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Abstract

This study investigates how municipal cash transfer programmes influence intra-urban inequality through the case of Maricá, Brazil. Using over 3.4 million transactions from a local digital currency, we trace the spatial flow of social benefits and assess economic spillovers via local multiplier (LM3) and urban scaling models. Despite a strong multiplier effect, spending is highly concentrated in a few retailers (Gini = 0.80), while neighbourhood-level business revenue scales superlinearly with social benefits (β = 1.15). These findings reveal how redistributive policies interact with spatial inequalities, offering new insights into the geography of welfare and the design of inclusive urban policy.

Keywords

urban inequality cash transfer programmes local currencies basic income territorial development local multiplier effect urban analytics

Introduction

Urban inequality is increasingly shaped not only by national policies but also by the spatial dynamics of local social policy interventions. In the wake of the COVID-19 pandemic, municipal cash transfer programmes have proliferated globally (Gentilini, 2022), offering new tools for redistribution and local economic development. Yet, their spatial effects within cities remain insufficiently understood. This article investigates how such programmes interact with intra-urban inequalities, focusing on one of the world’s largest municipal basic income schemes implemented in the city of Maricá (near Rio de Janeiro), Brazil, using a local digital currency (De Wispelaere et al., 2024).

While cash transfer programmes (CTPs) are widely recognised for reducing poverty (Bastagli et al., 2018), most evaluations focus narrowly on recipients, overlooking how benefits circulate spatially and economically within urban territories. This is particularly problematic in cities of the Global South, where socio-spatial inequalities are deeply entrenched in colonial and capitalist urbanisation processes (Barros et al., 2024; Ortiz, 2024). In such contexts, even progressive policies risk reinforcing unequal development patterns if they fail to account for the territorial logics of urban infrastructure, commerce, and mobility (Fix and Arantes, 2022; Roitman et al., 2024).

A key challenge in assessing the implications of cash transfers for intra-urban inequalities lies in the availability of indicators that adequately capture socio-spatial variations across neighbourhoods within cities: social inequalities are strongly associated with data inequalities (de Albuquerque et al., 2023). Most conventional datasets used to analyse the distribution of social benefits and related socio-economic indicators lack spatial granularity, as data are typically aggregated at the city level, thereby obscuring inequalities within urban areas. This limitation is particularly consequential in the most deprived areas of Global South cities, often referred to as “informal neighbourhoods,” which are typically self-built, continuously evolving, and poorly documented (Ulbrich et al., 2018).

This study addresses these gaps by analysing high-resolution transaction data from Maricá’s Mumbuca currency, which is used to disburse social benefits and restricts spending to local businesses. We ask: how do municipal cash transfers circulate within the city, and what are their implications for spatial inequality? Using a combination of local multiplier analysis (LM3), Gini coefficients, and urban scaling models, we show that while the programme stimulates local economic activity, it also concentrates spending in central neighbourhoods and among large retailers. These findings challenge assumptions about the spatial dynamics of redistributive policies and offer new insights into the territorial politics of welfare in urban settings.

This study makes two important contributions. First, it contributes to the literature on spatial justice, territorial development, and urban scaling laws with new evidence on how local currency-based cash transfers relate to local economic effects and intra-urban inequalities. Second, it proposes a framework for urban policymakers interested in implementing cash transfer and basic income policies to assess how the benefits circulate within a city. This can support more spatially inclusive social policy by providing a tool for evaluating and adjusting cash transfer policies to better achieve their intended objectives, such as reducing intra-urban inequalities and strengthening the local economy.

Cash transfer programmes, local currencies, and urban inequalities

The need to “stay local” fostered an innovative aspect in some municipal CTPs: the use of local currencies to disburse benefits (Howitt, 2019). This approach aims to boost local development by restricting spending to specific areas, thereby enhancing economic exchange within urban territories. While CTPs have been associated with a reduction in regional inequalities (Connolly et al., 2022; Doussard and Schrock, 2023; Manzi et al., 2023), local currencies have gained attention as a mechanism to reduce intra-urban inequalities (Ansorena et al., 2021). As local currencies keep money circulating within a specific territory, they influence territorial development (Blanc et al., 2025) and foster local consumption (Seyfang, 2006). Thus, local currencies in cash transfers potentially improve socioeconomic conditions while promoting small businesses (de Souza, 2024). Belmonte et al. (2021) add that CTPs in local currencies positively impact consumption habits, increasing trust in local businesses and fostering a sense of belonging to the local economy.

There are two key reasons to focus on municipal CTPs that use local currencies. First, these programmes have rapidly spread globally in recent years, gaining supporters in Europe, the USA, and Asia (Thompson, 2022). Second, by using their own currency to pay beneficiaries, cities can trace the circulation of money and assess its impact on the territory (Santos, 1999), “generating knowledge about the use, circulation, and destination of public spending linked to the currency” (Segura and Muns, 2019). Notable examples of municipal cash transfers with local currencies include Gyeonggi, South Korea (Chung, 2020; Lee et al., 2020), Barcelona, Spain (Belmonte et al., 2021), Maricá, Brazil (Gonzalez et al., 2020), and various initiatives in France, Belgium, Switzerland, and Canada (Blanc and Fare, 2022), showcasing the global adoption of CTPs with local currencies.

To assess the expected potential of CTPs with local currencies to improve socioeconomic conditions while promoting small businesses (de Souza, 2024), one possible approach is the LM3 method (Sacks, 2002). Developed by the New Economics Foundation (NEF), the LM3 approach has been used to study the impacts of local public procurement of hospitals in the UK (Thatcher and Sharp, 2008), environmental policies shortening supply chains in the Czech Republic (Březina et al., 2013), and the local effects of online retail on an English market town (Mitchell and Lemon, 2019).

Recent studies have begun using the LM3 approach to examine the circulation of local currencies. Lafuente-Sampietro (2021) used LM3 to investigate the multiplier effect of local currencies in France, while Belmonte et al. (2021) and Roca et al. (2023) used it to investigate a CTP case with local currency in Spain. These studies became a reference for establishing the use of LM3 in studies of the circulation of local currencies and helping to understand its implications for the local economies where they are used by measuring the level of recirculation, meaning the number of times the same unit of currency is used before being redeemed in fiat money.

Previous studies have shown that local currencies tend to promote higher monetary recirculation within a territory than conventional fiat currencies, which typically exit the local economy more rapidly. It is commonly assumed that cash transfers disbursed in local currency are more likely to support a broader range of small businesses and stimulate economic activity within the city, thereby generating multiplier effects that contribute to local and territorial development (Blanc et al., 2025; Roca et al., 2023). However, existing research has yet to explore how such recirculation interacts with socio-spatial inequalities – specifically, how the benefits are distributed across different types of businesses and urban areas.

The spatial implications of welfare policies are particularly salient at the municipal level, especially amid rising intra- and inter-regional disparities (Bathelt et al., 2024). Glaeser et al. (2009) argue that generous local welfare schemes may attract low-income populations from surrounding areas, potentially intensifying inequalities within cities. This concern is empirically supported by recent findings from China, where cash transfer programmes have been linked to increased rural-to-urban migration (Howell, 2023).

Analyses that focus solely on the direct effects of cash transfers for recipients risk overlooking how such programmes interact with pre-existing spatial inequalities and urban agglomeration dynamics (Ahlfeldt and Wendland, 2013). This raises critical questions about where economic value is created, and where it is ultimately captured or extracted (Thompson et al., 2020). At the intra-urban scale, inclusionary policies may indeed foster more socially integrated cities, but they also risk reinforcing spatial inequalities if the economic benefits become concentrated in already advantaged areas (Roitman et al., 2024). Municipal strategies that seek to stimulate local development through financial incentives must therefore reckon with the spatial logic embedded in urban form and infrastructure, lest they inadvertently reproduce entrenched territorial disparities (Diezmartínez and Short Gianotti, 2024; Souche et al., 2015).

Understanding the spatial implications of social policy is particularly critical in the context of cities in the Global South, where urban inequality is often both historically entrenched and spatially manifest. In Latin America, urban development has long been marked by acute socio-spatial disparities and patterns of segregation (Santos, 1978). Scholars of the region emphasise that these inequalities are deeply rooted in colonial, capitalist, patriarchal, and racialised urbanisation processes that continue to shape contemporary urban structures and outcomes (Barros et al., 2024). Despite the adoption of progressive territorial development policies at the municipal level, such legacies persist and are frequently reinforced through mechanisms of spatial exclusion and social control, which are often driven by private interests seeking to capture and valorise real estate (Fix and Arantes, 2022; Ortiz, 2024).

While municipal cash transfer programmes (CTPs) are typically designed to target the most socioeconomically vulnerable populations (often residing in the most deprived neighbourhoods), the spatial circulation of these benefits may not align with their distributive intentions. If spending predominantly occurs in more affluent or commercially central neighbourhoods, the programme may unintentionally reproduce or intensify intra-urban inequalities. According to theories of urban scaling (Arvidsson et al., 2023; Bettencourt et al., 2007; Brelsford et al., 2017; Lobo et al., 2020), economic indicators such as business revenue are expected to follow non-linear, heavy-tailed distributions, scaling superlinearly with population size. Scaling theory predicts that urban infrastructure scales sublinearly with population size due to economies of scale; whilst market activity scales superlinearly with population, since innovation and productivity intensify in denser urban environments (Bettencourt et al., 2007).

However, the effects of population concentration on the spatial distribution of cash transfer benefits and local currency payments are still underinvestigated. Although the use of local currencies in CTPs is often justified as a mechanism to foster decentralised spending and support smaller local retailers (Belmonte et al., 2021; Blanc et al., 2025), there remains limited quantitative evidence on whether such tools interact with agglomeration dynamics, particularly when programmes are deployed city-wide. As a case in point, Lafuente-Sampietro (2024) analyses nine French cases, showing positive business impacts and stronger consumer–enterprise ties, but is silent about quantitative spatial effects.

Taken together, the literature highlights both the promise and the limitations of municipal cash transfers implemented through local currencies. While existing studies demonstrate their potential to stimulate local economic activity and promote income redistribution, less is known about how these benefits spread across the urban space. This gap underscores the importance of examining not only whether local currencies multiply spending, but also where this circulation occurs and who captures its value. By studying the spatial dynamics of municipal cash transfers, our study addresses this lacuna and investigates their implications for urban inequality, contributing to answering our proposed research question.

Data and methods

This study addresses the knowledge gaps in the literature by conducting an empirical study of a cash transfer programme in Brazil that uses a digital local social currency. The analysis of data on digital payment transactions using this local currency enables us to trace, for the first time, the flow of cash-transfer benefits among the beneficiaries and the various businesses located in the different areas of the city, to tackle two specific sub-questions:

(1) To what extent do municipal cash transfers generate multiplier effects for the city? How do the benefits paid circulate across the various businesses?

(2) How are the monetary resources introduced by the cash-transfer programme distributed across the city neighbourhoods? How are they related to existing socio-spatial intra-urban inequalities?

The analysis methods include quantitative analysis using the LM3 method, calculation of distribution inequalities using the Gini index, spatial analysis, and urban scaling models. The next sections describe in detail the case study setting, the data sources used, and the analysis procedures.

Introducing the Maricá case

The coastal city of Maricá (population 197,300; area 360,000 m²) is located in the state of Rio de Janeiro, Brazil. Over the past decade, it has emerged as an important case study in urban development due to its Cash Transfer Programme (CTP), which uses a local currency called Mumbuca, pegged at 1:1 to the Brazilian Real, the national fiat currency. Due to the significance of this unique CTP, Maricá has become a focal point for researchers both in Brazil (Waltenberg and Katz, 2023) and internationally (De Wispelaere et al., 2024).

Maricá is part of Brazil’s offshore oil exploration area and receives the largest share of oil royalties. These royalties have aimed to promote economic development and benefit low-income families in the city (Waltenberg and Katz, 2023). Since a significant portion of Maricá’s population commutes to neighbouring cities for work, the decision to use a local currency in the CTP was intended to encourage the income earned through resources from Maricá to be spent within the city’s limits.

The programme began at the end of 2013, paying 130 Mumbucas per family, but was redesigned in 2019 to provide payments to individuals. During the COVID pandemic, the payment was increased to 300 Mumbucas per person, and additional benefits were introduced, such as incentives for informal workers to stay home and for small businesses to avoid layoffs, all paid in Mumbucas. Maricá’s success in navigating the challenges of the pandemic (Gonzalez et al., 2020; Leal and Araújo, 2023; Lopes et al., 2024) has inspired 10 neighbouring cities in the state of Rio de Janeiro to adopt what they call “the Maricá model,” establishing their own municipal CTPs with local currencies.

Table 1 summarises the state of Maricá’s social welfare policies related to CTPs in Mumbucas after the pandemic, when the RBC was adjusted to the current amount of 200 Mumbucas per person, along with the expanded scope of benefits. The variety of social programmes explains why they serve almost half of the city’s population, reaching over 90,000 accounts by the end of 2023 (Prefeitura de Maricá, 2023), including CTP beneficiaries, businesses, and other independent account holders. The evolution of the Mumbuca programmes over time into different categories and formats of benefits for the population of Maricá was largely responsible for keeping the city’s economy growing, even during the hardest times of the pandemic. Maricá was the only city in the state to increase the number of formal jobs between 2020 and 2021 (Pereira et al., 2023).

Table 1.

Municipal programmes paid in Mumbucas.

Programme	Brief description
Citizenship Basic Income (RBC)	Cash transfer for beneficiaries in social vulnerability.
Workers’ and Microentrepreneurs’ Support Programmes (PAT/PPT)	Cash transfer to workers and micro-entrepreneurs who lost their income during the pandemic. After the pandemic, the programme was redesigned to serve as working capital to enhance micro-business activities.
Meal vouchers	Cash transfers to City Hall employees provided as meal vouchers.
Other benefits	Several smaller CTPs were implemented to address specific situations, including support for women victims of violence, assistance for high school-aged children, social housing initiatives, and support for the homeless, among others.

Source: Melo (2023).

Table 2 shows the overall spending by programme in the 1 year analysed (July/2021–June/2022). Altogether, the social benefit payments exceed $300 million, corresponding to R$300 million (approximately $52.4 million US dollars in February 2025). The programmes with the highest spending are the Workers’ and Microentrepreneurs’ Support Programme (PAT/PPT) and the Citizenship Basic Income (RBC), which together account for nearly 80% of the total spending. The Meal Voucher programme also has significant value, accounting for slightly over 20% of total expenditure. The other municipal programmes are less significant in terms of their relative share of the total spending.

Table 2.

Absolute and relative government expenditure in each social benefit programme in the analysed period (between July 2021 and June 2022).

Programme	Absolute expenditure	Relative expenditure (%)
Workers’ and Microentrepreneurs’ Support Programmes (PAT/PPT)	$131.0 million	42.6
Citizenship Basic Income (RBC)	$101.8 million	33.1
Meal vouchers	$56.7 million	18.4
Other benefits	$17.9 million	5.8
Total	$307.3 million	100

Maricá has specific features that warrant explanation. Urbanised areas are concentrated along the coast, particularly in the central and eastern regions (see Figure 1). The municipality also boasts large lakes and green spaces, resulting in some neighbourhoods being largely uninhabited. Additionally, Maricá is home to seven protected sites, including areas designated for full protection and those for sustainable use. These geographic characteristics must be considered in the intra-urban analysis of currency flow.

Figure 1.

Population distribution in the city of Maricá in 2022.

Maricá has emerged in recent years as a relevant case for the study of CTPs using local currency for at least three reasons. First, the scale of Mumbuca usage reaches roughly half of the city’s population, making it one of the local currencies with the highest levels of adoption globally. Second, there are evident positive impacts of the CTP programme across the city, particularly when compared to other municipalities that also receive significant oil royalties. Third, the availability of data on currency circulation within the city – characteristic of CTPs paid in local currencies on digital platforms – allows for tracking its flow. This capability is essential for evaluating the implications of the currency to social and spatial inequalities, which would otherwise be untraceable.

While other studies on Maricá focused on different aspects of the cash transfer programme on the city as a whole (De Wispelaere et al., 2024; Waltenberg and Katz, 2023), our analysis examines the programme’s varying impacts on different business profiles and socio-spatial inequalities across various neighbourhoods within the city’s territory.

Data sources

The main data source for our study is the e-dinheiro digital platform, which is used to manage all payments of benefits and purchasing transactions using the local currency Mumbuca. Since these data include personally sensitive information, we sought approval from the ethics committees of our institutions. We analysed datasets comprising monthly benefit payments to 52,504 individuals, as well as 3,477,022 transactions involving 8097 businesses, between July 2022 and June 2023.

Further details about this dataset and corresponding processing, anonymisation, and aggregation procedures can be found in the Supplemental Material. The final processed version of the dataset is organised by transactions, with each row representing a payment transaction, and columns that identify the date, value, origin, and destination neighbourhoods, enabling the mapping of digital transaction flows. Additionally, the dataset indicates whether the transaction was conducted by an individual or a business, both at the origin and destination.

This study used the 2010 and 2022 demographic censuses to construct intra-urban indicators (IBGE, 2010; IBGE, 2022). Georeferenced household data (provided by IBGE, 2022) were used to interpolate and aggregate the census sector data to the neighbourhood level, using a neighbourhood grid provided by the Maricá City Hall.

For the analysis of intra-urban inequalities in the municipality of Maricá, data from UNDP, IBGE, and the Oswaldo Cruz Foundation (Fiocruz) were utilised (Allik et al., 2020). The Brazilian Index of Deprivation (IBP/Fiocruz), for 2010, was applied at the intra-urban level to identify and classify areas with varying degrees of social and economic deprivation, providing a detailed view of socio-spatial inequalities within the municipality (Allik et al., 2020). This indicator was initially obtained at the census sector level, and the same interpolation method was employed to aggregate the data at the neighbourhood level.

Analysis procedures

The data analysis consisted of two major steps, each corresponding to one of our sub-research questions and is described in the following sections.

Assessing local multiplier effects and distributions amongst businesses

LM3 was used to assess the circulation and retention of local currency in the city of Maricá from July 2022 to June 2023. LM3 (Sacks, 2002) calculates the local spending and monetary recirculation within a community across three rounds, which are called waves of spending:

Wave 1 (G2C: government to citizens): the amount of social benefits paid by the City Hall government to citizens in local currency (Mumbucas), which is converted 1:1 from Brazilian official currency (Reais).

Wave 2 (C2B: citizens to businesses): business revenue from beneficiary spending through payments in local currency.

Wave 3 (B2B: business to business): business revenue from payments made by other businesses using local currency.

Importantly, in the design of the Maricá local currency, citizens cannot convert the amounts they receive as social benefits in local currency (Mumbucas) into mainstream money (Reais), but must use the local currency to make payments within the network of businesses that accept the local currency. Another important restriction is that citizens cannot receive payments in local currency from other users, only from the government. In contrast, business users can receive payments from either individual citizens or other businesses and are free to convert any amount they receive into Reais (initially, they paid a small fee for each conversion, but this has been abolished during the study period). Therefore, the main multiplier effects in this case are achieved in Wave 3 by businesses that, instead of cashing out in Reais the amounts they receive in Mumbucas, decide to “recirculate” the Mumbucas through payments to other businesses. Theoretically, businesses could again recirculate the amount they receive, creating a fourth wave(and possibly higher ones), but in our data, all such transactions are business-to-business, and we classify them as Wave 3.

To calculate the multiplier effect of the overall circulation of local currency on the economy, we followed Roca et al. (2023) and calculated the total value of the three waves of payments, then divided this sum by the amount of the initial benefits paid out by the government. This results in an index ranging from 1 (indicating no recirculation in the local economy) to 3 (indicating full recirculation from the total of Waves 2 and 3) following the equation:

LM 3 = \frac{Wave 1 + Wave 2 + Wave 3}{Wave 1}

The index thus estimates the recirculation of resources in the local economy that generates the multiplier effect, calculated by summing resources circulating across three rounds and dividing by the initial investment. To illustrate this effect, let's look at a hypothetical example (Figure 2): In Wave 1, a basic income benefit of $100 Mumbucas (equivalent to R$100 Brazilian Reais) is distributed to beneficiaries by Banco Mumbuca on behalf of the local government. In Wave 2, the beneficiaries fully spend this amount within the local economy. In Wave 3, the local merchants who receive those $100 Mumbucas exchange $50 from them into Brazilian Reais for supplies from outside the local area, while using the remaining $50 Mumbucas to purchase goods and services from other local suppliers. To summarise the total Mumbucas spent locally, we calculate: 100 + 100 + 50 = 250. By dividing this total by the initial 100 Mumbucas received by the beneficiaries, we find that the multiplier effect is 2.50.

Figure 2.

Illustration of the LM3 flow applied to a basic income programme using the Mumbuca local currency convertible 1:1 to the Brazilian Real (R$).

In our case, we calculated the LM3 index to assess the multiplier effect of cash transfer programmes paid in local currency for each of the 12 months in our dataset (Jul/2022–Jun/2023).

To shed light on how businesses of different sizes contribute to multiplier effects by recirculating the amounts received in local currency, we calculated the recirculation rate for each business account in the dataset as the ratio of total local-currency payments made to other businesses (Wave 3) to the total revenue received in local currency (Wave 2). Some businesses in the dataset spent more than they received in payments in the period analysed (N = 1100, probably due to accumulations from previous years), so to facilitate the analysis, we saturated the recirculation rate at 100% when the spending was equal to or higher than the revenue for each business. To assess whether there were statistically significant differences in the recirculation rates among large, medium, and small receiver businesses, we employed the Kruskal–Wallis H test. This non-parametric method is appropriate for comparing more than two independent groups when the assumption of normality is violated, and is robust for skewed distributions and ordinal data (Conover, 1999), as was the case in our data.

Following a significant Kruskal–Wallis result, we conducted post-hoc pairwise comparisons using the Wilcoxon rank-sum test (also known as the Mann–Whitney U test), which is suitable for comparing two independent samples without assuming normality, which is ideal for our non-parametric context. We applied the Bonferroni correction, a conservative adjustment that divides the significance threshold by the number of comparisons to maintain the overall family-wise error rate. This approach is widely recommended in empirical research for its simplicity and effectiveness in controlling false positives (Bland and Altman, 1995).

To further understand the inequality in the distribution of the revenue from payments in local currency received by the 8097 businesses in our dataset, we employed one of the most common measures of income inequality: the Gini coefficient (Dorfman, 1979). The Gini coefficient was obtained based on a Lorenz curve depicting the cumulative share of revenue received by businesses (y-axis) that is cumulatively earned by fractions of the business population (x-axis). A line of 45 degrees would represent perfect equality, with all businesses receiving an equal share of transactions. The Gini coefficient is given by the ratio of the area that lies between the line of equality and the actual Lorenz curve, with a range from 0 (total equality) to 1 (absolute inequality).

Assessing relationships between cash transfer effects and spatial inequalities

The final phase of our analysis assessed differences in the amounts transacted across the 51 neighbourhoods of Maricá. For an initial exploratory analysis, we prepared choropleth maps using R depicting the spatial distribution of the key variables of our study – that is, social benefits paid (i.e. corresponding to Wave 1 transactions) and business revenue generated by local-currency payments (i.e. Waves 2 and 3) – in contrast with variables capturing existing spatial inequalities: Population (recorded by the 2022 Census) and Deprivation index of neighbourhoods derived from the IBP index. To facilitate visual comparisons, business revenue and social benefits for each neighbourhood were divided by its population to account for heterogeneity in population distribution across neighbourhoods. To visually analyse the flows exchanged among the different neighbourhoods, we plotted a flow map of transactions using the graph R package and a bar chart with values from Waves 1, 2, and 3.

We visually investigated the association between pairs of mapped variables and used Spearman correlation tests; the results for significant relationships are reported in the next section. Spearman’s method is well-suited for detecting monotonic relationships in non-normally distributed data and does not require linearity or homoscedasticity, which makes it preferable to Pearson’s correlation in this context.

To assess the distribution of social benefits and business revenue among neighbourhoods, we used scaling models based on previous work (Bettencourt et al., 2007; Brelsford et al., 2017), with the following standard formulation:

Y = α X^{β},

where Y can denote either material resources (such as water and sanitation infrastructure) or measures of social activity (such as wealth, income, or patents), X is the population size, $α$ is a proportionality constant, and $β$ is the scaling exponent. Building upon urban scaling theory (Bettencourt et al., 2007), we hypothesised that business revenue from local currency payments would scale superlinearly with the population size of neighbourhoods, whilst the amount of social benefits paid in local currency would scale sublinearly (since the need for social benefits payment should correlate with the distribution of urban basic infrastructure).

To test these hypotheses, we constructed the following two scaling models:

Y_{1} = α_{1} X^{β_{1}}

(1)

Y_{2} = α_{2} X^{β_{2}}

(2)

Where $Y_{1}$ is a vector with the total revenue of businesses in local currency for each neighbourhood, $Y_{2}$ is the amount of social benefits paid in local currency, $X$ is a vector with the population size for each neighbourhood, and $β_{1}$ and $β_{2}$ are the corresponding scaling exponents.

Additionally, we wanted to test whether scaling models can be useful for estimating how businesses’ revenue (in local currency) grows with the amount of social benefits paid in local currency per neighbourhood. To the best of our knowledge, this is the first usage of scaling theory to understand the multiplier effects of local currencies at the neighbourhood level, for which we propose the following model:

Y_{1} = α_{3} Y_{2}^{β_{3}}

(3)

Where $Y_{1}$ is a vector with the total revenue of businesses in local currency for each neighbourhood, $Y_{2}$ is the amount of social benefits paid in local currency, and $β_{3}$ is the corresponding scaling exponent.

Following previous work (Bettencourt et al., 2007), we estimated $β$ by linearising these equations via a log transformation, in the form $l o g Y = l o g α + β l o g X$ , and performing linear least-squares regressions on the log-transformed dependent and independent variables. Sensitivity analyses were conducted with alternative (non-log-transformed) linear models (Leitão et al., 2016), as well as with other covariates (population size, deprivation index), but these alternative models did not perform significantly better or change the directions of the associations found in the most parsimonious models. We also tested for spatial dependence by applying Moran’s I to the residuals of linear models and assessed spatial lag and spatial error dependence with Lagrange multiplier tests (Anselin, 1988). These tests did not reveal significant spatial dependence in any of our models (p-values > 0.5).

Findings

Our research findings are presented in line with our sub-questions as follows.

Local multiplier effect and revenue inequalities amongst businesses

To assess the multiplier effect to the city from the cash transfer benefits paid, we calculated the three waves of the LM3 methodology for each month (see Section “Analysis Procedures”), which are depicted in Figure 3. Note that in December 2022, a larger amount was paid to beneficiaries as a Christmas bonus, allowing them to save and spend in the following months. This generates a ripple effect, making the total spending in Wave 2 slightly larger than government payments of Wave 1 in the months from January to March 2026 (Figure 3).

Figure 3.

Government expenditure in monthly benefit payments and spending transactions in local currency (Mumbucas).

Table 3 shows the total amounts of local currency (Mumbucas) exchanged in each wave, as well as the LM3 index calculated monthly. In some months, the value spent in Wave 2 (payments from beneficiaries to businesses) exceeds the total from Wave 1 (benefits paid by the government), reflecting the retention of income from previous months. However, the amounts spent in Wave 2 are generally close to those in Wave 1, indicating that beneficiaries tend to spend almost the full amount of the benefits received in local currency, as expected. However, the data also show a significant recirculation rate, with a yearly LM3 of 2.10, indicating a 10% multiplier effect on the total amount paid by the government to beneficiaries through local circulation of currency. As visually depicted in Figure 1, due to this local recirculation, the sum of local currency exchanged in a month (green bars representing Waves 2 and 3) always exceeds the amount disbursed by the local government as social benefits (blue bar, representing Wave 1).

Table 3.

Total of local currency payments (Mumbuca) in Maricá and the LM3 per month for Waves 1, 2, and 3.

Months	Wave 1	Wave 2	Wave 3	LM3
Jul 22	$25,036,872	$25,267,720	$4,703,083	2.20
Aug 22	$25,374,804	$25,155,057	$3,743,427	2.14
Sep 22	$25,287,988	$25,086,254	$2,828,052	2.10
Oct 22	$25,234,619	$24,813,343	$2,431,509	2.08
Nov 22	$25,357,976	$24,585,933	$2,342,219	2.06
Dec 22	$39,888,550	$38,035,101	$3,091,477	2.03
Jan 23	$25,312,994	$26,470,994	$2,569,215	2.15
Feb 23	$25,274,391	$24,675,060	$2,193,969	2.06
Mar 23	$25,424,645	$26,092,934	$3,573,696	2.17
Apr 23	$21,669,475	$21,634,119	$2,145,755	2.10
May 23	$21,720,353	$21,683,305	$2,395,070	2.11
Jun 23	$21,696,824	$21,222,507	$2,187,733	2.08
Yearly total	$307,279,491	$304,722,327	$34,205,205	2.10

Table 4 breaks down revenue and spending by business type, using a three-tier statistical quantile: small receivers, medium receivers, and large receivers (N = 2699 in each group). The small receivers are businesses that received $4.1 million and spent $8.4 million between July 2022 and June 2023, meaning they recirculated more than they earned, likely due to the use of local currency acquired in previous years. The medium receivers handled $25.7 million and spent $6.9 million, and the large receivers earned $275 million and spent only $18 million during the same period. It is noteworthy that the mean recirculation rate decreases dramatically from more than 200% for small receivers to 27% for medium receivers and only 7% for large receivers, demonstrating that, in contrast with smaller establishments, large businesses tend to recirculate only a smaller fraction of the amount received using local currency.

Table 4.

Revenue and spending by different business sizes and corresponding recirculation rate.

Group	Annual revenue	Annual spending	Recirculation rate (%)
Small receivers	$4,091,855	$8,361,766	204
Medium receivers	$25,654,632	$6,945,519	27
Large receivers	$274,975,841	$18,897,919	7
Total	$304,722,328	$34,205,204	11

To further the recirculation of each business within the large groups, Figure 4 presents a violin plot of the distribution density (recirculation rates equal to or over 100% are saturated at 100% for visualisation purposes). A Kruskal-Wallis H test was conducted to determine whether the differences in the recirculation rate across three groups (Small, Medium, and Large receivers) were significant. The distributions were significantly different between the groups, χ² (2, N = 8097) = 389.27, p < 0.001. Post-hoc pairwise comparisons using the Wilcoxon rank sum test with Bonferroni correction revealed statistically significant differences (p < 0.001) in recirculation rate between small and medium receivers, small and large receivers, and medium and large receivers. These findings largely support the hypothesis (based on stakeholder insights) that larger receivers are less likely to recirculate using local currency. The analysis of the distributions of the different groups suggests that businesses with larger revenues in local currency tend to recirculate a smaller share of the amounts they receive in comparison to medium and small receivers, but there are many outliers in all groups concentrated around no recirculation at all or full recirculation of resources received (Figure 4).

Figure 4.

Violin plot showing the differences in the recirculation rate of local currency by different sizes of businesses.

Figure 5 presents a Sankey diagram that enables analysis of how each benefit programme (left-hand column) is spent by beneficiaries across different business profiles (Wave 2, middle column), and how these businesses spend the funds they receive in other businesses (Wave 3, right-hand column). For instance, beneficiaries tend to spend a larger share of the micro-entrepreneur support programmes (orange bar at the left-hand column) in medium and small receivers. In contrast, basic income and meal vouchers (light and dark blue, left-hand column) are predominantly spent in large receivers. The diagram also visually confirms that medium and especially small receivers tend to recirculate a larger share of their revenue.

Figure 5.

Sankey diagram showing transactions by benefit programme (on the left-hand columns), how beneficiaries spend across different business groups (middle column), and how these businesses spend the funds they receive (right-hand column).

To quantify the inequality of distribution of benefits received by the different businesses, Figure 6 presents the Lorenz Curve of the annual revenue received by businesses. The X-axis represents the accumulated proportion of businesses, while the Y-axis represents the accumulated proportion of received payments. The calculated Gini index was 0.80, which is very high and indicates a highly unequal distribution. Sixty per cent of businesses which receive less revenue (in Mumbucas) account for only 7.2% of the total transactions received by commercial establishments. On the other hand, the 20% of businesses with the highest revenue account for 82.4% of the transactions received, indicating strong inequality and concentration of spending among beneficiaries in a relatively small number of retailers.

Figure 6.

Lorenz curve showing the concentration of business revenue from beneficiary payments in local currency.

Spatial inequalities within the city

To investigate the relationship between the resources paid by the cash-transfer programmes and intra-urban inequalities, Figure 7 shows 6 choropleth maps with quantile spatial distributions across the city’s 50 neighbourhoods of the following variables: (a) Brazilian Deprivation Index; (b) Population growth rate (2010–2022); (c) Benefits paid per capita (Wave 1); (d) Business revenue normalised by population (Waves 2 and 3); (e) Recirculation rate (of payments received in the neighbourhood); (f) Number of registered receiving businesses. The most noticeable coincidence of visual patterns is between the spatial distribution of benefits (c), business revenue (d), and the number of registered businesses (f), which is an interesting indication that the payment of benefits in a given neighbourhood may be attracting business transactions to that neighbourhood. This impression is further reinforced by observing that these concentration patterns differ significantly from the baseline distributions of population (b) and deprivation (a). This is one of the key assumptions we would like to test, which is further investigated quantitatively below.

Figure 7.

Cloropleth maps showing the spatial distribution of several variables across neighbourhoods. (a) Brazilian Deprivation Index (IBP). (b) Population growth rate (2010–2022). (c) Social benefits paid in local currency (Wave 1). (d) Business revenue in local currency (Waves 2 and 3). (e) Recirculation rate of businesses. (f) Businesses receiving local currency.

Figure 7 also includes maps of the average recirculation rate (d) of payments made in each neighbourhood. However, this pattern does not seem to be directly associated with the spatial distributions of benefits paid or business payments in local currencies. In contrast, the maps of the Brazilian Deprivation Index (a) and Benefits per capita (c) present some visual coincidence, suggesting a possible association between them. Spearman’s rank correlation revealed a weak positive relationship, but this was not statistically significant, rs (98) = 0.25, p = 0.092. This provides some, although weak, evidence that the allocation of social programmes is directed towards neighbourhoods with higher social vulnerability in the municipality of Maricá.

Turning to the spatial dynamics of payments made in local currency, Figure 6 shows a flow map in which each node depicts a neighbourhood, with node size proportional to the value of Mumbucas received (dark blue dots represent the top 20% of receivers). The lines depict the money flows between the neighbourhood where benefits are paid and the neighbourhood where spending takes place, with line width proportional to the value exchanged. The neighbourhoods “Centro,” “Inoã,” and “São José do Embassaí” have the largest proportional size relative to the payments received and are hotspots for digital transaction flows between neighbourhoods. Notably, there is a substantial volume of flows between these neighbourhoods and those to the southwest of Maricá, which are characterised by high population density (Figure 8).

Figure 8.

Flow map showing spending transactions between neighbourhoods.

Figure 9 presents the results of a quantitative investigation into how the intra-urban distribution of the social benefits paid (Wave 1) and the business revenue they generate (Waves 2 and 3) scale with a neighbourhood's population size. The log-log regression model indicates that social benefits scale sublinearly with population size, with a statistically significant elasticity coefficient β₁ = 0.09 (95% CI: 076–1.05, p < 0.001), and a high degree of explanatory power (adjusted R² = 0.77). This indicates that the amount of benefits paid increases less than proportionally with the population, which is consistent with prior research showing that service-related variables typically scale sublinearly due to economies of scale and standardised allocation formulas (Bettencourt et al., 2007). In contrast, the relationship between business revenue and population size follows a superlinear scaling pattern, with an elasticity coefficient β₂ = 1.03 (95% CI: 0.85–1.22, p < 0.001), with the model accounting for a substantial portion of the variation in the dependent variable (adjusted R² = 0.72). These findings align with urban scaling theory, which posits that socioeconomic indicators such as income and productivity tend to exhibit superlinear behaviour in relation to population size. Together, these results reinforce the view that social benefit redistribution is constrained by demographics, but that business revenue increases with urban agglomeration.

Figure 9.

Scaling of social benefits paid in local currency (a) and business revenue in local currency (b) by population size in each neighbourhood.

Figure 10 now investigates how business revenue generated by the cash-transfer programme in a neighbourhood is associated with the amount of social benefits paid in that neighbourhood. The results indicate a strong superlinear relationship between social benefits paid and business revenue across neighbourhoods, with an elasticity of β₃ = 1.15 (95% CI: 1.03–1.27). The model explains a substantial proportion of the variation in business revenue (adjusted R² = 0.89), indicating that local cash transfers are a powerful predictor of neighbourhood-level commercial activity using local currency.

Figure 10.

Business revenue in local currency (log) scaled by social benefits paid (log) in local currency in each neighbourhood.

Discussion

Our study was designed to evaluate the intra-urban implications of municipal city cash transfer programmes by combining the local multiplier effect with a spatial analysis of the circulation of benefits paid in local currency. By analysing transactional data from the payment platform used to disburse a cash transfer programme and to make payments to local businesses, we could trace the flow of the benefit across different neighbourhoods within the city and thus evaluate the implications of money circulation in the local economy. Our findings align with Lafuente-Sampietro (2024) on the business benefits of local currencies, but we add that spatial inequalities shape where gains accrue, underscoring the need for spatial analysis in evaluations of such initiatives.

Considering the entire circulation of the local currency, we found an LM3 of 2.10, close to the LM3 of 2.09 reported by Roca et al. (2023) for the REC currency in Barcelona and within the 1.95–2.40 range of the two French cases studied by Lafuente-Sampietro (2021). However, in the Barcelona case, the REC programme explicitly promoted accreditation of smaller retailers, thereby intentionally steering spending towards them. In contrast, in our case, such size-based targeting of businesses was not implemented, which may account for differences in the distributional patterns of benefits across retailers. In any case, this multiplier effect is expected to be higher than the recirculation of money that is achieved by using mainstream currency, considering the usual values found in the literature for secondary cities, for example, Roca et al. (2023) considered that spending in euros would have LM3 of 1.94.

However, we found a starkly unequal distribution of local currency spent in businesses, with the 20% largest recipients accounting for as much as 82.4% of total local currency payments, whilst the bottom 60% of recipients receive only 7.4% of these payments. With a Gini coefficient of 0.8, the inequality of revenue distribution is significantly higher than what has been found in previous studies: Brelsford et al. (2017) found Gini coefficients between 0.6 and 0.7 for income distribution at the neighbourhood-level for Brazilian cities; de Sousa Filho et al. (2022), using a different methodology, found Gini coefficients between 0.4 and 0.7 for household income in 152 Brazilian cities. Additionally, this finding diverges considerably from the literature, which reports that local currency users switched from larger to smaller local shops and markets (Belmonte et al., 2021). The concentration of the local currency in a few large businesses also affects the general LM3 index, since large receivers are much less likely to recirculate the local currency than small receivers (only 7% of total revenue was recirculated, compared with more than 200% by small receivers).

As regards spatial inequalities, we found that spending of the benefits in local currency is strongly concentrated in a few central neighbourhoods, which have better infrastructure and host the largest businesses. This reveals novel insights into intra-urban disparities across cities, which, to the best of our knowledge, have not been reported in the literature.

Our analysis revealed contrasting patterns in how social benefits and business revenue respond to neighbourhood population size, offering important insights for social policy design. Social transfers scale sublinearly with population (elasticity = 0.90; 95% CI: 0.76–1.05), indicating that benefits increase less than proportionally in larger neighbourhoods. In contrast, business revenue scales superlinearly (elasticity = 1.03; 95% CI: 0.85–1.22), meaning that economic returns grow more than proportionally with population size. These results are both consistent with theory on urban scaling laws, initially proposed to compare different cities (Bettencourt et al., 2007) and later extended to compare different neighbourhoods within the same city (Brelsford et al., 2017). Our results, however, show, for the first time, the clear divergence between redistributive and market-based dynamics at the neighbourhood scale.

The concentration of local currency in certain neighbourhoods with better infrastructure might reflect what Roitman et al. (2024) note about the potential for inclusionary policies to concentrate resources captured by elite urban areas due to spatial inequalities. Although different inequality indicators can produce different results (Souche et al., 2015), our results raise the question about the contradictory effects that can increase urban inequalities (Glaeser et al., 2009) in cities with generous social protection programmes. It also reflects the inherited socio-spatial inequalities in Latin America (Barros et al., 2024; Fix and Arantes, 2022) that might not be changed in the short term by local distributive policies. Policy makers should be aware of the legacy of pervasive spatial inequalities, which persist even after the adoption of the progressive policies intending to promote territorial development (Ortiz, 2024).

Nevertheless, while prior research on urban scaling has primarily examined how population size relates to infrastructure, innovation, and income (e.g. Bettencourt et al., 2007; Lobo et al., 2020), our findings offer a novel theoretical contribution that predicts that redistributive social policies produce amplified local economic effects, extending urban scaling theory into the domain of welfare-driven economic dynamics. Our analysis shows that for every 1% increase in social benefits paid at the neighbourhood level, local business revenue rises by approximately 1.15%, with this relationship explaining nearly 90% of the variation observed across neighbourhoods.

Based on these empirical findings, we suggest an extension of urban scaling theory (Bettencourt et al., 2007) to predict that intra-urban spatial patterns of economic activity at the neighbourhood level follow the same regularities found in urban scaling laws at the city level as regards population concentration, adding that the induced neighbourhood-level commercial activity is predicted to scale superlinearly with social cash transfers to local residents. If confirmed through new empirical studies in other cities, this theoretical development means that social cash transfers do more than support individual and household welfare: they spill over to stimulate economic activity at the neighbourhood level, amplifying their impact through increased consumer spending in local businesses. These results contribute to recent investigations of within-city inequalities in urban scaling law literature (Arvidsson et al., 2023), suggesting that well-targeted cash transfer programmes can serve as powerful tools not only for social protection but also for promoting inclusive territorial development.

Our study brings important insights but also has limitations. It is important to note that the absence of the 2022 Census microdata precluded the analysis of the evolution of intra-urban inequalities in recent years, as well as the current characteristics of inequality in the municipality of Maricá. We were not able to access granular neighbourhood-level data on general economic activity in Maricá, but if available in the future, it could be used to understand whether spending is concentrated in areas of high business activity. Although robust, our analysis is limited to the observational data from transactions in the e-Dinheiro platform. Despite having established strong associations aligned with theory that would not be possible to be drawn from other methodological approaches, they need to be further investigated to establish causality. For example, the motivation for buying in larger recipient businesses might be related to many factors, from price to variety of offers, as well as the proximity to the workplace. A more nuanced understanding of the industrial classifications of businesses and how spending in local currency compares among them, and in contrast with general revenue patterns, is an interesting avenue for future work that could not be pursued here due to a lack of data. It is important to note that the use of a local currency affects how income from social benefits is spent, which implies that some of the results found here may not generalise to basic income programmes that are not disbursed in local currency. Further research should also be conducted to understand the reasons for high concentration levels in some neighbourhoods and the evolution of infrastructure quality that would incentivise small businesses to thrive closer to where most beneficiaries live.

Conclusion and policy implications

This study applied a novel data-intensive approach to assess the intra-urban dynamics of a municipal cash transfer programme in Maricá, Brazil, which operates through a local digital currency. By analysing millions of transaction records, we traced how social benefits circulate spatially and economically across the city, offering new insights into the territorial effects of local welfare policies.

Our results demonstrate that Maricá’s cash transfer programme, delivered via the Mumbuca digital currency, has a strong local economic impact. The estimated local multiplier (LM3 = 2.10) suggests that for every unit of local currency disbursed, more than twice that amount circulates within the local economy – a finding consistent with or exceeding those from similar initiatives globally. This confirms the potential of local currencies to amplify the reach of social transfers and retain economic value within city boundaries.

However, the distribution of these economic benefits is uneven. Spending is heavily concentrated among a small number of retailers, which tend to recirculate less of the currency. In contrast, a large number of small retailers, despite having a higher recirculation rate, capture a relatively small share of beneficiary spending. Spatial analysis also reveals that neighbourhoods with stronger commercial infrastructure attract disproportionate spending, while more peripheral or deprived areas benefit less from the economic ripple effects. Promoting the accreditation of smaller retailers was adopted in Barcelona, where programme design prioritised small businesses that tend to recirculate more. Our findings reinforce this lesson: without mechanisms that promote recirculation, benefits tend to concentrate unevenly.

One of the most important findings is the superlinear relationship between social benefits paid and business revenue at the neighbourhood level: a 1% increase in transfers is associated with a more than 1% increase in local business revenue. This indicates that social spending can generate disproportionately large economic returns, especially in areas with higher commercial density. This has two contrasting implications. On the one hand, it may reinforce existing concentrations of economic activity, further advantaging central neighbourhoods. On the other hand, it presents a strategic opportunity: targeted cash transfers to deprived areas could induce localised economic growth by stimulating commercial activity where it is currently limited. In other words, with the right policy design, redistributive programmes can serve not only as tools for poverty alleviation but also for inclusive territorial development.

Based on these findings, we offer three key recommendations for policymakers:

The design of local currency schemes must take into consideration existing spatial urban inequalities. Our study makes clear that local currency schemes should be accompanied by mechanisms that actively promote more equitable spatial outcomes to avoid entrenching existing urban inequalities. These could include incentives for beneficiaries to spend in underserved neighbourhoods and smaller-scale merchants. However, the design of such mechanisms should not penalise large receivers that recirculate local currency (as we have seen in our study). One possible alternative is to create incentives, such as reduced redemption fees or “pay back” bonuses, available exclusively to clients who purchase from businesses that recirculate local currency.

Cash transfer and basic income schemes should be accompanied by policies to address spatial inequalities in existing infrastructure across neighbourhoods. Neighbourhoods with better infrastructure tend to attract and concentrate more resources, as people prefer to shop and invest in areas that offer security, cleanliness, and well-maintained public spaces. Without a targeted policy, cash transfers will have a limited impact on achieving spatial equity, as they may inadvertently reinforce commercial activity only in areas that already concentrate existing businesses. Therefore, investments in basic public facilities can help to deconcentrate local spending by making other areas more attractive for consumption and business activity. Policymakers should thus consider infrastructure improvements as a strategic tool to stimulate the circulation of money in targeted areas of the city to enhance local economic development. Additionally, programmes that support small and medium-sized enterprises in disadvantaged neighbourhoods can help decentralise commercial infrastructure.

Local currencies not only help boost local economies but can also be a powerful tool for monitoring and improving the spatial equity of social benefit payments. As demonstrated in this study, spatial analysis that disaggregates digital payments data by neighbourhoods and business profiles can help identify imbalances and adjust programme design accordingly.

These findings and recommendations have been disseminated through workshops with local stakeholders and have already had an impact, informing policy in the City of Maricá, as the municipal agencies running the cash-transfer programme are considering improvements to the programme design based on the results of our study.

More broadly, our findings highlight that the spatial effects of cash transfers are not automatic or neutral. Even well-intentioned redistributive programmes can inadvertently reproduce spatial inequality if they fail to account for territorial dynamics. Maricá’s experience is already being replicated in other municipalities through a process that Barinaga et al. (2025) describe as “standardised malleability.” This concept captures how cities in diverse contexts adopt the core model of basic income distributed via a local currency while flexibly adapting the scheme’s design features, such as benefit levels, governance arrangements, and criteria for merchant inclusion. As more cities adopt innovative social protection schemes, coupling them with fine-grained spatial analysis, as exemplified in this article, can help adjust their design to ensure these policies promote more inclusive urban economies.

Supplemental Material

sj-docx-1-epn-10.1177_0308518X251413928 – Supplemental material for Tracing the flow of money to reveal spatial effects and inequalities in cash transfer programmes

Supplemental material, sj-docx-1-epn-10.1177_0308518X251413928 for Tracing the flow of money to reveal spatial effects and inequalities in cash transfer programmes by Eduardo H. Diniz, João Porto de Albuquerque, Jarvis Campos, Bruno Andrade de Figueiredo, João Akio Ribeiro Yamaguchi and Mozart Fazito in Environment and Planning A: Economy and Space

Footnotes

Acknowledgements

The authors are grateful to Joaquim Melo and the team at Instituto E-Dinheiro, and to the whole team at Banco Mumbuca, for making the primary data available for this research, as well as for discussing preliminary analysis results.

ORCID iDs

João Porto de Albuquerque

João Akio Ribeiro Yamaguchi

Ethical considerations

This research was approved by the Ethical and Compliance Committee from Fundação Getulio Vargas at October 9, 2023 under the number P.304.2023.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the University of Glasgow’s GCID Small Grants Fund. JPA and EHD also acknowledge funding by FAPESP through the Visiting Research Grant “Advancing Participatory Urban Analytics for Just and Sustainable Transformations in Brazilian Cities,” grant number 23/18054-1. JPA also acknowledges complementary funding by UKRI Economic and Social Science Research Council ES/L011921/1 “Urban Big Data Centre (Data Service).”

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The primary data used for this research cannot be made public due to privacy concerns and restrictions in the data-sharing agreement. Researchers interested in the anonymised and aggregated dataset can contact the corresponding author.*

Supplemental material

Supplemental material for this article is available online.

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