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Analysis of the determinants of fertility decline in the Czech Republic


In this paper, we analyze the decline in the total fertility rate (TFR) in the Czech Republic during the economic transition. To identify transition-specific features of this decline, we estimate a Heckman–Walker multistate model of the birth process using data from the 1998 Family and Fertility Survey. We find that the negative effect of transition on TFR is mostly driven by a sharply increased influence of higher education, limited ability to combine employment with childbearing and lack of adequate childcare facilities. We also detect a significant role of the increased use of contraception, motivated by both economic and demographic reasons.

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  1. For richer summary statistics calculating age- and cohort-specific fertility rates, refer to Billari and Kohler (2002). An overview of tempo-adjusted Bongaarts–Feeney rates can be found in Sobotka (2001).

  2. To avoid measurement error bias in the first cohort, we consider only those females who have completed their education. This simultaneously reduces the efficiency loss due to censoring.

  3. There are also some questions about the reasons for own fertility decisions, but very high nonresponse rates render these data useless.

  4. This assumption is particularly appropriate in our case since we do not have income data, and individuals are surely heterogeneous with respect to income.

  5. Identification of θ requires setting one of its elements to zero. Alternatively, one can suppress intercept term in x (see Heckman and Singer 1982, p.64).

  6. Keep in mind, though, that the effect of employment intensity is still negative and significantly contributes to the delay of first birth as described by Happel et al. (1984).

  7. This finding is more of a demographic nature and is quite similar to the results of Bonneuil (1997) on the dependence between fertility and urbanization. Analyzing cointegrating relationships between fertility and urbanization in France, Bonneuil (1997) demonstrates that with the fall of fertility rates, the positive effect of urbanization on birth postponement disappears.

  8. We also ran the model for the youngest cohort without the belief variables to see whether this would change the other covariates (particularly the employment and education variables). It has very little impact on them, so that the results for these variables are robust to inclusion or exclusion of the belief variables.

  9. Apart from purely economic reasons, the significant effect of the increased use of contraception may also have behavioral and demographic roots (see Bonneuil 1997).


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We would like to thank Michael Grimm, Siv Gustafsson, and the participants of ESPE 2004 and EEA 2005. Comments of an anonymous referee were especially useful. Funding from the German Science Foundation (DFG) within SFB 386: “Statistical Analysis of Discrete Structures” is greatly acknowledge.

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Correspondence to Andrey Launov.

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Responsible editor: Allesandro Cigno



Direction of the effect and marginal effects

Waiting time

First, consider the effect of the covariates on Weibull hazard function

$$h{\left( {t\left| {\theta _{i} } \right.} \right)} = \gamma {\left( {\exp {\left\{ {x\beta + \theta _{i} } \right\}}} \right)}^{\gamma } t^{{1 - \gamma }} $$

Since the first derivative with respect to x β is positive, the sign of the effect will always be equal to the sign of the estimated parameter. For instance, a positive estimated coefficient conveys that an increase in x leads to an increase in the hazard of having a child and, hence, to a reduction in the waiting time between births.

To compute the size of the effect, consider the expected value of a Weibull-distributed random variable

$$E{\left( {T\left| \phi \right.{\left( x \right)}} \right)} = \phi {\left( x \right)}^{{ - 1}} \Gamma {\left( {1 + \gamma ^{{ - 1}} } \right)},$$

where in our case, ϕ(x)=exp {x β+θ i }. In the presence of unobserved heterogeneity, this expectation modifies to

$$E{\left( {Tx,\theta } \right)} = {\sum\limits_i {\frac{{\pi _{i} }}{{\exp {\left\{ {x\beta + \theta _{i} } \right\}}}}} }\Gamma {\left( {1 + \gamma ^{{ - 1}} } \right)}.$$

Taking the first derivative of E(Tx,θ) with respect to x, we get

$$\frac{{\partial E{\left( {T\left| {x,\theta } \right.} \right)}}}{{\partial x}} = {\sum\limits_i { - \frac{{\pi _{i} \Gamma {\left( {1 + \gamma ^{{ - 1}} } \right)}}}{{\exp {\left\{ {x\beta + \theta _{i} } \right\}}}}} }\beta .$$

As covariates enter E(T x,θ) nonlinearly, the marginal effect at the different levels of one and the same variable will not be the same (e.g., an additional year of schooling for a person with university education will cause a change in waiting time different from that caused by an additional year for a person with only secondary school education). To ensure correct inference, the marginal effects must be calculated for all possible levels of the explanatory variables.

Quit probabilities

Identical inference can be made about changes in quit probabilities. With parameterization

$$p = {\left( {1 + \exp {\left\{ { - x\omega } \right\}}} \right)}^{{ - 1}} ,$$

an increase in the value of the variable will cause an increase in probability of quitting after the realized birth, provided that the corresponding parameter is positive. The marginal effect of the covariates on the expected quit probabilities is

$$\frac{{\partial E{\left( {Px} \right)}}}{{\partial x}} = \frac{{\exp {\left\{ { - x\omega } \right\}}}}{{{\left( {1 + \exp {\left\{ { - x\omega } \right\}}} \right)}^{2} }}\omega .$$


Table A1 Estimated coefficients with parameterized quit probabilities: first birth cohort (born 1972–1982)
Table A2 Estimated coefficients with parameterized quit probabilities: second birth cohort (born 1963–1971)
Table A3 Estimated coefficients with parameterized quit probabilities: third birth cohort (born 1954–1962)
Table A4 Test of no increase in the negative effect on timing of births
Table A5 Test of no increase in the negative effect on exit from childbearing

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Klasen, S., Launov, A. Analysis of the determinants of fertility decline in the Czech Republic. J Popul Econ 19, 25–54 (2006).

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  • Economic transition
  • Fertility decline
  • Czech Republic
  • Multistate model of birth process

JEL Classification

  • J13
  • J11