detailed information on labor supply, employment, unemployment, formal wages, informal wages, and self-employed income, and a number of socio-economic characteristics of individuals and households. The IES survey contains detailed data on household expenditure patterns, labor and non-labor incomes of household, and a number of socio-economic characteristics of households. When the two databases are combined and observations with missing sampling weights are dropped, the number of individuals in our database is 103,732 from 26,214 households. We rely on household weights from the IES data to generate economy-wide results.

The specification of our model of the income-generation process at the individual or household level is described in more details in Essama _et. al._ (2007) and Korman (2006). The model has three components: (a) a multinomial logit model of the allocation of individuals across occupational states, based on individual and labor characteristics; (b) a model of the determinants of earnings (such as education, gender, union membership, urban-rural location, head of household, marital status, etc.); and (c) an aggregation rule for computing household income from the contribution of its employed members. We assume that other types of non-labor income, such as interest and rent incomes or transfers, are exogenous. The sum of formal and informal wages and self-employment income by all wage earners and self-employed people in a household and other non-labor income make up total household income. The econometric modeling of the income generation processes includes the estimation of wage functions and occupational probability functions for formal labor, informal labor, and self-employed workers by skill-type and by economic sectors (see the Annex for details).

**_Macro-micro links._** The communication between the CGE model and the microsimulation model is a top-down approach. The CGE model translates the impact of the shocks and policies through changes in relative prices of commodities and factors, and through levels of employment.  The micro-simulation model takes these changes as exogenous and translates them into changes in household behavior which underpins changes in earnings, occupational status, and gains and losses of per capita income as indicative measures of welfare. A series of steps are taken to ensure outcomes from the micro-simulation model are consistent with the aggregate results from the CGE model both before and after the shock. In particular, the consistency constraints require that the occupational choices predicted by the micro-simulation model match the employment shares in the CGE model.  Similarly, simulated earnings at the micro level must match macro predictions.<sup>16</sup> Because the base years for the social accounting matrix (SAM,2003) and the survey data (2000) in our study of South Africa are different, we employ percent changes to communicate changes in employment, wages, and prices from the CGE to the micro simulation.  This allows us to retain the more recent numbers in the macro accounts as well as the familiar poverty and inequality measurements of the micro data.<sup>17</sup>

In the case of employment changes, the CGE model provides estimates of the percent change in employment by category for each simulation. The micro simulation model generates exactly the same percent changes in the individual labor force data set by moving individuals into (or out of) that specific labor category. For example, when a labor category expands, the

> 16 Bourguignon, Robilliard and Robinson (2002) explain that benchmark consistency could be achieved by ensuring that the calibration of the CGE is compatible with the consistency constraints.

> 17 See Essama _et. al._ (2007) for details.  Savard (2006) and  Robillard and Robinson (2006) also discussed approaches for achieving consistency  between household survey data  and the national accounts.

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