Tag Archives: Carl Shulman

Make More Singapores!

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I have advocated the DRITI policy– instead of coercively restricting migration, tax it and use the proceeds to compensate natives– for years, and Principles of a Free Society was written, if you like to think of it that way, as a political philosophy suited to undergird DRITI policies. By now, this has become a standard part of the case for open borders. Thus, in “Meant for Each Other: Open Borders and Western Civilization,” Bryan Caplan writes:

Still worried [about open borders undermining natives’ wages]?  There’s a cheaper and more humane remedy than keeping foreigners out: Charge them an admission fee or surtax, then use the proceeds to help displaced native workers.

Which is the same idea as DRITI. Not that I’m blaming Caplan for borrowing the idea from me without acknowledgment. Caplan knows I advocate migration taxes. He even wrote one of the blurbs for Principles of a Free Society. But Gary Becker proposed a migration tax to the IEA back in 2010. Actually, that was four years after I published the idea, but Becker had written about it before, in an op-ed in the 1980s. Recently, at the APEE conference in Las Vegas last April, Richard Vedder also proposed a migration tax. The idea is really too obvious to quibble over its paternity. It’s a simple cross-application of the standard free-trade advocacy truism– “yes, free trade has winners and losers, but tax the winners to compensate the losers”– to migration policy. I’m not sure whether Caplan got the idea from me. I’m pretty sure that if he hadn’t got it from someone, he’d easily have thought it up on his own. It’s a no-brainer for economists, but somehow policymakers are blind to it. Or so I thought.

What I didn’t realize is that Singapore already has something close to a DRITI policy in place, as Carl Shulman reports. From 1970 to 2010, the number of foreign workers in Singapore rose from just over 20,000 to more than a million, a third of the labor force, and still rising. Most of these are not the high-skilled workers that OECD democracies tend to privilege. Nearly a million are here on low-skilled “work permits,” including foreign domestic workers (214,500) or construction workers (319,100). How is the number of immigrants regulated? First, by Foreign Worker Levies, i.e., migration taxes, a policy variable. There are also Dependency Ceilings, maximum foreign shares of an employer’s workforce. Shulman doesn’t say how often these are binding, but they seem liberal, e.g., 87.5% of construction crews can be foreign. And how much do migration taxes raise?

Levies for unskilled workers range from $3,600-$9,000 per annum. With around a million workers subject to levies, the proceeds to Singapore should be in the billions of dollars (all figures are Singaporean dollars, about 0.8 $USD each).

Total levies collected amounted to $1.9 billion in 2010 and $2.5 billion in 2011. Total government operating revenues in 2010 were $45.5 billion and $50.5 billion for 2011, so worker levies alone accounted for 4-5% of operating government revenue. Since then the migrant population has grown substantially and levies have been hiked, by a third or more in many cases, with further increases scheduled, so the current percentage is likely significantly higher. Even so, the figure will be small compared to the economy, because low-skill workers contribute disproportionately little to economic output, but is high as a proportion of compensation costs. While Singaporean natives with such low incomes would pay no income tax, the top levy rates for unskilled workers (charged to employers) can be half or more of wages.

In short, while we can quibble about details, Singapore more or less understands and is applying and applies the citizenist case for open borders. Singaporeans benefit from cheap cleaners, bus drivers, and domestic workers. And they don’t just benefit themselves; foreign workers also gain opportunities. As Vipul Naik pointed out in conversation, if all developed countries adopted policies like this, migrant wages and wages in migrant source countries would be competed up. I might argue for slightly different moral side-constraints (e.g., I wouldn’t endorse deportation of pregnant women), but it would be a great thing for both economic development and freedom if other developed countries followed Singapore’s lead.

Lastly, a more general point. Singapore is amazing. Every time I read about policy in Singapore, I find myself involuntarily thinking Wow! This is the most enlightened regime on earth. Perhaps that’s a slight exaggeration, but few would deny that Singapore is a spectacular success. So why don’t we make more of them? Much of the key to Singapore’s success seems to be simply that it’s a sovereign city-state. In general, sovereign city-states make wildly disproportionate contributions to civilization. Think of the city-states of ancient Greece, Athens and Sparta and Corinth, birthplaces of philosophy, history, science, and democracy. Or the city-states of Renaissance Italy– Venice, Florence, Genoa– which were also gloriously accomplished in arts and letters and sciences. Today, Hong Kong has a kind of partial sovereignty. It, too, is a dazzling success, whose success has spilled over in a massive way. Hong Kong has been a major catalyst for China’s economic take-off. Yet, perversely, we have established a system of international sovereignty which makes it impossible to found new sovereign city-states. We need charter cities.

Implications of embracing low-end estimates of the global economic impact of open borders

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Carl Shulman’s recent post on upward and downward biases in the double world GDP estimates, as well as Nathan’s (forthcoming) post proposing his own model, led me to start thinking about the extent to which the case for open borders is tied to uncertain claims about the economic effects, and whether pushing the “double world GDP” idea as a slogan is epistemically unsound.

One can make a case that high estimates of the global effect of open borders have value in getting people initially interested in the subject, and that once they’re sufficiently invested they will not change their mind even if they learn that the estimates were too high. Indeed, something similar seems to have happened with the growth of the effective altruism movement: initial estimates of the money you needed to donate to save a life were in the range of a few hundred dollars, whereas current estimates are over 2000 dollars, and still rising. Jacob Steinhardt described this in his critique of effective altruism:

The history of effective altruism is littered with over-confident claims, many of which have later turned out to be false. In 2009, Peter Singer claimed that you could save a life for $200 (and many others repeated his claim). While the number was already questionable at the time, by 2011 we discovered that the number was completely off. Now new numbers were thrown around: from numbers still in the hundreds of dollars (GWWC’s estimate for SCI, which was later shown to be flawed) up to $1600 (GiveWell’s estimate for AMF, which GiveWell itself expected to go up, and which indeed did go up). These numbers were often cited without caveats, as well as other claims such as that the effectiveness of charities can vary by a factor of 1,000. How many people citing these numbers understood the process that generated them, or the high degree of uncertainty surrounding them, or the inaccuracy of past estimates? How many would have pointed out that saying that charities vary by a factor of 1,000 in effectiveness is by itself not very helpful, and is more a statement about how bad the bottom end is than how good the top end is?

People who may have been attracted initially by the lowball estimates have generally tended not to leave, perhaps due to an endowment effect or status quo bias, or because they found more compelling and robust reasons to stay once they became effective altruists.

On the other hand, “too-good-to-be-true” estimates can also make it difficult to get people to take the cause seriously in the first place, and can also lead to people getting disillusioned once they realize the estimates won’t work, thereby throwing out the proverbial baby with the bathwater. As Carl Shulman wrote in the context of effective altruism estimates:

Mainly, I think it’s bad news for probably mistaken estimates to spread, and then disillusion the readers or make the writers look biased. If people interested in effective philanthropy go around trumpeting likely wrong (over-optimistic) figures and don’t correct them, then the community’s credibility will fall, and bad models and epistemic practices may be strengthened. This is why GiveWell goes ballistic on people who go around quoting its old cost-effectiveness estimates rather than more recent ones (revisions tend to be towards less cost-effectiveness).

I have listed above some of the strategic pros and cons of embracing overly optimistic estimates, but I am personally more interested in the epistemic question of the extent to which the case for open borders, or for migration liberalization in general, hinges on the magnitude of the estimates, and what a reasonable case for open borders, and for open borders advocacy, might be in the lowball scenario.

Let’s first look at the lowball scenario. Here is a back-of-the-envelope calculation that Clemens does in his literature review paper (Pages 84-85) (emphasis mine):

Should these large estimated gains from an expansion of international migration outrage our economic intuition, or after some consideration, are they at least plausible? We can check these calculations on the back of the metaphorical envelope. Divide the world into a “rich” region, where one billion people earn $30,000 per year, and a “poor” region, where six billion earn $5,000 per year. Suppose emigrants from the poor region have lower productivity, so each gains just 60 percent of the simple earnings gap upon emigrating—that is, $15,000 per year. This marginal gain shrinks as emigration proceeds, so suppose that the average gain is just $7,500 per year.
If half the population of the poor region emigrates, migrants would gain $23 trillion—which is 38 percent of global GDP. For nonmigrants, the outcome of such a wave of migration would have complicated effects: presumably, average wages would rise in the poor region and fall in the rich region, while returns to capital rise in the rich region and fall in the poor region. The net effect of these other changes could theoretically be negative, zero, or positive. But when combining these factors with the gains to migrants, we might plausibly imagine overall gains of 20–60 percent of global GDP.

This 20-60% comes under assumptions that I think would seem reasonable to many critics of migration. For instance, it largely accords with the assumption of no closing of the skills gap between migrants and natives. Also, it doesn’t consider the long term (the children of migrants getting better education and therefore having more human capital than their counterfactuals in the home country). So it does not rely on beliefs about the closing of skill level and achievement gaps, which are controversial among many critics of migration. In particular, if you believe in intergenerational persistence of these gaps, the above estimation exercise should still seem reasonable to you. The only thing the above doesn’t account for is a radical form of killing the goose that lays the golden eggs (note that the lower end of the 20-60% estimate already accounts for moderate forms of goose-killing, as the original point estimate is 38%). So, setting such radical goose-killing aside for now as an important possibility worth separate investigation, let’s look at the 20-60% estimate. What would it mean?

The pessimistic end of the estimate, 20%, is still more than three times the total of the highest literature estimate of the gains from removing trade barriers and the gains from removing barriers to capital mobility (4.1% + 1.7% = 5.8%) among the papers cited by Clemens. So, free labor mobility still has higher upside — even with these pessimistic assumptions — than free trade.

But even though there’s bigger upside, the margin isn’t that huge. If you had originally believed that open borders would double world GDP, but you then revised the estimate downward to 20%, that would mean that the extent to which open borders advocacy is a compelling cause would reduce, ceteris paribus. However, there are a few countervailing considerations, even if you embrace the lowball estimate.

To help explain this, let me look at my Drake equation-like estimate of the social value of open borders advocacy. I expressed the value as a product:

$latex \text{Utility of a particular form of open borders advocacy} = Wxyz$

Here:

  • $latex W$ is the naive estimate of the gains from complete open borders (using, for instance, the double world GDP ballpark).
  • $latex x$ is a fudge factor to represent the idea that “things rarely turn out as well as we expect them to.” If we set $latex x = 0.1$, for instance, that’s tantamount to saying that, due to all the numerous problems that our naive models fail to account for, the actual gains from open borders would be only 10% of the advertised gains. The product so far, namely $latex Wx$, describes what we really expect the gains from open borders to be.
  • $latex y$ is the fraction to which the world can realistically move in the direction of open borders. The product $latex Wxy$ is total expected gain from however far one can realistically move in the open borders direction.
  • $latex z$ is the extent to which a particular effort at advocacy or discussions moves the world toward open borders, as a fraction of what is realistically possible. For instance, setting $latex z = 10^{-4}$ for Open Borders the website would mean that the creation of the website, and work on the website, has moved the world 1/10,000 of the way it feasibly could in the direction of open borders.

Now, note that we have at least two ways that a decline in $latex W$ might be compensated for:

  • Compensating increase in $latex x$: This would be tricky to argue, because we need to show that our current belief of how realistically our new model accounts for stuff is better than our past belief of how the old model accounted for stuff. In other words, if our original estimate of $latex x$ was based on the knowledge that that model is as crude as it turns out to be, then when we adjust $latex W$ downward and thus make our model realistic, we can compensatingly adjust $latex x$ upward. The effects would approximately, though not exactly, cancel out.
  • Compensating increase in $latex y$: In the specific case at hand, in fact, these arguments do apply. The main source of overestimation in the models predicting huge gains in world GDP is the large number of people that would need to move. Adjusting those numbers alone would get us in the 20-60% range. But, to the extent that this is true, the fraction in which we can move in the direction of open borders might also increase: if open borders involves “only” 300 million people moving instead of 3 billion, then allowing 30 million people to move moves us 10% (0.1) of the way to open borders, rather than 1% (0.01). Again, whether or not $latex y$ gets compensated in practice depends on whether we were aware a priori of the large numbers of people that the model needs to move — if we weren’t, then the adjustment might not happen.
  • Compensating increase in $latex z$: If open borders, or partial moves in that direction, aren’t as radical as they seemed, maybe partial advocacy efforts in that direction are more likely to move us toward them. We should be careful not to double-count this against $latex y$, though — if we’ve already made the adjustment for $latex y$, we probably don’t need to make the adjustment for $latex z$.

A couple of additional notes:

  • All the estimates ($latex x$, $latex y$, and $latex z$) are highly speculative. Combined with the fact that these estimates are related to our estimates for $latex W$ and the methods we used to arrive at those estimates, there’s a lot of room for fudging and very little that can be said conclusively. The order of magnitude of decline in our gain estimate (from 100% of current world GDP to 20%) is only a decline by a factor of five, so our estimates of utility go down by only one order of magnitude, whereas the range of uncertainty is about three orders of magnitude (the range I gave in the original blog post for the Open Borders website was $50,000 -$50,000,000).
  • That said, it would be surprising if the decline in $latex W$ were accompanied by no decline in the overall utility of the form of open borders advocacy. That could happen, based on the considerations listed above, but we should have a prior against it happening. Remember the one-penny proof whenever you’re tempted to believe that a specific change in the estimate of one value will not affect the estimate of another value that it is in general related to.

UPDATE: Diaspora dynamics might reconcile low short-run estimates of how many would move with large long-run estimates of the same. For more, see here.

Doubling world GDP versus doubling utility: a technical note

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Carl Shulman, one of the most impressive people I know, pointed me to a blog post he’d written a couple of weeks ago titled Turning log-consumption into a measure of short-run human welfare. Carl brought to my attention that a passage in my recent post titled how far are we from open borders?, used ambiguous language. Specifically, he pointed out that the passage:

These same estimates also suggest that much of the gain in production – and consumption – would be experienced by the world’s currently poorest people, leading to a significant reduction in, and perhaps an elimination of, world poverty. If we take utility to grow logarithmically with income, then this distributional aspect argues even more strongly in favor of the idea that open borders would increase global utility tremendously.

might suggest that I’m saying that taking utility as logarithmic points in the direction of the proportional gain in utility being higher than the proportional gain in world GDP. That was not my goal. Rather, my goal was to say that, if we take utility as the sum of logarithms of incomes, then for a given gain in world GDP, the gain in global utility resulting from that gain in world GDP would be higher if inequality was also reduced than if it wasn’t. Explicitly, having the poor’s income increase four-fold and the rich’s income stay the same, with overall GDP doubling, would give a higher utility gain than having everybody’s income double.

That’s the quick clarification. But Carl’s post raises a number of other points about the use of logarithms for considering utility, and I want to talk a bit more about some of the issues raised. The upshot, based on my reading, is that the considerations Carl raises point in favor of life-saving interventions (such as combating malaria) over interventions (such as open borders) that improve the quality of life of an existing population. But within the class of interventions that improve the quality of life of an existing population, the relative value of open borders to other interventions is not affected by the considerations Carl raises. Note also that the calculations in Carl’s original post explicitly adopt a short-run perspective, although he is elsewhere on the record stating that long-run considerations should dominate. Finally, population ethics is a fraught subject and there are a large number of issues that are somewhat related to this blog post that I do not get into, such as the question of how to value the potential existence of nonexistent people. See Nick Beckstead’s Ph.D. thesis for a detailed discussion of the far future and a summary of the philosophical literature on population ethics.

The rest of the post is fairly technical — following it properly requires a basic knowledge of calculus-level mathematics, though you can skip the quantitative statements and just consider the verbal statements.

I will consider six cases of progressively increasing complexity.

Case 1a: If you just have one person: taking logarithms is a monotone transformation that translates ratios into differences

Let’s begin with the case that we’re looking at just one person’s income. We want to understand, roughly, how the person’s “utility” grows with his or her income. We know that the greater the person’s income, the higher the person’s utility. In other words, utility is an increasing function of income. This in and of itself is good enough to tell us whether a given change in income leads to an increase or decrease in utility. What it doesn’t do is allow us to compare different changes in income with different starting and ending points. In other words, simply knowing that utility goes up with income says that income can be used as an ordinal scale for utility, but doesn’t allow us to answer questions such as: would increasing income from $10,000 to $11,000 matter more or increasing income from $100,000 to $101,000?

The assumption that utility grows logarithmically with income is an assumption that allows us to make cardinal comparisons between different changes in incomes. If we take utility to be logarithmic in income, then the increase in utility is the logarithm of the ratio of the final income by the initial income. This allows us to now meaningfully say that increasing income from $10,000 to $11,000 results in a bigger utility gain than increasing income from $100,000 to $101,000, because the ratio in the former case (1.1) exceeds that in the latter case (1.01). Note that we don’t need to take logarithms to answer the question of what gain is greater: we can just compare the ratios themselves.

The logarithm function is concave down, i.e., its second derivative is negative, so the average of the logarithms is less than the logarithm of the average. In other words, the gain in the logarithm for a given absolute gain in income is greater at lower income levels than at higher income levels. This can also be seen directly in terms of ratios as above: a $1,000 gain from $10,000 to $11,000 is larger as a proportional gain than a $1,000 gain the same absolute gain value) from $100,000 to $101,000.

There are two parameters to choose when setting up the logarithm-taking process, both of which are irrelevant for our purpose of comparing utility gains:

  1. The base to which logarithms are taken. Changing the base of logarithm from one value to another corresponds to a scaling transformation.
  2. The choice of “1” for income when taking logarithms, or equivalently, the choice of “0” for after taking logarithms, i.e., the income level whose logarithm we take to be zero. Changing this corresponds to a translation of the logarithmic scale, i.e., a change in the origin point.

Both choices are irrelevant for our main purpose: (2) is irrelevant because we are always looking at differences between points on the scale, so the location of the origin does not matter. (1) is irrelevant because we are comparing the differences with each other, not looking at their absolute magnitudes (in the same way as switching from meters to feet for length measurement will not change any of our fundamental analysis). (Technical note: We do need to impose the condition that the base of logarithms be greater than 1 for the analysis to hold, otherwise the scaling factor becomes negative and everything gets messed up).

A technical way of framing this is that we are treating the logarithm of income as an interval scale, i.e., a scale where it’s permissible to compare and take ratios of differences, but there is no natural zero, so it does not make sense to “double” a particular value of logarithm of income, nor does it make sense to add two values of logarithm of income. We can add, scalar-multiply, and take ratios between differences between logarithms of incomes, as these operations are invariant under the choice of origin. This is similar to how we treat temperature in practice: it does not make sense to add two temperatures and double a temperature, but we can perform the operations meaningfully on temperature differences.

However, once you delve deeper into physics, you discover that temperature actually does have an absolute zero and therefore can be measured on a ratio scale (the Kelvin scale being the standard choice in the case of temperature). If expressed in that scale, temperatures can legitimately be added and multiplied by scalars. Does there exist a similar natural choice of “absolute zero” for the logarithm of income? Not quite, but sort of. We now turn to some reasons for looking for such a zero.

Case 1b: Interpersonal utility comparisons

Let’s now consider the situation of comparing two people. We make the assumption not only that the utility functions of both are logarithmic in income, but also that the base of logarithms is the same. With these assumptions, we can compare an income change for one person to an income change for the other. If we also set an absolute zero for the log-income scale (i.e., a unit value for the income) for both the people (we could choose it to be the same for both) then we can also compare the income levels of the two people.

Case 1c: Considering the problem of zero income and non-existence

As income approaches zero, its logarithm approaches $latex -\infty$ (negative infinity). If we approximate death as switching to an income level of zero, then being dead corresponds to having a utility of negative infinity. This can pose problems when computing expected utilities in situations where there is a nonzero probability of death. Carl Shulman describes a standard way to get around the problem in his post. Explicitly, he suggests taking “subsistence income” as the absolute “1” for income, but with a twist: add a constant for the value of existing. Carl defines utility for a dead or non-existent person as 0, and utility for a living person as:

$latex s + \log(\text{income}) – \log(\text{subsistence income})$

where $latex s$ is the value of existence, and $latex \log(\text{income}) – \log(\text{subsistence income})$ is the additional value accrued from having income above the subsistence level. With this model, the effective “1” for income would be (subsistence income)/$latex b^s$ (where $latex b$ is the base of logarithms). People whose incomes are lower than that value have a negative value of existing. But in practice, we choose subsistence income and $latex s$ in a manner that nobody falls below subsistence income, let alone below (subsistence income)/$latex b^s$.

Once we have set up utility as a ratio scale as above, it makes sense to talk of the proportional change in utility. In particular, it makes sense to ask whether a given change in income causes utility to double, or more than double, or less. The answer to that would depend on the value of existence ($latex s$) and also on how far above subsistence income the person under consideration is. However, for reasonable choices, doubling income will lead to far less than a doubling of utility.

For instance, suppose we chose $latex b = 2$ as the base of logarithms, take subsistence income as $1/day, and take the existence value as $latex s = 5$. In this case, doubling income from $2/day to $4/day increases utility from 6 to 7, which is far less than doubling. Doubling income from $32/day to $64/day has an even smaller effect in terms of proportional utility gains: utility goes up from 10 to 11. (Technical aside: considering proportional gains in log-income is tantamount to taking differences of log-log-income.)

In fact, for a reasonably high choice of existence value, any change to the situation of an already existing person pales relative to a change that affects whether or not the person exists, such as birth and death. We’ll get back to this point once we consider the issue at the population level.

Case 2a: Getting multiple people into the picture, but abstracting away from the problem of people dying

We began by dealing with just one person who can earn different incomes, and then moved on to interpersonal utility comparisons, and also considered the possibility of death or non-existence, as well as . Let’s ignore the problem of death or non-existence right now, and consider a fixed population with more than one person.

The goal is to consider two different income configurations for this population, and compare them to find out which one is better. Now, at the individual level, the knowledge that utility is increasing in income was enough to say which of two income levels is greater, and the logarithmic assumption was necessary only to answer the question of how differences compared. However, in order to effectively aggregate the individual data, we do need to use a cardinal scale. In this case, since utility is assumed to be logarithmic in income, the “total utility” is the sum of all log-income values. We can then compare these totals across different configurations. Note that this case relies, albeit indirectly, on our being able to execute interpersonal utility gain comparisons (the case 1c above), and that reliance is reflected in our choice of using the same base for logarithms for all members of the population.

Now, although we are taking the sum, we are still using only the interval scale properties, and in particular, the location of the zero does not matter. This is because we are adding the same number of terms (corresponding to the members of the population) in all configurations. If we shift the location of the “zero” then that affects our “sum of log-incomes” for all configurations by an equal amount. Perhaps a better way to think than the sum is the average, for which the conclusion is clearer.

If open borders were to double every individual’s income, it would increase the average value of log-income by $latex \log 2$ and it would increase total utility by $latex \log 2$ times the population size. If, however, open borders doubled world GDP with its effect concentrated on people with low incomes, it would increase the average value of log-income by more than $latex \log 2$ (this follows from the remarks made in the discussion of Case 1a about the logarithm function being concave down).

The above is the situation that I consider by default and that is the context in which the quoted passage from my earlier post was written.

Case 2b: Comparing different populations

The remarks above continue to apply to the case of comparing improvements for different populations, including populations of different sizes, with the following catch: unless we fix an absolute zero, we can only compare changes in one population with changes in the other population. We cannot compare the absolute level of one population against the absolute level of the other, except in the following cases:

  • If both populations have the same size, we can compare absolute utility levels for the populations with one another assuming they have the same absolute zero, but we do not need to specify this absolute zero.
  • If the populations have different sizes, we need to specify absolute zeros for both populations in order to compare their absolute utility levels.

Case 2c: How the absolute zero allows us to compare absolute levels for different populations and introduce the possibility of death

If we embrace the model used by Carl described in Case 1c, we can tackle both the situation of comparing different populations and dealing with the problem of a nonzero probability of death. In this context, it actually does make sense to ask questions such as:

  • What is the ratio of the utility levels of two different populations?
  • What is the ratio of the utility levels of two different configurations for the same population?
  • What is the expected utility gain for a population in a scenario (where there is a nonzero probability of death or new births in the population)?

In this context, therefore, it makes sense to ask whether doubling world GDP would indeed double utility. The general answer would be that it falls far short of doing so, even if the gains are concentrated on the poorest people. This is for the reasons already discussed in Case 1c: for any individual, doubling income has a far smaller effect on utility than doubling. Note that the gains being concentrated on the poorest people still has more effect on utility than having the gains evenly distributed or concentrated on the richest people, but that “more” still isn’t sufficient to cause doubling.

The main reason why open borders gets to look a lot worse from the point of view of the ratios of utility levels than otherwise is simply that existence itself carries huge value, and open borders, by simply moving people, doesn’t add value commensurate with the value of existing. But this argument applies generally: for a reasonably high estimate of the value of existence, any measure that makes an existing population somewhat better (without increasing births or eliminating deaths) would look a lot worse than a measure that increased the size of the population. In fact, from the existence value perspective, there’s very little that’s more promising than pro-natalism and mortality reduction — which could range from combating malaria to ending aging. Thus, the comparative analysis of open borders with most other typically considered interventions, except interventions that directly and significantly affect mortality, still points strongly in favor of open borders.

With that said, it’s worth noting that it’s likely that, by reducing poverty, open borders would reduce child mortality and increase life expectancy worldwide, and therefore could also increase global utility more greatly through that channel than the GDP estimates indicate. Also, the effects on global population growth would need to be considered: it’s likely that open borders would lead to a short-term increase in population (as first-generation migrants resemble the fertility rates of their source countries but would have lower mortality rates), meaning a significant gain in utility, but within a few generations, migrants would assimilate to native fertility rates, which may well mean lower world population than in the status quo scenario. The effect of open borders on long-term demographic trends is an important topic but is outside the scope of the current post.