New victim stats reveal Pakeha privilege

On Friday the Police released the first of their new series of statistics, “Recorded crime victims statistics”. The media release says these are an improvement on the old recorded offences numbers, and aim to “count the victims behind the crimes” in order to “provide new information about victims and a more complete picture of who is affected by crime in New Zealand”. The victims statistics will be complemented by statistics on offenders, to be released next year.

This change in reporting is a positive step towards humanising talk about crime.

Instead of abstract offences, faceless criminals and invisible victims, we can now access a story in which there are human offenders, human victims, and decisions made that have lead to crime being committed. Releasing the victims statistics first is, hopefully, an acknowledgement that victims are real people, an attitude that could eventually help to undermine our culture of victim-blaming.

The data on victims are broken down along several dimensions. I’ve crunched the numbers in search of interesting patterns in gender, ethnicity and types of crime. The table below (click to enlarge) shows who the victims were, and which groups are over- and under-represented among victims, in the three months to September 2014 (see below for the caveats on these numbers).

victimisation-2014-q3

What do we learn from this?

Firstly, Census data erases genderqueer and trans people. The population proportions in grey are from the Census, which divides the population completely into “male” and “female”. The table I was using didn’t even have an undeclared category for gender, and yet there must have been people who didn’t answer this question on their Census form. Leaving them out of the results entirely isn’t just cisnormative, it also compromises the data quality.

The Police data include undeclared categories for both gender and ethnicity. For consistency with the Census, I’ve had to leave out those numbers from the totals.

Turning to the victims who are counted, four things jump out at me.
* European women and men are incredibly privileged. They were the victims in the majority of cases this quarter, but usually less than their share of the population. That means that European people became victims less often than they walked down the street. For most other ethnicities, it’s more often.
* Asian women are privileged too, at least this quarter. They’re under-represented as victims of every type of crime.
* No crime was as gendered as sexual assault. There’s a clear pattern of over-representation among women of all ethnicities (except Asian) as sexual assault victims. Even for European women, sexual assault is the one category where they are victims in proportion with their share of the population. Female oppression trumps Pakeha privilege.
* People of colour were generally over-represented as victims. The worst-affected groups are Maori men and “other” men and women, but all POC groups are over-represented as victims of several types of crime.

None of this is all that surprising, but it’s good to have it revealed in decent high-frequency statistics. The data will be coming out every month from the end of January, so we can keep an eye on how trends evolve.

I’m happy to provide my aggregated raw data as soon as I can figure out how to upload it.

Caveats:
* These figures are for reported crimes only, for a single three-month period. Under-reporting is significant and will affect the conclusions.
* Figures are totalled across the groups shown only. “Undeclared” categories and victims who are organisations are excluded.
* The gender and ethnicity categories from the Census add up to more than 100% of the population, possibly due to the practice of randomly rounding figures to preserve confidentiality. I’ve normalised them for comparison with the Police numbers.
* My definition of over-representation is a share in victimisation by one group more than 25% higher than that group’s share of the population. For under-representation, it’s less than 25% lower. I used a multiplicative threshold, not additive, in an attempt to treat large and small groups evenly.
* In the crime category for burglary, home invasion and breaking and entering, there were no reported victims this quarter, so I’ve left it out of the table.
* The measure used is the outcome of the investigation after 30 days. The 7-day figures are also available; the number of victims is lower after 30 days than after 7 days, so I’m treating the 7-day figures as provisonal.

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