The impact evaluation of a new policy or program aims to inform the decision on wider adoption and even, perhaps, national scale-up. Yet often the practice of IE involves a study in one localized area – a sample “site” in the terminology of a newly revised working paper by Hunt Allcott. This working paper leverages a unique program roll-out in the U.S. to explore the challenges and pitfalls that arise when generalizing IE results from a handful of sites to a larger context. And the leap from applying impact estimates taken in one site to the larger world is not always straightforward
As a research assistant working for David, I’ve had to create many, many regression and summary statistics tables. Just the other day, I sent David a draft of some tables for a paper that we are working on. After re-reading the draft, I realized that I had forgotten to label dependent variables and add joint significance tests in a couple regression tables. In an attempt to avoid forgetting these details in the future and potentially help future researchers, I thought I’d post a checklist for generating regression and summary statistics tables.
- Stata commands
- The latest Journal of Economic Perspectives has a symposium on Big Data.
- On the CGD blog, Matt Collins argues why RCTs in development should not be double-blind.
We all know that institutions matter for development. A really nice new paper by Daron Acemoglu, Tristan Reed and James Robinson shows us how political competition affects a wide range of development outcomes.
The discount rate used by individuals to trade off utility in the future against utility today is a fundamental parameter of decision theory. It is typically elicited in surveys by asking individuals to make choices between receiving an amount today, and a different amount at some point in the future. There are lots of key design issues involved in doing this (e.g.
- Using mobile phones for data collection efforts – some lessons from doing this in Uganda – from the World Bank’s EduTech blog.
Trends in income inequality are at the center of development policy discussions these days. Part of this renewed attention is no doubt a tribute to Thomas Piketty’s pioneering work to measure top income shares using income tax data, as well as his much-discussed new book. Piketty’s work shows some dramatic trends in inequality at the top end of the income distribution. For example, in countries such as C
- Andrew Gelman hosts a discussion on list randomization experiments to elicit sensitive information
- In Foreign Affairs Chris Blattman and Paul Niehaus discuss cash transfers.
Last week I attended the International Development Conference at the Kennedy School of Government, joining a session on social protection. The conference is organized by KSG students (kudos to the students for their hard work in making it happen and interesting!), and has a format with no presentations and informal panel discussions with invited speakers.