Both the physical and transition-related impacts of climate change pose substantial macroeconomic risks. Yet, markets still lack credible estimates of how climate change will affect debt sustainability, sovereign creditworthiness and the public finances of major economies. We present a taxonomy for tracing the physical and transition impacts of climate change through to impacts on sovereign risk. We then apply the taxonomy to the UK’s potential transition to net zero. Meeting internationally agreed climate targets will require an unprecedented structural transformation of the global economy over the next two or three decades. The changing landscape of risks warrants new risk management and hedging strategies to contain climate risk and minimise the impact of asset stranding and asset devaluation. Yet, conditional on action being taken early, the opportunities from managing a net zero transition would substantially outweigh the costs.

Climate change and weather shocks have multi-faceted impacts on food systems with important implications for economic policy. Combining a longitudinal household survey with high-resolution climate data, we demonstrate that both climate and weather shocks increase food insecurity; cash assistance and participation in Ethiopia’s Productive Safety Net Programme have reduced food insecurity; but food assistance has been ineffective. Importantly, households with savings, and those that stored their harvest to sell at higher prices rather than for home use, suffered less from food insecurity, yet both strategies are harder for the poorest and most food insecure households to adopt. Our paper provides micro-founded evidence needed to design policies that both improve agricultural yields in the context of a changing climate and target households’ abilities to cope with shocks that put upwards pressure on food prices.

Climate change is one of the most serious risks facing humanity. Temperature rises can lead to catastrophic climate and natural events that threaten livelihoods. From rising sea levels to flooding, bush fires, extreme temperatures and droughts, the economic and human cost is too large to ignore. More than 190 world leaders got together in Glasgow during November 2021 at the UN’s COP26 climate change summit to discuss progress on the Paris Agreement (COP21) and to agree on new measures to limit global warming. In Paris, countries agreed to limit global warming to well below 2° and aim for 1.5° as well as to adapt to the impacts of a changing climate and raise the necessary funding to deliver on these aims. However, actions to date were not nearly enough as highlighted by the IPCC (2018) special report. The world is still on track to reach warming above 3° by 2100. As evident from figure 1, global temperatures have been on a steadily increasing path since the start of the 20th century and this process has substantially accelerated since the beginning of the 1980s. This has been unevenly distributed, with temperatures in the Northern hemisphere being a full 1°C higher than for the 1961–1990 average, whilst temperatures in the Southern hemisphere have increased by almost 0.5°C.

Measured health output in the UK declined sharply during the Covid-19 pandemic, despite the evident large increase in some National Health Service (NHS) activities such as critical care, and the new test and trace and vaccination programmes. We explain the measurement methods applied to public services that produced the published decline, in the context of the inherent difficulties of defining and measuring health output and productivity; and describe the changes in practice that had to take place in NHS hospitals. We also discuss, on the basis of these changes including increased use of technology, the likely outlook for healthcare productivity. We conclude that within NHS England capacity, constraints have contributed to substantial falls in non-Covid-19 healthcare output and argue that increased capacity in the social infrastructure of the health service is essential to enable higher productivity in an uncertain environment.

In 2020–2021, the world has been gripped by a pandemic that no living person has ever known. The coronavirus pandemic is undoubtedly the greatest challenge the world has faced in over a generation. The imperative of statistical modelling is not only to manage the short-run crisis for the health services, but also to explain the pandemic’s course and establish the effectiveness of different policies, both non-pharmaceutical and with vaccines. This difficult task has been undertaken by the epidemiologists and others in the face of measurement data problems, behavioural complications and endogeneity issues. This paper proposes a simple taxonomy of the alternative different models and suggests how they may be used together to overcome limitations. This perspective may have important implications for how policy-makers cope with future waves or strains in the current pandemic, or future pandemics.

An often overlooked strategy for fighting the COVID-19 pandemic is group testing. Its main advantage is that it can scale, enabling the regular testing of the whole population. We argue that another advantage is that it can induce social distancing. Using a simple model, we show that if a group tests positive and its members are in close social proximity, then they will rationally choose not to meet. The driving force is the uncertainty about who has the virus and the fact that the group cares about its collective welfare. We therefore propose identifying socially connected groups, such as colleagues, friends and neighbours, and testing them regularly.

This article shows how new time series models can be used to track the progress of an epidemic, forecast key variables and evaluate the effects of policies. The univariate framework of Harvey and Kattuman (2020, Harvard Data Science Review, Special Issue 1—COVID-19, https://hdsr.mitpress.mit.edu/pub/ozgjx0yn) is extended to model the relationship between two or more series and the role of common trends is discussed. Data on daily deaths from COVID-19 in Italy and the UK provides an example of leading indicators when there is a balanced growth. When growth is not balanced, the model can be extended by including a non-stationary component in one of the series. The viability of this model is investigated by examining the relationship between new cases and deaths in the Florida second wave of summer 2020. The balanced growth framework is then used as the basis for policy evaluation by showing how some variables can serve as control groups for a target variable. This approach is used to investigate the consequences of Sweden’s soft lockdown coronavirus policy in the spring of 2020.

Can smart containment policies crowd out private efforts at social distancing? We analyse this question from the perspective of network formation theory. We focus in particular on the role of externalities in social distancing choices. We also look at how these choices are affected by factors such as the agents’ risk perception, the speed of the policy intervention, the structure of the underlying network and the presence of strategic complementarities. We argue that crowding out is a problem when the probability that an outbreak may spread undetected is relatively high (either because testing is too infrequent or because tests are highly inaccurate). This is also the case where the choice of relaxing social distancing generates the largest negative externalities. Simulations on a real-world network suggest that crowding out is more likely to occur when, in the absence of interventions, face-to-face contacts are perceived to carry relatively high risk.

Based on evidence gathered from a newly built large macroeconomic dataset (MD) for the UK, labelled UK-MD and comparable to similar datasets for the United States and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components). This and other crucial aspects of ML-based forecasting in unprecedented times are studied in an extensive pseudo-out-of-sample exercise.

Nowcasting, that is, forecasting the current economic conditions, is a key ingredient for decision making, but it is complex, even more so for a small open economy, due to the higher volatility of its GDP. In this paper, we review the required steps, taking Luxembourg as an example. We consider both standard and alternative indicators, used as inputs in several nowcasting methods, including various factor and machine learning models. Overall, mixed frequency dynamic factor models and neural networks perform well, both in absolute terms and in relative terms with respect to a benchmark autoregressive model. The gains are larger during problematic times, such as the financial crisis and the recent Covid period.

A major part of the 2010–15 UK government's education reforms in England was a focus on the curriculum that pupils study from ages 14–16. Most high profile was the introduction of the English Baccalaureate (EBacc) performance measure for schools, incentivising study of “subjects the Russell Group identifies as key for university study” (Gibb, 2011). However, there does not appear to be good quantitative evidence about the importance of studying such a set of subjects, per se. This paper sets out to analyse this question, considering whether otherwise similar young people who study specific sets of subjects (full set for EBacc-eligibility, two or more sciences, foreign languages, applied subjects) to age 16 have different probabilities of entering university, and specifically a high-status university. It compares results from regression modelling and propensity score matching, taking advantage of rich survey data from a recent cohort of young people in England. We find that conditional differences in university entry attributable to subject choice are, at most, small.

None of the existing models for the future trade policy relationship between the UK and the EU come with a predetermined foreign and security policy relationship. This article assesses how the future EU-UK foreign and security policy relationship might be organised post-Brexit. It provides evaluation of the current EU-UK interrelationship in the fields of the EU's Common Foreign and Security Policy (CFSP) and Common Security and Defence Policy (CSDP) and assesses the degree to which the UK is presently integrated into EU decision-making and implementation. It highlights that the UK needs to determine the degree to which it wants autonomy or even divergence from existing EU policies. The article concludes by rehearsing the costs and benefits of three possible future relationships between the UK and EU foreign, security and defence policy: integrated, associated or detached.

We estimate the changing value of workplace pensions in the UK and incorporate their value into an estimate of the public sector pay differential. Falling pension membership in the private sector and growing value of public service pensions led to a significant increase in the estimated public sector pay differential from 1997 to 2009, even though headline pay grew faster in the private sector. From 2009 to 2012, although pay grew faster in the public sector, reforms to public service defined benefit pensions, particularly indexation to the CPI rather than RPI, significantly reduced the public pay differential.

The level of private sector labour productivity has been particularly weak since the start of the crisis. In this paper we explore whether impairment to capital reallocation has been contributing to this weakness. The recent increase in the dispersion of output, prices and rates of return across firms and sectors is stark, and suggests that resources have had incentives to move. Efficient allocation requires that capital moves to firms and sectors where rates of return are relatively high. And the change in capital levels across sectors has been particularly low, suggesting there has been an unusually slow process of capital reallocation since 2008 compared to previous UK recessions and other banking crises. This result is also apparent within sectors. We use a simple and general model to show that increased price dispersion can be a consequence of frictions to efficient capital allocation. And the size of this dispersion can usefully inform us about the size of the associated output and productivity loss. We then find that – using firm level data – the relationship between rates of return and subsequent capital movements has changed since the financial crisis. Overall, our results suggest that impaired capital reallocation across the UK economy is likely to have been one factor contributing to the recent weakness in productivity growth.