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Abstract
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Before
Digital credit through mobile money has expanded rapidly in Mozambique. Yet many users do not know the basic terms of the products available to them, including the service fee, the repayment deadline, the automatic deduction of wallet inflows while a debt is unpaid, and the consequences of non-payment. This study tests whether accurate information about Txuna, the digital credit product offered through M-Pesa, changes how market micro-entrepreneurs in Maputo understand and use digital credit. We interview approximately 1,400 micro-entrepreneurs aged 21 or older who hold an active M-Pesa account registered in their own name, in 14 markets randomly drawn from the official municipal register of Maputo markets and stratified by municipal district and market category. Within each market, respondents are approached using a random walk protocol with a fixed skip interval and pre-defined starting points. After consent and the collection of all pre-treatment modules, the survey software assigns each respondent, with probability one-half, to treatment or control. The treatment group watches a video stating the range of loan amounts, the service fee at each repayment term with worked examples, the fact that the fee is identical for every user and that the debt stops growing after the deadline, the automatic deduction of a share of every wallet inflow while a debt is unpaid, and the reporting of non-payment to the Credit Registry of the Bank of Mozambique. The treatment group is then offered an optional assisted eligibility check, which each participant completes on their own handset. Treated respondents also keep a wallet-sized card that restates the same terms in printed form, namely the minimum and maximum loan amounts, the fee at each of the three repayment terms with the worked example on a loan of 1,000 meticais, and the two steps needed to check one's own eligibility in the M-Pesa menu. The card stays with the participant, so the information remains available whenever they want to check it while considering a loan. The control group watches a placebo video on the everyday role of mobile money that carries no information on any service, cost, product, or credit, and receives neither the card nor the assisted check. We measure effects on beliefs about the terms of the product, on comprehension of the pricing rule at a loan amount never shown, on spontaneous knowledge, on stated intentions, on choices in hypothetical credit scenarios, and, for participants who consent to data linkage, on take-up, repayment, and mobile money use observed in administrative records 30, 90, and 180 days after the interview. The sign of the effect on borrowing is not predicted. Accurate terms can raise take-up when borrowers overestimate the cost or wrongly believe they are ineligible, and lower it when borrowers underestimate the cost or are unaware of the automatic deduction and the credit registry consequences. We therefore measure the direction of each participant's initial belief gap before the intervention, which is what separates these two cases.
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After
Digital credit through mobile money has expanded rapidly in Mozambique. Yet many users do not know the basic terms of the products available to them, including the service fee, the repayment deadline, the automatic deduction of wallet inflows while a debt is unpaid, and the consequences of non-payment. This study tests whether accurate information about Txuna, the digital credit product offered through M-Pesa, changes how market micro-entrepreneurs in Maputo understand and use digital credit. We interview approximately 1,400 micro-entrepreneurs aged 21 or older who hold an active M-Pesa account registered in their own name, in the 27 markets of the official municipal register of Maputo markets that meet our inclusion criteria. Within each market, respondents are approached using a random walk protocol with a fixed skip interval and pre-defined starting points. After consent and the collection of all pre-treatment modules, the survey software assigns each respondent, with probability one-half, to treatment or control. The treatment group watches a video stating the range of loan amounts, the service fee at each repayment term with worked examples, the fact that the fee is identical for every user and that the debt stops growing after the deadline, the automatic deduction of a share of every wallet inflow while a debt is unpaid, and the reporting of non-payment to the Credit Registry of the Bank of Mozambique. The treatment group is then offered an optional assisted eligibility check, which each participant completes on their own handset. Treated respondents also keep a wallet-sized card that restates the same terms in printed form, namely the minimum and maximum loan amounts, the fee at each of the three repayment terms with the worked example on a loan of 1,000 meticais, and the two steps needed to check one's own eligibility in the M-Pesa menu. The card stays with the participant, so the information remains available whenever they want to check it while considering a loan. The control group watches a placebo video on the everyday role of mobile money that carries no information on any service, cost, product, or credit, and receives neither the card nor the assisted check. We measure effects on beliefs about the terms of the product, on comprehension of the pricing rule at a loan amount never shown, on spontaneous knowledge, on stated intentions, on choices in hypothetical credit scenarios, and, for participants who consent to data linkage, on take-up, repayment, and mobile money use observed in administrative records 30, 90, and 180 days after the interview. The sign of the effect on borrowing is not predicted. Accurate terms can raise take-up when borrowers overestimate the cost or wrongly believe they are ineligible, and lower it when borrowers underestimate the cost or are unaware of the automatic deduction and the credit registry consequences. We therefore measure the direction of each participant's initial belief gap before the intervention, which is what separates these two cases.
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Last Published
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Before
August 21, 2026 12:18 PM
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After
September 02, 2026 01:37 PM
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Field
Experimental Design (Public)
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Before
One-visit survey experiment with micro-entrepreneurs trading in Maputo markets. We sample markets first, then respondents within markets, and randomize treatment at the respondent level.
We drew fourteen markets from the official municipal register of Maputo markets, Resolution number 94/AM/2008 of the Maputo Municipal Assembly approving the Markets and Fairs By-law. Markets serve as sampling units only. Both arms are present in every market, so markets are sampling clusters and not treatment units, and all specifications include market fixed effects. We allocate interviews across markets in proportion to market size rather than equally.
Within each market, enumerators approach respondents by a fixed protocol with office-drawn starting points that leaves no selection to the enumerator, and they substitute neither refusals nor ineligible respondents. Eligibility requires being 21 years of age or older, owning or being self-employed in a business operating in the market, and holding an active M-Pesa account registered in the respondent's own name with the handset present at the interview.
After consent, and after we collect all baseline covariates and prior beliefs, the survey software assigns treatment at the individual level with probability one-half. The respondent then watches the video assigned to their arm. We measure all survey outcomes in the same visit, with no follow-up survey. For participants who consent to data linkage, we add M-Pesa administrative records from 30, 90, and 180 days after the interview date.
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After
One-visit survey experiment with micro-entrepreneurs trading in Maputo markets. We cover all markets that meet our inclusion criteria, select respondents within markets, and randomize treatment at the respondent level.
We cover the 27 markets of the official municipal register of Maputo markets, Resolution number 94/AM/2008 of the Maputo Municipal Assembly approving the Markets and Fairs By-law, that meet our inclusion criteria. Markets serve as sampling units only. Both arms are present in every market, so markets are sampling clusters and not treatment units, and all specifications include market fixed effects. We allocate interviews across markets in proportion to market size rather than equally.
Within each market, enumerators approach respondents by a fixed protocol with office-drawn starting points that leaves no selection to the enumerator, and they substitute neither refusals nor ineligible respondents. Eligibility requires being 21 years of age or older, owning or being self-employed in a business operating in the market, holding an active M-Pesa account registered in the respondent's own name with the handset present at the interview, and not working for M-Pesa.
After consent, and after we collect all baseline covariates and prior beliefs, the survey software assigns treatment at the individual level with probability one-half. The respondent then watches the video assigned to their arm. We measure all survey outcomes in the same visit, with no follow-up survey. For participants who consent to data linkage, we add M-Pesa administrative records from 30, 90, and 180 days after the interview date.
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Randomization Method
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Before
Market selection. Done by computer in the office, in Stata, before fieldwork began, with fourteen markets as a pre-specified target rather than a number derived from the frame. We drew the fourteen from a frame of 27 units, built from the official municipal register of Maputo markets, as set out in Resolution number 94/AM/2008 of the Maputo Municipal Assembly approving the Markets and Fairs By-law.
The frame is cross-classified on two dimensions. The first is the municipal district, namely KaMpfumo, Nlhamankulu, KaMaxakeni, KaMavota, and KaMubukwana, the five mainland districts of Maputo City. The second is the market category in the by-law, namely Class A, Class B, and Class C for formal markets, and a fourth category for informal markets. Together the two dimensions define 13 non-empty cells across the 27 units.
District is the explicit stratum. We allocated the fourteen markets across districts in proportion to the number of frame units in each, by largest remainders with a floor of one market per district, giving three to KaMpfumo, three to Nlhamankulu, two to KaMaxakeni, two to KaMavota, and four to KaMubukwana. Category is the implicit stratum. We sorted each district's units by category before a systematic selection with a random start, so the draw spreads across categories within district in proportion to their frequency in that district. Each unit has a known, non-zero inclusion probability equal to its district allocation divided by its district frame size, and carries the corresponding design weight. We fixed the seeds before running the draw, with a master seed and one derived seed per district, so the draw is reproducible from the do-file.
Respondent selection within market. No list of traders exists from which to draw a sample, so selection is systematic rather than a lottery. Enumerators follow a fixed traversal route from a pre-defined starting position, taking every fifth stall, which removes enumerator discretion, with a birthday rule to choose among eligible people at the same stall.
Treatment assignment. Done by the survey software, SurveyCTO, on the enumerator's tablet during the interview. A stored once(random()) call draws a uniform number once per respondent, only after consent and after every pre-treatment module is complete, and maps it to treatment with probability 0.5. The form retains the assignment timestamp. Enumerators cannot anticipate the arm while selecting, screening, or consenting a respondent, and they cannot redraw it. Treatment assignment is not stratified.
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After
Market selection. Done by computer in the office, in Stata, before fieldwork began. The study covers all 27 markets that meet the inclusion criteria, built from the official municipal register of Maputo markets, as set out in Resolution number 94/AM/2008 of the Maputo Municipal Assembly approving the Markets and Fairs By-law. We drew 14 of them on 14 August 2026 and visited those first, stratified by municipal district, namely KaMpfumo, Nlhamankulu, KaMaxakeni, KaMavota, and KaMubukwana, with market category as an implicit stratum imposed by sorting each district's units by category before a systematic selection with a random start. We fixed the seeds before running it, with a master seed and one derived seed per district, so it is reproducible from the do-file. The remaining 13 markets were added on 28 August 2026.
Respondent selection within market. No list of traders exists from which to draw a sample, so selection is systematic rather than a lottery. Enumerators follow a fixed traversal route from a pre-defined starting position, taking every third stall, which removes enumerator discretion, with a birthday rule to choose among eligible people at the same stall.
Treatment assignment. Done by the survey software, SurveyCTO, on the enumerator's tablet during the interview. A stored once(random()) call draws a uniform number once per respondent, only after consent and after every pre-treatment module is complete, and maps it to treatment with probability 0.5. The form retains the assignment timestamp. Enumerators cannot anticipate the arm while selecting, screening, or consenting a respondent, and they cannot redraw it. Treatment assignment is not stratified.
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Planned Number of Clusters
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Before
Treatment is randomized at the individual level, so there are no randomization clusters. The sample is drawn in 14 market clusters, with both arms present in each.
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After
Treatment is randomized at the individual level, so there are no randomization clusters. The sample covers 27 market clusters, the universe of urban markets with a GPS coordinate available, with both arms present in each.
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Planned Number of Observations
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Before
1,400 individuals aged 21 or older, owning or self-employed in a business in the market, and holding an active M-Pesa account registered in their own name. Interviews are allocated across the 14 markets in proportion to measured market size rather than equally.
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After
1,400 individuals aged 21 or older, owning or self-employed in a business in the market, holding an active M-Pesa account registered in their own name, and not working for M-Pesa. Interviews are allocated across the 27 markets in proportion to measured market size rather than equally.
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Power calculation: Minimum Detectable Effect Size for Main Outcomes
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Before
With 1,400 individuals split equally, 80 percent power, and a 5 percent two-sided test, the minimum detectable effect for standardized survey outcomes is 0.15 standard deviations without covariates, and about 0.12 standard deviations for posterior beliefs controlling for the prior of the same item, assuming the prior explains 35 percent of posterior variance. For administrative take-up, assuming a 10 percent control group take-up rate over 90 days, the minimum detectable effect is 4.5 percentage points. At a 20 percent control rate, it is 6.0 percentage points.
Because treatment is randomized within market, clustering at the market level does not inflate the standard error of the treatment coefficient once market fixed effects are included, so these calculations require no design effect correction for the treatment contrast. The 14-market design does limit precision for descriptive and weighted population statements, which use the market design weights and are reported with market-clustered standard errors. Market fixed effects are not credited in the calculations above. Detailed power calculations are reported in the pre-analysis plan.
The administrative minimum detectable effects apply to the subsample consenting to data linkage. If consent falls materially below the full sample, the realized detectable effect rises in proportion, and we will report the achieved figure rather than the planned one.
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After
With 1,400 individuals split equally, 80 percent power, and a 5 percent two-sided test, the minimum detectable effect for standardized survey outcomes is 0.15 standard deviations without covariates, and about 0.12 standard deviations for posterior beliefs controlling for the prior of the same item, assuming the prior explains 35 percent of posterior variance. For administrative take-up, assuming a 10 percent control group take-up rate over 90 days, the minimum detectable effect is 4.5 percentage points. At a 20 percent control rate, it is 6.0 percentage points.
Because treatment is randomized within market, clustering at the market level does not inflate the standard error of the treatment coefficient once market fixed effects are included, so these calculations require no design effect correction for the treatment contrast. Covering 27 markets rather than 14 raises the number of clusters available for descriptive and weighted population statements. Because the study covers every market that meets the inclusion criteria, market design weights are equal to one, and these statements are reported with market-clustered standard errors. Market fixed effects are not credited in the calculations above. Detailed power calculations are reported in the pre-analysis plan.
The administrative minimum detectable effects apply to the subsample consenting to data linkage. If consent falls materially below the full sample, the realized detectable effect rises in proportion, and we will report the achieved figure rather than the planned one.
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