Giving Incentives of Adult Children Who Care for Disabled Parents.
As people age, their chances of being disabled (i.e., reduced ability to perform basic activities of daily living, such as dressing, eating, or bathing) increase. This occurs at the same time their economic resources and earnings opportunities are declining. More than any other time in their lives, older persons with disabilities rely more heavily on public resources and/or family caregivers (primarily adult daughters) for financial, health, and lifestyle support.
This paper examines the economic incentives that form the basis of family members' decisions to provide caregiving support at a critical stage in an older person's life cycle, the stage of physical disability. The researchers investigate this perspective by addressing the following questions. Why do caregivers obtain satisfaction from spending time in the production of long-term care services for older disabled persons? Is caregiving an effort that is motivated solely by social and psychological factors, or are there also economic motivations? Do caregivers derive personal satisfaction or utility from knowing that the older person's needs are met, facilitating (in contrast to typical economics hypotheses) interdependent satisfaction or utility between the older person and the caregiver? Is the caregiver's decision to provide care motivated by an interest to enhance the future welfare of the caregiver?
Understanding what motivates family caregivers is important for framing effective public policy for older persons. It informs public policymakers whether enhanced caregiver support will be available when state/federal budget crises lead to decreased support of health and wellness programs for older persons. If potential family caregivers provide adequate caregiver support with an expectation to receive some form of personal gain, public policy strategies that provide compensation (e.g., tax credits or direct financial rewards) may be effective. Reliance on social and psychological forces to generate an adequate level of caregiving support may be inadequate.
The conceptual framework for providing insights on caregiving was guided by the theoretical perspectives of the intergenerational transfers and intergenerational giving literature, and these perspectives were further integrated with the general caregiving literature. The Health and Retirement Study (HRS) provides a unique database to examine these issues. This is because a sub-sample of the data includes persons who were adult children with disabled parents.
A PERSPECTIVE ON CAREGIVING PRESENTED IN THE GERONTOLOGY LITERATURE
Most gerontological research on caregiving begins with a sample of persons who have decided to provide support for older disabled persons. Within this literature, research on social-psychology and aging has shown that filial obligation and attachment are motivations for caregiving (Cicirelli 1993; Cantor 1989). Adult children believe that they have a duty or social obligation to care for their parents. These feelings of obligation lead to increased hours of assistance (Callahan 1985; Walker et al. 1990). Adult children also engage in caregiving to protect and prolong life of an attachment figure whose health is declining. Stronger attachment corresponds with more hours of assistance (Cicirelli 1993). The ways that families define their responsibilities affect caregiving efforts, as well as the sharing of responsibility among siblings (Piercy 1998).
Gerontological research on the economics and sociology of aging has addressed questions such as, how many hours of support will caregivers provide? How much monetary support will they provide? This research literature helps explain the efforts of these caregivers and the characteristics of those who are most likely to give their resources to older persons (e.g., Johnson and LoSasso 2000; White-Means and Chollet 1996; Smith and Wright 1994; Kemper 1992; Abel 1991; Liu, McBride, and Couglin 1990). It finds that caregivers are primarily middle-age daughters who provide significant support, including care hours comparable to part-time jobs. Some caregivers maintain full-time jobs in the labor market as well as elder care responsibilities. Moen, Robison, and Fields (1994) report that women between the ages of forty-five and fifty-four are more likely to combine caregiving and labor market employment than when they are in their thirties or age fifty-five and older.
A consistent finding is that caregivers who work outside the home provide less informal support. Using data from the 1982 National Long Term Care Survey and a two-stage least squares estimation procedure, Boaz and Mueller (1992) found that caregivers with full- and part-time employment spend less time providing elder care. Gibeau and Anastas (1989) found that when caregiving efforts and employment are conflicting interests, labor force adjustments, such as partial retirement, occur. Using simultaneous panel data models and data from the Health and Retirement Study, Johnson and LoSasso (2000) found that women and men who are ages fifty-three to sixty-five and spend two or more hours per week helping their parents, reduce paid employment by 43 percent if they are female and 28 percent if they are male.
For caregivers of disabled older persons, the gerontology literature has examined extensively the tradeoffs made by caregivers in allocating their time between market work and caregiving (e.g., Johnson and LoSasso 2000; Pavalko and Artis 1997; White-Means and Chollet 1996; Moen, Robison, and Fields 1994; Wolf and Soldo 1994; Boaz and Mueller 1992; Stone and Short 1990). The nature of the tradeoffs between giving care time and giving financial support to disabled older persons are less clear in the literature.
A PERSPECTIVE ON CAREGIVING PRESENTED IN THE INTERGENERATIONAL TRANSFERS LITERATURE
In contrast to the gerontology literature, the intergenerational transfers and intergenerational giving literature primarily focuses on the tradeoffs between giving time and money to parents. The research samples include caregivers for disabled and nondisabled parents. Using the 1988 National Survey of Families and Households, Freedman et al. (1991) examined intergenerational giving and documented that about one-fifth of adult children provided an older parent with assistance in household tasks (giving time), while 12 percent gave financial assistance. The estimated annual dollar values of time and money given to parents and parents-in-law were $13,294 and $18,276, respectively. For those who cared for both their own parents and parents-in-law, the value is $39,377 (in 1992 dollars). The total dollar value is higher for those who are caring for both parents with activities of daily living (ADLs) than those who are caring for parents with cognitive impairment (Fast, Hong, and Kolodinsky 1998).
What motivates these giving patterns? Are the patterns similar for parents who are disabled and nondisabled? The literature on intergenerational transfers provides answers to the first of these two questions only. The literature indicates that two motives affect giving: (1) motives to obtain bequests (i.e., future wealth accumulation) and (2) altruistic motives (conscientious efforts to show love to parents and desires to be loved later in life by one's own children). Conceptually, the literature suggests that caregivers' efforts to maximize long-term financial and human capital resources may influence (motivate) care efforts for their parents. This conceptualization suggests that there are economically motivated giving patterns of adult children when their parents face the disability stage of their life cycle (Altonji, Hayashi, and Kotlikoff 1996).
The intergenerational transfers literature has a primary emphasis on the theoretical conceptualization of giving, with testable hypothesis. However, because the corresponding empirical analysis typically uses a sample of adult children whose parents may or may not be disabled, empirical documentation of economic motives of caregivers for disabled parents is scanty. The literature reports that bequests from parents to children are a form of wealth transfers. Data from the 1983 to 1985 Survey of Consumer Finances indicate that over 75 percent of financial transfers involve parents giving to children (Bernheim, Shleifer, and Summers 1985). Furthermore, bequests represent 31 percent of aggregate net wealth transfers. Individuals over sixty-five years of age hold 32 percent of all family net worth in the U.S. (U.S. Bureau of the Census 1999), and baby boomers will inherit an estimated $10 trillion to $41 trillion over the next twenty years (Chatzky 1998).
Parents may use bequestable wealth to influence the behaviors of their children (Bernheim, Shleifer, and Summers 1985; Cox 1987). They may leave bequests to their heirs in exchange for services heirs provide. Or parents may pose a credible threat that bequests will be withdrawn if services are not provided. Siblings may even compete for bequestable wealth (Stark 1995). If caregivers support the needs of older disabled persons due to a bequest motive, caregivers will respond to conditional terms of bequests (Bernheim, Shleifer, and Summers 1985). Thus, to maximize their bequests, they must meet some specific service or goods requirement of the bequester. According to Bernheim, Shleifer, and Summers (1985), Cox (1987), and Zabner (1993), a testable hypothesis of the bequest motive is that as the parents' bequestable wealth increases, the amount of giving by the child should increase.
From this literature a second testable hypothesis is that wealthy children should be less easily influenced by bequest motives and less likely to give elder care support. A third testable hypothesis is that children give more to parents in families with large numbers of siblings, due to the competition for bequests. Alternatively, Wolf, Freedman, and Soldo (1997) posit that when siblings work as a team that is solely motivated by the desire to meet the needs of a disabled parent, the giving efforts of each child will decrease as the number of siblings increases. This is because there are more persons to share the workload. Wolf, Freedman, and Soldo (1997) provide empirical support for this hypothesis from the 1993 Asset and Wealth Dynamics among the Oldest Old (AHEAD) data base.
Similar to the bequest motive, one can conceptualize an altruism motive among caregivers that is based on an effort to enhance the long-term welfare of caregivers. (1) A seminal contribution to this literature was written by Stark (1995) who theoretically conceptualizes altruism among caregivers for elderly parents. He also provides an empirical test of this form of altruism by developing a concept of economically motivated altruism. He postulates that the bequest motive for elder care is an inadequate explanation for the time intensive care given to impoverished older persons with inadequate resources for current consumption and nothing to leave for heirs as a bequest. Stark suggests that showing love, i.e., altruism, is a human capital characteristic that parents want to instill in their children. The relationship among persons in three generations may reflect altruism incentives. Specifically, providing caregiving services for one's parents not only meets the need of a parent, but it is also a way to encou rage one's children to learn altruism. As the children of adult caregivers observe their parents caring for their grandparents, they are more likely to show altruism to their parents in later years.
In essence, showing altruism to parents is also preference shaping and development of a human capital characteristic (altruism) in one's children. This preference shaping may increase an adult child caregiver's resource base of time and money in later years when the caregiver faces disability. This is a do as I do approach to caregiving that assures one will have a caregiver in the future. It is conceptually linked to real world evidence that children emulate parents. For example, children of teen or divorced parents tend to become teen parents and divorcees when they are grown. An alternative to the demonstration effect for inculcating altruism in children is via religious affiliation (Stark 1995). Religious affiliations reinforce the value that it is important for children to love their parents. For example, Christian religions promote the Ten Commandments that teach the philosophy of honoring fathers and mothers. Religious training enhances parents' ability to develop altruism. Stark predicts that religion is positively associated with giving to parents.
Stark's (1995) altruism framework suggests that elder care services received by one's parents are greater when caregivers have children than when they do not. With more children, the likelihood that at least one child will emulate parents increases and giving to parents has greater benefits for caregivers. Thus, it is possible to test empirically for the altruism motive by examining the sign of the variable (number of children) in models of caregivers' activities. This positive predicted effect of children on giving to disabled parents differs from that of another economic approach to understanding caregiving (the Beckerian household time allocation analysis framework) and predictions based on other social science disciplines. The later perspectives predict that with more children, demands on the caregiver's time increase, the caregiver's roles increase, and, thus, the caregiver will provide less care to older parents. Therefore, caregiving competes for scarce time that is needed for other activities, i.e., c hild care.
This study makes two unique contributions to the literature. First, it bridges the foci of the gerontology and intergenerational transfers literatures. One literature (gerontology) documents that caregivers make tradeoffs between elder care time and work. The other literature (intergenerational transfers) documents that caregivers make tradeoffs between elder care time and giving money. Thus, this study examines the interrelationship among three forms of giving/caring for older parents-giving time, giving current financial resources, and giving future financial resources (reflected by decisions to not work in the labor market). (2) The second contribution of this research is to provide an explicit test of whether economic (altruism and/or bequest) motives influence decisions to provide care to disabled older parents. This issue is explored for a sample of adult children who range in age from fifty-one to sixty-one and have disabled parents. This study examines whether altruism and/or bequest motives influence each of the three forms of giving to parents.
The data for this study are from the 1992 Health and Retirement Study (HRS). The data were collected by the Institute for Social Research at the University of Michigan and the National Institute on Aging (Juster and Suzman 1994). It is a national longitudinal study that focuses on labor force participation, pensions, health insurance, health status, retirement, housing and mobility, family structure, and economic status of 12,654 individuals born between January 31, 1931 and December 31, 1941 and their spouses and partners. The HRS is ideal for this study because this age group (ages 51 to 61) is thought to provide the bulk of caregiving assistance to disabled parents. The data include one respondent per household.
A subsample of these data is used in this research. Because this study is concerned with giving to parents when they are limited physically or impaired in their ability to care for themselves, the sample includes 1,704 adult children who have at least one living parent or parent-in-law (either biological or step) who cannot be left alone more than one hour or who has a limitation in at least one of the following ADLs--dressing, eating, or bathing.
Estimation Procedure, Model Specification, and Identification of the Simultaneous System
The empirical model is a three equation, simultaneous system used to estimate the probability that an adult child spends time helping (GIVETIME), provides financial support (GIVEMONEY), and does not work in the laborforce (NOWORK), thus giving up future financial earnings opportunities and benefits in order to provide care for a disabled parent. The structural equations for this model are the following:
(1) NOWORK = [[beta].sub.0] + [[beta].sub.1] GIVETIME + [[beta].sub.2] GIVEMONEY
+ [[beta].sub.3] Children + [[beta].sub.4] Children*Catholic + [[beta].sub.5] Children*OC + [[beta].sub.6]Houses
+ [[beta].sub.7] Savings + [[beta].sub.8]Sibling ($) Transfers + [[beta].sub.9]Sibling Time Transfers
+ [[blank].sub.-10]ADLs + [[beta].sub.11]ADLS*Not alone + [[beta].sub.12]Inherited + [[beta].sub.13] Bequest 1
+ [[beta].sub.14]Bequest2 + [[beta].sub.15]Hours Volunteered + [[beta].sub.16]White
+ [[beta].sub.17]Unearned Income + [[beta].sub.18]Age + [[beta].sub.19]Male + [[beta].sub.20]Married
+ [[beta].sub.21] Education + [[beta].sub.22] Excellent Health + [[beta].sub.23]Very good/Good Health
+ [[beta].sub.24]Catholic + [[beta].sub.25]OC + [[beta].sub.26]Wage
+ [[beta].sub.27] Post-retirement Health Insurance + [[micro].sub.1]
(2) GIVETIME = [[beta].sub.0] + [[beta].sub.1] GIVEMONEY + [[beta].sub.2] NOWORK
+ [[beta].sub.3] + Children + [[beta].sub.4]Children*Catholic+ [[beta].sub.5] Children*OC + [[beta].sub.6]Houses
+ [[beta].sub.7]Savings + [[beta].sub.8] Sibling ($) Transfers + [[beta].sub.9] Sibling Time Transfers
+ [[beta].sub.10] ADLs + [[beta].sub.11] ADLs * Not alone + [[beta].sub.12] Inherited + [[beta].sub.13] Bequest 1
+ [[beta].sub.14] Bequest2 + [[beta].sub.15] Hours Volunteered + [[beta].sub.16] White
+ [[beta].sub.17] Unearned Income + [[beta].sub.18] Age + [[beta].sub.19] Male + [[beta].sub.20] Married
+ [[beta].sub.21] Education + [[beta].sub.22] Excellent Health + [[beta].sub.23] Very good/Good Health
+ [[beta].sub.28] Midwest + [[beta].sub.29] West + [[micro].sub.2]
(3) GIVEMONEY = [[beta].sub.0] + [[beta].sub.1] NOWORK + [[beta].sub.2] GIVETIME
+ [[beta].sub.3] Children + [[beta].sub.4] Children * Catholic + [[beta].sub.5] Children * OC + [[beta].sub.6] Houses
+ [[beta].sub.7] Savings + [[beta].sub.8] Sibling ($) Transfers + [[beta].sub.9] Sibling Time Transfers
+ [[beta].sub.10] ADLs + [[beta].sub.11] ADLs * Not alone + [[beta].sub.12] Inherited + [[beta].sub.13] Bequest 1
+ [[beta].sub.14] Bequest 2 + [[beta].sub.15] Hours Volunteered + [[beta].sub.16] White
+ [[beta].sub.17] Unearned Income + [[beta].sub.18] Age + [[beta].sub.19] Male + [[beta].sub.20] Married
+ [[beta].sub.21] Education + [[beta].sub.22] Excellent Health + [[beta].sub.23] Very good/Good Health
[[beta].sub.24] Poor parents + [[micro].sub.3]
A full description of the model variables and their measures are included in Table 1.
Because GIVETIME, GIVEMONEY, and NOWORK are simultaneously determined, each equation cannot be estimated separately using ordinary least squares (OLS). To do so would produce biased and inconsistent estimates. The appropriate estimation technique depends, in part, on the identification of the equation system. The above system of equations is overidentified. One technique that can be used to generate unbiased and consistent estimates is a two-stage estimator (Greene 1999). Such an estimator addresses the bias that results when the endogenous giving patterns are not independent of the regression error terms, as found in the above equations. Thus, the first stage estimation generates instrumental variable measures of giving by estimating the reduced form model of the equation system, with each endogenous variable estimated as a function of all the exogenous variables of the system. In the second stage, the structural model of giving is estimated. In this stage, each equation includes the instrumental variable me asures of the alternate forms of giving and exogenous variables uniquely associated with that form of giving.
Another caveat that must be considered is that each of the endogenous variables is dichotomous. Thus, a limited dependent variable estimation procedure is needed. This caveat and the desire to produce unbiased and consistent estimates for the model led the researchers to choose a two-stage maximum likelihood estimator and a two-stage logistic regression estimator (Greene 1999).
Via this model the researchers test the hypothesis that each form of giving is jointly determined with the two other forms of giving. The researchers also test the hypotheses that adult child giving is influenced by altruism and bequest incentives, the caregiving environment, and/or characteristics of the adult child.
The HRS provided three dichotomize indicators of adult child giving (see Table 1). The first is, GIVETIME, a variable that equals one if an adult child indicates that he or she or his or her spouse spent 100 hours or more helping with basic needs care of at least one disabled parent or parent-in-law during the last twelve months. The second variable, GIVEMONEY, equals one if the adult child and/or the adult child's spouse gave $500 or more to support the care of at least one disabled parent or parent-in-law during the last twelve months.
The third variable, NOWORK, equals one if the adult child is not currently employed. While incorporating some measurement error, NOWORK is the best available measure of the caregiver's loss of long-term financial resources. The sample of caregivers in this study, who are ages fifty-one to sixty-one, are in an age cohort that typically works in the labor market. Data from the 1992 Current Population Survey (CPS) indicates that 67.4 percent of persons who were ages fifty-five to fifty-nine participated in the labor force and 64 percent were employed. Whether or not they were employed in the labor market affected the availability and amount of their Social Security benefits and future retirement income (Wiatrowski 1993). Because they had not reached age sixty-five, employment type (or working part- or full-time, post retirement) could represent a viable option for some adult children, and caregiving responsibilities could lead some to not avail themselves of this option. For the NOWORK variable, the one category includes people who have permanently left the labor force due to caregiving responsibilities, those who have temporarily left the labor force due to caregiving responsibilities, and those who are in the labor force but not working because caregiving responsibilities absorb so much time that they cannot search extensively for a job. Because the one category also includes those who are not working for reasons other than caregiving, it overestimates the persons who have made caregiving choices that impact on their long-term financial resources. The NOWORK variable is used because (1) it is the best proxy measure available in the data source and (2) to exclude any measure of the work force decision would produce inordinate biases in the empirical model. While biased, NOWORK, is the measure commonly used in the caregiving literature, allowing comparability between the research reported in this paper and research findings in the literature.
Independent Variables (Included in all regression models)
Four variables are included to proxy bequest motives: number of houses owned by disabled parents, a prediction of expected savings at retirement for the adult child, and two dummy variable measures of whether a sibling of the adult child has transferred money or time to the disabled parent in the last twelve months. The hypotheses for these variables are derived from the intergenerational transfers literature. The researchers hypothesize that if the adult child is motivated by an incentive to establish him or herselves in the good graces of the older person to receive a financial or capital (housing) inheritance, there will be enhanced motives to care for a parent when the parent has greater resources (Bernheim, Shleifer, and Summers 1985). Thus, the researchers predict a positive relationship between the number of houses owned by parents and the probability of giving by adult children. On the other hand, the greater the adult child's expected savings, the less dependent they are on parents' bequests and the less likely they are to respond to a bequest motive (Bernheim, Shleifer, and Summers 1985). The researchers predict a negative relationship between the adult child's expected savings and the probability of giving. The intergenerational transfer literature suggests that an adult child will give more care to a disabled parent when siblings compete for bequests. Thus, when siblings give money, it is predicted that adult child respondents will be more likely to give money. Similarly, if siblings give time, it is predicted that more time contributions will be made by the adult child respondent.
Altruism motives are reflected by two variables: number of children and religion. Hypotheses for these variables are based on theoretical predictions and empirical findings in the intergenerational transfers literature. According to Stark's (1995) framework, if developing altruism in their children (preference shaping) motivates families, greater efforts of giving will occur when parents have more children. Thus, the researchers predict a positive relationship between number of children and giving to disabled parents. Religious affiliation serves as an alternate mechanism for developing altruism in children. Those who are religious (i.e., frequent churchgoers) or report themselves as Protestant or Catholic, tend to be more altruistic than those with no religion (Rossi and Rossi 1990). Thus, the researchers' prediction is that adult children with a Christian religious affiliation will be more likely to give to disabled parents. This study also includes interaction terms of children and religious affiliation to capture the intervening influence of religious affiliation on the motivation to preference shape children. Again a positive association is predicted.
In each giving equation, the researchers included variables to measure characteristics of the caregiving environment. This is because previous research has shown that the needs of the disabled parent influence the amount of support given by individual caregivers (e.g., White-Means and Thornton 1996; Altonji, Hayashi, and Kotlikoff 1996). The caregiving environment variables include the total number of parents' with ADLs, and an interaction term of the number of parents with ADLs and number of parents who cannot be left alone. It is predicted that as the disabled parent's needs increase, the probability of giving will increase.
The final group of independent variables for the giving equations are caregiver characteristics. They include the caregivers' nonwage income, self-assessed health status, age, gender, education, marital status, and race. The nonwage income measure incorporates all other sources of household income, including the spouse's income. A measure of the hours volunteered are included to capture the altruistic spirit of the adult child, a type of altruism that is independent of economic motivations to benefit the adult child in the future. The variable inherited (= 1 if the adult child has received an inheritance or was given substantial assets in the form of a trust at some previous time) captures an incentive of the adult children to give care to disabled parents when they have been recipients of gifts in the form of inheritances or trusts. Finally, this study includes a measure of the adult child's attitudes about bequests, a measure of whether the adult child plans to leave a bequest for his or her child.
Independent Variables Included for Identification of the Simultaneous System
For purposes of identification of the simultaneous system of equations, each equation of the system includes unique variable measures. Measures of the caregiving environment and caregiver characteristics vary in each equation. By doing so, the researchers obtain a system of simultaneous equations that is overidentified and for which it is possible to distinguish each equation of the system from the others.
The equation whose dependent variable indicates whether or not the adult child worked in the labor force (i.e., whether the adult child gave up future financial earnings opportunities and benefits) includes two unique measures that reflect the opportunity cost of leaving the workforce: postretirement health insurance (= 1 if have insurance that is sponsored by one's current or former employer and expect that the insurance will be available during postretirement) and an opportunity wage. The opportunity wage is a measure of the opportunity cost of the caregiver's time. This measure is imputed for all adult children by first estimating wage regressions for employed adult children. The natural log of wages per hour is predicted by education, race, potential experience ([age] -- [education -- 6]) and its square, region, and disability status. This equation is estimated separately for men and women. Coefficients from these regressions are used to impute values of the opportunity wage for all adult children. The op portunity wage should not be included in the equation whose dependent variable indicates whether the adult child gave money to his or her disabled parent. This is because, by giving money, the adult child does not lose the opportunity to earn wages in the labor force; the opportunity wage is a measure of the opportunity cost of time lost from labor force efforts.
The unique variables in the equation whose dependent variable indicates whether the adult child gave time to his or her disabled parent are regional dummy variables. These variables reflect variations in regional health care practices and financing policies for the aging population. For example, those who live in the South and West are less likely to have insurance. The South has the highest concentration of older persons who live in poverty (Ries 1987). Market-purchased home care may represent a care option that is unavailable or not affordable to some disabled parents in this region. Thus, it is predicted that structural characteristics of the community of residence may increase demands on adult children to give time to care for disabled parents. While it can be argued that there may be regional variations in all the endogenous variables, the researchers believe the effects of region on the NO WORK and money transfer decisions are small, after accounting for the role of other key variables. Specifically, mo st of the regional variation in the decision to work should be captured in the opportunity wage measure. Similarly, regional variations in poverty and the need for financial assistance from adult children are captured in the measure, number of poor parents.
The unique variable in the equation whose dependent variable indicates whether the adult child gave money to his or her disabled parent is a measure of the number of parents who are poor. When parents have limited financial resources, adult children must decide whether to supplement their parents' financial resources with their own. This describes the direct effect of the parent's poverty status. Because this variable is only included in the financial giving model, the researchers suggest that this variable impacts the time decision indirectly via the decisions about giving money to parents, that is, the coefficients for the GIVEMONEY variable in the GIVETIME and NOWORK regressions. Thus, given the decision of an adult child to give money to poor parents, the time decisions are adjusted. It is predicted that disabled parents who live in poverty are more likely to have adult children who contribute financial resources. However, the more poor parents that the adult child and his or her spouse have, the less lik ely that financial resources will be available for any particular disabled parent. Thus, the researchers predict a negative relationship between the number of poor parents and giving financial resources. Table I summarizes the measures of all dependent and independent variables.
Sample Characteristics: Adult Children With Disabled and Nondisabled Parents
Note that much of the intergenerational transfer literature considers adult child giving patterns to parents who may or may not be disabled. In this study, giving patterns are considered only among adult children who have disabled parents. How unique is the sample of adult children who provide support for disabled parents, when compared to adult children with nondisabled parents? To address this question, the researchers compare them to a similarly aged sample of adult children whose parents are not disabled. Table 2 reports the results and stratifies the sample of adult children who care for disabled parents according to the type of parental disability. Some children attend to the needs of parents with ADL limitations only or with the inability to be left alone, or both. The data show that adult children spend significantly more hours in support care for their parents if their parents have ADL disabilities and also cannot be left alone. When parents have both types of limitations, adult children average twen ty times the hours spent with parents who are not disabled. Adult children who assist with parents who are only limited in ADLs average almost sixteen times the hours spent with parents who are not disabled.
Additionally, Table 2 provides a contrast of time given, money given, and employment, according to whether the adult child gives time or money to disabled parents. Those who give time also give significantly more money and are less likely to hold jobs in the labor market than adult children who do not give time. Those who give money also give significantly more time, yet are more likely to hold jobs in the labor market than adult children who do not give money.
Based on the descriptive statistics of Table 2, giving money and giving time, not working in the labor market and giving time appear complementary. On the other hand, not working in the labor market and giving money appear substitutable. When adult children give time, they average higher monetary giving ($925.27) than those who do not give time ($93.34). They also are less likely to work in the labor market (61.57% versus 71.07%). Similarly, when adult children give money to disabled parents, they also average more annual hours in support of parents (71.82 versus 31.80), yet are slightly more likely to work in the labor market (77.13% versus 70.25%). These unexpected relationships require further investigation, particularly in a multivariate framework.
Table 3 reports descriptive statistics for model variables, according to the parents' disability status. Because the data in Table 2 suggest that the demands on adult children are more extensive when parents are limited in ADLs or have ADLs and also cannot be left alone, the researchers stratify the data in the empirical models to reflect these differences in care environments. In Table 4 and all remaining tables, the data are stratified according to whether parents are limited in ADLs (with ADLs only or ADLs and cannot be left alone) or whether parents cannot be left alone (either with or without ADLs).
Tables 4 and 5 report the results of the second stage logistic regressions for the simultaneous model of giving. (3) The results indicate that the different forms of giving are interrelated, with the nature of this relationship varying with the type of disabilities faced by parents. They provide support for the hypothesis that bequest motives influence caregiving among adult children. There is only weak support for an altruism motive. Moreover, the caregiver's altruistic spirit (altruism that is independent of economic motives) affects giving. The researchers also find that the type of disability faced by the parent affects giving patterns.
As hypothesized, when parents face ADL limitations or cannot be left alone, giving care time and giving up jobs in the labor market are complements. Caregivers who do not work provide more care time for disabled parents (Tables 4 and 5). Additionally, the higher the probability that adult children give money to their cognitively disabled parents, the more likely they are to work in the labor market (see Table 5).
There is some, although weak, support for an economically motivated altruism incentive. As predicted, non-Catholic Christians with more children are more likely to give money transfers to parents with ADL limitations. However, among non-Catholic Christians who have parents with ADL limitations (Table 4), having more children decreases the likelihood of not working (i.e., decreases the likelihood of giving up long-term resources). Among adult children whose parents cannot be left alone (Table 5), religious affiliation is significant but not of the predicted sign. Catholics are less likely than non-Christians to not work in the labor market.
In both Tables 4 and 5, the empirical findings provide strong support for and are consistent with the bequest motive hypothesis. As predicted, the researchers find that adult children are more likely to give money and time to parents when their siblings give to their parents. According to the bequest motives hypothesis, these findings provide support for the proposition that sibling rivalry motivates efforts to give time and money to parents who are disabled with ADLs or cannot be left alone. If siblings transfer money, adult children are more likely to transfer time and money to parents who have ADL limitations or cannot be left alone (Tables 4 and 5). If siblings transfer time, adult children are more likely to transfer time (Tables 4 and 5).
Bequest motives also seem to influence the employment decisions of adult children who care for parents who cannot be left alone; money transfers by siblings increase the likelihood that an adult child is not working in the labor market. In both tables, it is also noted that when adult children's expected savings are larger, the probability of not working decreases. This finding is consistent with the bequest hypothesis that if adult children have savings, they are less likely to engage in giving to enhance the probability of receiving bequests from parents.
The caregiving environment also influences giving. When parents have ADL disabilities (Table 4), neither the number of parents with ADLs nor the number of parents with ADLs and also cannot be left alone influences the giving decisions of adult children. In contrast, when parents cannot be left alone (Table 5), the number of parents with this limitation or this limitation and ADL disabilities influences giving. The more parents who cannot be left alone, the more likely an adult child does not work in the labor market, and the less likely he or she gives time or gives money. In essence, the adult child gives future resources (employment opportunities, earnings, and benefits) rather than current resources. Additionally, with both greater numbers of parents who have ADL limitations and cannot be left alone, time transfers and money transfers (giving current resources) increase.
If adult children have an altruistic spirit (as evidenced by the number of hours volunteered), they are more likely to give money to parents with ADL limitations or cannot be left alone. They also are more likely to reduce labor force participation when parents cannot be left alone. If adult children plan to leave a bequest to their children, they are more likely to make money transfers to parents with ADL limitations.
The opportunity cost of time (measured by the imputed wage) is not statistically significant for caregivers with disabled parents who have ADLs or cannot be left alone. Having postretirement health insurance makes the opportunity cost of leaving the labor force higher for adult children and is associated with a lower probability of not working in the labor market (i.e., decreases the probability of giving long-term financial resources). However, higher levels of other (nonwage) income are associated with higher probabilities of giving. That is, higher levels of nonwage income decrease the probability that adult children who have disabled parents will work in the labor market and increase the probability of time and money transfers. Among adult children who care for parents who cannot be left alone, higher levels of nonwage income are associated with a higher probability of not working. Adult children with greater levels of health (sometimes associated with higher productivity) are more likely to participate i n the labor market. This tendency to have a lower probability of giving among healthier adult children is found for both those who care for parents with ADL disabilities and for those whose parents cannot be left alone.
What motivates adult children to care for their disabled parents? Similar to Cox's (1987) analysis of private income transfers to all parents, the researchers find that bequest incentives dominate adult children's motivations to give time and money to their disabled parents. The researchers find little support for an economically motivated altruism incentive among adult children ages fifty-one to sixty-one and their spouses. Additionally, adult children who have a noneconomically motivated altruistic spirit (as evidenced by their efforts to serve as caregivers who are community volunteers) and opportunity cost are key factors. These factors influence adult children's decisions about employment, giving time, and giving money to support disabled parents.
By estimating giving patterns as a simultaneous system of choices, the researchers find that different forms of giving are interrelated. Giving money and not working are substitutes. Thus, an adult child who shares current financial resources with his or her parent is less likely to give up long-term financial resources by not working in the labor market. On the other hand, the choice to share current time resources with a disabled parent is positively associated with giving up long-term financial resources by not working in the labor market. This analysis does not indicate a relationship between giving care time and giving money. The analysis presented in this study extends Altoinji, Hayashi, and Kotlikoff's work (1996) that used single equation models with dummy variable measures of the alternate forms of giving, to provide preliminary measures of the interrelationship between time and money transfers. Their preliminary results contrast those reported in this study and suggest that giving time and giving mo ney to parents are complements.
In the decision to give care to an older disabled parent, the care environment is important for adult children ages fifty-five to sixty-one. The contrasting findings in the models, presented in Table 4 versus Table 5, indicate that there are significant differences in decisions of adult children who care for parents with ADL disabilities and those who cannot be left alone. The parent's health status (i.e., number of ADLs and ability to be left alone) significantly affect time and money transfers of adult children who care for parents who cannot be left alone and is not a significant factor for those who care for parents with ADL disabilities.
In much of the literature that contrasts altruism versus bequest motives for giving, a critical variable is income (Cox 1987; Altonji, Hayashi, and Kotlikoff 1996; McGarry and Schoeni 1995). The hypothesis is that the altruism motive exists if there is a positive relationship between an adult child's income and giving to a parent. If a negative relationship exists, this finding supports a bequest motive; that is, wealthier children are less easily influenced by the desire to obtain a bequest from disabled parents and, thus, are less likely to give elder care support. In this study, the researchers have grouped adult children's income/financial resources in three categories: (I) wage of adult child, (2) nonwage income, and (3) expected savings. The researchers examine the regression coefficient for the expected savings variable to test for altruistic versus bequest incentives. This third category of financial resources provides the best source of information about the adult child's wealth and lack of financial dependence on the parent's intention to leave bequests. The finding of a negative relationship between this variable and parental giving in the form of not working, along with the results for the sibling transfer measures, is consistent with the bequest incentives hypothesis. Nonetheless, it is important to note that the nonwage income measure is positively associated with money and time transfers and not working in the labor market, corresponding with the altruism hypothesis. The latter result is also consistent with McGarry and Schoeni (1995) who use measures of adult child income and wealth quartiles and find a positive association with financial giving to parents.
This study's findings for the role of the wage rate differ from those of Altonji, Hayashi, and Kotlikoff (1996) who used the 1988 Panel Study on Income Dynamics (PSID) to examine giving patterns among all adult children who have living parents. Wages were significant and negatively related to giving time to parents. Similarly, White-Means and Chollet (1996) used data from the 1982 and 1989 National Long-Term Care Survey (NLTCS) and found (in both time periods) a positive and significant relationship between wages and employment of caregivers for disabled elderly. White-Means and Chollet (1996) note that reductions in the unemployment rate between 1982 and 1989 partly explain the more significant role of wages in employment decisions in 1982 compared with 1989. Greater earnings opportunities in 1989 made leaving paid employment a difficult choice. The data for the present study are for 1992, correspond with a recessionary period with high unemployment, are for a relatively older sample, and may partly explain this study's finding of an insignificant effect of wages on not working and giving time to care for disabled parents. Additionally, measurement error in the wage variable may bias its coefficient toward zero. Imputed wages are highly correlated with age and education, both included in the regression models, with age significant in the NO WORK equation and education significant in the GIVETIME model.
The findings reported in this research have implications for public policy. Several federal and state policy initiatives have sought to decrease government responsibility for the financial support of disabled older persons. There are proposed changes to Medicare and Medicaid that include increasing age eligibility, increasing premiums and deductibles, giving nursing homes more flexibility in discharging patients to their homes, and allowing states to require spouses of nursing home residents to sell their homes and other assets. The results that are reported in this study suggest that these types of policy changes could lead to unexpected secondary effects. These policy changes would decrease the financial resources of disabled parents. However, if bequest motives influence caregivers' incentives to provide elder care support, then consumer policy that decreases the wealth of disabled older persons may lead to reduced caregiving efforts by adult children, particularly in providing time and monetary support to parents who have less bequestable resources.
In contrast, President Clinton's proposed long-term care tax credit for older persons and their caregivers (U.S. Senate, Special Committee on Aging 1999) is a public policy strategy that can provide needed financial resources, as well as sustain the older disabled parents' caregiving network. According to the findings of this study, the latter policy effect is likely to occur because Clinton's tax credit minimizes the drain on the disabled parents' bequestable resources. Clinton's proposed caregiver support program acknowledges that caregivers provide approximately $45 to $95 billion of cost savings ($4,800-10,400 per caregiver) by providing unpaid in-home support care. Thus, adequately motivating and sustaining the caregiver network is essential to the efficient provision of care for disabled older persons.
Long-term care insurance is another vehicle for protecting the resources of older disabled persons. Yet, long-term care insurance has been purchased by only 6 to 7 percent of older persons (U.S. Senate, Special Committee on Aging 2000). This is in part due to inadequate information and also delayed retirement and estate planning. Strategies are needed to inform employees early in their work life about the value of long term care insurance. While an unexpected outcome, this research suggests that the purchase of long term care insurance will help to preserve the adult child caregiving resource by preserving the older disabled parents' financial resources.
Shelley I. White-Means is Professor, Department of Economics, The University of Memphis, TN. Gong-Soog Hong is Professor and Head, Department of Human Environments, Utah State University.
(1.) It is important to note that this conceptualization of the altruism motive is distinct from that round in a portion of the intergenerational transfer literature. For example, Cox (1987) and other researchers have defined parental (care recipient) altruism, based on the parent's desire to maximize his or her utility, as a factor associated with adult children's giving. Cox predicts that parental altruism may imply that parents' income and giving by the child are negatively related or the adult child's (caregiver's) income and services to parents may be positively related. Both outcomes suggest that altruistic parents may compensate their children in amounts that are greater than the services given to them by their children.
This conceptualization of altruism is also distinct from one that views altruism as an emotional response that is focused on other's welfare (Schultz 1990).
(2.) A fourth form of giving, sharing physical space in one's home, is not examined due to the small numbers of persons who reside with parents (McGarry and Schoeni 1995).
(3.) First stage results are available from authors upon request.
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Table 1 Measuremen of Variables DEPENDENT VARIABLES: Variables Measurements NOWORK 1, if equals the adult child is not currently employed. GIVETIME 1, if an adult child indicates that he or she or his or her spouse spent 100 hours or more helping with basic needs care of at least one disabled parent or parent-in- law during the last twelve months. GIVEMONEY 1, if the adult child and/or the adult child's spouse gave $500 or more to support the care of at least one disabled parent or parent-in-law during the last twelve months. INDEPENDENT VARIABLES: Variables Measurements Ehat predicted probability of not working. Mhat predicted $ value of money transfer. That predicted amount of time transfer. Altruism # children number of children younger than eighteen. Religion Catholic 1, if Catholic; 0, otherwise. Other Christian 1, if non-Catholic Christian; 0, otherwise. # hours volunteered # hours volunteered in the past twelve months. Race 1, if Caucasian; 0, if African American. Bequest # houses owned by parents total # of houses owned by parents and parents-in-law. Expected savings at $ value of expected savings retirement ($) (excludes IRA, Keogh, and pension fund assets). Money transferred by 1, if $ was transferred to parent siblings by sibling(s) in the past twelve months; 0, otherwise. Time transferred by siblings 1, if time was transferred to parent by sibling(s) in the past twelve months; 0, otherwise. Caregiving Environment # ADL number of parents and parents-in- law with one or more ADLS. # Not alone number of parents and parents-in- law who ca not be left alone for more than one hour. Not alone 1, if parent(s) or parents-in-law cannot be left alone more than one hour; 0, otherwise. # poor parents total number of living parents who are poor. Region Northeast 1, if live in Northeast; 0, otherwise. Midwest 1, if live in Midwest; 0, otherwise. West 1, if live in West; 0, otherwise. South 1, if live in South; 0, otherwise. Caregiver Characteristics Inherited 1, if received an inheritance or was given substantial assets in th form of a trust; 0, otherwise. Plan to leave bequest Bequest 1 1, if yes definitely or probably expect to leave a sizable inheritance to heirs; 0, otherwise. Bequest 2 1, if yes possible or probably won't expect to leave a sizable inheritance to heirs; 0, otherwise. Unearned income ($) total income-earned income. Postretirement health 1, if have coverage after insurance retirement; 0, if not. Wage wage/hour. Age age in years. Gender 1, if male; 0, otherwise. Marital status 1, if married; 0 otherwise. Education years of formal education. Health status Excellent 1, if excellent 0, otherwise. Very good/good 1, if very good or good; 0, otherwise. Table 2 Differences in Characteristics of Adult Children by Presence of Parents with ADLs, Wheater Parents Can Be Left Alone, Giving Time, and Giving Money Time Given Money Given Variables (Hours) (a)(c)(d)(f) ($) (g)(h) With ADLs, Not Alone, ADLs and Not Alone, and No Disability ADL (N = 682) 125.89 162.99 Not Alone (N = 323) 15.95 77.01 ADL and Not Alone (N = 669) 162.89 270.43 No Disability (N = 3,214) 8.02 99.24 GIVETIME Yes (N = 216) 1045.27 925.27 No (N = 4,672) 0 93.34 GIVEMONEY Yes (N = 293) 271.82 2170.43 No (N = 4,595) 31.80 0 Employment Variables (%) (g)(h) With ADLs, Not Alone, ADLs and Not Alone, and No Disability ADL (N = 682) 65.95 Not Alone (N = 323) 67.07 ADL and Not Alone (N = 669) 67.85 No Disability (N = 3,214) 72.62 GIVETIME Yes (N = 216) 61.57 No (N = 4,672) 71.07 GIVEMONEY Yes (N = 293) 77.13 No (N = 4,595) 70.25 Note: For the general well-being measure, higher values indicate higher levels of satisfaction. For the measures of financial, family life, and overall life satisfaction, the means correspond with the raw data measures of these variables and not the recorded measures described in Table 1. The raw data ranges from values one through five, with one indicating very satisfied. (a)Significantly different at .05 or better between ADL and not alone. (b)Significantly different at .05 or better between ADL and ADL and not alone. (c)Significantly different at .05 or better between not alone and ADL and not alone. (d-f)Significantly different at .05 or better between none and (ADL, not alone, or ADL and not alone, respectively). (g)Significantly different at .05 or better between those who give time and those who did not. (h)Significantly different at .05 or better between those who give money and those who did not. Table 3 Descriptive Statistics ADL & Variables ADL NOT ALONE Continuous variabies with mean and standard deviation: # children 0.29 (0.69) 0.32 (0.74) # hours volunteered 26.09 (184.23) 32.24 (225.88) # houses owned by parents 0.97 (0.99) 1.06 (0.97) Expected Savings ($) 83,238 (369,224) 94,796 (415.806) Unearned income ($) 14,834 (22,107) 13,557 (21,777) # ADLs 1.09 (0.31) 0.75 (0.59) #Not alone 0.53 (0.60) 1.14 (0.39) Age 55.93 (4.83) 55.43 (5.11) Education 12.27 (3.21) 12.15 (3.17) # poor parents 0.22 (0.55) 0.24 (0.58) Wage 12.45 (4.97) 12.06 (4.67) Categorical variables with frequency and percentage: Money transferred by siblings 96 (8.1) 57 (6.9) (yes) Time transferred by siblings 168 (14.2) 88 (10.6) (yes) Not alone (yes) 769 (65.1) 559 (67.4) Religion Other Christian 769 (65.1) 559 (67.4) Catholic 304 (25.7) 206 (24.8) Other 109 (9.2) 64 (7.7) Caucasian 946 (80.0) 652 (78.6) Inherited 164 (13.9) 111 (13.4) Plan to leave bequest Yes, definitely or yes, 365 (30.9) 253 (30.5) probably Yes, possibly or probably not 494 (41.8) 358 (43.2) No, definitely 323 (27.3) 218 (26.3) Married 877 (74.2) 629 (75.9) Female 641 (54.2) 439 (53.0) Health Excellent 255 (21.6) 187 (22.6) Very good/good 631 (53.4) 437 (52.7) Fair/poor 296 (25.0) 205 (24.7) Region Northeast 211 (17.9) 131 (15.8) Midwest 271 (22.9) 185 (22.3) West 185 (15.7) 150 (18.1) South 515 (43.6) 363 (43.8) Postretirement health insurance 591 (50.0) 424 (51.1) Variables NOT ALONE Continuous variabies with mean and standard deviation: # children 0.28 (0.69) # hours volunteered 25.37 (174.2) # houses owned by parents 1.01 (0.98) Expected Savings ($) 82,494 (332,971) Unearned income ($) 14,142 (21,515) # ADLs 0.89 (0.51) #Not alone 0.65 (0.64) Age 55.72 (4.91) Education 12.24 (3.24) # poor parents 0.23 (0.56) Wage 12.37 (4.87) Categorical variables with frequency and percentage: Money transferred by siblings 112 (7.2) (yes) Time transferred by siblings 178 (11.4) (yes) Not alone (yes) 829 (53.2) Religion Other Christian 1,010 (64.9) Catholic 406 (26.1) Other 161 (9.0) Caucasian 1,241 (79.7) Inherited 221 (14.2) Plan to leave bequest Yes, definitely or yes, 469 (31.4) probably Yes, possibly or probably not 655 (42.1) No, definitely 413 (26.5) Married 1162 (74.6) Female 894 (57.4) Health Excellent 339 (21.8) Very good/good 846 (54.3) Fair/poor 372 (23.9) Region Northeast 276 (17.7) Midwest 359 (23.1) West 261 (16.8) South 661 (42.5) Postretirement health insurance 796 (51.1) Table 4 Second Stage Logit Estimates for the Models of No Work, Time Transfers and Money Transfers to Disabled Parents with ADLS (N = 1162) Variables No WORK Ehat 1 That 1 -1.20 (1.31) Mhat 1 -5.50 (3.91) Altruism: # children 0.56 (0.37) Religion Other Christian (OC) -0.09 (0.30) Catholic -0.39 (0.33) Children (*) (OC) -0.91 (0.39) (**) Children (*) Catholic -0.55 (0.42) Bequest: # houses owned -0.17 (.083) (**) Expected savings -5.25E-6 (1.l97E-6) (***) Money transferred 0.54 (0.4468) Time transfeered -0.03 (0.30) Caregiver Environ. #ADLs -0.28 (0.29) #ADLs (*) Not alone 0.13 (0.19) # poor parents Region Northeast Midwest West (South) Caregiver Characteristics Inherited 0.10 (0.22) Bequest 1 0.10 (0.21) Bequest 2 -0.05 (0.18) # hours volunteered .0007 (0.0005) Caucasian -.030 (0.20) Unearned income 0.00001 (3.96E-6) (**) Postret. health ins. -0.72 (0.16) (***) Wage -9.66E-6 (0.0002) Age 0.11 (0.02) (***) Male -1.24 (0.29) (***) Married -0.06 (0.20) Education -0.03 (0.03) Health Excellent -1.39 (0.23) (***) very good/Good -1.55 (0.18) (***) Fair/Poor Intercept -4.07 (1.14) (***) -2logliklihood 1132.58 Chi Squared 350.35 Variables Time Transfers Ehat 1 2.78 (1.26) (**) That 1 Mhat 1 -5.04 (3.96) Altruism: # children 0.39 (0.37) Religion Other Christian (OC) -0.29 (0.48) Catholic 0.61 (0.51) Children (*) (OC) -0.13 (0.40) Children (*) Catholic -0.63 (0.43) Bequest: # houses owned -0.0606 (0.1178) Expected savings 2.77E-6 (2.95E-7) Money transferred 0.88 (0.5055) (*) Time transfeered 0.89 (0.23) (***) Caregiver Environ. #ADLs -0.44 (0.39) #ADLs (*) Not alone 0.35 (0.27) # poor parents Region Northeast -0.36 (0.32) Midwest -0.61 (0.28) (**) West 0.005 (0.29) (South) Caregiver Characteristics Inherited 0.29 (0.12) Bequest 1 0.002 (0.29) Bequest 2 -0.22 (0.25) # hours volunteered 0.0002 (0.0005) Caucasian -0.18 (0.26) Unearned income 9.13E-6 (5.31E-6) (*) Postret. health ins. Wage -0.00004 (0.0002) Age -0.0002 (0.03) Male -3.56 (0.53) (***) Married -0.60 (0.25) (**) Education 0.07 (0.04) (*) Health Excellent 0.14 (0.35) very good/Good 0.29 (0.26) Fair/Poor Intercept 1.02 (2.43) -2logliklihood 639.29 Chi Squared 231.44 Variables Money Transfers Ehat 1 0.84 (1.30) That 1 0.52 (1.83) Mhat 1 Altruism: # children -1.03 (0.65) Religion Other Christian (OC) Catholic Children (*) (OC) 1.09 (0.67) (*) Children (*) Catholic 0.92 (0.70) Bequest: # houses owned 0.089 (0.14) Expected savings -1.11E-7 (4.497E-7) Money transferred 2.31 (0.29) (**) Time transfeered (0.41) Caregiver Environ. #ADLs -0.30 (0.46) #ADLs (*) Not alone 0.030 (0.27) # poor parents -0.56 (0.34) (*) Region Northeast Midwest West (South) Caregiver Characteristics Inherited -0.44 (0.41) Bequest 1 0.70 (0.34) (**) Bequest 2 0.22 (0.31) # hours volunteered 0.002 (0.001) (***) Caucasian 0.25 (0.31) Unearned income 0.000011 (5.86E.6) (*) Postret. health ins. Wage Age -0.0078 (0.03) Male -1.55 (0.53) (***) Married -0.99 (0.29) (***) Education 0.03 (0.05) Health Excellent 0.76 (0.49) very good/Good 0.27 (0.44) Fair/Poor Intercept -1.87 (2.53) -2logliklihood 504.91 Chi Squared 167.28 Note: (*)Significant at .1 (**)significant at .05 (***)significant at.01 or better. Table 5 Second Stage Logit Estimates for the Models of No Work, Time Transfers, and Money Transfers to Disabled Parents Who Cannot Be Left Alone (N = 815) Variables NO WORK Ehat 1 That 1 -1.24 (2.49) Mhat 1 -13.65 (5.90) (**) Altruism: # children 0.61 (0.46) Religion Other Christian (OC) 0.39 (0.41) Catholic -0.07 (0.44) (**) Children (*) (OC) -0.70 (0.49) Children (*) Catholic -0.44 (0.50) Bequest: House owned -0.07 (0.26) Expected savings -2.15E-6 (7.89E-7) (**) Money transferred 1.31 (0.61) (**) Time transferred -0.48 (0.44) Caregiver Environ. # Not alone 0.59 (.25) (**) # ADLs (*) Not alone -0.15 (0.11) # poor parents Region Northeast Midwest West (South) Caregiver Characteristics Inherited 0.21 (0.27) Bequest 1 0.11 (0.26) Bequest 2 -0.22 (0.23) # hours volunteered 0.0009 (0.0005) (*) -1.68 (0.36) (***) Caucasian Unearned income 0.00002 (4.89E-6) (***) Postret. health ins. -0.96 (0.20) (***) Wage -0.0002 (0.0002) Age 0.12 (0.02) Male -1.68 (.36) (***) Married -0.11 (0.26) Education -0.06 (0.04) Health Excellent -1.37 (0.28) (***) Very good/Good -1.73 (0.23) (***) (Fair/Poor) Intercept -4.44 (l.41) (***) -2logliklihood 759.81 Chi Squared 273.98 Variables Time Transfers Money Transfers Ehat 1 3.56 (l.72) (**) 1.36 (1.53) That 1 1.99 (3.54) Mhat 1 -9.27 (6.18) Altruism: # children 0.18 (0.49) -0.59 (0.59) Religion Other Christian (OC) -1.77 (0.67) (***) 0.25 (0.83) Catholic -0.72 (0.70) 0.39 (0.58) Children (*) (OC) 0.06 (0.53) 0.46 (0.65) Children (*) Catholic -0.28 (0.57) 1.04 (0.65) Bequest: House owned 0.03 (0.17) -0.06 (0.19) Expected savings 5.24E-7 (3.49E-7) 2.9E-7 (4.17E-7) Money transferred 1.59 (0.72) (**) 2.94 (0.38) (***) Time transferred 1.92 (0.36) (***) -0.75 (0.66) Caregiver Environ. # Not alone -1.59 (0.83) (**) -1.50 (0.87) (*) # ADLs (*) Not alone 0.83 (0.26) (***) 0.60 (0.24) (**) # poor parents -0.486 (0.4360) Region Northeast -0.46 (0.49) Midwest -0.62 (0.43) West -0.44 (0.42) (South) Caregiver Characteristics Inherited 0.64 (0.45) -0.09 (0.51) Bequest 1 0.11 (0.43) 0.20 (0.44) Bequest 2 -0.24 (0.34) -0.40 (0.41) # hours volunteered 0.0002 (0.0005) 0.001 (0.0005) (**) -0.1416 (0.4193) Caucasian -0.85 (0.36) (**) Unearned income 4.83E-6 (8.2E-6) 7.12E-7 (8.72E-6) Postret. health ins. Wage -0.0003 (0.0004) Age -0.0002 (0.04) 0.006 (0.04) Male -4.07 (0.86) (***) -1.31 (0.69) (**) Married -0.25 (0.38) -0.26 (0.40) Education 0.11 (0.06) (*) -0.007 (0.07) Health Excellent -0.40 (0.48) 0.59 (0.59) Very good/Good -0.31 (0.38) 0.49 (0.49) (Fair/Poor) Intercept 3.73 (3.42) -0.88 (3.33) -2logliklihood 321.51 297.99 Chi Squared 174.63 115.18 Note: (*)significant at .1. (**)significant at .05. (***)significant at .01 or better.
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|Author:||White-Means, Shelley I.; Hong, Gong-Soog|
|Publication:||Journal of Consumer Affairs|
|Article Type:||Statistical Data Included|
|Date:||Dec 22, 2001|
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