Ethnic minority youth employment evidence gap analysis
Data gaps can make it difficult to understand key outcomes for young people
from ethnic minority backgrounds.
Young people from minority ethnic backgrounds have long experienced higher
unemployment and lower wages than their white peers. The impact of the
Coronavirus pandemic has only exacerbated these inequalities, with research
showing an unequal impact on employment across ethnic groups. In addition,
previous work has identified substantial gaps in the collection and analysis
of data relating to young people from minority ethnic backgrounds. There are
particular concerns around systematic erasure in data collection, lack of
observations and small sample sizes, and a lack of culturally relevant data.
The data analysis and quantitative review have identified a range of gaps in
data and analysis of the employment outcomes of young people from minority
ethnic backgrounds, broadly relating to the three themes of systematic
erasure in data collection, lack of observations and sampling, and a lack of
culturally relevant data. This includes gaps in terms of availability of
ethnic breakdowns; demographic data; employment details and features of
surveys or datasets.
Two-way intersectional analysis refers to evidence that includes the analysis of an employment outcome by ethnic group by another variable, while three-way intersectional analysis refers to evidence that includes analysis of an employment variable by ethnic group by two other variables. Key: 3 = Fully met2 = Mostly met1 = Partially met0 = Not met
Capturing data about the experience of different ethnic groups, alongside
other key demographic information such as geographic region, socioeconomic
status, and religion is crucial to allowing intersectional analysis and
thereby fully understanding the experiences of different ethnic groups.
Few national datasets or other evidence include breakdowns by ethnic
groups at a sufficiently granular level to understand the impact of policy
changes or interventions, with some such as the Universal Credit
statistics not including ethnic breakdowns. This lack of data capture
impedes understanding of the uptake of specific benefits by different
ethnic groups, and is in contrast to statistics on Jobseekers Allowance or
Employment and Support Allowance.
Where datasets do include ethnicity, most focus on broad ethnic groupings
based on the standard five ethnic groups included in ONS research (white,
black, Asian, mixed, and other). Where more detailed ethnic groups are
used, this is often limited to the 16-18 detailed ethnic groups used in
ONS research, meaning that relevant subgroups, such Somali as a
subcategory of black African, are not included. This results in a lack of
understanding of the experience of these subgroups.
There are other ways in which ethnic identity is erased. For instance,
Gypsy, Roma and Irish Traveller groups are frequently excluded from
surveys and national data leading to a gap in data available for analysis,
and the latest wave of the Millennium Cohort Study did not ask young
people (aged 17) their ethnicity, instead using parental ethnicity in
analysis. This gap masks any changes in ethnic identification between
young people and their parents, and may lead to errors where parents are
from separate ethnic backgrounds. However, the next wave of the study will
ask young people their ethnicity.
Presence of culturally relevant and use of timely data by report
Culturally relevant data is any additional information that can provide background or cultural information for specific groups that may be faced by different communities, and may be missed or misunderstood without the collection of relevant data. Timeliness of data considers how up to date the data used in the evidence is, and if the data covers a suitable time for its aims. Key: 3 = Fully met2 = Mostly met1 = Partially met0 = Not met
While the timeliness of data is generally good, only 4 pieces of evidence
fully met this criteria by including a time period that covered both pre-
and post-pandemic data.
Many communities experience challenges or barriers not shared by the
majority population, or by other minority ethnic communities. There are
also differences in practices, experiences and systems of support across
different cultures and communities, such as different approaches to debt
or differences in access to personal protective equipment, that may be
missed or misunderstood without the collection of relevant data.
The review shows that culturally relevant data is generally lacking in
evidence on employment outcomes for young people from minority ethnic
groups, with many data collection instruments not covering questions or
considerations that are culturally relevant for specific ethnic groups.
This creates challenges as the data on employment outcomes does not
contain the nuance necessary to account for specific cultural experiences,
and leads to a lack of understanding about the specific challenges faced.
In addition to this, there is a lack of published local datasets and data
on culturally relevant regional issues.
Lack of different types of employment data
Evidence looked in at the quantitative review was assessed against eight
different types of employment grouping criteria.