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Economic Uses and Applications of AI and Big Data

Paper Session

Saturday, Jan. 7, 2023 2:30 PM - 4:30 PM (CST)

Hilton Riverside, Grand Salon D Sec 19 & 22
Hosted By: American Economic Association & Committee on the Status of Women in the Economics Profession
  • Chair: Laura Veldkamp, Columbia University

Consumer Privacy and the Value of Consumer Data

Mehmet Canayaz
Pennsylvania State University
Ilja Kantorovitch
Pompeu Fabra University
Roxana Mihet
Swiss Finance Institute & HEC Lausanne


We analyze how the adoption of the California Consumer Privacy Act (CCPA), which limits consumer personal data acquisition, processing, and trade, affects voice-AI firms. To derive theoretical predictions, we use a general equilibrium model where firms produce intermediate goods using labor and data in the form of intangible capital, which can be traded subject to a cost representing regulatory and technical challenges. Firms differ in their ability to collect data internally, driven by the size of their customer base and reliance on data. When the introduction of the CCPA increases the cost of trading data, sophisticated firms with small customer bases are hit the hardest. Such firms have a low ability to collect in-house data and high reliance on data and cannot adequately substitute the previously externally purchased data. We utilize novel and hand-collected data on voice-AI firms to provide empirical support for our theoretical predictions. We empirically show that sophisticated firms with voice-AI products experience lower returns on assets than their industry peers after the introduction of the CCPA, and firms with weak customer bases experience the strongest distortionary effects.

Venture Capital (Mis)Allocation in the Age of AI

Lea Stern
University of Washington
Victor Lyonnet
Ohio State University


We use machine learning to study how venture capitalists
(VCs) make investment decisions. Using a large administrative data set on French entrepreneurs that contains VC-backed as well as non-VC-backed firms, we use algorithmic predictions of new ventures'
performance to identify the most promising ventures. We find that VCs invest in some firms that perform predictably poorly and pass on others that perform predictably well. Consistent with models of stereotypical thinking, we show that VCs select entrepreneurs whose characteristics are representative of the most successful entrepreneurs (i.e., characteristics that occur more frequently among the best performing entrepreneurs relative to the other ones).
Although VCs rely on accurate stereotypes, they make prediction errors as they exaggerate some representative features of success in their selection of entrepreneurs (e.g., male, highly educated, Paris-based, and high-tech entrepreneurs). Overall, algorithmic decision aids show promise to broaden the scope of VCs' investments and founder diversity.

Control and Influence in Decentralized Autonomous Organizations

Jillian Grennan
University of California-Berkeley
Ian Appel
University of Virginia


Decentralized Autonomous Organizations (“DAOs”) are crypto-native organizational forms that are collectively owned and managed by their members. As headless organizations, administration is distributed among members and decision-making is designed to be made in a collective manner. Is this possible at scale? Or are DAOs marketing themselves as decentralized when that may not be the case?
Taking advantage of the data trail recorded on the blockchain, this study assembles novel data on DAOs and their improvement proposals to evaluate emerging governance patterns. Our findings suggest most DAOs have yet to achieve decentralization; importantly though, significant heterogeneity among DAOs exists and correlates with performance. We use these insights to inform broader debates on how organizational structure and ownership affect governance.

Measuring the Velocity of Money

Allison Luedtke
St. Olaf College
Crolina Mattsson
Leiden Institute for Advanced Computer Science
Frank Takes
Leiden Institute for Advanced Computer Science


The velocity of money is an important macroeconomic indicator that is conventionally measured indirectly and as an average for an economy as a whole. However, this measurement approach obscures heterogeneity in the underlying spending patterns. With the advent of large-scale micro-level transaction data comes the opportunity to measure the velocity of money at the level of individual spenders. In this paper, we propose a new measurement methodology that leverages big-data computational techniques. For a given payment system's transaction network, our method enables a systematic comparison of the velocity of money across different spatial, temporal, and demographic subgroups of spenders. We also allow for changes in the balance of funds in the system, which is commonly observed in real-world payment systems yet not accounted for by conventional measurement approaches. This allows us to observe how events such as a pandemic or targeted currency operations affect the velocity of money across relevant subgroups. Using data from a community currency in a developing country, we construct an intertemporal transaction network and find the following:
(1) transaction volume comes mostly from fast-moving money, while much of the balance at any particular point in time is slow-moving,
(2) transaction rhythms differ between rural and urban areas, in particular, money moves faster in urban communities, and
(3) community currency circulation picked up speed as COVID-19 unfolded.
The big-data approach described in this paper improves our understanding of heterogeneity in macroeconomic patterns and can inform policies that affect these patterns.

Daniel Rock
University of Pennsylvania
Romana Nanda
Imperial College London
Jason Sandvik
Tulane University
Wenhao Li
University of Southern California
JEL Classifications
  • D8 - Information, Knowledge, and Uncertainty
  • G3 - Corporate Finance and Governance