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Alice Lépissier

Year Admitted

Year Graduated

Research Areas
tropical deforestation, illicit financial flows, climate change policies, market-driven environmental governance, quantitative methods

Faculty Advisor
Matthew Potoski

Matto Mildenberger, Robert Heilmayr, Alexander Franks

Dissertation Title & Abstract

A Methodological Toolkit to Understand Complex Policy Problems: Applications to Climate Change and Illicit Finance

Complex policy problems like climate change and illicit finance require a diverse methodological repertoire and an agnostic approach to selecting the appropriate analytical tool to accomplish discrete inferential tasks. Drawing from the disciplines of political science, economics, and statistical data science, this dissertation tackles three distinct problems on causal evaluation, measurement, and missing data.

Chapter 1 evaluates the causal effect of a climate mitigation policy on the carbon emissions of the UK. Using a synthetic control estimator, this chapter finds that post-treatment emissions in the UK were 10% lower than what they would have been without the climate reform. The results imply that unilateral climate policies can meaningfully reduce emissions despite the absence of a legally binding global climate agreement.

Chapter 2 presents a novel methodology to measure illicit trade flows and originates the ""atlas of misinvoicing"", the first database to provide comprehensive bilateral estimates of the dollar amount of misinvoiced trade disaggregated by commodity sector for 167 countries during 2000-2018. Results show that African countries lost on average $86 billion a year in gross illicit outflows during that period, and that the biggest source of illicit trade on the continent was the natural resources sector. The findings suggest that combating illicit financial flows will be crucial to provide finance for sustainable development and to promote domestic resource mobilization in poor countries.

Chapter 3 proposes a machine learning approach to ameliorate the problem of missing data from developing countries, where administrative systems for data collection tend to be weaker. Some low-income countries do not provide customs declarations, which the ""atlas"" method requires as input data. This chapter predicts illicit trade using machine learning models that are trained on readily available data without relying on official trade statistics. Findings show that the models are able to recover 70% of the variation in illicit trade outcomes. This demonstrates the promise of predictive approaches to augment existing measures of illicit finance in data-constrained settings.

Broadly, the chapters in this dissertation can be understood as operating in the different scientific frameworks of causal, descriptive, and predictive inference, respectively. Tackling difficult environmental and developmental problems will require a willingness to traverse methodological siloes in order to identify the best tool for the job – this dissertation contributes to pushing the search for solutions forward.

MA Statistics, University of California Santa Barbara
MA Economics and Publicy Policy, Sciences Po Paris and Ecole Polytechnique
MA Economic History, London School of Economics
BA European Social and Political Studies, University College London

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