Volunteering and well-being Cristina Rosemberg New Directions in Welfare II 8 July, Paris

1. Volunteering and well-being Cristina Rosemberg New Directions in Welfare II 8 July, Paris

2. 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: • 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? • Self-selection: are there underlying characteristics that make individuals to selected themselves into the volunteering that are also correlated with their well-being? • 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).

3. 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: • Corr(X,Z)!=0 • Corr(Z,ni)=0

4. 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.

5. 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.

6. Volunteering Average 7 waves GHQ12: 36 point ‘Likert’ scale Wave 6 Average score: 11.20

7. 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.

9. Results (I)

10. Model1 Model 2 Model 3 IV OLS IV OLS IV OLS b/se b/se b/se b/se b/se b/se Volunteering - 0.513 - 0.281*** - 1.324 - 0.379*** - 1.246 - 0.383*** (0.377) (0.050) (2.251) (0.100) (2.254) (0.100) Age 0.017 0.016 0.030 0.026 0.027 0.023 (0.015) (0.015) (0.026) (0.023) (0.026) (0.023) Age2 - 0.000 0.000 - 0.000 - 0.000 - 0.000 - 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Base: living comfortably Finan.sit = doing alright 0.434*** 0.435*** 0.489*** 0.495*** 0.484*** 0.489*** (0.047) (0.047) (0.094) (0.092) (0.094) (0.092) Finan.sit= jus about getting by 1.410*** 1.408*** 1.576*** 1.574*** 1.575*** 1.573*** (0.057) (0.057) (0.112) (0.111) (0.112) (0.111) Finan.sit= finding it quiet difficult 3.004*** 3.000*** 3.231*** 3.216*** 3.226*** 3.212*** (0.091) (0.091) (0.177) (0.173) (0.178) (0.173) Finan.sit= finding it very difficul t 4.879*** 4.873*** 5.433*** 5.429*** 5.432*** 5.428*** (0.136) (0.136) (0.258) (0.257) (0.259) (0.258) Number of health problems 0.577*** 0.577*** 0.650*** 0.654*** 0.648*** 0.652*** (0.019) (0.019) (0.038) (0.036) (0.037) (0.036) Base: never married Marital status= married 0.300*** 0.302* ** 0.271 0.269 0.300 0.298 (0.109) (0.108) (0.198) (0.197) (0.198) (0.198) Marital status=separated 0.957*** 0.957*** 1.627*** 1.601*** 1.639*** 1.617*** (0.181) (0.181) (0.345) (0.339) (0.345) (0.339) Marital status= divorced - 0.117 - 0.115 - 0.123 - 0.110 - 0.098 - 0.085 (0.156) (0.1 56) (0.283) (0.280) (0.283) (0.281) Marital status= widowed 1.332*** 1.329*** 1.487*** 1.495*** 1.524*** 1.531*** (0.189) (0.189) (0.335) (0.333) (0.335) (0.334) L n( Income) 0.044*** 0.046*** 0.024 0.035 0.020 0.030 (0.013) (0.013) (0.037) (0.026) (0.037) (0.026) Trust - 0.384*** - 0.394*** - 0.388*** - 0.397*** (0.091) (0.088) (0.091) (0.088) Frequency talks to neighbours (weekly or more=1) - 0.234** - 0.238** (0.094) (0.093) Frequency meet s people ( weekly or more =1) 0.056 0.042 (0.110) (0.104) Constant 8.439*** 8.392*** 8.412*** 8.358*** 8.661*** 8.631*** (0.337) (0.328) (0.534) (0.516) (0.544) (0.537) Results (II) Fixed-effects (within) IV and GLS regressions

11. 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.

12. 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

13. 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

14. 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