If you build it, will they come? Incentivizing the Adoption of Digital Financial Services in Niger

As part of our baseline survey, we conducted an information experiment. The intervention involved a two-page flyer that explains mobile money, detailing how cash-in, cash-out, and transfer processes are executed. In addition to the flyer, treated respondents received a verbal explanation of mobile money and its services. To vary the treatment intensity, we randomly selected either 50% or 100% of respondents within a village to receive both the flyer and the verbal explanation.

To encourage the adoption and usage of mobile money, we implemented a financial incentive intervention. Households first received information about mobile money (as during the baseline survey), and were offered an incentive to use the service. The household was offered an incentive of 2,000 CFA (via mobile money), if they received a transfer via mobile money within one month from any person (i.e., a sender). The sender received a financial incentive of 1,000 CFA for making the mobile money transfer to the household. Transfer reception was verified through the network operator. The control group received no financial incentive.

We added reminder phone calls to test whether reminding households about the offer has an effect on their take-up of the financial incentive offer. We called half of the treated households to thank them for their participation in the last survey and to remind them that they only had two weeks left to receive a mobile money transfer from any person, in order to get the financial incentive. To test whether the continuous engagement with the households regarding our mobile money study had any effect, we also called half of the control households to thank them for their participation in the last survey and to remind them that we would be re-interviewing them shortly. Together, these three components allow us to assess both the independent and combined effects of addressing different barriers to mobile money adoption.

2025-05-11

2025-07-07

Mobile money adoption (extensive margin)

• Has the respondent heard of mobile money?
• Does the respondent or any household member have a mobile money account?
• Knowledge about mobile money
– Transfer costs (for 10,000 CFA transfer)
– Delivery of transfer
– Functionalities of mobile money


Mobile money usage (intensive margin)

• Did the respondent use mobile money at least once in their life?
• Did the respondent use mobile money in the last three months?
• How many mobile money transfers has the respondent received in the last three months?
• How many mobile money transfers has the respondent sent in the last three months?
• Which functionalities of mobile money has the respondent used?
• Did the respondent use the mobile money account to save money?
• How much money does the respondent normally save in the mobile money account?
• How was the respondent’s experience with mobile money?
• How was the respondent’s experience with mobile money agent(s)?

Migration and transfers

• Has any household members migrated (inter-)nationally in the last twelve months?
• What were the main destinations of the migrating household members?
• Did the household receive transfers from someone outside of the village in the last twelve months?
• How many transfers has the household received in the last twelve months? (categorical)
• What was the main purpose of the last transfer received?
• Which transfer channels was used for the last transfer received?
• What were the problems with using the respective transfer channel?
• Did the household send any transfer to someone outside of the village in the last twelve months?
• How many transfers has the household sent in the last twelve months? (categorical)
• Which transfer channel was used for the last transfer received?
• Which is the preferred transfer channel?


Household activities, consumption and well-being

• What are the main income sources for the household?
• How much income did the household generate in the last week?
• Household assets
– Ownership
– Quantity
• Household expenditures in the last week
– Food
– Education
– Health
– Ceremonies/celebrations
– Phone/airtime/battery charging
• Subjective well-being of own household (measured on a Likert scale ranging from 1 (poor) to 5 (rich))
• Subjective well-being of neighbors (measured on a Likert scale ranging from 1 (poor) to 5 (rich))
• Food insecurity
– In the last twelve months, were there any months when the household did not have enough food to meet the household’s needs?
– In which months did the household not have enough food to meet the household’s needs?


Phone usage

• Did the respondent use a mobile phone/smartphone in the last twelve months?
• How many phones does the household own in total?
• Which mobile network operator does the respondent or any household member use?


Shocks and health expenditures

• Which types of shocks did the household face in the last twelve months?
• What were the household’s strategies to cope with the shock(s)?
• In the last twelve months, has any member of the household needed medical treatment, but was unable to get it because of lack of money?
• In case of emergency, how many days would it take to get help from relatives/friends?

Sampling and data collection

Our study takes place in the Dosso region in Niger. Our sample of households is drawn from 61 villages located in nine communes in the departments of Dogon-Doutchi and Tibiri. The villages have been randomly chosen, but we had to avoid insecure villages while constituting the sample. The household baseline survey was conducted in February 2024 with 978 households. We recorded GIS locations of villages and households. We also implemented a phone survey in September 2024 interviewing the same households to assess the impact of a small randomized information treatment embedded in the baseline survey (see below for further details). The follow-up survey will take place in August 2025.

We also collect data on agents and agent networks to explore how local supply-side constraints impact adoption of mobile money. Our sample of agents was obtained through a listing of mobile money and airtime agents in 38 markets in the departments of Dogon-Doutchi and Tibiri. The markets were identified as the key markets associated with the villages from our household sample. The agent baseline survey was carried out in August 2024 and it was conducted with 190 agents from 36 markets. Out of the 190 agents, 92 are mobile money as well as airtime agents and 98 are only working as airtime agents. We will do an additional follow-up survey with the agents.


Randomization procedure

First, we randomly assigned the information treatment for the baseline survey. For this, we grouped communes into three regions: North, Middle, and South. Next, we created strata based on commune group and participation in a previous study on an adult literacy program. Villages were then randomly assigned to receive no information (1/3) or information (2/3). The villages that received information, the treatment intensity was either 50% or 100%. In the 50% treatment intensity villages, the random assignment of the information was stratified by respondents’ gender.

Second, we offered the financial incentive in the same 40 villages that received the baseline information treatment (pooling villages that received the 50% and 100% information treatments). The financial incentive was offered to all respondents in the 40 villages. We tested the treatment design in a pilot with a small urban sample beforehand. Taking the baseline information experiment into account, we have villages where all respondents received the information at baseline and during the intervention and the financial incentive (Full) and villages where only half of the respondents received the information at baseline, but then all respondents received the information during the intervention and the financial incentive (Half ).

Third, we randomly assigned households into the phone call component. This household-level randomization was stratified by village and espondents’ gender. Taking the reminder into account, we end up with these four groups: (1) a pure control group, (2) a control group that received a phone call, (3) a treatment group that received the information and the incentive, and (4) a treatment group that received the information, incentive and phone call reminder.


Spillovers

Treated households may share the information or the flyer that is handed out as part of the intervention with other households in the same village or other villages. Information sharing within the same village is not problematic for our identification, because we randomize the treatment at the village level. Since we will estimate intent-to-treat effects, information sharing across treated and control villages may only underestimate the effect of the information. However, we will monitor the extent of information sharing. Since from a policy perspective information sharing is a desired action which should increase the cost-effectiveness of the intervention, we will ask households whether they shared the information about mobile money with other households.

Given the personalized nature of our financial incentive and phone call and the in-person monitoring done by our implementing partner, we do not expect significant spillovers from our financial incentive treatment on control villages. The treatment and control villages are geographically dispersed, which minimizes the likelihood of interaction between treated and control units. These factors together suggest that any unintended influence of the treatment on control groups is likely to be small.

Randomization done in office by a computer

Village

Yes

61 villages

3,314 observations across three household survey rounds and two agent survey rounds. For the baseline survey 978 respondents from households and 190 mobile money and airtime agents were interviewed. For the households, with one baseline, phone and follow-up survey, the number of observations for a small number of outcomes is 2,934. It is 1,956 for the remaining outcomes. For the agents alone, with one follow-up the number of observations for some outcomes is 380, and 190 for the remaining outcomes. We will test for differential attrition in the phone and in-person follow-up surveys. If attrition is less than 10 percent and uncorrelated with treatment, we will proceed without making corrections. If attrition rates are greater than 10% and we find evidence of differential attrition by treatment status, we will estimate pairwise Lee bounds for our treatment effects.

Control 21 villages, Any treatment 40 villages

We use our baseline data to perform power calculations as it provides information on the means, standard deviations and intra-cluster correlation of key outcome variables. Our power calculations are for intention-to-treat effects. The MDEs are based on a 95% confidence interval and a power of 80%. The baseline data shows a mean of households using mobile money of 0.02, with a standard deviation of 0.16 and an intra-cluster correlation (ICC) of 0.125. Given our average cluster size of 16 households per cluster and 61 clusters in total, we are powered to detect a minimum effect of 0.06.

PAP_Mobile money adoption in Niger.pdf