Weekly links October 2, 2026: insurance and entrepreneurship, recycling bribes into public services, labor slack in health, scaling cash, and more...

· Johan Fourie on the way the state helped build Africa’s richest entrepreneur, and lessons from Denmark and China on how access to capital and insurance can help create more growth-oriented entrepreneurs. In China “after 1993, people whose spouse held a state job were 9.8 percentage points more likely to become entrepreneurs, more than double the pre-reform rate of 4.6%. And the effect scales with how safe the spouse’s job was....It is insurance. The effect is largest in families with volatile incomes, poor health or no health cover. The firms these couples start also choose riskier industries and spend more on research.” · On the IGL blog, Cher Li, Richard Kneller, Anwar Adem note that when they reanalyzed their experiment that had used DiD with Ancova instead, they were able to learn more. “The revised analysis did not simply turn null results into positive ones. It produced a more discriminating account of where benchmarking worked, where it did not and for whom.” · On VoxDev, Shan Aman-Rana, Clement Minaudier and Sandip Sukhtankar documents how public employees sometimes themselves end up paying for public services like food banks, flood relief, and police uniforms and vehicles themselves – and how this in part gets paid for by bribes that citizens get forced to pay: they refer to this as informal fiscal systems. “In a context in which governments are severely resource-constrained, allowing bureaucrats to take bribes and redistribute part of them can be an attractive option for governments. It enables them to provide the services that citizens need while keeping official taxes low. Moreover, when corruption monitoring is difficult, it can be easier to induce bureaucrats to redistribute the bribes they already take than to prevent them from taking bribes in the first place.” · On VoxEU, Chiara Fumagalli Alfonso Gambardella provide evidence from an experiment with Bocconi first-year undergrads that teaching causal reasoning can be a useful complement to the use of AI. “The two treatments work on different outcomes. GPT is what raised performance: students with ChatGPT wrote recommendations that the evaluators rated higher and that came closer to the experts’ solutions. Causal reasoning training did not improve performance, and adding it to GPT did not raise performance further. ...Deeper thinking, however, is another matter. Only the students who underwent training in first-principles thinking stated the conditions under which their recommendations would fail, explained the mechanisms behind them, and produced ideas that differed from those of their classmates.” · Labor slack in the health sector: On the CGD blog, Jishnu Das and colleagues have a nice post on how overworked are healthcare workers in developing countries? They note that there are very uneven loads – lots of unused capacity in many clinics, while the busiest ones have large waits – “The average healthcare provider has substantial unused capacity, but the average patient visits a relatively busy provider.” “Across each of these types of studies, there is little evidence that heavy workloads are a generalized, first-order constraint on the quality of primary care....health care providers in rural Senegal saw only 5–9 patients a day and spent less than 2 total hours on consultations. Interactions with providers are short, typically lasting between 3 and 6 minutes, despite substantial unused capacity, not because providers are busy.... in a study of two Indian cities in 45 percent of interactions no other patients were waiting and 65 percent had a queue of 1 or fewer waiting patients. But 5 percent did have more than 10 patients waiting” · A nice VoxDev podcast interview with Caitlin Tulloch on Give Directly’s thinking as it goes to scale – around general equilibrium issues, how to think about potential trade-offs between fraud and reach, and thoughts on delivering cash-plus at scale via some cheap mentoring/coaching. Also an interesting project in progress getting macroeconomists to fit a model to baseline data in Malawi and predict in advance the multiplier.