I'm currently based in West Africa (The Gambia). I work as a technical assistant, economist and ODI Fellow. Prior to this, I worked in a international development consultancy firm (with the Global Health & Education portfolio), where I focused on impact evaluation and M&E. I also worked at the Centre for Sustainable Employment. I love economics - specifically, applied econometrics, labour and the microeconomics of development and I am passionate about using data and causal inference to better understand our world. I enjoy making, using and learning from the different digital public goods that people make and share. I also enjoy data visualisation 🎨, graphs 📈 and neat presentations 👩‍🏫. You can view my academic research here. A new paper that I co-authored with JV Meenakshi and Deepti Goel has been published in the Review of Development Economics.

2025 Update: I have a new working paper, co-authored with Anand Srivastava and Girish Bahal, that can be viewed here!

✔️ Arsenal FC 🔴
✔️ Research methods & research design 📄
✔️ Teaching R & Stata 💻
✔️ Labour economics, applied econometrics & development 🤓
✔️ Supporting my local Negroni industry 🥃

Here are some of my personal contributions to the world of digital public goods - they are all built either in R or Stata. All the RShiny apps are freely accessible, while Stata packages can be installed via ssc.

1. ODK2Doc: A simple and easy to use tool that automatically converts ODK/Kobo/SurveyCTO xls survey files into formatted and editable Word documents.
2. CascadeR: A tool that uses micro data to build cascading lists that can be pasted directly into a ODK/Kobo/SurveyCTO xls survey files.
3. MergeR: A WYSIWYG no-coding tool for merging xls & csv datasets (1:1, 1:m).

1. ALLCATPLOT: allcatplot ensures that all predefined response categories for a variable, including those that are not present in the dataset, are included in a graph.
2. RATIO_PLOT: ratio_plot is designed to plot ratios and confidence intervals of specified variables with an option for disaggregation.
3. MEAN_CI_PLOT: mean_ci_plot offers a user-friendly command for visualizing means and confidence intervals of specified variables, with an option for disaggregation.
4. PLOT_CONFIDENTLY: plot_confidently allows users to visualize the mean and confidence interval of a specified variable, with options for up to two levels of disaggregation.

For those interested in applied econometrics using R - I have a series of applied problem sets from the Stock & Watson Textbook available here and slides on data visualisation using ggplot here.