Growing Resources for Growing Cities: Density and the Cost of Municipal Public Services in Brazil, Chile, Ecuador, and Mexico

Libertun de Duren, Nora; Guerrero Compeán, Roberto Working Paper Growing Resources for Growing Cities: Density and the Cost of Municipal Public Services in Brazil, Chile, Ecuador, and Mexico IDB Working Paper Series, No. IDB-WP-634 Provided in Cooperation with: Inter-American Development Bank (IDB), Washington, DC Suggested Citation: Libertun de Duren, Nora; Guerrero Compeán, Roberto (2015) : Growing Resources for Growing Cities: Density and the Cost of Municipal Public Services in Brazil, Chile, Ecuador, and Mexico, IDB Working Paper Series, No. IDB-WP-634, Inter-American Development Bank (IDB), Washington, DC, https://hdl.handle.net/11319/7332 This Version is available at: https://hdl.handle.net/10419/146442 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc-nd/3.0/igo/legalcode IDB WORKING PAPER SERIES Nº IDB-WP-634 Growing Resources for Growing Cities Density and the Cost of Municipal Public Services in Brazil, Chile, Ecuador, and Mexico Nora Libertun de Duren Roberto Guerrero Compeán Inter-American Development Bank Institutions for Development Sector November 2015 November 2015 Growing Resources for Growing Cities Density and the Cost of Municipal Public Services in Brazil, Chile, Ecuador, and Mexico Nora Libertun de Duren Roberto Guerrero Compeán Contact: Nora Libertun de Duren, [email protected]. Copyright © Inter-American Development Bank. 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Cataloging-in-Publication data provided by the Inter-American Development Bank Felipe Herrera Library Growing resources for growing cities: density and the cost of municipal public services in Brazil, Chile, Ecuador, and Mexico / Nora Libertun de Duren, Roberto Guerrero Compeán. p. cm. — (IDB Working Paper Series ; 634) Includes bibliographic references. 1. Municipal services—Brazil. 2. Municipal services—Chile. 3. Municipal services—Ecuador. 4. Municipal services—Mexico. 5. Cities and towns— Brazil. 6. Cities and towns—Chile. 7. Cities and towns—Ecuador. 8. Cities and towns—Mexico. I. Guerrero Compeán, Roberto. II. Inter-American Development Bank. Fiscal and Municipal Management Division. III. Title. IV. Series. IDB-WP-634 http://www.iadb.org 2015 1! Abstract* This paper finds that per capita municipal spending on public services is strongly and non-linearly correlated to urban population density. Optimal expenditure levels for municipal services are achieved when densities are close to 9,000 residents per square kilometer. In this study of approximately 8,600 municipalities in Brazil, Chile, Ecuador, and Mexico 85 percent are below this ideal density level. This analysis provides strong policy support for densification, particularly for medium-sized cities in developing countries, which are currently absorbing most of the world’s urban population growth. JEL Codes: R12, R58 Keywords: optimal density, Latin America, urban services, public expenditures *We gratefully acknowledge the Vice Presidency for Sectors and Knowledge for its support in this study; Vicente Fretes-Cibils for his extremely helpful comments; and José Joaquín López for his excellent research assistance. A previous version of this paper was published in the Urban Studies Journal in September 2015. 2! 1. Introduction Dense cities are a rational choice for the increasingly urban world, where concerns about environmental sustainability and urban sprawl are paramount (UN-Habitat, 2012). Among their many advantages, dense cities help preserve fertile rural lands (Jenks and Burgess, 2000), decrease overall commuting lengths (Gaigne, Riou, and Thisse, 2012), and contribute to reductions in greenhouse gas emissions (Stone et al., 2007). Along with its environmental benefits, density correlates positively with human capital accumulation (Glaeser, 1999), a higher rate of inventions (Carlino, Chatterjee, and Hunt, 2007), labor productivity (Ciccone and Hall, 1996), and social inclusiveness (Burton, 2000). On these grounds, and as development policies finally integrate environmental and social goals, urban policies are pushing for densification in both developed and developing countries. Multilateral organizations such as the IDB (2013), the OECD (2012), the United Nations (UN-Habitat, 2012), and the World Bank (2014a) are calling for denser cities. National development plans, including those of China (2011), Colombia (2011), Mexico (2013), and South Africa (2012), advocate urban densification. Even development plans of arguably already dense cities such as London (2013), Monterrey (2011), and New York (2011) pursue explicit policies for higher densities. However, sustaining dense populations has its costs. Urban density increases land prices (Glaeser, Kolko, and Saiz, 2001), the wage premium (Wheaton and Lewis, 2001), congestion (Wheaton, 1998), and crime (Glaeser and Sacerdote, 1999). Its impact on public spending is inconclusive in the literature. Some studies show densification leads to savings in fire protection, waste collection, and education services (Bollinger et al., 2001). Yet, analogous research correlated density to diseconomies of scale for those same services (Abrate et al., 2012). In other studies, high densities have no impact on expenditures on fire protection and solid waste while reducing expenses on capital facilities, roadways, police, and education (Carruthers and Ulfarsson, 2003; 2008). Finally, others propose a U-shaped relationship between density and spending, implying that after an optimal density, expenditures and density would rise (Holcombe and Williams, 2008; Ladd, 1992). Further, the literature disregards that density is endogenous to spending, assuming that public service spending is a function of density. Yet, it is also plausible that people move to places where public services are available. Latin American urban history provides specific examples for both scenarios: densification has led to investments in public service infrastructure; neighborhood upgrading programs in Brazil provide only one of many recent examples (Brakarz, Greene, and Rojas, 2002). Conversely, investments in public service infrastructure have led to densification, with the canonical example being planned cities such as Brasilia. Moreover, coverage of public services could be imperfect, which is often the case in developing countries. For example, about one-third of Latin American urban population suffers some deficit of urban services (Bouillon, 2012)—a condition that threatens smaller municipal ! 3! governments now responsible for urban services (Campbell, 2012). If history provides guidance, the lack of municipal services would not deter population growth, but it would rather foster informal arrangements to provide these services. Indeed, Latin America’s urbanization is an ideal case for exploring the questions of endogeneity and imperfect coverage of urban services. Between 1960 and 2010, the Latin American and Caribbean (LAC) region’s share of urban population rose from about 50 to 80 percent, making the region more urbanized than Europe and as urbanized as the United States (World Bank, 2014b). Although only a few cities currently account for the vast majority of urban population, that number is increasing. While in 1950 there were 12 cities with more than 500,000 residents, now there are almost 125. As expected, urbanization also increased the demand for urban services and the number of municipalities responsible for their provision (IDB, 2013). However, LAC municipalities’ fiscal capacity tends to lag behind (Bonet et al., 2013); yet, urban immigration has not been detracted (Feler and Henderson, 2011). Today, the basic-service provision gap is considerable; more than 13 million urban residents lack access to improved water sources, while almost 64 million lack improved sanitation facilities in their dwellings (World Bank, 2014b). Closing the incremental water deficit demand that arises from urbanization, formalizing households’ water connections, and eliminating deficits by 2030 will cost more than US$100 billion; another US$79 billion is needed just to close the current sanitation deficit (CAF, 2013). In light of the gravity of these deficits, this study considers how current urban growth patterns would impact such deficits. Do dense municipalities have better coverage of basic services? How does density impact the per capita expenditure of these services? Our main contribution is to answer these questions by taking into account the issues of imperfect provision of public services and endogeneity, and relying on data sets form understudied developing countries. Therefore, we model public service spending as a function of its demand and cost, considering actual coverage levels. We also use climate as an instrumental variable to establish the causal effect of increases of density on municipal spending. We apply our model to a panel of approximately 8,600 municipalities in Brazil, Chile, Ecuador and Mexico, for years 2000 and 2010 (for a total of nearly 17,000 observations).We consider three basic services— water, sewage, and waste collection—the provision of which is organized and fully financed by municipal governments, unlike other services, such as education and health, whose costs are partly paid by the state and federal governments, the private sector, or directly by the homeowners.1 2 !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 1 National constitutions mandate that municipalities ensure the provision of these services (see Article 30 of the Brazilian Constitution, Article 115 of the Mexican Constitution, Article 264 of the Ecuadorian Constitution, and Article 3 of the Chilean Constitutional Law for Municipalities). 2 We attempted to include additional urban services, but data limitations did not allow for further disaggregation of urban services, given that some service categories overlap for some countries and are missing in others. ! 4! In the countries considered for this study, municipalities spend approximately one-seventh of their budget in these services (see Panel C in Table 2). The diversity of these countries provide a good background to test our model. Brazil and Mexico are large federal countries; Chile and Ecuador are small and quite centralized. The urbanization rates of these countries also differ, ranging from 68 and 78 percent in Ecuador and Mexico, to 85 and 89 percent in Brazil and Chile. Approximately 28 percent of Brazilian urban residents live in informal settlements lacking some basic service; this figure is 9 percent in Chile, 21 percent in Ecuador, and 14 percent in Mexico. The median GDP per capita in 2010 was US$10,978 in Brazil, US$12,685 in Chile, US$4,637 in Ecuador, and US$8,916 in Mexico (UNDP, 2005; UN-Habitat, 2014; World Bank, 2014b).3 Significantly, the combined population of these four amounts for about 60 percent of LAC countries. 2. Population Density and the Cost of Public Services As municipalities struggle to serve their current population, a critical issue is whether spatial factors pose a fiscal impact on public service delivery—this is precisely the matter we will address in this paper. Population density is a common indicator of the spatial distribution of residents (Forsyth, 2003). Its prominence in empirical studies suggests that—notwithstanding its shortcomings in depicting urbanization vis-!à-vis more nuanced dimensions such as continuity, nuclearity, and centrality—it is still a useful metric of urban form (Angel, Sheppard, and Civco, 2005). While nuanced spatial dimensions reveal how specific urban policies affect land use patterns and other particular local phenomena in developable and non-developable areas, density is a clearer concept to operationalize and compare, less prone to misinterpretations and more intuitive in general (Rapoport, 1977). Additionally, focusing on density rather than on population size makes sense from a development perspective as it relates to economic performance (Henderson, 2003). Moreover, in a context of a democratic society, policy tools for managing urban density are easier to implement than those restricting population growth. One might think that because population density is so consequential for many aspects of urban processes it would mean that it is well understood, particularly when it comes to its effect on local public finance. This is not the case. The hypotheses that urban economists and planners have proposed for cost structure dynamics are remarkably inconsistent. The impact of population density on government spending patterns, albeit widely studied and documented, is the subject of empirical controversy. The literature on the effects of density is—ironically—notoriously sprawled, with clear discrepancies both in terms of magnitude and sign. Although the notion that there is an adequate density level that makes the !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 3 We harmonize the data based on IMF (2014). ! 5! provision and delivery of public services economically efficient is consensual, different studies diverge in their recommendations for the most efficient use of economic resources. Advocates for densification argue that population density decreases the per capita cost of service provision. Sprawl would require infrastructure to be expanded to sparsely populated locations, increasing per capita costs. Coyne (2003) reports that between 1980 and 2000, densification policies in Colorado led to a 27 percent increase in population density and a 7 percent reduction in per capita spending. This coincides with a report for the City of Calgary (IBI Group, 2009) claiming that a 25 percent densification would reduce public expenditure on the provision of roads, fire protection, and water by 36, 46, and 54 percent respectively. Burchell and Mukherji (2003) find that moving 11 percent of households from sprawling counties to denser ones decreases the costs of water and sewer infrastructure by 7 percent, local road costs by 12 percent, and housing costs by 8 percent. Carruthers and Ulfarsson (2003) show that a 1 percent rise in the population density of US counties is associated with a 2 to 4 percent decrease in the cost of police protection and education, and an overall 3 percent decrease in the combined cost of 12 urban services. Likewise, Hortas-Rico and Sole-Olle (2010) find that in Spain a twofold expansion of urbanized land—that is, sprawl, increases community facilities costs by 11 percent, local police costs by 9 percent, housing costs by 8 percent, culture and sports costs by 15 percent, and general administration costs by 11 percent. Conversely, some researchers argue density does not necessarily lead to economies of scale. Pineda (2005) indicates that labor-intensive urban services (e.g., police, fire protection, healthcare) increase their per capita costs with population density. Ladd and Yinger (1989) demonstrate that a higher average density increases the public services costs owing to a “harsher environment.” Cameron (1989) finds that higher density implies higher costs for police services. Holcombe and Williams (2008, 2010) show that in municipalities larger than 500,000 residents, higher population density is associated with higher per capita government expenditures, particularly for sewer, police and highway spending. Carruthers and Ulfarsson (2003) show that transportation cost increases with density when roads are excluded. A third view is that densification has efficiency advantages in the provision of public services, but these dissipate as city size continues to increase. This suggests a U-shaped relationship between urban density and spending, and consequently that there is an optimal density level. Werner Hirsch (1959) performed one of the first empirical analyses in support of this theory based on fire protection data. Ladd’s (1992) seminal piece demonstrates that the operating expenditure function of US counties is approximated by a parabola whose trough is at a population density of 250 residents per square mile. She finds that the average current spending per head in very lowand very high-density counties (i.e., up to 125, and more than 24,000 residents per square mile) is 14 and 43 percent higher than in counties within ! 12! Source: Authors’ elaboration. a We prefer INEGI’s data to ENOE’s data because it collects this information at the municipal level. 4.5 Climate Indicators We use monthly average daily temperature and precipitation data generated from the University of East Anglia Climatic Research Unit (CRU) (2014) time-series data sets spanning the period 1910–1930. These are calculated on high-resolution (0.5⁰ by 0.5⁰) grids. Similarly, we obtain monthly self-calibrated average Palmer Drought Severity12 Index values (Palmer, 1965) to proxy for soil moisture, obtained from NOAA’s National Center for Atmospheric Research. These are calculated on 2.5⁰ latitude by 2.5⁰ longitude global grid (NOAA, 2014). We construct our municipal data by applying a spherical interpolation routine: weighted averages of the 10-year climatology of temperature, rainfall, and soil moisture for every gridded point within 150 km of each municipality’s geographic center, with the inverse squared haversine distance between the grid point and the municipality centroid as the weighting factor. D. Urban indicators (2000–2010) Geographical coordinates UNDP Brazil Caracterização do Município Instituto Geográfico Militar. MAPAS IGM. INEC. División Político Administrativa del Ecuador INEGI. Marco geoestadístico 2010 versión 5.0 AGEM Territorial extension Urban territorial extension Empresa Brasileira de Pesquisa Agropecuária. Mape amento e estimativa da área urbanizada do Brasil Corporación Nacional Forestal. Sistema de Información Territorial INEC. Archivo Nacional de Datos y Metadatos Estadísticos. Censo de Información Ambiental Económica INEGI. Información Vectorial de Localidades Urbanas Primacy definition IBGE. RIDES and Regiões de Influência das Cidades INE. Ciudades, pueblos, aldeas y caseríos 2002 Gobierno de Ecuador. Constitución de Ecuador de 2008 CONAPO. Delimitación de las zonas metropolitanas de México 2000, 2010 Metropolitan area definition IBGE. Organização Territorial e Composição das Regiões Metropolitanas SENPLADES. Estrategia Territorial Nacional E. Climate indicators (1910–1930, 2010–2030) Monthly average daily temperature; rainfall; soil moisture UEA Climatic Research Unit NOAA’s National Center for Atmospheric Research ! 13! Table 2. Descriptive Statistics Mean Standard deviation Observations A. Demographics (2000–2010) Population density 175.90 919.20 17,092 Urban population density 13,121.19 85,583.85 15,439 Covered urban population density 8,447.19 47,152.56 14,795 Potential service users (all municipal residents) 36,574 174,349 17,096 Urban population (and) 54 29 17,092 Average household size 3.89 0.65 17,082 Poverty rate 0.37 0.24 17,105 B. Public service (2000–2010) Municipal coverage–three services (high quality) 0.51 0.24 17,087 Urban 0.69 0.21 15,268 Piped water 0.67 0.24 17,087 Urban 0.87 0.19 15,268 Network sanitation 0.31 0.31 17,087 Urban 0.44 0.38 15,268 Curbside trash collection 0.55 0.28 11,507 Urban 0.77 0.28 11,506 C. Fiscal and economic indicators (2000–2010) in USD Municipal spending 18.51 169.22 16,731 Municipal spending in public services 2.61 17.70 16,710 Water 0.76 5.69 16,210 Sanitation 0.70 6.74 11,130 Trash collection 0.88 9.97 16,210 Tax revenue 3.08 69.56 16,738 Property tax revenue 1.15 23.95 15,287 Intergovernmental transfers 12.49 73.00 16,738 Average wage 4,710 3,211 16,785 Unemployment rate 0.07 0.06 16,814 Income 5,089 5,733 17,014 D. Municipal characteristics: 2000–2010 Territorial extension (km2) 1,484 14,303 17,168 Urbanized area (and) 3 12 17,020 Metropolitan municipality indicator 0.11 0.31 17,172 Primate municipality indicator 0.05 0.22 16,588 E. Climate measures (1910–1930 and 2012) Lagged average annual temperature (°C) 21.0 4.3 17,042 Lagged soil moisture (Palmer Drought Index) 0.2 1.0 16,239 Lagged annual rainfall (mm) 1,197.0 490.1 17,042 Current average annual temperature (°C) 22.1 5.2 17,042 Current annual rainfall (mm) 1,102.5 870.9 17,042 Source: Authors’ elaboration (data sources are already clarified). ! 14! 5. Results 5.1 Density and Coverage We begin our analysis with a simple question: In which municipalities do households have more access to high-quality water, sanitation, and waste collection services? We estimate a nonparametric locally weighted regression (Fan, 1992) with an Epanechnikov kernel to display municipal coverage levels as a function of urban density. We restrict the sample to 90 percent of the observations. Figure 1 indicates that a larger share of the urban population with access to high-quality services is observed in denser urban areas. More than two-thirds of the municipalities whose coverage level is below 10 percent are in the first quartile of the urban population density distribution. Conversely, over 43 percent of the municipalities enjoying coverage levels above 90 percent are in the top quartile. This relation is consistent and holds when disaggregating coverage by type of service (Figure 2), but sanitation coverage is much lower than that of the other two and water coverage is high even for sparsely populated urban areas. From these figures alone, it is impossible to determine to which extent urban density affects municipal spending patterns, particularly its magnitude at different parts of the distribution. Figure 3 illustrates a locally weighted regression that shows the relationship between urban density (percentiles) and municipal expenditures in public services per head. It would seem that the relationship is U-shaped, yet the statistical significance of a causal effect of urban density on local spending should be verified, given the endogeneity of density to spending patterns. We turn now to our empirical strategy to address this issue. Table 3. Percentage of Residents with Access to Municipal Public Services, by Coverage Level and Urban Population Density Quartile Households covered Urban population density (pop/km2) Q1 0-2,333 Q2 2,334-3,960 Q3 3,961-6,378 Q4 over 6,378 A. All municipal services Under 10% 68.4 21.9 6.9 2.8 Over 90% 3.7 15.0 38.0 43.4 B. Piped water Under 10% 66.3 17.8 9.2 6.7 Over 90% 16.9 23.4 28.8 31.0 C. Network sanitation Under 10% 59.7 24.5 10.8 5.0 Over 90% 4.8 15.0 36.7 43.5 D. Curbside trash collection Under 10% 60.8 25.5 9.8 3.9 Over 90% 10.1 20.8 36.8 32.3 Source: Authors’ elaboration ! 15! Figure 1. High Quality Municipal Service Coverage on Urban Density Source: Authors’ elaboration Notes: Non-parametric fan locally weighted regression, using an Epanechnikov kernel and a bandwidth of 1 with bootstrapped standard errors, conditional on regional fixed effects and country-specific time trends. ! 16! Figure 2. High Quality Municipal Service Coverage on Urban Density, by Type of Service Source: Authors’ elaboration Notes: Non-parametric fan locally weighted regression, using an Epanechnikov kernel and a bandwidth of 1 with bootstrapped standard errors, conditional on regional fixed effects and country-specific time trends. ! 17! Figure 3. High Quality Municipal Public Service Coverage per Head on Urban Density, by Type of Service Source: Authors’ elaboration. Notes: Non-parametric fan locally weighted regression, using an Epanechnikov kernel and a bandwidth of 1 with bootstrapped standard errors, conditional on regional fixed effects and country-specific time trends. 6. First-Stage Relationship and Reduced-Form Results We now discuss the ability of our instruments to predict current population density. Remember that our instrument set includes lagged municipal temperature, rainfall, and soil moisture conditions. The firststage relationship between our set of instruments and population density is always significant, with the strongest association being observed between soil moisture, temperature, and urban density (Table 4). The relationship is also robust and equally significant when we add controls for municipal characteristics and two-way fixed effects, as well as country-specific time trends (Regressions 5–7, Table 4). Notice that the first-stage relationship remains strong and significant when rainfall substitutes soil moisture conditions as part of the instrument set. Although statistical tests show that climate instruments are moderately strong (F-statistics ranging from 9.0 to 18.9), we estimate as an identification check a false experiment specification in which future climatic conditions, which should be orthogonal to current urban density, are used as instruments. We find that coefficient estimates are indeed statistically equal to zero ! 18! (Regression 8, Table 4). Lower lagged temperatures are strongly associated with higher municipal spending in the reduced-form regressions. A 1 percent increase in lagged temperature is associated with a 10 percent decrease in per capita municipal spending in public services. Similarly, a 1 percent increase in lagged soil moisture is associated with a 0.1 percent increase in municipal spending in public services per head (Regressions 1 and 2, Table 5). These relationships are statistically significant at the 99 percent confidence. As expected, when only urban municipalities are considered6, the point estimates decrease in magnitude, but the relationship remains statistically strong (Regressions 6 and 7, Table 5). Similarly, reduced-form regressions indicate that our instrument sets are also associated with total municipal spending per head, but at lower magnitudes and somewhat lower statistical significance (Regressions 8 and 9, Table 5). 7. Main Empirical Results We perform both ordinary least squares (OLS) and instrumental-variable two-stage least squares (IV2SLS) estimations. Given our previous theoretical discussion and nonparametric analysis, a nonlinear IV 2SLS with municipal fixed effects, country-specific time trends, and controls specification are taken as our benchmark. We will focus on the results of this specification from this point forward. (Table 7b, column 10). Our results are similar when time fixed effects are included. We find that the relationship between urban density and municipal spending in public services per head is strong and U-shaped, suggesting there is an optimal density point (the vertex of the parabola) beyond which economies of scale are exhausted. An increase in urban density leads to lower per capita municipal public service spending in sparse and medium-sized urban areas, but a further increase in population density significantly raises the costs of providing public services in already dense jurisdictions. We identify an optimum density point at approximately 9,000 inhabitants/km2.14 Belem (Brazil), Santiago (Chile) and Puebla (Mexico), are among those municipalities near the optimal density range (see Table 6). We find that the average municipality exhibits economies of scale near 8,450 residents, spending US$75 per resident in basic municipal service provision. Our benchmark specification shows that a 1 percent point increase in population density leads to a 0.99 percent point decline in per capita expenditures in public services. This is equal to a decrease in current municipal service spending per resident from US$75 to US$67, given a 10 percent increase in urban density. In a municipality with lower-than-average densities, such as those in the first quartile (i.e., 2,334 inhabitants per km2), a 1 percent increase in urban density would decrease per capita spending by !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! 6 We consider a municipality as urban when at least 50 percent of its residents live in urban areas. We considered other cutoff points with virtually identical results. ! 19! almost 1.4 percent. Conversely, in a very dense municipality, such as those in the ninth decile (i.e., 9,659 inhabitants per km2), a 1 percent increase in urban density leads to an increase in per capita spending by almost 0.1 percent. All these associations are significant at the 95 percent level (see Table 7a/b). The impact of urban density on municipal public service spending per head is significant in alternative specifications. To further assuage potential violations to the exclusion restriction (i.e., climate should affect municipal spending patterns only through density), we restrict our sample to urban municipalities. In our view, the most serious violation to the exclusion restriction is a potential climate effect on income. However, while there is evidence that climate is robustly related to income in rural areas, it has not been found to exert a clear effect in urban centers (Guerrero Compeán, 2013). When nonurban municipalities are excluded, the elasticity of population density, based on our preferred benchmark framework, is approximately 21.5 for the average municipality (Regression 2, Table 8). The results remain statistically significant at the 95 percent level. Again, we find evidence in support of a U-shaped relationship, with low-(high-) density urban municipalities exhibiting economies (diseconomies) of scale. For sparsely populated urban municipalities—at the first decile—a 1 percent point increase in population density leads to a 3.4 percent point decline in per capita municipal spending in public services. Conversely, for the urban municipality at the ninth decile, a 1 percent point increase in population density leads to a 0.4 percent point increase in municipal public service expenditures per head. The IV-2SLS fixed-effects results are robust to an alternative dependent variable. When the relationship between urban density and total municipal spending per head—as opposed to per capita spending in public services—is considered, we find that most municipalities exhibit economies of scale, with the trough being at a population density of over 50,000 people per square kilometer (Regression 4, Table 8). Similarly, the choice of instruments does not change the statistical significance of our results. Urban population density does not have a statistically differential impact on public service spending per head (for either the pooled or urban-only specifications) when rainfall is included as an additional instrument (Regressions 1 and 3, Table 8). ! 20! Table 4. Climate and Population Density (first stage) Explanatory variable Ordinary least squares (1) (2) (3) (4) (5) (6) (7) (8) Temperature 0.0230** 0.162*** 0.173*** 0.133*** 0.0753*** 0.100*** 0.0701*** (0.0108) (0.0411) (0.0364) (0.0333) (0.0240) (0.0279) (0.0234) Soil moisture -0.267*** 0.0629*** 0.0517*** 0.0289*** 0.0235*** (0.0634) (0.00929) (0.00922) (0.00577) (0.00593) Rainfall 0.0002* 0.0002** (0.0001) (0.0001) Future temperature 0.0231 (0.0187) Future rainfall 0.0001 (0.0001) F-test of excluded instruments 9.05 23.23 18.92 14.25 13.26 10.35 10.02 1.12 Full controls No No No No Yes Yes Yes Yes Country-specific time trends No Yes No No Yes No No No Two-way fixed effects No No Yes Yes No Yes Yes Yes Observations 13004 12030 12030 12812 10024 10024 10722 10722 R2 0.0438 0.367 0.378 0.378 0.584 0.588 0.595 0.593 Root mean square error 1.241 0.912 1.028 1.399 0.926 1.090 1.103 1.026 Source: Authors’ elaboration. Notes: Dependent variable: Covered urban population density. Regression disturbance terms are clustered at the regional level. Huber-White robust standard errors in parentheses. * p<0.10 ** p<0.05 *** p<0.01. ! 21! Table 5. Climate and Municipal Spending in Public Services (Reduced Form)–Part 1 Explanatory variable Dependent variable Municipal spending in public services per capita Pooled (OLS) (1) (2) (3) (4) (5) Temperature -0.965*** -0.987*** -0.965*** (0.182) (0.180) (0.195) Soil moisture 0.129*** 0.0643* (0.0405) (0.0375) Rainfall 0.000425* 0.00069* (0.000274) (0.00037) Two-way fixed effects Yes Yes Yes Yes Yes Observations 15580 14839 15580 15580 14832 R2 0.107 0.0846 0.0768 0.109 0.114 Root mean square error 0.691 0.703 0.703 0.690 0.692 Source: Authors’ elaboration. Notes: Regression disturbance terms are clustered at the regional level. Huber-White robust standard errors in parentheses. * p<0.10 ** p<0.05 *** p<0.01. Table 5. Climate and Municipal Spending in Public Services (Reduced Form)–Part 2 Explanatory variable Dependent variable Municipal spending in public services per capita Total municipal spending per capita Urban (OLS) Pooled (OLS) (6) (7) (8) (9) Temperature -0.475*** -0.320* -0.278*** -0.284*** (0.153) (0.182) (0.0481) (0.0507) Soil moisture 0.0982* 0.00348 (0.0561) (0.0105) Rainfall 0.000954** 0.000264* (0.000433) (0.000144) Two-way fixed effects Yes Yes Yes Yes Observations 1129 1104 16149 15359 R2 0.191 0.189 0.279 0.277 Root mean square error 0.423 0.425 0.260 0.260 Source: Authors’ elaboration. ! 28! capital from operational costs; current data do not allow us to perform such studies. Last, an important future contribution will be to examine how density impacts the costs of delivering urban services in informal settlements, given that delivering services ex post is more expensive. From a policy perspective, the association between municipal spending and density is good news. We have better tools to control population density than to limit population sizes. Building codes and performance zoning are useful tools in regulating densities. On the other hand, the cost of changing the existing status-quo needs to be considered when planning for density. While densification should lead to economies of scale in sparsely populated areas, expanding coverage may be harder. Conversely, increasing coverage in a dense area might be easier, even if it is more expensive. This is true inasmuch as local governments fiscally capitalize the benefits of a more comprehensive coverage, and urban areas are already zoned for higher densities. The fact that density increases per capita public service spending in highly dense urban areas does not mean it is bad policy. It just underscores that growing cities need growing resources. A finer-grain study of where it makes sense to densify within a single municipality is needed. It may be more effective to promote density in certain neighborhoods with a given set of spatial features. This may require assembling land in low dense areas—where it should have a lower market price—and then rezoning it for higher densities. Tools for promoting density—such as master plans—should be reconsidered in light of their impacts in other dimensions, as well as the delay between their publication and actual implementation. It would be more effective to promote density by increasing the quality and access to urban services. In that sense, quantitative references here should be taken as part of a larger picture in the analysis of each city, in which history, culture, and geography play a key role. In all cases, governments should take a long, strategic view toward urban growth. Finally, this study calls for an integral approach to determining ideal densities. 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