ABSTRACT
We argue that the volume of remittances sent home by migrants is influenced by the exogenous likelihood that the duration of their migration will be cut short. A higher probability of reverse migration, brought about by the collapse of jobs in the wake of COVID-19, made migrants attach greater importance to the creation of a social and economic environment in their places of origin that can support them when they return. There are several ways in which this can be done. One is by instilling gratitude. When bigger remittances are responded to by greater gratitude, the support will be bigger. An impact of the COVID-19 pandemic on migrants’ perceived duration of their migration is an increase in the uncertainty of the duration. The good will of migrants’ families and communities at origin is a form of insurance. A standard response to uncertainty is to take out insurance, and when uncertainty is higher, insurance is more valuable, and there is a tendency to acquire more of it. As it happens, the link between the volume of remittances and the likelihood of return migration does not feature at all in Shastri’s (2022) paper, nor for that matter in related writings by the World Bank and the IMF. The purpose of this rejoinder is to draw attention to this link, inducing students of migrants’ remittances to explore the link.
ABSTRACT
In many countries, schools have responded to the COVID-19 pandemic by splitting up classes. While the purpose of dividing classes is clearly health-related, the process of doing so poses an interesting question: what is the best way to divide a class so as to maximize the incentive for students to perform better? Using a constructive example, we demonstrate how social-psychological unhappiness can be the basis for an incentive structure that optimally nudges students to improve their performance. The example is based on evidence that students aspire to improve their performance when it lags behind that of other students with whom they naturally compare themselves. For a given set of m students, we quantify unhappiness by the index of relative deprivation, which measures the extent to which a student lags behind other students in the set who are doing better than him. We examine how to divide the set into an exogenously predetermined number of subsets in order to maximize aggregate relative deprivation, so that the incentive for the students to study harder because of unfavorable comparison with other students is at its strongest. We show that the solution to this problem depends only on the students’ ordinally-measured levels of performance, independent of the performance of comparators. In addition, we find that when m is an even number, there are multiple optimal divisions, whereas when m is an odd number, there is only one optimal division.