This paper investigates ethnic disproportionality in the identification of Social, Emotional, and Mental Health (SEMH) needs, a category of Special Educational Needs (SEN), amongst primary school pupils in England. Disproportionality exists when pupils from an ethnic minority group are more (or less) likely to be identified with SEN compared to pupils in the ethnic majority.  Ethnic disproportionality in special education has long been a concern particularly in the US (Dunn, 1968) and England (Coard, 1971) and these concerns continue in more recent analyses of national data in in the US (e.g. Donovan & Cross, 2002) and in England (Strand & Lindsay, 2009; 2012). Similar issues have been identified in other European countries. For example in Denmark, learners from minority ethnic backgrounds are up to twice as likely as their peers to be placed in special education (Berhanu & Dyson, 2012) and in Germany students from an immigrant background (predominantly Turkey, Greece and Southern Europe) are again more likely than their peers to receive special education (Werning, Loser & Urban, 2008).

Some forms of SEN have a clear biological basis, for example sensory impairments, physical needs, or profound and multiple learning difficulties. These categories are often contrasted with categories like SEMH which are socially constructed in the sense that students’ behaviour is interpreted in terms of expected patterns or norms. Consequentially it is no surprise that the main explanation forwarded for the over-representation of Black students with SEN is the long history of ethnic stratification within education. Disproportionality is assumed to reflect inappropriate interpretation of ethnic and cultural differences including teacher racism, low expectations and a failure of schools to provide quality instruction or effective classroom management (e.g. Artiles et al, 2010; Waitoller et al, 2010).

An alternative hypothesis is that disproportionality reflects the fact that ethnic minority students are more at risk of SEN because of the greater socio-economic disadvantage (SED) they experience relative to the White majority. Most large scale US studies lack the individual pupil level data needed to evaluate this hypothesis, but Strand & Lindsay (2009) report that in England although controlling for SED reduced the extent of disproportionality Black Caribbean students remained over-represented for SEMH. However some recent analyses of the US Early Childhood Longitudinal Study-Kindergarten (ECLS-K) have claimed that Black and Latino students are actually under-represented for SEN at age 9 after control for educational achievement and teacher's ratings of students behaviour at Kindergarten entry (Hibel et al, 2010; Morgan et al, 2015). The ECLS-K includes 11,000 students so is not small, but given the US national incidence of Emotional Difficulties at 0.6% this represents just 66 students in the sample, before considering any splits by gender, ethnicity etc. These small numbers are a substantial obstacle to accurate determination of disproportionality and studies based on population data are urgently needed.

We focus on the earliest years at school and our study is, to our knowledge, the first to explore ethnic disproportionality in the emergence of SEMH identification in a longitudinal study over ages 4-11 with national pupil level data. Our approach allows us to account for the effects on identification of controlling for a range of socio-economic variables and educational achievement in the first year at school.

Specific research questions addressed are:

1. What ethnic groups, if any, are over-represented or under-represented for SEMH relative to the majority (White British) group?
2. Can any over- or under-representation be accounted for by socio-economic variables and/or prior achievement, given that some ethnic groups are more likely to experience socio-economic deprivation and low achievement?
3. To what extent are school composition variables associated with ethnic  disproportionality in SEMH identification?

The study is based on pupil level data drawn from the England National Pupil Database (NPD). The 562,274 students starting Reception class at age 4+ in September 2008 are the focal cohort, and we access their records from the Annual School Census every January between 2008 and 2015. Key data includes: • Ethnic group (18 categories) • Primary type of SEN (12 categories) • Level of SEN (school support or statemented) • Gender (Boy/Girl) • Birth season (autumn, spring or summer born) • Entitlement to a Free School Meal • Income Deprivation Affecting Children Index (IDACI) a measure of small area socio-economic deprivation in the neighbourhood where the child resides (32,000 in England) • Measures of Communication, Language and Literacy (CLL), Problem Solving, Reasoning & Numeracy (PSRN) and Personal, Social and Emotional Development (PSED), drawn from teachers’ ratings at the end of Reception Year. We focus on SEMH in particular in this paper because of it’s high prevalence: 18% of all pupils identified with SEN have SEMH as their primary SEN, second only to Moderate Learning Difficulties at 26%. Our dependent variable is whether, and in what year, the pupil is identified with SEN where the SEN primary type is SEMH. We utilise Cox’s regression (sometimes also called event history analysis or logit hazard modelling) to identify how the likelihood of SEMH identification cumulates over time. This more accurately reflects the likelihood of SEN identification which is not a single-point in time event but instead occurs over time as children age. We identify the Hazard Ratios (HR) of identification for each ethnic minority group against White British pupils. Given the huge size of the dataset (n=560,000+) statistical significance is a very poor indicator of educationally meaningful effects. We identify HRs >1.33:1, or <0.75:1 as evidence of substantial disproportionality. These thresholds indicate the ethnic group is one-third more likely (4:3), or one-third less likely (3:4), to be identified relative to the White British majority in any particular year of a pupil’s Primary education. We see how these Hazard Ratios change as we include controls for variables such as age, gender, socio-economic circumstances and educational achievement in the pupil’s first year of school. We also test school level variables, including school type, size and quintile bands for % FSM pupils and % Black pupils in the school.

Our analyses reveal the following findings: • Black Caribbean (HR=2.32), Mixed White and Black Caribbean (HR=1.85) and Black Other Groups (HR=1.54) are substantially more likely to be identified with SEMH than White British pupils. • Boys (HR=2.46) are more likely to be identified than girls, pupils entitled to FSM (HR=1.79) are more likely to be identified than those not entitled to FSM and a one SD increase in IDACI raised the odds of SEMH identification by 1.17. Literacy and mathematics score at Reception have a relatively small association with identification, but a one SD increase in Personal, Social & Emotional development (PSED) score is associated with a substantial reduction in the odds of identification (HR=0.39). • Even after the above pupil level controls, Black Caribbean (HR=1.42) and Mixed White & Black Caribbean (HR=1.46) remained more likely to be identified with SEMH than comparable White British pupils. • After adjusting for the pupil level variables, pupils in schools in the top quintile of %FSM had a greater risk of identification (HR=1.25) than those in the lowest %FSM quintile, and similarly pupils in schools in the top quintile for % Black pupils (HR=1.21) had higher risk than those in the bottom quintile of % Black pupils. However the increase in variance explained (pseudo R2 in similarly specified logistic regression models) by including school level variables was small (increase from 17.0% to 17.2%). We conclude that there is evidence of disproportionate identification of Black Caribbean and Mixed White & Black Caribbean students for SEMH. However this does not apply to the numerically larger Black African group, who are not over-represented in raw data (HR=1.12) and are significantly under-represented in the adjusted model (HR=0.65). The data militate against any simple Black/White interpretation and suggest a range of cultural factors are implicated in disproportionate identification.

Artiles, A. J., Kozleski, E. B., Trent, S. C., Osher, D., & Ortiz, A. (2010). Justifying and Explaining Disproportionality, 1968-2008: A Critique of Underlying Views of Culture. Exceptional Children, 76(3), 279-299. Berhanu, G., & Dyson, A. (2012). Special education in Europe, overrepresentation of minority students. In J. Banks (Ed.), Encyclopaedia of Diversity in Education (pp. 2070-2073). Thousand Oaks: SAGE Publications. Coard, B. (1971). How the West Indian child is made educationally subnormal in the British school system: The scandal of the Black child in schools in Britain. London: New Beacon for the Caribbean Education and Community Workers’ Association. Department for Education (DfE) (2017). Special educational needs: An analysis and summary of data sources. London: Department for Education. Dunn, L.M. (1968). Special education for the mildly retarded – Is much of it justifiable? Exceptional children, 23, 5-21. Donovan, S. & Cross, C. (2002). Minority students in special and gifted education. Washington, D.C: National Academy Press. Lindsay, G. & Strand, S. (2016). Children with language impairment: prevalence, associated difficulties and ethnic disproportionality in an English population. Frontiers in Education, 1(2),1-14. http://dx.doi.org/10.3389/feduc.2016.00002 Strand, S. & Lindsay, G. (2009). Evidence of ethnic disproportionality in special education in an English population. Journal of Special Education, 43(3), 174-190. Strand, S. & Lindsay, G. (2012). Ethnic disproportionality in the identification of Speech Language and Communication Needs (SLCN) and Autistic Spectrum Disorders (ASD): 2005-2011. London: Department for Education. Waitoller, F. R., Artiles, A. J., & Cheney, D. A. (2010). The Miner's Canary: A Review of Overrepresentation Research and Explanations. Journal of Special Education, 44(1), 29-49. Werning, R., Loser, J. & Urban, M. (2008). Cultural and Social Diversity: An Analysis of Minority Groups in German Schools. Journal of Special Education, 42, (1), 47-54.