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Hiring Domestic Help and Family Well-Being in Hong Kong: 1A Propensity Score Matching Analysis

*Adam Ka-Lok Cheung

INTRODUCTION

Over the past few decades, the female labor force participation rate has increased remarkably in post-industrial societies (Brewster & Rindfuss, 2000). The shift in the female labor force participation rate has had significant impacts on family relations. Work-family conflict for both men and women has increased in these societies (Winslow, 2005). Some studies have suggested that the increasingly conflicting demands from the work and the family domains are responsible for the declining marital quality over time (Amato, Johnson, Booth, & Rogers, 2003; Rogers & Amato, 2000). For couples to adapt to these changes, the outsourcing of household tasks has become an important strategy to avert the conflict between work and family roles (Hochschild, 2003).

An increasing trend of domestic outsourcing, including the use of paid domestic help and childcare services, has been documented in these societies (Bittman, Matheson, & Meagher, 1999; de Ruijter, 2004; Hochschild, 2003). Among the options of domestic outsourcing, the hiring of domestic help is now common among middle-class families in Asian societies such as Hong Kong, Singapore, and Taiwan (Constable, 2007; Yeoh, Huang, & Gonzalez, 1999; Cheng, 2003; Voyadnoff, 2005b). In Hong Kong, for example, the proportion of households hiring domestic help has tripled between 1987 and 2000 (Chan, 2005). In 2001, at least one-fifth of households in Hong Kong that earned HKD$40,000 or above hired a domestic helper (Hong Kong Census and Statistics Department, 2001). Despite the increasing popularity of outsourcing domestic tasks, the possible impact of such outsourcing on family relations remains an important gap in the literature. The contributions of hiring domestic help to the employers' family, such as averting marital conflict and improving marital quality, are often assumed in the literature (Anderson, 2000; Cheng, 2006) but are rarely examined with empirical data. This paper investigates the effects of

1 Acknowledgement: An earlier version of this article received an honorable mention for Early Stage Family Scholar Award, and was presented in the International Sociological Association CFR/RC06 Conference “Demographic and Institutional Change in Global Families” at Academia Sinica, Taipei, 28-30 March 2013. The author is grateful to Professor Rudy Seward and other board members of the Committee on Family Research of the International Sociological Association for their encouragement and comments on the paper. The author would like to thank Professor Kwok-fai Ting for his support and sharing of the survey data generously. Many thanks are also given to the editor and anonymous reviewers for their useful comments and suggestions on earlier drafts of this work.* Department of Social Sciences, Hong Kong Institute of Education, D3-1/F-22A, 10 Lo Ping Road, Tai Po, New Territories, Hong Kong ([email protected]).

©Journal of Comparative Family StudiesVolume XLV Number 4 Autumn 2014

hiring domestic help on the following two indicators of the employers' family well-being: marital conflict and marital quality. Analyzing data from a representative household survey in Hong Kong (N=974) using the propensity score matching method, which is currently gaining popularity in sociology (Brand & Halaby, 2006; Frisco, Muller, & Frank, 2007), the current study finds only weak positive effects of hiring domestic help. None of the estimated effects are statistically significant. The data from this study suggest, contrary to some previous claims, that the effects of hiring domestic help on two indicators of family well-being for employers are far from substantial. Previous studies which assumed that hiring domestic help enabled middle-class families to enjoy a better family life may have been overly optimistic.

PAST LITERATURE AND THE CURRENT STUDY

Household Labor and Family Well-Being

Household labor plays an important role in shaping family well-being (Coltrane, 2000; Shelton & John, 1996). According to the ecological systems perspective, marital well-being is affected not only by characteristics within the family domain, but also by how couples manage the demands from both work and family domains (Voydanoff, 2002). Role strain and conflict associated with work and family demands can negatively affect the marital well-being if the couples do not have the resources or effective strategies to manage the conflicting demands and to maintain a balance between the roles (Hill, 2005; Voydanoff 2002, 2005a, 2005b). In contrast, maintaining work-family fit and balance can positively affect the well-being of couples. Nevertheless, managing the conflict between work and family roles has become increasingly difficult, especially in those societies where high demand from the work domain meets the lack of a family-friendly policy environment. Empirical findings from previous studies have already shown that the conflict between work and family roles is negatively related to marital satisfaction, and can lead to marital discord (Coltrane, 2000; Shelton & John, 1996). The double burden of work and housework creates work-family conflict for many couples (Lee & Chao, 2001; Luk & Shaffer, 2005), which in turn affects their well-being (Aycan & Eskin, 2005; Voydanoff, 2005a). For both men and women, long work hours are positively related to work-family conflict, which has a substantial negative impact on family well-being (Aycan & Eskin, 2005; Lee & Chao, 2001).

From the perspective of symbolic interactionism, doing housework can carry emotional messages of caring and love (Erickson, 1993; Coltrane, 2000). Marital well-being is also affected by how couples evaluate their role performance, and the perceived and expected contributions made by the spouses (Coltrane, 2000). Besides the actual burden from work and housework, couples will also constantly evaluate the fairness of the division of household labor (Thompson, 1991). Marital quality and marital conflict can be thought of as reflecting the evaluation by couples of the role performance of one spouse and the other, and the perceived housework equity. For couples whose performance cannot meet each partner's expectations, conflict arises and marital quality deteriorates. Rogers and Amato (2000) find that perceived inequality in the division of household labor is significantly related to marital discord. In contrast, couples enjoy a higher level of marital happiness and a lower probability of divorce if they are satisfied with the division of household labor (Frisco & Williams, 2003). It has also been found that a greater share of housework done by the wife has a negative impact on the marital satisfaction of dual-earner couples (Wilkie, Ferree, & Ratcliff, 1998).

476 Journal of Comparative Family Studies

Domestic Outsourcing and Family Well-Being

Since the conflicting demands between family and work roles are an important source of role strain for couples, family researchers, with the guidance of ecological systems theory, hypothesized that a coping strategy or a boundary-spanning resource, which reduces the burden from work/housework or helps in managing the conflicting demands from the two domains, will also help reduce the risk of marital conflict and improve marital quality due to the removal of an important stressor and a reduction in work-family conflict (Anderson, 2001; Skinner, 1980; Voydanoff, 2005b). These researchers conceptualize the hiring of domestic help as a strategy for couples to relieve the housework burden and avert work-family role conflict, or as a resource to manage the conflicting demands from the work and the family domains (Hochschild, 2003; Voydanoff, 2005b). The household service sector has been growing rapidly in many societies. For example, the proportion of households in Hong Kong that have hired domestic help increased from 1.5% in 1981 to 10.9% in 2001 (Chan, 2006). Aside from allowing married women to participate in the labor force (Chan, 2006), the hiring of domestic help can help couples save time in doing specific household tasks. Previous studies show that hiring domestic help can significantly reduce the amount of time women spend on domestic tasks such as cleaning (van der Lippe, Tijdens, & de Ruijter, 2004). The reduction of the time spent by women on domestic tasks may also help change the couples' perception of unfairness in household labor arrangement. With reference to these timesaving effects, some studies suggest that hiring domestic help improves family well-being, because couples can avoid conflict over the division of household labor, spend quality time with each other, and enjoy more leisure time (Anderson, 2001; Chan, 2005; Gregson & Lowe, 1994; Yeoh, Huang, & Gonzalez, 1999). Chan (2005) also finds that the presence of a domestic helper acts as a buffer that diverts negative emotions for couples and reduces the chance of conflict. Drawing upon the literature from ecological systems theory and symbolic interactionism, this study first hypothesizes that the hiring of domestic help improves marital quality and reduces marital conflict for the couples who hire domestic help.

Nevertheless, some studies suggest that hiring domestic help creates another type of housework burden, one that replaces traditional housework and is often not measured in conventional surveys. Household members, especially women, are now responsible for recruiting, training, and monitoring the work of a domestic helper, and for turning themselves into managers at home (Groves & Lui, 2012). Moreover, some household tasks, such as childrearing, are not entirely delegated to a domestic helper (Chan, 2005). Therefore, the timesaving effects of hiring domestic help may be limited, and hiring domestic help does not necessarily lead to an equal division of household labor. Instead, a study reveals that men paid for the domestic help and treated that as a “gift” to the wife so that they could avoid doing housework and help maintain the hierarchy of division of household labor (Groves & Lui, 2012), which have already been found in past studies to be negatively associated with marital quality. Besides, for families that hire a full-time maid at home, the presence of a female stranger in the home may also bring new conflict to families because sexual tension or suspicions may arise (Chan, 2005). Therefore, the assumption that hiring domestic help substantially improves family well-being may have been overly optimistic, and may have over-simplified the complexity of family life for families with a domestic helper at home. Some studies, mainly focusing on female professionals and entrepreneurs, have found no significant correlation between the hiring of domestic help and the level of work-family conflict or marital satisfaction (Lee & Chao, 2001; Lo, Stone, & Ng, 2003; Luk & Shaffer, 2005; Warde, Moonesignhe, Allen, & Gelberg, 1999). Based on these cumulative findings, another hypothesis is formulated for this study: hiring domestic help does not have a substantial impact on family well-being as was previously assumed.

477Hiring Domestic Help and Family Well-Being

Research Question

Two competing hypotheses are therefore derived from the past literature. This study attempts to assess the effects of hiring domestic help on the family well-being for couples who hire domestic help. How would family relations be different for those employers of a domestic helper if they did not hire domestic help? This research question has received little attention in empirical research, but is very relevant to our understanding of family dynamics in modern societies in which work-family conflict is an important issue both empirically and theoretically. It should be noted that past studies have had several limitations. Firstly, most of their data do not come from representative samples, and the populations of these studies are limited solely to a narrow portion of professional workers and entrepreneurs only (Lee & Chao, 2001; Luk & Shaffer, 2005; Warde et al., 1999). Secondly, while some studies have paid attention to the western context, empirical studies with quantitative data that assess the impact of hiring domestic help on family relations in a non-western context are extremely lacking. This study aims to fill in the research gap by analyzing a territory-wide representative sample in Hong Kong.

Context for the Current Study: Hong Kong

Hong Kong is a well-situated location for the current study. On the one hand, a traditional pattern of the division of domestic labor is well rooted in this Chinese society. Women are responsible for most of the housework. In 2003, 50% of married women in Hong Kong had performed 60% to 100% of the housework. On average, women spent more time on housework compared with men (Hong Kong Census and Statistics Department, 2003). A substantial portion of Hong Kong's population still expects women to take on the role of homemaker and men to take on the role of breadwinner (Chow & Lum, 2008). On the other hand, the economy is facing rapid change. Due to economic pressure, dual-earner households are becoming more common (Hong Kong Census and Statistics Department, 2010). One strategy that allows women to work outside the home is to outsource the housework and hire a domestic helper.

Unlike in many western countries, family-friendly policies to promote work-family balance, such as family care leaves, flexible work-time, and standard/maximum work-hours, are almost absent in Hong Kong. Under such a context, hiring domestic help has become a popular choice for middle-class families to help release the housework burden so that the couples can concentrate on their careers. To allow employees to work longer hours at the workplace and concentrate on their work, some large corporations in Hong Kong have recently started providing allowances for their married employees holding professional and managerial positions to hire household and childcare services. Since the 1980s, the demand for full-time domestic helpers has been escalating sharply in Hong Kong. The number of full-time live-in foreign domestic helpers increased from 21,517 in 1982 to 237,104 in 2002 (Task Force on Population Policy, 2003).

According to the Census and Statistics Department (2001), 10.1% of households in Hong Kong hired domestic help. Nearly 90% of the households that hired domestic help hired full-time foreign domestic helpers. These figures are high, compared with many other societies. According to a survey conducted by the Census department, households that hire domestic help and intend to hire domestic help primarily want the following services: cleaning, tidying up the household, purchasing groceries, cooking, taking care of children, and washing and ironing clothes (Hong Kong Census and Statistics Department, 2001). Another survey also reveals that persons from households that hired domestic help are more likely to


be responsible for a smaller share of the housework (Hong Kong Census and Statistics Department, 2003).

METHODS

Data

The data for this study are from the Hong Kong Family Life Survey 2009, a household survey conducted by the Hong Kong Institute of Asia-Pacific Studies at the Chinese University of Hong Kong. A probability sample was drawn from the Frame of Quarters, which is a list of the most up-to-date residential addresses, covering 99% of the population of Hong Kong, and compiled by the Hong Kong Census and Statistics Department. Eligible respondents were ever-married Chinese adults who were between 18 and 60 years old. One respondent was drawn randomly from each valid address with eligible persons to complete a structured face-to-face interview. Trained interviewers were sent to the sampled addresses to conduct the interview. A total of 1,177 respondents completed the interview. The contact response rate for this study is 49.2%, which is comparable with some other household surveys conducted in Hong Kong. A total of 203 respondents were excluded from the analysis in this study because they were divorced or separated from their spouses. Data from 974 respondents, who were still married at the time of the interview, are analyzed.

Measures

Hiring domestic help. The dichotomous treatment variable in this study indicates if the respondent hired domestic help to share the housework (0 = no, 1 = yes). Respondents were asked who was primarily responsible for the following 10 housework tasks: 1) cooking; 2) washing dishes; 3) buying groceries; 4) washing and ironing clothes; 5) maintaining household appliances; 6) bringing the child to school; 7) childcare; 8) assisting the child with homework; 9) managing household expenses; and 10) tidying up, sweeping the floor, and cleaning. Responses for each items provided to the respondents include “respondent,” “spouse,” “respondent's child,” “parents/parents-in-law,” “domestic helper,” and “others.” Respondents who indicated that a domestic helper was primarily responsible for at least one of the housework tasks listed above were coded 1, indicating that they hired a domestic helper to share the housework. For respondents who did not mention that a domestic helper was responsible for any of the 10 housework tasks were coded 0, indicating that they did not hire domestic help to share the housework. Of the sampled households, 12.4% hired domestic help, which is comparable with the official figure (Hong Kong Census and Statistics Department, 2003).

Two outcome variables are used in this study to indicate the respondents' family well-being: marital conflict and marital quality.

Marital conflict. A three-item scale (summed from three 5-point Likert items) measures the frequency of marital conflict over “financial arrangement”, “housework”, and “spousal relationship” in the past year (1 = none, to 5 = more than once per week; alpha = .73). The scale ranged from 3 to 15 (M = 4.37; SD = 1.83), with a higher score indicating that marital conflicts are more frequent in the relationship.

Marital quality. An eight-item scale (summed from eight 7-point Likert items) measures the respondents' perceived marital quality. For each item, respondents were asked whether they agreed with the following statements: 1) “You are very satisfied with the relationship with


your spouse;” 2) “You can confide your personal feelings to your spouse;” 3) “Your spouse can share your burden when you feel upset;” 4) “You have common hobbies/interests with your spouse;” 5) “You have common activities with your spouse, such as watching a movie or lounging around;” 6) “You have intimate behaviors with your spouse, such as holding hands or hugging;” 7) “Your spouse gives you a strong sense of security;” 8) “You have great confidence in the decisions made by your spouse” (1 = strongly disagree, to 7 = strongly agree; alpha = .89). The scale ranged from 8 to 56 (M = 41.10; SD = 8.96), with a higher score indicating better perceived marital quality. Other covariates. Since the hiring of domestic help is a self-selected treatment, it is essential to consider the selection mechanism in the treatment assignment in order to estimate the effect of hiring domestic help. There is already a well-established literature that examines the factors of hiring domestic help and spending on housekeeping (Baxter, Hewitt, & Western, 2009; Cheung & Lui, forthcoming; Cohen, 1998; de Ruijter, Treas, & Cohen, 2005; Oropesa, 1993). Hiring domestic help is especially popular among middle- and upper-class families (Lee & Chao, 2001; Warde, Moonesignhe, Allen, & Gelberg, 1999). Previous studies show that socioeconomic indicators, such as couples' education, employment, work-hour, income, house ownership, and financial assets, are positively correlated with the expenses of hiring domestic help (Baxter, Hewitt, & Western, 2009; Cheung & Lui, forthcoming; Cohen, 1998; de Ruijter, 2004; de Ruijter, Treas, & Cohen, 2005; Oropesa, 1993). The objective demands of housework indicated by the presence of children, young child in particular, and co-residing elderly parents are also found to correlate with the hiring of domestic help in some samples (Chan, 2006; de Ruijter, Treas, & Cohen, 2005; Treas & de Ruijter, 2008). In addition, it is also argued that attitudinal variables, such as the respondent's gender ideology, are relevant to the expenditure on housekeeping and other options of domestic outsourcing, because these attitudes may affect the willingness of couples to outsource domestic labor to strangers (Cheung & Lui, forthcoming; de Ruijter & van der Lippe, 2009; Oropesa, 1993).

With reference to the above studies, the following variables are included in the propensity score model in this study: number of children, the presence of young child under the age of five, co-residing with the respondents' parents/in-laws, both husband's and wife's age, education, full-time employment status, work-hour per week, monthly income, house ownership, respondent's attitude toward gender roles. For the husband's and wife's age, squared-terms are also included. For male respondents, husband's variables are constructed with the respondents' characteristics. For female respondents, husband's variables are constructed with respondents' report on their spouse's characteristics. The same rule applies for the construction of the wife's variables.

Analytic Strategy

In this study, the effects of hiring domestic help refer to the average treatment effect for those people who are treated (ATT) and are estimated through the propensity score matching

2method.


2 Average Treatment Effect on the Treated (ATT) can be defined as:

ATT= E (Y - Y | X, D=1),1 0

Where X denotes a vector of covariates, and Y and D denote the outcome and treatment variables, respectively. Subscripts denote the treatment status, where Y is the outcome if the units were treated and Y is the outcome if the 1 0

units were not treated. For those treated units, Y is observed, and Y is the counterfactual outcome for these treated 1 0

units and therefore not observable. Estimating ATT can be seen as a missing data problem; therefore, Y for those 0

treated units are to be estimated. In propensity score matching analysis, Y is estimated with the information from 0

non-treated units that have the same propensity to receive treatment, compared with treated units, but did not receive the treatment.

481

Propensity score matching. Propensity score methods are often used in economics, evaluation studies, and public health to evaluate the effectiveness of a policy, program, or treatment, with observational data. Until recently, they have been less popular among sociologists (Brand & Halaby, 2006; Frisco, Muller, & Frank, 2007). Propensity score matching shares many similarities with multiple regression, but has some additional

3advantages in making causal inference (Stuart, 2010).

In this study, we use propensity score matching, a strategy for approximating the design of an experimental study using observational data, to estimate the average effect of hiring domestic help on marital well-being for those individuals who actually hired domestic help, an estimand known as the average treatment effect on the treated (ATT). To use observational data to estimate the effects of hiring domestic help, the underlying propensity of hiring domestic help for those who actually hired domestic help and those who did not hire should first be known, so that only comparable units will be compared. The true underlying propensity to hire a domestic helper is unknown; therefore, it is estimated with a logistic regression model, known as the propensity score model. Then, treated and non-treated individuals are matched with regard to their estimated propensity score values. The exact manner by which the matching is done depends on the matching algorithms. In the empirical literature, several matching algorithms are commonly used. The most commonly used methods include nearest neighbor matching within caliper, radius matching, and kernel matching (Epanechnikov kernel). All propensity score matching analyses presented in this study are performed with –pscore–, –psmatch2–, and –pstest– modules in Stata 12. This paper primarily reports the findings from kernel matching, because it achieves the best balance for most of the covariates used in this study, and it yields the smallest variance for the estimates. Standard errors for all matching estimators used in this study are derived using the bootstrapping method (with 1,000 bootstrap replicates).

Covariate balance. The goal of matching is to compare treated and non-treated individuals who are similar with respect to pre-treatment covariates. The standardized mean difference (SMD) is used to quantify covariate balance before and after matching; as a percentage, it reflects the mean difference for a given variable between the treated and non-treated individuals with respect to its standard deviation. As suggested in previous studies, the SMD after matching should be less than 25%, indicating that the mean differences between treated and non-treated units are less than 25% of their standard deviation, with smaller SMD values indicating better matching performance. To estimate the effects of hiring domestic help, the matching algorithm that achieves the smallest standardized differences across the covariates

4is chosen.

Additional analysis. To check if the conclusions of the current study are sensitive to the choice of matching methods, findings from radius matching and 1:1 nearest-neighbor propensity score matching with replacement and within caliper, setting the caliper at 0.25

Hiring Domestic Help and Family Well-Being

3 First, compared with regression adjustment to control bias, the propensity score matching method requires fewer modeling assumptions in order to sufficiently reduce selection bias. Second, researchers can easily check for the balance of covariates after matching with the propensity score and exclude those units that have extreme conditional probabilities in treatment assignment. In contrast, in regression analysis, researchers often unintentionally extrapolate the treatment effect estimates to units that actually do not have cases with similar background in the sample. Third, regression in the most general form, if not confounded, estimates the average treatment effect (ATE), which is conceptually different from average treatment effect on the treated (ATT), on which this study focuses. It is relatively complicated for regression to estimate ATT, whereas propensity score matching method estimates ATT in a relatively simple way. 4 For a detailed discussion on estimating treatment effect with propensity score matching, readers can see reviews from Caliendo and Kopeinig (2008), Gangl (2010), and Stuart (2010).


standard deviations of the propensity score (that is, cases will be matched only if units are within the range of probability of hiring domestic help set by the caliper), are also presented.

One may hypothesize that men and women benefit differently by hiring domestic help. Additional analysis was conducted with male and female subsamples to check whether the effects of hiring domestic help on men's and women's report of family well-being are different. For subgroup analysis, the propensity score models are fitted for the male and female subsamples separately. Male and female respondents are then matched within the subsamples, using the propensity scores estimated for the specific subsamples. Additional checks for covariate balance for each subsample are performed to ensure the matching is well implemented.

Some may also argue that the wife's employment, work hour and income should not be considered in the selection mechanism. These variables may be post-treatment outcomes, as some previous studies suggested that economic outcomes are affected by the availability of domestic outsourcing (Chan, 2006), but not the other way around (Chevalier & Vitanen, 2002). For robustness check, these variables are excluded in another set of analysis. The result is reported in the additional analyses section after the main results are reported.

Missing values. Although there is no missing value for the treatment variable, there are cases with missing values for the covariates in our study, mainly for the variables reporting spouse's information. The covariates with the largest amount of missing values are husband's work hour (16.94%) and income (17.56%) and wife's work hour (4.52%) and income (9.24%). A single imputation approach is adopted in this study, where missing values are imputed, using information from all other covariates in the current study. Although multiple imputation is generally considered a better method for handling missing values, past studies have shown that single imputation performs well in the context of propensity score matching (Stuart, 2010). In order to minimize the potential impacts from the differential distribution of missing cases between the treatment and control groups, we also additionally include dummy variables indicating the missing status of these covariates into the propensity score models so that the proportion of the missing cases will also be matched between the two groups. To check the robustness of our conclusion regarding the choice of strategies to handle missing data, listwise deletion is also used. With listwise deletion, the conclusion of this study remains the same. Due to space constraints, analysis with listwise deletion is not reported in the paper but is available upon request.

RESULTS

Covariate Balance Before and after Kernel Matching

The propensity score used in the matching process in this study is the predicted probability derived from the logistic regression model. Interested readers can see Appendix 1 for the detailed results of the propensity score model. All covariates jointly predict the propensity to

2hire domestic help fairly well (Pseudo-R = .36). On average, the estimated probability of hiring domestic help is 12%. Each treated case (couples who hired domestic help) is matched with one or more cases with a similar background in the control group (couples who did not hire domestic help), depending on the type of matching methods used. With kernel matching, three treated and 67 non-treated cases are excluded due to the lack of common support. These cases have extreme conditional probabilities of hiring domestic help (close to 0% for non-treated cases and 98% for the treated case), making it difficult, and most likely meaningless, to estimate the counterfactual outcome for these cases. Hence, they are excluded in the matching analysis.


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<.0

5.

p<.0

1.

p<.0

01. (

two-

tail

ed)

bA

fter

Mat

chin

gS

tand

ardi

zed

aa


Before estimating the effect of hiring domestic help on the employers' family well-being, the covariate balance is checked with the matched sample. Table 1 reports the standardized difference in percentage for each covariate between the treatment and the control groups before and after kernel matching with the propensity score. The treated group and the control group differ considerably before matching. Aside from the number of children, co-residence with parents/in-laws, the respondent's gender and the missing indicators, all covariates are significantly different between the treatment and the control groups. Specifically, couples who hired domestic help, as compared to those who did not hired help, are younger, having a higher socioeconomic status, more likely to work full-time, with longer work hours per week, with at least one young child, and have a less traditional gender ideology. Standardized differences in percentage for socioeconomic indicators, such as husband's income (125.9%) and education (118.2%), and wife's income (114.3%) and education (118.1%), are particularly large.

Kernel matching has largely reduced the differences of covariates between the two groups. After matching, none of the covariates has a remaining standardized bias larger than 25% (standardized differences after matching ranged from 1.5% to 14.5%). The covariates are well balanced with kernel matching in the current study.

Estimating Effects of Hiring Domestic Help on Family Well-Being

Table 2 reports the estimated average treatment effects of hiring domestic help on marital conflict and marital quality for the couples who hire domestic help. Even before matching, marital conflict did not significantly differ between those who hired domestic help and those who did not. Given the importance of socioeconomic background in hiring domestic help in Hong Kong, it may indicate that couples with more resources are not necessarily in a better position to avoid marital conflict. However, marital quality for those who hired domestic help is better than it is for those who did not hire domestic help (mean difference between treated and non-treated group =1.90; 95% confidence interval [95% C.I.] = 0.19 to 3.61). However, the differences in marital quality between the treatment and the control groups are largely attributed to the differences in their background characteristics. For the treated, after matching with the propensity score, the average treatment effects on marital conflict (ATT = 0.16; 95% C.I. = -0.31 to 0.62) and marital quality (ATT = 0.75; 95% C.I. = -2.32 to 3.81) are not significantly different from zero.

Using Cohen's d as a standardized effect size measure (dividing the mean difference on the outcome variables between groups by their standard deviations), the estimated effect sizes of hiring domestic help on the marital conflict (Cohen's d = 0.08) and marital quality (Cohen's d = 0.08) of employers are very weak. Taking the 95% confidence intervals of the ATT estimates into consideration, the effect sizes of hiring domestic help on family well-being are still likely weak and close to zero. The upper bounds of the 95% confidence intervals are far below the threshold of a moderate effect size (as a rule of thumb, moderate effect size is approximately 0.5 for a Cohen's d measure). That is, the data suggest that the effects of hiring domestic help are not likely to be substantial. The findings in this study suggest that the assumption about the substantial effects of hiring domestic help on family well-being may have been overly optimistic. These findings are consistent with previous research suggesting that a paid domestic helper does not substantially change the division of domestic labor and reduce work-family stress (Aycan & Eskin, 2005; Groves & Lui, 2012; Killewald, 2011).

In addition to kernel matching, nearest neighbor matching within caliper and radius matching are also used to estimate the ATT of hiring domestic help. As shown in Table 2, the


Tab

le 2

. A

TT

Est

imat

es o

f H

irin

g D

omes

tic

Hel

p o

n M

arit

al C

onfl

ict

and

Mar

ital

Qu

alit

yd

cM

ean

Est

imat

ed95

%C

.I.

Tre

ated

gro

up (

n)C

ontr

ol g

roup

(n)

AT

TL

ower

Upp

er

Mar

ital

Con

flic

t (U

nmat

ched

) tw

o-sa

mpl

e t-

test

4.

36 (

121)

4.37

(85

3)-0

.00

-0.3

50.

34a

Ker

nel

mat

chin

g 4.

40 (

118)

4.24

(78

6)0.

16-0

.31

0.62

b1:

1 N

N m

atch

ing

(wit

hin

cali

per)

4.

38 (

114)

4.23

(11

4)0.

15-0

.37

0.67

bR

adiu

s m

atch

ing

4.38

(11

4)4.

15 (

786)

0.23

-0.1

90.

64

Mar

ital

Qua

lity

*(U

nmat

ched

) tw

o-sa

mpl

e t-

test

42

.78

(121

)40

.88

(853

)1.

900.

193.

61a

Ker

nel

mat

chin

g 42

.66

(118

)41

.92

(786

)0.

75-2

.32

3.81

b1:

1 N

N m

atch

ing

(wit

hin

cali

per)

42.6

7 (1

14)

41.6

9 (1

14)

0.97

-2.5

64.

51b

Rad

ius

mat

chin

g 42

.67

(114

)41

.59

(786

)1.

08-2

.09

4.24

ab

cN

otes

:

Ker

nel

band

wid

th i

s 0.

06;

Cal

iper

for

nea

rest

nei

ghbo

r/ra

dius

mat

chin

g w

as s

et a

t 0.

25 s

tand

ard

devi

atio

n of

the

est

imat

ed p

rope

nsit

y sc

ore;

B

oots

trap

con

fide

nce

din

terv

als

(1,0

00 b

oots

trap

rep

lica

tes)

; 3

tre

ated

cas

es a

nd 6

7 no

n-tr

eate

d ca

ses

are

excl

uded

due

to

lack

of

com

mon

sup

port

in

the

mat

ched

sam

ple,

and

4 t

reat

ed c

ases

are

ad

diti

onal

ly e

xclu

ded

beca

use

of t

he l

ack

of n

on-t

reat

ed c

ases

wit

hin

cali

per/

radi

us f

or n

eare

st n

eigh

bor

and

radi

us m

atch

ing.

*

****

* p

<.0

5.

p<.0

1.

p<.0

01. (

two-

tail

ed)

choice of matching algorithms has little impact of the size of ATT estimates. The findings from kernel matching are robust to the choice of matching algorithms.

Additional Analyses

Tables 3 and 4 present the results of additional analyses, assessing whether the above conclusion is robust to the propensity score model specification and whether the effects of hiring domestic help are different for men and for women. Some previous studies show that women's income and employment are correlated with the expenditure on housekeeping and that it may affect the propensity to hire domestic help (Cheung & Lui, forthcoming; Cohen, 1998; Treas & de Ruijter, 2008). However, it is difficult to establish whether women's employment status and income are causes or consequences of hiring domestic help. In the context of Hong Kong, many women hire domestic help in order to continue to work outside the home and to focus on developing their careers. In the analysis reported above, the wife's employment, work-hour and income are included in the propensity score model. However, to test the sensitivity of the findings in this study with regards to the propensity score model specification, these variables are excluded in the propensity score model for another set of analysis. For marital conflict and marital quality, ATT estimates do not substantially differ with respect to whether women's income, employment and work hour are included in the propensity score model. The ATT estimates for marital conflict (ATT = -0.03 Cohen's d = -0.02) and marital quality (ATT = -1.05; Cohen's d = -0.13) are both very weak and not significantly different from zero. Besides, the upper bounds of the interval estimates are also far below the threshold of a moderate effect size. The findings of this study are robust to whether wife's income and employment are considered in the selection mechanism.

Kernel matching was performed again for the male and female subsamples respectively, using the propensity scores estimated for each subsample. Covariate balances are achieved in each subsample (the tables of covariate balance within each subsample are not reported here, but available upon request). Table 4 presents the results of additional analysis for male and female subsamples. ATT estimates of hiring domestic help on marital conflict and marital quality are both weak and non-significant. The effects of hiring domestic help on reported marital conflict are 0.35 (Cohen's d = 0.19) for men and -0.09 (Cohen's d = -0.05) for women. The effects of hiring domestic help on perceived marital quality are -2.13 (Cohen's d = -0.27) for men and -0.19 (Cohen's d = 0.02) for women. The differences in ATT estimates of hiring domestic help between male and female subsamples are not statistically significant, thus providing no evidence for a gender-specific effect of an effect of hiring domestic help on family well-being.

DISCUSSION AND CONCLUSION

Hiring domestic help has become very common in societies where work-family conflict is increasingly becoming a substantial issue for dual-earner couples. In order to promote and encourage women to devote themselves to their careers, some countries (such as Singapore) have started to provide tax relief for married couples to hire household services, while in some other countries an allowance is occasionally provided by companies for their senior-level employees to buy household and childcare services. However, family research on the impacts of hiring domestic help on family functioning is still lacking. Many issues related to this phenomenon remain unexplored. This paper attempts to answer a straightforward but rarely examined empirical question: Does the hiring of domestic help substantially reduce marital conflict and improve the marital quality for those couples who hire domestic help, as was previously assumed? The data from this study suggest a negative answer, at least in the



Tab

le 3

. A

dd

itio

nal

An

alys

is:

AT

T E

stim

ates

of

Hir

ing

Dom

esti

c H

elp

on

Em

plo

yers

’ M

arit

al C

onfl

ict

and

Mar

ital

Qu

alit

y (E

xclu

din

g W

ife’

s In

com

e, E

mp

loym

ent,

an

d W

ork

Hou

r in

th

e P

rop

ensi

ty S

core

Mod

el)

cb

Mea

n E

stim

ated

95%

C.I

.T

reat

ed g

roup

(n)

Con

trol

gro

up (

n)A

TT

Low

erU

pper

Mar

ital

Con

flic

t (U

nmat

ched

) tw

o-sa

mpl

e t-

test

4.

36 (

121)

4.37

(85

3)-0

.00

-0.3

50.

34a

Ker

nel

mat

chin

g 4.

36 (

121)

4.39

(69

9)-0

.03

-0.5

30.

47

Mar

ital

Qua

lity

*(U

nmat

ched

) tw

o-sa

mpl

e t-

test

42

.78

(121

)40

.88

(853

)1.

900.

193.

61a

Ker

nel

mat

chin

g 42

.78

(121

)43

.83

(699

)-1

.05

-3.3

71.

26a

bc

Not

es:

Ker

nel

band

wid

th i

s 0.

06;

Boo

tstr

ap c

onfi

denc

e in

terv

als

(1,0

00 b

oots

trap

rep

lica

tes)

; 1

54 n

on-t

reat

ed c

ases

are

exc

lude

d du

e to

lac

k of

com

mon

sup

port

in

the

mat

ched

sam

ple.

***

***

p<

.05.

p<

.01.

p<

.001

. (tw

o-ta

iled

)


Tab

le 4

. A

dd

itio

nal

An

alys

is:

AT

T e

stim

ates

of

Hir

ing

Dom

esti

c H

elp

on

Em

plo

yers

’ M

arit

al C

onfl

ict

and

Mar

ital

Qu

alit

y (M

ale

and

Fem

ale

Su

bsa

mp

les)

cb

Mea

n E

stim

ated

95%

C.I

.T

reat

ed g

roup

(n)

Con

trol

gro

up (

n)A

TT

Low

erU

pper

Mal

e S

ampl

eM

arit

al C

onfl

ict

(Unm

atch

ed) t

wo-

sam

ple

t-te

st

4.50

(52)

4.33

(339

)0.

17-0

.36

0.74

aK

erne

l mat

chin

g 4.

45 (4

7)4.

10 (2

70)

0.35

-0.3

71.

08

Mar

ital

Qua

lity

(Unm

atch

ed) t

wo-

sam

ple

t-te

st

44.1

7 (5

2)42

.55

(339

)1.

62-0

.57

3.89

aK

erne

l mat

chin

g 44

.23

(47)

46.3

7 (2

70)

-2.1

3-6

.68

2.41

Fem

ale

Sam

ple

Mar

ital

Con

flic

t (U

nmat

ched

) tw

o-sa

mpl

e t-

test

4.

26 (6

9)4.

39 (5

14)

-0.1

3-0

.58

0.32

aK

erne

l mat

chin

g 4.

30 (6

7)4.

39 (3

22)

-0.0

9-0

.78

0.60

Mar

ital

Qua

lity

(Unm

atch

ed) t

wo-

sam

ple

t-te

st

41.7

2 (6

9)39

.78

(514

)1.

95-0

.49

4.34

aK

erne

l mat

chin

g 41

.57

(67)

41.7

6 (3

22)

-0.1

9.-3

.54

3.15

ab

cN

otes

: K

erne

l ban

dwid

th is

0.0

6;

Boo

tstr

ap c

onfi

denc

e in

terv

als

(1,0

00 b

oots

trap

rep

lica

tes)

; 5

trea

ted

case

s an

d 69

non

-tre

ated

cas

es a

re e

xclu

ded

for

mal

e sa

mpl

e du

e to

lack

of

com

mon

sup

port

in th

e m

atch

ed s

ampl

e. 2

trea

ted

case

s an

d 19

2 no

n-tr

eate

d ca

ses

are

excl

uded

for f

emal

e sa

mpl

e du

e to

lack

of c

omm

on s

uppo

rt.

***

***

p<

.05.

p<

.01.

p<

.001

. (tw

o-ta

iled

)

context of Hong Kong. While previous studies show that the hiring of domestic help has substantial effects on couples' economic roles and on time spent on specific household tasks, the data from this study suggest that the hiring of domestic help is not likely to substantially affect the employers' marital well-being. This study shows that couples who hired domestic help have better marital quality than those who did not hire domestic help; however, the results of matching analysis suggest that they may be not causally related. The results of this study are consistent with the theory that couples who hired domestic help enjoy better marital quality not because of the domestic help they received, but because of their different socioeconomic, demographic and other background characteristics, as compared with families that did not hire domestic help.

Ecological systems theory (Hill, 2005; Voydanoff 2002, 2005b) suggests that work and family demands can increase the role strain and role conflict of couples. To cope with the negative spillover effects, boundary-spanning resources (such as work-to-family facilitation, supportive policies) that help meet the demand of each domain can help to improve overall work-family fit (Voydanoff, 2005b). However, it is not entirely clear in the literature which resources or strategies are most effective in helping couples. While past studies have already shown that family-friendly policies, such as flexible work-hours, and a supportive workplace culture can help improve the family well-being of employees, more empirical studies and theoretical discussions are needed to sort out different types of resources and strategies that are helpful to different individual and couple-level outcomes. Past studies assumed that domestic help is a type of boundary-spanning resource that can be used to improve work-family fit and therefore marital well-being (Anderson, 2000; Voydanoff, 2002, 2005b). Therefore, they hypothesized that couples with a domestic helper at home can benefit from it to achieve role balance, and have less role strain and conflict.

Given the importance of the housework burden on family dynamics, why are the effects of hiring domestic help on the employers' family well-being likely to be negligible? Past studies suggested two possible reasons for the absence of a substantial effect. The first possible reason is that the hiring of domestic help enables women to work longer hours outside the home, which may in turn have negative impact of marital quality. Indeed, one of the primary reasons for married couples in Hong Kong to hire domestic help is to fully pursue employment and careers outside the home. Although the hiring of domestic help may help women to spend less time on housework, it does lengthen their time spent on work outside the home. The couples who hire domestic help are mostly from middle-class background whose jobs are extremely time-demanding. According to the Census Department, 30% of professionals and managers, and 60% of service workers in Hong Kong work more than 50 hours per week, and 52% of professionals and managers work overtime (Hong Kong Census and Statistics Department, 2007). Long work hours can reduce the time for the couples to spend together, which can in turn negatively affect family well-being. However, the estimated effects in this study are not significant regardless whether the wife's employment status and work-hour are matched. This reason may not well explain the findings.

The second potential reason is that the availability of domestic help did not improve the inequality of housework within the family. Hiring domestic help is found to be a strategy for men to maintain the hierarchy of unequal division of household labor in Hong Kong (Groves & Lui, 2012). Women are still primarily responsible for taking care of the child and for recruiting, training, and monitoring the domestic helper at home. From a symbolic interaction perspective, one constantly evaluates the role performance of self and the spouse. The perception on the fairness of the division of household labor is a key factor of marital


well-being and discord (Rogers & Amato, 2000; Wilkie, Ferree, & Ratcliff, 1998). However, the hiring of domestic help does not change gender inequality in household labor arrangement (Groves & Lui, 2012; Killewald, 2011). Women now see themselves as managers at home and do not necessarily think that men have increased their contribution to the household. Hence, conflict over household labor may still exist. The results of this study hint that reducing the time spent by women on traditional household tasks without changing the overall gender inequality in the division of household labor may not be very helpful in improving family relations. This may also point to the neglected side of the ecological systems theory and cross-domain approach in the work-family literature.

Nevertheless, the limitations of the current study should not be overlooked. Although Hong Kong provides an interesting setting for the current study, the absence of an effect of hiring domestic help on family relations may be culturally specific. Recent studies suggest that work affects marriage differently between the Western context and the Asian context (Wong & Goodwin, 2009). More data are needed to examine the effect of hiring domestic help across different contexts.

Besides, measurement of the treatment variable is less than perfect. The data from this study did not allow us to differentiate between those households who hired full-time and part-time domestic help. As noted earlier, however, the most commonly chosen option in hiring domestic help in Hong Kong is to hire a foreign live-in domestic helper (accounting for nearly 90% of those households that hired domestic help). Hence, the research design in the current study mainly captures the effect of hiring live-in foreign domestic helpers in Hong Kong.

The current design also ignores the possible temporal dimension of the effect of hiring domestic help on family well-being. Families that hire domestic help may benefit by hiring domestic help at the beginning, and the effects could start to diminish as these families adapt to a life of having a domestic helper and raise their material aspirations. Some studies that discuss the relationship between income and well-being suggest that an individual's rising income does not affect one's happiness in the long-run because individuals will raise their material aspirations after earning more income (Easterlin, 2001). The same logic can potentially extend to domestic outsourcing. However, longitudinal data are needed to test this line of speculation.

Longitudinal data would also provide stronger evidence for the presence, or absence, of a causal effect of hiring domestic help. In particular, longitudinal data that provide information about the couple's income level, employment status and work hour before and after hiring domestic help could help researchers to disentangle the causes and effects of hiring domestic help, and hence, provide better estimates of the effects of hiring domestic help on family well-being. Without tracking the changes of income, employment and work hour, this study could only match with the current conditions. With the current data, the findings presented are suggestive but not definitive.

Lastly, readers should also take note that propensity score matching method can only reduce the treatment-selection bias by adjusting observed covariates. If an unobserved but significant suppressor (that is, a variable correlated with treatment status and outcomes in an opposite direction) exists, the ATT estimates in this study may be underestimated. Although the current study has included a reasonable set of covariates in the propensity score models, this study cannot definitely rule out the possibility of a positive effect of hiring domestic help on family well-being.


Despite the limitations mentioned above, this study contributes to the domestic outsourcing literature by making use of a territory-wide representative sample and focusing on the effects of hiring domestic help on family well-being in the Chinese context. To my knowledge, this is the first study primarily designed to focus on this topic with a territory-wide representative sample, despite its theoretical and empirical importance. The lack of a published result on this topic may lead to sustain our impression that hiring domestic help substantially improves family well-being. With the findings from the current study, claims of the benefits of hiring domestic help should be made more cautiously in the future.

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Appendix 1. The Propensity Score Model: Logistic Regression of Hiring Domestic Help (n=974)

Hiring domestic help

Logit coef. SE

Husband’s age 0.22 0.19Husband’s age (squared) 0.00 0.00Husband’s education 0.06 0.04Husband’s employment 0.04 0.60Husband’s income 0.25*** 0.05Husband’s income imputed flag 0.40 0.38Husband’s work hour/week -0.02* 0.01Husband’s work hour imputed flag -0.19 0.38Wife’s age 0.11 0.22Wife’s age (squared) 0.00 0.00Wife’s education 0.10* 0.04Wife’s employment 0.40 0.60Wife’s income 0.21*** 0.06Wife’s income imputed flag -0.46 0.50Wife’s work hour/week 0.00 0.01Wife’s work hour imputed flag -0.07 0.66House ownership 0.11 0.16Number of children 0.53*** 0.16Presence of young child 0.70* 0.36Co-residing with parents/in-law 0.51 0.37Respondent’s gender (1 = male) -0.32 0.26Respondent’s gender role attitude 0.01 0.02Constant -13.59*** 3.89

LR Chi-square (df) 263.14(22)***2Pseudo-R 0.36

* ** *** p<.05. p<.01. p<.001. (two-tailed)


hiring domestic help and family well-being in hong kong: a ... · hiring domestic help and family...

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