wealth returns persistence and heterogeneity · use population data on wealth and capital income...

Wealth Returns Persistence and Heterogeneity

A. Fagereng, L. Guiso, D. Malacrino, and L. Pistaferri

(Statistics Norway, EIEF, Stanford University, and Stanford University)

April 2017

A. Fagereng, L. Guiso, D. Malacrino, and L. Pistaferri Returns Heterogeneity

Wealth concentration around the world: Top 1%

010

2030

40S

hare

, top

1%

Finlan

d

Belgium

Austra

lia Italy

Spain

Canad

a

United

King

dom

France

Chile

Norway

Portug

al

Netherl

ands

Austria

German

y

United

Stat

es

Source: OECD

Norway case


What explains the long thick tail?

Idiosyncratic earnings risk/skewness and precautionary saving response

Savings increasing with wealth (Non-homothetic bequests)

Heterogeneity in discount rates

Entrepreneurship

These explanations, in isolation, have trouble fitting the data

If they do, it is at the cost of counterfactual assumptions (De Nardiand Fella, 2016)


Stochastic wealth returns

Benhabib et al. (2016)

heterogeneous persistent wealth returns (along with some of thefeatures above) can reproduce the long thick tail of the wealthdistribution (and the extent of intergenerational correlation)

Gabaix et al. (2015)

persistent heterogeneity in returns and correlation of returns withwealth (scale and type dependence) can explain the speed of changesin tail inequality

Merit of this literature: shift in focus from heterogeneity in returns tohuman wealth to heterogeneity in returns to financial wealth


Open questions

Not clear if features emphasized by this recent literature are realisticand whether they are quantitatively important

In particular:

Q1: How much heterogeneity in wealth returns?Q2: How much persistence?Q3: Are returns to wealth correlated with wealth itself?Q4: Is there intergenerational correlation in returns?Q5: Is there assortative mating on returns?

Addressing these questions have proved diffi cult

No administrative information on wealth and capital income for arepresentative sample of individuals or asset classes in the USPopulation surveys (SCF) lack a consistent longitudinal component andhave low response rates at the top


Our contribution and findings

Use population data on wealth and capital income (by broad assetclasses) for Norway over two decades

Tax records: Cover all tax-payers, including the very wealthy

We can construct returns to wealth for each individual tax-payer

Findings:

massive returns heterogeneitystrong correlation with wealthpersistence

within person (strong), across generations (weak), and intramaritally(weak)

Measurement and conceptual issues

This paper: Measurement


Roadmap

Data

Measurement

Facts


The Norwegian data

Population tax record data from 1993 to 2013

Besides income tax, Norwegians pay also a wealth tax, so tax recordsinclude:

Information on income earned (from labor and capital)

Capital income distinguished by “broad” source Details

Detailed information on asset holdings

Also distinguished by “broad” source Details

For most sources, tax value=market valueFor unlisted stocks, etc., tax value≤market value Details

Our definition of wealth excludes housing (for the time being),(rank)-correlation between two measures high.

Third-party reports

Limited scope for tax evasion


Wealth Returns: Simplest Measurement

Tax returns include capital income:

yit = interest income + dividends + realized capital gains/losses

They also include the stock of wealth at the beginning of year t (“endof year t − 1”): wit

If no accumulation/decumulation of wealth during the year ("passive"portfolio), the average return would simply be:

rit =yitwit


Wealth returns measurement: Limitations and Adjustments

1. We only observe snapshots of total financial wealth (beginning/end ofeach period)

We use multiple observation points, and redefine our baseline measureas: Example

rit =yit

(wit + wit+1) /2

2. Value of private equity may be understated

We show results for all individuals and for non-private equity owners

3. Capital gains/losses only observed when shares are sold

Our fixed effect strategy will partly remedy thisWe impute unrealized capital gains/losses


Future Drafts

Shareholder Registry (2004-16)

Can compute unrealized capital gains on a security-by-security level (asin Bach et al., 2016)

Private businesses’balance sheet data

Can improve return measure using information on retained earnings andshares owned through companies (as in Alstadsæter et al., 2016)

Housing Transaction Registry (2000-16)

Can use hedonic regressions to impute housing capital gains (as inMogstad et al., 2017)


Descriptive Statistics, 2013: (N=3,046,517)

Demographics

Mean Std P10 Median P90

Age 45.10 13.95 26 45 65

Less than high school 0.19 0.39 0 0 1

High school 0.44 0.50 0 0 1

University 0.37 0.48 0 0 1

Years of education 13.74 3.64 10 13 17

Fraction w/ econ/bus.-degree 0.12 0.32 0 0 1

Assets statistics

Mean Std P10 Median P90

Fraction w risky assets 0.45 0.50 0 0 1

Risky assets share 0.14 0.24 0 0 0.54

Cond. risky assets share 0.30 0.29 0.01 0.20 0.78

Fraction w private equity 0.11 0.32 0 0 1

Private equity share 0.05 0.18 0 0 0.05

Cond. private equity share 0.48 0.41 0.01 0.42 1

Fraction w. public equity 0.38 0.49 0 0 1

Public equity share 0.09 0.20 0 0 0.65

Cond. public equity share 0.24 0.27 0.01 0.14 0.65

More Statistics


Portfolio Composition, 2013

Position Industry Holdings


Descriptive Statistics: Wealth Returns

All years Distribution


Q1: How much heterogeneity should we expect?

In standard Merton-Samuelson model individuals have access to thesame investments opportunities.

Differences in preferences for risk determine the share of risky assetsin portfolio:

πit =rmt − r ft

γiσ2

The return on wealth is

rit = r ft + πit

(rmt − r ft

)Conditioning on the share of risky assets in portfolio, returns shouldbe similar across investors


Returns heterogeneity by share of risky assets in portfolio,2013

02

46

810

12St

.dev

. ret

urn

(%)

0 20 40 60 80 100Share risky assets

Baseline No PE


Q2: Are returns correlated with wealth levels?

"It is perfectly possible that wealthier people obtain higher averagereturns than less wealthy people.... It is easy to see that such amechanism can automatically lead to a radical divergence in thedistribution of capital" (Piketty, 2014).

Wealthy investors may be more risk tolerant

Wealthy investors can buy the services of “financial experts”(economies of scale in wealth management) or be moreexpert/sophisticated themselves

Wealthy investors may have access to different investmentopportunities than retail investors

Some (more lucrative?) mutual funds have an entry requirementReturn on safe assets have a premium for those depositing above athreshold Safe Assets


The correlation between wealth and returns to wealth,2013 (median)

.005

.01

.015

.02

.025

.03

Med

ian

retu

rn

0 20 40 60 80 100Percentile wealth distribution


The correlation between wealth and returns to wealth,2013 (median)

.005

.01

.015

.02

.025

.03

Med

ian

retu

rn


Baseline Baseline no PE

All years Alternative Tax inc. Averages by percentile


Sharpe ratio by initial wealth percentile

Compute Si =Ei(rit−r ft )√vari (rit )

0.2

.4.6

.81

1.2

0 20 40 60 80 100Wealth perc. in 1995

Baseline No PE


Q3: Is returns heterogeneity persistent?

Certain individuals may reap persistently higher/lower returns thanthe average

Preferences

High risk tolerance leading certain individuals to invest inhigh-risk/high-return financial instruments (and preferences for risk arevery stable over time).

Talent

Better at “stock-picking” or at timing the marketBetter financial educationBusiness income/private equity: entrepreneurial ability

Benhabib et al. (2016), Quadrini (2000), Lusardi et al. (2015),Cagetti and De Nardi (2006), Kaperczyk et al. (2015)


Modeling returns heterogeneity

We consider a simple panel data regression model

rit = X ′itβ+ uit

Observables:Lagged wealth =⇒ size effectsLagged share in risky assets, private equity, direct stocks =⇒ riskexposureTime dummies and interactions with shares =⇒ common movementsin returnsAge dummies =⇒ life cycle effects

Unobservables:

uit = fi + εit

How much returns heterogeneity is explained by observables, fixedeffects, and remaining unobservables?


Regression results (compact)

(1) (2) (3)Lagged risky share 0.64

(0.01)*** 1.02

(0.01)***

Lagged priv. eq. share 5.61(0.02)

*** 3.45(0.02)

***

Lagged mut. fund shareLagged direct stockh. shareMale −0.03

(0.00)*** −0.03

(0.00)***

Years of schooling 0.03(0.00)

*** 0.04(0.00)

***

Econ/Business educ. 0.11(0.00)

*** 0.11(0.00)

***

Individual FE no no yesYear FE yes yes yesAge FE yes yes yesCounty FE yes yes yesDemographic controls yes yes yesLag. wealth percentile yes yes yesLag. risky share*year no yes noLag. priv. eq share*year no yes noR2 0.079 0.117 0.232N 50,553,557


Empirical distribution of fixed effects

In baseline specification, fixed effects explain 24% of total variation inunobservablesRanking of fixed effects unchanged under alternative measures ofreturns to wealth Figures


Empirical distribution of fixed effects: Sub-groups

No evidence of serial correlation in residuals S/C


Sharpe Ratio Regressions


Q4 and Q5: Assortative mating in (returns to) wealth andintergenerational correlation in returns

Benhabib, Bisin and Luo (2016) assume that returns are stochastic,constant within a generation, and persistent across generations

We find a role for intergenerational effects both of returns and thepersistent component of returns. To intergenerational

Our data can also be used to study assortative mating by individualwealth and returns to wealth To assortative mating

we find assortative mating by wealthwe also find some (weaker) assortative mating on returns to wealth(conditional on assortative mating on wealth)both the high and low return spouse matters for the household returns


Implications of returns heterogeneity

Implications of the evidence presented so far:

Can it explain the extent of wealth inequality and concentration?

What does it say about whether capital income taxation is preferrableto wealth taxation? (Guvenen et al., 2016)

Does it have an impact on measurement of wealth inequality andconcentration based on the capitalization approach? (Saez andZucman, 2016)

Our previous paper (Fagereng et al., 2016) focuses on the latter.Summary

Another paper (in progress) focuses on the first question.


Conclusions

Not much is known about the distribution of returns to financialwealth across individuals and households

This paper provides evidence using population tax records fromNorway

Returns exhibit massive heterogeneity, are correlated with the level ofwealth, and are persistent over time for the same individual andacross generations

Various implications for the debate on the causes and consequences ofwealth inequality


Q4: Are returns to wealth correlated intergenerationally?

Benhabib, Bisin and Luo (2016) assume that returns are stochastic,constant within a generation, and persistent across generations

Persistence may be due to sharing a private business, orintergenerational transmission of preferences for risk or talent forinvestmentHowever, BBL find weak evidence for persistence

Our data can be used to study mobility (or intergenerationalcorrelation) in wealth-related variables

We focus on:

Wealth levels (Boserup et al., 2014)Overall returns on wealthPersistent component of wealth returns (fixed effects)


Intergenerational correlation: Wealth

3040

5060

7080

0 20 40 60 80 100Father's wealth percentile

Average son's wealth percentile Predicted son's percentile45degree line


Intergenerational correlation: Overall returns


Intergenerational correlation: Fixed effect returns


Regression evidence: Percentile ranks

Dep. var.: Son’s return percentile

(1) (2) (3) (4)Father’s return percentile 0.084∗∗∗

(0.000)0.056∗∗∗(0.000)

0.053∗∗∗(0.000)

0.037∗∗∗(0.000)

Constant 47.362∗∗∗(0.021)

47.346∗∗∗(0.140)

42.620∗∗∗(0.192)

55.131∗∗∗(0.187)

Wealth percentile dummies N Y Y YYear FE N Y Y YAge controls N N Y YEducation lenght and type controls N N Y NIndividual FE N N N YR2 0.007 0.055 0.060 0.352N 21,048,243 21,048,243 21,048,243 21,048,243


Intergenerational correlation: Sharpe ratios


Q5: Is there assortative mating on returns to wealth?

In the literature there is evidence of assortative mating by education,income, and parents’wealth (Eika et al., 2014; Lam, 1988; Charles etal., 2013)

Our data can be used to study assortative mating by individual wealthand returns to wealth

In the data:

we observe couples before they get married (or have children)we find assortative mating by wealthwe also find some (weaker) assortative mating on returns to wealth(conditional on assortative mating on wealth)


Assortative mating on wealth


Assortative mating on returns to wealth


Regression results


Assortative mating on wealth and returns to wealth

Why may people want to sort on returns to wealth?

Similarity of traits - preferences for risk, etc.To preserve whatever wealth they have

Whether this matters depends on who manages the householdresources

If rposti = max{rprew , rpreh

}, then assortative mating on returns

shouldn’t matter

We consider a simple regression:

rposti = β0 + β1max {rprew , rpreh }+ β2min {rprew , rpreh }+ ei


Regression results: Post-marital household wealth return


Definitions: Stocks (all as of 12/31)

Safe Assets:

Deposits in Norwegian banksDeposits in foreign banksCashCapital in bond funds and money market fundsOutstanding receivables

Risky assets

Taxable assets in unit trusts (mutual funds)Tax value of Norwegian shares, equity certificates, bonds in VPS(listed)Capital value of shares and other securities not in VPS (unlisted)


Definitions: Capital Income

Safe Assets:

Interest on bank depositsOther interest income received (from personal loans)Interest on loans to companiesYields from endowment insurance

Risky assets

Taxable share dividendsTaxable yields from unit trustsOther taxable dividendsTaxable gains from sale of sharesTaxable gains from sale of units in securities fundsOther taxable gains from sale of sharesLosses from sale of sharesLosses from sale of units in securities fundsOther losses from sale of shares


Valuation of unlisted stocks

In addition to balance sheet information, unlisted companies have tosubmit a statement to the tax authorities detailing the “Estimatedtotal value of the company” (“Beregnet samlet verdi bakaksjene iselskapet”)

This may differ from the company’s book value of equity (althoughρ = 0.88) Graph

The estimate does not include net present value calculations orgoodwill

Companies with >5M NOK (approx. $500k) are subject to an auditobligation in the following financial year

Back to Data


Tax value vs. Book value of equity

Back


The effect of return heterogeneity (for ρ = 0)

Back


The effect of corr(r,w) (for σ = 0.04)

Back


Correlation b/w returns and wealth: Means.0

2.0

4.0

6.0

8.1

0 5 10 15 20Ventile of the distribution

Avg. return safe assets

Avg. return risky assets

1995

0.0

5.1

.15




2000

0.1

.2.3

.4




2005

.02

.03

.04

.05

.06

.07




2010

.02

.04

.06

.08

.1




2013




0.2

.4.6

.81

1.2

0 20 40 60 80 100Wealth perc. in 1995

Baseline No PE




0.2

.4.6

.81

1.2

0 20 40 60 80 100Wealth perc. in 1995

Baseline No PE

Back


Wealth Mobility in Norway


Other years

.02

.04

.06

.08

0 10 20 30 40 50 60 70 80 90 100Percentile of the wealth distribution

Baseline Alternative

1995

.02

.04

.06

.08



20000

.05

.1.1

5



2005

0.0

2.0

4.0

6.0

8



2010


Position in the company


Industry Composition


Further Decomposition


Mean return by cohort


Sharpe ratio by cohort

BackA. Fagereng, L. Guiso, D. Malacrino, and L. Pistaferri Returns Heterogeneity

US vs. Norway (top 0.1% wealth share)

.05

.1.1

5.2

.25

1995 2000 2005 2010 2015year

Norway (net worth) Norway (net worth), est.US, SaezZucman (net worth)


Motivation: Wealth inequality and concentration

In many countries, and over long time periods, the wealth distributionis extremely skewed and displays a long thick tail

Figure: Top 0.1% wealth share in the US (Saez and Zucman, 2016).


Difference in average and st.dev. of returns for "All" and"No PE" groups

0.0

5.1

.15

0 10 20 30 40 50 60 70 80 90 100Percentile wealth distribution

1995 2000 2005 2013

Average return difference b/wall and no PE

0.0

2.0

4.0

6.0

8


1995 2000 2005 2013

St.dev. return difference b/wall and no PE


Returns over the life cycle


Participation and risky shares over the life cycle

Back


Explaining the decline in returns at the very top

At the top 1%, more than 60% of wealth is held in private equity(entrepreneurship)

Three possibilities:

tax evasion (Zucman, 2016)"pivate equity premium puzzle" (Moskowitz and Vissing-Jorgensen,2002)direct control over dividend policy (Alstadsæter, Kopczuk and Telle,2014)

Tests:

Return gradient for safe and risky assets (drop only visible for riskyassets)Return gradient for those with and without private equityReturn gradient before and after 2006 introduction of shareholder tax


Return gradient for those with and without private equity

.02

.03

.04

.05

.06

.07

.08

950 960 970 980 990 1000Permillile of the wealth distribution

Private equity ownersOnly public equity owners


The effect of the shareholder tax reform on top percentiles

Shareholder tax reform is announced in 2001, but delayed until 2006Before 2006, dividends are basically untaxed


Time variation: Correlation between wealth and returns

Divide into three periods: 1995-2000, 2001-2005, 2006-2013

0.0

2.0

4.0

6.0

8


median r 9500 median r 0105 median r 0613

Back


Correlation between wealth and returns, 2013

0.0

2.0

4.0

6.0

8M

edia

n re

turn



Other years


Measurement of wealth inequality in the US

Saez and Zucman (2016) have access to IRS tax records on capitalincome (yit = ritwit), but wealth data are not available

They impute wealth using a capitalization method, imposing returnsheterogeneity (within broad asset classes):

wit =yitrt

If there is returns heterogeneity, and in particular a positivecorrelation between returns and wealth, the capitalization methodoverstates the extent of wealth inequality and concentration

If the correlation increases over time, the rise in wealth inequality andconcentration may also be overstated

In our Norwegian data we can compare actual wealth inequality withimputed wealth inequality


Theoretical Results

With independence between returns to wealth and wealth levels, bothGini and top wealth shares are overstated Result 1

With correlation between returns to wealth and wealth levels, Gini stilloverstated, while top wealth shares may be overstated or understateddepending on the sign of ρ Result 2


How large are the biases in practice?

We replicate Saez and Zucman’s capitalization approach to imputewealth (excluding housing, which is of higher quality only after 2010)in the Norwegian case

We then compute Gini indexes, and shares of wealth owned by thetop 5%, 1%, 0.1%

Results:

Gini indexes systematically overstate the degree of wealth inequalityFor top shares, results depend on how far in the tail we go


Gini

The Gini based on imputed wealth captures suffi ciently well thelong-term trends in actual wealth inequalityHowever, it overstates true inequality by a 1.05 factor on averageIt tends to do significantly worse in the middle of the sample perioddue to the introduction of a shareholder tax in 2006 (with someannouncement effects at work since 2001)


Top shares

The evidence on top shares is more nuancedThe larger the share we consider, the larger the overestimationHowever, the degree of overestimation declines if we consider smallerand smaller fractiles


Regression evidence

G (w )− G (w ) S0.1 (w )− S0.1 (w )(1) (2) (3) (4)

St.dev. returns 0.81∗(0.44)

−0.15(0.24)

2.45∗(1.37)

−0.39(0.86)

Corr(returns, wealth) 0.69∗∗∗(0.09)

2.06∗∗∗(0.31)

Obs. 20 20 20 20R2 0.16 0.83 0.15 0.76

Between 1978 and 2012, the top 0.1% wealth share increases by 15p.p. in the US (Saez and Zucman, 2015)

An increase in the correlation between wealth and returns mayoverstate the increase in wealth concentration at the very top (i.e.,∆ρ = 0.07)

Back


Time variation: Mean and median return

0.0

1.0

2.0

3.0

4.0

5.0

6

1995 2000 2005 2010 2015Year

Average return Median return


Time variation: St.dev. of returns

.03

.04

.05

.06

.07

.08

.09

St.d

ev.

1995 2000 2005 2010 2015Year


Time variation: Safe and risky assets

.05

0.0

5.1

.15

.2.2

5.3

.35

.4.4

5

1995 2000 2005 2010 2015Year

Average St.dev.

Risky assets

.05

0.0

5.1

.15

.2.2

5.3

.35

.4.4

51995 2000 2005 2010 2015

Year

Average St.dev.

Safe assets

Back


Time variation: Correlation between wealth and returns

Report median return for selected percentiles of the wealthdistributionReturns are persistently higher when we move up in the wealthdistribution

0.0

2.0

4.0

6

1995 2000 2005 2010 2015Year

Median return, 5th pctl. Median return, 10th pctl.Median return, 25th pctl. Median return, 50th pctl.Median return, 75th pctl. Median return, 90th pctl.Median return, 95th pctl.


Regression evidence: Returns

Dep. var.: Son’s return

(1) (2) (3) (4)Father’s return 0.075∗∗∗

(0.001)0.050∗∗∗(0.001)

0.050∗∗∗(0.001)

0.046∗∗∗(0.001)

Constant 2.675∗∗∗(0.002)

3.388∗∗∗(0.022)

2.296∗∗∗(0.125)

3.087∗∗∗(0.031)

Wealth percentile dummies N Y Y YYear FE N Y Y YAge controls N N Y YEducation lenght and type controls N N Y NIndividual FE N N N YR2 0.007 0.051 0.052 0.249N 14,548,263 14,548,263 14,548,263 14,548,263


Serial correlation?

From uit = fi + εit , additional persistence in returns may in principlecome from εit

We plot E (∆uit∆uit−s ) = E (∆εit∆εit−s ) for all s ≥ 0

The moments for s ≥ 2 are all economically undistinguishable from 0

Consistent with returns being basically unpredictable once controllingfor demographics and fixed effects


Autocovariance of residuals in first difference

Back


Safe assets


Issue # 1: Snapshot bias

Capital income may partly come from assets sold or purchased overthe year.

Suppose individual has wit = 100 and invests it in a rit = 0.1 CD

In mid-year, she puts extra savings into it (say, 50)At the end of year, we observe yit = 12.5 and wit+1 = 162.5The naive return measure is: rit = 0.125 → too highThe adjusted measure is much closer to actual one: rit = 0.095

Consider again the same starting scenario

But after 8 months, individual cashes half of CD and spends itAt the end of the year, we observe yit = 8.33 and wit+1 = 58.33The naive measure of return is: rit = 0.0833 → too lowThe adjusted measure is again much closer to actual one: rit = 0.105


Correlation with alternative measure

01

23

4M

edia

n re

turn

0 20 40 60 80 100Wealth percentile in 2013

Baseline Baseline no PEAlternative no PE


Tax incentives

Reform announced

Reform implemented

.02

0.0

2.0

4.0

6.0

8D

iff. b

/w re

turn

at t

op 1

% a

nd p

revi

ous

5%

1995 2000 2005 2010 2015Year


Cross-sectional distribution of returns

Figure:


Offshore Wealth

Potentially important at the top. Can distort measures of wealthconcentration

Alstadseter, Johannesen and Zucam (2015): data from a tax amnestyin Norway

1419 taxpayers disclose assets hidden offshoreNone below the 99th percentile of wealthOnly 12% in the top 0.1% (so not all hide assets offshore)Can potentially understate wealth at the top 0.1% by 4 p.p.

Does this matter for measurement of returns?

If the wealthy hide money offshore to avoid taxes at home, no: ourmeasure of gross returns would be unaffectedIf they invest offshore to grab more favorable returns, our estimates area lower bound of the extent of heterogeneity and correlation withwealth


Ranking of fixed effects under alternative measures ofreturns to wealth


Average returns, pooling all years

02

46

810

Mea

n re

turn

on

asse

ts

0 20 40 60 80 100Wealth percentile

T otal assets Risky assets Safe assets


Descriptive Statistics, 2013: (N=3,046,517)


Descriptive Statistics, 2013: Assets Statistics


Correlation between returns and wealth by asset class, 2013

0.0

05.0

1.0

15.0

2.0

25.0

3M

edia

n re

turn

60 70 80 90 100Percentile wealth distribution

Risky Assets

0.0

05.0

1.0

15.0

2.0

25.0

3M

edia

n re

turn


Safe Assets


Correlation between returns and wealth by asset class, 2013

0.0

05.0

1.0

15.0

2.0

25.0

3M

edia

n re

turn

60 70 80 90 100Percentile wealth distribution

Baseline Basel. no PE

Risky Assets

0.0

05.0

1.0

15.0

2.0

25.0

3M

edia

n re

turn


Baseline Basel. no PE

Safe Assets

Averages Pooling Cohorts Life cycle


Regression results

(1) (2) (3) (4) (5)Lagged risky share 0.64

(0.01)*** 1.02

(0.01)***

Lagged priv. eq. share 5.61(0.02)

*** 3.45(0.02)

*** 4.47(0.04)

***

Lagged mut. fund share 0.41(0.03)

***

Lagged direct stockh. share 2.33(0.05)

***

Male −0.03(0.00)

*** −0.03(0.00)

***

Years of schooling 0.03(0.00)

*** 0.04(0.00)

***

Econ/Business educ. 0.11(0.00)

*** 0.11(0.00)

***

Individual FE no no yes yes yesYear FE yes yes yes yes yesAge FE yes yes yes yes yesCounty FE yes yes yes yes yesDemographic controls yes yes yes yes yesLag. wealth percentile yes yes yes yes yesLag. risky share*year no yes no yes noLag. priv. eq share*year no yes no yes noR2 0.079 0.117 0.232 0.267 0.268N 50,553,557


wealth returns persistence and heterogeneity · use population data on wealth and capital income...

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