The Empirics of the Places We Go Part I: Economic Effects of Immigration

Congratulations!

Today is your day.

You're off to Great Places!

You're off and away!

You have brains in your head.

You have feet in your shoes.

You can steer yourself any direction you choose.

You're on your own. And you know what you know.

And you are the guy who'll decide where to go.
 

Oh the Places You’ll Go by Dr Seuss

Believe it or not, this post was supposed to be about terrorism. But as I began writing, I realised that I couldn’t make any policy prescriptions on the basis of the link between immigration and terrorism without considering the wider effects. This is a post about immigration and why, at least for now, we should keep yelling for more immigration. I cannot provide a figure and I don’t know when we’ll have to stop (and we may well have to stop one day), but we are nowhere near.  

I have become less pro-immigration as I have written this post. I don’t want to give the wrong idea, compared to the average person, I’m probably extremely pro-immigration but I’ve definitely become less certain and more restrictive than I thought I was. I’ve read quite a lot for this post but if you feel I’ve missed anything, please let me know.  Here is a structure of the essay below:

1. Shunning Sylvester McMonkey McBean: Economic Effects of Immigration  
2. The Predicaments of a (Douglas) North-Going Zax: Institutional Effects of Immigration
3. The Honourable Fifth Column: Crime, Terrorism and Immigration
4. Defining the Priors of Star Bellied Sneeches: The (Tenuous) Compatibility of Liberal Nationalism and Utilitarianism
5. How To Tell a Klotz from a Glotz: A Conclusion on Public Policy and Uncertainty

This series will likely be in two or three parts, I’m not sure how the above will be divided exactly yet. There are end notes which aren’t integral to the overall argument at the bottom of the post so feel free to ignore those (in this part they cover irrelevant things like a non-nationalist justification for the existence of State of Israel, the signalling model of education, globalisation’s effect on workers in high-wage countries and voting Leave, and the Chinese hukou system). Section 1 does not cover institutional arguments that could affect economic output, this will be handled in section 2.

Thanks to Sam for giving me some thoughtful comments on an early draft of this post. Any mistakes are his, and his alone.

1. Shunning Sylvester McMonkey McBean: Economic Effects of Immigration  

It is a truth universally acknowledged that the British public must be in want of less immigration, a fundamental value of 21st century Britain:  

There are four reasons why this truth should not be overstated – and, in my view, should not be heeded. First, it’s important to decompose the results. According to a recent Ipsos Mori poll, when you ask whether immigration has affected them personally, a slim majority (51%) say no effect and further quarter say it’s affected them positively. As Chris Dillow notes, ‘whilst most people think immigration is a national problem, they don't believe it to be one in their own area.’

Moreover, the areas with the highest levels of immigration, are the most pro-immigration (see graph above from Markaki (2012)). The less experience you have of immigration, the more you are likely to oppose it. This is not caused by white flight (i.e., white people who don’t like immigrants moving away from high immigration areas)[1]  

Second is that the reasons people are anti-immigration matter – especially if, like me, you buy into the Burkean trustee model of representation. The table below shows the results from a recent ComRes poll:

This interesting not least because it is strong evidence that anti-immigration views are based on the economy, rather than perceived socio-cultural changes or racist concerns.[2] 68% of people are not willing to reduce their income at all even if meant reduced immigration. It also means that, just like swathes of people in favour of nationalisation, if you buy into the trustee model of representation, MPs can legitimately ignore their views if immigration is actually good for the economy.

This is linked to the point that people may be anti-immigration because theyre wrong about the economic effects. Indeed, when people are made aware of the benefits, support for immigration rises as noted by The Guardian:

A YouGov poll found that the proportion of those questioned who viewed immigration negatively dropped from 63% to 54% after they were told that the government's financial watchdog believes that higher immigration will help the economy grow and ease pressure to cut spending…

YouGov found that, after being told the "full facts", the proportion of respondents holding positive views about net migration grew from 32% to 39%. The numbers wanting an end to all immigration dropped from 16% to 12%, and those advocating a small number of skilled migrants fell from 47% to 42%.

Third, we shouldn’t underestimate the value of the trade-off. The public may not like immigration, but they may vote for it because of the other things it brings. Brexit has given us an opportunity to see an example of this.[3] Despite high levels of opposition to more immigration, people are willing to forego this in exchange for access to the single market (source, see also here and here):

Fourth, relates to the perception of numbers. Sam noted a study which showed that ‘most people know very little about the extent and direction of income inequality in their societies, or where they fit in to the income distribution.’ Sam went onto argue that, as a result, ‘we can only solve [this problem] by making people feel less unequal – not by making them less unequal in fact’ because perceptions are unhinged from reality. I think it’s a good argument and it can be applied to immigration.

We can see from the graph above (source) above that in the UK, the perception is that just over 30% of the population are immigrants. The reality is that around 12% are. The perceptions are far from reality – given that immigration curbs may be costly and we cannot rely on people’s perceptions about the levels of immigration, we need not give premium to people’s preferences – especially given the aforementioned three points.[4] A more PC argument that Sam might make is that we need to inform people more about immigration levels – either way, the lust for lower immigration doesn’t carry much weight with me.

1.1 Domestic Economic Impact

I’m not convinced that there is a good economic case against high levels of immigration. For an overview of why, Lisenkova et al (2013) look at what would happened if we really did reduce migration to the “tens of thousands.” Their model splits people up into foreign born and native born. These two groups typically have different wages, consumption and life spans (that impact on the former two). They find significant negative effects for natives:

...by 2060 in the low migration scenario aggregate GDP decreases by 11% and GDP per person by 2.7% compared to the baseline scenario... this policy has a significant negative impact on public finances, owing to the shift in the demographic structure after the shock. The total level of government spending expressed as a share of GDP increases by 1.4 percentage points by 2060. This effect requires an increase in the effective labour income tax rate for the government to balance its budget in every period. By 2060 the required increase is 2.2 percentage points

The results are conservative in sense that they model for just below 100,000 (I’m not sure if this is what the Conservative Party means when they say they want immigration in the “tens of thousands”) and they don’t take into account the effects of immigration on things like total factor productivity. These results are fairly representative of the literature but its worth considering in details the specific impacts of immigration on employment, wages and public finances.  

On employment, Luchino et al (2012) find ‘no association between migrant inflows and claimant unemployment.’ The correlation actually goes the other way so that a 2 percentage point increase in the amount of migrants leads to a 0.02 percentage point reduction in the claimant (i.e., unemployment) count but this result is not significant. They go one step further and look at whether this association changes depending on the business cycle – still no association.  Note that this study was looking at the UK – but when you look at cross-sectional evidence, you find the same thing (see Figure 2 below).

The literature does raise some issues about whether this positive effect extends to everyone. For example, Dustmann et al (2005) find no significant effect on the overall level of unemployment but they did find it had a negative impact on those with only O-levels but a positive impact for those with A levels or university degrees. I’m not persuaded by this line of research for two reasons. First, when you look at the impact over long periods of time, the effect diminishes.  As Devlin et al (2014) note:

... our assessment is that there is relatively little evidence that migration has caused statistically significant displacement of UK natives from the labour market in periods when the economy has been strong... The evidence also suggests that where there has been a displacement effect from a particular cohort of migrants, this dissipates over time – that is, any displacement impacts from one set of new arrivals gradually decline as the labour market adjusts

And this ties quite nicely with a similar but quite distinct point: the impact of immigration tends to be tamed by how flexible your labour markets are. As Sam says ‘the more rigid a labour market, the worse immigration is for native workers.’ Looking at European data, Angrist and Kugler (2003) show that ‘increasing the severity of labour standards by one standard deviation, about the difference between Denmark and Belgium, would increase the negative effect of immigration from -0.027 at the median to about -0.042.’  Just to really hammer the point, the OECD produces this helpful graph:

The countries on the right hand side are likely to be integrating migrants into their labour markets better than those on the left. I decided to look at how these countries tacked on the Heritage Foundation’s measures of Labour Freedom (100 being the highest levels of labour freedom and 0 being the lowest). The average score for the ten countries with the highest risk is 56. The average score for the ten countries with the lowest risk is 70. That is a significant difference. You can check my working out by downloading the data here.

Fortunately in the Anglosphere, where the average Labour Freedom score is 81, we do have dynamic labour markets which moderate the effect – and, if anything this should lead you to favour economic freedom rather than restrictions on immigration. Why would flexible labour markets matter? The obvious is that people who do lose jobs can more easily find jobs in a labour market with less minimum wages, hiring and firing regulation and centralised collective wage bargaining etc. Di Tella and MacCullock (2005) find that if France had a labour market as flexible as the U.S., ‘its employment rate would increase 1.6 percentage points, or 14% of the employment gap between the two countries.’

But there’s an even more interesting mechanism that I wasn’t aware of until I read Brian and Kovak (2016): immigrants respond to labour market demands in a way that equilibrates the labour market. How? Well, natives don’t appear to be as geographically responsive to labour market demands: if employment declines and you lose your job, as a native, you are (relatively) not likely to get up and go somewhere where there is demand. But that is exactly what immigrants do: they will get up and go (see Figure 1 above). And this makes a huge difference for natives:

We find that in metro areas where the Mexican-born comprised a substantial share of the low-skilled workforce prior to the Recession, there was a much weaker relationship between labour demand shocks and native employment probabilities than in areas with relatively few Mexican workers. Natives living in metro areas with many similarly skilled Mexicans were thus insulated from local shocks, as the departure (arrival) of Mexican workers absorbed part of the relative demand decline (increase).

Changes in employment probabilities for natives living in cities with large Mexican populations are much less related to local demand conditions than are changes in cities with few Mexicans. The relationship in above-median cities is 61 percent weaker than in below-median cities. Thus, native employment probabilities were insulated from local shocks in the presence of substantial numbers of Mexican-born workers, with improved native outcomes in the hardest hit cities and diminished ones in more favorable markets

The key section to look at in Table 5 above is Panel B. You can legitimately question whether this result should be applied outside of the U.S. or Mexican immigrants, but it’s a fascinating study which shows the economic benefits of immigration. This effect seems to apply to “refugees” as well. I will be discussing the literature on refugees and native wages a little further below, but just in terms of outcomes on employment levels, Foged and Peri (2015) is one study. They conclude:

... the increase in refugee-country immigrants pushed less educated native workers to change occupation. This move was significant and towards non-manual occupations, and particularly strong when workers changed establishment.

The idea is that they were ousted by immigrants but then pushed into better jobs. Indeed, ‘an increase in refugee-country immigrants by 1 percentage point of employment increases the complexity of native jobs between 1.3 and 3.1%.’ Just to clarify, in this section I’m talking about the overall, net employment effect (even for those at the bottom). There are some who will lose out. One recent study undertaken by the Federal Reserve of Boston finds that for every foreign nurse hired in city in California, one to two fewer native nurses are employed in that same city (Cortes and Pan (2014)). As should be clear, the general effect on employment to me seems to be positive.

On wages, much of the general conclusions above relating to employment apply: generally small positive effects (Ottaviano and Peri (2012), Longhi et al (2005)) and Wadsworth et al (2016)) – the graph below is taken from the latter).

There are some studies which find negative average effects but these don’t seem to withstand scrutiny. The most famous is Borjas (2003) which found a 3.8% decline in wages. When you include an expanded data series, this drops to 2.2%. Even more interestingly, Borjas uses a weekly wage variable. Brauw and Russell (2014) multiple the weekly wage by the amount of weeks actually worked and then re-run the regression. You’d think it shouldn’t make a difference – but it does: its no longer statistically significant! The finding that there are modestly positive effects on average wages, therefore, seems to stand.  

However, this average increase in wages potentially impacts people in different parts of the distribution in different ways. I’m not sure I can do any better than Flipchart Rick’s post “It would be surprising if people didn’t worry about immigration.” I recommend that everyone read this post, and everything ever written by Rick. The Migration Observatory notes that:

The effects of immigration on workers within specific wage ranges or in specific occupations are more significant. The greatest wage effects are found for low-waged workers. Dustmann et al (2013) find that each 1% increase in the share of migrants in the UK-born working age population leads to a 0.6% decline in the wages of the 5% lowest paid workers and to an increase in the wages of higher paid workers. Similarly, another study focusing on wage effects at the occupational level during 1992 and 2006, found that, in the unskilled and semi-skilled service sector, a 1% rise in the share of migrants reduced average wages in that occupation by 0.5% (Nickell and Salaheen 2008).

There are some interesting studies on the effect of refugees on wages. The main studies look at Cuban refugees in Miami after the Mariel Boatlift in 1980 (over 120,000 Cubans arrived in Miami). Here is Card’s (1990) headline results:

Apart from the temporary increase in relative wages of workers in the lowest quartile between 1979 and 1981,the distribution of non-Cubans' wages in the Miami labor market was remarkably stable between 1979 and 1985 .. the influx of Mariel immigrants had virtually no effect on the wage rates of less-skilled non-Cuban workers. Similarly, there is no evidence of an increase in unemployment among less-skilled blacks or other non-Cuban workers. Rather, the data analysis suggests a remarkably rapid absorption of the Mariel immigrants into the Miami labor force, with negligible effects on other groups.

Enter Borjas (2015). He says that the Card study didn’t measure the right group that was likely to be affected by the influx of Cuban refugees (i.e., those without a high school education). When looking at this group of people, he found that wages were depressed 10-30%. I’m inclined to believe that Card’s finding is more robust for the following reasons:

1. Borjas’ study used four cities as controls: if every state (including those that did not experience an inflow of Ciban refugees) did not have a comparable fall, then it makes sense to attribute the decline to the immigrants. Which brings us to Peri and Yasenov (2015) - they use the ‘synthetic control method’. To get the point: the method allowed them use a larger pool of control states (43, rather than 4) – and when using this larger pool, they find that ‘the change in wages and unemployment of Miami highschool dropouts relative to the control group in 1979-1982 was, by no means, unusual andwell within the distribution of other cities’ idiosyncratic variation.’

2. Borjas’ sample consists of 17 to 24 workers in Miami each year. That is ridiculously small, and makes me question whether the results can really be statistically significant: his findings rely on wage falls for 16 people. Card’s sample covers people aged 16-61, Borjas’ covers 25-59.  Peri and Yasenov note that the smaller CPS dataset (used by Borjas) rather than the larger ORG dataset (used by Card) the measurement error for average log has a standard deviation of 0.15 log points – and so 10-30% depression of wages in the data really is insignificant.

3. When using a Synthetic Miami vs Miami model using the ORG dataset, Peri and Yasenov find that there was ‘no significant difference in the post-1979 labor market outcomes between Miami and Synthetic Miami (the control group). Neither wages (annual, weekly or hourly) nor unemployment of high school dropouts differ between Miami and the control after 1979, up to 1983.’

 

4. Card includes women, Borjas excludes them. When David Roodman applied Borjas’ model to women, he found the opposite result (see graph above showing the rise in wages). And when you combine the data for men and women, you find that its essentially a flat line (see graph below).

5. As Sam points out, ‘the relative wages of high school dropouts recover entirely by 1990 – the effect Borjas has found only holds in the short-run’ (more on this below).

6. The aforementioned Brauw and Russell make another significant point: large scale immigration is a mass entrance of new people into the labour and accordingly, essentially, a labour shock. They look at the largest labour shock: the entrance of women into the labour market in the U.S. post 1960s.  And their results don’t match Borjas’ (2003 or 2015) model. They find that ‘insignificant coefficients on weekly wages among men and positive, statistically significant coefficients on annual wages.’ It is more likely that Borjas’ findings are wrong than there being a discrepancy in labour market shocks

7. The preponderance of the limited evidence that we have on refugees seems to confirm Card, rather than Borjas. Foged and Peri (2015) (already mentioned above), for example, looked at refugees arriving in Denmark between 1986 and 1998. We have seen that low-wage earners were pushed into higher paying jobs. This explains their other significant finding: there is a 1 to 1.8% native wage increase following a 1 percentage point increase in share of immigrants from the refugee-sending countries studied.  

Borjas has responded to some of these claims in an article for the National Review. His response is largely unpersuasive. What he calls the most egregious flaw in Peri and Yasenov is including 16-61 year old because ‘your 16-, 17-, or 18-year-old son or daughter who is now a sophomore, junior, or senior in high school is classified as a high-school dropout because he or she does not yet have that diploma.’ That seemed to me to be a fair criticism – except it only accounts for about 20% of the increase in Peri and Yasenov’s study. I e-mailed Peri about this and he confirms what I suspected:

The key point is that none of his point really matters for our results of no effect on wages. We now [in a working paper due to be published soon] drop all workers below 19 in our main sample and the results are totally unchanged… when one uses the larger, more reliable sample, with lower measurement  error which is the  May-ORG sample, one never finds the impact on Miami less educated wages.

Only with SOME samples in the much smaller March-CPs data, which rely on 15-20 observations per city in the relevant group (and so massive measurement error) one can find some negative effects on some subgroups. This is why Borjas is so obsessed in the sample choice. Only in one small sample, only with March-CPS data one can find a negative impact. But given the large standard  error of those averages that occurrence can be by pure chance.

The aforementioned Wadsworth et al study of EU immigration finds ‘no apparent link between changes in the real wages of UK nationals and changes in EU immigration’ – even for the least skilled native workers. Figure 11 below shows the effect on real wages as against the change in immigration levels from the EU. So much for the negative effect on wages of the lower rung of the economic ladder.

Even if you wanted to maintain that there is a negative effect for low-earners, the studies that find negative results really are minimal. Nickell and Saleheen (2015) looking at the impact of a 10 percentage point rise in the proportion of immigrants working in semi/unskilled services, find a 1.88 percent reduction in pay (see Table 7). What does this actually mean? Here’s how Jonathan Portes explains it:

Well, the first thing to note is that a 10 percentage point rise in the proportion of migrants working in a sector – the amount needed to generate the “nearly 2 percent” wage impact is very large.  Indeed, it is larger than the entire rise observed since the 2004-06 period in the semi/unskilled services sector, which is about 7 percentage points…

Moreover the estimated impact is partly simply a compositional one – reflecting the fact that migrants earn less, as well as the impact on native wages. Allowing for this, we can calculate that the new paper implies that the impact of migration on the wages of the UK-born in this sector since 2004 has been about 1 percent, over a period of 8 years. With average wages in this sector of about £8 an hour, that amounts to a reduction in annual pay rises of about a penny an hour.

But here’s another interesting reason why you shouldn’t be that concerned about the wage effects. Those low-earners whose wages are probably affected are themselves immigrants. Manacorda et al (2012) find that ‘immigration has primarily reduced the wages of immigrants - and in particular of university educated immigrants - with little discernible effect on the wages of the native-born.’ Studies that look at wage effects without disaggregating groups are borderline worthless: Ottaviano and Peri (2012) find that there are positive wage effects for average native works (including those without a high school degree) in the range of 0.6 to 1.7% - but there is a 6.7% decrease for existing immigrants.

@ByrneTofferings Same thing that happened to coal miners. And may more communities be devastated for the good of us all.

— Mugwump (@anonmugwump) May 16, 2016

Even more important is that the effect on wages seems to be short lived (see above, and Devlin et al). As far as the compositional effects on the poorer people go, I have to say quite bluntly: even if there is an effect (unlikely) and it’s not short term (unlikely):  you can be worried, but don’t expect the rest of us to heed your concerns[5]. There are more significant reasons why I believe this that will be handled below, but even just looking at the domestic side of things, I can’t put it any better than Portes:

Yes, we're doing this for the good of our country, and yes you may lose out, but ultimately we still have to do this… Just as we said to the coal miners 30 years ago: 'Sorry we can get our coal a lot cheaper abroad. We can't afford to keep on propping you up.'… In the case of coal mining and immigration relatively liberal policies had benefited people, on average, even if some individuals had lost out, and that these policies had therefore been broadly sensible: the human consequences, overall, were positive.

On public finances, the Office of Budget Responsibility (2013) looked at the question of how much immigration affects debt levels and finds what the Lisenkova et al (2013) found:

By 2062, our debt as a percentage of GDP will be 175% with no immigration, it would be 100% with 140,000 a year and 75% with 260,000. There are two reasons for this. First, immigrants just don’t drain resources: they pay taxes, they create wealth. It is little surprise that the OBR has said that for Chancellor Osborne to reach his deficit target he must miss his target to reduce net migration. Indeed, revising immigration up from 105,000 to 185,000, led to the OBR revising output up by 0.9% (see this Guardian report). As the Financial Times noted in 2014, migrants ‘created one in every seven UK companies’ and ‘are responsible for creating 14 per cent of British jobs.’  The report goes onto say:

... entrepreneurial activity among the migrant community was nearly double that of UK-born individuals: 17.2 per cent had launched their own businesses, compared with 10.4 per cent of those born here. They are also, on average, eight years younger than indigenous entrepreneurs at 44.3 years old, compared to 52.1. This is despite the extra challenges they face, including access to finance and cultural and language barriers

This finding is not unique to the U.K. As Anderson (2016) notes, in the U.S., immigrants have ‘started more than half (44 of 87) of America’s startup companies valued at $1 billion dollars or more and are key members of management or product development teams in over 70%’ of them. The value of just these 44 companies is ‘$168 billion, which is close to half the value of the stock markets of Russia or Mexico.’

The second reason why the effect on public finances shouldn’t be surprising is because the pull of welfare states is often overstated. Evidence from the U.S. seems to suggest that the welfare state is really not a magnet or, at the very least, immigrants are not using welfare states any more than the native population.

…immigrants generally participate in the safety net at lower rates than natives once we restrict ourselves to comparisons within the set of lower‐income families. This is true for almost all programs we consider and is true both before and after [the 1996] welfare reform [which restricted welfare for immigrants]… our results show that immigrants rely more on earnings as a source of income (than do natives) and the degree of reliance has increased post‐welfare reform

On the question of immigrants coming to Western countries to use our welfare states and draining them, the general argument, therefore, seems to fail. However, there may be a differential depending on the origin of the immigrant. Which brings us to my favourite Radio 4 programme, More or Less. In an episode from 2014, they investigated the claim that immigrants create public debt. They note that:

…between 1995 and 2011, on average each European immigrant put about £6,000 more into the public purse than they took out. Non-European immigrants, on the other hand, each took out about £21,000 more than they put in. on average, each native Briton took out roughly £11,000 more than they put in between 1995 and 2011.

FullFact (an under-appreciated site) has a nice summary of the research on the fiscal effects of immigration. I suggest you ignore the studies put out by MigrationWatch because their methodology is so flawed, I barely know where to begin. But, even then, the broader picture is precisely this: European immigration seems to be okay for public finances, non-European immigration does not.

It is worth considering the Dustmann and Fratitini research in a bit more detail. As you can see, they find that non-EEA migrants cost the public purse. However, when you take into account that some public expenditures are fixed, even non-EEA migrants outperform natives when it comes to contributions to the public purse:

Aside from the origin of the immigrant, their reason for coming may matter: refugees, for example, may be less likely to be able to work because of their age and/or skills and therefore their contributions to GDP and to public finances may be smaller. The evidence here does seem to show a negative impact on public finances. Ruist (2015) looks at the effect of the recent refugee flow into Sweden on public finances. The table below is worth considering:

Ruist finds that refugees account for 5.6% of public spending. Refugees in Sweden constitute 5.1% of the population so this isn’t hugely disproportionate. But, when you look at the table, you see that they account for 55.4% of social assistance and 25.3% of the spending on “crime and justice.” Refugees are a drag on public finances: the net fiscal redistribution is 1% of Sweden’s GDP. Ruist explains this finding:

... they performed much worse than the rest of the population on the revenue side, where they contributed only an estimated 3.4% of total public revenue, essentially through direct and payroll taxes. The reason is clear: the employment rate among adult refugees was 20 percentage points lower than that among all adults. The reasons include poor language skills, lack of applicable training, lower female labour force participation rates, and so on... Four-fifths of the redistribution was due to lower public per capita revenues from refugees compared with the total population, and one-fifth to higher per capita public costs.

There still may be a drag but Ruist’s finding that around a pretty insignificant 1.35% of GDP being redistributed should dampen the weight of this argument. The more important point about Ruist’s finding relates to the labour market. With flexible labour markets, refugees can be integrated into employment far quicker – this has been addressed above, but when you put the points together you get a simple maxim: If you want immigrants not to be a drag, free your labour markets!

If you only cared about the public finances side of things, you should have no qualms, at the least, with European (and, let’s be honest, Anglosphere) immigration but you might want restrictions on non-Europeans. Leaving aside the impacts on public finances in the past, the OBR’s model is clear: you should want to encourage immigration to a level near 260,000. You might even cut out the nationality element and prefer that immigrants must have (1) a job or (2) a high paying job. This policy prescription has to be weighed against further factors below.  

The public finances argument is not that conclusive for me (GDP, GDP per capita and wages matter more in my opinion) but here’s an interesting idea. If you do care about public finances and the impact from non-European immigrants – why not say you are anti-legal immigration but you are pro-illegal immigration? Why not support worker programmes? I do not support these and I will explain why and return to the issue of public finances below. But, as we’ll see, the benefits to immigrants are so large, that the answer should almost never be restriction of immigration but changes of other policies.

As a concluding point to this subsection, the above broadly positive economic findings are consistent and significant. Ed West’s book The Diversity Illusion is an anti-immigration book worth reading (full disclosure: I consider Ed a friend) but it does contain a common incorrect refrain of the anti-immigration writers when he says ‘certainly some economists believe that immigration benefits the economy… but just as many economists argue to the contrary’ (my emphasis) (p.73).

This is not true. The weight of the evidence is behind the positive effects of immigration (a point that Ed accepts when he says that ‘economist tend to approve’ of immigration on p.84). There is a better argument that these effects are closer to zero and only marginally positive, but that doesn’t seem to be the argument of most anti-immigration writers. Below is the IGM Economics Experts Panel view of immigration’s effects on the average worker.

10% is not “just as many” as 63%. As accepted, there will be losers but we cannot make policy on the basis that there are some losers. I use the word “some” very specifically. Aubrey et al (2016) look at the labour market effects (employment, wages etc.), the fiscal effects and what they call ‘market size effects.’ Market size effects are aimed at measuring ‘aggregate demand for goods and services in the receiving and sending countries’ as this ‘determines firms’ entry and exit decisions and in turn, the numbers of entrepreneurs and goods available to consumers’. They then try to measure who exactly benefits on the basis of these effects:

The set of winners represents 6 9.1% of OECD non-migrant population aged 25 and over. This share increases to 83.0% if one considers the 22 countries whose GDP per capita was above USD 30,000 in the year 2010. Contrary to popular perceptions, winners mainly reside in net immigration countries; their gains can be important and are essentially due to the entry of immigrants from non-OECD countries, which has a drastic effect on market size… The average effect on non-migrants is positive in 28 OECD countries, nil in France, and negative in 5 traditional countries of emigration.

In the second figure above (labelled (b)), the average effect of migration from outside the OECD on natives within the OECD are considered. You can plainly see that ‘effect of extra-OECD migration is positive in 22 countries.’ How does this compare with intra-OECD migration? Aubrey et al identify ‘17 winners and 16 losers (the effect is nil in Sweden)’. Their main conclusion on total welfare is as follows:

Overall, extra-OECD migration flows increase the average utility of non-migrants by 1.2% in the OECD (and by 0.9% if older cohorts of migrants are included in the average)... Intra-OECD migration flows decrease the average utility of non-migrants by 0.1% in the OECD. Hence, the bulk of welfare gains from global migration is driven by extra-OECD migration, in line with Di Giovanni etal. (2015) or Iranzo and Peri (2009).

This is not me picking and choosing studies, the bulk of the economic literature and economists support the idea that immigration is a good for the economy and the domestic state with minimal, short-term downsides. Anti-immigration activists would do better to make a cultural or institutional case – something I’ll consider in the next part.

1.2 International Economic Impact

Of course, the domestic impact is not all we care about –  the impact on global economic outcomes is similarly important. The different weight we should give to domestic and international factors will not be explored in this part. The first impact to consider is the net impact on GDP. The most famous paper is Clemens (2011) in which he states:

For the elimination of trade policy barriers and capital flow barriers, the estimated gains amount to less than a few percent of world GDP. For labor mobility barriers, the estimated gains are often in the range of 50–150percent of world GDP

Clemens notes that if you assume that the average gain for an immigrant is $7,500pa (which is highly conservative), then you would have overall gains of 20 to 60% of global GDP. Clemens’ paper is a literature review which is why it’s unsurprising he’s not alone: Hamilton and Whalley (1984) suggest a gain of 147%, Moses and Bjorn (2004) suggest 96.5%, Klein and Ventura (2007) suggest 121%.

Clemens’ review has not gone unchallenged and here’s where we meet Borjas again – he provides the only critique worth talking about. In his book Immigration Economics (2014) he argues that the costs of moving essentially wipes out the gains to world GDP. Borjas states that ‘“breakeven” cost of migration [is]… around $140,000’ and that ‘much of the presumed gains from open borders would disappear if migration costs were roughly of the same magnitude as the estimates in the literature’ (p.169, 262). It clearly doesn’t cost immigrants $140,000 to move across borders so what does Borjas mean? He explains:

In a world of income-maximizing agents, the stayers are signaling that there are substantial psychic costs to mobility, perhaps even on the order of hundreds of thousands of dollars per person, and that they are willing to leave substantial wage gains on the table (p.168).

You read that right. Borjas is saying the fact that some people stay means that its because there are “psychic costs” to moving. These costs are hundreds of thousands of dollars. I realise I’ve probably just repeated the extract above but I’m hoping you’re as incredulous as I am. Milton Friedman’s phrase about ‘looking how people vote with their feet’ comes to mind. What can we show by way of the empirical literature? Well, the first point is that Borjas is relying on the slightly ironic position that immigration controls don’t matter – people are choosing not to come. Clemens and Pritchett (2016) explain:

This claim implies that the existing global limits on international migration—passports, visa restrictions, limits on recognition of professional credentials, all deportations, all sea patrols, all fences—do not collectively have important effects on workers’ decisions about where to locate. This would be the case if, as Borjas asserts is possible, migration itself generally conveys sufficient disutility that those policy barriers do not substantially alter workers’ choice of location. This opinion is incompatible with existing evidence.

Second, we can compare wage differentials (i.e., the amount you could make if you moved to X-land from Y-land) in contexts where there are border controls and contexts where there aren’t – and we don’t need to guess about this as Clemens and Pritchett (2016) have looked into it. Their findings are, for me, the strongest economic argument for looser immigration controls. They find that when you have strong immigration controls, you get abnormally high wage differentials:

[Borjas’] Compensating Differential Case encounters further difficulty in explaining why there is a 600–800% real wage gap between Haiti and the United States (which are separated by tight visa restrictions and naval interdictions), but historically similar Guadeloupe exhibits only a 60% difference in real wages with metropolitan France (to which Guadeloupian workers may move at will). Similarly there is a 300% real wage gap between observably identical Filipinos in the United States and the in the Philippines [where restrictions are high], but only a 50% wage gap between ethnic Guamanians in the mainland United States and in Guam [where restrictions are less]

This discrepancy suggests that it’s not that there are “psychic costs” but that there are barriers to immigration that are stopping people.

Third, a point linked to the first, the preferences of people would likely change in response to changes in border controls so that when Borjas claims that people don’t move now, it doesn’t mean anything for what people would do if they had fewer restrictions. There is a large literature about ‘reference-dependent preferences’. The gist of these studies show that people value things they have over things they don’t because, for example, losing something you have (your homeland) is worse than losing something you don’t (life in the West) especially when you think it is improbable to get the gain (i.e., are you actually likely to get access to the West? If not, the loss aversion is high).

And this is precisely why Borjas is wrong: immigration restrictions lead to the same kind of loss aversion (which Borjas takes as evidence of “psychic costs”) that impact preferences (Koszegi and Rabin (2006)) and accordingly the “cost” is not as high as stated, certainly nowhere near wiping out the consistent gain found in the literature.

That said, it is right to have some scepticism over the higher estimates of world GDP especially because not everyone who is pro-immigration wants the elimination of all barriers, they may simply want high levels of immigration. Doquier et al (2015) find that the global GDP gains when you actually look at more realistic migration levels (and desires) is between 7 and 18% increase in global GDP. Table 3 below shows the income gains for workers in a country (including immigrants following liberalisation), natural (natives in the country receiving immigrants) and stayers (those who stay in their country of birth).

The Dcoquier et al study is, in my mind overly pessimistic, but it does two good things: first, it measures potential migrants and the educational composition of such migrants. Education in Fiji is worth less than education in the West (see endnote 7), so a skilled migrant with the same qualification as a native is not worth as much and their income gains wont be as high as some of the previous studies assume. But even on that basis, it is clear that immigration is good for the immigrants themselves and global GDP.  

This leaves three things to discuss. First, the brain drain; immigrants who come here may have better outcomes but this may have negative outcomes for those left behind. The logic of the argument is clear: immigrants are likely to be productive members, perhaps the most productive and when they migrate they deprive their home nations of their productive goodness. And it’s borne out by several studies in the literature – to take one of the recent examples, Ha et al (2016)[6] look at the migration that happens within China and find that ‘one permillage increase in the emigration rates will decrease the growth rate of that province by 0.4%.’

Here’s the interesting thing though: Ha et al (2016) find something else that it prevalent in the literature: migration increases the human capital of those who stay behind. Chands and Clemens (2008) find that a large exit of (skilled) Indians was associated with a large increase in human capital investment that human capital stocks rise, even net of large departures of skilled workers.’ Why is this the case? It’s because, rather sadly, watching people move out encourages people’s to want to do the same – and so they also go through the same steps that the migrant has but don’t end up leaving. This line of research has some interesting implications for the signalling model of education, please see [7] if you are at all interested.

Paul Collier (2013) in his book, Exodus, states the following:

For developing countries as a group the [brain drain] concern is clearly misplaced: gains outweigh the losses… [However] Intractable poverty as a problem.. is becoming concentrated in small, poor countries that have suffered significant net losses of their skilled population. As their diasporas build up, their rate of emigration is likely to increase. For these societies, “brain drain” unfortunately remains the right concern (p.203)

It’s clear that Collier’s statement is driven by Doquier and Rapoport (2012). Here are their findings, I’ve highlighted something which Collier neglects to (an unfortunate feature throughout his book):

On the whole, the simulations results reveal that the countries experiencing a positive net effect (the “winners”) generally combine low levels of human capital (below 5%) and low high-skill emigration rates (below 20%), whereas the “losers” are typically characterized by high high-skill migration rates and/or high enrollment rates in higher education. There appear to be more losers than winners, and the losers tend to lose relatively more than the winners gain.

The main "globalizers" (e.g., China, India, Indonesia, Brazil) all experience modest gains while many small and medium- size sub-Saharan Africa and Central American countries experience significant losses. However, the absolute gains of the winners exceed the absolute losses of the losers, resulting in an overall gain for the developing world as a whole.

I think this a neat finding but I’m not sure how much the ‘small, poor’ vs ‘developing nations’ dichotomy works. The aforementioned Chands and Clemens paper was based on Fiji and similar findings have been found in Cape Verdi (Batista et al (2011)), Papa New Guinea and Tonga (Gibson and McKenzie (2011)). It’s worth bearing in mind that the Doquier and Rapoport paper relies on not on real data but simulations using quite complicated algorithms and formulas that, to be quite honest, I don’t fully understand. They and Collier are probably nonetheless right that some countries suffer from brain drain whilst others do not – but I really don’t want to over-egg it because of that last line above. Beine et al (2003) find the same thing in their sample of 50 countries, they find

…the brain drain appears to have negative growth effects in countries where the migration rate of the highly educated is above 20% and/or where the proportion of people with higher education is above 5%. [By contrast] We found that most countries combining low levels of human capital and low emigration rates of their highly-educated are positively affected by the brain drain.

In that latter category of winners are 80 to 94% of the developing world (the former is from the Beine et al study, the latter is from Legrain (2002), p.181). And that’s without even taking into account returnees. There is evidence of massive human capital gains for the I.T. industry in Indian (Commander et al (2004)). But the returnees argument isn’t very strong, it’s just a slight mitigator. Why? Dustman and Weiss (2007) look at the UK and find that

….remigration for immigrants from Europe, the Americas and Australasia as well as the Middle East, other Asia and other countries is substantial (more than 45 per cent have returned after five years since arrival, compared with those who are still there after year 1), and seems to continue after five years, return migration for the other two groups is much less pronounced. There is little indication of any return for immigrants from Africa and the Indian Sub-Continent

The evidence from the U.S. is similar: Mayr and Peri (2008) find that Asian and European migrants have a return rate of around 20%  (or 0.8) but the Latin America return rate is close to 1 (both this study and the Dustmann and Weiss study use the same research design hence you can compare with Figure 3 above). The point is not that “don’t worry about immigration, a lot of them will leave” – but “the brain drain isn’t as bad because some people go back”. And what makes return migration a particularly good mitigator of the brain drain is that there is an emerging consensus those that do return are the mostly highly skilled (Mayr and Peri (2008), Dustmann and Weiss (2007), Gundel and Peters (2008)).

Another way to mitigate the effect of the brain drain brings me onto the second thing to discuss: the effect of remittances. di Giovanni et al (2014) has one of the best papers on the overall welfare gains (in terms of looking at market size effects, productivity gains and population effects and, importantly, remittances).

… the long-run impact of observed levels of migration is large and positive for the remaining natives of both the main sending countries and the main receiving ones. Relative to the counterfactual scenario in which no migration takes place, some countries in both groups are as much as 10% better off. Interestingly, while the overall numbers are similar, the salient reason for the welfare changes is different. For the countries with the highest immigration rates (Australia, New Zealand, Canada), migration raises welfare through increased equilibrium variety. For the countries with the highest emigration rates (El Salvador, Jamaica), the staying natives are better off because of remittances

Hidden in these averages are important nuances (which, again, shouldn’t be over-egged):

 

…. in the long run the large majority of OECD countries would be worse  in the absence of migration. The average OECD country would experience a welfare change of -2.38%, with substantial dispersion in outcomes (standard deviation of 3.07%). In this group, the largest losses are experienced by the natives of the countries with the largest observed shares of the foreign-born in the population: Australia (-11:63%), Canada (-7:07%), and New Zealand (-6:89%). However, it is worth noting that a handful of OECD countries would experience welfare gains: Greece, Korea, and Portugal would all be about 1.1-1.4% better off in the no-migration counterfactual

…. the majority of non-OECD countries also have lower welfare in the no-migration counterfactual, although dispersion in country outcomes is substantial. The average welfare change is -2.00% with an associated standard deviation of 3:55%. The highest welfare losses are to native stayers in El Salvador, the Dominican Republic, Jamaica, and the Philippines, at around -7-10%. Interestingly, a handful of non-OECD countries experience welfare gains: mainly, Trinidad and Tobago (5:70%), Mexico (1:32%), and Turkey (1:07%).

It is particularly interesting to compare the predictions for El Salvador and Trinidad and Tobago. These two countries would experience similar population gains due to return migration, at 19% and 17:9% respectively. But while the former would suffer a welfare loss of -8.72%, the latter would experience a welfare gain of 5.70%... the diverging effects of return migration on these two countries are explained by the role of remittances. 

These results are because of the fact that remittances help mitigate the effect of leaving populations – but not all countries get the same amount of remittances. The Table above shows the percent change in the real average income of natives of that country in the no migration scenario relative to the benchmark.

The effect, however,  of remittances also stands alone as an argument in favour of high levels of immigration. The literature on remittances is large and so I’m endeavouring to go through all of it. The graph above shows how remittances compare to other forms of transfers and flows. One thing that no one will question is the sums involved, see graph above (taken from Anghel et al in Handbook of the International Political Economy of Migration (2015), p.236).

Collier (2013) makes the laughable claim that ‘remittances largely offset the loss of output [from the immigrant leaving]’ (p.208). The difference is that there are now a few less mouths to feed and so per capita expenditure can be a little higher.’ This, of course, does not take into account the human capital effects explained above. But more importantly, the effect that his on poverty is phenomenal:

The World Bank has calculated what would happen to poor people’s incomes in a cross-section of thirty-seven countries if remittances dried up. In the countries where remittances account for a large share of the economy – 11 per cent of [GDP], on average – the share of the population living on less than a dollar a day would rise by half, from 24.8 per cent to 37 percent… Household surveys suggest that remittances have reduced the share of people living in poverty by 11 percentage points in Ugdana, 6 percentage points in Bangladesh and 5 percentage points in Ghana (Immigrants, Legrain (2007), p.164-165)).

Adams and Page (2005) looking at 71 countries find that ‘remittances do in fact, reduce the level, depth, and severity of poverty.’ Their estimate is that estimates that a 10 per cent increase in official remittances leads to a 3.5 per cent decline the share of people living in poverty. Jongwanich (2007) looking at Asia and Pacific countries find that a 10 per cent increase in remittances leads to a 2.8 per cent reduction in poverty incidence.

Ahmad et al (2010) finds that for Pakistan ‘the probability of households becoming poor decreases by 12.7% if they receive remittances’ and that the poverty headcount ratio declines by 7.8%. Yang and Choi (2007) have a neat research design using rainfall in the Philippines. Bad weather can cause dramatic decreases in income and remittances are found by Yang and Choi to offset 62.9% of this decline.  

And on and on the literature goes. In a perfect example of Collier’s back and forth, he does conclude his section on remittances by admitting remittances ‘have been beneficial and substantial for the people left behind in some of the poorest countries of origin.’ The picture on remittances isn’t all rosy in four respects. First, for those who care about inequality, as Anghel et al (2015) note the literature is quite mixed:

A large number of studies show that remittances have a negative impact on income ine-quality, as measured by the Gini coefficient (Barham and Boucher 1998;Rodriguez 1998; Adams et al. 2008b; Adams and Cuecuecha 2010a), and this effect is more pronounced when remittances come from international as opposed to internal migrants… However, De and Ratha (2005) found that the Gini coefficient drops for the effect of remittances; while McKenzie and Rapoport (2007) found that even though the initial effect of migration is to increase income inequality, as the level of migration increases, income inequality decreases. Given the mixed findings, the relation between remittances and income inequality is still a topic of debate (Handbook of the International Political Economy of Migration (2015), p.240)

Second, what are remittances actually spent on? My favourite study of the effect of remittances is Yang (2008). In 1998, the East Asia crisis meant that there was a devaluation of currency in some countries and the converse in others. This extra money (for countries who had their currencies devalued) went toward business investment and education. But again, the literature on this point is not as emphatic as the poverty-reducing effects, as Anghel et al note, there is a view that remittances ‘cause behavioural changes and are spent on consumption rather than investment goods’ (p.240).  

The third concern about remittances is that they may reduce labour force participation. To cut to the chase, the effect is likely around zero but the composition changes in response to remittances. Amuedo-Dorantes and Pozo (2006) show that Mexicans receiving remittances increase their work in the informal sector although there is some negative effect for women’s participation in the labour market in rural parts of Mexico.

The fourth concern is the effect on growth. Some studies show negative effects, some show positive effects, most show no effect (again, I can do not better than the summary in Anghel et al (2015)). My view goes the plurality of studies. I’m not particularly concerned about this because of the overall effects of migration on growth, trade and globalisations. Here are the results of a meta-analysis undertaken by Genc et al (2011):

…we analyzed the distribution of immigration elasticities of imports and exports across48 studies that yielded 300 estimates. The results confirm that immigration boosts trade, but theimpact is less on trade in homogeneous goods. An increase in the number of immigrants by 10percent increases the volume of trade by about 1.5 percent.

Why would this be the case? Well, migrants create networks in the destination country that have fewer costs to transacting with their origin state. Rauch and Trinidade (2002) find that the presence of a migrant Chinese population can increase in biltareral aid by 63% (their upper estimate is 102%). There’s also some evidence that the presence of migrants increases foreign direct investment into the origin country (Javorick et al (2011), for example, find that ‘a 1-percent increase in the migrant stock is associated with a 0.35–0.42% increase in the FDI stock’).

The aforementioned four concerns are worth talking about, but I’m not sure they would affect my calculus much in the face of the aforementioned poverty reduction effects. There is however one finding that bothers me and its one of the reasons I am for high levels of immigration but not open borders. How does the level of restrictiveness of immigration interact with the level of remittances sent? Doquier, Rapoport and Salomone (2012) look at this question.

Using a new database obtained by merging various second hand sources on bilateral remittances for a large set of country-pairs over the period 1985–2005.. [they find] The results strongly support the theoretical analysis, suggesting that immigration policies in the migrants' host countries determine whether the home countries receive relatively more or less remittances from their skilled emigrants

[The] more restrictive destinations are associated with skilled migrants sending relatively more remittances). The marginal effect of costly family reunion immigration policies on the propensity to send remittances by skilled people is estimated to be equal to 0.267… As expected, the sign of the coefficient of the interaction term is negative and highly significant. Our estimate of the marginal effect of skill biased immigration policies on the propensity to send remittances is equal to 0.79.

This makes sense: if immigration is less restricted, the ‘left behind’ are more likely to come over themselves. I can imagine a migrant thinking “why should I send money, when they can just come over?”. I think this is a devastating finding to those arguing for open borders. Nine times out ten when you hear people talk in favour of it, they’ll talk about remittances. But their system would likely destroy a significant amount of those gains! It’s also worth noting that what changes is the preferences of the migrants, rather than the number of people in need of remittances.

The final thing to consider, but only in a preliminary fashion, is the effect of national IQ on economic outcomes. Like I said, I am only looking at economic outcomes independent of institutions in this section. Garret Jones’ The Hive Mind has the following graph:

Jones doesn’t make this argument, but I will because it provides a good Segway to Part II of this series. National IQ has a positive correlation with GDP and GDP per capita. What if importing immigrants with lower IQ damages GDP? Well, the evidence on GDP and GDP per capita from the domestic section above would seem to refute this, but a more interesting argument is that even if it did affect GDP per capita, these are compositional effects. For example, if the average wage is 25k, and we let in a bunch of people whose average productivity / IQ is worth £16k, the average wage will drop. But the average for non-migrants is unlikely to have been changed.  

Jones does make an argument about the long term effect on institutions of immigrants. The following extract is a nice way to round up the literature above and conclude this part:

The economics of less-skilled immigration to richer, more productive countries are reasonably clear: life-changing good news for the immigrant with only fairly small effects one way or the other on so-called “native” less-skilled workers. That’s true when we look at the short run or when we look across towns and cities within the same country…

[However] the possible—I emphasize possible—effect on long-run institutions. Will less-skilled immigrants tend to vote for policies that will weaken the wealth-creating opportunities they’ve enjoyed? Or will less-skilled immigrants and their descendants instead build up high levels of human capital, perhaps raising the average information levels of voters? Conversely, might more skilled immigrants bring a focus on the long run, a more informed perspective, into political discussions? (p.161-162).

These are questions I’ll be looking at in the next part. Thanks for reading!

Endnotes

 [1] Eric Kauffman undertook an analysis of the BHPS and found that ‘there is no difference between pro-immigration whites and anti-immigration whites in their propensity to leave a diverse area.’ He goes onto conclude:

…local diversity does lead to more tolerant white attitudes and this is not the result of white flight. As more locales become diverse, this should lead to interethnic contact and more positive white attitudes to outgroups.

Kauffman, in an earlier post, noted the following:

We netted around 1700 white British respondents of whom about 200 said they had moved to a more or less diverse ward over the past decade. Whether the question asked about comfort with a boss of a different race or a Prime Minister of a different race, anti-immigration views or neighbourhood minority comfort thresholds, the result was the same. Namely, that racial and immigration attitudes had almost no effect on white mobility. Only at the conservative extremes did attitudes affect behaviour, but this was a marginal effect operating on 1 or 2 percent of the sample.

[2] I am not convinced that the predominant reason for the majority of people being anti-immigration is racism. Consider the fact that of the foreign born population, a majority is still for reducing immigration “a lot”. If you look at the graph below from Ipsos Mori, you can see that more recent immigrants agree with this anti-immigration position less.

Ipsos says that this may be a function of their time in the UK and the ‘type’ of immigration. It is more difficult for a recent immigration to say “put up the drawbridge” than a more established one – but in relation to the ‘type’ of immigrant’, the most persuasive confounder is age. I would like to see a breakdown of the foreign born population’s views broken down by age. In any event, the point is that given that a foreign population is anti-immigration, the argument that anti-immigration views are xenophobic or racist is more difficult to sustain. Admittedly, it might be possible that all the previous immigrants have non-racist reasons and the natives have racist reasons but I’m unconvinced by this given the downward trend of racism and the fact that only approximately 20-30% of people say they’re prejudiced themselves. Indeed, we merely need to look at why anti-immigration people give, from the same Ipsos poll:

They could all be liars, or maybe, just maybe, people can be reasonably opposed to immigration without it being racist despite it being wrong. The idea of being reasonable but wrong is an idea I’ve gone on and on about and I’m sure I will return to it at some point.

[3] On the subject of the impact of immigration on voting Leave, the main graph to take heed of it the one below provided by the Economist. They note that ‘The proportion of migrants may be relatively low in Leave strongholds such as Boston, Lincolnshire, but it has soared in a short period of time. High numbers of migrants don’t bother Britons; high rates of change do.’
 

However, Colantone and Stanig (2016) (for more on this study see endnote 5) find that:
 

…when London (which contains five regions) is included in the analysis, we find a negative correlation between arrivals and support for Leave — meaning that on average, regions with more new arrivals have lower support for Brexit. But once we remove those five regions from the analysis, there is really no pattern left in the data. This means that the correlation is driven by London, which is historically more cosmopolitan and diverse. For this reason we do not want to make much of this negative correlation — but are, at a minimum, confident that there is really no detectable relationship between how many immigrants arrived in recent years and how much support the Leave option received in the referendum.
 

[4] If you believe in rights, there is a very interesting meta-argument about whether we should give precedence to rights to self-determination or rights of association (which is, in essence, an application of loose immigration controls). I will return to this argument in a future part to this series. However, it’s worth thinking about what this means for mandate-era Palestine. If you believe that rights of association should trump the will of the people, then you must agree with British policy during 1917-1933, 1945-1947 of permitting Jewish immigration into Palestine. It’s likely that the local population, had they been in control of their country, would have voted to stop immigration.
 

My own view is that post-WW1, the British needed to provide stable institutions for the Ottoman Empire’s breakup. Indeed, the purpose of the Class A mandate was that they required “the rendering of administrative advice and assistance by a Mandatory until such time as they are able to stand alone”. World War II provides a later of justification for Israel not because of the Holocaust but because British rule of Palestine became doubly justified in attempting to avert Nazi rule of strategically important areas. Accordingly, during the period of legitimate British rule, the policy of open immigration (if that is indeed the objectively correct policy), was justified. When it then came to the establishment of states in a state-less area, precedence should be given to self-determination – and that is a liberal (relatively) non-nationalist justification for the State of Israel’s legitimate existence and establishment.
 

This gives moral justification for the existence State of Israel but it does not say anything about the creation of the refugee crisis between 1947-1948. During the 1980s following a series of books published by the New Historians, most notably Benny Morris’ The Birth of the Palestinian Refugee Problem, showed that refugee crisis was not borne about because, as Zionist historians claimed, they were ordered to leave by Arab commanders but through a combination of leaving through fear, direct involvement in the war and Israeli expulsion orders.* I’ve come to the view that the debate about the causes of 700,000 or so refugees doesn’t matter: whether they left voluntarily or not, post-1948, Israel stopped them from returning. There’s a discussion to be had about whether this was right, but it makes a discussion about the causes of the refugee crisis of academic interest and nothing more.
 

* The precise breakdown of the first wave is provided in Morris, ‘The Causes and Character of the Arab Exodus from Palestine: The Israel Defence Forces Intelligence Branch Analysis of June 1948’, Middle Eastern Studies (1986) Vol 22, Issue 1, 5 available at https://docs.google.com/file/d/0B-5-JeCa2Z7hMXpOaWotOTY0Wm8/edit
 

[5] The same argument could be made about globalisation. Take China’s move toward a market economy in the 1980s. The move toward a capitalistic model has lifted millions of people out of poverty. However, we know from the work of Autor et al (2016) that it has cost some people in the West. He finds that around 1 million of the 5.5 million jobs lost in manufacturing in the U.S. is down to the “China shock” – trading with China. Those whose jobs are displaced don’t find it easy to adjust: equilibration is ‘remarkably slow, with wages and labor-force participation rates remaining depressed and unemployment rates remaining elevated for at least a full decade after the China trade shock commences.’
 

However, as the above graph shows, we know that free trade and globalisation is a good – for those in China and the U.S (see, generally, Norberg (2003), Bhagwati (2007) and Bhagwait and Panagariya (2014). To take the most recent TPP deal, estimates of raising American incomes by 0.5% (on average) makes trade deals worth doing, TTIP will raise U.S. GDP by 3% (see here). These are not trivial gains. But most importantly, global utility is raised: China's poverty rate since it has been following more market-orientated has lifted millions of people from poverty.
 

The particular relevance of the globalisation discussion is a more recent study by Colantone and Stanig (2016). They summarise their findings in a Monkey Cage post:

We found a strong and statistically significant relationship between the strength of the import shock and the Leave share in the referendum... Let’s look at Inner London, where 28 percent voted Leave, and Derbyshire/Nottinghamshire, where 56 percent did. That’s a difference of 28 percentage points in support for Brexit. According to the data and our statistical analysis, of these 28 points, at least 18 are attributable to the difference in the intensity of the “import shock” between the two regions.
 

[6]Incidentally, one of many of Mao’s devastating policies was the establishment of the hukou system which, essentially, branded people as agricultural or non-agricultural and stopped the movement of people who lived in rural areas to urban areas. As Ha et al note the ‘gross loss induced by the labor market segmentation from 1960 to 1978 amounted to 20%–60% of GDP.’ Commies are the worst.
 

[7] If you look at Docuqier’s model he says that one of the reasons the benefits of immigration is overstated is because we don’t take into account their education levels. It’s simply the case that, with some exceptions, most university educated immigrants come from universities that aren’t as good as Western universities. Accordingly, in Docquier’s model, he gives the following example: ‘each college graduate from Angola is considered as a combination of 0.73 of an actual college-educated worker and 0.27 of a less-educated worker.’
 

The issue is put more starkly by Mattoo et al (2008) which looks at the discrepancy across immigrant groups:
 

Even after we control for age, experience and education level, we find that highly educated immigrants from certain countries are less likely to obtain skilled jobs. For example, a hypothetical 34 year old Indian college graduate who arrived in 1994 has a 69% probability of obtaining a skilled job while the probability is only 24% for a Mexican immigrant of identical age, experience and education.
 

It’s interesting to consider is what this means for the signalling model of education debate. We have the following options as to explain this disparity:

1. The market discriminates against immigrants irrationally / based on taste; or
2. Signalling model is wrong: the immigrants are not as skilled despite having equivalent qualifications because their universities have not given them said skills; or
3. Signalling model is correct: the universities aren’t as good and so the signal it sends to the market is “immigrants are not as good”.  

We can discount (1) assuming Becker’s model of discrimination holds true: the market punishes employers who discriminate. If you don’t want to hire someone on the basis of taste discrimination, you essentially impose a cost on yourself. I believe this model to be true, see for example Weber and Zulehner (2014) who look at discrimination against women and how this impacts the survivability of said companies. They find that there is a

strong indication for a negative effect of relative female share on exit rates [i.e., going out of business], which is not diminished by the inclusion of a rich set of other productivity relevant variables in the regression model. This effect is mainly concentrated at the bottom of the distribution: firms with relative female shares in the bottom quartile exit about 18 months earlier than firms with median share of females… highly discriminatory firms that manage to survive submit to market powers and increase their female workforce over time.
 

Levine et al (2012) is another excellent example. In the 1990s, there was a liberalisation of the U.S banking system. It may surprise many to know that nationwide banking was only permitted in from the 1990s. Levine looks at the effect of the deregulation in the 1990s on the disparity between black and white Americans. The deregulation had two impacts: first, it allowed the entry of non-financial firms which could expand credit and second, consistent with Becker’s model of taste discrimination, the market forced those with bigoted views to incur financial cost where they hadn’t before. The result was a rise in relative wage rates between blacks and whites:

The graph on the right shows the percentage change in relative wages of blacks in states where racism was effectively higher than the median and the right shows the percentage change in relative wages of blacks in states where was racism was below the median. Importantly, there is an increase in both thereby proving the benefits of increased competition and the validity of Becker’s model.  

So, what's true out of (2) and (3)? Annoyingly, I haven’t seen papers that directly address the question in a convincing way. Freidberg (2000) looks at the influx of immigration into Israel. As with the other studies, she finds the same discrepancy (see graph above). She also finds that 'the earnings gap between immigrants and natives can be fully explains by the lower value placed on the immigrants' human capital' and, indeed, once you account for this immigrants in Israel earn roughly 37% more than native Israelis. This doesn’t tell us much about the origins of the human capital though.

Mattoo et al (2008) mentioned above also look at this question. They look at “nominally identical” degrees among immigrant groups. They find that:

a large part of this country-level variation can be explained by certain country attributes. Some of these attributes affect the quality of human capital accumulated at home, such as expenditure on tertiary education and the use of English as a medium of education. Other attributes lead to a selection effect, i.e. variation in the abilities of migrants because they are drawn from different sections of the skill distribution of their home countries, and include the GDP per capita, the distance to the US, and the openness of US immigration policies to residents of a given country.
 

The fact that returns to education are impacted by the amount spent on tertiary education could be because under-funded schools don’t give people the skills they need or it could be that the market sees under-funded schools as not giving the right signals (or it could be both: lacks of skills -> no good market signal). Mattoo et al also find one other variable that matters: the effect of military conflict. As they go onto say:

the negative sign on the coefficient of the military conflict variable implies that the average quality of immigrants seem to increase with political stability… The existence of military conflict in the home country can have both a quality effect, because it weakens institutions that create human capital, and a selection effect, because it lowers the threshold quality of immigrants.
 

Again, military conflict leading to crapper schools makes sense but disentangling this effect from the signal that such education systems gives is difficult. Finally, I recently read The Knowledge Capital of Nations: Education and the Economics of Growth by Hanushek and Woessmann (2015). In one part of the book, they compare immigrants who have been taught in the U.S., vs those who have been taught in their countries of origin. Their main results show that being educated in the country or origin reduces average earnings in the U.S by 6 to 13% except for English speaking immigrants (where there is no reduction) after a series of controls. An increase in average test scores for those educated in their country of origin leads to 16% increase in earnings in the U.S. Hanushek and Woessman then go onto say:
 

The estimates do, however, provide direct support for the production view of schooling as contrasted with the signalling or screening view. As indicated above, one approach to identifying production versus signalling is to rely on what happens during schooling as opposed to the market returns to school attainment. The results above provide just such evidence because they show that the quality of different schools and the cognitive skills related to different schooling have direct payoffs within the same market (p100-101).  
 

I could be being incredibly dim but I’m not sure if this follows. It could be that the market is looking at signals for good schools – and this could account for the results. I do not have a view of whether the signalling model of education is correct, but I think future research designs utilising immigrants is an under-explored way of obtaining an answer.

 

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