Studies using national IQs for predicting immigration outcomes

Below I list all the studies I am aware of that use national IQs in studies of immigrants, usually as an estimate of their IQ levels in the host country. Most of these studies are authored by me and coauthors, but not the first two from 2010, which were seemingly independently conceived by two sets of academics. I count 14 papers in this literature so far, and 1 unpublished meta-analysis of these.

Search was done this way:

  1. Looked over the citing studies of Jones & Schneider 2010, and Vinogradov., & Kolvereid 2010 on Google Scholar, looking for anything that seemed relevant.

  2. Copied in all my own listed using my website front page as list.

  3. Looked in Lynn’s Race difference book (2nd ed) for any mentions of immigration/immigrants. Since Lynn is the originator of national IQs, he presumably would be on the lookout for any study using them.

So it is possible I missed studies that do not cite the two 2010 studies, but do cite some of Lynn’s work. Furthermore, it’s possible there are some studies that use other datasets to do essentially the same thing, e.g. Altinok’s scores, Rindermann’s scores, or other datasets.

  • Jones, G., & Schneider, W. J. (2010). IQ in the production function: Evidence from immigrant earnings. Economic Inquiry, 48(3), 743-755.

We show that a country’s average IQ score is a useful predictor of the wages that immigrants from that country earn in the United States, whether or not one adjusts for immigrant education. Just as in numerous microeconomic studies, 1 IQ point predicts 1% higher wages, suggesting that IQ tests capture an important difference in cross‐country worker productivity. In a cross‐country development accounting exercise, about one‐sixth of the global inequality in log income can be explained by the effect of large, persistent differences in national average IQ on the private marginal product of labor. This suggests that cognitive skills matter more for groups than for individuals. (JEL J24, J61, O47)

The level of self-employment varies significantly among immigrants from different countries of origin. The objective of this research is to examine the relationship between home-country national intelligence and self-employment rates among first generation immigrants in Norway. Empirical secondary data on self-employment among immigrants from 117 countries residing in Norway in 2008 was used. The relevant hypothesis was tested using hierarchical regression analysis. The immigrants’ national intelligence was found to be significantly positively associated with self-employment. However, the importance of national IQ for self-employment among immigrants decreases with the duration of residence in Norway. The study concludes with practical implications and suggestions for future research.

Many recent studies have corroborated Lynn and Vanhanen’s worldwide compilation of national IQs; however, no one has attempted to estimate the mean IQ of an immigration population based on its countries of origin. This paper reports such a study based on the Danish immigrant population and IQ data from the military draft. Based on Lynn and Vanhanen’s estimates, the Danish immigrant population was estimated to have an average 89.9 IQ in 2013Q2, and the IQ from the draft was 86.3 in 2003Q3 (against a ‘Danish’ IQ of 100). However, after taking account of two error sources, the discrepancy between the measured IQ and the estimated IQ was reduced to a mere 0.4 IQ. The study thus strongly validates Lynn and Vanhanen’s national IQs.

We discuss the global hereditarian hypothesis of race differences in g and test it on data from the NLSF. We find that migrants country of origin’s IQ predicts GPA and SAT/ACT.

Criminality rates and fertility vary wildly among Danish immigrant populations by their country of origin. Correlational and regression analyses show that these are very predictable (R’s about .85 and .5) at the group level with national IQ, Islam belief, GDP and height as predictors.

A previous study found that criminality among immigrant groups in Denmark was highly predictable by their countries of origin’s prevalence of Muslims, IQ, GDP and height. This study replicates the study for Norway with similar results.

We obtained data from Denmark for the largest 70 immigrant groups by country of origin. We show that three important socioeconomic variables are highly predictable from the Islam rate, IQ, GDP and height of the countries of origin. We further show that there is a general immigrant socioeconomic factor and that country of origin national IQs, Islamic rates, and GDP strongly predict immigrant general socioeconomic scores.

I present new predictive analyses for crime, income, educational attainment and employment among immigrant groups in Norway and crime in Finland. Furthermore I show that the Norwegian data contains a strong general socioeconomic factor (S) which is highly predictable from country-level variables (National IQ .59, Islam prevalence -.71, international general socioeconomic factor .72, GDP .55), and correlates highly (.78) with the analogous factor among immigrant groups in Denmark. Analyses of the prediction vectors show very high correlations (generally ±.9) between predictors which means that the same variables are relatively well or weakly predicted no matter which predictor is used. Using the method of correlated vectors shows that it is the underlying S factor that drives the associations between predictors and socioeconomic traits, not the remaining variance (all correlations near unity).

We argue that if immigrants have a different mean general intelligence (g) than their host country and if immigrants generally retain their mean level of g, then immigration will increase the standard deviation of g. We further argue that inequality in g is an important cause of social inequality, so increasing it will increase social inequality. We build a demographic model to analyze change in the mean and standard deviation of g over time and apply it to data from Denmark. The simplest model, which assumes no immigrant gains in g, shows that g has fallen due to immigration from 97.1 to 96.4, and that for the same reason standard deviation has increased from 15.04 to 15.40, in the time span 1980 to 2014.

Immigrants can be classified into groups based on their country of origin. Group-level data concerning immigrant crime by country of origin was obtained from a 2005 Dutch-language report and were from 2002. There are data for 57 countries of origin. The crime rates were correlated with country of origin predictor variables: national IQ, prevalence of Islam and general socioeconomic factor (S). For males aged 12-17 and 18-24, the mean correlation with IQ, Islam, and S was, respectively, -.51, .37, and -.42. When subsamples split into 1st and 2nd generations were used, the mean correlation was -.74, .34, and -.40. A general crime factor among young persons was extracted. The correlations with the predictors for this variable were -.80, .34, and -.43. The results were similar when weighing the observations by the population of each immigrant group in the Netherlands. The results were also similar when using crime rates controlled for differences in household income. Some groups increased their crime rates from the 1st to 2nd generation, while for others the reverse happened.

Two datasets with grade point average by country of origin or parents’ country of origin are presented (N=13 and 19). Correlation analyses show that GPA is highly predictable from country-level variables: National IQ (.40 to .64), age heaping 1900 (.32 to .53), Islam prevalence (-.72 to -.75), average years of schooling (.41 to .74) and general socioeconomic factor (S) in both Denmark (.72 to .87) and internationally (.38 to .68). Examination of the gap sizes in GPA between natives and immigrants shows that these are roughly the size one would expect based on the estimated general cognitive ability differences between the groups.

Number of suspects per capita were estimated for immigrants in Germany grouped by citizenship (n=83). These were correlated with national IQs (r=-.53) and Islam prevalence in the home countries (r=.49). Multivariate analyses revealed that the mean age and sex distribution of the groups in Germany were confounds.

The German data lacked age and sex information for the crime data and so it was not possible to adjust for age and sex using subgroup analyses. For this reason, an alternative adjustment method was developed. This method was tested on the detailed Danish data which does have the necessary information to carry out subgroup analyses. The new method was found to give highly congruent results with the subgrouping method.

The German crime data were then adjusted for age and sex using the developed method and the resulting values were analyzed with respect to the predictors. They were moderately to strongly correlated with national IQs (.46) and Islam prevalence in the home country (.35). Combining national IQ, Islam% and distance to Germany resulted in a model with a cross-validated r2 of 20%, equivalent to a correlation of .45. If two strong outliers were removed, this rose to 25%, equivalent to a correlation of .50.

Employment rates for 11 country of origin groups living in the three Scandinavian countries are presented. Analysis of variance showed that differences in employment rates are highly predictable (adjusted multiple R = .93). This predictability was mostly due to origin countries (eta = .89), not sex (eta = .25) and host country (eta = .20). Furthermore, national IQs of the origin countries predicted employment rates well across all host countries (r’s = 0.74 [95%CI: 0.30, 0.92], 0.75 [0.30, 0.92], 0.66 [0.14, 0.89] for Denmark, Norway and Sweden, respectively), and so did Muslim % of the origin countries (r’s =-0.80 [-0.94,-0.43],-0.78 [-0.94,-0.37],-0.58 [-0.87,-0.01]).

The relationships between national IQs, Muslim% in origin countries and estimates of net fiscal contributions to public finances in Denmark (n=32) and Finland (n=11) were examined. The analyses showed that the fiscal estimates were near-perfectly correlated between countries (r = .89 [.56 to .98], n=9), and were well-predicted by national IQs (r’s .89 [.49 to .96] and .69 [.45 to .84]), and Muslim% (r’s -.75 [-.93 to -.27] and -.73 [-.86 to -.51]). Furthermore, general socioeconomic factor scores for Denmark were near-perfectly correlated with the fiscal estimates (r = .86 [.74 to .93]), especially when one outlier (Syria) was excluded (.90 [.80 to .95]). Finally, the monetary returns to higher country of origin IQs were estimated to be 917/470 Euros/person-year for a 1 IQ point increase, and -188/-86 for a 1% increase in Muslim%.

The European Union has seen an increased number of asylum seekers and economic migrants over the past few years. There will be request to assess some of these individuals to see if they have an intellectual disability (ID). If this is to be done using the current internationally recognized definitions of ID, we will need to be confident that the IQ tests we have available are able to accurately measure the IQs of people from developing countries. The literature showing substantial differences in the mean measured IQs of different countries is considered. It is found that, although there are numerous problems with these studies, the overall conclusion that there are substantial differences in mean measured IQ is sound. However, what is not clear is whether there are large differences in true intellectual ability between different countries, how predictive IQ scores are of an individual from a developing country ability to cope, and whether or not an individual’s IQ would increase if they go from a developing country to a developed one. Because of these uncertainties, it is suggested that a diagnosis of ID should not be dependent on an IQ cut-off point when assessing people from developing countries.

This is borderline with regards to inclusion. It does not look at prediction differential immigrant group performance using national IQs, but it does discuss the national IQs at length with regards to immigration.

Not a paper yet, but I have done a meta-analysis on most of these results, which was presented at LCI 2017. It is available on Youtube. For technical output, see