This website uses cookies.

By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device to enhance site navigation and analyze site performance and traffic. For more information on our use of cookies, please see our Privacy Policy.

  1. Home
  2. ASSA Annual Meeting
  3. 2024
  4. Program
  5. The Data Economy
« Back to Results

The Data Economy

Paper Session

Friday, Jan. 5, 2024 2:30 PM - 4:30 PM (CST)

Grand Hyatt, Travis B
Hosted By: Econometric Society
  • Chair: Laura Veldkamp, Columbia University

Data Deserts and Black Boxes: The Impact of Socio-Economic Status on Consumer Profiling

Catherine Tucker
,
Massachusetts Institute of Technology

Abstract

Data brokers use black-box methods to profile and segment individuals for ad targeting, often with mixed success rates. We present evidence from five complementary field tests and 15 data brokers that differences in profiling accuracy and coverage for these attributes mainly depend on who is being profiled. Consumers who are better off---for example, those with high incomes---have personal backgrounds that are profiled more accurately or ensure that there is any available profile information. In addition, occupational status (white-collar vs. blue-collar jobs), the ethnic background, gender, and household arrangements often affect the accuracy and likelihood of having a profile that is covered by data brokers, although this varies by country.

Our analyses suggest that successful consumer-background profiling is driven by the scope of the digital footprint (online activities and the number of electronic devices). %, whereas the likelihood of being profiled depends primarily on how many electronic devices a consumer uses.
Those who come from lower-income backgrounds have a lower digital footprint, leading to a `data desert' for such individuals. In contrast to consumer variables, vendor-specific effects (capturing possible technology differences in profiling methods) explain virtually no variation in profiling accuracy for our data, but explain a variation in the likelihood of who is profiled. Vendor differences due to unique networks and partnerships also affect profiling outcomes indirectly due to differential access to individuals that vary in their background. We discuss the implications of our findings for policy and marketing practice.

Production, Trade, and Cross-Border Data Flows

Qing Chang
,
Central University of Finance and Economics
Lin William Cong
,
Cornell University
Liyong Wang
,
Central University of Finance and Economics
Longtian Zhang
,
Central University of Finance and Economics

Abstract

We build a tractable general equilibrium model to analyze the effects of cross-border data flows and pre-existing development gaps in data economies on each country's production and international trade. Raw data as byproducts of consumption can be transformed into various types of working data (information) to be used by both domestic and foreign producers. Because data constitute a new production factor for intermediate goods, a large extant divide in data utilization can reduce or even freeze trade. Cross-border data flows mitigate the situation and improve welfare when added to international trade. Data-inefficient countries where data are less important in production enjoy a ``latecomer's advantage'' with international trade and data flows, contributing more raw data from which the data-efficient countries generate knowledge for production. Furthermore, cross-border data flows can reverse the cyclicity of working data usage after productivity shocks, whereas shocks to data privacy or import costs have opposite effects on domestic and foreign data sectors. The insights inform future research and policy discussions concerning data divide, data flows, and their implications for trade liberalization, the data labor market, among others.

Data Sales and Data Dilution

Laura Veldkamp
,
Columbia University & NBER
Ernest Liu
,
Princeton University & NBER
Song Ma
,
Yale University & NBER

Abstract

The market power of data sellers is a topic of concern for policymakers. While most data
sellers have a monopoly for their data set, they also cannot commit not to sell data to other
customers. We find that limited commitment, combined with the fact that data’s strategic value
declines in the number of users, makes data sellers behave more competitively. Monopolist
data sellers do not have much monopoly power, but data subscription sellers do. While subscriptions allow firms to credibly commit to restrict their supply of data, they also incentivize
firms to invest in higher-quality data. Using evidence from online data markets to quantify the
model, we find that data subscriptions are better for consumers. While subscriptions benefit
firms and consumers, financially constrained firms tend to front-load their revenue by selling
data. Regulation to eliminate data market power may backfire, because without rents, the incentive to invest in high-quality data disappears.

Estimating the Value of Offsite Data to Advertisers on Meta

Nils Wernerfelt
,
Meta
Anna Tuchman
,
Northwestern University
Brad Shapiro
,
University of Chicago
Rob Moakler
,
Meta

Abstract

We study the extent to which advertisers benefit from data that are shared across applications. These types of data are viewed as highly valuable for digital advertisers today. Meanwhile, product changes and privacy regulation threaten the ability of advertisers to use such data. We focus on one of the most common ways advertisers use offsite data and run a large-scale study with hundreds of thousands of advertisers on Meta. Within campaigns, we experimentally estimate both the effectiveness of advertising under business as usual, which uses offsite data, as well as how that would change under a loss of offsite data. Using recently developed deconvolution techniques, we flexibly estimate the underlying distribution of treatment effects across our sample. We find a median cost per incremental customer using business as usual targeting techniques of $43.88 that under the median loss in effectiveness would rise to $60.19, a 37% increase. Similarly, analyzing purchasing behavior six months after our experiment, ads delivered with offsite data generate substantially more long-term customers per dollar, with a comparable delta in costs. Further, there is evidence that small scale advertisers and those in CPG, Retail, and E-commerce are especially affected. Taken together, our results suggest a substantial benefit of offsite data across a wide range of advertisers, an important input into policy in this space.
JEL Classifications
  • C1 - Econometric and Statistical Methods and Methodology: General