Transcript Dealer Price Discrimination in New Car Purchases: Evidence

Dealer Price Discrimination in New
Car Purchases: Evidence from the
Consumer Expenditure Survey
Pinelopi Goldberg (JPE, 1996)
Presented by Jake Gramlich October 12, 2004
1
Introduction
• Is there price discrimination in the new car
market?
• Ayres & Siegelman (1995)
– Audit Study: yes
• Goldberg (1996)
– Microdata: no
• How can we reconcile these two findings?
– Second moments of reservation prices
2
Two-part paper:
1. Present evidence from the Consumer
Expenditure Survey (CES) that contradicts Ayres
& Siegelman’s findings of racial and gender
discrimination
2. Reconcile the two studies by looking at second
moments of discounts (and thus implied
reservation prices)
3
Microdata approach
• Instead of audit method, use microdata (CES)
on actual purchases and transaction prices of
new cars
• Advantages relative to audit method:
– Data are on actual purchases
– Nationwide (not Chicago area)
– More car models (not just 9 representative models)
• Disadvantage relative to audit method
– No controlled environment
• Only household data
• No dealership data
4
Data
• CES, 1983-1987, quarterly, pooled
• Household’s asked:
–
–
–
–
–
–
Household characteristics
Household car purchase activity
Household’s stock of owned vehicles
Disposal of old cars
Trade-in
Financing
• Representative of U.S. population
• 32,000 households; 3,000 bought cars; 1,279 bought
from dealers for personal use
• 67 minorities (Black, Hispanic, American Indian)
5
Model
• Estimation Equation:
D ijt     H it   Z jt   X t   ijt
–
–
–
–
–
–
–
–
D = discount
i = individual
j = model
t = time
H = household characteristics (vector)
Z = model characteristics (vector of dummies)
X = time dummies
ε = iid error term
6
7
Discounts
D ij  L j  T ij
L j  LB j 
O
k
* PO kj  DF j  DPF j  C d
k
List = base + options + destination fees + dealer prep fees
+ dealer specific costs
Tij 
( EXP
ij
 EXi j )
 TRD
i
Si
Transaction = (Expenditure – Expenses) / Sales Tax
+ Trade-in value
• Absolute (not relative) – profit, not power
8
Measurement Error:
Measurement error of LHS vars
Variables: model info, smaller options, tradein allowance, sales tax, financing, fees.
Solutions:
1. Imputation
2. Lack of correlation with RHS
variables (so we still have
consistent results)
3. Tests for above
Measurement error of RHS
Variable: Race, Gender of bargainer
Solution: Race correlated, Gender biased towards
finding discrimination
9
Regression Results (Table 2)
• Dependent Variable = D
• R-Square = .18, Obs = 1,279
• Significant:
–
–
–
–
–
–
–
–
Intercept (-)
Rural (-)
Midwest (+)
dealer financing (+)
first time buyer (+)
trade-in (-)
Q3/4p (+), Q4s (-)
CLAO*Minority (-)
• Not Significant:
– minority (-)
– female (-)
– minority female (-)
– Wealth controls (-)
10
Take-home from CES Regression
•
Conclusion from microdata is no price
discrimination due to race or gender
•
Then why bargain?
1. Bargaining power relevant, just not predictable
2. There is variation in prices paid: optimal for seller to
bargain
•
How to explain Ayres & Siegelman?
1. Minorities choose stores with systematically lower prices
2. Sample Selection Bias: Discriminated drop out of market
3. Second Moments: Wider spread of reservation prices for
minorities
11
Possibility 2: Sample Selection Bias
•
Discriminated household’s don’t purchase, or
purchased used cars
Arguments against this explaining difference between
two studies:
•
–
–
–
Ayres & Siegelman find same discrimination pattern in 20% of
sample reaching agreement
Visiting dealership indicates willingness to pay approximately
equal to retail price – you might visit another dealership, but
you wouldn’t leave the market
Re-estimate model with Selection Equation (used, drop out)
•
•
Similar to OLS results
The correlation coefficient between the error terms of the
selection and regression equations is statistically insignificant =>
“no selection bias” hypothesis unrejected
12
Possibility 3: Second Moments
•
•
•
Blacks’ distribution of reservation prices is spread out
Bargaining theory predicts sellers use whole distribution
of buyer reservation prices in making offers
Example
– Reservation prices: $4k, $6k (type A) v. $3k, $7k (B)
– Initial offers higher of $6k and $7k (respectively; types
costlessly observed)
– Final offers depend on parameters, strategies, but likely that
$3k will receive lower (using patience to bargain longer)
•
If blacks have higher spread of reservation prices,
bargaining theory predicts:
1. First round offers to blacks higher
2. In equilibrium, low-value blacks receive lower final offers than
low-value whites (and vice-versa)
3. For some parameters, groups pay same average prices
•
Econometric Evidence i-iii…
13
i. Variances in Discounts Paid
14
15
ii. Empirical Discount Distributions
16
17
iii. Quantile Regression:
• Dependent Variable = D
• R-Square = .18, Obs = 1,279
OLS
Median
10% Quant
90% Quant
Minority
-248
(-1.04)
-49
(-.27)
-784
(-2.87)**
453
(1.81)*
Female
-130
(1-.10)
-115
(-1.39)
190
(1.52)
1
(.08)
MinFem
-22
(-.05)
-98
(-.34)
446
(1.06)
-380
(-.86)
18
Summary of i - iii
• Empirical discount distributions for minorities is
more spread out than the distribution for white
males
– Explains initial offer disparity
• What about final offer disparity?
– Ayres & Siegelman “final offers” are poor indicators of
transaction prices (since they do not lead to sales)
– Ayres & Siegelman imposed uniform bargaining
strategy. This indicates from where on the distribution
you come
• Systems analyst at a bank
• Wealthy suburb of Chicago
19
Summary
• Ayres & Siegelman, Audit, price
discrimination
• Goldberg, microdata, no price
discrimination
• Reconciliation: Second moments
20
Comments
• CES Regression?
– Signs were headed in right direction (increase N,
increase R-square)
– Especially few minorities
• Story of wider spread in minority reservation
prices?
– Not income (controlled for)
– Aggressive v. Unaggressive heterogeneity?
– Aggressive v. Uninformed?
• Link between reservation prices and discounts?
– More careful treatment of bargaining theory
21