Financial Constraints to Exporting: Experimental Evidence from Rwanda

The ECGF is an initiative conceived by Rwanda's Ministry of Trade and Industry (MINICOM), implemented by the Development Bank of Rwanda (BRD), and externally financed by the German Development Bank (KfW). The facility's primary objective is to provide loan guarantees for firms along export supply chains—including current exporters, potential exporters, and upstream firms supplying inputs to these exporters (and potential exporters). The ECGF aims to enhance these firms’ access to credit from private financial institutions by addressing collateral constraints.
1) We will use administrative data from the Rwandan Revenue Authority (RRA) including customs data, VAT tax data, corporate income tax data, and other datasets to estimate a model predicting likely adoption of the ECGF based on firm characteristics. This analysis will be accessed from an on-site secure RRA data facility. Given the focus of the ECGF, we included firms in tradable goods and services. Our final pool of firms will consist of ~5,000 that are i) current exporters, ii) potential exporters, iii) upstream firms that are selling to exporters or potential exporters.
2) We will then conduct a listing exercise with these ~5,000 firms. This exercise will be conducted mostly on the phone, with an in-person follow-up for a subset of those unreachable via phone. Firms will be given a short survey to collect additional contact information and gauge general interest in expanding access to credit and exporting. This will be used to screen out firms who (1) are hard to reach/unwilling to be surveyed; (2) do not express interest in increasing access to credit or (3) do not express interest in exporting in the future or supplying to exporters. Based on additional ECGF eligibility criteria (e.g. only domestic firms may participate) we expect this to result in a reduced target sample of ~2,000 firms.
3) We will randomize these ~2,000 firms into ~500 treatment and ~1,500 control. As detailed below in the Power Calculation section, we have chosen to have more controls than treated firms in this experiment due to the high cost of treatment relative to data collection, especially for outcomes available in the administrative data. In these cases, a greater number of controls is optimal for power given a fixed project budget (Duflo et al, 2006).

We will use the administrative data to stratify our randomization by value chain position, firm size and sector. Value chain position here is particularly important, as we intended to explore effects of treating upstream suppliers vs. downstream exporters along value chains, to speak to optimal targeting of industrial policies and propagation of effects through networks.

This administrative data from the pre-experiment period will be used to test for balance among treatment and control groups, as well as to serve as baseline controls in our analysis.

We will then conduct a door-to-door campaign in conjunction with the BRD for treatment firms. This campaign will be comprised of the following components:

(i) Pre-visit engagement: Firms will first be contacted via phone to confirm contact details, identify appropriate decision-makers, and schedule an in-person meeting. Follow-up calls will reconfirm the appointment, ensuring effective coordination.

(ii) In-person visits: Field officers will visit the firms, providing a detailed presentation on the ECGF program, including eligibility criteria, benefits, and application processes. Firms will receive informational handouts and contact details for further assistance. Officers will conduct a brief survey assessing the firms’ existing banking relationships, interest in ECGF financing, and readiness regarding required documentation. The officers will finally explain the available tailored assistance and schedule follow-up sessions.

(iii) Tailored application support: Interested firms will receive intensive, hands-on support to provide tailored assistance to interested firms, guiding them through each step of the loan application process to maximize the submission of high-quality, complete applications. This phase includes help with basic documentation and specialized assistance for sector-specific requirements. Support involves follow-ups to address technical or strategic challenges in coordination with private financial institutions (PFIs), continuing until application submission.

Once completed, the firms will submit their finalized applications through PFIs, with the BRD following the ECGF eligibly review and approval processes. This structured outreach and intensive assistance program serves as "randomized encouragement," enabling evaluation of ECGF's impact on firm outcomes.

2025-11-11

2026-10-13

Using a combination of administrative data (customs, VAT, corporate income tax, firm-level employee information from PAYE) and firm surveys we will measure the impact of the ECGF over several key outcomes:

1) Total firm credit access.
2) Firm revenue, employment and export performance
3) Spillover effects through forward and backward linkages

1) Total firm credit access: we will measure total borrowing from firm-level surveys on the loan portfolio. We may supplement this with data from the National Credit Registry, if we are granted data access by the Central Bank of Rwanda. The objective is to analyze whether firms substitute away from commercial loans or increase their total borrowing.

2) Firm revenue, employment and export performance: we will measure these outcomes in administrative data, using CIT, PAYE and customs data, respectively. Export performance will be separated into the extensive margin (any exporting) and several intensive margin measures (total exports, number of export destinations, number of products exported).

3) Spillover effects through forward and backward linkages: We will estimate the effects of spillovers along the value chain, e.g. if the treatment of a supplier has an effect on downstream (potential) exporters and, equivalently, if exporter treatment impacts outcomes of upstream suppliers. We will explore these network effects along the dimensions listed in 1) and 2). We do this by leveraging pre-existing firm-to-firm linkages in the VAT data.

1) Mechanisms affecting firms' performance outcomes: quality upgrading, certification, export promotion, capacity expansion, etc.

(1) Mechanisms affecting firms' performance outcomes: this will be measured a using a combination of firm surveys and administrative data. This will include measures of quality upgrading (measured using self-reported quality measures), number and type of certifications obtained (obtained from the Rwandan Standards Board (RSB) and self- reported in firm surveys), total investment and expenses in training and research (from surveys and/or administrative data), export promotion activities (as reported in firm surveys) and capacity expansion (as reported by self-assessed total capacity in firm-level surveys).

Experimental Design (Public) *
1) We will use administrative data from the Rwandan Revenue Authority (RRA) including customs data, VAT tax data, corporate income tax data, and other datasets to estimate a model predicting likely adoption of the ECGF based on firm characteristics. This analysis will be accessed from an on-site secure RRA data facility. Given the focus of the ECGF, we included firms in tradable goods and services. Our final pool of firms will consist of ~5,000 that are i) current exporters, ii) potential exporters, iii) upstream firms that are selling to exporters or potential exporters.

2) We will then conduct a listing exercise with these ~5,000 firms. This exercise will be conducted mostly on the phone, with an in-person follow-up for a subset of those unreachable via phone. Firms will be given a short survey to collect additional contact information and gauge general interest in expanding access to credit and exporting. This will be used to screen out firms who (1) are hard to reach/unwilling to be surveyed; (2) do not express interest in increasing access to credit or (3) do not express interest in exporting in the future or supplying to exporters. Based on additional ECGF eligibility criteria (e.g. only domestic firms may participate) we expect this to result in a reduced target sample of ~2,000 firms.

3) We will randomize these ~2,000 firms into ~500 treatment and ~1,500 control. As detailed below in the Power Calculation section, we have chosen to have more controls than treated firms in this experiment due to the high cost of treatment relative to data collection, especially for outcomes available in the administrative data. In these cases, a greater number of controls is optimal for power given a fixed project budget (Duflo et al, 2006).

We will use the administrative data to stratify our randomization by value chain position, firm size and sector. Value chain position here is particularly important, as we intended to explore effects of treating upstream suppliers vs. downstream exporters along value chains, to speak to optimal targeting of industrial policies and propagation of effects through networks.

This administrative data from the pre-experiment period will be used to test for balance among treatment and control groups, as well as to serve as baseline controls in our analysis.

We will then conduct a door-to-door campaign in conjunction with the BRD for treatment firms. This campaign will be comprised of the following components:

(i) Pre-visit engagement: Firms will first be contacted via phone to confirm contact details, identify appropriate decision-makers, and schedule an in-person meeting. Follow-up calls will reconfirm the appointment, ensuring effective coordination.

(ii) In-person visits: Field officers will visit the firms, providing a detailed presentation on the ECGF program, including eligibility criteria, benefits, and application processes. Firms will receive informational handouts and contact details for further assistance. Officers will conduct a brief survey assessing the firms’ existing banking relationships, interest in ECGF financing, and readiness regarding required documentation. The officers will finally explain the available tailored assistance and schedule follow-up sessions.

(iii) Tailored application support: Interested firms will receive intensive, hands-on support to provide tailored assistance to interested firms, guiding them through each step of the loan application process to maximize the submission of high-quality, complete applications. This phase includes help with basic documentation and specialized assistance for sector-specific requirements. Support involves follow-ups to address technical or strategic challenges in coordination with private financial institutions (PFIs), continuing until application submission.

Once completed, the firms will submit their finalized applications through PFIs, with the BRD following the ECGF eligibly review and approval processes. This structured outreach and intensive assistance program serves as "randomized encouragement," enabling evaluation of ECGF's impact on firm outcomes.

Not available

Randomization will be done by the PIs.

Firms will be randomized into treatment. We will stratify by value chain position, with three categories: (i) exporter, (ii) potential exporter and (iii) supplier of exporter or potential exporter, as we intended to explore effects of treating upstream suppliers vs. downstream exporters along value chains, to speak to optimal targeting of industrial policies and propagation of effects through networks. We will also stratify by aggregate sector: (i) agribusiness, (ii) manufacturing, (iii) services and (iv) wholesale and retail. Within each value chain position-sector stratum, we will rank firms by revenue and match by quadruplets—grouping every four consecutive firms into blocks. Within each quadruplet, one firm is randomly assigned to treatment (25% treatment rate), ensuring treatment and control firms are comparable in revenue within strata.

No

NA

2143 Firms

1605 Firms Control, 538 Firms Treatment

We conduct power calculations using administrative data from the RRA, along with the results from a retrospective difference-in-difference analysis of a previous non-experimental round of a predecessor BRD program, the Export Growth Fund (EGF). As the ECGF and EGF are very similar in nature, the results from our initial difference-in-difference estimates provide a reasonable proxy for the treatment effects we expect to see in this study. The average revenue of the firms in our sample is 369 million Rwandan Franc (RWF) ($250,000 USD). The standard deviation is 426 million RWF ($290,000 USD). Since we will be using panel administrative data to evaluate the impact of the intervention, we will have access to several pre- and post-intervention measurements. For the purpose of this power calculation, we analyze the impact with 3 follow-up measurements and 1 baseline. The within-firm correlation of revenues across periods in the administrative data is 0.81. As explained above, we will use a larger control set than our treatment, taking advantage of the limited cost of additional control units using administrative data. We use a treatment-to-control ratio of 0.25, with 1605 firms in the control and 538 in the treatment. To be conservative, we assume a take-up rate of 25%, below the average of 30% from 5 microcredit studies in Banerjee et al. 2015. Our simulations suggest that with 538 treated firms, we will be able to detect an increase in revenues of 0.26 standard deviations. This is less than half of what we find in our difference-in-difference analysis of EGF, where we find an average increase of 0.6 standard deviation 2 years after receiving a loan. In fact, with 538 treated firms, we would be able to detect an impact like the one we find in the DiD retrospective analysis with a take-up rate as low as 10.5%.