1. Social Interaction and Stock Market Participation: Evidence from British Panel Data Sarah Brown and Karl Taylor Department of Economics University of Sheffield July 2011

2. Dept of Economics, University of Sheffield, UK Introduction and Background  R elationship between social interaction & participation in the stock market at the individual level.  Growing interest in the role of social capital & social interaction in the economy.  Social interaction & social capital might influence financial & economic decision- making at the individual or household level;  Investment in risky financial assets & social interaction: Hong et al. (2004).

3. Introduction and Background  Word-of-mouth or observational learning relating to opportunities or how to invest;  Satisfaction from talking about stocks & shares with fellow investors – ‘hobby’;  Social norms of the group – ‘keeping up with the Jones’. Dept of Economics, University of Sheffield, UK

4. Introduction and Background  Ivkovic & Weisbenner (2007): positive relationship between a household’s stock purchases & those made by neighbours.  Brown et al. (2008): a causal link between an individual’s decision to own stocks & the average stock market participation of the individual’s community;  Hong et al. (2004): a positive association between social interaction & stock market participation in the U.S.;  Christelis et al. (2010): socially active households are more likely to own shares. Dept of Economics, University of Sheffield, UK

5. Introduction and Background  We explore the relationship between social interaction & the propensity to invest in risky financial assets for the UK;  We employ a relatively wide range of measures of social interaction as well as a measure of generalised trust.  Our main contribution lies in exploiting the panel nature of our data;  We analyse the probability of stock market participation over time via a fixed effects framework;  We also analyse the dynamics of stock market participation over time to investigate the role of state dependence. Dept of Economics, University of Sheffield, UK

6. Data  British National Child Development Study (NCDS).  Cohort study: target sample of all children born in Great Britain during a given week – March 3 rd to March 9 th – in 1958;  The NCDS was conducted at ages 7, 11, 16, 23, 33, 42, 46 and 50 (2008/09).  Age 50, 35% of the sample own stocks/and or shares in 2008. Dept of Economics, University of Sheffield, UK

7. Measurement of Social Interaction  The NCDS includes four measures of social interaction in 2008; 1. Three binary indicators for whether the individual is a member of one club, two or three clubs, or four or more clubs; 2. Binary dummy variable equal to unity if the individual currently attends church two or three times a month or more frequently; Dept of Economics, University of Sheffield, UK

8. Measurement of Social Interaction (continued) 3.Binary indicator equal to unity if the individual has visited their friends three or more times in the last two weeks; 4. Binary indicator equal to unity if the individual is currently an active member of a sports club and attends once a month or more frequently; Plus: a binary dummy variable equal to unity if the individual believes that most people can be trusted Dept of Economics, University of Sheffield, UK

9. Measurement of Social Interaction (continued) % of sample investing in stocks and/or shares in 2008/09 SocialNon Social Clubs41%27% Friends38%34% Church44%35% Sport47%33% Trust39%31% Observations7,286 Dept of Economics, University of Sheffield, UK

10. Social interaction & the probability of stock market participation in 2008/09  Cross-section analysis;  Logit model;  Stock market participation and social interaction are measured concurrently, i.e. in 2008. Dept of Economics, University of Sheffield, UK

11. Dependent variable= stock market participation in 2008/09; marginal effects ClubsChurchFriendsSportTrust Member of 1 club 0.032 (2.12) Member of 2-3 clubs 0.092 (5.51) Member of > 3 clubs 0.122 (3.64) Attends church 0.024 (2.18) Visits friends 0.042 (3.20) Member of sports club 0.074 (4.60) Most can be trusted 0.011 (0.93)

12. Social Interaction and Stock Market Participation: Panel Data Analysis, Fixed Effects Analysis  Information on stock and/or share ownership was also available in 1991 (age 33) & 1981 (age 23).  %s participating in the stock market in 1981, 1991 and 2008 are 4%, 22% % 35%, respectively.  Consistent binary measures of social interaction over time relating to the number of clubs that the individual is a member of, church attendance, and participation in sport. Dept of Economics, University of Sheffield, UK

13. Social Interaction and Stock Market Participation: Panel Data Analysis, Fixed Effects Analysis  Binary fixed effects logit model;  Unbalanced panel, 11,673 observations. Dept of Economics, University of Sheffield, UK

14. Social Interaction and Stock Market Participation: Panel Data Analysis, Fixed Effects Analysis (Continued) ClubsChurchSport CoefTStatMECoefTStatMECoefTStatME 1 club 0.1772.360.009 2-3 clubs 0.2823.420.013 4+ clubs 0.3112.140.016 Church 0.0834.000.014 Sport 0.1522.220.009 Dept of Economics, University of Sheffield, UK

15. Social Interaction and Stock Market Participation: Panel Data Analysis, Dynamic Analysis  Dynamic model which takes into account past experience;  Probability of participating in the stock market may be more likely if the individual has purchased stocks in the past;  Alessie et al. (2004): unobserved heterogeneity & state dependence play a large role in stock ownership for Dutch households between 1993 & 1998. Dept of Economics, University of Sheffield, UK

16. Social Interaction and Stock Market Participation: Panel Data Analysis, Dynamic Analysis (continued)  We model the ownership of stocks over the period 1981 to 2008;  Balanced panel over three time periods (1981, 1991 and 2008) and 7,467 individuals giving total observations of 22,401. Dept of Economics, University of Sheffield, UK

17. Social Interaction and Stock Market Participation: Panel Data Analysis, Dynamic Analysis (continued) Stocks at time tStocks at t+1 1981No stocks in 198123% hold stocks in 1991 Holds stocks in 198154% hold stocks in 1991 1991No stocks in 199126% hold stocks in 2008 Holds stocks in 199158% hold stocks in 2008 Dept of Economics, University of Sheffield, UK Transition rates in stock market participation

18. Social Interaction and Stock Market Participation: Panel Data Analysis, Dynamic Analysis (continued) ClubsChurchSport CoefT-StatCoefT-StatCoefT-Stat Stock t-1 0.98015.780.97015.370.98515.55 1 club0.1033.30 2-3 clubs0.2056.52 4+ clubs0.2674.80 Church0.0963.50 Sport0.0843.41 RE term1.6042.801.4102.101.6791.80 Dept of Economics, University of Sheffield, UK

19. Social Interaction and Stock Market Participation: Panel Data Analysis, Dynamic Analysis (continued)  Initial conditions: static reduced form equation for period 1 using the same covariates & excluding the lagged dependent variable;  Plus binary controls for the occupation that the individual was first employed in as identifying variables;  Significance of the random effect term indicates that unobserved heterogeneity is important;  Coefficient on stock t-1 indicates state dependence. Dept of Economics, University of Sheffield, UK

20. Conclusion  First empirical study of stock market participation & social interaction for the UK;  Our findings support a positive relationship between social interaction & stock market participation when both are measured concurrently;  Cohort data allows us to contribute to the existing literature in this area by exploring the panel aspect of the data. Dept of Economics, University of Sheffield, UK

21. Conclusion  We firstly control for unobserved fixed effects and, secondly, state dependence in stock ownership.  Timing difference & reverse causality: positive effect of social interaction remains when social interaction is measured prior to stock market participation.  Our findings lend further support for the importance of social interaction for stock market participation. Dept of Economics, University of Sheffield, UK