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Parental labor market penalties during two years of COVID-19

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Abstract

We use a matched employer-employee dataset covering the universe of employees in the Italian private sector to compare labor market outcomes of mothers and fathers during the pandemic. We find that mothers experienced a larger penalty in terms of reduced labor market earnings compared to fathers (−14.1 vs. −6.9 %) in 2020 and the first half of 2021. In contrast, starting from July 2021, we observe similar trends in mothers’ and fathers’ earnings. Evidence highlighting differences in penalties according to the sector of activity (essential vs. non-essential and easiness of access to work from home), the type of contract, the age of children, and the pre-pandemic mother-father pay gap suggests that both demand and supply factors have played a role in explaining the gendered impact of COVID-19.

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Notes

  1. An important limitation of the data is that we have no information on education. Moreover, differently from survey data, we are unable to capture informal work, which in Italy is estimated to be about 12% of total employment, according to the Italian National Statistical Institute (Istat).

  2. Essential activities include agriculture, some manufacturing, energy and water supply, transports and logistics, ICT, banking and insurance, professional and scientific activities, public administration, education, healthcare and some service activities. Non-essential activities include most of manufacturing activities, wholesale and retail trade, hotels, restaurants and bars, entertainment and sport activities.

  3. We cannot investigate whether this fall has been compensated with additional work in the informal sector, as our data only record formal employment. However, consistently with the evidence found by Leyva and Urrutia (2023) for Latin America, the pandemic has produced a relevant fall in informality rates in Italy, which—coupled with the fact that women are overrepresented in the informal activities that were mostly affected by the crisis (ISFOL, 2007)—likely implies that our estimates are a lower bound of the gendered impact of the pandemic.

  4. The decrease in parental leave use probably follows the adoption of remote working arrangements that may have allowed an easier work-life balance. However, the literature highlights that even with remote working opportunities mothers struggled to participate to the labor market in the absence of affordable childcare services (Heggeness and Suri, 2021).

  5. Such heterogeneity likely reflects the changing composition of firms requesting subsidized hours’ reductions: while the nationwide lockdown in the first wave imposed larger constraints on firms’ activities, sector-specific lockdowns in the subsequent waves hit particularly firms in services, that may have different policies regarding short-time work use.

  6. Among others, Chetty et al. (2020), Cortes and Forsythe (2023a, 2023b), Dalton et al. (2021), Petroulakis (2023) have examined the heterogeneous effects of the pandemic across demographic and occupational groups.

  7. In Germany and Canada, the differentiated impact was smaller but still substantial: in Germany, from February to May there was a rise of 18% in female unemployment rate, whereas for men the increase was 14% (Bundesagentur für Arbeit, 2020); in Canada, from February to April labor supply dropped by 30.1% for women, compared to 27.7% for men (Lemieux et al., 2020). Lambert et al. (2020) and Farré et al. (2022) report similar evidence for France and Spain, respectively. Hupkau and Petrongolo (2020), instead, find no increase in the gender gap in paid employment for the UK. For the Netherlands, Zimpelmann et al. (2021) and Meekes et al. (2020) find little overall widening of the gender gap in employment or hours.

  8. There is also evidence of negative effects on labor productivity with relevant consequences on women career prospects. Fuchs-Schundeln (2020) find a reduction during the first 5 months of 2020 in paper submissions to the Review of Economic Studies by female economists (-2 p.p.), while male submissions have slightly increased. Similar results are also found by Amano-Patino et al. (2020).

  9. D.L. 18, March 2020. In 2021 (D.L. n. 30/2021) the policy was extended to parents of children aged up to 14 years old.

  10. In Italy, before the pandemic parents had two measures helping them with child bearing after the end of the compulsory maternity leave, which has a duration of 5 months. The first measure consists in an optional parental leave scheme entitling each household with children aged 12 or younger to a total of 10 months of leave (the first 6 months paid with a 30% replacement rate and the remaining months unpaid). The system is designed to encourage parents to share the leave: each parent can take up at most 6 months of parental leave and when the father uses at least 3 months of parental leave, the household is entitled to one additional month for a total of 11 months. The second measure, Bonus Asili Nido, is a yearly childcare subsidy of 3000 Euros (Law 11/12/2016 n. 232). This policy became first available in 2012 – Bonus Infanzia, Law 28/06/2012 n. 92 – implemented experimentally in the years 2013-2015, and confirmed for 2016, 2017 and 2018.

  11. Similar measures were adopted in other countries. For example, in France working parents with children under age 16 affected by school closure and/or self-isolation were entitled to paid sick leave (paid at 90% of gross earnings for the first 30 days) if no alternative care or work arrangements could be found. In Germany, working parents with children under age 12 who have not been able to work due to school or child care closures have been entitled to six weeks of paid leave, paid at 67% of earnings. See OECD (2020) for further details.

  12. In 2021 the age limit was raised to 14 (D.L. n. 30/2021).

  13. This measure was available for private employees, self-employed, as well as for some specific categories of public employees (doctors, nurses, biomedical and radiological laboratory technicians, staff of the law enforcement officers engaged for emergency-related needs) with children under the age of 12.

  14. The maximum amount of the voucher was firstly set at 600 and 1000 euros for private employees and medical\security sectors’ employees, respectively. Later, these ceilings were increased to 1200 and 2000 euros.

  15. The short-time work (STW) compensation scheme, Cassa Integrazione Guadagni, is a subsidy, granted by the government, for partial or full-time hours reduction, which preserves employment relationships and replaces 80% of the earnings forgone due to hours reduction, up to a threshold. At the onset of the pandemic, the Italian government introduced a special COVID-related STW compensation scheme of the duration of 9 weeks that applied retroactively starting from 23 February. The COVID-related STW scheme extended the coverage of the regular STW to firms with less than 15 employees, which were not covered normally, and to those already using the extra-ordinary STW, one of the sub-species of STW granted by the Italian employment protection legislation, which in normal times cannot be cumulated with the regular one. Moreover, firms using the COVID-related STW could renew temporary contracts, waiving to the norms of standard regulation.

  16. Mothers who had their child when unemployed cannot be identified and are not included in the sample, as they do not apply for maternity leave. For this reason, we cannot reliably identify women and men without children in the data, as they could be working and not have applied to maternity or parental leave if they were non-employed around childbirth.

  17. As explained in footnote 10, according to the Italian law, each parent is entitled to a maximum of 6 months of parental leave individually, with households granted a total of 10 or 11 months of leave. Given this framework, it becomes important for administrative purposes to gather information on the identity of both parents independently, irrespective of their relationship status. Then, we are confident that our dataset does not suffer from an over-representation of married fathers.

  18. Fathers who are partners of women who do not apply for parental leave might be either more or less involved in childcare responsibilities compared to fathers with a partner who applies for parental leave. It is therefore unclear what kind of bias can derive from this type of selection. In contrast, the selection deriving from including fathers who have applied for parental leave would imply that our estimates of gender differences in labor market outcomes during the pandemic can be interpreted as lower bounds.

  19. We therefore measure a 37% gender gap in monthly earnings in 2019. To understand potential selection of fathers, we compute the same statistic from Labor Force Survey data, which records self-reported net monthly earnings, top coded at 3000 euros. We measure a 20% gender pay gap between mothers and fathers employed in the non-agricultural private sector. The difference with administrative social security data is likely a consequence of using net, instead of gross, earnings and, more importantly, top coding. The share of fathers with top coded earnings is more than double that of mothers, likely biasing downwards the estimated gender pay gap.

  20. Our analyses are based on full-time equivalent days, which coincide with raw days for full-time workers and raw days multiplied by half for part-time workers.

  21. According to the INPS Annual Report (2023), ordinary parental leave is predominantly utilized by mothers, who, in 2019 accounted for approximately 80% of the requests, while for Covid-19 leave, they represented 79%. The slightly increased utilization of Covid-19 leave by fathers (21%) might be related to the fact that the allowance for this leave was higher compared to that paid for the ordinary leave (50% of the salary, instead of 30%).

  22. We do not include individual fixed effects, as otherwise we would only be exploiting within worker variation in labor outcomes, while we are interested in more aggregate trends within cells defined by the covariates included in \({X}_{{itm}}\) and by regions and sector effects. For a similar approach, see Basso et al. (2023).

  23. An alternative approach would be to run a dynamic difference-in-differences using the full longitudinal data in one regression. With calendar month dummies, this approach is equivalent to running the separate cross-sectional regressions. We have estimated this model for 2020 vs. 2019 in a previous unpublished draft and the results remain indeed the same.

  24. Choosing 2019 as a baseline year is innocuous, as the Italian economy was roughly on the same trend as in the previous years. Figure A1 reports the number of employees from the quarterly Labor Force Survey. The figure superimposes a linear trend estimated over the period 2013-2018. The graph suggests that 2019 was in line with such trend.

  25. Results are very similar if, instead of using earnings in the main job, we use total earnings across different jobs (as workers may be employed by more than one employer in a given month).

  26. This difference is even more pronounced when we match fathers and mothers in a household (see Fig. A6).

  27. It is worthwhile to notice that, under some specific circumstances such as school quarantine or contagion, during 2021 parents could still apply for a special leave with an allowance equal to 50% of the salary.

  28. Note that we are only able to study differences in short-time work use on the extensive margin. However, there might be differences on the intensive margin: for example, women with children may use more hours of short-time work relative to fathers (or women without children). Unfortunately, our data does not record hours spent in short-time work, but only whether a worker ever uses short-time work in any given month.

  29. Depalo and Viviano (2022) show that workers, especially older ones, quit their jobs moved by fear of contagion, especially in essential (continuing) activities. Fear-induced quits by gender were larger in magnitude for men than for women.

  30. We do not consider the probability of being fired as the Italian government in March 2020 introduced a ban on the dismissal of employees for economic reasons.

  31. For employment to non-employment transitions, we define a dummy variable taking the value of one when the worker is observed in our dataset in any month \(m\) and is not observed in month \(m+1\).

  32. The figure again displays how the baseline reduction in days worked (related for example to ordinary leave taking or ending of fixed-term contracts that are not conducive to a move to non-employment) is larger for women than for men during the first wave (March-April 2020) and, to a lesser extent, during the second (November–December 2020) and third (March-April 2021) waves.

  33. E-NE transitions between April and June are mostly driven by endings of fixed-term contracts and retirements; those between October and November are driven by quits as shown in Fig. 1, panel e.

  34. Seasonally adjusted participation rates, age group 15-64. Istat, Quarterly Labor Force Survey.

  35. With intensive margin we mean here days worked. One additional margin would be hours within days, which we cannot measure as the social security data do not record hours worked.

  36. Our measure of non-employment refers to the non-agricultural private sector only. Hence, we cannot exclude that workers non-employed in a given year-month pair are self-employed or employed in the public sector, although these transitions in a period of shutdown of economic activities and general economic downturn are quite unlikely to happen.

  37. In other terms, we report \({\widetilde{\beta }}_{t}^{m}=\frac{{\beta }_{t}^{m}}{E[{\widetilde{y}}_{{itm}}]},\) where \({\beta }_{t}^{m}\) are the coefficients estimated from Eq. (1) for \(t=\{2020,\,2021\}\) and \({\widetilde{y}}_{{itm}}\) is the predicted outcome in 2019, when omitting the contribution of the year dummies for 2020 and 2021 dummies, i.e., \({\widetilde{y}}_{{itm}}={\hat{\alpha }}^{m}+{\hat{\delta }}^{m}{X}_{{itm}}+{\hat{\eta }}_{J\left(i,t\right)}^{m}\).

  38. The arbitrary transformation of the dependent variable by adding one, in order to use log values, is not ideal for a number of reasons. First, coefficients do not have a percent interpretation comparable to that of the log transformation. Second, the transformation introduces non-linearity, which could cause negative weighting problems (see, e.g., Cohn et al, 2022). As they serve the only purpose of providing a robustness check, we still report estimates with the log of one plus outcomes.

  39. The relatively larger drop in log earnings during the first wave in 2020 is again consistent with the decline in participation rates in the first half of 2020. The contribution of the latter to non-employment spells is, however, short-lived, as by the end of 2021 participation rates fully recovered the pre-pandemic levels.

  40. We use daily wages instead of monthly earnings to divide households, in order not to categorize data based on the outcome (monthly earnings, in this case). Also, note that in this case we make a further sample restriction by requesting that both household members are observed at least once in 2019. Therefore, there could be some differences between the disaggregated patterns shown in Fig. 3 and the aggregate ones in Figure A6.

  41. These results are not driven by low-wage mothers, who may stay at home in the presence of negative shocks irrespective of considerations on household-level bargaining. To verify this, we did replicate the analyses excluding mothers in the first decile of the 2019 daily wage distribution, obtaining very similar conclusions to those reported in the text.

  42. Hence, the points shown in the Figure can be interpreted as difference-in-differences, the first difference being that between mothers and fathers, and the second one that between each month and January 2020.

  43. We distinguish workers in subgroups based on whether they have a part-time or full-time contract in February of each year, in order to avoid dividing workers on a potentially endogenous variable.

  44. Results are available upon request.

  45. For example, part-time fathers are more likely be on temporary contracts than the average father (14 vs. 6%). This difference is larger than the one observed for mothers (9 vs. 8%). Part-time fathers are also more likely to be migrants (15 vs. 9%; for mothers, 9 vs. 10%), have one-year lower labor market experience (no such difference is observed for mothers) and are disproportionately employed in services (23 vs. 14%; for mothers, 25 vs. 23%).

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Acknowledgements

We would like to express our gratitude to the editor and two anonymous referess, Fabrizio Balassone, Tito Boeri, Giulia Bovini, Francesca Carta, Federico Cingano, Francesco D’Amuri, Luigi Federico Signorini, Paolo Pinotti, Eliana Viviano, Fabrizio Zilibotti, and participants to the XXIII FRDB European Conference for useful comments and discussion. The views expressed in this paper are those of the authors only and should not be attributed to the Bank of Italy nor to the Eurosystem. Contacts: De Paola, m.depaola@unical.it; Lattanzio, salvatore.lattanzio@bancaditalia.it.

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These authors contributed equally: Maria De Paola, Salvatore Lattanzio

Appendix – Additional Figures

Appendix – Additional Figures

Figures 611

Fig. 6
figure 6

Employees, Quarterly Labor Force Survey, 2013–2021. Notes. The figure reports the number of male and female employees from the quarterly Labor Force Survey between 2013Q1 and 2021Q4. The dashed lines are linear trends estimated over the period 2013Q1-2018Q4

Fig. 7
figure 7

Dynamic estimates of the impact of the pandemic on labor market outcomes of mothers and fathers – log daily wages. Notes. The figure reports the estimated coefficient \({\beta }_{0}^{m}\) and \({\beta }_{1}^{m}\) (the difference in log daily wages for workers in 2020 and 2021 relative to 2019 in each month) from Eq. (1) for mothers and fathers. Control variables include: labor market experience, age, dummy for white-collar workers, the number of children, dummies for workers taking the parental leave and COVID-19 leave, dummy for workers in short-time work compensation schemes, sector and region dummies

Fig. 8
figure 8

Decomposition of the reduction in days worked. Notes. The figure reports the marginal effects for mothers in panel a and fathers in panel b from an augmented Eq. (1) in which year dummies are interacted with dummies for being on short-time work, taking parental leave or moving to non-employment, for mothers. “Baseline” reports the coefficients on the year dummies, representing the baseline change in days worked in 2020 or 2021 relative to 2019 for a worker that does not: work short-time hours, take parental leave, move to non-employment. “Short-time work”, “Parental leave”, “E-NE” are the sum of the baseline effect plus the one for workers reducing their labor supply because of one of these reasons

Fig. 9
figure 9

Dynamic estimates of the impact of the pandemic on labor market outcomes of mothers and fathers, sample including non-employment spells. Notes. Each panel reports the estimated coefficient \({\beta }_{0}^{m}\) and \({\beta }_{1}^{m}\) (the difference in each outcome for workers in 2020 and 2021 relative to 2019 in each month) from Eq. (1) for mothers and fathers. The outcomes are monthly earnings in panel a and monthly days worked in panel b. The sample includes year-month observations when a worker is not employed, imputing a value of zero in the outcome variable. The coefficients are rescaled by the average predicted outcome excluding the contribution of year dummies for 2020 and 2021. Control variables include: labor market experience, age, dummy for white-collar workers, the number of children, dummies for workers taking the parental leave and COVID-19 leave, dummy for workers in short-time work compensation schemes, sector and region dummies

Fig. 10
figure 10

Dynamic estimates of the impact of the pandemic on labor market outcomes of mothers and fathers, sample including non-employment spells. Notes. Each panel reports the estimated coefficient \({\beta }_{0}^{m}\) and \({\beta }_{1}^{m}\) (the difference in each outcome for workers in 2020 and 2021 relative to 2019 in each month) from Eq. (1) for mothers and fathers. The sample includes year-month observations when a worker is not employed, imputing a value of zero in the outcome variable. The dependent variables are the log of one plus monthly earnings in panel a and monthly days worked in panel b. Control variables include: labor market experience, age, dummy for white-collar workers, the number of children, dummies for workers taking the parental leave and COVID-19 leave, dummy for workers in short-time work compensation schemes, sector and region dummies

Fig. 11
figure 11

Dynamic estimates of the impact of the pandemic on labor market outcomes of mothers and fathers, household matched data. Notes. Each panel reports the estimated coefficient \({\beta }_{0}^{m}\) and \({\beta }_{1}^{m}\) (the difference in each outcome for workers in 2020 and 2021 relative to 2019 in each month) from Eq. (1) for mothers and fathers in the sample with household information. The outcomes are: log monthly earnings in panel a; log monthly days worked in panel b; and binary indicators for workers taking parental leave in panel c, being on short-time work compensation schemes in panel d, and quitting their job in panel e, in any given month. Control variables include: labor market experience, age, dummy for white-collar workers, the number of children, dummies for workers taking the parental leave and COVID-19 leave (except panel c), dummy for workers in short-time work compensation schemes (except in panel d), sector and region dummies

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De Paola, M., Lattanzio, S. Parental labor market penalties during two years of COVID-19. Rev Econ Household 23, 327–355 (2025). https://doi.org/10.1007/s11150-024-09728-3

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  • Issue Date:

  • DOI: https://doi.org/10.1007/s11150-024-09728-3

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