigrant entrepreneurship to differentiate to see which of these models best explains the evidence. [↩]
3. He’s also a model academic. Besides his volumes of academic papers, he’s written a well-used textbook, several popular books on immigration and dozens of editorials on the subject [↩]

Here’s the cross-state plot of the effect of immigrants on native wages:

This time you’ll notice that I plotted the changes in the proportion of immigrants against the change in wages (these are changes from the 1990 to 2000 censuses). In effect, I’m controlling for fixed features of states. This makes the effect size (a slope of about 0.25 and statistically insignificant) a little closer to the average effect size reported by Longhi, Nijkamp and Poot.

Angus Deaton recently said that all the attention that natural (and actual) experiments are getting is over blown. He claims experimental data has no special status in a hierarchy of evidence. I agree to the extent that I don’t think we should favor one form of evidence to the exclusion of other types of evidence¹. Evidence is evidence.

A readily available form of evidence about the relationship between native employment opportunities and immigration is cross-section data². These data describe various geographical regions or worker skill groups. For each region or skill group, the analysts assigns average wages (or other employment outcome) and the percentage of the group that is immigrants. Then the analyst checks to see if there’s a correlation among the groups between wages and the number of immigrants.

As you can imagine, there’s a lot for the interested analyst to play with. Every country has its own data sources. You can change the definition of skill group. You can look at larger geographic regions like states or smaller ones like cities. And, like always, you can choose from the palette of statistical techniques to calculate your estimated correlation and effect size. Longhi, Nijkamp and Poot did a meta-analysis of 18 papers that reported 348 estimates of this correlation.

As a quick demonstration of what these papers look like, I’ve downloaded some Census 2000 data from IPUMS USA. For each state, I calculated the percent of workers that are foreign born and the average wage for native workers. Here’s the plot:

I’ve drawn the regression line. Surprisingly, the line has an upward slope, suggesting a positive correlation. The slope of the line is about 1.5.

One thing that’s wrong with this plot, besides the fact that I haven’t controlled for a bunch of obvious things, is that this simple correlation conflates the impact of immigration on native wages with the shared economic incentives of natives and immigrants to move to states that have positive wage growth. Both immigrants and natives will want to move to states that have good wage prospects; they select themselves, to use the jargon. We really only care about the first thing, the impact of immigrants on natives, and so we’d like to wash this correlation to get the stain of “selection” out.

A neat regularity among immigrants is that they tend to move to regions where previous immigrants had already called home. We’ll leave it to sociologists to tell us why this might be the case and for the moment just exploit this fact for our statistical purposes. We can predict the percentage of immigrants in a state in the year 2000 by looking at the percentage of immigrants in that state several years before. Here’s a plot:

The red line is the regression line and the black line is the 45 degree line. As you can see, the percentage of immigrants has uniformly increased in those 40 years, but the red line is positively sloped and the dots cluster pretty well around the regression line. The immigrate ratios in 2000 are predicted pretty well by their ratios in 1960(!).

So what? Well, suppose the percentage of immigrants in a state does not have an impact on the relative wage prospects in that state 40 years later. The prediction of the year 2000 immigration ratios using the red line, then, should be unrelated to the wage prospects for immigrants (and natives) in that year. This prediction is just the detergent we needed to get rid of the stain of selection. Basically, we’re taking the variation of immigrant ratios due to selection out and only looking at the variation due to immigrant clustering. Here’s a plot of native wages versus predicted year 2000 immigrant ratios:

The slope on the regression line is 1.8. That this slope is close to the slope of the one where I didn’t correct for selection suggests that selection isn’t that big of a deal.

While its size is a bit big and so makes me think I did something wrong, the sign of the slope I’ve estimated isn’t surprising. Longhi, Nijkamp and Poot found that almost as many estimates of the effect of immigration on native wages are positive as negative. Here’s their figure 1 which shows the distribution of estimates across analyses:

The estimates seem to cluster around zero. My estimates are 1.5 standard deviations away from the mean; not too bad for a quick and dirty analysis!

So even the non-experimental evidence suggests immigrants have little impact on native wages.

1. I tenured member of the Cult of Identification told me once that she wouldn’t write a paper about a topic unless there was a clear source of exogenous variation. She proudly told me that she hadn’t used an instrumental variable in years. [↩]
2. In applied micro seminars, you often hear Cult members hiss something to the effect, “But those estimates are from cross-sectional data”. With grimaces around the table at the mention of the taint. [↩]

There’s more experimental-like evidence that immigrants have little or no negative effect on native employment opportunities, if you need it.

In 1962, 900,000 pied-noirs repatriated to France after fleeing Algeria following their loss in the country’s war of independence. These repatriates settled in the warmer departments of France with climates more similar to their former home country. And as evidence that this was an exogenous shock to the labor force, they tended to settle in those departments with the higher unemployment and lower wages.

This exogenous shock to the French labor force that increased the number of workers by 1.7% was studied by Jennifer Hunt in her 1992 paper. She found a small but significantly negative effects on native unemployment six years after the mass repatriation. However, she found no effect on native participation rates. Her data on wages is a bit of a mess, but with what she had she found at most a small negative effect¹

Another crumbling empire provides us with another immigration experiment: the returnados from Angola and Mozambique in the mid-1970s increased Portugal’s work force by 10%. In a study design similar to Card’s Mariel paper, Carrington and Delima find the Europe-wide recession of those years swamps out any effect the the immigrants might have had.

1. She worries that given her data limitations, she’s not able to properly control for the fact that the repatriates happened to have chosen to migrate to departments that were having bad economic outcomes unrelated to the mass migration. Interestingly, the migrants didn’t seem to have an effect on internal migration patterns, i.e. their presence didn’t discourage the native French from migrating to departments heavily populated by the new immigrants. [↩]

Нет!¹

An easy criticism of “experimental” data, like that from the Mariel boatlift, is it only applies to the particular situation under which the experiment was run. Immigrants may have had no effect on employment outcomes in the very special circumstances of Miami in the early 1980’s, but in other places and times immigration would have an effect. Card, himself, says that one of the reasons the Mariels had no effect on the Miami labor market was because the city had had ample experience in the preceding decades absorbing Hispanic and, in particular, Cuban immigrants. Maybe Miami is just an extremely efficient melting pot.

The only way around this criticism of experiments is to do as laboratory scientists do, replicate them. Of course, in the economic study of immigration, this is not something that can be done in the lab. Economists studying immigration can not run their own experiments; they can not produce their own data. They have to wait for nature run experiments for them. Lucky for economists, nature provided such an experiment in the collapse of the Soviet Union.

Between 1990 and 1994, Israel’s population grew by 12% due to immigration. Most of these immigrants were Jews from the becoming-former Soviet Union. Like the Mariel boatlift, this mass migration was almost entirely due to circumstances in the sending country and at least at first, it was largely unanticipated by the native population. The instability in the Soviet Union at that time encouraged many people to leave. Israel was chosen as the destination country simply because of shared religion and open immigration policies.

When something external to the system causes a change to that system, like these particular mass migrations, economists call it an “exogenous shock”. Laboratory scientists use random assignments into control and treatment groups, for example, as their exogenous shock. The great thing about exogenous shocks is that they remove the mystery from what causes what. If conditions in the system under study do not affect the timing or magnitude of the shock, then any changes to the system that come after the shock must be due to the shock itself. Because unlike laboratory scientists they can not induce exogenous shocks to the systems they study, the challenge for economists is to look for data that was generated from exogenous shocks or to use various, and sometimes complicated, techniques to analyze data in a way that makes it look like it was generated by exogenous shocks.

The mass migration from the Soviet Union to Israel, then, was an exogenous shock to the Israel labor force. However, the occupations chosen by Russian immigrants once they entered Israel are not exogenous. Because Rachel Friedberg wanted to study the effect of the mass migration on wages within occupations — her question was “Did Russian immigrants entering an occupation cause wages in that occupation to decline?” — she was worried about the non-random choice of occupation. Specifically, she was worried that the immigrants had chosen occupations that were experiencing above average growth in wages. In this case, if immigrants were depressing wages by increasing labor supply and so competing with natives for jobs, the raw correlation would show no effect of immigration. In the raw correlation the bad effects of immigrants on wages would be cancelled out by the fact that immigrants were disproportionately choosing occupations with higher wage growth. In a hypothetical world without immigrants, then, native wages would have been higher.

To test to see if Russian migrants were choosing occupations with higher wage growth and so creating an artificial zero correlation between immigration and Israeli native wages, she came up with a clever way to see what would have happened if those immigrants did not get to chose their occupations in Israel. What she did was to assign each immigrant to the occupation they had back in Russia. She reasoned that because of training and skills accumulated over the career, immigrants would prefer to have occupations in Israel that were similar to their old ones in Russia. Because some occupations get paid more than others, that the old occupation is similar to the new one means the old occupation is correlated with immigrant wages in Israel. The old occupations, however, do not depend on the growth in wages in Israeli occupations. So assigning the immigrant to their old occupation instead of their chosen one essentially removes the problem of occupation choice that was screwing up the raw correlation in the previous paragraph. Getting rid of the choice problem means we can see the real effect of immigration on wages.

The results are surprising. The immigrants that choose occupations different from their old occupations actually had lower wages than if they had stayed in their old occupation. Maybe “choose” is the wrong word here. It may be that immigrants, with their poor language skills or lacking social networks or because of discrimination, were forced into occupations with lower wages. In fact, when you control for this downward mobility of immigrants, the actual correlation between immigration and native wages is positive! Occupations that saw a disproportionate amount of immigration had higher wage growth in the period of the study.

This potentially throws the simple model of the affect of immigration on native employment opportunities on its head. Not only can we not confirm its implications, but its implications seem to be backwards. How is it possible that immigrants could increase wages of native workers!?

1. Thanks Google Translate! [↩]

¡No!

David Card’s famous paper studied the impact of the sudden arrival of 120,000 refugees to Miami in 1980. He estimates that the total labor supply in Miami suddenly increased by 7% and the Cuban work force increased by 20%.

Because labor is one undifferentiated mass doing the same tasks, with the same skills, creating the same nondescript widget with a fixed number of machines, this increase in supply induces a decrease in wages and an increase in unemployment among native-born workers. At least this is the mental model most observers at the time carried around in their heads: people conjectured that unemployment spiked to 7.1% in the summer of 1980 because of the arrival of the refugees. It was presumed that the influx of workers created other systematic problems. For example, the boatlift was said to be partially to blame for riots in black neighborhoods that killed 13 people. Also crime, and specifically the homicide rate, spiked in 1980. The Mariel’s themselves participated in a number of crimes; they were responsible for about 10% of homicides. But presumably through their bad affects on the labor market, they caused higher crime rates even among the native population.

To a certain group of economists, to which Card is a high priest, the most important question to ask of correlations like the one found between unemployment and the Mariel boatlift is the following: is there some third factor that is correlated with bad market outcomes that is also, but independently, correlated with the influx of Cubans? Because the stories being told at the time suggested the boatlift caused bad labor opportunities for native-born Americans and then other social ills, this question is particularly important. If a third factor explains the worsening labor conditions in Miami around the time of the boatlift, then that would be the cause of so many social ills and not the boatlift itself. The obvious candidate for this third factor is the deepening national and international recession in 1980¹.

By comparing Miami’s experience with other similar cities that were not affected by the boatlift but were affected by the general economic slowdown, Card was able to show that wages and unemployment in Miami were not affected by the boatlift. He, in effect, subtracted out the effects of the recession on the Miami labor market and got zero. This suggests that the real culprit in Miami in 1980 was the recession. The boatlift just happened to have happened at the same time the economy was contracting.

Another group of economists cares more about the mental model used to understand the Miami labor market. In the case of the Mariel boatlift, for them the most important question to ask is: what’s wrong with the simple mental model that its obvious implication — more workers leads to lower wages and higher native unemployment — broke down? Here’s a number of possibilities:

• Workers are not an undifferentiated mass; they have different and complimentary tasks or they have different skills
• Workers are not all producing the same good
• The number and quality of machines is not fixed

To the degree that any of those three things are true, the implications of the simple model break down. If Cuban refugee workers have skills that are complimentary to native-born workers or if Cuban workers are making products that natives don’t make, then those two groups will not compete with each other in the labor market. Just like an increase in the number of dentists would not have an effect on the wages of construction workers, refugees with different skills from natives would not have an effect on native wages.

But even if workers are an undifferentiated mass, if the machines and production processes they use can be quickly installed or upgraded to accommodate new workers, then the refugee workers will increase the total amount produced by the Miami economy and they will not affect wages or employment of native workers.

1. The recession that year was later dated by the NBER to have started in January 1980, several months before the boatlift. Its also interesting to note that a primary reason for the boatlift was a bad economy in Cuba. [↩]

Work with me here. In normal times, say 2004 through 2007, suppose 10% of the working population are looking for jobs while still employed (do you know of a better estimate?). This means about 13 to 14 million employed workers are “job seekers” in normal times.

“Quits” are voluntary separations from jobs and folks do that because they’re leaving the work force (e.g. retiring) or because they found another job. Now look at quit rates over the last couple of years:

Quits have declined by about 40% compared to normal times. From here, about 50% of quits are retirements. If the retirement rate has stayed the same, then quits due to job changes went from 1.1% to about 0.5%. This suggests the number of “job seekers” among the employed has gone down by at least half.

How many job seekers are there right now? Supposing all unemployed workers are “job seekers” then the total number is about 20 million people. In normal times, that number is about… 21 or 22 million people (unemployed plus 10% of the working population). By this measure, the job market now is less congested than usual!

Making the assumptions that I made above, I constructed a “job seekers” per job opening time series:

If you assume none of the currently employed workers are “job seekers” then the graph above looks like the one put up by EPI. If you assume 20% of the currently employed are “job seekers” then even the up-tick seen in the later part of the time series goes away.