[NoahCarl]

Journal:
Open Quantitative Sociology & Political Science

Authors:
Noah Carl

Title:
Net Opposition to Immigrants of Different Nationalities Correlates Strongly with Their Arrest Rates

Abstract:
Public beliefs about immigrants and immigration are widely regarded as erroneous. For example, members of the public typically overestimate the immigrant fraction of the population by ~10–15 percentage points. On the other hand, popular stereotypes about the respective characteristics of different groups (e.g., sexes, races, nationalities) are generally found to be quite accurate. The present study shows that, in the UK, net opposition to immigrants of different nationalities correlates strongly with the log of immigrant arrests rates (r = .77; p = 0.00002; 95% CI = [.52, .90]) and with the log of their arrest rates for violent crime (r = .77; p = 0.00001; 95% CI = [.52, .90]). This is particularly noteworthy given that Britons apparently think that an immigrant’s criminal history should be one of the most important characteristics when considering whether he or she should be allowed into the country. Moreover, the correlations are not accounted for by a general opposition to non-Whites, non-Westerners, or foreigners who do not speak English. While circumstantial in nature, the study’s findings suggest that public beliefs about immigrants are more accurate than is often assumed.

Key words:
Immigrants; attitudes; stereotypes; crime rates

Length:
~2,400 words; 7 pages

Files:
https://osf.io/mpq5n/

[Emil]

I read the paper. I have no particular objection to the methods or interpretation.

However, it's a shame that this data would only be used for this small analysis. This paper is, to my knowledge, the first to present and analyze immigrant performance data for broken down by country of origin/nationality for the UK, and so it would be very relevant to see if the usual predictors -- national IQ and Islam prevalence -- perform well in the UK sample as well. It would also be very relevant to see if there is an S factor, as there was for Denmark and Norway.

Thus, I propose that you extend this study to include more diverse outcome data and analyze whether these can be predicted by national IQ/Islam prevalence as previously reported. For this reason, I spent some time searching for data for you. I used search terms like "united kingdom", "country of birth", "country of origin", "nationality", "statistics" and then added a word for the particular type of outcome I was looking for, e.g. crime, income, employment. This produced the following results:

You have crime rates, but you can get prisoners per capita by nationality:

https://www.gov.uk/government/collection...-quarterly

These numbers cover a lot more countries. See table 1.11 (in the yearly version spreadsheet). There's prisoner rate data for about 200 countries. You just need to pair it with matching population count data for the same periods. The data covers 2002 to 2016, so one can average out a lot of the randomness for the smaller groups.

This source has welfare use by country of origin for a small number of countries.

https://www.migrationwatchuk.org/briefing-paper/367

It's probably possible to find some more data. I particularly would like to see:
- (un)employment rate
- average income
- educational attainment -- most relevant when they attended school in the UK
- cognitive/scholastic scores, GCSE for instance
- health indicators, such as smoking

It is possible to impute data for groups that have e.g. 2/3 datapoints.

Let me know what you think. I realize this would take a lot more work, but I think it is worth it. Recall how much your previous terrorism paper improved by using multiple data sources.

[NoahCarl]

However, it's a shame that this data would only be used for this small analysis. This paper is, to my knowledge, the first to present and analyze immigrant performance data for broken down by country of origin/nationality for the UK, and so it would be very relevant to see if the usual predictors -- national IQ and Islam prevalence -- perform well in the UK sample as well. It would also be very relevant to see if there is an S factor, as there was for Denmark and Norway.

Thus, I propose that you extend this study to include more diverse outcome data and analyze whether these can be predicted by national IQ/Islam prevalence as previously reported.

While I agree an analysis of how well country-of-origin variables predict immigrant performance in the UK would be very interesting, the purpose of my paper was somewhat different, namely to explore whether Britons' immigration attitudes are based on well-informed beliefs about the respective characteristics of different immigrant groups––which I believe is also an interesting question. I would therefore prefer to keep the focus of the analysis roughly as it is now.

For this reason, I spent some time searching for data for you. I used search terms like "united kingdom", "country of birth", "country of origin", "nationality", "statistics" and then added a word for the particular type of outcome I was looking for, e.g. crime, income, employment. This produced the following results:

You have crime rates, but you can get prisoners per capita by nationality:

https://www.gov.uk/government/collection...-quarterly

These numbers cover a lot more countries. See table 1.11 (in the yearly version spreadsheet). There's prisoner rate data for about 200 countries. You just need to pair it with matching population count data for the same periods. The data covers 2002 to 2016, so one can average out a lot of the randomness for the smaller groups.

I considered using prison population data in my analysis, but the sample sizes were indeed very small for some countries. For example, there are only 3 Argentinian prisoners and 4 Japanese prisoners in 2012. Because prison population is a stock measure, averaging over multiple years doesn't make that much difference––many of the prisoners from a given country who are incarcerated in one year will also be incarcerated in the next year. And obviously, with n = only 23 cases in the analysis, measurement error on just a few cases can make a lot of difference to the estimated correlations and regression coefficients. The arrest data, which I chose to utilise instead, have the virtue of much larger sample sizes: no country in the sample had fewer than 100 total arrests over the time period in question.

This source has welfare use by country of origin for a small number of countries.

https://www.migrationwatchuk.org/briefing-paper/367

It's probably possible to find some more data.

These data are probably of reasonably high quality. However, they are only available for about half of the 23 countries in my analysis. And, obviously, n = 23 already represents a small sample size.

I particularly would like to see:
- (un)employment rate
- average income
- educational attainment -- most relevant when they attended school in the UK
- cognitive/scholastic scores, GCSE for instance
- health indicators, such as smoking

It is possible to impute data for groups that have e.g. 2/3 datapoints.

Let me know what you think. I realize this would take a lot more work, but I think it is worth it. Recall how much your previous terrorism paper improved by using multiple data sources.

I agree an S-factor analysis of UK data would be highly interesting and informative. But I would claim that investigating how opposition to immigrants from different nationalities relates to their involvement in crime––the purpose of my study––is also interesting and informative. My hunch is that many social scientists, especially those ignorant of the stereotype accuracy literature, would be quite surprised to learn that net opposition to different immigrant groups is reasonably well-calibrated to their rates of criminality.

Obtaining reliable data at the level of the immigrant nationality appears to be quite difficult. The UK census only reports figures at the level of the broad region (e.g., South Asia), which isn't particularly helpful. And most social surveys don't have enough cases to get a reliable average for some of the smaller immigrant groups.

I would reiterate that, if reliable data on a large number of immigrant groups within the UK do become available, it would certainly be worth conducting an S-factor analysis. But in the meantime, I believe my analysis of public attitudes merits publication in its own right.

[Emil]

Fair enough. Let me replicate your analysis in R and then you have my approval.

[Emil]

Some comments.

Raw support data
Raw data for net support/oppose are not given. Please give the raw support numbers, so that others may calculate other metrics for net support. Your operationalization, while reasonable, is not the only reasonable one.

More exact sources
The spreadsheet gives sources in Sheet 2, but there are no links. There should be. If the source files are xlsx files or similar (and not e.g. a web browser interface), please include them as well.

White
The White majority variable is not explained in the paper. Was this filled out by the author's judgment or how was it done? Note that "White" is a kind of sociological category, not necessarily a homogeneous genetic category. For instance, persons who self-identify as non-Hispanic White in the United States are about 99% European genetically. However, persons who self-identify as Blanca (White) in Latin America are often only about 80% European genetically. You could use the World Migration Matrix to fill in the data. It's the Putterman and Weil dataset.

http://www.brown.edu/Departments/Economi...matrix.htm

English language
No source is given for the categorical English language variable. This variable is also not explained in the paper. Since it's coded as categorical, presumably it reflects official language status. Official language status, however, is a poor correlate of actual speakers. Consider using proportion of English speakers by country, such as:

https://en.wikipedia.org/wiki/List_of_co...population

It's doubtful that most people know that English is an official language in e.g. Pakistan, as you have coded it.

https://en.wikipedia.org/wiki/List_of_te...l_language

Reversely, English is not an official language in any Nordic country (+Netherlands), but these countries have the best English speakers of non-native countries. Of course, English ability is to some degree confounded with cognitive ability.

https://en.wikipedia.org/wiki/EF_English...ency_Index

Analytic replication
No inconsistencies.

[NoahCarl]

Raw data for net support/oppose are not given. Please give the raw support numbers, so that others may calculate other metrics for net support. Your operationalization, while reasonable, is not the only reasonable one.

These data have now been included in the supplementary data file.

The spreadsheet gives sources in Sheet 2, but there are no links. There should be. If the source files are xlsx files or similar (and not e.g. a web browser interface), please include them as well.

Links to data sources have now been included in the supplementary data file.

The White majority variable is not explained in the paper. Was this filled out by the author's judgment or how was it done? Note that "White" is a kind of sociological category, not necessarily a homogeneous genetic category. For instance, persons who self-identify as non-Hispanic White in the United States are about 99% European genetically. However, persons who self-identify as Blanca (White) in Latin America are often only about 80% European genetically. You could use the World Migration Matrix to fill in the data. It's the Putterman and Weil dataset.

Percentage of the population that is white has now been used in place of white majority. These data were taken from Wikipedia and the CIA World Factbook.

No source is given for the categorical English language variable. This variable is also not explained in the paper. Since it's coded as categorical, presumably it reflects official language status. Official language status, however, is a poor correlate of actual speakers. Consider using proportion of English speakers by country

Percentage of the population that speaks English has now been used in place of English as an official language. These data were taken from the Wikipedia page recommended by Emil. Note that the coding for Western country has now been altered slightly (Israel = non-Western), in accordance with Huntingdon's (1996) classification.

[Emil]

All changes seem good to me.

I had a look a the datafile too. Seems to be in order.

I saw that the numbers were updated in the paper for the regression models. I re-run my code and my numbers fit again. The only discrepancy was that you changed the variable name from "Western" to "West", which broke my code.

I approval of publication.

[Emil]

One final suggestion. Add some identifier for the UK in the title. I imagine that I will run a replication study about this for Denmark some time next year, so it would be nice if they could be easily grouped with similar, but distinctive titles.

[NoahCarl]

One final suggestion. Add some identifier for the UK in the title. I imagine that I will run a replication study about this for Denmark some time next year, so it would be nice if they could be easily grouped with similar, but distinctive titles.

I have changed the title to: 'British Opposition to Immigrants of Different Nationalities Correlates Strongly with Their Arrest Rates'.

[Emil]

Sounds good.