Association for Public Policy Analysis & Management

We implemented a nation-wide RCT in Peru to test a novel low-cost strategy aimed at reducing teacher-school sorting, based on insights from behavioral economics. The experiment consisted in two different arms designed to induce teachers to apply to disadvantaged schools (e.g., remote, low-performing and typically understaffed schools). In the first arm, we primed teachers’ altruistic facet of their identity by making it more salient, while in the second arm we increased the salience of an existing government monetary-incentive scheme that rewards teachers that work in disadvantaged institutions, just before teachers made their job application decisions through an standardized platform. Our results show that both interventions were similarly successful in triggering them to apply for jobs in - and finally be allocated to - disadvantaged schools. The results in both arms seemed to have been driven by male teachers, arguably more likely to be mobile and thus more willing to work farther away. We also found that the likelihood of being allocated to more disadvantaged schools among high-performing teachers increased substantially. The relevance of our results in terms of policy is crucial, as we show that low-cost behavioral interventions can increase the pipeline and the quality of professionals teaching in high-need and under-performing areas. This is fundamental to improve equity in the distribution of resources.