Achieving the United Nations Sustainable Development Goals and the European Union's policies on environmental and social sustainability requires a comprehensive measure of human progress that does not focus solely on GDP. However, the evidence on alternative approaches is fragmented and the lack of consensus on competing indicators and policy frameworks is a mAchieving the United Nations Sustainable Development Goals and the European Union's policies on environmental and social sustainability requires a comprehensive measure of human progress that does not focus solely on GDP. However, the evidence on alternative approaches is fragmented and the lack of consensus on competing indicators and policy frameworks is a major obstacle to setting policy goals that promote multi-dimensional well-being and to monitoring and measuring progress. MERGE addresses these challenges by providing a forum for dialogue, co-creation and knowledge exchange, and by linking cutting-edge research and policy practice. A consortium of leading researchers and key communities in the field, MERGE brings together three recently launched higher education research consortia (SPES, ToBe, WISE Horizons) and an ERC grant (REAL). To scale up results, MERGE provides a framework for creating and strengthening a multidisciplinary community of researchers, a technical and knowledge network, a policy network and a network of civil society actors. Through these networks, MERGE aims to build a broad consensus on easy-to-use and acceptable indicators and frameworks for measuring multidimensional well-being within planetary boundaries in the EU and Member States, as well as in global organisations and civil society. MERGE participants will benefit from collaborative and training events, analyses, indicators, datasets and policy briefings. Through knowledge exchange, stakeholders and researchers can adopt and develop a systematic and coherent understanding of the sustainable economy paradigm in their own work.

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.

The project will create, assess, document and support use of a research-ready dataset, generated by linking personal data from the Census to employee records from the longitudinal Annual Survey of Hours and Earnings (ASHE). This will provide the research and policy communities with a uniquely powerful longitudinal dataset, which can create new insights into the influence of personal and household characteristics, job characteristics and employers on labour market outcomes. The UK censuses provide a wealth of information on individuals in the population but do not collect data on earnings, limiting their value for labour market research. ASHE is an annual panel survey based on a 1% sample of employee jobs in the UK, collecting detailed and highly-reliable information on employees' earnings and paid hours. However, the survey collects few personal characteristics for the employee (restricted to sex, age and residential location). The project will build on the respective strengths of these datasets to provide the largest longitudinal dataset for research on earnings and hours in the UK. In the ADR-funded Wage and Employment Dynamics Strategic Impact Project (WED), the proposing team partnered with the Office for National Statistics (ONS) to link person records from ASHE to person records from the 2011 Census for England and Wales (CEW11). This has added Census data on personal characteristics to around three-quarters (74%) of the 2011 ASHE records held by employees in England and Wales. The ASHE-CEW11 linked dataset is already enabling researchers in government and academia to explore how factors such as ethnicity, disability, education, country of birth and household circumstances affect individual's wage levels and pay progression. However, the value of the dataset would be significantly enhanced if the link was extended to the most recent census. In the proposed project, we will work with ONS to link person records from the 2021 Census for England and Wales to the longitudinal ASHE, using a new and improved linkage protocol (ONS' Reference Data Management Framework, RDMF). The RDMF will also be used to relink ASHE to the 2011 Census for England and Wales, generating a longitudinal ASHE-Census dataset spanning at least a decade. The linkages between ASHE and Census will be quality assessed and documented. The project will focus on generating research-ready data, supported by a program of training and user support. The resulting longitudinal ASHE-Census dataset will represent a significant improvement in the UK's data infrastructure for labour market research, providing more reliable earnings data, larger samples, and better employer information, than is currently available from existing household surveys. This dataset will support the aims of the program by using Census 2021 to provide fresh insights into the experiences of employees, enabling policymakers to make better-informed decisions.

The proposal outlines a project geared towards revolutionizing data accessibility and security through innovative data synthesis techniques. We first highlight one bottleneck in the data discovery process: the scarcity of good teaching datasets, particularly for data that sit in virtual research environments where access restrictions impede their creation. The creates a discoverability challenge for new users, who are unable to explore data before going through an approval process, increasing barriers to entry. While synthetic data is a potential solution, concerns about risk and utility exist. Data services often grapple with assessing the disclosure risk associated with synthetic data, as it deviates from the scope of conventional output disclosure control rules. Moreover, there is uncertainty about its utility, especially when specific analyses might yield results diverging from real data, diminishing the training process's effectiveness. The project has three objectives: (1) investigate tailored teaching datasets for restricted data access, (2) develop a systematic approach to assess disclosure risk in analytical outputs from restricted data sources, and (3) assess the feasibility of producing linked synthetic data from different sources (using the same methodology). The project spans from April 2024 to March 2025 and falls primarily under Theme 2: Data discovery using machine learning or other AI technologies, but also has the potential to add value under the other two themes (with objective 3 speaking to the federated services agenda and objective 2 providing a tool for augmenting the skills of output checkers). A preliminary study conducted at Manchester University, in collaboration with Administrative Data Research UK, demonstrates the feasibility of generating synthetic datasets with both high utility and low risk. The methodology involves leveraging cleared analytical outputs from data services as the basis for generating synthetic data using a genetic algorithm. The goal is to provide trainees with data that not only closely resembles real-world data but also yields analytical output very similar to that of the real data, enhancing the training experience. Beyond merely this replication of analytical properties, the approach also offers a route to formalise assessment the disclosure risk associated with analytical outputs from safe settings. By embodying statistical outputs in synthetic data, it enables a systematic evaluation of disclosure risk, addressing the informality and potential inconsistencies present in current output checking procedures. Furthermore, the project aims to bolster the federated services agenda by exploring the creation of synthetic linked data from using analytical outputs from data of multiple services. This approach expands the possibilities of data synthesis without the need for actual linkage and elaborate governance of infrastructure, such as trusted third parties. Deliverables include open-source code, example synthetic datasets, and academic papers aimed at knowledge dissemination and skill development. The project emphasizes collaboration among data providers, services, and stakeholders to address challenges in data accessibility and security. In essence, the project aims to redefine data accessibility by providing tailored teaching datasets and systematic disclosure risk assessment methods. It will also foster a collaborative ecosystem for transformative advancements in data synthesis and access management, and contributes to the broader research data landscape.

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.