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Record W3123002806 · doi:10.3386/w19133

Some Simple Economics of Crowdfunding

2013· preprint· en· W3123002806 on OpenAlexafffund
Ajay Agrawal, Christian Catalini, Avi Goldfarb

Bibliographic record

VenueNational Bureau of Economic Research · 2013
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEquity crowdfundingDue diligenceReputationInformation asymmetryEquity (law)Seed moneyEconomicsTransaction costBusinessMarketingPublic economicsFinancePolitical science

Abstract

fetched live from OpenAlex

It is not surprising that the financing of early-stage creative projects and ventures is typically geographically localized since these types of funding decisions are usually predicated on personal relationships and due diligence requiring face-to-face interactions in response to high levels of risk, uncertainty, and information asymmetry. So, to economists, the recent rise of crowdfunding -raising capital from many people through an online platform -which offers little opportunity for careful due diligence and involves not only friends and family but also many strangers from near and far, is initially startling. On the eve of launching equity-based crowdfunding, a new market for early-stage finance in the U.S., we provide a preliminary exploration of its underlying economics. We highlight the extent to which economic theory, in particular transaction costs, reputation, and market design, can explain the rise of non-equity crowdfunding and offer a framework for speculating on how equity-based crowdfunding may unfold. We conclude by articulating open questions related to how crowdfunding may affect social welfare and the rate and direction of innovation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.260
GPT teacher head0.433
Teacher spread0.172 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations26
Published2013
Admission routes2
Has abstractyes

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