给我一个好故事，我可以借给你钱 ——论自我叙述在P2P网贷中的作用外文翻译资料

贷款资金的对数

利率降低百分比的对数

beta;

SE

t

P

beta;

SE

t

P

常数

-6.60

.20

-32.52

.00

-5.64

.16

-36.00

.00

身份数量

.11

.04

2.81

.01

.12

.03

4.04

.00

申请贷款金额（千）

-.10

.01

-16.16

.00

-.03

.01

-6.78

.00

初始利率

21.72

.77

28.15

.00

7.26

.60

12.19

.00

AA

4.23

.18

23.16

.00

1.59

.14

11.29

.00

A

3.65

.18

20.43

.00

1.38

.14

9.98

.00

B

2.85

.17

16.85

.00

1.08

.13

8.28

.00

C

1.91

.16

11.94

.00

.63

.12

5.14

.00

D

1.30

.16

8.27

.00

.46

.12

3.81

.00

E

.27

.15

1.78

.00

.18

.12

1.54

.12

男性

.51

.13

4.00

.00

.16

.10

1.63

.10

女性

.38

.14

2.83

.00

.13

.10

1.21

.23

已婚

.25

.12

2.07

.04

.20

.09

2.16

.03

离异

-.19

.21

-.92

.36

.04

.16

.24

.81

单身

.49

.20

2.42

.02

.29

.16

1.85

.07

订婚

-.10

.24

-.43

.67

.21

.19

1.13

.26

白种人

-.11

.11

-1.04

.30

.12

.08

1.44

.15

非裔美国人

-.37

.18

Tell Me a Good Story and I May Lend You

Money: The Role of Narratives in

Peer-to-Peer Lending Decision

Abstract

This research examines how identity claims constructed in narratives by borrowers influence lender decisions about unsecured personal loans. Specifically, do the number of identity claims and their content influence lending decisions, and can they predict the longer-term performance of funded loans? Using data from the peer-to-peer lending website Prosper.com, the authors find that unverifiable information affects lending decisions above and beyond the influence of objective, verifiable information. As the number of identity claims in narratives increases, so does loan funding, whereas loan performance suffers, because these borrowers are less likely to pay back the loan. In addition, identity content plays an important role. Identities focused on being trustworthy or successful are associated with increased loan funding but ironically are less predictive of loan performance than other identities (i.e., moral and economic hardship). Thus, some identity claims aim to mislead lenders, whereas others provide true representations of borrowers.

Keywords: identities, narratives, peer-to-peer lending, decision making under uncertainty, consumer financial decision making

STUDY

Data

Our data set consists of 1,493 loan listings posted by borrowers on Prosper in June 2006 and June 2007. We extracted this data set using a stratified random sampling strategy. Using a web crawler, we extracted all loan listings posted in June 2006 and June 2007 (approximately 5,400 and 12,500 listings, respectively). A significant percentage of borrowers on Prosper have very poor credit histories, and most loan requests do not receive funding. To avoid overweighting high-risk borrowers and unfunded loans, we sampled an equal number of loan requests from each credit

grade. To do so, we first separated funded loan requests from unfunded ones, then divided each group by the seven credit grades assigned by Prosper. We also eliminated all loan requests without any narrative text, for three reasons.First, including loan requests without narratives could confound the borrowers choice to write something other than narratives in the open text box with the choice to write nothing at all. Second, the vast majority of listings lacking a narrative do not receive funding. Third, loan requests without text represent only 9% of all loans posted in June 2006 and 4% of those posted in June 2007. We nevertheless used the 'no text' loans in our robustness check.

We randomly sampled posts from the 14 subgroups (2 funding status x 7 credit grades). In 2006, we sampled 40 listings from each subgroup (until data were exhausted) to obtain 513 listings; in 2007, we sampled 70 listings from each subgroup to obtain 980 listings, for a total of 1,493 listings. Each listing includes the borrowers credit grade, requested loan amount, maximum interest rate, loan funding, final interest rate of funded loans, payback status of funded loans after two years, and open-ended text data. Before combining the data from 2006 and 2007, we tested for a year effect but found none, which supports their combination.

Results and Discussion

In H₁ we posited that a lower credit grade would result in more identities claimed in a borrowers narrative. We employed a trend analysis to test this hypothesis; as Dawes and Corrigan (1974) suggest, a linear function can account for almost all the variance in any conditionally monotonic relationship. Thus we coded the credit grade information as one variable, with the following possible values: 1 if the credit grade is HR, 2 = E, 3 = D, 4 = C, 5 = B, 6 = A, and 7 = AA. We employed an analysis of covariance that treats this variable as categorical. When we control for demographic and loan characteristic variables, the trend analysis, as we expected, showed a significant linear relation between borrowers credit score and the number of identities that borrowers constructed in their narratives (t(1472) = 2.63, p lt; .01). Higher-order polynomial trends did not account for a significant proportion of the variance in the number of identities (p_quadratic = .17, P_cubic = .81, P_order4 = .16). In Figure 1, we provide the estimated marginal means of the number of identities as a function of credit grade, controlling for demographics and loan characteristics.

notes: These values are after controlling for demographics and loan financial characteristics. Covariates are evaluated at the following values: requested loan amount=8,310.24, initial interest rate=.169081, male=.32,female=.24, married=.23, divorced=.04, single=.05, engaged =.03, Caucasian =.35, African American=.07, Hispanic=.01, children=.27.

In H_2a, we posited that more identities claimed in a narrative would have a positive effect on loan funding. To test this hypothesis, we regressed the number of identities as the main predictor and credit grade, requested amount, gender, marital status, race, and family status as covariates on the natural log of loan funding. We present the results in Table 3,

notes: Regarding the reference categories for the predictors, for gender, it is “no gender”(borrowers did not disclose their gender);for marital status, it is 'no marital status or widow' (only two widows in the sample); for race, it is 'no race or race other than Caucasian, African American, or Hispanic' (i.e., there were a few Asian and Indian respondents); and for credit grade, it is HR (high risk).

which shows that when we control for borrowers demographics and loan characteristics, the number of identities borrowers clai

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