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Dealer Price Discrimination in New Car Purchases: Evidence from the Consumer Expenditure Survey Pinelopi Goldberg (JPE, 1996). Presented by Jake Gramlich October 12, 2004. Introduction. Is there price discrimination in the new car market? Ayres & Siegelman (1995) Audit Study: yes

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Presented by Jake Gramlich October 12, 2004


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    1. Dealer Price Discrimination in New Car Purchases: Evidence from the Consumer Expenditure SurveyPinelopi Goldberg (JPE, 1996) Presented by Jake Gramlich October 12, 2004

    2. 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

    3. 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)

    4. 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

    5. 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)

    6. Model • Estimation Equation: • 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

    7. Discounts List = base + options + destination fees + dealer prep fees + dealer specific costs 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, trade- in 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? • Minorities choose stores with systematically lower prices • Sample Selection Bias: Discriminated drop out of market • 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: • First round offers to blacks higher • In equilibrium, low-value blacks receive lower final offers than low-value whites (and vice-versa) • For some parameters, groups pay same average prices • Econometric Evidence i-iii…

    13. i. Variances in Discounts Paid

    14. ii. Empirical Discount Distributions

    15. iii. Quantile Regression: • Dependent Variable = D • R-Square = .18, Obs = 1,279

    16. 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

    17. Summary • Ayres & Siegelman, Audit, price discrimination • Goldberg, microdata, no price discrimination • Reconciliation: Second moments

    18. 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