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Measures of Help Available to Households in Need: Their Relation to Well-being, Welfare, and Work

By Kurt J. Bauman and Barbara A. Downs

Population Division
U. S. Bureau of the Census
Washington, D.C. 20233

May 2000

Population Division Working Paper No. 42


An earlier version of this paper was presented at the annual meetings of the Population Association of America, Los Angeles, CA, March 24, 2000.

DISCLAIMER:

This paper reports the results of research and analysis undertaken by Census Bureau Staff. It has undergone a more limited review than official Census Bureau publications. This report is released to inform interested parties of research and to encourage discussion.


TABLE OF CONTENTS

ABSTRACT

Introduction

Networks

Networks and employment

Other influences on work entry and exit

Plan of this paper

Data

Results

Influences on network indicators

Effects of networks on welfare and work

Conclusion

Notes

References

Figure 1. It's Who You Know: Resources, Networks, and Support

Tables

  1. Percent of People Who Expect Help When in Need By Demographic and Economic Characteristics

  2. Percent of People Who Expect Help When in Need By Demographic and Economic Characteristics

  3. The Effect of Economic and Demographic Factors on Indicators of Networks Relationships

  4. The Effect of Networks on Work and Welfare Transitions -- No Controls

  5. The Effect of Networks and Other Factors on Work and Welfare Transitions

Population Division Technical Working Papers Series


ABSTRACT

Quantitative research on support networks, poverty and welfare has been limited to only a few available measures. We introduce a new quantitative measure of family and non-family support available to people in need - expectation of assistance available when in need. We find this measure has a strong relationship to material well being, employment and the ability to avoid welfare use. The measure provides stronger evidence than has been available in the past that resource networks affect employment and welfare. Our work also confirms that poverty has a strong negative impact on the ability to participate in resource networks, and therefore illustrates a mechanism through which poverty is linked to family functioning.


Measures of Help Available to Households in Need: Their Relation to
Well-being, Welfare, and Work

Introduction

Recent sociological work has demonstrated that family and other social networks can help people enter employment and meet basic needs. Many types of resources flow to individuals that have strong network attachments, including child care, help in emergencies, and resources that allow people to undertake new endeavors such as school enrollment or the purchase of a new home. Ethnographic work has repeatedly described the ways in which people with low incomes rely on family and friends to provide instrumental and financial assistance to help them make ends meet and engage in productive activities. Quantitative research has provided some evidence that network support can aid entry into the workforce. However, the findings of quantitative research are not as strong as they could be, due to the limited number of quantitative measures available to measure network strength.

Past research on network effects has often been limited to using one or two indicators to measure network resources, and these indicators contained a number of confounded influences. Among these influences are (1) network strength in terms of resources and norms of assistance, (2) need for assistance on the part of the individual receiving support, (3) the ability of the individual to participate fully in the network by reciprocating when support is received, and (4) the nature of behavioral expectations associated with network participation as they affect future material outcomes. The complexity of network effects can be dealt with more effectively when more than one measure of network assistance is used in any given analysis.

In this paper, we explore the interrelationships between expectations of support from social networks and the well being of households -- their ability to meet basic needs, enter into work, maintain labor force attachments and negotiate welfare transitions. In so doing, we make use of several indicators of network attachment, including a new quantitative measure, expectation of help available when in need, which provides new insights into the decision-making processes individuals navigate as they manage their family and work affairs.


Networks

Social networks are comprised of people who are available to provide support (Antonucci 1990). Ideally, it would be possible to measure the characteristics of these networks directly. However, direct measurement has typically been difficult. Much past work has relied on indicators of individual use of networks (such as coresidence and proximity to kin) as a major indicator of network availability. While such indicators reliably reflect the presence of network connections, they cannot measure network characteristics independently of individual characteristics which affect network use. These individual characteristics need to be considered explicitly. There are at least three types of individual characteristics that influence network relationships -- resources, needs and characteristics that reflect on the normative standards of the network. 1

Access to resources appears to be a motivating factor in the development of social support networks (Corin 1987; Krause 1987). These support relationships are reciprocal in nature (Wellman and Hall 1986; Roberto 1989; Rossi and Rossi 1990). Thus groups with scarce resources are likely to expect fewer supports than groups with greater resources (Antonucci 1990). Married people, who have greater material resources, also have larger support networks, both in the number and the variety of sources of support (Antonucci 1990; Flowers et al. 1996). Access to resources is usually represented by money income and assets. Those who have more money are presumably in a better position to reciprocate when network assistance is provided, and are likely to be in contact with people who have the ability to provide assistance. A distinction needs to be drawn, however, between indicators of short-term financial difficulties and long-term economic position. The latter can be expected to have a larger effect on the ability to participate in resource networks. Assets, housing and other factors that reflect long-term living situation can be expected to have a stronger effect than income. Even less impact should be expected from indicators of short-term difficulty meeting basic needs, which may reflect short-term problems (Bauman 1998, Beverly 1999).

The existence of social networks is demonstrated by the flow of resources, and these usually flow to those who have particular needs. Research has documented that those with greater need for assistance (young, poor, minority, and single parents) are more likely to adopt strategies such as residing with other family members, which may alleviate some of their financial distress (Stack 1974; Hogan et al. 1990). Similarly, people facing difficult or expensive transitions in their lives -- such as attending college, purchasing a home, moving to a nursing home or recovering from illness or injury -- have traditionally been the recipients of resources from family members and others concerned with their well-being. Needs and resources, of course, are opposites. Those who have greater needs will generally be less able to reciprocate, even if they are in contact with networks with substantial resources. So, most indicators of needs (and of resources) will have offsetting effects, meaning that their net influence on assistance from networks cannot be predicted in advance. The exception to this rule would be situations where the need for assistance is temporary or associated with predictable life-course events. Parenthood and age (both youth and old age) are examples of such events.

The normative content of network relations does not correspond as clearly to identifiable individual characteristics. People who belong to stronger, resource rich networks presumably have a greater incentive to adhere to norms. However, the incentive will be less if their own resources permit them to live independently of network support. In addition, it is not clear how the rules and expectations associated with network participation might vary. For example, Edin and Lein (1997) argued that network support was a critical ingredient that allowed poor single mothers to maintain employment and remain off of welfare. Their position is supported by the findings of Parish et al. (1991) who found that proximity to other working adults increased the likelihood a young, single parent will enter the workforce. A contrasting position is taken by Stack (1974) who observed that in low-income communities, networks discourage workforce entry because exchange activities of the group focus on non-work based exchanges.

A reasonable reconciliation of these opposing views is that networks reinforce behaviors that contribute to the maintenance of the network. Thus an individual will be drawn into a network not only when he or she has the resources to reciprocate, but also when he or she has characteristics and behaviors prevalent in the network -- such as employment, as well as ethnicity, appropriate age, childbearing within marriage, etc. In some networks, employment may be rare, or contingent on particular extended family circumstances or needs (Stack and Burton 1993). However, it should be expected that the most resource-rich networks would have normative behaviors that include regular employment.

From the preceding discussion, it should be apparent that aspects of network relations work in offsetting ways. Greater individual income allows access to assistance, but indicates low need for assistance. Network attachment may encourage employment for some while discouraging employment for others. It is perhaps not surprising that past findings on network effects have not always shown them to be very strong predictors of behavior.

When several network indicators are available, closer examination of offsetting effects is possible. In the present paper, the indicators can be classified into three types. The first set are indicators of network usage by those who are in need. These indicators include coresidence with others and financial assistance from outside the household. Past research has shown that these are related to need for assistance. Those who have never married and have lower human capital are most likely to reside with relatives or friends, as are black and Hispanic women (Hao and Brinton 1997; Hogan et al. 1990; Hofferth 1984; Tienda and Angel 1982). Resources from family and others outside the household are an important income source for low-income single mothers (Edin and Lein 1997).

Expectations of assistance from family and friends, on the other hand, are not dependent on actual experience of material need in order to be expressed. They should, therefore, more directly express network strength and the ability to participate in the network through reciprocation.

Finally, there are other indicators that must be treated as special cases due to question wording or other factors. An indicator of expectation of help from groups or agencies in the community could be an indicator of network strength (as with strong church ties) or network weakness (as with welfare dependence).

From the discussion above, the direction of effects of individual or household characteristics on indicators of network availability cannot be predicted from theoretical considerations alone. On the other hand, relative effects can be predicted. That is to say, we can expect stronger effects on some outcomes than on others. Households with greater income, resources, or human capital would be relatively more likely to express the expectation of assistance from family and friends than they would be to have received financial assistance or to have co-resided with friends or with kin. Indicators of need, such as disability, the presence of children, single parent status, membership in a disadvantaged minority group, or age extremes (young, old) would have a strong positive effect on use of network assistance relative to their effect on expectations.


Networks and employment

Network help is obviously important in cases where people run into emergencies or temporary problems. Over the long-term, however, it is not self-evident that the presence of strong social networks will contribute to overall well-being. For example, when a person loses a job the financial assistance they receive from relatives may be better than no help at all, but may not go very far towards meeting major expenses. A family loan to help buy a house may make it possible to buy a home that would otherwise be unaffordable, but may only move the time of ownership forward a year or two. Across the entire population, moreover, these events may be rare enough that they don't provide a significant upward boost in recipients' well-being.

One way in which households can reach long-term economic security is through employment. Networks may be important in helping obtain and maintain employment. However, the role played by social networks in work entry and exit is indeterminate. Once again, analysis is hampered by problems of simultaneous causality -- i.e., the need for assistance is often associated with employment problems, making assistance seem less encouraging of employment. Those with the greatest difficulty finding work often have the greatest need, and thus may be likely to get network assistance, creating a negative relationship between networks and employment. On the other hand, those who are working are likely to be in contact with networks that have sufficient resources, and are also likely to conform to normative expectations favoring employment. Of the indicators of network connection, expectations of assistance most reflect network strength. Expectations of assistance, therefore, should be positively associated with employment. Indicators of network use, however, would be less frequently associated with work because of the offsetting influences just described.

We have examined one mechanism through which employment is related to network support -- normative expectations of the network itself. There are other mechanisms as well. Networks may provide material resources to facilitate the undertaking of new activities, such as moving or purchasing a home. They may provide ongoing assistance to maintain productive activities, such as emergency child care, assistance with everyday tasks, and normative support. People make different use of networks depending on their situation (Wellman and Hall 1986). Networks may also be important as information sources, as sources of direct financial assistance, as "insurance" against financial or other problems, or as sources of normative support (Wellman 1979; Corin 1987; Krause 1987; Roberto 1989). Networks may provide critical resources to prevent temporary problems or emergencies from ballooning into permanent setbacks.

If networks act mainly to prevent catastrophe rather than to help people recover, then it may be that expectations of assistance will be important, because they represent potential resources available to people who have not experienced problems. Actual assistance received may have less of an impact on employment due to the offsetting impacts of assistance and need. If networks act mainly to prevent catastrophe rather than to help people recover, they may also be more important for maintaining employment than for entering employment. would be more affected by networks than the entry into employment, on the presumption that many of those needing network assistance to enter work have already experienced a setback.


Other influences on work entry and exit

Predictors of work entry and exit can be categorized as indicators of: resources, human capital, family structure, demographic characteristics, and social networks. Resources are factors such as income, child care availability, or transportation assistance. Each of these factors increases work readiness or provides a cushion against unexpected financial downturns, thus they increase the likelihood of entering or staying in the workforce.

Human capital, frequently measured as education, increases the likelihood of being in the workforce, according to economic theory (e.g. Pencavel 1986). Those with greater educational attainment have more marketable skills and are able to obtain higher paying work. More work income makes it less likely an individual would leave the workforce, as the costs of doing so are greater.

Family structure, defined as number of children and presence of other adults in the household, have contradictory effects on workforce entry and exit (Hao and Brinton 1997; Harris 1996). Children are, in general, a burden in terms of financial and time commitments. It is more difficult for a woman with several children to make and pay for child care arrangements for all of her children than it is for a woman with no children or one child. The woman with more children would therefore have more difficulty coordinating her activities such that she would be able to seek and maintain employment. The presence of other adults in the household, conversely, could be a resource which allows a person access to child care or money, which help with entering and staying in the working world. However, if the other adults in the household are living there because of their own financial problems, they become a burden similar to children. Thus, the effect of living with other adults on workforce behavior is somewhat ambiguous.

Finally, demographic characteristics, in particular, age, are related to workforce entry and exit (Clark and Summers 1991). Older people, at least those below the retirement age, are more likely to enter and stay in the workforce for several reasons. They have higher earnings due to experience. They are in general, more mature and stable than younger people. Their children are most likely older, so they have fewer child care constraints. They also have been exposed to work as a normative behavior for a longer time, and they have had more time to develop connections with potential employers.


Plan of this paper

This paper examines the factors affecting social network size and strength. It uses this information to predict workforce entry and exit. The results provide a broad explanation of the effects of person and social characteristics and behavior. Our main hypotheses are as follows.

Hypothesis 1: Households with greater income and resources will have greater expectations of assistance from networks.

Hypothesis 2: Flow of resources is associated with need.

Hypothesis 3: Networks provide resources primarily to those with short-term (rather than long-term) needs.

Hypothesis 4: Households with access to networks are more able to obtain and maintain employment.

Hypothesis 5: Actual assistance received from networks will have a stronger impact on entry into work, while expectations of assistance will be morestrongly felt at the point of exit.

The next section discusses the data used in this paper. This will be followed by an examination of influences of household characteristics on network support indicators. The influence of network support on subsequent labor market behavior and welfare use will then be examined. This will be followed by a brief examination of the relationship of network support to difficulty meeting basic needs. Finally, we will discuss the results and present conclusions.


Data

The data for this study are from the 1991 and 1992 panels of the U.S. Census Bureau's Survey of Income and Program Participation (SIPP). The regression analyses make use of the topical module on "Extended Measures of Well-being" administered in wave 6 of the 1991 panel and wave 3 of the 1992 panel. The topical module was administered to the reference person in each household (or his/her proxy), providing data on many conditions affecting the household -- including housing quality, neighborhood conditions and material well-being. An identical questionnaire was administered in each panel. These panels overlapped so that both sets of questionnaires were in the field at the same time -- winter of 1992-1993. There were 13,162 households in the 1991 panel and 18,634 in the 1992 panel for a total of 31,796 cases for analysis. In many analyses, missing data on some variables reduced the sample somewhat.

The 1991 and 1992 SIPP surveys asked respondents several questions that touched on their ability to draw on social network support. One form of such support was coresidence with relatives outside the immediate family or with housemates. Three types of living situations were determined form the complete household roster recorded on a monthly basis in these SIPP panels. Households where any member resided with their own child and with a parent were classified as "three generation" households. Any other coresidence by relatives outside the family was classified as residence with relatives. If non-relatives lived in the household, it was classified as one with housemates. A second measure of network support was financial assistance from outside the household. As part of the accounting of all income sources in the SIPP, household respondents were asked to report any financial contributions made by relatives or friends. These contributions are recorded separately from child support and alimony. Another indicator of network support was obtained for those people who experienced difficulty meeting basic needs (described below). Those who had such difficulties were asked if they received help from others outside the household. Finally, respondents were asked whether they expect they would receive assistance in a situation where they needed help, such as sickness or moving. They were asked about help expected from family, friends, or their other people in the community such as a social agency or a church. Responses were coded on a four-point scale running from "all the help we need" to "none of the help we need." For the purposes of this analysis, these variables were treated in two ways. For many analyses, the expectation variables were converted into dichotomous indicators separating the highest two levels on the scale from the lowest two. In addition, a scale was used that averaged the scores of responses to the two questions on help from family and from friends.

In these two panels of the SIPP, respondents were asked a variety of questions about material circumstances of their households. Questions on material hardship asked in the 1991 and 1992 SIPP were very similar to those used by Mayer and Jencks in their analysis of poverty and material hardship in Chicago (Mayer and Jencks 1989). Household heads were asked about instances when the household did not pay rent, mortgage, or utility bills, use of medical services and food sufficiency. A summary measure of hardship indicated households where at least two types of hardship were reported. Respondents were also asked about neighborhood conditions. These included street noise, streets in need of repair, litter in the streets, rundown or abandoned buildings, industrial or other nonresidential uses, and smoke or fumes. Questions on housing conditions focused on the number of rooms, the state of repair and satisfaction with the home. Questions on crime related to perceptions of safety of the home and neighborhood.

For this research, single parenthood was treated as a characteristic of a household. Any household that contained the unmarried parent of a child under age 18 during the interview period was considered a single parent household. All other households containing parents were considered married parent households. Households where no parents were identified were non-parent households.

Receipt of several different program types was considered 'welfare' for the purposes of this analysis. These included all means tested income support programs included on the survey: AFDC, SSI, general assistance and several less common programs. Households that relied on one or more of these programs and contained no members who worked were classified as on welfare. Households that contained at least one member who worked, and received no welfare were considered working.

Because there is only one respondent per household, individual characteristics of all household members were not used in the construction of control variables. Instead, characteristics of the household itself (household income, home ownership) and characteristics of the household head (age, race, sex, education) were used. Disability status, however, covered the entire household -- if any member reported a work disability the entire household so classified. Health insurance status was recorded into three categories: complete coverage for all household members in all months, coverage for at least some household members in some months, or no coverage for any household member. Household income (in logarithmic form) was measured both with and without food stamps, with little difference in results. The analyses using the former version were included here. After some experimentation with others, two measures of asset holdings were included: tenure and number of asset types. The latter was measured by adding together the total number of types of asset income received (there were 12 types, including such things as money market accounts, US government securities, dividends, rental income).

The SIPP is based on a multistage sample, rendering standard statistical tests inappropriate. In the tabulations and regression results presented below, standard errors are adjusted to reflect a design effect of 3.0.


Results

Table 1 shows bivariate tabulations of expectations of help when in need with several economic and demographic variables. All of these had significant relationships to expectations of help when in need. Households with greater resources were more likely to expect help. Poor households and households having difficulty meeting basic needs were less likely to expect help than other households (with the exception of assistance from groups or agencies in the community expected by the poor).

Family structure and life cycle variables were also significantly related to expectations of help when in need. Single parent households were less likely to expect help. On the other hand, other households with children were more likely to expect help. Households headed by younger people were more likely to expect help from family and friends. Households with heads age 65 or higher were also more likely to expect help from family, but less likely to expect help friends. Education is associated with greater expectations of assistance from friends, but less of assistance from family and others.

Table 2 examines the degree to which expectations of help available are related to the actual receipt of help. Expectations are only loosely correlated with other indicators of network assistance. The only significant effect is negative -- households with members outside the nuclear family tend to expect less help from friends.


Influences on network indicators.

To more fully explore these relationships, Table 3 provides the results of logistic regressions of six indicators of network assistance on a set of control variables similar to those just examined. The control variables are arranged in several groups, corresponding to the discussion above. As hypothesized, measures of immediate (short-term) resources, such as income and health insurance coverage, have a relatively weak relationship with expected assistance. By contrast, indicators of longer-term economic stability, including assets, home ownership, and neighborhood conditions, have strong, significant relationships with expected assistance. The exception to this observed pattern is the effect of difficulty meeting basic needs (paying bills and meeting expenses). There are at least two possible explanations for this seeming anomaly. In previous work, we have noted that difficulty meeting basic needs may be an indicator of long-term financial instability (Bauman 1999). In regressions not shown here income has a large impact on expectations of assistance until indicators of longer-term well-being are introduced. Assets and neighborhood conditions may not adequately control for long-term difficulty meeting basic needs. More plausible, perhaps, is the explanation that difficulty meeting basic needs forces people to rely on network assistance, and that experience makes them pessimistic about future help. Alternatively, the type of household that is unable to pay bills or meet other basic needs is also unable to call upon help from outside the household, due to characteristics not observed here.

Measures of assistance received relate to economic influences in a fashion that is complementary to that of assistance expected. Indicators of short-term economic position have a large impact while indicator of long-term position have much less of an effect. The positive coefficient of income on coresidence runs counter to the hypothesized direction. This may be due to the fact that households with coresident extended family members contain those who are able to provide assistance along with those who are in need.

Family composition has no significant effect on expected help or on help received from outside the household. However, the impact on coresidence is very large. Households with children, and especially single parent households, are much more likely to coreside with relatives than other types of households.

Expectations of help from family have the expected curvilinear association with age (higher for households headed by the young and the old). Expectations of help from friends, income from relatives and coresidence, however, do not conform to this pattern, each having monotonic changes with age. Income from relatives and coresidence may be thought of as working together to form a curvilinear shape, however. Income assistance is prevalent among young households, coresidence among those that are older.

Other results, however, do not follow the hypothesized direction. If education is thought of as an indicator of long-term economic well-being, then the pattern of effects observed here are quite unexpected. Contrary to our hypothesis, education has a negative effect on expectations of assistance from family, and a positive effect on income from relatives. The negative effect of education on coresidence is as hypothesized, although its strong significance and large magnitude relative to other effect of education does not immediately follow from the reasoning above. The reason for these anomalous effects may have to do with network norms. People with greater education may be less likely to subscribe to traditional, ascriptive, kin-based, notions of solidarity, and therefore eschew close association with the types of networks examined here. They are also more likely to be geographically distant from relatives, making immediate help and coresidence more difficult to accomplish. It is perhaps in this set of parameters that we find evidence that the poor have stronger kinship networks than those with greater resources.

The remaining indicators do not test our hypotheses very strongly, but are included for completeness. Households containing a person with a work disability are much more likely to consist of extended family. Male-headed households are less likely to coreside this way, and also less likely to receive assistance from outside the household. Black and Hispanic households are significantly more likely to coreside with relatives even after controls, but are significantly less likely to expect help from friends -- perhaps as a result of the lack of resources in many African-American and Latino communities. Hispanic households seem to be less likely than others to expect help from family as well. This may have to do with geographic dispersal, or with other factors common to Hispanic households that are not measured here.


Effects of networks on welfare and work

The results considered thus far seem to conform to a reasonably coherent story. On the other hand, we have been unable to provide a strong test of our overall model. Perhaps the most nettlesome question that remains after the preceding analysis is whether the observed network connections have any impact on overall well being. One may wonder whether expectations of help when in need have any practical impact, or whether network assistance provides limited help without markedly changing people's circumstances. The beginnings of a test of such an impact is shown in Tables 4 and 5. We make use of the longitudinal structure of the SIPP to predict future labor market and welfare participation based on current answers about network assistance, household composition and resources. The models are discrete-time hazard regression models using a 4-month period as the time unit for analysis.2 The data for these analyses include only households with at least one member under the age of 55, because we are less interested in retirement than in other labor force transitions. We have further restricted the sample to households who were eligible for the transition in question at the time of the baseline interview (i.e., when the topical module questions on network assistance were administered).

Table 4 shows the relation of the network assistance variables to work entry and exit and to welfare entry and exit. Controls for household composition and resources have not been included. Most of the network indicators we have considered have an impact on at least one of these transitions. The expectations measure is no exception. Help expected from family and friends has an impact on exit from work and on welfare entry. Welfare entry is affected by nearly every indicator.

In Table 5 we see that two of the social network effects remain significant even after control for the full set of household composition and resource indicators used in earlier regressions. Help expected from family and friends has a significant negative impact on leaving work. This result indicates that expectations of help may indeed be an indicator of network resources that impact behavior. Apparently this impact is felt among those who are already well established, preventing them from falling into potentially worse situations. On the other hand, anticipated help might well not be a measure of network resources but of household characteristics not captured by other variables. A test of this proposition must wait for another paper.

Membership in a three-generation household has a significant positive impact on entering welfare. Coresidence seems to facilitate a transition to welfare use -- though only coresidence of families with children with grandparents. This may be due to use of welfare by the older generation (SSI is included in our definition of welfare in these analyses; it could be separated out in future work). Separating out single parent families in three-generation situations did not produce different results (i.e. the coefficients for both single and married parent families were very close to the same).


Conclusion

The empirical work in this paper has supported the hypotheses laid out at the beginning, albeit not without qualifications. The households anticipating the greatest access to outside resources were those in the least need for those resources. Actual receipt of resources from outside the household, however, responded primarily to need. It is also clear that short-term rather than long-term needs are those that attract the greatest support from network sources.

If this picture of network relations is confirmed by future work, it points to a limited role of family and friends in providing help to people who are experiencing more than temporary setbacks. If income support programs continue to be replaced by programs designed to steer people into self-support, the danger is that those who have the least ability to survive on their own will be the least likely to receive support from existing social networks.

Three exceptions to the empirical regularities just mentioned may provide insight into the processes by which network support are provided. The first involves difficulty meeting basic needs as an indicator of short-term need. The second involves the role of coresidence as a means of providing support. The third involves the effect of education.

In earlier work, we have argued that disruptions are serious impediments to maintaining labor force attachment and that difficulty meeting basic needs is an indicator of this type of disruption or instability of living conditions (Bauman 1999). Difficulty meeting basic needs is affected by both short-term and long-term indicators of household instability. Moreover, the large effect of difficulty meeting basic needs on help expected might have two parts: (1) expected assistance from family and friends helps cushion against difficulties making basic needs, and (2) the experience of difficulty meeting basic needs makes people less optimistic about getting help from family and friends in future cases of difficulty. It is clear that additional work needs to be done to sort out these effect.

The strong relationship between coresidence and long-term indicators of need was not anticipated. Coresidence stands out as the one means people have to deal with long-term needs and disabilities. Unfortunately, the role of age has not been adequately handled in this analysis. Even though we controlled for the age of household head, the age of other household members is not controlled. The strong effect of single parent family status cannot be explained by age, however. Moreover, the effects of low education and of disability are stronger than might be expected if the primary phenomenon were simply a matter of coresidence by elderly kin. Again, more work is needed.

Education of household head was the only indicator of long-term resources that had a negative impact on help expected from family. This provides some indication that education is not a stand-in for permanent income in this model. As mentioned above, the role of education may be to change norms rather than resource relationships. However, there is a serious weakness to the original question on help from family, in that it asks specifically about family "living nearby." So family solidarity might be just as strong among the highly educated as it is among the rest of the population if all we are really measuring is geographic proximity to kin. There are no direct measures of kin proximity in the SIPP data, but measures of geographic mobility are available to provide a partial test of this interpretation.

Overall, then, as much work needs to be done as has been accomplished at this point. However, we believe we have shown that measures of expected help can indeed provide information about the nature of social network assistance available to the poor (and others), and that the importance of having resources in order to engage in network exchange is an important consideration in considering the role of these networks.


Notes

  1. A flowchart showing the model that is implicit in our discussion is included as Figure 1. Missing from this model is any representation of the normative aspect of networks.

  2. The four-month period is related to the four-month recall period in the SIPP questionnaire. A monthly time unit could also be used with these data, but our attempts to do so yielded poor results. This probably has to do with the fact that most changes in status are reported to have occurred in months 4, 8 and 12 (i.e., the "seam bias" problem), and there was not enough variation in other months to support analysis of simple multiplicative covariate effects.


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FIGURE 1

Figure 1:  It's Who You Know:  Resources,
Networks, and Support


Table 1

Percent of People Who Expect Help When in Need
By Demographic and Economic Characteristics

  Source of help expected
Family Friends
Below poverty 55.93* 44.38*
Above poverty 62.27 60.31
     
Difficulties meeting basic needs 50.06* 41.12*
No difficulties 63.09 60.95
     
Single parent household 57.27* 49.49*
Other household types 62.16 59.84
     
Own child in household 63.29* 60.28*
No own child 61.01 58.17
     
Age 15-34 66.37* 61.74*
Age 35-64 58.98* 58.75
Age 65 or more 64.82 58.88
     
HS or less educ 63.55* 55.18*
Some college or more 59.23 63.36
* Percentage is significantly different from the percentage listed directly below.


Table 2

Percent of People Who Expect Help When in Need
By Demographic and Economic Characteristics

  Source of help expected
Family Friends
Income from relatives, friends 63.22 62.07
No income from rels, friends 61.61 58.68
     
Three generation household 60.76 46.01*
Other living situations 61.68 59.12
     
Lived with relatives 58.15 51.56
Other living situations 61.71 58.87
     
Lived with housemates 57.12 52.86*
Other living situations 61.78 58.93
* Percentage is significantly different from the percentage listed directly below.


Table 3

The Effect of Economic and Demographic Factors on Indicators of Network Relationships

  Help expected from Help
received
Income from
relatives
Coresidence
w/ relatives
Family Friends
coef.
(t-stat)
coef.
(t-stat)
coef.
(t-stat)
coef.
(t-stat)
coef.
(t-stat)
Resources/economic needs
Household income (log) 0.018 0.065* -0.521* -0.131 1.146*
(0.5) (2.0) (-5.9) (-1.8) (9.7)
Difficulty with needs -0.790* -0.717* 0.797* 0.324 0.344*
(-11.4) (-10.5) (6.0) (1.9) (2.2)
Partial health insurance 0.057 0.076 0.142 -0.529* 0.293
(0.6) (0.9) (0.8) (-2.7) (0.9)
Complete hhld. health ins. 0.015 0.086 0.008 -0.745* 0.024
(0.1) (0.8) (0.0) (-2.8) (0.1)

Assets/long-term economic situation
Number of assets 0.019 0.041* -0.001 -0.035 -0.029
(1.0) (2.4) (-0.0) (-0.6) (-0.5)
Undesirable neighborhood -0.195* -0.267* -0.033 -0.205 -0.084
(-2.7) (-3.9) (-0.2) (-1.0) (-0.5)
Rent home -0.195* -0.125* 0.029 0.269 0.158
(-3.5) (-2.4) (0.2) (1.7) (1.1)
Live in subsidized housing -0.140 -0.204* 0.225 -0.404 -0.419
(-1.3) (-2.0) (1.2) (-1.4) (-1.6)
Home in disrepair -0.376* -0.377* 0.241 0.521* 0.232
(-5.9) (-6.1) (1.9) (3.2) (1.5)
Problems with crime -0.173* -0.198* 0.012 0.199 -0.103
(-2.9) (-3.5) (0.1) (1.2) (-0.7)

Family situation/life cycle
Single parent household -0.042 -0.097 0.195 0.072 6.228*
(-0.4) (-0.9) (0.9) (0.3) (17.3)
Own children in hhld 0.104 -0.095 0.003 -0.394 4.688*
(1.1) (-1.0) (0.0) (-1.5) (13.2)
Number of children 0.007 -0.021 0.135 0.006 0.202*
(0.2) (-0.6) (2.0) (0.1) (3.5)

Life cycle (age)
Age 15 to 25 0.498* 0.440* -0.057 2.428* -1.781*
(4.0) (3.9) (-0.2) (8.9) (-6.0)
Age 25 to 34 0.372* 0.428* -0.040 1.501* -2.516*
(4.8) (5.9) (-0.2) (5.8) (-11.8)
Age 35 to 44 -0.123 0.205* -0.147 1.229* -2.561*
(-1.7) (2.9) (-0.7) (4.7) (-12.8)
Age 45 to 54 -0.253* 0.132 0.039 0.841* -1.661*
(-3.6) (1.9) (0.2) (3.0) (-8.4)

Other characteristics
Education -0.045* 0.008 0.032 0.105* -0.140*
(-5.4) (1.1) (1.4) (3.7) (-6.6)
Work disability -0.140* -0.028 0.329* 0.294 0.768*
(-2.3) (-0.5) (2.4) (1.6) (5.1)
Male household head -0.110* 0.026 -0.182 -0.564* -0.363*
(-2.0) (0.5) (-1.3) (-3.9) (-2.4)
Non-Hispanic Black -0.037 -0.325* 0.072 -0.240 0.933*
(-0.5) (-4.7) (0.5) (-1.1) (6.0)
Hispanic -0.279* -0.431* -0.107 -0.410 0.412*
(-3.3) (-5.3) (-0.6) (-1.5) (2.3)
 
Intercept 1.605* -0.142 2.328* -4.176* -16.331*
(5.0) (-0.5) (2.7) (-5.6) (-13.2)
Observations 31308 31308   31308 31308


Table 4

The Effect of Networks on Work and Welfare Transitions -- No Controls

  Entered work Left work Entered welfare Left Welfare
Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-stat
Help expected: fam/friends 0.017 0.2 -0.268* -4.6 -0.210* -2.5 -0.117 -1.0
Help expected: social agency -0.301 -1.9 -0.068 -0.6 0.125 0.8 0.294 1.3
Income from relatives/friends 0.633* 2.7 0.291 1.1 0.599* 2.0 0.740 1.7
Three generation household 0.026 0.1 0.338 1.5 1.800* 8.6 0.593* 2.3
Coreside with other relatives -0.036 -0.1 0.671* 2.6 0.826* 2.2 0.605 1.5
Coreside with housemates 0.394 1.4 0.418 1.9 1.000* 3.5 0.626 1.9
                 
Intercept -3.406* -17.0 -4.404* -30.9 -5.802* -25.3 -4.641* -13.1
Months 7 to 9 1.352* 7.6 0.977* 8.1 1.278* 6.5 1.725* 5.4
Months 10 to 12 0.611* 3.1 0.323* 2.4 0.707* 3.3 1.158* 3.4
Missing value: family help -0.311 -1.2 -0.086 -0.5 -0.333 -1.2 0.026 0.1
Missing value: agency help -0.236 -1.1 0.029 0.2 0.182 0.9 0.415 1.4
                 
Number of observations 5809   56243   59109   4421  
Likelihood due to model 85.1   119.5   125.8   53.8  
Degrees of freedom 10   10   10   10  


Table 5

The Effect of Networks and Other Factors on Work and Welfare Transitions

  Entered work Left work Entered welfare Left Welfare
Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-stat
Help expected: fam/friends 0.013 0.2 -0.126 -2.1 0.024 0.3 -0.166 -1.3
Help expected: social agency -0.221 -1.4 -0.069 -0.6 0.123 0.7 0.316 1.4
Income from relatives/friends 0.332 1.3 0.143 0.5 0.389 1.3 0.503 1.1
Three generation household 0.077 0.2 -0.414 -1.6 1.065 4.1 0.435 1.3
Coreside with other relatives -0.253 -0.5 0.362 1.3 0.240 0.6 0.255 0.6
Coreside with housemates 0.123 0.4 -0.149 -0.6 0.226 0.7 0.059 0.2
                 
Household income (log) 0.195 2.3 -0.323 -7.7 -0.244 -4.6 0.168 1.0
Number of assets -0.065 -1.0 0.075 1.8 -0.068 -0.9 -0.066 -0.4
0.292 1.9 0.338 2.7 0.449 2.7 -0.118 -0.5
0.025 0.1 0.245 1.9 0.118 0.6 0.231 1.0
0.019 0.1 0.083 0.7 0.212 1.3 0.070 0.3
-0.102 -0.6 0.076 0.6 0.110 0.6 0.013 0.1
Rent home -0.154 -0.9 0.572 5.2 0.186 1.2 0.169 0.6
Live in subsidized housing -0.147 -0.7 0.182 0.9 0.626 2.5 -0.564 -2.0
                 
Single parent household 0.464 1.7 0.556 2.7 0.850 3.1 0.511 1.3
Own children in household 0.670 2.9 -0.113 -0.6 0.600 2.6 0.249 0.6
Number of children -0.087 -1.1 0.082 1.2 0.155 2.0 -0.047 -0.5
                 
Age 15 to 25 0.797 2.6 -0.775 -3.5 0.518 1.7 0.714 1.5
Age 25 to 34 0.745 3.0 -0.851 -5.2 -0.064 -0.2 0.099 0.2
Age 35 to 44 0.637 2.7 -0.920 -5.8 -0.422 -1.6 -0.007 -0.0
Age 45 to 54 0.490 2.1 -0.815 -5.3 -0.072 -0.3 -0.084 -0.2
                 
Education 0.042 1.6 -0.063 -3.7 -0.078 -3.1 0.023 0.6
Work disability -0.375 -2.4 0.585 5.1 0.636 3.9 -0.517 -2.2
Male 0.326 1.9 -0.089 -0.8 -0.041 -0.3 0.612 2.4
Non-Hispanic Black -0.431 -2.3 0.051 0.4 0.479 2.6 -0.350 -1.4
Hispanic -0.192 -0.9 0.001 0.0 0.240 1.2 -0.554 -1.8
Partial health insurance -0.477 -2.3 -0.239 -1.6 0.270 1.2    
All have health insurance -0.504 -1.9 -0.745 -3.9 -0.375 -1.4    
                 
Intercept -5.782 -6.6 -0.059 -0.1 -3.602 -5.4 -6.508 -3.9
Months 7 to 9 1.395 7.8 0.999 8.2 1.301 6.6 1.749 5.5
Months 10 to 12 0.679 3.4 0.351 2.6 0.736 3.5 1.193 3.5
Missing value: family help -0.337 -1.3 0.019 0.1 -0.187 -0.7 -0.018 -0.0
Missing value: agency help -0.193 -0.9 0.042 0.3 0.212 1.0 0.399 1.3
                 
Number of observations 5809   56243   59109   4421  
Likelihood due to model 170.7   453.5   329.2   86.7  
Degrees of freedom 32   32   32   30  

Source: U.S. Census Bureau | Well-Being |  Last Revised: 2012-05-22T15:23:58.93-04:00