Redistributing Loyalty? Political Determinants of Municipal Funding in Chile (2009–2023)

Redistributing Loyalty? 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Political Determinants of Municipal Funding in Chile (2009–2023) Ignacio Cienfuegos, Luis Garrido-Vergara, Cristóbal Cabezas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6865910/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Transparent and objective criteria for the distribution of resources from central government to local government are essential to mitigate territorial inequality. On the contrary, discretionary power can deepen inequalities and promote corruption. This study examines the influence of political factors on the distribution of government resources to municipalities in Chile during 2009 and 2023. Using panel models with fixed effects, robust standard errors and interactive terms, the analysis reveals that municipalities governed by mayors from the same political party as the president received on average significantly higher transfers during that period from central government. These findings also underline the role of electoral strategy, highlighting how central government resources were distributed to reinforce political alliances during local government elections. political budget cycles distributive politics local governments incumbency advantage Introduction In general terms, the explanations of the factors that would influence the allocation of public resources lie somewhere between government ideology (Alesina et al., 1989 ) and technical considerations derived from bureaucratic decision-making standards. However, in recent years, the academic literature on distributive politics has suggested the importance of electoral motives in allocating resources above ideological factors or technical issues (Albertus, 2019 ). The underlying hypothesis is that politicians are motivated by the desire to retain power and, as a result, policymakers allocate certain goods to specific groups at particular times in the electoral cycle (Golden & Min, 2013 ). These practices are constitutive elements of the distributive game in democracy since, on the one hand, politicians seek to remain in power, and on the other, the beneficiaries enjoy the favors at the expense of the inefficiencies it entails for society as a whole. Considered by Livert et al. ( 2023 ), these expenditure variations are closely related to electoral cycles, what the literature known as Political Budget Cycles (PBC), which generate expenditure variations either according to a pre-election strategy (Drazen & Eslava, 2010 ; Meloni, 2016 ; Veiga & Veiga, 2007 ) or strategies during the election year itself (Hanusch & Keefer, 2014 ; Pierskalla & Sacks, 2018 ; Stolfi & and Hallerberg, 2016) that generate increased public spending. In the case of Chile, there has been evidence of resource transfers from the central government to the local government, favoring political coalitions to gain an electoral advantage (Corvalan et al., 2018 ; Livert & and Gainza, 2018) benefiting, for example, local stakeholders from the same governing coalition (Gainza & Livert, 2021 ; Lara E. & Toro M., 2019 ). In recent years, there has been a proliferation of analyses that account for bias in the distribution of resources specifically towards municipalities (Corvalan et al., 2018 ; Livert & and Gainza, 2018; Veiga & Veiga, 2007 ). According to these works, rulers manipulate fiscal variables to obtain electoral advantages in the next election. In the case of Chile, there is evidence of politically biased transfers from the central to the local level (Corvalan et al., 2018 ; Lara E. & Toro M., 2019 ; Livert et al., 2022 ; Livert & and Gainza, 2018) in particular periods of the electoral cycle. Therefore, previous research relies heavily on or small sample size (Corvalan et al., 2018 ; Livert et al., 2022 ), which might justify a replication using a longitudinal study. The latter also raises an opportunity for a theoretical contribution, to identify in a more formal way, casual mechanisms that could give further explanation to the issue of political distribution, especially for developing countries. In our research we consider the current transfers from the Subsecretaría de Desarrollo Regional (SUBDERE) to the Chilean municipalities from 2009 to 2023. This is an appropriate expenditure category to analyze for several reasons. On the one hand, it is a public good that is territorially excludable, i.e., it can be used to benefit one municipality(ies) and exclude another(s). This allows politicians to select jurisdictions to provide benefits based on political criteria, such as, for example, the partisan affiliation of local officials (Diaz-Cayeros et al., 2016 ). On the other hand, this is a fund to support decentralization, which helps us to understand what interests the allocating agency pursues in the spatial distribution of collective goods. These central transfers are relevant for Chilean municipalities since they are highly dependent on this type of income. In Chile, transfers from the central level to municipalities represent 51.1%, of local government income, while the average among OECD countries is 38% (OECD, 2019 ). While current transfers constitute the dependent variable, electoral information is our primary independent variable. We use municipal outcomes to estimate whether mayors of the same party as the President systematically benefited and to understand whether the political inertia follows a top-down or bottom-up logic. Our paper presents two main contributions to literature. First, we used panel data and multiple linear regression models to study the distribution of transfers during 2009–2023 which shows a methodological improvement from previous research. Second, we show how the distribution of the current expenditure transfer was used to benefit the municipalities where the mayor and the President are from the same political party. Additionally, we identify that in the case of one of the programs studied, the lower the margin of victory, the higher the transfers were, and for the other program, the higher the margin, the more transfers, which would indicate a strategic behavior on the part of the central government in the distribution of public resources. This last result suggests that, despite being a decentralized fund, there was a top-down interaction between the interests of the central government and the mayors. Consequently, even though we rely on previous research considering discretionary transfers received by municipalities in Chile from the central government, advancing in the literature by extending the work developed by Corvalán et al., (2018); Livert et al. ( 2022 ) and Livert et al. ( 2023 ), we have considered in our research, a longitudinal study with a large panel data and causal mechanisms, methodological approach that was absent in the existent literature, which provided a strong argument to justify the replications of a similar perspective for the case of Chile. We believe that our research and findings advance our empirical knowledge of Latin American political distribution literature, highlighting the political bias in the distribution of resources transferred from the central government during 2009–2023. These results can also set a comparative benchmark for studying other regional countries and their potential opportunistic practices by the central government in order to improve the options of candidates related to the party or coalition in the next election. While several studies have examined discretionary transfers to municipalities in Chile (Corvalán et al., 2018; Livert & Gainza, 2018 ; Gainza & Livert, 2021 ), our work contributes to this literature in at least three novel ways. First, we employ an extended longitudinal panel (2009–2023), which captures multiple full presidential terms and offers greater leverage for identifying political cycles and enduring partisan effects across different governing coalitions. Second, we incorporate interactive terms and third-degree polynomial trends to account for complex temporal dynamics, an approach largely absent in prior studies. Third, and most importantly, we refine the theoretical framework by explicitly modeling how discretionary transfers respond not only to mayoral alignment but also to electoral competitiveness, municipal fiscal autonomy, and socioeconomic conditions. This allows us to uncover conditional effects that prior literature has not systematically tested. As such, our findings do not merely replicate earlier results, but offer deeper insights into the causal mechanisms that shape partisan allocation patterns, providing new empirical and theoretical value to the field of distributive politics in Latin America. The rest of the paper follows. First, we review academic literature on political distribution and some insights about the Chilean context. Then, we present the data and the methodology on which the empirical analysis is based, while the fourth section details the results. The article ends with the main conclusions and a discussion of the implications for public policy and mechanisms to limit the margin of arbitrariness in the distribution of resources. Literature review Distributive policy Considering the discussion of intergovernmental relations, there are mechanisms through which the political system transfers resources are used to reduce the socio-territorial inequality gap between territories (Ruiz-Porras & García-Vázquez, 2014 ). In general, these transfer mechanisms, having different degrees of formality, conditionality, composition, and criteria, should be relatively institutionalized, leaving little room for discretion to the government authority. However, there are other resources in the hands of the national executive that may be distributed in a discretionary manner. Thus, there is a critical analysis of government resource usage and its impact on electoral outcomes (Corvalan et al., 2018 ; Livert & and Gainza, 2018). This perspective focuses on legislative elections (in this case, the objective is that the allocation of more resources in the commune has a positive impact on the future composition of the legislative branch) or on subnational levels (municipalities, districts, regions, provinces, states) to improve the reelection options of the incumbent authority (Livert et al., 2022 ). In this dimension, two main models explain the distribution of discretionary resources. On the one hand, there is the partisan model, in which the objective is to achieve a majority in the legislative branch, which in turn can have two logics: more resources in those districts where more voters are willing to change their preference in favor of the government or more transfer in the territories where the ruling party or coalition has more votes. On the other hand, there is the non-partisan model, where the interests of the territorial legislator guide the allocation of discretionary funds (Veiga & Veiga, 2007 ). The specialized literature calls distributive politics when public authorities confer geographically concentrated benefits for political purposes. At the same time, the costs of inefficiency in collection and allocation are spread among all voters (Weingast et al., 1981 ). This form of allocation could be considered as some degree of corruption on the part of the rulers, with essential costs for efficiency, equity, and democratic quality (Vergara-Garrido and Cienfuegos, 2025). Nonetheless Stokes et al. (2013) consider that a distinction should be made between distributive strategies depending on whether this is part of the political program of governments. The rules must be formalized and public in a programmatic distribution, while in a non-programmatic distribution, the criteria are not public. There is also evidence that Political factors and the institutional framework play a relevant role in the allocation of resources (Kroth, 2014), as they condition the political choices of the ruler. According to Alt and Rose ( 2009 ), two conditions are essential: the first refers to the incentives politicians have to stay in office, while the second condition is associated with the ability of ruling politicians to manipulate fiscal instruments (Livert et al., 2023 ). There are several ways in which politicians can try to influence voters' behavior. One is by manipulating fiscal variables throughout the legislature, known as Political Budget Cycles (PBC). The central assumption is that the electorate is myopic and will evaluate the government based on its most recent actions (Alesina et al., 1989 ). As a result, rulers have incentives to manipulate fiscal instruments as the race approaches. The analysis of cycles has more recently been transferred to the municipal level. Corvalán et al. (2018) consider that transfers mechanisms from central government to municipalities may be an indirect way to reinforce their position. By increasing the resources of aligned mayors, they can rally their support to mobilize the electorate in the next national election. For their part, Drazen and Eslava ( 2010 ) argue that certain expenditures can be targeted more effectively and, therefore, voters can more clearly reward whoever is responsible for them. Indeed, local services are apparent, while the evaluation of national public goods -defense, health, the legal system- becomes more blurred (Livert et al., 2022 ; Veiga & Veiga, 2007 ). According to Livert, et.al ( 2022 ), one of the criteria for increasing the chances of success at the ballot is by geographically distributing resources to areas with higher returns. This includes financial transfers from the central government to the municipal government and sometimes goods targeted to specific populations groups in a discretionary manner -e.g., jobs- or territories -e.g., equipment- (Brollo & Nannicini, 2012 ). One of the most critical questions is determining which strategy provides a more significant advantage, focusing on core voters (Cox & McCubbins, 1986 ) or targeting swing voters (Dixit & Londregan, 1996 ). The first approach argues that politicians transfer resources to their strong electorate, following a risk-averse strategy. On the other hand, the second approach argues that politicians distribute resources to both the traditional and the swing electorate to maintain and expand their support base. The difference between both explanations is based on the ability of the electorate to change their preferences, on competition, and on the willingness to change their vote (Livert & and Gainza, 2018). Political bias and transfers in Chile While Chile has always been considered one of the least corrupt countries in Latin America (Nord et al., 2024 ) would not be a corruption-free country (Lopez, 2019). Political corruption at the local level has particularly exploded during the last years, with a series of corruption scandals involving the embezzlement of public funds by local authorities. Three cases in particular—two large urban municipalities of the metropolitan region, Vitacura and Maipú, and the touristic middle size municipality of Viña del Mar, all of which surfaced in 2022–2023—have stirred public debate over the country’s problems with corruption at the local level (Garrido-Vergara and Cienfuegos, 2025). The debate on decentralization in Chile focuses on enhancing the competencies and resources of municipal and regional governments. Nonetheless and despite some institutional advancements, Chile remains one of the most centralized countries in South America and the least decentralized among OECD nations (OECD, 2014 ). Literature indicates a direct relationship between decentralization processes and territorial inequality. For instance, Giraudy and Pribble ( 2020 ) illustrate how the nature of the decentralizing coalition (top-down versus bottom-up) and the presence of mechanisms for coordinating fiscal management significantly influenced territorial inequality reduction in Brazil, led to a moderate decrease in Mexico, and resulted in a minimal decrease in Argentina. An index reflecting these dynamics shows a correlation with the participation of subnational governments in public spending in Chile. Recent data reveal that 18.7% of state spending is implemented through municipalities in Chile, with only 7% coming from expenditures funded by their own permanent revenues and unconditional transfers from the Municipal Common Fund (OECD, 2020). According to Pribble ( 2015 ), building effective local institutions requires economic resources. As the primary revenue sources for municipalities in Chile include property taxes (which are set and collected by the central government), vehicle and permit fees (mainly from commercial activities), and user fees for services like waste collection, it is likely that wealthier municipalities achieve more effective institutional outcomes and services compared to poorer ones. There is broad agreement on the assessment and proposals surrounding this issue, as well as a consensus on the need for resource transfers to be conducted transparently and equitably (Letelier Saavedra, 2002 ; Pineda et al., 2018 ). In Chile, the transfer system to municipalities has two key components: Common Municipal Fund (FCM): Funded by municipal contributions and the national budget. Transfers for Municipalized Primary Health and Education: Conditional funds based on public health system users or municipal school enrollment. Transparency in the FCM's distribution is generally acceptable, as well as transfer related to Municipalized Primary Health and Education, while concerns exist regarding the central government’s discretionary allocation of resources, raising corruption risks. As a consequence, both national and international institutions have raised the necessity to improve transparency and reduce discretion in these transfers from central government in Chile (OECD, 2017; Government of Chile, 2009). Critics point out that SUBDERE controls resources for discretionary allocations and specific projects like the Urban Improvement Program (PMU) and Neighborhood Improvement Program (PMB). Although funding for these programs has increased, there are no clear guidelines and formal rules for allocation. PMUs focus on minor urban projects, while PMBs target sanitation in rural areas. SUBDERE's funding can significantly impact municipal budgets, averaging around 7.7% of municipal income (Livert et al., 2022 ). Previous studies have suggested that such discretionary funds improve the reelection chances of incumbent mayors, though evidence varies across different resources. Cuevas (2012) highlighted those incumbents had a 37–42% higher probability of winning in closely contested municipalities, where the central government allocated more funds to its coalition. These findings are supported by recent research (Corvalan et al., 2018 ; Livert et al., 2022 , 2023 ), emphasizing that municipal spending significantly impacts mayors' reelection chances. This highlights the importance of central government transfers in electoral success (Núñez, 2007, cited in Acuña et al., 2017, p. 46). However, as we have mentioned, previous studies have focused on a limited time frame of analysis and did not use causal mechanisms in their research. Methodology Variables, data and hypothesis The dependent variables are the logarithms of per capita transfers from two discretionary programs: the Urban Improvement Program (PMU) and the Neighborhood Improvement Program (PMB) (see Appendix, Table 1 ). These transfers are vertical and discretionary; they originate from the Ministry of the Interior and Public Safety and are allocated directly to municipalities without a predetermined formula or conditionality. The independent variables are categorized into two dimensions: political and control variables. The political variables include (1) the party alignment between the mayor and the President -a binary variable coded 1 when the mayor belongs to the same political party as the President of the Republic, and 0 otherwise- and (2) the electoral victory margin, measured as the percentage point-difference in votes between the elected mayor and the runner-up in the most recent municipal election. The first variable has been used in similar studies, both in the context of Chile and across Latin America (Gainza & Livert, 2021 ; Livert et al., 2022 ; Livert & and Gainza, 2018), whereas the latter serves as a proxy for electoral competitiveness. A positive relationship between higher transfers and a higher margin of victory would suggest that the allocations of resources benefit core voters, while a negative one could indicate strategic allocations to competitive (swing) municipalities. Control variables include key municipal financial indicators -such as the logarithm of per capita revenues from the Municipal Common Fund (FCM) and the logarithm of per capita autonomous permanent own revenue- and variables that account for structural differences and socioeconomic need across municipalities -poverty and population density-. In the case of the FCM, it indicates the level of vulnerability of the municipalities; since it is a horizontal transfer based on a formula related to local vulnerability, the greater the dependence on the FCM, the greater the vulnerability of the municipality. Accordingly, and aiming at covering the gaps left on previous research we have considered the following hypothesis: H1: After controlling time-invariant municipal characteristics, the allocation of per capita transfers from the central government to municipalities between 2009 and 2023 was influenced by discretionary partisan criteria. H2: Holding municipal fixed effects constant, mayors from the president's political party received, on average, higher per capita transfers from the central government than opposition mayors between 2009 and 2023. H3: Conditional on time-invariant municipal characteristics, municipalities where the mayor won by a narrow margin received higher per capita transfers from the central government compared to municipalities with less competitive elections between 2009 and 2023. Table 1 Descriptive statistics of the variables Variable Observations Minimum Maximum Median Mean Standard Deviation PMB Revenue (M $ ) 5175 0.00 5,242,589.00 67,691.00 179,215.42 324,293.69 PMB Revenue pc (log) 5175 0.00 7.75 1.32 1.58 1.48 PMU Revenue (M $ ) 5175 0.00 7,250,083.00 198,416.00 317,520.59 532,080.65 PMU Revenue pc (log) 5175 0.00 7.51 2.24 2.29 1.25 FCM Revenue (M $ ) 5149 720,414.00 82,467,207.00 2,796,114.00 4,795,329.03 6,274,788.70 FCM Revenue pc (log) 5149 1.37 9.22 5.00 5.05 0.96 Population Density (hab/km2) 5025 0.01 24,367.68 30.27 951.00 2,935.67 Poverty (%) 5165 0.00 59.74 14.02 15.39 8.53 Autonomous permanent own revenue pc (M $ ) 4086 7.00 5350.00 73.00 135.56 278.60 Autonomous permanent own revenue pc (log) 4086 2.08 8.59 4.30 4.42 0.84 Electoral Victory Margin (%) 5175 -4.79 88.65 16.55 21.16 16.92 President's Party (dummy) 5175 0.00 1.00 0.00 0.11 0.31 Note pc = per capita; log = logarithmic transformation. Source: Own elaboration Table 1 shows the descriptive analysis of the data considered in this article. The dataset consists of 5,175 observations for most variables, with some minor variations in specific cases. In the case of the dependent variables related to the municipal revenues, the PMB revenue (M $ ) ranges from 0 to 5,242,589, with a mean of 179,215.42 and a high standard deviation (324,293.69), indicating considerable variation across municipalities. On the other hand, the PMU revenue (M $ ) has a broader range (0 to 7,250,083), with a mean of 317,520.59, suggesting a more substantial allocation compared to PMB. The FCM revenue (M $ ) shows extreme variability, ranging from 720,414 to 82,467,207, with a very high mean (4,795,329.03) and standard deviation (6,274,788.70), indicating substantial differences among municipalities. Finally, the per capita measures (log-transformed) exhibit lower variability, with means between 1.32 and 5.05, showing the normalization effect of log transformation. Regarding socioeconomic and political characteristics, the population density (hab/km²) is extremely skewed, ranging from 0.01 to 24,367.68, with a median of 30.27, but a mean of 951.00, suggesting that a few municipalities have very high density. Poverty (%) varies from 0 to 59.74%, with an average of 15.39%, indicating significant socioeconomic disparities across municipalities. Electoral victory margin (%) ranges from − 4.79–88.65%, with a median of 16.55%, reflecting diverse electoral competitiveness. Finally, concerning partisan affiliation of mayors, the mean value of 0.11 for president’s party suggests that only a small fraction of municipalities have mayors from the president’s party in a given year. The descriptive analysis shows that, concerning observations and data distribution, most financial variables exhibit high standard deviations, indicating significant disparities in revenue distribution across municipalities. Political and socioeconomic variables show a wide range, suggesting heterogeneity in competitiveness, poverty, and population density. Analysis and results Statistical analysis was based on data collected for the 345 communes of Chile between 2009 and 2023. The research employed panel models with fixed effects to evaluate the relationship between political and socioeconomic variables in the distribution of municipal transfers. The data were obtained from official sources such as the National Municipal Information System (SINIM), Electoral Service (SERVEL) and Ministry of Health (MINSAL). Fixed-effects models were employed using the plm package in R. Panel data combines cross-sectional and temporal dimensions, allowing the analysis of units over time (Baltagi, 2021 ; Hsiao, 2014 ; Wooldridge, 2010 ). This structure helps control unobserved heterogeneity and improves the accuracy of econometric estimates. The fixed effects (FE) model is a technique for estimating relationships in panel data when there is a suspicion that unobserved factors may be correlated with the explanatory variables. We acknowledge that fixed effects models (FEM), while widely used to control for unobserved time-invariant heterogeneity, do not address all possible sources of endogeneity—particularly those stemming from time-varying omitted variables or simultaneity. Our use of FEM follows well-established practices in the analysis of panel data in distributive politics, especially when data availability limits the use of stronger causal designs. To further mitigate potential bias, we included year fixed effects and polynomial time trends to control for unobserved national-level shocks and nonlinear dynamics. While we interpret our findings with appropriate caution, we believe our model specification offers a robust foundation for identifying systematic associations between political alignment and the allocation of discretionary transfers. The general structure of the estimated fixed-effects model is: \(\:{Y}_{it}\) = \(\:{{\beta\:}}_{0}\) + \(\:{{\beta\:}}_{1}{\text{X}}_{it}\) ​+ \(\:{{\beta\:}}_{2}{\text{Z}}_{it}\) + \(\:{{\alpha\:}}_{i}\) ​+ \(\:{{\gamma\:}}_{t}\) + \(\:{\text{ϵ}}_{it}\) where: \(\:{Y}_{it}\) ​ = log of either per capita PMU or PMB transfers for commune i in year t . \(\:{\text{X}}_{it}\) = Vector of explanatory variables, including: \(\:{presiden{t}^{{\prime\:}}s\:party}_{it}\) : dummy (0,1) for mayor aligned with the president’s party. \(\:{victory\:margin}_{it}\) : margin of victory (%) between the elected mayor and the second majority for the given municipal election. \(\:{poverty}_{it}\) : poverty percentage (%). \(\:{pop\:density}_{it}\) : population density \(\:\left(\frac{hab}{{km}^{2}}\right).\) \(\:pc\:{FCM\:revenue\:\left(log\right)}_{it}\) : Log of per capita municipal revenue from the common fund. \(\:{pc\:autonomous\:permanent\:own\:revenue\:\left(log\right)}_{it}\) : Log of per capita autonomous permanent own revenues. \(\:poly\left(year,3\right)\) : polynomial trend to capture non-linearity in time trends. \(\:{{\alpha\:}}_{i}\) = Commune-specific fixed effect (time-invariant factors). \(\:{{\gamma\:}}_{t}\) = Time fixed effect (to control for period shocks). \(\:{\text{ϵ}}_{it}\) = Error term, Equation for PMU Transfers (log scale): \(\:{log(pc\:PMU\:transfer}_{it}+1)\) = \(\:{\beta\:}_{0}\) \(\:+\) \(\:{\beta\:}_{1}{presiden{t}^{{\prime\:}}s\:party}_{it}\) ​ \(\:+\) \(\:{\beta\:}_{2}{victory\:margin}_{it}\) \(\:+\) \(\:{\beta\:}_{3}{poverty}_{it}\) \(\:+\) \(\:{\beta\:}_{4}{pop\:density}_{it}+{\beta\:}_{5}{log(pc\:FCM\:revenue}_{it})+{\beta\:}_{6}{log\:(pc\:autonomous\:permanent\:own\:revenue}_{it})+{\sum\:}_{k=1}^{3}{\beta\:}_{7k}{\left(year\right)}^{k}{\:+\:\alpha\:}_{i}\) ​+ \(\:{\gamma\:}_{t}\) + \(\:{ϵ}_{it}\) Equation for PMB Transfers (log scale): \(\:{log(pc\:PMB\:transfer}_{it}+1)\) = \(\:{\beta\:}_{0}\) \(\:+\) \(\:{\beta\:}_{1}{presiden{t}^{{\prime\:}}s\:party}_{it}\) ​ \(\:+\) \(\:{\beta\:}_{2}{victory\_margin}_{it}\) \(\:+\) \(\:{\beta\:}_{3}{poverty}_{it}\) \(\:+\) \(\:{\beta\:}_{4}{pop\:density}_{it}+{\beta\:}_{5}{log(pc\:FCM\:revenue}_{it})+{\beta\:}_{6}{log(pc\:autonomous\:permanent\:own\:revenue}_{it})+{\sum\:}_{k=1}^{3}{\beta\:}_{7k}{\left(year\right)}^{k}{\:+\:\alpha\:}_{i}\) ​+ \(\:{\gamma\:}_{t}\) + \(\:{ϵ}_{it}\) Since in panel data trends in the dependent variable (log of per capita transfers) can be non-linear, a linear or quadratic trend might not fully capture fluctuations over time, especially if there are periods of increase, decline, and recovery. Considering this fact, a third-degree polynomial (cubic polynomial) allows for three inflection points, making it more flexible in capturing different phases of variation over time. Tests were conducted to ensure the validity of the models. Concerning autocorrelation, the Wooldridge test is used, indicating the need for robust errors; the Breusch-Pagan test is applied, revealing heteroskedasticity; considering cross-sectional dependence, the Pearson test suggests correlation between communes. These tests justify the use of robust standard errors for model adjustments, applying the Driscoll-Kraay type. The final model shows that the political alignment of the mayor with the president significantly influences the distribution of resources (see also Appendix, Fig. 1 ). The inclusion of polynomial trends improves the model fit, and robust errors ensure the statistical validity of the results. Interaction Effects were also considered: The interaction between president’s party and victory margin : This tested whether political alignment influences the effect of electoral competitiveness on transfers. The interaction between president’s party and poverty : This tested whether the effect of political alignment on transfers depends on municipal poverty levels. The interaction between president’s party and pc permanent autonomous own income : This tested whether municipalities with more own revenue receive different levels of transfers based on political alignment. Equation for PMU Transfers (log scale) with interactions: \(\:{log(pc\:PMU\:transfer}_{it}+1)\) = \(\:{\beta\:}_{0}\) \(\:+\) \(\:{\beta\:}_{1}{presiden{t}^{{\prime\:}}s\:party}_{it}\) ​ \(\:+\) \(\:{\beta\:}_{2}{victory\:margin}_{it}\) \(\:+\) \(\:{\beta\:}_{3}{poverty}_{it}\) \(\:+\) \(\:{\beta\:}_{4}{pop\:density}_{it}+{\beta\:}_{5}{log(pc\:FCM\:revenue}_{it})+{\beta\:}_{6}{log(pc\:permanent\:autonomous\:own\:revenue}_{it})+\:{\beta\:}_{7}{(presiden{t}^{{\prime\:}}s\:party}_{it}*{victory\:margin}_{it})+{\beta\:}_{8}{(presiden{t}^{{\prime\:}}s\:party}_{it}*{poverty}_{it})\:\:+{\beta\:}_{9}{(presiden{t}^{{\prime\:}}s\:party}_{it}*{log(pc\:permanent\:autonomous\:own\:revenue}_{it}\left)\right)+{\sum\:}_{k=1}^{3}{\beta\:}_{10k}{\left(year\right)}^{k}{\:+\:\alpha\:}_{i}\) ​+ \(\:{\gamma\:}_{t}\) + \(\:{ϵ}_{it}\) Equation for PMB Transfers (log scale) with interactions: \(\:{log(pc\:PMB\:transfer}_{it}+1)\) = \(\:{\beta\:}_{0}\) \(\:+\) \(\:{\beta\:}_{1}{presiden{t}^{{\prime\:}}s\:party}_{it}\) ​ \(\:+\) \(\:{\beta\:}_{2}{victory\:margin}_{it}\) \(\:+\) \(\:{\beta\:}_{3}{poverty}_{it}\) \(\:+\) \(\:{\beta\:}_{4}{pop\:density}_{it}+{\beta\:}_{5}{log(pc\:FCM\:revenue}_{it})+{\beta\:}_{6}{log(pc\:permnent\:autonomous\:own\:revenue}_{it})+\:{\beta\:}_{7}{(presiden{t}^{{\prime\:}}s\:party}_{it}*{victory\:margin}_{it})+{\beta\:}_{8}{(presiden{t}^{{\prime\:}}s\:party}_{it}*{poverty}_{it})\:\:+{\beta\:}_{9}{(presiden{t}^{{\prime\:}}s\:party}_{it}*{log(pc\:permanent\:autonomous\:own\:revenue}_{it}\left)\right)+{\sum\:}_{k=1}^{3}{\beta\:}_{10k}{\left(year\right)}^{k}{\:+\:\alpha\:}_{i}\) ​+ \(\:{\gamma\:}_{t}\) + \(\:{ϵ}_{it}\) Table 2 Fixed effects models Variable PMU pc (log) PMB pc (log) PMU pc (log) PMB pc (log) President’s Party 0.176** (0.064) 0.271** (0.105) 0.904*** (0.197) 0.196 (0.553) Victory Margin -0.001* (0.001) 0.005** (0.002) -0.002* (0.001) 0.004* (0.002) Poverty -0.011 (0.007) 0.005 (0.004) -0.011 (0.008) 0.004 (0.004) Population Density 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) 0.000 (0.000) pc FCM Revenue (log) 0.202+ (0.111) -0.260** (0.098) 0.190+ (0.113) -0.262** (0.098) pc Permanent Autonomous Own Income (log) 0.307* (0.120) 0.458*** (0.139) 0.327** (0.114) 0.462** (0.151) \(\:{\varvec{P}\varvec{o}\varvec{l}\varvec{y}(\varvec{y}\varvec{e}\varvec{a}\varvec{r},\:3)}^{1}\) -11.592** (3.912) 16.606*** (4.906) -11.523** (3.895) 16.620*** (4.880) \(\:{\varvec{P}\varvec{o}\varvec{l}\varvec{y}(\varvec{y}\varvec{e}\varvec{a}\varvec{r},\:3)}^{2}\) 13.930*** (2.961) 14.473** (4.725) 13.927*** (3.011) 14.381** (4.710) \(\:{\varvec{P}\varvec{o}\varvec{l}\varvec{y}(\varvec{y}\varvec{e}\varvec{a}\varvec{r},\:3)}^{3}\) 10.768* (4.284) -6.567 (5.365) 10.741* (4.259) -6.640 (5.391) President’s Party x Poverty - - -0.005 (0.006) 0.012 (0.011) President’s Party x Victory Margin - - 0.002 (0.002) 0.004 (0.003) President’s Party × pc Autonomous Own Income (log) - - -0.154*** (0.042) -0.042 (0.122) Num. Obs. 4662 4662 4662 4662 AIC 20081.0 22582.7 20068.1 22572.6 BIC 47920.0 50421.7 47887.8 50392.3 + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001 Note pc = per capita; log = logarithmic transformation. Source: Own elaboration The fixed effects models evaluate the three hypotheses regarding the allocation of per capita transfers from the central government to municipalities between 2009 and 2023 to PMU and PMB. The hypotheses consider partisan and electoral criteria in the distribution of resources (H1, H2, and H3). To test these hypotheses, municipal-level fixed effects models were analyzed with interactive terms and additional controls. The estimated models included two dependent variables: PMU pc (log) and PMB pc (log), representing different categories of per capita transfers (see also Appendix, Figs. 2 and 3 ). Basic models and models with interactions between the presidential party and other explanatory variables were considered. To evaluate H1 (partisan influence on transfer allocation), the variable president’s party , was observed, which indicates whether the mayor belongs to the president's party. In the basic model, president’s party has a positive and significant coefficient in PMU (0.176, p < 0.01) and PMB (0.271, p < 0.01), indicating that municipalities with mayors from the president’s party receive more transfers. In the interactive model, the coefficient in PMU increases significantly (0.904, p 0.1). These results support H1, confirming the existence of partisan discretionary allocation of resources. To evaluate H2 (differences in transfers based on party affiliation, which proposes that ruling party mayors receive more transfers than opposition mayors), the results in the basic models show a significant and positive coefficient for president’s party , supporting the idea that ruling party mayors receive more transfers. The interactive models reveal that while this effect remains highly significant in PMU (coefficient 0.904, p < 0.001), it becomes non-significant in PMB. This suggests that the partisan advantage in transfers may be stronger in certain types of funding sources but not in others. In conclusion, H2 is supported for PMU, but the evidence is weaker for PMB when interactions are considered. To evaluate H3 (impact of victory margin on transfer allocation), the variable victory margin was analyzed. In PMU, the coefficient is negative and significant (-0.001, p < 0.05), suggesting that mayors who won by a smaller margin received higher transfers. On the other hand, in PMB, the coefficient is positive and significant (0.005, p < 0.01), indicating that municipalities with less competitive elections received more funds. The interaction between president’s party and victory margin is not significant in any model, suggesting that the margin of victory does not differentially affect the ruling party and opposition mayors. Therefore, H3 receives partial support, as the effect of the margin of victory varies by transfer type. The models also include interactions between the president's party and other variables. Interaction between president’s party and poverty was not significant, suggesting that municipal poverty does not alter the partisan advantage in transfers. The interaction between president’s party and pc autonomous own income had a negative and significant effect (-0.154, p < 0.001 in PMU), indicating that the advantage of ruling party mayors decreases in municipalities with higher per capita income. Therefore, H1 is supported, as mayors from the president’s party receive higher transfers in fixed effects models, particularly in PMU. H2 is partially supported, as the partisan effect is clear in PMU but weaker in PMB when interactions are included. H3 receives partial support, as the margin of victory influences transfers, but the direction depends on the type of transfer. The interactions show that the partisan advantage is moderated by municipal income, decreasing in wealthier municipalities. These results suggest that the distribution of transfers in Chile responds to partisan and electoral criteria, although with variations depending on the source of funding and municipal characteristics. The inclusion of interaction terms highlights that these effects are not uniform across all municipalities and are influenced by economic factors such as municipal income levels. Discussion and conclusions Using data collected from the universe of municipalities in Chile between 2009 and 2023, the article provides a comprehensive analysis of how resources are distributed from the central to local governments. Our findings reveal that the allocation of funds is significantly affected by the political affiliations of local mayors and the president. Therefore, our evidence supports previous research (Corvalan et al., 2018 ; Livert et al., 2022 ) showing that municipalities led by mayors from the same political party as the president tend to receive disproportionately higher transfers, especially in the lead-up to elections. The results align as well, with studies from different context that demonstrates that local or regional governments controlled by the same party as the central government receive more benefits than those governed by opposition parties (Bertelli & John, 2010 ; Livert & and Gainza, 2018; Solé-Ollé, 2013 ). This trend highlights the strategic use of public resources for electoral advantage, which can perpetuate existing inequalities among Chilean municipalities. We appreciate the relevance of political budget cycles (PBC) in the Chilean context, as documented by Corvalán et al. (2018) and Livert & Gainza ( 2018 ). While our empirical models do not include a separate dummy variable for election years, the inclusion of year fixed effects and a third-degree polynomial trend allows us to account for time-specific shocks—electoral or otherwise—that may systematically affect transfer patterns across all municipalities. Moreover, our focus on political alignment and electoral competitiveness indirectly captures key mechanisms theorized in the PBC literature. We interpret our findings in light of this broader framework, and future research could explicitly disentangle election-timing effects by integrating data on central and local election calendars. However, and by employing a longitudinal approach and robust empirical methods, this research advances from previous research, while also offering a foundation for comparative studies in other Latin American countries. The implications of this study are critical for public policy, as they call for mechanisms to enhance transparency and limit discretionary power in resource allocation. Such measures could help mitigate territorial inequalities and ensure that resources are distributed based on need rather than political affiliation. Accordingly, a well-designed transfer system between levels of government should avoid creating perverse incentives for beneficiary jurisdictions. The allocation mechanism must align with the intended objectives. For instance, in equalization transfer formulas, it is crucial to identify indicators of relative spending needs—such as population demographics and geographic characteristics—as well as cost variations in the provision of subnational services (Ahmad & Searle, 2006 ; Litvack et al., 1998 ). The chosen indicators should be resistant to manipulation, not create inappropriate incentives, and be easily verifiable. Moreover, the formulas should be stable over several years, though they should be subject to review and adjustment as necessary, ideally based on technical analyses of their impact on the transfer objectives (Sutherland et al., 2018 ). Generally, municipalities with higher spending needs and lower relative fiscal capacity should be expected to be net beneficiaries of transfers. A critical design criterion is that the indicators of spending needs and fiscal capacity should be "exogenous" to the municipalities receiving the transfers (Ahmad & Searle, 2006 ). In that sense, arrangements to regulate the territorial equalization process must be accepted by the involved subnational governments. This acceptance is essential for ensuring that the reform process is perceived as fair and beneficial for all parties. International experience shows a variety of practices in this area, ranging from independent commissions embedded within the institutional framework to centralized processes where the decision to allocate transfers and implement the respective formulas is made by the central government (Ahmad & Brosio, 2006 ; Kinda, 2012 ; Liu & Waibel, 2009 ; Ter-Minassian, 2012 ). Political bias significantly impacts how resources are distributed, as politicians are motivated by their desire to stay in office. Meanwhile, voters tend to appreciate receiving benefits, even when such benefits lead to inefficiencies that affect the majority (Livert & and Gainza, 2018). As a result, attempts to eliminate electoral influence are likely to be unsuccessful. However, certain policy proposals could help reduce unwarranted political discretion and create a fairer system for distributing fiscal resources. Therefore, it is vital at least that the information and procedures for transfer allocation are transparent and clearly communicated to all stakeholders, especially subnational governments. This means that the outcomes of allocation decisions should be publicly accessible knowledge. From a political economic perspective, such transparency is crucial for fostering "competition" among municipalities and promoting "convergence" and effective "equalization" (Livert et al., 2022 ). In conclusion, while the central government plays a pivotal role in supporting local governance through financial transfers, the politicization of these resources poses challenges to equitable development. While this paper provides valuable insights into the political bias in the distribution of central government resources to Chilean municipalities, its limitations might be related to just focus on a single country context and the need for further exploration of causal mechanisms. In that sense, future research could expand to comparative studies in other Latin American countries and investigate additional institutional factors influencing resource allocation, as well as explore the impacts of these dynamics on governance and public trust. Declarations Funding Author a* wish to acknowledge the financial support from the National Fund for Scientific and Technological Development of Chile, FONDECYT INICIACIÓN, Grant No. 11240400. Author b* wish to acknowledge the financial support from the by the Fondo Nacional de Desarrollo Científico y Tecnológico, Agencia Nacional de Investigación y Desarrollo de Chile-COES: ANID/FONDAP/1523A0005. 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Econometric analysis of cross section and panel data (Second edition). MIT Press. Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6865910","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472728266,"identity":"c4dc6639-b812-42a6-a304-6c61636821f3","order_by":0,"name":"Ignacio Cienfuegos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYBACNgbGBgYGGxCT+QCYDQQGQGxBQEsamJkAZvNAtEgQsAushceAOC187IcbHxckHJaTd+/5JvFzx+F8e/bDGx8w1ODWwsaT2Gw8I+GwseGZs9ske88ctuzhSSs2YDiGR4sEY5s074/DiRtn5G6T4G07bMDDkGMmwdiAV0v7b56Ew/UbZ+Q8k/wL0sL/hqCWNmaglgR5iRw2abAtEoRsAfpFmich3XADzzFja9m2dAOeG8+KDRLw+EW+/fjDzzwJ1vLy7c0Pb75tszZg70/e+OBDjQ1OLXBgcACZl0BYA9C6BmJUjYJRMApGwYgEANeITRUk+paMAAAAAElFTkSuQmCC","orcid":"","institution":"Alberto Hurtado University","correspondingAuthor":true,"prefix":"","firstName":"Ignacio","middleName":"","lastName":"Cienfuegos","suffix":""},{"id":472728267,"identity":"cf526d67-6238-46a4-aa94-0cfc242ae78d","order_by":1,"name":"Luis Garrido-Vergara","email":"","orcid":"","institution":"University of Chile","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"","lastName":"Garrido-Vergara","suffix":""},{"id":472728268,"identity":"7dc1749f-323c-460e-84a5-3b2b57612570","order_by":2,"name":"Cristóbal Cabezas","email":"","orcid":"","institution":"Alberto Hurtado University","correspondingAuthor":false,"prefix":"","firstName":"Cristóbal","middleName":"","lastName":"Cabezas","suffix":""}],"badges":[],"createdAt":"2025-06-10 19:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6865910/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6865910/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102963901,"identity":"94738988-d94c-479b-a7c2-cac885bdcddd","added_by":"auto","created_at":"2026-02-19 04:20:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":647012,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6865910/v1/dfd4e480-47ad-4593-bd84-4d4404169272.pdf"},{"id":84880013,"identity":"5fd39d4f-f416-43c4-bb2d-dfa836bc47ae","added_by":"auto","created_at":"2025-06-18 10:45:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":306108,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-6865910/v1/5395b9d6f05c298fcbafb2a7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Redistributing Loyalty? Political Determinants of Municipal Funding in Chile (2009–2023)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn general terms, the explanations of the factors that would influence the allocation of public resources lie somewhere between government ideology (Alesina et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) and technical considerations derived from bureaucratic decision-making standards. However, in recent years, the academic literature on distributive politics has suggested the importance of electoral motives in allocating resources above ideological factors or technical issues (Albertus, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The underlying hypothesis is that politicians are motivated by the desire to retain power and, as a result, policymakers allocate certain goods to specific groups at particular times in the electoral cycle (Golden \u0026amp; Min, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These practices are constitutive elements of the distributive game in democracy since, on the one hand, politicians seek to remain in power, and on the other, the beneficiaries enjoy the favors at the expense of the inefficiencies it entails for society as a whole.\u003c/p\u003e \u003cp\u003eConsidered by Livert et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), these expenditure variations are closely related to electoral cycles, what the literature known as Political Budget Cycles (PBC), which generate expenditure variations either according to a pre-election strategy (Drazen \u0026amp; Eslava, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Meloni, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Veiga \u0026amp; Veiga, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) or strategies during the election year itself (Hanusch \u0026amp; Keefer, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pierskalla \u0026amp; Sacks, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stolfi \u0026amp; and Hallerberg, 2016) that generate increased public spending. In the case of Chile, there has been evidence of resource transfers from the central government to the local government, favoring political coalitions to gain an electoral advantage (Corvalan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Livert \u0026amp; and Gainza, 2018) benefiting, for example, local stakeholders from the same governing coalition (Gainza \u0026amp; Livert, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lara E. \u0026amp; Toro M., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent years, there has been a proliferation of analyses that account for bias in the distribution of resources specifically towards municipalities (Corvalan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Livert \u0026amp; and Gainza, 2018; Veiga \u0026amp; Veiga, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). According to these works, rulers manipulate fiscal variables to obtain electoral advantages in the next election. In the case of Chile, there is evidence of politically biased transfers from the central to the local level (Corvalan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lara E. \u0026amp; Toro M., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Livert et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Livert \u0026amp; and Gainza, 2018) in particular periods of the electoral cycle. Therefore, previous research relies heavily on or small sample size (Corvalan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Livert et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which might justify a replication using a longitudinal study. The latter also raises an opportunity for a theoretical contribution, to identify in a more formal way, casual mechanisms that could give further explanation to the issue of political distribution, especially for developing countries.\u003c/p\u003e \u003cp\u003eIn our research we consider the current transfers from the Subsecretar\u0026iacute;a de Desarrollo Regional (SUBDERE) to the Chilean municipalities from 2009 to 2023. This is an appropriate expenditure category to analyze for several reasons. On the one hand, it is a public good that is territorially excludable, i.e., it can be used to benefit one municipality(ies) and exclude another(s). This allows politicians to select jurisdictions to provide benefits based on political criteria, such as, for example, the partisan affiliation of local officials (Diaz-Cayeros et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). On the other hand, this is a fund to support decentralization, which helps us to understand what interests the allocating agency pursues in the spatial distribution of collective goods. These central transfers are relevant for Chilean municipalities since they are highly dependent on this type of income. In Chile, transfers from the central level to municipalities represent 51.1%, of local government income, while the average among OECD countries is 38% (OECD, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile current transfers constitute the dependent variable, electoral information is our primary independent variable. We use municipal outcomes to estimate whether mayors of the same party as the President systematically benefited and to understand whether the political inertia follows a top-down or bottom-up logic.\u003c/p\u003e \u003cp\u003eOur paper presents two main contributions to literature. First, we used panel data and multiple linear regression models to study the distribution of transfers during 2009\u0026ndash;2023 which shows a methodological improvement from previous research. Second, we show how the distribution of the current expenditure transfer was used to benefit the municipalities where the mayor and the President are from the same political party. Additionally, we identify that in the case of one of the programs studied, the lower the margin of victory, the higher the transfers were, and for the other program, the higher the margin, the more transfers, which would indicate a strategic behavior on the part of the central government in the distribution of public resources. This last result suggests that, despite being a decentralized fund, there was a top-down interaction between the interests of the central government and the mayors.\u003c/p\u003e \u003cp\u003eConsequently, even though we rely on previous research considering discretionary transfers received by municipalities in Chile from the central government, advancing in the literature by extending the work developed by Corval\u0026aacute;n et al., (2018); Livert et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Livert et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), we have considered in our research, a longitudinal study with a large panel data and causal mechanisms, methodological approach that was absent in the existent literature, which provided a strong argument to justify the replications of a similar perspective for the case of Chile. We believe that our research and findings advance our empirical knowledge of Latin American political distribution literature, highlighting the political bias in the distribution of resources transferred from the central government during 2009\u0026ndash;2023. These results can also set a comparative benchmark for studying other regional countries and their potential opportunistic practices by the central government in order to improve the options of candidates related to the party or coalition in the next election.\u003c/p\u003e \u003cp\u003eWhile several studies have examined discretionary transfers to municipalities in Chile (Corval\u0026aacute;n et al., 2018; Livert \u0026amp; Gainza, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gainza \u0026amp; Livert, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), our work contributes to this literature in at least three novel ways. First, we employ an extended longitudinal panel (2009\u0026ndash;2023), which captures multiple full presidential terms and offers greater leverage for identifying political cycles and enduring partisan effects across different governing coalitions. Second, we incorporate interactive terms and third-degree polynomial trends to account for complex temporal dynamics, an approach largely absent in prior studies. Third, and most importantly, we refine the theoretical framework by explicitly modeling how discretionary transfers respond not only to mayoral alignment but also to electoral competitiveness, municipal fiscal autonomy, and socioeconomic conditions. This allows us to uncover conditional effects that prior literature has not systematically tested. As such, our findings do not merely replicate earlier results, but offer deeper insights into the causal mechanisms that shape partisan allocation patterns, providing new empirical and theoretical value to the field of distributive politics in Latin America.\u003c/p\u003e \u003cp\u003eThe rest of the paper follows. First, we review academic literature on political distribution and some insights about the Chilean context. Then, we present the data and the methodology on which the empirical analysis is based, while the fourth section details the results. The article ends with the main conclusions and a discussion of the implications for public policy and mechanisms to limit the margin of arbitrariness in the distribution of resources.\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003eDistributive policy\u003c/p\u003e \u003cp\u003eConsidering the discussion of intergovernmental relations, there are mechanisms through which the political system transfers resources are used to reduce the socio-territorial inequality gap between territories (Ruiz-Porras \u0026amp; García-Vázquez, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In general, these transfer mechanisms, having different degrees of formality, conditionality, composition, and criteria, should be relatively institutionalized, leaving little room for discretion to the government authority.\u003c/p\u003e \u003cp\u003eHowever, there are other resources in the hands of the national executive that may be distributed in a discretionary manner. Thus, there is a critical analysis of government resource usage and its impact on electoral outcomes (Corvalan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Livert \u0026amp; and Gainza, 2018). This perspective focuses on legislative elections (in this case, the objective is that the allocation of more resources in the commune has a positive impact on the future composition of the legislative branch) or on subnational levels (municipalities, districts, regions, provinces, states) to improve the reelection options of the incumbent authority (Livert et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this dimension, two main models explain the distribution of discretionary resources. On the one hand, there is the partisan model, in which the objective is to achieve a majority in the legislative branch, which in turn can have two logics: more resources in those districts where more voters are willing to change their preference in favor of the government or more transfer in the territories where the ruling party or coalition has more votes. On the other hand, there is the non-partisan model, where the interests of the territorial legislator guide the allocation of discretionary funds (Veiga \u0026amp; Veiga, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe specialized literature calls distributive politics when public authorities confer geographically concentrated benefits for political purposes. At the same time, the costs of inefficiency in collection and allocation are spread among all voters (Weingast et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). This form of allocation could be considered as some degree of corruption on the part of the rulers, with essential costs for efficiency, equity, and democratic quality (Vergara-Garrido and Cienfuegos, 2025). Nonetheless Stokes et al. (2013) consider that a distinction should be made between distributive strategies depending on whether this is part of the political program of governments. The rules must be formalized and public in a programmatic distribution, while in a non-programmatic distribution, the criteria are not public. There is also evidence that Political factors and the institutional framework play a relevant role in the allocation of resources (Kroth, 2014), as they condition the political choices of the ruler. According to Alt and Rose (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), two conditions are essential: the first refers to the incentives politicians have to stay in office, while the second condition is associated with the ability of ruling politicians to manipulate fiscal instruments (Livert et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are several ways in which politicians can try to influence voters' behavior. One is by manipulating fiscal variables throughout the legislature, known as Political Budget Cycles (PBC). The central assumption is that the electorate is myopic and will evaluate the government based on its most recent actions (Alesina et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). As a result, rulers have incentives to manipulate fiscal instruments as the race approaches. The analysis of cycles has more recently been transferred to the municipal level. Corvalán et al. (2018) consider that transfers mechanisms from central government to municipalities may be an indirect way to reinforce their position. By increasing the resources of aligned mayors, they can rally their support to mobilize the electorate in the next national election. For their part, Drazen and Eslava (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) argue that certain expenditures can be targeted more effectively and, therefore, voters can more clearly reward whoever is responsible for them. Indeed, local services are apparent, while the evaluation of national public goods -defense, health, the legal system- becomes more blurred (Livert et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Veiga \u0026amp; Veiga, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to Livert, et.al (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), one of the criteria for increasing the chances of success at the ballot is by geographically distributing resources to areas with higher returns. This includes financial transfers from the central government to the municipal government and sometimes goods targeted to specific populations groups in a discretionary manner -e.g., jobs- or territories -e.g., equipment- (Brollo \u0026amp; Nannicini, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). One of the most critical questions is determining which strategy provides a more significant advantage, focusing on core voters (Cox \u0026amp; McCubbins, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) or targeting swing voters (Dixit \u0026amp; Londregan, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The first approach argues that politicians transfer resources to their strong electorate, following a risk-averse strategy. On the other hand, the second approach argues that politicians distribute resources to both the traditional and the swing electorate to maintain and expand their support base. The difference between both explanations is based on the ability of the electorate to change their preferences, on competition, and on the willingness to change their vote (Livert \u0026amp; and Gainza, 2018).\u003c/p\u003e \u003cp\u003ePolitical bias and transfers in Chile\u003c/p\u003e \u003cp\u003eWhile Chile has always been considered one of the least corrupt countries in Latin America (Nord et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) would not be a corruption-free country (Lopez, 2019). Political corruption at the local level has particularly exploded during the last years, with a series of corruption scandals involving the embezzlement of public funds by local authorities. Three cases in particular—two large urban municipalities of the metropolitan region, Vitacura and Maipú, and the touristic middle size municipality of Viña del Mar, all of which surfaced in 2022–2023—have stirred public debate over the country’s problems with corruption at the local level (Garrido-Vergara and Cienfuegos, 2025).\u003c/p\u003e \u003cp\u003eThe debate on decentralization in Chile focuses on enhancing the competencies and resources of municipal and regional governments. Nonetheless and despite some institutional advancements, Chile remains one of the most centralized countries in South America and the least decentralized among OECD nations (OECD, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLiterature indicates a direct relationship between decentralization processes and territorial inequality. For instance, Giraudy and Pribble (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) illustrate how the nature of the decentralizing coalition (top-down versus bottom-up) and the presence of mechanisms for coordinating fiscal management significantly influenced territorial inequality reduction in Brazil, led to a moderate decrease in Mexico, and resulted in a minimal decrease in Argentina. An index reflecting these dynamics shows a correlation with the participation of subnational governments in public spending in Chile. Recent data reveal that 18.7% of state spending is implemented through municipalities in Chile, with only 7% coming from expenditures funded by their own permanent revenues and unconditional transfers from the Municipal Common Fund (OECD, 2020). According to Pribble (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), building effective local institutions requires economic resources. As the primary revenue sources for municipalities in Chile include property taxes (which are set and collected by the central government), vehicle and permit fees (mainly from commercial activities), and user fees for services like waste collection, it is likely that wealthier municipalities achieve more effective institutional outcomes and services compared to poorer ones. There is broad agreement on the assessment and proposals surrounding this issue, as well as a consensus on the need for resource transfers to be conducted transparently and equitably (Letelier Saavedra, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Pineda et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Chile, the transfer system to municipalities has two key components:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e Common Municipal Fund (FCM): Funded by municipal contributions and the national budget.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTransfers for Municipalized Primary Health and Education: Conditional funds based on public health system users or municipal school enrollment.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eTransparency in the FCM's distribution is generally acceptable, as well as transfer related to Municipalized Primary Health and Education, while concerns exist regarding the central government’s discretionary allocation of resources, raising corruption risks. As a consequence, both national and international institutions have raised the necessity to improve transparency and reduce discretion in these transfers from central government in Chile (OECD, 2017; Government of Chile, 2009). Critics point out that SUBDERE controls resources for discretionary allocations and specific projects like the Urban Improvement Program (PMU) and Neighborhood Improvement Program (PMB). Although funding for these programs has increased, there are no clear guidelines and formal rules for allocation. PMUs focus on minor urban projects, while PMBs target sanitation in rural areas. SUBDERE's funding can significantly impact municipal budgets, averaging around 7.7% of municipal income (Livert et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have suggested that such discretionary funds improve the reelection chances of incumbent mayors, though evidence varies across different resources. Cuevas (2012) highlighted those incumbents had a 37–42% higher probability of winning in closely contested municipalities, where the central government allocated more funds to its coalition. These findings are supported by recent research (Corvalan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Livert et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), emphasizing that municipal spending significantly impacts mayors' reelection chances. This highlights the importance of central government transfers in electoral success (Núñez, 2007, cited in Acuña et al., 2017, p. 46). However, as we have mentioned, previous studies have focused on a limited time frame of analysis and did not use causal mechanisms in their research.\u003c/p\u003e "},{"header":"Methodology","content":"\u003cp\u003eVariables, data and hypothesis\u003c/p\u003e\u003cp\u003eThe dependent variables are the logarithms of per capita transfers from two discretionary programs: the Urban Improvement Program (PMU) and the Neighborhood Improvement Program (PMB) (see Appendix, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These transfers are vertical and discretionary; they originate from the Ministry of the Interior and Public Safety and are allocated directly to municipalities without a predetermined formula or conditionality. The independent variables are categorized into two dimensions: political and control variables. The political variables include (1) the party alignment between the mayor and the President -a binary variable coded 1 when the mayor belongs to the same political party as the President of the Republic, and 0 otherwise- and (2) the electoral victory margin, measured as the percentage point-difference in votes between the elected mayor and the runner-up in the most recent municipal election. The first variable has been used in similar studies, both in the context of Chile and across Latin America (Gainza \u0026amp; Livert, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Livert et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Livert \u0026amp; and Gainza, 2018), whereas the latter serves as a proxy for electoral competitiveness. A positive relationship between higher transfers and a higher margin of victory would suggest that the allocations of resources benefit core voters, while a negative one could indicate strategic allocations to competitive (swing) municipalities.\u003c/p\u003e\u003cp\u003eControl variables include key municipal financial indicators -such as the logarithm of per capita revenues from the Municipal Common Fund (FCM) and the logarithm of per capita autonomous permanent own revenue- and variables that account for structural differences and socioeconomic need across municipalities -poverty and population density-. In the case of the FCM, it indicates the level of vulnerability of the municipalities; since it is a horizontal transfer based on a formula related to local vulnerability, the greater the dependence on the FCM, the greater the vulnerability of the municipality.\u003c/p\u003e\u003cp\u003eAccordingly, and aiming at covering the gaps left on previous research we have considered the following hypothesis:\u003c/p\u003e\u003cp\u003eH1: After controlling time-invariant municipal characteristics, the allocation of per capita transfers from the central government to municipalities between 2009 and 2023 was influenced by discretionary partisan criteria.\u003c/p\u003e\u003cp\u003eH2: Holding municipal fixed effects constant, mayors from the president's political party received, on average, higher per capita transfers from the central government than opposition mayors between 2009 and 2023.\u003c/p\u003e\u003cp\u003eH3: Conditional on time-invariant municipal characteristics, municipalities where the mayor won by a narrow margin received higher per capita transfers from the central government compared to municipalities with less competitive elections between 2009 and 2023.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDescriptive statistics of the variables\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePMB Revenue (M\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,242,589.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67,691.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e179,215.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e324,293.69\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePMB Revenue pc (log)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePMU Revenue (M\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7,250,083.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e198,416.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e317,520.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e532,080.65\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePMU Revenue pc (log)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCM Revenue (M\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5149\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e720,414.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82,467,207.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,796,114.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,795,329.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6,274,788.70\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCM Revenue pc (log)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5149\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation Density (hab/km2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5025\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24,367.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e951.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,935.67\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5165\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.53\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutonomous permanent own revenue pc (M\u003cspan\u003e$\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4086\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5350.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e73.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e135.56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e278.60\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutonomous permanent own revenue pc (log)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4086\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectoral Victory Margin (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.55\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.16\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.92\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresident's Party (dummy)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5175\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003c/p\u003e\u003cp\u003epc = per capita; log = logarithmic transformation.\u003c/p\u003e\u003cp\u003eSource: Own elaboration\u003c/p\u003e\u003cp\u003e Table\u0026nbsp; \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive analysis of the data considered in this article. The dataset consists of 5,175 observations for most variables, with some minor variations in specific cases. In the case of the dependent variables related to the municipal revenues, the PMB revenue (M\u003cspan\u003e$\u003c/span\u003e) ranges from 0 to 5,242,589, with a mean of 179,215.42 and a high standard deviation (324,293.69), indicating considerable variation across municipalities. On the other hand, the PMU revenue (M\u003cspan\u003e$\u003c/span\u003e) has a broader range (0 to 7,250,083), with a mean of 317,520.59, suggesting a more substantial allocation compared to PMB. The FCM revenue (M\u003cspan\u003e$\u003c/span\u003e) shows extreme variability, ranging from 720,414 to 82,467,207, with a very high mean (4,795,329.03) and standard deviation (6,274,788.70), indicating substantial differences among municipalities. Finally, the per capita measures (log-transformed) exhibit lower variability, with means between 1.32 and 5.05, showing the normalization effect of log transformation. \u003c/p\u003e\u003cp\u003eRegarding socioeconomic and political characteristics, the population density (hab/km²) is extremely skewed, ranging from 0.01 to 24,367.68, with a median of 30.27, but a mean of 951.00, suggesting that a few municipalities have very high density. Poverty (%) varies from 0 to 59.74%, with an average of 15.39%, indicating significant socioeconomic disparities across municipalities. Electoral victory margin (%) ranges from − 4.79–88.65%, with a median of 16.55%, reflecting diverse electoral competitiveness. Finally, concerning partisan affiliation of mayors, the mean value of 0.11 for president’s party suggests that only a small fraction of municipalities have mayors from the president’s party in a given year.\u003c/p\u003e\u003cp\u003eThe descriptive analysis shows that, concerning observations and data distribution, most financial variables exhibit high standard deviations, indicating significant disparities in revenue distribution across municipalities. Political and socioeconomic variables show a wide range, suggesting heterogeneity in competitiveness, poverty, and population density.\u003c/p\u003e\u003cp\u003eAnalysis and results\u003c/p\u003e\u003cp\u003eStatistical analysis was based on data collected for the 345 communes of Chile between 2009 and 2023. The research employed panel models with fixed effects to evaluate the relationship between political and socioeconomic variables in the distribution of municipal transfers. The data were obtained from official sources such as the National Municipal Information System (SINIM), Electoral Service (SERVEL) and Ministry of Health (MINSAL). Fixed-effects models were employed using the \u003cem\u003eplm\u003c/em\u003e package in R.\u003c/p\u003e\u003cp\u003ePanel data combines cross-sectional and temporal dimensions, allowing the analysis of units over time (Baltagi, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hsiao, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wooldridge, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This structure helps control unobserved heterogeneity and improves the accuracy of econometric estimates. The fixed effects (FE) model is a technique for estimating relationships in panel data when there is a suspicion that unobserved factors may be correlated with the explanatory variables. We acknowledge that fixed effects models (FEM), while widely used to control for unobserved time-invariant heterogeneity, do not address all possible sources of endogeneity—particularly those stemming from time-varying omitted variables or simultaneity. Our use of FEM follows well-established practices in the analysis of panel data in distributive politics, especially when data availability limits the use of stronger causal designs. To further mitigate potential bias, we included year fixed effects and polynomial time trends to control for unobserved national-level shocks and nonlinear dynamics. While we interpret our findings with appropriate caution, we believe our model specification offers a robust foundation for identifying systematic associations between political alignment and the allocation of discretionary transfers. The general structure of the estimated fixed-effects model is:\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{it}\\)\u003c/span\u003e \u003c/span\u003e = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{0}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{1}{\\text{X}}_{it}\\)\u003c/span\u003e\u003c/span\u003e ​+ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\beta\\:}}_{2}{\\text{Z}}_{it}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\alpha\\:}}_{i}\\)\u003c/span\u003e\u003c/span\u003e ​+ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{\\gamma\\:}}_{t}\\)\u003c/span\u003e\u003c/span\u003e + \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{ϵ}}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003ewhere:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{it}\\)\u003c/span\u003e \u003c/span\u003e ​ = log of either per capita \u003cem\u003ePMU\u003c/em\u003e or \u003cem\u003ePMB\u003c/em\u003e transfers for commune \u003cem\u003ei\u003c/em\u003e in year \u003cem\u003et\u003c/em\u003e.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{X}}_{it}\\)\u003c/span\u003e \u003c/span\u003e = Vector of explanatory variables, including:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{presiden{t}^{{\\prime\\:}}s\\:party}_{it}\\)\u003c/span\u003e \u003c/span\u003e: dummy (0,1) for mayor aligned with the president’s party.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{victory\\:margin}_{it}\\)\u003c/span\u003e \u003c/span\u003e: margin of victory (%) between the elected mayor and the second majority for the given municipal election.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{poverty}_{it}\\)\u003c/span\u003e \u003c/span\u003e: poverty percentage (%).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{pop\\:density}_{it}\\)\u003c/span\u003e \u003c/span\u003e: population density \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\frac{hab}{{km}^{2}}\\right).\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:pc\\:{FCM\\:revenue\\:\\left(log\\right)}_{it}\\)\u003c/span\u003e \u003c/span\u003e: Log of per capita municipal revenue from the common fund.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{pc\\:autonomous\\:permanent\\:own\\:revenue\\:\\left(log\\right)}_{it}\\)\u003c/span\u003e \u003c/span\u003e: Log of per capita autonomous permanent own revenues.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:poly\\left(year,3\\right)\\)\u003c/span\u003e \u003c/span\u003e: polynomial trend to capture non-linearity in time trends.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cp\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{{\\alpha\\:}}_{i}\\)\u003c/span\u003e \u003c/span\u003e = Commune-specific fixed effect (time-invariant factors).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{{\\gamma\\:}}_{t}\\)\u003c/span\u003e \u003c/span\u003e = Time fixed effect (to control for period shocks).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{ϵ}}_{it}\\)\u003c/span\u003e \u003c/span\u003e= Error term,\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eEquation for PMU Transfers (log scale):\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{log(pc\\:PMU\\:transfer}_{it}+1)\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e=\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}{presiden{t}^{{\\prime\\:}}s\\:party}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e​\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}{victory\\:margin}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{3}{poverty}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{4}{pop\\:density}_{it}+{\\beta\\:}_{5}{log(pc\\:FCM\\:revenue}_{it})+{\\beta\\:}_{6}{log\\:(pc\\:autonomous\\:permanent\\:own\\:revenue}_{it})+{\\sum\\:}_{k=1}^{3}{\\beta\\:}_{7k}{\\left(year\\right)}^{k}{\\:+\\:\\alpha\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e​+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eEquation for PMB Transfers (log scale):\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{log(pc\\:PMB\\:transfer}_{it}+1)\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e=\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}{presiden{t}^{{\\prime\\:}}s\\:party}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e​\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}{victory\\_margin}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{3}{poverty}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{4}{pop\\:density}_{it}+{\\beta\\:}_{5}{log(pc\\:FCM\\:revenue}_{it})+{\\beta\\:}_{6}{log(pc\\:autonomous\\:permanent\\:own\\:revenue}_{it})+{\\sum\\:}_{k=1}^{3}{\\beta\\:}_{7k}{\\left(year\\right)}^{k}{\\:+\\:\\alpha\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e​+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eSince in panel data trends in the dependent variable (log of per capita transfers) can be non-linear, a linear or quadratic trend might not fully capture fluctuations over time, especially if there are periods of increase, decline, and recovery. Considering this fact, a third-degree polynomial (cubic polynomial) allows for three inflection points, making it more flexible in capturing different phases of variation over time.\u003c/p\u003e\u003cp\u003eTests were conducted to ensure the validity of the models. Concerning autocorrelation, the Wooldridge test is used, indicating the need for robust errors; the Breusch-Pagan test is applied, revealing heteroskedasticity; considering cross-sectional dependence, the Pearson test suggests correlation between communes. These tests justify the use of robust standard errors for model adjustments, applying the Driscoll-Kraay type.\u003c/p\u003e\u003cp\u003eThe final model shows that the political alignment of the mayor with the president significantly influences the distribution of resources (see also Appendix, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The inclusion of polynomial trends improves the model fit, and robust errors ensure the statistical validity of the results.\u003c/p\u003e\u003cp\u003eInteraction Effects were also considered:\u003c/p\u003e\u003cul\u003e \u003cli\u003e \u003cp\u003eThe interaction between \u003cem\u003epresident’s party\u003c/em\u003e and \u003cem\u003evictory margin\u003c/em\u003e: This tested whether political alignment influences the effect of electoral competitiveness on transfers.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe interaction between \u003cem\u003epresident’s party\u003c/em\u003e and \u003cem\u003epoverty\u003c/em\u003e: This tested whether the effect of political alignment on transfers depends on municipal poverty levels.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe interaction between \u003cem\u003epresident’s party\u003c/em\u003e and \u003cem\u003epc permanent autonomous own income\u003c/em\u003e: This tested whether municipalities with more own revenue receive different levels of transfers based on political alignment.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e\u003cp\u003eEquation for PMU Transfers (log scale) with interactions:\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{log(pc\\:PMU\\:transfer}_{it}+1)\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e=\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}{presiden{t}^{{\\prime\\:}}s\\:party}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e​\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}{victory\\:margin}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{3}{poverty}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{4}{pop\\:density}_{it}+{\\beta\\:}_{5}{log(pc\\:FCM\\:revenue}_{it})+{\\beta\\:}_{6}{log(pc\\:permanent\\:autonomous\\:own\\:revenue}_{it})+\\:{\\beta\\:}_{7}{(presiden{t}^{{\\prime\\:}}s\\:party}_{it}*{victory\\:margin}_{it})+{\\beta\\:}_{8}{(presiden{t}^{{\\prime\\:}}s\\:party}_{it}*{poverty}_{it})\\:\\:+{\\beta\\:}_{9}{(presiden{t}^{{\\prime\\:}}s\\:party}_{it}*{log(pc\\:permanent\\:autonomous\\:own\\:revenue}_{it}\\left)\\right)+{\\sum\\:}_{k=1}^{3}{\\beta\\:}_{10k}{\\left(year\\right)}^{k}{\\:+\\:\\alpha\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e​+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eEquation for PMB Transfers (log scale) with interactions:\u003c/p\u003e\u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{log(pc\\:PMB\\:transfer}_{it}+1)\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e=\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}{presiden{t}^{{\\prime\\:}}s\\:party}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e​\u003c/em\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{2}{victory\\:margin}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{3}{poverty}_{it}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:+\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{4}{pop\\:density}_{it}+{\\beta\\:}_{5}{log(pc\\:FCM\\:revenue}_{it})+{\\beta\\:}_{6}{log(pc\\:permnent\\:autonomous\\:own\\:revenue}_{it})+\\:{\\beta\\:}_{7}{(presiden{t}^{{\\prime\\:}}s\\:party}_{it}*{victory\\:margin}_{it})+{\\beta\\:}_{8}{(presiden{t}^{{\\prime\\:}}s\\:party}_{it}*{poverty}_{it})\\:\\:+{\\beta\\:}_{9}{(presiden{t}^{{\\prime\\:}}s\\:party}_{it}*{log(pc\\:permanent\\:autonomous\\:own\\:revenue}_{it}\\left)\\right)+{\\sum\\:}_{k=1}^{3}{\\beta\\:}_{10k}{\\left(year\\right)}^{k}{\\:+\\:\\alpha\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e​+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\gamma\\:}_{t}\\)\u003c/span\u003e\u003c/span\u003e \u003cem\u003e+\u003c/em\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{it}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eFixed effects models\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePMU pc (log)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMB pc (log)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePMU pc (log)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePMB pc (log)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresident’s Party\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.176** (0.064)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.271** (0.105)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.904*** (0.197)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.196 (0.553)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVictory Margin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001* (0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005** (0.002)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.002* (0.001)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004* (0.002)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.011 (0.007)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005 (0.004)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.011 (0.008)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004 (0.004)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation Density\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000 (0.000)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000 (0.000)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000 (0.000)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000 (0.000)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epc FCM Revenue (log)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.202+ (0.111)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.260** (0.098)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.190+ (0.113)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.262** (0.098)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epc Permanent Autonomous Own Income (log)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.307* (0.120)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.458*** (0.139)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.327** (0.114)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.462** (0.151)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{P}\\varvec{o}\\varvec{l}\\varvec{y}(\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r},\\:3)}^{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-11.592** (3.912)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.606*** (4.906)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-11.523** (3.895)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.620*** (4.880)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{P}\\varvec{o}\\varvec{l}\\varvec{y}(\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r},\\:3)}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.930*** (2.961)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.473** (4.725)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.927*** (3.011)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.381** (4.710)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{P}\\varvec{o}\\varvec{l}\\varvec{y}(\\varvec{y}\\varvec{e}\\varvec{a}\\varvec{r},\\:3)}^{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.768* (4.284)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6.567 (5.365)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.741* (4.259)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.640 (5.391)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresident’s Party x Poverty\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.005 (0.006)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012 (0.011)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresident’s Party x Victory Margin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002 (0.002)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004 (0.003)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePresident’s Party × pc Autonomous Own Income (log)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.154*** (0.042)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.042 (0.122)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNum. Obs.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4662\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4662\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4662\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4662\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20081.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22582.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20068.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22572.6\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47920.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50421.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47887.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50392.3\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e+ p \u0026lt; 0.1, * p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001\u003c/p\u003e\u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003c/p\u003e\u003cp\u003epc = per capita; log = logarithmic transformation.\u003c/p\u003e\u003cp\u003eSource: Own elaboration\u003c/p\u003e\u003cp\u003eThe fixed effects models evaluate the three hypotheses regarding the allocation of per capita transfers from the central government to municipalities between 2009 and 2023 to PMU and PMB. The hypotheses consider partisan and electoral criteria in the distribution of resources (H1, H2, and H3). To test these hypotheses, municipal-level fixed effects models were analyzed with interactive terms and additional controls.\u003c/p\u003e\u003cp\u003eThe estimated models included two dependent variables: PMU pc (log) and PMB pc (log), representing different categories of per capita transfers (see also Appendix, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Basic models and models with interactions between the presidential party and other explanatory variables were considered. To evaluate H1 (partisan influence on transfer allocation), the variable \u003cem\u003epresident’s party\u003c/em\u003e, was observed, which indicates whether the mayor belongs to the president's party. In the basic model, \u003cem\u003epresident’s party\u003c/em\u003e has a positive and significant coefficient in PMU (0.176, p \u0026lt; 0.01) and PMB (0.271, p \u0026lt; 0.01), indicating that municipalities with mayors from the president’s party receive more transfers. In the interactive model, the coefficient in PMU increases significantly (0.904, p \u0026lt; 0.001), while in PMB it loses significance (0.196, p \u0026gt; 0.1). These results support H1, confirming the existence of partisan discretionary allocation of resources.\u003c/p\u003e\u003cp\u003eTo evaluate H2 (differences in transfers based on party affiliation, which proposes that ruling party mayors receive more transfers than opposition mayors), the results in the basic models show a significant and positive coefficient for \u003cem\u003epresident’s party\u003c/em\u003e, supporting the idea that ruling party mayors receive more transfers. The interactive models reveal that while this effect remains highly significant in PMU (coefficient 0.904, p \u0026lt; 0.001), it becomes non-significant in PMB. This suggests that the partisan advantage in transfers may be stronger in certain types of funding sources but not in others. In conclusion, H2 is supported for PMU, but the evidence is weaker for PMB when interactions are considered.\u003c/p\u003e\u003cp\u003eTo evaluate H3 (impact of victory margin on transfer allocation), the variable \u003cem\u003evictory margin\u003c/em\u003e was analyzed. In PMU, the coefficient is negative and significant (-0.001, p \u0026lt; 0.05), suggesting that mayors who won by a smaller margin received higher transfers. On the other hand, in PMB, the coefficient is positive and significant (0.005, p \u0026lt; 0.01), indicating that municipalities with less competitive elections received more funds. The interaction between \u003cem\u003epresident’s party\u003c/em\u003e and \u003cem\u003evictory margin\u003c/em\u003e is not significant in any model, suggesting that the margin of victory does not differentially affect the ruling party and opposition mayors. Therefore, H3 receives partial support, as the effect of the margin of victory varies by transfer type.\u003c/p\u003e\u003cp\u003eThe models also include interactions between the president's party and other variables. Interaction between \u003cem\u003epresident’s party\u003c/em\u003e and \u003cem\u003epoverty\u003c/em\u003e was not significant, suggesting that municipal poverty does not alter the partisan advantage in transfers. The interaction between \u003cem\u003epresident’s party\u003c/em\u003e and \u003cem\u003epc autonomous own income\u003c/em\u003e had a negative and significant effect (-0.154, p \u0026lt; 0.001 in PMU), indicating that the advantage of ruling party mayors decreases in municipalities with higher per capita income.\u003c/p\u003e\u003cp\u003eTherefore, H1 is supported, as mayors from the president’s party receive higher transfers in fixed effects models, particularly in PMU. H2 is partially supported, as the partisan effect is clear in PMU but weaker in PMB when interactions are included. H3 receives partial support, as the margin of victory influences transfers, but the direction depends on the type of transfer.\u003c/p\u003e\u003cp\u003eThe interactions show that the partisan advantage is moderated by municipal income, decreasing in wealthier municipalities. These results suggest that the distribution of transfers in Chile responds to partisan and electoral criteria, although with variations depending on the source of funding and municipal characteristics. The inclusion of interaction terms highlights that these effects are not uniform across all municipalities and are influenced by economic factors such as municipal income levels.\u003c/p\u003e"},{"header":"Discussion and conclusions","content":"\u003cp\u003eUsing data collected from the universe of municipalities in Chile between 2009 and 2023, the article provides a comprehensive analysis of how resources are distributed from the central to local governments. Our findings reveal that the allocation of funds is significantly affected by the political affiliations of local mayors and the president. Therefore, our evidence supports previous research (Corvalan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Livert et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showing that municipalities led by mayors from the same political party as the president tend to receive disproportionately higher transfers, especially in the lead-up to elections. The results align as well, with studies from different context that demonstrates that local or regional governments controlled by the same party as the central government receive more benefits than those governed by opposition parties (Bertelli \u0026amp; John, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Livert \u0026amp; and Gainza, 2018; Sol\u0026eacute;-Oll\u0026eacute;, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This trend highlights the strategic use of public resources for electoral advantage, which can perpetuate existing inequalities among Chilean municipalities.\u003c/p\u003e \u003cp\u003eWe appreciate the relevance of political budget cycles (PBC) in the Chilean context, as documented by Corval\u0026aacute;n et al. (2018) and Livert \u0026amp; Gainza (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While our empirical models do not include a separate dummy variable for election years, the inclusion of year fixed effects and a third-degree polynomial trend allows us to account for time-specific shocks\u0026mdash;electoral or otherwise\u0026mdash;that may systematically affect transfer patterns across all municipalities. Moreover, our focus on political alignment and electoral competitiveness indirectly captures key mechanisms theorized in the PBC literature. We interpret our findings in light of this broader framework, and future research could explicitly disentangle election-timing effects by integrating data on central and local election calendars.\u003c/p\u003e \u003cp\u003eHowever, and by employing a longitudinal approach and robust empirical methods, this research advances from previous research, while also offering a foundation for comparative studies in other Latin American countries. The implications of this study are critical for public policy, as they call for mechanisms to enhance transparency and limit discretionary power in resource allocation. Such measures could help mitigate territorial inequalities and ensure that resources are distributed based on need rather than political affiliation.\u003c/p\u003e \u003cp\u003eAccordingly, a well-designed transfer system between levels of government should avoid creating perverse incentives for beneficiary jurisdictions. The allocation mechanism must align with the intended objectives. For instance, in equalization transfer formulas, it is crucial to identify indicators of relative spending needs\u0026mdash;such as population demographics and geographic characteristics\u0026mdash;as well as cost variations in the provision of subnational services (Ahmad \u0026amp; Searle, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Litvack et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe chosen indicators should be resistant to manipulation, not create inappropriate incentives, and be easily verifiable. Moreover, the formulas should be stable over several years, though they should be subject to review and adjustment as necessary, ideally based on technical analyses of their impact on the transfer objectives (Sutherland et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Generally, municipalities with higher spending needs and lower relative fiscal capacity should be expected to be net beneficiaries of transfers. A critical design criterion is that the indicators of spending needs and fiscal capacity should be \"exogenous\" to the municipalities receiving the transfers (Ahmad \u0026amp; Searle, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn that sense, arrangements to regulate the territorial equalization process must be accepted by the involved subnational governments. This acceptance is essential for ensuring that the reform process is perceived as fair and beneficial for all parties. International experience shows a variety of practices in this area, ranging from independent commissions embedded within the institutional framework to centralized processes where the decision to allocate transfers and implement the respective formulas is made by the central government (Ahmad \u0026amp; Brosio, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kinda, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liu \u0026amp; Waibel, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Ter-Minassian, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePolitical bias significantly impacts how resources are distributed, as politicians are motivated by their desire to stay in office. Meanwhile, voters tend to appreciate receiving benefits, even when such benefits lead to inefficiencies that affect the majority (Livert \u0026amp; and Gainza, 2018). As a result, attempts to eliminate electoral influence are likely to be unsuccessful. However, certain policy proposals could help reduce unwarranted political discretion and create a fairer system for distributing fiscal resources. Therefore, it is vital at least that the information and procedures for transfer allocation are transparent and clearly communicated to all stakeholders, especially subnational governments. This means that the outcomes of allocation decisions should be publicly accessible knowledge. From a political economic perspective, such transparency is crucial for fostering \"competition\" among municipalities and promoting \"convergence\" and effective \"equalization\" (Livert et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, while the central government plays a pivotal role in supporting local governance through financial transfers, the politicization of these resources poses challenges to equitable development. While this paper provides valuable insights into the political bias in the distribution of central government resources to Chilean municipalities, its limitations might be related to just focus on a single country context and the need for further exploration of causal mechanisms. In that sense, future research could expand to comparative studies in other Latin American countries and investigate additional institutional factors influencing resource allocation, as well as explore the impacts of these dynamics on governance and public trust.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eAuthor a* wish to acknowledge the financial support from the National Fund for Scientific and Technological Development of Chile, FONDECYT INICIACI\u0026Oacute;N, Grant No. 11240400.\u003c/p\u003e \u003cp\u003eAuthor b* wish to acknowledge the financial support from the by the Fondo Nacional de Desarrollo Cient\u0026iacute;fico y Tecnol\u0026oacute;gico, Agencia Nacional de Investigaci\u0026oacute;n y Desarrollo de Chile-COES: ANID/FONDAP/1523A0005.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eI.C. and L.GV . conceived and designed the study. I.C. performed the experiments and collected the data. L.V.G. and C.C. analyzed and interpreted the results.C.C. prepared Figures 1\u0026ndash;3 and contributed to the data visualization. I.C. and L.G.V. wrote the main manuscript text. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData that support the findings of this study have been deposited in the following link:https://drive.google.com/drive/folders/1onw1oFomLIG0fkWa22KwYc4sON8Fblen?usp=sharing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmad, E., \u0026amp; Brosio, G. (Eds.). (2006). \u003cem\u003eHandbook of Fiscal Federalism\u003c/em\u003e. Edward Elgar Publishing.\u003c/li\u003e\n\u003cli\u003eAhmad, E., \u0026amp; Searle, B. (2006). On the Implementation of Transfers to Subnational Governments. \u003cem\u003eChapters\u003c/em\u003e. https://ideas.repec.org//h/elg/eechap/3584_15.html\u003c/li\u003e\n\u003cli\u003eAlbertus, M. (2019). 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MIT Press.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"political budget cycles, distributive politics, local governments, incumbency advantage","lastPublishedDoi":"10.21203/rs.3.rs-6865910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6865910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTransparent and objective criteria for the distribution of resources from central government to local government are essential to mitigate territorial inequality. On the contrary, discretionary power can deepen inequalities and promote corruption. This study examines the influence of political factors on the distribution of government resources to municipalities in Chile during 2009 and 2023. Using panel models with fixed effects, robust standard errors and interactive terms, the analysis reveals that municipalities governed by mayors from the same political party as the president received on average significantly higher transfers during that period from central government. These findings also underline the role of electoral strategy, highlighting how central government resources were distributed to reinforce political alliances during local government elections.\u003c/p\u003e","manuscriptTitle":"Redistributing Loyalty? Political Determinants of Municipal Funding in Chile (2009–2023)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-18 10:37:25","doi":"10.21203/rs.3.rs-6865910/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9abed0f7-5f29-430c-b8ca-eaf4ff8377d1","owner":[],"postedDate":"June 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-17T17:25:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-18 10:37:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6865910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6865910","identity":"rs-6865910","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}