3.1 Empirical aim and approach

Most existing studies in the area of digital financial services use discrete choice models to examine consumers’ preferences for various types of payments and other financial services [29–31]. However, recent studies have shown that capturing changes in behavior from traditional choices to digital options involves several factors, including socio-economic, behavioral and institutional characteristics [32]. These multifaceted patterns suggest that a multidisciplinary approach combining economic, behavioral and data analytics is required to address such changes [33]. Machine learning methods are powerful data analysis tools that hold promise for generating new insights into payment behavior [34–36]. [37] surveys a number of methods used in studies of changes in demand behavior and concludes that machine learning techniques are effective and, often, more powerful for this type of analysis.

The advantages of a machine learning approach where there are many potential influences at the macroeconomic and microeconomic levels, such as digital payments, has increased its use for such analysis. For example, [38] uses a random forest model to discover hidden patterns that may be valuable for decision-making in bank marketing. Machine learning approaches are also employed to estimate consumer preferences for technology products [39], to examine travel choices [40], and, more generally, to model consumer response [34].

Building on this growing literature, our empirical analysis employs machine learning techniques to analyze a combination of payment ecosystem (macro) and payment system participant specific (micro) factors. Payment ecosystem factors include improvements to payment and nonpayment infrastructure, national ID programs, technological advancements in communication and computing technologies, and policies to reduce the shadow economy. Payment system participant factors include government policies to increase transaction account ownership, payment service provider (PSP) incentives, fiscal incentives to consumers and merchants to adopt digital payments, and technological advances that allow PSPs to provide payment services to more consumers and businesses often at lower costs that impact electronic payment acceptance (EPA) and usage. These incentives are defined in line with previous World Bank research [41].

The machine learning approaches that we employ carry three primary advantages over traditional parametric statistical methods. First, machine learning is a data-driven, bottom-up analytical approach or commonly referred to as “let the data speak” approach. It requires no preestablished or strict assumptions regarding the structure of the data or the functional relationships. Second, because we rely on tree-based machine learning mechanisms, our analysis may offer some insights on combinations of incentives and decision sequencing. While standard parametric models in econometrics (e.g. multilevel logit and propensity score matching) can identify behavioral patterns among clusters of individuals of firms, they are unable to offer predictions based on a sequence of actions. In other words, for some of the results obtained, the order of implementation (or a specific combination) of factors matters. In our context, previous empirical analyses and the policy experience in several countries seem to suggest that some actions or conditions can only be effective in promoting merchant EPA when other actions and conditions are already in place. For example, technology such as mobile phones or QR codes or payment infrastructure such as fast payment or card networks as a pre-condition for some payments to be accepted. However, when the number of actions and conditions is large (as in our setting) machine learning can be used to try to find sequences that increase EPA. Taken together, these first two advantages yield an analytical approach that uncovers patterns in real world interactions that induces greater adoption of merchant digital payment acceptance infrastructure and digital payment usage than what could be captured with a top-down statistical model that imposes a global structure on the data. Such an approach is also important to extract policy lessons for further merchant acceptance of electronic payments in countries with current low levels of acceptance as the global treatment of the data somehow elucidates a number of benchmarks or paths for these countries to follow. Given the treatment of data in our machine learning approach, it would not make much statistical sense to conduct specific sub-sample analyses for a smaller set of countries (e.g., low-income countries) as variability within a single group would not provide much information on benchmarking and potential routes for merchant acceptance development. Lastly, from a predictive standpoint, our machine learning approaches are more accurate than traditional econometric models used in the banking and payments literature.

However, the primary disadvantage of machine learning is that it typically emphasizes prediction over inference. Economists have traditionally been interested in studying what factors cause a change in a dependent variable as opposed to what factors predict the movement in a dependent variable. In our analysis, we are not only interested in what factors or preconditions predict digital payment acceptance and usage but also what incentives would change the behavior of merchants and consumers to increase their usage and adoption of digital payments. We partially overcome this limitation primarily because of data limitations by utilizing three complementary machine learning models, the respective features of which allow us to draw inferences about the drivers of digital payment acceptance. Ideally, we would like to use a panel dataset where we could also study adoption over time within a country. Also as mentioned above, we would prefer greater merchant heterogeneity across the incentives that we study.

Specifically, our analysis follows a three-step process. In the first step, we use the random forest algorithm [42] to analyze and narrow down the list of variables that are most important for predicting our dependent variables. For digital payment acceptance, we use POS terminal adoption because merchants generally must have POS terminals to accept payment cards. Unfortunately, we do not have reliable data for non-card merchant acceptance infrastructure. To account for the growth of non-card-based payments, we also use MSMRs’ share of P2B digital payments. While not a direct approach, the share of digital payments also captures the adoption of payment infrastructure because acceptance infrastructure must be in place before payments can be made. In addition, share of digital payments allows us to capture the usage of digital payments which is the eventual goal of policymakers and digital PSPs.

In the second step, we use the most important variables identified by the random forests to build conditional inference trees [43], which more specifically identify the sequential paths of adoption. In other words, we identify the likelihood of greater POS terminal adoption and usage based on a combination or a sequence of factors. These conditional inference trees isolate important sets of factors for prediction.

In the last step, we leverage the recently developed causal forest [44] and [45]—an adaptation of random forests for more inferential purposes—to estimate treatment effects of relevant incentives on payment acceptance. Causal forests are frequently implemented as randomized experiments in a controlled sample. In these cases, causal forests may assist to sort the information and calibrate the average treatment effects when the number of covariates is large. For example, [46] explores the impact of a randomized implementation of a microcredit program on the money borrowed and pension plan eligibility. While randomized experiments may be applied in the assessment of public policies, they cannot always be implemented. This occurs when the experimentation protocol is too expensive or complex. However, causal forests can also be used (with the necessary interpretation caveats) in the context of quasi-natural experiments. In particular, if some policies are exogenously implemented and identified as a potential shock or driver of change of certain behavior. We study the effects of groups that are treated or receive a type of incentive to increase EPA versus groups that do not receive that incentive. For the country-level sample, we study the impact of incentives on a treated country and on an untreated country. For the merchant-level sample, we study the impact on merchants within a treated country and merchants in an untreated country (see [47], on the use of causal forest to evaluate policies at the country-level).

In exploring the mechanisms underlying digital payment acceptance and usage, we take a general-to-specific approach. That is, we first analyze a “general mechanism,” that consists of identifying and rank the predicting power of country-level indicators to identify incentive mechanisms broadly. In particular, we strive to explain the absolute or conditional weight of some factors on the success of digital payment acceptance and usage incentives. This exercise provides a general framework with respect to successful implementation steps for different incentives. From a data perspective, the country level data allows us to study 106 countries rather than only the seven countries that we have merchant survey data for.

Second, in analyzing a merchant-level sample, we explore a more “detailed mechanism,” which describes the individual path followed by merchants in accepting various forms of payments and identifies the role of some incentives specific to merchants. As with the country-level analysis, the merchant level analysis investigates the effects of incentives on EPA. The following subsections expand on the data, we use the three sequential analytical approaches that we employ—the random forest, the conditional inference tree, and the causal forest.

3.2 Data

In this subsection, we describe how we construct our dataset. We combine a cross-section of country level explanatory variables with merchant survey data to identify and quantify the impact of incentives on electronic payment acceptance (EPA) infrastructure and usage.

3.3 Identifying variable importance using random forest

The random forest [42] is a tree-based, recursive partitioning machine learning approach. Within the forest, each tree depends on the values of a random vector sampled independently and with the same distribution for all other trees. The algorithm splits trees with the goal of reducing impurity between clusters of observations. Impurity is generally measured by information content metrics, such as the Gini score and residual sum of squares. Ultimately, the random forest gathers hundreds or thousands of trees to make predictions.

In predicting POS terminal adoption and share of P2B electronic payments at both the country- and merchant-level, we feed the random forest models with all the country- and merchant-level predictor variables, respectively (see S1 Appendix). This approach is common when data comes from surveys specifically designed to examine changes in banking and payment instruments [48, 49].

With the random forest estimation, our primary goal is to identify the variables that are most predictive of digital payment acceptance and usage. As explained further in the results section, the algorithm generates variable importance metrics for the model’s predictor variables. These model diagnostics provide inferential value, but they lack directionality and economic interpretation, as they are not in the units of response variables. Thus, perhaps the most important function of the random forest is that it helps us narrow down a core group of variables that are most predictive of digital payment acceptance and usage. We are able to use these predictors to more thoroughly explore the drivers of digital payment acceptance and usage by using the conditional inference tree and the causal forest.

3.4 Decision sequencing with conditional inference trees

After identifying predictors using random forest algorithms, we use the characteristics and determinants with the largest discriminant power to build a decision tree for each dimension by estimating a conditional inference tree [43]. This technique estimates a regression relationship through binary recursive partitioning in a conditional inference framework. In particular, the algorithm tests the global null hypothesis of independence between each of the input variables and the response and selects the input variable with the strongest association to the response. The algorithm then implements a binary split in the selected input variable and recursively repeats this process for each of the remaining variables. Importantly for our purposes, the conditional inference tree shows the sequential combination of factors that explain the EPA and usage decision process. This approach does not require any linearity assumptions, which is important because many of the digital payment acceptance and usage determinants could be related nonlinearly.

The conditional inference tree is similar in many ways to typical regression and classification trees, including those underlying random forests, in that it is a tree-based, recursive partitioning machine learning approach. A key difference, though, is that it is more statistical in nature, since it uses chi-square tests of independence to determine tree splits. As its name implies, the conditional inference tree is more geared toward “inference” than other tree-based methods. This technique provides insights into examining the tree structure in addition to focusing on predictive accuracy.

3.5 Estimating impact of incentives with causal forest estimation

Since machine learning models have not, historically, been designed to estimate causal effects, a new field of study has emerged over the last few years that combines the advances from machine learning with the theory of causal inference [50]. The aim of these techniques is to complement, rather than to serve as a substitute for, traditional machine learning methods, by helping researchers leverage the data-driven nature of machine learning to estimate causal effects [51, 52]. The main advantage of causal machine learning is that it can be used after the modeling phase in order to confirm some of the relations between predictors and the response variables.

In our context, by employing a causal learning method, we aim to examine the impact of incentives or conditions with the largest predictive power on the digitalization process. In a broad sense, it consists of comparing an outcome in a treated group (e.g., countries/merchants exposed to an incentive) with an untreated group (e.g., countries/merchants not exposed to an incentive). This departs to some extent from the sampling and treatment analysis in field experiments where sample selection and treatment implementation is under the control of the researchers, using randomization. In our setting, the treatment is associated with a quasi-natural experiment as we can combine a sample of merchants in different countries where some EPA incentives have been implemented as opposed to other countries or sub-samples of merchants with no access to such incentives. While the extension of our results to other jurisdictions not considered in our analysis should be done cautiously, we believe the causal forest analysis may help understand the impact of the incentives and conditions that impact EPA and usage controlling for a large number of covariates at both the country and the merchant level.

Among the recent methods developed in the causal machine learning literature, causal forest has gained particular relevance [44, 51, 52]. [53] shows that causal forests perform consistently well across different data generating processes and aggregation levels. The algorithm allows for a tractable asymptotic theory and valid statistical inference by extending the random forest algorithm. Methodologically, causal forests maintain the main structure of random forests, including recursive partitioning, subsampling, and random split selection. However, instead of averaging over the trees, causal forests allow for the estimation of heterogeneous treatment effects [45] by identifying how different treatments (e.g., incentive vs no incentive) affect the outcome (e.g., digital payment acceptance and usage). One important requirement for a proper identification is the so-called ‘honesty’ condition. This is the basic idea is that you cannot use the same outcome data to both partition the tree and estimate the average impact. This is particularly important when, rather than the standard randomization of samples, we use data to explore an exogenous change (i.e. policy) on a number of subsamples [52]. The ‘honesty’ condition is satisfied in our sample.

Compared to a normal decision tree, the causal tree uses a splitting rule that explicitly balances two objectives: (1) finding the splits where treatment effects differ the most; and (2) estimating the treatment effects most accurately. In order to obtain consistent estimates of the treatment effects (in our case, the features that may have an impact on digital payment acceptance and usage), the algorithm splits the training data into two subsamples: a splitting subsample and an estimating subsample [44, 53]. The splitting subsample is used to perform the splits and grow the tree, while the estimating subsample is used to make predictions. All observations in the estimating subsample are dropped down the previously grown tree until they fall into a terminal node. Ultimately, the prediction of the treatment effects is given by the difference in the average outcomes between the treated and the untreated observations of the estimating subsample in the terminal nodes. [45] provide a full mathematical explanation on how causal forests are built for causal inference.

Using this empirical methodology, we are able to examine the impact of those features with the largest predictive power on digital payment acceptance and usage. All analyses are carried out using the R package grf [54]. In running the algorithm, in the case of the country-level sample, we take a conservative approach by assuming that the level of digital payment acceptance and usage can be arbitrarily correlated within a country. Hence, the errors are clustered at the country-level.

4.1 Random forest results

Employing the random forest algorithm, we identify the best predictors for POS terminal adoption and MSMRs’ share of P2B digital payments. From over the 190 factors, we identify 14 variables with the largest predictive power of MSMRs adoption of POS terminals and consumer usage of digital payments at MSMRs. The random forest algorithm generates a variable importance ranking. The relative statistical importance of each factor in predicting the impact on the dependent variables is estimated. We measure the importance of each predictor by mean decrease in accuracy and mean decrease in Gini [55]. The mean decrease in accuracy reflects the mean loss in accuracy when each specific variable is excluded from the algorithm. Therefore, the determinants and characteristics with the greater mean decrease in accuracy are the most relevant. Additionally, the mean decrease in Gini is a measure of how each feature contributes to the homogeneity between the decision trees used in the resulting random forest.

This analysis provides eight variable importance plots from multiplying two accuracy methods (mean decrease in accuracy and mean decrease in Gini) by two dependent variables (POS terminal adoption and share of P2B electronic payments) covering two samples (country-level and merchant-level). For simplicity, Table 1 offers the factors with the largest prediction power for the two dependent variables that are consistently shown at the country- and merchant-level sample. The detailed variable importance plots are available upon request. In addition, Table 1 shows the predictors with mean decrease in accuracy larger than 10 percent and mean decrease Gini larger than 2 percent. Furthermore, our selection is consistent with the procedure proposed by [3]. It consists of 1) running the random forest algorithm and returns the mean decrease in accuracy and the mean decrease in Gini of each variable, 2) ranking every variable using the mean decrease in accuracy and the mean decrease in Gini, respectively, 3) scoring each variable, 4) computing the total score of each variable, 5) reordering them by the total score.

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Table 1. Main predictors of POS adoption and share of P2B electronic payments (combined results from the country-level and merchant-level samples).

https://doi.org/10.1371/journal.pone.0276203.t001

For POS terminal adoption, the main predictors correspond to three variable groups: merchant payment chain, ICT infrastructure and account ownership, and institutional and policy actions. Merchant payment chain includes the MSMRs’ share of P2B digital payments, merchant perceptions on consumers payment instrument preferences, and the percentage of wages paid digitally at the merchant level. For actual usage of P2B digital payments at MSMRs, the merchant’s perception of consumer willingness to use payment cards, and merchant usage of digital payments to pay their workers are the main predictors of greater POS terminal adoption and greater share of digital payments. In addition, we might expect that over time, as the total MSMR’s share of P2B payments increases, more MSMRs would adopt POS terminals given the popularity of payment cards. However, in some countries, digital payments not requiring POS terminals have leapfrogged payment cards.

For infrastructure, our results suggest that ICT infrastructure, transaction account ownership, and national ID programs are strong predictors of POS terminal adoption. ICT infrastructure increases the ability to open transaction accounts especially remotely and access accounts to make digital payments. Because debit cards are linked to transaction accounts, account ownership is necessary for the adoption of debit cards by both consumers and MSMRs. Not surprisingly, national IDs are a strong predictor of POS terminal adoption because IDs enable widespread ownership of transaction accounts which are necessary for consumer card adoption which is a critical factor for merchants when deciding to install POS terminals.

We also find institutional and policy actions taken by policymakers and the size of the shadow economy are important predictors of POS terminal adoption. Our empirical results regarding the impact of the shadow economy are robust to the use of some alternative measures of economic informality. In particular, our results remain very similar when we use alternative indicators of informality as the informality measures based on dynamic general equilibrium models and on the combination of multiple indicators, as provided by [56]. Correlation across the 106 countries in our sample between our shadow economy metric and these economic informality indicators ranges from 87 percent to 89 percent. Public authorities have implemented financial inclusion programs to increase transaction account ownership often with access to debit cards. Fiscal incentives for merchants are also strong predictors for greater POS terminal adoption. Furthermore, payment of wages into transaction accounts is also a strong predictor of greater adoption of POS terminals by MSMRs. Finally, the size of the shadow economy is also a strong predictor MSMR POS terminal adoption. The larger the size of the shadow economy the lower the likelihood of POS terminal adoption.

We also identify strong predictors of the MSMRs’ share of P2B electronic payments of which four of them are the same for POS terminal adoption. We categorize these predictors into four groups: merchant payment chain, payment instrument developments, ICT infrastructure and account ownership, policy variables, and access points. In the MSMR payment chain category, three variables are strong predictors of the MSMRs’ share of P2B electronic payments: MSMRs’ beliefs about consumer payment preferences, the percentage of the total value of electronic wage payments, and the proportion of the electronic payments made to suppliers. As discussed before, as MSMRs become more digitally liquid, they will tend to adopt digital payments for all incoming and outgoing payments. Given that cards are still a popular digital payment, POS terminal adoption continues to be a strong predictor of MSMRs’ share of P2B digital payments.

In the payment instruments group, previous (debit and credit) card penetration (measured as the penetration in 2011) are strong predictors of greater the share of MSMRs’ P2B electronic payments. This result suggests that consumers may require time to change their payment habits and merchants will install POS terminals as consumer demand increases over time. There may also be spillover effects between some merchant sectors into others. For example, in many countries, high-end merchants and merchants located in tourist locations are likely to be early adopters of payment cards. As consumer and merchant awareness of digital payments increases, the adoption of POS terminals by other types of MSMRs may also increase.

As expected, the level of development of ICT infrastructure is a strong predictor for the MSMRs’ share of P2B digital payments. This result is more general than the result with POS terminal adoption because usage of noncard digital payment instruments is included. In the future, we would expect that payment cards will continue to face greater competitive pressure from alternative payments such as those based on fast payment networks and closed-loop digital payment networks.

In the policy and access points categories, wages being paid in a transaction account, presence of a killer app, POS terminal adoption, and presence of agents for payment service providers are strong predictors for MSMR P2B digital payments. This result suggests that there are adoption and usage synergies between digital payments across the MSMRs’ payment chain even if the payment instruments themselves may differ. In addition, workers that are receiving payments digitally may prefer to pay digitally at MSMRs if given the opportunity.

One key finding is that each of the dependent variables is a predictor for the other dependent variable. In the case of MSMRs’ share of electronic P2B payments, POS terminal adoption provides a means to accept payment cards, the most popular digital payment option at MSMRs during our sample period. For predicting POS terminal adoption, the MSMRs’ share of P2B electronic payments is a strong predictor. The result can be interpreted as a confirmation of the feedback mechanism, whereas POS adoption increases, more MSMRs adopt POS terminals. In other words, as more MSMRs install POS terminals, other MSMRs also adopt because if they do not, they may lose business to MSMRs that accept digital payments. However, as new digital payment instruments that do not rely on POS terminals have greater market penetration, we would expect this effect to lessen.

4.2 Conditional inference tree results

While the random forest estimation techniques identified predictors in isolation, conditional inference trees allow us to study how a combination of factors and their sequence increase the likelihood of POS terminal adoption and MSMRs’ digital share of P2B payments. Based on the predictors from the variance importance analysis, we estimate conditional inference trees that identify the interactions among predictors and their sequences.

An example of a conditional inference tree is shown in Fig 1. In this example, we observe that if MSMRs believe that consumers prefer electronic payments and percent of wages paid electronically is greater than 70 percent, the likelihood of MSMR POS terminal adoption is 70 percent. Alternatively, if MSMRs believe that consumers do not prefer digital payments and less than 40 percent of wages are paid electronically by MSMRs, the likelihood of MSMR POS terminal adoption is only 10 percent. If consumers do not prefer electronic payments, the percentage of wages paid digitally is greater than 40 percent and if long-term fiscal incentives are implemented, the likelihood of MSMR POS adoption is 60 percent suggesting the importance of fiscal incentives especially in the absence of merchant beliefs that consumers do not prefer digital payments.

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Fig 1. Example of a conditional classification tree.

Note: This conditional inference tree is obtained from a merchant-sample estimation using the parameters provided by the variable importance plot from the random forest.

https://doi.org/10.1371/journal.pone.0276203.g001

For simplicity, we offer a summary of the most impactful prediction relationships in Tables 2 and 3. These estimates are derived from numerous conditional classification tree analysis similar to Fig 1 that are not shown but available upon request. In Table 2, we summarize the conditional paths that have the most impact on MSMR POS terminal adoption. Interestingly, POS terminal adoption is 200 percent more likely when the MSMRs’ share of electronic P2B payments and bank account ownership are above the median country (23.2 percent and 62 percent, respectively). These thresholds may be challenging for a number of countries within the sample. The 25th percentile for share of electronic P2B payments and bank account ownership are 9.2 percent and 44.2 percent respectively. The median percentage of electronic P2B transactions at MSMRs is a relatively low threshold. This result reinforces the dominant role of payment cards among the digital payment choices generally available.

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Table 2. Factors influencing POS terminal adoption.

https://doi.org/10.1371/journal.pone.0276203.t002

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Table 3. Factors influencing share of electronic P2B payments.

https://doi.org/10.1371/journal.pone.0276203.t003

We also find that if wages are paid electronically above the median (35 percent) and merchants believe that consumers prefer electronic payments is also above the median (52 percent), POS terminal adoption is 100 percent more likely. In addition, there may be a feedback loop whereby as more consumers prefer digital payments, more merchants install POS terminals leading to more consumers adopting and so forth. As consumers become more accustomed to using payment cards, the demand for merchants that did not previously adopt payment infrastructure increases. In addition, payment card acceptance may be a strategic tool to steal customers from merchants that do not accept cards. Unfortunately, our data does not allow us to study business stealing. For more on business stealing in a theoretical context, see [57]. Consumer preferences toward using digital payments have likely improved because of their access to transaction accounts and payment cards.

Also, if at least 35 percent of wages are paid by a given MSMR into a transaction account at a bank (the median in our sample) and ICT infrastructure is higher than the median country (above 5.3 on a 10-point scale). The 25^th percentile values for wages paid in a financial institution account and the ICT index are 10 percent and 3.5, respectively. POS terminal adoption is twice as likely. This result suggests that if merchants pay a sufficient number of workers digitally into a bank account (35% of more), they are more likely to accept card payments from their customers if there is a sufficient level of ICT infrastructure. Our results suggest that public policy should not only focus on P2B but also consider incentives to increase B2P payments such as wages.

We also find that with ICT infrastructure above 5.3 on a 10-point scale or national ID implementation being above median (94 percent) and the proportion of the shadow economy to the whole economy being no greater than 15 percent result in a 50 percent greater likelihood of MSMR POS terminal adoption. As we discussed above, ICT infrastructure is important for POS terminal adoption, but other factors may be necessary. In this case, a national ID system enables greater ownership of transaction accounts debit card.

Furthermore, implementing a national financial inclusion strategy or merchant fiscal incentive initiative (at a national level) and having a proportion of the shadow economy to the whole economy below 20 percent results in the likelihood of POS terminal adoption increasing by 20 percent. The implementation of a financial inclusion strategy is another variable that captures a necessary condition for card ownership which in turn increases the likelihood of MSMR POS terminal adoption. The merchant fiscal incentive reduces the benefits of tax evasion.

In Table 3, we report our results on the factors that impact the MSMRs’ share of electronic P2B payments. When wages are paid digitally and card penetration in the previous five years are above the median (35 percent and 11 percent, respectively), the likelihood of P2B electronic payments increases by 100 percent. This result suggests that greater awareness by consumers in the form of greater access to payment cards or by greater payment of their wages digitally, increases the likelihood of digital payments increases substantially.

Given the popularity of cards as a digital alternative to cash, if POS terminal adoption is over the median (10,005 terminals per 100,000 adults) the predicted share of P2B digital payments increases by 60 percent. Alternatively, it is less likely that merchants will adopt POS terminals if they believe that consumers do not have access to them or will not use them.

If the level of ICT is over the sample median (5.4 out of 10) is combined with the development of killer apps, or with a significant use of agents of payment service providers (1.2 per 1,000 inhabitants), P2B electronic payments’ likelihood increases by 50–60 percent. Killer apps allow payments to be made by mobile phones and may serve as an alternative to payment cards. However, sufficient ICT infrastructure is likely necessary along with innovative mobile phone-based solutions to increase MSMR’s P2B digital share of payments. In the treatment effects section, our results suggest a negative relationship between leapfrogging and POS terminal adoption. However, card payments could also benefit from mobile phone technology and QR codes but this likely occurs when a robust card ecosystem already exists. Also, the importance of agents suggests that digital payments do not immediately lead to digital liquidity, but cash agents are generally required for consumers and businesses to convert digital funds to cash and vice versa. We would expect a greater reliance on cash agents when significant parts of the population are unbanked or do not use banks, or a lack of merchant acceptance of digital payments.

When payments to direct suppliers of MSMRs are above the median sample value (45.9 percent) and the wages are paid electronically (over the median value), the MSMRs’ share of P2B electronic payments is predicted to increase by 30 percent. This result suggests that digital payments in other parts of the merchant’s payment chain likely increases usage of digital P2B payments.

4.3 Causal forest results

While the random forest and conditional inference trees offer insights into strong predictors of digital payment acceptance and usage along with their sequencing and interactions with each other, we also investigate causal effects. Specifically, we estimate the impact of a change in each explanatory variable on one of our two dependent variables. The treatment effect is given by the difference in the average outcomes between the treated and the untreated observations. The standard procedure is to compute the “average treatment effects” (ATE) when the treatment is defined based on a binary outcome as is the case for some incentives in our database. For example, if we compute the impact of financial inclusion policies on POS terminal adoption, we can estimate how POS adoption is impacted on average in countries with these policies compared with countries where no such policies exist, controlling for the rest of explanatory factors. Our analysis is not based on a randomized experiment but, instead, our analysis is a natural experiment based on exogeneous EPA incentive policies in various countries for different types of merchants.

We need to consider that our data also have a number of continuous variables. In this case, instead of the ATE, we compute the “average partial effect” (APE), which shows the percent change in the dependent variable due to a unit change in the treatment variables. Given the four-level (1 = low; 2 = mid-low; 3 = mid-high; and 4 = high) transformation of the continuous explanatory variables in our setting, the APE will show the average change in the dependent variable of going from one level of the treatment to the next one. For example, if we select the “ICT development index” as a treatment for POS adoption, we will be showing the average partial effect on POS adoption of a country moving from one level to a higher one (e.g., from mid-low to high ICT group).

As an initial test, we consider the top thirty explanatory factors in the variable importance plot as a reference. We find significant relationships for 23 variables. We group these variables into six categories: access points, economic formality, ICT infrastructure and account ownership, policy variables, instruments, and merchant payment chain. We run a causal forest for each one of the 23 variables and replicate the process for both dependent variables in the country-level and the merchant-level samples. All the results are shown as point estimates with 95 percent level confidence intervals.

In Fig 2, we report the results for the country-level sample where POS terminal adoption is the dependent variable. In the case of access points, the use of “mobile phone or the internet to access a financial institution account” is found to have a significant impact (16.2 percent) on average across the four levels of POS terminal adoption and treated and untreated countries, suggesting that consumers’ financial digitalization significantly influences merchants’ adoption of POS terminals. Among more traditional channels, bank branches per capita in 2011 seem to have a more limited positive effect.

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Fig 2. Treatment effects on POS adoption (country-level sample).

https://doi.org/10.1371/journal.pone.0276203.g002

MSMRs may have a greater incentive to accept cash as a means to evade taxes. On the other hand, greater reliance on cash transactions may result in MSMRs and consumers. In the case of economic formality, the treatment effect of the “shadow economy over GDP” on POS adoption is, as expected, negative with a relatively tight confidence interval illustrating that a large shadow economy reduces the incentive for MSMRs to adopt POS terminals on average. The effect of “crime” (measured as robbery and assaults per inhabitant) is positive with very tight confidence interval suggesting that safety concerns for consumers and MSMRs leads to greater adoption of digital payment acceptance infrastructure.

Account ownership indicators are among those with the larger marginal impact on POS terminal adoption, suggesting that financial inclusion policies are impactful and should be encouraged. We capture long-term transaction account ownership at banks by considering 2011 data. This is a proxy for a time dimension. Changing consumers’ and MSMRs’ payment behavior requires time and these variables are able to provide some insights. For short-term bank transaction account ownership, we use 2014. Interestingly, we find that the impact for long-term account ownership is higher than short-term account ownership suggesting that consumers need time to change from their preference for cash transactions at POS. As for technology-driven changes, while there is a positive APE on POS terminal adoption of mobile money accounts per capita possibly suggesting that similar infrastructures are used for noncard and card payments.

Two policy actions are economically significant and positive. These include mandates to pay wages into transaction accounts and the presence of a national financial inclusion strategy. The marginal impacts of both these public policy initiatives are significant.

From a price regulation perspective, we tried to capture the impact of regulating interchange fees, the fees that the merchant’s bank pays the cardholder’s bank and comprises the bulk of the merchant cost to accept payment cards. The two variables that we consider are: “authorities have taken actions or are considering taking action to address interchange fees” and “authorities consider interchange fees to be high.” We would expect both of these variables to have negative impact on POS terminal adoption because high fees would deter merchant adoption of POS terminals. However, the treatment effects of these variables do not have the expected sign and are insignificant. This result suggests that interchange fees may not be the main deterrent for the lack of POS terminal adoption. However, we caution that these results are averages across countries in different stages of financial development. In some cases, interchange fee regulation occurs in countries where merchant adoption is near complete. In these cases, interchange fee regulation is not likely to increase adoption by merchants or usage by consumers. In some cases, interchange fee regulation was implemented to reduce certain types of payment card transactions. In other cases, interchange fee regulation occurs in countries where the adoption and usage of cards by consumers is very low. Decreasing interchange fees may not provide incentives to consumers to adopt and use payment cards. Alternatively, the lack of a payment card infrastructure may result in new payment technologies being adopted such as noncard based mobile payments. This result is also consistent with the premise that there are other factors besides payment card fees that determine whether consumers will use payment cards even if merchants accept them.

Our next category is payment instruments. While debit and credit card ownership have a significant impact on POS terminal adoption, the latter seems to have a larger effect and to increase over time. The link between payment cards and POS terminals should not be surprising since payment card acceptance for the most part requires a terminal. Alternative acceptance infrastructure has recently been introduced but the card form factor remains the dominant one. In many countries, credit cards are accepted at tourist locations and certain high-end stores well before adoption of debit cards by the masses. Consumer awareness and card infrastructure grows from credit card acceptance in certain sectors which in turn may allow for broader acceptance of debit cards.

As we have seen before, digital payment usage in other parts of the MSMR’s payment chain positively impacts POS terminal adoption. The impact of the share of P2B, the share of B2B and the share of B2P electronic payments over total transactions on POS terminal adoption is positive and significant. The P2B results suggest a reinforcing feedback loop.

The impact on the MSMRs’ share of P2B electronic payments at MSMRs for the country-sample are shown in Fig 3. For access points, not surprisingly, using the internet or mobile phone to access a transaction account, POS terminal per capita, and ATMs per capita in 2011 are significant and positively impact MSMR share of digital P2B payments. However, branches per capita is found to have a negative impact, suggesting the persistence of cash usage has a significant effect in countries with large bank physical networks also the lower shoe leather costs may help maintain the demand for cash transactions.

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Fig 3. Treatment effects on the share of P2B electronic payments (country-level sample).

https://doi.org/10.1371/journal.pone.0276203.g003

For the infrastructure variables, there is one main difference between the POS terminal adoption and MSMR digital share of P2B payments. The effect of ICT infrastructure for the latter is large and positive suggesting that all digital payments require sufficient ICT infrastructure.

For government policy actions, the national financial inclusion strategy variable does not remain significant suggesting that other factors such as technological advancements can reduce the importance of a national financial inclusion strategy. While we discuss the impact of a considerable number of incentives, others (such as terminal subsidies) or lotteries could not be empirically analyzed due to data availability. In addition, the percentage of population with a bank account is insignificant suggesting that bank accounts may not be a precursor in all countries for greater digital payment usage.

In the case of payment instruments, debit card ownership in 2011 does not remain significant while the other variables remain positive and significant. Given new data that captures noncard based payments, we would expect the importance for card adoption to decrease even more. Card networks are also processing other types of payments such as fast payments using their infrastructure.

The magnitudes for the payment chain variables increase suggesting that merchants may adopt other digital payments besides cards. As for the merchant payment chain, P2B payments seem also to be substantially driven by B2B payments and, in particular, by B2P payments.

The merchant-level sample allows us to compare treated and untreated merchants for each incentive. Because our incentives occur at the national level, our comparison will be between merchants receiving the incentive in one or more countries (treated group) against merchants not receiving the incentive in other countries (untreated group).

For expositional simplicity, we only report the impact of those incentives not previously included in the country-level sample (Fig 4). Implementing merchant fiscal incentives will increase the likelihood of POS terminal adoption. Consumer fiscal incentives also have a positive and significant effect. The impact is lower for mandated acceptance and for cash limits. We also find that in those cases where a leapfrogging strategy develops, e.g. a killer mobile app for payments, the effect is negative because POS terminal adoption may no longer be necessary. The adoption of dongles on mobile phones and quick response codes may eventually eliminate the need for terminal adoption.

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Fig 4. Impact of incentives: Treatment effects on pos terminal adoption (merchant level sample).

https://doi.org/10.1371/journal.pone.0276203.g004

Fig 5 reports the merchant-level effects of the different incentives on the share of P2B electronic payments. In this case, the effect of the killer app is positive and the largest among the incentives considered showing the effects of mobile-related technology adoption on payment usage. Merchant and consumer fiscal incentives are also found to have significant effects and the effect is also considerable in the case of mandated use of electronic payments. The impact of short-term and long-term cash limits are also positive but not as large.

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Fig 5. Impact of incentives: Treatment effects on the share of merchant P2B electronic payment transactions (merchant-level sample).

https://doi.org/10.1371/journal.pone.0276203.g005

A summary of all treatment effects for the country-level sample and the merchant-level sample is shown in Tables 4 and 5, respectively. Overall, the empirical analysis provides some generalized conclusions to increase merchant acceptance and usage of digital payments. First, merchant and consumer decisions are interlinked when it comes to adoption and usage of digital payments. Second, digital payment acceptance and usage are positively impacted when any part of the MSMR’s payment chain uses digital payments. Third, in cases where well-functioning payment infrastructure does not exist or is not widely used, certain technologies help MSMRs bypass traditional acceptance infrastructure and adopt digital payments via alternative routes (e.g., leapfrogging via mobile payments). Fourth, while several strategies and incentives can be effective to increase digital payment acceptance and usage, their impact depends critically on the formality of the economy.

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Table 4. Summary of treatment effects from the country-level sample.

https://doi.org/10.1371/journal.pone.0276203.t004

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Table 5. Summary of treatment effects from the merchant-level sample.

https://doi.org/10.1371/journal.pone.0276203.t005

All the estimated treatment effects at the country-level and merchant-level are shown in Tables 4 and 5, respectively. We have also conducted a number of robustness tests to check the accuracy and stability of our results compared to other methods and model specifications. These are shown in S2 Appendix for exposition simplicity.