| Field | Before | After |
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| Field Last Published | Before August 02, 2023 04:02 PM | After August 02, 2023 04:23 PM |
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| Field Paper Abstract | Before Interventions to promote learning are often categorized into supply- and demand-side approaches. In a randomized experiment to promote learning about COVID-19 among Mozambican adults, we study the interaction between a supply and a demand intervention, respectively: teaching via targeted feedback, and providing financial incentives to learners. In theory, teaching and learner-incentives may be substitutes (crowding out one another) or complements (enhancing one another). Experts surveyed in advance predicted a high degree of substitutability between the two treatments. In contrast, we find substantially more complementarity than experts predicted. Combining teaching and incentive treatments raises COVID-19 knowledge test scores by 0.5 standard deviations, though the standalone teaching treatment is the most cost-effective. The complementarity between teaching and incentives persists in the longer run, over nine months post-treatment. | After Interventions to promote learning are often categorized into supply- and demand-side approaches. In a randomized experiment to promote learning about COVID-19 among Mozambican adults, we study the interaction between a supply and a demand intervention, respectively: teaching via targeted feedback, and providing financial incentives to learners. In theory, teaching and learner-incentives may be substitutes (crowding out one another) or complements (enhancing one another). Experts surveyed in advance predicted a high degree of substitutability between the two treatments. In contrast, we find substantially more complementarity than experts predicted. Combining teaching and incentive treatments raises COVID-19 knowledge test scores by 0.5 standard deviations, though the standalone teaching treatment is the most cost-effective. The complementarity between teaching and incentives persists in the longer run, over nine months post-treatment. Replication Package: https://doi.org/10.7910/DVN/BCMVJT |
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| Field Paper Abstract | Before Can informing people of high community support for social distancing encourage them to do more of it? We randomly assigned a treatment correcting individuals' underestimates of community support for social distancing. In theory, informing people that more neighbors support social distancing than expected encourages free-riding and lowers the perceived benefits from social distancing. At the same time, the treatment induces people to revise their beliefs about the infectiousness of COVID-19 upwards; this perceived infectiousness effect as well as the norm adherence effect increase the perceived benefits from social distancing. We estimate impacts on social distancing, measured using a combination of self-reports and reports of others. While experts surveyed in advance expected the treatment to increase social distancing, we find that its average effect is close to zero and significantly lower than expert predictions. However, the treatment's effect is heterogeneous, as predicted by theory: it decreases social distancing where current COVID-19 cases are low (where free-riding dominates), but increases it where cases are high (where the perceived-infectiousness effect dominates). These findings highlight that correcting misperceptions may have heterogeneous effects depending on disease prevalence. | After Can informing people of high community support for social distancing encourage them to do more of it? We randomly assigned a treatment correcting individuals' underestimates of community support for social distancing. In theory, informing people that more neighbors support social distancing than expected encourages free-riding and lowers the perceived benefits from social distancing. At the same time, the treatment induces people to revise their beliefs about the infectiousness of COVID-19 upwards; this perceived infectiousness effect as well as the norm adherence effect increase the perceived benefits from social distancing. We estimate impacts on social distancing, measured using a combination of self-reports and reports of others. While experts surveyed in advance expected the treatment to increase social distancing, we find that its average effect is close to zero and significantly lower than expert predictions. However, the treatment's effect is heterogeneous, as predicted by theory: it decreases social distancing where current COVID-19 cases are low (where free-riding dominates), but increases it where cases are high (where the perceived-infectiousness effect dominates). These findings highlight that correcting misperceptions may have heterogeneous effects depending on disease prevalence. Replication package: https://doi.org/10.7910/DVN/TMARZT |