Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Environ Psychol. Author manuscript; available in PMC 2012 Mar 1.
Published in final edited form as:
J Environ Psychol. 2011 Mar 1; 31(1): 62–69.
doi:  10.1016/j.jenvp.2010.11.003
PMCID: PMC3079923

Neighborhood Conditions and Helping Behavior in Late Life


The purpose of this study is to develop and test a latent variable model that explores the ways in which social structural factors influence the amount of social support that older adults provide to their social network members. Neighborhood conditions play a key role in this conceptual scheme. The findings provide support for the following conceptual linkages: (1) low parental education is associated with low respondent education; (2) older people with less education encounter more economic difficulty; (3) greater financial problems are associated with living in a rundown neighborhood; (4) older individuals who live in dilapidated neighborhoods are more hostile; and (5) older adults who are hostile are less likely to provide social support to their social network members. Research indicates that helping others is a key to successful aging. Ways must be found to help economically disadvantaged elders provide support to their social network members.

Keywords: Neighborhood, hostility, social support, latent variable models

1. Introduction

An intriguing body of research suggests that in the process of helping others, support providers often find that their own health has improved, as well (Krause, 2007). Specifically, this research reveals that people who provide assistance to informal social network members tend to have better physical health (Schwartz & Sendor, 2000), less psychological distress (Midlarsky, 1991), improved psychological well-being (Silverstein & Bengtson, 1994), an enhanced sense of self-esteem (Krause & Shaw, 2000), and lower odds of dying over the course of a study follow-up period (Brown, Nesse, Vinokur, & Smith, 2003). Many of the studies on helping others are based on data that have been provided by older people.

If health-related benefits arise from helping others, than researchers need to learn more about the factors that encourage older adults to become support providers in the first place. Put another way, studies are needed that treat providing support to others as a dependent variable. The information emerging from this research can be useful for developing interventions that are designed to improve and maintain the health of our aging population. Several important factors that are associated with helping others have been identified in the literature. For example, research by Boerner and Reinhardt (2003) reveals that older people with higher levels of educational attainment, women, and individuals who are relatively young (i.e., the young-old) are more likely to provide assistance to their social network members. In contrast, a study by Liang, Krause, and Bennett (2001) that was based on the principles of social exchange theory, indicates that older people are more likely to help other individuals if they have received support from them in the past. And yet other investigators have found that helping behavior may also be motivated by religious beliefs and practices (Krause & Bastida, 2009).

Although the studies that have been done so far provide valuable insight into the factors that shape helping behavior, there has been little research on the potentially important influence of the built environment. More research is needed in this area because a number of studies indicate that living in rundown neighborhoods can influence other aspects of social relationships in a number of ways. For example, research by Scharf, Phillipson, and Smith (2005) reveals that older people who reside in deprived urban environments are more socially isolated.

The purpose of the current study is examine the relationship between living in deteriorated neighborhoods and helping behavior in late life. This is research is cast in a wider social structural framework by developing and testing a latent variable model that evaluates how the interplay between intergenerational (i.e., parental education), financial, and neighborhood factors influence the extent to which older people help others. This conceptual scheme, and the theoretical rationale upon which it is based, is presented in the following section.

1.1 The social structural foundation of helping behavior in late life

The conceptual model that is evaluated in this study is presented in Figure 1. Two steps were taken to make this latent variable model easier to comprehend. First, the elements of the measurement model (i.e., the factor loading and measurement error terms) are not depicted graphically. Second, the relationships among the constructs that are depicted in this conceptual scheme are evaluated after the effects of age and gender have been controlled statistically.

Figure 1
A Conceptual Model That Shows How Social Structural Factors Influence Helping Behavior

The theoretical core of this conceptual model is captured in the following linkages: (1) older people whose parents had little schooling will have fewer years of educational attainment, as well; (2) older adults with less education will be more likely to encounter financial difficulty in late life; (3) older people who experience ongoing economic problems will be more likely to reside in rundown neighborhoods; (4) older adults who live in deteriorated neighborhoods will tend to feel more hostile; and (5) older adults with higher levels of hostility will be less likely to provide support to their social network members. The theoretical underpinnings of each linkage are briefly reviewed below.

1.2 Parental education and respondent education

Ever since the classic studies by Duncan and his colleagues appeared nearly 50 years ago, sociologists have been exploring the factors that influence educational and occupational mobility (Duncan & Hodge, 1963). One of the most consistent findings in this literature suggests that the level of education that is achieved by parents has a substantial impact on the number of years of schooling that their offspring are able to complete. Re-examining this relationship in the current study adds a dimension to research on helping behaviors that has not been examined previously. More specifically, bringing parental education to the foreground makes it possible to cast research on helping behaviors in a broader life course perspective by showing that older people’s proclivity to help others may be shaped by factors that were set in motion before they were born (i.e., parental education) and then carried forward through their own level of educational attainment.

1.3 Respondent education and financial strain

In their insightful book on social status and health, Mirowsky and Ross (2003) present a compelling argument for why education is the root cause of successful aging. In this work, they succinctly argue that, “Education develops the habits and skills of communication: reading, writing, inquiring, discussing, looking things up, and figuring things out. It develops analytic skills of broad use such as mathematics, logic . . .” (Mirowsky & Ross, 2003, p. 26). When these general skills are combined with occupational-specific training, it is not surprising to find that people who have a better education are more likely to find higher paying jobs (Mirowsky & Ross, 2003). And research consistently shows that people with higher paying jobs are less likely to encounter financial problems as they grow older (Schulz, 2001). The participants in the current study were all retired when they were first interviewed. Consequently, their current economic situation was determined to a large extent by the decisions they made much earlier in life regarding their own education. Viewed from this perspective, the model that was developed for the current study provides yet another way to show how providing support to people in late life is influenced by factors that were set in place decades earlier.

1.4 Financial strain and neighborhood deterioration

Research has consistently shown that people who encounter ongoing economic difficulties are more likely to live in neighborhoods that are dilapidated and rundown than individuals who do not experience financial problems (Evans, Wethington, Coleman, Worms, & Frongillo, 2008; Krause, 1993). Moreover, this literature further reveals that once they are in rundown neighborhoods, individuals with more economic problems are less likely to escape their residential problems by moving to better neighborhoods (South, Crowder, & Chavez, 2005). This suggests that like financial strain, neighborhood deterioration may also be thought of as a type of chronic or ongoing strain.

Specifying a causal link between chronic financial strain and neighborhood deterioration makes it possible to evaluate a principle that has been discussed in the stress literature for some time. As Wheaton (1994) argues, people are not randomly exposed to stressors over the course of their lives. Instead, his research indicates that some individuals tend to experience a series of different stressors over time, thereby demonstrating a tendency toward stress proneness. Finding a statistically significant relationship between financial strain and neighborhood deterioration in the current study would provide support for this perspective. However, one limitation in the work of Wheaton (1994) arises from the fact that he does not provide a clear sense of why some people are more likely to experience a series of stressful events. An effort is made in the current study to contribute to this literature by showing that stress proneness may arise from a person’s socioeconomic standing (as reflected in low levels of respondent educational attainment) and the socioeconomic standing of his or her parents (as reflected in low levels of parental educational attainment).

1.5 Neighborhood deterioration and hostility

For decades, urban sociologists have argued that the physical condition of a neighborhood serves as a lexicon that people rely on to evaluate the quality of the built environment. For example, Schorr (1970) maintains that housing represents an extension of oneself: it makes a statement to others about the nature and character of the resident. Schorr further proposes that housing can also affect the attitudes of the occupants - when dwellings are in poor condition, residents tend to become passive, pessimistic, and cynical. It follows from this that when a neighborhood is dominated by buildings, sidewalks, and roads that are in a state of disrepair, it will exude negative cues that foster a sense of mutual pessimism, cynicism, and mistrust among local residents. Empirical support for this perspective is provided by Ross and Mirowsky (2001), who report that people who live in disadvantaged neighborhoods tend to feel powerless, threatened, and mistrustful of others (see also Krause, 1993).

Although rundown neighborhoods may foster a range of negative emotions, attitudes, and perspectives, the model that was developed for the current study focuses on one emotion that may be especially important for research on the determinants of helping behavior B feelings of hostility. Findings from several studies suggest that people who live in dilapidated neighborhoods tend to feel more angry (Schieman, Pearlin, & Meersman, 2006) and hostile (Wen, Hawkley, & Cacioppo, 2006) than individuals who live in higher quality neighborhoods. However, these findings are far from new. One of the first studies on community psychiatric epidemiology was carried out by Alexander Leighton and his colleagues (Hughes, Tremblay, Rapoport, & Leighton, 1960). They conducted detailed research in three different neighborhood environments, one of which was especially rundown. In addition to conducting quantitative research, these investigators also sent teams of anthropologists into each area to evaluate the social environment. The team reports that people who lived in the dilapidated community, “. . . find difficulties in family and community relationships and expect inconsistency of affection from their fellows. Meeting hostility from their nearest human contacts, they react in many situations with avoidance or antipathy” (Hughes et al., 1960, p. 250).

Even though there is ample evidence to support the notion that rundown neighborhoods tend to promote feelings of hostility, an additional factor comes into play that is especially relevant for studying the relationship between neighborhood conditions and hostility among older people. This factor may be found by turning to the widely-cited developmental theory that was developed by Erikson (1959). According to this perspective, the life span is divided into eight stages. Each stage presents a person with a unique developmental challenge. The final stage is characterized by the crisis of identity versus despair. This is a time of deep introspection when the individual begins to accept the kind of person he or she has become over the years. This is accomplished by reconciling what one set out to do in life with what has actually been accomplished. If this crisis is resolved successfully, older people are thought to develop a deep sense of meaning in life, but if it is not resolved successfully, they slip into despair. If older people reside in neighborhoods that are rundown and dilapidated, it is not difficult to see why they may have trouble reconciling the way their lives have turned out. And, as Erikson (1959) notes, they will likely slip into despair. It is important to point out that in the process of describing despair, Erikson (1959) comes quite close to specifically mentioning hostility: “Such despair is often hidden behind a show of disgust, a misanthropy, or chronic contemptuous displeasure with particular institutions and particular people . . .” (p. 98).

1.6 Hostility and providing social support to others

A fairly extensive number of studies have been conducted to assess the relationship between hostility and social support. However, the wide majority of these studies focus solely on support that is received from social network members (e.g., Vranceanu, Gallo, & Bogart, 2006). In contrast, there do not appear to be any studies that examine the relationship between hostility and providing support to others in samples comprising older people. Even so, it is easy to see why older people who feel hostile would be less likely to provide assistance to their family members and friends. Evidence of this may be found by exploring the basic nature and characteristics of hostility. In their extensive review of the literature, Smith and his colleagues argue that hostility involves “. . . a devaluation of the worth and motives of others, an expectation that others are likely sources of wrong-doing, a relational view of being in opposition toward others, and a desire to inflict harm or see others harmed” (Smith, Glazer, Ruiz, & Gallo, 2004, p. 1218). If older people view other individuals in this way, then it would be highly unlikely that they would provide support to significant others, even if they were requested to do so.

2. Methods

2.1 Sample

The data for this study come from a nationwide longitudinal survey of older adults in the United States. Altogether, six waves of interviews have been conducted. The population for this study was defined as all household residents who were noninstitutionalized, English-speaking, 65 years of age or older, and retired (i.e., not working for pay). In addition, residents of Alaska and Hawaii were excluded from the study population.

The sampling frame consisted of all eligible persons contained in the beneficiary list maintained by the Centers for Medicare and Medicaid Services (CMS). Study participants were selected at random from the CMS files. All interviews were conducted face-to-face in the homes of the study participants by interviewers from Harris Interactive (New York). The first three waves of data were collected between 1992 and 1999. A total of 1,103 interviews were completed at the baseline in 1992–1993. The response rate was 69.1%. Following this, 605 of the Wave 1 study participants were re-interviewed in 1996–1997. Then, a third wave of interviews was conducted in 1998–1999. A total of 530 older people who participated in earlier rounds of interviews were successively re-interviewed at Wave 3.

In 2002–2003, a fourth wave of interviews was conducted. However, the sampling strategy for the Wave 4 survey was complex. Two groups of older people were interviewed at this time. All survivors from Waves 1–3 were interviewed first (N = 269). This group was then supplemented with a sample of new study participants who had not been interviewed previously. This supplementary sample was also selected at random from the CMS files. However, in this case, an effort was made to select the sample so that there would be approximately equal numbers of older people in the following age groups: Young-old (ages 65–74, N = 491); old-old (ages 75–84; N = 515); and the oldest-old (ages 85 and older; N = 509). Altogether, the Wave 4 sample consisted of 1,518 older adults. The overall response rate for the Wave 4 survey was 54%.

A fifth wave of interviews was completed in 2005. A total of 1,166 of the Wave 4 study participants were successfully re-interviewed. Not counting those who had moved to a nursing home or had died, the re-interview rate for the Wave 5 survey was 83.9%.

Wave 6 was completed in 2007. A total of 1,011 older people were re-interviewed at this time. Not counting older adults who had moved to a nursing home or older people who died, the re-interview rate for Wave 6 was 76.9% of the older people who participated at Wave 4.

The data that are used in the current study come from the Wave 6 survey because this was the first time that questions on hostility were administered. The analyses provided below are based on the portion of the sample who completed the Wave 6 survey (N = 1,011). Full information maximum likelihood estimation (FIML) was used for any incomplete data among this subset of cases. Simulation studies suggest that the FIML procedure is preferable to listwise deletion of missing values (Enders, 2006). Moreover, this literature reveals FIML estimates that are virtually identical to estimates provided by more time-consuming multiple imputation procedures (Graham, Olchowski, & Gilreath, 2007). Preliminary analysis of the imputed Wave 6 data suggests that the average age of the study participants was 77.2 years (SD = 6.3 years; range: 69 – 101 years), 46% were older men, and their average level of educational attainment was 12.5 years (SD = 3.3 years). These estimates, as well as the data that are provided below, have been weighted.

2.2 Measures

Table 1 contains the measures that were used to assess the core constructs that are depicted in Figure 1. The procedures that are used to code these indicators are provided in the footnotes of this table.

Table 1
Core Study Measures

2.21 Parental education

The measure of parental education was taken from the Midlife in the U.S. study (MIDUS) (Brim, Ryff, & Kessler, 2004). Respondents were asked to report the highest grade of school or year of college that was completed by their fathers and (separately) their mothers. If data on the educational attainment of fathers was present, then it was used as the measure of parental education. However, if data on the father’s level of educational attainment was not available, then the mother’s level of educational attainment was used in its place. In either case, the level of educational attainment was coded into 12 ordinal categories. A high score denotes more years of schooling.

2.22 Respondent education

Two items were used to measure a respondent’s level of educational attainment. First, study participants were asked to report the highest grade or level of schooling they ever attended. Then, following this, they were asked if they finished that grade and received credit for it. A single indicator was developed from this information to represent the total number of years of schooling that an older person completed successfully.

2.23 Chronic financial strain

Ongoing economic difficulties were evaluated with three items that were taken from the work of Pearlin and his colleagues (Pearlin, Menaghan, Lieberman, & Mullan, 1981). As shown in Table 1, these indicators assess difficulties paying monthly bills, whether study participants have money left over at the end of the month, and how they would rate their overall financial situation. A high score represents more financial difficulty.

2.24 Neighborhood deterioration

The physical environment of the neighborhood was evaluated with five items that were taken from the work of Krause (1993). These indicators assess the overall conditions of buildings and sidewalks in the neighborhood as well as the amount of noise, the quality of the air, and the condition of the streets. A high score on these measures stands for more dilapidated residential conditions. It should be emphasized that the neighborhood ratings were made by the interviewers and not by the study participants. This is important because research reveals that self-reported neighborhood ratings may be biased by the psychological state of study participants (Winkel, Saegert, & Evans, 2009). This raises the possibility that older people who experience more hostility may rate their neighborhood less favorably than older adults who are not as hostile.

2.25 Hostility

Hostility was measured with a shortened form of the widely used Cook-Medley Hostility Scale (Cook & Medley, 1954). Using Rasch modeling procedures, Strong and his colleagues identified a 17-item version of the original 50-item scale (Strong, Kahler, Greene, & Schinka, 2005). Nine items from this 17-item version were administered in the current study. However, when using latent variable modeling procedures, it is not necessary to have nine observed indicators to measure a single latent construct. Instead, as Kline (2005) and others point out, three to four items will typically suffice. An exploratory factor analysis (not shown here) was used to select four indicators from the nine available items. The correlation between the four items that were used in the analyses presented below and the nine available indicators is .95 (p < .001). A high score on these measures denotes greater hostility.

2.26 Emotional support provided to others

Four indicators were also used to assess how often older study participants provided emotional support to the people they know during the year prior to the interview. These indicators were taken from the work of Krause (2004). A high score on these items identifies study participants who provide emotional support to significant others more often.

2.27 Demographic control variables

As noted earlier, the relationships among the constructs in Figure 1 were evaluated after the effects of age and sex were controlled statistically. Age is measured continuously in years and sex is coded in a binary format (1 = men; 0 = women).

3. Results

The model that is depicted in Figure 1 was estimated with Version 8.80 of the LISREL statistical software program (du Toit & du Toit, 2001). Recall that FIML estimation procedures were used in the analysis to deal with item non-response. The LISREL software program only provides two goodness-of-fit indices when the FIML estimator is used. The first is the Full Information Maximum Likelihood Chi-Square value. The chi-square value that was derived in the present study is 480.98 (with 146 degrees of freedom; p < .001). The second goodness-of-fit index is the root mean square error of approximation (RMSEA). This absolute fit measure has a built in correction for model parsimony. Kelloway (1998) reports that RMSEA estimates of .05 or less indicate an excellent fit of the model to the data. The RMSEA estimate for the model in Figure 1 is .05.

The findings that were derived from estimating the study model are provided below in two sections. The psychometric properties of the multiple-item study measures are presented in the first section. Following this, the substantive relationships among the latent constructs are reviewed in section two.

3.1 Psychometric properties of the study measures

Table 2 contains the factor loadings and measurement error terms that were derived from estimating the model presented in Figure 1. These coefficients are important because they provide preliminary information about the reliability of the multiple-item measures. Kline (2005) recommends that observed indicators with standardized factor loadings in excess of .60 tend to have good reliability. As the data in Table 2 indicate, the standardized factor loadings range from .67 to .94. This suggests that the measures used in the current study have good psychometric properties.

Table 2
Measurement Model Parameter Estimates for Core Study Measures (N = 1,011)

Although the factor loadings and measurement error terms associated with the observed indicators provide useful information about the reliability of each item, it would be helpful to know something about the reliability of the scales as a whole. Fortunately, it is possible to compute these reliability estimates with a formula provided by DeShon (1998). This procedure is based on the factor loadings and measurement error terms in Table 2. Applying the procedures described by DeShon (1998) to these data yields the following reliability estimates for the multiple-item constructs: financial strain (.80), neighborhood deterioration (.94), hostility (.86), and providing emotional support to others (.86). Taken as a whole, these estimates suggest that the items used in the current study have an acceptable level of reliability.

3.2 Substantive study findings

Estimates of the substantive relationships among the study measures are provided in Table 3. Taken as a whole, these data provide support for the theoretical rationale that was developed for this study. Specifically, the findings indicate that older adults whose parents had fewer years of schooling tend to have lower levels of educational attainment, themselves (Beta = .39; p < .001). The results further reveal that older people with fewer years of education tend to experience more financial difficulty in late life than older individuals with higher levels of educational attainment (Beta = −.33; p < .001). The data also suggest that older adults who encounter more economic problems are, in turn, more likely to reside in rundown neighborhoods (Beta = .37; p < .001). Moreover, the findings in Table 3 indicate that older people who reside in deteriorated neighborhoods have higher levels of hostility than older adults who reside in more favorable living environments (Beta = .20; p < .001). Finally, the data reveal that older people who experience greater hostility are less likely to provide emotional support to their social network members than older adults who do not feel as hostile (Beta = −.27; p < .001).

Table 3
Socioeconomic Correlates of Helping Behaviors (N = 1,011)

One of the advantages in working with latent variable models arises from the fact that in addition to estimating the direct effects that are presented in Table 3, this data analytic procedure also provides estimates of the indirect and total effects that operate through a study model. These additional estimates add greater depth to study findings and provide another way to assess the overall utility of a conceptual model. Table 4 contains a decomposition of effects for the core constructs in Figure 1. Two sets of findings from these analyses are discussed briefly below.

Table 4
Decomposition of Effects (N = 1,011)

Initially, the data presented in Table 3 may create the impression that parental education is not significantly associated with the amount of hostility that people experience in late life (Beta = −.01; n.s.). However, when the indirect effects of parental education on hostility are taken into account (Beta = −.09; p < .001; see Table 4), the resulting total effect (Beta = −.11; p < .001) reveals that the level of educational attainment achieved by parents influences the amount of hostility that their offspring experience decades later. Moreover, the fact that 82% of this total effect operates through the study model (−.09/−.11 = .82) suggests that the conceptual model that was developed for the current study provides significant insight into the way the effects of parental education on hostility are transmitted. More specifically, these data suggest that the genesis of hostility in late life may be attributed, in part, to socioeconomic factors that operate across the life course (i.e., respondent education, financial strain, and residence in rundown neighborhoods).

The decomposition of effects also provides additional insight into the ways in which financial difficulty may influence the extent to which older people are willing to help others. Taken at face value, the data in Table 3 appear to suggest that ongoing economic problems are not associated with the amount of emotional support that older adults provide to their social network members (Beta = −.05; n.s.). However, a different picture emerges when the data in Table 4 are taken into account. These findings indicate that when the indirect effects that operate through the model (Beta = −.06; p < .01) are added to the direct effects, the resulting total effect (Beta = −.11; p < .01) reveals that older people who experience greater financial difficulty are indeed less likely to help others. Moreover, 55% of this effect (−.06/−.11 = .55) is explained by deteriorated neighborhoods and the feelings of hostility that arise from living in them.

4. Discussion

One of the central principles of the life course paradigm specifies that people live linked lives (Elder, 1995). The findings from the current study provide a specific example of how this core tenet operates. The data reveal that the level of education that older people have been able to attain is determined to a significant extent by the level of education that their parents were able to achieve. But more than this, the findings show that this fundamental intergenerational link sets in motion a cascade of events that shape the way study participants relate to their social network members in late life. More specifically, the results indicate that people who are not well educated tend to encounter greater financial difficulty as they grow older and these economic problems force them to live in dilapidated neighborhoods. This is noteworthy because deteriorated living conditions, in turn, create a cauldron of negative emotions that culminate in deep seated feelings of hostility toward others. And older adults who view others with contempt and harbor feelings of ill will toward them are less likely to engage in the very behaviors that may improve their own lives (i.e., providing assistance to others). The fact that educational decisions that were made by parents can leave a visible imprint on the way their offspring relate to people over half a century later is remarkable. But more than this, the fact that this parental influence is transmitted primarily through wider socioeconomic factors underscores the pervasive way in which social structural forces ripple across the life course.

Although these findings may be illuminating, there are still gaps in the conceptual model that was developed for this study. For example, the precise ways in which rundown neighborhoods generate feelings of hostility were not evaluated empirically. Two possible explanations should be evaluated in the future. First, perhaps older people who live in deteriorated living environments are faced with inescapable proof that their lives have not turned out the way they planned and, as Erikson (1959) suggests, these feelings of despair become directed toward others through feelings of resentment and hostility. Second, the feelings of hostility that older adults who reside in deteriorated neighborhoods experience may arise in reaction to the hostility that is directed toward them by their neighbors (Hughes et al., 1960).

Another important issue to examine in the future involves developing more elaborate and complex specifications of the neighborhood environment. This can be accomplished in at least three ways. First, the current study focuses solely on the way in which life in a rundown neighborhood influences social relationships. However, as research by Scharf, Phillipson, and Smith (2005) indicates, people who reside in deteriorated neighborhoods also experience exclusion from material resources, civic activities, and basic community services. Second, researchers should also take into account aspects of the built environment that are especially relevant for older people, such as the extent to which homes are accessible (Oswald et al., 2007). Third, only the negative aspects of residing in deteriorated neighborhoods were assessed in the current study. However, as a number of studies reveal, there are positive aspects that are associated with living in rundown neighborhoods, as well. Many older people have lived in the same dwelling for a long time and as a result, they have strong feelings of belonging in their neighborhood (Phillipson, 2007), they have a strong sense of attachment to their homes (Rollero & Piccoli, 2010), and they derive a deep sense of identity with the neighborhood in which they reside (Phillipson, 2007).

In the process of expanding the scope of neighborhood assessments, researchers should also focus on the ways in which older people cope with the effects of the environmental conditions that confront them. Many older people do not suffer ill effects from residing in rundown neighborhoods because they are able to confront the challenges that face them. For example, research by Krause (1998) suggests that some older people are able to cope successfully with living in deteriorated neighborhoods by relying on religion. In order to more fully understand the influence of neighborhood conditions, these as well as other sources of resilience should be taken into account.

In the process of pursuing these issues, researchers would be well advised to pay attention to the limitations in the current study. Three shortcomings are discussed briefly below. First, the data that were analyzed in this study were obtained at a single point in time. As a result, decisions about the direction of causality were based on theoretical considerations alone. However, it is possible to reverse some of the causal specifications by arguing, for example, that people who are hostile in late life may have experienced similar feelings when they were adolescents, and as a result, feelings of hostility during adolescence may have influenced the amount of education they were able to obtain. Clearly this, as well as other causal assumptions, should be evaluated with data that are obtained at different points in the life course. Second, it would have been helpful to more fully delineate neighborhood conditions by including measures of population density as well as residence in multi-family buildings in the study model. Unfortunately, measures of these constructs were not available in the data that were used in the current study. Third, research indicates that there may be differences in the nature and functioning of social support networks among people from different cultural and ethnic backgrounds (Kaniasty & Norris, 2000). Measures that capture cultural variations in support were not available in the current study. However, exploring cultural differences in neighborhood support should be a high priority in the future.

Vaillant (2002) presents impressive findings from his research on the factors that promote successful aging. This work is noteworthy because it is based upon data that were gathered from the same respondents across a substantial portion of the life course. In summarizing the key findings, Vaillant places a special emphasis on the role that is played by the ability to care for others. However, as the findings from the current study suggest, social structural factors may inhibit the ability of some older people to engage in these life-enhancing behaviors. The challenge that awaits investigators who wish to work on this issue involves finding ways to enhance the ability of disadvantaged elders to become active support providers as well as support recipients.

Research Highlights

  1. The level of parental education influences whether older people will reside in rundown neighborhoods
  2. Living in rundown neighborhoods tends to make older people more hostile
  3. Older people who are more hostile are less likely to provide support to family members and friends


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