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					      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:


 ln ( C it )   0   a g e A g e it   c C o h o r t it   t D t        F a m ily it   it
        k                                                                                       k
                                                                           fs




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.
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.
                                  t j               1 
           T t
                   1                    (C t  j )       
m ax E t                              
                                         
                                                            
                                                            
           j0    1                     1            


                  
    C                                
        t 1
Et                    (1  rt  1 )   1
    C t                              
                                       


                                  1
 ln C t  1         t 1
                                     ln (1  rt  1 )   t  1
                                  
     Graphical Illustration – No Substitution Effect
C

                                                 High interest rate

                                   ΔC2 = X

               ΔC1 = X                           Low interest rate




           1                                 2       period



With only an income effect – consumption growth rate will not respond to interest
rate changes. Estimate of (1/ρ) = 0.
    Graphical Illustration – With Substitution Effect
C                                                  High interest rate



                                    ΔC2 > X

                                                   Low interest rate
                ΔC1 < X




            1                                  2       period



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.
                                             1
                   ln C t  1   t  1        ln (1  rt  1 )   t  1
                                             


•   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).
                                  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

                                                                 
               N

m a x E    u (C t ,  )  
               t                        N 1
                                           VN   1
                                                     (W   N   1
                                                                 )
         t 1                                                    


W t  1  (1  r ) (W t  Y t  C t )


                            1 
                        C
u (C , Z )  v ( )
                        1 


Y t  Pt V t

Pt  G t Pt  1 N   t
                              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:

                               t j             1 
             T t
                       1             (C t j )      
    m ax Et                                        
                                       1            
                j0   1                           



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.
               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:


    ln (W it )   0   1                2                  3 ln ( y it )  Z it   u it
                               p erm y            tr a n s y
                              it                  it



•    Precautionary savings model predicts wealth will be higher the more risk
     that households face.
       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
                                     Permanent   Transitory    Percent
Group                                 variance    variance    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          47.5%         13.3%
       Precautionary Savings

       Dependent Variable (Log)                      Total         Total
                                                   Net Worth     Net Worth

       Permanent Income Measure (Averaged)         Non-capital   Non-capital
                                                    Income        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/Tract
    ableBufferStock/

    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:

                                                              2                                2
W k = a + d1 W p + a 1 k A g e k + a                2k   A g ek + a 1 p A g e p + a   2p   Age p + ek



W k     2W p   1 k A g e k   2 k A g e k   1 p A g e p   2 p A g e p   k Z k   p Z        uk
                                                2                             2
                                                                                                     p




  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.
                           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 Persistence
                                                        I                        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 Measure
                             Very 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
               Proportion of households with positive debt levels

80%


70%
                                              Any Mortgage
60%


50%
                                        Credit Card
40%


30%


20%


10%


0%
      25-29   30-34   35-39   40-44   45-49     50-54   55-59   60-64   65-69   70-74   75-79
                   Average debt levels, conditional on having positive debt

160,000                                                                                              32,000


140,000                                                                                              28,000


120,000                                                                                              24,000
                                                             Mortgage Levels (Left Axis)
100,000                                                                                              20,000


 80,000                                                                                              16,000


 60,000                                                                                              12,000

                                          Credit Card (Right axis)
 40,000                                                                                              8,000


 20,000                                                                                              4,000


     0                                                                                               0
          25-29   30-34   35-39   40-44    45-49   50-54   55-59     60-64   65-69   70-74   75-79
    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
    Are the people that hold debt at older ages representative?
•   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?
    Are the people that hold debt at older ages representative?


•   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*
    Are the people that hold debt at older ages representative?


•   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
                             st
           1                    1                      
                                      C  C      
                                                         2
m ax Et                                                  
         st  1                2                      
                                                            


B t  1  ( B t  Y t  C t ) (1  r )



w h e r e C is b lis s p o in t c o n s u m p tio n , B is b e g i n n in g o f p e r io d

w e a lth , a n d Y is la b o r in c o m e .



N o te :   A s s u m p tio n o f " lo g u tility "



F o r s im p lic ity :   A ssum e   r.
        Income Shocks and Consumption Growth

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

       C t 1   t 1 ,           E t [ t  n ]  0  n  0

• Suppose, income process is as follows:

                  Pt  1  Pt       t 1

                 Y t  1  Pt  1   t  1



                  E t [ t  n ]  E t [     tn
                                                    ] 0


• Optimal Consumption Growth:

                          r 
             C t 1           t 1            t 1
                         1 r 
                                                                     64
                     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 income
                 o    Insurance mechanisms available to households.


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



                                                                              65
    Deaton and Paxson Methodology (U.S. Application)

•     Variance of Residual Variation

    ln C it   0   a g e A g e it   c o h o r t C o h o r t it   t D t   fs F a m ily it   it
          k               k                 k                         k           k                    k




•     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).
                                                                                                           66
                               Figure 1b: With and With Out Housing Services
                            0.24



                            0.20
Log Deviation From Age 25




                            0.16



                            0.12



                            0.08



                            0.04



                            0.00
                                   25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
                               Figure 1b: With and With Out Housing Services
                            0.24

                                      Cross Sectional Variance of Total Nondurables for 25 Year Olds = 0.16
                            0.20



                            0.16
Log Deviation From Age 25




                            0.12



                            0.08



                            0.04



                            0.00
                                    25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75


                            -0.04
                               Figure 1b: With and With Out Housing Services
                            0.24

                                      Cross Sectional Variance of Total Nondurables for 25 Year Olds = 0.16
                            0.20



                            0.16
Log Deviation From Age 25




                            0.12



                            0.08



                            0.04



                            0.00
                                    25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75


                            -0.04
                 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 income
       o Insurance mechanisms available to households
                                                                    70
  Dispersion of Propensity to Work Over Life Cycle
0.60




0.50




0.40




0.30




0.20




0.10




0.00
       25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
                                            Age
Cross Sectional Lifecycle Dispersion: Entertainment
                                     0.4



                                     0.2
Difference in Variance From Age 25




                                     0.0



                                     -0.2



                                     -0.4



                                     -0.6



                                     -0.8
                                            25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
                                                  Non Increasing Dispersion Categories
                                     0.5


                                     0.0
Difference in Variance From Age 25




                                     -0.5


                                     -1.0


                                     -1.5


                                     -2.0


                                     -2.5


                                     -3.0


                                     -3.5
                                            25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75


                                     Entertainment      Utilities   Housing Services   Food At Home   Other Non Durable
                                     Where is the Increase in Dispersion Coming From?
                                     4.0

                                     3.5

                                     3.0
Difference in Variance From Age 25




                                     2.5

                                     2.0

                                     1.5

                                     1.0

                                     0.5

                                     0.0

                                            25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
                                     -0.5

                                     -1.0
                                             Clothing     Transportation   Domestic Services    Food Away From Home
         Lifecycle Variation in Standard Deviation
                       Variance at   Change    Change    Change    Change
Consumption Category     Age 25      25 - 44   45 - 59   59 - 68   25 - 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
                                         0.50



                                         0.40
Percentage Point Deviation From Age 25




                                         0.30



                                         0.20



                                         0.10



                                         0.00
                                                 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75


                                         -0.10



                                         -0.20
                                                                             Core Nondurable
                                                    Cross Sectional Dispersion Over Lifecycle
                                         0.50



                                         0.40
Percentage Point Deviation From Age 25




                                         0.30



                                         0.20



                                         0.10



                                         0.00
                                                 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75


                                         -0.10



                                         -0.20
                                                          Core Nondurable       Work Related      Food At Home
                                         Cross Sectional Dispersion Over Lifecycle: Figure 6b
                                         0.50



                                         0.40
Percentage Point Deviation From Age 25




                                         0.30

                                                                                                       Total
                                         0.20



                                         0.10



                                         0.00
                                                 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
                                                                                                            Core
                                         -0.10



                                         -0.20
                                         Cross Sectional Dispersion Over Lifecycle: Figure 6b
                                         0.50



                                         0.40
Percentage Point Deviation From Age 25




                                         0.30

                                                                                                       Total
                                         0.20



                                         0.10



                                         0.00
                                                 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75
                                                                                                            Core
                                         -0.10



                                         -0.20
                       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 production
   one good (transport, clothes) are complements to market work
   there is a time budget constraint

   Assumptions:

   o    conditional on work, income process is uncertain
   o    take the lifecycle process of work as exogenous
   o    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
         Average Consumption in CEX
9.8500


9.8000


9.7500


9.7000


9.6500


9.6000


9.5500

                                      84
  Percent Change in Consumption in CEX (from 1981)
   0


-0.02


-0.04


-0.06


-0.08


 -0.1


-0.12


-0.14


-0.16
                                                 85
           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).
                                                                              86
Kevin Murphy’s Web Page




                          87
Kevin Murphy’s Web Page




                          88
         Consumption Inequality (Time Series)
0.5600


0.5500


0.5400


0.5300


0.5200


0.5100


0.5000


0.4900

                                                89
   Consumption Inequality: Adjusting For Family Size
0.06

0.05

0.04

0.03

0.02

0.01

   0

-0.01

-0.02


         No Controls   Family Size Dummies   Family Size Adjustment
                                                                      90
Trends in CEX Consumption (Attanasio et al, 2004)




“What really happened to consumption inequality in the US?”
                                                              91
         Trends in CEX Consumption Inequality
                  (Attanasio et al, 2004)




“What really happened to consumption inequality in the US?”
                                                              92
                            Aguiar and Hurst (2009)
                           Change in the Cross Sectional Variance of Log Expenditure Over Different
                                                         Time Ranges

                                     I.                        II.                       III.
                          1981-    1990-    1981-    1981-    1990-   1981-    1981-    1990-   1981-
Log Expenditure Measure   1990     2003     2003     1990     2003    2003     1990     2003    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
                                                                                                93
       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



                               Income




                                 Time
          t                T




                                         96
                       Uncertain Income



                                                      Income
                            Shock (as identified by econometrician)




                                                        Time
                       t                          T



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

                                                                           97
                         No Insurance


                     Consumption (dotted line)
                                                     Income




                                                       Time
                     t                           T



Household consumption responds completely to permanent shock to
income.

                                                                  98
                    Complete Insurance



                                                 Income
                                                 Consumption




                                                   Time
                     t                       T



Household consumption will not respond to the permanent income shock.


                                                                   99
    The Conceptual Issue: Deterministic Income



                                                    Income




                                                      Time
                       t                        T



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

                                                                         100
    The Conceptual Issue: Deterministic Income



                                                     Income
                           Shock (as identified by econometrician only)




                                                       Time
                       t                         T



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

                                                                          101
    The Conceptual Issue: Deterministic Income



                                                  Income
                                                  Consumption




                                                    Time
                      t                       T



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

                                                                    102
    The Conceptual Issue: Deterministic Income



                                                  Income
                                                  Consumption

                              No change in consumption growth




                                                    Time
                      t                       T



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

                                                                    103
     The Conceptual Issue: Deterministic Income



                                                    Income
                                                    Consumption

                               No change in consumption growth




                                                      Time
                       t                        T


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



                                                                      104
     The Conceptual Issue: Deterministic Income



                                                    Income
                                                    Consumption

                               No change in consumption growth




                                                      Time
                       t                        T


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.
                                                                         105
        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:

     ln ( C        )  k  vt    yt  
                i                             i       i
                t                                     t




    w h e r e f u ll r i s k s h a r i n g i m p li e s t h a t  = 0

                                                                                 110
    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
                    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 Roussanov
             QJE 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 Jewelry
    o      Personal Care
    o      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             57,800   63,800   38,400   39,800
(Conditional Inc > 0 )

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

Visible Expenditures            2,029    2,176    1,538     1,681
(Quarterly)


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
                                                    Black         Hispanic
Regression Controls Included                      Coefficient    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)
  8
  6
  4
  2




      7                   8                      9         10
                         Log Quarterly Total Expenditure

                                 Black             White




Estimated Difference at sample mean income ~ 0.3
      Separately Analyzing Visible Components (Table 3)
                              I. Full Sample        II. Positive Car
                                                        Spending
Visible Consumption Sub-    Black       Hispanic    Black    Hispanic
Category                   Dummy        Dummy      Dummy     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)
      Separately Analyzing Visible Components (Table 3)
                              I. Full Sample        II. Positive Car
                                                        Spending
Visible Consumption Sub-    Black       Hispanic    Black    Hispanic
Category                   Dummy        Dummy      Dummy     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
                                           Log Expenditure      Black    Hispanic

Housing                 0.03      0.13
                       (0.02)    (0.03)    Entertain Services   -0.29     -0.36
Utilities               0.09     -0.02                          (0.03)    (0.05)
                       (0.03)    (0.02)    Entertain Durables   -0.35     -0.17
Food                    -0.06     0.06                          (0.05)    (0.05)
                       (0.02)    (0.02)    Health               -0.51     -0.48
Other Transport.        -0.15    -0.02                          (0.05)    (0.06)
                       (0.03)    (0.04)    Alc./Tobacco         -1.04     -1.04
Home Furnishings        -0.18     0.09                          (0.05)    (0.05)
                       (0.04)    (0.05)
                                           Other                -0.08     -0.38
Education               -0.16    -0.30
                                                                (0.04)    (0.08)
                       (0.10)    (0.12)
                  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)

                             ( yi  ci )  u (ci )  w ( si )
                                 k     k          k            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, ykmax]

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


    s i  E  y i | c i , k  , w h e re c i
     k             k   k*                   k*
                                                 is e q u ilib riu m v is ib le c o n s u m p tio n
                           
                      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:

                       s i ( c i ( y i ))  y i
                        k    k*     k         k




•    Similar spirit to Glazer and Konrad (1996).
                           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
      (cik* 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 cik* 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)
.00001 .00002 .00003   Black vs. White Permanent Income Distribution (Fig 2a)
    Density




                          0




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

                                                              White
                                                              Black




                         Permanent Income Measured by Total Expenditure (CEX data)
            Black vs. White Permanent Income Distribution (Fig 2b)
   .00002
Density
.00001



              0




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

                                                White
                                                Black




              Permanent Income Measured by Average Income (PSID data)
                    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?

ln ( v is ib le isr )   0   sr (  s g r )   ln ( T o ta lE x p e n d itu r e i )   X i   i


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).
                                       Figure 3

      1
                                        AL

                             KY



                                      MANVNV
                                           NJ
.5




                                 AR         KS SC
                AR               IL     VA
                                     TX
                                 OH OK WI KS
                                     TX
                                    WA
                      AL
                                 MI
                                   DC
                            OR NYDC CA
                               MA           MD CO MD
                          PA           CO CA                    AK
                          IATN
                         WI FL
                              SC NY AZINNJHI AK
                                    AZ
                     LA           MN CT IL
                                  NC MN KY
                                    NC           OH
                                MO
                             OK IN GA        CT MI IA
                                           FL         VA
                                                     WA
      0




                                           PA OR HI    AL
                                               AR KY        PA
                              IA            MO           SC
                                                         LA
                                                        IN OH
                                                          KS
                                       TN                 NC
                                             GA LA TN MO WI MI MA TX
                                                                  IL
                                                             AZ GA
                                                          OR MNWA     CT   DC
                                                     OK   FL NVVA CA NJ
                                                                  NY
                                                                 COAK

                                                        HI           MD
-.5




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

                                   White      Black       Hispanic
                   Examining Within Race Regressions


  ln ( v is ib le is )   0   1 (  k )   2 ( D k )   ln ( T o ta lE x p e n d itu r e i )
                                      y              y



                               X i  i


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).
                     Table 6: Within White Results

                                                     Dependent Variable
                                                                                Log All
                                                                                 Less
                                                                                Visible
                                    Log Visible Expenditure         Log Food      and
                                                                                Housing
                                   (1)         (2)          (3)           (4)     (5)

Log of Mean Income of Own         -0.60       -0.70         -0.58      0.23       -0.01
Race in State                     (0.14)     (0.14)        (0.13)     (0.06)     (0.05)

Coefficient of Variation of                   -0.72        -0.63       0.59      -0.06
Income for Own Race in State                 (0.30)        (0.28)     (0.10)     (0.03)

Log of Individual Housing                                   -0.13      0.01       -0.15
Expenditures *                                             (0.06)     (0.03)     (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 All
                                                                                        Less
                                                                                       Visible
                                                                                         and
                                           Log Visible Expenditure            Log Food Housing
                                   (1)         (2)        (3)         (4)        (5)     (6)

Log of Mean Income of Own          -0.44       -0.51     -0.45        -0.64     0.12     -0.02
Race in State                     (0.13)      (0.12)    (0.13)       (0.15)    (0.08)   (0.03)

Coefficient of Variation of                    0.25      0.26         0.26      -0.14    -0.02
Income for Own Race in State                  (0.17)    (0.18)       (0.17)    (0.07)   (0.04)

Log of Individual Housing                                -0.09        -0.16     0.16     -0.14
Expenditures *                                          (0.08)       (0.09)    (0.04)   (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
                           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
                           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!

				
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