Digital Credit and Borrowers’ Welfare in Ethiopia: A Pilot Random Control Trial Study

Last registered on March 15, 2024

Pre-Trial

Trial Information

General Information

Title
Digital Credit and Borrowers’ Welfare in Ethiopia: A Pilot Random Control Trial Study
RCT ID
AEARCTR-0013130
Initial registration date
March 04, 2024

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
March 15, 2024, 2:46 PM EDT

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

Locations

Region

Primary Investigator

Affiliation
Addis Ababa University

Other Primary Investigator(s)

PI Affiliation
FDRE Policy Studies Institute (PSI)
PI Affiliation
FDRE Policy Studies Institute (PSI)
PI Affiliation
Addis Ababa University
PI Affiliation
University of Copenhagen
PI Affiliation
FDRE Policy Studies Institute (PSI)

Additional Trial Information

Status
In development
Start date
2024-03-15
End date
2024-12-31
Secondary IDs
00011347
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In a unique experimental setting in digital credit for micro and small enterprises (MSEs), we will randomize eligible digital credit applicants into treatment and control groups (stratified by gender), where the former will receive digital credit while the control group will not get digital credit for at least four months. In addition, previous studies have also documented that MSEs face multiple constraints, including limited financial knowledge and skills, and addressing only a single credit constraint may not be enough to improve their performance. Therefore, we introduce a second treatment where we combine financial literacy training with digital credit access to explore whether providing financial literacy training could amplify the impacts of digital credit. We will investigate the impacts of digital credit on the welfare of borrowers. To guide the design of our interventions, we will leverage administrative data on existing borrowers of digital credit. The findings are expected to provide insight into policy formulation, product development and modification, customer protection, and default reduction mechanisms in Ethiopia, where digital credit is a new phenomenon, and beyond.
External Link(s)

Registration Citation

Citation
Belay, Dagim et al. 2024. "Digital Credit and Borrowers’ Welfare in Ethiopia: A Pilot Random Control Trial Study ." AEA RCT Registry. March 15. https://doi.org/10.1257/rct.13130-1.0
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Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
Ethiopia lags behind Sub-Saharan countries in terms of key financial inclusion indicators (Achew et al., 2021, Demirgüç-Kun et al., 2022). For instance, around 81.4% of the rural adult population has been unbanked (CSA, 2020). The standard banking service has been offering loans to a limited number of customers, mostly to large firms (NBE, 2019). All banks in the country combined offered loans to only around 300 thousand customers in 2018/19 (NBE, 2020), which is infinitesimal for the country of over 109 million population. Around 70% of MSEs had no credit access, and 61% of them never applied for loan due to primarily lack of collateral (Gebreeyesus et al., 2018. Almost all of the MSEs (95.7%) used their savings to establish their businesses (Gebreeyesus et al., 2018). That could be why credit constraint is one of the major barriers for the growth for MSEs in the country (Assefa et al., 2014; Endris and Kassegn, 2022; Gebreeyesus et al., 2018). To curtail the problem, the Ethiopian government has recently promoted digitalization of the financial sector, including the introduction of mobile money and digital credit services (Ndulu et al., 2023). Digital credit has a promising potential to improve living standards by smoothing consumption for consumers, leveraging liquidity constraints of farmers and small businesses (Brailovskaya, 21; Johnen et al, 2021). An apt example is when Cooperative Bank of Oromia (COOP) launched the first bank-based digital credit in Ethiopia in January 2022, it received over 160 thousand digital credit applications from MSEs, and offered uncollateralized digital credit to over 92 thousand customers since its launch (COOP, 2023).
Digital credit brings both opportunity and risk in Ethiopia. On the one hand, it offers great opportunity to access credit for consumers, farmers and MSEs which otherwise have had scant credit access from the banks. The credit access may increase income, profit, employment, resilience and consumption among the target population who are mostly subsistent and self-employed households engaged in micro and small enterprises. On the other hand, digital credit may have a net negative impact on the households’ welfare since (i) the interest rate is very high when compared with the interest fee from the standard loans from financial institutions, (ii) the duration of the credit period is short in that the loan period may be insufficient to do a profitable business and return the credit along with the interest, and (iii) most of the people in Ethiopia have had very limited financial literacy in that the borrowers may use the credit unwisely, leading to loss, default and being blacklisted on their future credit applications (Johnen et al., 2021; Robinson et al., 2022). Hence, it is essential to systematically investigate the impact of digital credit on borrowers’ welfare.

To this end, we randomize digital credit using random control trial approach to investigate the impact on borrowers.
We propose to randomize two interventions. First, we propose to randomize the digital credit itself. Credit constraint is a major problem for MSEs in Ethiopia, hindering their growth (Assefa et al., 2014; Endris and Kassegn, 2022). Digital credit may leverage this constraint. However, little is known about the impact of digital credit on borrowers’ welfare. Second, we propose complementing the digital credit with financial literacy training since financial literacy is poor in Ethiopia (CSA, 2020; Demirgüç-Kun et al., 2022). Evidence shows that addressing a bundle of constraints simultaneously has a multiplier effect, greater than the sum of the effects of addressing each constraint separately (J-PAL and CEGA, 2022; Suri and Udry, 2022). Hence, this study allows to investigate if provision of digital credit improves the borrowers’ welfare and if combining digital credit with financial skill trainings improves the performance digital credit. Our primary outcome variable will be profitability of the digital credit for the borrowers. Secondary outcome variables include consumption and employment.
Intervention Start Date
2024-03-20
Intervention End Date
2024-04-15

Primary Outcomes

Primary Outcomes (end points)
The primary outcome variable is profitability of the digitl credit.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Employment and household consumption.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Sample draws from digital credit applicants who are eligible for credit and who are in the waiting list will be randomly assigned into treatment and control groups. The first treatment group will receive digital credit; the second treatment group will receive digital credit plus financial literacy training; while the control group will not receive any treatment for at least four months. The randomization unit will be at individual MSE level. The main potential identification challenge will be compliance problem from both the treatment and control groups. Analysis of administrative data from our partner bank show that, on average, 9% of the customers rejected the digital credit after being offered in the last 16 months of COOP’s digital service provision period, where the percentage of customers who rejected the credits remained fairly the same across time. Similarly, the control group whose application will be rejected may apply for credit somewhere else. These non-compliant customers may systematically differ with the customers who comply, causing potential bias. In this case, we propose three approaches. The first approach is to estimate intent-to-treat. The second approach is to consider the time period that the customers actually receive the credit as treatment indicator, particularly if the non-compliance rate will be very high. The third approach is to use propensity score matching technique and evaluate the impact only on customers who comply, for that we increased the sample size by about 20% as discussed below.
Experimental Design Details
Randomization Method
Randomization will be done in office by a computer. Eligible SMEs will be divided in to three groups by generating a random number (Bernoulli distribution) corresponding to the list of SMEs.
Randomization Unit
The randomization unit will be individual SMEs.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1,980 small and medium sized formal business enterprises
Sample size: planned number of observations
1,980 small and medium sized formal business enterprises
Sample size (or number of clusters) by treatment arms
The sample size will be equally divided in to two treatment arms and a control group, each treatment arm having 660 sample size. That is, 660 SMEs will receive digital credit, 660 will receive digital credit and training about financial literacy and the remaining 660 SMEs will not receive digital credit and finincial literacy training. We will stratify the sample by gender: 31% female and 65% male, implying a random sample of 614 female and 1366 males. The gender distribution of the sample is based on information from previous digital credit applicants administrative data.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We calculate the minimum detectable effect (MDE) to be 0.16 standard deviations with significance level of 0.05 and power of 0.8. Given that we have multiple treatment arms, we use Bonferroni correction and divide the level of significance by the number of experimental arms. Thus, with our new level of significance (0.0166), we can detect the proposed effect size if we have 1,650 sample size. To account the potential compliance problem and to have a sufficient sample size, we will increase the sample size by 10%, implying 1,815 sample size. Moreover, there could be another potential compliance problem from the control group who may apply for digital credit in other digital credit providers. We do not have data on this issue, nevertheless, will increase the sample size by another 10% to have sufficient sample size without the compliance group, implying a total sample size of 1,980. The sample size will be equally divided into treatment and control groups. In addition, evidence from administrative data shows that gender is an important factor for decision making in digital credit application and individual’s life outcomes. Hence, we stratify our sample using gender. Administrative data from COOP shows that 31% of digital credit borrowers were women. Using the gender composition of our target population, (31% female and 65% male), we propose a stratified randomization, implying a random sample of 614 female and 1366 males, totaling a sample size of 1,980 number of beneficiaries. Given the magnitude of treatment effects in the latest studies, we believe that this study is well powered for economically meaningful comparisons against control.
IRB

Institutional Review Boards (IRBs)

IRB Name
Institutional Review Board of the College of Business and Economics of Addis Ababa University
IRB Approval Date
2023-12-21
IRB Approval Number
CBE/ADean/RTT/004/2023

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