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Selection effects for migrants: some a priori possibilities

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This post combines many different threads I’ve explored in earlier posts. Back in July 2013, I wrote a post arguing that it’s important to get a handle on both the quantity and the selectivity of migration. Recently, I wrote a series of blog posts laying out a detailed conceptual framework for the empirical analysis of migration (introductory post here, describes and links to other posts). While laying out this conceptual framework, I noted that, under any policy regime other than complete closed borders, there is likely to be both a selection effect and a treatment effect for migrants. Specifically, in part 3, I considered a situation where we assume for simplicity that the people who do not migrate are not affected by the act of migration. In that case, we can concentrate on selection and treatment effects for migrants and ignore the treatment effects on non-migrants. Our goal was to discuss the rank-ordering and quantitative comparison of the following four values (where X is the indicator of interest):

  1. Performance of natives of target country B on indicator X.
  2. Performance of natives of source country A (who would not move under either policy) on indicator X.
  3. Performance of potential migrants on indicator X if they were allowed to migrate (i.e., in the migration scenario).
  4. Performance of potential migrants on indicator X if they were not allowed to migrate (i.e., in the no-migration scenario).
  5. Ability to plan and execute a move.

Note that:

  • The difference between (2) and (4) measures the selectivity of migration relative to the source country.
  • The difference between (1) and (3) measures the selectivity of migrants relative to the target country, or equivalently, to their failure of assimilation (the assimilation may be “upward” or “downward” depending on how the migrants compare with the target country natives).
  • The difference between (3) and (4) refers to the treatment effect of migration on migrants.

The goal of this blog post is to come up with a priori arguments on how migrants might be selected on various parameters. We’re not concentrating on the treatment effect directly, except insofar as beliefs about the treatment effect affect the selection of migrants. In some cases, we cite empirical evidence to support the claim. But the goal is not to make concrete empirical predictions, but to lay out general considerations that would help make concrete predictions for specific migration policy regimes.

Because of the vagueness of our analysis, we don’t distinguish heavily between selectivity of migrants relative to source countries and selectivity relative to target countries. However, our arguments, as stated here, apply a priori far more to selection relative to source countries than to selection relative to target countries. This follows from the nature of the analysis: we’re trying to figure out who, from a given bunch of people in the same environment, would end up moving. Therefore, these are best thought of as arguments about emigrant selectivity (comparing (2) and (4)) than about immigrant selectivity (comparing (1) and (3)). At the end of the blog post, we’ll discuss what we can infer about immigrant selectivity from the information.

Also, for the most part, I restrict attention here to considerations that would be relevant even under open borders. There are some forms of selectivity that arise from fiat: migration policy dictates that migrants must satisfy a set of conditions in order to be allowed in. We discuss these only in passing here, and will return to explicit policy selection in a separate post (more remarks on this at the end of this post). Note that I ignore explicit policies in the post but I certainly consider them quite important. I am not an economic determinist.

Costs of moving

Migrants are moving to a new place. Even under an open borders regime, moving requires nontrivial fixed costs in terms of time, money, and emotional energy. The magnitude of the costs depends on the geographic distance moved, the cost differential (moving to a place with a higher cost of living means one’s savings are less use for covering the initial costs of setup, even if one expects to eventually recoup those costs through higher earnings), as well as the cultural and linguistic distance between the source and destination. Note that all these apply even under open borders. In a regime with migration restrictions, there are additional costs of time, money, and uncertainty in applying for permission to move. Depending on the feasibility of return migration, one may also need to dispose off assets before making the move. Those crossing borders illegally need to incur coyote fees and undertake time-consuming and dangerous journeys to reach their destination.

What attributes does the high cost of moving select for? It’s hard to say, but here are some guesses:

  • Money: People who have more money can afford the costs of moving more easily.
  • Strong future orientation (i.e., lower discount rate): People who think of life a few years ahead are more likely to be willing to migrate than people who engage in hyperbolic discounting.
  • Willingness to break ties: People who are heavily attached to their family and home culture would find the move more difficult, whereas people who define themselves less by their present relationships can move more easily.
  • Adventurousness, openness to experience, and willingness to take risks

Opportunity costs of migration

When people migrate, they leave behind their home, family connections, and a culture that they are more familiar with and may be attached to. What sort of people are willing to leave that behind? Here are some guesses:

  • People who have little to lose by leaving are most likely to do so. This could be because they are at high risk of being victimized by violence, are heavily discriminated against by people where they live, are cultural misfits, or cannot find any use of their job skills where they currently live.
  • People who have skills or assets that cannot be transported easily and can be leveraged most in the homeland are least likely to leave. For instance, people who are good-looking by the standards of their culture may have the best prospects in their homeland (however, if a huge diaspora from the country already exists, they might be able to marry a member of the diaspora settled elsewhere through a family connection or other introduction). People who inherit a big family business that they can continue running, but aren’t particularly entrepreneurial, may just prefer to stay where they are to keep running the business. People with skills in politics have the best shot at politics in their home country, given that voters everywhere are likely to discriminate in favor of people who were born in the country and fit in culturally and linguistically. People who have completed expensive location-specific qualifications (such as in law) may prefer to stay in the home country because they’d need to re-qualify to practice law in a new country.

Note that there’s a contradiction of sorts between the little to lose criterion (which suggests that poor people may be more keen to migrate) and the observation that wealthier people can more easily afford to migrate. We’ll talk more about this later.

Benefits of migration

Migration generates huge benefits for some people, and scant benefits for others. The main benefit of migration from lower-productivity regions to higher-productivity regions is the place premium: one can earn more with the same skills simply by migrating to a new country.

The following attributes predict the benefits of migration:

  • Larger absolute wage gains predict migration. Highly skilled individuals, who command high incomes in general, are likely to have large absolute wage gains.
  • Larger proportional wage gains predict migration. Increasing one’s income from $1000/year to $10,000/year looks a lot more attractive than increasing one’s income from $30,000/year to $40,000/year.
  • Greater knowledge of, affinity for, or ability to learn, the language, customs, and culture of the new place predicts more migration.
  • Greater ideological or political affinity with the place they’re moving to (see Ilya Somin’s blog post on the subject).
  • Presence of diasporas from the source country in the target country can make migration more attractive. This is part of the diaspora dynamics model developed by Paul Collier.
  • Other geographical and health-related considerations could play a role. For instance, I’ve been told that in the 19th/20th century US there was a wave of migrants with lung diseases to the southwest to benefit from the dry air there. This was intranational migration, but presumably there could be international migration for similar reasons under open borders. The selection effects here are unclear, for instance, it may be that emigration of the unhealthy makes it such that the people who stay on in inhospitable climates are unusually healthy and fit.

Migration for one’s children

In my blog post on whether there might be too much or too little migration, I talked about how the costs of migration are borne by the migrant, but the benefits are shared by the descendants. I had noted at the time that, because migrants may not fully take into account the benefits to their descendants (even though they care somewhat about their descendants) this might lead to too little migration.

I want to bring up the same point, but with a focus on selectivity rather than raw quantity. People who strongly care for the future of their children and later descendants (including unborn descendants) are more likely to be willing to migrate. This probably selects for two things:

  • Strong future orientation (we already talked about this in the context of overcoming the costs of migration, but it takes on added importance if one is thinking of one’s children or grandchildren, particularly the unborn ones).
  • Greater love or concern for one’s future family. Note that this is in some tension with the fact that migrants are generally more willing to break ties with their existing families in order to migrate.

I’m planning to do a post on how migration can be considered a sacrifice for future generations, where I’ll explore this in more detail.

Selectivity of migration on income and wealth

The a priori considerations provided above paint a mixed picture of the role that income and wealth play. The following emerge:

  • Higher wealth allows people to fund their move more easily.
  • On the other hand, less wealth means people have less to lose and are more desperate to migrate.
  • Huge wage gains attract more migration. But huge wage gains in absolute terms are linked to higher incomes, whereas huge wage gains in proportional terms are linked to lower incomes.
  • Higher wealth may be correlated with other traits that predict greater or lesser ability to migrate. This is particularly the case for self-acquired wealth, but might also apply for inherited wealth to the extent that parental wealth correlates with parental attributes and via that with the person’s attributes.

As noted above, the nature of the income and wealth pattern may matter more than the amount. People whose income and wealth is heavily tied to their current location are likely to stay, whereas those whose income and wealth are tied to transportable skills or assets are more likely to move to places where their skills and assets can be best used to earn more.

One example of a “wealthy with little to lose” combination is (relatively) wealthy members of minority groups that are forcefully evicted as path of ethnic cleansing, or anticipate that this will happen. This was the case with Indians in Uganda, businessmen who found themselves on the wrong side of the border in the runup to the Partition of India, Tamils in Sri Lanka, and many others. Market-dominant minorities may in general fear hostile political environments and may be eager to leave when populist political parties or opinions are ascendant.

What does empirical evidence suggest about the relation between income/wealth and emigration? This blog post by Michael Clemens reviews the evidence and concludes that for countries below something like $6,000–8,000 GDP per capita (at US prices), countries that get richer have more emigration. The plot of emigration flow in terms of GDP per capita peaks at this income range, as does the plot of emigrant stock in terms of GDP per capita. Clemens writes:

Social scientists have six theories for this “mobility transition”. I review these theories and the evidence for them in the paper. Briefly: 1) Development is usually accompanied by a demographic transition that favors a corresponding mobility transition, 2) development means that more people can afford to emigrate, 3) development means that more people can access the information they need to emigrate, 4) development tends to disrupt economic structures that keep people immobile, 5) development shapes domestic inequality in ways that foster migration, and 6) development in country A means that people in country B are more likely to give visas to migrants from A.

The Zelinksy model of mobility transition is also relevant.

Selectivity of migration on criminality

The following are some considerations:

  1. The strong future orientation needed to migrate suggests that migrants will be less criminal, because crime generally involves short-term benefits and long-term costs, and criminals generally discount the future heavily. In addition to future orientation, the ability to execute the move might also filter for other relevant positive traits that predict lower criminality.
  2. The fact that migrants are likely to have more money (in order to fund their moves) and the fact that richer people commit fewer violent and property crimes, argues in favor of migrants being less criminal.
  3. The fact that migrants often need to cheat and lie in their visa applications in order to be able to migrate, or that they cross borders illegally, might lead to migrants being selected for higher levels of criminality.

In a later post series on crime and open borders, we’ll weigh these considerations against one another. The general bottom line will be that emigrants have substantially lower crime rates than natives of their source countires, and this is attributable in large part to selection.

Selectivity of migration on enterprisingness

People who move have strong future orientation, adventurousness, openness to experience, and willingness to take risks. This suggests that they are more likely to be enterprising in the general sense. In some cases, this translates to being more entrepreneurial (see here for more on existing research). The following are some other considerations:

  • To the extent that regulations on migrants make it easier for them to stay in standard, steady jobs, they are less likely to engage in entrepreneurship. This is a major issue for migration to the US: it’s much easier for high-skilled migrants to get a work visa working at a big company than to start a company. Note that this effect could operate at both a selection and a treatment level: entrepreneurial people may shy away from migrating to a place where it’s not that easy to start a business, and people who’ve already migrated may prefer to continue in an existing company than start a business.
  • To the extent that regulations (or societal discrimination) inhibit work in the formal sector, migrants are more likely to start their own small businesses. For instance, it may be easier for families to start a restaurant and have family members work at it so that younger members can contribute and they can circumvent labor laws. Note that this type of entrepreneurship isn’t the “create a billion-dollar business” type, and has lower value per entrepreneur, but it is still important to society. At the same time, artificial restrictions on formal sector employment may lead to too many family businesses and a more inefficient economy overall because family businesses cannot avail of the economies of scale.
  • The amount of wealth that migrants have affects whether they can afford to experiment with entrepreneurial ventures. As we saw, the relationship between migration and wealth is unclear.

Selectivity of migration on political attitudes

While there are many migrants who leave because of political persecution, this political persecution often has more to do with ethnic identity and religious beliefs than with specific political beliefs. (There is some relation between religious beliefs and political beliefs, but it’s very tenuous). The following are some general remarks:

  • The very fact that migrants left their home country suggests that they are not overly attached to the institutional or policy framework of that country. This doesn’t mean they actively dislike it. This creates a prior against migrants replicating the policies of their home countries. Empirically, there is little evidence of home country policy replication: people from communist countries aren’t noticeably in favor of communism and don’t seem to want to impose communism on other countries. At any rate, they haven’t been successful doing so. On a related note, see Ilya Somin’s blog post on immigration and political freedom.
  • People who have a strong aptitude or interest in politics (in the sense that they want to become political activists or politicians) are likely to stay in their home countries, because it’s easier to make headway in politics as a native.

Remarks about the distinction between selectivity with respect to source and target countries

The arguments above concentrate on what we expect regarding selectivity relative to the migrants’ source countries, because we’re trying to answer the question: of a given set of people in a given environment, who’d be most willing and able to leave? But people in the receiving countries are more interested in comparing immigrants to natives, in order to figure out how immigration affects the overall societal composition.

To what extent can the above arguments make predictions about how immigrants compare with natives? We need to know both how much the countries differ (the (1) versus (2) gap) and how large the treatment effect of migration is (the (3) versus (4) gap).

For instance, let’s say we have a low-productivity poor country A and a high-productivity rich country B. By our general arguments, we think that the people who migrate from A to B are likely to be more enterprising, more future-oriented, and more adventurous than those who stay behind in country A. How do they compare with country B? The conclusion we draw depends on what we think of the relative levels of these traits in the two countries.

One school of thought is that the distribution of traits in the populations of both countries is similar, so that the (conjectured) fact that emigrants do better on these traits than natives also implies that immigrants will do better on these traits. For instance, one might argue that there isn’t any difference between the levels of future orientation in China and Taiwan. Therefore, immigrants from (low-income) China to (high-income) Taiwan, who are selected relative to their source country with respect to future orientation, are probably also selected relative to their target country.

Another school of thought is that the reason country B is richer and has higher productivity is that the people there are more enterprising, more future-oriented, more adventurous, etc. For instance, one might argue that the United States is more entrepeneurial than the United Kingdom, and this accounts for the difference in their per capita levels of income and wealth. In this case, even though emigrants from country A score higher on these traits than natives of country A, it’s unclear how they compare with natives of country B. There are two separate issues to consider to figure this out:

  • How strong is the selection effect of migrants relative to their source countries, in comparison with the difference between source and target countries? Even if the US is more entrepreneurial than the UK, that difference on average might be much smaller than the selection effect for migrating.
  • How much of a treatment effect is there on migrants? The strength of the treatment effect arguably depends on the age of migration. Those who migrate as young children, and do not grow up in an isolated culture, are likely to be exposed to similar cultural influences as the natives of the target country, though they still experience a different home culture and prenatal environments, and are genetically close to country A. Note that though treatment effects are stronger for young people, selection effects may be weaker, because young people are often dragged along by their parents rather than being active participants in the decision to move.

Remarks on differences in selectivity of different migration policy regimes

The extent of selectivity depends heavily on the nature of the migration policy regime. Thus, the level of selection under open borders is likely to be quite different (and in general, much weaker) than the level of selectivity under the status quo.

The majority of the considerations outlined in this post apply to migration even under open borders. The main difference is that rigid legal constraints on whether one can migrate, and the amount of bureaucratic red tape one has to go through to migrate, both reduce under open borders. If we are trying to quantitatively ballpark the level of selectivity, we need to keep in mind its sensitivity to the policy regime. In a future post, I’ll explore ways that governments can (and do) affect the selectivity of migration through explicit migration policy.

A conceptual framework for empirical analysis of migration (part 4: models for migrant performance)

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This post is part 4 of a series outlining a conceptual framework for the empirical analysis of migration. Read the introductory post to the series here, part 1 here, part 2 here, and part 3 here.

Migrant performance as a combination of source and target country performance?

A simple model against which we could compare reality is that migrant performance is a function of the native performance in their source and target countries. In other words, if we knew the performance of source country natives and we knew the performance of target country natives, we would be able to predict how migrants perform.

Qualitatively, here are some possibilities:

  1. Migrant performance falls somewhere in between the performance of their source and target countries. For instance, perhaps the performance of migrants falls midway between the source and target countries. Note in particular that if the source and target countries have identical values for natives, then migrants are also identical to them, suggesting that there is no effect coming from being a migrant per se.
  2. Migrant performance is nearly identical to that of natives in the target country, and is independent of the source country.
  3. Migrant performance is nearly identical to that of natives in the source country, and is independent of the target country.
  4. Migrant performance is determined by performance in the target country, but is not equal to it. For instance, perhaps migrants have incarceration rates that are 0.7 times the incarceration rates of natives in the target country, regardless of their source and target countries.
  5. Migrant performance is determined by performance in the source country, but is not equal to it. For instance, perhaps migrants have fertility rates that are 1.2 times those of their source countries, regardless of where they come from and where they go.

Mathematical digression: a linear combination model

As in part 1, denote by $latex x_{ij}$ the performance of migrants from country $latex i$ to country $latex j$ on indicator X, and denote by $latex x_{ii}$ and $latex x_{jj}$ respectively the performance of natives of the countries who stay put. We claim that, to a reasonable approximation, there is a (nice enough) function $latex F$, independent of $latex i$ and $latex j$, such that:

$latex x_{ij} = F(x_{ii},x_{ij})$

The simplest possible example of such a function is a linear combination. In this model, we have the following, where $latex \alpha$ and $latex \beta$ are nonnegative reals:

$latex x_{ij} = \alpha x_{ii} + \beta x_{jj}$

We now revisit the five cases above in terms of the linear combination model:

  1. $latex \alpha + \beta = 1$, i.e., the performance of migrants is a convex combination of that of natives from the source and target countries, and therefore in particular lies somewhere in between those two values. In that case, we can write $latex x_{ij} = \alpha x_{ii} + (1 – \alpha) x_{jj}$. The special case $latex \alpha = 0.5$ is the one where migrant performance is midway between the natives of the source and target countries.
  2. $latex \alpha$ is close to 0 and $latex \beta$ is close to 1.
  3. $latex \alpha$ is close to 1 and $latex \beta$ is close to 0.
  4. $latex \alpha$ is close to 0 and $latex \beta$ is positive but not close to 1.
  5. $latex \alpha$ is positive but not close to 1, and $latex \beta$ is close to 0.

Linear models are not the only ones possible: one can imagine more complicated functional relationships, including power relationships (which would be linear once you take the logarithm). Linear models are the ones people generally look for when predicting performance, and that’s what linear regressions are generally used for. Anyway, the best type of model to use depends on the type of indicator we have and what we understand about how it’s determined, i.e., we need a phenomenological story first (more on this later in the post).

End mathematical digression

Separating selection and treatment: potential migrant performance and actual migrant performance in terms of source and target country performance

The above discusses the performance of people who actually migrate in terms of their source and target countries. But, building on the discussion in part 2 and (more directly relevant) part 3, we’re also interested in how potential migrants would perform if they weren’t allowed to migrate. This allows us to separate out the selection and treatment effects.

Unlike the case of people who do migrate, it’s not a priori clear why the indicator value in the target country should be a predictor for people who don’t migrate. One argument that it should: the very fact that they are considering migration to the target country, or that a potential migration policy is considering them, suggests potential affinity with the target country. It may happen in some cases that the function doesn’t depend on $latex x_{jj}$ at all.

Mathematical digression: two linear combinations

To stay similar to the earlier notation (from parts 2 and 3 of the series), we denote the “how migrants would do if they were’t allowed to migrate” quantity as $latex x_{ij}^{n,o}$. We are thus interested in understanding the function $latex G$ such that:

$latex x_{ij}^{n,o} = G(x_{ii},x_{jj})$

The simple case is a linear function, i.e., we have:

$latex x_{ij}^{n,o} = \alpha^{n,o}x_{ii} + \beta^{n,o}x_{jj}$

We can now make cases based on the values of these numbers. We list some possibilities:

  • Suppose $latex \alpha/\beta < \alpha^{n,o}/\beta^{n,o}$. This means that for people who do migrate, their performance is predicted more by the target country than if they were not allowed to migrate.
  • $latex \beta^{n,o} = 0$ suggests that the performance of potential migrants, if they stay in their source country, is determined completely by their source country. In the case $latex \alpha^{n,o} = 1$, the potential migrants are indistinguishable on the indicator from others in their source country. In other cases, migrants differ from others in their source country, but by a constant factor.
  • $latex \alpha = 0$ suggests that the performance of people who actually migrate is determined completely by their target country. In the case $latex \beta = 1$, the migrants become indistinguishable from natives of the target country. In other cases, they differ by a constant factor.
  • If $latex \alpha < \alpha^{n,o}$ and $latex \beta < \beta^{n,o}$, that implies that migrants score lower on the indicator if they're allowed to migrate than if they're not, regardless of how the source and target country compare on the indicator. The opposite conclusion holds if $latex \alpha > \alpha^{n,o}$ and $latex \beta > \beta^{n,o}$.

End mathematical digression

Phenomenological stories

The above were purely mathematical models of migrant performance, and didn’t provide a story as to why a particular functional expression works, of why particular parameter values are right. But what’s going on? Why might we expect a functional relationship, linear or otherwise, between migrant performance and the performance of natives in the source and target countries?

Some possible stories:

  1. Migration policy explicitly selects for people based on how they fare relative to the native population of the recipient country, so that the similarity across countries between the relative performance between natives and migrants is largely because most countries’ migration policies revolve around similar explicit objectives in terms of how the migrants should compare with the natives.
  2. Immigrants self-select for countries where their performance will be at a particular level relative to natives.
  3. People self-select to emigrate if their performance relative to their source country is at a particular level relative to the natives of that source country.
  4. People’s intrinsic characteristics (that they transport with themselves when they migrate) only determine their performance relative to where they live, rather than in absolute terms. For instance, a person’s inclination to criminality may determine how much crime the person commits relative to natives of the region. Similarly, a person’s skill level may determine how much money the person can earn relative to natives of whatever country he or she is in, rather than in absolute terms.

Mathematical digression: translating the phenomenology to the linear combination model

(1) and (2) explain the parameters $latex \beta^{n,o}$ and $latex \beta^n$. (3) explains the parameters $latex \alpha^{n,o}$ and $latex \alpha^n$. In the extreme case that (4) holds completely, $latex \alpha^{n,o} = \beta^n$ and $latex \beta^{n,o} = \alpha^n = 0$.

End mathematical digression

A conceptual framework for empirical analysis of migration (part 2: comparative statics, multiple matrices)

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This post is part 2 of a series outlining a conceptual framework for the empirical analysis of migration. Read the introductory post to the series here and part 1 here.

The questions discussed in this post are often difficult or impossible to resolve empirically, because one or more of the scenarios being compared is counterfactual. Techniques used include comparison of different time periods or different regimes. Regression analysis may be used to isolate the relevant factors. Conclusions drawn here are suspect even if the data collected is impeccable, because the theoretical model used for analysis may be invalid.

The simplest form of comparison is to consider the indicator values for various (source country, target country) pairs under the different possible migration policy regimes, and compare corresponding indicator values between the two regimes. For instance, how do French natives who stay in France under the pre-EU migration policy regime compare with French natives who stay in France under the EU migration policy regime?

Mathematical digression: multiple matrices

The earlier static framework considered a single matrix that encapsulated information on the performance of migrants as well as people who stay put for various source and target countries. Now, we’re trying to compare different scenarios. Now, each scenario has its own matrix. Our goal then is to compare the entry in one matrix with the corresponding entry in another matrix. In some cases, what we’re interested in is not a single entry, but a weighted average, or ratio, or difference, of entries. We then compute and compare that expression for the different countries.

For instance, consider the three-country scenario with France, Germany and the UK again (from part 1). Now, consider two policy regimes: the pre-EU regime and the EU regime. These are qualitatively different regimes: in the former, migration between the countries is not completely free, so there are stronger selection effects for migrants. Therefore, the matrices for the two regimes are probably different.

Suppose the matrix with the pre-EU regime is as follows (the superscript $latex {}^o$ is not an exponent, but indicates that the matrix refers to indicator values under the old policy regime):

$latex \begin{pmatrix} x^o_{11} & x^o_{12} & x^o_{13} \\ x^o_{21} & x^o_{22} & x^o_{23} \\ x^o_{31} & x^o_{32} & x^o_{33} \\\end{pmatrix}$

and the matrix with the EU regime is as follows (the superscript $latex {}^n$ is not an exponent, but indicates that the matrix refers to indicator values under the new policy regime):

$latex \begin{pmatrix} x^n_{11} & x^n_{12} & x^n_{13} \\ x^n_{21} & x^n_{22} & x^n_{23} \\ x^n_{31} & x^n_{32} & x^n_{33} \\\end{pmatrix}$

We can then compare the entries. For instance:

  • The comparison of $latex x^o_{11}$ and $latex x^n_{11}$ reveals how the French who stay in France under the pre-EU regime compare with the French who stay in France under the EU regime.
  • The comparison of $latex x^o_{12}$ and $latex x^n_{12}$ reveals how the people from France and in Germany under the pre-EU regime compare with the people from France and in Germany under the EU regime.
  • The comparison of $latex x^o_{13}$ and $latex x^n_{13}$ reveals how the people from France and in the UK under the pre-EU regime compare with the people from France and and in Germany under the EU regime.

End mathematical digression

Note that any such comparison between different policy regimes has two components:

  • Selection effect: The set of people in each of the categories is different under the two regimes. In particular, people who might not have been able to migrate under the pre-EU regime can migrate under the EU regime. Thus, even if the indicator value is the same between the two regimes for every individual (i.e., the changes to migration patterns don’t actually affect how any individual performs on the indicator), the difference in the labels means a different matrix for the two regimes.
  • Treatment effect: The marginal migrants under the new policy experience changes relative to what they would have if they had stayed put, and they may also influence the indicator values for the people who stay put, or the others who would have migrated under the old regime as well.

Separating the selection and treatment effects requires us to consider separate matrices of indicator values using groupings from one regime, but measurements from the other regime. For instance, we ask: how do the people who would have stayed in France under the EU migration policy regime fare under the non-EU migration policy regime? We then compare these matrices to the matrices where the grouping and performance are measured for the same regime.

Mathematical digression: the matrices that use grouping and indicator values from different regimes

We continue with our three-country representation: country 1 (France), country 2 (Germany) and country 3 (the UK). Recall that the superscript $latex {}^o$ was used for the old policy regime (the pre-EU regime) and the superscript $latex {}^n$ was used for the new policy regime. We now consider some new matrices that can be constructed in principle but are hard to measure because they require a mix of information about two policy regimes.

Consider the matrix that uses grouping from the EU regime but indicator values from the pre-EU regime, denoted with superscript $latex {}^{n,o}$.

$latex \begin{pmatrix} x^{n,o}_{11} & x^{n,o}_{12} & x^{n,o}_{13} \\ x^{n,o}_{21} & x^{n,o}_{22} & x^{n,o}_{23} \\ x^{n,o}_{31} & x^{n,o}_{32} & x^{n,o}_{33} \\\end{pmatrix}$

The matrix is interpreted as follows: it represents the average values of the indicators under the pre-EU regime but using the groupings under the EU regime. For instance, the entry $latex x^{n,o}_{12}$ measures how the people who would migrate from France to Germany under the EU regime fare under the pre-EU regime. We can similarly consider another matrix with entries denoted $latex x^{o,n}$ that uses the groupings from the pre-EU regime but the indicator values from the EU regime. Entry comparisons between the four matrices reveal different types of information. The various combinations are discussed below:

  • A direct comparison of $latex x^o$ and $latex x^n$ is comparing different regimes, using the grouping for each regime when considering it. This incorporates both a compositional selection effect arising from the difference in grouping and the treatment effect arising from a different set of people being able to migrate, affecting themselves and others.
  • The comparison of $latex x^n$ and $latex x^{n,o}$ isolates for the treatment effect using the grouping of the new regime.
  • The comparison of $latex x^n$ and $latex x^{o,n}$ isolates for the selection effect using the grouping of the new regime.
  • The comparison of $latex x^o$ and $latex x^{o,n}$ isolates for the treatment effect using the grouping of the old regime.
  • The comparison of $latex x^o$ and $latex x^{n,o}$ isolates for the selection effect using the grouping of the old regime.

End mathematical digression

Changes in weights

The number of migrants, as well as the number of non-migrants, differs under the various policy regimes. Therefore, the weights needed to take a weighted average (when computing average indicators — “per natural” for people born in a country or “per resident” for people living in a country) differ between the policy regimes.

Mathematical digression

The choice of weights depends on the grouping, so $latex x^n$ and $latex x^{n,o}$ use the same weights as each other, whereas $latex x^o$ and $latex x^{o,n}$ use the same weights as each other, but different from the other two.

End mathematical digression

Same set of people in the two regimes?

One of the points we’ve elided somewhat in our framing above is that we’re assuming that the set of people is the same in both regimes, and in fact, that the set of naturals for each country (i.e., the set of people with that source country) is the same in both regimes. What differs between the regimes is what country they land up in (the compositional selection effect) and how this affects the value of the indicator for them (the treatment effect).

But the assumption that the set of people itself is the same doesn’t actually hold water. People have children, and their decision of whether or not to migrate affects the identity and affiliation of the children. It might also affect how many children they have. Similarly, people may die, and migration policies may affect how long people live. We’re abstracting away from these issues for now, but will return to them in parts 5 and 6, before we start applying the framework in earnest to real-world migration questions.

Different normative perspectives

The individualist utilitarian universalist perspective is concerned with the weighted average of the indicator over the whole matrix for the two different policy regimes.

Once we leave the utilitarian universalist perspective, however, we have a bewildering array of normative choices. There are three big dimensions to the normative choices:

  1. The dimension of what particular indicator or weighted combination of indicators we care about. One may care about:
    • A particular (source country, target country) combination.
    • All naturals of a country (all people with that source country, including those who stay and those who leave).
    • All residents of a country (all people with that target country, including natives and immigrants).
    • All immigrants to a country.
    • All emigrants from a country.
  2. The method used for grouping:
    • We could use, for each regime, the grouping of that regime. For instance, we could compare the performance on indicator X of the French who stay in France under the EU regime, with the performance on indicator X of the French who stay in France under the pre-EU regime. This is problematic because selection effects can lead to the compositional effects paradoxes where all individuals are better off but some indicators still get worse due to the change in grouping. Territorialism has this flavor in practice, though it could in principle be of the other type below.
    • We could privilege a particular regime to determine the grouping. For instance, we could say “I’m interested in maximizing the welfare of the set of people who would be French natives staying in France under the pre-EU regime, regardless of where they go under the EU regime.” Citizenism, though it isn’t exactly in this framework (since it favors citizenship and not necessarily birthplace) has this flavor: citizenists explicitly reject changing the idea of “who are we” in the face of new migration policy when deciding ex ante what policy regime is favorable.
  3. Whether one looks at only a single instance, or at all. For instance, we could imagine somebody who cares about French natives only, or German natives only, versus somebody who cares about “natives” as a reference class, or “whoever gets to be resident in a country” as what we’re trying to improve, for each country. This could well be universalist (if the set of things we care about encompass everybody) and yet be different from individualistic utilitarian universalism, because we care about averages for particular groupings rather than about individuals qua individuals. While these different forms of universalism often agree, they don’t always do, thanks to compositional effects paradoxes.

First-order and second-order effects

The most direct treatment effect of migration is on migrants: they move to a new place, and experience a new environment. Assuming that migrants are a relatively small share relative to both their source and target countries, this effect will dominate at a per capita level, though possibly not at the aggregate (total) level.

An indirect, second-order, treatment effect is on the natives of the sending countries and receiving countries. Migrants leave the sending countries, thereby changing the nature of the society in these countries. They enter the receiving countries. and similarly change the societies there. Effects here are likely to be small on a per capita basis, but comparable in the aggregate to the effects on migrants themselves.

Note also that individual migrants affect other migrants, because a lot of migrants interact with fellow migrants to a greater extent than would be predicted by their proportion in the population. There is some terminological ambiguity on whether to consider this a first-order or a second-order effect. On the one hand, it’s an effect directly experienced by “migrants” as a class. On the other hand, it is an effect that people’s migration has on other migrants. This idea is closely related to diaspora dynamics, and we’ll get to it somewhere in parts 5 and 6.

Crossed dependencies: how the migration policy regime of one country affects migration between other pairs of countries

When we talk of a particular policy regime or scenario, we’re talking of a particular combination of immigration and emigration policy regimes for all countries. For any given country, its own migration policy is the most relevant when considering migration flows to and from that country. But the migration policies of other countries matter too:

  • The immigration policies of countries that may receive migrants from the country, and the emigration policies of the countries that may send migrants to the country, matter.
  • The immigration policies of countries that may “compete” with the given country for migrants also matter. Similarly, the emigration policies of countries that may compete with the country for sending migrants to a third country also matter.

To complicate matters even further, migration policies of countries are often linked with each other based on reciprocity and multilateral agreements (the EU is one example; temporary visa programs around the world are another).

Policies not directly related to migration affect migration

In a sense, all policies are relevant to migration, because they affect the economic, social, and cultural indicators of the country, and these in turn affect how attractive a destination it is for potential migrants. Some policies more directly affect migrants. For instance, high minimum wage laws might deter migration from places where workers are unlikely to have sufficient skills to get jobs that command the high minimum wage.

A conceptual framework for empirical analysis of migration (part 1: direct empirical measurement)

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This post is part 1 of a series outlining a conceptual framework for the empirical analysis of migration. Read the introductory post to the series here. This post focuses on a particular form of comparison that can be carried out through direct empirical measurement. The questions directly answered this way aren’t the ones we are usually most interested in. But at least these are questions for which we can obtain precise answers in principle. That’s a start.

Questions about how different groups of people compare for a given regime at a given point in time (or over an interval of time) can be answered by direct empirical measurement, at least for existing regimes. They cannot be directly answered for hypothetical regimes. But the fact that they can be answered at all differentiates them from other, more speculative, questions.

(Source country, target country) pairs as the basis of aggregation

The conceptual model we use identifies two attributes of a person: the person’s source country (also known as the sending country, and defined as the country that person was born in) and the person’s target country (also known as the receiving country or recipient country, and defined as the country the person now lives in). For non-migrants, the source and target country coincide. For migrants, the source and target country differ. For every individual, therefore, we can write down a (source country, target country) pair. For instance, somebody born in Mexico who stays in Mexico gets the pair (Mexico,Mexico). Somebody born in Nepal who moves to India gets the pair (Nepal,India). (This is obviously a very crude simplified model, because some people migrate temporarily, some migrate to one country and then to another, etc. But it’s good enough to get us started).

We’re interested in the performance on indicator X both for people who stay put in their countries, and for people with particular (source country, target country) combinations. For instance, we may be interested in asking: how does the (Nepal, India) combination fare on indicator X? Explicitly, that’s asking: how do people who are from Nepal and living in India perform on indicator X?

Mathematical digression: using a matrix representation to store the information

We can use a matrix representation where the rows correspond to source countries and the columns correspond to target countries (both rows and columns should be the same list of countries in the same order for the observations below to hold). The entry in a given cell provides information on indicator X about the collection of people whose source country is the row country and whose target country is the column country.

Let’s explicitly consider the case of three countries. Let’s say country 1 is France, country 2 is Germany, and country 3 is the United Kingdom. The indicator X values for these source and target countries can be codified via a matrix:

$latex \begin{pmatrix} x_{11} & x_{12} & x_{13} \\ x_{21} & x_{22} & x_{23} \\ x_{31} & x_{32} & x_{33} \\\end{pmatrix}$

The nine entries are interpreted as follows:

  • $latex x_{11}$ is the performance on indicator $latex X$ of the people in country 1 (France) who stay in France.
  • $latex x_{12}$ is the performance on indicator $latex X$ of the people who migrate from country 1 (France) to country 2 (Germany).
  • $latex x_{13}$ is the performance on indicator $latex X$ of the people who migrate from country 1 (France) to country 3 (the UK).
  • $latex x_{21}$ is the performance on indicator $latex X$ of the people who migrate from country 2 (Germany) to country 1 (France).
  • $latex x_{22}$ is the performance on indicator $latex X$ of the people in country 2 (Germany) who stay in Germany.
  • $latex x_{23}$ is the performance on indicator $latex X$ of the people who migrate from country 2 (Germany) to country 3 (the UK).
  • $latex x_{31}$ is the performance on indicator $latex X$ of the people who migrate from country 3 (the UK) to country 1 (France).
  • $latex x_{32}$ is the performance on indicator $latex X$ of the people who migrate from country 3 (the UK) to country 2 (Germany).
  • $latex x_{33}$ is the performance on indicator $latex X$ of the people in country 3 (the UK) who stay in the UK.

Note that the entries on the main diagonal (the one from top left to bottom left), namely $latex x_{11}$, $latex x_{22}$, and $latex x_{33}$, correspond to the non-migrants, i.e., the people who stay put in their country. The off-diagonal entries, i.e., the entries $latex x_{ij}, i \ne j$, correspond to migrants. In this case, there are six such entries: $latex x_{12}, x_{13}, x_{21}, x_{23}, x_{31}, x_{32}$.

The three countries in the example above weren’t ordered in any particular way, so there is no significance of an entry being above or below the diagonal. If the countries had been ordered based on some criterion (such as GDP (PPP) per capita), then the entries above and below the diagonal would reflect different types of migration based on whether the sending or receiving country had higher GDP (PPP) per capita.

The simplified example here considers migration between three countries. However, if we want to study migration worldwide, we’d need to include all countries. If there are 200 countries, then we’d have a $latex 200 \times 200$ matrix, with a total of 40,000 entries. In general, if there are $latex n$ countries, the matrix is a $latex n \times n$ matrix with a total of $latex n^2$ entries, of which there are $latex n$ diagonal entries (corresponding to the people who stay put in their respective countries) and $latex n^2 – n = n(n-1)$ off-diagonal entries (corresponding to people who migrate from one country to another). Half of them ($latex n(n – 1)/2$) are above the diagonal. and the other half are below the diagonal, but the above/below distinction is of importance only if the countries are ordered according to some criterion.

Now, there may be cases where migration between two countries is so quantitatively small, or even actually zero, that it’s not meaningful to compute that particular matrix entry. For instance, I think there is zero migration from North Korea to Somalia. So, some entries of the matrix are not defined. This means that we need to be careful if we intend to subject the matrix to techniques of linear algebra. However, we’re using the matrix only to store information, and we don’t perform matrix operations.

End mathematical digression

Totals versus averages

In some cases, we care about the per capita level of an indicator. This is usually the case for indicators such as GDP per capita, crime, or unemployment. In cases where fixed resources are being used up, however, we may care more about the total use. An example may be water use in a country that has a fairly limited water supply. If we’re concerned about total use, then in addition to knowing the per capita value on indicator X for (source country, target country) pairs, we also need to know the size of the population.

The relative size of different populations may matter even if we are concerned only about averages, because we need relative sizes to compute weighted averages.

Weighted averages for residents, naturals, immigrants, and emigrants

In some cases, we are interested not in a particular (source country, target country) combination, but in combining information for all people in a particular source or target country. The following are four typical weighted averages we are interested in. If we are looking at a total of $latex n$ countries, then there are $latex n$ weighted averages of each type (one for each country) and therefore a total of $latex 4n$ weighted averages to consider.

  • The weighted average for all residents of a country, including natives of the country who stay put and migrants from other countries to that country.
  • The weighted average for all naturals of a country, including natives of that country who stay put and people from that country who migrate to other countries.
  • The weighted average for all immigrants to a country, i.e., people who have that as their target country but are from other source countries.
  • The weighted average for all emigrants from a country, i.e., people who have that as their source country but now live in other countries.

Typical forms of comparison

After figuring out how various (source country, target country) combinations, or weighted averages thereof, fare, we can then ask how they compare with one another. Here are some typical questions that can be asked. We’ll use the letter A to denote a hypothetical source country and the letter B to denote a hypothetical target country, but you can replace these with concrete instances (such as France and the United Kingdom):

  1. How do migrants from country A to country B compare with natives of country B (the target country) on indicator X?
  2. How do migrants from country A to country B compare with natives of country A (the source country) on indicator X?
  3. How do migrants to country B compare with resident natives of that country on X?
  4. How do migrants from country A compare with resident natives of that country on X?
  5. How do migrants from country A compare with the natives of the countries they go to on X (combined analysis for all countries they go to)?
  6. How do migrants to country B compare with the natives of their source countries on X (combined analysis for all source countries)?
  7. How do migrants in general compare with non-migrants in general on X?
  8. How do natives of a country receiving migrants compare with natives of a country sending migrants on X? One advantage of this question is that it can be asked without collecting separate statistics on migrants, and can also be asked prior to implementation of migration policies, although the answer might change after implementation of the migration policies.

Mathematical digression: interpretation of the questions in matrix terms

Here is how each of the questions would look like in terms of the matrix representation. For illustrative purposes, we will continue to draw on the three-country setup above with country 1 as France, country 2 as Germany, and country 3 as the United Kingdom.

  1. Compare a matrix entry with the diagonal entry in its column. If we’re interested in studying migration from the UK to France, we compare the entry $latex x_{31}$ (migrants from the UK to France) with the entries $latex x_{11}$ (French natives who stay put).
  2. Compare a matrix entry with the diagonal entry in its row. If we’re interested in studying migration from the UK to France, we compare the entry $latex x_{31}$ (migrants from the UK to France) with the entries $latex x_{33}$ (UK natives who stay put).
  3. Compare the (weighted) average of the off-diagonal entries in a column with the diagonal entry of that column. If we are interested in understanding migration to Germany, we need to compare the entries $latex x_{12}$ and $latex x_{32}$ (migrants from France and the UK to Germany) with $latex x_{22}$ (Germans who stay put). We would usually compute the average of $latex x_{12}$ and $latex x_{32}$ weighted by the respective population sizes.
  4. Compare the (weighted) average of the off-diagonal entries in a row with the diagonal entry of that row. If we are interested in understanding migration from France, we need to compare the entries $latex x_{12}$ and $latex x_{13}$ (migrants from France to Germany and to the UK) with the entry $latex x_{11}$ (French who stay put).
  5. A bunch of pairwise comparisons of the type seen in Question 1 (with pairs in the same column). If we’re interested in figuring out how migrants from France compare with the natives wherever they go. Then, we will compare $latex x_{12}$ with $latex x_{22}$ (comparing French migrants to Germany with Germans who stay put), and separately compare $latex x_{13}$ with $latex x_{33}$ (comparing French migrants to the UK with UK natives who stay put).
  6. A bunch of pairwise comparisons of the type seen in Question 2 (with pairs in the same row). If we’re interested in figuring out how migrants to the UK fare relative to the natives of their source country. Then, we will compare $latex x_{13}$ with $latex x_{11}$ (French who move to the UK versus French who stay put), and separately compare $latex x_{23}$ with $latex x_{22}$ (Germans who move to the UK versus Germans who stay put).
  7. The off-diagonal entries represent migrants, and the diagonal entries represents people who do not migrate. This question therefore involves a comparison of the off-diagonal entries and the diagonal entries.
  8. This compares two diagonal entries. If we’re interested in comparing Germany and the UK, we’ll compare $latex x_{22}$ and $latex x_{33}$.

End mathematical digression

Remarks on selection and treatment effects

We’ll return to this in more depth in part 2, but here are a few preliminary remarks.

The significance of the migration policy regime and other aspects of the scenario (economic policies, economic performance, linguistic differences, etc.) on the indicator matrix is two-fold:

  • A compositional selection effect (for short, we’ll call this a selection effect or a compositional effect) for the groupings, i.e., the choice of the migration policy scenario determines who migrates and who doesn’t, and therefore affects what set of people get included in various (source country, target country) pairs.
  • A treatment effect for the groupings, i.e., some people being able to migrate affects their own performance on indicator X, and also affect the performance on the indicator of others who stay behind in their own countries.

In Part 2, we will look more closely at how to isolate selection and treatment effects when comparing different policy regimes.

Remarks on measurability

For existing policy regimes, the performance on particular indicators of particular (source country, target country) pairs can be computed in principle. Some methods involve complete measurement: for instance, census data that asks people to identify their country of origin, or computerized records of all residents along with their source country. Other methods involve the use of partial data along with sampling techniques to extrapolate to the general population.

Some challenges:

  • In some cases, there is ambiguity, both conceptual and empirical, on the source country of individuals, or on what it means to be a resident (for instance, do we count crimes by tourists?)
  • In some cases, people deliberately conceal or misrepresent information about themselves where the stakes are high. For instance, a foreign-born person may claim to be a native-born when arrested for a misdemeanor, in order to avoid deportation. On the other hand, those who prefer deportation to another country to spending time in prison may misrepresent themselves as foreign-born. People may lie to get access to welfare benefits. False identity documentation may be produced in order to be eligible to work.
  • In some cases, the population involved is so small that the indicator cannot be measured from small samples of the overall population. For instance, there are about 100 people in the US who were born in North Korea. A random sample would probably not pick any of them. Even if it did, statistical averages for the population would not be robust.
  • There are challenges when considering the comparability of indicators across different target countries (and in some cases even within a particular country), because different countries (and different jurisdictions within a country) use different protocols for measurement and have different sources of bias. For instance, the rate of crime reporting may differ considerably between countries, particularly for rape and minor theft. Similarly, when comparing income values, purchasing power parity estimates are not necessarily reliable.

Normative significance of comparisons

The measurements and comparisons here offer only a starting point for investigating the effects of migration: we’d need comparative statics between different regimes in order to tease out the effects of migration. We’ll talk about this more in part 2 and in part 3. But in many cases, our only reliable empirical measurements are the direct ones discussed here, and people often draw conclusions based on this evidence. The following are three typical styles of crude conclusion people draw.

  • Immigrants to country B do better (respectively, worse) on the indicator than natives of country B who stay in their country $latex \implies$ immigration “good” (respectively, “bad”) for country B.
  • Emigrants from country A do better (respectively, worse) on the indicator than natives of country A $latex \implies$ emigration “bad” (cf. brain drain) (respectively, “good”) for country A.
  • Natives of country A worse on the indicator than natives of country B $latex \implies$ Migration from country A to country B good for country A and bad for country B.

Of course, put so bluntly, the claims seem obviously ill-substantiated, and they often break down in practice.

But apart from the need to do more sophisticated counterfactual analysis to actually talk about the effects of migration, there’s another important point: the overall levels of an indicator might matter more than how different groups compare on it. The relative crime rates of natives and migrants are not as important as knowing whether either group has a high crime rate. The relative fertility rates are similarly less important than the overall fertility level. Too much focus on the question of “are immigrants better than natives?” can lead us to ignore other questions of greater moral and practical relevance.

A conceptual framework for empirical analysis of migration (introduction)

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Is migration good or bad for indicator X (here, X could be wages, employment levels, self-reported happiness, crime, welfare state use, moral virtue, etc.)? The question, as posed, is ill-defined. The ambiguity could arise from different meanings or interpretations of indicator X. But there’s also considerable ambiguity in the “Is migration good or bad” part of the question. Good or bad for whom? Compared to what?

I was moved to write this post series after an aborted attempt at trying to synthesize what different people had said about the effects of migration. Often, the people were talking past each other, measuring slightly different things. There’s a question of what we should measure, i.e., what measurement is the most appropriate one. But a first step is knowing that it’s possible to be measuring many different things. This post series attempts to clarify the range of things one could be measuring and how they relate to one another.

The series is structured as follows:

  1. Part 1: direct empirical measurement focuses on something that can be computed through direct empirical measurement: the performance of people who stay put in their countries, and the performance of people who migrate from one country to another.
  2. Part 2: comparative statics, multiple matrices discusses how to compare different policy regimes or scenarios for migration. Such comparisons typically involve counterfactuals and cannot be settled completely by empirical data: we need a model, and there’s considerable model uncertainty even if the data is excellent.
  3. Part 3: simplified model assuming no changes to non-migrants considers a simplified situation where we assume that migration at the margin primarily affects migrants and not the natives of either sending or receiving countries. The question is then about how migrants fare relative to the counterfactual where they are not allowed to, or were unable to, migrate. We’ll consider rank-ordering and quantitative comparison of natives of the source country, natives of the target country, potential migrants if they can migrate, and potential migrants if they cannot migrate.
  4. Part 4: Models for migrant performance considers different models for how migrant performance might be predictable in terms of the performance of the source and target countries.
  5. Part 5 discusses the descendants of migrants, and in particular the interaction with diaspora dynamics.
  6. Part 6 wraps up by considering some subtleties that were omitted in the preceding discussion.

The series also includes a number of minor mathematical digressions. If you have a reasonable background in mathematics (up to basic calculus and linear algebra, to the level generally needed for social scientists) you should be able to follow these. But you can otherwise skip the mathematical digressions without loss of continuity.

Choice of analytical focus

Despite the bewildering array of possibilities we’ll consider, there’s a high chance that the model used in the series will remain wanting. We’ll defer a detailed (but still partial!) discussion of the shortcomings to part 6, but a few preliminary remarks might be helpful.

A broad remark worth making is that the analysis in the coming parts will focus heavily on the migrant’s country of origin/birth (the “source country”) as well as the migrant’s country of current residence (the “target country”). We’ll also consider the distinction between migrants and non-migrants. This suggests that there are only three important components to the person’s identity that carry importance in statistical aggregation: the source country, the target country, and whether the person is a migrant. While other attributes can vary, we’re not interested in using them as the basis for grouping, since we’re aggregating over them.

But the reality is more complicated. Religious and ethnic identities can be subnational or supranational. Lebanese Muslims and Lebanese Christians may be best viewed as separate groups (though there are some cultural similarities and they’re probably genetically close to identical). In the United States, the Native Americans (American Indians) may be better viewed as a separate subgroup. On the other hand, sometimes it may be better to consider ethnic or ideological groupings that cut across national lines, such as Scandinavian, Western European, Anglo-American, Arab Muslim, Sunni Mulim, Shia Muslim, sub-Saharan African, Hindu, or ethnically Chinese.

The reason for our singular focus on nationality is simply that immigration law as it currently stands gives extreme importance to national boundaries and national membership. It may be ironic that, on a website devoted to critiquing the existing global regime of borders and migration controls, and the rigidity of national identity enforced by laws, a series of blog posts so meekly follows the status quo. My only excuse is that one needs to start somewhere. But you should feel free to fill in your own variations of the ideas based on forms of identity that do not coincide with one’s place of birth and one’s current residence, rather than wait to get to part 6.

Where’s the data?

As I go over different aspects of the model, you might be tempted to ask: can one actually construct the data that’s needed to do the quantitative comparisons and answer the various questions I pose? Data does exist for some things but not for others. The data for the model discussed in part 1 is relatively good. For the model in part 2, there is considerable model uncertainty, so rather than standardized data, we generally have to rely on individual pieces of research that attack specific instances. Often, the absence of data will illustrate the underlying point, namely, that obtaining clear answers to some questions is hard. It’s best to view this conceptual framework more as a tool to encourage clear thinking than as something in which we can plug in numerical values and answer questions.

If you’re interested in learning about existing data sets on migration, take a look at the migration information web resources page on this website.