Economic Uses and Applications of AI and Big Data
Paper Session
Saturday, Jan. 7, 2023 2:30 PM - 4:30 PM (CST)
- Chair: Laura Veldkamp, Columbia University
Venture Capital (Mis)Allocation in the Age of AI
Abstract
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
Abstract
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
Abstract
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.
Discussant(s)
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