Advertisement

Quantitative Marketing and Economics

, Volume 12, Issue 4, pp 331–377 | Cite as

The Joint identification of utility and discount functions from stated choice data: An application to durable goods adoption

  • Jean-Pierre Dubé
  • Günter J. Hitsch
  • Pranav Jindal
Article

Abstract

We present a survey design that generalizes static conjoint experiments to elicit inter-temporal adoption decisions for durable goods. We show that consumers’ utility and discount functions in a dynamic discrete choice model are jointly identified using data generated by this specific design. In contrast, based on revealed preference data, the utility and discount functions are generally not jointly identified even if consumers’ expectations are known. The separation of current-period preferences from discounting is necessary to forecast the diffusion of a durable good under alternative marketing strategies. We illustrate the approach using two surveys eliciting Blu-ray player adoption decisions. Both model-free evidence and the estimates based on a dynamic discrete choice model indicate that consumers make forward-looking adoption decisions. In both surveys the average discount rate is 43 percent, corresponding to a substantially higher degree of impatience than the rate implied by aggregate asset returns. The estimates also reveal a large degree of heterogeneity in the discount rates across consumers, but only little evidence for hyperbolic discounting.

Keywords

Conjoint analysis Diffusion models Durable goods adoption Dynamic discrete choice models Identification of discount factors 

JEL Classifications

C14 D9 D12 M31 

References

  1. Abbring, J.H. (2010). Identification of dynamic discrete choice models. Annual Review of Economics, 2, 367–394.CrossRefGoogle Scholar
  2. Aguirregabiria, V., & Suzuki, J. (2012). Identification and Counterfactuals in Dynamic Models of Market Entry and Exit. manuscript.Google Scholar
  3. Arcidiacono, P., & Ellickson, P.B. (2011). Practical methods for estimation of dynamic discrete choice models. Annual Review of Economics, 3, 363–394.CrossRefGoogle Scholar
  4. Bajari, P., Chernozhukov, V., Hong, H., Nekipelov, D. (2009). Nonparametric and Semiparametric Analysis of a Dynamic Discrete Game. manuscript.Google Scholar
  5. Bass, F.M. (1969). A new product growth model for consumer durables. Management Science, 15(5), 215–227.CrossRefGoogle Scholar
  6. Bass, F.M., Gordon, K. , Ferguson, T.L., Githens, M.L. (2001). DIRECTV: forecasting diffusion of a new technology prior to product launch. Interfaces, 31(3 Part 2 of 2), S82–S93.CrossRefGoogle Scholar
  7. Briesch, R.A., Chintagunta, P.K., Matzkin, R.L. (2010). Nonparametric discrete choice models with unobserved heterogeneity. Journal of Business & Economic Statistics, 28(2), 291–307.MathSciNetCrossRefGoogle Scholar
  8. Bronnenberg, B.J., Dubé, J.-P., Mela, C.F., Albuquerque, P., Erdem, T., Gordon, B., Hanssens, D., Hitsch, G., Hong, H., Sun, B. (2008). Measuring long-run marketing effects and their implications for long-run marketing decisions. Marketing Letters, 19, 367–382.CrossRefGoogle Scholar
  9. Chevalier, J., & Goolsbee, A. (2005). Are durable goods consumers forward looking? Evidence from college textbooks. NBER Working Paper 11421.Google Scholar
  10. Chung, D., Steenburgh, T., Sudhir, K. (2014). Do bonuses enhance sales productivity? A dynamic structural analysis of bonus-based compensation plans. Marketing Science (forthcoming).Google Scholar
  11. Cochrane, J.H. (2001). In Asset Pricing. Princeton: NJ.Google Scholar
  12. Dubé, J.-P., Hitsch, G.J., Rossi, P.E. (2009). Do switching costs make markets less competitive?. Journal of Marketing Research, 46, 435–445.CrossRefGoogle Scholar
  13. Fang, H., & Wang, Y. (2013). Estimating Dynamic Discrete Choice Models with Hyperbolic Discounting, with an Application to Mammography Decisions . manuscript.Google Scholar
  14. Fox, J.T., Kim, K.I., Ryan, S.P., Bajari, P. (2012). The random coefficients logit model is identified. Journal of Econometrics, 166, 204–212.MathSciNetCrossRefGoogle Scholar
  15. Frederick, S., Loewenstein, G., O’Donoghue, T. (2002). Time discounting and time preference: a critical review. Journal of Economic Literature, 40(2), 351–401.CrossRefGoogle Scholar
  16. Gowrisankaran, G., & Rysman, M. (2012). Dynamics of consumer demand for new durable goods. Journal of Political Economy, 120(6), 1173–1219.CrossRefGoogle Scholar
  17. Green, P.E., Krieger, A.M., Wind, Y. (2001). Thirty years of conjoint analysis: reflections and prospects. Interfaces, 31(3), S56—S73.Google Scholar
  18. Green, P.E., & Rao, V.R. (1971). Conjoint measurement for quantifying judgmental data. Journal of Marketing Research, 8(3), 355–363.CrossRefADSGoogle Scholar
  19. Green, P.E., & Srinivasan, V. (1990). Conjoint analysis in marketing: new developments with implications for research and practice. Journal of Marketing, 54(4), 3–19.CrossRefGoogle Scholar
  20. Horsky, D. (1990). A diffusion model incorporating product benefits, price, income and information. Marketing Science, 9(4), 342–365.CrossRefGoogle Scholar
  21. Hotz, V.J., & Miller, R.A. (1993). Conditional choice probabilities and the estimation of dynamic models. Review of Economic Studies, 60(3), 497–529.MathSciNetCrossRefGoogle Scholar
  22. Huber, J. (1997). What We Have Learned from 20 Years of Conjoint Research: When to Use Self-Explicated, Graded Pairs, Full Profiles or Choice Experiments. Sawtooth Software Research Paper Series.Google Scholar
  23. Magnac, T., & Thesmar, D. (2002). Identifying dynamic discrete decision processes. Econometrica, 70(2), 801–816.MathSciNetCrossRefGoogle Scholar
  24. Matzkin, R.L. (2007). Nonparametric Identification. In J. J. Heckman & E. E. Leamer (Eds.), Handbook of Econometrics (Vol. 6B, chap. 73, pp. 5307–5368). Elsevier B.V.Google Scholar
  25. Melnikov, O. (2013). Demand for differentiated durable products: the case of the U.S. computer printer market. Economic Inquiry, 51(2), 1277–1298.CrossRefGoogle Scholar
  26. Miller, R.A. (1984). Job matching and occupational choice. Journal of Political Economy, 92(6), 1086–1120.CrossRefGoogle Scholar
  27. Nair, H.S. (2007). Intertemporal price discrimination with forward-looking consumers: application to the US market for console video-games. Quantitative Marketing and Economics, 5, 239–292.CrossRefGoogle Scholar
  28. Newton, M.A., & Raftery, A.E. (1994). Approximate bayesian inference with the weighted likelihood bootstrap. Journal of the Royal Statistical Society, Series B, 56(1), 3–48.MathSciNetGoogle Scholar
  29. Norets, A., & Takahashi, S. (2013). On the surjectivity of the mapping between utilities and choice probabilities. Quantitative Economics, 4(1), 149–155.MathSciNetCrossRefGoogle Scholar
  30. Pakes, A. (1986). Patents as options: some estimates of the value of holding european patent stocks. Econometrica, 54(4), 755–784.CrossRefGoogle Scholar
  31. Pesendorfer, M., & Schmidt-Dengler, P. (2008). Asymptotic least squares estimators for dynamic games. Review of Economic Studies, 75, 901–928.MathSciNetCrossRefGoogle Scholar
  32. Phelps, E.S., & Pollak, R.A. (1968). On second-best national saving and game-equilibrium growth. Review of Economic Studies, 35(2), 185–199.CrossRefGoogle Scholar
  33. Rossi, P., Allenby, G., McCulloch, R. (2005). Bayesian statistics and marketing, Wiley.Google Scholar
  34. Rust, J. (1987). Optimal replacement of GMC bus engines: an empirical model of Harold Zurcher. Econometrica, 55(5), 999–1033.CrossRefGoogle Scholar
  35. Rust, J. (1994). Structural estimation of Markov decision processes. In R. F. Engle & D. L. McFadden (Eds.), Handbook of Econometrics (Vol. IV, pp. 3081–3143). North-Holland: Elsevier.CrossRefGoogle Scholar
  36. Song, I., & Chintagunta, P.K. (2003). A micromodel of new product adoption with heterogeneous and forward-looking consumers: application to the digital camera category. Quantitative Marketing and Economics, 1, 371–407.CrossRefGoogle Scholar
  37. Viscusi, W.K., Huber, J., Bell, J. (2008). Estimating discount rates for environmental quality from utility-based choice experiments. Journal of Risk and Uncertainty, 37, 199–220.CrossRefGoogle Scholar
  38. Yao, S., Mela, C.F., Chiang, J., Chen, Y. (2012). Determining consumers’ discount rates with field studies. Journal of Marketing Research, 49(6), 822–841.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Booth School of BusinessUniversity of Chicago and NBERChicagoUSA
  2. 2.Smeal College of BusinessPennsylvania State UniversityUniversity ParkUSA

Personalised recommendations