Spatial inequality within countries is a pervasive and persistent phenomenon. How does the connectivity of place determine underlying spatial inequality of opportunity? To answer this question, I derive a sufficient statistic result linking local opportunity to market access terms, developing a framework consistent with a broad class of spatial general equilibrium models. I empirically validate this result using a novel not-on-least-cost-path identification strategy and data from historical road maps in Benin, Cameroon, and Mali covering 1970 to 2020, that I digitize. Using these estimates to parameterize a structural case of the spatial model, I show that road building alters the spatial distribution of opportunity. By considering each possible road upgrade I show that although some roads decrease the standard deviation of opportunity by more than 2%, others increase inequality by a similar amount. By fixing the spatial distribution of opportunity I show that to achieve similar reductions in inequality by moving people, a prohibitively large number would need to migrate from low- to high-opportunity areas - between 13% and 44% of the low-opportunity areas' population. Policymakers also face an equity-efficiency trade-off: 4 out of the top 10 aggregate opportunity-increasing roads also increase spatial inequality of opportunity.

Presentations

(2023) RIEF conference (awarded best article prize, joint with Lea Bou Sleiman), Brussels (2022) LEAP, Bocconi; EWMES, Berlin; NEUDC, Yale; CEP Junior trade workshop, LSE; OxDev, Oxford; American Urban Economics Association Summer Meeting, World Bank DC; Infra4Dev (World bank/ IGC), online; 7th Lindau Meeting on Economic Sciences, Lindau; Development Economics Workshop, ARE UC Berkeley; European Urban Economics Association Meeting, LSE; Royal Economic Society Young Economist Symposium, Online; African Meeting of the Econometric Society, Online; Warwick Ph.D. conference, Warwick; Applied Microeconomics Workshop, Oxford; (2021) CSAE Conference, Oxford; Development Economics Workshop, Oxford; Newcastle Ph.D. Economics Conference, Newcastle; (2020) American Urban Economics Association Workshop, Online; (2019) Applied Microeconomics Workshop, National University of Singapore; CSAE Conference, Oxford.

High-ability individuals work in high-paying occupations in high-paying firms, that is, they work in high-paying jobs. Such interactions between occupations and firms in determining pay and pay inequality have previously been mainly overlooked by literatures focusing on either in isolation. We decompose log wage inequality using a two-way fixed effect model allowing for unrestricted firm-occupation interactions and quantify the relative importance of firms and occupations. Using data from the universe of French workers, we find that between-occupation variance in pay premia is roughly as large as within-occupation variation. However, sorting of workers between occupations is substantially larger than sorting of workers between firms within occupations. Standard worker-firm fixed effect decompositions overstate individual heterogeneity and miss the importance of between-occupation wage dispersion and worker-occupation sorting.

Using data from the Doomsday Book, I find that areas of England that were 10% richer in 1086 are on average almost 2% richer today. Using a natural experiment and a dynamic quantitative spatial economics model I show that this persistence is not due to path dependency. Instead, the 1086 economy was moving towards a different, but correlated, long-run spatial equilibrium than that observed in 2020. This correlation in spatial equilibria can in part be explained by local market access. Modern place-based policies aiming to shift the spatial distribution of economic activity should focus on changing location fundamentals if they are to have long-run impacts. 

This paper asks what impact a large, but temporary, productivity shock can have on the spatial distribution of economic activity across cities in the short and long run. To answer this question I use a dynamic quantitative spatial economics model and the natural experiment of a sudden river, the Zwin, that connected Bruges to the North Sea in the 12th century. I show that despite dramatic short-run impacts in Bruges and across the Low Countries during the period the Zwin was navigable as well as in the centuries after, this shock failed to alter the prevailing long-run spatial equilibrium. Simulating alternative shock magnitudes or locations also doesn't result in a change in spatial equilibrium, but a permanent shock would have. However, convergence is slow, in 1800 some 300 years after the Zwin became impassable aggregate welfare remains on average 2% higher across the Low Countries as compared to the counterfactual world where the Zwin had never existed.

Exposure to Chinese import competition led to significant manufacturing job losses in the United States. Local labor markets, however, differ significantly in how they fared with respect to manufacturing employment. An important question is whether labor market institutions have an impact on the dynamic response of manufacturing employment to rising import penetration. We contribute to this debate by showing that minimum wages amplified the negative effect of Chinese import penetration on manufacturing employment in US local labor markets between 2000 and 2007. We develop a rigorous double-edged identification strategy. First, we construct shift-share instrumental variables to address the endogeneity of import penetration. Second, we use a border identification strategy to distinguish the effects of minimum wage policies from the effects of other local labor market characteristics that are unrelated to policy. Specifically, we rely on comparing commuting zones that are contiguous to each other but located in different states with different minimum wage policies. The approach essentially considers what happens to the response of manufacturing employment to import penetration when one crosses a policy border.

We examine how cross-border syndication ties reduce information frictions and positively impact exports of equity underwriting services. Using a panel data set from 2000–15, we develop a measure of information flows based on ‘core syndication ties’ where the lead underwriter is in either the importing or exporting country. We find that new core syndication ties have a significant and positive effect on exports. This finding is supported by evidence from ‘peripheral syndication ties’, which are associated with smaller information flows, and an instrumental variable approach which focuses on plausibly exogenous supply-side shocks. Furthermore, the effect of new core syndication ties is stronger when information frictions between trading partners are more severe and for more information-sensitive transactions like IPOs.

In this paper, I study spatial inequality of opportunity measured in terms of the causal effect place has on primary completion, in Benin, Cameroon, and Mali. Using a movers design I find that on average moving to a one standard deviation better location at birth increases a child’s probability of completing primary school by 15 percentage points. Estimating locality-year and gender-locality-year place effects I show that place matters more for girls than boys and place effects are correlated with local characteristics such as urbanization, access to electricity, and employment in agriculture. Religion is weakly correlated with average causal place effects but strongly correlated with the gender gap in the effect place has on educational attainment.

The COVID-19 pandemic has seen the emergence of digital contact tracing to help to prevent the spread of the disease. A mobile phone app records proximity events between app users, and when a user tests positive for COVID-19, their recent contacts can be notified instantly. Theoretical evidence has supported this new public health intervention, but its epidemiological impact has remained uncertain. Here we investigate the impact of the National Health Service (NHS) COVID-19 app for England and Wales, from its launch on 24 September 2020 to the end of December 2020. It was used regularly by approximately 16.5 million users (28% of the total population), and sent approximately 1.7 million exposure notifications: 4.2 per index case consenting to contact tracing. We estimated that the fraction of individuals notified by the app who subsequently showed symptoms and tested positive (the secondary attack rate (SAR)) was 6%, similar to the SAR for manually traced close contacts. We estimated the number of cases averted by the app using two complementary approaches: modelling based on the notifications and SAR gave an estimate of 284,000 (central 95% range of sensitivity analyses 108,000–450,000), and statistical comparison of matched neighbouring local authorities gave an estimate of 594,000 (95% confidence interval 317,000–914,000). Approximately one case was averted for each case consenting to notification of their contacts. We estimated that for every percentage point increase in app uptake, the number of cases could be reduced by 0.8% (using modelling) or 2.3% (using statistical analysis). These findings support the continued development and deployment of such apps in populations that are awaiting full protection from vaccines.

We observed significant decreases in incidence and R on the Isle of Wight immediately after the launch of the Test and Trace programme. The Isle of Wight had a marked reduction in R, from 1·3 before the Test and Trace programme to 0·5 after by one of our measures, and went from having the third highest R before the Test and Trace programme, to the twelfth lowest afterwards compared with other UTLAs. Our results show that the epidemic on the Isle of Wight was controlled quickly and effectively after the launch of Test and Trace.  

Epidemiological evidence shows that app-based contact tracing can suppress the spread of COVID-19 if a high enough proportion of the population uses the app and that it can still reduce the number of infections if uptake is moderate. We found strong support for the app under both regimes, in all countries, across all subgroups of the population, and irrespective of regional-level COVID-19 mortality rates. We investigated the main factors that may hinder or facilitate uptake and found that concerns about cybersecurity and privacy, together with a lack of trust in the government, are the main barriers to adoption.