· Oliver Hanney on the development economics he’d like to see – “We are missing a whole thriving ecosystem working on growth….Across education, health, microfinance, trade and many other areas, we are making lots of progress. I’m particularly bullish on education …And these do all have indirect implications for growth. But if we are thinking directly about economic growth, and the type of questions a policymaker would ask on that subject – something along the lines of: how can I get growth going before the next election, or replicate the success of East Asian economies – development economists have little to say on one of the main levers they can pull, industrial policy….On the last day, I visited an industrial park with policymakers and practitioners from across Africa. Their questions were almost entirely practical, on implementation, e.g. how does power come through, what does it cost, how do you make people deliver on their contracts to build the necessary infrastructure, which government agency is really in charge, how do they coordinate the different actors, where does the money come from.” – there has been more recent work on the what of industrial policy, as in the new policy research report at the Bank, but these detailed how questions are ones where definitely we don’t have systematic evidence.
· Interesting to compare the above, with the NBER Reporter summarizing the themes that have received the most recent attention in the NBER development economics program: social protection, the psychology of poverty, using microdata to answer macro questions, and environmental economics. Although the Chat-GPT summary of themes identifies state capacity and firms and productivity as other areas that have received more attention, which are more closely linked to growth.
· A new magazine called “In Development” has its first article up, which is an interview with Paul Niehaus writing on Give Directly’s journey to $1 billion delivered in cash. “One way to tell GiveDirectly’s story is thus as a bellwether for evidence-based decision-making. To win over skeptics we invested heavily, as I will describe, in causal evidence. And we benefited from the growth around us of an ecosystem that took that evidence seriously. If even a nutty idea like giving away money for nothing could survive and thrive in this environment, this bodes well for other efforts to elevate evidence over anecdote…. it was not so obvious what role program evaluation should play. If the money really is for nothing—free not just of strings, but of any particular sought-after result—then what exactly should one evaluate?.... When Sir Ronald Fisher pioneered experimental methods at the Rothamsted Experimental Station, one of the world’s oldest centers for agricultural research, in order to figure out which fertilizers or seeds worked best, his “subjects” had no ethically significant agency: they were plants. But the subjects in a cash transfer experiment do. When a researcher documents the choices they make, we learn something about their preferences, their priorities, their vision of a good life. These insights have no analogue in a purely technical matter like agriculture productivity. And they have been an essential part of the story.” Our readers may also enjoy this sidenote “Our initial idea had been to conduct the study near Busia, which had become a hotbed for RCTs after the pioneering early collaboration there between Michael Kremer (among others) and the NGO Investing in Children and their Societies (ICS). But Busia turned out to be too hot of a bed: so many other RCTs were running nearby that we could not find a place to work without stepping on someone’s toes, inadvertently cross-cutting their randomization or contaminating their control group. So we packed our bags and went elsewhere.”
· On Monday this week I took part in an IGL Masterclass Series on Designing Field Experiments, where Oliver Hauser and I discussed collaborating with firms and policymakers. The material for that session and previous ones is up on their website (you have to do the free enrolment link), and the last session is this coming Monday April 20.
· I have a policy brief out in the LISER Policy lab series on unlocking migration’s potential through better development financing: “International migration remains widely framed as a challenge, yet growing evidence highlights its potential as a driver of development. A review of projects funded by the World Bank shows a stark imbalance: most resources support refugee hosting, while very limited funding targets economic migration”
· Paul Goldsmith-Pinkham continues his series of videos and posts on using AI tools for empirical work with a video and post on writing and thinking with AI assistance. “to be honest, I think it’s quite challenging to perfectly preserve your writing voice through these tools. It’s just not the same. Some aspects may come close, but we’re all humans who vary from day to day. The output won’t consistently reflect how you’d want to write a particular thing….While writing is an important vehicle for thinking, there’s also a large amount of entirely banal and painful writing that we do for our jobs. Think about boilerplate emails, memo summaries, and routine documentation. Think about even the initial write-ups of regression coefficient summaries….My view is LLMs are quite good at this kind of work, and this type of time-saving is immensely valuable. The real trick is knowing what bright line we put between the banal and the important.”
· In PNAS (ungated), Jens Hainmueller and co-authors conduct a DiD analysis of Germany’s Job-Turbo program that aimed to fast track refugees from Ukraine and other countries into work through intensified employment counselling from public employment service caseworkers. They note that some countries prioritize education and language acquisition first, whereas this was more of a hybrid approach, with a less intensive integration course and then effort to get immediate labor market entry. Their DiD compares refugees to other unemployed immigrants who weren’t eligible for the program, with unemployed Germans as a robustness check. They estimated this program increased the monthly rate of getting a job by 1.8 percentage points, more than doubling the pre-treatment average of 1.6%, which they estimate led to 58,000 extra job placements over their 23-month follow up period. A policy brief also has more details.
· On the World Bank Data blog, the sort of getting into the weeds of measurement discussion we like to highlight – Yonzan and co-authors discuss the need to bottom-code income and consumption when measuring distribution-sensitive welfare measures. “When using the raw data, wealthy countries like Austria, Norway, and Sweden would be classified as less prosperous (a higher Prosperity Gap) than Peru, whose average income is 6 times lower. This is because these countries have a small share of the population that report extremely low incomes…. All summary measures that are sensitive to the income or consumption of the poor respond strongly to extremely small values, as they should. Many measures also cannot incorporate zero and negative incomes. For both these reasons, researchers and data providers commonly use bottom codes.”. They bottom code at PPP 25 cents per day.
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