TITLE:
A Supply-Side Pricing Model for Personal Data: Privacy Compensation, Sensitivity, and Information Entropy
AUTHORS:
Juguang Chen
KEYWORDS:
Personal Data Markets, Privacy Compensation, Willingness to Accept, Information Entropy, Data Pricing
JOURNAL NAME:
Theoretical Economics Letters,
Vol.16 No.5,
October
15,
2026
ABSTRACT: Personal-data markets face a pricing problem because data buyers value information content while data providers bear heterogeneous privacy costs. This paper develops a supply-side reference-pricing framework that combines entropy-based information content, information-category sensitivity weights, and stylized privacy-compensation regimes. A Wenjuanxing survey conducted in Guangdong Province, primarily Guangzhou, from March to May 2024 retained 103 of 130 submissions after documented quality screening. The six-item privacy-compensation preference measure showed acceptable internal consistency (Cronbach’s alpha = 0.81; M = 3.81, SD = 0.52). Exploratory comparisons indicate differences by age, F(5,97) = 3.884, p = 0.003, and prior privacy-breach experience, F(2,100) = 5.159, p = 0.007, whereas gender and education are not significant at the 5% level. All respondents rated the full 16-profile orthogonal conjoint set, yielding normalized sensitivity coefficients highest for biometric information (0.190). The entropy component is calculated from a verified 12-profile, 39-attribute matrix (468 binary attribute opportunities) using observed category frequencies. After correcting the compensation schedules so that L = 7 is used consistently and every benchmark factor follows Equation (4), a 1500-yuan baseline produces reference prices from approximately 1331 to 3155 yuan. The output is a transparent supply-side negotiation benchmark rather than a market-clearing price.