While policymakers have been concerned with the financial exclusion of indigenous peoples, little is known about its magnitude and determinants. Indeed, for Latin America, we found scant and mostly descriptive literature on the topic. In this preliminary attempt to characterize the policy challenges faced, we explore only one dimension of the many barriers encountered: the comparatively limited extent of the financial infrastructure available in areas with high incidence of indigenous peoples, for the cases of Colombia, Ecuador, and Peru¹.

To accomplish this narrow objective, we first report the extent of the disparities in the financial infrastructure available to indigenous peoples namely, we describe the facts about uneven outreach. Next, we explore how much explanatory value can be added in the assessment and interpretation, by three types of quantitative analyses: a descriptive study, an econometric evaluation, and an exploration of potential linear and nonlinear relationships with machine learning (ML) tools. This allows us to highlight the diverse impact that these methodological choices have on the results. Our aim is to identify relationships that might not be recognized by traditional methods. Our findings suggest the presence of interesting and significant associations and correlations, with actionable policy implications, but they do not necessarily affirm any specific causation.

As starting points, we adopt the notions that access to finance is important (Shaw, 1973; Levine, 1997; Cermeño, Roa Garcia & Gonzalez-Vega, 2016; Demirgüç-Kunt, Klapper, & Singer, 2017; Popov, 2017), and that it matters the most for poor and vulnerable populations (Collins et al., 2009; Soria, Hernandez & Ciacci, 2020). Moreover, while the emergence of financial transactions is always difficult, it is particularly challenging in the case of these populations. Both parties incur high costs and face significant risks, which prevent many financial transactions from emerging, leading to exclusion (Jaffee & Stiglitz, 1990; Armendariz & Murdock, 2010; Hartarska & Cull, 2023).

Diverse informal financial arrangements and some community-based initiatives are available to indigenous peoples in the three countries (Gomez-Soto et al., 2023). While valuable, they nevertheless offer a limited range of services and do not lead to transformative outcomes (Maldonado et al., 2023). At the same time, indigenous peoples enjoy notably limited access to institutional financial services, including deposit facilities, diverse types of loans, insurance, and efficient tools for payments and transfers. Even when these services are available, frequently these peoples do not use them (Davis & Partridge, 1994; Hall & Patrinos, 2005). This exclusion from formal financial markets limits their capacity to deal with risks -including their vulnerability to climate change— and constrains their ability to take advantage of income—enhancing opportunities, which would allow higher productivity and living standards, financial health, and resilience (Klapper & Kajag, 2022; Niño Zarazua, Larquemin & Castellani, 2024).

Our purpose here is not to fully evaluate the determinants of the financial exclusion/inclusion of indigenous peoples. Rather, based on the premises listed above, we attempt to highlight existing disparities in the financial infrastructure available to these peoples, suggest why this matters, and illustrate key associations with population density and other regional distinctions, while using a small number of census and financial infrastructure data.

Financial exclusion/inclusion are market outcomes (Heimann et al., 2009). Access to, the use of, and the cost and quality of financial services result from the convergence of supply and demand, given specific market environments and regulatory frameworks. Moreover, for access to emerge and for use to materialize, the parties in financial contracts must meet and interact. This required meeting place is provided by the availability of an appropriate -physical, institutional, and digital- infrastructure. This infrastructure is more valuable, the lower the resulting transaction costs.

The key idea is that a financial infrastructure that offers an affordable and convenient meeting place is a precondition for any financial inclusion to emerge and be sustainable over time. Thus, the adequacy of this infrastructure is a necessary but not a sufficient condition for inclusion. There are many other determinants of inclusion, which we do not address here. The nature and extent of this infrastructure matters because it influences the country’s financial inclusion profile and evolution. This intimate relationship between such meeting places and financial inclusion, through its dimensions of access, use, and cost, has not being explicitly studied.

Such meeting places traditionally arose from the development -by financial intermediaries- of a system of branches, with the eventual addition and growing importance of a network of correspondent agents (Boada Serret, & Rodríguez Ferrari, 2015; Faz & Garcia Arabéhéty, 2015). With the increasing availability of on-line services, digital meeting places have rapidly expanded. Here, we explore the presence of an infrastructure of branches and correspondent agents, in territorial units of different administrative levels (ATUs), such as counties and municipalities, for which census and financial outreach data are available in the three countries.

Digital banking offers new and attractive opportunities for increased financial inclusion, particularly for populations in remote areas and with limited access to physical infrastructures (Khera et al., 2021; Demirgüç-Kunt et al. (2022); Brogeras et al., 2023; Dias et al., 2023). However, several distortions and unanticipated adverse effects have emerged in some of these efforts (Siwale & Godfroid, 2021; Kandie & Islam, 2022). It may be too early to assess the nature and extent of the potential contributions of digital infrastructures to the specific inclusion of indigenous peoples. To achieve this, numerous barriers must be overcome, including connectivity shortcomings, illiteracy, limited affordability, lack of trust and cultural factors, which may create additional disparities for indigenous peoples in their financial access (Castells, Corvalan & Rattel, 2023; Gomez-Soto et al., 2023).

Even with the growing use of digital wallets and internet banking, a physical infrastructure will continue to be needed. In Africa, for example, digital wallets have achieved some success precisely because there is a digital correspondent agent in each corner (Suri, 2017; Van Hove & Dubus (2019). If, in effect, the rate of participation in digital banking by the non-indigenous population becomes higher than the adoption rate for indigenous peoples, this will increase the gaps observed in the financial inclusion of the two groups. Specially targeted interventions may be needed to avoid these potential additional gaps. Unfortunately, given scant information, we have had to ignore this new channel for outreach in the exercises undertaken here.

Financial exclusion is the outcome of insufficient convergence of supply and demand. It reflects both universal and idiosyncratic barriers to the emergence of financial transactions. Universal barriers restrict access for all types of vulnerable populations, and are related to various dimensions of distance, poverty, lack of knowledge, risks, incomplete institutions, and small market size, among others. Idiosyncratic barriers reflect restrictions based on ethnic identities, cultural practices, and cognitive biases.

In the three countries, indigenous peoples frequently live in remote, sparsely populated areas. Although their territory is often rich in biodiversity, it is endowed with limited education, health and sanitation services, fragmented roads, and poor communications. Investment opportunities are scarce, and universal barriers to financial inclusion dominate, but they cannot be removed merely by financial interventions (Von Pischke & Adams, 1980; Gonzalez-Vega, 1998). Identifying the extent to which idiosyncratic barriers are present and how to overcome them is a pending research task.

In conclusion, if a convenient meeting place does not exist or if access to existing points of service is prohibitively expensive (given the transaction costs incurred by either party in the contract), the resulting exclusion will reflect a missing market. Any financial inclusion intervention must start, therefore, from an assessment of the existing infrastructure and a plan to strengthen it. Additional types of infrastructure may be desirable, as the emergence of digital meeting places seems to offer other opportunities for demand and supply to converge.

There is considerable heterogeneity across and within indigenous peoples, such that the extent and determinants of exclusion show substantial disparities (Gomez-Soto et al., 2023). Broad distinctions separate the peasant indigenous populations of the Andean regions, from those that inhabit the tropical forests of the Amazon and similar areas (Hartl, 2019). We also find substantial heterogeneity among the financial infrastructure available to these peoples and reasons for differentiated and tailored strategies to reduce the gaps.

We undertake a preliminary investigation to show three categories of results to address different dimensions of the problem. Descriptive analyses illustrate features of the distributions of financial outreach and of the incidence of indigenous populations and their correlations, laying the groundwork for the other approaches. Econometric analyses include models with various covariates and adjusted inference. Visual inspections using machine learning techniques -such as regression tree analyses- feature importance plots and dependency plots. These visualizations utilize a new explainability tool called SHAP values, which helps to understand the association between different variables. This comprehensive combination of methodological approaches allows us a more robust cross-validation of results, enables us to discern the degree of heterogeneity found across countries, and helps us illustrate the pronounced financial exclusion observed in regions with higher incidence of indigenous populations. This integration ensures robust and relevant analysis.

The rest of the paper is structured as follows. In Methods outlines the methods used. In Data describes the data sources and variables. In Results presents the results and discussion, divided into descriptive statistics analysis, econometric results, and machine learning outputs. It focuses on the Amazon region and on territorial units where most of the population is indigenous. Finally, conclusions and additional offers reflections.

In the prior belief that universal barriers might be reflected by population density, as high concentrations lead to a greater availability of public services and the creation of trading opportunities, markets, and the exploitation of economies of scale, scope, and agglomeration, we attempted to use this variable to capture their influence. Thus, we looked for associations among population density (PD), the incidence (%) of indigenous population (IIP), and the extent of the financial infrastructure in the various ATUs. We assessed the latter by the degree of financial outreach (FO), measured by the number of financial service points per 10,000 inhabitants in the ATU.

Our initial failure to identify significant correlations posed two issues. First, it questioned the value of census data on population density in answering these kinds of enquiries, since density figures are averages for the ATU and (implicitly) assume that the population is evenly distributed in the territory, when clearly neither the population nor FO are. Second, traditional analytical tools may fail in identifying the underlying complex and non-linear relationships, since FO is characterized by heterogeneity and complexity (Vivalt, 2015; Bamberger, 2016; Gonzalez-Vega, Mo & Di Placido, 2023).

We study both country-level and Amazon-region data (site of the highest share of indigenous population, difficult access, and limited government presence). When the data are available, in addition to PD, other covariates are used (per capita incomes), and some segmentations are examined (by degrees of rurality). While further segmentation would be useful, given the wide-ranging heterogeneity of the indigenous peoples, we keep the modelling simple, and focus on some key questions for policymakers and lessons for researchers.

First, for the descriptive analysis, we examine the distributions and percentile features of the target variable, in comparison to potential covariates, accompanied by inferential tests. We look at the extent and potential regressiveness of the financial infrastructure in the three countries.

Second, for the econometric analysis, we estimate regression models via ordinary least squares (OLS), to evaluate the statistical significance of the indigenous incidence and population density variables. Seeking to improve model specification, we study the importance of other variables, when data were available for a particular country. We examine the fit of the models by correcting for selection bias, by using the two-stage Heckman approach whereas, in the first stage, the selection process is based on a probit model (for municipalities with indigenous population). For the first selection stage, we use PD as a variable. For this, we utilize the developments of Toomet and Henningsen (2008) with their R sample Selection package: (https://cran.r-project.org/web/packages/sampleSelection/index.html).

Third, for the ML alternatives, we use complementary post hoc explanatory graphical tools for the decision tree techniques (Lundberg & Lee, 2017; Lundberg et al., 2020), developed in the rpart library in R by Therneau and Atkinson (2022), based on the work of Breiman (2017). Also, the eXtreme Gradient Boosting (XGBoost) model is used for its predictive capacity (Chen & Guestrin, 2016). Chen et al. (2024) developed this in the XGBoost library in R.

To provide explainability to the models and identify possible nonlinear relationships, we use tools -to recognize the importance of the variables- developed by Greenwell and Boehmke (2020). We also use SHAP values, which are based on the definition of shapley values developed in game theory (Shapley, 1953). SHAP values allow us to estimate the contribution of each variable and its importance, to identify monotonicity and relatedness, and even to recognize nonlinear relationships derived from ML proposals. We use SHAP values mainly to identify how IIP is related to FO, in the presence of other variables. For their application, we use the libraries developed by Mayer and Watson (2023), Kernel SHAP, and the shapviz library (Mayer, 2024).

In summary, we present three classes of results. First, we show a table with descriptive analyses, highlighting the main findings related to the distributions of FO, PD, IIP, and their relationships. Second, we discuss the results of the econometric analyses, including coefficients and inferences from the proposed models. We designate OLS1 as the model involving PD and IIP in the ATUs. For the case of Colombia, OLS2 includes control covariates, such as dummy variables for ATUs with a majority (over 50%) of indigenous population (mpi), ATUs in the Amazon region, and gross domestic product per capita. We also include a Heckman selection bias adjustment (OLS3), with a probit model for the presence of indigenous population per ATU. Finally, we build a model (OLS4) regarding FO, incorporating covariates specific to each country but excluding the incidence of indigenous population, and instead utilizing a dummy variable indicating a dominant presence of indigenous population (above 50%) per ATU.

Third, using ML models, we include visual analyses, comprising graphs resulting from regression tree scrutiny on the FO variable, and we feature importance plots and dependency plots (using SHAP values). Negative SHAP values are associated with low FO, and high values with high FO. These analyses allow visualization of monotonic relationships and potential non-linear relationships, focusing primarily on the association between FO and IIP per ATU.

Our data come from official sources within each country². Information on the indigenous population per ATU was extracted from population censuses. Financial outreach statistics were obtained from the regulatory authorities. In Colombia, however, Banca de las Oportunidades provided the data. Each dataset includes usual core variables: total and indigenous population numbers, financial service points, and ATU area, in square kilometers (km²). The Colombia data include an estimated per capita gross domestic product, and a classification of ATUs based on degrees of rurality (National Planning Department). All datasets contain a variable identifying territories within the Amazon region. We have data for 1,118 municipalities in Colombia, 224 counties in Ecuador, and 1,872 municipalities in Peru. The Annex shows maps simultaneously reporting the extent of financial outreach and the incidence of indigenous population in each country. This visualization offers an attractive way of showing the spatial distribution of the variables.

We now present the main results and discussion of the three types of analysis: descriptive, econometric, and machine learning visualization tools.

To further specify the context in which our results emerge and to highlight potential policy implications, here we complement the conclusions with several reflections, not necessarily derived from the exercises but relevant for the interpretation. First, our results show considerable dispersion and heterogeneity, across and within countries. Despite common historical roots and shared cultural practices, the financial infrastructures developed in Colombia, Ecuador and Peru exhibit important differences in extent and location across the space. Because the range and types of financial services supplied also differ, the dissimilar evolution of the financial infrastructures has had consequences on levels of financial outreach, financial inclusion, and financial health, in general and for the indigenous peoples in particular.

Further, these heterogeneity and location-specific results justify the selection of three countries for our research. Because these three Andean countries share several common features, this may make it easier to look for other determinants of the observed disparities. We have not found equivalent studies for other countries with similar presence of indigenous peoples (Bolivia, Guatemala), that would help in confirming our results.

Some observers have attributed a strong influence on these results to the diverse evolution of the regulatory frameworks (The Economist Intelligence Unit, several years; Rojas & Ruesta, 2019). Indigenous peoples have been reached mostly by state-owned banks and private microfinance banks in Colombia, by cooperatives in Ecuador, and by a broad range of private microfinance banks, financial companies, and NGOs in Peru. Both diverse initial conditions and path dependency have resulted in the dissimilar set of experiences reflected in our results (Quirós, González-Vega and Fardella, 2019).

Second, our analysis reveals profound regional disparities in financial outreach within each country, particularly pronounced in Colombia and Ecuador, where the incidence of the indigenous population emerges as a critical potential determinant of differences in financial outreach. These disparities are reflected in the maps that combine incidence of indigenous population and financial outreach, presented in the Annex. These maps are borrowed from a research activity at Fundación Capital, in which some of us participated (Gomez-Soto et al., 2023).

Third, on the one hand, the data suggest that the indigenous peoples often reside in territories characterized by socio-economic conditions that do not facilitate financial inclusion, such as high poverty rates, limited literacy and educational achievements, a precarious infrastructure, long distances from major urban centers and limited market access, and inadequate connectivity. Given the importance of the universal barriers resulting from these circumstances, there is a major role for the supply of the required public goods to overcome those barriers as well as for comprehensive financial inclusion efforts.

On the other hand, the data underscore the desirability of complementing broad financial inclusion policies with interventions that address idiosyncratic barriers and are tailored to the unique socio-economic challenges faced by indigenous communities in these regions. When these communities are concentrated in specific areas, as in Colombia, regional efforts may be sufficient. When the indigenous population is present throughout the country, as in Peru, a national approach will be needed.

In effect, the lower levels of financial infrastructure associated with a higher incidence of indigenous population might reflect cognitive and cultural biases that constrain both the demand and supply of financial services. While our research elsewhere found that the indigenous worldview (cosmovision) does not significantly constrain the demand for financial services, current and potential indigenous clients show little trust in government programs and financial institutions, possibly in reflection of adverse experiences in the past (Maldonado et al. (2023). At the same time, staff members of financial institutions reveal both prejudice and insufficient knowledge about the potential indigenous clientele. Financial education efforts and culturally aware communications may help increase the trust of indigenous households in financial institutions, while efforts directed at increasing the sensitivity and knowledge of their staff about the circumstances of the indigenous peoples will strengthen both trust and information.

Fourth, Maldonado et al. (2023) and Gomez-Soto et al. (2023) also found considerable heterogeneity, within indigenous communities, among individual households, including specific demographic dimensions (gender, age) as well as resource endowments and productive opportunities. Such heterogeneity suggests the importance of using segmentation as a critical tool of a financial technology that attempts to reach these populations (Gonzalez-Vega, Mo and Di Placido, 2023).

Fifth, the linkages and associations among the variables considered here do not necessarily imply a causal relationship. Most likely, both financial outreach and the socioeconomic circumstances associated with the universal and idiosyncratic barriers that constrain it have been determined by underlying factors, such as natural resource endowments, geography, and historical shocks (Hartl, 2019).

We now focus on the results from our current exercises. In general, territories with a higher incidence of indigenous population are associated with lower financial outreach. In Colombia, other circumstances, such as belonging to the dispersed rural segment, are associated with lower financial outreach (in part due to the absence of economies of agglomeration). In Ecuador, this is the case for territories located on the coast, most likely related to other types of vulnerability experienced by different ethnic groups, such as people of African descent.

Regions with low population density are consistently associated with lower financial outreach. This suggests that sparsely populated areas face additional challenges in facilitating access to financial services, possibly due to dimensions of physical, social, and cultural distance from financial institutions and a lack of basic infrastructure, which increases transaction costs for all parties. Also, a lower density of community support networks reduces the ability to cope with risk. Financial policies should address the specific needs of these areas, implementing strategies that consider geographic features and local socio-economic conditions, to ensure inclusive access to financial services and foster economic empowerment and social inclusion.

The consistently lower financial outreach observed in the Amazon region, across these countries, underscores the need for targeted policies to bolster financial access and inclusion in these marginalized areas. While this feature is evident across Colombia and Peru, it is worth noting that the Amazon barrier is less pronounced in Ecuador, where important cooperatives are found in this region. The indigenous communities residing in the Amazon face additional challenges from limited access to a financial infrastructure, linguistic and cultural barriers, and environmental vulnerabilities.

The analysis highlights three key contributions. First, descriptive and econometric analyses identify regional priorities, such as Amazonian municipalities with high indigenous incidence and low population density. Second, ML tools, such as SHAP scores, provide actionable insights by identifying critical thresholds. Finally, the consistency between econometric and ML results enhances confidence in policy findings and recommendations. Future research could incorporate spatial autocorrelation analysis to further strengthen the findings. While regional controls address potential spatial dependencies, explicitly modelling spatial effects using spatial econometrics or geospatial machine learning could reveal localized interactions and spillover effects. This approach would validate the robustness of the results and uncover new patterns, enhancing the design of geographically targeted financial inclusion.

Despite the valuable insights gained from our research (enhanced with the use of machine learning tools), it is important to acknowledge several limitations. First, the analysis relied on secondary data sources, which may have limitations in accuracy and completeness. However, a national census is subject to strict review and the financial data reflect prudential regulation requirements. Second, the scope of our study was limited to specific countries in Latin America, potentially reducing the ability to generalize our findings to other contexts.

Third, the multifaceted nature of financial inclusion involves numerous influencing factors (complexity), beyond those considered in our analysis, such as cultural norms, government policies, and historical context, which we still need to explore more fully. Fourth, while our study identified associations between various factors and financial outreach, it is crucial to recognize that correlation and association, even monotonic relationships, do not imply causality. Further research is needed to elucidate causal relationships.

Fifth, our study did not incorporate qualitative data or perspectives from indigenous communities, which could provide valuable insights into their unique experiences and challenges related to financial inclusion. These limitations highlight the need for continued research, to deepen our understanding of financial outreach complexities and inform more targeted and effective policy interventions.