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Correcting Measurement Bias in Online Auction Research: A Relative Value Benchmark for Strategic Underpricing

Tanishk Garg

Subject area: Management and Commerce  ·  Area of research: Applied Econometrics and Pricing Strategy

Abstract

Empirical research in e-commerce and online auction dynamics frequently evaluates 'strategic underpricing' using category-level quantile cutoffs (e.g., opening bids in the bottom 10th or 25th percentiles). In this paper, I demonstrate that threshold-based quantile indicators introduce severe measurement error by confounding low-value products with strategically underpriced products. To resolve this measurement bias, I introduce the Relative Underpricing Index (RUI), a continuous metric benchmarked against item-level expected market reference values (Vc). Applying heteroskedasticity-robust (HC3) Ordinary Least Squares (OLS) regressions and structural path mediation models to 628 completed eBay auctions across standard product categories, I show that quantile thresholds yield misclassified, spurious estimates. Under the benchmarked RUI metric, strategic underpricing exhibits a strong, statistically significant negative association with net revenue realization (β = -117.42, p < 0.001). Furthermore, while relative underpricing lowers entry barriers and increases unique bidder participation (α = +5.71, p < 0.001), a formal Sobel mediation test confirms that the resulting indirect revenue boost (+51.18, p < 0.0001) fails to recover the direct revenue loss incurred from opening bid discounts. This study provides a corrected econometric framework for marketplace researchers and warns sellers against relying on naive low opening bids.

Keywords

Strategic Underpricing, Relative Underpricing Index (RUI), Measurement Error, Online Auctions, Mediation Analysis, Econometric Methods.

References

[1] Bajari, P., & Hortaçsu, A. (2003). The winner's curse, reserve prices, and endogenous entry: Empirical insights from eBay auctions. The RAND Journal of Economics, 34(2), 329–355. Crossref

[2] Cabral, L., & Hortaçsu, A. (2010). The dynamics of seller reputation: Evidence from eBay. The Journal of Industrial Economics, 58(1), 54–78. Crossref

[3] Klemperer, P. (2004). Auctions: Theory and Practice. Princeton University Press.

[4] Lucking-Reiley, D. (1999). Using field experiments to test equivalence between auction formats: Magic on the Internet. American Economic Review, 89(5), 1063–1080. Crossref

[5] MacKinnon, J. G., & White, H. (1985). Some heteroskedasticity-consistent covariance matrix estimators with improved finite sample properties. Journal of Econometrics, 29(3), 305–325. Crossref

[6] Myerson, R. B. (1981). Optimal auction design. Mathematics of Operations Research, 6(1), 58– 73. Crossref

[7] Roth, A. E., & Ockenfels, A. (2002). Last- minute bidding and the rules for ending second- price auctions: Evidence from eBay and Amazon auctions on the Internet. American Economic Review, 92(4), 1093–1103. Crossref

[8] Vickrey, W. (1961). Counterspeculation, auctions, and competitive sealed tenders. The Journal of Finance, 16(1), 8–37. Crossref

How to cite this paper

Tanishk Garg "Correcting Measurement Bias in Online Auction Research: A Relative Value Benchmark for Strategic Underpricing" Iconic Research And Engineering Journals Volume 10 Issue 3 2026 Page 1773-1777
Tanishk Garg "Correcting Measurement Bias in Online Auction Research: A Relative Value Benchmark for Strategic Underpricing" Iconic Research And Engineering Journals, vol. 10, no. 3, Sep. 2026
Tanishk Garg (2026). Correcting Measurement Bias in Online Auction Research: A Relative Value Benchmark for Strategic Underpricing. Iconic Research And Engineering Journals, 10(3).
Tanishk Garg "Correcting Measurement Bias in Online Auction Research: A Relative Value Benchmark for Strategic Underpricing" Iconic Research And Engineering Journals, vol. 10, no. 3, Sep. 2026.
@article{1723071,
      author = {Tanishk Garg},
      title = {Correcting Measurement Bias in Online Auction Research: A Relative Value Benchmark for Strategic Underpricing},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {1773-1777},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723071.pdf},
      abstract = {Empirical research in e-commerce and online auction dynamics frequently evaluates 'strategic underpricing' using category-level quantile cutoffs (e.g., opening bids in the bottom 10th or 25th percentiles). In this paper, I demonstrate that threshold-based quantile indicators introduce severe measurement error by confounding low-value products with strategically underpriced products. To resolve this measurement bias, I introduce the Relative Underpricing Index (RUI), a continuous metric benchmarked against item-level expected market reference values (Vc). Applying heteroskedasticity-robust (HC3) Ordinary Least Squares (OLS) regressions and structural path mediation models to 628 completed eBay auctions across standard product categories, I show that quantile thresholds yield misclassified, spurious estimates. Under the benchmarked RUI metric, strategic underpricing exhibits a strong, statistically significant negative association with net revenue realization (β = -117.42, p < 0.001). Furthermore, while relative underpricing lowers entry barriers and increases unique bidder participation (α = +5.71, p < 0.001), a formal Sobel mediation test confirms that the resulting indirect revenue boost (+51.18, p < 0.0001) fails to recover the direct revenue loss incurred from opening bid discounts. This study provides a corrected econometric framework for marketplace researchers and warns sellers against relying on naive low opening bids.},
      keywords = {Strategic Underpricing, Relative Underpricing Index (RUI), Measurement Error, Online Auctions, Mediation Analysis, Econometric Methods.},
      month = {September},
  }