In my last post, I used World Bank data to look at the effect of net migration on economic growth. Net migration is defined by the World Bank as the number of immigrants (coming into a country) less the number of emigrants (leaving the country). I showed that net migration as a share of the population in 2012 (the last year with for which this data has been reported so far) is negatively correlated with growth of PPP GDP per capita from 2012 to 2015. In other words, countries where the share of immigrants as a percent of the population was larger grew more slowly than countries with a smaller proportion of immigrants.
The natural question is… does this relationship hold over a longer period of time? In this post, I will show that the answer is yes.
As to data… I will use three series compiled by the World Bank: net migration, population, and PPP GDP per capita. Net migration data is reported every fifth year beginning in 1962, and it covers five years of activity. In other words, the net migration figure for 1962 is the sum total net migration for the years 1958 through 1962. Similarly, the net migration figure for 1967 is the total for the years from 1963 through 1967. Population is available annually going back to 1960. PPP GDP per capita is available annually, but only begins in 1990. To maximize the use of the available data, and still avoid situations where growth could be leading immigration, I looked at total migration from 1958 to 1992 as a share of the population in 1992, and compared it to growth in PPP GDP per capita from 1993 to 2015.
In other words, I took a look at (roughly) the percentage of the population that had migrated over 34 years, and compared that to the growth rate from the following year to 2015, which is a period of 22 years.
There are 155 countries for which the full sample of data is available. Here’s what that graph looks like:
The correlation is -0.10. Not huge, but negative. In other words, on average, for this large sample of countries, the the more immigrants arrived over a 34 year period (relative to the country’s population), the slower that country grew over the next 22 years. This was a weak relationship, but it certainly doesn’t support the notion that increased immigration leads to more growth.
Nevertheless, the sample contains a lot of countries that won’t provide many useful insights to industrialized nations like the US. Much of the sample is made up of countries that are very small. As of 2015, 59 countries have fewer than a million people, 24 have fewer than 100,000 people, and one (Tuvalu) had less than 10,000 residents. Additionally, many of the countries have very, very different economic systems, and in a few sad cases, nothing resembling an economic system whatsoever. The next graph includes only countries that the World Bank defines as being High Income, and which had at least 1 million residents in the year 2015.
Notice that the sample size drops to 36, and the correlation becomes -0.67. But… you’d be forgiven if you decided this result must be driven by outliers. For example, the point sitting to the far right of the graph is the United Arab Emirates. According to the CIA World Factbook,
immigrants make up almost 85% of the total population, according to 2015 UN data (2016).
On the far left side of the graph, we see Trinidad & Tobago, whose net migration from 1958 to 1992 equaled -23.5% of its 1992 population. In other words, roughly a quarter of its people had left the country over a generation and a half. More colorfully, Trinidad & Tobago lost the equivalent of the population of Tobago several times over from 1958 to 1992.
Limiting our sample to countries with a more normal range of net migration / population (pruning outliers one at a time suggests the range should be from -15% to 15%) gives us this graph for wealthy countries with population in excess of 1 million:
Our sample drops to 29 countries, and the correlation between the two variables is -0.6. This tells us that the UAE and Trinidad (with or without Tobago) were not really outliers but rather the most extreme observations on the curve. And what the curve shows is that among large, high income countries, on average, nations with the most immigration as a share of their population from 1958 to 1992 also happened to be the countries with the slowest growth from 1993 to 2015. This is similar to the result of immigration on growth that we saw in the previous post when looking at the short term. Coincidence? I don’t think so.
I note that I played around with the data a bit, changing the dates, etc. Doing so moves the correlation up or down, but the negative correlation remains with any sample bearing a resemblance to the one selected. Of course, violently heroic cherry picking can provide the desired, politically correct results, but they resemble violently heroic cherry picking when you do it. I will happily send you my spreadsheet if you want to try it yourself. Or, you don’t have to trust me at all; nothing I did is difficult to replicate.
Moving along, what policy prescriptions does this suggest? Well, it is clear (not from this data, but it is obvious) that some immigration is very, very good for a country. Many an immigrant have contributed unbelievable benefits to their new homes, both for the US and for every other country in the sample. But in recent decades, the data seems to show that the positive impact of many immigrants seems to be more than outweighed by the negative impact of many other immigrants. The trick is to figure out what causes some immigrants to have negative outcomes and others to enjoy positive outcomes. Is it cultural values? The skills an immigrant brings? Family connections? Whatever it is, we should gear our immigration programs toward generating growth and benefiting the country. Right at the moment, it seems most wealthy, large countries, to the detriment of their citizens, are doing very much the opposite.
As always, if you want my spreadsheet, drop me a line at mike dot my last name (only 1 m in my last name!!!) at gmail. I’ve made the spreadsheet interactive so you can modify dates, weed out countries by various factors, select what goes on what axis, etc. If you contact me within a month of the publication of this post, I will send you the spreadsheet. If you contact me several years later, I reserve the right to have a computer crash n the interim, etc.
1. Net migration, by country available here. The most recent data is from 2012. Net migration is defined as the “total number of immigrants less the annual number of emigrants, including both citizens and noncitizens. Data are five-year estimates.” As an example, the US reportedly had net migration of 5,007,887 (i.e., positive) in 2007 through 2012, while Bangladesh had a figure of -2,226,481 (i.e., negative) in the same years.
2. PPP GDP per capita. Data available here. The last year for which data is available is 2015.