Lorraine Dearden

Work Related training is currently at the top of the political and public policy agenda. The report looks at who gets work related training in Britain, the effect it has on the subsequent employment prospects of men and women, the wage payoffs to different types of work related training, and whether it improves the wages prospects of relatively low skilled individuals

Introduction: This article summarises lessons learnt from the Health Outcomes for young People throughout Education (HOPE) Study and serves as a real world, transferable application for addressing causal questions using administrative data. The HOPE study applied causal methods to analyses of administrative data in Education and Child Health Insights from Linked Data (ECHILD) aimed at studying the effectiveness of provision for special educational needs and disability (SEND) on health and education outcomes. Methods: Defining causal questions regarding the impact of SEND provision required judicious mapping of the question onto the data, leading to the selection of appropriate measures of effect, transparent handling of the data and control of confounding factors to estimate effects. We adopted the target trial emulation framework to guide these steps. Having encountered specific computational challenges in estimating the effects of interest, we simulated data that resembled the HOPE study and used them to practice the implementation of alternative estimation methods and to study impact of some of their assumptions. Results: The creation and analysis of the simulated data provided valuable insights. First, we learned the importance of aligning the target of estimation with the causal question at hand. Second, we observed how deviations from assumptions specific to each estimation method can affect results. Third, we highlighted the benefits of employing alternative estimation methods as sensitivity tools that can aid the interpretation of the resulting estimates. Finally, we offer user-friendly code in two programming languages (R and Stata) and accompanying simulated data to facilitate the implementation of these methods for similar causal questions. Conclusion: We recommend users of administrative data to fully specify -and possibly revise- the causal questions they wish to address and to carefully examine and compare assumptions, implementation and results obtained using alternative estimation methods.

Using a large and novel administrative dataset, this paper investigates variation in returns to different higher education ‘degrees’ (subject-institution combinations) in the United Kingdom. Conditioning on a rich set background characteristics, it finds substantial variation in returns, even within subject, across universities with very similar selectivity levels, suggesting degree choices matter a lot for later-life earnings. Selectivity is weakly related to returns through most of the distribution but strongly positively correlated at the top end. Other than selectivity, returns are poorly correlated with observable degree characteristics, which has implications for student choices and the incentives of universities.

Higher education (HE) participation has expanded dramatically in England over the last half century; however, access to HE amongst particular groups of students remains a major policy concern. This report makes use of newly linked administrative data to better understand the determinants of participation in HE - and participation in high status universities - amongst those facing socio-economic disadvantage, those from poorly educated families and ethnic minorities. It is unique in being able to follow a particular cohort of children (all state school children in England in Year 11 in 2001-02) from age 11 right through to HE participation at age 18 (in 2004-05) or age 19 (in 2005-06). The findings documented in this report show that while there are large raw gaps in HE participation rates by socio-economic status, these differences disappear once controls for prior attainment are included. This suggests that it is not barriers arising at the point of entry into HE (e.g. borrowing constraints) that are most problematic, but poor attainment in secondary schools. Indeed, if results can be improved between age 11 and age 16, those from poorer backgrounds are at least as likely to go on to university as their more advantaged peers. By contrast, most ethnic minority groups are now significantly more likely to participate in HE than their White British counterparts

Children born at the end of the academic year have lower educational attainment, on average, than those born at the start of the academic year. Previous research shows that the difference is most pronounced early in pupils' school lives, but remains evident and statistically significant in high-stakes exams taken at the end of compulsory schooling. To determine the most appropriate policy response, it is vital to understand which of the four possible factors (age at test, age of starting school, length of schooling and relative age without cohort) lead to these differences in attainment between those born at different points in the academic year. However, research to date has been unable to adequately address this problem, as the four potential drivers are all highly correlated with one another, and three of the four form an exact linear relationship (age at test = age of starting school + length of schooling). This paper is the first to apply the principle of maximum entropy to this problem. Using two complementary sources of data we find that a child's age at the time they take the test is the most important driver of the differences observed, which suggests that age-adjusting national achievement test scores is likely to be the most appropriate policy response to ensure that children born towards the end of the year are not at a disadvantage simply because they are younger when they take their exams

Children born at the end of the academic year have lower educational attainment, on average, than those born at the start of the academic year. Previous research has shown that the difference is most pronounced early in pupils' school lives, but remains evident and statistically significant in high-stakes exams taken at the end of compulsory schooling. Those born later in the academic year are also significantly less likely to participate in post-compulsory education than those born at the start of the year. We provide the first evidence on whether these differences in childhood outcomes translate into differences in the probability of employment, occupation and earnings for adults in the UK. We also examine whether there are differences in broader measures of well-being such as self-perceived health and mental health. We find that the large and significant differences observed in educational attainment do not lead to pervasive differences in adulthood; those born towards the end of the academic year are more likely to experience unemployment (which is particularly true for females and those that don't achieve a degree level qualification) but in general there are few substantial or statistically significant differences in terms of occupation, earnings and self-perceived health and mental health. It is not clear why this should be the case, but if employers reward productivity equally as they learn more about their workers, irrespective of their educational attainment, then this lack of significant differences may not be surprising

This paper uses data from a rich UK birth cohort to estimate the differences in cognitive and non-cognitive skills between children born at the start and end of the academic year. It builds on the previous literature on this topic in England by using a more robust regression discontinuity design and is also able to provide new insight into the drivers of the differences in outcomes between children born in different months that we observe. Specifically, we compare differences in tests that are affected by all three of the potential drivers (age at test, age of starting school and relative age) with differences in tests sat at the same age (which are therefore not affected by the age at test effect) as a way of separately identifying the age at test effect. We find that age at test is the most important factor driving the difference between the oldest and youngest children in an academic cohort; highlighting that children born at the end of the academic year are at a disadvantage primarily because they are almost a year younger than those born at the start of the academic year when they take national achievement tests. An appropriate policy response in this case is to appropriately age-adjust these tests. However, we also find evidence that a child's view of their own scholastic competence differs significantly between those born at the start and end of the academic year, even when eliminating the age at test effect. This means that other policy responses may be required to correct for differences in outcomes amongst children born in different months, but not necessarily so: it may be that children's view of their scholastic competence would change in response to the introduction of appropriately age-adjusted tests, for example as a result of positive reinforcement