Abstract
Previous research has found that financial transfers from parents to young adult children decline as children age and that age is one of the strongest predictors of support. Using data collected from young adults (ages 18 to 34) and their parents (ages 40 to 60; N=536 parent-child dyads), we explore the possibility that the relationship between age and financial support is mediated by offspring needs, acquisition of adult roles, or geographical and emotional closeness. We find that age-related declines in offspring’s needs help to explain why financial support falls with age. However, offspring age remains a robust predictor of financial support after controlling for a wide range of factors, suggesting that age norms condition support from parents to offspring.
Keywords: Intergenerational transfers, Transition to adulthood, Social support, Aging
American parents are important sources of financial support for their young adult children. The American government provides relatively little support compared with its European counterparts, and it is difficult for young adults who have not transitioned to full-time work to support themselves (Cook & Furstenberg, 2002). Therefore, the burden falls on parents, who spend an average of $38,340 on each of their children as they age from 18 to 34 (Schoeni & Ross, 2005). These financial contributions are increasingly influential: the amount of support parents give children during this period has grown over time (Ibid). The increase in parent-to-child support coincides with delays in the transitions that traditionally signaled adult status and independence, including marriage and completing school (Fussell & Furstenberg, 2005). Although offspring do provide some support to their parents, support generally flows downward – from parents to children – until parents are elderly (Albertini, Kohli, & Vogel, 2007; Hill, 1970). The monetary value of parent-to-child transfers is substantial; however, the factors shaping these transfers are not fully understood. In this paper, we explore one of the most important predictors of support – the age of offspring – in order to better understand parents’ motivations for giving.
Age is one of the central factors determining whether offspring receive financial support from parents, with support declining as offspring grow older (Cooney & Uhlenberg, 1992; Eggebeen, 1992; Eggebeen & Hogan, 1990; Fingerman, Miller, Birditt, & Zarit, 2009; Rossi & Rossi, 1990). Using the National Survey of Families and Households, Cooney & Uhlenberg (1992) found that receipt of support falls between the early twenties and the late thirties, and that this general pattern holds for various types of support, such as gifts and advice. They also found that controlling for several status characteristics (including schooling, work, and marriage) did little to change the age coefficient. In this paper, we take their analysis further by exploring how the effect of age on financial support is mediated by a wide range of variables.
Researchers have long been interested in identifying the factors that shape transfer relationships. Two reasons for this include a) that within-family transfers can act as an important safety net for those in need, and b) that intergenerational transfers can contribute to the reproduction of inequality across generations (Swartz, 2009). Prior research has found a number of factors that condition transfers. The age of the recipient is one of these factors, but it is not clear whether age has a direct effect on transfers (due to age norms, for example), or whether age simply acts as a proxy for factors such as offspring needs, acquisition of adult social roles and maturity, and geographical and emotional closeness.
The literature on intergenerational transfers generally identifies three motivations for parental investment in offspring: altruism, evolution, and exchange (Antonucci & Akiyama, 1987; Becker, 1981). Parents support children because they derive satisfaction from seeing their children happy and successful (altruism), because they want to maximize their children’s reproductive success (evolution), or because they want their children to reciprocate, especially when parents are elderly (exchange) (Ibid). These theories would not predict that increasing age would have a strong effect on support, unless age were acting as a proxy for other characteristics.
We use data from the Family Exchanges Study, in which middle-aged adults in the metropolitan area of Philadelphia were surveyed about exchange behavior, along with their offspring. Previous research that has examined the determinants of intergenerational exchange has mostly relied on the National Survey of Families and Households (NSFH) and the Panel Study of Income Dynamics (PSID). These data sources have the drawback of only including transfers between parents and children who live in separate households. This is an important limitation since a large and increasing proportion of young adults live with one or both parents (Schoeni & Ross, 2005). Our research builds on previous literature by examining financial support between parents and children, whether they live together or not. Moreover, our study uses more complete measures of financial transfers compared with previous research. We examine two dimensions of financial support – frequency and monetary value – and utilize reports of transfers from both the giver (the parent) and the recipient (the offspring). We focus on financial support because unlike other types of support it does not depend on physical proximity or having a particular need. This analysis contributes to our understanding of what motivates family members to transfer financial resources to one another, and uses a data source that allows for a well-rounded picture of exchange relationships.
THEORETICAL FRAMEWORK AND HYPOTHESES
There are several possible explanations for why parent-to-offspring financial transfers decrease as offspring age. We developed a framework for organizing these explanations (see Figure 1), which include a) declining offspring needs, b) increasing adult identity, c) declining proximity and affinity, and d) a possible direct effect of age on transfers.
Figure 1.
Conceptual Diagram
Offspring needs
Parents may give to offspring because of altruism – that is, because they derive satisfaction from seeing their children happy and successful (Becker 1981). If intergenerational support is governed by altruism, support may decline with age simply because older offspring have fewer needs and may request less support. Factors that are associated with higher need on the part of offspring include student status, unemployment, parenthood, poor health, and low income. Previous research supports the idea that students receive more gifts and other types of support than non-students (Aquilino, 2006; Cooney & Uhlenberg, 1992). Health problems may also elicit increased support from parents and may decline as offspring leave their teenage years (Fingerman et al., 2009; Suitor, Pillemer, & Sechrist, 2006). Results are mixed on the question of whether having children of one’s own is associated with higher or lower levels of parental support (Belsky & Rovine, 1984; Cooney & Uhlenberg, 1992; Nomaguchi & Milkie, 2003).
Adult identity
Alternatively, parents and offspring might make decisions about whether to engage in transfers based on whether offspring have adopted adult social roles. Status transitions, particularly entering marriage and other serious partnerships, are traditional markers of adulthood and exchange patterns might respond to norms around these social roles. The likelihood that offspring have partnered increases with age, so a negative association between age and support may be explained by these role transitions. Shanahan, Porfeli, Mortimer, and Erickson (2005) found that completing status transitions is a strong predictor of labeling oneself an adult. Moreover, prior research has found that unmarried offspring receive more support than married offspring (Suitor, Pillemer, & Sechrist, 2006).
The age pattern might also exist because age acts as a proxy for more subjective measures of adult status. Some research has found that individualistic criteria, particularly the idea of being responsible for oneself, are more important to conception of adult identity than are status transitions (Arnett 1997, 2001; Greene, Weatley, & Aldava, 1992). An increase in these subjective criteria with age might help explain the age-support pattern.
Proxmity and affinity
The decline in support with age might also be due to decreases in geographical proximity or emotional closeness: younger offspring might live closer to parents or be closer emotionally, and it is this closeness that fosters exchange relationships. Silverstein & Bengtson (1997) refer to these factors as “affinity” and “proximity.” Prior research has found that emotional closeness and frequent contact are associated with higher levels of exchange (Hogan, Eggebeen, & Clogg, 1993; Suitor, Pillemer, & Sechrist, 2006). Likewise, Rossi and Rossi (1990) reported that there is more exchange among family members who are geographically close. When offspring and parents are more involved in one another’s lives, offspring are probably more likely to turn to parents when they need help. Likewise, parents are probably more aware of their offspring’s needs and feel more moved to help. Age of adult offspring has been shown to be negatively associated with geographical and emotional closeness with parents (Lawton, Silverstein, & Bengtson, 1994; Silverstein &. Bengtson, 1997).
Age norms
In the absence of mediating factors, we might conclude that age has a direct effect on support. It is possible that norms around biological age play a role in shaping transfer patterns: prior research has found evidence for norms tied to biological age in various domains. Neugarten and colleagues (1965) and Settersten and Hagestad (1996) found that there is some consensus around when men and women should finish school, marry, and have children. According to Smith (2004), GSS respondents believed on average that young adults should achieve financial independence by 21. Many, including Settersten and Hagestad, have argued that people have a general idea of when family transitions should be completed (a “normal biography”) but that the ages are considered flexible.
OTHER CORRELATES OF FINANCIAL SUPPORT
There are several other factors that both change as offspring age and influence the amount of support that young adult offspring receive. First, parents’ ability to support offspring may decline as their children age. For example, as offspring age, their parents’ incomes eventually decline, and previous research has shown that parents with higher incomes are more likely to be engaged in intergenerational exchanges than their less wealthy counterparts (Henretta, Grundy, & Harris, 2002; Hogan, Eggebeen, & Clogg, 1993). Also, as children grow older, the likelihood that their parents are married to one another drops. Adult children whose parents aren’t married are less likely to receive support (Aquilino, 2005; Eggebeen, 1992; Furstenberg, Hoffman, & Shrestha, 1995), a fact that could be the result of lower relationship quality, particularly with fathers (Kaufman & Uhlenberg, 1998; Shapiro, 2003) or fewer resources on the part of unmarried parents (Uhlenberg, Cooney, & Boyd, 1990). Moreover, parents’ health and age are associated with the amount of support that adult children receive (Cooney & Uhlenberg, 1992; Henretta, et al., 2002). As children grow older, their parents also age and their health declines. Parents may also become more concerned with the need to support themselves as they come closer to their own retirement years.
Overall, the present study examines parents’ and offspring’s reports of financial transfers to test four competing hypotheses:
Hypothesis 1: Parent-to-child transfers decrease with age because the needs of offspring decline as they get older.
Hypothesis 2: Parent-to-child transfers decrease with age because offspring increasingly acquire markers of adult identity, both subjective and objective.
Hypothesis 3: Parent-to-child transfers decrease with age because as offspring get older, they lose the proximity and affinity with parents that facilitate transfer relationships.
Hypothesis 4: There is a direct relationship between offspring age and financial transfers, possibly because age norms dictate what is appropriate for parents to give offspring.
METHOD
The Family Exchanges Study offers a unique opportunity to examine the determinants of intergenerational exchange. A sample of adults age 40–60, along with their offspring were interviewed in 2008 regarding exchange dynamics.
Middle aged respondents aged 40 to 60 were recruited from the Philadelphia Primary Metropolitan Statistical Area (PMSA), which includes urban, suburban, and rural areas in five counties (Pennsylvania State Data Center, 2001). Respondents had to have at least one offspring age 18 or over to participate. After oversampling in areas with large numbers of minorities, the final sample has a similar average income but a higher level of education compared with the Philadelphia PMSA as a whole (Pennsylvania State Data Center, 2001; US Census, 2008).
A total of 633 middle-aged adults were surveyed, along with nearly 600 of their offspring, age 18 and over. Response rates were 75% for the middle-aged adults who were sampled. These respondents reported having 2.16 offspring, on average, and provided contact information for 63% of those 18 and older (parents who provided contact information for offspring did not differ systematically from those who did not). A total of 75% of offspring whose parents offered contact information for them responded to the survey, a response rate that is comparable to other studies (Suitor & Pillemer, 2009; Sweet & Bumpass, 1996). Offspring who chose to participate were on average younger (mean age of 23.7 compared to 25.7), more likely to be female (45% compared to 43%) and lived nearer to parents (120 miles compared to 270 miles) than offspring who did not participate.
We analyzed exchange data from parent-offspring pairs. The parent was the “target” respondent originally selected as the main respondent of the study. Both the parent and the offspring reported how much financial support was exchanged and our analysis focused solely on “downward” transfers – those from parents to offspring.
We limited our analytic sample to offspring between 18 and 34 years old, in keeping with the boundaries of young adulthood put forward by Schoeni and Ross (2005) and others; this led us to drop 26 offspring. In addition, a small percentage of cases were missing data on one or more control variables. These missing values were filled in using regression-based, single imputation. A small number of cases were also missing data for one offspring characteristic: geographical distance from parents. These missing values were imputed using frequency of visits with parents and demographic characteristics. Out of 567 offspring ages 18–34 in the original dataset, 31 (5%) were excluded due to missing data, leaving an analytic sample of 536.
Outcome variables
We differentiated between two dimensions of financial support: frequency of support and monetary value of support.
Frequency of support
Frequency of support from the target parent to the offspring was assessed with one item. Offspring respondents were asked, “Please think about financial support. Financial support involves giving you money, loaning you money, or helping you purchase goods, services, insurance, or education. How often does your mother/ stepmother/ father/ stepfather) provide you with financial support? (Include holiday/birthday gifts).” Response options included: less than once a year or never (= 1), once a year (= 2), a few times a year (= 3), monthly (= 4), a few times a month (= 5), weekly (= 6), a few times a week (= 7), and daily (=8). The same question was asked of the target parent regarding assistance given to the offspring. One variable was created for the parent’s report and one variable was created for the offspring’s report. Each of these reports was treated as an 8-point scale. Similar items have been used in prior studies that examine financial transfers (e.g. Eggebeen, 1992).
Amount of support
The amount of support given by the target parent was based on the following prompt given to offspring respondents: “Now, think about the amount of financial support your parent(s) have given you in the past twelve months – including any loans (they/he/she) (have/has) provided. Did your (mother/ stepmother/ father/ stepfather) give you at least $500 in financial support in the past twelve months?” Offspring respondents who received at least $500 in the past 12 months were then asked if they received at least $1,000, while respondents who did not were asked if they received at least $100 in the past 12 months. The same series of questions was asked of the target parent regarding assistance given to the offspring. The resulting variable was a 4-category ordinal variable for the amount transferred from parent to offspring in the prior 12 months: less than $100 (= 1), $100–$499 (= 2), $500–999 (= 3), and $1000 or more (= 4). One variable was created for the parent’s report and one variable was created for the offspring’s report.
Independent variables
Variables describing the offspring’s characteristics came from the offspring’s report (though parental reports are used in cases of missing data). Likewise, variables describing the parent’s characteristics came from the parent’s report. Offspring’s age was the primary independent variable of interest and was captured using a second degree polynomial (variables for age and age-squared were both included in the models). In addition, the analysis examined three groups of variables that predict transfers: variables describing offspring need, variables describing adult identity, and variables describing proximity and affinity.
Variables related to “offspring need” included offspring employment status, offspring’s student status, whether the offspring had children, whether the offspring had a recent health problem, and offspring’s household income. Offspring’s employment was captured with a series of dummy variables for part-time work, not working, homemaker, and “other” (the omitted category was full-time work). The “not working” category included those actively looking and those who were not working because they were disabled or students. The “other” employment category included those who were self-employed or had erratic work schedules. The student variable was coded 1 if the offspring was enrolled in school at the time of the survey, and 0 if not. Respondents were coded 1 if they had any children and 0 if they had no children. The variable for offspring’s “recent health problem” referred to the previous 12 months and was coded as 1 if the respondent experienced any health problem during this time (0 if not). The coresidence variable was coded as 1 if the offspring lived with one or both parents at the time of the survey and 0 if not.
“Adult identity” variables included partnership transitions and an individualistic measure: whether the offspring’s parents considered him or her an adult. Partnership status was measured with dummy variables indicating whether the respondent was married, cohabiting, or “other” (who were mostly separated or divorced). The omitted category was single (never married and not cohabiting). If offspring said that their parents think of them as an adult always or often, they were coded 1 for “offspring is considered an adult by parents,” and 0 if not.
“Proximity and affinity” variables included the geographical distance between the offspring and the target parent and emotional closeness between the offspring and target parent. The variable for living near parents was coded 1 if offspring lived within 50 miles of the target parent at the time of the survey (including those who coreside with the parent), and 0 if they lived farther away. Offspring were asked whether the parent is the most important, or among the [3, 6, 10, or 20] most important, or less important than that. Those who reported that the target parent was one of the three most important people to them were coded 1 for the emotional closeness variable; those who do not were coded 0.
Our data lacked some important indicators of offspring need and parental resources. These included the individual incomes of offspring (separate from household income), the savings of offspring and parents, and offspring’s home ownership.
Analytic strategy
First we calculated descriptive statistics to describe the study variables. Then, to examine whether age was associated with financial support and whether it was mediated by other factors, we estimated a series of models in steps (non-cumulative). The steps included: 1) age and controls only 2) age and controls plus offspring needs, 3) age and controls plus adult identity, and 4) age and controls plus proximity and affinity. We compared the coefficients for age and age squared in each model to evaluate whether any set of factors act as mediators. We estimated linear regression models to examine the frequency of support1 and estimated ordered logistic regression models to examine the amount of support. We analyzed four dependent variables in total: two variables for frequency of support (offspring’s report and parent’s report) and two variables for amount of support (offspring’s report and parent’s report).
In the model controlling for offspring’s needs (Model 1 / Hypothesis 1), the variable for offspring’s household income was interacted with residence status (whether offspring lives with one or both parents) to account for the fact that household income for coresiding offspring included parental income.
In all models we controlled for offspring’s sex and offspring’s race-ethnicity. We also controlled for parental characteristics that affect their ability to provide support, including age and sex, recent health problems, household income, employment status, number of children the parent had, and whether the offspring’s parents were married to each other. These characteristics were controlled since they are likely to be correlated with both the likelihood of support and the key independent variables being tested (Fingerman et al., 2009; Hogan et al., 1993). In all cases, parent variables referred to the “target” parent (the person selected in the initial sample). Because the dataset included multiple offspring per family, our analysis used robust standard errors to account for the non-independence of observations.
RESULTS
Descriptive statistics for our independent and control variables are shown in Table 1. We found expected associations with age: older offspring were more likely to be employed, be out of school, have children, have higher incomes, live apart from parents, be married or cohabiting, and be emotionally distant from parents, compared with younger respondents.
Table 1.
Descriptive Statistics for Parents and Offspring
| Means or %
|
||||
|---|---|---|---|---|
| All | Age categories | |||
| 18 to 22 | 23 to 26 | 27 to 34 | ||
|
|
||||
| Offspring’s age | 23.2 | 19.9 | 24.3** | 29.9** |
| Parent’s age | 50.7 | 49.1 | 51.1** | 54.2** |
| Parent sex (male) (%) | 47.0 | 49.1 | 46.5 | 42.6 |
| Parent had recent health problem (%) | 36.9 | 35.4 | 41.1 | 36.1 |
| Parent’s household income (%) | ||||
| <$40k | 15.3 | 13.3 | 10.9 | 24.6* |
| $40k–100k | 50.0 | 46.0 | 56.6* | 52.5 |
| >$100k | 34.7 | 40.7 | 32.6 | 23.0** |
| Parent’s employment status (%) | ||||
| Full-time | 67.5 | 71.6 | 71.3 | 54.1** |
| Part-time | 10.1 | 8.8 | 10.9 | 12.3 |
| Retired | 2.1 | 1.1 | 1.6 | 4.9^ |
| Homemaker | 5.4 | 3.9 | 5.4 | 9.0 |
| Not working | 12.1 | 11.6 | 9.3 | 16.4 |
| Other employment status | 2.8 | 3.2 | 1.6 | 3.3 |
| Parent’s total number of offspring (%) | 3.1 | 3.0 | 3.0 | 3.2 |
| Offspring’s parents are married (%) | 67.2 | 71.2 | 67.4 | 57.4* |
| Offspring’s sex (male) (%) | 46.5 | 49.8 | 48.1 | 36.9* |
| Offspring’s race/ethnicity (%) | ||||
| White only | 66.8 | 70.9 | 65.9 | 58.2* |
| Black only | 22.4 | 17.9 | 24.0 | 31.1* |
| Hispanic, any race | 2.4 | 2.8 | 3.1 | 0.8 |
| Multi-racial or other race | 8.4 | 8.4 | 7.0 | 9.8 |
| Offspring’s employment status (%) | ||||
| Full-time | 43.8 | 18.9 | 69.8** | 74.6** |
| Part-time | 34.0 | 50.5 | 16.3** | 13.9** |
| Not working | 17.5 | 25.6 | 10.1** | 6.6** |
| Homemaker | 3.7 | 3.9 | 3.9 | 3.3 |
| Other employment status | 0.9 | 1.1 | 0.0^ | 1.6 |
| Offspring is a student (%) | 48.1 | 75.1 | 20.2** | 14.8** |
| Offspring has children (%) | 21.1 | 4.9 | 25.6** | 54.1** |
| Offspring had recent health problem (%) | 31.9 | 32.6 | 31.0 | 31.1 |
| Offspring’s household income (%) | ||||
| <$40k | 22.2 | 27.4 | 18.6* | 13.9** |
| $40k–100k | 42.7 | 34.0 | 57.4** | 47.5* |
| >$100k | 35.1 | 38.6 | 24.0** | 38.5 |
| Offspring coresides with parents (%) | 50.4 | 71.6 | 34.9** | 17.2** |
| Offspring’s marital status (%) | ||||
| Single, never married | 71.6 | 89.5 | 62.0** | 40.2** |
| Married | 13.4 | 1.8 | 14.7** | 39.3** |
| Cohabiting | 11.6 | 7.7 | 17.1* | 14.8^ |
| Other marital status | 3.4 | 1.1 | 6.2* | 5.7* |
| Offspring is considered an adult by parents (%) | 4.1 | 3.8 | 4.3** | 4.3** |
| Offspring lives <50 miles from parent (%) | 76.5 | 74.4 | 76.7 | 81.1 |
| Offspring emotionally close with parent (%) | 46.6 | 66.0 | 34.9** | 13.9** |
| N | 536 | 285 | 129 | 122 |
p<0.01,
p<0.05,
p<0.10 for difference from 18–22 age group.
Parent’s and Offspring’s Reports of Financial Transfers to Offspring
Table 2 presents offspring’s and parents’ reports of financial transfers. Both parents and offspring reported more frequent transfers for younger offspring compared with older offspring. However, within each age group, parents reported a higher frequency of transfer than the offspring did. This is consistent with other research which finds that the giver in an exchange relationship tends to report more transfers than does the recipient (Kim, Zarit, Eggebeen, Birditt, & Fingerman, 2011; Mandemakers & Dykstra, 2008).
Table 2.
Parent’s and offspring’s reports of parent-to-offspring financial transfers
| Means or %
|
||||
|---|---|---|---|---|
| All | Age categories | |||
| 18 to 22 | 23 to 26 | 27 to 34 | ||
|
|
|
|||
| Frequency of support scale1 (mean) | ||||
| Offspring’s report | 4.0 | 5.0 | 3.0** | 2.7** |
| Parent’s report | 4.4 | 5.4 | 3.5** | 2.9** |
| Amount of support (%) | ||||
| Offspring’s report | ||||
| $0–99 per year | 28.0 | 18.6 | 33.3** | 44.3** |
| $100–499 per year | 20.9 | 17.9 | 25.6^ | 23.0 |
| $500–999 per year | 18.8 | 17.5 | 19.4 | 21.3 |
| $1,000 per year | 32.3 | 46.0 | 21.7** | 11.5** |
| Parent’s report | ||||
| $0–99 per year | 15.1 | 3.5 | 20.2** | 36.9** |
| $100–499 per year | 12.5 | 7.0 | 17.1** | 20.5** |
| $500–999 per year | 18.8 | 17.9 | 21.7 | 18.0 |
| $1,000 per year | 53.5 | 71.6 | 41.1** | 24.6** |
| N | 536 | 285 | 129 | 122 |
1 = less than once a year or never, 2 = once a year, 3 = a few times a year, 4 = monthly, 5 = a few times a month, 6 = weekly, 7 = a few times a week, 8 = daily.
p<0.01,
p<0.05,
p<0.10 for difference from 18–22 age group.
The data on the value amount of transfers revealed a similar pattern. The monetary value of transfers was higher at the younger ages compared with the older ages, according to both parents’ and offspring’s reports. Offspring ages 18–22 most often received $1,000 or more from parents during the prior 12 months, while those ages 27–34 most often received less than $100. As with transfer frequency, parents’ estimates of the transfer amount were generally higher than offspring’s estimates – 72% of parents reported giving the offspring at least $500 in the previous year, while only 51% of offspring reported receiving at least $500 from the parent.
Predictors of support and mediation of the age-support association
The analyses presented in Tables 3 and 4 evaluate the factors that may mediate the association between age and support using four sets of regressions. In each table, predictors are added in steps – each corresponding to one of the hypotheses laid out above – and we evaluated change in the age and age squared coefficients.
Table 3a.
Coefficients from OLS Regressions Predicting Frequency of Parent-to-Offspring Financial Transfers (1= Less Than Once a Year or Never, 8 = Daily) (Offspring’s Reports of Frequency)
| Model 0 Baseline | Model 1 Offspring needs | Model 2 Offspring adult identity | Model 3 Proximity and affinity | Model 4 Full model | |
|---|---|---|---|---|---|
|
|
|||||
| Offspring’s age | −1.331 (0.200) ** | −0.817 (0.244) ** | −1.251 (0.211) ** | −1.204 (0.205) ** | −0.814 (0.248) ** |
| Offspring’s age squared | 0.022 (0.004) ** | 0.013 (0.005) ** | 0.021 (0.004) ** | 0.020 (0.004) ** | 0.013 (0.005) ** |
| Offspring’s employment status (Reference = full-time) | |||||
| Part-time | 0.335 (0.246) | 0.272 (0.242) | |||
| Not working | 0.953 (0.303) ** | 0.900 (0.292) ** | |||
| Homemaker | 0.315 (0.414) | 0.379 (0.403) | |||
| Other employment status | 0.031 (0.899) | −0.185 (0.930) | |||
| Offspring is a student | 0.179 (0.224) | 0.202 (0.219) | |||
| Offspring has children | 0.572 (0.272) * | 0.662 (0.292) * | |||
| Offspring had recent health problem | −0.157 (0.176) | −0.072 (0.179) | |||
| Offspring’s household income (Reference = <$40k) | |||||
| $40k–100k | −0.660 (0.313) * | −0.655 (0.310) * | |||
| >$100k | −0.712 (0.336) * | −0.611 (0.339) ^ | |||
| Offspring coresides with one or both parents | 0.460 (0.379) | 0.133 (0.579) | |||
| Offspring’s household income x coresidence interaction | |||||
| $40k–100k x coresides with parent(s) | 0.499 (0.437) | 0.432 (0.432) | |||
| >$100 x coresides with parent(s) | 0.324 (0.464) | 0.160 (0.473) | |||
| Offspring’s marital status (Reference = single) | |||||
| Married | −0.596 (0.263) * | −0.446 (0.285) | |||
| Cohabiting | −0.889 (0.230) ** | −0.800 (0.249) ** | |||
| Other marital status | 0.251 (0.597) | 0.163 (0.559) | |||
| Offspring is considered an adult by parents | −0.062 (0.106) | −0.056 (0.104) | |||
| Offspring lives <50 miles from parent | 0.293 (0.207) | 0.298 (0.195) | |||
| Offspring emotionally with parent | 0.609 (0.212) ** | 0.164 (0.511) | |||
| R2 | 0.324 | 0.380 | 0.343 | 0.343 | 0.394 |
| N | 536 | 536 | 536 | 536 | 536 |
p<0.01,
p<0.05,
p<0.10.
Note: All models control for parent’s sex, parent’s age, parent’s recent health problem, parent’s household income, parent’s employment status, parent’s total number of offspring, whether offspring’s parents are married to each other, offspring’s sex, and offspring’s race-ethnicity. Standard errors are in parentheses below coefficients.
Table 4a.
Coefficients from Ordered Logistic Regressions Predicting Amount of Parent-to-Offspring Financial Transfers (1= <$100, 4 = >$1,000) (Offspring’s Reports of Amount)
| Model 0 Baseline | Model 1 Offspring needs | Model 2 Offspring adult identity | Model 3 Proximity and affinity | Model 4 Full model | |
|---|---|---|---|---|---|
|
|
|||||
| Offspring’s age | −0.499 (0.222)* | −0.061 (0.252) | −0.494 (0.231)* | −0.441 (0.224)* | −0.073 (0.262) |
| Offspring’s age squared | 0.008 (0.004)^ | 0.000 (0.005) | 0.008 (0.005) | 0.007 (0.004) | 0.000 (0.005) |
| Offspring’s employment status (Reference = full-time) | |||||
| Part-time | 0.456 (0.250)^ | 0.450 (0.253)^ | |||
| Not working | 0.702 (0.277)* | 0.612 (0.276)* | |||
| Homemaker | 0.015 (0.346) | 0.039 (0.365) | |||
| Other employment status | 0.007 (1.419) | −0.103 (1.383) | |||
| Offspring is a student | 0.310 (0.225) | 0.276 (0.228) | |||
| Offspring has children | 0.509 (0.258)* | 0.591 (0.278)* | |||
| Offspring had recent health problem | −0.105 (0.189) | −0.087 (0.192) | |||
| Offspring’s household income (Reference = <$40k) | |||||
| $40k–100k | −0.577 (0.306)^ | −0.596 (0.316)^ | |||
| >$100k | −0.553 (0.360) | −0.525 (0.385) | |||
| Offspring coresides with one or both parents | −0.044 (0.341) | −0.005 (0.544) | |||
| Offspring’s household income x coresidence interaction | |||||
| $40k–100k x coresides with parent(s) | 0.392 (0.423) | 0.396 (0.434) | |||
| >$100 x coresides with parent(s) | 0.471 (0.490) | 0.419 (0.516) | |||
| Offspring’s marital status (Reference = single) | |||||
| Married | −0.383 (0.265) | −0.330 (0.316) | |||
| Cohabiting | −0.219 (0.248) | −0.206 (0.254) | |||
| Other marital status | 0.747 (0.413) ^ | 0.565 (0.427) | |||
| Offspring is considered an adult by parents | 0.019 (0.102) | 0.014 (0.106) | |||
| Offspring lives <50 miles from parent | −0.339 (0.207) | −0.346 (0.212) | |||
| Offspring emotionally with parent | 0.299 (0.196) | 0.027 (0.497) | |||
| Pseudo R2 | 0.091 | 0.109 | 0.096 | 0.094 | 0.113 |
| N | 536 | 536 | 536 | 536 | 536 |
p<0.01,
p<0.05,
p<0.10.
Note: All models control for parent’s sex, parent’s age, parent’s recent health problem, parent’s household income, parent’s employment status, parent’s total number of offspring, whether offspring’s parents are married to each other, offspring’s sex, and offspring’s race-ethnicity. Standard errors are in parentheses below coefficients.
Tables 3a and 3b present coefficients for models predicting frequency of support, as reported by offspring (Table 3a) and parents (Table 3b). Because the magnitude of change in the age and age-squared coefficients across models is difficult to interpret, we present graphs of predicted values. Figure 2a corresponds with Table 3a and Figure 2b corresponds with Table 3b. First, we describe the predictors of parent-to-offspring transfers, then we evaluate each of the four hypotheses.
Table 3b.
Coefficients from OLS Regressions Predicting Frequency of Parent-to-Offspring Financial Transfers (1= Less Than Once a Year or Never, 8 = Daily) (Parent’s Reports of Frequency)
| Model 0 Baseline | Model 1 Offspring needs | Model 2 Offspring adult identity | Model 3 Proximity and affinity | Model 4 Full model | |
|---|---|---|---|---|---|
|
|
|||||
| Offspring’s age | −1.606 (0.201)** | −1.118 (0.226)** | −1.498 (0.203)** | −1.420 (0.201)** | −1.116 (0.231)** |
| Offspring’s age squared | 0.028 (0.004)** | 0.020 (0.004)** | 0.027 (0.004)** | 0.025 (0.004)** | 0.020 (0.004)** |
| Offspring’s employment status (Reference = full-time) | |||||
| Part-time | 0.728 (0.204)** | 0.649 (0.199)** | |||
| Not working | 0.685 (0.258)** | 0.610 (0.250)* | |||
| Homemaker | 1.114 (0.497)* | 1.148 (0.481)* | |||
| Other employment status | −0.002 (0.791) | −0.371 (0.838) | |||
| Offspring is a student | 0.115 (0.185) | 0.136 (0.177) | |||
| Offspring has children | 0.075 (0.235) | 0.296 (0.221) | |||
| Offspring had recent health problem | −0.175 (0.160) | −0.096 (0.154) | |||
| Offspring’s household income (Reference = <$40k) | |||||
| $40k–100k | −0.609 (0.270)* | −0.425 (0.272) | |||
| >$100k | −0.895 (0.308)** | −0.570 (0.310) ^ | |||
| Offspring coresides with one or both parents | 0.351 (0.341) | −0.958 (0.369)** | |||
| Offspring’s household income x coresidence interaction | |||||
| $40k–100k x coresides with parent(s) | 0.299 (0.398) | 0.004 (0.390) | |||
| >$100 x coresides with parent(s) | 0.725 (0.403) ^ | 0.261 (0.408) | |||
| Offspring’s marital status (Reference = single) | |||||
| Married | −1.185 (0.228)** | −0.987 (0.239)** | |||
| Cohabiting | −0.301 (0.253) | −0.219 (0.242) | |||
| Other marital status | −0.097 (0.386) | −0.138 (0.337) | |||
| Offspring is considered an adult by parents | −0.252 (0.092)** | −0.224 (0.092)* | |||
| Offspring lives <50 miles from parent | −0.027 (0.179) | −0.042 (0.176) | |||
| Offspring emotionally with parent | 0.889 (0.198)** | 1.552 (0.312)** | |||
| R2 | 0.366 | 0.431 | 0.405 | 0.397 | 0.470 |
| N | 536 | 536 | 536 | 536 | 536 |
p<0.01,
p<0.05,
p<0.10.
Note: All models control for parent’s sex, parent’s age, parent’s recent health problem, parent’s household income, parent’s employment status, parent’s total number of offspring, whether offspring’s parents are married to each other, offspring’s sex, and offspring’s race-ethnicity. Standard errors are in parentheses below coefficients.
Figure 2.
Figure 2a. Predicted Frequency of Parent-to-Offspring Transfers (Offspring report)
Note: All variables other than age and age-squared are held constant at the mean
Figure 2b. Predicted Frequency of Parent-to-Offspring Transfers (Parent report)
Note: All variables other than age and age-squared are held constant at the mean
Tables 3a and 3b show that a large number of the variables used in our models were significant predictors of parent-to-child financial transfers. Offspring received more frequent support (according to both offspring’s and parent’s reports of support) when the offspring was not working (compared with working full time), when the offspring had lower income, and when there was emotional closeness between parent and offspring. Some variables were only significant predictors when the dependent variable was the offspring’s report of frequency (rather than the parent’s report). Offspring reported receiving more frequent support when they had children and when they were single (rather than cohabiting). Likewise, some variables were only significant predictors when the dependent variable is the parent’s report of frequency (as opposed to the offspring’s report). Parents reported giving more frequent support to offspring working less than full time and offspring who were unmarried.
The models that included offspring needs in Tables 3a and 3b provided support for our first hypothesis: that parent-to-child transfers may decline with age due to declines in need. When variables related to offspring needs were added in Model 1, the coefficients for age and age squared were smaller than they had been in the baseline model. This is evident in Figures 2a and 2b: the strong negative association between offspring age and support frequency revealed in the baseline model (Model 0) was attenuated in the model that controls for offspring needs (Model 1). The findings were consistent across models predicting parent and offspring reports.
Tables 3a and 3b provide weak support for our second hypothesis, that transfers decline with age because offspring acquire adult status as they move through their adult years. Controlling for partner transitions (marital and cohabitation status) and subjective or perceived adult status (whether parents consider the offspring an adult) in Model 2 did little to attenuate the age-support coefficients. This pattern was consistent across models predicting parent and offspring reports.
Similarly, Model 3 in each of the tables offers weak support for our third hypothesis, that a decline with age in proximity and affinity between parents and offspring is responsible for the reduction in support. Controlling for geographical and emotional closeness between parent and offspring led to only a small attenuation of the negative age-support association. Again, this finding was consistent across our two dependent variables (parent’s and offspring’s reports), though the amount of attenuation varied slightly.
As predicted by our final hypothesis, we see that the age pattern of support remained strong no matter what predictor variables were included in the model. Model 4 in Tables 3a and 3b included all potential mediating variables. When these variables were included, the age coefficients were somewhat reduced compared with the baseline age coefficients, but they remained large and highly significant. The predicted values displayed in Figures 2a and 2b reveal that the decline in support with age continued to be robust, even when controlling for a wide range of parent and offspring characteristics.
Tables 4a and 4b present coefficients for models predicting the monetary value of parent-to-offspring transfers, as reported by offspring (Table 4a) and parents (Table 4b). Figure 3a presents predicted probabilities corresponding to the models in Table 4a and Figure 3b presents predicted probabilities corresponding with Table 4b.
Table 4b.
Coefficients from Ordered Logistic Regressions Predicting Amount of Parent-to-Offspring Financial Transfers (1= <$100, 4 = >$1,000) (Parent’s Reports of Amount)
| Model 0 Baseline | Model 1 Offspring needs | Model 2 Offspring adult identity | Model 3 Proximity and affinity | Model 4 Full model | |
|---|---|---|---|---|---|
|
|
|||||
| Offspring’s age | −1.096 (0.265)** | −0.886 (0.307)** | −0.926 (0.269)** | −1.039 (0.264)** | −0.783 (0.325)* |
| Offspring’s age squared | 0.018 (0.005)** | 0.015 (0.006)* | 0.015 (0.005)** | 0.017 (0.005)** | 0.013 (0.006)* |
| Offspring’s employment status (Reference = full-time) | |||||
| Part-time | 0.797 (0.258)** | 0.792 (0.260)** | |||
| Not working | 0.297 (0.344) | 0.156 (0.335) | |||
| Homemaker | 0.498 (0.511) | 0.592 (0.518) | |||
| Other employment status | −0.376 (0.982) | −0.660 (0.935) | |||
| Offspring is a student | 0.127 (0.241) | 0.146 (0.247) | |||
| Offspring has children | −0.160 (0.283) | 0.104 (0.294) | |||
| Offspring had recent health problem | −0.047 (0.215) | −0.015 (0.214) | |||
| Offspring’s household income (Reference = <$40k) | |||||
| $40k–100k | −0.772 (0.395)^ | −0.593 (0.418) | |||
| >$100k | −1.197 (0.432)** | −0.813 (0.453)^ | |||
| Offspring coresides with one or both parents | −0.740 (0.410)^ | −1.135(0.615) ^ | |||
| Offspring’s household income x coresidence interaction | |||||
| $40k–100k x coresides with parent(s) | 0.880 (0.487)^ | 0.640 (0.517) | |||
| >$100 x coresides with parent(s) | 1.670 (0.526)** | 1.186 (0.543)* | |||
| Offspring’s marital status (Reference = single) | |||||
| Married | −1.282 (0.302)** | −1.242 (0.338)** | |||
| Cohabiting | −0.167 (0.306) | −0.276 (0.319) | |||
| Other marital status | 1.295 (0.630)* | 1.142 (0.629)^ | |||
| Offspring is considered an adult by parents | −0.407 (0.119)** | −0.392 (0.126)** | |||
| Offspring lives <50 miles from parent | −0.410 (0.251) | −0.489 (0.279)^ | |||
| Offspring emotionally with parent | 0.340 (0.203)^ | 0.598 (0.498) | |||
| Pseudo R2 | 0.162 | 0.184 | 0.196 | 0.165 | 0.216 |
| N | 536 | 536 | 536 | 536 | 536 |
p<0.01,
p<0.05,
p<0.10.
Note: All models control for parent’s sex, parent’s age, parent’s recent health problem, parent’s household income, parent’s employment status, parent’s total number of offspring, whether offspring’s parents are married to each other, offspring’s sex, and offspring’s race-ethnicity. Standard errors are in parentheses below coefficients.
Figure 3.
Figure 3a. Predicted Amount of Parent-to-Offspring Transfers (Offspring report)
Note: All variables other than age and age-squared are held constant at the mean
Figure 3b. Predicted Amount of Parent-to-Offspring Transfers (Parent report)
Note: All variables other than age and age-squared are held constant at the mean
Tables 4a and 4b reveal that a large number of variables were significant predictors of receiving higher value support. The value of support received by offspring is greater (according to both offspring’s and parent’s reports of support amount) when the offspring works part time (rather than full time) and when the offspring has a lower household income. Some variables were significant predictors of the amount of support only when the dependent variable was the offspring’s report (not the parent’s report). Offspring reported receiving more when they were not working and when they had children. Other variables were only significant predictors when the dependent variable was the parent’s report of transfer amount (as opposed to the offspring’s report). Parents reported giving more support when the offspring did not coreside with them, when the offspring was unmarried, when the offspring said they were not perceived as an adult, when the offspring lived more than 50 miles away, and when the offspring reported an emotionally close relationship with the parent.
When the dependent variable was the amount of support (Tables 4a and 4b), we generally found the same patterns regarding the mediation of the age-support association we found when the dependent variable was the frequency of support (Tables 3a and 3b). Tables 4a and 4b offer some support for our first hypothesis. Controlling for offspring’s needs reduced the age coefficients compared with the baseline model, so we conclude that offspring needs partially mediate the association between offspring age and the receipt of support. For both parent’s and offspring’s reports of support, the needs of offspring were the most powerful mediators of the age-support association. In the models predicting the offspring’s report, controlling for offspring needs made the age coefficients non-significant.
Tables 4a and 4b provide weaker support for the second and third hypotheses: controlling for offspring’s adult identity (Model 2) or proximity and affinity (Model 3) did very little to reduce the age coefficients. This finding was consistent across models predicting parent and offspring reports.
Finally, the results presented in Tables 4a and 4b are consistent with a direct relationship between offspring age and receipt of transfers from parents. After controlling for a wide range of factors in the full model (Model 4), the age coefficients remained large and significant in the table predicting parent’s report of transfer amount. The age coefficients remained large (but become non-significant) in the table predicting the offspring’s report.
Post-hoc tests
We tested a number of other specifications to verify the stability of our results. Since the “other employment status” category is heterogeneous and therefore difficult to interpret, we estimated the regressions without these observations and found that our pattern of results did not change. Because coresidence may substitute for financial support, we estimated the models separately for offspring who live with their parents and those who do not. The coefficients were not identical, but the main results were the same using this specification. A small number of parents included in the analysis were step-parents (32 out of 536). After excluding parent-offspring dyads where the parent was a step-parent, the pattern of results was unchanged.
We considered whether to include offspring’s education as a mediating variable; however, a large proportion of respondents were too young to have completed college (45% were between 18 and 21). We estimated models limited to respondents age 23 and over, controlling for highest level of education, and found that including education in the model for offspring needs (Model 1) resulted in little or no additional attenuation of the age-support pattern. In sum, the post hoc tests showed that the results were stable.
In all four graphs, it was clear that the decline in support with age was attenuated in the models that control for offspring needs. This group of variables included offspring’s employment, offspring’s student status, whether offspring had children of their own, offspring’s health problems, and the interaction of offspring’s household income and coresidence with parents (Model 1). We disaggregated these variables to see which variables, specifically, were the most powerful mediators of the age-support relationship. In the models predicting frequency of support, offspring’s household income (interacted with coresidence) had the largest attenuating effect on the age coefficients, compared with the other offspring need variables (not shown). Meanwhile, in the models predicting amount of support, three variables – offspring’s employment, status, student status, and household income – had the largest attenuating effects on the age coefficients.
DISCUSSION
We built on existing literature by: a) exploring what factors underlie the age pattern of support, b) using a unique dataset that includes parent-to-offspring transfers that occur both within and across households, c) examining both parents’ and offspring’s reports of transfers, and d) differentiating between two dimensions of financial support: frequency and amount.
We found that a wide variety of factors, including offspring’s age, needs, acquisition of adult roles, perceived adult status, and their parents’ abilities to provide support, all play a role in determining the financial support that parents provide their young adult offspring. This portrait is generally consistent with findings from previous research (Fingerman et al., 2009; Suitor, Pillemer, & Sechrist, 2006). These findings suggest that parents and children respond to many factors in negotiating the transfer of resources, including their children’s needs, perceived adult status, and parents’ ability to provide support.
Our central research question was why support from parents to young adult offspring declines as offspring age. We find that this decline is partially explained by the fact that the needs of offspring decline with age. This fits with prior research that has found that offspring need is one of the most powerful predictors of support from parents to young adult offspring (Fingerman et al., 2009; Attias-Donfut & Wolf, 2000). The literature on intergenerational transfers identifies altruism as one of the central motivations for parental investment in offspring (Becker, 1981). Our finding that the decline in support with age is partially explained by decreasing needs adds support to the idea that parents are motivated in part by altruism.
We also found that age is very robust as a predictor of support to young adults and seems to be a main factor rather than simply a proxy for other factors. It is possible that the age-support association is mediated by characteristics we did not measure, but we have controlled for a wide range of important factors in our analysis and the age coefficient was not greatly diminished. The residual “effect” of age is large – a five-year increase in age has a larger effect than most variables in the model, including being single (versus being married) and having a health problem in the past year, for example.
The robustness of the age coefficient could be the result of a cohort effect: support to young adult offspring could be increasing for successive cohorts over time, rather than diminishing as offspring age. In other words, offspring currently in their thirties may have lower levels of support at the time of the survey in part because those cohorts have always had lower levels of support, compared to those offspring currently in their late teens and early twenties. We are not able to evaluate this hypothesis with our cross-sectional data. Basic analyses using longitudinal data in the Fragile Families and Child Wellbeing study reveals that among urban mothers between 18 and 34, the likelihood of receiving financial support from friends and family declines as they get older (results not shown). This suggests that cohort differences do not provide a satisfying explanation for the age pattern of support.
Alternatively, our results may point to the idea that there are social norms around biological age that dictate when it is considered desirable for parents to give support and for offspring to request and accept support. Consistent with this interpretation, we find a particularly large drop in support between age 29 and age 30, an age change that is considered important socially (results not shown). Settersten and Hagestad (1996) have argued that Americans have opinions about when family transitions should be completed but that the timing is considered flexible. Our results suggest that these types of flexible norms extend to financial self-sufficiency – there are guidelines that dictate how support should decline with increasing age, but age is not the only predictor of support. In other words, age has a strong and independent effect on support, but support also responds to parental resources, offspring needs, and other characteristics.
Our results add nuance to research by Settersten and Hagestad (1996) on age norms and support from parents. They found that unlike other family transitions like leaving home, marriage, and childbearing, most people in their survey did not believe that there is an age that is “too late” to move back in with parents. They concluded that support from families responds to the offspring’s needs and individual situation, rather than age norms. We would argue that although there may not be a fixed deadline for ending parent-to-child support, there is a strong correlation between age and support that is not explained by individual needs and circumstances.
The robustness of the age-support association also points to the idea that current theories of exchange are not adequate for understanding transfers. The three motivations commonly given for intergenerational transfers – altruism, evolution, and exchange – would not predict that increasing age would have a strong negative effect on support, unless age were acting as a proxy for offspring needs or other characteristics. Intergenerational exchange theory should make room for the idea that other factors are important in shaping exchange behaviors. For example a parent might be altruistically motivated to want to help an older out-of-work child, but may not (or help may be refused) because of age-related norms discouraging support at older ages.
We lacked some important indicators of offspring need and parental resources, including wealth indicators for offspring and parents. We controlled for a wide range of important factors in our analysis, however, and the age coefficient was not greatly diminished, so we are confident in our results. A second limitation of our analysis is that financial transfers, even when measured well, exclude relevant information. Most notably, parents might provide non-financial help to offspring that nevertheless result in a direct financial savings for the offspring, such as a parent allowing a child to live in his or her home, providing free babysitting, or lending the family car. Finally, we lacked direct reports from parents and offspring about age norms and how age influences their thinking about financial transfers. Future research should seek to bring together data on exchange behavior with data on the way people think about age norms.
In order to assess the role of age norms in shaping parent-to-offspring transfers, future work might benefit from taking a cross-national approach. This paper is based on a U.S. sample that reflects a culture that has traditionally encouraged early autonomy (Cook & Furstenberg, 2002). In Europe, this cultural standard most closely resembles the Northern European pattern, which is distinct from what occurs in the South of Europe or parts of Eastern Europe (Newman & Aptekar, 2006). Thus, it would be interesting to examine the age-support pattern with similar data in Europe as a way of examining normative differences. A slower drop-off of support, given similar circumstances of youth and their parents, in more familistic European nations might reveal normative differences across nations.
In sum, our findings confirm the importance of age as a predictor of support from parents to young adult children and reveal that the age-support pattern is partially explained by changes in offspring’s needs, but remains robust even after controlling for a wide range of relevant factors. Our results indicate that traditional explanations for intergenerational exchange, such as altruism, are useful but probably not sufficient for understanding parent-to-child transfers in early adulthood. Exploring how parents and offspring think about age in the decision to offer or accept support, respectively, is an important next step for understanding the dynamics of intergenerational exchange.
Acknowledgments
This research was supported in part by training grants from the National Science Foundation, the University of Pennsylvania, and the National Institute on Aging (T32 AG000221). Additionally, the data used in this analysis was funded by grants from the National Institute on Aging, including: R01 AG027769 and 2R01 AG027769, The Psychology of Intergenerational Transfers and Family Exchanges Study 2, Karen Fingerman, Principal Investigator and a grant from the MacArthur Network on Transitions to Adulthood (Frank Furstenberg, Director).
Footnotes
The transfer frequency variable is an ordinal, not a continuous variable, so we estimated our models using both OLS and ordered logistic regressions. The pattern we observed was the same, so we chose to present the results of OLS regressions since they are easier to interpret, following Fingerman et al (2009).
Contributor Information
Caroline Sten Hartnett, Email: carohart@isr.umich.edu, University of Michigan, Population Studies Center, ISR, 426 Thompson Street, Ann Arbor, MI 48104, T: 401-965-4213, F: 215-898-2124.
Frank Furstenberg, Email: fff@ssc.upenn.edu, University of Pennsylvania, 3718 Locust Walk, Room 239, Philadelphia, PA 19103, T: 215-898-6718, F: 215-898-2124.
Kira Birditt, Email: kirasb@isr.umich.edu, University of Michigan, Life Course Development Program, Institute for Social Research, 426 Thompson St., Ann Arbor, MI 48106-1248, T: 734-647-4855, F: 734-647-0861.
Karen Fingerman, Email: kfingerman@austin.utexas.edu, University of Texas at Austin, 1 University Station, A2702, Austin, TX 78712, T: 512-471-0618, F: 512-475-8662.
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