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Price Differentials Across Outlets in CPI Data, 2002-2007 PowerPoint Presentation
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Price Differentials Across Outlets in CPI Data, 2002-2007

Price Differentials Across Outlets in CPI Data, 2002-2007

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Price Differentials Across Outlets in CPI Data, 2002-2007

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  1. Price Differentials Across Outlets in CPI Data, 2002-2007 John Greenlees Robert McClelland May 15, 2008

  2. New Outlets Bias • New Outlets Bias can arise from the failure of the CPI to adequately reflect the gains to consumers from the appearance of new types of product outlets • These welfare gains can arise from: • Greater convenience (e.g., Internet shopping) • Greater product variety (e.g., Tuscan restaurants) • Lower prices (e.g., Wal-Mart, Costco) • This paper focuses only on the lower-price effect

  3. Price Effects of New Outlets • The CPI does not reflect differences in prices between products at different sample outlets • Differences across outlets are implicitly treated as entirely reflecting quality differentials • Currently, the most interest concerns the low prices offered at discount department stores like WalMart and warehouses and club stores like Costco

  4. Empirical Studies New Outlets Bias is a long-recognized issue • Hoover and Stotz (1964) • Reinsdorf (1993) • White (2000) • Hausman and Leibtag (2004, 2005)

  5. Empirical Approach • Use CPI Research Database for 2002-2007 • Select relatively, but not completely, homogeneous CPI food item categories • Regress price on item characteristics, with dummies for time and for outlet fixed effects • Estimate changes in average outlet premium or discount, and average item quality, over time • Decompose outlet effects within and across outlet categories

  6. Key Advantages of Our Approach • Uses actual CPI microdata • Uses regression estimation to incorporate variations in item characteristics • Examines outlet differentials in general, not just across pre-specified outlet categories • Analyzes item quality change, not just outlet effects • Compares hedonic to matched-model approach

  7. CPI Sample • 14 Food ELIs corresponding to categories used earlier by Reinsdorf and Hausman/Leibtag • Some more homogeneous than others • 69 months from January 2002 through September 2007 • About 8,000 outlets • About 16,000 “quote strings” • About 360,000 price quotes

  8. Sample Shares by Outlet Type, 2002-2007

  9. Hedonic Regression Models • We estimate 14 regressions, one for each of our item categories • Dependent variable is lnPijt, the log-price of item i in outlet j in time t. • RHS variables include item characteristics, outlet fixed effects, and dummies for month • Regressions yield a monthly price index for each item category with January 2002=100.

  10. Price Trends with Outlet Fixed Effects

  11. Index log-changes by Item Category, 2002-07

  12. Overall Results Outlet effect = -0.26 percent/year Item characteristics effect = +0.20 percent/year Difference between hedonic and matched-model index = -0.25 percent/year

  13. Weighted Sample AverageOutlet Effects by Outlet Category

  14. Conclusions (1) • We find significant new outlet effects • Averaging -0.26 percent per year • Lowering prices for 10 of 14 items • This result is after adjusting for differences in item characteristics across outlets • Remember, some of these effects may be due to differences in outlet quality

  15. Conclusions (2) • Most of the outlet effects do not arise from growth in the discount department store category • Warehouse category growth is also important • About 1/3 of total outlet effect comes from changes in average outlet premiums within categories

  16. Conclusions (3) • We also identify large effects of changes in item characteristics • Even within our relatively homogeneous item categories • Hedonic model estimates average quality increase at 0.20 percent per year • Differences between hedonic and matched model estimates warrant further study of how CPI adjusts for quality