arXiv 28 Apr 2021 · Econometrics
arXiv:2104.13865 · PDF · DOI · OpenAlex · Extracted main text
This paper studies sequential search models that (1) incorporate unobserved product quality, which can be correlated with endogenous observable characteristics (such as price) and endogenous search cost variables (such as product rankings in online search intermediaries); and (2) do not require researchers to know the true distribution of the match value between consumers and products. A likelihood approach to estimate such models gives biased results. Therefore, I propose a new estimator -- pairwise maximum rank (PMR) estimator -- for both preference and search cost parameters. I show that the PMR estimator is consistent using only data on consumers' search order among one pair of products rather than data on consumers' full consideration set or final purchase. Additionally, we can use the PMR estimator to test for the true match value distribution in the data. In the empirical application, I apply the PMR estimator to quantify the effect of rankings in Expedia hotel search using two samples of the data set, to which consumers are randomly assigned. I find the position effect to be $0.11-$0.36, and the effect estimated using the sample with randomly generated rankings is close to the effect estimated using the sample with endogenous rankings. Moreover, I find that the true match value distribution in the data is unlikely to be N(0,1). Likelihood estimation ignoring endogeneity gives an upward bias of at least $1.17; misspecification of match value distribution as N(0,1) gives an upward bias of at least $2.99.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ursu, R. M (2018) The power of rankings: Quantifying the effect of rankings on online consumer search and purchase decisions | 1.000 | 14 | 4 | 100% |
| 2 | Chen, Y. and S. Yao (2017) Sequential search with refinement: Model and application with click-stream data | 1.000 | 7 | 4 | 100% |
| 3 | Chung, J. H., P. K. Chintagunta, and S. Misra (2019) Estimation of sequential search model | 1.000 | 7 | 3 | 100% |
| 4 | Yavorsky, D., E. Honka, and K. Chen (2021) Consumer search in the US auto industry: The role of dealership visits | 1.000 | 5 | 3 | 100% |
| 5 | Gu, N. and Y. Wang (2021) Consumer online search with partially revealed information | 0.928 | 4 | 4 | 100% |
| 6 | Dong, X., I. Morozov, S. Seiler, and L. Hou (2020) Estimation of preference heterogeneity in markets with costly search | 0.928 | 4 | 3 | 100% |
| 7 | Moraga-González, J. L., Z. Sándor, and M. R. Wildenbeest (2018) Consumer search and prices in the automobile market | 0.928 | 4 | 3 | 100% |
| 8 | Koulayev, S (2014) Search for differentiated products: identification and estimation | 0.928 | 4 | 3 | 100% |
| 9 | Hortacsu, A. and C. Syverson (2004) Product differentiation, search costs, and competition in the mutual fund industry: A case study of S&P 500 index funds | 0.843 | 3 | 3 | 100% |
| 10 | Kim, J. B., P. Albuquerque, and B. J. Bronnenberg (2010) Online demand under limited consumer search | 0.843 | 3 | 3 | 100% |
Showing the top 10 of 48 scored citations.