The proposal draws on work to better understand conditions facing British businesses by the universities of Nottingham and Stanford, who established the Decision Maker Panel (DMP) in partnership with the Bank of England (Bank) in 2016. This was funded by the ESRC through standard grants from 2017 - 2022 and it is recognized within HM Government as essential data infrastructure. To date, DMP has been essential to gaining a better understanding of the effects of Brexit, Covid and the Ukraine war during a period of enormous economic, geo-political and social change, delivering actionable insights for policymakers at the highest levels of government. It has delivered policy-focused research that is an essential resource for the Bank of England, HM Treasury, 10 Downing St and the Cabinet Office, Office for National Statistics (ONS) and the Office for Budget Responsibility (OBR) since 2016. It has received the ESRC Celebrating Impact Prize (second prize) 2021, it was described by the Deputy National Statistician as 'a game changer for business surveys', and 'world leading' 4* impact case study by REF2021. The ability to maintain a large, representative survey of senior business executives and to address the impacts and longer term effects of economic disruptions has made it vital infrastructure for policy. It maintains a corpus of questions that provide longitudinal quantitative information on business conditions, expectations and uncertainty and has the flexibility to adapt survey questions to new conditions as they emerge e.g. during Covid. The DMP has developed into a strategic data asset that we have made accessible to researchers beyond the core DMP team. In this proposal we will i) gather data from key decision makers in business; ii) provide a delivery plan to analyze the data for actionable insights; and iii) make the data available to other researchers. We will build capacity in survey data collection and analysis that sustains and expands economic and societal impact. The grant will support ongoing data collection of DMP data making it: > One of the largest business surveys in the UK, which is maintained by minimizing attrition and continuously recruiting new firms. > A regular source of information to businesses, journalists and academics through monthly letters sent to all the participants and the website www.decisionmakerpanel.co.uk > An essential input to Bank of England Policy Committees (MPC, FPC), HM Treasury, BEIS and OBR drawing on DMP research analysis to support the actions and communication of policy The research we undertake will address six vital questions: 1. What are the effects of Brexit on employment, investment, R&D, productivity growth, trade, prices, and wages of UK firms? 2. What are the uncertainty impacts of the COVID pandemic on firms? 3. How will firms respond to the resurgence of inflation? 4. How will investment growth policies announced in several Budgets and fiscal statements incentivize UK firms? 5. What effects will climate change make to firm investment and risk exposure? 6. What effect does CEO leadership have on performance? We draw on a highly-experienced established team of international researchers that have created a 'world-leading' survey to address We draw on a highly-experienced established team of international researchers that have created 'world-leading' surveys to address similar policy related research since 2016 in collaboration with the Bank of England, HM Government and the Office for National Statistics. Drawing on senior academics from Nottingham and Stanford we offer benefits to academics and users of the research through academic papers in top five economics journals, blogs, podcasts and media outputs, impact for policy making. We will create a publicly accessible dataset and build capacity in survey design and analysis by training a new cohort of early career researchers, giving them opportunities for secondment and career advancement.

Brexit is the biggest change to the UK's external relations for at least a generation. Leaving the EU will lead to the introduction of new barriers to trade between the UK and the EU, while also creating opportunities for the UK to develop its own independent trade policy. This project studies the impact of Brexit on the UK's trade and economic performance. The analysis will consider how the UK economy is changing in response to Brexit and will provide estimates of the effect of Brexit on productivity, trade, output, wages and living standards. A key contribution of the research will be the creation of a new dataset matching trade and production data at the firm level. Using this dataset, the project will analyse which firms, workers, regions and industries are most exposed to Brexit, how Brexit affects firms' participation in the global economy, whether COVID-19 alters how firms respond to Brexit and how changes in trade impact firm performance and productivity. The project will also develop a trade model of the UK economy to quantify the aggregate economic consequences of Brexit and evaluate alternative post-Brexit trade policies. The project's findings will help policy makers and the general public understand the economic consequences of Brexit and will inform debate on UK trade and economic policy after Brexit. For example, the project will shed light on the costs and benefits of alternative trade agreements with the EU and other partners; what types of trade barriers are most important for UK firms, and; how the effects of Brexit differ across workers, firms, regions and industries.

The outbreak of COVID-19 pandemic is likely to cause the worst recession the world economy has experienced since the Great Depression. Millions of people have already lost their jobs, and the functioning of the labour market has been profoundly disrupted by social distancing measures. In this context, it is fundamental to quantify the impact of the pandemic on job creation. This project will use a unique data set of daily online job postings to provide answers to key questions: which firms and sectors are expanding or contracting during the pandemic? Which jobs are being demanded? What skills and tasks are required in these jobs and how are work activities being delivered? How fast will the dynamics of job creation change as lockdown measures are eased? To answer these questions, our project will carry out an articulated analysis, employing multiple econometric techniques. Firstly, we will provide a detailed descriptive analysis on the evolution of job creation across occupations, sectors, and regions in order to deliver essential insights on the economic consequences of the pandemic, including the crucial distributive impacts across regions and types of jobs. Second, we will make use of advanced techniques in text analysis to study the wording of job postings in order to shed light on whether and how the structure of jobs changes as a result of the COVID-19 shock. In light of the intensity of the COVID-19 induced economic disruption, we may expect to see persistent structural changes to the design of work activities and the remuneration patterns associated with different jobs. The granular and high frequency data that we will employ will allow us to comprehensively assess the occurrence and importance of such changes. Finally, we plan to identify the firm-level characteristics that play a crucial role in ensuring firms' production continuity, and labour demand resilience. Among other factors, the degree of automated work may be crucial to ensure firms' production continuity under lockdown restrictions. For example, robots assembling product components or production processes that are compatible with remote work may allow firms to remain more active while social distancing measures are in place. Coupled with a detailed analysis on the skills demanded, the study will be provide essential inputs for the design and roll-out of targeted interventions that support the most severely affected areas, jobs and industries. These inputs will also be useful for the informing longer run investment decisions on skill training programs and government assistance.

Labour productivity fell sharply at the start of the 2008 financial crisis. Output produced per worker remains around 14 percentage points below the pre-crisis trend. The scale of the productivity fall and the continued stagnation has been the defining feature of the UK's Great Recession and is commonly termed the 'productivity puzzle'. Headway has been made in solving the puzzle, much of it by work at the Bank of England and the Institute for Fiscal Studies. We know that the solution does not lie in the changing composition of the work force or in an industrial shift in the economy. At the start of the recession productivity was depressed as employers with sufficient resources retained skilled and experienced workers even though they were less productive at that time (this is known as 'labour hoarding'). Since then, multiple factors have likely contributed to the continued weakness, including: a fall in real wages (that may have facilitated labour hoarding and led firms to substitute labour for capital, but that may also be a result of lower productivity); a fall in investment (such that workers have less, and less good capital to work with); higher firm survival than in previous recessions. However, it has been difficult to account for the entire puzzle by appealing to these factors alone. In this project we will explore the role of capital allocation. Previous evidence shows that the overall productivity of the economy - Total Factor Productivity (TFP) - is improved substantially when resources flow to the uses that have the highest return. We have found preliminary evidence that the link between investment and rates of return in the UK has broken down since 2008. The recession was characterised by relative demand shocks and an increasing dispersion in rates of return to capital across firms. Despite this, there has been very little adjustment in the allocation of capital; capital is not flowing to the projects with the highest returns. This is perhaps not surprising given that the key channels through which capital flows across the economy (new investment and the entry and exit of firms) have been notably weak since 2008. The UK experience stands in contrast to previous UK recessions, and to the current experience of the US. An increase in uncertainty and financial market impairment are important candidates for why capital has not been adjusted in response to shocks. In this project we view such factors as distorting firms' choices over how much capital to invest in. Our objective is to quantify the scale of such distortions and to estimate how important they are in impeding the allocation of capital and thereby lowering aggregate TFP (and therefore labour productivity). This project will be novel in applying previously developed methods from the academic literature to the context of an advanced economy recession; most of existing literature on resource misallocation focuses on developing countries and seeks to explain their relatively poor productivity performance. In addition, we plan to extend the existing modelling framework to explicitly account for a process of dynamic capital adjustment. That is, to account for the presence of costs that inhibit the adjustment of capital even in normal times. Productivity growth is the source of rising living standards and economic growth. Understanding the causes of weak productivity is therefore central to identifying the policy challenges and designing the solutions. For example, the extent to which low productivity is a temporary or permanent problem is central to judging the UK's supply capacity, and whether expansionary policies are called for. This is of particular importance to monetary policy, which must consider the inflationary consequences of economic expansion. This project will bring together expertise from the Bank of England and the IFS with a view to advancing the solution to the productivity puzzle and directly informing policy makers.

Small and medium-sized enterprises (SMEs) constitute a critical pillar of the UK economy. More than 99% of the roughly 6 million businesses in the UK are SMEs and they employ more than 16 million workers. As the impact of the COVID-19 pandemic becomes clearer, it is evident that SMEs are facing serious and unprecedented challenges, including declining revenues, defaulting on loans, inability to retain employees and postponing growth plans. However, many SMEs in the UK find it extremely difficult to obtain funding through standard banking channels as the lack of financial information about SMEs makes it difficult to evaluate SMEs' credit risk and debt repayment capacity. Hence, to meet all these pressing needs, it is critical to develop an efficient protocol to assess SMEs' pandemic risk exposure and SMEs' resilience towards funding shortages caused by COVID-19. This project will use Artificial intelligence (AI) techniques including Machine Learning (ML), Deep Learning (DL), and Big Data to develop two novel analytical tools: 1) The Pandemic Risk Index of UK SMEs (PRI): In this strand, the project will develop a novel Pandemic Risk Index (PRI) to model the potential economic, financial, and reputational effects of COVID-19 on UK SMEs in the short and long run. The academic and professional literature emerging in the wake of the COVID-19 crisis has considered several factors in isolation. However, this index aims to combine as many COVID-19- relevant variables as possible into one holistic multidimensional set of metrics. This is to have a better informed understanding of the big picture by accounting for and explaining the various weights and interrelationships of these variables. The main variables (but not exclusively) of this index would be (all of them are at the firm-level): exposure to global supply chains, exposure to international capital markets, corporate governance, financial flexibility, and geographical proximity to COVID-19 hotspots. 2) AI-based Programme Suite to assess the Credit Risk of Borrowing UK SMEs (AI_CREDIT): In this strand, the project will develop an effective AI-based Python programme suite (AI_CREDIT) using Machine Learning (ML) and Deep Learning (DL) to provide policymakers in the UK government and financial intermediaries with an accurate and timely evaluation of an SME borrower's credit risk profile. With this, policymakers and lenders can make prompt decisions in providing appropriate emergency loans to SMEs to overcome their funding shortages and mitigate the impact of COVID-19. Based on the cutting-edge application of ML/DL to corporate credit risk, this project will develop a novel programme suite by integrating innovative methods. The innovations introduced by this project will extend the application of ML/DL in the estimation of SMEs' credit profiles by training ML/DL with a large amount of seemingly irrelevant data about large firms. The research impact of this project is relevant to many stakeholders. Policymakers and lenders can directly benefit by gaining access to novel tools to allocate funds and support SMEs efficiently. Other financial institutions including Insurance companies and private equity funds will benefit from the tools in assessing the risk related to SMEs in terms of insurance policies and investment decisions, respectively. All these are likely to lead to efficient allocation of funds and reduction of cost of funds allocated to SMEs which in turn will help SMEs to survive and thrive the current and any future pandemic disruptions. The planned project is UK wide, and it will be applicable to all UK SMEs. The project is in collaboration with the Bank of England and the Confederation of British Industry (CBI). CBI is a leading business lobby group that promotes business interests within public bodies and deals with the impact of policy on businesses in the UK. The engagement with the project partners and other stakeholders is crucial to scale up the implement