Volunteering and
well-being
Cristina Rosemberg
New Directions in
Welfare II
8 July, Paris
Motivation
• Explore potential positive effects of participating on civic
engagements and of taking a more active role in society.
• Literature have established a positive correlation between
volunteering and well-being (Li&Ferraro, 2005, Helliwell&Putman,
2004):
– Formal volunteering have beneficial effects on subjective well-being, particularly
on depression among older people.
– Civic engagements have a robust positive correlation with happiness and life
satisfaction
• However, the positive correlation found in the literature could be
spurious given three main problems:
1. Reverser causality: does volunteering increases subjective well-being, or is it
that people with higher levels of well-being is more willing to engage in this type
of activities?
2. Self-selection: are there underlying characteristics that make individuals to
selected themselves into the volunteering that are also correlated with their wellbeing?
3. Omitted variables: are there factors –which can not observed- that determines
a both, a higher propensity to volunteers and to report higher levels of well—
being? (e.g. personality traits).
Methodology (I)
• Instrumental variables
• Need to find an instrument (Z) that affects Mental Health indirectly
just through its effects on volunteering.
• More precisely, the instrument has to full-fill two requirements:
1.
2.
Corr(X,Z)!=0
Corr(Z,ni)=0
Methodology (II)
•
Data
–
–
–
•
British Household Panel Survey (BHPS)
18 waves, random sample of aprox. 10,000 individuals (5,500 British households), 15 years and
older.
Includes measures of well-being, volunteering, social characteristics
How to measure well-being?
–
–
Preference satisfaction, hedonic accounts, evaluation accounts
Combined measures: mental health GHQ12
•
•
•
•
•
•
•
Brief self-report measure, with ‘excellent’ properties as a screening instrument for psychiatric disorders in nonclinical
settings (Goldberg & Williams, 1988).
Use extensively in medical, psychological and sociological research.
GHQ-12 comprises six ‘ positive ’ and six ‘negative’ items concerning the past few weeks. Presence or intensity of the state
is ranked by the respondent using a 4-point scale.
It cover issues of social functioning (feeling capable of making decisions), anxiety and depression (being able to sleep well
) and confidence (thinking of oneself as worthless).
Likert GHQ score: obtained by assigning the value of 3 to the ‘most negative ‘ answer and the value of 0 the ‘most positive’
ones.
Score: from 0 (most posittive outcome) to 36.
How to measure volunteering?:
–
–
–
–
Memberships (W1-W5, W7, W9, W11, W13, W15, W17):
Q.: Are you currently a (n active) member of any of the kinds of organisations [...]?
It is not clear what are the resources (money, time) that individuals contribute to these organisations: what
does ‘active’ mean?
Variable seems to be capturing a broad measure of social capital better than volunteering.
Methodology (III)
• How to measure volunteering? (cont’):
– Unpaid voluntary work (W6,W8,W10,W12,W14,W16,W18):
– Q: We are interested in the things people do in their leisure time, I'm
going to read out a list of some leisure activities. Please look at the
card and tell me how frequently you do each one... Do unpaid
voluntary work.
– Main concern: ‘unpaid voluntary work’ questions could be capturing
participation in informal volunteering or the existence of family
strategies such as caring for a family member that lives inside or
outside the household. According to the literature, this kind of
volunteering might be detrimental to carers’ mental health
(Li&Ferraro, 2005).
– However, ‘caring for a family member’ does not seem to driven the
responses to this question:
• Volunteering among individuals that do care for a household
member is similar to volunteering among individuals that do not
report providing that kind of support (20.6% and 20.7%
respectively). And the difference is not statistically significant.
GHQ12: 36 point ‘Likert’ scale
Wave 6
Average score: 11.20
Volunteering
Average 7 waves
Methodology (IV)
• Instrument:
– Percentage of people in the region that engages in volunteering,
per year.
• Positively correlated with volunteering
• ...but not reason to believe that it is correlated with any
underlying factors determining individual mental health.
• Other controls:.
– Second stage (Mental health): sex, age, age^2, physical
health, marital status, financial strain, log annual income.
– First stage: instrument and covariates of 2nd stage.
inner london
outer london
r. of south east
south west
east anglia
east midlands
west midlands conurbation
r. of west midlands
greater manchester
merseyside
r. of north west
south yorkshire
west yorkshire
r. of yorks & humberside
tyne & wear
r. of north
wales
scotland
northern ireland
.1
.2
.3
.4
.1
.2
.3
.4
.1
.2
.3
.4
.1
.2
.3
.4
missing or wild
5
10
15
20
5
10
15
20
5
10
15
wave
Graphs by region
20
5
10
15
20
5
10
15
20
Results (I)
First-stage within regression
Fixed-effects (within) regression
Group variable: Individual ID
b/se
Instrument: Proportion of volunteers in the region, per year 1.009***
(0.030)
Controls 2nd stage
Yes
N
F(13,64191)
Prob > F
88779
97
0.000
Results (II)
Fixed-effects (within) IV and GLS regressions
Volunteering
Age
Age2
Base: living comfortably
Finan.sit=doing alright
Finan.sit=jus about getting by
Finan.sit=finding it quiet difficult
Finan.sit=finding it very difficult
Number of health problems
Base: never married
Marital status=married
Marital status=separated
Marital status=divorced
Marital status=widowed
Ln(Income)
Model1
IV
b/se
-0.513
(0.377)
0.017
(0.015)
-0.000
(0.000)
OLS
b/se
-0.281***
(0.050)
0.016
(0.015)
0.000
(0.000)
Model 2
IV
b/se
-1.324
(2.251)
0.030
(0.026)
-0.000
(0.000)
OLS
b/se
-0.379***
(0.100)
0.026
(0.023)
-0.000
(0.000)
Model 3
IV
b/se
-1.246
(2.254)
0.027
(0.026)
-0.000
(0.000)
OLS
b/se
-0.383***
(0.100)
0.023
(0.023)
-0.000
(0.000)
0.434***
(0.047)
1.410***
(0.057)
3.004***
(0.091)
4.879***
(0.136)
0.577***
(0.019)
0.435***
(0.047)
1.408***
(0.057)
3.000***
(0.091)
4.873***
(0.136)
0.577***
(0.019)
0.489***
(0.094)
1.576***
(0.112)
3.231***
(0.177)
5.433***
(0.258)
0.650***
(0.038)
0.495***
(0.092)
1.574***
(0.111)
3.216***
(0.173)
5.429***
(0.257)
0.654***
(0.036)
0.484***
(0.094)
1.575***
(0.112)
3.226***
(0.178)
5.432***
(0.259)
0.648***
(0.037)
0.489***
(0.092)
1.573***
(0.111)
3.212***
(0.173)
5.428***
(0.258)
0.652***
(0.036)
0.300***
(0.109)
0.957***
(0.181)
-0.117
(0.156)
1.332***
(0.189)
0.044***
(0.013)
0.302***
(0.108)
0.957***
(0.181)
-0.115
(0.156)
1.329***
(0.189)
0.046***
(0.013)
0.271
(0.198)
1.627***
(0.345)
-0.123
(0.283)
1.487***
(0.335)
0.024
(0.037)
-0.384***
(0.091)
0.269
(0.197)
1.601***
(0.339)
-0.110
(0.280)
1.495***
(0.333)
0.035
(0.026)
-0.394***
(0.088)
8.439***
(0.337)
8.392***
(0.328)
8.412***
(0.534)
8.358***
(0.516)
0.300
(0.198)
1.639***
(0.345)
-0.098
(0.283)
1.524***
(0.335)
0.020
(0.037)
-0.388***
(0.091)
-0.234**
(0.094)
0.056
(0.110)
8.661***
(0.544)
0.298
(0.198)
1.617***
(0.339)
-0.085
(0.281)
1.531***
(0.334)
0.030
(0.026)
-0.397***
(0.088)
-0.238**
(0.093)
0.042
(0.104)
8.631***
(0.537)
Trust
Frequency talksto neighbours (weekly or more=1)
Frequency meets people (weekly or more=1)
Constant
Results (III)
• Validity of the instruments:
• Weakness: first-stage regression shows a strong (positive) correlation
between the instrument and volunteering.
• Over identification: We cannot reject the null that the instruments are valid.
• Hausman test of endogeneity: There are no systematic differences
between IV and OLS estimates.
• If endogeneity is ruled out, then OLS provides consistent and
efficient estimators, while IV provides consistent but inefficient
estimators.
• Fixed effects seem to be removing problems of omitted variables
and reversed causality.
Results (III)
• What about self-selection?
– A ‘treatment effect’ model
– The idea behind the model is to regress two equations simultaneously:
• The first is the probability of volunteering controlling by personality traits (Big
5: extraversion, openness, neuroticism, agreeableness and conciousteness).
• The second is the outcome regression (mental health) as a function of the
treatment variable (volunteering).
– To simultaneously estimate the two regressions we have to assume that the error
terms are jointly normally distributed.
– Estimate ‘treatment effect’ model using Wave 16.
– Wald-test tests the null that the correlation between the error terms of the two
equations is biased towards zero. With a chi2(1)= 119.26, p-value=0.000, we can
conclude that there is selection bias in our model.
– However, once the model have been corrected, volunteering is still positive
and significantly correlated with mental health.
E(Mental Health ¦ volunteering=1)= 11.25
E(Mental Health ¦ volunteering=0)= 11.53
Results (IV)
• What are the mechanisms through which volunteering generates a
positive effect on mental health?
• Hypothesis: Volunteering as a buffer mechanism to deal with
potentially negative personal episodes/situations:
– Retirement
– Financial strain
– Termination of marriage
Models
Subsample:
Volunteers
b/se
Panel A: Retirement
Dummy (yes=1)
Panel B: Financial strain
Dummy (finding it quiet or very different)
Panel C: Termination of a marriage
Dummy (separated, divorced, widowed)
Mental health
Subsample: Non
Volunteers
b/se
-1.206***
(0.199)
-0.517***
(0.108)
2.186***
(0.203)
2.508***
(0.084)
-0.329
(0.271)
0.320***
(0.108)
Conclusions
• Fixed effect models seem to be successfully dealing with issues of
reverse causality and omitted variables.
• Self-selection problem is not tackle with OLS estimations, however:
– ‘Treatment effects’ model provide similar OLS estimators once estimation have
been corrected by selection bias.
• Volunteering has a positive effect on mental health.
• Volunteering seems to be playing a role on alleviating potential
negative effects of personal episodes/situations:
– It increases well-being among retirees:
• Hypothesis: Helps volunteers to find a sense of purpose after their working life.
– Decreases the negative effects of being on financial strain:
• Hypothesis: Helps volunteers to see things in perspective/Helps volunteers to
achieve personal satisfaction that is not related to monetary rewards.
– Deludes the negative effect of being separated, divorced or widowed (as opposed
to being married).
• Hypothesis: Helps volunteers to see things in perspective
• Further research:
– Test this results with other measures of well-being such as life satisfaction.
– More in-depth analysis needed to understand how those mechanism work in the
field work