effects amounts to 2.7 percent of the national GDP.

The migration response to migrant death suggests that potential migrants update their beliefs on mortality rate abroad after they observe a migrant death in their district. A rational Bayesian, who believes that migrants deaths are generated by an i.i.d process, will also update their beliefs in response to migrant deaths. However, for such a rational Bayesian, the sequencing of migrant deaths in previous periods does not affect the extent of updating in response to a current signal. Contrary to this prediction, the migration response to a migrant death in a district-destination cell is much larger if there have been more deaths in the past 6 months in the cell. In fact, the migration response to deaths is almost completely driven by district-destination cells that have experienced more than one migrant death in the past 6 months. Interestingly, the migration response is not sensitive to changes in the actual underlying death rates in the destination countries. This indicates that the updating rule that potential migrants use to form their beliefs on mortality rate depends upon the sequencing of migrant deaths in the recent past.

This suggests that potential migrants are committing a fallacy in their updating behavior. One possibility is that potential migrants are using some heuristic rule to form their beliefs (à la Kahneman and Tversky, 1974; Tversky and Kahneman, 1973). It could be that they think recent deaths represent the scenario more accurately, or that the past few months of migrant deaths is what is on the top of their mind while making their migration decision. Another possibility, not necessarily mutually exclusive, is that the potential migrants expect the probability rules to apply exactly even for the ‘small’ samples of migrants from a district (à la Tversky and Kahneman, 1971; Rabin, 2002). Indeed, in his mathematical formulation of this fallacy, Rabin (2002) shows that such individuals, even when they are Bayesian in updating their beliefs, tend to over-infer from short sequence of signals and conclude that the true rate generating the sequence of signals to be more extreme than the truth. The finding that the amount of updating, and hence the migration response, depends upon the number of recent migrant deaths is consistent with Rabin’s prediction.

Furthermore, I compute the change in perceived mortality risk for potential migrants following a single death event to show that the amount of updating is, in fact, too high. Using the estimates of earnings elasticity of migration from Shrestha (2017), I find that earnings need to go up by 15 percent to offset the migration response of a single death event. Further, using the estimate of the VSL from the same study, I find that the change in earnings translates to an increase in perceived two-year mortality rate of migration by 6.7 per thousand. This amount of updating from a single migrant death in the district is too high relative to the true rate of 1.3 per thousand. It is also too high compared to the updating behavior of a rational Bayesian, who believes the migrant deaths are generated by an i.i.d binomial process.

Moreover, this paper presents a context where fallacious learning, albeit about extreme and rare events, could have large welfare costs. Many studies find migration to be welfare improving for the marginal migrant and their family (see Bryan, Chowdhury, and Mobarak, 2014; McKenzie, Stillman, and Gibson, 2010, for a few examples). These studies also find the levels of migration to be suboptimally low. This paper, through a stark example, shows how fallacious learning could potentially lead to lower levels of migration.

The remainder of the paper is organized as follows: Section 2 describes the migration process in 3