Active Pension Participation & Household Wealth Accumulation:

1. Active Pension Participation & Household Wealth Accumulation: Evidence of Learning by Doing Jason S. Seligman Carl Vinson Institute of Government University of Georgia & Rana Bose Department of Economics University of Georgia

2. Pension Savings Risk & DC Plans Protect workers from some DB risks • Greater mobility for workers • Less dependence on firm as trustee Have emerged as equity market participation increased • Empirics Suggest Lack of Perfect Information • Persistent Non Participation Puzzle however (50%) • Less than optimal use of risky assets • Average balances in DC Plans may be smaller… Big question remains: What are totalimpacts of these plans for savings?

3. DC Impacts on Non-Pension Savings? Two Related Questions: -1- Do DC Plan Participants Save Differently? -2- Do Particular Plan Features Impact Savings? • Self Direction (Choice) • Plan Sponsored Education Efforts Two Basic Outcome Measures: -1- Portfolio Allocations -2- Wealth Outcomes

4. Pension Savings Literature -- Common Themes CHOICE: underutilized -- misapplied Inertia -- Default Bias Madrian & Shea (2001) Naïve (1/N) Diversification Benartzi & Thaler (2001) EDUCATION: some promise of improving Participation Bernheim & Garrett (1996) Participation & Diversification Choi Laibson Madrian (2002) DC & PRIVATE SAVINGS impact outside of plan Increases in Equity Holdings Weisbrenner (2002) Impact on Allocations Friedberg & Webb (2006)

5. Data • HRS RAND version D -- Public Use Data • Self Reported Pension Characteristics: • Plan Type • Choice • Financial Education (in 1992, and 2000) • Respondent Characteristics: • Risk Preferences (Low, Med, High) • Planning Horizon • Bequest Probabilities • Subjective Retirement Age Probabilities • Usual Socio Economic Controls • Follow a sample from the Initial Cohort through 6 Waves (‘02)

6. Basic Correlates Risky Asset Ownership & Plan Features Risky Assets ≡ stocks, bonds, mutual funds

7. Non-Plan Financial Asset Levels at RetirementDistributions by Plan Attributes

8. Random Effects Probit Estimator Where: -1- yit measures ownership of an asset class: {risky, safe, retirement} -2- Xincludes: age, wealth quartile, income, income squared, age cohort, socio-economic characteristics, pension plan participation by type, IRA ownership, occupation, industry, Census region, and time dummies -3- Di is our binary indicator for attending financial education or having choice in the individuals DC plan (s). -4- With the RE Probit we have: and:

9. RE Probit Results Impacts of Employer Plans by Plan Type Notes: -1- t statistics reported below estimated correlates. -2- Controls included from model in previous slide.

10. RE Probit Results Interacting Choice & Financial Education • Notes: • -1- t statistics reported below estimated correlates. • -2- Saving preference controls do not significantly impact patterns above: • Risk Preference • Planning Horizon • Bequest Motive.

11. Multivariate ProbitSimultaneous Asset Allocation Decisions Notes: -1- SE’s statistics reported below estimated correlates. -2- Controls again included but suppressed in presentation above. -3- Estimation employs Geweke, Hajivassiliou &Keane (GHK) simulator,

12. Multivariate ProbitSimultaneous Asset Allocation DecisionsHousing Included Notes: -1- t statistics reported below estimated correlates. -2- Controls again included but suppressed in presentation above. -3- Estimation employs Geweke, Hajivassiliou &Keane (GHK) simulator,

13. Propensity Score Matching Estimator Ideally we wish to measure the average treatment effect: E(Y1|D = 1) − E(Y0|D = 0) Where Y1 is the treated subset of households Y. However the treatment should not be assumed to be random. We thus first estimate a household’s probability of treatment, given its observed covariates. Propensity Score Estimator

14. PSM: Matching Observations • We match our treated and untreated households on: • age cohort, education, race; • marital status; • a binary indicator for dual earner couples; • DC plan, DB plan, and/or IRA plan participation; • an indicator for poor health; • preferences for risk, and financial planning horizon; • earnings, and net household wealth. • We employ kernel matching: • This weights our match so as to make the very best use of a relatively large non-treatment group to create composites

15. PSM Estimation Simultaneous Asset Allocation DecisionsHousing Included Notes: -1- t statistics reported below estimated correlates.

16. Summary of Findings • Exposure to pension plans and their features are associated with changes in savings habits outside of plan. • increases in portfolio diversity • positive impacts on wealth • Impacts are found with a variety of methods and controls • Implications: • ‘Failing to Plan => Planning to Fail’ • Low levels of saving and inertia • Concern for how different groups might manage public pension program changes

17. Thank You Please contact us with further comments and questions Corresponding Author: Jason Seligman seligman@cviog.uga.edu 706. 542. 6252