Department of Quantitative Social Science The use (and misuse) of statistics in understanding social mobility: regression to the mean and the cognitive development of high ability children from disadvantaged homes

The use (and misuse) of statistics in understanding social mobility: regression to the mean and the cognitive development of high ability children from disadvantaged homes DISCLAIMER Any opinions expressed here are those of the author(s) and not those of the Institute of Education. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. DoQSS Workings Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may be available directly from the author. The use (and misuse) of statistics in understanding social mobility: regression to the mean and the cognitive development of high ability children from disadvantaged homes Abstract. Social mobility has emerged as one of the key academic and political topics in Britain over the last decade. Although economists and sociologists disagree on whether mobility has increased or decreased, and if this is a bigger issue in the UK than other developed countries, both groups recognise that education and skill plays a key role in explaining intergen-erational persistence. This has led academics from various disciplines to investigate how rates of cognitive development may vary between children from rich and poor backgrounds. A number of key studies in this area have reached one particularly striking (and concerning) conclusion – that highly able children from disadvantaged homes are overtaken by their rich (but less able) peers before the age of 10 in terms of their cognitive skill. This has become a widely cited " fact " within the academic literature on social mobility and child development, and has had a major influence on public policy and political debate. In this paper, we investigate whether this finding is due to a spurious statistical artefact known as regression to the mean (RTM). Our analysis suggests that there are serious methodological problems plaguing the existing literature and that, after applying some simple adjustments for RTM, we obtain dramatically different results. ‡ We would like to thank John Micklewright for particularly helpful comments on an initial draft. We also acknowledge the extremely helpful feedback we received from participants at a seminar at the University of Southampton, particularly the comments of Patrick Sturgis, along with the assistance provided by Paul Clarke. This work has been produced as part of the ESRC ALSPAC large grant scheme.