Weekly links October 15: what does a coach do, beyond services vs manufacturing, what DiD to use, migration and jobs, and more...

· What is the magic sauce in coaching? On the Agency Fund blog, Patricia Andrews Fearon and Mahreen Khan summarize four different roles that “coaching” can play in programs like graduation programs (aspirational, behavioral and skill building, relational, and administrative), and how some of them are more easily scaled or substituted by technology than others. · Can we skip the factory? asks Francisco Amodio in In Development. “Countries can succeed in attracting modern factories, integrating into global value chains and expanding exports, but that no longer seems to generate the broad-based employment growth that characterized earlier episodes of industrialization...If factories no longer absorb labor on the scale they once did, perhaps they should no longer sit at the center of governments’ development strategies.” He argues what matters is increasing consumer demand and multiplier effects “Recent research has shown that productivity improvements in consumer services can generate self-reinforcing growth...[but] Manufacturing’s historical contribution was that, in raising the productivity of firms, it raised the productivity of ordinary workers, at scale. It took millions of people with limited formal education and placed them in organizations where technology, specialization and scale made them dramatically more productive. That was the foundation on which the broader multiplier was built. There are reasons to believe that, while present, non-tradable services cannot generate virtuous cycles that are sufficiently large, broad and predictable to anchor a national development strategy.” He then argues to look beyond sector “But if traditional manufacturing no longer works as the primary engine of development, and if labor-absorbing services are unlikely to become one, governments need to think differently about industrial policy. They should not ask which sectors employ the most workers today but start asking a different question: Which sectors create opportunities for everyone else? The sectors that matter most may not be creating jobs within themselves but can trigger a chain reaction of investment, production and consumption far beyond their own boundaries.” · Migration and jobs: On the CGD blog, Dany Bahar argues that “Development institutions should acknowledge that many of those jobs they want to create already exist, except that in other countries” – “without migration, the rich democracies would lose 121 million working-age people between 2020 and 2050, while Africa and South Asia together would gain more than a billion. Connecting the two requires visas, language preparation and recognition of qualifications.” · A searchable archive of all job market posts: In case you missed it, we launched our annual blog your job market paper call. Pierre Biscaye (who had a post in our 2023 series) has helpfully put together a searchable database of all 260 of our posts, along with those in the ETRM series – so you can search by topic, year, country, etc. · What DiD estimator should you use? A dashboard and Stata command to tell you: Clement de Chaisemartin shared this very nice dashboard which helps you think through which design case you are in, which estimators you can use, the relevant Stata, R and python commands, and a link to the relevant section of his textbook. He also notes a Stata command whichDIDshouldIuse that will use your data to characterize your design and suggest estimators. · Overeducated and underemployed – this JPAL piece on Egypt’s labor market mismatch is a useful summary of the issues around educated youth wanting only certain types of jobs. But the conclusions made in the piece are to change the type of training- when there also needs to be a focus on entrepreneurship, competition policy, and how to increase the demand for educated labor. · Using AI to enhance your survey instrument/lab-in-the-field experiment: on the ETRM blog Francisco Flores shares his experiences using Claude Code to assist him in his data collection through a lab experiment.