Inside the Production Function: The Effect of Financial Contracts on Growing Firms' Technology Use

Last registered on July 08, 2019


Trial Information

General Information

Inside the Production Function: The Effect of Financial Contracts on Growing Firms' Technology Use
Initial registration date
July 04, 2019

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
July 08, 2019, 10:18 AM EDT

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



Primary Investigator

Trinity College Dublin

Other Primary Investigator(s)

PI Affiliation
Stockholm University
PI Affiliation
Ben Gurion University of the Negev
PI Affiliation
Stockholm University

Additional Trial Information

On going
Start date
End date
Secondary IDs
We examine how key aspects of the most common form of financing-debt-may inhibit young firms' expansion. While access to
credit is crucial for firm growth, SMEs in developing countries are often credit constrained. Even when they have access to credit, the types of loans available to them are not very suitable for productive investments. Starting or expanding a business entails learning how to use inputs efficiently, such as hiring additional workers. There is often uncertainty about demand that is harmful for small business without adequate resources. This implies that revenues not only are volatile but that it takes time to build up a revenue stream, as returns are back-loaded. Another concern is large and indivisible startup costs in the form of bulky investments such as machines. Meanwhile, most debt contracts available to micro-entrepreneurs in developing countries (often offered through MFIs) involve constant repayments starting shortly after loan disbursement and loan sizes that are capped because of information asymmetries. The implication is that these contractual features, together with firms’ production technology, may distort investment toward inputs that involve less learning, less uncertainty, and smaller projects; hampering firm growth.

To shed light on the extent to which these theoretical mechanisms limit the effectiveness of credit, we collaborate with BRAC Uganda's Small Enterprise Lending Program to study the effect of the credit terms on starting firms' input use, profits, and repayment performance. Small and medium-sized firms are the engines of the Ugandan economy, comprising over 90 % of the private sector and BRAC Uganda has been lending to such firms since 2008 through its Small Enterprise Lending Program. The loans range from 2.5 million to 20 million Ugandan Shillings and are repaid monthly with a maturity of 12 months at an annual interest rate of 25%. We collaborate with the program in order to examine the effect of credit contract terms on starting and newly established firms’ use of inputs, profits, and repayment performance. Using a randomized-controlled trial methodology we ask whether standard contractual terms, such as constant and monthly repayments and small initial loan amounts, are particularly restrictive for firms with specific constraints.
External Link(s)

Registration Citation

Gulesci, Selim et al. 2019. "Inside the Production Function: The Effect of Financial Contracts on Growing Firms' Technology Use." AEA RCT Registry. July 08.
Former Citation
Gulesci, Selim et al. 2019. "Inside the Production Function: The Effect of Financial Contracts on Growing Firms' Technology Use." AEA RCT Registry. July 08.
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Experimental Details


Firms approved for BRAC loans were randomly allocated to receive different modifications to the credit contract, and became part of a randomized controlled trial. Specifically, the following interventions were implemented among the 2,340 firms in our study: (i) changes to repayment frequency to distinguish the effects of uncertain project returns from those of backloaded returns; (ii) subsidies to ease the purchase of indivisible goods.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Firm outcomes (e.g. profits, revenues, costs, production), firms inputs (e.g. capital, labor, technology), household outcomes (income, consumption, labor)
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The borrowers were randomly allocated into one of the following 6 groups (treatment arms):
T1. “Early repayment voucher”: Firms in treatment group 1 were allowed to skip the first 2 repayments (months 1-2 in the 1-year loan cycle);
T2. “Late repayment voucher” firms in treatment group 2 were allowed to skip the last 2 repayments (months 11-12 in the 1-year loan cycle);
T3. “Flexible repayment voucher” firms in treatment group 3 were allowed to skip any 2 repayments of their own choosing (any 2 months in the 1-year loan cycle);
T4. “Flat repayment voucher” firms in treatment group 4 received rebates on all repayments such that the total loan repayment over the 1-year loan cycle is equivalent to the total repayment in each of treatments T1-T3;
T5. “Subsidy voucher” firms in treatment group 5 received a cash grant equivalent in value to 2 repayments (one sixth of the principal plus the interest payment). This grant was paid to the firms on the same day as their loan disbursement (i.e. at the beginning of their loan cycle);
C. “Control” firms in the control group received the standard BRAC loan contract as described above.

Experimental Design Details
In addition, we implement a cross-cutting treatment whereby 50% of firms in groups T1-T4 are provided cash subsidies in order to compensate them for the fact that their rebates are delayed relative to group T5 who receive an upfront cash grant. The intuition behind this cross-cutting treatment is to control for any income effect that may arise from the fact that some firms receive their subsidies earlier than others. Only firms in the control group and the T5 group are not included in this cross-cutting treatment arm.
Randomization Method
The randomization was conducted by the researchers on a computer. The randomization was stratified (Bruhn and McKenzie, 2009) by region, sector (manufacturing vs. retail), and previous experience with BRAC SEP loans (new vs. repeat borrower). Furthermore, firms that entered into the sample consecutively within a stratum were assigned blocks and the randomization was conducted within each block. In this way, we effectively stratify the sample by the order in which firms enter into our sample within an area, sector, and previous loan experience.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
2340 firms
Sample size (or number of clusters) by treatment arms
390 firms per treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The sample size (390 firms per treatment group and 390 for the control group) is based on calculations to detect a 0.20 S.D. shift in key outcomes with 80 percent power under a 95 percent confidence level.

Institutional Review Boards (IRBs)

IRB Name
Mildmay Uganda Research Ethics Committee (MUREC)
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials