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Abstract

The authors investigate the moderating role of three dimensions of economic hardship on the relationship between maternal nonstandard work schedules (working evening, nights, or weekends) and children’s behavioral and cognitive outcomes at age five in the United Kingdom. The literature on the relationship between nonstandard work and child development in early childhood has not taken into consideration the potentially important role of families’ economic circumstances. Economic circumstances may reduce or amplify the potential consequences of maternal nonstandard work schedules for young children. Using the Millennium Cohort Study, a nationally representative birth cohort from the United Kingdom, and residualized change models, the authors test associations between children’s cognitive and behavioral outcomes at age five from contemporaneous maternal nonstandard work schedules. Mothers who worked nonstandard schedules had more economic hardship relative to mothers working standard schedules. Nonstandard work schedules were related to higher internalizing behavior scores at age five. The authors examined if observed associations were moderated by income poverty, financial stress, and material hardship, separately, and found that the interaction of nonstandard work with higher levels of financial stress at age five was related to higher internalizing behavior scores. The results highlight a potentially challenging work-family interface in the context of working nonstandard schedules and experiencing economic hardship.

Keywords

nonstandard work schedules child outcomes poverty material hardship subjective financial stress

Over recent decades, the growth of the service sector, reduced costs of labor due to technological changes and globalized labor markets, and access to global consumer markets have precipitated precarity in working conditions (Kalleberg 2009). In postindustrial economies, employers’ focus on worker productivity has led to the rise of “just-in-time” and on-call scheduling practices as well as zero-hour contracts (Kalleberg and Vallas 2018). A common element of the aforementioned working conditions is nonstandard work schedules, which is defined as regularly working during the evening, night, and weekend (Presser 2003). Research has documented that nonstandard work schedules are pervasive in developed economies, and particularly ones that are service based. For example, using population-based surveys and considering that definitions of nonstandard work and samples vary, the prevalence of such work schedules is nearly 16 percent to 20 percent in North America (Bureau of Labor Statistics 2019; Enchautegui 2013), about 36 percent of Australian workers (Australian Bureau of Statistics 2009), and ranges from 15 percent to 40 percent within the European Union (Eurostat 2021; Gracia, Han, and Li 2021; Zilanawala 2021). Such work schedules are more common among but not exclusive to low-wage and income-poor working mothers relative to their higher wage earning peers (Enchautegui, Johnson, and Gelatt 2015), raising questions about whether there could be differential impacts on children’s early development according to families’ economic well-being.

With few exceptions (Castillo et al. 2020; Gassman-Pines 2011), the extant research on the consequences of nonstandard work, which has focused almost exclusively on the United States context, suggests that the mothers’ nonstandard work schedules are generally linked to worse child behavior and cognitive outcomes (Daniel et al. 2009; Dunifon et al. 2013; Han 2008; Odom, Vernon-Feagans, and Crouter 2013). However, limited attention has been given to the links between maternal nonstandard work schedules and child outcomes outside of North America.

There is also reason to believe that the economic circumstances of a family are a salient moderating context that influences the relationship between mothers’ nonstandard work schedules and children’s outcomes; although no studies have yet scrutinized economic hardship as a moderator. Nonstandard work schedules contribute both challenges and opportunities to families’ lives. For example, nonstandard work schedules may disrupt parenting behaviors by creating stress, interrupting family routines, and compromising social and emotional resources. At the same time, these work schedules may create opportunities for parents to maximize time with their children, while also maximizing their income. It is possible that the challenges of nonstandard work schedules are more pronounced among low-income families who cannot easily buy services to support family needs than among medium- or high-income families (Li et al. 2020). Thus, it is the intersection of nonstandard work schedules and economic hardship that may be problematic for children’s outcomes, rather than nonstandard work schedules alone. Understanding the potential moderating role of economic hardship is critical to identifying appropriate avenues for intervention.

Specifically, we ask three research questions: (1) What is the economic well-being of families in which mothers work nonstandard schedules relative to families in which mothers work standard schedules? We explicitly measure economic hardship by including income poverty, material hardship, and subjective financial stress. (2) Do young children of mothers who work nonstandard work schedules in the United Kingdom have worse behavioral and cognitive outcomes than their peers whose mothers work standard schedules? We add to the very limited literature on the relationship between mothers’ nonstandard work schedules and child outcomes in contexts other than the United States. (3) Are the associations between nonstandard work schedules and child outcomes the same for children in families that do and do not experience each of the three dimensions of economic hardship? We answer these research questions using nationally representative data from the UK Millennium Cohort Study (MCS).

Mothers’ Nonstandard Work and Child Cognitive and Behavioral Outcomes

A review on the effects of nonstandard work schedules on child outcomes broadly concluded mothers’ nonstandard work, net of other correlates of such work schedules (e.g., low wages, instability), is associated with worse child behavior and cognitive outcomes for children (Li et al. 2014). Using data from the United States on economically disadvantaged families, Joshi and Bogen (2007) found that children of mothers who worked nonstandard schedules in early childhood (three to five years old) experienced worse child behavior, as measured by externalizing (e.g., conduct problems, hyperactivity) and internalizing (e.g., emotional and peer problems) behaviors. These findings are consistent with other studies that investigate nonstandard work schedules’ associations with child behavior but in more advantaged or nationally representative U.S. samples (Daniel et al. 2009; Han 2008; Rosenbaum and Morett 2009). Furthermore, working night schedules or evening schedules have been associated with worse child behavioral and lower reading and math scores (Dunifon et al. 2013; Han and Fox 2011) whereas the evidence on irregular work schedules is more mixed (Grzywacz et al. 2016; Walther and Pilarz 2024).

A minority of studies provide a counterpoint to the otherwise consistent findings of negative associations. For example, Leibbrand (2018) found that mothers’ nonstandard work schedules were related to fewer behavioral problems among younger children and others have found no significant relationship between maternal nonstandard work schedules and children’s behavioral problems in early childhood (Gassman-Pines 2011; Han 2005). Unlike child behavior, prior evidence investigating the relationship between nonstandard work and child cognitive outcomes is more limited, particularly when focused on young children. Two U.S. studies using nonrepresentative data revealed that maternal nonstandard work schedules in the first two years of a child’s life were associated with lower verbal comprehension and expressive language skills among toddlers (Han 2005; Odom et al. 2013).

Most studies examining the association between maternal nonstandard work schedules and children’s outcomes focus on U.S. samples. Country context matters if supportive family-friendly policies, such as flexible working hours, parental leave, and subsidized child care, offer protection for families with relatively scarce resources (Rönkä et al. 2017; Täht and Mills 2012). One study considered the association between maternal work schedules and child outcomes in the United Kingdom albeit without an exclusive focus on early childhood. Using nonrepresentative data from child care centers in the United Kingdom, Finland, and the Netherlands, Rönkä et al. (2017) found that mothers’ nonstandard work schedules were associated with increased mental health problems among 3- to 12-year-olds. Evidence from other countries appears more mixed. Using Canadian population data, researchers have found more behavioral problems among preschoolers (aged 2–4 years) when mothers worked nonstandard schedules (Strazdins et al. 2004, 2006) whereas Han (2020) found that mothers’ nonstandard work schedules in China were related to fewer internalizing and externalizing problems. On the other hand, a recent study using a German family panel study revealed no associations between fixed nonstandard work schedules and child behavior during middle childhood and adolescence (Li et al. 2020).

Maternal nonstandard work schedules are thought to influence child outcomes through multiple pathways, including investments through time, maternal well-being, and parenting. Mothers who work nonstandard schedules may invest less time with their children (Gassman-Pines 2011; Pilarz and Awkward-Rich 2024), which in turn may affect children’s cognitive and behavioral outcomes (Fiorini and Keane 2014). Such work schedules could interfere with parental well-being and the family environment, for example, by increasing the risk for marital instability and lower relationship quality (Davis et al. 2008; Zilanawala and McMunn 2024) and hindering the development and maintenance of family routines (Wight, Raley, and Bianchi 2008). Equally, nonstandard work schedules may lead to depressive symptoms due to mental stress and fatigue (Grzywacz et al. 2016; Strazdins et al. 2006), possibly resulting from difficulties in maintaining family and social routines. Working outside of standard hours may compromise sensitive and cognitively stimulating parenting, either directly or indirectly due to increased stress and depression (Odom et al. 2013; Prickett 2018), which in turn have long-term effects on children’s life courses (Raby et al. 2015).

Economic Hardship as a Moderating Context for the Impact of Mothers’ Nonstandard Work

Prior studies have highlighted that the impacts of mothers’ nonstandard work on children’s outcomes vary by families’ existing economic and demographic characteristics. For example, the association between nonstandard work schedules and children’s time with parents varies by family structure (Pilarz and Awkward-Rich 2024) and associations with child outcomes may be moderated by child gender, child age, maternal education and low income (Han 2008; Leibbrand 2018; Wang 2023). Such findings highlight the importance of looking at heterogeneity in the effects of mothers’ work schedules on child outcomes.

We investigate the interaction between nonstandard work schedules and economic hardship to identify whether nonstandard work schedules pose a greater risk to children’s outcomes among relatively more economically vulnerable families. A wide range of industries and occupations are represented by nonstandard work schedules and offer a range of incomes (Enchautegui 2013). For example, health care providers, such as nurses and doctors, frequently work nonstandard shifts, as do office cleaners, grocery workers, and security services. Some of these professions offer medium to high wages and employment security whereas industries such as food and accommodation, which have the highest share of workers with nonstandard work schedules, are associated with low wages, instability in work hours, and lower levels of job control. Individuals with more social and economic resources may be in a better position to confront the challenges and negative sequalae of nonstandard work schedules (Gerstel and Clawson 2018).

Qualitative research provides some indication that the association between maternal nonstandard work schedules and early childhood outcomes is moderated by a family’s level of economic situation. Families consider the financial ability to purchase childcare important to managing the stress resulting from nonstandard work schedules (Li et al. 2020). This suggests that families with fewer economic resources have less protection against the job demands and stressors associated with nonstandard work schedules. In turn, such resulting challenges could lead to more pronounced negative outcomes among children in families with economic vulnerabilities.

A handful of quantitative studies have also examined the moderating relationship of low income among samples of school-aged children, but the evidence is mixed. Although several studies showed that the association between maternal nonstandard work and worse child behavior is strongest among income-poor families (Han 2006, 2008; Strazdins et al. 2004), others did not find stronger associations among low-income families (Dunifon, Kalil, and Bajracharya 2005; Hsueh and Yoshikawa 2007; Strazdins et al. 2006). No studies have yet considered low income as a moderator in the relationship between maternal work schedules and early childhood outcomes, but a few studies have examined the relationship between maternal nonstandard work schedules and child outcomes among income-poor families in the United States. In low-income samples (i.e., <200 percent of the federal poverty line) of preschool children (ages two to four), Joshi and Bogen (2007) found that nonstandard schedules were related to more externalizing and internalizing problems whereas Gassman-Pines (2011) found no relationship between nonstandard work and child externalizing and internalizing behaviors. Odom et al. (2013) found associations between mothers’ nonstandard work schedules and lower expressive language ability in toddlers among low-income families (nearly 150 percent of the federal poverty level).

The moderating role of economic hardship is likely to be more complex than simply how much income flows into a family. Therefore, it is important that research on the moderating role of economic hardship looks beyond only low income. A growing body of evidence supports the existence of three related, but distinct dimensions of families’ economic circumstances that can influence children’s development (Leininger and Kalil 2014; Schenck-Fontaine and Panico 2019; Schenck-Fontaine et al. 2020; Zilanawala and Pilkauskas 2012). Income poverty, the most widely studied dimension, captures the amount of financial input available to a family. Material hardship refers to the inability to afford basic living expenses that are considered necessary, such as adequate housing, food, or health care. Finally, financial stress refers to psychological distress related to worries about current or future economic circumstances. Although these three dimensions of economic hardship are related to each other, they should be treated as being potentially distinct experiences.¹ Research consistently shows that these correlations are modest at best, generally ranging between 0.15 and 0.35 (Bradshaw and Finch 2003; Schenck-Fontaine and Panico 2019). These modest correlational relationships are consistent with evidence that many families who are poor may not experience material hardship or financial stress and vice versa. Recent nationally representative evidence shows that material hardship is nearly twice as prevalent as income poverty in the United States (Rodems and Shaefer 2020). Similarly, evidence from the United States and United Kingdom suggest that financial stress is also more prevalent than income poverty (Gauthier and Furstenberg 2010; Schenck-Fontaine and Panico 2019). Therefore, the three dimensions taken together provide a more complete picture of a family’s economic circumstances than information about one dimension alone.

The family stress model (Conger and Donnellan 2007; Elder 1999) and the family investment model (Conger and Donnellan 2007) provide a framework for understanding how these different types of economic hardship affect children’s outcomes. The family investment model defines economic hardship as low income which influences children’s development through reduced parental investments in material, social, and time resources. On the other hand, the family stress model proposes material hardship and subjective financial stress as mediators of low income and posits that economic hardship influences children by adversely affecting parenting behavior through family conflict, parental depression, and parental stress.

Building on these two frameworks, Gershoff et al. (2007) showed that income and material hardship each are associated with different child outcomes. Specifically, low income without the presence of material hardship was more influential for cognitive outcomes, whereas material hardship, as a result of low income, primarily affects child behavior. Consistent with this conclusion, another study found that, regardless of a household’s income, material hardship and financial stress are more predictive of adverse behavioral outcomes among children than income poverty, but this study did not examine cognitive outcomes (Schenck-Fontaine and Panico 2019). Several studies have also shown that income, material hardship, and financial stress affect children through different pathways. Low income affects children through decreased parental investments in resources, such as time or money (Dräger and Pforr 2022; Gershoff et al. 2007; Li and Chzhen 2024). On the other hand, material hardship and financial stress are typically found to affect children through parental stress and mental health, family conflict and routines, and parenting behavior (Lee et al. 2023; Liu and Merritt 2018; Liu et al. 2022; Masarik and Conger 2017; McConnell, Breitkreuz, and Savage 2011).

It is notable that there is significant overlap in the mediating pathways between those that explain the effects of nonstandard work and the effects of economic hardship dimensions on children’s outcomes. Therefore, we expect that economic hardship is likely to exacerbate the negative effects of nonstandard work on children. Given the empirical evidence and theoretical background, we expect the moderation of nonstandard work schedules on child outcomes is likely to vary by economic hardship dimension. Specifically, we expect material hardship and financial stress to exacerbate the negative effects of nonstandard work on children. Material hardship and financial stress are both known to increase family conflict, mental health problems, and distress, which are pathways by which nonstandard work schedules also affect children. In other words, we expect that the added stress and chaos related to such work schedules are an additional risk factor for children in a family that is already experiencing some family conflict and depression, resulting from not being able to make ends meet, relative to a family who is not economically strained. Without the additional factor of experiencing economic hardship, families may be better able to manage the potential stress resulting from nonstandard work schedules.

We do not expect income poverty to be a significant moderator of the effects of nonstandard work, because studies suggest resource investments, relative to psychological distress and parental conflict, are not as important as a mediating pathway between nonstandard work and child outcomes (Li et al. 2014). On the basis of the prior work showing that different domains of economic hardship operate through different pathways, we also expect that the moderating effects of material hardship and financial stress are likely to matter for children’s behavioral outcomes, but not cognitive outcomes, which are better predicted by resource investments related to income. Although the nonstandard work literature does not find income poverty as a key pathway to child outcomes, to the degree that results indicate income poverty is an important moderator, we expect it to only magnify the impacts of nonstandard work schedules on cognitive outcomes but not behavioral outcomes.

The UK Context

The UK provides a useful case in which to examine nonstandard work schedules and child outcomes, because its economic and cultural context is similar to the United States, but its family and economic policies are notably more protective. For example, in the early 2000s, policies supporting families with young children expanded in the United Kingdom in the areas of parental leave, tax credits, and child care benefit. European Union legislation ensures that employees who are not in standard employment enjoy the same benefits and protections as those in standard work schedules (Hook and Wolfe 2013). Additionally, access to health care is universal and not contingent on employment status.

In contrast, in the United States, labor market regulations and family policy provisions are low, and workers in nonstandard schedules lack protection. It is conceivable that the increased stress, fatigue, and compromised family time may be universal responses to working unsociable hours. Equally, child care is expensive in both the United States and UK (Hook and Wolfe 2013), but there is a greater provision of publicly funded childcare in the United Kingdom for three- to five-year-olds. Nevertheless, after-school care is expensive and harder to find outside regular daytime hours (Sandstrom, Giesen, and Chaudry 2012). Thus the challenges of securing evening, night, or weekend child care and the potential lack of choice in working nonstandard hours may be features that affect family life in both contexts. However, because of differences in policy supports in the labor market, the adverse associations with nonstandard working may be buffered by strong labor market protections and benefits, and a more generous welfare state.

Methods

Data

We use data from the MCS, an ongoing population-based birth cohort study following a representative sample of infants born to 19,244 families in the United Kingdom between September 2000 and January 2002 (Joshi and Fitzsimons 2016). The MCS data are freely available under standard access conditions via the UK Data Service (http://ukdataservice.ac.uk) after registering and submitting a data request. Economically disadvantaged and minority families were oversampled by stratifying by the child poverty index and the proportion of ethnic minority population of each electoral ward, an administrative unit level. Northern Ireland, Scotland, and Wales were also oversampled relative to England. At each wave, an interview is carried out with the main parent (normally the mother). Of particular interest to our study is that the MCS is one of the only longitudinal studies of children that collected information about mothers’ employment characteristics, including the timing and regularity of work schedules, different dimensions of economic hardship, and child behavior and cognitive measures.

This study used data from the third wave when cohort members were on average five years old. We limit the sample to families with singleton births, given that families with multiple births likely have unique economic hardship experiences. We used a sample of cohort members whose mothers were interviewed at age five (n = 14,548) and had information on at least one of our dependent variables of interest. We further restricted our sample to mothers who were employed in the last week (n = 7,925 at age five), because we are interested in features of maternal employment and whether such elements interact with economic hardship to influence child development. To minimize potential bias from excluding cases with incomplete data on our analytical variables, we rely on full-information maximum likelihood to handle missing values. This method computes model parameters with all available information by treating missing observations as a function of all available information from the variables in the model. This approach produces efficient and unbiased results comparable with those gained via multiple imputation (Allison 2012). The resulting analytic sample consisted of 7,925 mothers for the age five regressions. Descriptive statistics on employment and control variables were conducted on all available data (n = 7,375–7,925; see Table 1). To ensure that findings are representative of the population we apply overall survey weights at age five to take account of interwave attrition and survey design (Fitzsimons 2017).

Table 1.

Descriptive Statistics of Analysis Variables (n = 7,375-7,925).

Variable	Nine Months		Age Three		Age Five
Variable	Mean or %	SD	Mean or %	SD	Mean or %	SD
Child characteristics
Child outcomes
Naming Vocabulary (20–80)					56.66	10.01
Picture Similarity (20–80)					56.36	9.77
Pattern Construction (20-80)					51.49	9.64
Externalizing behavior (0–20)					4.40	3.19
Internalizing behavior (0–18)					2.15	2.23
Naming Vocabulary (20–80)			51.82	10.27
School readiness (56–149)			107.40	14.96
Externalizing behavior (0–20)			6.24	3.58
Internalizing behavior (0–18)			2.50	2.25
Child is male					51.24
Time-varying employment variables
Nonstandard work schedule					43.06
Work hours					23.32	11.48
Time-varying economic variables
Income poverty					13.54
Severe financial stress					7.12
Material hardship					14.33
Mother’s baseline sociodemographics
Age at birth (years)	29.82	5.38
Mental health (0–9)	1.45	1.56
Education
No qualifications	5.23
Overseas qualifications	1.25
Less than GCSE	6.35
GCSE	29.82
A-level	15.96
College degree or more	41.39
Ethnicity
White	92.95
Indian	1.95
Pakistani	.71
Bangladeshi	.24
Black Caribbean	1.39
Black African	1.27
Other	1.50
Country of interview
England	81.17
Wales	5.14
Scotland	9.58
Northern Ireland	4.11
Household characteristics
Household size					4.07	1.00
Family structure
Single					14.47
Cohabiting partner					18.25
Married partner					67.28

Note: All percentages and means are weighted with attrition weights from the UK Millennium Cohort Study Age 5 interview. Sample sizes vary because all available data are used for means and percents. Employment and economic variables are conditional on employment at age five interview. GCSE = General Certificate of Secondary Education.

Measures

Child Cognitive and Behavioral Outcomes

We examined five measures of child outcomes at the age five interview. Each measure is standardized to have a mean of 0 and a standard deviation of 1; thus, coefficients in regression models may be interpreted as effect sizes. To assess cognitive outcomes, we included measures of verbal and spatial ability. The verbal assessment used the British Ability Scales Naming Vocabulary test, which assesses expressive language skills by requiring the child to name out loud the object shown in a single picture. We also used two dimensions of spatial ability: the British Ability Scales Picture Similarities and Pattern Construction tasks. The Picture Similarities test assesses children’s nonverbal reasoning ability, by asking them to identify which one of four pictures shares a similar concept or element with a fifth response card. The Pattern Construction test also assesses nonverbal reasoning and spatial visualization and requires children to replicate patterns using colored foam squares or blocks.

To assess child behavior, we used the externalizing and internalizing behavior problems scales from the maternal report of the Strengths and Difficulties Questionnaire. The externalizing behavior scale includes the conduct problems (α = .76) and hyperactivity (α = .77) subscales and captures behavior problems such as restlessness, tantrums, or lying. The internalizing behavior subscales includes the emotional symptoms (α = .70) and peer problems (α = .59) subscales and captures behavior problems such as worrying, loneliness, or unhappiness.

Finally, we included lagged measures of child cognitive and child behavior to adjust for time-invariant child-level omitted variables (discussed further later). These lagged child outcomes were drawn from the age three survey. Lagged measures of verbal ability and child behavior were used in models predicting age five verbal and child behavior outcomes, respectively. There were no equivalent lagged measures of spatial ability. We used a school readiness measure assessed from the Bracken School Readiness Assessment test. This test assesses the readiness of a child for formal education by testing their knowledge and understanding of basic verbal and spatial concepts (Bracken 1998).

Maternal Work Schedules and Work Hours

We measured mothers’ work schedules and work hours using employment information among employed mothers from the age five wave of MCS. At the interview, mothers were asked if they were engaged in paid work in the last week and, if yes, were asked if they regularly (daily or weekly) worked each type of nonstandard work schedule: evenings (6 p.m. to 10 p.m.), nights (10 p.m. to 7 a.m.), and weekends. These three schedule categories were predetermined options and were not mutually exclusive. The designated options and allowing mothers to choose multiple options for nonstandard work schedules are similar to measurements of nonstandard work in other cohort studies (Dunifon et al. 2013). However, unlike the National Longitudinal Survey of Youth 1979, the Future of Families and Child Wellbeing Study, and the Early Childhood Longitudinal Studies, there are not questions on irregular or rotating schedules.

Following previous practice in these data (Zilanawala and McMunn 2023), we created a dichotomous variable to indicate if mothers worked any nonstandard schedule; mothers were coded 1 if they reported working any nonstandard schedule (i.e., evenings, nights, or weekends) and 0 if they reported paid work in the last week and did not report working any of the aforementioned nonstandard schedules (the omitted category).

Mothers reported the number of hours they worked per week at their job. We used a continuous variable to assess weekly work hours. Mothers were asked about work hours only in regard to their primary job; therefore, our measure of work hours may underestimate the total hours mothers worked.

Economic Hardship

All three economic hardship variables were assessed at the age five interview. We used a dichotomous indicator, created by the MCS survey team, for relative income poverty if equivalized net family income was below 60 percent of the median sample income. Imputation using interval regression was conducted by the MCS survey team to address item nonresponse on income variables.

Mothers rated their subjective financial stress on a Likert-type scale indicating how well the household was managing financially (1 = “living comfortably,” 2 = “doing alright,” 3 = “just about getting by,” 4 = “finding it quite difficult,” 5 = “finding it very difficult”). We created a binary variable for severe financial stress which was coded 1 if mothers reported “finding it quite difficult” or “finding it very difficult” and 0 otherwise.

Material hardship was measured using five items that capture three domains of material hardship. Hardship related to essential items for children was measured using mothers’ reports about whether they were unable to afford a warm, waterproof coat for the cohort member. We measured utility (fuel) hardship using information from mothers’ reports about whether they were behind on their utility bill payments (i.e., electricity, gas, fuel, or other fuel). Additionally, mothers were asked to what degree damp or condensation on the walls of their home was a problem in rooms other than the kitchen and bathroom (1 = “no damp,” 2 = “not much of a problem,” 3 = “some problems,” 4 = “great problem”), which is an indicator of fuel hardship in the UK climate. A binary variable indicating damp was a problem was coded 1 if mothers reported “some problems” or “great problem” and 0 otherwise. Relatedly, mothers also indicated whether they had no central heating in their home. To capture housing hardship, we used information on crowded housing. Following the standard definition of crowded housing (Blake, Kellerson and Simic 2007), we constructed a binary variable coded 1 if mothers reported more than one person per room, excluding kitchens and bathrooms, and 0 otherwise. Following previous research in these data, we created a dichotomous variable which was coded 1 if a mother reported at least one of these five hardships and 0 if a mother reported no hardships (Schenck-Fontaine and Panico 2019). The correlations between each of the three dimensions of economic hardship are relatively low, ranging between .19 and .34, but all are statistically significant (see Table A1 in the Appendix).

Control Variables

In all models, we included a set of individual, family, and household characteristics from the nine-month interview that may be associated with maternal employment decisions and child outcomes. We included a binary variable for child gender (coded 1 if the child is male). Maternal characteristics include mother’s age at birth and mental health using the sum score from the Malaise Inventory, a set of nine self-completion questions measuring levels of psychological distress (α = .70) (Rutter, Tizard, and Whitmore 1970). Mother’s education level was measured with a series of dummy variables indicating none, overseas, less than General Certificate of Secondary Education, General Certificate of Secondary Education, A-level, and college degree or more (the reference category), including equivalents for each. Mother’s race and ethnicity was measured with indicators that the mother was White (the reference category), Indian, Pakistani, Bangladeshi, Black Caribbean, Black African, or other. Models controlled for country of interview. We also included two characteristics of the household, measured at the age five interview, to capture changing circumstances in the household: household size and family structure (whether the mother is married (the reference category), cohabiting, or living without a partner).

Descriptive statistics for all analysis variables in our regression models are presented in Table 1. Of note is that the prevalence of nonstandard work schedules among employed mothers was 43 percent at age five. Nearly 14 percent of mothers experienced income poverty which was nearly as common as material hardship (14 percent), whereas severe financial stress (nearly 7 percent) was the least prevalent.

Data Analysis

We aimed to reduce selection bias in addressing our research questions about the relationship between nonstandard work schedules and child outcomes by implementing residualized change models (National Institute of Child Health and Human Development Early Child Care Research Network and Duncan 2003). These models account for time-invariant child and family characteristics by adjusting for unobserved variables associated with the lagged outcome. The regression models estimate the associations between nonstandard work schedules and child outcomes when children were five years old, adjusting for a prior measure of the child outcome from the three-year wave. Residualized change models are preferred if there is persistence over time in the outcome measure (i.e., the outcome at one time point is strongly associated with the outcome at a subsequent time point) and they are appropriate when prior levels of a child outcome may influence mothers’ selection into particular types of work schedules (Allison 1990). An advantage of residualized change models is they can provide more power than simple change models or child fixed effects models to detect associations (Cronbach and Furby 1970). The drawback of residualized change models is that they cannot control for time-varying unobservable factors nor can they account for bias from unobserved characteristics that have differential impacts on child development at ages three and five. In addition to including the lagged outcome measures in regression models, we also included all control variables listed in Table 1.

First, we examined weighted prevalence estimates of each economic hardship dimension by work schedule at the age five interview. Second, we estimated a series of regression models predicting children’s cognitive and behavioral outcomes at age five from the maternal work schedule variable when children were five years old. Third, we used the residualized change model to determine whether the associations between maternal work schedules and child outcomes varied by each of the three types of economic hardship. To examine this possibility, we separately interacted the variable for nonstandard work schedule with income poverty, severe financial stress, and material hardship. The three interaction models for each child outcome take the generic form of estimating coefficients for the two main effects, nonstandard work schedules and the hardship indicator, and the interaction between nonstandard work and the binary measure of economic hardship, adjusting for the lagged child outcome and covariates. For example, in the case of interaction models for income poverty, the addition of the coefficients for the main effect on income poverty and the interaction of poverty and work schedule indicates the difference in the level of the outcome at age five for children in families with mothers who work nonstandard work schedules and are in income poverty relative to those whose mothers also work nonstandard work schedules but are not income poor, conditional on the child’s lagged outcome from age three. Equally, such interaction models allow the comparison of children in families who are income poor but differ in their mothers’ work schedule type, that is, nonstandard versus standard. For ease of interpretation of findings, predicted mean outcomes from the interaction regression models are presented in a figure and regression models are presented in appendix tables (see Tables A2 –A4). To reduce the possibility of type 1 error resulting from multiple tests, we used a Bonferroni correction.

In supplementary analyses, we assessed whether our results were robust to using continuous measures of material hardship (zero to five hardships) and financial stress (from one to five). We also considered the partner’s (if resident) employment status at the age five interview (residential partner is working, residential partner is not working, no residential partner). Last, we created an alternative measurement of nonstandard work schedules to create mutually exclusive categories. Specifically, we created a variable with three categories for employed mothers: working multiple nonstandard work schedules, working a single type of nonstandard work schedule, or worked a standard schedule.

Results

Descriptive Differences in Economic Hardship by Work Schedules

Table 2 shows mean cross-sectional prevalence of each dimension of economic hardship within maternal work schedules among employed mothers at the age five interview. Overall, mothers who worked nonstandard schedules had higher prevalence of income poverty, severe financial stress, and material hardship. Although, the differences between mothers’ economic hardship experiences by work schedule were only statistically significant for income poverty (14.6 percent vs. 12.8 percent) and material hardship (16.2 percent vs. 13.0 percent).

Table 2.

Cross-Sectional Prevalence of Income Poverty, Financial Stress, and Material Hardship within Work Schedule, Age Five.

	Nonstandard Work	Standard Work	Difference
Poverty
Age five	14.59	12.81	*
n	552	622
Severely financially stressed
Age five	7.57	6.73
n	257	300
Material hardship
Age five	16.15	13.02	**
n	503	572

Note: Cross-sectional weighted prevalence estimates are presented as percents. Sample sizes are not weighted. The “Difference” column indicates statistical significance in prevalence estimates between nonstandard and standard working mothers. Sample sizes for nonstandard and standard work schedules varied by economic hardship dimension: 3,125 to 3,425 for nonstandard work and 4,354 to 4,601 for standard work.

p < .05. **p < .01.

Predicting Children’s Outcomes from Maternal Nonstandard Work Schedules

Table 3 presents regression results predicting child cognitive and behavioral outcomes from contemporaneously measured maternal nonstandard work schedules and shows that, relative to standard schedules, nonstandard work schedules did not predict verbal nor spatial outcomes at age five. Instead, nonstandard work schedules were related to 0.06 SD higher internalizing behavioral scores. This effect size is small in magnitude, suggesting relatively small associations between maternal nonstandard work schedules and child development. There were null associations between nonstandard work schedules and externalizing behavioral scores.

Table 3.

Residualized Change Regression Models Predicting Child Outcomes at Age Five from Nonstandard Work Schedules at Age Five.

	Cognitive			Behavior
	Naming Vocabulary	Picture Similarity	Pattern Construction	Externalizing	Internalizing
	B (SE)	B (SE)	B (SE)	B (SE)	B (SE)
Working any nonstandard schedule at age five	−.02(.02)	−.01(.02)	−.01(.03)	.03(.02)	.06**(.02)
n	7,925	7,925	7,925	7,925	7,925

Note: The reference category for work schedule variable is standard schedule. Regression coefficients are in standard deviation units and are weighted with attrition weights from the UK Millennium Cohort Study Age 5 interview. Sample sizes are not weighted. All models control for covariates shown in Table 1. The lagged outcome at age three corresponded to the child outcome at age five and measured either verbal ability, school readiness, or child behavior.

p < .01.

Association between Work Schedule and Economic Hardship Interactions and Child Outcomes

We did not find any evidence that the associations between children’s outcomes and nonstandard work schedules varied by income poverty or by material hardship (see Tables A2 and A3). However, we did find that associations between children’s internalizing behavioral scores and nonstandard work schedules varied by financial stress at age five (see Table A4). In the presence of no financial stress, children of mothers who work nonstandard work schedules have 0.05 SD higher internalizing behavioral scores relative to their peers of mothers who work standard schedules. Where families experience financial stress, the association between nonstandard work and internalizing behavioral scores increases by 0.18 SD. There is no significant association between financial stress and internalizing scores when mothers work standard schedules.

To ease interpretation of the findings, predicted means of internalizing scores from the interaction model with nonstandard work schedule and financial stress are shown in Figure 1. Among mothers who worked nonstandard schedules, children whose mothers reported financial stress had nearly 0.25 SD higher internalizing scores compared with children whose mothers did not experience financial stress (see Figure 1). These children (of mothers in nonstandard work schedules and who had financial stress) also had higher internalizing scores relative to children whose mothers worked standard schedules, irrespective of experiencing financial stress (0.23–0.29 SD higher scores). Among mothers who worked standard schedules, there were no differences in the predicted means of financial stress.

Figure 1.

Predicted means of child internalizing behaviors at age five from interaction model of nonstandard work schedule and financial stress at age five.

Supplementary Analyses

To test the sensitivity of our main results to various analytic decisions, we conducted a series of specification checks. First, we considered an alternative approach in measuring economic hardship by using a continuous measure of material hardship and financial stress. Our descriptive results on the prevalence of these two economic hardship dimensions and the conclusions from the interaction models did not change (results available upon request). Second, although we adjusted for family structure in our regression models, we tested the robustness of our findings to including partners’ employment status. A partner’s time availability could influence the family dynamics and processes which have implications for children’s development. However, adjusting for partners’ (if resident) employment status did not alter the findings from our regression models (results available upon request).

Finally, we estimated our main models without interactions using two specifications of nonstandard work schedules. First, we explored if different types of nonstandard work schedules were related to child outcomes (results available upon request). Broadly, most coefficients, in magnitude and significance, were substantially similar to results in Table 3 with the exception of two differences. Regularly working night schedules were associated with 0.08 SD (p < .05) lower verbal scores and regularly working weekend schedules were associated with 0.06 SD (p < .05) higher externalizing scores. Secondly, we explored the possibility that mothers who work a single nonstandard work schedule may have more stability in the home environment compared with their peers who work more than one type of nonstandard work schedule (results available upon request). Working multiple nonstandard work schedules could be associated with more economic strain, compromised quality parenting time, and more stress. We did not see differences in effect sizes between mothers who worked a nonstandard work schedule in isolation and those who worked multiple schedules. Nor did we see consistently stronger associations for multiple nonstandard work schedules to warrant a different conclusion from our current set of results.

Discussion

This study examines whether maternal nonstandard work schedules and their interactions with three types of economic hardship are associated with a constellation of early childhood cognitive and behavioral outcomes. Our findings extend the literature by using nationally representative data on a birth cohort of UK-born children to investigate the moderating role of types of economic hardship, which in combination with working nonstandard schedules could together exert more strain and pressure on child outcomes. We also build on the existing literature by giving greater attention to children’s cognitive outcomes.

First, our descriptive results show that more than 40 percent of working mothers engage in nonstandard work schedules when their children are age five. Our results are within the wide range in prevalence found elsewhere examining such work schedules in early childhood. Using nationally representative data from the US and approximate birth cohort year to the MCS, Ballentine and Pilarz (2024) found that 17 percent of working mothers in nonstandard schedules among kindergarteners. Other studies, which use U.S. data that overrepresent low-income families or mothers who are economically advantaged, report 67 percent when children are age four (Han 2008), 27 percent when children are age three (Han 2005), and nearly one third when children are ages three to five (Dunifon et al. 2013).

Our study is the first to descriptively document the different types of economic hardship among mothers of young children who work nonstandard schedules. We found that income poverty, material hardship, and financial stress were higher among mothers in nonstandard work schedules compared with mothers working standard schedules. Although, the differences were only statistically significant for income poverty and material hardship. Few studies examine the economic well-being of parents who work nonstandard schedules. However, our findings are in line with evidence suggesting higher levels of financial stress and a greater propensity to fall into poverty among parents with nonstandard work schedules compared with those who work standard hours (Han and Zhang 2021; Rönkä et al. 2017). To further understand the economic circumstances of parents who participate in nonstandard work, we encourage a more refined, multidimensional approach to assess the economic circumstances of parents in such work schedules.

Second, we found the associations between nonstandard work schedules and child outcomes had small, statistically significant effect sizes or null associations. Specifically, results suggest that nonstandard work schedules at age five are associated with contemporaneous increases in internalizing behavioral scores (i.e., emotional and peer problems) and null associations with externalizing behavioral scores (i.e., conduct problems and hyperactivity) relative to working standard schedules, even after adjusting for covariates and prior levels of child behavior. Effect sizes were less than one-tenth of a standard deviation. We consider our results to show lower bounds for the effects of nonstandard work schedules on internalizing behaviors compared with prior studies examining associations between mothers’ nonstandard work schedules and children’s internalizing scores (between one tenth and one third of a standard deviation) (Daniel et al. 2009; Dunifon et al. 2013; Joshi and Bogen 2007). In contrast to studies finding that such work schedules can compromise cognitive outcomes (Han 2005; Odom et al., 2013), we found null associations between nonstandard work schedules and children’s verbal and spatial abilities, the latter of which is particularly understudied in the literature.

We offer two potential explanations to understand the small effect sizes and null associations. One explanation is related to the policy context in which we investigate our research questions. Our reference group compromises of mothers who are untouched by nonstandard work schedules but may nonetheless face the same flexible work policies and family policy supports as mothers who work nonstandard schedules. For example, parents in the United Kingdom have the right to reduce their working hours and enjoy more control and discretion over their work hours compared with parents in the United States (Lyness et al. 2012). The relative generosity of paid and unpaid leave policies can also be associated with more perceived schedule control which could translate to facilitating the integration of work and family responsibilities. The smaller effect sizes could mean nonstandard work schedules offer flexibility in arranging child care responsibilities and/or provide opportunities for mothers to maximize their time with children (Täht and Mills 2012).

Another explanation is accumulation of stressors is more deleterious to child outcomes than a single stressor (Evans and Kim 2013). Thus, our single point in time measure of nonstandard work schedules may underestimate the cumulative effects of nonstandard work. Studies suggest that adverse associations between variable work shifts and child outcomes accumulative over time (Grzywacz et al. 2016; Walther and Pilarz 2024), but this has not been tested in the context of cumulative nonstandard work schedules in the early childhood period. Future scholarship should consider extending the literature to understand how the effects of nonstandard work schedules develop over time.

Third, the most important contribution this study makes is analyzing the moderating role of the three dimensions of economic hardship. We found that the association between nonstandard work and internalizing behavior is 0.18 SD higher in the presence of financial stress compared with not experiencing financial stress. However, income poverty and material hardship did not moderate the associations between work schedules and child cognitive and behavioral outcomes. On one hand, it is surprising that material hardship did not moderate associations between nonstandard work and child behavior given that it affects children through similar mechanisms to financial stress (i.e., family conflict, psychological distress). On the other hand, evidence suggests that it is the combination of material hardship and financial stress that has the greatest impact on levels of child behavior rather than experiencing a single dimension of hardship (Schenck-Fontaine and Panico 2019). Another possible explanation is that material hardship, as operationalized here, may reflect long-term economic hardship for which mothers have put coping strategies in place (Schenck-Fontaine and Ryan 2022) whereas mothers may not be able shield their children from feelings of economic stress if financial stress is short term or relatively new to the family. Collectively, our study reinforces that not all experiences of economic hardship affect children in the same way. Research to identify the potentially different mechanisms, including coping strategies, by economic hardship dimension would provide clarity into why some hardship dimensions do and do not moderate associations between nonstandard work and children’s behavior.

How can country policies protect families against material hardship and financial stress? Undoubtedly extensive leave provisions and publicly subsidized childcare support all working families. Although in the face of rising income inequality, inflation, and bouts of recession, reducing financial stress may also require interventions that directly target material deprivation (e.g., food or housing subsidies) and are universally available, in addition to or in concert with interventions solely focused on increasing income. If families feel more economically insecure and national income inequality is worse, we expect children to be more sensitive to the added stress in a family because the economic hardship stressors are likely to be higher. Bolstering the ability for parents to cope with financial stress and using effective problem skills could reduce psychological distress and harsh parenting which in turn affect child outcomes (Masarik and Conger 2017).

There are some limitations to note. The relationship between nonstandard work schedules and child outcomes may be due to characteristics that we could not address using these data. For example, we were not able to discern between mothers who chose to work a nonstandard schedule or considered them beneficial to their family routines compared with mothers whose schedules are mandated by the job itself. However, future data collection should consider collecting information on work schedule choice to better understand family dynamics. More specifically regarding variable measurement, our operationalization of work schedules is in line with previous literature but is limited because of self-reporting and the use of predetermined categories (e.g., evenings 6 p.m. to 10 p.m.). Though these data are ideal for studying nonstandard work schedules, they are limited in making conclusions about mothers who work rotating or irregular and unpredictable work schedules. These details could provide more nuanced information on the relationship between nonstandard work schedules and child outcomes. Because of small cell sizes, we were unable to examine heterogeneity in the associations between economic hardship and child outcomes by types of nonstandard work schedules (e.g., evenings, nights, and weekends). However, we expect that the moderation of different types of economic hardship on child outcomes do not vary by type of work schedules, because experiencing any nonstandard schedule is predicted to have negative consequences for child outcomes. Although, the associations between economic hardship and child outcomes could vary by type of hardship and child outcome. Future research has the potential to investigate these inquiries with larger cell sizes.

Other measurement limitations relate to the measurement of economic hardship. The measures of material hardship are limited and do not include information about food insufficiency. Moreover, several of the measures are specific to the UK context and may not be relevant to other climates and policy contexts. We used dichotomous indicators of income poverty, financial stress, and material hardship to answer our research questions. The drawback in using binary variables is the potential loss in statistical power to detect associations (Altman and Royston 2006). However, in supplementary analyses, our conclusions did not change when using continuous variables.

Notwithstanding these limitations, our results raise the profile of mothers who face economic hardship, in its varied dimensions, and work outside of daytime hours, as well as the unique challenges for children’s outcomes resulting from this combination. Our results highlight that income poverty is a salient but insufficient measure of economic hardship for families in which mothers engage in nonstandard work, and that there is a continuous need to distinguish between the diverse experiences of economic hardship. That we find the association between nonstandard work and young children’s internalizing behavior to be more pronounced among mothers who experience financial stress clearly indicates that there is notable heterogeneity in the experience and effects of nonstandard work schedules and that this is driven by the degree of financial stress parents experience. This is an important addition to the conceptual frameworks that guide this body of literature, as financial stress has not previously been considered in research on the implications of nonstandard work schedules on child outcomes. In line with a growing literature on the subjective perception of inequality and hardship (Edin et al. 2019; Schneider and Schenck-Fontaine 2021), future research should consider not only the objective circumstances, but also individuals’ own perceptions of their work schedules as central to defining what impacts these work schedules may have on the family. Building on these findings, future research should examine how, when, and why financial stress is associated with mothers’ nonstandard work schedules.

Footnotes

Appendix

Table A4.

Residualized Change Regression Models Predicting Child Outcomes at Age Five from Nonstandard Work Schedules and Financial Stress at Age Five.

Interaction with Financial Stress	Cognitive			Behavior
	Naming Vocabulary	Picture Similarity	Pattern Construction	Externalizing	Internalizing
	B (SE)	B (SE)	B (SE)	B (SE)	B (SE)
Working any nonstandard schedule	−.02(.02)	−.00(.03)	−.01(.03)	.03(.02)	.05*(.02)
Financial stress	−.07(.05)	.04(.07)	−.03(.07)	.12(.06)	.05(.06)
Nonstandard × financial stress	.11(.07)	−.06(.10)	−.05(.09)	.05(.09)	.18*(.09)
n	7,925	7,925	7,925	7,925	7,925

Note: The reference category is standard schedule and no hardship. Regression coefficients are in standard deviation units and are weighted with attrition weights from the UK Millennium Cohort Study Age 5 interview. Sample sizes are not weighted. All models control for covariates shown in Table 1. The lagged outcome at age three corresponded to the child outcome at age five and measured either verbal ability, school readiness, or child behavior.

p < .05.

Acknowledgements

The authors are grateful for helpful comments on analyses from participants at the Center for Longitudinal Studies Seminar, feedback from Heather Hill, and inspiration from Harriet Presser.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Research support for this work was provided by the UK Economic and Social Research Council under grants ES/R003114/1, ES/R003114/2, and ES/R003114/3.

ORCID iD

Afshin Zilanawala

Notes

Author Biographies

Afshin Zilanawala is an associate professor of demography at the University of Southampton. A particular focus of her research is investigating the proximate family setting in which children live and examining factors, such as parental employment, mental health, and economic hardship, which are implicated in the (re)production of inequalities in children’s development. Much of her research considers the consequences of systems of stratification (e.g., race/ethnicity and gender) on children’s well-being across early childhood and adolescence. Additionally, her research agenda incorporates young adults’ romantic relationship experiences and feelings of loneliness.

Anika Schenck-Fontaine is the managing director of new data spaces for the Social Sciences at Leibniz Institute for Educational Trajectories. Her research focuses on how material hardship, financial stress, and economic losses affect children’s well-being across different countries. She also investigates how societal factors or events, such as recessions, rising inequality, pandemics, and climate change might contribute to the emotional and financial stress experienced by children and families.

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Mothers’ Nonstandard Work Schedules,Economic Hardship,and Children’s Outcomes

Abstract

Keywords

Mothers’ Nonstandard Work and Child Cognitive and Behavioral Outcomes

Economic Hardship as a Moderating Context for the Impact of Mothers’ Nonstandard Work

The UK Context

Methods

Data

Measures

Child Cognitive and Behavioral Outcomes

Maternal Work Schedules and Work Hours

Economic Hardship

Control Variables

Data Analysis

Results

Descriptive Differences in Economic Hardship by Work Schedules

Predicting Children’s Outcomes from Maternal Nonstandard Work Schedules

Association between Work Schedule and Economic Hardship Interactions and Child Outcomes

Supplementary Analyses

Discussion

Footnotes

Appendix

Acknowledgements

Funding

ORCID iD

Notes

Author Biographies

References