I am currently a PhD student in the Department of Economics at the University of Michigan.
I was previously a research assistant in development economics at the the Blavatnik School of Government, University of Oxford where I worked at Digital Pathways at Oxford and the Mind and Behaviour Research Group (MBRG), Centre for the Study of African Economies (CSAE). Prior to this, I was an economic advisor on the ODI Fellowship Scheme at the Rwandan Ministry of Finance and Economic Planning where I worked at the Macroeconomic Policy Unit in the Office of the Government Chief Economist between 2017 and 2019. I completed an MPhil in Economics at the University of Oxford in 2017 and a BA in Economics at the University of Cambridge in 2015.
My CV can be found here.
Research in Progress
Abstract: Rapid urbanisation in Africa and other developing regions increases demand for urban services, which local governments often struggle to provide. Land value capture and taxation through zoning presents an under-exploited opportunity to mitigate this problem. Rwanda, with its fast-growing cities, first adopted a comprehensive land use zoning law in 2013. Focusing on the urban fringe in Rwandan cities, we compare the value of (previously unbuilt) land zoned for residential use to nearby land zoned for agricultural use, allowing us to assess the potential for revenue generation from zoning.
Valuing Property and Buildings in Kigali and Secondary Cities for an Operational CAMA
with Patrick McSharry, Kaspar Kundert, Felix Bachofer and Andreas Braun
Using Machine Learning and Remote Sensing to Value Property in Rwanda, International Growth Centre Working Paper
with Patrick McSharry, Felix Bachofer, Andreas Braun and Jonathon Bower
Abstract: Property valuation models can achieve mass valuation transparently and cheaply. This paper develops a number of property valuation models for Kigali, Rwanda, and tests them on a unique dataset combining remote sensing data and infrastructure and amenities data for properties in Kigali, with sales transaction data for 2015. We use a machine learning approach, Minimum Redundancy Maximum Relevance, to select from 511 features those that minimise ten-fold cross validated Mean Absolute Error. Cross validated diagnostics are used to eliminate overfitting given that our goal is to generate a model that can be used to extrapolate value estimates out of sample.
Production Networks and International Trade
Abstract: This paper examines how increases in international trade and global value chain participation impact world economic volatility. I develop and apply a theoretical model of intermediate input trade to data on production networks in order to assess how global network structures affect aggregate volatility. Due to network interconnections between country-sectors, idiosyncratic shocks to specific country-sectors transmit downstream towards their direct and indirect customers, leading to a potential synchronised expansion of economic activity. This mechanism generates sizeable aggregate fluctuations when the importance of country-sectors is significantly asymmetric, leading to the existence of dominant country-sectors which effectively propagate shocks originating elsewhere in the network to the entire economy. By mapping global production networks to data from world input-output tables, I obtain measures for country-sector importance which capture the centrality of each country-sector in the network. The distribution of these centrality measures then provides information about the structure of the global production network and the implications for aggregate volatility. The key empirical result shows that the network structure of global production is highly interconnected with production fragmented across the world economy. This finding implies that idiosyncratic shocks can propagate through the global production network, leading to sizeable aggregate fluctuations. Understanding the underlying structure of global production networks can help policymakers accommodate for international disruptions to global value chains.
The Macroeconomic Impact of Dollarisation Adjustments in Cambodia: a Bayesian DSGE Approach
Abstract: This paper develops and estimates a quantitative dynamic stochastic general equilibrium model with partial dollarisation of the Cambodian economy, to evaluate the volatility implications of de-dollarisation. We find that as the level of dollarisation falls, macroeconomic volatility exhibits a U-shape effect. For the initial stage of de-dollarisation, the economy’s improved ability to accommodate asymmetric foreign shocks drives down volatility. However, for the final stages of de-dollarisation, the vulnerability from foreign debt in dollars leads to a rise in volatility. This implies that some degree of dollarisation can achieve a favourable balance between these two opposing effects, minimising macroeconomic volatility.