Lecture 2: Consumption (Continued). Wrapping Up from Last Time: Non Separabilities My belief: U(C,N)...

Lecture 2:Consumption (Continued)

Wrapping Up from Last Time: Non Separabilities

My belief:

U(C,N) can be written as u(C) + v(N)

However – we do not measure C directly:

C = f(x,h) where h is directly related to N (through time budget constraint).

We measure X and N in the data.

X = f-1(C,h(N))

Implication:

U(X,N) cannot be written as U(X) + V(N).

Take Away

Non Separabilities between X and N (expenditure and labor supply) are important.

When is it important to implicitly model the home production sector?

When changes to home production technology are important!

When care about cross good predictions.

When have actual consumption (intake) measures.

For most applications, a reduced form assumption that X and N are non-separable can be important.

Wrapping Up from Last Time: Synthetic Cohorts

From last time, we estimate:

What is the intuition of this regression? (I went through it fast last time).

Underlying the estimation is repeated cross section of regressions.

Hard to identify lifecycle or time series effects from cross sectional data.

Use the repeated cross sections to create “synthetic cohorts” based on observables.

0ln( )k kit age it c it t t fs it itC Age Cohort D Family

Examples (Done in class)

Another Data Set: Survey of Consumer Finances (SCF)

Detailed data on household balance sheets.

Cross sectional in design (small panel 1983 – 1989).

Data: 1983, 1986, 1989, 1992, 1995, 1998, 2001, 2004, and 2007

Sample Size: ~5,000 households per wave

Quality of Data:

Assets (general balance sheet, housing, some pension) – very good

Demographics and Income – very good

Some data imputed (need to account for the imputations – each observation has 5 “records” given the imputations).

Other Wealth Surveys: Health and Retirement Survey

• Surveys “Older Households” (not too old – over the age of 50)

• Panel data (same households are tracked)

• Years: Every two years starting in 1992.

• Detailed wealth and pension data (along with very good income, health and demographic data).

• Can apply to get the social security earnings records of participants!

• Have full income data and detailed wealth data on the eve of retirement. Can explore retirement saving adequacy in detail.

Scholz, Seshadri, and Khitatrakun (JPE 2006)

“Are Americans Saving ‘Optimally’ For Retirement?”

• Great use of HRS data (I love this paper)

• Writes down an individual optimization problem (with stochastic income, stochastic length of life, imperfect capital and insurance markets, realistic government programs, and a bequest motive).

• Solves the optimal saving rule for each household given their actual income (from their social security records), health trajectories, and expected length of life (from life tables based on observables).

• Assumes everyone has the same preferences and preference parameters.

• Computes the optimal amount of wealth they should have (on the eve of retirement) and compares that to the households actual wealth.

Scholz, Seshadri, and Khitatrakun: Key Findings

Other Wealth Surveys: PSID

• Panel Study of Income Dynamics (PSID) – Discussed in last class

• Panel data (same households are tracked)

• Years: 1984, 1989, 1994, 1999, 2001, 2003, 2005, 2007 and 2009

• Detailed wealth data for broad asset classes “stocks”, “checking accounts”, “debt”, etc. Until recently, pension data is not that good.

• Housing wealth (and mortgage debt) asked every year.

• Very good income and demographic data.

• See description in Hurst, Luoh, and Stafford (1996 – Brookings Papers on Economic Activity).

Topics for Today (May Extend Into Next Week)

Part 1. Estimating preference parameters using consumption data

Part 2. Discuss parts 1 and 3 of homework

Part 3. Discuss how consumption data can be used to learn about the income process households are facing.

Part 4. Discuss CEX data (part 2 of homework) and link to measures of changing consumption inequality.

Part 5. Discuss risk sharing and consumption

Part 6. Discuss my favorite of my papers (which empirically documents the importance of “status” in determining household

consumption decisions).

Part 1:Estimating Household Preferences

Part 1: Estimating Preferences

• Intertemporal elasticity of substitution (I.E.S.)

• Risk Aversion

• Time discount rates

Note: Risk aversion = (1/I.E.S.) with CES preferences

Note: Using notation from last week:

(1/ρ) = I.E.S.

δ = time discount rate

Why is the I.E.S. important?

• The intertemporal elasticity of substitution determines how levels of consumption respond over time to changes in the price of consumption over time (which is the real interest rate – or more broadly – the real return on assets).

• This parameter is important for many macro applications.

• Economics:

Raising interest rates lowers consumption today (substitution effect)

Raising interest rates raises consumption today (income effect – if net saver)

Consumption tomorrow unambiguously rises

Estimating I.E.S.

1

0

11

1 1 1 1

( )1max

1 1

(1 ) 1

1ln ln(1 )

t jT tt j

tj

tt t

t

t t t t

CE

CE r

C

C r

Graphical Illustration – No Substitution Effect

1 2 period

C

Low interest rate

High interest rate

ΔC2 = X

ΔC1 = X

With only an income effect – consumption growth rate will not respond to interestrate changes. Estimate of (1/ρ) = 0.

Graphical Illustration – With Substitution Effect

1 2 period

C

Low interest rate

High interest rate

ΔC2 > X

ΔC1 < X

As the substitution effect gets stronger, the growth rate of consumption increases more as interest rates increase. Estimate of (1/ρ) > 0.

Issues With Estimating I.E.S.

• Use of data source (micro or aggregate)

• Forecast of future interest rates?

• Correlation of forecast of interest rate with error term (things that make interest rates go up could be news about permanent income – which effect consumption).

1 1 1 1

1ln ln(1 )t t t tC r

Hall 1988

“Intertemporal Substitution in Consumption” (JPE)

Uses aggregate data (National Accounts)

Attempts to deal with time aggregation

Uses various measures of interest rates (stock market return, t-bill, etc.)

Instruments interest rate with lag interest rates and lags of consumption.

Estimate: 1/ρ ≈ 0.00

Attanasio and Weber 1993

“Consumption Growth, the Interest Rate and Aggregation” (ReStud)

Uses micro data (cohort data – British Family Expenditure Survey)

- Aggregate the micro data appropriately to aggregate data

Use aggregate data (from National Accounts)

Uses building society deposit rate as measure of interest rate

Instruments interest rate with lag interest rates.

Estimate: 1/ρ ≈ 0.35 (National Accounts)

≈ 0.60 (FES Data - aggregating)

≈ 0.75 (FES Data – micro data)

Vissing-Jorgensen (2002)

“Limited Asset Market Participation and the Elasticity of

Intertemporal Substitution” (JPE)

Data: CEX

Innovation: Split sample to those who are “saving” in financial markets

Bond returns should only apply to bond holders

Stock returns should only apply to stock holders

Others are not on the margin because of fixed costs of participating.

Estimate: 1/ρ ≈ 0.8 (Bond holders)

≈ 0.3 (Stock holders)

Gourinchas and Parker (2002)

“Consumption Over the Lifecycle” (Econometrica)

You should read this paper.

Estimates lifecycle consumption profiles in the presence of realistic labor income uncertainty (via calibration).

Use CEX data on consumption (synthetic cohorts).

Estimates the riskiness of income profiles (from the Panel Study of Income Dynamics) and feeds those into the model.

Use the model and the observed pattern of lifecycle profiles of expenditure to estimate preference parameters (risk aversion and the discount rate).

Gourinchas and Parker Structure

11 1

1

1

1

1

max ( , ) ( )

(1 )( )

( , ) ( )1

Nt N

t N Nt

t t t t

t t t

t t t t

E u C V W

W r W Y C

Cu C Z v

Y PV

P G P N

Methodology

Find in the income process (use different education and occupation groupings)

Using PSID

•Computed “G” from the data (mean growth rate of income over the lifecycle).

•Estimated the variances from the data.

Using CEX

•Compute lifecycle profiles of consumption

•Compute lifecycle profile of wealth/income (at beginning of life)

Intuition

No Uncertainty:

No “Buffer Stock Behavior”

Consumption growth determined by Rβ (where β = 1/(1+δ))

With Income Uncertainty

Buffer stock behavior takes place (household reduce consumption and increase saving to insure against future income shocks).

Consumption will track income if households are sufficiently “impatient”

Sufficiently Impatient with Uncertainty: RβE[(GN)-ρ] < 1

Results

Estimates (Base Specification):

δ = 4.2% - 4.7% (higher than chosen r = 3.6%)

ρ = 0.5 – 1.4 (1/ρ = 0.6 – 2.0)

Interpretation

Early in the lifecycle, households act as “buffer stock households”. As income growth is “high”, consumption tracks income (do not want to accumulate too much debt to smooth consumption because of income risk)

In the later part of the lifecycle, consumption falls because households are sufficiently impatient such that δ > r.

Barsky, Juster, Kimball, and Shapiro (1997)

Preference Parameters and Behavior Heterogeneity: An Experimental Approach in the Health and Retirement Survey (QJE)

“Suppose that you are the only income earner in your family, and you have a good job guaranteed to give you (and your current (family)) income every year for life. You are given the opportunity to take a new and equally good job, with a 50-50 chance it will double your (family) income and a 40-40 chance that it will cut your (family) income by a third. Would you take the new job?”

If answer yes to base question, give a new question changing “third” to “half”.

If answer no to base question, give a new question changing “third” to “20 percent”.

Barsky, Juster, Kimball, and Shapiro (1997)

Have four sets of answers:

No – No ‘Low Risk Tolerance’

No – Yes ‘Medium Low Risk Tolerance’

Yes – No ‘Medium High Risk Tolerance’

Yes – Yes ‘High Risk Tolerance’

Use Survey Evidence to Measure Risk Parameters

Risk Grouping Percent

Low Tolerance “reject all gambles” 64.6%

Medium Low Tolerance 11.6%

Medium High Tolerance 10.9%

High Tolerance “accept 50-50 gamble” 12.8%

Using some structure (on distributions and preferences), estimate the coefficient of relative risk aversion (ρ) to be about 4.0 (standard error of 5 or so).

Implication: (1/ρ) = 0.25 (lower than other estimates)

Summary of Estimated I.E.S and Risk Aversion

For those that ignore non-separabilities (or labor supply broadly), researchers usually use CES utility such that:

ρ = 1.5 – 2.0 (1/ρ = 0.5 – 0.66)

δ = r = 3.0 – 3.5% (sometimes δ > r )

We will talk about preferences with non-separable leisure in a few weeks.

1

0

( )1max

1 1

t jT tt j

tj

CE

A Separate Question:The Importance of Precautionary/Buffer Stock Savings

• How much of total wealth accumulation can be attributed to a precautionary motive?

• Carroll and Samwick “How Important is Precautionary Saving?” (ReStat, 1998)

• Hurst et al “The Importance of Business Owners in Assessing the Size of Precautionary Savings” (ReStat, forthcoming).

• Use panel data from the PSID and estimate:

• Precautionary savings model predicts wealth will be higher the more risk that households face.

0 1 2 3ln( ) ln( )permy transyit it it it it itW y Z u

The Importance of Precautionary Savings

• Use income data to predict the transitory and permanent shocks to income by occupation and industry (specifically, we compute the variances for each individual and then instrument the two variances with income and occupation)

• Identifying assumption:

Occupation and Industry are independent of wealth aside from their effect on the variances of income.

• Focus on households aged 26 – 50 (years 1984 and 1994)

The Importance of Precautionary Savings Group

Permanent variance

Transitory variance

Percent of sample

Total sample 0.0162 0.0513 100 (0.0023) (0.0040) Professional and technical workers 0.0135 0.0404 23.74 (0.0042) (0.0069) Managers (non self-employed) 0.0171 0.0305 14.60

(0.0048) (0.0083)

Managers (self-employed) 0.0272 0.0866 5.27 (0.0163) (0.0270) Clerical and sales workers 0.0192 0.0541 13.25 (0.0075) (0.0128) Craftsmen 0.0129 0.0524 20.10 (0.0043) (0.0079) Operatives and laborers 0.0199 0.0592 15.35 (0.0055) (0.0094) Farmers and farm laborers 0.0079 0.1414 2.01 (0.0209) (0.05) Service workers 0.0126 0.0547 5.69 (0.0096) (0.0184)

Results Variables

Pooled

Pooled

Variance of Permanent Income Shocks (α1) 15.91 -1.57 (2.98) (4.35) Variance of Transitory Income Shocks (α2) 7.52 -0.27 (1.48) (1.87) Percentage of Net Worth Explained by Precautionary Savings

47.5% 13.3%

Dependent Variable (Log) Total

Net Worth Total

Net Worth Permanent Income Measure (Averaged) Non-capital

Income Non-capital

Income Sample Size 2,144 1,729

• Carroll/Samwick results (our replication) in column I• Our results (controlling for business owners) in column II

• Our results ranged from 0.0 – 14% of total wealth.

An Aside

• Here are some good notes from Chris Carroll on the underpinnings of the “Buffer Stock Saving Model”

http://econ.jhu.edu/people/ccarroll/public/lecturenotes/Consumption/TractableBufferStock/

They can be found on Chris Carroll’s Johns Hopkins web site.



Things I am Interested In: Heterogeneity of Preferences

• “Grasshoppers, Ants, and Pre-Retirement Wealth” (Erik Hurst ; permanent working paper) – my dissertation

“It was wintertime, the ants’ store of grain had got wet and they were laying it out to dry. A hungry grasshopper asked them to give it something to eat. ‘Why did you not store food in the summer like us?’ the ants asked. ‘I hadn’t time’, it replied. ‘I was too busy making sweet music.’ The ants laughed at the grasshopper. ‘Very well’, they said. ‘Since you piped in the summer, now dance in the winter’.”

• Permanent income hypothesis (broadly defined) describes well roughly 80% of the population. Roughly 20% appear “rule of thumb” or “time inconsistent”.

Discussion of My Dissertation (including origins)

• Discuss in class

Things I am Interested In: Stability of Preferences

• “The Correlation of Wealth Across Generations” (Kerwin Charles and Erik Hurst ; JPE 2002)

• Do high saving parents have high saving kids? (Not the question I was originally interested in).

• Real question of interest:

“Can shocks to “preferences” today have long lasting effects on economic decisions?”

“If we disenfranchise a group (Blacks) in the past – and then stop – how long will differences between two groups persist”

Estimating Parent-Child Correlations

Use data from the Panel Study of Income Dynamics and estimate:

δ1 ≈ 0.40

δ2 ≈ 0.20 (where Z vectors include permanent income, direct transfers, education, etc.)

δ2 can be interpreted as the correlation in saving rates (conditional on income, how similar are parent and child wealths)

Can be do to “active” component or “passive” component.

2 21 1 2 1 2k p k k k k p p p p kW W Age Age Age Agea d a a a a e= + + + + + +

2 22 1 2 1 2k p k k k k p p p p k k p p kW W Age Age Age Age Z Z u

Wealth Persistence

Parental Age-Adjusted Log Wealth Quintile (1984-1989)Child Age-Adjusted Log Wealth

Quintile (1999) 1 2 3 4 5

1 36 26 16 15 11

2 29 24 21 13 16

3 16 24 25 20 14

4 12 15 24 26 24

5 7 12 15 26 36

Total 100 100 100 100 100

Persistence in Portfolio PersistenceI II III

Child Owns Stock? Child Owns Business? Child Owns Home?

A B C A B C A B C

Parent Owns Stock 0.133 0.057 0.058

(0.039) (0.041) (0.041)

Parental Owns Business 0.110 0.081 0.065

(0.033) (0.034) (0.034)

Parental Owns Home 0.245 0.145 0.147

(0.073) (0.072) (0.073)

Parent and Child Age Controls a Yes Yes Yes Yes Yes Yes Yes Yes Yes

Parent and Child Income Controls b No Yes Yes No Yes Yes No Yes Yes

Parent and Child Risk Tolerance Controls c No No Yes No No Yes No No Yes

Adjusted R-Squared 0.030 0.115 0.138 0.029 0.062 0.072 0.087 0.180 0.181

Persistence in Portfolio Persistence

Child’s Risk Tolerance MeasureVery Low Low Medium High

Regressors A B A B A B A B

Parental Risk Tolerance

Low Risk Tolerance 0.059 0.064 0.008 -0.021 -0.054 -0.042 -0.012 -0.001(0.065) (0.066) (0.051) (0.052) (0.054) (0.054) (0.057) (0.058)

Medium Risk Tolerance -0.117 -0.125 0.072 0.039 0.081 0.107 -0.037 -0.021(0.079) (0.083) (0.062) (0.065) (0.065) (0.068) (0.069) (0.072)

High Risk Tolerance -0.138 -0.098 -0.005 -0.013 -0.010 -0.012 0.154 0.123(0.057) (0.057) (0.045) (0.047) (0.047) (0.049) (0.050) (0.053)

Part 1: Thoughts/Conclusions

• Use consumption data to estimate preference parameters

• Precautionary savings is an important feature in modern macro models

• The importance of precautionary saving depends on household risk aversion, their impatience, and the risk they face.

• Empirically, the importance of “precautionary savings” in explaining aggregate wealth holdings is mixed. Recent evidence suggests that it is small.

• Preference heterogeneity seems to exist in the data. How important is it?

• Are preferences stable?

Part 2:Homework Part 1 and Part 3

Discussion of “The Age of Reason: Financial Decisions Over the Lifecycle”

Erik Hurst

University of Chicago, GSB

Paper Synopsis

• The main findings

– Focus on a cross section of households

– Within the cross section, interest rates paid (fees, inverse of financial sophistication) is U-shaped

– Holds in a wide variety of settings

• Emphasized interpretation

– Financial learning and declining cognitive ability

• Other interpretations offered (differing risk, opportunity cost of time, medical expenses, sample selection, cohort effects, etc.)

My comments

• I will focus on the “old” vs. “middle age” results (the upward sloping portion of the U-shaped profiles). I am going to ignore the young.

• Comment 1: The importance of selection?

Use data from existing nationally representative surveys to show that selection issues are very important (the 60-70 year olds that are borrowing are not random 60-70 year olds).

• Comment 2: Are these magnitudes big?

Maybe….Aggregating across all different debt types, difference in rates/fees paid by 55 year olds relative to 75 year olds is about $175 per year (~$3.50/week).

Two (related) Questions

• Why do people hold debt? (people do not hold debt randomly)

– Smooth out consumption over their lifecycle

– Borrowing will be peak when household income profiles are “low” or household consumption needs are “high”

– Who borrowers when they are 20? when they are 50? when they are 70?

• What interest rate will borrowers pay? (interest rates not charged randomly)

– Function of default probabilities

– Function of collateral amounts

– Function of borrower search (opportunity cost of time and value of lower interest rate)

– Function of financial sophistication

Issue 1: Examining Selection

• Use data from 2003 PSID

• Nationally representative

• Cross sectional comparisons (just like in this paper)

• Focus on 25 – 75 year olds.

• Look at:

Lifecycle profile of credit card debt and mortgage debt

Differences in the types of people (based on observables) that hold debt over the lifecycle

Are the people that hold debt at older ages representative?

• Not Really - For example, sizeable differences by race:

Black Head

Have Mortgage debt

Age Non-Borrower Borrower Difference

50s 0.150 0.082 -0.068

60s 0.134 0.100 -0.034

0.035 *

Have Credit Card debt

Age Non Borrower Borrower Difference

50s 0.122 0.100 -0.021

60s 0.102 0.146 0.044

0.065*

* Indicates significance at the 1% level


• Why is the racial composition important?

Blacks are found to pay higher interest rates than Whites in many markets adjusting for a full vector of demographics (including age).

Charles, Hurst and Stephens (2008) – presented in a AEA session earlier today.

Blacks pay higher rates for car loans than otherwise comparable whites (using SCF data). The effect is entirely due to the type of establishments frequented by blacks.

Consistent with a plethora of recent lawsuits against vehicle finance service companies (GMAC, Ford Credit, etc.)

Discrimination or financial sophistication?


• Not Really - Health Differences:

Report Health Deterioration in Prior Two Years

Have Mortgage debt

Age Non-Borrower Borrower Difference

50s 0.246 0.141 -0.105

60s 0.223 0.203 -0.020

0.085 *

Have Credit Card debt

Age Non Borrower Borrower Difference

50s 0.179 0.190 0.011

60s 0.186 0.259 0.073

0.062*


• Additional differences by age (conditional on borrowing):

- Self reported health much worse (mortgage and credit card)

- Hospitalization more likely in the prior two years (credit card)

- Gross wealth much lower (mortgage and credit card)

• No difference by education (interesting)

Summary: Is selection important? --- Yes

• Probability of holding debt (and conditional levels of debt) diminish rapidly with age.

• Who holds debt among older households?

Much more likely to be Black.

Much more likely to have received an adverse health shock (even if health spending is not put on the credit card, health shocks have occurred).

Poorer individuals.

Issue 2 – Examining Magnitudes

• Cost of interest burden between 55 and 75 year olds: Annual Cost*

Home equity loan interest gap: (~25 basis points) $100

Home equity line interest gap: (~30 basis points)$180

Credit card interest gap: (~5 basis points) $4

Auto interest rate gap: (~5 basis points) $2

Mortgage interest rate gap: (~12 basis points) $53

Total $350/year

* All costs valued at the mean level of debt (as reported in the Appendix)

Magnitudes?

• Numbers on the previous page are likely way overstated!

• As seen above, the amount of debt holdings seem to fall with age by roughly 50% (so my estimated costs should fall by 50%).

• Suggestion: Why not compute exact dollar differences by different age ranges using data from SCF, PSID, HRS, AHEAD which tells the amount of debt of each type held at each age.

• Prediction: For those holding debt, my guess is that the annual difference in expenditures is going to be less than $175/year (between 55 and 75 year olds). (About 50 minutes a month valued at pre-retirement wages)

• Note: This number again would still be biased upwards if borrower composition is changing between 55 and 75.

Conclusion

• The policy prescription (particularly for the aged) depend on the reasons for the upward sloping interest rate profile.

Are the old unable to process complex interest rate tasks (relative to their young selves)? I am not sure.

• Selection seems to be important – much more work can be done on this (the data sets to address this are readily available). Moreover, interest rate data exists in some of these other data sets.

• Race and health composition changes over the lifecycle!

• The magnitudes are pretty small (not zero – just small). Would a cost benefit analysis recommend a policy intervention (again – particularly for the old)? A table of costs would be a great addition to the paper.

Thoughts on “Depression Babies”

Why Has The U.S. Saving Rate Declined?

Part 3:Consumption and Income

Consumption and Income Shocks

2

1

1 1max

1 2

( )(1 )

C is bliss point consumption, B is beginning of period

wealth, and Y is labor income.

Note: Assumption of "log utility"

For simplic

s t

ts t

t t t t

E C C

B B Y C r

where

ity: Assume .r

64

Income Shocks and Consumption Growth

• Given above preferences, consumption is a random walk such that:

• Suppose, income process is as follows:

• Optimal Consumption Growth:

1 1, [ ] 0 0t t t t nC E n

1 1

1 1 1

[ ] [ ] 0

t t t

t t t

t t n t t n

P P

Y P

E E

1 1 11t t t

rC

r

65

Deaton and Paxson (1994)

“Intertemporal Choice and Inequality” (JPE)

Hypotheses: PIH implies that for any cohort of people born at the same time, inequality in both consumption and income should

grow with age.

How much consumption inequality grows informs researchers about:

o Lifecycle shocks to permanent incomeo Insurance mechanisms available to households.

Data: U.S., Great Britain, and Taiwan

66

Deaton and Paxson Methodology (U.S. Application)

• Variance of Residual Variation

• Compute variance of εkit at each age and cohort

• Regress variance of εkit on age and cohort dummies (equation

(2))

• Plot age coefficients (deviation from 25 year olds)

Note: This is my application of the Deaton/Paxson Methodology (very similar in spirit to theirs).

0ln k k k k k kit age it cohort it t t fs it itC Age Cohort D Family

Figure 1b: With and With Out Housing Services

Figure 1b: With and With Out Housing Services

Cross Sectional Variance of Total Nondurables for 25 Year Olds = 0.16

70

More Aguiar/Hurst (2009)

• Examine lifecycle profile of cross sectional inequality by category

• Goods which have expenditures that increase with market work (due to home production or complementarity) should experience increasing dispersion when the dispersion of work increases.

• Portion of lifecycle profile of cross sectional inequality due to these goods does NOT inform researchers about:

o Lifecycle profile of shocks to permanent incomeo Insurance mechanisms available to households

Dispersion of Propensity to Work Over Life Cycle

Cross Sectional Lifecycle Dispersion: Entertainment

Non Increasing Dispersion Categories

Where is the Increase in Dispersion Coming From?

Lifecycle Variation in Standard Deviation

Consumption CategoryVariance at

Age 25Change25 - 44

Change45 - 59

Change59 - 68

Change25 - 75

Increasing Transportation 0.70 -0.14 0.11 0.04 0.38 Clothing/P. Care 0.63 0.18 0.53 0.09 0.91 Food Away 1.54 0.00 1.29 0.42 1.91 Alcohol /Tobacco 5.80 1.53 2.62 1.05 4.82 Domestic Services 6.82 0.84 1.15 0.47 2.85

Non Increasing Housing Services 0.41 -0.07 -0.12 -0.07 -0.27 Utilities 0.89 -0.56 -0.09 -0.05 -0.76 Entertainment 1.29 -0.31 -0.10 -0.17 -0.69 Other Non-Durable 9.57 -0.71 -0.91 -0.27 -2.39 Food at Home 0.41 -0.05 0.02 0.01 -0.02

Cross Sectional Dispersion Over Lifecycle

Cross Sectional Dispersion Over Lifecycle: Figure 6b

Core

What Does it Mean?

• Aguiar and Hurst (2009)

Write down a model where households maximize utility with three consumption goods (and leisure) with the following constraints:

one good (food) is amenable to home productionone good (transport, clothes) are complements to market workthere is a time budget constraint

Assumptions:

o conditional on work, income process is uncertaino take the lifecycle process of work as exogenouso assume that individual receives no utility for the lifecycle component of work related expenses.

Implications

When use disaggregated consumption data to match moments of model, get:

• The estimated uninsurable/unanticipated permanent income volatility gets reduced by more than half (increases transitory volatility)

Reason: The consumption volatility of “core nondurables” increases by roughly 50% less than “total nondurables”

• The estimated importance of precautionary savings due to income fluctuations in explaining the wealth holdings of individuals is reduced.

Reason: Permanent income volatility is lower

• Agents are estimated to be significantly more patient

Reason: Mean spending on core nondurables does not fall over the back half of the lifecycle.

Conclusions

• Beckerian model of consumption is important for explaining not only lifecycle profile of mean expenditures but also lifecycle profile of cross sectional dispersion in expenditures.

- Explains decline in mean during back half of the lifecycle.

- Explains increase in cross sectional dispersion post middle age.

• The assumption that consumption (expenditure) and leisure are non-separable is not a valid assumption.

Part 4:Homework Part 2

and Time Series of Consumption Inequality

84

Average Consumption in CEX

85

Percent Change in Consumption in CEX (from 1981)

86

Income and Consumption Inequality

• Large literature documenting the increase in income inequality within the U.S. during the last 30 years (Katz and Autor, 1999)

• Consumption is a better measure of well being than income (utility is U(C) not U(Y)).

• Does income inequality imply consumption inequality?

Depends on whether income inequality is “permanent”

Depends on insurance mechanisms available to households

Depends on other margins of substitution (home production, female labor supply, etc.).

• Topic taken up by Attanasio and Davis (1994, JPE), Krueger and Perri (2006, ReStud), and Attanasio, Battistin, and Ichimura (2004, orazio’s web page).

87

Kevin Murphy’s Web Page

88

Kevin Murphy’s Web Page

89

Consumption Inequality (Time Series)

90

Consumption Inequality: Adjusting For Family Size

91

Trends in CEX Consumption (Attanasio et al, 2004)

“What really happened to consumption inequality in the US?”

http://www.homepages.ucl.ac.uk/~uctpjrt/abi_0204.pdf

92

Trends in CEX Consumption Inequality (Attanasio et al, 2004)

“What really happened to consumption inequality in the US?”

http://www.homepages.ucl.ac.uk/~uctpjrt/abi_0204.pdf

93

Aguiar and Hurst (2009)Change in the Cross Sectional Variance of Log Expenditure Over Different

Time Ranges

I. II. III.

Log Expenditure Measure1981-1990

1990-2003

1981-2003

1981-1990

1990-2003

1981-2003

1981-1990

1990-2003

1981-2003

Log Total Non Durable Expenditures 0.055 0.039 0.094 0.042 0.045 0.087 0.036 0.037 0.073

Log Core Non Durable Expenditures 0.063 0.027 0.090 0.037 0.031 0.068 0.034 0.023 0.058

Log Work Related Expenditures 0.104 0.041 0.145 0.076 0.038 0.114 0.052 0.020 0.072

Log Food at Home Expenditures -0.047 -0.020 -0.066 -0.005 0.003 -0.002 -0.004 0.000 -0.003

First Stage Controls None None None Full Full Full Full Full Full

Second Stage Controls None None None None None None Age Age Age

Part 5:Consumption and Insurance

Consumption Insurance

• The Broad Question of Interest:

Are households “insured” against “shocks” to their income process?

• The Problem at Hand:

How does one measure a “shock” from the household’s perspective? What we (the econometricians) label as a shock may be anticipated from the household’s perspective. Given that, households may react little to our identified “shocks”.

• The Methodology:

Use the joint distributions of income and consumption (and sometimes expectations) to analyze the extent of consumption insurance.

95

The Conceptual Issue: Uncertain Income

96

TimeTt

Income

Uncertain Income

97

TimeTt

Income

Shock (as identified by econometrician)

Suppose, from individual perspective, the household truly did receive an unexpected permanent shock to income.

No Insurance

98

TimeTt

Income

Household consumption responds completely to permanent shock to income.

Consumption (dotted line)

Complete Insurance

99

TimeTt

Income

Household consumption will not respond to the permanent income shock.

Consumption

The Conceptual Issue: Deterministic Income

100

TimeTt

Income

Suppose, from individual perspective, the income process is completely deterministic.


101

TimeTt

Income

Shock (as identified by econometrician only)

Suppose, from individual perspective, the income process is completely deterministic.


102

TimeTt

Income

Forward looking consumers will incorporate the expected change in income into their current consumption decisions.

Consumption


103

TimeTt

Income

Forward looking consumers will incorporate the expected change in income into their current consumption decisions.

Consumption

No change in consumption growth


104

TimeTt

Income

From the perspective of the econometrician, households appear to be completely insured against permanent “shocks” to income.

Consumption



105

TimeTt

Income

From the perspective of the econometrician, households appear to be completely insured against permanent “shocks” to income.

Results from not properly identifying unanticipated changes in income.

Consumption


Blundell, Pistaferri, and Preston (AER, 2008)

• Write down and estimate an econometric model to uncover the extent to which households are insured against both transitory and permanent income shocks.

• They use data on actual income and consumption data.

• Using data on only observed income and consumption does not allow the econometrician to separately identifying unanticipated changes in income from anticipated changes in income. (Akin to the simplified example above).

• Blundell, Pistaferri and Preston made the implicit assumption that variance of anticipated permanent changes in income and the variance of the anticipated transitory changes in income were zero (i.e., there was no uncertainty over the anticipated changes in income).

• As seen above, if that assumption fails to hold, the estimated extent of household insurance would be over stated (change in consumption understated). 106

Kaufmann and Pistaferri (AER P&P, 2009)

• Use data on:

Actual income realizations

Actual consumption data

Expected income changes

• Use the moments of these three series to identify how consumption responds to the unexpected permanent and transitory innovations in income.

• The key is using data on expected income changes to better isolate income “shocks” from the perspective of the household.

107

Some More Preliminaries

• Data is from the Italian Survey of Household Income and Wealth

• Survey questions on individual expectations of future income.

• With a tad bit of structure, can compute the expected future income for all households who report answers to the survey questions.

• Strong correlation between expected income and actual income (~0.5).

108

Key Results

• As theory predicts, the amount of insurance is OVERSTATED with respect to permanent income shocks when econometricians ignore the fact that individuals have superior information about their own income process.

- Some of our identified “shocks” are expected by the household resulting in a muted consumption response.

• Key results from these paper:

BPP KP

Response to Transitory Income Shocks 0.14 0.31

(0.05) (0.43)

Response to Permanent Income Shocks 0.69 0.94(0.27) (0.51)

109

Benefits of Risk Sharing

• An important implication of complete markets, full insurance model is that allows the construction of a “representative” consumer.

• Good for aggregating individuals

• Aggregate consumption moves as if it were determined by a representative consumer who only responds to aggregate risk (no need to worry about idiosyncratic risk).

Formalize the test:

110

ln( )

where full risk sharing implies that = 0

i i it t t tC k v y

Important Earlier Empirical Papers Testing Full Risk Sharing

• Townsend (1994) “Risk and Insurance in Village India” (Econometrica)

• Cochrane (1991) “A Simple Test of Consumption Insurance” (JPE)

• Attanasio and Davis (1996) “Relative Wage Movements and the Distribution of Consumption” (JPE)

All papers reject perfect risk sharing. Some limited evidence of partial risk sharing (government transfers, self insurance for transitory shocks, family transfers).

111

Something You Should Read

Job Market Paper from Greg Kaplan (out of NYU – now at Penn Economics Department)

“Moving Back Home: Insurance Against Labor Market Risk”

Had offers from Booth, Wharton, Penn Econ, Berkeley Econ, Sloan, Michigan, and 6 others.

Dissertation looked at the role families play (particularly the ability to move back home) in insuring labor market risk for young low educated workers.

http://homepages.nyu.edu/~gwk210/Greg_Kaplan/Home.html

All of you could have written this dissertation.112

http://homepages.nyu.edu/~gwk210/Greg_Kaplan/Home.html

Conclusions on Risk Sharing

• There is some risk sharing (within families).

• However, we are far from perfect risk sharing.

• Permanent idiosyncratic shocks have permanent effects on household consumption.

113

Part 6

“Conspicuous Consumption and Race”

Charles, Hurst, and RoussanovQJE 2009

Racial Differences in Economic Outcomes

• Large literature documenting differences in wealth holdings, savings rates, and portfolio allocation between Blacks and Whites. (e.g., Barsky et al. (2002), Hurst et al. (1998), Charles and Hurst (2001), etc.)

Question: Why do Blacks save less (hold less wealth) than otherwise similar Whites?

• Likewise, there is some work documenting racial differences in individual consumption categories such as education and health insurance.

Question: Why do Blacks spend less on health insurance and education than similar Whites?

• Related Question: What are Blacks spending more on?

• Question: Can racial differences in spending patterns on these goods explain (at least partially) racial differences in savings rates or racial

differences in education or health spending?

Conspicuous (Visible) Consumption

• Veblen (1899) : Consumption communicates information about economic status.

“Consumption is evidence of wealth, and thus becomes honorific, and…failure to consume a mark of demerit.”

o The argument does not necessarily apply to “total consumption” – only the portion of consumption that is observable by others.

• Theoretically, models of conspicuous consumption have been explored by many.

• Empirically, the signaling value of consumption is relatively unexplored in economics.

Some Preliminaries: An Overview of Main Data Set

• Use data from Consumer Expenditure Survey (CEX)

o Use data from 1986 – 2002 (pooled).

o Include one observation per household (collapse multiple observations throughout the year into a single observation).

o Restrict the primary analysis sample to households with a head aged 18 to 49 (inclusive).

o Include households with a head being either Black, Hispanic, or White (we also look at Asians in some cuts of the data).

Sample includes roughly 37,300 Whites; 6,800 Blacks; 5,300 Hispanics

Will use other data (PSID) to confirm the CEX findings

An Overview of the Data (continued)

• Summary: We define visible goods to include expenditures on:

o Clothing and Jewelryo Personal Careo Spending on vehicles (excluding maintenance)

• Treat housing separately

o Hard to separate the quantity from the price effect. o Evidence of discriminatory practices.

• Note: Racial differences in visible spending get slightly LARGER if we include housing as a visible good.

Some Descriptive Statistics (Tables 1 and A2)

All White Black Hispanic

Total Annual Income

(Conditional Inc > 0 )

57,800 63,800 38,400 39,800

Total Expenditure (Quarterly) 10,700 11,600 7,700 8,400

Visible Expenditures (Quarterly)

2,029 2,176 1,538 1,681

Vis Expend/Total Expend 0.12 0.12 0.12 0.12

All in 2005 dollars

Part 1: Documenting the Facts

Estimate:

ln(Visible Exp) = βo + β1 Black + β2 Hispanic + φ Permanent Income + θ X + η

Additional Controls (X):

o Year dummies ; o Sex dummy ; o Quadratic in age;o Family structure dummies (number of adults, number of children,

married) ;o Location dummies (urban dummy, MSA dummy, census region

dummies, city size dummies (post-1996)) ; o Wealth controls (in some specifications)

Measuring Permanent Income

Approach 1:

Use current income controls (current income, education dummies, and occupation dummies) to proxy for permanent income.

CEX current income data is notoriously bad (27% of sample had missing income – no imputations).

Racial gaps in income using CEX data do not match the racial gaps in income using CPS data (although the CEX expenditure gaps match the CPS income gaps).

Approach 2:

Use CEX total expenditure as a proxy for permanent income.

Potential Issues with Approach 2

Potential problems with using total expenditure as a proxy for permanent income:

1. Total expenditure is not exogenous (expenditure components are jointly determined).

2. Measurement error in visible expenditure will cause a correlation between visible expenditures and total expenditures.

Solution:

Instrument total expenditure with our current income controls (either current income or current income, education and occupation dummies).

Verify our results in the PSID where we can use panel aspect to create a better measure of permanent income.

Preferred Specification

Estimate:

ln(Visible Exp) = βo + β1 Black + β2 Hispanic + φ ln(Total Exp) + θ X + η

Notes:

Instrument Total Expenditure with: a dummy for whether current income was zero, a cubic in current income (or the log of current income) if income was positive, education and occupation dummies.

Included non-linear total expenditure controls as a robustness.

Similar to standard “consumption demand system” model.

Will estimate separately by race and plot the visible expenditure Engel curves.

Table 2: Base Regression Results

Regression Controls IncludedBlack

CoefficientHispanic

Coefficient

1. No Additional Controls -0.38 (0.04) -0.23 (0.04)

2. Specification 1 plus current income controls -0.03 (0.03) 0.14 (0.04)

3. Specification 1 plus ln(Total Expenditure) 0.31 (0.03) 0.26 (0.06)

4. IV Regression of Specification 3 0.23 (0.03) 0.20 (0.05)

5. Specification 4 plus time dummies 0.24 (0.03) 0.21 (0.05)

6. Specification 5 plus rest of X vector 0.26 (0.02) 0.23 (0.05)

Magnitudes

• Blacks Hispanics spend roughly 26% (23%) more on visible consumption than comparable whites.

• Average household in sample spends roughly $2,100 per quarter on visible consumption.

• Blacks (Hispanics) spend roughly $2,200 ($1,900) a year more on visible goods than comparable Whites.

• The level is likely an under estimate (research shows that the CEX under reports total expenditures relative to NIPA).

• Mean total pre-tax family income for Blacks (Hispanics) during the 1990s (March CPS): $42,500 ($48,300)

Estimated Engel Curves (Figure 1)

Estimated Difference at sample mean income ~ 0.3

24

68

Lo

g Q

uart

erly V

isib

le E

xpen

diture

s

7 8 9 10Log Quarterly Total Expenditure

Black White

Separately Analyzing Visible Components (Table 3)

I. Full Sample II. Positive Car Spending

Visible Consumption Sub-Category

Black Dummy

Hispanic Dummy

Black Dummy

Hispanic Dummy

Clothing/Jewelry 0.38 0.41 0.36 0.37

(0.03) (0.03) (0.04) (0.02)

Personal Care 0.73 0.43 0.81 0.42

(0.05) (0.03) (0.06) (0.05)

Cars (Limited) -0.43 -0.29 0.12 0.09

(0.07) (0.10) (0.04) (0.06)

Cars (Expanded) -0.46 -0.34 0.09 0.04

(0.10) (0.17) (0.03) (0.05)

Table 4: Racial Differences in All Spending Categories

Log Expenditure Black Hispanic

Housing 0.03 0.13

(0.02) (0.03)

Utilities 0.09 -0.02

(0.03) (0.02)

Food -0.06 0.06

(0.02) (0.02)

Other Transport. -0.15 -0.02

(0.03) (0.04)

Home Furnishings -0.18 0.09

(0.04) (0.05)

Education -0.16 -0.30

(0.10) (0.12)

Log Expenditure Black Hispanic

Entertain Services -0.29 -0.36

(0.03) (0.05)

Entertain Durables -0.35 -0.17

(0.05) (0.05)

Health -0.51 -0.48

(0.05) (0.06)

Alc./Tobacco -1.04 -1.04

(0.05) (0.05)

Other -0.08 -0.38

(0.04) (0.08)

Table 5: Robustness Exercise Using PSID

Log Expenditure Black

Clothing Expenditures, No Controls -0.07

(0.07)

Clothing Expenditures, Full Controls 0.24

(0.07)

Price of Recent Car Purchase, Full Controls 0.12

(0.09)

Food Expenditures, Full Controls -0.12

(0.03)

Entertainment Expenditures, Full Controls -0.33

(0.08)

Other Transportation, Full Controls -0.09

(0.06)

Summary of the Facts

• Large evidence that relative to economically similar Whites, both Blacks and Hispanics consume considerably more “visible” goods.

o The magnitudes are large: roughly 26% more which translates to about $2,100 more per year in visible spending for blacks.

o The findings are very robust – within different sub-groups of the population, across different time periods, across different specifications.

o The percentage differences are much smaller for older Black households (off a much smaller base).

o Aside from housing, all other consumption categories are lower for Blacks and Hispanics (including health spending and education)

Part 2 – A Model of Conspicuous Consumption

• Preference differences could explain the differences in consumption patterns across races.

• Question 1:

Is there any model that does not rely on differences in preferences between races that can explain the documented consumption patterns?

• Question 2:

If so, can the predictions of this model be distinguish from a model of preference differences?

Part 2 – A Signaling Model of Conspicuous Consumption

• Glazer and Konrad (1996) study the signaling value of observable charitable giving.

• Other models include Mailath (1987) and Ireland (1994).

• Similar in implications to the classic Spence model (1973) of job market signaling.

• Our goal is to draw on the implications of these theoretical models.

Part 2 – Model Components

• Preferences (household i drawn from group k)

where: ci is consumption of all visible goods

yi is the total household income endowment

y-c is consumption of all non-visible goods (static model)

• Income is not observable (only c is observable to others)

• Income is drawn from known distribution fk(y) with support [ykmin, yk

max]

• Define: Status (sik) is society’s inference about i’s income based upon things observed about the person.

( ) ( ) ( )k k k ki i i iy c u c w s

* *| , , where c is equilibrium visible consumptionk k k ki i i is E y c k

Part 2 – Model Components

Notes:

• All preferences are constant across all groups.

• v(.), u(.), and w(.) are each concave and twice continuously differentiable.

• We do not take a stand on the benefits of “status” .

Focus on separating equilibrium such that:

• Similar spirit to Glazer and Konrad (1996).

*( ( ))k k k ki i i is c y y

Signaling Predictions

1. cik* is strictly increasing in yi (relationship can be concave or convex

depending on the relative concavity of w(.) with u(.) and v(.)).

2. In equilibrium, the poorest individual in group k has no incentive to signal (ci

k* will be the same regardless of whether or not w(.) = 0).

How does cik* relate with moments of the income distribution, f(.)?

3. The relationship between group income dispersion and cik* is

theoretically ambiguous (holding own income constant).

Depends on curvature of ∂c*/∂y

4. If poorer persons are added to the group such that the support of the group’s income distribution becomes [ymin – θ, ymax] and average group income falls, then ci

k* increases at every level of income.

Comments

• Framework is quite general. Reference groups k represent, in theory, any type of groupings into which the population can be sorted.

• Depending on the situation, observers will know more or less about the distribution from which other individual’s unobserved income is drawn.

• Key insight: Information about one’s reference group influences observer’s inferences about one’s income and thus interacts with the optimal choice of signaling expenditures.

A leftward shift in the distribution of reference group income:

cik* ↑ (holding yi constant)

An increase in dispersion of reference group income:

cik* ? (holding yi constant)

Black vs. White Permanent Income Distribution (Fig 2a)

0.0

00

01

.000

02

.000

03

De

nsity

0 20000 40000 60000 80000 100000 120000 140000 160000Total Expenditure (Annual)

White

Black

Permanent Income Measured by Total Expenditure (CEX data)

Black vs. White Permanent Income Distribution (Fig 2b)

Permanent Income Measured by Average Income (PSID data)

0.0

00

01

.000

02

De

nsity

0 25000 50000 75000 100000 125000 150000 175000 200000Average Family Income

White

Black

Relevant Questions at Hand

• Are moments of the reference group income distribution (mean and variance) systematically related to visible consumption?

Can we see such a relationship within a race?

For example, do Whites from poorer reference groups consume more visible goods than otherwise similar Whites from richer reference

groups?

Note: Use mean as proxy for the leftward shifting of the income distribution.

• Does controlling for moments of the reference group income distribution explain the racial differences in visible consumption?

As seen above, the black distribution of income is, on average, to the left of the white income distribution.

How Do We Define Reference Group Income Distribution

• Main approach (when assessing CEX data)

Define reference group at the state/race level

States is the lowest level of geographic location available in the CEX.

• Robustness approach (when assessing PSID data)

Define reference group at the MSA/race level

Use PSID confidential geo-code data to get MSA info for each household.

For the state/race moments of the income distribution, we use CPS data from 1990-2002 (total income of men aged 18-49).

For the MSA/race moments of the income distribution, we use census data from 2000 (total income of men aged 18-49).

We explored many different income measures as a robustness exercise.

An Important Caveat

• Throughout our analysis, we are taking the choice of reference group as being “exogenous”.

• We believe that there are many interesting potential implications that may arise if we endogenize residential choice patterns (i.e., allow people to choose their reference group).

• We are thinking about these implications in future work.

Reference Group Income Distribution and Visible Spending

• How do moments of the reference group income distribution interact with visible spending?

where Γs and Γr are vectors of state and race fixed effects, respectively.

• Regression estimated via IV (as described above) where current income, education and occupation controls are used as instruments for total expenditure.

• Figure 3 plots the estimated δsr against the mean state income for the particular race/state cell (from the CPS as described above).

Key results: Systematic negative relationship between mean income of state and the propensity to consume visible goods (all else

equal).

0 gln( ) ( ) ln( )isr sr s r i i ivisible TotalExpenditure X

Figure 3

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CACOCT DC

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HI

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MD

MAMI

MN

MO

NV NJNY

NCOH

OK

OR

PASC

TN TX

VA

WA

WI

AL

AKAZ

AR

CACO

CT

DC

FL

GA

HI

IL

IN

IA

KS

KY

LA

MD

MA

MI

MNMO

NVNJ

NY

NC

OH

OK

OR

PASC

TN

TXVA

WA

WI

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AKAZ

AR

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DC

FL

GA

HIILIN

IA

KS

KY

LA

MDMA

MIMN

MO

NV

NJNY

NC OH

OKOR

PA

SC

TN

TX

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pen

din

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lativ

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hite

Ala

bam

ans

9.6 10 10.4 10.8 11.2Log of Mean Income of Race-State Cell

White Black Hispanic

Examining Within Race Regressions

where:

μ is the log of the mean income for persons race/state cell (from CPS)

D is the dispersion of income in a race/state measured by the coefficient of variation (from CPS).

Note: We also control directly for “housing” costs (which are location specific).

0 1 2ln( ) ( ) ( ) ln( )

y yis k k i

i i

visible D TotalExpenditure

X

Table 6: Within White Results

Dependent Variable

Log Visible Expenditure Log Food

Log All Less

Visible and

Housing(1) (2) (3) (4) (5)

Log of Mean Income of Own Race in State

-0.60 (0.14)

-0.70(0.14)

-0.58(0.13)

0.23(0.06)

-0.01(0.05)

Coefficient of Variation of Income for Own Race in State

-0.72(0.30)

-0.63(0.28)

0.59(0.10)

-0.06(0.03)

Log of Individual Housing Expenditures *

-0.13(0.06)

0.01(0.03)

-0.15(0.02)

* We also instrument individual housing expenses with state housing prices (from 1990 and 2000 census)

Table 7: Within Black and Hispanic Results

Dependent Variable

Log Visible Expenditure Log Food

Log All Less

Visible and

Housing(1) (2) (3) (4) (5) (6)

Log of Mean Income of Own Race in State

-0.44(0.13)

-0.51(0.12)

-0.45(0.13)

-0.64(0.15)

0.12(0.08)

-0.02(0.03)

Coefficient of Variation of Income for Own Race in State

0.25(0.17)

0.26(0.18)

0.26(0.17)

-0.14(0.07)

-0.02(0.04)

Log of Individual Housing Expenditures *

-0.09(0.08)

-0.16(0.09)

0.16(0.04)

-0.14(0.03)

Log Mean Income of All in State

0.60(0.31)

Explaining the differences across races

How much of the race gap can be explained by differences in reference group income?

Specifically, compare:

ln(Visible Expenditure) = βo + β1 Black + β2 Hispanic

+ φ ln(Total Expenditure) + θ X + η

with

ln(Visible Expenditure) = βo + β1 Black + β2 Hispanic + β3 Mean Incomeik

+ β4 Coefficient of Variationik + γ ln(Total Expenditure) + δ X + η

Table 8 (The Payoff)

Variable 1 2 3 4 5

Black Coefficient 0.26 0.28 -0.03 -0.005 -0.04

(0.02) (0.02) (0.07) (0.07) (0.07)

Hispanic Coefficient 0.23 0.26 -0.01 -0.01 -0.04

(0.03) (0.03) (0.08) (0.06) (0.07)

Log of Mean Own Group -0.53 -0.51 -0.52

State Income (0.12) (0.11) (0.11)

Coefficient of Variation 0.17

(0.12)

State Fixed Effects No Yes No Yes Yes

Table 8 (The Payoff)

Variable 1 2 3 4 5

Black Coefficient 0.26 0.28 -0.03 -0.005 -0.04

(0.02) (0.02) (0.07) (0.07) (0.07)

Hispanic Coefficient 0.23 0.26 -0.01 -0.01 -0.04

(0.03) (0.03) (0.08) (0.06) (0.07)

Log of Mean Own Group -0.53 -0.51 -0.52

State Income (0.12) (0.11) (0.11)

Coefficient of Variation 0.17

(0.12)

State Fixed Effects No Yes Yes Yes Yes

Summary

• Document a set of facts that both Blacks and Hispanics spend a considerable more on visible consumption items than similar Whites.

• This behavior is persistent within all sub groups and exists in the data since 1984. There is some evidence that this behavior dissipates with age.

• A model of conspicuous consumption and signaling fits the data very well.

• Controlling for the mean income of the group from which the individual is drawn explains the majority of the racial gap in visible consumption.

• Moreover, the model is race blind. The model is supported when looking at behavior within races (either Whites or Blacks).

Part 4. Potential Implications

• How does the propensity to spend on visible goods effect the spending on other categories?

o If we wish to promote Black spending on items such as education or health care, we need to understand the incentives to purchase status by investing in visible consumption.

o May effect they way we design social programs.

o Question: To what extent is visible spending differences correlated with spending differences in spending on other categories, like health care and education?

o Question: Can conspicuous consumption be a potential explanation for observed saving/wealth differences across races?

Unresolved Questions

• How does conspicuous consumption affect saving in a dynamic model?

Need to take a stance on why people value the “status”

• Can any of the observed “saving” gaps between blacks and whites be explained by differences in spending on conspicuous spending?

• How do people signal status in different settings? Do these finer models of signaling and status matter for anything “bigger”.

• How are residential sorting patterns affected by conspicuous consumption motives? The reference groups – along some dimension – is endogenous!

Lecture 2: Consumption (Continued). Wrapping Up from Last Time: Non Separabilities My belief: U(C,N)...

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