characteristics between any two arms of each type of intervention. The results show that randomization balances these characteristics across treatment cells.<sup>18</sup> Since most of my analysis focuses on subgroups of inexperienced and experienced migrants, I also check for balance within these subgroups. Within each subgroups, the joint test across all outcomes fails to reject equality across the treatment arms at conventional levels.<sup>19</sup>

## **3.4 Empirical specification**

The randomized nature of the intervention implies that the basic empirical specification to estimate the effect of the programs is quite straightforward. I estimate where _yi_ is the outcome for individual _i_ , _DeathLoi_ , _DeathHii_ , _WageLoi_ and _WageHii_ are indicators of whether individual _i_ receives any of these treatments. _Xi_ are a set of controls which includes full set of interactions between education categories, age categories and location, indicators for the chosen destination, and enumerator fixed effects. _εi_ represents the error term, and I allow arbitrary correlation across individuals at the date of initial survey _×_ enumerators level. The standard errors remain quantitatively similar with alternative clustering specifications.

# **4 Does providing information affect perceptions?**

Using data from the control group (which does not receive any information on wages or deaths), the first part of this section establishes that potential migrants are indeed misinformed about earnings and mortality risks of migration. To do so, I only use the data on the subjects that did not receive any informational intervention. In the second part of this section, I estimate the impact of the informational treatment on perceptions about mortality and earnings.

## **4.1 Descriptive evidence on the extent of misinformation**

### **Misinformation in expected earnings**

Misinformation about earnings abroad may persist even in cases where a large share of the population is a migrant. As discussed earlier, local agents and recruitment companies have an incentive to exaggerate earnings information to induce potential migrants to go. Moreover, previous migrants may also provide biased information. They may lie about their earnings to their social network if they fear social taxation, or feel pressure to maintain any social prestige they gain from having migrated abroad (as in McKenzie, Gibson, and Stillman, 2013, Seshan and Zubrickas, 2015, and Sayad, Macey, and Bourdieu, 2004). This has fueled concern among policymakers that potential migrants may overestimate their earning potential abroad.

However, systematic evidence on the degree of such misinformation is rare. To date, there are no credible surveys of migrants in the destination countries to determine the actual earnings of Nepali migrants.<sup>20</sup> Further, the government does not have a way to track actual earnings abroad. The Department of Foreign Employment only receives reports of contractual earnings from potential migrants when they apply for permits to work abroad, and even this data is not publicly available. In this section, I use the survey data I collected to compare potential migrants’ expectations with a few benchmarks to establish that potential migrants are misinformed on their earning potential.

> 18The joint tests across all comparisons have a p-value of 0.65 for comparisons within death information treatment arms and 0.48 for comparisons within wage information treatment arms. Detailed results available on request.

> 19Results available upon request.

> 20The closest to this approach is the Nepal Migration Survey of 2009 conducted by the The World Bank (2011), which asked household members about the earnings of the foreign migrants. It also asked the returnees the actual earnings they made during their migration episode. Other than the fact that this data was collected almost six years ago, it also suffers from reporting biases of the household members, and reflects the misinformation within the household as highlighted in Seshan and Zubrickas (2015).

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