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# SIPHER Inclusive Economy (local authority level) ## Welcome to the SIPHER Inclusive Economy (local authority level) OSF repository ### About the dataset The SIPHER Inclusive Economy (local authority level) dataset, developed within the [SIPHER Consortium][2] and supported by the [UK Prevention Research Partnership][11] (MR/S037578/2), provides a meaningful collection of data that can be used to explore the extent and nature of economic inclusion across local authorities (LAs) in Great Britain. SIPHER is a major UKPRP investment which brings together scientists across seven universities, four government partners at local, regional and national level, and multiple practice partner organisations. Some background information on the motivation behind the Inclusive Economy I dataset can be found at the [SIPHER Blog - What is an inclusive economy – and how do you know if you’ve got one?][3] The selection of data sources used in this dataset is described in detail in a technical report, which you can find in the [Metadata and information folder][4]. The dataset consists of 13 indicators. ### What the dataset contains and how to use it The main dataset, i.e. the full set of 13 indicators in a consistent format, can be found in the [Dataset / IE Dataset Latest Version folder][5]. In addition to the 13 inclusive economy indicators, the dataset contains additional information on the demographic structure (Total Dependency Ratio & Sex Ratio) and health outcomes (Life Expectancy & Lifespan Variation) for each local authority. For each of the 13 indicators, as well as all additional demographic indicators and health outcomes, we have created a folder that contains (a) the primary data from which the metric for the indicator was calculated, (b) the code used to calculate the metric and (c) the processed data. In the same folders you will also find manuals containing a detailed description of the indicators, variables and metrics used. In the [Dataset / Collation scripts folder][6] you will find (a) an R program for collating all indicators in one final dataset, and (b) code for the utilised imputation. As some of the indicators were not available for all local authorities, we used Amelia II, a Bayesian multiple imputation algorithm, to fill gaps in the data. Further information can be found at [Amelia II: A Program for Missing Data][7]. Some details of the *k*-means clustering method used to present the dataset can be found in the [Dataset / Clustering folder][8], where an *n* = 4 clustering was found to be optimal. The [Dataset / Comparators folder][9] provides a comparison of each of the 13 inclusive economy indicator with the concept of multiple deprivation. The results of this comparison highlight that our indicators capture a heterogeneity that cannot be summarised in a single number, as would be the case with a deprivation index. ### Other resources - [Inclusive Growth Indicators for Cities: considerations and options - Inclusive Growth Analysis Unit, University of Manchester][10], Briefing Paper 6, August 2017 - pdf. - [Existing Indicator Sets Workbook][12], a compilation of how the Inclusive Economy indicators were developed and compiled, University of Glasgow, 2020-2021. ### Acknowledgements This work by the [SIPHER Consortium][2] was supported by the [UK Prevention Research Partnership][11] (MR/S037578/2), which is funded by the British Heart Foundation, Cancer Research UK, Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Health and Social Care Research and Development Division (Welsh Government), Medical Research Council, National Institute for Health Research, Natural Environment Research Council, Public Health Agency (Northern Ireland), The Health Foundation and Wellcome. This research was conducted as part of the [Systems Science in Public Health and Health Economics Research - SIPHER Consortium][2] and we thank the whole team for valuable input and discussions that have informed this work. [2]: [3]: [4]: [5]: [6]: [7]: [8]: [9]: [10]: [11]: [12]:
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