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Net Migration and Economic Growth Around the World

This post uses data from the World Bank to look at the effect of migration on countries around the world. I will begin by looking at all countries for which the World Bank has data, then drill down.

So to begin, the data used in this post:
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. That should fit with your intuition.

2. PPP GDP per capita. Data available here. The last year for which data is available is 2015.

3. Data on population through 2015.

I started by looking at immigration relative to the size of the population. I assumed that the net migration figure was the same in each of the five years. (I know – not correct, but reasonable.) I then divided the Net Migration from 2012 by Population from 2012. I then compared that to the annualized growth in PPP GDP per capita from 2012 to 2015. In other words, I looked at the Net Migration as a share of the Population in 2012 and the growth rate in the subsequent three years. I put both series up on a scatter plot.

Before I put up the graph, I would also note that I did leave some data out. It goes without saying that if a country did not report information, I did not include it. Additionally, countries reporting zero net migration were left out. After all, even North Korea has escapees, er, migrants, even if they won’t admit to it. Otherwise, everything went into the pot leaving a sample of 176 countries. Here’s what the relationship between Net Migration (from as a share of the Population in 2012 and the growth in PPP GDP per capita from 2012 to 2015 looks like for them:

Figure 1.  Net Migration div Pop 2012 v. Growth from 2012 to 2015, 176 Countries 20170112
Figure 1

The correlation is -0.32. That is, countries with higher Net Migration as a share of their Population tended to perform less well over the subsequent three years. In other words, it is better to give than to receive, at least when it comes to migrants.

Of course, if we want to understand the effect of Net Migration in the US and other Western Countries, perhaps it makes sense to narrow things down. The next graph uses only countries deemed to be “High Income” by the World Bank. I also restricted the sample to countries with populations exceeding 1 million people to avoid trying to learn life lessons based on recent happenings in Monaco or Andorra. Here’s what that looks like:

Figure 2.  Net Migration div Pop 2012 v. Growth from 2012 to 2015, 44 Countries 20170112
Figure 2

The population sample dropped from 176 countries to 44, and the correlation tightened up a bit to -0.48.

Frankly, I think the sample still needs cleaning up. Most of the points on the graph look bunched up because there are a few countries with very, very high Net Migration. For example, Oman is at 6.8%(!!!!), Qatar 3.6%, Kuwait 3.0% and Singapore 1.5%. These are mostly special cases, even for high income countries, and I would venture to say, provide very few lessons on immigration that are applicable to the US or most of the West. Limiting the sample to countries with Net Migrants to Population under 1.4%, the graph now looks like this:

Figure 3.  Net Migration div Pop 2012 v. Growth from 2012 to 2015, 40 Countries 20170112 - with corrected axis
Figure 3

This doesn’t change the outcome much, but it makes things easier to see. If desired, we can cut out one more outlier – this one on account of excessive economic growth. The point on the far right side of the graph is Ireland, bouncing back (in PPP GDP) from the monster collapse in 2007-8. Removing Ireland as well gives us this:

Figure 4.  Net Migration div Pop 2012 v. Growth from 2012 to 2015, 39 Countries 20170112 - with axis corrected
Figure 4

The absolute value of the correlation drops, but the fact remains: we are still left with a negative correlation between Net Migration as a percentage of the Population in 2012 and the growth in PPP GDP per capita between 2012 and 2015. We can do a bit more pruning, but frankly, the data simply refuses to support Holy Writ. Sure, these graphs don’t prove that immigration is bad for growth. However, they make it very, very hard to argue that immigration had a positive effect on growth during the past few years. Of course, that isn’t what we hear from our betters.

I will follow up this post with looks at other periods for which data is available from the World Bank. Meanwhile, I put together a spreadsheet that allows the user to make changes to the dates or downselect the data through income level, population, etc. It’s a bit large, but I will send it to anyone who contacts me for it within a month of the publication of this post.  I can be reached at mike and a dot and my last name (note – just one “m” in my last name) and the whole thing is at gmail.com.

 

Updated about fifteen minutes after original posting.  Figures 3 and 4 needed an additional significant digit on the Y-axis.

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US wages trail 10 OECD countries, but with higher unemployment than 9 of them

by Kenneth Thomas

US wages trail 10 OECD countries, but with higher unemployment than 9 of them

Contra Eric Cantor, Labor Day celebrates the importance of labor and the labor movement in American history. But the bluster of Cantor, where he celebrates the so-called job creators, does illustrate that organized labor has been in decline in this country for quite some time.

One result of having a weak labor movement is that average wages in the United States have fallen behind those of 10 other industrialized democracies that are members of the Organization for Economic Cooperation and Development (OECD). What is most confounding, for Republicans at least, is that nine of these countries also have lower unemployment, which contradicts their view that high wages (and high minimum wages) harm employment.

The table below below is constructed from data at OECD StatExtracts, showing the average earnings of all wage and salary workers in each country, as well as its most recent unemployment rate (usually July 2012).
Source: OECD StatExtracts.


For average wages, select data by theme, then labour, then earnings, then average annual wages, and use “2011 USD exchange rates and 2011 constant prices” for each country. For unemployment, select data by theme, then labour, then labour force statistics, then short-term statistics, then short-term labour market statistics, then harmonized unemployment rates.
 
This table does not make use of purchasing power parity (PPP) conversions to wages (and the U.S. in fact has the highest wages when adjusted for PPP), for a very important reason. Essentially, the PPP calculation adjusts actual exchange rates for differences in the cost of living between countries. In practice, this means downward adjustments for expensive countries like Norway (where I had a personal pan pizza for $25 on my honeymoon six years ago; the New York Times recently published more examples) and upward adjustments for developing countries and even Eastern European countries. As I note in Investment Incentives and the Global Competition for Capital, gross national income per capita for the Czech Republic in 2006 was $12,680 at actual exchange rates, but $21,470 at PPP (page 99).
 
The reason we should ignore PPP when dealing with wages and jobs is that a company deciding to invest in one place rather than another has to pay the wages using the actual exchange rate and is not affected by PPP. Thus, if there is an effect of wages on employment, that will be a response to what an employer actually has to pay to hire someone, not a hypothetical measure of how well off the worker is in terms of PPP-adjusted dollars. The data here does not show any negative effect of wages on unemployment.
 
Moreover, I would argue that living in a high-wage, high-cost location has distinct advantages over living in a low-wage, low cost location, even if after adjusting for cost of living (via PPP or within a single country) the lower wage location has “higher” pay. One important reason is that having extra cash gives you extra options. You will have a higher retirement benefit and will keep it if you move to a lower-cost area, whereas the reverse is not possible. You will have better quality services on average, particularly health care. It is far easier for you to vacation in a low-cost location than it will be for someone in a low-cost location to vacation to a high-cost location ($25 personal pan pizzas!).

Your high salary will be the benchmark if you take a job in a lower-cost location. If you economize from the standard basket of goods used to measure cost of living, your benefit will be higher in the high-cost area. Of course, a full treatment of this issue requires another post, but the big point is that high wages do not necessarily create unemployment and reducing wages is not the route to middle class prosperity.

cross posted with  Middle Class Political Economist

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