The Financial trailblazing of Digital Finance in Rural China: The Stepping Stone Effect

Last registered on June 28, 2023
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Pre-Trial

Trial Information

General Information

Title
The Financial trailblazing of Digital Finance in Rural China: The Stepping Stone Effect
RCT ID
AEARCTR-0011539
Initial registration date
June 26, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
June 28, 2023, 4:55 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country
China
Region

Primary Investigator

Name
Longyao Zhang
Affiliation
College of Finance, Nanjing Agricultural University
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
On going
Start date
2020-09-01
End date
2023-08-31
Keywords
Finance & Microfinance
Additional Keywords
Digital Finance；Financial Trailblazing；Stepping Stone
JEL code(s)
G14, G20, G24
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study draws from the approach of Karlan et al. (2011), using an algorithm (a computer program) to seek out a group of "marginal reject lenders" among customers applying for digital finance for the first time. Subsequently, a Randomized Controlled Trial (RCT) is conducted on this group of "Marginal Reject Lenders", randomly approving digital finance applications to assess the marginal effect of digital finance.

The reason for focusing on "marginal reject lenders" is that directly carrying out an RCT targeting all loan applicants would be costly. Moreover, the RCT may reject some applicants who could have been approved in regular business procedures, thus harming the welfare of these applicants.

To address this, this study aims to find among those rejected by normal business rules, a group of loan applicants who exhibit similar characteristics, have comparable credit qualifications, but due to minor issues, fall slightly below the loan approval threshold (the "marginal reject lenders" group). By implementing an additional randomized digital finance approval intervention (either approval or continued rejection), we form corresponding digital finance treatment and control groups. By comparing the relative changes in welfare performance indicators between the two groups, we attribute these changes to the marginal contribution of digital finance.
External Link(s)

Registration Citation

Citation
Zhang, Longyao. 2023. "The Financial trailblazing of Digital Finance in Rural China: The Stepping Stone Effect." AEA RCT Registry. June 28. https://doi.org/10.1257/rct.11539-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
This study, in conjunction with the sample financial institution, develops a credit scoring algorithm to identify "marginal reject lenders". The algorithm uses credit-related business indicators, combined with the requested loan amount, term, and repayment method. It trains and formulates this algorithm based on the default or non-default history of the financial institution's previous customers. This algorithm is then used to credit score first-time digital credit applicants.

Labels of "major reject lenders" and "marginal reject lenders" are assigned based on this algorithm to those who apply for digital credit for the first time but have their applications rejected. "Major reject lenders" are loan applicants whose qualifications are far below the loan threshold due to historical defaults, fraud, poor credit records, and other factors. "Marginal reject lenders", on the other hand, are a group of loan applicants whose credit qualifications are slightly below the loan threshold.

In the group of "marginal reject lenders", digital credit loan applications are randomly approved, constituting the treatment group; the remaining digital credit loan applications continue to be rejected, forming the control group.
Intervention Start Date
2021-04-01
Intervention End Date
2021-05-31

Primary Outcomes

Primary Outcomes (end points)
Farmers' access to formal credit; Farmers' access to formal credit; Borrowing costs for farmers
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Farmers' financial literacy
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study, in conjunction with the sample financial institution, develops a credit scoring algorithm to identify "marginal reject lenders". The algorithm uses credit-related business indicators, combined with the requested loan amount, term, and repayment method. It trains and formulates this algorithm based on the default or non-default history of the financial institution's previous customers. This algorithm is then used to credit score first-time digital credit applicants.

Labels of "major reject lenders" and "marginal reject lenders" are assigned based on this algorithm to those who apply for digital credit for the first time but have their applications rejected. "Major reject lenders" are loan applicants whose qualifications are far below the loan threshold due to historical defaults, fraud, poor credit records, and other factors. "Marginal reject lenders", on the other hand, are a group of loan applicants whose credit qualifications are slightly below the loan threshold.

In the group of "marginal reject lenders", digital credit loan applications are randomly approved, constituting the treatment group; the remaining digital credit loan applications continue to be rejected, forming the control group.
Experimental Design Details
Randomization Method
The "marginal reject lenders" group is identified through a computer-developed algorithm. Furthermore, the division of the "marginal reject lenders" into the treatment group and control group is also achieved through random assignment by a computer.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
4000 "marginal reject" digital credit applicants
Sample size: planned number of observations
4000 "marginal reject" digital credit applicants
Sample size (or number of clusters) by treatment arms
680 applicants in treatment group and 3320 applicants in control group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Research Ethics Board- Nanjing agricultural university
IRB Approval Date
2021-05-11
IRB Approval Number
N/A
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials