states. To identify six parameters, 168 home production moments are calculated,<sup>29</sup> and nonparticipation rates are used. **To identify , the nonparticipation and unemployment proportions are mainly used. Setting this value to zero will place everyone in the labor market.** As mentioned before, the level of a spouses’ AA ss home productivity will cause different time allocation and different incentives for participating in the labor market. As the current specification has three home production types per gender, the model has nine household types. Without any heterogeneity, every couple will make the same decisions in the same labor market and children state. This means there is no variance in home production, conditional on the same labor market and children state. If the home productivity levels are similar, then there will be less variance in home production and maybe not a lot of heterogeneity on the time allocations <mark>from spouses that</mark> participate or not in the labor market. Thus, identification for γ<sup>HH</sup> comes from the mean and standard deviation of home production per gender. Additionally, the NP-NP couples helps MM_γγ WW_γγ , μμ , μμ to identify the proportion of high home producers.

**Given that in the Roma survey one person per household is surveyed regarding time usage, correlation of spouses’ times cannot be used in the current estimation; consequently, the identification of is compromised.** Hopefully, the 168 home production moments provide enough information to estimate accurately these two parameters. Future estimations must include both 33 θθ, ββ spouses’ time allocations. **All the parameters form the vector of parameters, .** To estimate the optimal vector of parameters (θ� ), the method of simulated methods of moments is used as follows: θθ SSM θ�= arg min (6) θ<sup>(QQ(θ) −qqss)′ WW−1(QQ(θ) −qqss),</sup> SSM where are the simulated moments, are the sample moments, is the bootstrap weighting matrix, and the diagonal is the inverse of the bootstrapped standard errors and zeros everywhere else. ss QQ(θ) qq WW<sup>−1</sup>

# **5. Estimation Results and Model Fit**

Table 3 shows the arrival rates of shocks, the primitives of the lognormal distribution on wage offer parameters, the gender’s probability to receive a part-time offer, the search costs, and the flow utility function the fertility parameters, and the home production parameters.

**In terms of the leisure and household utility parameters, when the household has no children, the male’s leisure utility parameter is higher than the female parameter** . When having children, males’ parameters decrease. Conversely, household production utility parameters increase when children are present (see table 3).

**The curvatures of the CRAA terms show that Roma households are more risk averse in terms of income, followed by home production male’s leisure, and female’s leisure, respectively.** This hierarchy means that males are less willing to change their leisure time when changing among labor market states than females.

**Jointly, these two sets of parameters have the potential to replicate households’ decisions where husbands enjoy more leisure, and having children increases spouses’ housework time, increases**

> 29 168 moments= 8 unconditional, 32 conditional on labor status, and 128 conditional on own and spouse labor status.

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