Our objective is to engage in innovative research that extends the frontiers of the discipline, deepening our understanding of the operation of modern economies. Research spans almost all the major sub-fields of economics with particular strengths in microeconomic theory, including behavioural economics; econometrics, both micro-econometrics and time series; economic history and development and international economics.

The University of Oxford is ranked 8th in the world and 2nd in Europe in the most recent Tilburg University ranking of Economics departments, based on research contribution for the period between 2012-2016.

In the most recent Research Excellence Framework (REF 2014) to evaluate the research output of UK Universities, Oxford was first in overall research strength in Economics and Econometrics, with more research ranked as ‘world-leading’ than any other participating institution. In a submission of 84 FTE academics, 56% of our research was rated as ‘world-leading’ (4*) and a further 33% rated as ‘internationally excellent’ (3*).

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Policy and Impact

As one of Europe’s leading Economics departments, Oxford aims to inform and improve the development and implementation of economic and public policy in the UK and around the world. We do this by producing innovative research that extends the frontiers of the discipline and deepens our understanding of the operation of modern economies.

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Working Papers

Authors: Hamish Low, Michaela Benzeval, Jon Burton, Thomas F. Crossley, Paul Fisher, Annette Jäckle, Brendan Read

Jun 2020

Using new data from the Understanding Society: COVID 19 survey collected in April 2020, we show how the aggregate shock caused by the pandemic affects individuals across the distribution. The survey collects data from existing members of the Understanding Society panel survey who have been followed for up to 10 years. Understanding society is based on probability samples and the Understanding Society Covid19 Survey is carefully constructed to support valid population inferences. Further the panel allows comparisons with a pre-pandemic baseline. We document how the shock of the pandemic translates into different economic shocks for different types of worker: those with less education and precarious employment face the biggest economic shocks.Some of those affected are able to mitigate the impact of the economic shocks: universal credit protects those in the bottom quintile, for example. We estimate the prevalence of the different measures individuals and households take to mitigate the shocks. We show that the opportunities for mitigation are most limited for those most in need.

JEL Codes: C83, D31, G51, I31, J31, J63

Keywords: COVID-19, job loss, inequality, mitigation, financial distress

Individual View

Authors: Adam Brzezinski, , David Van Dijcke, Valentin Kecht

Jun 2020

In combating the spread of COVID-19, some governments have been reluctant to adopt lockdown policies due to their perceived economic costs. Such costs can, however, arise even in the absence of restrictive policies, if individuals’ independent reaction to the virus slows down the economy. This paper finds that imposing lockdowns leads to lower overall costs to the economy than staying open. We combine detailed location trace data from 40 million mobile devices with difference-in-differences estimations and a modification of the epidemiological SIR model that allows for societal and political response to the virus. In that way, we show that voluntary reaction incurs substantial economic costs, while the additional economic costs arising from lockdown policies are small compared to their large benefits in terms of reduced medical costs. Our results hold for practically all realistic estimates of lockdown efficiency and voluntary response strength. We quantify the counterfactual costs of voluntary social distancing for various US states that implemented lockdowns. For the US as a whole, we estimate that lockdowns reduce the costs of the pandemic by 1.7% of annual GDP per capita, compared to purely voluntary responses.

JEL Codes: I12, I18, H12, D04, C33, H51

Keywords: COVID-19, difference-in-differences, SIR model, social distancing, lockdown, big data

Individual View

Authors: Sander Barendse, Andrew J. Patton

May 2020

We develop tests for out-of-sample forecast comparisons based on loss functions that contain shape parameters. Examples include comparisons using average utility across a range of values for the level of risk aversion, comparisons of forecast accuracy using characteristics of a portfolio return across a range of values for the portfolio weight vector, and comparisons using a recently-proposed “Murphy diagrams” for classes of consistent scoring rules. An extensive Monte Carlo study verifies that our tests have good size and power properties in realistic sample sizes, particularly when compared with existing methods which break down when then number of values considered for the shape parameter grows. We present three empirical illustrations of the new test.

JEL Codes: C53, C52, C12

Keywords: Forecasting, model selection, out-of-sample testing, nuisance parameters

Individual View

Authors: Gustavo Mellior

May 2020

This paper analyses theoretically and quantitatively the effect that different higher education funding policies have on welfare (on aggregate and at the individual level) and wealth inequality. A heterogeneous agent model in continuous time, which has uninsurable income risk and endogenous educational choice is used to evaluate five different higher education financing schemes. Educational investments can be self financed, supported by government guaranteed student loans - that may come with or without income contingent support - or be covered by the public sector. When educational costs are small, differences in outcomes amongst systems are negligible. On the other hand, when these costs rise to realistic levels we see that there can be large gains in welfare and significant drops in inequality by moving to a system with more public sector support. This support can come in the form of tuition subsidies and/or income contingent student loans. However, as the cost of education and the share of debtors in society gets larger, it is preferable to increase public support in the form of tuition subsidies. The reason is that there is a pecuniary externality of debt that gets magnified when student loans become excessive. While I identify large steady state welfare gains from more public sector financing, I show that the transition costs can be large enough to justify the status quo.

JEL Codes: D52, D58, E24, I22, I23

Keywords: Incomplete markets, Higher education funding, Human capital

Individual View

Authors: Alex Teytelboym, , Maximilian Kasy

May 2020

We show how to efficiently use costly testing resources in an epidemic, when testing outcomes can be used to make quarantine decisions. If the cost of false quarantine and false release exceed the cost of testing, the optimal myopic testing policy targets individuals with an intermediate likelihood of being infected. A high cost of false release means that testing is optimal for individuals with a low probability of infection, and a high cost of false quarantine means that testing is optimal for individuals with a high probability of infection. If individuals arrive over time, the policy-maker faces a dynamic tradeoff: using tests for individuals for whom testing yields the maximum immediate benefit vs. spreading out testing capacity across the population to learn prevalence rates thereby benefiting later individuals. We describe a simple policy that is nearly optimal from a dynamic perspective. We briefly discuss practical aspects of implementing our proposed policy, including imperfect testing technology, appropriate choice of prior, and non-stationarity of the prevalence rate.

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