Second, we model labor earnings for all employed individuals ages 15 to 64 as a function of individual and job characteristics and use a standard Mincerian OLS regression to estimate the parameters of the model, as given by (2) (similar to Ferreira et al. 2008). We use a fairly broad definition of labor earnings for the purpose of the exercise that includes wages and salaries, but also income from self‐employment. This is particularly important in the case of agriculture and for economies with large informal sectors, such as Bangladesh, since wage and salaried workers constitute a limited fraction of those employed in these sectors. It may lead however to a loss in precision and/or predictive power given that the structural relationship between individual and job characteristics and earnings could be different for salaried and non‐salaried workers. To allow for maximum flexibility and (indirectly) account for some of these differences we estimate the model separately for agriculture, industry and services and for low and high‐skill workers.<sup>7</sup> _where x =gender, age, education, region, land ownership, and indicators for export industry, salaried and public employment._ The results of the estimation of equations (1) and (2) and a full description of all variables can be found in Annex 1.

Finally, we model non‐labor income with a focus on international remittances and make some minimal assumptions about other sources of non‐labor income. Ideally, we would estimate a probit model to estimate the probability of how likely a household would be to receive international remittances, given its characteristics. However, if the migration‐related information in the survey is poor or insufficient and/or the predictive power of probability model is low (as is the case for Bangladesh), we are better‐off relying on a simple non‐parametric assignment rule that is consistent with the existing evidence (the specific rule used for Bangladesh is discussed in more detail when we describe the simulation process).

**Simulation.** Simulation is the process by which information on aggregate projected **changes** in output, employment and remittances is used to generate **changes** in labor and non‐labor income at the micro level using the structural models developed as part of the calibration.<sup>8</sup> This is done in four steps.<sup>9</sup> First,

> 7 Notice that, although we could estimate separate models for salaried and non‐salaried workers based on the information from the household survey, we would not be able to use these models for the purpose of predicting future earnings since we do not have earnings and employment information disaggregated by salaried/non‐ salaried workers from macro data.

> 8 <mark>We do not assure consistency (i.e that absolute aggregate magnitudes are equal) between the data set used at the two modeling stages (see Bourguignon et. al 2008). Additionally, we assume equal changes at macro and micro levels. We cannot run a test if macro changes are similar or not to micro changes because of lack of a panel data at micro level (see Deaton 2001 and Bourguignon et al 2008).</mark>

> 9 This sequence for introducing changes in the model is based on Vos et al (2002)

7