counterparts (EU average around 80% for both men and women, WB WDI, year 2012). The following section includes a discussion of women’s activity status, differentiated by child rank, measured by SILC data (tables A to C in the appendix).

Uraz et al. (2010) show that while there is little or no significant difference in female labor market participation between regions (women work in rural areas as well as in urban areas), the impact of the number of children present in the household on women’s labor market participation is more negative in urban areas, in particular for low-skilled women.

# **4. The impact of women’s activity status on the probability of child arrival**

# _Data construction_

Our econometric analysis of child arrival probabilities in Turkey serves to see which kind of families are most likely to decide for or against child birth, while we differentiate births by rank. We focus our analysis on women’s and their partners activity status, but also control for other socioeconomic and demographic characteristics.

To get information about the correlation between child birth and women’s employment situation in Turkey, we observe women’s activity status _during a certain period before the potential conception of a child_ , which allows us to reduce a potential bias caused by inverse causality between labor market participation and fertility decisions (women’s degree of labor market attachment might be influenced by family formation- or family enlargement-intentions to). Simply differentiating completed fertility rates by women’s activity status (observed in the SILC data, but not in the census data), as we did for education, is not possible here, as women’s activity status observed at the time of the survey (at age 37 to 42) is likely to be influenced by their preceding fertility history. For education, this is less the case, as the SILC data reports that most women have obtained their highest educational level before the arrival of a first child (even though we acknowledge that even here, endogeneity issues cannot be completely ruled out).

We estimate women’s probability of having a child (differentiated by rank one, two and three) as a function of their activity status (observed before potential procreation) with a logit regression model. We do not analyze the arrival of births of higher ranks as the number of observations of women ‘at risk’ (having at least three children) gets too small to obtain robust results.

Following Greulich et al. (2014), the data are compiled as follows: To observe women’s characteristics _before_ potential procreation, we use the _longitudinal_ data set of the SILC covering the years 2006 to 2011 for women aged 15 to 45 years in Turkey. The longitudinal data set is a rotational panel with individuals and households observed for a maximum period of four years. A dummy variable indicating the arrival of a child during the observed period serves as endogenous variable, while we observe the women’s and their (if existing) partners’ characteristics during a certain period before potential procreation. This data transformation allows us to apply a simple logit estimation model (with robust standard errors).

In order to obtain the information needed, individuals have to be observed over a period of at least three years. Children born in the third and the fourth quarters of each year are generally declared at the interview of the year after as interviews usually take place during the first half of each year. Births that 14