Consumption and “Full Wealth” Since Modigliani () we have known that consumption should depend on all current and future resources, including all components of wealth I call this PDV of all resources “full wealth” = M

HRS Data: Unprecedented Ability to Measure Full Wealth Expected present value of resources deterministic for older households: M = Human Wealth + Net Worth Human Wealth= Earnings+Pensions+Social Security+Other Transfers Net Worth = 10 categories of assets less 3 categories of debt

HRS: New Consumption Data 2001 & 2003 Consumption and Activities Mailout Survey (CAMS) –Covers >90% of total expenditures as measured by the CEX Consumption & Full Wealth Together –enables household level analysis of the propensity to consume using the right measure of resources

Full Wealth Looks Different Age Profile of Consumption/WealthAge Profile of Wealth

Dispersion of Full Wealth Coefficients of Variation Full Wealth 0.96 (mean $825,000) Net Worth 1.67 (mean $322,000) Income 1.21 (mean $61,600) Consumption 0.77 (mean $40,300) C/M 0.78 (mean 0.077)

Average Propensity to Consume out of Full Wealth Construct full wealth and total consumption measures to get C/M Test predictions about C/M from a simple neoclassical model Evaluate additions to the model

Findings – Part 1 Given an appropriate time preference parameter, a simple neoclassical model with mortality hazard can approximately match the mean age profile of C/M

Findings Part 2: Heterogeneity Observed heterogeneity across households suggests heterogeneous time preferences –Time preference and C/M are tightly linked in the model –With identical time preferences, the model cannot explain much heterogeneity across households. –Heterogeneous time preferences do well in explaining observed variation by level of income or wealth –Some additions to the model matter, but still leave a role for heterogeneous preferences.

Literature HRS wealth measures: other papers have constructed wealth/pension measures (Gustman & Steinmeier) Lifecycle and buffer stock: find that households closer to retirement act more like neoclassical/certainty equivalence (Carroll ; Gourinchas&Parker 2002…) –Labor income uncertainty: many papers in this literature focus on effects of stochastic income; not relevant here given my model and age of households Optimal wealth accumulation: don’t use consumption data (Engen et al1999; Scholtz et al 2002)

Neoclassical Model Only uncertainty in model is mortality and rate of return (Merton) Implications: C/M depends only on preferences, stochastic return characteristics, and mortality. Does not depend on level of full wealth, income profile, or outcome of past income shocks

Neoclassical Model with Stochastic Returns Subject to: Solution for optimal portfolio share: The value function: The Bellman equation: Estimate mortality with Gompertz function: age =  1 e (  2 *age)

Average Propensity to Consume Infinite Horizon: In the special case where r, μ, and σ are constant, then α * is constant and dA/dt=0 which gives: Finite Horizon:

Data - HRS Health and Retirement Survey Nationally representative panel of ages 50+ Seven waves since 1992 Sample updated with new year-olds in 1998 Detailed socioeconomic, income, wealth, health, employment history, family Some attitudes, subjective expectations, plans etc. Complete Social Security earnings histories Employer-reported pension formulas

Data – Human Wealth  Components  Expected earnings for non-retired – projected from current earnings based primarily on experience and tenure  Present value of defined benefit and defined contribution pension plans – HRS pension calculator with adjustments & using survey report of expected retirement age  Present value of Social Security benefits  Government benefits – veterans, disability, approximate “income floor” based on SSI  All human capital is after-tax (approximate year-specific tax rates) and discounted, including an age and gender-specific mortality hazard

Data - Consumption 2001 & 2003 Consumption and Activities Mailout Survey (CAMS)  Sent to approximately ½ of the households in the HRS sample  2001 response rate 77% = 3,866 househoId obs  My sample: approx 2,000 households In CAMS, in the HRS or WB cohorts, Social Security match, and still in the sample for the 2002 wave 26 expenditure categories covering equivalent of >90% of total expenditures measured by the CEX I also impute rental equivalence and vehicle consumption with predicted values based on CEX

Predicted vs. Actual C/M Mean Age Profile r=.02; μ=.06;σ=.2; ρ=.02; γ=2 Sequence of pictures just changing lines: 1.mean, median, 2. “It’s all about rho” Mean & to match mean 3. Heterogeneity: add confidence intervals to 1 st pic

Invariance Model implies invariance to past income shocks and C linear in M, so C/M independent of level of M. ∆C/M does not depend on past income shocks But…C/M does depend on level of wealth

C/M Varies by Income or Wealth Level Changing ρ Doesn’t Help Unless Heterogeneous Note: do without confidence intervals

Heterogeneous Preferences People’s preferences differ, not controversial Evidence for heterogeneous preferences (Barsky et al, others…) Here have enough data to make progress on time preference heterogeneity –Investigate and eliminate other possible sources of variation within the model –Evaluate some alternatives outside the model

Few Sources of Variation Within the Model (other than preferences) Model allows for variation by age (through mortality) and differing rate of return expectations –Mortality: estimated with a Gomperts function based on life tables –Returns: estimated using HRS questions on stock return expectations → predicted C/M covaries only slightly with actual C/M (R 2 of.02, simple correlation.20) Changing values of r or using actual versus optimal risky asset share doesn’t help

Measurement Error

Additions/Alternatives: Liquidity Constraints Common addition to a lifecycle model (although often found not binding at older ages) Of particular concern here since full wealth includes illiquid assets that can’t be borrowed against But, using full wealth, theory says the effect of liquidity constraints is unambiguous (Carroll&Kimball 2001): push C/M ↓

No Evidence of Liquidity Constraints

Most common addition to lifecycle model Work in correct direction to help explain variation by income level HRS asks series of questions on probability of leaving bequests (any, >$10K, >$100K) Bequests matter, but can’t explain substantial portion of variation Additions/Alternatives: Bequests

Bequests Dependent variable: ln(C/M)-ln(predC/M) CoefficentT-stat Expect to leave any bequest **-2.5 Large Bequest (>50% Chance of leaving bequest >$100,000) **-4.8 Means for Large Bequest=1 Apply coefficient C/M *.151=.0092 M$1,182,000 Net Worth $534,000 Imagine Household’s Calculation: C/(M-b)= =.0518 → b ≈ $200,000 Fraction of Net Worth? 200,000/534,000=38% Hurd & Smith (2002) estimate actual bequests of 39% of net worth

Recent literature questioning whether all households have the ability, financial literacy, or propensity to plan optimal savings and consumption strategies for retirement (Lusardi, Willis/Lillard, Caplin/Leahy…) HRS asks questions on basic cognition (recall, counting, subtraction) plus planning horizon and subjective expectations –Willis & Lillard “fraction of exact answers”; precision of expectations formation related to financial decisions Measures matter such that lower cognition, less precision, and shorter planning horizons all imply higher propensities to consume Additions/Alternatives: Cognition & Planning

Regression Results Note: Can’t fit on one slide – point out that complete results in paper, pick highlights

Dependent variable: ln(C/M) -ln(predC/M) CoefficentT- stat Additional people in household0.067**4.8 Subjective Life Expectancy-0.058*-1.7 Race/Ethnicity: Black or Hispanic 0.089*1.9 Education: No HS Education: Some College Education: College Age of Head-0.119**-4.6 Age of Head Squared0.001**4.4 Fair/ Poor Health Retired-0.098**-2.9 Widowed0.186*1.9 Never Married0.269**4.1 Entrepreneurs-0.341**-4.9 CoefficentT- stat Word Recall Low0.051*1.6 Counting Backwards-0.092**-1.9 Easiest Subtraction Problem Hardest Subtraction Problem **-2.4 Fraction of Precise Answers Horizon for financial planning **-2.4 Smoker0.077**2.5 R 2 =0.272; N=1611 Survey Measure of Risk Aversion (midpoint of range) *-1.6 Stats for OLS w/ Risk Aversion R 2 =0.278N= 1227