metadata: authors: 0: author: Lo Bue,Maria C.
1: author: Perova,Elizaveta
2: author: Reynolds,Sarah
count: World,East Asia and Pacific
docna: 0: docna: Maternal Work and Children’s Development :
            Examining 20 Years of Evidence
docty: Policy Research Working Paper
theme: Gender
majtheme: Social dev/gender/inclusion
abstracts: cdata!: Maternal work may affect children
            positively through increased household income, higher
            control of mothers over available income, and expansion of
            maternal information networks through work contacts and
            greater decision-making power of mothers as they become more
            economically empowered. However, maternal work may reduce
            maternal time spent with children. If maternal time is not
            substituted for time of equal quality by other caregivers,
            children&#x2019;s development may be penalized. Stress
            associated with work may also decrease the quality of
            parenting. This review summarizes causal evidence on the
            relationship between maternal work and children&#x2019;s
            development. The majority causal studies find positive or 0
            impacts of maternal work on children&#x2019;s development.
display_title: Maternal Work and Children’s
            Development : Examining 20 Years of Evidence
listing_relative_url: /research/2023/02/34002345/maternal-work-children’s-development-examining-20-years-evidence
topicv3: Gender and Human Development m1326908 919,Child Development m1327025 2151,Labor Force Participation m1504050 2746
publishtoextweb_dt: 2023-02-14T00:00:00Z
dois: 10.1596/1813-9450-10305
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: NA
1: author_key: 943628
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model_extractions: Policy Research Working Paper 10305 Maternal Work and Children’s Development Examining 20 Years of Evidence _Maria C. Lo Bue Elizaveta Perova Sarah Reynolds_ East Asia and the Pacific Region Office of the Chief Economist February 2023
Policy Research Working Paper 10305

# **Abstract**

Maternal work may affect children positively through increased household income, higher control of mothers over available income, and expansion of maternal information networks through work contacts and greater decision-making power of mothers as they become more economically empowered. However, maternal work may reduce maternal time spent with children. If maternal time is not substituted for time of equal quality by other caregivers, children’s development may be penalized. Stress associated with work may also decrease the quality of parenting. This review summarizes causal evidence on the relationship between maternal work and children’s development. The majority causal studies find positive or null impacts of maternal work on children’s development.

This paper is a product of the Office of the Chief Economist, East Asia and the Pacific Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at eperova@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Maternal Work and Children’s Development: Examining 20 Years of Evidence

Maria C. Lo Bue, Elizaveta Perova and Sarah Reynolds J13, J21, J16, E24 Keywords: child development, maternal labor force participation, gender equality, review of evidence
# Complex links between maternal work and children’s development

Maternal work is linked to children’s development through several channels. On the one hand, maternal work may affect children’s development positively. First, children benefit from living in households with higher income, and mothers—in comparison to fathers—spend a higher proportion of their resources on children (Lundberg 1997; Ermish and Francesconi 2013; Dunbar, Dunbar, Lewbel, and Pendakur, 2013). There are also intangible benefits of maternal work, such as mothers’ exposure to a larger social network through work contacts, greater access to information, and a potential increase in mothers’ decisionmaking power, all of which may allow for better choices around monetary and non-monetary investments in children (Currie, 2009; Doss, 2013; Lépine and Strobl, 2013).

On the other hand, maternal work may affect children’s development negatively through reduction in maternal time spent with children and, consequently, fewer opportunities for interactions and consistent engagement. Child development would be penalized by maternal work if institutional or other nonmaternal childcare is of inferior quality than maternal care and the increase in income does not compensate with nutritional and safety expenses, for example. Stress associated with work may reduce the quality of parenting (Baker, Gruber, Milligan, 2008). The combination of these factors may lead to less secure attachment with the mother - an important factor for children’s socio-emotional development and subsequent academic success (Alto and Petrenko, 2017; Allen, 2008). Maternal work may also increase the likelihood that older children take on more domestic responsibilities and spend less time on homework or enrichment activities (Afridi et al., 2016).

With both positive and negative theoretical impacts of mothers’ work on child development, it is important to examine whether up-to-date empirical evidence suggests overall positive, negative, or null impacts. Our study summarizes the evidence of maternal work on child and adolescent health and intellectual development from the past 20 years. We note strengths and limitations of the studies and highlight common threads relevant to different facets of policy design, including considerations for emerging economies.

# Broad coverage of contexts and measures

Using a variety of search terms, we retreived 1,181 studies from databases that cover medical, psychological, economic and other social science literature. We identified a total of 613 relevantly-titled articles published between 2002 and 2021, and further limited them to 312 which examined the links between maternal work and development of children ages 0 to 18 years. We further narrowed our selection to 80 papers, which utilized statistical methods to plausibly establish causal impact. Within these studies, we limited our analysis to a set of 26 articles, which used the most rigourous methods: instrumental variables, sibling fixed effects, and multiple analytic approaches (most commonly individual fixed effects and lagged dependent variables combined).

Twenty-six causal papers cover a range of contexts: 21 papers are from Western countries (Australia, Denmark, Italy, Norway, the United Kingdom, and the United States) and 5 are from non-Western: three lower-middle income (the Arab Republic of Egypt, India, and Indonesia) and one high income (Chile). Among papers on high-income countries (all Western countries and Chile), 5 focus on low-income population s (such as mothers of welfare in the United States), and 22 rely on nationally representative surveys or censuses.

2
Overall, the 26 causal studies examined 20 different measures of child development. To reduce multiple dimensions of this body of evidence, we combined them into 6 main outcomes: schooling, cognitive, behavioral and 3 types of health measures. Schooling outcomes included years of schooling, current enrollment, grade attaintment and high school grades. Cognitive outcomes included test scores from a range of psycometrically validated exams. Behavioral outcomes are largely comprised of self-reported risky beahviors for adolescents and parent-reported standardized indexes for behavioral problems for younger children. We grouped health outcomes into three categories: (1) height outcomes, which capture long-term nutrition, (2) weight outcomes, which consider short-term nutrition and physical activity,<sup>1</sup> and (3) other health measures, most of which were indicators of medical concerns such as lung capacity, hemoglobin levels, asthma episodes, ear infections and overnight hospitalization.

Several studies examined more than one group of outcomes. To facilitate the discussion of results, we refer to separate groups of outcomes within a study as a substudy. A study can have one substudy or multilple substudies. Twenty-six causal studies yielded 40 substudies with evidence on 6 outcomes (height, weight, health, schooling, behavior and cognition).

For each substudy, we categorized whether it detected null, positive or negative impacts of maternal work on child development for each group of outcomes it examined. If at least one outcome in a group is negative/positive in a statistically significant way, while impacts on other outcomes in that group are not statistically significant, we count it as evidence of negative/positive impact for the substudy.<sup>2</sup> If there was no statistically significant impact on any outcomes in the group, we count it as evidence of null effect. There were no papers which detected a statistically significant positive impact on one outcome in a group, and negative on a different one.

# Main findings: Impacts on children’s development

The most abundant evidence is on weight-related outcomes, followed by cognitive and behavioral outcomes (12, 8 and 7 substudies, respectively). The evidence is more scant on schooling, health and height outcomes, with 5, 4 and 4 substudies, respectively.

The majority of causal evidence from 10 countries over the last 20 years suggests that maternal work does not hurt children’s development. Of the substudies, 68% (27 out of 40) do not find evidence of negative impact, with 6 finding positive, and 21 null effects (Figure 1).

An interesting feature of the studies reviewed is that many of them use a wide range of outcomes, and only 2 corrected for multiple hypotheses testing, or adjusted for the probability of discovering a non-null effect purely by chance. While common practice in medicine, correction for multiple hypotheses testing started making forays into social science only recently.<sup>3</sup> Thus the 20-year span of our analysis incorporates many papers that were not yet submitted to this additional check.

> 1 Weight outcomes include deviations from the norm in both directions: underweight as well as obesity.

> 2 For example, the s t udy in Indonesia examines 2 indicators for health: hemoglogin and lung capaicty. The authors find statistically signficant impacts on hemoglobin, but not on lung capacity. We categorize this result as evidence of positive impact on health.

> 3 Some of the earliest papers that raised the question of correcting for multiple hypotheses testing and demonstrating sensitivity of results include Anderson (2008), List, Shaikh and Xu (2019), Kling, Liebman and Katz (2007).

3
Using the values of coefficients and standard errors in the papers, we have carried out two types of checks for multiple hypotheses testing, using both Bonferroni correction and the less conservative BenjaminiHochberg (1995) correction for False Discovery Rate. Application of these corrections reduces the number of substudies with negative results to 9,<sup>4</sup> with 78% of the substudies yielding non-negative results (Figure 2).

Understanding when maternal work may have a negative impact If one of the objectives of the policy maker is to do no harm, it is important to focus on the negative substudies and understand whether there are specific features of maternal work that may have negative impact on children. A closer examination of papers with negative outcomes reveals several trends.

First, the environment in which children of working mothers are while mothers are working matters. Both Rashad and Sharaf (2019) and Shajan and Subbyamoola (2020) find that maternal work increases the likelihood that children are stunted in Egypt and India, respectively. These two countries have low presence of early childhood education facilities, compared to other countries analyzed in the papers reviewed. Thus, the children of working mothers are likely to be with them in the field or in the market, rather than in center-based care. Indeed, Shajan and Subbyamoola (2020) point out that their results are driven by children of women employed in agricultural and manual, rather than professional jobs. Gennetian et al. (2010) examine the impacts of maternal work for low-income populations in the United States, focusing on women eligible for the public welfare program. The earnings from the public welfare program may not be sufficient to cover the costs of high-quality childcare.

Second, specific aspects of maternal work matter. Felfe and Hsin (2012) examine the impacts of characteristics of maternal work, comparing working mothers exposed to more or less hazardous environments. Thus, their study suggests the negative impact of work-related stress and hazards, rather than maternal work per se. James-Burdumy (2005) finds that maternal work results in negative cognitive impacts at age 9 only during the first year of life. However, work during the second and third years of life does not have this effect, suggesting that children's vulnerability or sensitivity to mothers' working is in very early childhood. Additionally, the cognitive impact is not all-around: only math scores were impacted, while reading and vocabulary scores had positive, albeit not significant association with maternal work.

Third, in developed countries, when viable childcare options are present, the impacts are generally low. James-Burdumy (2005) notes that the size of the "loss" to mathematics skill is relatively small at a 0.03 effect size. Furre Haaland, Rege and Votruba (2013) find that 5 additional years of full-time employment reduce a child’s education by 0.065 year (or 4 percent of the standard deviation in their sample).

Lastly, one study pertains to a relatively narrow context, which allows for a potentially different interpretation of outcomes. Morrill (2011) focuses on the impact on hospitalizations in the United States. Notably, her analysis does not control for whether the mother has insurance. In the United States, the availability of insurance may be an important factor in the decision whether to take the child to the doctor or not, conditional on the same level of severity of incident. Mothers who work are more likely to have insurance. Thus, the paper may be capturing a different treatment for the same level of severity of

> 4 The sub-studies with non-null results which did not withstand correction for multiple hypotheses testing found negative behavioral impact (Felfe and Hsin, 2012 and Aughinbaugh and Gittleman, 2003); negative impact on being underweight (Rashad and Sharaf, 2012), and increase in the likelihood of obesity (Von Hinke Kessler Scholder, 2008).

4
<!-- Start of picture text --> Maternal Work & Child Development: Number of Findings by Outcome<br>Behavioral Cognitive Schooling Health Weight Height<br>9-<br>8-<br>7-<br>o 6- gs<br>&Ts= S- ||findingNegative<br>bet 4- | Positive<br>8 i<br>3-<br>2-<br>neg null:pos neg nullipos neg nullipos neg null+pos neg nullipos neg null+pos<br>40 findings from 26 papers<br>Source: Authors' systematic review of papers 2002-2022 without corrections for multiple hypotheses testing<br>Weight category includes BMI measures<br><!-- End of picture text -->
<!-- Start of picture text --> Maternal Work & Child Development: Number of Findings by Outcome<br>Behavioral Cognitive Schooling Health Weight Height<br>10-<br>9-<br>8-<br>Fa23o 7-6- findingLJ Negative<br>33E5 4.5- ywPositive<br>1 a<br>i Le a<br>neg' null+pos' neg' null+pos' neg' null+pos' neg' null+pos' neg1 null+pos' neg' null+pos'<br>40 findings from 26 papers<br>Source: Authors' systematic review of papers 2002-2022<br>Weight category includes BMI measures<br><!-- End of picture text -->
# References

Afridi, F., Mukhopadhyay, A. & Sahoo, S. Female labor force participation and child education in India: evidence from the National Rural Employment Guarantee Scheme. IZA J Labor Develop 5, 7 (2016). https://doi.org/10.1186/s40175-016-0053-y Allen, Joseph P. 2008. “The Attachment System in Adolescence.” In Handbook of Attachment: Theory, Research, and Clinical Applications, 2nd Ed, 419–35. New York, NY, US: The Guilford Press. Alto, Michelle E., and Christie L. M. Petrenko. 2017. “Fostering Secure Attachment in Low- and MiddleIncome Countries: Suggestions for Evidence-Based Interventions.” Evaluation and Program Planning 60 (February): 151–65. https://doi.org/10.1016/j.evalprogplan.2016.11.003.

Baker, Michael, Jonathan Gruber, and Kevin Milligan. “Universal Child Care, Maternal Labor Supply, and Family Well‐Being.” Journal of Political Economy 116, no. 4 (August 2008): 709–45. <u>https://doi.org/10.1086/591908.</u> Currie, J. (2009). ‘Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development’. Journal of Economic Literature, 47(1): 87–122. <u>https://doi.org/10.1257/jel.47.1.87</u> Cheryl Doss, Intrahousehold Bargaining and Resource Allocation in Developing Countries, The World Bank Research Observer, Volume 28, Issue 1, February 2013, Pages 52–78, https://doi.org/10.1093/wbro/lkt001 Duflo, Esther. 2003. “Grandmothers and Granddaughters: Old Age Pension and Intra-Household Allocation in South Africa.” World Bank Economic Review 17 (1): 1–25.

Dunbar, Geoffrey R, Arthur Lewbel, and Krishna Pendakur. “Children’s Resources in Collective Households: Identification, Estimation, and an Application to Child Poverty in Malawi.” American Economic Review 103, no. 1 (February 1, 2013): 438–71. https://doi.org/10.1257/aer.103.1.438.

Haerpfer, C., Inglehart, R., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano J., M. Lagos, P. Norris, E. Ponarin & B. Puranen (eds.). 2022. World Values Survey: Round Seven - Country-Pooled Datafile Version 5.0. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat. doi:10.14281/18241.20 Kling, J., Liebman, J., and Katz, L. (2007). Experimental analysis of neighborhood effects. Econometrica, 75(1):83–119.

Lépine, A., and E. Strobl (2013). ‘The Effect of Women’s Bargaining Power on Child Nutrition in Rural Senegal’. World Development, 45: 17–30. https://doi.org/10.1016/j.worlddev.2012.12.018 John A. List & Azeem M. Shaikh & Yang Xu, 2019. "Multiple hypothesis testing in experimental economics," Experimental Economics, Springer;Economic Science Association, vol. 22(4), pages 773-793, December.

Lundberg, S. J, R. A Pollak, and T. J Wales. 1997. “Do Husbands and Wives Pool Their Resources? Evidence from the United Kingdom Child Benefit.” _Journal of Human Resources_ 32 (3): 463–80.

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# Reviewed Papers

Afridi, F., Mukhopadhyay, A., & Sahoo, S. (2016). Female labor force participation and child education in India: Evidence from the National Rural Employment Guarantee Scheme. _IZA Journal of Labor & Development_ , _5_ (1), 7. https://doi.org/10.1186/s40175-016-0053-y Agiro, B. T., & Huang, W.-C. (2020). Re-Examining the Effect of Maternal Employment on Child Overweight: The Case of School-Age Children. _Journal of Family and Economic Issues_ , _41_ (1), 140–157. <u>https://doi.org/10.1007/s10834-019-09658-8</u> Anderson, P. M., Butcher, K. F., & Levine, P. B. (2003). Maternal employment and overweight children. _Journal of Health Economics_ , _22_ (3), 477–504. https://doi.org/10.1016/S0167-6296(03)00022-5 Aughinbaugh, A., & Gittleman, M. (2004). Maternal employment and adolescent risky behavior. _Journal of Health Economics_ , _23_ (4), 815–838. https://doi.org/10.1016/j.jhealeco.2003.11.005 Baum, C. L. (2015). Does Early Maternal Employment Harm Child Development? An Analysis of the Potential Benefits of Leave Taking. _Journal of Labor Economics_ . https://doi.org/10.1086/345563 Bishop, J. (2011). The Effect of Maternal Employment on Youth Overweight in Australia. _Economic Record_ , _87_ (s1), 92–104. https://doi.org/10.1111/j.1475-4932.2011.00747.x Del Boca, D., Pasqua, S., & Suardi, S. (2016). Child Care, Maternal Employment, and Children’s School Outcomes. An Analysis of Italian Data. _European Journal of Population_ , _32_ (2), 211–229. <u>https://doi.org/10.1007/s10680-015-9370-0</u> Dervisevic, E., Perova, E., & Lo Bue, M. C. (2021). _Maternal employment and children’s outcomes: Evidence from Indonesia_ . The World Bank.

Dunifon, R., Kalil, A., Crosby, D., & Su, J. H. (2013). Mothers’ Night Work and Children’s Behavior Problems. _Developmental Psychology_ , _49_ (10), 1874–1885. https://doi.org/10.1037/a0031241 Ermisch, J., & Francesconi, M. (2013). The Effect of Parental Employment on Child Schooling. _Journal of Applied Econometrics_ , _28_ (5), 796–822. https://doi.org/10.1002/jae.2260 Felfe, C., & Hsin, A. (2012). Maternal work conditions and child development. _Economics of Education Review_ , _31_ (6), 1037–1057. <u>https://doi.org/10.1016/j.econedurev.2012.06.008</u> Gennetian, L. A., Hill, H. D., London, A. S., & Lopoo, L. M. (2010). Maternal employment and the health of low-income young children. _Journal of Health Economics_ , _29_ (3), 353–363. <u>https://doi.org/10.1016/j.jhealeco.2010.02.007</u> Greve, J. (2011). New results on the effect of maternal work hours on children’s overweight status: Does the quality of child care matter? _Labour Economics_ , _18_ (5), 579–590. <u>https://doi.org/10.1016/j.labeco.2011.03.003</u> 8
Haaland, V. F., Rege, M., & Votruba, M. (2013). _Nobody Home: The Effect of Maternal Labor Force Participation on Long-Term Child Outcomes_ (SSRN Scholarly Paper ID 2363912). Social Science Research Network. https://doi.org/10.2139/ssrn.2363912 Hubbard, M. N. (2009). _Effects of maternal employment and child care on the health of young children_ [Ph.D., The University of North Carolina at Chapel Hill]. <u>https://www.proquest.com/docview/304960202/abstract/4C8AF9FF2D534249PQ/1</u> James‐Burdumy, S. (2005). The Effect of Maternal Labor Force Participation on Child Development. _Journal of Labor Economics_ , _23_ (1), 177–211. https://doi.org/10.1086/425437 Mendolia, S. (2016). Maternal Working Hours and the Well-Being of Adolescent Children: Evidence from British Data. _Journal of Family and Economic Issues_ , _37_ (4), 566–580. https://doi.org/10.1007/s10834- <u>015-9480-1</u> Morrill, M. S. (2011). The effects of maternal employment on the health of school-age children. _Journal of Health Economics_ , _30_ (2), 240–257. https://doi.org/10.1016/j.jhealeco.2011.01.001 Pekkurnaz, D. (2014). _Dynamic analysis of maternal employment, child care, quality and early childhood development_ [Ph.D., The University of North Carolina at Chapel Hill]. <u>https://www.proquest.com/docview/1613182648/abstract/D6F7129E26342F5PQ/1</u> Pilkauskas, N. V., Brooks-Gunn, J., & Waldfogel, J. (2018). Maternal Employment Stability in Early Childhood: Links with Child Behavior and Cognitive Skills. _Developmental Psychology_ , _54_ (3), 410–427. <u>https://doi.org/10.1037/dev0000438</u> Rashad, A. S., & Sharaf, M. F. (2019). Does maternal employment affect child nutrition status? New evidence from Egypt. _Oxford Development Studies_ , _47_ (1), 48–62. <u>https://doi.org/10.1080/13600818.2018.1497589</u> Reynolds, S. A., Fernald, L. C. H., & Behrman, J. R. (2017). Mothers’ labor market choices and child development outcomes in Chile. _Social Science and Medicine - Population Health_ , _3_ , 756–766. <u>https://doi.org/10.1016/j.ssmph.2017.08.010</u> Shajan, J., & Sumalatha, B. S. (2020). Maternal employment and children’s health in India: An exploratory analysis. _Journal of Public Affairs_ , _n/a_ (n/a), e2580. https://doi.org/10.1002/pa.2580 von Hinke Kessler Scholder, S. (2008). Maternal employment and overweight children: Does timing matter? _Health Economics_ , _17_ (8), 889–906. https://doi.org/10.1002/hec.1357 Waldfogel, J., Han, W.-J., & Brooks-Gunn, J. (2002). The effects of early maternal employment on child cognitive development. _Demography (Pre-2011)_ , _39_ (2), 369–392.

Ziol-Guest, K. M., Dunifon, R. E., & Kalil, A. (2013). Parental employment and children’s body weight: Mothers, others, and mechanisms. _Social Science & Medicine_ , _95_ , 52–59. <u>https://doi.org/10.1016/j.socscimed.2012.09.004</u> 9
### Supplementary Materials for **Maternal Work and Children’s Development: Examining 20 Years of Evidence** By Maria C. Lo Bue, Elisaveta Perova and Sarah Reynolds

# 1. Methods

## 1.2 Search

To identify previous studies, we first listed search terms determined from an informal scoping review: we considered the main types of outcomes researched and used the authors’ knowledge of common realms of outcomes in the child development. (All authors had published on this topic previously.) The following three sets of search terms emerged: “Maternal” OR “Mother”; “Work” OR “employment” OR “labor”; and “child development” OR “health” OR “nutrition” OR “obesity” OR “stunting” OR “wasting” OR “underweight” OR “overweight” OR “behavior” OR “socio-emotional” OR “cognition” OR “schooling” OR “education” OR “illness” OR “asthma accident”.

In October 2021 a research assistant used the 104 search term permutations to collect citations from EconLit and Web of Science.<sup>5</sup> From this list, we selected the top three permutations with the most results and searched them in PubMed. This search did not provide additional documents to include in the review, so we did not continue with the full search in PubMed. We did find some additional citations using this approach with PsychInfo, so we also repeated the citation search using this database. We compared the citations from our search to the citations included in the systematic review by Lucas-Thompson, Goldberg, and Prause (2010). As a result, we added the additional search terms “outcomes” OR “achievement” OR “adolescent” OR “adolescence,” which increased our total to 1182 records. Duplicates were removed, reducing our total to 614 records.

Titles were screened for topic relevance by a research assistant and one author of this review. We excluded studies focusing on samples of children with disabilities from birth, premature children, or teen mothers. We excluded outcomes on usage of health services (i.e., vaccinations, nutrition related behaviors, doctor visits). Similarly, we excluded outcomes that are considered mediators leading to the final outcomes in our list (e.g., dietary intake, sedentary behaviors, sleep, work-family conflict). We excluded employment relating to migration or sex work. This screening provided us with 313 records.

For the final decision regarding inclusion, the abstracts and/or methods sections of papers were examined by an author of this review, with the other authors consulted regarding the empirical quality of the studies. We limited the studies to those published between 2002 and 2021. Studies were included if they had quantitative analysis, a measure of maternal work as a causal variable in the paper, at least one child or adolescent development outcome (cognition, schooling, behavior, anthropometrics, and health). Moreover, we restrict the analysis to studies analyzing natural or quasi-natural experiments and using the following methodologies: individual fixed or random effects, lagged dependent variables,<sup>6</sup> sibling fixed

> 5 The search was restricted to the titles of articles, since when the entire content of the articles was searched, it returned tens of thousands of papers. Several keyword searches (in contrast to title searches) of the permutations that yielded the most results did not provide additional papers for the review, so we did not pursue a keyword search.

6 Even though many papers analyzed longitudinal data on maternal work, we excluded studies that only controlled for observables. One exception is the lagged dependent variable approach, which controls for the baseline value of the outcome variable. Although the lagged outcome variable is an observable, including it accounts for unobserved maternal or child characteristics that affected the earlier outcome, which reduces selection bias due to unmeasured child and family characteristics. However, if the study had controls for child development measures at baseline and these were from the same realm as the outcome variable, even if not exactly the same measure, we 10
<!-- Start of picture text --> 1182 studies imported<br>= 614 titlesNyscreened ——_— 568 duplicates removed<br>© Ny lS 301 titles deemed irrelevant<br>~ 313 abstracts read<br>— > 232 studies excluded<br>Q 12925 Cross-sectional,Not accessible viacorrelationalinternet analysis<br>21 Not studies on maternal work<br><x 24 ~=Not empirical — e.g. review paper<br>9 Not child development outcomes<br>> 17 _ Duplicate or earlier versions<br>Y 22 SampleNot in Englishsize too small for study<br>— 1 Aggregate macro study<br>ac 81 methods assessed 11 NotProblematic rigorous data on child outcomes<br>ny, _——_ 55 studies excluded<br>26 causal studies<br><!-- End of picture text -->
instrument and maternal work: as this assumption is testable, we only include papers with strong first stage in the review.<sup>8</sup> The exclusion restriction – or assumption that the instrument only affects children’s development through its impact on maternal employment only – is impossible to test. The range of papers that we use exploits a variety of instruments, from local unemployment rates to eligibility of the youngest child for kindergarten to changes in tariffs for female-dominated industries to child’s health at birth. Exclusion restriction is more likely to be satisfied in some cases than in other. For example, children’s birth weight is likely to affect children’s development outcomes directly, in addition to maternal employment decisions. Several papers construct instrumental variables based on economic variables at the location of residence, such as local unemployment rates, female labor force participation, per capita governmental transfers. Variables reflecting local economic conditions may also affect children’s development directly–for example, through higher quality educational establishments and medical services. Although we note that plausibility that exclusion restriction holds varies in papers we reviewed, we did not exclude any based on these criteria, as it is not testable, and exclusion would rely on our subjective judgement.

# Fixed effects

Fixed effects strategies identify causal impacts through controlling for unobserved but fixed omitted variables. This approach relies on two core identifying assumptions. The first assumption is that conditional on a set of fixed time-invariant characteristics, maternal decision to work is as good as randomly assigned. The second assumption is that the effect of maternal work is additive and constant.

There are two variations of this identification strategy in the papers which we reviewed: a subset of papers uses maternal (or family) fixed effects, another set uses child fixed effects. In the former case, identification relies on comparing development outcomes of at least two children born to the same mother, when her work status varies by child. To identify causal impacts, we need to assume that unobservable differences between children do not affect maternal decision to work. In the latter case, researchers estimate impacts from comparison of outcomes of the same child overtime, with mother working in some periods and not working in other. Identification of causal impacts requires assuming that maternal decision to work does not respond to time-variant changes in child’s development.

Assumptions in both cases can be easily violated: for example, mothers may choose to stay at home when their less academically able children are at school to help them with homework. Similarly, in the case of child fixed effects, mothers may adjust their work in response to children’s development: for example, they may withdraw from the labor market in critical years, or in response to illness. Of course, the likelihood that the identifying assumptions are violated depends on the set of controls included in the estimating regression: for instance, it is more plausible that maternal decision to work in maternal fixed effects framework is as good as randomly assigned when conditional on her children’s ability.

Lastly, fixed effects estimation is susceptible to attenuation bias from measurement error. Instrumenting independent endogenous variable can reduce the measurement error, and indeed several papers in our review rely on a combination of fixed effects and instrumental variables.

Several papers use random effects approach, which also relies on identifying causal impacts by purging the influence of time-invariant variables, but, unlike fixed effects, allows residuals for a given child to be correlated across periods.

> 8 All selected papers either presented the first stage or stated that it was strong.

12
### Lagged dependent variables

Lagged dependent variables also exploit variation in outcomes of the same child observed over time. However, identifying assumption is different: to establish causal relationship between maternal work and child’s development in the lagged dependent variables framework, one needs to assume that maternal decision to work is as good as random, conditional on child’s past development outcomes (not unobserved time-invariant characteristics). This assumption is likely to be violated if maternal decisions to work also respond to unobserved environmental variables, which may also affect children’s outcomes such as employment of other household members.

Notably, fixed effects and lagged dependent variables can be thought of as bounding the causal effect of interest; and several studies included in this review include both FE and LDV. Consequently, we included studies that rely on lagged dependent variables when combined with child FE.

# 2. Samples

Our review covers 10 countries: 7 high income (Australia, Chile, Denmark, Italy, Norway, UK and US), and 3 lower-middle income (the Arab Republic of Egypt, India and Indonesia). Only four countries are nonWestern: Chile, Egypt, India and Indonesia. Table A1 presents the distribution of the number of papers by country and method.

#### <mark>Table A1 <title>?></mark>

|Country|Number of<br>||Number ofpape<br>|rs bymethod<br>||
|---|---|---|---|---|---|
||studies|IV|Child FE & LDV|Mother FE|Mixed methods|
|Australia|1||||1|
|Chile|1||||1|
|Denmark|1|1||||
|Egypt,Arab Rep.|1|1||||
|India|2|1|||1|
|Indonesia|1|1||||
|Italy|1|1||||
|Norway|1|||1||
|United Kingdom|3||1|1|1|
|United States|14|4|4|3|3|
|Total|26|9|5|5|7|

The papers rely on a wide range of data, including survey and administrative data. A significant share of surveys are nationally representative, some focus on low-income populations. Some studies focus on a specific subsample – due to methodological demands (e.g., children with younger siblings only). <mark>Table AX <table 1?></mark> presents the details for each study.

# 3. Scope of the measures

## Maternal employment

There was a broad spectrum of how maternal employment was operationalized. 6 studies used exclusively a binary variable indicating whether the mother is employed and 3 studies differentiate between parttime and full-time employment with full-time usually defined as more than 35 hours per week ~~.~~ 13
Work intensity was considered in 13 studies and it was generally measured by continuous variables indicating weekly working hours<sup>9</sup> or employment duration, defined as the number of years, months, weeks or quarters in which the mother worked. In one instance (Reynolds et al. 2017) employment duration was measured as the number of weekly hours worked between the two surveys as a fraction of weekly working hours under full-time employment.

Many studies considered multiple employment variables simultaneously. For example, being employed and work intensity. Thus, the interpretation holds constant intensity while considering when the work occurred.

Finally, in 2 studies additional qualitative aspects of maternal employment were considered while controlling for working hours. Felfe and Hsin (2012) analyzed the impact of mothers’ exposure to workrelated hazards and stress on child cognitive and behavioral outcomes. Dunifon et al. (2013) considered standard in contrast to non-standard work schedules. These were measured as dummy variables, and could include night work, weekend work, or hours that changed weekly.

# Child development outcomes

The studies reviewed cover a wide range of outcomes. To better organize the results, we grouped them in 6 categories: behavioral, cognitive, schooling, health, height and weight.

Behavioral outcomes are the most subjective as they are most often parent-reported often using a set of questions provided in the Child Behavior Checklist (CBCL), a validated checklist consisting of 99 items assessing a range of problem behaviors relating to socio-emotional well-being. In one study (Mendolia, 2016) different behavioral outcomes such as self-reporting on smoking, low self-esteem, high life satisfaction and intention to leave education were examined. Overall, 8 studies reported behavioral outcomes.

Cognitive outcomes, reported in 8 studies, vary as different tests for different realms (reading, mathematics, and receptive vocabulary, for example) are used. These were typically applied by a trained psychologist. The scores are generally not comparable across tests or children’s ages (older children are expected to have higher scores than younger children), so results are standardized; sometimes standardization is done by child age and other times child age is a control variable.

Schooling outcomes were examined in 5 studies. The type of measures chosen differed according to whether children are observed during school age or not. In the first case, indicators of enrolment and being on track are used together with continuous variables such as the years of schooling (Dervisevic et al. 2022) or the time spent at school (Afridi et al., 2016). In the latter case, school achievement was measured by respondents’ final grades at high school (Del Boca, 2016), achievement (yes/no) of advanced level qualification for admission in universities (Ermish and Francesconi, 2013) or by the years of education at age 27 (Haaland, Rege, Votruba, 2013).

Four studies included additional health outcomes. These cover a broad range from hospitalizations, injury and asthma episodes (Morrill, 2011) to parent reported perception of general health (Gennetian et al. 2010; Pekkurnaz, 2014) to more specific indicators such as levels of hemoglobin or lung capacity

> 9 This was defined in most papers as average hours per week. Alternative definitions are present in two articles: Pekkurnaz, 2014 used the number of hours worked in the past week ̧ Dunifon et al. 2013 recoded the number of hours worked into <mark>five <four?></mark> dummies (1-19; 20-34; 35-44; 45+).

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(Dervisevic et al. 2022). The breadth of sources from administrative data to surveys make this category of outcomes the most disparate.

Height outcomes include height for age z-scores (HAZ) and a binary variable for stunting, defined as low height-for-age, which is a proxy for chronic or recurrent malnutrition. Height is usually a direct observation, measured by a trained interviewer. Height measures cumulative nutrition and is particularly valuable prior to age 5, when growth distributions in healthy populations have been found to be similar worldwide (WHO, 2006). Three studies, all of them from non-Western lower middle-income countries use height outcomes (Rashad and Sharif, 2019; Shajan and Sumalatha, 2020; Dervisevic et al., 2022).

Weight outcomes are based on weight for age z-scores (WAZ) or body mass index (BMI), and – depending on the context – capture deviation from normal towards malnutrition or obesity.

Seven studies (Von Hinke Kessler Scholder 2008; Hubbard 2009; Anderson et al 2003; Bishop 2011; Pekkurnaz 2014; Greve 2011; Agiro and Huang 2020) considered binary indicators for obesity and overweight, which in most cases where constructed based on information on BMI. Two studies (Haaland, Rege, Votruba 201 and Ziol-Guest, Dunifon & Kalil 2013) use BMI. Three studies included other weight measures such as WAZ (Shajan and Sumalatha 2020) or an indicator of “underweight” (Dervisevic et al., 2022; Rashad and Sharif, 2019). Unlike height, weight fluctuates over time and therefore reflects current and acute as well as chronic malnutrition. The studies had a trained professional take this data.

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# 4. Correcting for multiple hypotheses testing

**Table 1: Bonferroni p-values and FDR q-values for papers with multiple outcomes and without multiple hypotheses corrections**

|**Paper**|Outcome|Coefficient|Standard<br>error|Naïve p-value|Bonferroni<br>p-value|FDR q-value|
|---|---|---|---|---|---|---|
||Overnight hospitalization|0.037|0.013|0.005|0.043|0.035|
||Overnight hospitalization|0.045|0.017|0.009|0.081|0.035|
||Overnight hospitalization|0.092|0.038|0.015|0.133|0.035|
||Injury/poisoning|0.056|0.028|0.047|0.423|0.081|
|**Morill (2011)**|Injury/poisoning|0.050|0.030|0.099|0.890|0.111|
||Injury/poisoning|0.074|0.060|0.220|1.000|0.220|
||Asthma episode|0.116|0.060|0.054|0.484|0.081|
||Asthma episode|0.143|0.059|0.016|0.141|0.035|
||Asthma episode|0.192|0.110|0.081|0.733|0.105|
|**Rashad & Sharaf (2019)**|Stunted|0.186|0.088|0.034|0.069|0.069|
||Underweight|0.073|0.046|0.113|0.225|0.113|
|**Shajan and Sumalatha**|Height for age|-0.208|0.066|0.002|0.003|0.002|
|**(2020)**|Weight for age|-0.173|0.054|0.001|0.003|0.002|
||Letter word score|-0.055|0.024|0.022|0.066|0.059|
|**Felfe & Hsin (2012)**|Passage completion|-0.049|0.026|0.059|0.178|0.059|
||Appliedproblem solving|-0.048|0.025|0.055|0.165|0.059|
|**Felfe & Hsin (2012)**|Internal behavioral<br>problems|0.048|0.025|0.055|0.110|0.110|
||External behavioral<br>problems|0.007|0.024|0.771|1.000|0.771|
|**Von Hinke Kessler (2008)**|Pre-school|0.004||Above 0.1|Above 0.3|Above 0.1|

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||Age 7<br>Age 11|0.038<br>0.019||Above 0.01 and<br>below 0.05.<br>Above 0.1|Above 0.03<br>and below<br>0.15.<br>Above 0.3|Above 0.03 and<br>below 0.15.<br>Above 0.1|
|---|---|---|---|---|---|---|
||Years of education|0.013|0.005|0.012|0.050|0.012|
|**Furre Haaland (2013)**|Completed high school|0.003|0.001|0.003|0.011|0.004|
||Attended colleage|0.004|0.001|0.001|0.003|0.002|
||Loginc 29|0.005|0.002|0.001|0.003|0.002|
||Time at school|6.506|1.102|0.000|0.000|0.000|
|**Afridi et al. (2016)**|Enrollment|0.472|0.138|0.001|0.002|0.001|
||Gradeprogression|0.406|0.101|0.000|0.000|0.000|
||PIAT reading, year 1|-0.00069|0.00064|0.281|1.000|0.422|
|**J-Bd 2005**|PIAT reading, year 2|0.00064|0.00059|0.278|1.000|0.422|
|**amesurmuy, ;**<br>**weeks worked**|PIAT reading, year 3|0.00039|0.00047|0.407|1.000|0.488|
||PIAT math, year 1|-0.00117|0.00067|0.081|0.485|0.422|
||PIAT math, year 2|0.00034|0.00061|0.577|1.000|0.577|
||PIAT math, year 3|0.0006|0.00051|0.239|1.000|0.422|
||PPVT, year 1|0.03651|0.5222|0.944|1.000|0.944|
||PPVT, year 2|-0.05182|0.05137|0.313|1.000|0.580|
||PPVT, year 3|-0.00949|0.0469|0.840|1.000|0.944|
|**James-Burmudy, 2005;**|PIAT reading, year 1|-0.0526|0.02506|0.036|0.322|0.129|
|**hours worked**|PIAT reading, year 2|-0.02109|0.02131|0.322|1.000|0.580|
||PIAT reading, year 3|0.01945|0.0864|0.822|1.000|0.944|
||PIAT math, year 1|-0.07385|0.02595|0.004|0.040|0.036|
||PIAT math, year 2|0.01512|0.02275|0.506|1.000|0.759|
||PIAT math, year 3|0.04108|0.02026|0.043|0.383|0.129|
|**Aughinbaugh and**|Any risky behavior|1.018|0.546|0.062|0.685|0.294|
|**Gittelman (2003)**|Drank alcohol|0.629|0.246|0.011|0.116|0.116|
||Smoked cigarettes|0.184|0.269|0.494|1.000|0.764|

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||Used marijuana|0.315|0.257|0.220|1.000|0.485|
|---|---|---|---|---|---|---|
||Used other drugs|0.22|0.373|0.555|1.000|0.764|
||Had sexual intercourse|0.058|0.243|0.811|1.000|0.892|
||Convicted of crime|0.141|0.355|0.691|1.000|0.845|
||drank alcohol at least<br>several tiems a month|0.039|0.325|0.904|1.000|0.904|
||smoked cigarettes<br>everyday|0.438|0.315|0.164|1.000|0.452|
||used marijuana at least 1-<br>2 tiems per week|-0.511|0.489|0.296|1.000|0.543|
||used no birth control at<br>last sex|-0.544|0.311|0.080|0.883|0.294|
||Externalizing behavior|-0.004|0.003|0.182|0.730|0.243|
|**Pilkauskas et al. (2018)**|Internalizing behavior|0.001|0.002|0.617|1.000|0.617|
||PPVT|0.007|0.002|0.000|0.002|0.001|
||Woodcock-Johnson|0.008|0.002|0.000|0.000|0.000|

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