Our Management and Expectations Survey (MES), cited in the ESRC call, arose from a partnership between the ONS and ESCoE: it is the largest ever survey of UK management capabilities, executed on a population of 25,000 firms across industries, regions, firm sizes and ages documenting the variable quality of management practices across UK businesses. Our analysis found a significant relationship between management practices and labour productivity amongst UK firms, and examined whether certain types of firms have poor management practices and stagnant productivity, drawing conclusions about the links between them, ONS (2018). This team, with two seminal contributors to management practice and performance (Bloom, Stanford, and Van Reenen, MIT) who initiated the World Management Survey, partners from the ONS (Awano, Dolby, Vyas, Wales), and the Director and Fellows of the ESCoE (Riley, Mizen, Senga, Sleeman) at the NIESR, will investigate five issues: 1. Longitudinal changes in management practices and performance The initial MES offers a cross section of variation in management practices and expectations between firms, but it does not explore variations within businesses through time due to the missing longitudinal dimension to the data. A second wave of the MES will expand our scope of analysis so that we can interpret how management practices in the UK have varied over time. This extension addresses the 'broad consensus' from the recent ESRC-ONS workshop that 'there is not enough longitudinal data around productivity that allows for consistent, ongoing analysis, and in particular data that enables researchers to identify, isolate and accurately measure changes over time.' 2. International comparisons Drawing on our links through Bloom and Van Reenen with the US Management and Organizational Practices Survey (MOPS) at the US Census Bureau will enable us to i) test identical hypotheses using their methods and variables to draw research insights that help identify causal drivers of productivity at the firm level, and compare and contrast the UK and US data; ii) draw together a unique joint ONS-Census Bureau methodological forum for collecting the most useful micro-data for measuring management, investment and hiring intentions for UK and US firms. Similar data collection exercises have been taking place across other countries. We have established links with German and Japanese teams and we intend to discuss key differences, e.g. between the US and European business environments, and similarities, e.g. the Japanese experience of low productivity. 3. Analysis of linked business surveys and administrative data Partnership between academic researchers and ONS facilitates the matching of data from other sources to answer key questions around: a) management and firms' ability to cope with uncertainty by linking MES responses to trade data, administrative data on VAT, R&D expenditure, and patenting data, and exploiting variation across firms in exposure to EU markets through supply chains and export destination of goods; b) evidence of superior innovation, R&D and export performance from evidence of how business innovation and exporting varies across firms and over time in response to management practices and cultures. This will directly inform practical lessons for UK businesses. 4. Experimental analysis using big data We will use natural language processing and machine learning to investigate big data from job-search companies to objectively identify the factors that affect staff satisfaction and performance in the UK. Matching to the MES and other micro datasets we will examine links between mental health and management practices. 5. Randomised control trials Nearly 9,000 responding businesses in the MES sought 'feedback' on their management score. By varying feedback to respondents we will observe in collaboration with BIT (the 'Nudge Unit') and CMI the impact on firm's subsequent adaptation and performance.

ADR UK (Administrative Data Research UK) is a partnership transforming the way researchers access the UK’s wealth of public sector data, to enable better informed policy decisions that improve people’s lives. By linking together data held by different parts of government, and by facilitating safe and secure access for accredited researchers to these newly joined-up data sets, ADR UK is creating a sustainable body of knowledge about how our society and economy function – tailored to give decision makers the answers they need to solve important policy questions. ADR UK is made up of three national partnerships (ADR Scotland, ADR Wales, and ADR NI) and the Office for National Statistics (ONS), which ensures data provided by UK government bodies is accessed by researchers in a safe and secure form with minimal risk to data holders or the public. The partnership is coordinated by a UK-wide Strategic Hub, which also promotes the benefits of administrative data research to the public and the wider research community, engages with UK government to secure access to data, and manages a dedicated research budget. ADR UK is funded by the Economic and Social Research Council (ESRC), part of UK Research and Innovation. To find out more, visit adruk.org or follow @ADR_UK on Twitter. The Office for National Statistics (ONS) plays a crucial role in sourcing, linking and curating public sector data for ADR UK (Administrative Data Research UK), ensuring that all data is accessed by researchers in a safe and secure form. To support the ADR UK partnership, ONS is expanding and improving its established Secure Research Service (SRS) – the organisation’s facility for providing secure access to de-identified public sector data for research – and significantly increasing the range of administrative data available. ONS will focus on increased data reuse to deliver efficiencies to government departments (who only need to provide data once), and maximise the use of this data by identifying shared priorities and objectives with government departments.

ADR UK (Administrative Data Research UK) is a partnership transforming the way researchers access the UK’s wealth of public sector data, to enable better informed policy decisions that improve people’s lives. By linking together data held by different parts of government, and by facilitating safe and secure access for accredited researchers to these newly joined-up data sets, ADR UK is creating a sustainable body of knowledge about how our society and economy function – tailored to give decision makers the answers they need to solve important policy questions. ADR UK is made up of three national partnerships (ADR Scotland, ADR Wales, and ADR NI) and the Office for National Statistics (ONS), which ensures data provided by UK government bodies is accessed by researchers in a safe and secure form with minimal risk to data holders or the public. The partnership is coordinated by a UK-wide Strategic Hub, which also promotes the benefits of administrative data research to the public and the wider research community, engages with UK government to secure access to data, and manages a dedicated research budget. ADR UK is funded by the Economic and Social Research Council (ESRC), part of UK Research and Innovation. To find out more, visit adruk.org or follow @ADR_UK on Twitter. The Office for National Statistics (ONS) plays a crucial role in sourcing, linking and curating public sector data for ADR UK (Administrative Data Research UK), ensuring that all data is accessed by researchers in a safe and secure form. To support the ADR UK partnership, ONS is expanding and improving its established Secure Research Service (SRS) – the organisation’s facility for providing secure access to de-identified public sector data for research – and significantly increasing the range of administrative data available. ONS will focus on increased data reuse to deliver efficiencies to government departments (who only need to provide data once), and maximise the use of this data by identifying shared priorities and objectives with government departments.

This application seeks continued financial support from the ESRC for LIS, a cross-national data archive and research institute. LIS is a data infrastructure of income and wealth data whose primary purpose is to enable cross-national, interdisciplinary primary research into socio-economic outcomes and their determinants. Whilst LIS is physically located in Luxembourg, users of the LIS microdata come from about 100 countries including the UK. The work of acquiring and harmonising diverse datasets from multiple countries is labour intensive; by centralising this task, LIS saves time for researchers carrying out comparative analyses, avoiding the repetition of these tasks every time a scholar starts a project; in addition, thanks to its expertise over many years, LIS can ensure users the best comparability of the data. In order to avoid having to charge individual user fees, LIS is seeking financial support to be able to continue providing researchers with access to high-quality data. This application seeks support from ESRC to help cover LIS' basic operating costs, which primarily consists of staff salaries and computer equipment. LIS contains the Luxembourg Income Study (LIS) Database, which includes income data, and the Luxembourg Wealth Study (LWS) Database, which focuses on wealth data. Since its founding, LIS datasets have been used by 8,000 researchers from around the world to analyse economic and social policies and their effects on outcomes including poverty, income inequality, employment status, wage patterns, gender inequality, family formation, child-wellbeing, health status, immigration, political behaviour and public opinion. The newer LWS datasets enable research on wealth portfolios, asset levels, and the interplay between household income and wealth. According to the Publish and Perish software that retrieves and analyses academic citations, the Hirsch's h-index is above 160 for LIS and 55 for LWS. LIS is a unique resource not only with respect to the breath of its data offering (it is the only data archive in existence that includes income, wealth and labour market microdata, over time and in one place from such diverse geographic regions and at such varied income levels), but also because it is the only archive providing access to confidential microdata through a secure remote execution system, that allows thousands of registered users all over the world to receive the logs of their statistical queries in real time (an average of about 70,000 requests are processed every year). LIS has also long operated as a venue for researchers and practitioners to exchange research ideas, results, and methods. These exchanges take place through the widely accessed Working Paper Series (now including 840 papers), the Visiting Scholar program, pre- and postdoctoral postings, annual workshops and conferences. The participating countries are high-income and middle-income countries. LIS will continue to grow to include many more middle-income countries' datasets, enabling greater comparative research opportunities. Additionally, it is now seeking to expand its data offerings in terms of increased frequency of data availability, and improved tools for data access and meta data. The UK has always had an important role in LIS since its very inception in the 1980s, when British economist Tony Atkinson gave a fundamental contribution to its construction and development (he later become the president of its Board). Individuals and organisations in the UK have been actively engaged with LIS for over three decades, providing data, contributing financing and serving as board members. Researchers in the UK have queried the microdata; produced publications, government reports and working papers using the LIS data; attended summer workshops; participated in the Visiting Scholar program; contributed to research conferences and conference volumes; and provided invaluable intellectual guidance and direction regarding LIS' activit

Previous research conducted at UCL has demonstrated that a name very often provides an open and accessible statement of the cultural, ethnic and linguistic characteristics of its bearer (e.g. Mateos et al 2011). Additional light may be shed upon these characteristics by parental choice of fore-(given) name, while changing fashions often render forenames a valid indicator of age and other geographic and social characteristics. This information has been used to develop working classifications of names, and they have been successfully used to augment incomplete data records for audit purposes - for example in gauging the success of NHS preventive care initiatives across different ethnic groups. However, these classifications have been developed using incomplete address registers (such as the public version of the Electoral Roll) and telephone directories. There are a number of shortcomings to the data sources hitherto used in this kind of research that limit the usefulness of the resulting classifications when applied to new datasets: 1. The data sources underlying the classifications provide incomplete and probably biased representations of the population-at-large. For example, public electoral registers do not include (young and immigrant) non-voters or (privacy sensitive) 'opt out' individuals, and public telephone directories provide less than universal coverage and few given names. 2. Commercial classifications of the age profiles of given names are typically restricted to the 16+ age cohorts, and supplementation with ONS birth name data (e.g. www.ons.gov.uk/ons/rel/vsob1/baby-names--england-and-wales/2012/stb-baby-names-2012.html) is error prone because young children may move abroad and immigrants may bring young children with them. Thus these sources do not allow an inclusive snapshot of the population resident in the UK at any specific moment in time. 3. Whilst 'crowd sourced' validation is possible (e.g. www.onomap.org), there is no comprehensive means of comparing predicted and objective (e.g. age) or self-assigned (e.g. ethnicity) characteristics. 4. Little focus has been developed upon refining attempts to classify 'hard to reach' groups, such as Caribbeans, whose ethnicity can likely only be ascertained through subtle associations between forename-surname pairings. 5. The clustering procedure has been largely aspatial, in significant part because of unevenness of geographical coverage and the absence of highly granular location information. This research will address these shortcomings through use of the best available secondary dataset for developing an enriched classification and conducting sensitivity analysis to refine and improve its universal application across the UK. Individual level Census data will be used in order to develop a classification of given and family names into cultural, ethnic and linguistic groups, by extending the methodology of Mateos et al (2011). Crucially, and for the first time, the results of this classification will be compared to individual and household data on Ethnicity, National Identity, Country of Birth, First and Second Language Spoken and Nationality. This will make it possible to investigate the causes of apparent errors in the classification, and to identify the small geographic areas in which they are concentrated. Through an iterative procedure, secure online facilities will be used to improve the classification in the light of these results. Comparison of household and individual classification results with self assignments in terms of Census measures of identity will make it possible to make the classification tool sensitive to indicators of cultural assimilation, whether through inter-marriage or duration of residence, at scales from the local to the national. The 'surname regions' will also be used to add regional context to the 2011 ONS Output Area Classification.