Abstract
The peer economy, such as crowdfunding, democratizes access to tasks available only to professionals. Although the peer economy has gained great popularity in practice, how crowds infer information from their peers, especially from experts, is still under minimal study in academia. Using data from a debt-based crowdfunding platform in China, this study investigates the impact of seasoned predecessors’ bids on subsequent investors' decisions and how seasoned and unseasoned investors respond differently to herding signals. We discover that the cumulative lending amount from seasoned predecessors is positively associated with the lending amount of a successor, and such an association is greater if the successor is seasoned. In the repayment process, we find that the lending amount from seasoned investors is positively associated with loan performance, while the lending amount from unseasoned investors is not. Our results contribute to the literature on crowds of wisdom, implying that in a context that requires sophisticated knowledge, extracting hidden talents from experts rather than from crowds is more appropriate.
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Funding was provided by Natural Science Foundation of China (Grant No. 71771159, 72071160).
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Appendices
Appendix A: Results of investor participation decision
(1) | |
|---|---|
Participate | |
Seasoned (1 = yes) | − 0.0088*** (0.0024) |
Amount requested | 0.2713*** (0.0025) |
Interest rate | 0.0110*** (0.0003) |
Loan duration | − 0.0291*** (0.0004) |
Credit risky | − 0.2841*** (0.0030) |
Title length | − 0.0003 (0.0002) |
Purpose (enterprise startup) | 0.0538*** (0.0042) |
Purpose (other purpose) | 0.1000*** (0.0040) |
Purpose (short-term turnover debt) | 0.0387*** (0.0028) |
Purpose (not reported) | 0.4559*** (0.0071) |
Borrower age | 0.0008*** (0.0002) |
Education (low) | − 0.0902*** (0.0034) |
Education (not reported) | − 0.2517*** (0.0029) |
Gender (male) | − 0.0804*** (0.0034) |
Num of child | − 0.0059** (0.0030) |
Marriage (not married) | − 0.0156*** (0.0036) |
Marriage (not reported) | − 0.1131*** (0.0079) |
Intercept | − 4.1472*** (0.0239) |
N | 6,520,938 |
AIC | 1,191,191.3033 |
BIC | 1,191,437.7328 |
Log Likelihood | − 595,577.6516 |
Appendix B: Loan level characteristics
Mean | SD | Min | Max | ||
|---|---|---|---|---|---|
\({\mathrm{Y}}^{\mathrm{s}}\): Amount from seasoned | Lending amount from seasoned investors | 4940.770 | 5353.760 | 100.000 | 77,727 |
\({\mathrm{Y}}^{\mathrm{u}}\): Amount from unseasoned | Lending amount from unseasoned investors | 1786.219 | 1773.565 | 0.000 | 20,072 |
Amount requested | Request amount of a loan (RMB) | 6473.993 | 6156.625 | 3000.000 | 88,000 |
Interest rate | Interest rate provided by a borrower (%) | 16.144 | 4.127 | 5.000 | 22 |
Loan duration | The number of months the borrower makes payments | 5.561 | 3.477 | 1.000 | 12 |
Credit risky | 1 if a loan’s credit rating is E or HR, 1 otherwise | 0.153 | 0.360 | 0.000 | 1 |
Title length | Number of characteristics in the title of a loan | 18.519 | 5.846 | 10.000 | 30 |
Late | Whether a loan is late for more than 30 days | 0.062 | 0.242 | 0.000 | 1 |
Default | Whether a loan is late for more than 90 days | 0.048 | 0.213 | 0.000 | 1 |
Purpose (base) | The purpose of a loan is for goods consumption | 0.305 | 0.461 | 0.000 | 1 |
Purpose (enterprise startup) | The purpose of a loan is for enterprise startup | 0.111 | 0.314 | 0.000 | 1 |
Purpose (other purpose) | The purpose of a loan is for other purpose | 0.177 | 0.382 | 0.000 | 1 |
Purpose (short-term turnover debt) | The purpose of a loan is for short-term turnover debt | 0.407 | 0.491 | 0.000 | 1 |
Age | The age of the borrower | 30.537 | 5.337 | 20.000 | 54 |
Num of child | Number of children that the borrower has | 1.492 | 0.589 | 1.000 | 4 |
Education (base) | The borrower has a college diploma or higher | 0.468 | 0.499 | 0.000 | 1 |
Education (low) | The borrower has a high school diploma or lower | 0.142 | 0.350 | 0.000 | 1 |
Education (not reported) | The borrower education level is not reported | 0.389 | 0.488 | 0.000 | 1 |
Gender (base) | The borrower is a female | 0.172 | 0.377 | 0.000 | 1 |
Gender (male) | The borrower is a male | 0.828 | 0.377 | 0.000 | 1 |
Marriage (base) | The borrower has married | 0.578 | 0.494 | 0.000 | 1 |
Marriage (single) | The borrower has not married | 0.401 | 0.49 | 0.000 | 1 |
Marriage (not reported) | The borrower marriage status is not reported | 0.021 | 0.143 | 0.000 | 1 |
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Chen, D., Huang, C., Liu, D. et al. The role of expertise in herding behaviors: evidence from a crowdfunding market. Electron Commer Res 24, 155–203 (2024). https://doi.org/10.1007/s10660-022-09570-8
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DOI: https://doi.org/10.1007/s10660-022-09570-8