But Net Migration Isn’t Enough

Let’s imagine a prison in Cook County is closed and the prisoners are transferred to southern Illinois. The data we have will show this as outflows from Cook County. But surely nobody in their right mind would suggest this is because of tax policy, or bad standard of living, or excessive housing costs, or regulation, or poorly funded schools, or whatever. We’d say it’s because of, well, whatever led to a prison being closed!

Or consider if a large number of non-religious Chicagoans converted to a new religion, and began recruiting spiritual leaders for their congregation from outside the region. How would we model this? No economic model of migration of which I’m aware includes a religious component. There wouldn’t be an observable income shock. This would just be a pure change in consumption preferences among Chicagoans resulting in a sudden demand for people trained at the Non-Chicago Religious Seminary.

Or consider the person who moves to Chicago for a job offer without doing much research then, upon arrival, discovers that holy cow sales taxes are really high and their paycheck doesn’t go as far as they thought. Time to pick up stakes and move on! This person would have a tax-motivated migration… without any change in tax policy! Residence in Chicago allowed them to acquire better information about local tax policy!

In all these cases, net migration balances would wrongly inform us about what’s happening in the region in question. Less-than-voluntary migratory flows (prisoners, but also school-age children, some religious orders, military personnel, etc) certainly tell us little or nothing about amenities and costs in an area. Shocks to migration driven by idiosyncratic preference changes are also hard to see as being driven by specific local costs or amenities. And of course, different migrants have different sets of information available to them when they assess Chicago’s fitness for their preferences.

The way we try to get at this as economists is by assessing “revealed preferences.” So we try to control for things that we don’t consider core regional preferences; that is, things people will almost always prefer or disprefer. So, more jobs should attract people. More crime should repel people. Better schools should attract people. Expensive cost of living should repel people. High wages should attract people. High taxes should repel people. When we control for these, we can get an unexplained residual; idiosyncratic migratory factors for a region.

Some researchers call this the amenity value of a region. But that’s wrong too. Consider a place with a university. It will have high 18 year-old inflows and high 22-year-old outflows. Depending on graduating class sizes, you may get substantial changes in the net migration across these age groups despite literally zero change in the underlying amenities. And since this group has a very high migration rate, this tail can wag the net migration dog.

Some things are amenities to some people but costs to others. Some things are amenities of shifting value over the life cycle. These are complicated effects, but by controlling for migratory sub-groups, we can usually sort this out and get a subgroup-specific residual, so we can do a kind of subgroup-weighted-regional-amenity-score. In practice nobody does this because it’s a pain to calculate, but it’s what researchers should do if they want to be precise.

But even that isn’t quite right. Economists today generally suggest that economic agents respond within certain information parameters. That is, people don’t respond to signals they do not receive. Obvious, but important.

The signals received by an outflow vs. an inflow can vary widely. A person who leaves has already collected large amounts of residency-based information than a person who is just arriving. That is, outflows reflect better-informed preferences than inflows. This is a really nifty quirk of information-based models: inflows are a less reliable indicator of local amenities than outflows! Inflows don’t have as much information about local conditions as outflows, so they can’t be seen as being as reliable of indicators.

This model is now widely used in the economics of migration. Studies of repeat migration, failed migration, iterative migration, etc are now the workhorses of the discipline, and they include explicit or implicit models about information acquisition and preference formation. For a fantastic example of these information-based models of migration which I’ve covered before which also happens to be the best recent paper on why migration is falling, see here.

There’s no really good way to test how much information migrants have. But what we do know is that repeat- and return-migration has fallen in the last two decades more than migration generally, suggesting inflows have been obtaining information which better enables them to avoid dis-preferred locations. Now, this information may also be causing potential migrants to miss out on potentially preferable migrations… but the bigger effect is fewer people moving to places they’re likely to dislike.

The point of this is simple.

If you want to assess what places people “prefer” or not; places they are “voting” for or against via migratory revealed preferences, net migration isn’t actually enough.

Net migration is a non-negotiable starting point. But it is not the ending point. It is a necessary component for explaining revealed preferences, but not a sufficient one.