Journal of Aging Research and Healthcare
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Research Article | Open Access
  • Available online freely | Peer Reviewed
  • Social Capital and Health Outcomes of Elderly People

    Bosu Seo 1      

    1University of the Fraser Valley, British Columbia, Canada

    Abstract

    Greater social capital has been shown to be associated with improved mental health, general wellbeing and reduced risk of premature mortality, cancer mortality and cardiovascular mortality. However, most of these studies found a positive relationship between social capital and health are limited to descriptive studies. This project is performing a theoretical approach to the role of social capital in producing health outcome based on Becker’s household production function.

    We are testing whether social capital has a positive impact on health both directly through a more effective production of health and indirectly through utilizing the health care system better, using several measurements of social capital from ‘social support’ module in the National Health and Nutrition Examination Survey (NHANES) 2007-2008 for a sample of those 60 years old and above. NHANES is a unique data set in terms of collecting both subjective self-rated health status and several objective health outcome measurement through medical and laboratory examination.

    Finding from 2SLS with instrumental variable was a bit surprising – various social capital measures do not show significant results in different experiments. The only exception is that more resources of emotional support can promote better overall health status.

    Received 01 Dec 2018; Accepted 19 Jan 2018; Published 12 Feb 2018;

    Academic Editor:Pengcheng Han, Dignity Health St Joseph Hospital and Medical Center, USA

    Checked for plagiarism: Yes

    Review by: Single-blind

    Copyright©  2018 Bosu Seo, et al.

    License
    Creative Commons License    This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

    Competing interests

    The authors have declared that no competing interests exist.

    Citation:

    Bosu Seo (2018) Social Capital and Health Outcomes of Elderly People. Journal of Aging Research And Healthcare - 2(3):1-16.
    Download as RIS, BibTeX, Text (Include abstract )
    DOI10.14302/issn.2474-7785.jarh-17-1886

    Introduction

    “Why treat people’s illness without changing what makes them sick in the first place?” World Health Organization poses this question, suggesting that without modifying the social determinants of health, health care and medicine may be useless 1. Social determinants of health examines why, in countries all over the world, there is a social gradient when it comes to health status and outcomes. Individuals higher up in the social hierarchy consistently have better health outcomes than those lower down.

    Social capital was explored as a social determinant of health in the public health domain and has become a popular topic in the past decade particularly with the publication of Putnam’s Bowling Alone (2000). Robert Putnam defined social capital as social networks and the associated norms of reciprocity, which is inhering both in the individual and the collective 2, 3. Ichiro Kawachi attempted to normalized definitions and methodologies of measurement of social capital in the health field 4, 5. In Kawachi’s definition, social capital can be examined as a group or community-level characteristic, called “social cohesion”, or as an individual characteristic using network theory. He also separated bonding and bridging social capital. Bonding social capital refers to the social connectedness within a group whose members are alike in some ways (i.e. race or ethnicity, class, language) and bridging social capital is social connectedness that crosses groups or other boundaries of social attribute 6.

    A growing body of literature has analyzed the concept of social capital and its impact on health outcomes and has attracted the attention of both the academic and the policy communities. For example, greater social capital has been shown to be associated with better levels of general health and (subjective) well-being, lower cardiovascular and cancer mortality, and lower suicide rates 7, 8, 9, 10, 11, 12, 13.

    In this paper, we will explore social capital from the perspective of an individual resource and social connectedness, which refers to the relationships people have with others a. People enjoy constructive relationships with others in their families, communities, churches, and workplaces. Families support and nurture those in need of care. Social connectedness is integral to wellbeing. People are defined by their social roles, whether as partners, parents, children, friends, caregivers, teammates, staff or employers, or a pile of other roles. Relationships give people support, happiness, contentment and a sense they belong and have a role to play in society 14. They also mean people have support networks in place that they can call on for help during times of illness or poor health.

    Most of the recent studies found a positive relationship between social capital and health in general, but they are limited to descriptive studies. The focus in this paper is on a theoretical approach to the role of social capital in producing health based on Becker’s household production function. This study is testing whether social capital has a positive impact on health status both directly through a more effective production of health and indirectly through utilizing the health care system better, using several measurements of social capital from the National Health and Nutrition Examination Survey (NHANES) 2007-2008 for a sample of those 60 years old and above b.

    A main reason to consider social capital in light of social networks/ connectedness of elderly people is that networks might enhance positive outcomes for seniors. Previous research reflects strong themes about the importance of family members and friends in the lives of older adults. Social ties have been linked to beneficial health and social outcomes, to the maintenance of independence in later life, and to responsive care for seniors with chronic long-term health problems 15, 16, 17. It is also timely to examine the relationship between social capital and better heath in elderly people with the advent of the baby-boom generation’s aging and retirement. However, there has been little research on the impact of social connectedness in older adults, except Keating et al. 18.

    In the literature, studies utilize subjective self-rated health status to explore the relationship between social capital and health. However, NHANES 2007-2008 allows us to use several objective measures, including medical and laboratory examination results as well as self-rated health status. These objective measures will allow us to conduct a more rigorous study about the impact of social capital on health outcomes.

    Theoretical Background

    The proximate determinants of an individual’s health usually are decisions made by the individual or by the household in which people live- given assets, prices, and community endowments. Therefore, a natural starting point is the determination of individual health at the household level. Similar to Behrman and Deolalikar (1988), this project is based on the standard household model with constrained maximization of a joint utility function 19, 20. It is assumed that the household behaves as if it maximizes a utility function, which is a function of the goods and services consumed, health status of household members, and leisure c.

    A household behaves as if maximizing a utility function:

    (1)

    where

    is the health of household member i ,

    is the consumption of household member i ,

    is the leisure of household member i , and

    is the number of individuals in the household.

    (All of these variables and others defined below may be vectors with multiple dimensions.)

    Health is a household-produced commodity. The health of the given i th individual is produced by a number of choices relating to the commodities consumed, health inputs, which do not affect utility except through health (e.g. health insurance), and the individual and household endowments:

    (2)

    where

    is the health outcome of the i th individual,

    is the consumption of the i th individual that affects health,

    is the observable characteristics including socio-demographic variables of the i th individual

    is the unobservable attributes, such as genetic endowment of the i th individual,

    is the characteristics of household, and

    is the social capital of i th individual .

    To analyze the basic correlation between social capital and health, we estimate the following regression:

    (3)

    where H*i is the individual's actual health, x is a vector of explanatory variables, S is a vector of social capital, β' is the vector of coefficients, and is the error term. Explanatory variables include socio-demographic variables and genetic endowment variables (X), and social capital (S). Detailed variable lists are found in the appendix.

    Data

    The National Center for Health Statistics (NCHS), part of the Centers for Disease Control and Prevention (CDC), has collected nationally representative health and nutrition surveys since the early 1960’s. In each survey a nationally representative sample of the US civilian non-institutionalized population was selected using a complex, stratified, multistage probability cluster sampling design. Primary sampling units (PSU) are generally single counties, although small counties are combined to meet a minimum population size. Clusters of households are selected, the households are screened for demographic characteristics, a sample of households is selected, and one or more persons per household are selected d.

    Survey workers collected demographic data and information on general health, use of health services, and housing characteristics in an interview in the home. Nearly three-quarters of the participants also received a four-hour medical examination at a mobile Medical Exam Center (MEC). The MECs, including 12 physicians and other persons involved with the examinations, moved from city to city, preserving consistency in the medical exam. In addition to the MEC examinations, a small number of survey participants receive an abbreviated health examination in their homes because they are not able to come to the MEC. The survey included many tools to induce those selected for the study to participate, especially those selected for the medical exam portion of the survey.

    For NHANES 2007-2008, 10,149 persons were interviewed and 9,762 were examined in the MEC. Data were collected between January 2007 and December 2008. The data and corresponding documents for the survey interview and examination components are available from the CDC website.

    Methods and Variables

    NHANES household, interview, and examination data files were merged using the unique sequence number given to each participant. Samples were weighted using the procedure recommended in the NHANES documentation. In this study, a sample of those 60 years old and above will be analyzed. The total sample size is 1,684 and 815 of them are males and 869 are females. Table 1 shows the definition of variables used in this study.

    Table 1. Definition of variables used in the analyses
    VARIABLES DEFINITIONS Mean (or percentage in decimal)
    Dependent Variables    
    1) Overall =1 if overall health status is excellent, very good, or good; else=0 (fair or poor) 0.700
    2) Phyhealth =1 if numbers of physical health was not good during the past 30 days >0; else =0 0.363
    3) Menhealth =1 if numbers of mental health was not good during the past 30 days >0; else =0 0.249
    4) Biorisk summary index of biological risk (inflammation, metabolic, and cardiovascular factors) 1.927
    Independent Variables    
    Age Age at Screening 72.774
    Male =1 if survey participant (SP) is male 0.476
    Race    
    White (reference variable) =1 if SP is Non-Hispanic White 0.623
    Black =1 if SP is Non-Hispanic Black 0.168
    Mexican =1 if SP is Mexcican American 0.155
    Other =1 if SP is Other Hispanic American, Asian, or Multirace 0.053
    Education    
    LSHS (reference variable) =1 if level of educationis less than high school 0.394
    HS =1 if level of education is high school,inlcuding GED 0.235
    MTHS =1 if level of education is more than high school 0.363
    Country of Birth    
    USborn (reference variable) =1 if country of birth is US 0.858
    Mexicobn =1 if country of birth is Mexico 0.067
    Otherbn =1 if country of birth is somewhere else 0.073
    Married =1 if marital status is either married or lived with partners 0.563
    HHINC Annual Household Income (Recode) 5.720
    Famhis =1 if either blood relatives have disbetes or blood relatives have Alzheimer's 0.495
    Social capital measures    
    Ssnum Numers of sources that give emotional support 1.947
    Emoss =1 if anyone to help with emotional support 0.941
    Finss =1 if anyone to help with financial support 0.826
    Anyss =1 if either Emoss=1 or Finss=1 0.960
    Numfriends Number of close friends 7.103
    Instrumental variable    
    Longres =1 if years of residence at the current address >2 years 0.867

    Source: NHANES 2007-2008, age 60 and above

    Dependent Variable

    We use several health outcome measures as dependent variables. The first measure is peoples’ self-rated health status. Measures of self-rated health are based on individual and robust predictors that have gained in popularity to forecast individual health outcomes, even in persons without prior health problems. Previous research has shown that self-rated health status has predicted such important patient outcomes as mortality and health system utilization 21, 22, 23.

    In the NHANES data, people were asked: “How is your health in general? Would you say it is excellent, very good, good, fair, or poor?” We converted the original 5-point scale to a dichotomous variable, with the value 1 representing excellent, very good, or good health, and the value 0 representing fair and poor health. A probit model is used for the empirical analysis.

    Second, the current health status section (variable name prefix HSQ) of the NHANES questionnaire provides personal interview data on recent illness for the past 30 days, blood donations, and AIDS testing. We chose select recent illness measures, which indicated the number of days that a person’s health condition was not good during the past 30 days. It was collected based on physical and mental health separately.

    Third, data based on nine biomarkers were used to create an overall summary index of biological risk, to reflect the cumulative effect of physiological problems across multiple systems. We used three subscales based on subsets of biomarkers reflecting inflammatory, metabolic and cardiovascular parameters. The inflammation subscale included C-reactive protein (mg/dL) and albumin (g/dL). The metabolic subscale included glycated hemoglobin (%), total cholesterol (mg/dL), HDL cholesterol (mg/dL), and Body mass index (kg/m2). The cardiovascular subscale included systolic blood pressure (mm Hg), diastolic blood pressure (mm Hg), and heart rate (bt/min). For each of the variables, a dichotomous indicator was created, reflecting those with “high risk” values (assigned a score of “1”) and “lower risk” values (assigned a score of “0”). Values assigning high and low risk were based on clinically accepted “high risk” criteria. The summary, multi-system score was created by summing the subscale scores.

    Table 2. Clinically-defined “high risk” criteria for biologic risk factors
    Indicators High-risk cutoff point
    Inflammation  
    Albumin < 3.8 g/dL
    C-reactive protein ≥ 0.3 mg/dL
    Metabolic  
    Body mass index ≥ 30.0 kg/m2
    Total cholesterol ≥ 240 mg/dL
    HDL cholesterol < 40 mg/dL
    Glycated Hemoglobin ≥ 6.4 %
    Cardiovascular  
    Heart Rate ≥ 90 bt/min
    Systolic Blood pressure ≥ 140 mm Hg
    Diastolic Blood pressure ≥ 90 mm Hg

    Independent variables

    A key independent variable is the social capital measure. NHANES 2007-2008 includes ‘social support’ module. Table 3 shows the questionnaire lists for the ‘social support’ module collected in the NHANES 2007-2008 e. Measures of social capital are number of emotional support sources, emotional/financial support from any source, and number of close friends f, g.

    Other independent variables include socio-demographic variables and genetic endowment variables. First, a number of socio-demographic variables were controlled in the equation. The variables to be included are: Gender, Age, Race/Ethnicity, Country of Birth (Foreign born or not), Education, Annual Household Income, and Marital status. Second, we also include a few genetic endowment variables, such as family disease history. These variables are h:

    Blood relatives have diabetes

    Blood relatives have Alzheimer’s

    Table 3. Social support questionnaire variable list
    Item # Data File Component Questionnaire
    980 SSQ-B Social support Anyone to help with emotional support
    981 SSQ-B Social support Spouse gives most emotional support
    982 SSQ-B Social support Daughter gives most emotional support
    983 SSQ-B Social support Son gives most emotional support
    984 SSQ-B Social support Sibling gives most emotional support
    985 SSQ-B Social support Parent gives most emotional support
    986 SSQ-B Social support Other relative gives most emotional support
    987 SSQ-B Social support Neighbors give most emotional support
    988 SSQ-B Social support Co-workers give most emotional support
    989 SSQ-B Social support Chorch members give most emotional support
    990 SSQ-B Social support Club members give most emotional support
    991 SSQ-B Social support Professional give most emotional support
    992 SSQ-B Social support Friends give most emotional support
    993 SSQ-B Social support Others give most emotional support
    994 SSQ-B Social support No one gives most emotional support
    995 SSQ-B Social support Needed more support last year
    996 SSQ-B Social support How much more support needed
    997 SSQ-B Social support Anyone to help with financial support
    998 SSQ-B Social support Number of close friend

    Source: NHANES 2007-2008

    Results

    Descriptive Statistics

    The descriptive statistics of all variables are presented in Table 4. The columns of Table 4 break out a sample of those 60 years old and above into three groups: total group, males only, and females only. The total sample size is 1,684 and 815 of them are males and 869 are females.

    Table 4. Descriptive Statistics: NHANES 2007-2008
    Variables Total group Males Females
      N Mean or % Stdev N Mean or % Stdev N Mean or % Stdev
    Dependent Variables                  
    Overall Health 1684     815     869    
    Excellent   9.5%     10.6%     8.5%  
    Very Good   24.3%     24.4%     24.2%  
    Good   36.2%     35.8%     36.6%  
    Fair   23.8%     22.9%     24.6%  
    Poor   6.1%     6.1%     6.0%  
    Don't know   0.1%     0.1%     0.1%  
    # of Days not good                  
    Physical health 1683 5.95 12.25 815 5.26 11.79 868 6.61 12.63
    Mental health 1682 3.44 10.10 815 2.44 7.49 867 4.38 11.98
    # of Inactive Days 1681 2.53 9.04 815 2.25 8.04 866 2.80 9.89
    Independent Variables                  
     Age 1872 71.06 1138.00 891 70.44 1033.99 981 71.52 1220.00
    HH Annual Income (coded)a) 1643 6.63   804 7.13   839 6.24  
    Married 1868 54.0%   889 72.4%   979 37.2%  
    Race/Ethnicty                  
    Non-Hispanic White 1164 62.2%   557 62.5%   607 61.9%  
    Non-Hispanic Black 310 16.6%   147 16.5%   163 16.6%  
    Mexican American 291 15.5%   137 15.4%   154 15.7%  
    Other Hispanic 61 3.3%   27 3.0%   34 3.5%  
    Other Race - Including Multirace 46 2.5%   23 2.6%   23 2.3%  
    Education                  
    LT HS 738 39.5%   362 40.7%   376 38.4%  
    HS Grad (Including GED) 439 23.5%   175 19.7%   264 27.0%  
    MT HS 679 36.4%   349 39.3%   330 33.7%  
    Refused 4 0.2%   1 0.1%   3 0.3%  
    Don't know 8 0.4%   2 0.22   6 0.61  
    Emotional Support                  
    Anyone helps 1727 92.5%   813 91.5%   914 93.5%  
    Spouse 817 43.6%   550 61.7%   267 27.2%  
    Daughter 801 42.8%   300 33.7%   501 51.1%  
    Son 620 33.1%   260 29.2%   360 36.7%  
    Sibling 286 15.3%   104 11.7%   182 18.6%  
    Parent 27 1.4%   14 1.6%   13 1.3%  
    Relatives 224 12.0%   78 8.8%   146 14.9%  
    Neighbor 74 4.0%   25 2.8%   49 5.0%  
    Co-worker 21 1.1%   10 1.1%   11 1.1%  
    Church 149 8.0%   62 7.0%   87 8.9%  
    Club member 9 0.5%   6 0.7%   3 0.3%  
    Professional 30 1.6%   9 1.0%   21 2.1%  
    Friends 454 24.3%   172 19.3%   282 28.7%  
    Others 56 3.0%   21 2.4%   35 3.6%  
    Needed more emotional support 233 13.5%   87 10.7%   146 16.0%  
    How much more                  
    A lot 50 21.5%   19 21.8%   31 21.2%  
    Some 93 39.9%   34 39.1%   59 40.4%  
    A little 90 38.6%   34 39.1%   56 38.4%  
    Financial support 1872 79.2%   891 75.7%   981 82.4%  
    How many close friends 1840 7.94 1122.27 878 8.32 1214.42 962 7.64 1029.12

    Source: NHANES 2007-2008
    Sample: Adults who are 60 years old and above a) Codes are following:
    1 - $0 to $4,999; 2 - $5,000 to $9,999
    3 - $10,000 to $14,999; 4 - $15,000 to $19,999
    5 - $20,000 to $24,999; 6 - $25,000 to $34,999
    7 - $35,000 to $44,999; 8 - $45,000 to $54,999
    9 - $55,000 to $54,999; 10 - $65,000 to $74,999
    11 - $75,000 and over

    Self-rated health status shows similar patterns between males and females. About 70% of study participants evaluate themselves as either in excellent, very good, or good health (70.8% for males and 69.3 % for females).

    However, other health outcome measurements have a different distribution between males and females. Males usually show better health outcome than females. Males have 5.26 days of physical health that was not good during the past 30 days and 2.44 days of mental health that was not good during the past 30 days, while females have 6.61 days for poor physical health and 4.38 days for poor mental health. Males have less days of inactive days due to physical/mental health during the past 30 days (2.25 for males and 2.80 for females). Less than 5% of men had stomach or intestinal illness during the past 30 days, but almost twice as many women experienced it (7.6%). Regarding flu, pneumonia, or ear infection, in contrast to 4.1% of women, only 3.2% of males experienced these ailments during the past 30 days.

    Females are generally older than males by one year, have lower household annual income, and fewer females completed education surpassing high school (33.7% for females and 39.3% for males). One interesting finding from the socio-demographic variables is marital status. Only 37.2% of females are married while 72.4% of males are married and this is mainly due to the fact that the sample is adults 60 years old and above: women live longer than men and some females stay widowed once they lose their spouse. Also, a lower rate of second marriage for females may explain the gap.

    The race/ethnicity variable is derived by combining responses to questions on race and Hispanic origin. Sixty two percent of total group are Non-Hispanic White, 16.6% Non-Hispanic Black, 15.5% Mexican American, 3.3% Other Hispanic, and 2.5% of them are other race, including multi-race. This distribution still applies when the total sample is divided into males only and females only.

    Regarding social capital related factors, both males and females express similar responses. First, 91.5% of males and 93.5% of females have someone to help with emotional support in the last 12 months. Common resources of emotional support are spouse, children, and friends. More women needed more emotional support than males (16.0% for females and 10.7% of males) and around 60% of both males and females needed either a lot or some more emotional support (60.9% for males and 61.6% for females). Women also received more financial support in the past year than men (75.7% for males and 82.4% for females). Males have more close friends than females (8.32 for males and 7.64 for females).

    Endogeneity Issues

    Social capital measurements should be treated as endogenous variables in the analysis, since a person’s health is likely to affect their social interaction. An estimation approach that does not explicitly address the simultaneous process will bias the estimated relationship between health outcome and the explanatory variables.

    The standard econometric procedure for handling endogeneity is some type of instrumental variables (IV) estimator, which is often employed in cross-sectional studies. Mostly two-stage least squares (TSLS) is employed, assuming an appropriate instrument is available. Instrumental variables should be theoretically correlated with the endogenous explanatory variables but not correlated with the error terms.

    One potential instrumental variable available in NHANES is the number of years the person has lived at their current address i. The longer the person has lived at their current address, the more likely they build social capital. However, this variable reflects past choices by the individual at certain point of time, so it is not correlated with the error term.

    Conclusion and Discussion

    We have obtained MLE estimates of the coefficients for the probit regressions with instrumental variables predicting overall health status, physical health and mental health during the past 30 days separately j. The last two dependent variables can imply recent illness. 2SLS was utilized to obtain the estimate of the coefficients predicting index of biological risk factors. Regression results of the health demand equation are presented in Table 5-1 to Table 5-4.

    Table 5-1. Health demand equation (Dependent variable: Overall Health Status)
      Equation 1 Equation 2 Equation 3 Equation 4 Equation 5
      ( Obs =1,474) ( Obs =1,456) ( Obs =1,419) ( Obs =1,463) ( Obs =1,462)
      Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z|
    age -0.011 0.097* -0.006 0.857 -0.027 0.207 -0.027 0.858 -0.023 0.929
    male 0.221 0.215 -1.023 0.721 0.359 0.459 2.235 0.885 -1.051 0.939
    black -0.417 0.003*** -0.474 0.547 -0.619 0.058* -0.223 0.897 -16.247 0.941
    mexican -0.495 0.004*** -1.128 0.616 -0.381 0.076* 1.841 0.904 -9.539 0.939
    otherrace -0.545 0.033** 0.825 0.824 -0.418 0.196 -2.722 0.865 -7.905 0.938
    mexicoborn -0.759 0.001*** -4.194 0.679 -0.558 0.070* 5.513 0.895 -6.256 0.934
    otherborn 0.036 0.873 0.005 0.996 0.116 0.669 0.102 0.965 -7.461 0.943
    hs (dropped)a) (dropped) (dropped) (dropped) (dropped)
    mths (dropped) (dropped) (dropped) (dropped) (dropped)
    married -0.439 0.038** 1.157 0.751 -0.145 0.322 -2.363 0.877 8.970 0.942
    hhinc 0.114 0.001*** 0.347 0.528 0.049 0.586 -0.372 0.913 0.967 0.932
    famhis -0.009 0.927 -0.218 0.728 -0.069 0.563 0.107 0.946 0.245 0.959
    Social capital measures                    
    ssnum 0.851 0.080*                
    emoss     -39.213 0.725            
    finss         4.843 0.354        
    anyss             107.935 0.883    
    numfriends                 -5.824 0.941

    Source: NHANES 2007-2008, age 60 and above
    * Denotes significance at the 10% level
    ** Denotes significance at the 5% level
    *** Denotes significance at the 1% level 1.
    Dependent variable is overall health (=1 if oveall health status is excellent, very good, or good; = 0 if overall health status is fair or poor). Independent variables are age, gender, race (ref=non-hispanic white), country of born (ref= us born), education (ref=less than high school), marital status, household income, family disease history, and a social capital measure. Probit model with instrumental variable (years of residence at the current address) was utilized for an analysis. a. It was dropped due to collinearity in SAS.
    Table 5-2. Health demand equation (Dependent variable: Physical Health)
      Equation 1 Equation 2 Equation 3 Equation 4 Equation 5
      ( Obs =1,468) ( Obs =1,450) ( Obs =1,413) ( Obs =1,457) ( Obs =1,456)
      Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z|
    age 0.005 0.374 0.001 0.992 0.020 0.360 0.013 0.825 0.004 0.937
    male -0.357 0.023** 0.438 0.841 -0.558 0.256 -1.139 0.802 0.069 0.971
    black 0.006 0.963 0.082 0.883 0.189 0.544 -0.124 0.867 4.875 0.884
    mexican 0.345 0.021** 0.739 0.655 0.291 0.128 -0.647 0.884 2.796 0.872
    otherrace 0.169 0.435 -0.657 0.801 0.074 0.800 0.96 0.825 2.381 0.88
    mexicoborn 0.037 0.85 2.181 0.767 -0.147 0.623 -2.448 0.833 1.95 0.886
    otherborn 0.124 0.507 0.149 0.846 0.091 0.706 0.045 0.961 2.371 0.882
    hs (dropped) (dropped) (dropped) (dropped) (dropped)
    mths (dropped) (dropped) (dropped) (dropped) (dropped)
    married 0.205 0.273 -0.766 0.771 -0.145 0.322 0.879 0.834 -2.761 0.884
    hhinc -0.018 0.271 -0.156 0.723 0.049 0.586 0.164 0.858 -0.283 0.872
    famhis -0.034 0.669 0.109 0.817 -0.069 0.563 -0.117 0.862 -0.107 0.913
    Social capital measures                    
    ssnum -0.514 0.222                
    emoss     24.323 0.764            
    finss         -4.173 0.427        
    anyss             -42.537 0.834    
    numfriends                 1.783 0.884

    Source: NHANES 2007-2008, age 60 and above
    * Denotes significance at the 10% level
    ** Denotes significance at the 5% level
    *** Denotes significance at the 1% level 1.
    Dependent variable is physical health (=0 if numbers of physical health was nood good during the past 30 days is zero; else = 1). Independent variables are age, gender, race (ref=non-hispanic white), country of born (ref= us born), education (ref=less than high school), marital status, household income, family disease history, and a social capital measure. Probit model with instrumental variable (years of residence at the current address) was utilized for an analysis.
    Table 5-3. Health demand equation (Dependent variable: Mental Health)
      Equation 1 Equation 2 Equation 3 Equation 4 Equation 5
      ( Obs =1,469) ( Obs =1,451) ( Obs =1,414) ( Obs =1,458) ( Obs =1,457)
      Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z|
    age -0.009 0.149 -0.005 0.848 -0.024 0.284 -0.02 0.841 -0.01 0.906
    male -0.117 0.471 -1.097 0.640 0.095 0.850 1.319 0.899 -0.824 0.882
    black -0.129 0.309 -0.201 0.762 -0.335 0.318 0.038 0.978 -9.271 0.923
    mexican -0.140 0.381 -0.627 0.727 -0.082 0.715 1.527 0.880 -5.004 0.922
    otherrace -0.165 0.476 0.917 0.765 -0.169 0.618 -1.755 0.872 -4.348 0.923
    mexicoborn 0.048 0.824 -2.704 0.748 0.271 0.406 4.616 0.871 -3.379 0.927
    otherborn 0.022 0.913 -0.002 0.999 0.104 0.708 0.069 0.968 -4.263 0.926
    hs (dropped) (dropped) (dropped) (dropped) (dropped)
    mths (dropped) (dropped) (dropped) (dropped) (dropped)
    married -0.325 0.096* -0.766 0.771 -0.112 0.463 -1.738 0.868 5.121 0.925
    hhinc -0.031 0.088* -0.156 0.723 -0.099 0.296 -0.386 0.868 0.487 0.927
    famhis 0.142 0.103 0.109 0.817 0.130 0.299 0.242 0.830 0.348 0.897
    Social capital measures                    
    ssnum 0.634 0.159                
    emoss     -31.171 0.735            
    finss         4.786 0.379        
    anyss             -42.537 0.834    
    numfriends                 -3.379 0.923

    Source: NHANES 2007-2008, age 60 and above
    * Denotes significance at the 10% level
    ** Denotes significance at the 5% level
    *** Denotes significance at the 1% level 1.
    Dependent variable is physical health (=0 if numbers of mental health was not good during the past 30 days is zero; else = 1). Independent variables are age, gender, race (ref=non-hispanic white), country of born (ref= us born), education (ref=less than high school), marital status, household income, family disease history, and a social capital measure. Probit model with instrumental variable (years of residence at the current address) was utilized for an analysis.
    Table 5-4. Health demand equation (Dependent variable: Biological Risks)
      Equation 1 Equation 2 Equation 3 Equation 4 Equation 5
      ( Obs =1,438) ( Obs =1,419) ( Obs =1,386) ( Obs =1,427) ( Obs =1,430)
      Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z| Estimate Pr > |Z|
    age -0.013 0.009*** -0.002 0.985 -0.021 0.258 -0.011 0.046** -0.015 0.316
    male -0.054 0.679 0.497 0.925 0.069 0.877 -0.041 0.844 -0.124 0.509
    black 0.309 0.003*** 0.511 0.775 0.212 0.489 0.349 0.011** -0.702 0.879
    mexican -0.122 0.129 0.374 0.928 -0.145 0.314 -0.016 0.948 -0.704 0.796
    otherrace -0.268 0.113 -0.719 0.865 -0.227 0.322 -0.271 0.237 -0.832 0.755
    mexicoborn 0.206 0.558 3.533 0.902 0.324 0.240 0.564 0.526 -0.192 0.920
    otherborn -0.070 0.247 0.154 0.936 -0.035 0.854 -0.031 0.866 -0.642 0.812
    hs (dropped) (dropped) (dropped) (dropped) (dropped)
    mths (dropped) (dropped) (dropped) (dropped) (dropped)
    married -0.023 0.865 -0.572 0.918 0.049 0.611 -0.001 0.999 0.663 0.818
    hhinc -0.065 0.001*** -0.232 0.874 -0.099 0.224 -0.081 0.145 -0.003 0.991
    famhis 0.254 0.001*** 0.003 0.999 0.228 0.008*** 0.211 0.127 0.297 0.302
    Social capital measures                    
    ssnum 0.173 0.614                
    emoss     28.386 0.908            
    finss         2.025 0.673        
    anyss             4.100 0.698    
    numfriends                 -0.397 0.825

    Source: NHANES 2007-2008, age 60 and above
    * Denotes significance at the 10% level
    ** Denotes significance at the 5% level
    *** Denotes significance at the 1% level 1.
    Dependent variable is biological risks (=summation of inflammation, metabolic, and cardiovascular risk factors). Independent variables are age, gender, race (ref= non-hispanic white), country of born (ref= us born), education (ref=less than high school), marital status, household income, family disease history, and a social capital measure. 2SLS model (; instrumental variable=years of residence at the current address) was utilized for an analysis.

    In general, blacks and Mexican Americans are in poorer health than whites. A similar pattern holds for people who were born in Mexico compared with U.S. born. Each table includes 5 separate regressions with one of five social capital measures: numbers of emotional support sources, emotional support from any source, financial support from any source, either emotional or financial support from any source, and number of close friends. Surprisingly, the social capital measures do not show significant results except in one case. The only exception is that more resources of emotional support can promote better overall health status, as shown in equation 1 in Table 5-1.

    Social capital, at least in terms of the variables that are available to measure in the NHANES 2007-2008, do not affect health outcomes of elderly people, at least the ones analyzed in this study. This unexpected finding does not necessarily imply social capital has little impact on health of elderly people. Rather, it opens further questions of refining the models and statistical analysis, including exploring other omitted variables. Maybe our instrumental variable was not suitable for the analyses. Or we may have to accept that the basic hypothesis regarding the effect of social capital on health may simply be rejected in this particular case.

    In terms of future research on this topic, we plan to use factor analysis to extract common factors in defining social capital. Factor analysis is a method of data reduction. It does this by seeking underlying unobservable (latent) variables that are reflected in the observed variables (manifest variables). In this study, we used a summary index of biological risk factor using nine indicators. We will utilize other indexing methods and define separate inflammation risk, metabolic risk, and cardiovascular risk with other measures k.

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