A year in blogs: Reflecting on 2019 through the eyes of staff, affiliates, and partners
In 2019 we launched new initiatives to spur innovative research, admitted the first-ever cohort of blended Masters’ students, celebrated our founders’ and affiliate’s Nobel Prize win, and much more. It’s almost impossible to capture the breadth and depth of what our staff, affiliates, and partners have learned and achieved over the last year.
But we’ll try—with contributors from across industries and around the world, the J-PAL blog captures evidence insights, research tips and resources, big announcements, and reflections from partners and affiliates on working and researching with J-PAL. We’re revisiting some of our most-read posts from 2019:
1) Welcoming our first class of blended master’s students. The first cohort of the new Master’s in Data, Economics, and Development Policy, the first master’s degree to be offered by MIT’s Department of Economics, will arrive at MIT in January to commence classes.
2) Targeting extreme poverty in Egypt: A national priority. Almost one-third of Egypt’s population lived below the poverty line in 2015. Can an evidence-based program in Upper Egypt help tackle this issue?
3) Announcing J-PAL’s new Innovations in Data and Experiments for Action (IDEA) Initiative. Relatively little administrative data is accessed, analyzed, or used in impact evaluations to improve decision-making. J-PAL’s IDEA Initiative will enable us to greatly expand the use of administrative data in randomized evaluations, and do so in a much more systematic way.
4) How can RCTs help us reduce violence and conflict? More innovative responses to address the changing nature of violent conflict are necessary. Several promising lines of inquiry have emerged as new research probes the mechanisms behind successful interventions—i.e. how they work.
5) Six rules of thumb for understanding statistical power. What should policymakers and practitioners keep in mind to ensure that an evaluation is high powered? Read our six rules of thumb for determining sample size and statistical power.