The perils of pre-filling: lessons from the UK’s Annual Survey of Hours and Earning microdata

The role of the National Statistical Institution (NSI) is changing, with many now making microdata available to researchers through secure research environments. This provides NSIs with an opportunity to benefit from the methodological input from researchers who challenge the data in new ways. This article uses the United Kingdom’s Annual Survey of Hours and Earnings (ASHE) to illustrate the point. We study whether the use of prefilled forms in ASHE may create inaccurate values in one of the key fields, workplace location, despite there being no direct evidence of it in the data supplied to researchers. We link surveys to examine the hypothesis that employees working for multi-site employers making an ASHE survey submission are more likely to have their work location incorrectly recorded as the respondent fails to correct the work location variable that has been pre-filled. In the short-term, suggestions are made to improve the quality of ASHE microdata, while longer-term, we suggest that the burden of collecting additional data could be offset through greater use of electronic data capture. More generally, in a time when statistical budgets are under pressure, this study encourages NSIs to make greater use of the microdata research community to help inform statistical developments

Turning data into research-ready data

Objectives Governments acquire extensive data holdings and face increasing pressure to make these available as record-level microdata for research. However, turning data into research-ready data (RRD) is not a straightforward exercise. We demonstrate how even in simple cases researcher involvement can bring substantial rewards for effective RRD development. Methods This paper reports on an ADRUK-funded project to take a dataset originally collected by the Office for National Statistics for official statistics (the UK Annual Survey of Hours and Earnings, ASHE), formally review its microanalytical characteristics, link it to Census 2011 data, and prepare a new ‘research ready dataset’ with appropriate documentation and coding. This should have been straightforward as the datasets had already been widely used as research microdata. However, the involvement of academic researchers in the production of research-ready data led to many important new insights. Results The research programme had 3 aims: testing assumptions about the data; reviewing data quality; and adding value. Because of its sampling model, ASHE is assumed to have random non-response both longitudinally and in cross section. The research team showed that was untrue: there was higher attrition than expected, and both longitudinal and cross-sectional non-response appeared non-random.. The data quality review showed further concerns about the accuracy of some geographical indicators, and some variables of opaque provenance; in contrast, we confirmed the accuracy of administrative variables created by ONS. As well as being important for researchers, these findings have the potential for significant effects on official statistics produced from the source data, enhancing the value of the source data. Finally, value was added from new variables which reflected the team’s wide research interests Conclusion Often in government the assumption is that creating RRDs is a matter of creatign files and giving access to the researchers. Insights from our work show that the deep involvement of the research community can bring rewards for both data holders and researchers. For RRDs, researcher-led construction is vital

How common is low pay in Britain? New findings from linked data

Objectives The Annual Survey of Hours and Earnings (ASHE) is the main source of public statistics on low pay in Britain. As part of the ADR-funded Wage and Employment Dynamics Project, we identify and adjust for non-response biases in ASHE and generate new estimates of the incidence of low pay. Methods We linked the ASHE data to the Business Structure Database – a research-ready version of the UK’s official register of businesses. This linked dataset enabled us to identify which types of employers were more or less likely to respond to ASHE in a given year, and to generate non-response adjustments to the existing ASHE weights. We then used the unique personal identifier on ASHE to link observations across years. We compared rates and correlates of longitudinal attrition in ASHE with rates and correlates of employment exit observed in the ONS Annual Population Survey, generating longitudinal weights to account for non-random attrition. Results We find that jobs in smaller organisations, younger organisations and those in the private sector are under-represented in the annual achieved samples from ASHE, relative to their prevalence in the wider economy. The percentage of jobs paid at or below the National Minimum Wage is under-estimated by around one fifth if one does not take account of these cross-sectional response biases. We find that longitudinal attrition is more likely to affect younger employees and those with low job tenure. However, we do not find that estimates of the rate at which employees move off the National Minimum Wage to higher rates of pay are biased by non-random patterns of longitudinal attrition. Conclusion Data linking enables us to identify observable response biases in the UK’s official source of earnings statistics (ASHE). These biases affect our view of the bottom of the wage distribution, and have the potential to affect decisions around a key area of government labour market policy

The perils of pre-filling: lessons from the UK’s Annual Survey of Hours and Earning microdata

The role of the National Statistical Institution (NSI) is changing, with many now making microdata available to researchers through secure research environments. This provides NSIs with an opportunity to benefit from the methodological input from researchers who challenge the data in new ways. This article uses the United Kingdom’s Annual Survey of Hours and Earnings (ASHE) to illustrate the point. We study whether the use of prefilled forms in ASHE may create inaccurate values in one of the key fields, workplace location, despite there being no direct evidence of it in the data supplied to researchers. We link surveys to examine the hypothesis that employees working for multi-site employers making an ASHE survey submission are more likely to have their work location incorrectly recorded as the respondent fails to correct the work location variable that has been pre-filled. In the short-term, suggestions are made to improve the quality of ASHE microdata, while longer-term, we suggest that the burden of collecting additional data could be offset through greater use of electronic data capture. More generally, in a time when statistical budgets are under pressure, this study encourages NSIs to make greater use of the microdata research community to help inform statistical developments

Can we identify students in ASHE?

ASHE is a key dataset in the UK, the only one which allows long-term analysis of flows in labour market status and earnings, and hence vitally important in the understanding of low pay and wage progression. Separating out students from non-student workers therefore has considerable value. This study has tried to create a proxy for ‘student working’ using the ASHE dataset, and then triangulating with the Census 2011 data which has some of the same people but with an accurate marker for student status. Unfortunately, triangulating this with accurate student information on the Census suggested that our preferred method was not notably the ‘best’

Enriched ASHE Quick Start Guide

The Wage and Employment Dynamics (WED) project was funded by Administrative Data Research 2019-2022 to review, quality assure and enhance ASHE data. The project was also provided with ASHE data linked to the 2011 Census for England and Wales. This document describes the ‘Enriched ASHE’ dataset created by the code, which can be applied to the standard ASHE dataset. The code can also be applied to the ASHE-2011 Census dataset, creating an ‘Enriched ASHE – 2011 Census’ dataset. A separate document describes the ASHE - 2011 Census base dataset. The code is available to researchers within the Secure Research Service (SRS). Note that the ASHE – 2011 Census dataset does not hold geography for individuals below the level of Local Authority

Longitudinal attrition in ASHE

The Annual Survey of Hours and Earnings (ASHE) provides many of the UK’s official earnings statistics. The survey operates on an annual 1% sample of employee jobs. However, the method of sampling - based on the final two digits of an employee’s National Insurance number – means that records are linkable longitudinally. Manygovernment and academic studies have utilised the dataset in this way. However, the longitudinal integrity of the ASHE sample has been the subject of little prior investigation, with the panel sample generally assumed free of any attrition biases that might compromise longitudinal analysis. We explore the validity of this assumption by comparing rates of year-on-year sample retention in ASHE with ratesof employment retention estimated from a reference dataset (the Longitudinal Annual Population Survey). Our analysis confirms the existence of systematic patterns of longitudinal attrition in ASHE, which have the potential to introduce bias into longitudinal analyses of these data. We go on to construct longitudinal weightsthat correct for estimated attrition biases over adjacent years in ASHE. In an illustrative analysis, the application of these weights brings about a small widening of the distribution of individual wage growth

Using ASHE to examine trends in low pay: Initial exploration of the data

Using the Annual Survey of Hours and Earnings (ASHE) 2004-2019 we report consistent time-series estimates of the percentage of jobs on and around the minimum wage; low paid jobs above the minimum; and ‘high paid’ jobs. In doing so we report on some important methodological considerations including the construction of hourly pay in ASHE; the identification of ‘main’ and ‘other’ jobs; the incidence of missing data; and the use of rounding. We show the percentage of jobs paying around the minimum wage has risen over the period, but so too has the number of ‘high paid’ jobs

The incidence of low pay is falling in Britain, but why – and can we trust the figures?

Recent research indicates that the percentage of employees in Britain who are low paid – earning below two-thirds median hourly earnings – has been falling in the last 6-7 years. It points to the increased ‘bite’ of the adult National Minimum Wage (NMW) and its replacement in 2016 by the more generous National Living Wage (NLW) as a driver of this change, raising pay rates at the bottom end of the earnings distribution. However, new research from the ADR UK funded www.wagedynamics.com project, published for the first time today, revisits estimates of the incidence of low pay over the period 2004-2018 using new methods. The study indicates that the incidence of low pay among those aged 25 and above has been falling since 2013, predating an increase in the NMW/NLW bite. Although there are a number of reasons why low pay might be declining we show the decline appears to be driven by an increase in the probability of leaving low pay for higher pay, an increase that began in 2012/13. Ours is the first study to account for non-response and sample attrition in the key data set used by the Low Pay Commission and others to estimate the incidence of low pay. We show that the incidence of low pay is over-estimated and the decline in low pay under-estimated if one does not account for non-response