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The Harvard Center for International Development is home to faculty affiliates from each school at Harvard University, working across sectors in developing nations around the world.

Faculty research is published in a wide range of academic and policy venues and can be found through the feed and filters below. Select faculty research papers are highlighted in our Faculty Research Insights series on our blog, CID Voices.

CID working papers published by Harvard faculty, graduate students, and research fellows prior to 2024 can be found here

Showing results 1 - 10 of 25

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David Yang
Working Paper No. 32993
Autocracy 2.0, exemplified by modern China, is economically robust, technologically advanced, globally engaged, and controlled through subtle and sophisticated methods. What…
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Melissa Dell
Working Paper No. 32768
Deep learning provides powerful methods to impute structured information from large-scale, unstructured text and image datasets. For example, economists might wish to detect the…
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Stefanie Stantcheva
We survey the recent literature in economics measuring what is on top of people’s minds using open-ended questions. We first provide an overview of studies in political economy,…
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Luis M. Viceira
Using a large panel of U.S. brokerage accounts trades and positions, we show that a large fraction of retail investors trade as contrarians after large earnings surprises,…
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Catherine Elizabeth Snow
Vol. 9
Children from historically marginalized racial/ethnic and socioeconomic groups, on average, score lower on widely used assessments of academic, executive functioning, and social-…
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Charles Taylor
Fertilizer is critical to agricultural supply chains, but its use results in downstream externalities in the form of aquatic hypoxic zones and algal blooms. Quantifying farm…
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Jane Kim
Vol. 26, Issue 8, Pages 1217-1224
Objectives: Model-based cost-effectiveness analyses can inform decisions about screening guidelines by quantifying consequences of alternative algorithms. Although actual…
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Melissa Dell
Abstract: A diversity of tasks use language models trained on semantic similarity data. While there are a variety of datasets that capture semantic similarity, they are either…