Child health and parental paid work

Child health and parental paid work

Peter Burton • Kelly Chen • Lynn Lethbridge •

Shelley Phipps

Received: 16 September 2013 / Accepted: 14 May 2014

� Springer Science+Business Media New York 2014

Abstract We ask how the paid work of Canadian married mothers and fathers is

affected when a child has a physical/mental condition or health problem that leads

to restrictions in daily activities. Using the Statistics Canada National Longitudinal

Survey of Children and Youth, we find that married mothers of children with

disabilities are less likely to engage in paid work and/or work fewer paid hours per

week. No statistically significant changes in paid work participation or hours are

apparent for fathers of the same children. We find, moreover, evidence that the

degree of specialization within families increases when there is a child with a

disability. These responses are consistent with traditional gender roles within

families, and may make sense as a ‘household’ coping strategy. However, such a

division of labor may generate economic vulnerability for mothers compared to

fathers.

P. Burton � S. Phipps

Department of Economics, Dalhousie University, Halifax, NS B3H 3JH, Canada

e-mail: [email protected]

S. Phipps


K. Chen (&)

Digonex Technologies Inc., 150 West Market Street, Indianapolis, IN 46204, USA


L. Lethbridge

Community Health and Epidemiology, Dalhousie University, 5790 University Ave, Halifax,

NS B3H 1V7, Canada


S. Phipps

Department of Economics, Dalhousie University and the Canadian Institute for Advanced Research,

Halifax, NS, Canada

123

Rev Econ Household

DOI 10.1007/s11150-014-9251-z

Keywords Child disability � Maternal labor force participation � Within-

household � Intra-household � Family � Gender

JEL Classification I14 � J14 � J16

1 Introduction

In this paper, we ask how the paid work of Canadian married1 mothers and fathers is

affected when a child has a physical/mental condition or health problem that leads

to an activity limitation.2 This is an important question given both the growth in the

number of children living at home with disabilities3 and the growth in the number of

married-couple families in which both mother and father are engaged in paid work.

Although demands placed upon families vary with the nature and severity of the

child’s health problem, it is often the case that there are both time and financial

pressures to be faced (Gould 2004; Stabile and Allin 2012).

Children with disabilities often have frequent hospitalizations, may need to

attend many medical appointments or therapy sessions, require extra help with

schoolwork or parental advocacy for their special needs and/or require additional

physical care (Beagan et al. 2005). At the same time, supports commonly available

to other parents engaged in paid work may not be available when a child has a

disability or serious health problem (Thyen et al. 1999). For example, daycare for

children with disabilities is often very limited; even friends and family may be

reluctant to take on the care of a child who requires specialized treatment. Some

parents may thus be unable to continue in paid work (or in paid work of the same

kind and quantity) if a child has serious health problems, with negative implications

for family financial well-being.

At the same time, even in Canada where doctor or hospital bills are not an issue,

many other expenses are not covered by public health insurance. For example, extra

money may be needed to build wheelchair ramps, buy hearing aids, travel from a

rural area to visit specialists in the city, or pay ‘deductibles’ on drugs. US studies

document extra financial costs incurred by families of children with disabilities or

chronic conditions (e.g., Hobbs and Perrin 1985; Lukemeyer et al. 2000; Meyers

et al. 1998). While these may be lower in Canada, data from the Statistics Canada

Participations and Activity Limitations Survey nonetheless indicate that 67 % of

1 Throughout the paper, ‘married’ refers to both legal and common-law marriages.2 Starting from 2000, there has been a change in the definition of ‘disability’ in Canadian national

surveys (Human Resources Development Canada 2003). In this paper, we focus on children reported to

experience functional limitations at home, at childcare, at school or in any other activities such as

transportation, play, sports or games, for a child of his/her age, in order to construct consistent measure

across survey years.3 Powers (2003) presents US data. Trends across time in child disability rates are harder to identify with

Canadian data, given changes in definitions used by nationally representative surveys (Human Resources

Development Canada 2003), but are likely to follow a similar trend. This may, paradoxically, be partially

due to advances in medical science which mean that children survive but live with health problems; it is

also the case that fewer children with serious disabilities are institutionalized than was previously the case

(Salkever 1982a).

P. Burton et al.

123

children with severe to very severe disabilities have ‘unmet needs’ for specialized

aids; in the majority of cases, needs were unmet due to cost (Burton and Phipps

2009; Statistics Canada 2001). Thus, families in which a child is not well may be

caught in a ‘double bind’ of needing both more time at home and more money.

1.1 Theoretical perspectives on household behavior

Since the work of Mincer (1962) and Becker (1965), theoretical models of

household behavior have recognized that families combine time and money to

produce domestic goods and services (e.g., clean clothes and hot meals) that

ultimately generate utility for family members. Both authors argue that the parent

with the higher market wage (typically the father) is likely to specialize in the paid

work necessary to earn money needed to purchase inputs to household production

while the parent with the lower market wage (typically the mother) devotes her time

to work at home. How non-market production is actually carried out by any given

family is hypothesized to depend upon available technology as well as the relative

costs of ‘production inputs’ (e.g., mother’s time versus purchased market

substitutes).

Extending these initial insights, Leibowitz (1974), in the context of child health

production and Browning (1992), in the context of parents deriving utility from the

well-being of their children both argue that the health and well-being of children

also depends upon both purchased inputs (food, clothing) and parental time. If a

child becomes disabled or develops a chronic condition, time and money needs will

both increase and one solution to the increased pressure may be increased

specialization within the family, even if this was not previously the case.4 Since

most wives earn less than their husbands, relative opportunity costs are likely to

mean that, from a family perspective, it makes most sense for the mother to reduce

paid work. Women who are mothers may also have chosen more ‘family-friendly’

jobs in order to accommodate ‘regular’ care-giving needs (e.g., chicken pox or the

‘flu) which could also make it relatively easier for them to reduce hours when faced

with more serious child health problems.

Even if the mother is not the parent with lower earnings or a more flexible job,

the ‘identity’ model proposed by Akerlof and Kranton (2000) suggests that she may

be the one who reduces paid work to do the care-giving. In this context, behavior

consistent with a ‘good mother’ identity might be to prioritize the care-giving role.

The mother of a child who is seriously ill may feel she ‘should’ be his or her

principal care-giver (and this may be reinforced if extended family and health-care

workers also think this should be the mother’s role). Qualitative evidence is

consistent with such reasoning. In a study of parents of children with high-

functioning autism, Gray (2003) quotes a mother as saying: ‘‘Yes, I do work but…of course, again [my son] dominated that’’ (p. 636).

4 Lone parents obviously do not have this option and so are faced with extraordinarily difficult

circumstances. However, we do not study lone mothers in this paper given our focus on ‘household’

responses with potentially different roles for mothers and fathers connected to gendered norms.


123

On the other hand, behavior consistent with a ‘good father’ identity when a child

is very ill or disabled might be to prioritize bread-winning (e.g., by not reducing

current paid hours or even possibly increasing them, though options for increasing

hours are likely to be limited if most fathers are already working full time). Again,

such behavior may be reinforced through the re-actions of outsiders, including

extended family and health-care professionals. Evidence from qualitative studies is

also consistent with this idea: ‘‘So, I’d basically come home and have my tea,

shower, bit of rest, change, go back to the office and do another 3 h of work, which

was quite stressful’’ (father quoted by Gray 2003, p. 635).

Notice, too, that policies and institutions may, perhaps inadvertently, serve to

reinforce adherence to traditional norms of behavior. For example, if standard paid

work weeks involve very high hours or if there is no daycare available for children

with special needs, it will be difficult for parents to share bread-winning and care-

giving; to behave as a ‘dual-earner/dual-carer’ couple (Gornick and Meyers 2003).

While specialization may make sense as a family coping strategy, it can have

negative implications for the personal economic well-being of the mothers. This

point is also noted in the literature on bargaining models of household behavior

which emphasize the relative earnings of husband and wife as key predictors of

bargaining power (e.g., Chen and Woolley 2001; Lundberg et al. 1997). Thus, role

specialization as a means of coping with a child disability has the potential of

reducing the mother’s bargaining power within marriage if it leads to the erosion of

job-related human capital and hence earnings potential over time. Second, reducing

paid hours or withdrawing completely from paid work may have negative long-term

implications for the mother’s financial well-being if the couple should divorce,5 or

even if they remain married but she does not have the opportunity to gain labor

market experience, pension entitlements, etc. This might be viewed as a more

extreme version of the ‘child penalty’ documented for women with children

compared to women without, regardless of the child’s health (see, for example,

Waldfogel 1998).

In summary, we hypothesize that married mothers and fathers will not respond in

the same way to a reduction in child health status. Instead, it seems likely that: (1)

mothers will reduce paid hours or even withdraw from the labor market; (2) fathers

will not reduce paid hours (and indeed may, if anything, do more hours of paid

work); (3) there will, as a result, be increased specialization according to traditional

gender roles within the family. To investigate these hypotheses, we use a sample of

children with married-couple parents drawn from Statistics Canada’s National

Longitudinal Survey on Children and Youth (NLSCY).

Our main contributions to the literature are as follows: first, since we have labor

market data for both mother and father, we can study differences in their responses.

Results suggest increased specialization within the family if there is a child with a

disability present. We thus highlight the ‘household’ nature of parental decision

5 Pollak (1985) notes that complete specialization in non-market production might be regarded as an

extreme investment in ‘marriage-specific’ human capital, which would both increase the ‘payoff’ to

remaining married, but also reduce the ‘payoff’ to leaving the marriage if no market human capital is

acquired (Pollak 1985). .

P. Burton et al.

123

making when a child develops a disability, an aspect of the situation which has thus

far received very little attention in the literature.

Second, we use longitudinal data to study the onset of child disability on parental

paid work in order to compare families in which a child develops a disability with

other families with similar observable characteristics. We find that mother’s

participation in paid work and usual weekly hours fall, controlling for her labor

market behavior before the activity limitation appeared; no association appears to

exist for fathers of the same children.

Third, since much of the existing literature on child disability and parental paid

work uses US data, but policies and institutions for families of children with

disabilities differ across countries, it is important to examine the impact of child

disability on parental paid work in other contexts. Policy differences between

Canada and the US which may be relevant in this context are: (1) medical expenses

connected with child disability are likely lower in Canada given universal public

health insurance; (2) nearly all families with children receive cash transfers and

families of children with disabilities receive extra benefits (see Burton and Phipps

2009).6 Thus, the need to increase paid work in order to cover additional expenses

may be somewhat less urgent for Canadian families.7

The remainder of the paper is organized in the following way: Sect. 2 provides a

brief review of the relevant literature. Section 3 describes the data. Contempora-

neous estimates of the association between child disability and parental paid work

are presented in Sect. 4. Section 5 discusses the onset models. Section 6 provides

discussion and conclusions.

2 Previous empirical literature

A small early literature using primarily US data studied cross-sectional associations

between maternal paid work and child disability, thus mingling health conditions

that have just appeared with those that have existed for many years. Findings from

these early studies are fairly consistent, indicating reduced probabilities of

participating in paid work by married mothers of children with disabilities (e.g.,

Breslau et al. 1982; Gould 2004; Kimmel 1998; Powers 2003; Salkever 1982a, b) as

well as lower paid hours, given participation (Gould 2004; Powers 2003; Salkever

1982b).8 Larger negative impacts are apparent for lower-income mothers (Breslau

et al. 1982; Salkever 1982b); smaller associations are apparent when young children

6 As well, some medical expenses can be deducted from taxes owing and tax credits are also available.

See Burton and Phipps (2009).7 Six weeks of ‘Compassionate Care’ benefits as part of the Canadian Employment Insurance program

were also available during our study period; however, parents in our sample would not generally be

eligible since these benefits were only available if the child is ‘was at significant risk of death’.

Compassionate Care take-up rates were thus very low. .8 In contrast, a slightly larger literature focussed on lone mothers generates more ‘mixed’ results,

sometimes finding no impact on paid work (e.g., Salkever 1982a; Kimmel 1997, 1998), sometimes finding

a negative impact (e.g., Baydar et al. 2007; Breslau et al. 1982; Lukemeyer et al. 2000; Salkever 1990;

Wolfe and Hill 1995).


123

are present in the family (Powers 2001; Salkever 1982b). Several studies point out

that size of the estimated association between child disability and mother’s paid

work will vary with the nature and severity of the child’s health problem, with larger

reductions when the condition is more severe or when multiple disabilities are

present (e.g., Powers 2003; Salkever 1982b).

More recent studies have examined the dynamics of maternal response to a

child’s disability (see also Stabile and Allin 2012 for a recent review). For example,

Powers (2003) uses pooled SIPP panels to estimate changes in work activity

(‘dropping out’ of paid work; changes in paid hours) for mothers whose children

have disabilities in the starting year of the analysis. All explanatory variables take

starting-year values. Powers is able to estimate changes in maternal labor market

activity one year later and two years later. Results for her dynamic models are

somewhat weaker than for her static, cross-sectional models in the sense that fewer

variables are statistically significant. For married mothers, she finds little evidence

that having a child with a disability affects hours or participation in paid work (in

fact, the only cases of statistical significance for child disability variables indicate,

somewhat non-intuitively, increases in hours).

Using the U.S. ‘Fragile Families’ data, focused on low-income unmarried

mothers, Corman et al. (2005) find that mothers of children born with health

problems are less likely to be in the labor force when the child is one year old,

controlling for baseline characteristics of both mother and father. Baydar et al.

(2007) estimate probabilities of withdrawing from full-time employment for

mothers of children with asthma and find that single mothers (though not married

mothers) of children with asthma had increased odds of leaving full-time work.

Finally, Kvist et al. (2013) use Danish register data to estimate labor supply for

parents of first-born ten-year old children diagnosed with ADHD. The particular

strengths of their study are that they need not rely on parental assessments of child

health and can condition on a rich set of covariates measured prior to the birth of the

child. They find, for both mothers and fathers, a reduction in paid work. The size of

the association is larger for mothers than fathers (e.g., 5–8 days per year vs. 4–6).

Mothers of children with ADHD also have a 2 % point lower probability of

participation in paid work.

Gould (2004) very usefully emphasizes that some disabilities are demanding of

parental time, others are very expensive and some require both time and money.9

She develops an individual model of mother’s behavior reflecting the different

implications of these aspects of child disability, conducts focus groups with medical

experts to ascertain which sorts of disabilities will require more time compared to

money and supports her model with an empirical analysis using the PSID.

As is evident from the discussion above, much of the research on the paid work

implications of parenting a child with a disability has focussed on implications for

the mother. However, Salkever (1982b) symmetrically studies father’s responses,

and finds no statistically significant association with child disability status; Noonan

9 Hobbs and Perrin (1985) provide discussions of the nature and implications of individual childhood

chronic conditions.

P. Burton et al.

123

et al. (2005) find reductions in the labor supply of fathers of newborns with health

problems in a US population of mostly unwed parents.

There has thus far been relatively little explicit discussion in the economics

literature of the possible ‘household’ nature of responses to the onset of child health

problems. Although most empirical studies of the paid work of married mothers of

children with disabilities control for the husband’s characteristics (e.g., his earnings,

health status), the idea that both parents may respond, while perhaps specializing

according to traditional roles is not explicitly considered. The relative lack of

attention to what happens to the paid work of fathers of children10 with disabilities

may also in itself be indicative of implicit assumptions that married parents will

behave in accordance with traditional roles—that fathers will specialize as bread-

winners and not reduce paid hours or withdraw from paid work when a child has

health problems.

Following on from Gould’s (2004) argument that child health problems can be

demanding in terms of both time and/or money, it is plausible to suppose that for

married-couple parents, deciding how to cope with the onset of a child’s serious

health problem will require household decision-making. Although not the focus of

her paper, Gould recognizes the possibility of ‘household’ responses to a child’s

health problem by married-couple parents by summing mother’s and father’s hours

and estimating the impact on total family hours (Table 6, p. 536). She finds no

statistically significant relationships between total family hours and her child

disability measures. Her interpretation, not pursued, is that there is ‘potential

substitution between mother’s and father’s work hours’ (p. 538).

3 Data

The data set employed is the Statistics Canada National Longitudinal Survey of

Children and Youth (NLSCY), a nationally representative survey of Canadian

children conducted every 2 years from 1994 to 2008). The surveys we use were

answered by the ‘person most knowledgeable’ about the child (or, pmk). Children in

our sample range from age 0 to 17 (18 is the age of majority in Canada).

Our parental paid work variables are derived from a question about ‘usual’ hours

in the year before the survey: ‘‘During the past 12 months, how many weeks did %

you/he/she % do any work at a job or business? Include weeks on paid vacation

leave, paid maternity or parental leave,11 paid sick leave. About how many hours a

week did % you/he/she % usually work?’’

These questions are answered by the pmk, for both parents. Since in 96 % of our

sample, the pmk is the mother of the child, it is possible that mother’s paid work

time is more accurately reported than father’s, though we would argue that

10 Some surveys may not provide details on father’s labor market behaviour, if, for example he is not

present in the household (i.e., does not reside with the child).11 Since maternity and parental leaves are not counted as time outside the labor market, we should not

expect any impact of the 2001 extension of parental benefits on the reported paid work of new parents.


123

participation in paid work is easily observable and even paid hours per week can be

quite accurately reported by one’s spouse.

We define a child to have a disability if he or she has an activity limitation that

prevents his/her activities at home, at childcare, at school or in any other activities,

for example, transportation, play, sports or games, for a child of his/her age.12 Note

that these are not mutually exclusive categories—a child could be limited in

multiple functional domains. Averaged across all cycles, 6.1 % of children meet one

or more of these criteria.

As noted earlier, the ‘usual weekly hours of paid work’ refer to the year

preceding the survey while child activity limitations are reported for the survey

year. Since we want to be certain that the child’s disability status is contempo-

raneous with reported labor market status, we use child’s activity limitation status

from 1 cycle earlier (and exclude children who ‘recovered’). Thus, for example, a

survey conducted in 2008 would report labor force status for calendar year 2007.

We would then use child’s disability status as reported for 2006 (with any children

activity limited in 2006 but no longer limited in 2008 excluded).13

Since all information about the child’s health is reported by the pmk, the question

of reliability of such reports may arise. While there is some evidence of

inconsistencies between medical records and mother reports (e.g., Miller et al.

2001), in general, consistency between medical records and self reports appears to

increase with the severity of the condition (e.g., Baker et al. 2001). We argue that

the activity limitation measure we study is both relatively severe and that the

questions are easier to answer than questions such as ‘how healthy is your child?’

Powers (2001) argues that reports of the severity of child disability status may be

endogenous to maternal labor market behavior (e.g., because mothers who withdraw

attempt to justify their behavior by emphasizing the severity of the child’s

condition). However, Powers also argues that maternal reports of specific

impairments are more likely objective. Since the NLSCY does not ask pmks to

assess the severity of the condition, but only to report if there is any restriction of

activities, we believe that reporter bias is likely to be fairly small in our case.14

Given our interest in comparing labor market responses of both mothers and

fathers, we also select only children in married-couple households. It is, however,

important to recognize the possibility that parenting a child with a disability or

chronic condition may increase the probability of parental divorce, and that, if this

12 The survey questions that we used to construct child disability variable are as follows: ‘‘Does child

have any long term conditions or health problems which prevent or limit % his/her % participation in

school, at play, or in any other activity for a child of % his/her % age?’’ (cycles 1–3), or ‘‘Does a physical

condition or mental condition or health problem reduce the amount or the kind of activity this child can

do: (1) at home? (2) at childcare? (3) at school? (4) in other activities, for example, transportation, play,

sports or games? (cycles 4–8)’’.13 We have also estimated all models retaining the ‘recovering’ children. Results are not affected.14 A limitation of the NLSCY is that it is not possible to provide separate estimates for children more or

less severe activity limitations or with specific conditions, though as emphasized by Salkever (1982a, b),

Powers (2003) and Gould (2004), results may be sensitive to the definition of child disability employed,

since both time and financial demands will vary with the nature and severity of the disability. Observed

patterns of specialization could differ depending upon whether the child’s health problem is more

demanding of time or money (Gould 2004).

P. Burton et al.

123

is so, then we are probably under-stating some of the potential negative

consequences of parenting a child with a disability. Evidence on this point appears

mixed. Hoddap and Krasner (1994); Lehrer (2003) and Mauldron (1992) find

evidence of reduced family functioning and/or increased probability of divorce for

families with a disabled child; whereas, Haven (2005) and Seltzer et al. (2001) do

not.

After excluding children with missing information for any analysis variable, our

basic sample consists of 40,656 observations.

All estimates are carried out using longitudinal survey weights. Since some

children can appear twice and/or siblings can be present in the data, standard errors

are adjusted to take account of the non-independence of these observations (i.e.,

clustering at the household level).

3.1 Descriptive statistics for ‘contemporaneous’ sample

Table 1 provides a first indication of the associations between parental labor market

activity and child functional status. Unconditionally, mothers15 of children with

disabilities, are slightly less likely to engage in paid work than mothers of children

with no reported disabilities (82 % compared to 84 %—a small but statistically

significant difference); mean current weekly hours are also lower (27.5 compared to

28.2). There are no statistically significant differences in participation or paid hours

for fathers of the same children.

Finally, for each family we construct a measure of the difference between father

and mother paid hours (father hours minus mother hours) and find that this

difference is higher for families with a disabled child (15.3 vs 13.8).

Table 1 Parental paid work by contemporaneous child disability statusa

Child without

activity limitation

Child with activity

limitation

Mothers

In labor force participation 0.84 (0.003) 0.82 (0.01)

Usual weekly hours (including zeros) 28.15 (0.13) 27.49 (0.53)

Fathers

In labor force 0.96 (0.002) 0.96 (0.005)


Difference in usual weekly parental paid

hours (father minus mother, including zeros)

13.78 (0.17) 15.32 (0.62)

Number of observations 38,186 2,470

Standard errors are in parentheses

15 Recall that we only study children with two parents. If the pmk is female, we code her as the mother

and her spouse as the father. If the pmk is male, we code him as the father and his spouse as the mother.

We did not identify any same-sex couples in the data. .


123

4 ‘Contemporaneous’ models

We first use probit models to estimate the probability that the mother is engaged in

paid work and GLM models with gamma error distributions for her usual weekly

paid hours.16 Our key explanatory variable is the dummy indicating that the child

has a disability. Consistent with other empirical studies in this area (e.g., Powers

2003; Gould 2004), we control for personal characteristics of the mother as reported

in the same cycle as the labor force variables. These include: age (mean of 39) and

age squared; education level (52.9 % have at least some post-secondary education);

immigrant status (15.2 % are born outside of Canada); own self-assessed health

status (19.6 % have an activity limitation, chronic condition, or fair/poor health).

We also control for family characteristics likely to affect a mother’s reservation

wage: father’s health status (18.3 % have an activity limitation, chronic condition,

or fair/poor health status); and number of children (mean of 2.3). Finally, we control

for whether the family resides in a rural area (14.5 %) and for the provincial

unemployment rate (mean of 7.6 %), both likely indicative of local employment

opportunities.

The same models of participation and hours are estimated for fathers (substituting

‘mother’ for ‘father’ as appropriate); and, OLS models of differences in parental

paid hours are estimated, again using the same explanatory variables. Means and

frequencies of control variables are reported in Table 2.

Estimation results for participation in paid work are reported in Table 3;

estimates for weekly hours are reported in Table 4; and, estimates for differences

between father and mother hours are reported in Table 5. In each case, we estimate

three model specifications, a basic model [column (1)] that controls only for the

child’s functional status and survey year fixed effects, an enhanced model [column

(1)] that additionally controls for parental and family characteristics except for self-

reported health status and a most comprehensive model [column (3)] that includes

everything. Although controlling for own and spouse health is common in the this

literature, we report separate estimates for models that include these variables given

that parental health is also be affected by child disability (see, for example, Burton

et al. 2008a, b).

For mothers, both participation in paid work and weekly hours of paid work are,

other things equal, lower for mothers of children with activity limitations.17 For

fathers of the same children, no statistically significant association between child

health and labor market behavior is evident. Finally, the difference between father

and mother hours is larger if the child is activity limited.

16 Given the non-negative and right skewed nature of the parental paid hour variable, the GLM models

with a gamma distribution appear most appropriate. We also estimated Tobit models. Despite yielding

qualitatively similar results, the Tobit assumption of normality is strongly rejected by the Lagrange

multiplier test (at 1 % significance level) in all cases. Since there is no clear theoretically best choice for

the link function, we follow Hardin and Hilbe (2012) and used a power analysis to determine the optimal

link. The result suggests that the preferred one is the canonical inverse (power = -1). We thus present

results only from inverse-gamma models in the paper, although they are generally similar among different

links such as the log (power = 0).17 The coefficient on child disability in the labor force participation model for mothers is, however, no

longer statistically significant when we control for mother’s health status.

P. Burton et al.

123

5 Estimation of ‘onset’ models

There are several potential criticisms of contemporaneous estimates of the

association between child disability and parental labor market behavior. First, child

disability status in any given year mingles together children who have always had a

disability with children who have just developed a problem. Thus, a first criticism is

that labor market implications may be different during the initial adjustment period

than over the longer-term (and it isn’t obvious which would be larger—daycare for a

special needs child might be found with enough time, for example, but the parent

might ‘burn out’ and feel less able to continue with both paid work and care-giving).

Also, since some studies suggest that women may increase labor supply prior to a

divorce (e.g., Johnson and Skinner 1986), and, as noted earlier, child disability has

been found to increase the probability of divorce, this may also serve to obscure the

connection between child disability and labor supply in a cross-sectional analysis.

Since our data set does not provide health histories from birth for most children,

we cannot know when a currently existing problem began. It is thus not an option to

study labor market implications of complete child health histories. We can,

however, start with a sample of children who all have a ‘clean bill of health’ and

trace the implications for parental labor market activity of a health problem that first

emerges during our study period. We refer to this as our ‘onset’ model.

A second limitation of estimates of contemporaneous associations between child

health and parental labor market behavior is that we know there are likely to be

unobservable differences between parents in degree of career motivation,

Table 2 Means for independent variables

All

children

No activity

limitation

Activity

limited

Child’s age 9.7 9.7 10.9

Mother’s age 39 38.8 40

Father’s age 41.1 41.0 42.1

Mother has post-secondary diploma or university degree (%) 52.9 52.7 56.1

Father has post-secondary diploma or university degree (%) 52.2 52.0 55.5

Mother has an activity limitation, chronic condition or fair/

poor health status (%)

19.6 18.6 35

Father has an activity limitation, chronic condition or fair/

poor health status (%)

18.3 17.6 28.1

Pmk is an immigrant (%) 15.2 15.6 9.9

Father’s total earnings (2008 constant dollars) 59,718 59,872 57,363

Mother’s total earnings (2008 constant dollars) 30,979 31,112 29,002

Number of children in the family 2.3 2.3 2.3

Rural residence (%) 14.5 14.4 15.3

Provincial unemployment rate (%) 7.6 7.6 7

Number of observations 40,656 38,186 2,470

Contemporaneous sample


123

Ta

ble

3M

argin

alef

fect

sfr

om

pro

bit

model

sof

par

enta

lla

bor

mar

ket

par

tici

pat

ion

Moth

erF

ather

(1)

(2)

(3)

(1)

(2)

(3)

Dum

my=

1if

chil

d

has

adis

abil

ity

-0.0

243*

(0.0

88)

-0.0

310**

(0.0

34)

-0.0

187

(0.1

81)

0.0

00192

(0.9

78)

0.0

00494

(0.9

35)

0.0

0375

(0.4

49)

Moth

er’s

age

0.0

226***

(0.0

05)

0.0

223***

(0.0

05)

0.0

0620*

(0.0

86)

0.0

0610*

(0.0

57)

Moth

er’s

age

squar

ed-

0.0

00288***

(0.0

05)

-0.0

00285***

(0.0

06)

-0.0

000821*

(0.0

70)

-0.0

000832**

(0.0

37)

Fat

her

’sag

e0.0

145**

(0.0

38)

0.0

139**

(0.0

48)

0.0

131***

(0.0

00)

0.0

118***

(0.0

00)

Fat

her

’sag

esq

uar

ed-

0.0

00212***

(0.0

10)

-0.0

00203**

(0.0

14)

-0.0

00175***

(0.0

00)

-0.0

00152***

(0.0

00)

Moth

erhas

post

-sec

ondar

y

dip

lom

aor

univ

ersi

ty

deg

ree

0.0

850***

(0.0

00)

0.0

828***

(0.0

00)

0.0

0692

(0.1

62)

0.0

0570

(0.1

87)

Fat

her

has

post

-sec

ondar

y

dip

lom

aor

univ

ersi

ty

deg

ree

0.0

0446

(0.6

43)

0.0

0353

(0.7

10)

0.0

199***

(0.0

00)

0.0

166***

(0.0

00)

Pm

kis

anim

mig

rant

-0.0

457**

(0.0

49)

-0.0

448*

(0.0

50)

-0.0

108

(0.0

93)

-0.0

103

(0.1

10)

Rura

lre

siden

ce-

0.0

234**

(0.0

41)

-0.0

253**

(0.0

28)

0.0

0766*

(0.0

71)

0.0

0633*

(0.0

83)

Num

ber

of

chil

dre

nin

the

fam

ily

-0.0

415***

(0.0

00)

-0.0

428***

(0.0

00)

-0.0

0415*

-0.0

0379*

(0.0

78)

Chil

d’s

age

0.0

0726***

(0.0

00)

0.0

0755***

(0.0

00)

0.0

0233***

(0.0

01)

0.0

0234***

(0.0

00)

Pro

vin

cial

unem

plo

ym

ent

rate

-0.0

0866***

(0.0

00)

-0.0

0892***

(0.0

00)

-0.0

0335***

(0.0

00)

-0.0

0315***

(0.0

00)

Hea

lth

stat

us

of

moth

er-

0.0

938***

(0.0

00)

-0.0

0234

(0.6

35)

Hea

lth

stat

us

of

fath

er-

0.0

137

(0.2

70)

-0.0

675***

(0.0

00)

N40,6

56

40,6

56

40,6

56

40,6

56

40,6

56

40,6

56

Conte

mpora

neo

us

chil

ddis

abil

ity

stat

us

(1)

Mar

gin

alef

fect

s(i

.e.

inst

anta

neo

us

rate

sof

chan

ge

for

conti

nuous

var

iable

san

ddis

cret

ech

ange

of

dum

my

var

iable

from

0to

1)

from

pro

bit

regre

ssio

ns

are

report

ed;

(2)

two-

tail

edp

val

ues

inpar

enth

eses

wit

hS

Ecl

ust

ered

atth

ehouse

hold

level

.(3

)C

ycl

ean

dco

hort

dum

mie

sar

ein

cluded

but

not

report

ed

*p

\0.1

,**

p\

0.0

5,

***

p\

0.0

1

P. Burton et al.

123

attachment to paid work, etc. In addition to sorting out the history/dynamics of the

child’s health condition, a key advantage of the ‘onset’ estimation approach is that

we are able to control for parental labor market activity before the child’s health

status falls, thus helping us deal with the issue of unobserved heterogeneity.

Finally, estimates using only contemporaneous information might be criticized

insofar as values for some control variables may, in part, reflect responses to the

child’s health condition. For example, families may have moved from the country to

the city in order to be nearer to needed specialists/therapists; parents’ own health

may have been compromised. The onset estimation approach allows us to set all

control variables at their ‘pre-child disability’ values.

To estimate the impact of the onset of child health problems on the labor market

behavior of their parents, we again pool panels of data constructed from cycles 1–8.

Sample selection criteria are as previously described with the addition that we now

select only children who had no reported disability at the first observation.

Although, as outlined above, they solve some technical problems, one important

limitation of the onset models is thus that we are excluding some of the most

seriously disabled children from our sample (e.g., those who have had disabilities

from birth or early life).

After the additional exclusion, we have a sample of 32,767 children who did not

have an activity limitation at the first observation.18

5.1 Descriptive statistics

From our sample of children in married-couple families who were healthy at the first

observation, 4.4 % experienced the onset of disabilities between the first and second

observations (i.e., an activity limitation not reported at the first observation appeared

at the second). For children who remained healthy, 87 % of mothers engaged in

some paid work compared to 79 % of mothers whose children developed health

problems (see Table 6). Mother’s current weekly hours are also lower following a

fall in child health status, 29.8 h per week compared to 27.3 h. For fathers, a slight

increase in the probability of engaging in paid work is evident (97 % compared to

96 % were engaged in paid work,); mean hours are also slightly higher for the

sample where child health status declined (43.5 h per week compared to 43 h per

week).

18 It is possible that the incidence of child disability is not a ‘random event’ that is equally likely to

happen to any child in the population. For example, if reductions in health status are more likely in rural

areas and labor force participation is also lower in rural areas, then we might observe an association

between incidence of child disability and low rates of participation without necessarily any causal

connection. To help address this concern, we use a ‘propensity score reweighting’ technique (Rosenbaum

1987; Hirano and Imbens 2001) that involves constructing a scalar weight based on estimated propensity

scores to create a ‘balanced’ sample in order to compare the labor market behavior of parents of children

with disabilities to parents whose children remain healthy but are otherwise as similar as possible in terms

of other observable characteristics. Results obtained using propensity score reweighting are qualitatively

very similar to the onset results reported here. They are available in an earlier version of the paper,

available on request.


123

Ta

ble

4M

argin

alef

fect

sfr

om

GL

M-g

amm

am

odel

sof

par

enta

lw

eekly

pai

dhours

Mo

ther

Fat

her

(1)

(2)

(3)

(1)

(2)

(3)

Du

mm

y=

1if

chil

d

has

ad

isab

ilit

y

-1

.735

**

*(0

.00

4)

-1

.730

**

*(0

.00

2)

-1

.45

1*

**

(0.0

10)

0.2

63

(0.5

04)

-0

.039

4(0

.91

4)

0.1

83

(0.6

19)

Mo

ther

’sag

e1

.372

**

*(0

.00

9)

1.3

41

**

(0.0

11)

-0

.134

(0.7

55)

-0

.10

6(0

.80

0)

Mo

ther

’sag

esq

uar

ed-

0.0

18

3*

**

(0.0

07)

-0

.01

79

***

(0.0

00)

0.0

00

94

0(0

.86

7)

0.0

00

49

1(0

.92

9)

Fat

her

’sag

e0

.903

*(0

.06

2)

0.9

08

*(0

.06

0)

2.5

72

**

*(0

.00

0)

2.5

13

**

*(0

.00

0)

Fat

her

’sag

esq

uar

ed-

0.0

12

6*

*(0

.03

1)

-0

.01

26

**

(0.0

30)

-0

.033

0*

**

(0.0

00

)-

0.0

32

0*

**

(0.0

00)

Mo

ther

has

po

st-s

eco

nd

ary

dip

lom

ao

ru

niv

ersi

tyd

egre

e

4.0

05

**

*(0

.00

0)

3.9

33

**

*(0

.00

0)

0.4

30

(0.1

31

)0

.405

(0.1

45)

Fat

her

has

po

st-s

eco

nd

ary

dip

lom

ao

ru

niv

ersi

tyd

egre

e

-0

.707

*(0

.09

8)

-0

.70

2*

(0.0

97

)0

.90

0*

**

(0.0

03)

0.7

91

**

*(0

.00

7)

Pm

kis

anim

mig

ran

t0

.247

(0.7

92

)0

.259

(0.7

80)

-0

.488

(0.3

38)

-0

.46

4(0

.36

5)

Rura

lre

sid

ence

-0

.681

(0.1

94)

-0

.71

4(0

.17

0)

1.3

60

**

*(0

.00

0)

1.3

22

**

*(0

.00

0)

Nu

mb

ero

fch

ild

ren

inth

efa

mil

y-

2.9

24

**

*(0

.00

0)

-2

.95

0*

**

(0.0

00)

-0

.281

(0.1

31)

-0

.28

1(0

.12

5)

Chil

d’s

age

0.3

25

**

*(0

.00

0)

0.3

33

**

*(0

.00

0)

0.2

58

**

*(0

.00

0)

0.2

58

**

*(0

.00

0)

Pro

vin

cial

un

emp

loy

men

tra

te-

0.1

42

*(0

.07

3)

-0

.15

1*

(0.0

57

)-

0.4

21

**

*(0

.00

0)

-0

.42

7*

**

(0.0

00)

Hea

lth

stat

us

of

mo

ther

-2

.33

4*

**

(0.0

00)

-0

.17

0(0

.63

9)

Hea

lth

stat

us

of

fath

er-

0.0

92

2(0

.84

3)

-3

.33

0*

**

(0.0

00)

N4

0,6

56

40

,65

64

0,6

56

40

,65

64

0,6

56

40

,65

6

Co

nte

mp

ora

neo

us

chil

dd

isab

ilit

yst

atu

s

(1)

Mar

gin

alef

fect

s(i

.e.

inst

anta

neo

us

rate

so

fch

ang

efo

rco

nti

nu

ou

sv

aria

ble

san

dd

iscr

ete

chan

ge

of

du

mm

yv

aria

ble

from

0to

1)

fro

mg

amm

are

gre

ssio

ns

are

report

ed;

(2)

two

-tai

led

pv

alu

esin

par

enth

eses

wit

hst

andar

der

rors

clu

ster

edat

the

ho

use

ho

ldle

vel

.(3

)C

ycl

ean

dco

ho

rtd

um

mie

sar

ein

clu

ded

bu

tn

ot

rep

ort

ed

*p

\0

.1,

**

p\

0.0

5,

**

*p\

0.0

1

P. Burton et al.

123

5.2 Multivariate results for onset models

Using our sample of children in married-couple families who, at the first observation,

did not have any reported disabilities, we again estimate probit models of the

probability that the mother (father) engaged in any paid work as well as GLM-gamma

models of weekly hours of paid work at the third observation. The key explanatory

variable is now a dummy equal to one if the child developed an activity limitation

between the first and second observations and we control both for whether or not the

parent engaged in paid work at the first observation and how many hours she/he

worked per week (before any child disabilities were apparent). Other explanatory

variables are as described above, except that we use characteristics of the parent,

family and region at the first rather than the second observation (means are reported in

Table 7). We also additionally control for spouse’s earnings at the first observation.

Marginal effects for estimated probit models of labor force participation are

reported in columns 2 and 3 of Table 8. We find that married mothers of children

who develop disabilities between the first and second observations are less likely to

be engaged in paid work, controlling for both labor market participation and weekly

Table 5 OLS models of difference between usual weekly hours of parents (father minus mother)

Difference in weekly hours

(1) (2) (3)

Dummy=1 if child has

a disability

2.050*** (0.004) 1.830** (0.010) 1.732** (0.016)

Mother’s age -1.212** (0.038) -1.153** (0.049)

Mother’s age squared 0.0154** (0.039) 0.0146* (0.051)

Father’s age 1.221*** (0.007) 1.165** (0.011)

Father’s age squared -0.0148*** (0.006) -0.0139*** (0.009)

Mother has post-secondary

diploma or university degree

-3.703*** (0.000) -3.668*** (0.000)

Father has post-secondary

diploma or university degree

1.751*** (0.001) 1.644*** (0.002)

Pmk is an immigrant -0.775 (0.500) -0.757 (0.511)

Rural residence 2.018*** (0.001) 2.032*** (0.001)

Number of children in the family 2.441*** (0.000) 2.474*** (0.000)

Child’s age -0.0694 (0.389) -0.0775 (0.332)

Provincial unemployment rate -0.272*** (0.003) -0.270*** (0.004)

Health status of mother 2.472*** (0.000)

Health status of father -3.252*** (0.000)

N 40,656 40,656 40,656

Contemporaneous Child Disability Status

(1) Regression coefficients from OLS regressions are reported; (2) two-tailed p values in parentheses with

SE clustered at the household level. (3) Cycle and Cohort dummies are included but not reported

* p \ 0.1, ** p \ 0.05, *** p \ 0.01


123

hours prior to the onset of the child’s condition.19 For married fathers, there is no

association between current labor force participation and the onset of health

problems for the child.

Results for estimated GLM-gamma models of weekly paid hours are reported in

columns 4 and 5 of Table 8, for married mothers and fathers, respectively. As was

also true for the contemporaneous models, for mothers we find that weekly paid

hours are lower, controlling for baseline hours, if a child develops a disability

between the first and second observations. Moreover, the size of this effect is

relatively large if we use having an additional child as a basis of comparison.

Specifically, the onset of an activity limitation is associated with a 2.2 % point

reduction in the probability that a mother will participate in paid work, versus a

3.1 % reduction with the addition of an additional child. Hours of work are

estimated to fall by 1.1 h if the child develops an activity limitation compared to

1.3 h for the addition of another child to the family.

For fathers of the same children, we do not find statistically significant

associations between paid work and the onset of a child’s activity limitation.

Column 6 of Table 8 reports results for estimated OLS model of differences in

weekly paid hours between married mothers and fathers, which is larger (about 2 h)

if the child develops an activity limitation, versus a 1.6 h increase in the gap when a

new child appears.

This is not consistent with our theoretical prediction that fathers of children who

develop disabilities are likely to increase specialization in market production.

Table 6 Parental paid work by child disability onset

No child disability develops

in the 2nd observation

Child disability develops

in the 2nd observation

Mothers

In labor force (%) 0.87 (0.004) 0.79 (0.019)


Fathers

In labor force (%) 0.96 (0.002) 0.97 (0.006)


Difference in hours (including zeros) 13.42 (0.19) 15.32 (0.89)

Number of observations 31,310 1,456

No children with disabilities in first period

Standard errors in parentheses

19 Although some authors have found larger negative impacts for lower-income mothers (Breslau et al.

1982; Salkever 1982b), we find no difference in effect for mothers with high school or less education,

controlling for prior labor market behavior (i.e., the interaction between low education and onset of child

disability is statistically insignificant). Also, we find no statistically significant difference for older versus

younger children (whereas Salkever 1982b found smaller associations for younger children using cross-

sectional US data). This may reflect higher rates of labor force participation for women with young

children in the late 1990’s and 2000’s.

P. Burton et al.

123

However, almost all the fathers in our sample already do many hours of paid work

per week regardless of child functional status. For example, 83 % of fathers in our

sample work more than 40 h per week before the onset of the child’s health

problem; indeed, 30 % already work more than 50 h per week. Thus, it may difficult

for them to obtain (or do) more hours. The key point is perhaps simply that fathers

do not appear to have lower participation in paid work when there is a child with a

disability in the family.

Our results of ‘no change’ for Canadian fathers are consistent with Salkever

(1982b) though not with Noonan et al. (2005). A potential explanation for the

difference between our findings and those of Noonan, Reichman and Corman could

be that they use the US ‘Fragile Families’ survey with a high proportion of younger

unwed fathers who may have more marginal attachment to the labor force than a

nationally representative sample of married Canadian fathers with strong labor force

attachment.

Table 7 Means for independent variables

Average No activity

limitation

Activity

limited

Child’s age 8.0 7.9 8.9

Mother’s age 37.3 37.2 38

Father’s age 39.5 39.5 40

Education of mother post-secondary diploma

or university degree %

46.4 46.6 42.4

Education of father post-secondary diploma

or university degree %

47.1 47.3 42.6

Mother has an activity limitation, chronic

condition or fair/poor health status %

21.8 21.5 27.4

Father has an activity limitation, chronic

condition or fair/poor health status %

20.4 20.3 22.5

Pmk is an immigrant % 15.1 15.3 11.8

Father’s total earnings (2008 constant dollars) $49,494 $49,381 $51,908

Mother’s total earnings (2008 constant dollars) $25,824 $25,782 $26,732

Number of children in the family 2.3 2.3 2.4

Change in the number of children -0.03 -0.03 -0.05

Rural residence % 14.5 14.5 14.7

Provincial unemployment rate % 8.2 8.2 7.5

Change in provincial unemployment rate -0.87 -0.88 -0.55

Dummy=1 if mother is in the labor force 82.6 82.7 79.1

Mother’s usual weekly hours of paid work 28.1 28.2 27.3

Dummy=1 if father is in the labor force 97.0 97.0 97.2

Father’s usual weekly hours of paid work 43.2 43.2 43

Number of observations 32,767 31,455 1,312

First period. Onset sample

All level covariates measured at the baseline year


123

Ta

ble

8P

aren

tal

lab

or

mar

ket

par

tici

pat

ion

and

the

on

set

of

chil

dd

isab

ilit

y

Mo

ther

LF

PF

ath

erL

FP

Mo

ther

ho

urs

Fat

her

ho

urs

Dif

fere

nce

inh

ou

rs

Du

mm

y=

1if

chil

dd

evel

op

s

dis

abil

ity

-0

.021

7*

(0.0

81)

0.0

01

64

(0.7

31)

-1

.08

0*

**

(0.0

09)

0.1

13

(0.7

45

)1

.720

**

*(0

.00

4)

Du

mm

y=

1if

moth

eris

inth

e

lab

or

forc

e(b

asel

ine)

0.2

41

**

*(0

.00

0)

12

.57

**

*(0

.00

0)

Mo

ther

’sw

eek

lyh

ou

rso

f

pai

dw

ork

(bas

elin

e)

0.0

02

46

**

*(0

.00

0)

0.3

00

**

*(0

.00

0)

Du

mm

y=

1if

fath

eris

inth

e

lab

or

forc

e(b

asel

ine)

0.1

05

**

*(0

.00

7)

16

.64

**

*(0

.00

0)

Fat

her

’sw

eek

lyh

ou

rso

fp

aid

wo

rk(b

asel

ine)

0.0

01

04

**

*(0

.00

0)

0.4

62

**

*(0

.00

0)

Dif

fere

nce

inm

oth

er’s

ver

sus

fath

er’s

wee

kly

ho

urs

(bas

elin

e)

0.4

25

**

*(0

.00

0)

Fat

her

’sto

tal

earn

ings

(20

08

con

stan

td

oll

ars)

(bas

elin

e)

-5

.30e-

08

(0.5

42)

-0

.00

00

086

0(0

.13

4)

0.0

00

03

43

**

*(0

.00

0)

Mo

ther

’sto

tal

earn

ings

(20

08

con

stan

td

oll

ars)

(bas

elin

e)

9.9

5e-

08

*(0

.05

6)

0.0

00

00

13

3(0

.59

2)

-0

.00

00

786

**

*(0

.00

0)

Nu

mb

ero

fch

ild

ren

inth

e

fam

ily

(bas

elin

e)

-0

.009

44

**

(0.0

27)

0.0

00

23

1(0

.88

8)

-0

.67

5*

**

(0.0

00)

-0

.047

0(0

.70

7)

0.6

20

**

*(0

.00

7)

Chan

ge

inn

um

ber

of

chil

dre

n-

0.0

31

0*

**

(0.0

00)

0.0

00

14

2(0

.96

4)

-1

.32

4*

**

(0.0

00)

-0

.096

0(0

.68

7)

1.6

13

**

*(0

.00

0)

Mo

ther

’sag

e(b

asel

ine)

0.0

04

37

(0.4

91

)0

.00

76

3**

(0.0

11)

0.0

52

5(0

.87

6)

0.0

47

1(0

.90

1)

0.2

61

(0.6

09)

Mo

ther

’sag

esq

uar

ed(b

asel

ine)

-0

.000

07

41

(0.3

70)

-0

.000

09

52

**

(0.0

17)

-0

.00

17

6(0

.69

1)

-0

.000

60

3(0

.90

8)

-0

.00

13

6(0

.84

1)

Fat

her

’sag

e(b

asel

ine)

0.0

10

2*

*(0

.03

2)

0.0

02

07

(0.2

72)

0.8

16

**

*(0

.00

4)

1.5

66

**

*(0

.00

0)

0.2

49

(0.4

25)

Fat

her

’sag

esq

uar

ed(b

asel

ine)

-0

.000

14

0*

*(0

.01

2)

-0

.000

04

49

**

(0.0

29)

-0

.01

08

***

(0.0

02)

-0

.021

8*

**

(0.0

00

)-

0.0

04

45

(0.2

36

)

Mo

ther

has

po

st-s

eco

nd

ary

dip

lom

ao

ru

niv

ersi

ty

deg

ree

(bas

elin

e)

0.0

25

1*

**

(0.0

01

)0

.00

44

7(0

.15

3)

1.0

29

**

*(0

.00

0)

0.2

17

(0.3

07

)-

0.6

16

(0.1

49

)

P. Burton et al.

123

Ta

ble

8co

nti

nu

ed

Mo

ther

LF

PF

ath

erL

FP

Mo

ther

ho

urs

Fat

her

ho

urs

Dif

fere

nce

inh

ou

rs

Fat

her

has

po

st-s

eco

nd

ary

dip

lom

ao

ru

niv

ersi

ty

deg

ree

(bas

elin

e)

0.0

08

50

(0.2

78

)0

.00

63

4**

(0.0

40)

-0

.23

8(0

.33

0)

0.3

08

(0.1

26

)0

.505

(0.2

00)

Pm

kis

anim

mig

ran

t(b

asel

ine)

-0

.021

9(0

.13

2)

-0

.003

82

(0.3

95

)0

.663

(0.1

30)

-0

.024

7(0

.94

8)

-0

.74

4(0

.31

2)

Rura

lre

sid

ence

(bas

elin

e)-

0.0

11

7(0

.12

1)

0.0

04

67

(0.1

03)

-0

.46

1*

(0.0

88)

0.8

77

**

*(0

.00

0)

1.3

79

**

*(0

.00

1)

Chil

d’s

age

(bas

elin

e)0

.002

29

**

(0.0

26

)0

.00

13

5**

*(0

.00

5)

0.0

78

9*

*(0

.03

3)

0.1

94

**

*(0

.00

0)

0.0

71

3(0

.27

6)

Pro

vin

cial

un

emp

loy

men

t

rate

(bas

elin

e)

-0

.002

53

*(0

.06

5)

-0

.000

84

2(0

.10

0)

0.0

57

8(0

.17

4)

-0

.172

**

*(0

.00

0)

-0

.24

8*

**

(0.0

01)

Chan

ges

inp

rov

inci

al

un

emp

loy

men

tra

te

0.0

02

16

(0.6

05

)-

0.0

00

02

62

(0.9

87

)-

0.0

50

9(0

.71

5)

-0

.219

**

(0.0

35)

-0

.29

2(0

.17

9)

Hea

lth

stat

us

of

mo

ther

(bas

elin

e)

-0

.031

7*

**

(0.0

01)

-0

.002

44

(0.4

92

)-

0.6

17

**

(0.0

15)

-0

.121

(0.5

92)

0.6

84

(0.1

37)

Hea

lth

stat

us

of

fath

er

(bas

elin

e)

-0

.012

1(0

.16

4)

-0

.020

1*

**

(0.0

00

)0

.030

2(0

.90

5)

-1

.449

**

*(0

.00

0)

-1

.72

6*

**

(0.0

00)

N3

2,7

67

32

,76

73

2,7

67

32

,76

73

2,7

67

(1)

Mar

gin

alef

fect

s(i

.e.

inst

anta

neo

us

rate

so

fch

ang

efo

rco

nti

nu

ou

sv

aria

ble

san

dd

iscr

ete

chan

ge

of

du

mm

yv

aria

ble

fro

m0

to1

)fr

om

pro

bit

and

gam

ma

regre

ssio

ns;

reg

ress

ion

coef

fici

ents

from

OL

Sre

gre

ssio

ns

are

rep

ort

ed;

(2)

Tw

o-t

aile

dp

val

ues

inp

aren

thes

esw

ith

stan

dar

der

rors

clu

ster

edat

the

ho

use

ho

ldle

vel

.(3

)C

ycl

ean

dco

ho

rt

du

mm

ies

are

incl

ud

edb

ut

no

tre

po

rted

*p

\0

.1,

**

p\

0.0

5,

**

*p\

0.0

1


123

Ta

ble

9E

stim

ates

of

the

Imp

act

of

gen

eral

mea

sure

so

fch

ild

dis

abil

ity

on

lab

or

mar

ket

beh

avio

ro

fm

arri

edm

oth

ers

usi

ng

lon

git

udin

ald

ata

Dis

abil

ity

defi

nit

ion

Sam

ple

Lab

or

forc

e

mea

sure

Est

imat

edim

pac

to

fch

ild

hea

lth

Th

is pap

er

Funct

ional

lim

itat

ion

athom

e,at

chil

dca

re,

atsc

hool

or

in

any

oth

erac

tiv

itie

ssu

chas

tran

spo

rtat

ion

,p

lay,sp

ort

so

r

gam

es,

rela

tiv

eto

oth

erch

ild

ren

of

sam

eag

e

Can

ada,

NL

SC

Y,

19

94

–2

00

8C

hil

dre

n,

0–

17

Usu

al

ho

urs

per

wee

k

last

yea

r

Cro

ss-s

ecti

on

alre

sult

s:3

.3%

pts

less

lik

ely

tob

ein

lab

or

forc

e;2

.3fe

wer

wee

kly

ho

urs

On

set

mo

del

:2

.7%

pt

red

uct

ion

inlf

pan

d

redu

ctio

no

f1

.7in

wee

kly

ho

urs

Po

wer

s

20

03

Defi

nit

ion

2:

chil

dre

nle

ssth

an6

:‘‘

any

lim

itat

ion

sat

all

in

usu

alkin

dof

acti

vit

ies

done

by

most

chil

dre

nof

thei

r

age

bec

ause

of

physi

cal,

lear

nin

gor

men

tal

hea

lth

con

dit

ion

?ch

ild

ren

6–

21

wit

h‘‘

alo

ng

-las

ting

con

dit

ion

that

lim

its

thei

rab

ilit

yto

wal

k,

run

or

use

stai

rs’’

or

ali

mit

atio

nin

the

abil

ity

tod

ore

gu

lar

sch

ool

work

bec

ause

of

‘‘a

physi

cal,

lear

nin

gor

men

tal

hea

lth

con

dit

ion

’’

US

SIP

P,

19

92/9

3ch

ild

ren

0–

21

Usu

al

wee

kly

ho

urs

Cro

ss-s

ecti

on

alre

sult

s:5

.9%

pt

less

lik

ely

tob

ein

lab

or

forc

e;3

.7h

few

erw

eek

lyp

aid

ho

urs

Fix

edef

fect

s:no

signifi

cant

effe

ct(t

hough

un

expec

ted

po

siti

ve

asso

ciat

ion

s)

Go

uld

20

04

28

spec

ific

illn

ess

or

med

ical

con

dit

ions

iden

tifi

ed

thro

ug

h:

‘‘H

asy

ou

rd

oct

or

or

hea

lth

pro

fess

ion

alev

er

said

yo

uch

ild

had

…’’

?‘‘

or

any

oth

eril

lnes

s/

condit

ion?’

’ch

arac

teri

zed

asm

oney

inte

nsi

ve

and

tim

e

inte

nsi

ve

US

19

97

PS

ID,

chil

d

dev

elo

pm

ent

sup

ple

men

t,

chil

dre

n0

–1

2

Av

erag

e

wee

kly

ho

urs

No

sign

ifica

nt

asso

ciat

ion

ifco

nd

itio

no

nly

‘mo

ney

inte

nsi

ve

‘or

on

ly‘t

ime

inte

nsi

ve

(th

oug

hp

osi

tiv

e

coef

fici

ents

);’

17

%p

tlo

wer

pro

bab

ilit

yo

f

par

tici

pat

ion

ifco

ndit

ion

both

tim

ean

dm

oney

inte

nsi

ve

Wee

kly

hours

1.7

low

erif

condit

ion

both

tim

ean

d

mon

eyin

ten

siv

e

Corm

an

etal

.

20

05

‘Po

or

hea

lth

’ch

ild

isco

nsi

der

edto

be

inp

oo

rh

ealt

hif

at

leas

t1

of

the

foll

ow

ing

istr

ue:

chil

dw

eig

hed

\4

po

und

s

atb

irth

;m

oth

erre

po

rted

ap

hy

sica

ld

isab

ilit

yat

12

mon

thfo

llo

w-u

p;

chil

dh

adn

eith

erw

alk

edn

or

craw

led

by

12

mo

nth

s

US

frag

ile

fam

ilie

ssu

rvey

,

sam

ple

of

most

lyu

nw

ed

par

ents

,1

99

8–

200

0;

Ch

ild

ren

age

0–

1.5

8%

po

int

red

uct

ion

inp

rob

abil

ity

moth

eris

emplo

yed

at1

2m

on

thfo

llo

w-u

p(c

on

troll

ing

init

ial

emplo

ym

ent)

;3

.2fe

wer

ho

urs

of

pai

dw

ork

Ple

ase

see

Po

wer

s(2

00

3)

for

asi

mil

ardis

cuss

ion

of

earl

ier

rese

arch

.E

stim

ates

for

single

,sp

ecifi

cco

ndit

ions

are

not

report

edher

e(e

.g.,

Kvis

tet

al.

20

13

for

AD

HD

;

Bay

dar

etal

.2

00

7fo

ras

thm

a)

P. Burton et al.

123

5.3 Canadian compared to US findings

As noted in the introduction, since most earlier research in this area uses US data,

one of our contributions is to provide estimates of the impact of child disability on

the labor supply of married mothers in a different institutional setting. Table 9

presents a summary comparison of key differences in data, methods and results for

several recent studies all of which use general definitions of child disability (rather

than condition-specific) and all of which use longitudinal data. In all cases, the size

of impact is slightly smaller when longitudinal rather than cross-sectional methods

are used. However, for Canadian mothers, results change very little whereas for two

of the US studies, the impact of child disability of mother hours mostly disappears

(or even becomes positive). This could reflect greater increases in income needs for

families of children with disabilities living in the US, both given that some families

will not have adequate health insurance and that families of children with

disabilities do not receive special cash transfers to help offset the additional costs in

the US as they do in Canada. Perhaps fewer US mothers can afford to reduce paid

hours under these circumstances?

6 Discussion and conclusions

Using longitudinal microdata from the Statistics Canada National Longitudinal

Survey of Children and Youth, we find that married mothers of children with

disabilities are less likely to engage in paid work and/or work fewer paid hours per

week than otherwise similar women whose children do not have health problems.

This finding is apparent in contemporaneous estimates of the association between

child health and mother’s labor market behavior and in models of the onset of child

health problems that control for prior labor market attachment of the mother. And,

these results seem large enough to have policy relevance (about three quarters the

size of estimates for the addition of another child to the family).

No statistically significant changes in paid work participation or hours are

apparent for fathers of the same children (though the point estimate for hours is

positive). Not surprisingly, then, we find a statistically significant increase in the

difference between father paid hours and mother paid hours in families where a

child develops a disability.

These results suggest a ‘household’ coping strategy, with increased specialization

according to traditional gender roles. While this probably makes sense for many

families, it is important to keep in mind that such a division of labor may generate

economic vulnerability for mothers compared to fathers. For example, some

household models emphasize relative earnings as important predictors of bargaining

power; the literature of ‘family gap’ demonstrates long-run earnings penalties

associated with labor-market withdrawal for mothers; such role specialization can

leave mothers particularly vulnerable in the event of divorce.

How could policy help? Certainly, more flexible job schedules as well as

childcare/afterschool care programs that accommodate children with special needs

could help parents balance care-giving and paid work. As well, income supplements


123

for families of children with special needs would help to relieve some of the

financial costs often associated with raising a child with a disability. In Canada, paid

maternity and parental leave to support the care-giving of new mothers and fathers

(including adoptive parents) are available. Since reductions in maternal paid work

associated with the onset of a child’s disability are in the same order of magnitude

as those estimated for women with a new child, we could provide similar benefits

for families caring for children with disabilities such as are now offered in many

European countries (see Gornick and Meyers 2003). Ideally, these benefits would be

available to mothers and fathers and would allow for periodic absences from paid

work to accommodate medical appointments, therapy sessions, etc. However,

regardless of the type of policy adopted, it would be important that it not

inadvertently entrench mothers as care-givers (e.g., a low benefit ceiling would

make effective replacement rates significantly lower for fathers) rather offering

parents the option of sharing bread-winning and care-giving responsibilities.

Acknowledgments We would like to thank both the Canadian Institute for Advanced Research and the

Canadian Institutes for Health Research through the ‘‘Healthy Balance Research Program: A Community

Alliance for Health Research on Women’s Unpaid Caregiving’’ for funding this work.

References

Akerlof, G. A., & Kranton, R. E. (2000). Economics and identity. The Quarterly Journal of Economics,

65(3), 715–753.

Baker, M., Stabile, M., and Deri, C. (2001). What Do Self-Reported, Objective, Measures of Health

Measure? National Bureau of Economic Research Working Paper. 8419.

Baydar, N., Joesch, J. J., Kieckhefer, G., Kim, H., & Greek, A. (2007). Employment behaviors of mothers

who have a child with asthma. Journal of Family Economic Issues, 28, 337–355.

Beagan, B., Stadnyk R., Loppie, C., MacDonald, N., Hamilton-Hinch, B., MacDonald, J. (2005). I do it

because I love her and I care: Snapshopts of the lives of caregivers. www.healthyb.da.ca/.

Becker, G. (1965). A theory of the allocation of time. The Economic Journal, 75(299), 493–517.

Breslau, N., Salkever, D., & Staruch, K. S. (1982). Women’s labor force activity and responsibilities for

disabled children. Journal of Health and Social Behavior, 23(2), 169–183.

Browning, M. (1992). Children and household economic behavior. Journal of Economic Literature,

30(September), 1434–1475.

Burton, P., Lethbridge, L., & Phipps, S. (2008a). Mothering children with disabilities and chronic

conditions: long-Term Implications for self-reported health. Canadian Public Policy, 34(3),

359–378.

Burton, P., Lethbridge, L., & Phipps, S. (2008b). Children with disabilities and chronic conditions and

longer-term parental health. Journal of Socioeconomics, 37(3), 1168–1186.

Burton, P., & Phipps, S. (2009). Economic costs of caring for children with disabilities in Canada.

Canadian Public Policy, 35(3), 269–290.

Chen, Z., & Woolley, F. (2001). A Cournot-Nash model of family decision making. Economic Journal,

111, 722–748.

Corman, H., Noonan, K., & Reichman, N. E. (2005). Mothers’ labor supply in fragile families: The role

of child health. Eastern Economic Journal, 31(4), 601–616.

Gornick, J., & Meyers, M. (2003). Families that work: Policies for reconciling parenthood and

employment. New York: Russell Sage Foundation.

Gould, E. (2004). Decomposing the effects of children’s health on mother’s labor supply: Is it time or

money? Health Economics, 13(October), 525–541.

Gray, D. E. (2003). Gender and coping: The parents of children with high functioning autism. Social

Science and Medicine, 56, 631–642.

P. Burton et al.

123

http://www.healthyb.da.ca/

Hardin, J. W., & Hilbe, J. M. (2012). Generalized linear models and extensions. StataCorp LP: Third

Edition.

Haven, C.A. (2005). Becoming a resilient family: child disability and the family system. Access Today,

17 (Spring). http://www.ncaonline.org (Accessed March 10, 2006).

Hirano, K., & Imbens, G. W. (2001). Estimation of causal effects using propensity score weighting: An

application to data on right heart catheterization. Health Services and Outcomes Research

Methodology, 2(3), 259–278.

Hobbs, N., & Perrin, J. M. (Eds.). (1985). Issues in the care of children with chronic illness. San Franciso:

Jossey-Bass Publishers.

Hodapp, R. M., & Krasner, D. V. (1994). Families of children with disabilities: Findings from a National

Sample of Eighth-Grade Students. Exceptionality, 5(2), 71–81.

Human Resources Development Canada. (2003). Office for Disability Issues. Defining Disability: A

Complex Issue. http://publications.gc.ca/collections/Collection/RH37-4-3-2003E.pdf.

Johnson, W. R., & Skinner, J. (1986). Labor supply and marital separation. The American Economic

Review, 76(3), 455–469.

Kimmel, J. (1997). Reducing the welfare dependence of unmarried mothers: Health-related employment

barriers and policy responses. Eastern Economic Journal, 23(2), 151–163.

Kimmel, J. (1998). Child care costs as a barrier to employment for single and married mothers. Review of

Economics and Statistics, 80(2), 287–299.

Kvist, A. P., Nielsen, H. S., & Simonsen, M. (2013). The importance of children’s ADHD for parents’

relationship stability and labor supply. Social Science and Medicine, 88, 30–38.

Lehrer, E. (2003). The economics of divorce. In S. Grossbard (Ed.), Marriage and the economy (pp.

55–74). Cambridge: University Press.

Leibowitz, A. (1974). Home investments in children. Journal of Political Economy, 82(2), S111–S131.

Lukemeyer, A., Meyers, M., & Smeeding, T. (2000). Expensive children in poor families: Out-of-pocket

expenditures for the care of disabled and chronically ill children in welfare families. Journal of

Marriage and Family, 62(2), 399–415.

Lundberg, S., Pollak, R. A., & Wales, T. (1997). Do husbands and wives pool their resources? Evidence

from the United Kingdom child benefit. Journal of Human Resources, 32, 463–480.

Mauldron, J. (1992). Children’s risks of experiencing divorce and remarriage: Do disabled children

destabilize marriages? Population Studies, 46(2), 349–362.

Meyers, M., Lukemeyers, A., & Smeeding, T. (1998). The cost of caring: Childhood disability and poor

families. The Social Service Review, 72(2), 209–233.

Miller, J. E., Gaboda, D., Davis, D. (2001). Early childhood Chronic Illness: Comparability of Maternal

Reports and Medical Records. Department of Health and Human Services, Centers for Disease

Control and Prevention, National Center for Health Statistics. Publication No. 2001- 1331, Series 2,

No. 131.

Mincer, J. (1962). Labor Force Participation of Married Women: A Study of Labor Supply. in Aspects of

Labor Supply. http://nber.org/books/univ62-2.

Noonan, K., Reichman, N. E., & Corman, H. (2005). New fathers’ labor supply: Does child health matter?

Social Science Quarterly, 86(Supplement), 1399–1417.

Pollak, R. A. (1985). A transaction cost approach to families and households. Journal of Economic

Literature, 13(June), 581–608.

Powers, E. T. (2001). New estimates of the impact of child disability on maternal employment. American

Economic Review Papers and Proceedings, 91(2), 135–139.

Powers, E. T. (2003). Children’s health and maternal work activity: Estimates under alternative disability

definitions. The Journal of Human Resources, 38(3), 522–556.

Rosenbaum, P. R. (1987). Model-based direct adjustment. Journal of the American Statistical

Association, 82(398), 387–394.

Salkever, D. (1982a). Children’s health problems and maternal work status. Journal of Human Resources,

17(1), 94–109.

Salkever, D. (1982b). Children’s health problems: Implications for parental labor supply and earnings. In

Victor Fuchs (Ed.), Economic aspects of health (pp. 221–251). Chicago: University of Chicago

Press.

Salkever, D. (1990). Child health and other determinants of single mothers’ labor supply and earnings. In

Ismael Sivageldin, Alan Sorkin, & Richard Frank (Eds.), Research in human capital and

development 6 (pp. 147–181). London: JAI Press.


123

http://www.ncaonline.org

http://publications.gc.ca/collections/Collection/RH37-4-3-2003E.pdf

http://nber.org/books/univ62-2

Seltzer, M. M,, Greenberg, J. S., Floyd, F. J., Pettee, Y., & Hong, J. (2001). Life course impacts of

parenting a child with a disability. American Journal on Mental Retardation, 106(3), 265–286.

Stabile, M., & Allin, S. (2012). The economic costs of childhood disability. The Future of Children,

22(1), 65–96.

Statistics Canada. (2001). Participation and Activity Limitation Survey. Children with disabilities and

their families. Cat No. 89-585-XIE.

Thyen, U., Kuhlthau, K., & Perrin, J. M. (1999). Employment, child care, and mental health of mothers

caring for children assisted by technology. Pediatrics, 103(6), 1235–1242.

Waldfogel, J. (1998). Understanding the family gap in pay for women with children. Journal of Economic

Perspectives, 12(1), 137–156.

Wolfe, B. L., & Hill, S. C. (1995). The effect of health on the work effort of single mothers. The Journal

of Human Resources, 30(1), 41–62.

P. Burton et al.

123

Date post:	24-Jan-2017
Category:	Documents
Upload:	shelley
View:	214 times
Download:	1 times

Child health and parental paid work

Documents