# **6 and Estimation**

Several components of our model are directly observed in the data. These include the cost _Cit_<sup>_d_ofmigrationtodestination</sup><sup>_d_,earnings</sup><sup>_w_</sup> _it_<sup>_l_andprofits</sup><sup>_πit_byindividualcharacteristics</sup> at location _l_ , saving rates _ρ_<sup>_l_</sup> , and earnings expected prior to emigration, _w_ ̃ _it_<sup>_d_.Furthermore,</sup> the survey contains information on the reason for return migration, including whether a work contract has been terminated. We use this information to compute the corresponding probabilities _δit_<sup>_d_withineachgroupofmigrants.AppendixB,andinparticularTablesA4</sup> and A5 provide further details. In the Bangladesh Informal Firms Survey 2010, we observe the share _q_ of businesses that are inherited. Finally, _rA_ and _rL_ are, respectively, the interest rates earned on savings, or paid on migrant loans; these are obtained from Mallick (2012) and Berg, Emran, and Shilpi (2013).

We estimate the remaining structural parameters of the model with by method of simulated moments, minimizing the distance between informative moments computed for a population of agents simulated from the model, and the counterpart of these moments observed in the data. In total, we jointly estimate 52 parameters pertaining to the demand in each destination country for workers of a given education level (parameters determining the share _λ_<sup>_d_</sup> _et_<sup>ofwould-bemigrantswhoobtainaworkvisa),agents’destination-education-</sup> specific disutility from migration _ηe_<sup>_d_andtheirinitialstockofassets</sup><sup>_Ae,_0,theshare</sup><sup>_pL_</sup> _e_<sup>of</sup> would-be migrants who can finance migration on credit, as well as the cost _Ce_<sup>_I_ofsetting</sup> up a business. The latter three sets of parameters are education-specific, and identified by having the model match the observed asset level, the fraction of migrants who report having financed their migration on credit, and the self-employment share in our sample. The remaining parameters are identified by observed migration patterns. Note that the intensity and distribution of emigration from Bangladesh to different destination countries are affected both by labor demand _λ_<sup>_d_</sup> _et_<sup>andbyagents’preferences</sup><sup>_η_</sup> _e_<sup>_d_.Todisentanglethetwoeffects,we</sup> target both emigration shares and migration durations conditional on having migrated to a given destination. The disutility _ηe_<sup>_d_from staying abroad determines both the emigration and</sup> the migration duration decision. Motivated by Figure 1, the share _λ_<sup>_d_</sup> _et_<sup>primarilyaffectsemi-</sup> gration. The termination or expiration of work permits is observed directly and accounted for through _δit_<sup>_d_.</sup> Building on the evidence reported in Figure 1, we let _λ_<sup>_d_</sup> _et_<sup>vary with destination countries’</sup> fuel revenues. We proceed under the following assumptions: (i) Fuel revenues are exogenous; (ii) The labor supply of Bangladeshi migrants always exceeds labor demand in destination countries (or is very elastic, as suggested by the wage trend depicted in Figure 1); (iii) The elasticity of the probability of locating a job in a destination with respect to fuel revenues 38