The Role of Gender and Education in Peer-to-peer Lending Activities: Evidence from a European Cross-country Study
Keywords:
Peer-to-peer lending, education, gender gap, financial literacy
Abstract
The wide use of peer-to-peer lending platforms coupled with the Fintech global race has emphasized the role of social lending activities and their impact on consumers in recent years. Starting from the publicly available Bondora database, we analyse determinants of loan default during the 2013-2021 period by studying individual economic and social factors of borrowers. We apply a Logit model to estimate the ex-post probability of default on both original variables provided by the database and factors obtained by Principal Component Analysis. Results show the fundamental role of borrowers’ education in reducing the probability of default, as with financial awareness obtained by loan characteristics. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending.
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References
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University of Chicago. https://doi.org/10.1093/qje/qjw024
6. Barasinska, N., & Schäfer, D. (2014). Is crowdfunding different?
Evidence on the relation between gender and funding success from a
German peer-to-peer lending platform. German Economic Review,
15(4), 436-452. https://doi.org/10.1111/geer.12052
7. Berger, S. C., & Gleisner, F. (2009). Emergence of financial
intermediaries in electronic markets: The case of online P2P lending.
Business Research, 2(1), 39-65 https://doi.org/10.1007/BF03343528
8. Carmichael, D. (2014). Modeling default for peer-to-peer loans.
Available at SSRN 2529240. http://dx.doi.org/10.2139/ssrn.2529240
9. Chen, H., Chong, T. T. L. & She, Y. (2014). A principal component
approach to measuring investor sentiment in China. QuantitativeFinance Volume 14, 2014 - Issue 4: Special Issue on Behavioral
Finance https://doi.org/10.1080/14697688.2013.869698
10. Chen, D., and C. Han. (2015). A Comparative Study of Online P2P
Lending in the USA and China. The Journal of Internet Banking and
Commerce 2012: 101-116.
11. Chen, X., Huang, B., & Ye, D. (2020). Gender gap in peer-to-peer
lending: Evidence from China. Journal of Banking & Finance, 112,
105633. https://doi.org/10.1016/j.jbankfin.2019.105633
12. Demyanyk, Y., Loutskina, E., & Kolliner, D. (2017). Three myths
about peer-to-peer loans. Economic Commentary, 2017, 1-6.
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banks: Cream skimming or bottom fishing?. The Review of
Corporate Finance Studies, 11(2), 213-262.
https://doi.org/10.1093/rcfs/cfab026
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Economic Review, 105(2), 906-20. https:// DOI:
10.1257/aer.20130683
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variance swap rates and optimal variance swap investment. Journal of
Financial and Quantitative Analysis, 45, 1279–1310.
https://doi.org/10.1017/S0022109010000463
16. Eid, N., Maltby, J., & Talavera, O. (2016). Income rounding and loan
performance in the peer-to-peer market. Available at SSRN 2848372.
http://dx.doi.org/10.2139/ssrn.284837
17. Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating
credit risk and loan performance in online Peer-to-Peer (P2P)
lending. Applied Economics, 47(1), 54-70.
https://doi.org/10.1080/00036846.2014.962222
18. Feng, Y., Fan, X., & Yoon, Y. (2015). Lenders and borrower’s
strategies in online peer-to-peer lending market: an empirical analysis
of PPDAI.com. Journal of Electronic Commerce Research, 16(3),
242.
19. Gomez, R., and E. Santor. (2003). Do Peer Group Members
Outperform Individual Borrowers? A Test of Peer Group Lending
Using Canadian Micro-Credit Data. General Information.
20. Guo, Y., Zhou, W., Luo, C., Liu, C., & Xiong, H. (2016). Instancebased credit risk assessment for investment decisions in P2P lending.
European Journal of Operational Research, 249(2), 417-426.
https://doi.org/10.1016/j.ejor.2015.05.050
21. Hastie, T. and Tibshirani, R., & Friedman, J., (2009). The Elements
of Statistical Learning. Springer Series in Statistics.22. Huston, S., J., (2010). Measuring Financial Literacy, The Journal of
Consumer Affairs, Vol. 44, No. 2, 2010 ISSN 0022-0078
23. Hsu J. W. (2016). Aging and Strategic Learning: The Impact of
Spousal Incentives on Financial Literacy. The Journal of human
resources, 51(4), 1036–1067. https://doi.org/10.3368/jhr.51.4.1014-
6712r
24. Iyer, R., Khwaja, A. I., Luttmer, E. F., & Shue, K. (2016). Screening
peers softly: Inferring the quality of small borrowers. Management
Science, 62(6), 1554-1577. https://doi.org/10.1287/mnsc.2015.2181
25. Jiang, C., Wang, Z., Wang, R., & Ding, Y. (2018). Loan default
prediction by combining soft information extracted from descriptive
text in online peer-to-peer lending. Annals of Operations Research,
266(1), 511-529. https://doi.org/10.1007/s10479-017-2668-z
26. Jin, G. Z. and Freedman, S. (2014). The Information Value of Online
Social Networks: Lessons from Peer-to-Peer Lending. NBER
Working Papers. https://doi.org/10.1016/j.ijindorg.2016.09.002
27. Kgoroeadira, R., Burke, A., & van Stel, A. (2019). Small business
online loan crowdfunding: who gets funded and what determines the
rate of interest? Small Business Economics, 52(1), 67-87.
https://doi.org/10.1007/s11187-017-9986-z
28. Klafft, M. (2008). Online peer-to-peer lending: a lenders' perspective.
In Proceedings of the international conference on E-learning, Ebusiness, enterprise information systems, and E-government, EEE
(pp. 371-375). http://dx.doi.org/10.2139/ssrn.1352352
29. Lattin, J. M., Carroll, D. J. & Green, P. E. (2003). Analyzing
Multiveriate data, Thomson Brooks/Cole
30. Lee, J. Y. (2020). Prediction of Default Risk in Peer-to-Peer Lending
Using Structured and Unstructured Data. Journal of Financial
Counseling and Planning. DOI:10.1891/JFCP-18-00073
31. Lee, J. and Kim, K. T. (2017). The Increase in Payday Loans and
Damaged Credit after the Great Recession , Journal of Family and
Economic Issues, June 2018, v. 39, iss. 2, pp. 360-69.
https://doi.org/10.1007/s10834-017-9557-0
32. Lenka, S. K., (2015). Measuring financial market development in
India: a PCA approach. Theoretical and Applied Economics, Volume
XXII (2015), pp. 187-198.
33. Lin, X., Li, X., & Zheng, Z. (2017). Evaluating borrower’s default
risk in peer-to-peer lending: evidence from a lending platform in
China. Applied Economics, 49(35), 3538-
https://doi.org/10.1080/00036846.2016.1262526
34. Litterman, R., and J. Scheinkman (1991). Common factors affecting
bond returns. Journal of Fixed Income, June, 54–6135. Lyócsa, Š., Vašaničová, P., Hadji Misheva, B., & Vateha, M. D.
(2022). Default or profit scoring credit systems? Evidence from
European and US peer-to-peer lending markets. Financial Innovation,
8(1), 1-21. https://doi.org/10.1186/s40854-022-00338-
36. Ma, H. Z., & Wang, X. R. (2016). Influencing factor analysis of
credit risk in P2P lending based on interpretative structural modeling.
Journal of Discrete Mathematical Sciences and Cryptography, 19(3),
777-786. https://doi.org/10.1080/09720529.2016.1178935
37. Siddhartha, M., November 6, 2020, Bondora Peer-to-Peer Lending
Data. IEEE Dataport, doi: https://dx.doi.org/10.21227/33kz-0s65.
38. Milne, A., & Parboteeah, P. (2016). The business models and
economics of peer-to-peer lending.
http://dx.doi.org/10.2139/ssrn.2763682
39. Nigmonov, A., Shams, S., & Alam, K. (2022). Macroeconomic
determinants of loan defaults: evidence from the US peer-to-peer
lending market. Research in International Business and Finance, 59,
101516. https://doi.org/10.1016/j.ribaf.2021.101516
40. Omarini, A. E. (2018). Peer-to-peer lending: business model analysis
and the platform dilemma.
41. Pengnate, S., Riggins, F.J. (2020). The role of emotion in P2P
microfinance funding: A sentiment analysis approach, International
Journal of Information Management Volume 54, October 2020,
102138
42. Polena, M., & Regner, T. (2018). Determinants of borrowers’ default
in P2P lending under consideration of the loan risk class. Games,
9(4), 82. https://doi.org/10.3390/g9040082
43. Ravina, E., Gabriel, S. P., Galak, J., Gokli, A., Munro, A., Patel, H.,
& Qian, D. (2008). Love & loans: the effect of beauty and personal
characteristics in credit markets,’SSRN Working Paper 1101647.
http://dx.doi.org/10.2139/ssrn.1107307
44. Santoso, W., Trinugroho, I., & Risfandy, T. (2020). What determine
loan rate and default status in financial technology online direct
lending? Evidence from Indonesia. Emerging Markets Finance and
Trade, 56(2), 351-369.
https://doi.org/10.1080/1540496X.2019.1605595
45. Stiglitz, J. E. and Weiss, A. (1981). Credit rationing in markets with
imperfect information. The American economic review, 71(3), 393-
410.
46. Stock, J. H. and Watson, M. W. (1999). Forecasting inflation. Journal
of Monetary Economics, 44(2), 293-335.
https://doi.org/10.1016/S0304-3932(99)00027-647. Serrano-Cinca, C., Gutiérrez-Nieto, B., & López-Palacios, L. (2015).
Determinants of default in P2P lending. PloS one, 10(10), e0139427.
https://doi.org/10.1371/journal.pone.0139427
48. Tao, Q., Dong, Y., & Lin, Z. (2017). Who can get money? Evidence
from the Chinese peer-to-peer lending platform. Information Systems
Frontiers, 19(3), 425-441. https://doi.org/10.1007/s10796-017-9751-5
49. Wang, C., Zhang, W., Zhao, X., & Wang, J. (2019). Soft information
in online peer-to-peer lending: Evidence from a leading platform in
China. Electronic Commerce Research and Applications, 36, 100873.
https://doi.org/10.1016/j.elerap.2019.100873
50. Wardrop, R. and Ziegler, T. (2016). A Case of Regulatory Evolution–
A Review of the UK Financial Conduct Authority’s Approach to
Crowdfunding. CESifo DICE Report, 14(2), 23-32.
51. Yan, J., Yu, W., & Zhao, J. L. (2015). How signaling and search
costs affect information asymmetry in P2P lending: the economics of
big data. Financial Innovation, 1(1), 1-11.
https://doi.org/10.1186/s40854-015-0018-1
52. Yang, L., Rea, W. & Rea, A., (2017). Financial insights from the last
few components of Stock Market PCA. International Journal of
Financial Studies, doi:10.3390/ijfs5030015
53. Yoon, Y., Li, Y., & Feng, Y. (2019). Factors affecting platform
default risk in online peer-to-peer (P2P) lending business: an
empirical study using Chinese online P2P platform data. Electronic
Commerce Research, 19(1), 131-158 https://doi.org/10.1007/s10660-
018-9291-1
54. Zhou, L., Fujita, H., Ding, H., & Ma, R. (2021). Credit risk modeling
on data with two timestamps in peer-to-peer lending by gradient
boosting. Applied Soft Computing, 110, 107672.
https://doi.org/10.1016/j.asoc.2021.107672
55. Zou, Z., Chen, H. & Zheng, X. (2017). “A Study of Non-performing
Loan Behaviour in P2P Lending under Asymmetric Information”.
Transformations in Business and Economics, 2017, v. 16, iss. 3, pp.
490-504
factor models for credit scoring in P2P systems. Physica A: Statistical
Mechanics and its Applications, 522, 112-121.
https://doi.org/10.1016/j.physa.2019.01.130
2. Atkinson, A. and F. Messy (2012), Measuring Financial Literacy:
Results of the OECD / International Network on Financial Education
(INFE) Pilot Study, OECD Working Papers on Finance, Insurance
and Private Pensions, No. 15, OECD Publishing, Paris,
https://doi.org/10.1787/5k9csfs90fr4-en.
3. Bachmann, A., Becker, A., Buerckner, D., Hilker, M., Kock, F.,
Lehmann, M., ... & Funk, B. (2011). Online peer-to-peer lending-a
literature review. Journal of Internet Banking and Commerce, 16(2),
1.
4. Baker, M., and J. Wurgler (2006). Investor Sentiment and the CrossSection of Stock Returns. The Journal of Finance, 61(4), 1645–1680.
https://doi.org/10.1111/j.1540-6261.2006.00885.x
5. Baker, S. R., N. Bloom, & S. J. Davis (2013). Measuring Economic
Policy Uncertainty. Discussion paper, Stanford University and
University of Chicago. https://doi.org/10.1093/qje/qjw024
6. Barasinska, N., & Schäfer, D. (2014). Is crowdfunding different?
Evidence on the relation between gender and funding success from a
German peer-to-peer lending platform. German Economic Review,
15(4), 436-452. https://doi.org/10.1111/geer.12052
7. Berger, S. C., & Gleisner, F. (2009). Emergence of financial
intermediaries in electronic markets: The case of online P2P lending.
Business Research, 2(1), 39-65 https://doi.org/10.1007/BF03343528
8. Carmichael, D. (2014). Modeling default for peer-to-peer loans.
Available at SSRN 2529240. http://dx.doi.org/10.2139/ssrn.2529240
9. Chen, H., Chong, T. T. L. & She, Y. (2014). A principal component
approach to measuring investor sentiment in China. QuantitativeFinance Volume 14, 2014 - Issue 4: Special Issue on Behavioral
Finance https://doi.org/10.1080/14697688.2013.869698
10. Chen, D., and C. Han. (2015). A Comparative Study of Online P2P
Lending in the USA and China. The Journal of Internet Banking and
Commerce 2012: 101-116.
11. Chen, X., Huang, B., & Ye, D. (2020). Gender gap in peer-to-peer
lending: Evidence from China. Journal of Banking & Finance, 112,
105633. https://doi.org/10.1016/j.jbankfin.2019.105633
12. Demyanyk, Y., Loutskina, E., & Kolliner, D. (2017). Three myths
about peer-to-peer loans. Economic Commentary, 2017, 1-6.
13. De Roure, C., Pelizzon, L., & Thakor, A. (2022). P2P lenders versus
banks: Cream skimming or bottom fishing?. The Review of
Corporate Finance Studies, 11(2), 213-262.
https://doi.org/10.1093/rcfs/cfab026
14. Eckel, C. C. and Füllbrunn, S. C. (2015). Thar she blows? Gender,
competetion, and bubbles in experimental asset markets. American
Economic Review, 105(2), 906-20. https:// DOI:
10.1257/aer.20130683
15. Egloff, D., M. Leippold, and L. Wu (2010). The term structure of
variance swap rates and optimal variance swap investment. Journal of
Financial and Quantitative Analysis, 45, 1279–1310.
https://doi.org/10.1017/S0022109010000463
16. Eid, N., Maltby, J., & Talavera, O. (2016). Income rounding and loan
performance in the peer-to-peer market. Available at SSRN 2848372.
http://dx.doi.org/10.2139/ssrn.284837
17. Emekter, R., Tu, Y., Jirasakuldech, B., & Lu, M. (2015). Evaluating
credit risk and loan performance in online Peer-to-Peer (P2P)
lending. Applied Economics, 47(1), 54-70.
https://doi.org/10.1080/00036846.2014.962222
18. Feng, Y., Fan, X., & Yoon, Y. (2015). Lenders and borrower’s
strategies in online peer-to-peer lending market: an empirical analysis
of PPDAI.com. Journal of Electronic Commerce Research, 16(3),
242.
19. Gomez, R., and E. Santor. (2003). Do Peer Group Members
Outperform Individual Borrowers? A Test of Peer Group Lending
Using Canadian Micro-Credit Data. General Information.
20. Guo, Y., Zhou, W., Luo, C., Liu, C., & Xiong, H. (2016). Instancebased credit risk assessment for investment decisions in P2P lending.
European Journal of Operational Research, 249(2), 417-426.
https://doi.org/10.1016/j.ejor.2015.05.050
21. Hastie, T. and Tibshirani, R., & Friedman, J., (2009). The Elements
of Statistical Learning. Springer Series in Statistics.22. Huston, S., J., (2010). Measuring Financial Literacy, The Journal of
Consumer Affairs, Vol. 44, No. 2, 2010 ISSN 0022-0078
23. Hsu J. W. (2016). Aging and Strategic Learning: The Impact of
Spousal Incentives on Financial Literacy. The Journal of human
resources, 51(4), 1036–1067. https://doi.org/10.3368/jhr.51.4.1014-
6712r
24. Iyer, R., Khwaja, A. I., Luttmer, E. F., & Shue, K. (2016). Screening
peers softly: Inferring the quality of small borrowers. Management
Science, 62(6), 1554-1577. https://doi.org/10.1287/mnsc.2015.2181
25. Jiang, C., Wang, Z., Wang, R., & Ding, Y. (2018). Loan default
prediction by combining soft information extracted from descriptive
text in online peer-to-peer lending. Annals of Operations Research,
266(1), 511-529. https://doi.org/10.1007/s10479-017-2668-z
26. Jin, G. Z. and Freedman, S. (2014). The Information Value of Online
Social Networks: Lessons from Peer-to-Peer Lending. NBER
Working Papers. https://doi.org/10.1016/j.ijindorg.2016.09.002
27. Kgoroeadira, R., Burke, A., & van Stel, A. (2019). Small business
online loan crowdfunding: who gets funded and what determines the
rate of interest? Small Business Economics, 52(1), 67-87.
https://doi.org/10.1007/s11187-017-9986-z
28. Klafft, M. (2008). Online peer-to-peer lending: a lenders' perspective.
In Proceedings of the international conference on E-learning, Ebusiness, enterprise information systems, and E-government, EEE
(pp. 371-375). http://dx.doi.org/10.2139/ssrn.1352352
29. Lattin, J. M., Carroll, D. J. & Green, P. E. (2003). Analyzing
Multiveriate data, Thomson Brooks/Cole
30. Lee, J. Y. (2020). Prediction of Default Risk in Peer-to-Peer Lending
Using Structured and Unstructured Data. Journal of Financial
Counseling and Planning. DOI:10.1891/JFCP-18-00073
31. Lee, J. and Kim, K. T. (2017). The Increase in Payday Loans and
Damaged Credit after the Great Recession , Journal of Family and
Economic Issues, June 2018, v. 39, iss. 2, pp. 360-69.
https://doi.org/10.1007/s10834-017-9557-0
32. Lenka, S. K., (2015). Measuring financial market development in
India: a PCA approach. Theoretical and Applied Economics, Volume
XXII (2015), pp. 187-198.
33. Lin, X., Li, X., & Zheng, Z. (2017). Evaluating borrower’s default
risk in peer-to-peer lending: evidence from a lending platform in
China. Applied Economics, 49(35), 3538-
https://doi.org/10.1080/00036846.2016.1262526
34. Litterman, R., and J. Scheinkman (1991). Common factors affecting
bond returns. Journal of Fixed Income, June, 54–6135. Lyócsa, Š., Vašaničová, P., Hadji Misheva, B., & Vateha, M. D.
(2022). Default or profit scoring credit systems? Evidence from
European and US peer-to-peer lending markets. Financial Innovation,
8(1), 1-21. https://doi.org/10.1186/s40854-022-00338-
36. Ma, H. Z., & Wang, X. R. (2016). Influencing factor analysis of
credit risk in P2P lending based on interpretative structural modeling.
Journal of Discrete Mathematical Sciences and Cryptography, 19(3),
777-786. https://doi.org/10.1080/09720529.2016.1178935
37. Siddhartha, M., November 6, 2020, Bondora Peer-to-Peer Lending
Data. IEEE Dataport, doi: https://dx.doi.org/10.21227/33kz-0s65.
38. Milne, A., & Parboteeah, P. (2016). The business models and
economics of peer-to-peer lending.
http://dx.doi.org/10.2139/ssrn.2763682
39. Nigmonov, A., Shams, S., & Alam, K. (2022). Macroeconomic
determinants of loan defaults: evidence from the US peer-to-peer
lending market. Research in International Business and Finance, 59,
101516. https://doi.org/10.1016/j.ribaf.2021.101516
40. Omarini, A. E. (2018). Peer-to-peer lending: business model analysis
and the platform dilemma.
41. Pengnate, S., Riggins, F.J. (2020). The role of emotion in P2P
microfinance funding: A sentiment analysis approach, International
Journal of Information Management Volume 54, October 2020,
102138
42. Polena, M., & Regner, T. (2018). Determinants of borrowers’ default
in P2P lending under consideration of the loan risk class. Games,
9(4), 82. https://doi.org/10.3390/g9040082
43. Ravina, E., Gabriel, S. P., Galak, J., Gokli, A., Munro, A., Patel, H.,
& Qian, D. (2008). Love & loans: the effect of beauty and personal
characteristics in credit markets,’SSRN Working Paper 1101647.
http://dx.doi.org/10.2139/ssrn.1107307
44. Santoso, W., Trinugroho, I., & Risfandy, T. (2020). What determine
loan rate and default status in financial technology online direct
lending? Evidence from Indonesia. Emerging Markets Finance and
Trade, 56(2), 351-369.
https://doi.org/10.1080/1540496X.2019.1605595
45. Stiglitz, J. E. and Weiss, A. (1981). Credit rationing in markets with
imperfect information. The American economic review, 71(3), 393-
410.
46. Stock, J. H. and Watson, M. W. (1999). Forecasting inflation. Journal
of Monetary Economics, 44(2), 293-335.
https://doi.org/10.1016/S0304-3932(99)00027-647. Serrano-Cinca, C., Gutiérrez-Nieto, B., & López-Palacios, L. (2015).
Determinants of default in P2P lending. PloS one, 10(10), e0139427.
https://doi.org/10.1371/journal.pone.0139427
48. Tao, Q., Dong, Y., & Lin, Z. (2017). Who can get money? Evidence
from the Chinese peer-to-peer lending platform. Information Systems
Frontiers, 19(3), 425-441. https://doi.org/10.1007/s10796-017-9751-5
49. Wang, C., Zhang, W., Zhao, X., & Wang, J. (2019). Soft information
in online peer-to-peer lending: Evidence from a leading platform in
China. Electronic Commerce Research and Applications, 36, 100873.
https://doi.org/10.1016/j.elerap.2019.100873
50. Wardrop, R. and Ziegler, T. (2016). A Case of Regulatory Evolution–
A Review of the UK Financial Conduct Authority’s Approach to
Crowdfunding. CESifo DICE Report, 14(2), 23-32.
51. Yan, J., Yu, W., & Zhao, J. L. (2015). How signaling and search
costs affect information asymmetry in P2P lending: the economics of
big data. Financial Innovation, 1(1), 1-11.
https://doi.org/10.1186/s40854-015-0018-1
52. Yang, L., Rea, W. & Rea, A., (2017). Financial insights from the last
few components of Stock Market PCA. International Journal of
Financial Studies, doi:10.3390/ijfs5030015
53. Yoon, Y., Li, Y., & Feng, Y. (2019). Factors affecting platform
default risk in online peer-to-peer (P2P) lending business: an
empirical study using Chinese online P2P platform data. Electronic
Commerce Research, 19(1), 131-158 https://doi.org/10.1007/s10660-
018-9291-1
54. Zhou, L., Fujita, H., Ding, H., & Ma, R. (2021). Credit risk modeling
on data with two timestamps in peer-to-peer lending by gradient
boosting. Applied Soft Computing, 110, 107672.
https://doi.org/10.1016/j.asoc.2021.107672
55. Zou, Z., Chen, H. & Zheng, X. (2017). “A Study of Non-performing
Loan Behaviour in P2P Lending under Asymmetric Information”.
Transformations in Business and Economics, 2017, v. 16, iss. 3, pp.
490-504
Published
2023-02-09
How to Cite
Aliano, M., Alnabulsi, K., Cestari, G., & Ragni, S. (2023). The Role of Gender and Education in Peer-to-peer Lending Activities: Evidence from a European Cross-country Study. European Scientific Journal, ESJ, 14, 95. Retrieved from https://eujournal.org/index.php/esj/article/view/16433
Section
ESI Preprints
Copyright (c) 2023 Mauro Aliano, Khalil Alnabulsi, Greta Cestari, Stefania Ragni
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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