Tag Archives: prediction

Migration: how many, what kind, and why it matters

This post is an introduction to a planned series of posts (some by me, some perhaps by others) that explore questions related to how many people might move under various changes that lead towards open borders (locally or globally). The goal of the current post is to explain why I consider the question extremely important. A subsequent post will take a somewhat opposite stand: namely, try to sketch a case for open borders that is independent of how many people might move. Later posts will look at specific policy changes, some of them realistic and others less so, and estimates of what might happen under these. Another area I plan to explore is the question of what the most critical bottlenecks are to large-scale migration (the most obvious candidates are housing and infrastructure) and what limits they set on migration rates.

A while back, I wrote a blog post critical of what I called “economic determinism”: the idea that migration flows are determined completely by economic conditions and that legal barriers to migration have practically zero effect on the magnitudes of migration flows. There are factors other than economic conditions and legal barriers to consider as well, of course: factors such as cultural connections between the sending and receiving country. Alvaro Vargas Llosa’s recent book Global Crossings: Immigration, Civilization, and America drove this point home for me. So, I guess the position I am broadly critical of could be expanded beyond economic determinism to what I might call “restriction irrelevantism”: the idea that restrictions imposed by nation-state governments on migration are largely irrelevant in terms of their effect on the magnitude and nature of migration flows.

Vargas Llosa is definitely not an economic determinist, but whether or not he’s a restriction irrelevantist remains to be seen. This article seemed to suggest that he might be, but op-eds tend to be oversimplifications, and his book, which I haven’t completed reading, may offer a more nuanced picture. I was nonetheless somewhat disappointed by the fact that Chapter 3 of the book, titled “Why They Move” and otherwise excellent at considering the motivators for migration, gave short shrift to the idea that different degrees of restrictionism in different target countries might significantly affect people’s decision of whether or where to migrate.

In any case, this blog post is not about the somewhat extreme position of restriction irrelevantism, which may or may not have real proponents. The majority of proponents and opponents of open borders do not subscribe to restriction irrelevantism. Rather, I think there’d be general agreement that millions more would move, temporarily or permanently, under open borders. But “millions” is a vague term. It could range from an extra five million people over the next two decades to an extra 100 million migrants (many of them temporary) within 2-3 years of global open borders (the main data for how many would move under open borders in terms of the stated preferences of potential migrants are the polling data on migration, which suggest that over a billion people want to go to other countries temporarily or permanently, and about 500-700 million people think they would make long-term moves if they were allowed to). One could come up with a fairly diverse (albeit less so) range of estimates for the number of people who might move under a more targeted open borders regime. For instance, if the United States announced open borders for Haiti (population about 10 million), one might envision a scenario of anything between 2 million people moving to the United States over the next year to a roughly equal number moving to the United States over the next two decades.

The closer the proposed change is to the status quo, the less likely the range of disagreement, but since we are talking here about relatively radical ideas such as open borders, there could be considerable divergence of opinion.

Closely related to the question of how many is the question of who. Many arguments offered by open borders advocates rely on the crucial idea that migrants self-select, i.e., it is not all that easy to migrate to a new land, and therefore migrants are not representative of the populations they hail from, but rather, are selected for positive qualities. As BK has pointed out in the comments (see for instance here and here) you can’t have your cake and eat it too for selectivity: if your estimate says that 25% of the population from region A will move under open borders, you can’t assume that the average migrant who moves will be selected to be in the top 1% of people from region A.

While the precise economics behind double world GDP estimates tends to be complicated, an important point is that all the estimates of huge economic gains also predict that this happens through large numbers of people moving. If, in fact, large numbers of people do not move, then at any rate these specific estimates are not applicable (there may be other mechanisms by which world GDP might increase considerably, such as innovation, but at any rate the specific estimation exercises of the papers would be flawed if very few people moved). For instance, in his blog post about John Kennan’s paper on Open Borders, Nathan Smith writes:

In predicting the volume of migration, Kennan does not assume that humans are strict homini economici who will go wherever they can earn the most. He writes:

One might initially expect that in a world with open borders, everyone would move to the most productive location. But this ignores the strong attachment to home locations that is evident in the data.

He takes this into account by making the migration decision probabilistic, such that the proportion of people who stay in a country is the same as the proportion of the rich-country wage that is paid in that country. For example, if there are open borders between the US and Puerto Rico, and Puerto Rican wages are 2/3 of those in the US, then 2/3 of Puerto Rican adults would stay in Puerto Rico. This roughly fits the data in that particular case, but there is no theoretical motivation for that particular functional form. Relative to a homo economicus model in which everyone who could earn more elsewhere migrated, this assumption causes Kennan to understate the economic benefits of open borders. On the other hand, it also makes Kennan’s version of open borders less scary than it would be if all who stood to gain economically from migration migrated.

This still posits a large number of people who’d move under open borders. Nathan writes later (emphasis mine):

Now, two big things we would like to know about open borders are (a) how many people would move, and (b) how much would world GDP actually increase. If I’m not mistaken, Kennan could easily derive estimates of these things from his model. But he doesn’t. He doesn’t tell us how world GDP would rise under open borders, in the short or the long run. He doesn’t tell us how many people would move, or where they would come from. I think Kennan’s model implies a short-run increase in world GDP of about 65%, and I’m pretty sure in the long run world GDP would double. Since the increase in the effective labor supply comes from growth in the populations of rich countries where labor productivity is high, I think Kennan’s model implies that rich countries’ populations would more than double due to immigration under open borders.

Another related concern is swamping. One of the main concerns of people ranging from hardcore restrictionists to moderate pro-immigrationers and even some who identify as being pro-open borders is that true open borders would lead to very large numbers of people moving over short time periods in a manner that would strain housing, electricity, water supplies, and other infrastructure in the countries receiving the immigrants. The typical response is to point out that (i) borders can be opened somewhat gradually to minimize the possibility of an immediate flood of people (see here for instance), and (ii) in any case, migration flows will tend to be self-regulating and people are likely to plan ahead at least somewhat before making a big move. Evaluating the legitimacy of swamping as a concern is part of the reason why it’s important to get a handle on how many might move under migration regimes that move the needle considerably towards open borders.

Finally, in addition to the direct relevance of understanding migration counts and selectivity, making correct predictions, or at any rate, refraining from making laughably wrong predictions, can help build one’s credibility as an advocate or analyst of migration regimes in the eyes of others approaching the matter from the outside view.

Prediction records and open borders

I recently finished reading The Signal of the Noise by prediction guru and stats wizard Nate Silver (here’s the book on Amazon, and here’s Silver’s FiveThirtyEight blog-cum-website). Silver is well known for his extremely accurate predictions and commentary related to US elections, but his knowledge of and interest in issues related to prediction range far and wide. His book deals with many subtleties associated with prediction. The book manages to go quite deep into the statistical issues without pulling any punches, yet manages to be broadly accessible to readers.

Silver does not discuss anything as radical as open borders, and in general, does not discuss normative questions at all, preferring to stick to his area of expertise: the accuracy and precision of predictions and forecasts and the problems associated with trying to make good predictions and forecasts. Nonetheless, my guess after reading Silver’s book is that he would be extremely skeptical of any claims regarding the effects of open borders, which are way “out of sample.” In particular, I’m guessing Silver would be unimpressed with claims that open borders would double world GDP. At any rate, reading Silver makes me more skeptical of claims made about the effects of open borders with allegedly high confidence. If you believe in Knightian uncertainty as a concept, you may well take the view that the uncertainty associated with open borders is Knightian in nature, and that most attempts at quantifying its impact are flawed. This might also explain why, even though there is a broad economist consensus supporting somewhat more open borders, few economists commit to going all the way to open borders. My co-blogger Nathan noted this explicitly in a comment on another blog post.

Even in areas where we are looking at “out of sample” predictions, however, all is not lost. One idea that Silver repeatedly reiterates throughout his book is that one should keep and use every piece of data. Judging the effects of open borders might be very difficult, and we may end up with a huge range (i.e., low precision). But we can still use some data points. The type of question that somebody like Silver, starting from the outside view, would ask is: “Of the people making predictions regarding the effects of changes in migration policy regimes, who has the better prediction track record?” Or “of the various methods used to predict the effects of changes in migration policy regimes, which methods have the better prediction track record?” Ideally, what we’d need to make this kind of judgment is:

  • A large number of data points,
  • all of which have outcomes that can be agreed upon clearly,
  • with information about what prediction each side made prior to the event, and
  • with information about what the outcome was.

Weather prediction is one such example. There are a large number of data points (the daily maximum and minimum temperature and precipitation statistics in many cities over half a century). The final value of each data point is broadly agreed upon, though there are measurement error issues. The values predicted by organizations such as the National Weather Service and Weather Channel are also available. All the conditions for an analysis are therefore available, and Silver in his book mentions one such analysis. The analysis finds that both the National Weather Service and the Weather Channel are fairly accurate, but that the Weather Channel (deliberately, it turns out), inflates the probability of precipitation on days when that probability is extremely low. This phenomenon is now known as wet bias.

Predictions in the political and economic realm don’t fare as well. There are a reasonably large number of data points regarding the outcomes of various electoral races, which satisfy the necessary conditions (lots of data points, clear outcomes, information about each side’s predictions, and information about the outcome) that allow us to get a sense of the quality of political predictions. The data isn’t as extensive as for weather, but it is still quite extensive. Silver finds that while predictions that relied on statistically valid polling techniques tended to do well, predictions made by political pundits on television didn’t. Silver finds a similar disappointing story of prediction when it comes to economic forecasting. He is also critical of people who make predictions and forecasts without specifying the margin of error or the distribution, but simply give a point estimate. In the discussion, Silver alluded to Tetlock’s study of prediction records and his distinction between “foxes” and “hedgehogs” (see here for an article co-authored by Tetlock with a summary of the idea).

When two sides are debating an issue and relying heavily on empirical claims about the future to make their respective cases, you’d naturally be curious about the prediction records of the two sides with respect to past predictions. There are two additional complications over and above the obvious measurement difficulties that apply particularly to political debates such as migration policy debates:

  • The specific people engaging in the debate are usually different each time. Most pro-immigration groups and people around today weren’t there when the Immigration and Nationality Act of 1965 was passed. The same is true of the anti-immigration groups and people. Given this complication, each side can happily claim allegiance to the correct claims made historically by their side, and disown the incorrect claims as having been made by others they don’t support. This can be partly overcome by trying to come up with objective metrics of just how similar arguments offered today are to the failed arguments of the past, but there are many then versus now “outs” to deflect claims of objective similarity between the present and the past.
  • Relatedly, it can be argued that proponents of an argument weren’t saying it because they actually believed it, but rather, they were just trying to rally public support to our cause, knowing that they would need to lie to (or at any rate, exaggerate their case to) a public that did not share their normative views. (I discussed incentives to lie about immigration enforcement in an earlier post).

Although these two difficulties present a challenge, there is probably much to be gained from a retrospective analysis of past changes in migration regimes and the predictions made by various people during those changes. Significant changes are better because (a) more people are likely to make explicit predictions of the effects of significant changes, and (b) the larger effect size makes it easy to determine what actually happened. Unfortunately, significant changes are also fewer in number, so we do not have the “large number of data points” that would allow for good calibration of the accuracy of predictions. But we’ve just got to deal with that uncertainty. It’s better than completely ignoring the past.

Relatedly, looking at migration regime changes sufficiently far back in the past also gives us some idea of the more long term effects of the changes. BK, one of the skeptics of open borders in our comments, has argued that the benefits of migration are front-loaded, while the costs take decades to unfold (see for instance here and here). Evaluating such concerns would require us to look at the long-term effects of past migration regime changes.

My co-blogger Chris Hendrix plans to begin a series that looks at various instances of open borders becoming more closed, along with the predictions and rationales offered at the time (expect to read Chris’s introductory post soon!). Later, one of us (perhaps Chris again, perhaps I, or perhaps one of our other bloggers) will be looking at instances of immigration liberalization and the predictions and arguments accompanying and opposing them. I’m particularly interested in the Immigration and Nationality Act of 1965 in the United States and the Rivers of Blood Speech by Enoch Powell in 1968 in the UK. The historical analysis will hopefully help us better calibrate the accuracy of predictions and forecasts about changes to migration regimes, hence better enabling us to evaluate the plausibility of claims such as “double world GDP” or end of poverty from the outside view.