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When would online platforms pay data dividends

Sukanya Kudva, Anil Aswani

TL;DR

The paper develops a Stackelberg principal-agent model to analyze when online platforms would pay data dividends to users and how privacy valuation and data-protection investment shape this decision. By modeling platform utility that trades off data-revenue against breach risk and dividend payments, and user utility that depends on privacy costs and dividends, the authors derive optimality conditions under two homogeneous sharing equilibria (all high-sharing or all low-sharing). The results reveal threshold-based strategies for dividends and protection investment: platforms pay higher dividends when net user losses from breaches are salient, yet may invest heavily in protection and refrain from dividends when users value free services more than data. These insights inform policymakers about potential outcomes if data dividends become mandatory and highlight the trade-offs between privacy, sharing incentives, and cybersecurity investments.

Abstract

Online platforms, including social media and search platforms, have routinely used their users' data for targeted ads, to improve their services, and to sell to third-party buyers. But an increasing awareness of the importance of users' data privacy has led to new laws that regulate data-sharing by platforms. Further, there have been political discussions on introducing data dividends, that is paying users for their data. Three interesting questions are then: When would these online platforms be incentivized to pay data dividends? How does their decision depend on whether users value their privacy more than the platform's free services? And should platforms invest in protecting users' data? This paper considers various factors affecting the users' and platform's decisions through utility functions. We construct a principal-agent model using a Stackelberg game to calculate their optimal decisions and qualitatively discuss the implications. Our results could inform a policymaker trying to understand the consequences of mandating data dividends.

When would online platforms pay data dividends

TL;DR

The paper develops a Stackelberg principal-agent model to analyze when online platforms would pay data dividends to users and how privacy valuation and data-protection investment shape this decision. By modeling platform utility that trades off data-revenue against breach risk and dividend payments, and user utility that depends on privacy costs and dividends, the authors derive optimality conditions under two homogeneous sharing equilibria (all high-sharing or all low-sharing). The results reveal threshold-based strategies for dividends and protection investment: platforms pay higher dividends when net user losses from breaches are salient, yet may invest heavily in protection and refrain from dividends when users value free services more than data. These insights inform policymakers about potential outcomes if data dividends become mandatory and highlight the trade-offs between privacy, sharing incentives, and cybersecurity investments.

Abstract

Online platforms, including social media and search platforms, have routinely used their users' data for targeted ads, to improve their services, and to sell to third-party buyers. But an increasing awareness of the importance of users' data privacy has led to new laws that regulate data-sharing by platforms. Further, there have been political discussions on introducing data dividends, that is paying users for their data. Three interesting questions are then: When would these online platforms be incentivized to pay data dividends? How does their decision depend on whether users value their privacy more than the platform's free services? And should platforms invest in protecting users' data? This paper considers various factors affecting the users' and platform's decisions through utility functions. We construct a principal-agent model using a Stackelberg game to calculate their optimal decisions and qualitatively discuss the implications. Our results could inform a policymaker trying to understand the consequences of mandating data dividends.
Paper Structure (18 sections, 8 theorems, 10 equations, 3 figures, 1 table)

This paper contains 18 sections, 8 theorems, 10 equations, 3 figures, 1 table.

Table of Contents

  1. INTRODUCTION
  2. Cybersecurity on online platforms
  3. Privacy and data dividends
  4. Contributions and outline
  5. OUR MODEL
  6. Platform's utility
  7. User's utility
  8. Principal-agent formulation
  9. Optimal choices
  10. Case 1: $c_i^* = 1 \;\; \forall i = \{1, \cdots , k\}$
  11. Sub-case 1 - If $\mathcal{V} \geq 0$ then $p_1^* = \mathcal{V}$
  12. Sub-case 2 - If $\mathcal{V} \leq 0$ then $p_1^* = 0$
  13. Final results of Case 1
  14. Case 2: $c_i^* = 0 \;\; \forall i = \{1, \cdots , k\}$
  15. Insights from optimal choices
  16. ...and 3 more sections

Key Result

Proposition III.1

When $\Bar{V} \geq 0$, $I^* = I_1$ if $I_1$ exists. Else, $I^* =0$.

Figures (3)

  • Figure 1: Here, users choose the high level of data sharing, $I_1$ exists, and $\Bar{V}\leq 0$. Data dividend $p_1$ is plotted for increasing values of $L$, that is users' loss from a data breach. The platform pays a data dividend beyond a threshold when $\mathcal{V}>0$, that is users face a total loss.
  • Figure 2: Here, users choose the high level of data sharing and $\Bar{V}\geq 0$. Data dividend $p_1$ is plotted for increasing values of $L$, that is users' loss from a data breach. The platform always pays a data dividend of at least $\Bar{V}$.
  • Figure 3: Here, users choose the low level of data sharing, $I_4$ exists, and $\Bar{V}\leq 0$. Data dividend $p_0$ is plotted for increasing values of $L$, that is users' loss from a data breach. Interestingly, the platform doesn't pay a data dividend only when $\mathcal{V}=0$, that is users neither have a net loss nor gain.

Theorems & Definitions (16)

  • Proposition III.1
  • proof
  • Proposition III.2
  • proof
  • Proposition III.3
  • proof
  • Proposition III.4
  • proof
  • Theorem III.5
  • proof
  • ...and 6 more