We measure whether religious people in the UK cope better during a large negative shock - the nationwide pandemic lockdowns. We use data from the Understanding Society longitudinal dataset, including self-reports on religion and religiosity taken before the pandemic, and mental health data on unhappiness and depression, collected both before and during the lockdown periods. We find evidence that religious people coped better during the lockdowns. In terms of magnitude, we found that religious people (in that it makes a difference to their life) were around one-fifth less likely to suffer an increase in unhappiness or depression. Our results for those who belong to a religion (regardless if it makes a difference to their life) were higher in magnitude, but lower in significance. We found little difference in coping across religions; with the results for Christians, Muslims and Hindus all being broadly similar. However, we did find some difference within Christian denominations, with ‘Christian Other’ (those belonging to mainly Protestant churches other than the Church of England) coping relatively worse among those who belong to a religion. We also found that when places of worship were closed, religious Muslims and Catholics suffered disproportionately - the two religious groups from our study that normally require weekly communal attendance from their followers.

The purpose of this paper is to revisit an intriguing finding. Although over the last few decades leading up to the financial crisis there was a marked reduction in the volatility of aggregate output and inflation, there appears to have been a corresponding increase in the sales volatility of individual firms. Here we argue that a significant reason for this apparent increase in firm level volatility was an increase in churning of firm activity through the acquisition and disposal of businesses. This created an increase in observed negative covariances between firms, so even if the volatility of underlying organic growth has also fallen, observed volatility has risen

Option-implied risk-neutral densities are widely used for constructing forward-looking risk measures. Meanwhile, investor risk aversion introduces a multiplicative pricing kernel between the risk-neutral and true conditional densities of the underlying asset’s return. This paper proposes a simple local estimator of the pricing kernel based on inverse density weighting, and characterizes its asymptotic bias and variance. The estimator can be used to correct biased density forecasts, and performs well in a simulation study. A local exponential linear variant of the estimator is proposed to include conditioning variables. In an application, we estimate a demand-based model for S&P 500 index options using net positions data, and attribute the U-shaped pricing kernel to heterogeneous beliefs about conditional volatility. 

Using data on the external assets and liabilities of global banks based in the UK, the world’s largest centre for international banking, we identify exogenous cross-border banking flows by constructing novel Granular Instrumental Variables. In line with the predictions of a new granular international banking model, we show empirically that cross-border flows have a significant causal impact on exchange rates. A 1% increase in UK-based global banks’ net external US dollar-debt position appreciates the dollar by 2% against sterling. While we estimate that the supply of dollars from abroad is price-elastic, our results suggest that UK-resident global banks’ demand for dollars is price-inelastic. Furthermore, we show that the causal effect of banking flows on exchange rates is state dependent, with effects twice as large when banks’ capital ratios are one standard deviation below average. Our findings showcase the importance of banks’ risk-bearing capacity for exchangerate dynamics and, therefore, for insulating their domestic economies from global financial shocks.

Amidst the recent resurgence of inflation, this paper investigates the interplay of corporate profits and income distribution in shaping inflation and aggregate demand within the New Keynesian framework. We derive a novel analytical condition for profits to be procyclical and inflationary. Furthermore, we show that the cyclicality of profits is a key determinant of the propagation properties of these models under household heterogeneity, but there is a catch: for aggregate-demand fluctuations and inflation to be amplified by heterogeneity, profits have to be countercyclical—an implication that is at odds with the data. Adding physical capital investment to the model can resolve this conundrum, generating aggregate-demand amplification even under procyclical profits. However, the amplification works through an investment channel and not through profits, inconsistent with the narrative attributing elevated inflation to corporate greed.

It has been known since Elliott (1998) that standard methods of inference on cointegrating relationships break down entirely when autoregressive roots are near but not exactly equal to unity. We consider this problem within the framework of a structural VAR, arguing this it is as much a problem of identification failure as it is of inference. We develop a characterisation of cointegration based on the impulse response function, which allows long-run equilibrium relationships to remain identified even in the absence of exact unit roots. Our approach also provides a framework in which the structural shocks driving the common persistent components continue to be identified via long-run restrictions, just as in an SVAR with exact unit roots. We show that inference on the cointegrating relationships is affected by nuisance parameters, in a manner familiar from predictive regression; indeed the two problems are asymptotically equivalent. By adapting the approach of Elliott, Müller and Watson (2015) to our setting, we develop tests that robustly control size while sacrificing little power (relative to tests that are efficient in the presence of exact unit roots).

Covid-19 and the resulting lockdowns affected various aspects of people’s lives, including their mental health. Recent literature suggests a causal link between religiosity and mental health. Using data from an online survey, we investigate the role of religiosity in mediating the effect of Covid-19 on mental health. From February–March 2021, we conducted online surveys in the USA among 5178 individuals. These surveys elicited responses on (i) the incidence of Covid-19 infections among the respondents or their immediate social networks, (ii) religious beliefs and practices, and (iii) mental health. Employing the CESD scale, which tests for depression in clinical settings, we find that while the incidence of a Covid-19 infection is associated with significantly worse mental health, this negative association is significantly smaller for religious people. We posit that the mental health benefits of being religious emanate from the ability to participate in religious activities. Indeed, the ameliorative effect of religion is higher in low-strictness counties, where Covid-related lockdown policies were enforced less strictly, but not in high-strictness counties. We also document an increased uptake of online religious services, a substitute for in-person religious gatherings during the lockdown. Crucially, the ability to attend online religious services weakens the association between Covid-19 and worse mental health.

Modern production frequently involves teamwork among employees specialized in different tasks. I develop a model of teams in which firms assign tasks to workers who are heterogeneous in their overall quality and whose efficiency varies across different tasks. In addition to productivity gains, the division of labor generates coworker complementarities: the marginal productivity of one employee’s quality is increasing in other team members’ quality. This interdependence is stronger when variation in worker-task specific efficiencies is high. In frictional labor markets, coworker complementarities carry macroeconomic implications for both productivity and inequality. Coworker quality mismatch lowers team productivity, leading employers to search for workers of similar quality. In equilibrium, firms with "superstar teams" pull away in terms of productivity and pay. I validate the model’s key mechanisms using administrative micro data. Paralleling a shift in the nature of tasks, a theory-informed measure of coworker complementarities has doubled since 1990. A structural estimation exercise suggests that this rise explains between one quarter and one half of the increase in the between-firm share of wage inequality in Germany (1990-2010).

In this paper, we establish a causal effect of workers’ perceived probability of losing one’s job due to automation on preferences for redistribution and intentions to join a union. In a representative sample of the US workforce, we elicit the perceived fear of losing one’s job to robots or artificial intelligence. We document a strong relationship between fear of automation and intentions to join a union, retrain and switch occupations, preferences for higher taxation, higher government handouts, populist attitudes, and voting intentions. We then show a causal effect of providing information about job loss probabilities on preferred levels of taxation and handouts. In contrast, our information treatment does not affect workers’ intentions to self-insure by retraining or switching occupations, but it increases workers’ self-reported likelihood of joining a union to seek more job protection. The treatment effects are mostly driven by workers who are informed about larger job loss probabilities than they perceived.

Energy research seeking to influence policy in low- and -middle-income countries (LMICs) is often funded by – and conceptualised by authors in - institutions from high-income countries (HICs). Research agendas and policy recommendations determined in HICs potentially yield the most influence on policymaking for LMICs. This leaves a multidimensional gap in how LMICs frame, contextualise, evidence and enact policy processes. The unique contribution of this paper is analysing the dynamics of prevalent energy research on LMICs through a multi-method approach using bibliometric, network science and regression-based techniques. An innovative data-driven framework was established using a sample of 6,636 papers from the Web of Science database, combined with journal impact data from Scimago Journal Ranking and country economic data from the World Bank. Results show the existence of a cycle of imbalances across research practices. Most papers recommending energy policy for LMICs have a first author based in a HIC, funded by a HIC institution. Total citations of articles on energy policy in LMICs increase with the GDP of the first author’s country (a 1% increase in GDP is correlated with a 0.68% increase in total citations). Funders support authors based in countries of the same income band as them, or higher. Therefore, we recommend revising research practices and HIC funding policies to place local actors and knowledge at the heart of energy policy research, enabling high-impact policymaking in LMICs.