Partial Identification of Hedonic Demand Functions

1. Partial Identification of Hedonic Demand Functions Congwen Zhang (Virginia Tech) Nicolai Kuminoff (Arizona State University) Kevin Boyle (Virginia Tech) 10/23/2011

2. Endogeneity problem with hedonic demand estimation • Endogeneity arises because people choose prices and quantities/qualities simultaneously. • Example: we are interested in X, an environmental good. Hedonic price function: (non-linear in X ) Implicit price of X: ( is function of X ) Choice of X no based on an exogenous price. • Why worry? Most policies result in nonmarginal changes in X.

3. “Imperfect” Instrumental Variables (Nevo & Rosen, 2010) • X: endogenous variable; Z: instrumental variable (IV) “perfect” IV: and “imperfect” IV : We allow correlation between IV and error (unobserved components of preferences! Z is “perfect”: Z is “imperfect”: is bounded by and

4. 1-SIDED AND 2-SIDED BOUNDS Proposition (Nevo & Rosen, 2010): Suppose both and Case 1: If , then Case 2: If , then

5. “Imperfect” IVs in demand estimation • Potential “imperfect” IVs: IV1. market indicator (M) IV2. interaction between M and income (M*INC) • Why “imperfect” ? 1. sorting across markets 2. uncertainty about the spatial extent of a market • Correlation Direction: cov(X, U)>0, cov(M, U)>0, cov(M, X)>0 cov(X, U)>0, cov(M*INC, U)>0, cov(M*INC, X)>0 both IVs give us one-sided bound !

6. Partial Identification of Marshallian Consumer Surplus (MCS) • Bounds on βBounds on MCS • Suppose we obtain a 2-sided bound: MCSl (slope = ) MCS2 (slope = )

7. Partial Identification of MCS (slope = ) (slope = )

8. Partial Identification of MCS • Suppose we obtain a 1-sided bound: S (slope = ) (slope = )

9. An Empirical Demonstration • Water quality in markets for lakefront properties. • Data description: (1) House transactions: from multiple markets in VT, ME, and NH. (2) Water clarity data: associated w/ each house. (3) Demographic data: associated w/ each home owner. • Important features: (1) Each state includes data from multiple markets. (2) The spatial extent of a market is difficult to determine with certainty.

11. Two-Stage Hedonic Model • 1st stage: Estimate hedonic price function (market-specific) implicit price of water clarity: • 2nd Stage: Estimate demand function parameters (pooled)

12. Table . Demand Estimation with Pooled Data • Boyle et al. (1999)’s point estimates fall into our bounds !

13. Conclusions and future research • Partial identification provides a more credible way to estimate demand and welfare. • Provides approach to uncertainty analysis. How big can the injuries or benefits be? • One-side bounds not always helpful. • Partial identification logic can be a robustness check on point estimates. • Implicit prices are plausible.

14. Preferences for Stormwater Control in Residential Developments Jessica Boatright Kurt Stephenson Kevin J. Boyle Sara Nienow Virginia Tech 11/1/2011

15. Application • Subdivision infrastructure that affects stormwater runoff. • Hanover County, Virginia • Residential home sales between 1995-1996 • Mean sales price = $148,950

16. Variables • CUL = 1 if cul-de-sac and 0 otherwise • CURBGUTTER = 1 if curb-and-gutters and 0 otherwise • STW20 = 1 if street width 20 feet or less and 0 otherwise • STW25 = 1 if street width 20 to 30 ft and 0 otherwise • street width greater than 30 ft is omitted category

17. Results

18. Implications • Cul-de-sacs and curb and gutters channel and rapidly transport stormwater, which can exacerbate nonpoint-source pollution of surface waters. • Narrower streets mean less impervious surface, which can reduce some of the residential stormwater effects, but the benefits to home owners are less that being on a cul-de-sac or having a curb and gutter on their street.