The performance of immigrants from Yugoslavia in Denmark and Norway

This is actually an older post, but by accident I posted it on the Danish language sister blog.

I don’t know what study that is, however, I do have numbers for the performance of Yugoslavians in Denmark and Norway. There are both numbers for persons from Yugoslavia when that was one legal entity (actually multiple different with the same name) as well as for some of the constituent countries.

First a brief review. Many studies have looked at immigrant performance by macro-origin and recently country of origin. The country of origin studies are more useful because immigrants from e.g. “Asia” (which may or may not include the Muslim countries such as Afghanistan) are not very homogeneous. Cambodians and Chinese are different, but both are East Asians. Afghans are very different, but are sometimes included in the category. Mixing these together in a hodgepodge makes for uninterpretable results, especially when who is included changes over time and from study to study. For instance, whether a country is considered Western may depend on EU membership, which means that lots of non-Western became Western recently.

Because I was unsatisfied with the existing macro-origin studies for Denmark (most only found in Danish, but I guess I should do an English-language review some day) I began carrying out a series of such immigrants by country of origin studies with the underlying goal being to test the spatial transferability hypothesis (Fuerst’s name), which is that 1) when people move, they generally retain their mean levels of psychological traits, 2) and as a consequence, the effects of these traits follow them as well. Selective immigration and emigration makes this more difficult to test.

The two best such studies cover Denmark and Norway. They are the best because they include a large number of countries of origin and have data for many socioeconomic outcome variables. In brief, the S factors were extracted from available information regarding. income, educational attainment, crime, employment and use of social benefits. See the original papers for details (e.g. with regards to imputation).

The correlation between the S scores from DK and NO is .78 [CI .64 to .86], N=55. N’s by country are 70 and 67, DK and NO respectively. The table below shows all the data.

Abbrev. Country S in DK S in NO
AFG Afghanistan -1.38 -1.09
ARG Argentina 0.75
AUS Australia 1.131 1.03
AUT Austria 0.947 1.02
BDI Burundi -0.54
BEL Belgium 1.089 1.16
BGR Bulgaria 0.811 0.17
BIH Bosnia and Herzegovina -0.913 0.49
BRA Brazil 0.457 -0.34
CAN Canada 1.145 1.03
CHE Switzerland 1.119 1.13
CHL Chile 0.279 0.25
CHN China 0.627 0.61
COG Congo Rep. -1.07
COL Colombia 0.26
CZE Czech Republic 0.249 0.43
DEU Germany 0.846 1.04
DNK Denmark 1
DZA Algeria -0.775 -1.52
EGY Egypt Arab Rep. -0.241
ERI Eritrea -0.43
ESP Spain 0.788 0.52
EST Estonia 0.717 0.19
ETH Ethiopia -0.586 -0.16
FIN Finland 0.891 0.78
FRA France 1.098 0.97
GBR United Kingdom 0.848 1.14
GHA Ghana 0.162 0.03
GMB Gambia The -0.84
GRC Greece 0.613 0.61
HRV Croatia -0.12 0.54
HUN Hungary 0.837 0.45
IDN Indonesia 0.126 0.33
IND India 0.528 0.63
IRL Ireland 0.876
IRN Iran Islamic Rep. -0.688 -0.35
IRQ Iraq -1.654 -2.26
ISL Iceland 0.555 0.76
ISR Israel -0.061
ITA Italy 0.775 0.86
JOR Jordan -1.191
JPN Japan 1.018
KEN Kenya 0.088 -0.24
KSV Kosovo -0.43
KWT Kuwait -2.619
LBN Lebanon -2.027 -1.03
LKA Sri Lanka -0.749 -0.14
LTU Lithuania 0.897 -0.08
LVA Latvia 0.685 0.06
MAR Morocco -1.031 -0.63
MKD Macedonia FYR -0.439 -0.19
MMR Myanmar -1.812 -0.27
NGA Nigeria 0.336 -0.53
NLD Netherlands 1.118 1.11
NOR Norway 0.842
NPL Nepal 0.75
PAK Pakistan -0.679 -0.87
PER Peru 0.1
PHL Philippines 0.362 0.58
POL Poland 0.463 -0.02
PRT Portugal 0.631 0.54
PSE West Bank and Gaza -3.8
ROU Romania 0.703 0.31
RUS Russian Federation 0.447 -0.44
SDN Sudan -1.52
SOM Somalia -2.054 -3.06
SRB Serbia -1.931 0.46
SUN USSR 0.166
SVK Slovak Republic 0.42
SWE Sweden 0.766 1.03
SYR Syrian Arab Republic -1.997 -1.62
THA Thailand -0.233 -0.03
TUN Tunisia -0.825
TUR Turkey -1.42 -0.52
TZA Tanzania -0.254
UGA Uganda -0.341
UKR Ukraine 0.686 0.34
USA United States 1.259 0.97
VNM Vietnam -0.582 -0.11
YU2 Former Yugoslavia2 (Found in some Danish sources) -1.611
YUG Former Yugoslavia -1.247
ZAF South Africa 0.731


I have marked the Yugoslavian countries in italics above. The table below shows the Yugoslavian subset table:

Abbrev. Country S in DK S in NO
BIH Bosnia and Herzegovina -0.913 0.49
YUG Former Yugoslavia -1.247
YU2 Former Yugoslavia2 (Found in some Danish sources) -1.611
KSV Kosovo -0.43
MKD Macedonia FYR -0.439 -0.19
SRB Serbia -1.931 0.46


In both countries, the immigrants don’t perform well Well here means around native levels which is around +1. The natives are not found in the tables above because they are not immigrants. They perform worse in Denmark, in some cases by no small amount, which is somewhat puzzling. An S difference of 2.4 is case of Serbia is much larger than would be expected by sampling error (1.4 for BIH). Maybe differential selection. Looks like Denmark received more refugees than Norway despite similar population size, consistent with lower selection threshold for DK.


Crime, income, educational attainment and employment among immigrant groups in Norway and Finland

Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark.