Dev links coming to you weekly from now on!
Week #6: Oct 17
“How does a locally-managed conditional cash transfer program impact trust in government?”
- Cash transfers increased trust in leaders and perceptions of leaders’ responsiveness and honesty
- Beneficiaries reported higher trust in elected leaders but not in appointed bureaucrats
- Government record-keeping on health and education improved in treatment communities
2. Kinda random: sand dams
Read a WB blogpost on sand dams as a method for increasing water sustainability in arid regions … but that did not explain how the heck you store water in sand, so watched this cool video from Excellent Development, a non-profit that works on sand dam projects.
3. USAID increasingly using “geospatial impact evaluations” ft. MAPS!
Outlines example of a GIE on USAID West Bank/Gaza’s recent $900 million investment in rural infrastructure
BONUS: In other geospatial news
LSE blog post on the work of ground-truthing spatial data in Kenya
Week #5: Oct 10
Health Round-Up Edition
1. Dashboards for decisions: Immunization in Nigeria
A new dashboard is being used to improve data on routine immunizations … but doesn’t look like the underlying data quality has been improved. Is this just better access to bad data?
2. Norway vs. Thailand vs. US
A comparative study of health services for undocumented migrants
3. Traditional Midwives in Guatemala
Aljazeera on the complicated relationship between traditional midwives providing missing services and the gov’t trying to provide those services in health centers
BONUS: Visualizing fires + “good”
- Satellite imagery of crop burning in India in 2017 vs 2018
- How good is good? 6.92/10. The YouGov visualization on how people rate different descriptors on a 0-10 scale is really interesting if you look at the distributions – lots of agreement on appalling, average (you’d hope there would be clustering around 5!), and perfect. Then, pretty wide variance for quite bad, pretty bad, somewhat bad, great, really good, and very good. Shows how you should cut out generic good/bad descriptions in your writing and use words like appalling or abysmal that are more universally evocative.
Week #4: Oct 3
“Consider a simple policy rule: if a government’s statistics cannot be questioned, they shouldn’t be trusted. By that rule, the Bank and Fund would not report Tanzania’s numbers or accept them in determining creditworthiness—and they would immediately withdraw the offer of foreign aid to help Tanzania produce statistics its citizens cannot criticize.”
2. 12 Things We Can Agree On About Global Poverty?
In August, a CGDev post proposed 12 universally agreed-upon truths about global poverty. Do you agree? Are there other truths we should all agree on?
3. Food for thought on two relevant method issues
- Peter Hull released a two-page brief on controlling for propensity scores instead of using them to match or weight observations
- Spillover and estimands: “The key issue is that the assumption of no spillovers runs so deep that it is often invoked even prior to the definition of estimands. If you write the “average treatment effect” estimand using potential outcomes notation, as E(Y(1)−Y(0))E(Y(1)−Y(0)), you are already assuming that a unit’s outcomes depend only on its own assignment to treatment and not on how other units are assigned to treatment. The definition of the estimand leaves no space to even describe spillovers.”
BONUS: New head of IMF
Dr. Gita Gopinath takes over.