We conduct an adaptive randomized controlled trial to evaluate the impact of a SMS-based information campaign on the adoption of social distancing and handwashing in rural Bihar, India, six months into the COVID-19 pandemic. We test 10 arms that vary in delivery timing and message framing, changing content to highlight gains or losses for either one's own family or community. We identify the optimal treatment separately for each targeted behavior by adaptively allocating shares across arms over 10 experimental rounds using exploration sampling. Based on phone surveys with nearly 4,000 households and using several elicitation methods, we do not find evidence of impact on knowledge or adoption of preventive health behavior, and our confidence intervals cannot rule out positive effects as large as 5.5 percentage points, or 16%. Our results suggest that SMS-based information campaigns may have limited efficacy after the initial phase of a pandemic.
The central fact that has motivated the empirics of economic growth—namely unconditional divergence—is no longer true and has not been so for decades. Across a range of data sources, poorer countries have in fact been catching up with richer ones, albeit slowly, since the mid-1990s. This new era of convergence does not stem primarily from growth moderation in the rich world but rather from accelerating growth in the developing world, which has simultaneously become remarkably less volatile and more persistent. Debates about a "middle-income trap" also appear anachronistic: middle-income countries have exhibited higher growth rates than all others since the mid-1980s.
Understanding the causes and consequences of international differences in human capital is a central concern of economics. But how can we accurately measure the global distribution of skills when people in different countries take different tests? We develop a new methodology to non-parametrically link scores from distinct populations. By administering an exam combining items from different assessments to 2,300 primary students in India, we estimate conversion functions among four of the world’s largest standardized tests spanning 80 countries. Armed with this learning “Rosetta Stone,” we revisit various well-known results, showing, inter alia, that learning differences between most- and least-developed countries are larger than existing estimates suggest. Applying our translations to microdata, we match pupils’ socio-economic status to moments of the global income distribution and document several novel facts: (i) students with the same household income score significantly higher if they live in richer countries; (ii) the income-test score gradient is steeper in countries with greater income inequality; (iii) girls read better than boys at all incomes but only outperform them in mathematics at the lowest deciles of the global income distribution; and (iv) the test-score gap between public and private schools increases with inequality, partially due to a rise in socio-economic sorting across school types.