Which immigrant groups do well in Denmark and Norway and why?

Examining the evidence

Emil O. W. Kirkegaard, April 2014

Immigration in Denmark and Norway

• Denmark
• Total immigrant population of ~626k, ~12.5% of population.
• From >200 countries
• Growing rapidly:

• Norway
• Total immigrant population of ~759k, ~14.9% of population.
• From >200 countries
• Growing rapidly:

What others have done

• Divide immmigrants into western/non-western or developed/non-developed origin
• Calculate crime rates, employment rate, educational levels, etc. by macro-origin
• Typical results:

Crime rate

Employment rate

Working model: Global hereditarianism

• Dividing into macro-origin misses essential differences. It does not make sense to group China, Peru, Uganda and Japan. Must look at the country level.
• g differences between races/countries in the world are substantially genetic, e.g. >30%.
• Spatial transferability (a) when people move, they retain their relative g level to some degree.
• Spatial transferability (b) they keep their correlates of g too.

Crime rates by country of origin: Denmark

• Huge differences between countries. N=71
• Crime rate per 100 persons, age 20-29

Top 10:

Kuwait 19.2

Lebanon 18.3

Jordan 17.4

Morroco 14.2

Yugoslavia 13.8

Syria 13.8

Iraq 12.6

Somalia 12.3

Iran 11.8

Pakistan 11.5

Bottom 10:

Spain 1.5

Austria 1.5

France 1.3

Philippines 1.3

Canada 1.3

Belgium 1.2

Indonesia 1.1

USA 0.9

Australia 0.8

Japan 0.7

Crime rates by country of origin: Norway

• Lack of good data. Only N=21
• Crime rate per 1000 persons, age >15

Top 10

Somalia 47.8

Iraq 47

Iran 41

Afghanistan 36.3

Sri Lanka 36.3

Morocca 34.6

Russia 30.4

Chile 30.1

Pakistan 27

Turkey 26.7

Bottom 10

Bosnia-Hercegovina 19.6

Kosovo 19.6

Poland 19

India 11.5

China 9.6

Germany 9

Netherlands 9

Thailand 8.9

United Kingdom 7.4

Phillippines 4

Predictors of crime: Denmark

IQ

Islam

GDP

Crime: Norway

IQ

Islam

... and correlations with each other

Use of social benefits: Denmark

Islam

IQ

Employment rate: Norway

Islam

IQ

... and correlations with each other

Income: Denmark

Islam

IQ

Adult educational attainment: Denmark

Islam

IQ

General Socioeconomic factor: Denmark

Islam

IQ

General Socioeconomic factor: Norway

Islam

IQ

... and correlations with each other

Multiple regression

• Predictors can be combined to yield even better predictions.

Islam+IQ

Causation?

• Causes of differences in group behavior are aggregations of causes in individual behavior.
• g and crime.
• Group level correlations larger due to averaging out of other causes.
• Islamic countries have lower IQ, so some of the predictiveness of Islam is probably due to that.
• But Islam still has great predictive power controlling for both GDP and IQ. Culture conflict?
• Bad environment at home have lasting effects?

Future studies

• More tests of spatial transferability.
• Find more predictors.
• Path modeling.
• If voters knew this information, how would it affect their views on immigration policy?
• ... ideas?

References

2:

Danmarks Statistik. Indvandring i Danmark 2013.

Danmarks Statistik. FOLK2: Folketal 1. januar efter køn, alder, herkomst, oprindelsesland og statsborgerskab.

Statistisk Sentralbyrå. Tabell: 07459: Folkemengde, etter kjønn og ettårig alder. 1. januar (K), Tabell: 05182: Fem ulike avgrensninger av personer med innvandringsbakgrunn/utenlandsk bakgrunn, etter kjønn (F) Logg inn

3:

Emil O. W. Kirkegaard. Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immmigrant groups in Denmark. Open Differential Psychology. Submitted.

4:

John Fuerst, Emil O. W. Kirkegaard. Do National IQs Predict U.S. Immigrant Cognitive Ability and Outcomes? An Analysis of the National Longitudinal Survey of Freshman. Open Differential Psychology. 2014.

5:

Emil O. W. Kirkegaard. Criminality and fertility among Danish immigrant populations. Open Differential Psychology. 2014.

6:

Emil O. W. Kirkegaard. Criminality among Norwegian immigrant populations. Open Differential Psychology. 2014.

7:

See 5.

8:

See 6.

9:

See 5+6

10:

See 5.

11:

See 6.

12:

See 5+6.

13:

See 3.

14:

See 3.

15:

See 3.

16:

See: 6.

17:

See 3+6.

18:

See 3.

19:

Lubinski, David, and Lloyd G. Humphreys. "Seeing the forest from the trees: When predicting the behavior or status of groups, correlate means." Psychology, Public Policy, and Law 2.2 (1996): 363.

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