Changing Industrial Classification to SIC (2007) at the UK Office for National Statistics

Smith, Paul A.; James, Gareth

doi:10.1515/jos-2017-0012

Cited by 11 publications

(14 citation statements)

References 16 publications

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“…In the next step, the indices can be recalculated using the backcast input variables. See Smith & James () & Nolan et al () for details and issues with backcasting indices. Alternatively, the synthetic methods summarised under (C3) and correction methods based on structural time series methods (C4) can also be used.…”

Section: Adjusting Time Series For Discontinuitiesmentioning

confidence: 99%

“…For backcasting purposes, it might therefore also be useful to have at least the sample units from previous editions of the survey recoded according to the new classification or to use other external information to make more realistic backcasts. More technical details about the implementation of a new classification system can be found in Smith & James (), van den Brakel (), & ABS ().…”

Section: Adjusting Time Series For Discontinuitiesmentioning

confidence: 99%

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Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys

Brakel

Zhang

Tam

2020

Int Statistical Rev

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Summary A key requirement of repeated surveys conducted by national statistical institutes is the comparability of estimates over time, resulting in uninterrupted time series describing the evolution of finite population parameters. This is often an argument to keep survey processes unchanged as long as possible. It is nevertheless inevitable that a survey process will need to be redesigned from time to time, for example, to improve or update methods or implement more cost‐effective data collection procedures. It is important to quantify the systematic effects or discontinuities of a new survey process on the estimates of a repeated survey to avoid a disturbance in the comparability of estimates over time. This paper reviews different statistical methods that can be used to measure discontinuities and manage the risk due to a survey process redesign.

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Section: Adjusting Time Series For Discontinuitiesmentioning

confidence: 99%

Section: Adjusting Time Series For Discontinuitiesmentioning

confidence: 99%

Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys

Brakel

Zhang

Tam

2020

Int Statistical Rev

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“…2 Given that women make up nearly half the medical workforce, 3 it is disappointing that this is about twice as large as the gender pay gap for professional employees in the UK. 4 The Review arguably represented the most detailed analysis ever of gender pay gaps in a profession. However, it did not explore the interaction between ethnicity and gender in detail.…”

Section: Introductionmentioning

confidence: 99%

Retrospective observational study of ethnicity-gender pay gaps among hospital and community health service doctors in England

et al. 2021

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ObjectivesTo identify differences in average basic pay between groups of National Health Service (NHS) doctors cross-classified by ethnicity and gender. Analyse the extent to which characteristics (grade, specialty, age, hours, etc.) can explain these differences.DesignRetrospective observational study using repeated cross-section design.SettingHospital and Community Health Service (HCHS) in England.ParticipantsAll HCHS doctors in England employed by the NHS between 2016 and 2020 appearing in the Digital Electronic Staff Record dataset (average N=99 953 per year).Main outcome measuresHours-adjusted full-time equivalent pay gaps; given as raw data and further adjusted for demographic, job, and workplace characteristics (such as grade, specialty, age, whether British nationality, region) using multivariable regression and statistical decomposition techniques.ResultsPay gaps relative to white men vary with the ethnicity-gender combination. Indian men slightly out-earn white men and Bangladeshi women have a 40% pay gap. In most cases, pay gaps can largely be explained by characteristics that can be measured, especially grade, with the extent varying by specific ethnicity-gender group. However, a portion of pay gaps cannot be explained by characteristics that can be measured.ConclusionsThis study presents new evidence on ethnicity-gender pay gaps among NHS doctors in England using high quality administrative and payroll data. The findings indicate all ethnicity-gender groups earn less than white men on average, except for Indian men. In some cases, these differences cannot be explained giving rise to discussions about the role of discrimination.

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“…The Neyman allocation for stratified sampling (Neyman 1934) is a popular method often used in business surveys (Smith et al 2003, Smith & James 2017 and is often regarded as the most important benchmark in this field. This popularity stems from the fact that information from the sample is sufficiently retained and estimate efficiency is guaranteed also when facing challenging sampling issues, for example when both the overall and the stratum sample sizes are small (Kozak et al 2007, Särndal et al 2013, Hidiroglou & Kozak 2018.…”

Section: Introductionmentioning

confidence: 99%

Revisiting sample allocation methods: a simulation-based comparison

Chiodini

Manzi

Martelli

et al. 2019

Communications in Statistics - Simulation and Computation

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In stratified sampling the problem of optimally allocating the sample size is of primary importance, especially in business surveys when reliable estimates are required both for the overall population and for the domains of studies. To this purpose, in this paper we compare allocation methods via a simulation engine highlighting the effects on the reliability of the estimates due only to the sample allocation design. Allocation methods considered in this comparison are: the Neyman allocation, the uniform and proportional allocations, the Costa allocation, the Bankier allocation, the Interior Point Non Linear Programming allocation and the Robust Optimal Allocation with Uniform Stratum Threshold, an allocation method recently adopted by the Italian National Statistical Institute. The last two methods outperform the others at the stratum level. At the overall sample level they perform better than the others together with the Neyman allocation method.

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Changing Industrial Classification to SIC (2007) at the UK Office for National Statistics

Cited by 11 publications

References 16 publications

Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys

Measuring Discontinuities in Time Series Obtained with Repeated Sample Surveys

Retrospective observational study of ethnicity-gender pay gaps among hospital and community health service doctors in England

Revisiting sample allocation methods: a simulation-based comparison

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