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# Redistributive Preferences in a Center-Periphery Perspective: The Case of Latin America

Sébastien Carrère

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## To cite this version:

Sébastien Carrère. Redistributive Preferences in a Center-Periphery Perspective: The Case of Latin America. 2025. hal-05389700

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https://hal.science/hal-05389700v1

Preprint submitted on 30 Nov 2025

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Distributed under a Creative Commons Attribution 4.0 International License
Redistributive Preferences in a Center-Periphery Perspective:
the Case of Latin America

Sébastien Carrère*

November 30, 2025

**Abstract**

Despite high socio-economic and territorial disparities, Latin America shows little polarization in redistributive demands between the richest and the poorest. This study relies on a center-periphery approach to explore the role of geography in shaping citizens' redistribution preferences in the Latin American region, using nationally representative LAPOP opinion survey data from 16 countries between 2012 and 2018. A classification of respondents' cities is carried out to identify metropolises and peripheral areas, as well as their level of development using remote sensing data. Findings reveal stronger demands for redistribution among citizens living outside metropolises, and particularly those living in small cities and villages. These demands do not primarily stem from the poorest citizens, but rather from those who can be considered part of the consumer or middle class. The analysis also shows that while there has been a decline in strong redistributive demand since 2012, the gap between the center and periphery has tended to narrow since 2016, but with heterogeneous country-specific results. This decline is mainly driven by small cities and villages and the least affluent citizens, converging with those of metropolises.

**Keywords:** center-periphery, redistribution, inequality, Latin America

**JEL Codes:** R11, R12, R58, I31

<footnote>*University Grenoble-Alpes, sebastien.carrere@univ-grenoble-alpes.fr</footnote>
# 1 Introduction

The Meltzer and Richard's (1981) seminal theory of the size of government predicts that redistributive pressures should increase with the level of income inequality, and by extension, should be higher for the poorest (beneficiaries), and lower for the richest (contributors). In fact, as wealth increases in the hands of a minority, it is in the interest of the majority of the poorest to put pressure on governments to ensure a fairer distribution of the wealth created. Yet, the relationship between the level of inequality and support for redistribution is not so clear-cut. In her recent book, Cavaillé (2023) shows that rising inequalities in Western countries since the Great Recession did not lead to higher support for redistribution, especially among the worse-off. Support for redistribution has remained stable in most countries over the past few decades, despite rising inequality, recessions and mass unemployment.

This apparent paradox is even more pronounced in Latin America (LA), which is well-known for its high levels of income inequality, but also of access to quality education and healthcare (Busso and Messina 2020). A striking example comes from income inequality: in 2022, the poorest 50% held 7.5% of the national income before redistribution, while the top 10% captured almost 60% (instead of 18% and 40% respectively for Europe).<fnref n="1" /> Despite these high levels of inequality, redistribution policies in LA do little to reduce the gap between the richest and the poorest. A comparison of absolute redistribution levels – i.e. the difference between the pre-redistribution and post-redistribution Gini indicators – between OECD and LA countries provides a good illustration (Figure 1). For fairly similar levels of income inequality on the market, redistributive mechanisms in LA countries are mostly ineffective (and even regressive for Honduras, Ecuador, Bolivia, and El Salvador) compared with those in OECD countries.

At the same time, a large proportion of LA citizens are dissatisfied with the current income distribution, with around eight out of ten finding it unfair in their country.<fnref n="2" /> Busso et al. (2023) show that this high level of dissatisfaction for eight countries – whether in terms of income, quality of education or health, or justice – also materializes in a strong general demand for redistribution. Nevertheless, several studies

<footnote n="1">1. See the World Inequality Database for 2022: https://wid.world/ (access in July 2024).</footnote>

<footnote n="2">2. Average over the period 2001-2023 from the Latinobarómetro opinion survey: https://www.latinobarometro.org (last access in July 2024).</footnote>
point that the poorest in the region are generally no more demanding of redistribution from their governments than the richest (Dion and Birchfield 2010; Blofield and Luna 2011; Holland 2018).

<figure><img src="image_4.png" /><figcaption>**Figure 1:** Redistribution in Latin America and OECD countries (2000-2020)</figcaption></figure>

Source: Author's elaboration based on the Standardized World Income Inequality Database 5.1 (SWIID) for the period 2000 to 2020.

Note: Absolute redistribution refers to the difference between the pre and post Gini index. Chile, Colombia, Costa Rica and Mexico are also OECD countries.

A large body of literature explores the determinants of citizens' redistributive preferences (Guillaud 2013; Haggard et al. 2013), with most explanations of the underlying mechanisms based on the individual level. Some studies highlight a potential bias in citizens' knowledge of actual levels of inequality and their place in society and attempt to correct it (Kuziemko et al. 2015; Hoy and Mager 2021). Cavaillé (2023) argues that a combination of fairness beliefs, self-interest, and the existing distribution level influences individuals' redistributive preferences. This process of fairness reasoning can sometimes lead to demands that are seen as paradoxical. Holland (2018) offers an explanation for this phenomenon on a more aggregated scale. The author claims that the historically truncated nature of welfare states in the developing world (through informal access barriers, exclusionary social protection designs, and
flat or regressive benefits) explains citizens' lack of redistributive demands. In particular, the author emphasizes citizens' diminished expectations of the State's ability to improve their conditions, especially among the poorest facing exclusion, explaining the low polarization of redistributive demands in society.

While citizens' redistributive demands (or lack thereof) are influenced by many individual factors depending on their level of wealth or life experiences, the geographical context in which individuals evolve also appears to matter. The notion of "left behind" places, depicting unequal territorial development, has become increasingly popular over the past decade as an explanation of citizens' political attitudes in a context of rising anti-system and extreme political parties (Rodríguez-Pose 2018; Connor et al. 2024; Fiorentino et al. 2024). In a historical analysis, Cagé and Piketty (2023) study the political attitudes and abstention of citizens in France. The authors stress that an explanation of political behaviors based solely on a class struggle explanation is not sufficient, and requires a multidimensional and spatial vision of social class. They introduce the concept of "geo-social" classes, which anchors individuals in their specific territory with uneven and evolving territorial development.<fnref n="3" /> While previous studies have examined the relationship between redistributive demands and urban-rural affiliation or big-city residence (Haggard et al. 2013), little is known about the specific role of territory in shaping citizens' redistributive preferences, more specifically between central and marginalized areas.

This article aims to explore whether citizens' redistributive demands are influenced by their territorial anchorage, beyond individual characteristics. Our analytical framework has been largely motivated by the latest contributions of Cagé and Piketty (2023), which place the *geo-social* class at the heart of the debate. On a much more modest scale, this work aims to extend the analysis to the redistributive preferences of citizens in the (admittedly different) context of developing countries. Far from contradicting the relevance of analyses based on individual approaches, this present study proposes to complement them at a more aggregated level of analysis.

We rely on four waves (2012, 2014, 2016, 2018) of nationally representative opinion surveys from the

<footnote n="3">3. The notion of “geo-social classes” was initially developed by Latour (2021) and Latour and Schultz (2022), consisting in rethinking social classes as socio-ecological classes, taking into account their relationship and attachment to earthy living conditions. Cagé and Piketty (2023) take up this concept more broadly, emphasizing the geographical dimension, which also includes environmental issues and natural resources.</footnote>
Latin American Public Opinion Project (LAPOP) on 16 LA countries, covering the vast majority of the region's inhabitants (excluding the Caribbean). Based on the geolocation of 1,114 respondent' city centers, a center-periphery approach is adopted to reflect their relative position within the territory. Each city is categorized according to its size and estimated population, distance from a city over 100,000 inhabitants, and the surrounding urban density. Primary cities (over 500,000 inhabitants) and their nearby suburbs are assigned as metropolises, while secondary cities and smaller cities and villages are assigned as peripheral areas. In addition, remote sensing nighttime lights data are mobilized to account for cities' level of development (i.e., the median brightness level in each city group).

The overall findings reveal that citizens located in peripheral areas are more likely to express strong support for redistribution than those residing in metropolises. These results remain stable even when controlling for socio-economic characteristics or individual perceptions (such as inter-personal trust, perceived insecurity, or corruption). This is especially the case for citizens located in small cities or villages. Differentiating cities according to their level of development tends to show that citizens of less developed cities (i.e., the least bright 50%) have slightly stronger redistributive demands. Household wealth, measured by an indicator of disposable assets, also plays a significant role on redistributive demands in peripheral areas. Most of the redistributive differences between peripheral areas and metropolises originate from individuals belonging to the second and last terciles of the household wealth indicator. Redistributive preferences from those in the bottom tercile remain fairly similar across zones. These results suggest that differences in the redistributive demands of citizens in peripheral areas and metropolises do not seem to emanate from the poorest citizens, but from a consumer class relatively well endowed with durables, who could be assimilated to the middle class.

Finally, the analysis reveals a noticeable evolution in the redistribution preferences of the region's citizens over the period studied. While strong support for redistribution declined from 2012 onwards, the center-periphery redistributive demand gap previously identified progressively narrowed after 2014. This reversal trend is particularly striking in the case of the small cities and villages, but also among the poorest citizens. As expected, results are highly heterogeneous when the analysis moves from the regional to the
national scale, demonstrating the importance of the local and regional context in determining citizens'
redistributive demands.

The results highlighted here do not claim to be causal, as many mechanisms are involved in shaping the redistributive demands of individuals, which cannot be verified in this framework nor with the data used. However, these results contribute to the literature on the determinants of redistributive preferences in developing countries and shed light on the importance of geographical and territorial factors in explaining the paradox of redistributive preferences in LA, in addition to individual factors. The general decline in redistributive demand conceals two distinct trends. On the one hand, the redistributive demands of the poorest people in peripheral areas are diminishing and converging with those of metropolitan dwellers. On the other, the redistributive demands of other wealthier citizens in peripheral areas remain higher than those of metropolises. These behaviors seem to explain at least part of the low polarization of redistributive demands in the region (Holland 2018), suggesting a form of fatalism for the poorest.

The rest of the paper is structured as follows. Section 2 briefly discusses the role of geography on citizens' attitudes, and more specifically on the formation of redistributive demands. Section 3 presents the data and the methodology adopted. Section 4 describes the estimation strategy. Section 5 presents the main results, and Section 6 concludes.

## 2 The role of geography in citizens' attitudes

The uneven development of territories has been widely discussed in geographic economics, at least since Lipton's (1977) pioneering work on the *urban bias* of the development process. It states that investment choices made by policy makers tend to prioritize urban and industrial areas at the expense of rural territories, with adverse consequences for economic growth and poverty reduction in these areas in developing countries (Bezemer and Headey 2008), or more specifically in LA (Berdegué et al. 2011). Ferré et al. (2012) have extended the *urban bias* reasoning by showing a *metropolitan bias* in developing countries, favoring very large cities over smaller and medium ones, in which most urban poor reside. Their analysis, based on eight developing countries (including Brazil and Mexico), suggests an inverse
relationship between city size and poverty levels, where the largest cities have the greatest availability of services and infrastructure. A more recent study by Gómez-Lobo and Oviedo (2023) also confirms the extent of these spatial inequalities in the LA region compared to other regions of the world, notably between urban–rural areas, but also within small urban areas or on the outskirts, where poverty and informality are prevalent.

Uneven territorial development also affects for citizens’ perceptions, trust and political attitudes. Córdova and Layton (2016) provide evidence that contextual factors shape citizens’ trust in local representatives in El Salvador. They find that high neighborhood income inequality undermines local governance and increases distrust in local government among the poor. Their results, in line with the Fairness Heuristic Theory (Van den Bos 2001), reveal that individuals mainly draw on the most salient information from their local social context to judge the distributive fairness and performance of their elected representatives.<fnref n="4" /> Bland et al. (2023) also show that geography influences the accountability of political representatives in the LA region. Citizens in remote areas are less likely to turn their dissatisfaction with public services into sanctions against nationally elected officials than their urban counterparts, despite a lower quality of public services in rural areas. A similar study carried out in the African context for 17 countries reveals analogous results (Brinkerhoff et al. 2018). Regarding redistributive demands, Haggard et al. (2013) find in a cross-country study of 41 developing countries that while income is a key factor in citizens’ redistributive preferences, the latter also vary greatly according to other factors such as occupation, unionization, and the place of residence. In particular, job opportunities and urban cultural influence can explain the differences in redistributive preferences between large cities and rural areas. These results, suggesting an urban–rural divide in citizens’ attitudes, are also well illustrated in the work of Gimpel et al. (2020) for the case of the US. These authors conclude that spatial location plays a key role in determining and structuring political opinions.

Other studies dealing with territorial disparities also add the notions of *center* and *periphery*, distinguishing attractive, dynamic territories (in economic, political, and cultural terms) from marginal or

<footnote n="4">4. The Fairness Heuristic Theory posits that in cases where information is not fully available, people rely on simple heuristics or mental shortcuts to assess fairness (Van den Bos 2001).</footnote>
excluded areas. Treib (2021) argues that the (perceived) increase in spatial inequalities between core and periphery territories has consequences on people's political attitudes in Europe, and explains the rise of euroscepticism in several countries such as France, the UK, Italy or Poland. With different words, Rodríguez-Pose (2018) also shows that territories lagging behind or with "no future", have been the main sources of popular revolts and contributing to the rise of political populism in Europe or the US. These territorial disparities also have repercussions on citizens' preferences in terms of public policy. Using a center-periphery approach, Arndt et al. (2023) identify a cleavage in support for environmental policies (taxation, energy transitions) in Western Europe, between citizens in metropolises and those in small towns or rural areas, the latter being the most reluctant to adopt them.

Thus, one explanation for the low polarization of redistributive demands between the richest and poor-
est citizens in Latin America could lie in the spatial or territorial factor, beyond many other possible
individual causes. While the link between redistributive preferences and urban-rural residence has been
documented (Haggard et al. 2013), the differentiation between central and peripheral areas, where
economic attractiveness, employment opportunities and productive structures differ, has been poorly
studied in the region. The definition and contours of the concept of periphery, originating from geo-
graphic economics, can be the subject of debate in the literature (Kühn 2015; Pugh and Dubois 2021).<fnref n="5" />
Although these are beyond the scope of this study, it is worth clarifying its meaning throughout this
present work. As the aim of this study is to explore citizens' redistributive preferences under a center-
periphery perspective on a regional scale – including a large number of cities in different countries –, the
categorization of territories will be mainly based on geographical or urban aspects (distance to large
city, population, administrative role) for practical reasons and data availability (more details about the
method in the next Section 3.2). Neither the historical or political dimensions of territories, nor their
evolution, will be addressed here. Nevertheless, this approach makes it possible to consider territorial
disparities more precisely than an urban-rural distinction.

Regardless of individual characteristics, there are reasons to believe that territorial and spatial factors

<footnote n="5">5. For instance, between the terms periphery and peripheralization. Periphery refers mainly to a static geography notion such as distance from a center (large city), whereas peripheralization describes a dynamic process with social, economic or political relations that have spatial implications (Kühn 2015; Pugh and Dubois 2021).</footnote>
could influence citizens' redistributive demands. Based on the literature, two main hypotheses can be postulated. The first hypothesis (H1) is that citizens' redistributive preferences in peripheral areas might be higher than those of those located in metropolises. The potential urban or metropolitan bias of investment decisions (Ferré et al. 2012; Berdegué et al. 2015), particularly in terms of public services and infrastructure, should increase the feeling of unfairness among citizens, leading to higher redistributive demands.

H1: *Citizens in peripheral areas, due to the urban bias in public investments, are more likely to have stronger redistributive demands than those in metropolises.*

The second hypothesis (H2), echoing the theory of diminished expectations by Holland (2018), could precisely produce opposite effects. In fact, strong spatial inequalities between territories could reduce the polarization of demands for redistribution between citizens inside and outside metropolitan areas, due to lower expectations of the State's ability to effectively redistribute resources.

H2: *Citizens in peripheral areas have similar or lower redistributive demands than their metropolitan counterparts due to diminished expectations about the State.*

Other factors related to the center-periphery expansion may also generate alternative outcomes. Firstly, the diversity of contexts in which individuals evolve could affect citizens' perceptions and demands (Cór-dova and Layton 2016). Consequently, cities' size and development level should also influence citizens' redistributive preferences. Secondly, individual characteristics, particularly household wealth, could influence redistributive demands according to their center-periphery situation (Haggard et al. 2013). Thus, the effects of these potential interactions will be verified subsequently.
# 3 Data and methodology

## 3.1 Main data

This study draws on four waves (2012, 2014, 2016, 2018) of the LAPOP survey, which has full regional coverage.<fnref n="6" /> The study provides nationally representative information on citizens’ values, behavior and perceptions on a wide range of subjects. Common questionnaires and standardized techniques allow for comparison across countries and over time.<fnref n="7" /> We use data from 16 LA countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Peru, Paraguay and Uruguay), representing almost all the countries of the sub-continent (excluding the Caribbean). All selected countries are surveyed in the four waves.

We exploit the geographical dimension of the LAPOP dataset by geolocating respondents using information about their place of residence. Although the precise location of LAPOP respondents is not possible for anonymity reasons, it is possible to obtain the *municipio* – the smallest administrative scale – to which they belong.<fnref n="8" /> The geolocation of respondents is therefore approximated by the main city of the corresponding municipio. Open Street Map is mainly used to locate cities and villages, as well as the open source GeoNames database, which contains over eleven million unique placenames worldwide with information about geographical coordinates or population estimates.<fnref n="9" /> While this method does not allow us to pinpoint exactly where the respondent lives, it does identify the geographical context in which the respondent lives, while maintaining a fairly good geographic dispersion within the countries studies. Figure A1 in Appendix shows the geographical dispersion of cities covered by the LAPOP survey from 2012 to 2018. We identify 1,114 cities in the surveyed countries, with 86% of them present in each survey wave. The largest number of cities surveyed are Peru (157), Mexico (114) and Brazil (111), while the smallest are Nicaragua (46), Panama (30) and Costa Rica (29).

<footnote n="6">6. The questionnaires for these four waves are very similar, ensuring strong comparability. The 2008 and 2010 waves are not used here due to differences in city coding. Wave 2021, very different due to the COVID-19 epidemic, is not included in the analysis, nor is wave 2023, due to the absence of important variables from the model.</footnote>

<footnote n="7">7. More details in the LAPOP survey methodological notes (https://www.vanderbilt.edu/lapop/survey-designs.php).</footnote>

<footnote n="8">8. In some cases, it is possible to obtain more precise information, such as the city or district where the respondent lives.</footnote>

<footnote n="9">9. Last access in May 2024 (https://www.geonames.org/).</footnote>
## 3.2 Center-periphery measurement

In LAPOP, information on respondents' place of residence in is limited. The two variables available describe the urban-rural nature of the area, on the one hand, and its size (national capital, large city, medium-sized city, small town, rural area), on the other. However, definitions of urban and rural places are not common across countries, nor are definitions of city size. Similarly, it is not possible to understand the city's geographical context, such as its location in relation to a densely urbanized area, or its relative isolation. Yet, the distance from a major metropolitan area has a substantial influence on the economic, social and cultural context in which the respondent lives (Gimpel et al. 2020), which may also influence individuals' redistributive demands.

We propose here to distinguish the respondents' living areas according to a center-periphery criterion. We define centers here as large metropolises, including both primary cities and their immediate suburbs, while other areas are, therefore, considered peripheral. Due to their size, population density, and economic activity, metropolitan areas often have a higher bargaining power with the State than other territories, with a disproportionate allocation of resources (Ferré et al. 2012). Similarly, localities within the extended area of the metropolis or in direct geographical proximity – referred to as suburbs for convenience – are included because of their strong economic dependence on the primary city, and the associated benefits in terms of infrastructure or services.

First, we set a population threshold of 500,000 inhabitants to distinguish primary cities among the 1,114 in the database. This threshold corresponds to the OECD definition of metropolises, which is well suited to the LA context. Second, a multiple correspondence analysis (MCA) is performed in each country to provide a finer differentiation of the other localities based on the estimated population, the city size (defined by LAPOP), the distance to a city of over 100.000 inhabitants, and the average distance from the three neighbouring cities of more than 30.000 inhabitants.<fnref n="10" /> The score obtained from the first two MCA dimensions enables a cluster analysis grouping together homogeneous cities, with a manual check to identify any classification errors or particular geographical contexts. We use a second population

<footnote n="10">10. We use the OpenRouteService API to calculate distances between geographical points, using available road infrastructure (https://openrouteservice.org/).</footnote>
threshold to form the group of secondary cities between 100,000 and 500,000 inhabitants. For the rest of the localities, distance criteria help identify suburbs close to primary cities from small cities and villages in more remote areas.

Thus, two indicators are constructed from the groups previously established. The first resulting indicator is a straightforward binary indicator between metropolises (primary cities and suburbs) and peripheral areas. The second indicator adds the differentiation of peripheral areas by size between secondary cities and smaller cities and villages. Additional results also test the exclusion of suburban cities from the metropolise group, treating them as a fourth distinct group (Figures S2 and S3, and Table S2 in Supplementary Materials). Table 1 shows the proportion of cities in the different categories, and the relative importance of each group in the total sample, while Figure 2 shows the distribution of center-periphery cities for the LA region.<fnref n="11" /> As shown below, roughly half of the respondents are located in metropolises, the other half in peripheral areas. When decomposed by city type, around a third of respondents in metropolises are in the inner suburbs, and two-thirds of those classified as living in peripheral areas are in small towns and villages.

<table><caption>**Table 1:** Distribution of LAPOP respondents by city category</caption><thead><tr><th>City category</th><th>(a) Share of total number of cities (%)</th><th>(b) Share of total respondent population (%)</th></tr></thead><tbody><tr><td colspan="3"><em>Panel A: Binary center-periphery</em></td></tr><tr><td>Metropolises</td><td>26.5</td><td>39.8</td></tr><tr><td style="padding-left: 2em;">of which suburbs</td><td>12.5</td><td>12.4</td></tr><tr><td>Periphery</td><td>73.5</td><td>60.2</td></tr><tr><td colspan="3"><em>Panel B: Categorial center-periphery</em></td></tr><tr><td>Metropolises</td><td>26.5</td><td>39.8</td></tr><tr><td style="padding-left: 2em;">of which suburbs</td><td>12.5</td><td>12.4</td></tr><tr><td>Secondary cities</td><td>16.8</td><td>16.6</td></tr><tr><td>Small cities or villages</td><td>56.7</td><td>43.6</td></tr><tr><td>Observations</td><td>1,114</td><td>103,098</td></tr></tbody></table>

Source: Author's elaboration based on the LAPOP database (2012, 2014, 2016 2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: Weights provided by LAPOP are used for cross-country comparisons in column (b).

<footnote n="11">11. The proportion of respondents by city category for each country is available in Appendix (Figure A2).</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 2:** Classification of cities in Latin America (LAPOP survey)</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012, 2014, 2016, 2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: N = 1,114. Each point corresponds to a city covered by the LAPOP survey. Countries shown in dark gray are not included.
## 3.3 City development measurement

While the center-periphery approach may play a role in individuals' demands for redistribution, the city's development level may also be a determining factor. In particular, living in areas that are economically lagging behind and lacking in infrastructure or local public services may influence the level of citizens' demands through a feeling of relative deprivation. However, obtaining official information on economic development at the city level is not possible for most countries in the region. It is nevertheless possible to use remote sensing data to proxy the local level of development. We rely on night-time lights (NTL) to approximate the level of economic activity and wealth of each city. The NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS) dataset provides satellite-based visualization of brightness levels throughout the earth's surface.<fnref n="12" /> Annual time series (2013-2021) are provided from monthly cloud-free average radiance from orbital segments with a resolution of 15 arc seconds, which corresponds roughly to 500m at the Equator (Elvidge et al. 2021). Several studies have shown that NTL data are positively associated with human development indicators (Bruederle and Hodler 2018), and are effective in tracking poverty reduction efforts in specific areas (Andreano et al. 2021). In particular, Pérez-Sindín et al. (2021) compare several NTL data sources for the case of Colombia and conclude that these data are a good proxy for the level of municipal development, with VIIRS data offering the best level of prediction.

The average radiance over a distance of 2km around each geolocated point is calculated to approximate the level of development of the city over the period.<fnref n="13" /> As we have cities of very different sizes, we compare the brightness of city centers with the median luminosity of their group (metropolises including suburbs, secondary cities and small villages and towns). We provide an illustration of the NTL data for the bay of São Paulo and Rio de Janeiro for the period 2012-2018 in Supplementary Materials (Figure S1). The City's Relative Development (CRD) indicator takes the value 1 if the city belongs to the 50% least bright cities in its category, indicating relatively low economic activity, and 0 otherwise for cities with relatively high economic activity.

<footnote n="12">12. The VIIRS V2.1 database is available at the following address: https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_ANNUAL_V21#bands.</footnote>

<footnote n="13">13. The chosen distance of 2km, though arbitrary, allows us to calculate the luminosity of a city center over an area that is neither too large nor too small. Other thresholds are tested on the main results in Supplementary Materials (Table S3).</footnote>
## 3.4 Redistributive preference measurement

In LAPOP data, the measurement of redistributive preferences is based on the following statement: "The government should implement strong policies to reduce inequality between the rich and the poor", where respondents are asked to give a score on a Likert scale from 1 (strongly disagree) to 7 (strongly agree). Figure 3 shows the distribution of redistributive preferences for the LA region from 2012 to 2018. Global support for redistribution is strong in LA, especially before 2014, corresponding to the period when poverty declined the most in the region, i.e. during the 2000s. In 2012, almost one in two LA citizens strongly supports (score of 7) the need for governments to reduce inequalities between the richest and poorest, and almost 7 out of 10 when those answering a score of 6 are included.<fnref n="14" />

Although levels of demand for redistribution are relatively stable over the period, the percentage of citizens strongly agreeing nevertheless dropped noticeably from 2014 onwards, an observation in line with Franetovic and Castillo (2022). Among the many possible explanations for this phenomenon, Holland and Schneider (2017) argue that the weakening of redistributive demands in the region could come from the exhaustion of social models over the previous decade mainly based on the implementation of "easy" social policies (targeted transfers, basic social protection) without entering into the "hard" stages of redistribution (quality of public services, unemployment insurance), particularly among the middle and wealthier classes. In line with Busso et al. (2023) results, clear disparities exist in citizens' level of support for redistribution when moving from regional to national level countries (Figure 4), with Uruguay, Nicaragua, and Paraguay leading the way. In constrast, Bolivia, Honduras and Guatemala show the lowest support (around 53%). However, for all countries, redistribution support levels decreased in 2018 compared with their 2012 levels (except Costa Rica).

<footnote n="14">14. Citizens' redistribution demands for 2008 and 2010 are very similar to those for 2012. However, they are not presented here as they are not used in the analysis for the reasons given in Section 3.1.</footnote>
<figure><img src="image_6.png" /><figcaption>**Figure 3:** Support for redistribution in Latin America over time</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: N = 99,490. The question asked is the following: "The government should implement strong policies to reduce inequality between the rich and the poor". Weights provided by LAPOP are used for cross-country comparisons.

<figure><img src="image_7.png" /><figcaption>**Figure 4:** Support for redistribution by country in 2012 and 2018</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012 and 2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: N = 99,490. The question asked is the following: "The government should implement strong policies to reduce inequality between the rich and the poor". Respondents who strongly agreed answered 6 or 7, and those who strongly disagreed answered 1 or 2. Weights provided by LAPOP are used for cross-country comparisons.
Given that the aim of this analysis is to differentiate individuals in favor of more redistributive policies, the original variable is recoded in a binary form, taking 1 when respondents attribute a score of 6 or 7, and 0 otherwise. Both scores on a 7-point scale reflect a strong desire for change on inequality issues. It should be noted, however, that the question posed remains very general, and does not allow for a more precise understanding of the redistribution method that may be employed by policy-makers (pre- or post-redistribution). This is a recurring problem in research into redistributive preferences, ignoring the dimension of fairness that alters people's preferences (Cavaillé 2023). This drawback could overestimate the real proportion of individuals supporting redistribution. Busso et al. (2023) show in particular that when individuals are asked about more specific policies to be implemented to reduce inequality, support for redistribution weakens, although this largely depends on the national context.

# 4 Estimation strategy

To investigate the relationship between geographical periphery and individuals' redistributive preferences and test the previously exposed hypotheses, we adopt a simple linear regression model.<fnref n="15" /> The associated specification is of the following form:

$$Y_i = \alpha_i + \beta_1 Periphery_i + \beta_2 CRD_i + \theta X'_i + \eta_c + \eta_t + \epsilon_i \quad (1)$$

With $Y_i$ the dependent variable indicating if an individual strongly support inequality reduction between the rich and the poor (a 6 or 7 on the Likert scale). The Periphery_i variable corresponds to one of the indicators defined previously, either the binary indicator (1 if living in a peripheral area, 0 in a metropolis), or the categorical indicator (1 if living in a secondary city, 2 in a small city or village, and 0 in a metropolis). The variable CRD_i indicator takes the value 1 if the city is among the 50% least developed cities in its category (i.e. below the group's brightness median). We also add interaction between both indicators in additional specifications.

<footnote n="15">15. Since the dependent variable is binary, i.e. strong support for redistribution, we also provide results using a probit regression model in Supplementary Materials, which yields similar results (Table S1). For simplicity, we report coefficients from the linear regression in the main results.</footnote>
$X_i'$ is a vector of control variables that influence individuals' redistribution preferences often suggested in the literature.<fnref n="16" /> A first group of variables includes socioeconomic characteristics such as gender, age and its squared, employment status (working, unemployed, student, at home without job, retired and disabled), occupation (public employee, private employee, employer, self-employed, inactive), education level (from primary incomplete to post-secondary education), and whether household income has fallen over the past two years. A second group of variables adds experience and perception variables such as having witnessed corruption by a public employee in the previous year, perceived safety and interpersonal trust in the neighborhood, whether participated in a demonstration during the year, perceived political knowledge of the individual, and participation in religious events. Furthermore, a Household Wealth Index (HWI) is constructed from a MCA of household durable goods as a proxy for income, and divided into terciles corresponding to the poorest, middle and richest groups.<fnref n="17" /> Rather than an indicator of social class, it primarily serves to identify a class of consumers of goods, which can be interpreted as a middle class. On the other hand, it is difficult to differentiate the wealthiest citizens on the basis of the durable goods available in the survey, as we have no more precise information on luxury goods or real estate assets. Finally, country (or region) $\eta_c$ and year $\eta_t$ fixed effects are included, and the error term is clustered at city level to take into account the correlation between individuals surveyed in the same city.

# 5 Results

## 5.1 Periphery and demand for redistribution

Results of the relationship between individuals' territorial anchorage and redistributive preferences are shown in Table 2. Specification (1) tests the relationship between the binary center-periphery indicator and strong support for redistribution, including socio-economic controls with country and time fixed effects. Specification (2) adds all experience and perception controls that may affect individuals' redis-

<footnote n="16">16. The descriptive statistics table can be found in Appendix (Table A1).</footnote>

<footnote n="17">17. The HWI is constructed for each country and each survey wave to take into account the national context and changes in the use of goods over time. The list of durable goods is as follows: refrigerator, conventional telephone, cell phone, number of vehicles (none, one, two and more), washing machine, microwave/oven, computer, internet in the home, indoor plumbing, indoor bathroom, television (none, television, flat screen), and a motorcycle. For 2012, only the internet item is not available.</footnote>
tributive demands. In specification (3), we decompose the periphery indicator to consider the different sizes of cities in peripheral areas, while specification (4) includes region fixed effects, allowing for the consideration of finer invariable characteristics within countries.<fnref n="18" />

The results reveal a positive relationship between living outside large metropolises and strongly supporting the reduction of inequalities in LA for the period 2012-2018. The coefficient associated with the binary peripheral variable of interest (column 1) is significantly different from zero at 1% level, and remains stable with the inclusion of additional control variables (column 2). However, the magnitude of the effect remains relatively modest, which is not surprising given the scale of the regional analysis. All other things being equal, citizens living in peripheral areas are roughly 2 percentage points more likely to strongly support redistributive measures than those living in or near large metropolises (i.e. a 4% increase). The CRD indicator has a positive and small impact on citizens' redistribution demands, indicating slightly higher demands in least developed areas. When the periphery indicator is decomposed by sub-groups (columns 3 and 4), results indicate that it is only the smaller cities and villages that drive this result.<fnref n="19" />

These initial results are in contradiction to those of (Haggard et al. 2013), who found that citizens of big cities – also defined as cities over 500,000 – in 41 developing countries (including 9 in LA) are more supportive of redistribution than the rest. Although the coefficient is positive for citizens living in secondary cities, the difference is not significantly different from those living in metropolitan areas. Our results tend to confirm the H1 hypothesis, which postulates that individuals outside metropolises show stronger redistributive preferences, especially in smaller localities. Indeed, these overall results conceal a high degree of heterogeneity between countries. To make these results more concrete, a Bolivian woman living in a metropolise has a 39% probability of strongly supporting redistributive demands. This probability rises to 46% if she lives in a small city or village. Similar results are found in Ecuador (51% to 57%), Colombia (61% to 65%) or Panama (43% to 65%). On the other hand, no major differences are found in most Southern Cone or Central American countries.

<footnote n="18">18. We also provide additional results by excluding the suburbs from the center group, leaving the primary cities as the reference groups. See Table S2 in Supplementary Materials.</footnote>

<footnote n="19">19. Using the original coding of the dependent variable of redistribution preferences (scale from 1 to 7) with an ordered probit model does not change the results overall, but reduces the significance of the coefficients due to the high number of outcomes. (See Table S4 in Supplementary Materials).</footnote>
<table><caption>**Table 2:** Periphery and strong support for redistribution (2012-2018) – Linear regression</caption><thead><tr><th rowspan="2">Variables</th><th colspan="4">Strong support for redistribution</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td><strong>City characteristics</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Periphery (ref: metropolises)</td><td><strong>0.0218</strong>***<br>(0.0079)</td><td><strong>0.0198</strong>***<br>(0.0075)</td><td></td><td></td></tr><tr><td>Secondary city</td><td></td><td></td><td>0.0074<br>(0.0099)</td><td>0.0124<br>(0.0097)</td></tr><tr><td>Small cities or villages</td><td></td><td></td><td><strong>0.0255</strong>***<br>(0.0075)</td><td><strong>0.0244</strong>***<br>(0.0082)</td></tr><tr><td>CRD (ref: 50% richest)</td><td><strong>0.0115</strong>*<br>(0.0067)</td><td>0.0094<br>(0.0065)</td><td>0.0086<br>(0.0065)</td><td><strong>0.0090</strong>*<br>(0.0051)</td></tr><tr><td><strong>Socio-economic controls</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>-0.0006<br>(0.0036)</td><td><strong>0.0101</strong>***<br>(0.0037)</td><td><strong>0.0103</strong>***<br>(0.0037)</td><td><strong>0.0097</strong>***<br>(0.0037)</td></tr><tr><td>Age</td><td><strong>0.0992</strong>***<br>(0.0316)</td><td><strong>0.0592</strong>*<br>(0.0334)</td><td><strong>0.0601</strong>*<br>(0.0334)</td><td><strong>0.0668</strong>**<br>(0.0330)</td></tr><tr><td>Age<sup>2</sup></td><td><strong>-0.1113</strong>**<br>(0.0459)</td><td><strong>-0.0854</strong>*<br>(0.0494)</td><td><strong>-0.0853</strong>*<br>(0.0494)</td><td><strong>-0.0994</strong>**<br>(0.0484)</td></tr><tr><td>HWI (ref: 3rd tercile)</td><td></td><td></td><td></td><td></td></tr><tr><td>1st tercile</td><td><strong>0.0165</strong>***<br>(0.0054)</td><td><strong>0.0263</strong>***<br>(0.0056)</td><td><strong>0.0251</strong>***<br>(0.0054)</td><td><strong>0.0281</strong>***<br>(0.0053)</td></tr><tr><td>2nd tercile</td><td><strong>0.0194</strong>***<br>(0.0044)</td><td><strong>0.0248</strong>***<br>(0.0044)</td><td><strong>0.0244</strong>***<br>(0.0044)</td><td><strong>0.0259</strong>***<br>(0.0044)</td></tr><tr><td>Negative income shock</td><td>0.0063<br>(0.0043)</td><td><strong>0.0104</strong>**<br>(0.0043)</td><td><strong>0.0104</strong>**<br>(0.0043)</td><td><strong>0.0108</strong>**<br>(0.0044)</td></tr><tr><td>Occupation (ref: worker)</td><td></td><td></td><td></td><td></td></tr><tr><td>Unemployed</td><td>0.0278<br>(0.0212)</td><td>0.0283<br>(0.0219)</td><td>0.0292<br>(0.0220)</td><td>0.0302<br>(0.0219)</td></tr><tr><td>Student</td><td>0.0069<br>(0.0215)</td><td>0.0024<br>(0.0223)</td><td>0.0033<br>(0.0223)</td><td>0.0036<br>(0.0223)</td></tr><tr><td>At home</td><td>-0.0036<br>(0.0209)</td><td>-0.0016<br>(0.0214)</td><td>-0.0011<br>(0.0214)</td><td>-0.0005<br>(0.0214)</td></tr><tr><td>Retired/disabled</td><td>0.0097<br>(0.0218)</td><td>0.0065<br>(0.0225)</td><td>0.0072<br>(0.0225)</td><td>0.0091<br>(0.0226)</td></tr><tr><td>Employment (ref: public employee)</td><td></td><td></td><td></td><td></td></tr><tr><td>Private employee</td><td><strong>-0.0269</strong>***<br>(0.0088)</td><td><strong>-0.0203</strong>**<br>(0.0087)</td><td><strong>-0.0196</strong>**<br>(0.0087)</td><td><strong>-0.0211</strong>**<br>(0.0084)</td></tr><tr><td>Employer</td><td><strong>-0.0672</strong>***<br>(0.0149)</td><td><strong>-0.0604</strong>***<br>(0.0152)</td><td><strong>-0.0599</strong>***<br>(0.0152)</td><td><strong>-0.0617</strong>***<br>(0.0150)</td></tr><tr><td>Self-employed</td><td><strong>-0.0283</strong>***<br>(0.0089)</td><td><strong>-0.0228</strong>***<br>(0.0087)</td><td><strong>-0.0225</strong>***<br>(0.0087)</td><td><strong>-0.0236</strong>***<br>(0.0085)</td></tr><tr><td>Other inactives/Unpaid Worker</td><td><strong>-0.0392</strong>*<br>(0.0217)</td><td>-0.0317<br>(0.0224)</td><td>-0.0319<br>(0.0224)</td><td>-0.0336<br>(0.0222)</td></tr><tr><td>Education (ref: primary incomplete)</td><td></td><td></td><td></td><td></td></tr><tr><td>Primary complete</td><td><strong>0.0249</strong>***<br>(0.0068)</td><td><strong>0.0210</strong>***<br>(0.0068)</td><td><strong>0.0212</strong>***<br>(0.0068)</td><td><strong>0.0189</strong>***<br>(0.0067)</td></tr><tr><td>Secondary incomplete</td><td><strong>0.0406</strong>***<br>(0.0062)</td><td><strong>0.0290</strong>***<br>(0.0063)</td><td><strong>0.0296</strong>***<br>(0.0063)</td><td><strong>0.0279</strong>***<br>(0.0061)</td></tr><tr><td>Secondary complete</td><td><strong>0.0356</strong>***<br>(0.0063)</td><td><strong>0.0193</strong>***<br>(0.0065)</td><td><strong>0.0200</strong>***<br>(0.0064)</td><td><strong>0.0182</strong>***<br>(0.0063)</td></tr><tr><td>Post-secondary</td><td><strong>0.0468</strong>***<br>(0.0068)</td><td><strong>0.0147</strong>*<br>(0.0068)</td><td><strong>0.0156</strong>**<br>(0.0068)</td><td><strong>0.0130</strong>*<br>(0.0067)</td></tr></tbody></table>
<table><thead><tr><th></th><th>(0.0075)</th><th>(0.0077)</th><th>(0.0077)</th><th>(0.0074)</th></tr></thead><tbody><tr><td><em>Experience and perception controls</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Feeling of insecurity (ref: weak)</td><td></td><td>-0.0050<br>(0.0077)</td><td>-0.0042<br>(0.0076)</td><td>-0.0053<br>(0.0075)</td></tr><tr><td>Inter-personal trust (ref: high)</td><td></td><td><strong>-0.0638</strong>***<br>(0.0063)</td><td><strong>-0.0632</strong>***<br>(0.0063)</td><td><strong>-0.0591</strong>***<br>(0.0061)</td></tr><tr><td>Political knowledge (ref: low)</td><td></td><td><strong>0.1775</strong>***<br>(0.0068)</td><td><strong>0.1775</strong>***<br>(0.0068)</td><td><strong>0.1757</strong>***<br>(0.0067)</td></tr><tr><td>Participation in a protest</td><td></td><td><strong>0.0448</strong>***<br>(0.0073)</td><td><strong>0.0449</strong>***<br>(0.0073)</td><td><strong>0.0424</strong>***<br>(0.0073)</td></tr><tr><td>Witness to corruption</td><td></td><td><strong>-0.0232</strong>***<br>(0.0084)</td><td><strong>-0.0229</strong>***<br>(0.0084)</td><td><strong>-0.0210</strong>**<br>(0.0083)</td></tr><tr><td>Religious ceremonies (ref: none)</td><td></td><td><strong>0.0090</strong>*</td><td><strong>0.0090</strong>*</td><td><strong>0.0087</strong>*</td></tr><tr><td></td><td></td><td>(0.0047)</td><td>(0.0047)</td><td>(0.0047)</td></tr><tr><td>Observations</td><td>95,782</td><td>90,791</td><td>90,791</td><td>90,791</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.032</td><td>0.045</td><td>0.045</td><td>0.050</td></tr><tr><td>Experience and perception controls</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Fixed effects</td><td>Country &amp; Time</td><td>Country &amp; Time</td><td>Country &amp; Time</td><td>Region &amp; Time</td></tr></tbody></table>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: Coefficients are derived from a linear regression model. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. Ref corresponds to omitted reference category. Time and country (or region) fixed effects are included but not reported. Values in bold correspond to coefficients significantly different from 0. Weights provided by LAPOP are used for cross-country comparisons. Clustered standard errors are in brackets (by city). *** p<0.01, ** p<0.05, * p<0.1.

Regarding control variables, most of the expected effects on redistributive demand are confirmed here. Employees working in the public sector are more inclined to support redistribution policies than those in the private sector or the employers. Similarly, an intermediate or high level of schooling is associated with a greater demand for redistribution than individuals with no or low schooling. However, the effect is smaller for the higher education group, probably reflecting an income or class effect (Haggard et al. 2013). For the HWI variable, the coefficients indicate that those belonging to the two lower terciles show a relatively higher support for redistribution than the top tercile. Finally, perception and experience variables significantly influence redistributive preferences: a negative effect for those with low interpersonal trust or who have experienced corruption at the hands of a public employee, and a positive one for those with a strong understanding of the country's problems, or when mobilized at a demonstration.
## 5.2 City Relative Development and Household Wealth Index

Next, we decompose the marginal effects into the different groups of cities according to their relative development level (Figure 5). The models used are based on specification (4) of the previous table, plus an interaction between the periphery and the CRD indicators. The reference group here corresponds to the 50% richest or most developed metropolises. Figure 5 still shows similar results, where most peripheral territories exhibit higher probability of strongly supporting redistribution compared to the most developed metropolises. The demand for redistribution among citizens is positively associated (significant at 10%) with residing in less developed secondary cities compared to those in wealthier metropolitan areas.<fnref n="20" /> Those living in less wealthy metropolitan areas also have slightly higher demand than those in wealthier metropolitan areas. Building on these results, additional analyses modifying the reference group to include only primary cities and treating suburbs as a separate group show that citizens in the poorest suburbs also have higher demand for redistribution than their counterparts in primary cities (Figure S2 and Table S5 in Supplementary Materials).

We now examine how the interaction between indicators of city periphery and household wealth – measured by household assets – affects citizens’ redistributive preferences. In fact, the previously identified differences in redistributive demands between individuals in metropolises and peripheral areas may also differ according to their wealth level. Figure 6 shows the marginal effects of the two periphery indicators by wealth tercile, on the probability of strongly supporting redistribution. The first graph shows that differences in citizens’ redistributive demands between peripheral areas and metropolises stem mainly from the wealthiest citizens, located in terciles two and three of the HWI. Yet the preferences of those at the bottom are broadly similar.

A similar pattern can be seen when the indicator is broken down by city size. Citizens of small cities and villages consistently express higher redistributive preferences depending on household wealth, with the exception of the poorest. In contrast, redistributive demands of those in secondary cities are fairly

<footnote n="20">20. In additional results, we test several threshold values for calculating brightness (500m, 1,000m, 1,500m) instead of 2km. The results remain stable for small cities and villages, but vary slightly for secondary cities when the threshold values are smaller. See Table S3 in Supplementary Materials.</footnote>
similar to those in metropolises, except for the better-off citizens. Adding the suburbs as a separate
group or differentiating cities according to their development levels (Figures S3 and S4 in Supplementary
Materials) does not change our conclusions. To sum up, citizens' call for more redistribution outside
metropolises seems to come mainly from what might be likened to the middle and wealthier classes.

These disaggregated results show that while citizens outside large metropolises are globally more
supportive for redistribution (H1 hypothesis), the gap is mainly driven by the wealthiest ones. The
demand for redistribution among the least affluent citizens remains relatively homogeneous across
city groups and is not much higher than in the other HWI tertiles. This result resonates with Holland's
(2018) explanation of the low polarization of redistribution demand between the richest and poorest
individuals in the LA region (H2 hypothesis), namely of diminished expectations of the poorest on the
state's ability to effectively redistribute resources. This lower degree of polarization is consistent with
lower expectations from the state in areas where access to infrastructure and public services may be
more difficult.
<figure><img src="image_8.png" /><figcaption>**Figure 5:** Marginal effect of periphery and CRD on strong support for redistribution</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).
Note: N = 90,791. CRD = City Relative Development. Coefficients are derived from a linear regression model. The 95% confidence intervals are shown. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. All controls are included with region and time fixed effects. Weights provided by LAPOP are used for cross-country comparisons. Standard errors are clustered at city level.
<figure><img src="image_9.png" /><figcaption>**Figure 6:** Marginal effect of periphery and HWI on strong support for redistribution</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).
Note: N = 90,791. HWI = Household Wealth Index. Coefficients are derived from a linear regression model. The 95% confidence intervals are shown. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. All controls are included with region and time fixed effects. The HWI is derived from a multiple correspondence analysis of household durables for each country and year, and divided into terciles. Weights provided by LAPOP are used for cross-country comparisons. Standard errors are clustered at city level.

**5.3 Temporal and spatial heterogeneity**

As previously mentioned, support for redistribution has fallen steadily after 2012 in the LA region, and strong disparities exist across countries. The global analysis carried out so far between the center-periphery divide and redistributive demands hides possible temporal heterogeneity, as well as specificities of the countries studied. Figure 7 illustrates the marked decline in strong support for redistribution over time, adding the division between metropolises and peripheral areas. From 2014 onwards, there has been a large drop in strong redistributive demands from citizens both in metropolises and peripheral
areas. Although the gap between the two groups previously highlighted is clear for the two first waves, the picture is quite different for 2016 and 2018. The gap has largely narrowed over the last two years, reflecting a convergence of redistributive preferences, a particularly striking fact for the group of small cities or villages in remote areas, which had the strongest support at the start of the period, and the weakest in 2018 (Figure 8). Over the period 2012-2014, redistributive demands from citizens in small cities and villages were 7% higher than those in metropolitan areas, and disappeared over the following period.

On the other hand, the redistributive preferences of the poorest changed the most over the period (Figure 8). While a gap was well identified for the poorest citizens between those living in small cities and villages and metropolises in the first period, the opposite is true in the following period, with redistributive demands being very consistent across territories. The gradual decline in the redistributive demands of the poorest suggests a kind of fatalism on the part of these citizens, particularly in the least developed peripheral areas, in line with the H2 hypothesis. This trend also helps to explain the low magnitude of the results obtained from the global analysis.
<figure><img src="image_10.png" /><figcaption>**Figure 7:** Predicted values of the probability of strongly supporting redistribution by periphery indicator over time</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).
Note: N = 90,791. Predicted values are derived from a linear regression model. The 95% confidence intervals are shown. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. All controls are included with region and time fixed effects. Weights provided by LAPOP are used for cross-country comparisons. Standard errors are clustered at city level.
<figure><img src="image_11.png" /><figcaption>**Figure 8:** Predicted values of the probability of strongly supporting redistribution by periphery indicator over time</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: N = 90,791. Predicted values are derived from a linear regression model. The 95% confidence intervals are shown. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. All controls are included with region and time fixed effects. Weights provided by LAPOP are used for cross-country comparisons. Standard errors are clustered at city level.
Finally, redistributive demands also vary significantly when moving from regional to national scales of analysis. Figure 9 shows the marginal effects of the city's periphery and relative development by country for periods before and after 2014. The coefficients presented are derived from separate regressions for each country, including all controls and regional fixed effects. The significance of the coefficients is reduced for most countries, as the analysis is carried out on a smaller number of observations and cities. Overall, citizens' redistributive preferences are highly country-specific, and are sometimes contradictory even within peripheral areas and between the most and least developed territories. Prior to 2016, eight out of sixteen countries showed higher redistributive demands for a peripheral territory (especially in small cities or villages). For the period 2016-2018, the balance has reversed in many countries, and demands between territories converged and decreased in most cases. Only Bolivia, Colombia and Costa Rica continue to ask more redistribution in peripheral areas. These results, which are mainly specific to the contexts of the countries studied, nonetheless show the profound divergences in redistributive preferences that can exist in the LA region, not only between different city sizes, but also between metropolises and peripheral areas. Moreover, they show the complexity of the interactions between individual determinants and territorial contexts.
<figure><img src="image_12.png" /><figcaption>**Figure 9:** Marginal effect of decomposed periphery and city's relative wealth on strong support for redistribution by country and time periods</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Paraguay, Peru, Uruguay).

Note: Coefficients are derived from a linear regression model for each country. The 95% confidence intervals are shown. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. All controls are included with with region and time fixed effects. Weights provided by LAPOP are used for cross-country comparisons. Standard errors are clustered at city level.
# 6 Summary and conclusion

This article investigates the relationship between citizens' redistributive demands and the role of territo-
rial anchorage and geography. To do this, nationally representative data from LAPOP opinion survey
are mobilized, covering 16 major countries of the LA region between 2012 and 2018. The role of the
spatial factor on the redistributive preferences of citizens is assessed using a center-periphery approach
by geolocating 1,114 survey respondents' cities and categorizing them into metropolises (primary cities
and nearby suburbs) and peripheral areas (secondary cities and small cities or villages in remote areas)
to identify their relative position in the territory. The level of development of cities is controlled by
determining the median brightness level of city groups, based on remote sensing data. First, our results
identify a gap between the redistributive demands of citizens in metropolitan areas and those in periph-
eral areas. Citizens outside metropolitan areas are more likely to be strongly in favor of redistribution
policies, controlling for socio-economic characteristics. This trend is particularly pronounced driven by
the smallest cities or villages in remote areas.

Second, household wealth – measured by an index of durables assets – also plays a role in these differ-
ences. The gap identified for peripheral areas is stronger for households in the second and third wealth
tertiles, compared to their counterparts in metropolitan areas. Redistributive preferes for those in the
bottom, however, are fairly similar between territories. These results suggest that mainly middle class
or better-off citizens in peripheral areas are most in demand for redistribution in LA, unlike those in
metropolises. Nevertheless, these results remain to be confirmed, particularly with more complete
information on the real wealth of individuals (income from work and capital, assets, etc.).

Third, the analysis confirms temporal and country-specific heterogeneity. While there has been a decline
in strong redistributive demands in the region since 2012, center-periphery gap have also shifted sharply
since 2014, towards convergence. This is particularly the case for the small cities and villages, but also for
the poorest citizens, where the trend has reversed. It should be noted, however, that these conclusions
vary greatly depending on the country and local context. There are, however, several limitations to this
work. Further analysis is needed to consider the context and local specificities of the areas studied here. In
particular, the definition of periphery used in this framework does not consider the social, historical and
political relationships maintained by players within territories, as well as their evolution over time (Kühn
2015). A promising direction for future research would be to study potential redistributive divergences
within the neighborhoods of these areas, where inequality levels are most pronounced (Chauvin and
Messina 2020). In the absence of a more precise location of respondents, this study does not allow us to
verify this. Similarly, support for redistribution can vary widely depending on the economic policy options
to be implemented (Busso et al. 2023). A more detailed analysis of the diversity of citizens' redistribution
preferences is needed to better understand the divergent demands of territories (Cavaillé 2023).

These present results highlight the importance of considering territorial and spatial factors, in addition to
individual characteristics, when analyzing citizens' redistribution preferences. While citizens' demands
for redistribution remain strong overall in the region, with high voter turnout, the demands of vulnerable
populations are still lagging behind, with lower participation and interest in the democratic process
(Vlaicu 2020). Similarly, the growing dissatisfaction of populations, symbolized by the rise of populism
and the increase in social protest movements over the last decade, shows that the weakening of democ-
racy remains a major concern in the LA region, particularly to ensure effective redistribution (Vlaicu
2020; Welp 2022). Efforts to reduce socio-spatial inequalities and the center-periphery gap in the LA
region are needed to restore citizens' confidence in political power and institutions, particularly among
the most vulnerable. A growing literature on urban-rural linkages emphasizes the role of secondary
cities within territories in enhancing economic growth and achieving poverty reduction (Berdegué et
al. 2015; Christiaensen and Kanbur 2017). Adopting public policies favoring small and medium sized
towns outside the major urban centers could help meet citizens' redistributive expectations and rebuild
citizens' trust in their political representatives, where much work remains to be done.
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# Appendix

<table><caption>**Table A1:** Descriptive statistics</caption><thead><tr><th>Variables</th><th>N</th><th>Proportion (%)</th></tr></thead><tbody><tr><td><strong>Gender of respondent</strong></td><td>103,085</td><td></td></tr><tr><td>Female</td><td>50,698</td><td>49.2</td></tr><tr><td>Male</td><td>52,387</td><td>50.8</td></tr><tr><td><strong>Age</strong></td><td>103,098</td><td></td></tr><tr><td>Less than 30 years old</td><td>33,509</td><td>32.5</td></tr><tr><td>Between 30 and less than 60 years old</td><td>54,050</td><td>52.4</td></tr><tr><td>60 years old and more</td><td>15,539</td><td>15.1</td></tr><tr><td><strong>Education</strong></td><td>102,019</td><td></td></tr><tr><td>No education or primary incomplete</td><td>18,013</td><td>17.7</td></tr><tr><td>Primary complete</td><td>13,240</td><td>13.0</td></tr><tr><td>Secondary incomplete</td><td>24,940</td><td>24.4</td></tr><tr><td>Secondary complete</td><td>24,098</td><td>23.6</td></tr><tr><td>Post-secondary</td><td>21,728</td><td>21.3</td></tr><tr><td><strong>Occupation</strong></td><td>101,850</td><td></td></tr><tr><td>Worker</td><td>53,462</td><td>52.5</td></tr><tr><td>Unemployed</td><td>8,269</td><td>8.1</td></tr><tr><td>Student</td><td>7,902</td><td>7.8</td></tr><tr><td>At home</td><td>24,957</td><td>24.5</td></tr><tr><td>Retired/disabled</td><td>7,260</td><td>7.1</td></tr><tr><td><strong>Employment status</strong></td><td>101,850</td><td></td></tr><tr><td>Public employee</td><td>6,957</td><td>6.8</td></tr><tr><td>Private employee</td><td>17,811</td><td>17.5</td></tr><tr><td>Employer</td><td>1,781</td><td>1.7</td></tr><tr><td>Self-employed</td><td>26,038</td><td>25.6</td></tr><tr><td>Other inactives/unpaid worker</td><td>49,263</td><td>48.4</td></tr><tr><td><strong>Income evolution (past 2 years)</strong></td><td>101,378</td><td></td></tr><tr><td>Negative</td><td>28,224</td><td>27.8</td></tr><tr><td>Same or positive</td><td>73,154</td><td>72.2</td></tr><tr><td><strong>Redistributive preferences</strong></td><td>99,490</td><td></td></tr><tr><td>Strong (6-7)</td><td>57,450</td><td>57.7</td></tr><tr><td>Weak (1-5)</td><td>42,040</td><td>42.3</td></tr><tr><td><strong>Perception of safety in the neighborhood</strong></td><td>102,004</td><td></td></tr><tr><td>Very safe</td><td>19,822</td><td>19.4</td></tr><tr><td>Somewhat safe</td><td>39,320</td><td>38.5</td></tr><tr><td>Somewhat unsafe</td><td>28,763</td><td>28.2</td></tr><tr><td>Very unsafe</td><td>14,099</td><td>13.8</td></tr><tr><td><strong>Interpersonal trust in the neighborhood</strong></td><td>100,970</td><td></td></tr><tr><td>Very trustworthy</td><td>24,359</td><td>24.1</td></tr><tr><td>Somewhat trustworthy</td><td>38,839</td><td>38.5</td></tr><tr><td>Not very trustworthy</td><td>27,900</td><td>27.6</td></tr><tr><td>Untrustworthy</td><td>9,872</td><td>9.8</td></tr><tr><td><strong>Feeling of political knowledge</strong></td><td>100,510</td><td></td></tr><tr><td>Strong (6-7)</td><td>20,495</td><td>20.4</td></tr><tr><td>Weak (1-5)</td><td>80,015</td><td>79.6</td></tr><tr><td><strong>Participation in a demonstration (1 year)</strong></td><td>102,633</td><td></td></tr><tr><td>Yes</td><td>9,416</td><td>9.2</td></tr><tr><td>No</td><td>93,217</td><td>90.8</td></tr></tbody></table>
<table><tr><td>Bribery by a government employee (1 year)</td><td>102,649</td><td></td></tr><tr><td>Yes</td><td>5,989</td><td>5.8</td></tr><tr><td>No</td><td>96,660</td><td>94.2</td></tr><tr><td>Religion attendance</td><td>102,413</td><td></td></tr><tr><td>Once a week</td><td>30,608</td><td>29.9</td></tr><tr><td>Once or twice a month</td><td>16,883</td><td>16.5</td></tr><tr><td>Once of twice a year</td><td>9,888</td><td>9.6</td></tr><tr><td>Never</td><td>45,034</td><td>44.0</td></tr></table>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay)
<figure><img src="image_13.png" /><figcaption>**Figure A1:** Cities of LAPOP survey respondents</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012, 2014, 2016-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: N = 1,114. Each point corresponds to a city covered by the LAPOP survey. Countries shown in dark gray are not included.
<figure><img src="image_14.png" /><figcaption>**Figure A2:** Share of LAPOP respondents by country and city category</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).
Note: N = 103,098.
# Supplementary Materials

## Figures
<figure><img src="image_15.png" /><figcaption>**Figure S1:** Nighttime lights for Sao Paulo Bay and Rio de Janeiro (Brazil, 2012-2018)</figcaption></figure>

Source: Author's elaboration based on the VIIRS V2.1 database collected by the NASA/NOAA for the period 2012-2018.

Note: Each point corresponds to the city center of cities covered by the LAPOP survey. The classification of most or least developed cities is based on the median brightness of respondents' cities in the country studied on a 2-km radius. The most (least) developed cities are the 50% brightest (darkest) in their city category in their reference country (metropolises, secondary cities, small cities or villages). Metropolises include suburbs when calculating median brightness, but suburbs are represented as separated in the figure for greater clarity. The cities represented are: Belford Roxo, Carapicuíba, Duque de Caxias, Embu das Artes, Guarulhos, Indaiatuba, Ipeúna, Jandira, Ouro Fino, Pindamohangaba, Pinheiral, Pouso Alegre, Queimados, Rio de Janeiro, Santa Rita do Sapucaí, São Bernardo do Campo, São José dos Campos, São Paulo, São Pedror da Aldeia, and Volta Redonda.
<figure><img src="image_16.png" /><figcaption>**Figure S2:** Marginal effect of periphery and CRD on strong support for redistribution – Suburbs apart</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).
Note: N = 90,791. Coefficients shown are marginal effects from a probit model. The 95% confidence intervals are shown. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. All controls are included, as well as country and year fixed effects. Weights provided by LAPOP are used for cross-country comparisons. Standard errors are clustered at city level.
<figure><img src="image_17.png" /><figcaption>**Figure S3:** Marginal effect of periphery and HWI on strong support for redistribution – Suburbs apart</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).
Note: N = 90,791. HWI = Household Wealth Index. Coefficients shown are marginal effects from a probit model. The 95% confidence intervals are shown. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. All controls are included, as well as country and year fixed effects. The HWI is derived from a multiple correspondence analysis of household durables for each country and year, and divided into terciles. Weights provided by LAPOP are used for cross-country comparisons. Standard errors are clustered at city level.
<figure><img src="image_18.png" /><figcaption>**Figure S4:** Marginal effect of decomposed periphery and HWI on strong support for redistribution</figcaption></figure>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).
Note: N = 90,791. HWI = Household Wealth Index. Coefficients are derived from a linear regression model. The 95% confidence intervals are shown. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. All controls are included with region and time fixed effects. The HWI is derived from a multiple correspondence analysis of household durables for each country and year, and divided into terciles. Weights provided by LAPOP are used for cross-country comparisons. Standard errors are clustered at city level.
# Tables

<table><caption>**Table S1:** Periphery and strong support for redistribution (2012-2018) – Probit regression</caption><thead><tr><th rowspan="2">Variables</th><th colspan="4">Strong support for redistribution</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td><u>City characteristics</u></td><td></td><td></td><td></td><td></td></tr><tr><td>Periphery (ref: metropolises)</td><td><strong>0.0215</strong>***<br>(0.0078)</td><td><strong>0.0194</strong>***<br>(0.0075)</td><td></td><td></td></tr><tr><td>Secondary city</td><td></td><td></td><td>0.0071<br>(0.0099)</td><td>0.0120<br>(0.0097)</td></tr><tr><td>Small cities or villages</td><td></td><td></td><td><strong>0.0251</strong>***<br>(0.0074)</td><td><strong>0.0240</strong>***<br>(0.0082)</td></tr><tr><td>CRD (ref: 50% richest)</td><td><strong>0.0116</strong>*<br>(0.0067)</td><td>0.0095<br>(0.0065)</td><td>0.0086<br>(0.0065)</td><td><strong>0.0091</strong>*<br>(0.0051)</td></tr><tr><td><u>Socio-economic controls</u></td><td></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>-0.0007<br>(0.0036)</td><td><strong>0.0100</strong>***<br>(0.0037)</td><td><strong>0.0101</strong>***<br>(0.0037)</td><td><strong>0.0094</strong>**<br>(0.0037)</td></tr><tr><td>Age</td><td><strong>0.0992</strong>***<br>(0.0314)</td><td><strong>0.0605</strong>*<br>(0.0332)</td><td><strong>0.0614</strong>*<br>(0.0332)</td><td><strong>0.0686</strong>**<br>(0.0328)</td></tr><tr><td>Age<sup>2</sup></td><td><strong>-0.1111</strong>**<br>(0.0456)</td><td><strong>-0.0864</strong>*<br>(0.0489)</td><td><strong>-0.0862</strong>*<br>(0.0489)</td><td><strong>-0.1010</strong>**<br>(0.0480)</td></tr><tr><td>HWI (ref: 3rd tercile)</td><td></td><td></td><td></td><td></td></tr><tr><td>1st tercile</td><td><strong>0.0167</strong>***<br>(0.0054)</td><td><strong>0.0265</strong>***<br>(0.0056)</td><td><strong>0.0253</strong>***<br>(0.0055)</td><td><strong>0.0284</strong>***<br>(0.0053)</td></tr><tr><td>2nd tercile</td><td><strong>0.0195</strong>***<br>(0.0044)</td><td><strong>0.0250</strong>***<br>(0.0044)</td><td><strong>0.0245</strong>***<br>(0.0044)</td><td><strong>0.0261</strong>***<br>(0.0044)</td></tr><tr><td>Negative income shock</td><td>0.0064<br>(0.0043)</td><td><strong>0.0104</strong>**<br>(0.0044)</td><td><strong>0.0105</strong>**<br>(0.0044)</td><td><strong>0.0109</strong>**<br>(0.0044)</td></tr><tr><td>Occupation (ref: worker)</td><td></td><td></td><td></td><td></td></tr><tr><td>Unemployed</td><td>0.0275<br>(0.0206)</td><td>0.0283<br>(0.0214)</td><td>0.0291<br>(0.0214)</td><td>0.0298<br>(0.0213)</td></tr><tr><td>Student</td><td>0.0067<br>(0.0211)</td><td>0.0024<br>(0.0219)</td><td>0.0033<br>(0.0219)</td><td>0.0033<br>(0.0219)</td></tr><tr><td>At home</td><td>-0.0035<br>(0.0205)</td><td>-0.0014<br>(0.0211)</td><td>-0.0009<br>(0.0211)</td><td>-0.0006<br>(0.0210)</td></tr><tr><td>Retired/disabled</td><td>0.0093<br>(0.0214)</td><td>0.0063<br>(0.0221)</td><td>0.0070<br>(0.0221)</td><td>0.0087<br>(0.0221)</td></tr><tr><td>Employment (ref: public employee)</td><td></td><td></td><td></td><td></td></tr><tr><td>Private employee</td><td><strong>-0.0266</strong>***<br>(0.0088)</td><td><strong>-0.0203</strong>**<br>(0.0088)</td><td><strong>-0.0197</strong>**<br>(0.0088)</td><td><strong>-0.0214</strong>**<br>(0.0085)</td></tr><tr><td>Employer</td><td><strong>-0.0667</strong>***<br>(0.0149)</td><td><strong>-0.0604</strong>***<br>(0.0152)</td><td><strong>-0.0599</strong>***<br>(0.0152)</td><td><strong>-0.0618</strong>***<br>(0.0150)</td></tr><tr><td>Self-employed</td><td><strong>-0.0281</strong>***<br>(0.0089)</td><td><strong>-0.0230</strong>***<br>(0.0088)</td><td><strong>-0.0227</strong>***<br>(0.0088)</td><td><strong>-0.0240</strong>***<br>(0.0086)</td></tr><tr><td>Other inactives/Unpaid Worker</td><td><strong>-0.0389</strong>*<br>(0.0212)</td><td>-0.0319<br>(0.0220)</td><td>-0.0321<br>(0.0220)</td><td>-0.0338<br>(0.0218)</td></tr><tr><td>Education (ref: primary incomplete)</td><td></td><td></td><td></td><td></td></tr><tr><td>Primary complete</td><td><strong>0.0250</strong>***<br>(0.0068)</td><td><strong>0.0210</strong>***<br>(0.0068)</td><td><strong>0.0213</strong>***<br>(0.0068)</td><td><strong>0.0189</strong>***<br>(0.0067)</td></tr><tr><td>Secondary incomplete</td><td><strong>0.0404</strong>***<br>(0.0062)</td><td><strong>0.0286</strong>***<br>(0.0063)</td><td><strong>0.0292</strong>***<br>(0.0063)</td><td><strong>0.0275</strong>***<br>(0.0061)</td></tr><tr><td>Secondary complete</td><td><strong>0.0354</strong>***<br>(0.0068)</td><td><strong>0.0190</strong>***<br>(0.0068)</td><td><strong>0.0197</strong>***<br>(0.0068)</td><td><strong>0.0179</strong>***<br>(0.0067)</td></tr></tbody></table>
<table><thead><tr><th></th><th>(0.0063)</th><th>(0.0065)</th><th>(0.0065)</th><th>(0.0064)</th></tr></thead><tbody><tr><td>Post-secondary</td><td><strong>0.0465</strong><sup>***</sup><br>(0.0075)</td><td><strong>0.0147</strong><sup>*</sup><br>(0.0077)</td><td><strong>0.0155</strong><sup>**</sup><br>(0.0077)</td><td><strong>0.0128</strong><sup>*</sup><br>(0.0074)</td></tr><tr><td><em>Experience and perception controls</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Feeling of insecurity (ref: weak)</td><td></td><td>-0.0050<br>(0.0077)</td><td>-0.0042<br>(0.0076)</td><td>-0.0054<br>(0.0075)</td></tr><tr><td>Inter-personal trust (ref: high)</td><td></td><td><strong>-0.0629</strong><sup>***</sup><br>(0.0062)</td><td><strong>-0.0623</strong><sup>***</sup><br>(0.0062)</td><td><strong>-0.0583</strong><sup>***</sup><br>(0.0061)</td></tr><tr><td>Political knowledge (ref: low)</td><td></td><td><strong>0.1778</strong><sup>***</sup><br>(0.0067)</td><td><strong>0.1778</strong><sup>***</sup><br>(0.0067)</td><td><strong>0.1757</strong><sup>***</sup><br>(0.0066)</td></tr><tr><td>Participation in a protest</td><td></td><td><strong>0.0463</strong><sup>***</sup><br>(0.0075)</td><td><strong>0.0464</strong><sup>***</sup><br>(0.0075)</td><td><strong>0.0437</strong><sup>***</sup><br>(0.0076)</td></tr><tr><td>Witness to corruption</td><td></td><td><strong>-0.0229</strong><sup>***</sup><br>(0.0083)</td><td><strong>-0.0225</strong><sup>***</sup><br>(0.0083)</td><td><strong>-0.0206</strong><sup>**</sup><br>(0.0082)</td></tr><tr><td>Religious ceremonies (ref: none)</td><td></td><td><strong>0.0089</strong><sup>*</sup><br>(0.0047)</td><td><strong>0.0089</strong><sup>*</sup><br>(0.0047)</td><td><strong>0.0084</strong><sup>*</sup><br>(0.0047)</td></tr><tr><td>Observations</td><td>95,782</td><td>90,791</td><td>90,791</td><td>90,791</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.025</td><td>0.034</td><td>0.034</td><td>0.039</td></tr><tr><td>Experience and perception controls</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Fixed effects</td><td>Country &amp; Time</td><td>Country &amp; Time</td><td>Country &amp; Time</td><td>Region &amp; Time</td></tr></tbody></table>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: Coefficients are marginal effects derived from a probit regression model. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. Ref corresponds to omitted reference category. Time and country (or region) fixed effects are included but not reported. Values in bold correspond to coefficients significantly different from 0. Weights provided by LAPOP are used for cross-country comparisons. Clustered standard errors are in brackets (by city). *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>**Table S2:** Periphery and strong support for redistribution (2012-2018) – Suburbs apart</caption><thead><tr><th rowspan="2">Variables</th><th colspan="4">Strong support for redistribution</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td><strong>City characteristics</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Periphery (ref: primary cities)</td><td><strong>0.0273</strong><sup>***</sup><br>(0.0093)</td><td><strong>0.0246</strong><sup>***</sup><br>(0.0088)</td><td></td><td></td></tr><tr><td>Suburbs</td><td></td><td></td><td>0.0179<br>(0.0125)</td><td><strong>0.0170</strong><sup>*</sup><br>(0.0096)</td></tr><tr><td>Secondary city</td><td></td><td></td><td>0.0134<br>(0.0110)</td><td><strong>0.0189</strong><sup>*</sup><br>(0.0105)</td></tr><tr><td>Small cities or villages</td><td></td><td></td><td><strong>0.0319</strong><sup>***</sup><br>(0.0090)</td><td><strong>0.0313</strong><sup>***</sup><br>(0.0092)</td></tr><tr><td>CRD (ref: 50% richest)</td><td><strong>0.0111</strong><sup>*</sup><br>(0.0062)</td><td>0.0099<br>(0.0061)</td><td>0.0101<br>(0.0064)</td><td>0.0062<br>(0.0052)</td></tr><tr><td><strong>Socio-economic controls</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Female</td><td>-0.0006<br>(0.0036)</td><td><strong>0.0101</strong><sup>***</sup><br>(0.0037)</td><td><strong>0.0104</strong><sup>***</sup><br>(0.0036)</td><td><strong>0.0097</strong><sup>***</sup><br>(0.0037)</td></tr><tr><td>Age</td><td><strong>0.0987</strong><sup>***</sup><br>(0.0316)</td><td><strong>0.0590</strong><sup>*</sup><br>(0.0334)</td><td><strong>0.0601</strong><sup>*</sup><br>(0.0333)</td><td><strong>0.0669</strong><sup>**</sup><br>(0.0330)</td></tr><tr><td>Age<sup>2</sup></td><td><strong>-0.1108</strong><sup>**</sup><br>(0.0458)</td><td><strong>-0.0851</strong><sup>*</sup><br>(0.0493)</td><td><strong>-0.0843</strong><sup>*</sup><br>(0.0492)</td><td><strong>-0.0992</strong><sup>**</sup><br>(0.0484)</td></tr><tr><td>HWI (ref: 3rd tercile)</td><td></td><td></td><td></td><td></td></tr><tr><td>1st tercile</td><td><strong>0.0165</strong><sup>***</sup><br>(0.0054)</td><td><strong>0.0261</strong><sup>***</sup><br>(0.0055)</td><td><strong>0.0243</strong><sup>***</sup><br>(0.0054)</td><td><strong>0.0280</strong><sup>***</sup><br>(0.0053)</td></tr><tr><td>2nd tercile</td><td><strong>0.0192</strong><sup>***</sup><br>(0.0044)</td><td><strong>0.0246</strong><sup>***</sup><br>(0.0044)</td><td><strong>0.0238</strong><sup>***</sup><br>(0.0044)</td><td><strong>0.0257</strong><sup>***</sup><br>(0.0044)</td></tr><tr><td>Negative income shock</td><td>0.0063<br>(0.0043)</td><td><strong>0.0104</strong><sup>**</sup><br>(0.0043)</td><td><strong>0.0104</strong><sup>**</sup><br>(0.0043)</td><td><strong>0.0108</strong><sup>**</sup><br>(0.0044)</td></tr><tr><td>Occupation (ref: worker)</td><td></td><td></td><td></td><td></td></tr><tr><td>Unemployed</td><td>0.0278<br>(0.0213)</td><td>0.0284<br>(0.0220)</td><td>0.0295<br>(0.0220)</td><td>0.0303<br>(0.0219)</td></tr><tr><td>Student</td><td>0.0069<br>(0.0215)</td><td>0.0025<br>(0.0223)</td><td>0.0037<br>(0.0224)</td><td>0.0038<br>(0.0223)</td></tr><tr><td>At home</td><td>-0.0034<br>(0.0209)</td><td>-0.0013<br>(0.0214)</td><td>-0.0008<br>(0.0215)</td><td>-0.0003<br>(0.0214)</td></tr><tr><td>Retired/disabled</td><td>0.0099<br>(0.0218)</td><td>0.0067<br>(0.0225)</td><td>0.0075<br>(0.0226)</td><td>0.0093<br>(0.0226)</td></tr><tr><td>Employment (ref: public employee)</td><td></td><td></td><td></td><td></td></tr><tr><td>Private employee</td><td><strong>-0.0269</strong><sup>***</sup><br>(0.0088)</td><td><strong>-0.0201</strong><sup>**</sup><br>(0.0087)</td><td><strong>-0.0191</strong><sup>**</sup><br>(0.0087)</td><td><strong>-0.0210</strong><sup>**</sup><br>(0.0084)</td></tr><tr><td>Employer</td><td><strong>-0.0669</strong><sup>***</sup><br>(0.0150)</td><td><strong>-0.0601</strong><sup>***</sup><br>(0.0152)</td><td><strong>-0.0595</strong><sup>***</sup><br>(0.0152)</td><td><strong>-0.0617</strong><sup>***</sup><br>(0.0150)</td></tr><tr><td>Self-employed</td><td><strong>-0.0281</strong><sup>***</sup><br>(0.0089)</td><td><strong>-0.0226</strong><sup>***</sup><br>(0.0087)</td><td><strong>-0.0221</strong><sup>**</sup><br>(0.0087)</td><td><strong>-0.0234</strong><sup>***</sup><br>(0.0085)</td></tr><tr><td>Other inactives/Unpaid Worker</td><td><strong>-0.0392</strong><sup>*</sup><br>(0.0217)</td><td>-0.0318<br>(0.0224)</td><td>-0.0319<br>(0.0224)</td><td>-0.0337<br>(0.0222)</td></tr><tr><td>Education (ref: primary incomplete)</td><td></td><td></td><td></td><td></td></tr><tr><td>Primary complete</td><td><strong>0.0245</strong><sup>***</sup><br>(0.0067)</td><td><strong>0.0207</strong><sup>***</sup><br>(0.0068)</td><td><strong>0.0211</strong><sup>***</sup><br>(0.0068)</td><td><strong>0.0189</strong><sup>***</sup><br>(0.0067)</td></tr><tr><td>Secondary incomplete</td><td><strong>0.0401</strong><sup>***</sup><br>(0.0062)</td><td><strong>0.0287</strong><sup>***</sup><br>(0.0063)</td><td><strong>0.0297</strong><sup>***</sup><br>(0.0063)</td><td><strong>0.0279</strong><sup>***</sup><br>(0.0061)</td></tr><tr><td>Secondary complete</td><td><strong>0.0354</strong><sup>***</sup><br>(0.0062)</td><td><strong>0.0191</strong><sup>***</sup><br>(0.0063)</td><td><strong>0.0202</strong><sup>***</sup><br>(0.0063)</td><td><strong>0.0183</strong><sup>***</sup><br>(0.0061)</td></tr></tbody></table>
<table><tbody><tr><td>Post-secondary</td><td>(0.0063)</td><td>(0.0065)</td><td>(0.0065)</td><td>(0.0063)</td></tr><tr><td></td><td><strong>0.0468</strong><sup>***</sup></td><td><strong>0.0148</strong><sup>*</sup></td><td><strong>0.0159</strong><sup>**</sup></td><td><strong>0.0132</strong><sup>*</sup></td></tr><tr><td></td><td>(0.0076)</td><td>(0.0077)</td><td>(0.0077)</td><td>(0.0074)</td></tr><tr><td colspan="5"><em>Experience and perception controls</em></td></tr><tr><td>Feeling of insecurity (ref: weak)</td><td></td><td>-0.0052<br>(0.0077)</td><td>-0.0040<br>(0.0076)</td><td>-0.0053<br>(0.0075)</td></tr><tr><td>Inter-personal trust (ref: high)</td><td></td><td><strong>-0.0639</strong><sup>***</sup><br>(0.0063)</td><td><strong>-0.0629</strong><sup>***</sup><br>(0.0062)</td><td><strong>-0.0591</strong><sup>***</sup><br>(0.0061)</td></tr><tr><td>Political knowledge (ref: low)</td><td></td><td><strong>0.1774</strong><sup>***</sup><br>(0.0068)</td><td><strong>0.1775</strong><sup>***</sup><br>(0.0068)</td><td><strong>0.1756</strong><sup>***</sup><br>(0.0067)</td></tr><tr><td>Participation in a protest</td><td></td><td><strong>0.0450</strong><sup>***</sup><br>(0.0073)</td><td><strong>0.0451</strong><sup>***</sup><br>(0.0073)</td><td><strong>0.0426</strong><sup>***</sup><br>(0.0073)</td></tr><tr><td>Witness to corruption</td><td></td><td><strong>-0.0235</strong><sup>***</sup><br>(0.0084)</td><td><strong>-0.0231</strong><sup>***</sup><br>(0.0084)</td><td><strong>-0.0210</strong><sup>**</sup><br>(0.0083)</td></tr><tr><td>Religious ceremonies (ref: none)</td><td></td><td><strong>0.0090</strong><sup>*</sup><br>(0.0047)</td><td><strong>0.0089</strong><sup>*</sup><br>(0.0047)</td><td><strong>0.0086</strong><sup>*</sup><br>(0.0047)</td></tr><tr><td>Observations</td><td>95,782</td><td>90,791</td><td>90,791</td><td>90,791</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.033</td><td>0.045</td><td>0.045</td><td>0.050</td></tr><tr><td>Experience and perception controls</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Fixed effects</td><td>Country &amp; Time</td><td>Country &amp; Time</td><td>Country &amp; Time</td><td>Region &amp; Time</td></tr></tbody></table>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: Coefficients are derived from a linear regression model. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. Ref corresponds to omitted reference category. Time and country (or region) fixed effects are included but not reported. Values in bold correspond to coefficients significantly different from 0. Weights provided by LAPOP are used for cross-country comparisons. Clustered standard errors are in brackets (by city). *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>**Table S3:** Periphery and strong support for redistribution (2012-2018) – NTL radius</caption><thead><tr><th rowspan="2">Variables</th><th colspan="4">Strong support for redistribution<br>Radius NTL</th></tr><tr><th>500</th><th>1000</th><th>1500</th><th>2000</th></tr></thead><tbody><tr><td><em>Periphery and CRD</em></td><td></td><td></td><td></td><td></td></tr><tr><td>Poorer metropolises</td><td>-0.0108<br>(0.0105)</td><td>-0.0030<br>(0.0110)</td><td>0.0102<br>(0.0100)</td><td><strong>0.0186</strong><sup>*</sup><br>(0.0102)</td></tr><tr><td>Richer secondary cities</td><td><strong>0.0186</strong><sup>*</sup><br>(0.0110)</td><td><strong>0.0200</strong><sup>*</sup><br>(0.0110)</td><td><strong>0.0210</strong><sup>*</sup><br>(0.0124)</td><td>0.0165<br>(0.0121)</td></tr><tr><td>Poorer secondary cities</td><td>-0.0008<br>(0.0117)</td><td>0.0032<br>(0.0123)</td><td>0.0111<br>(0.0124)</td><td><strong>0.0225</strong><sup>*</sup><br>(0.0125)</td></tr><tr><td>Richer small cities or villages</td><td><strong>0.0192</strong><sup>**</sup><br>(0.0085)</td><td><strong>0.0236</strong><sup>***</sup><br>(0.0085)</td><td><strong>0.0285</strong><sup>***</sup><br>(0.0096)</td><td><strong>0.0296</strong><sup>***</sup><br>(0.0096)</td></tr><tr><td>Poorer small cities or villages</td><td><strong>0.0250</strong><sup>***</sup><br>(0.0091)</td><td><strong>0.0256</strong><sup>***</sup><br>(0.0091)</td><td><strong>0.0285</strong><sup>***</sup><br>(0.0100)</td><td><strong>0.0335</strong><sup>***</sup><br>(0.0100)</td></tr><tr><td>Observations</td><td>90,791</td><td>90,791</td><td>90,791</td><td>90,791</td></tr><tr><td>Adjusted R<sup>2</sup></td><td>0.050</td><td>0.050</td><td>0.050</td><td>0.050</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Fixed effects</td><td>Region & Time</td><td>Region & Time</td><td>Region & Time</td><td>Region & Time</td></tr></tbody></table>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: Coefficients are derived from a linear regression model. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. Ref corresponds to omitted reference category. Time and region fixed effects are included but not reported. Values in bold correspond to coefficients significantly different from 0. Weights provided by LAPOP are used for cross-country comparisons. Clustered standard errors are in brackets (by city). *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>**Table S4:** Periphery and support for redistribution (2012-2018) – Ordered Probit model</caption><thead><tr><th>Variables</th><th>(1)</th><th>(2)</th><th>(3)</th><th colspan="2">Support for redistribution</th><th>(6)</th><th>(7)</th></tr><tr><th></th><th></th><th></th><th></th><th>(4)</th><th>(5)</th><th></th><th></th></tr></thead><tbody><tr><td colspan="8"><strong>Panel A: Binary Periphery</strong></td></tr><tr><td>Periphery (ref: metropolises)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Periphery (ref: metropolises)</td><td>-0.0026*</td><td>-0.0016*</td><td>-0.0024*</td><td>-0.0030*</td><td>-0.0020*</td><td>0.0000</td><td><strong>0.0116*</strong></td></tr><tr><td></td><td>(0.0015)</td><td>(0.0009)</td><td>(0.0013)</td><td>(0.0017)</td><td>(0.0011)</td><td>(0.0001)</td><td>(0.0065)</td></tr><tr><td colspan="8"><strong>Panel B: Categorial Periphery</strong></td></tr><tr><td>Periphery (ref: metropolises)</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Secondary city</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Secondary city</td><td>-0.0003</td><td>-0.0002</td><td>-0.0003</td><td>-0.0003</td><td>-0.0002</td><td>0.0000</td><td>0.0013</td></tr><tr><td></td><td>(0.0020)</td><td>(0.0012)</td><td>(0.0018)</td><td>(0.0023)</td><td>(0.0015)</td><td>(0.0001)</td><td>(0.0087)</td></tr><tr><td>Small cities or villages</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Small cities or villages</td><td>-0.0037***</td><td>-0.0022**</td><td>-0.0033**</td><td>-0.0043***</td><td>-0.0029***</td><td>0.0000</td><td><strong>0.0164***</strong></td></tr><tr><td></td><td>(0.0015)</td><td>(0.0009)</td><td>(0.0014)</td><td>(0.0017)</td><td>(0.0011)</td><td>(0.0001)</td><td>(0.0065)</td></tr><tr><td colspan="8"><strong>Observations</strong></td></tr><tr><td>Controls</td><td>90,791</td><td>90,791</td><td>90,791</td><td>90,791</td><td>90,791</td><td>90,791</td><td>90,192</td></tr><tr><td>Fixed effects</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td></td><td>Country & Time</td><td>Country & Time</td><td>Country & Time</td><td>Country & Time</td><td>Country & Time</td><td>Country & Time</td><td>Country & Time</td></tr></tbody></table>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Mexico, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: All reported coefficients are marginal effects from an ordered probit model. The dependent variable measures citizens' demand for redistribution to reduce inequality ranging from 1 (minimum) to 7 (maximum). Ref corresponds to omitted reference category. Country and time fixed effects are included but not reported. Values in bold correspond to coefficients significantly different from 0. Weights provided by LAPOP are used for cross-country comparisons. Clustered standard errors are in brackets (by city). *** p<0.01, ** p<0.05, * p<0.1.
<table><caption>**Table S5:** Periphery by CRD and strong support for redistribution (2012-2018)</caption><thead><tr><th rowspan="2">Variables</th><th colspan="2">Support for redistribution</th></tr><tr><th>(1)</th><th>(2)</th></tr></thead><tbody><tr><td colspan="3"><strong>Periphery x CRD (ref: richer metropolises)</strong></td></tr><tr><td>Poorer metropolises</td><td>0.0138<br>(0.0136)</td><td><strong>0.0186*</strong><br>(0.0102)</td></tr><tr><td>Richer secondary cities</td><td>0.0061<br>(0.0114)</td><td>0.0165<br>(0.0121)</td></tr><tr><td>Poorer secondary cities</td><td>0.0204<br>(0.0152)</td><td><strong>0.0225*</strong><br>(0.0125)</td></tr><tr><td>Richer small cities and villages</td><td><strong>0.0294***</strong><br>(0.0090)</td><td><strong>0.0296***</strong><br>(0.0096)</td></tr><tr><td>Poorer small cities and villages</td><td><strong>0.0321***</strong><br>(0.0096)</td><td><strong>0.0335***</strong><br>(0.0099)</td></tr><tr><td colspan="3"><strong>Periphery x CRD (ref: richer primary cities)</strong></td></tr><tr><td>Poorer primary cities</td><td>0.0190<br>(0.0171)</td><td>0.0090<br>(0.0148)</td></tr><tr><td>Richer suburbs</td><td>0.0192<br>(0.0173)</td><td>0.0158<br>(0.0124)</td></tr><tr><td>Poorer suburbs</td><td><strong>0.0324**</strong><br>(0.0140)</td><td><strong>0.0270**</strong><br>(0.0129)</td></tr><tr><td>Richer secondary cities</td><td>0.0140<br>(0.0121)</td><td><strong>0.0203*</strong><br>(0.0122)</td></tr><tr><td>Poorer secondary cities</td><td><strong>0.0282*</strong><br>(0.0160)</td><td><strong>0.0260**</strong><br>(0.0126)</td></tr><tr><td>Richer small cities and villages</td><td><strong>0.0376***</strong><br>(0.0102)</td><td><strong>0.0336***</strong><br>(0.0101)</td></tr><tr><td>Poorer small cities and villages</td><td><strong>0.0405***</strong><br>(0.0109)</td><td><strong>0.0374***</strong><br>(0.0103)</td></tr><tr><td>Observations</td><td>90,791</td><td>90,791</td></tr><tr><td>Controls</td><td>Yes</td><td>Yes</td></tr><tr><td>Fixed effects</td><td>Country &amp; Time</td><td>Region &amp; Time</td></tr></tbody></table>

Source: Author's elaboration based on the LAPOP database (2012-2018) for 16 countries (Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay, Peru, Uruguay).

Note: CRD = City Relative Development. Coefficients are derived from a linear regression model. The dependent variable is a binary measure indicating a strong demand for redistribution from government to reduce inequality. Ref corresponds to omitted reference category. Time and country (or region) fixed effects are included but not reported. Values in bold correspond to coefficients significantly different from 0. Weights provided by LAPOP are used for cross-country comparisons. Clustered standard errors are in brackets (by city). *** p<0.01, ** p<0.05, * p<0.1.