When a person goes missing several pieces of demographic information are collected; age, gender, and ethnicity. However, there can be large discrepancies between police forces regarding the methods they use to collect this information, leading to gaps in the statistics and our understanding of who goes missing.
Currently police use the following categories when collecting a persons’ ethnicity: White, North European; White, South European; Black; Asian; Chinese, Japanese, or South East Asian; Middle Eastern; and Unknown. Based on a 2019/2020 report by the National Crime Agency, in the following cities over 60% of missing persons ethnicity were categorised as unknown: Greater Manchester (100%), Durham (100%), Sussex (100%), Cumbria (91.2%), City of London (90.7%), Hampshire (72.5%), and Avon and Somerset (61.5%). However, people from Unknown/Other ethnic groups only make up 1.00% of the total population, according to the Office of National Statistics. Therefore, these large figures raise concerns about the quality of ethnicity data.
Ethnicity data is used to help law enforcement understand the experiences of different ethnic groups and to develop appropriate strategies to address and mitigate systematic bias. Inaccurate or incomplete data therefore impairs policy makers’ ability to protect vulnerable groups and threatens public confidence in police data collection process. However, when it comes to unifying the information of individual police forces for the national data report, it is not always a straightforward matter.
Louise Newell, Operations Manager of the National Crime Agency Missing Persons Unit, explained that, “all forces record ethnicity and age; however, all forces use different computer systems and even those that use the same ones, use different versions of that system.” Therefore, certain forces are unable to pull specific data off their systems for collation, creating gaps in the data when all of the information is aggregated together. In the case of Greater Manchester (100%), Durham (100%), Sussex (100%), the appropriate demographic information might be recorded in their individual systems, but because they cannot separate the data as requested, “they might not provide a return at all,” Newell says. In which case the total number of missing incidents are reported as Unknown. However, for forces who can provide the appropriate data sets, the cause behind their large figure of unknown cases remains unexplained.
Ethnicity data can be self-reported, reported by the individual, or proxy-reported, reported about an individual by someone else. In the case of a missing person, ethnicity data is often proxy-reported. While this can lead to issues because ethnicity is complex, informed by parts of a person's identity, race, language, religion, place of birth, traditions and practices, ethnicity data which is proxy reported by someone who is related to the person or knows them well is the regarded as a viable alternative when self-reporting is not possible. Therefore, if proxy-reported data from related individuals is reliable why, large figures from police forces, like Cumbria (91.2%), City of London (90.7%), Hampshire (72.5%), and Avon and Somerset (61.5%), remains concerning. However, sometimes officer's perception of an individual's ethnicity must be used because it is the only source for information, as is the case with unidentified bodies.
This collection method is also referred to as “Officer-identified data”. A report by the Race Disparity Unit, indicates that “Officer-identified data can raise issues because the data is only based on visual appearance, it assumes ethnicity can be determined by someone’s race.” For individuals who self-report as White or Black, officer-identified ethnicity often matches to a reasonable quality for these ethnic groups. However, people who self-report as mixed are often wrongly identified as Black, while people who self-report as Chinese or Other are often incorrectly classified as White. This misclassification or under classification of certain demographics could mask the need for individualised support for specific ethnic groups or heighted police efforts towards protecting vulnerable populations.