Finally, we focus on non‐labor income. For this purpose we design an assignment rule for changes in international remittances and make some minimal assumptions about other sources of non‐labor income. Ideally, we would estimate a probability model to predict 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 Philippines), we are better‐off relying on a simple non‐parametric assignment rule that is consistent with the existing evidence (the specific rule used for Philippines 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>5</sup> This is done in four steps.<sup>6</sup> First, we use population growth projections to adjust for demographic changes between 2006 (base year) and 2009‐2010. This adjustment is particularly important in the case of Philippines because fertility rates are still high, which implies that the number of labor market entrants rises faster than overall population, and the baseline household survey is relatively old. In practical terms, doing this allows us to explicitly take into account changes in the size of the working age population, and hence to distinguish between employment growth driven (or rather absorbed) by demographic trends and net (or additional) employment growth.

Secondly, we use the projections from the labor force status and labor earnings models to replicate projected changes in aggregate total and sectoral output and employment. We start with employment and calculate the total number of individuals that need to be reassigned between employment and non‐ employment and across employment sectors in order to match projected aggregate changes in total and sectoral employment. We then use the estimated probabilities from the multinomial model to select candidates for reassignment.<sup>7</sup> The direction and magnitude of flows between employment and non‐ employment and across sectors of employment is given by changes in the relative shares of different status with respect to the reference population. For instance, whether individuals must be reassigned from non‐employment to employment or vice‐versa depends on whether the employment rate of individuals ages 15 to 64 is increasing or decreasing. Similarly, workers are expelled from sectors whose relative share in total employment are declining and absorbed into sectors whose relative share in total employment is increasing.

The sequence in which individuals are reassigned across states and sectors matters for the final simulation results so we briefly describe here the procedure we follow:

> 5 <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>

> 6 This sequence for introducing changes in the model is based on Vos et al (2002)

> 7 We add error terms which represent the unobserved heterogeneity of agents’ labor supply behavior. These lead to some disparateness in responses to a change in the labor demand, capturing the fact that in the real world individuals who are observationally equivalent (i.e. have identical observable characteristics) might still respond differently the same change in labor demand.

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