I conduct applied empirical research that addresses and advances our understanding of key public policy challenges, such as poverty, health and nutrition. My research has primarily involved the analysis of large-scale national survey and census data, but I have also worked with the proprietary transaction data of private banks and start-ups for applications in financial inclusion.

These are some of the principles I try to follow in my work:

• Applied research should be motivated by a clear set of policy-relevant questions, grounded in science. A starting point for any project I work on is a survey of the literature in order to understand of the current state of knowledge and the contours of the key scientific or policy debates on a specific issue or topic.

• Sound data analysis requires understanding where data comes from and exploring its properties, patterns and anomalies. What was the sampling procedure? Were there any selection criteria or non-response patterns that limit a datasets generalizability or bias it in a certain direction? Who are the respondents and how do they compare to a reference population? I invest significant time up front employing data exploration techniques to identify the errors, gaps and limitations in the data I work with.

• Research should be transparent and reproducible. I value creating a chain of documented code and reusable functions that link and describe how raw data inputs are transformed into final outputs (e.g. a table or chart) and am increasingly committed to making that code public.

• Research communication should be clear and engaging. Given that much of my work involves conveying ideas, data and insights to a general or policy audience, I strive to develop straight-forward and engaging visual and written material. However, social science is messy, and rarely provides clear answers, so communication should also be honest about the limitations of data and knowledge.

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