Research Fellows using ADR England flagship datasets

Status: Active

Two Research Fellows will use the Annual Survey of Hours and Earnings (ASHE) linked to the 2011 Census – England and Wales. This dataset allows for insight into the dynamics of wage and employment issues, and how characteristics such as gender, disability, and ethnicity influence these. 

Two Research Fellows will use data from the Department for Education’s Longitudinal Education Outcomes (LEO) Standard Extract which allows analysis of educational pathways within and after education and longer-term labour market outcomes. It can provide unique insights into the transitions of individuals from education to the workplace. 

The Growing Up in England (GUIE) and Grading and Admissions Data for England (GRADE) datasets will form the basis of the remaining two fellowships. The GUIE dataset can be used to better understand how factors such as household circumstances and geography shape educational outcomes for children in England. GRADE allows researchers to evaluate the judgements made in awarding grades in the 2020 Covid-19 pandemic, ensuring that lessons are learned. 

All these datasets will be accessed via the Office for National Statistics (ONS) Secure Research Service

Find out about the Research Fellows and their projects below.

Dr Ezgi Kaya

Exploring the nexus between immigration, integration and labour market outcomes

Using the ASHE linked to 2011 Census dataset

Ezgi Kaya is a Senior Lecturer in Economics at Cardiff Business School, Cardiff University. Her project aims to provide new and high-quality evidence on the labour market performance and integration of immigrant workers in England and Wales​. 

View project details

This project aims to explore the following research questions:

  1. How do labour market outcomes including pay, hours of work, occupational skill level and type of employment contract differ between UK-born and immigrant (non-UK-born) employees? ​
  2. What are the main drivers of any differences in labour market outcomes; and what is the influence of other personal and work-related characteristics, or the role played by individual employers?​
  3. Does the labour market performance of immigrants vary by factors related to immigration and integration (language proficiency, years of residence in the UK, year of arrival, holding a UK passport or self-defined national identity)?​
  4. How do labour market outcomes of immigrant employees progress over time; and how do the labour market dynamics of immigrants compare to those born in the UK?

The methodology used in this study: 

This research will use established econometric methods with three core elements. 

  • First, regression analysis will be used to explore the labour market performance of immigrant workers in each labour market outcome, in comparison to that of UK-born workers. This will be followed by a more detailed analysis of the drivers of any observed differences using an established decomposition method. This method isolates the contribution of observable characteristics and the role of individual employers from unobserved influences, where the latter will include unequal treatment in the labour market. 
  • Second, the analysis will focus on the role of factors related to immigration and integration, and regression analysis will be used to explore whether and how these factors determine the labour market performance of non-UK-born workers. 
  • Third, a dynamic analysis will be undertaken to explore how the labour market performance of immigrant workers progresses over time in terms of considered outcomes, in comparison to UK-born workers. This part of the analysis will exploit the longitudinal nature of the data which allows us to follow the same individuals over time. 

Throughout the analysis, differences between people will be explored, specifically to determine whether the findings vary based on gender and country of origin.

Funded value: £158,441 (full economic cost)

Duration: August 2023 – November 2024 

Damian Whittard    

Exploring the value of green jobs in England and Wales 

Using the ASHE linked to 2011 Census dataset

Damian Whittard is an Associate Professor in Economics at the University of West England. His research will generate and test alternative measures of green jobs, applying these to better understand the earnings and career prospects of those working in them in England and Wales. 

View project details

This project aims to explore the following research questions:

  1. What constitutes a green job and how should it be measured in different contexts?
  2. What is the extent of green jobs growth in England and Wales and how have economic conditions and environmental policies affected this?
  3. What are the characteristics of those working in green jobs in England and Wales, and do they vary by type of green job?
  4. Is there a pay premium or pay penalty for working in a green job in England and Wales, and does this outcome differ across demographic groups, such as gender, ethnic group, disability, and across regions?
  5. How do the early labour market experiences of green job employment affect an individual's subsequent labour market experience and wage progression?

The methodology used in this study: 

  • By combining the high-quality employment data in the Annual Survey of Hours and Earnings (ASHE) with the detailed demographic information in the 2011 Census, the project will use advanced mathematical and statistical modelling techniques (e.g., panel data estimation) to describe and analyse the characteristics and relationship of green jobs in England and Wales, and those that work in them. 
  • The ASHE survey collects data on the wages and employment of the same individual each year. This enables the characteristics of jobs undertaken to be explored and the career progression of individuals to be tracked over time. Using the industrial and occupational variables it is possible to construct alternative, but meaningful measures of green jobs. While the linked 2011 Census data will enable the results to be analysed by cohorts, including those with protected characteristics (e.g., disability, race, religion). 

Funded value: £141,986 (full economic cost)

Duration: September 2023 – February 2024

Xiaowei Xu

Understanding the link between geographical inequalities, geographical mobility and social mobility​

Using the LEO dataset 

Xiaowei Xu, a Senior Research Economist at the Institute for Fiscal Studies is examining the relationship between local opportunities, geographical mobility, social mobility and skills.

View project details

This project aims to explore the following research questions:

  1. How do labour market opportunities facing young people differ across England?​ 
  2. How geographically mobile are young people in England, and how does this affect inequalities between places and socio-economic groups?​
  3. Do differences in local economic opportunities affect young people’s educational choices?​

The methodology used in this study:

  • The project will use quantitative methods. It will use regression analysis to estimate earnings differences across local labour market areas, going beyond static, average effects by examining differences in career progression across places and the value of experience in London and other cities. It will document patterns of geographical mobility and estimate the extent to which young people move to places offering better labour market opportunities, considering differences between groups in terms of educational and socio-economic background. 
  • Furthermore, it will consider how local opportunities shape young people’s decisions to acquire education in the first place, using regression analysis to examine whether students who grow up in places with few universities (who face high costs of higher education) and those who grow up in places where degrees are not rewarded in the labour market (who face low returns) are less likely to pursue higher education.
  • The project will be supported by an advisory group including representatives from the Department for Education, the Department for Levelling up, Housing and Communities, HM Treasury and the Social Mobility Commission. The group will help co-design the research and ensure policymakers understand the findings.

Funded value: £180,508 (full economic cost)

Duration: August 2023 – December 2024

Dr Francesca Foliano

Understanding the internal migration of young adults in England and its effects on social mobility

Using the LEO dataset

Francesca Foliano, a Senior Research Associate at the UCL Social Research Institute is studying the residential mobility of young adults in England including the impact of economic opportunities and subsequent effects on social mobility.

View project details

This project aims to explore the following research questions:

  1. What are the migration flows of young adults across local labour markets?
  2. What are the internal migration decisions of young adults in England and do they respond to economic opportunities​?
  3. To what extent is internal migration in early adulthood associated with social mobility at individual and regional levels?

The methodology used in this study: 

  • The project will start with a descriptive analysis of internal mobility patterns of young adults (up to the age of 30) that will compare migration rates by region, gender and socio-economic characteristics. This analysis will be complemented by interactive maps showing the migration flows from and to all local labour markets in England. Local labour markets will be approximated by travel to work areas, a geography derived by the ONS to reflect self-contained areas in which most people both live and work.
  • To investigate whether young adults’ decisions to migrate are responses to local economic opportunities, the project will use linear probability and Poisson models. These will include a wide set of individual and local labour market characteristics and fixed effects. 
  • Regression analysis will be used to study how residential mobility contributes to explaining differences in social mobility among young adults and between local labour markets. The analysis will estimate the association between young adults’ earnings rank and the rank in the measure of their family disadvantage while also including indicators for the highest qualification achieved and residential mobility. 

Funded value: £180,456 (full economic cost)

Duration: August 2023 – January 2025

Dr Xiaohui Zhang   

Understanding the impacts of time in care on the educational attainment of young people in England

Using the GUIE dataset

Xiaohui Zhang is a Senior Lecturer in economics at the University of Exeter Business School. Xiaohui is investigating if young people being in care causes poor educational attainment, and how different factors of care experience influence looked-after children's educational performance.

View project details

This project aims to explore the following research questions:

  1. What factors influence the educational attainment of young people at key stages 4 and 5 in England?​
  2. What is the association between being in care and young people’s educational attainment at key stages 4 and 5 and is this association causal?
  3. What factors of care experience may influence looked-after children's educational performance and attainment?

The methodology used in this study:

  • Descriptive statistics will provide a first insight into the data and preliminary comparisons across different groups of people. This will help to understand the data and identify potential factors influencing educational attainments across different groups with respect to care status and qualification types.  
  • Multiple regression models will be applied to examine the effect of being in care on educational attainment, with all other factors controlled.
  • This project will apply Blinder Oaxaca decomposition to examine the differences in educational attainment between looked-after children and their comparable peers, i.e. children in need and general children not in need or care. Blinder Oaxaca decomposition is a statistical method that explains differences in the dependent variable between two groups. 
  • In this project, two strategies, i.e., the fixed-effect model and propensity score matching method, will be applied to examine the causal effect of being in care on educational attainment.

Funded value: £157,352 (full economic cost)

Duration: September 2023 – February 2025

Dr Oliver Cassagneau-Francis

Exploring the gaps in teacher judgements and the implications for university admissions

Using the GRADE dataset 

Oliver Cassagneau-Francis is a Research Fellow at the Centre for Education Policy and Equalising Opportunities at UCL. He is examining the extent of discrepancies in teacher judgements (measured by comparing predicted and awarded grades) in the English education system and the implications for student outcomes.

View project details

This project aims to explore the following research questions:

  1. Are there systematic differences in how teachers predict the grades of different groups of students, for example by socio-economic status, ethnicity or gender? 
  2. Do differences in teacher predictions across different groups of students differ by the subject of A-level qualification? 
  3. Do teacher-predicted grades and centre-assessed grades (CAGs) affect the university course students ultimately enrolled in?

The methodology used in this study:

  • This study will use a mostly descriptive approach to address research questions 1 and 2, estimating the distributions of predicted and achieved grades for the whole population and conditional on being in certain groups (e.g., by ethnicity, gender and socio-economic status). The project will compare these predicted and achieved grade distributions within and across groups and will also compare actual grades (2018 and 2019) with CAGs (2020) within groups. 
  • These descriptive analyses will provide an overview of any discrepancies across groups in teacher predictions. However, if the underlying distributions of “true” grades are very different across groups, this exercise might be less insightful. To mitigate against this, a regression discontinuity design will be used focusing only on students who were ranked very close to grade boundaries by their teachers in 2020. Comparing the characteristics of the students (e.g., their ethnicity, gender, socio-economic status) who were just awarded a higher grade, to those who just missed out will provide insights on whether certain groups are systematically over- or underpredicted. The same approach will also be used to compare the outcomes of students who look similar across a range of characteristics (including being similarly ranked) but who were awarded different grades.

Funded value: £176,314 (full economic cost)

Duration: 15 August 2023 – 15 December 2024

Categories: Research using linked data, Datasets, ADR UK Research Fellows, ADR England, Office for National Statistics, Children & Young People, Climate & Sustainability, Crime & Justice, Health & Wellbeing, Housing & Communities, Inequality & Social Inclusion, World of Work

Share this:

You are currently offline. Some pages or content may fail to load.