time to home production, especially in the labor participation states. Interestingly, Roma wives do not change their labor market decisions significantly when having children; nonparticipation goes from 82 percent to 83 percent; and, conditioning on labor market state, the share of time spent on home production time increases.

**Some descriptive statistics relevant to the present exercise are the assortative mating**<sup>**21**</sup> **of spouses in terms of the labor market and the mean observed wages** . Table 2 (left panel) presents the male and female labor market states. The data inside the borders is the percentage of households with spouses in the associated labor market states, while the 5<sup>th</sup> column and row represents the percentages of males and females in each labor market state, without conditioning on the spouse labor market state. That panel shows that in 35.2 percent of the Roma households at least one spouse works, in only 2.0 percent both spouses work, and in 28.3 percent only the husband works. The share of households where both spouses are nonparticipants is high (39 percent) compared to international standards.

**Table 2: Regional spouses labor status and mean wages**

|||Fe|males' la|bor state||||
|---|---|---|---|---|---|---|---|
|||NP|UE|PT|FT|All males|**Mean hourly wages (USD 2011 PPP)**|
|te|NP|39|4|1|2|47|PT Males<br>1.5|
|'<br>sta|UE|19|3|1|1|23|FT Males<br>0.6|
|ales<br>or|PT|9|1|0|0|11|PT Females<br>1.2|
|M<br>lab|FT|16|2|1|1|19|FT Females<br>0.5|
|All fe|males|83|10|2|4|100||

_Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey.

_Note:_ Abbreviations: FT= employed full-time; PT= employed part-time; U= unemployed; NP= nonparticipant. The numbers next to the labor market labels are the proportion of husbands and wives in each labor market state for each country. Per panel, the proportion of husbands and wives should sum up to 100. Because of approximation, for some panels, the sum is equal to 101.

**Table 2, (right panel) shows that in terms of hourly wages, there is a wage gender gap of 27 and 24 percent for part-time and full-time jobs, respectively.** Additionally, irrespective of gender, parttime workers earn in hourly terms more than twice the salary of full-time workers.

# **4. Estimation and Identification**

**The identification is provided by groups of parameters; first, we selected some moments from the RRS, and we assumed nine functional forms to identify 35 model parameters.** As mentioned, the model aims to explain spouses’ behavior in terms of their labor market status (full-time, part-time, unemployment, and nonparticipation), as well as their time allocation (leisure, work, and home production). Presence of children in the household is used as a shifter for spouses’ nonparticipation and intensive margin decisions. All the gender-specific parameters rely on gender differences found in participation rates, labor market participation, the ratio of people working on FT and PT work, wages, and time usage found among the marginalized Roma population, as in most of the countries worldwide. P **First, to identify the gender parameters for labor market frictions** , , **, and ), we use a similar identification strategy as in Flinn and Heckman (1982).** In this identification strategy, FFPP AA AA AA AA (λλ γγ ηη ηη

> 21 Assortative mating is the process by which people with similar characteristics choose a partner. In the present study, we used that definition to present the proportion of couples married on each of the cells defined by both spouses’ labor market. <mark>Table 2</mark> shows that, besides NP-NP, the Roma population does not practice much assortative mating in the labor market.

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