Suchergebnisse

Abstract
As the world has become more digitally dependent, questions of data governance, such as ethics, institutional arrangements, and statistical protection measures, have increased in significance. Understanding the economic contribution of investments in data sharing and data governance is highly problematic: outputs and outcomes are often widely dispersed and hard to measure, and the value of those investments is very context-dependent. The "Five Safes" is a popular data governance framework. It is used to design and critique data management strategies across the world and has also been used as a performance framework to measure the effectiveness of data access operations. We report on a novel application of the Five Safes framework to structure the economic evaluation of data governance. The Five Safes was designed to allow structured investigation into data governance. Combining this with more traditional logic models can provide an evaluation methodology that is practical, reproducible, and comparable. We illustrate this by considering the application of the combined logic model-Five Safes framework to data governance for agronomy investments in Ethiopia. We demonstrate how the Five Safes was used to generate the necessary context for a more traditional quantitative study, and consider lessons learned for the wider evaluation of data and data governance investments.

ObjectivesThe 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.
MethodsWe 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.
ResultsWe 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.
ConclusionData 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.

ObjectivesGovernments 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.
MethodsThis 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.
ResultsThe 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
ConclusionOften 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.