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The effect of women’s participation in the labour market on the postponement of first childbirth: a comparison of Italy and Hungary

Abstract

This paper analyses the effect of increasing female participation in the labour market on the transition to first childbirth. Regional perspectives are considered to help us understand how postponement behaviour is changing over time and at different paces in each region. The analysis is based on the first wave of the Generations and Gender Survey of Italy and Hungary. We use a multilevel event history random intercept model to examine the effect of individuals’ positions in the labour market on the transition to motherhood, controlling for differences in macrolevel factors related to regional backgrounds in the two countries. The regional data for Italy came from the Italian National Statistical Institute, and for Hungary from our imputation developed from the time series available at the national and the regional levels (Hungarian Central Statistical Office, KSH). The postponement of first childbirth is strongly linked to the increasing involvement of women in paid work, but with opposite effects in the two countries. Even if we control for changes in women’s levels of education over time and for shifts in women’s aspirations and levels of attainment in the labour market, we find that being employed remains a risk factor for the postponement of the first birth among Italian women, and a strong protective factor among Hungarian women. At the contextual level, the variables that take into account the regional socio-economic changes provides evidence of important effects on individual behaviour among Italian women, and of only minor effects among Hungarian women. All of the regional breakdowns in both Italy and Hungary show that the postponement of motherhood goes hand-in-hand with the acceptance of deep cultural and socio-economic changes.

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Notes

  1. 1.

    For a discussion on fertility trends see Spéder and Kamaràs (2008) for Hungary and Caltabiano et al. (2009) for Italy.

  2. 2.

    The European countries with the lowest fertility levels also have relatively low levels of female participation in the labour force, while countries with higher fertility levels tend to have relatively high female labour force participation rates.

  3. 3.

    For a review of the ‘work-family conflict’ literature see Voydanoff (1988).

  4. 4.

    The high percentages of youth unemployment and the low female labour market participation rate in many southern European countries, such as Italy, Greece, and Spain, seems to confirm the negative relationship between unemployment and fertility.

  5. 5.

    The distinction was based on the European Values Study data from 1999 published in Halman (2001).

  6. 6.

    Data show that in Italy in 2009 only 1.6 % of GDP was spent on family and motherhood, compared to the EU average of 2.6 %. The percentage of GDP spent on childcare in Hungary in 2009 was lower than the EU average, but greater than the share in Italy (2.2 %).

  7. 7.

    We calculated male rates using data form KSH MEF and ISTAT. The gender gaps are not presented here for space limits.

  8. 8.

    The young unemployment rates are been calculated using the same sources of data. They are not presented here for space limits.

  9. 9.

    As Spéder (2006) pointed out the age women had in 1990 is particularly important in understanding behavioural changes in a former socialist society such as Hungary.

  10. 10.

    See the “Appendix” for the classification of regional variables used both for Italy and Hungary.

  11. 11.

    To test whether the effect of age varies across regions, we used a likelihood ratio test in which the null hypothesis was that the two new parameters (u 0j and u 1j ) in the first model were simultaneously equal to zero. The likelihood ratio test statistic was calculated with and without the random slope for age. We could therefore conclude that the effect of age did indeed vary across regions.

  12. 12.

    To describe the changes in the reproductive behaviour of different individuals who grew up during the same period with similar historical experiences and opportunities, the women were grouped into five-year generations.

  13. 13.

    Details on how the covariates are built are available in the “Appendix”.

  14. 14.

    It should be noted that since this is a discrete-time logit model, there is a second-level, regional error (u 0j ) associated with the model, but none at the individual level (cf. Hox 2002).

  15. 15.

    More information on the survey is in Spéder (2001) and Kapitány (2003).

  16. 16.

    For example, for a woman born in 1977 who reached age 13 in 1990, we assigned the percentage of the women with secondary education recorded in 1990 for the first year in which they are at risk of first motherhood, that of 1991 for the second year, and so on; whereas for a second woman born in 1970 who reached age 13 in 1983, the percentage of women with secondary education will be that of 1983 for the first year, 1984 for the second year, and so on.

  17. 17.

    We are conscious that fertility support policies have a strong effect on the postponement of first motherhood, and that these policies differ greatly between the two countries: until 1990, Hungary maintained its fertility rate thanks to numerous policies that were modified in the 1990 s [for a brief survey of Hungarian policies on the family and fertility, see UNECE 1998], while Italy’s policy actions in this area were sporadic and ineffective. Unfortunately, due to the lack of data, we were not able to include in the model any data on this topic.

  18. 18.

    All estimation was done with the multilevel mixed-effects logistic regression (xtmelogit) in STATA.

  19. 19.

    The estimates related to Models 1, 2, and 3, produced with a stepwise procedure, are shown in the “Appendix”.

  20. 20.

    It is based on a linear predictor that includes both the fixed effects and the random effects, and the predicted mean is conditional on the values of the estimated random effects.

  21. 21.

    The selection of regions was based on their estimated means, also incorporating the random effects.

  22. 22.

    Aassve at al. (2006b) argued that when fertility and partnership formation and living in the parental home are very closely interrelated, considering partnership status only as a covariate in the fertility equation also captures the influence that some other covariates have on fertility, thus biasing the estimates of interest. To avoid this potential problem of endogeneity, we conducted a sensitive analysis, excluding the marital status and the living in the parental home variables, which strengthened the credibility of our results for both Italy and Hungary.

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Acknowledgments

The authors would like to thank the two anonymous reviewers for their extremely useful comments, which have helped us to significantly improve the paper, and Giovanni Boscaino for helping with the regional maps of Hungary and Italy. The authors gratefully acknowledge the financial support provided by University of Palermo [Grant No. ORPA06YH89 under the responsibility of Ornella Giambalvo]. Although this paper is the result of the joint effort of both authors, introduction and lowest-low fertility sections are attributable to O. Giambalvo whereas all the other sections to A. Busetta.

Author information

Correspondence to Annalisa Busetta.

Appendix: Data

Appendix: Data

Micro-dimension (individual variables)

Age:

Calculated from the difference between the month and the year of the interview and the interviewee’s year and month of birth

Regional Area:

For Hungary the regional variable is the result of cross-classification of the type of settlement (classified as capital, city, town, and village) and the Nuts 2 region where women lived, whereas for Italy is it simply the Nuts 3 (note that the survey does not allow for the consideration of regional and municipal levels simultaneously)

Generation:

We grouped women born from 1967 to 1981 into five-year age groups according to their age at the start of the political transition (i.e., 1990), as was proposed by Spéder (2005) (see Scheme 1). To compare the information provided by the two surveys and to describe the differences and similarities in the changes observed in reproductive behaviour for various cohorts, we also used Spéder’s classification to group Italian women

Scheme 1—Respondents’ year of birth, age at interview, and age in 1990

Year of birth Age at interview Age in 1990 (year)
1967–1971 30–34 19–23
1972–1976 25–29 14–18
1977–1981 20–24 9–13
Leaving parental home:

In both surveys, the information regarding the time of leaving the parental home was not assessed exactly. The age/year at ‘leaving the parental home’ was defined only for women for whom the change of family or home did not coincide with the time of first marriage or first cohabitation

Marriage and cohabitation:

For the purposes of the analysis, only the first marriage or first cohabitation was considered

Level of education:

The year in which the level of study was achieved was classified as follows: ‘no qualifications,’ ‘primary school,’ ‘professional school’ (only for Hungary, where vocational school lasts 1 year less than secondary school), ‘secondary school,’ or ‘university and beyond’). When interviewees provided their level of study but not the year, we considered it to correspond to the mean duration necessary to obtain the same qualifications in the country (calculated by the survey)

Activity status:

For Italy we created a dichotomic variable with value 1 if in the t year the woman was in a job, or 0 otherwise. For Hungary the variable used was equal to 0 if we did not know her effective job position, 1 if she was not employed and 2 if the woman was in a job

Parents:

The questions asked about family background were slightly different in the two surveys but it was therefore possible to deduce whether the parents were living together when the respondent was 15 years old

Number of siblings:

The Italian survey asked how many brothers and sisters the interviewee had, while the Hungarian survey was concerned with the number of brothers and sisters with whom the interviewee was brought up

Macro-dimension (contextual variables)

Hungary

Employment rate:

The time regional series from 1992 are available (Source: KSH MEF CSO Labour Force Survey). Using known time series we fitted a regressive linear model for the imputation of missing data (both for the year and regions)

Unemployment rate:

The time regional series from 1992 are available (Source: KSH MEF CSO Labour Force Survey). To estimate the missing data, an increasing or decreasing rate was obtained as the rate of the average of the known data, and the average of the known data minus the last one (for the year and for regions) was used

Female participation rate:

The time regional series from 1992 are available (Source: http://www.ksh.hu/docs/eng/xstadat/xstadat_long/h_qlf013a.html). To estimate the missing data an increasing or decreasing rate was obtained as the rate of the average of the known data, and the average of the known data minus the last one (for the year and for regions) was used

Graduate female rate:

Using the GGS database, the number of female university graduates and the female/male rate was estimated. At the end, the female graduates and the females interviewed were calculated. Finally, this rate was used as an expansive rate for the entire female graduate population

Secondary school female rate:

We adopt the same method for the point d. Starting with 1990 we used data available on http://www.ksh.hu/docs/hun/xftp/idoszaki/pdf/kozoktter03.pdf

Italy

Employment rate:

The time regional series are available (Source: ISTAT, http://dati.istat.it/). Using known time series for 1975 and 1976, the rates were estimated using the average imputation method considering data of 5 years

Unemployment rate:

The time regional series are available (Source: ISTAT, http://dati.istat.it/). Using known time series for 1975 and 1976, the rates were estimated using the average imputation method considering data of 5 years

Female participation rate:

The time regional series are available (Source: ISTAT, http://dati.istat.it/). Using known time series for 1975 and 1976, the rates were estimated using the average imputation method considering data of 5 years

Graduate female rate:

Svimez DATA are available since 1996. We estimated the missing data using the following method: \( y_{t} = y_{t - 1} + \frac{{(y_{t + 1} - y_{t - 1} )}}{2} \). Sampling data from ISTAT graduate placement surveys are used for the regional distributions

Secondary school female rate:

We adopted the same method for the graduate female rate

See Tables 3 and 4.

Table 3 Estimates of models for Italy (odds ratio and significance level)
Table 4 Estimates of models for Hungary (odds ratio and significance level)

See Figs. 13, 14 and 15.

Fig. 13
figure13

Transition to the first birth by generation in Italy and in Hungary. Source: (left) Istat 2003 survey Family and Social Subjects; (right) Hungarian Central Statistical Office—Demographic Research Institute, 2001–2002 survey Turning point of the life course

Fig. 14
figure14

Transition to the first birth by regions (NUT2), by generation in each region in Italy. Source: Istat 2003 survey Family and Social Subjects

Fig. 15
figure15

Transition to the first birth by regions (NUT2), by type of settlement, and by generation in each region in Hungary. Source: Hungarian Central Statistical Office—Demographic Research Institute, 2001–2002 survey Turning point of the life course

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Busetta, A., Giambalvo, O. The effect of women’s participation in the labour market on the postponement of first childbirth: a comparison of Italy and Hungary. J Pop Research 31, 151–192 (2014). https://doi.org/10.1007/s12546-014-9126-4

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Keywords

  • Low fertility
  • Postponement
  • First job
  • Education
  • Multilevel event history models