The alleged need for ‘crime rates broken down according to country of origin’: the Dutch government’s reasoning

In a letter shown below, three cabinet ministers present their reasoning behind the government’s practice of combining data on crime rates and people’s country of birth.

English translation (original text in Dutch is shown below this translation):

Dear Mr Leerdam, Mr Saadane and Mr Baran,

Also on behalf of the Ministers of Justice and Security and the Interior and Kingdom Relations, I would like to thank you very much for your letter with recommendations on the way in which Dutch government organisations publish about crime figures and migration backgrounds. I hereby send you my response to the letter, with apologies for the delay.

First of all, I would like to state that we in the Netherlands generally find it unacceptable that a person’s place of birth — or that of his or her parents — determines the possibilities, opportunities and social position. This applies without prejudice to persons with a migration background.

In your letter you indicate that, since the 1990s, various government publications have linked figures on criminal behaviour to the country of origin of the inhabitants of the Netherlands, which are also broken down into generations (first, second and third generations). You have also conducted research into this form of categorisation in relation to crime figures in government publications. As part of this study, you spoke with some administrators and researchers of organisations that publish such figures. You indicate that the questions about necessity, proportionality, falsifiability and distribution of responsibilities have been answered unsatisfactorily and sometimes insufficiently. Finally, you call for a reconsideration and revision of the way in which government organisations communicate about the origin of suspects of crime. You state that with continued government communication about crime and origin, a number of conditions must apply. Among other things, the government should try to include some other relevant variables in (data) analyses, such as the extent to which networks (families and/or friends) play a role, ethnic profiling and the educational level of parents.

Reading Guide
With this letter I am answering your letter — also on behalf of the Minister of JenV and the Minister of the Interior and Kingdom Relations. I would like to address the following points:
1. The need to break down crime figures by origin;
2. the objective of government policy and the role of categorisation by origin;
3. the importance of explanatory analyses for the deployment of effective policy.

Need for a breakdown of crime figures by origin

After careful consideration of the pros and cons, I come to the conclusion that crime figures broken down by countries of origin per population group are necessary for an effective and efficient policy. I do think it is important, as you indicate in your letter, to always make a critical assessment of whether and when these figures are really necessary, and only compile them in those situations when this is really the case. As far as I’m concerned, the necessity may lie in policy and political-social considerations. With regard to the latter, I would like to refer to the motion by MP Van der Staaij. This motion notes that the crime figures still show a worrying over-representation of people with a non-Western migration background and therefore asks the government to recognise and investigate this problem and to develop a targeted approach to reduce both the crime in these groups as well as to reduce crime in general.

Although the descriptive statistics that contain a breakdown by ethnic background are therefore necessary for policy making, and more particularly for evidence-based policy making, I fully realise that they can have the unintended side effect of confirming and reinforcing stereotypes and prejudices. In addition to descriptive statistics, police registrations are also an important source of policy information. Incidentally, these registrations themselves do not contain origin data, since these are sensitive data that may only be registered under strict conditions (such as subsidiarity and purpose limitation), but by enrichment with birth data from the Personal Records Database (BRP) it is possible — within the legal preconditions of privacy legislation — to compile anonymous statistics that form a reliable basis for policy development and evaluation.

Your letter of 8 April 2019 has prompted further reflection, especially on crime figures in relation to ethnic background. After all, such data can contribute to a self-reinforcing mechanism through the possible confirmation of stereotypical images. At the same time, a more detailed interpretation of the figures can actually contribute to disproving stereotypes, because then it shows that overrepresentation of certain ethnic groups can often be traced back to generic background characteristics.
Therefore, based on your recommendations, I intend to combine these descriptive insights as much as possible with analyses that provide insight into the factors with which a — possible — over- or underrepresentation of groups in crime can be explained. A good example of this are the so-called decomposition analyses that were recently carried out by Statistics Netherlands and published in the Annual Report Integration 2020. After all, they showed that for almost all country-of-origin groups the overrepresentation of young adults with a second-generation migration background in the conviction for crimes can be traced back to generic background characteristics such as education, parental and family structure, material wealth of the parents, living environment and (previous) parental convictions. If these categories with a migration background are compared with Dutch people without a migration background with the same background characteristics, the over-representation in the conviction rates disappears. In other words, no specific (country-of-origin) factors play a role in crime in those categories. These kinds of insights are fundamental for the instrumentation of policy: after all, when this is the case, this means that the policy goal of equal positions (as outlined later in this letter) does not require a specific (target group) policy, but that a generic approach will suffice. I intend to draw attention to this insight more clearly, in future government communication.

Objective of government policy and the role of categorisation by country-of-origin
Since the 1980s, the Dutch government has pursued a policy aimed at combating inequalities in opportunities and social positions. The ultimate goal of the policy is equal positions for all Dutch people, regardless of background. However, because Dutch people with a migration background are on average less educated than the group without a migration background, it is unrealistic to assume that they can achieve an equal position on the labor market, for example. That is why the policy has an intermediate objective, namely proportional positions. Proportionality exists if the different origin groups with the same background characteristics (eg age, gender and education) occupy a comparable position in society. In that case, these background factors — just as in the group without a migration background — explain the difference in factual position and someone’s migrant background therefore no longer plays an autonomous role.

In order to be able to monitor the progress of this policy, the Ministry of Social Affairs and Employment (SZW) periodically collects data, amongst others by Statistics Netherlands (CBS), about the position of population groups with different (migration) backgrounds, about the differences in those positions in relation to each other, and about how those differences develop over time. This descriptive information about the actual positions that groups take in relation to each other is indispensable for the monitoring of this policy. Insights into the extent to which these differences diminish over time and positions move towards each other provide information about target goals and the effects of the policy pursued. This applies to areas such as work and income, education and housing, but this type of information is also of equal importance in the area of crime. This provides important insight into the position of individuals and groups.

In this respect, the data collected with regard to the position of people with a migration background is limited to the first and second generations. Data on the third generation are not collected in this context.

Importance of explanatory analyses for the deployment of effective policy
At the same time, it should be noted that although these statistics play an indispensable role in a policy process, they ‘only’ provide descriptive information. In addition, for policy development, adjustments and instruments a desire exists for explanatory analyses that provide insight into the ‘how and why’ of the developments identified. This could include, for example, the insight or the realisation that generic policy instruments will suffice for policy objectives, or whether specific targeted instrumentation makes sense. The first is the case if there is proportionality: after all, generic background characteristics fully explain the differences in position — just as in the group without a migration background. If that is not the case, that’s a cause for targeted attention. A good example of this can be found in the labor market: background characteristics such as education, age, gender and work experience explained only part of the less favourable position of groups with a non-Western migration background in the labor market. Specific attention to ethnic background in the collection of data is necessary in this case to be able to explain the difference in position. This insight was the direct reason for the development and implementation of the policy program Further Integration in the Labor Market (VIA).

Final remarks
In conclusion, I would like to point out that considerations regarding which data are supportive for policy are not static. We shape our policy using scientific research and analyses, which can be generic or specific. As mentioned, I will make an effort to bring explanatory background characteristics more clearly into the limelight in future government communication about crime figures. As the policy goal of equal or proportional positions regardless of origin increasingly comes within reach, the need for such a classification of data will diminish. That is why, together with my colleagues in this cabinet, I will continue to fight for equal opportunities to enable full social participation of everyone in our diverse society. If there is reason to do so, I would like to involve various interested civil society organisations in a dialogue about the use and necessity of such categorisation of data in the future.

Yours faithfully,
The Minister of Social Affairs and Employment