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What Determines the Child Penalty in the Netherlands? The Role of Policy and Norms

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Having children can result in large earnings penalties for mothers. Using extensive administrative data from the Netherlands, we assess the magnitude and drivers of the effects of first childbirth on parents’ earnings trajectories in the Netherlands. We show that mothers’ earnings are 46% lower compared to their pre-birth earnings trajectory, whereas fathers’ earnings are unaffected by child birth. We examine the role of two potential determinants of the unequal distribution of parents’ labour market costs by gender: childcare policies and gender norms. We find that while child care availability is correlated with lower child penalty, the immediate short-term causal effect of increasing child care availability on the earnings penalty of becoming a mother is small. By taking advantage of variation in gender norms in different population groups, we show that gender norms are strongly correlated with child penalty for mothers.

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Source LISS panel 2008–2019

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  1. The OECD reports that in 2018, men earned 13% more than women in the Netherlands (OECD, 2021). It is close to the OECD average of 12.8%.

  2. Including Kleven et al. (2019b) for Austria, Germany, the US, the UK, Denmark and Sweden, Sieppi & Pehkonen (2019) for Finland, Andresen & Nix (2019) for Norway, Pora & Wilner (2019) for France, and de Quinto et al. (2020) for Spain.

  3. An alternative explanation for the child penalty is preferences, i.e. that women have a preference for child care and men do not. Whereas a part of our preferences is likely to be determined genetically and hence may vary by biological sex, another part is determined by the environment we are surrounded by. Since the world we live in is highly gendered, it is impossible to separate preferences from norms.

  4. Gender norms of the parents also affect sons. Farré & Vella (2013) finds that mothers’ attitude is correlated with sons’ attitude; and men with less egalitarian gender norms during their youth are more likely to have spouses with a lower labour supply during adult life.

  5. While Dutch men are the champions in part-time work compared to the other OECD countries (19.4%), the Netherlands is also champion in the gender gap in part-time work: with 38 percentage points, it is the largest among OECD countries (OECD, 2021).

  6. As the oldest cohort we consider are born in 1960, we observe high education attainment for a large majority of our sample.

  7. Self-employment is not included because of a change in recording of income from self-employment. We use an alternative measure including income from self-employed as a robustness check. This is not included in the main analysis because it is available for fewer years.

  8. The imputation of the full-time reference hours is based on the hours worked in the polis data, which is available from 2006 onward and which covers the full universe of jobs.

  9. This implies that the child penalty measure represents the total impact of all children independent of the total number of children. Kleven et al. (2019c), Sieppi & Pehkonen (2019) show that the penalty tends to be larger the more children a woman has, but that there is no difference by the number of children for men.

  10. This change in earnings trajectories may for example be explained by women sorting into more family friendly jobs when planning to have a family, pregnancy-related sick leave or employers reacting to the pregnancy.

  11. For example, the calendar year estimate of the year 2010 onwards are only based on already-parents, because by then everyone in our sample has had a child.

  12. In this robustness test we estimate the child penalty for the same cohort over a shorter period of time, such that fewer coefficients are estimated based on already-parents only.

  13. Since the child penalties of these three margins do not control for the other margins in the regression, they do not sum up to the overall earnings child penalty (Kleven et al., 2019c). It is possible to decompose the earnings child penalty, see for example Pora & Wilner (2019), but this is beyond the scope of this paper.

  14. Even if they are all computed according to the methodology from Kleven et al. (2019c), differences in the reported penalties can be partly driven by differences in data or time window used in the estimation.

  15. See Appendix B for a description of this data source.

  16. A detailed presentation of the institutional setting can be found in Bettendorf et al. (2015) or Adema et al. (2019).

  17. We limit the analysis to mothers, since father’s child penalty is non-existent, and hence child care policies are unlikely to matter for father’s child penalties.

  18. An 0.05 percentage point increase implies, for example, moving from the median municipality to the 75th percentile pre-reform and thus represents a large increase in child care availability.

  19. Reverse causality may arise if mothers who want to work more push municipalities to provide more child care by voting for politicians promising to provide more child care subsidies. One possibility for omitted variable bias would be that richer cities provide more childcare and mothers living there are working more.

  20. They find a 3.3% increase in the employment rate and 6.6% increase for hours worked for parents with a youngest child 0–11 years of age.

  21. We are aware that women in same-sex couples earn on average more than the rest of the population, in many countries (Drydakis, 2022) and in the Netherlands as well (Plug & Berkhout, 2004). This difference in level is not an issue since we compare the evolution of earnings at childbirth for different groups.

  22. The reference time to distinguish between the groups is t − 3, well before the child is born.

  23. Figure 14 in the appendix shows religiosity and child penalty by municipality.

  24. The polis datasets include a richer set of variables but only start from 2006 on, which limit the time window for the estimation of the penalty. This is the reason why we do not use it as main data source in the paper.

  25. Link to gbapersoontab documentation in Dutch

  26. Link to gbaoverlijdentab documentation in Dutch

  27. Link to gbamigratiebus documentation in Dutch

  28. Link to gbahuishoudensbus documentation in Dutch

  29. Link to gbaadresobjectbus documentation in Dutch

  30. Link to baankenmerkenbus documentation in Dutch

  31. Link to baansommentab documentation in Dutch

  32. Link to polis documentation in Dutch

  33. Link to polis documentation in Dutch

  34. The data can be found here


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This research was partly funded by the Dutch ministry of Education (OCW).

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Correspondence to Simon Rabaté.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We thank Egbert Jongen for the continuous support and the insights he provided for this project. We also thank Yvonne Adema and Derk Visser for their contribution to earlier versions of this analysis. We are grateful to Pieter Bakx, Jonneke Bolhaar, Anne Boring, Jeanette Duin, Pilar García-Gómez, Willem Huijnk, Nora Kuijper, Jasper Lukkezen, Gerben Muskee, Marianne Tenand, Teresa Bago d’Uva, Maria Zumbuehl, Annemarie van der Zeeuw, Jan van Ours and two anonymous referees for their valuable comments and suggestions. Remaining errors are our own.


Additional Tables and Figures

1.1 Additional Tables

See Tables 2, 3.

Table 2 Descriptive statistics by subgroups

1.2 Additional Figures

See Figs. 12, 13, 14, 15, 16, 17.

Fig. 12
figure 12

Impact of children on mothers’ and fathers’ careers. This figures present the counterfactual and predicted earnings used for the computation of the child penalty, for mothers and fathers and for the different labour market outcomes we consider. Predicted earnings is the predicted value of the outcome based on the estimation of Eq.(1). Counterfactual outcome is the average predicted labour market outcome at event time q, without the event dummies: \(\tilde{y}_{it}^q= \frac{1}{N}\sum _i\big [\sum _k{\hat{\beta }}_k \mathbbm{1}[age_{it}=k]+ \sum _t{\hat{\gamma }}_t\mathbbm{1}[time_{it}=t]\big ]\)

Fig. 13
figure 13

Distribution of the years of large childcare expansion. This figure present the distribution of the years for which we observe a childcare expansion (see Sect. 4 for details about the definition of the event)

Fig. 14
figure 14

Mothers’ child penalty and religiosity by city. This figure presents, for each municipality, the earnings child penalty for mothers (a) and the average religiosity (b). Religiosity is defined as the share of individual going to a religious place at least once a month. See text of Sect. 5 for details

Fig. 15
figure 15

Child penalty for high and low religious group, with and without matching. This figure presents the estimated earnings child penalty for mothers in high and low religiosity, without weights (A) and with weights (B). High (resp. low) religiosity group is composed of municipalities in which more than 25% (resp. less than 15%) of the population visit a religious place at least once a month. Weights are computed using a coarsened exact matching (CEM) procedure based on education and employment trajectories in the 5 years preceding childbirth (see text in Sect. 5 for details)

Data Construction

1.1 Construction of the Hours Worked Variable

In the baan datasets that we use there is no direct information available on the hours work. We do have access to a variable on the full time equivalent associated to a given job spell. For example, a person working 20 hours when the full-time reference is 40 hours, the % of FTE worked is equal to \(\frac{20}{40}=0.5\). The problem is that we do not observe the full-time reference hours, which would make it possible to retrieve the hours worked from the FTE. We therefore adopt the following approach: we use another data source, the polis datasets (see Sect. C above) which contains information on hours actually worked, to impute hours worked in our main estimation sample.Footnote 24 More precisely, we compute for each each the distribution the yearly hours worked in each sector using the polis data, and take the mode of the distribution as the reference work duration in the sector. This approach generates some measurement errors since the reference duration is determined at a more disaggregated level (collective agreement or even firm) than the one we are using (sectors, split in 70 categories). Another potential source of measurement errors comes from the fact that the polis dataset starts in 2006, and that we use the distributions observed at that year for the computation of the reference durations for years 2001–2005.

Figure 16 compares the observed and imputed distribution, based on year 2010, for which we can observe the hours worked in the polis dataset. Panel (a) compares the observed and imputed distribution of yearly hours worked, and panel (b) presents the distribution of the difference between the observed and imputed values. These figures confirm that we have measurement error in our measure of hours worked, but that the imputed values are on average very close to the observed one for the comparison sample.

1.2 Construction of the Childcare Index

For the construction of our childcare exposure index (Eq. 4 in Sect. 4), we need to compute the number of childcare jobs existing in each municipality. We do not have direct information on that, for example from municipalities’ expenditures as in Andresen and Nix (2019) or Kleven et al. (2020). We therefore use firm-level information on job location combined with sector information to compute our index. More precisely, we compute for each year and city the number of jobs in two different 5-digit sectors: 88911 (day nurseries for pupils) and 88912 (Kindergartens). We then divide this number of jobs by the number of below five years in old by year and city to compute our index.

Panel (a) of Fig. 17 presents the evolution of the index at the country level. We observe a sharp increase in the number of childcare jobs from year 2006. The overall childcare exposure more than doubles between 2006 and 2012, and then slightly decreases. This evolution is consistent with the timing of implementation of the childcare reform described in Sect. 4.1, as illustrated in panel (b) presenting the evolution of the overall public childcare expenditure. Reassuringly, our index closely follow the evolution of aggregate public expenditure, which suggests that we are capturing the effect of the childcare reform through it.

Fig. 16
figure 16

Hours worked variable. These figures compare the observed and imputed hours for workers observed in 2010 in both the baan and polis datasets. a shows the overlapping distributions of imputed and observed yearly hours. b shows the distribution of the gap between observed and imputed hours, computed for each individual

Fig. 17
figure 17

Evolution of childcare exposure index and childcare expenditure. The red line presents the yearly evolution of the aggregated childcare exposure index, measured as the ratio between the number of childcare jobs and the number of children below five years old. The green line presents the evolution of the total public spending for childcare (source: Ministry of social affairs). Both time series are shown relative to the 2005 value

Description of the Databases

In this appendix we describe the different data-sources used in the paper. Table 3 lists all the sources we use, and we then provide more detailed information about each of the data we use.

Table 3 Description of the datasets used in the analyses

1.1 Dataset Provided by Statistics Netherlands

Information about the administrative micro data sets can be found at (available in Dutch only)

1.1.1 gbapersoontabFootnote 25

It contains demographic background data (e.g. gender, year of birth, migration background) for the universe of the Dutch population, that is all persons who appear in the registered in the population register (Basic Register of Persons, BRP) since 1 October 1994.

1.1.2 gbaoverlijdentabFootnote 26

Contains the date of death of all persons who have died since 1 October 1994 and were registered in the population register (Basic Register of Persons, BRP) at the time of death. It also contains the date of death of persons who are not residents but were once residents of the Netherlands since 1 October 1994 and whose information about the death is received in the Register of Non-Residents (RNI). The main source of information for this dataset is the municipal registries (Gemeentelijke Basisadministratie Persoonsgegevens, GBA).

1.1.3 gbamigratiebusFootnote 27

It contains all migration spells for the full universe of the Dutch population (as defined in the gbapersoontab). For each immigration (resp. emigration) spell, a date of beginning and end is registered, as well as the country of origin (resp. destination). For each individual, we have as many spells as migration events occurring since 1994. The main source of information for this dataset is the municipal registries (Gemeentelijke Basisadministratie Persoonsgegevens, GBA).

1.1.4 gbahuishoudensbusFootnote 28

For the full universe of the Dutch population (as defined in the gbapersoontab), it contains information about the household composition: their place in the household, and the details of the household they belong to (e.g couple or not, married or not, with or without children, etc.). Retrospective information is available, as the data is presented as spells (one additional line when one characteristic of the household changes). The main source of information for this dataset is the municipal registries (Gemeentelijke Basisadministratie Persoonsgegevens, GBA).


For the full universe of the Dutch population (as defined in the gbapersoontab), it contains information about the individual address, with a unique object identifiers at the level of the building. Retrospective information is available, as the data is presented as spells (one additional line when one characteristic of the address changes). The main source of information for this dataset is the municipal registries (Gemeentelijke Basisadministratie Persoonsgegevens, GBA).

1.1.6 baankenmerkenbusFootnote 30

A spell database with information on job characteristics (contract, sector) for the full universe of the jobs. It is available for years 1999-2016.

1.1.7 baansommentabFootnote 31

A yearly database with information on wages (gross wage, fiscal wage, full time equivalent) for the full universe of the jobs. It is available for years 1999-2016.

1.1.8 polisbus Footnote 32 and spolisbus Footnote 33

It contains information on the full universe of job in the Netherlands, available from year 2006. There is one line by employment spells, with information on both the individual (wage, hours worked, contributions, etc) and the firm (sector, collective agreement, etc).

1.1.9 gemstpl

A yearly database containing information on job location at the municipality level for the full universe of job in the Netherlands existing at the end of the year (31/12). The address is based on the firm address. When there are several establishment for a given firm, the location is imputed by Statistic Netherlands as follows. The location attributed to a given job is the one which is the closest to the home address of the worker.

There are different version of this dataset, depending on the years considered: GEMSTPLAATSBUS for 1999-2005, GEMSTPLBUS for 2006-2014, GEMSTPSBUS for 2015-2019.

1.2 Other Datasets

LISS panel The LISS (Longitudinal Internet Studies for the Social sciences) panel administered by CentERdata (Tilburg University, The Netherlands) is a representative sample of Dutch individuals who participate in monthly Internet survey on household background information. The panel is based on a true probability sample of households drawn from the population register. Households that could not otherwise participate are provided with a computer and Internet connection. A longitudinal survey is fielded in the panel every year. The information we use in this paper is retrieved from the religion and ethnicity; and the political views and value module.

1.3 Survey on Religiosity

We use a survey on religious practices conduct by CBS to construct our religiosity index.Footnote 34 See Schmeets (2016) for a description of the data and a more complete analyses of religious practices in the Netherlands.

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Rabaté, S., Rellstab, S. What Determines the Child Penalty in the Netherlands? The Role of Policy and Norms. De Economist 170, 195–229 (2022).

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