WPS8696 Policy Research Working Paper 8696 Institutional Fragmentation and Metropolitan Coordination in Latin American Cities What Consequences for Productivity and Growth? Juan C. Duque Nancy Lozano-Gracia Jorge E. Patino Paula Restrepo Social, Urban, Rural and Resilience Global Practice January 2019 Policy Research Working Paper 8696 Abstract This paper provides empirical evidence on the impact of of urbanized areas with more that 500,000 inhabitants. The institutional fragmentation and metropolitan coordination initial results suggest that the presence of multiple local on urban productivity in Latin American cities. The use governments within metropolitan areas generates opposite of night-time lights satellite imagery and high-resolution effects on urban productivity. On the one hand, smaller population data allow the use of a broader definition of governments tend to be more responsive and efficient, metropolitan area. Thus, metropolitan area consists of the which increases productivity. But, on the other hand, mul- urban extent that results from the union between the for- tiple local governments face coordination costs that reduce mally defined metropolitan area and the contiguous patches productivity. This paper is a product of the Social, Urban, Rural and Resilience Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at nlozano@worldbank.org and prestrepocadavid@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Institutional Fragmentation and Metropolitan Coordination in Latin American Cities: What Consequences for Productivity and Growth? Juan C. Duque ( ) Research in Spatial Economics (RiSE-group), Department of Economics, Universidad EAFIT, Medellin, Colombia.e-mail: jduquec1@eafit.edu.co Nancy Lozano-Gracia, Social, Urban, Rural and Resilience. The World Bank. Washington, USA. e-mail: nlozano@worldbank.org Jorge E. Patino, Research in Spatial Economics (RiSE-group), Department of Economics, Universidad EAFIT, Medellin, Colombia.e-mail: jpatinoq@eafit.edu.co Paula Restrepo , Social, Urban, Rural and Resilience. The World Bank. Washington, USA. e-mail: prestrepocadavid@worldbank.org Keywords: productivity ꞏ institutional fragmentation ꞏ metropolitan coordination JEL Classification: R11 ꞏ R14 ꞏ R50 ꞏ H70 2 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   1 Introduction Cities are central to the productivity and growth prospects of the Latin American and Caribbean region (LCR). The vast majority of the region’s output is generated in cities – Mexican cities generate 87 percent of gross value added (Kim and Zangerling, 2016)), while Argentine cities contributed almost 80 percent of national GDP in 2007.1 Yet it is unclear whether LCR’s cities are realizing their full potential – for the region’s major economies, GDP per capita levels are well below what one would predict based on their urbanization levels,2 and recent policy reports by, for example, the McKinsey Global Institute have argued that cities in the region are being weighed-down by excessive diseconomies of agglomeration (Dobbs et al., 2011; Cadena et al., 2011). According to the United Nations, Latin America and the Caribbean is the most urbanized region on the planet with 80% of its population living in cities (UN, 2012). The rapid urbanization of cities, together with the loss of density, has caused the expansion of cities beyond their administrative borders, absorbing surrounding urban areas and creating large urban extents (metropolitan areas) covering several administrative units. Since no local government has the tools to address all challenges and opportunities within a metropolitan area on its own, this form of urbanization poses new challenges to local authorities in terms of governance and integration. The way in which these large, multicity, urban extents manage aspects such as transportation, urban planning, infrastructure provision and other social and economic affairs, can make the difference between enjoying the benefits of prosperous economies of agglomeration or suffering the consequences of the diseconomies of agglomeration (Ahrend et al., 2014). Unfortunately, there are two aspects that make it difficult for academics to make public policy recommendations for Latin American cities (LAC). First, the existing empirical evidence is mostly concentrated on metropolitan areas in developed countries; and second, there is supporting evidence in favor of the three models of metropolitan governance: The polycentrist model, which advocates                                                              1 McKinsey Global Institute Cityscape database, version 1.1 (http://www.mckinsey.com/insights/urbanization/urbanworld). 2 This conclusion is based on GDP per capita data from WDI and urban population share data from the UN’s World Urbanization Prospects (2014 Revision) database (http://esa.un.org/unpd/wup/). One issue that arises with the urban population data from the WUP database, however, is that it is based on official national definitions of urban areas, which differ from country to country. An important question to address, therefore, is the extent to which the apparent under- performance of LCR countries is driven by this inconsistent measurement of urban population shares. This will be addressed in another of the report’s background papers. J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 3   for the coexistence of multiple small and coordinated local governments; the centrist model, which argues that a single governance body takes advantage of reduced transactions costs and economies of scale and scope in providing public goods and services; and the regionalist view which recognizes the benefits of local governments while highlighting the importance of coordinated governance. This paper will examine the interaction between local governance and economic performance, looking specifically at the effects of the fragmentation of functional urban areas across administrative entities. It will empirically test whether the economic performance of LAC is affected by the degree of institutional fragmentation that they exhibit. The paper will also seek to explore which, if any, of the different forms of metropolitan coordination observed in the region leads to a reduction of the penalties/advantages imposed by institutional fragmentation on economic performance. Finally, the paper explores whether results vary across city sizes (i.e. is there a specific threshold after which fragmentation penalties appear?) and whether they depend on the level of population concentration in a city’s center or the overall spatial population distribution across administrative units (i.e., does it matter if most of the metropolitan population is concentrated in the city center?). Understanding these links is also important from a policy perspective. Improving the understanding of the strength and direction of these links can suggest whether national governments should support or incentivize metropolitan coordination mechanisms or entities, or whether they should support the consolidation of local governments. Further, there is also the question of whether national governments should support the fragmentation of local governments, or on the contrary, whether there are specific sectors (i.e. transport, environment, local economic growth) for which it makes sense to align fragmented local governments. The rest of the paper is organized as follows. Section 2 provides a literature review. Section 3 presents the empirical models. Section 4 describes the source data. Section 5 presents the empirical results. Finally, section 6 presents our conclusions and ideas of future work. 2 Literature review As mentioned by Nelson and Foster (1999), there exist three lines of thought when looking at the links between the governance structures of cities and their economic performance: polycentrist, centrist and regionalist. The polycentrist view is in line with Fisher´s argument in favor of dividing a 4 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   region into sub-regions to facilitate the planning and distribution of resources (Fischer, 1980). At the city level, the polycentrist view argues that institutional fragmentation of cities is equivalent to creating additional layers of decentralization, which can, in fact, enhance economic growth. This is thought to be achieved through two mechanisms. First, decentralized authorities are better informed of local needs and, therefore, can be more efficient in the provision of public goods (Ostrom, 2010). Second, increased competition between individual local governments constrains their ability to extract monopoly rents, thereby enhancing economic efficiency and, hence, economic growth (Stansel, 2005). The second line of thought, the centrist, argues that the presence of multiple local governments within metropolitan areas may generate coordination failures that reduce efficiency in the provision of transport infrastructure and land use planning, and therefore affect economic performance (see Ahrend et al., 2014). Fragmentation may also reduce the metropolitan area’s ease of doing business because of the additional bureaucracy (Kim et al., 2014). According to Cheshire and Gordon (1996) and Feiock (2009), the presence of administrative boundaries within the functional region generates higher transaction costs and barriers to the diffusion of growth-promoting policies. As additional evidence in favor of the centrist view, Foster (1993) found a negative association between the proportions of population unincorporated to the metropolitan area and income growth. Finally, the regionalist view can be seen as a middle way between the polycentrist and the centrist views. The regionalist view recognizes the benefits of local governments while highlighting the importance of metropolitan coordination defined as the efforts of governmental institutions to manage and solve problems in common between municipalities. According to Grassmueck and Shields (2010), more important than the existence of multiple local governments is the way in which they interact and perceive each other. Ahrend et al. (2014) found that the presence of a governance body that coordinates municipalities halved the penalty associated with fragmentation. Foster (1993) and Nelson and Foster (1999) also found empirical support for the regionalist view as a positive association between income growth and the presence of overarching decision-making mechanisms such as multi-jurisdictional, multipurpose regional governments. Also, the existence of single- purpose districts associated with large-scale infrastructure provision (e.g. water and wastewater systems) fosters income growth. J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 5   Regarding the measure of institutional fragmentation and metropolitan coordination, Table 1 and Table 2 present a summary of the variables commonly used in empirical studies. In Table 1 we classify the variables using the five categories proposed by Hendrick and Shi (2015). Based on the conceptualization of fragmentation as those urban extents that spread out over several, and independent, administrative units, the most common variables are those that measure the number of local governments included in the urban extent (sometimes standardized by population or land area). Other measures focus on the degree of concentration, the dominance relationship between the central city and the periphery. As considered by the centralist view, the potential drawbacks of having institutional fragmentation can be mitigated through proper channels of metropolitan coordination. This coordination can be reached via institutions (governance), coordinated planning and infrastructure (land use planning and mobility), the presence of overlapping governments with special of general purpose (provision of public utilities), or can be the result of tight linkages between the administrative units, which intensifies human interactions, generates spatial dependence, and facilitates coordination (functional region). Table 2 classifies the potential variables according to those proposed channels of coordination. The scarcity of empirical studies, in addition to the differences found when taking different approaches and using different economic performance indicators, suggests that there is a need to further test the empirical links between the institutional fragmentation of cities and their productivity/economic growth. This paper makes multiple contributions to the literature. First, and foremost, it will allow for testing of whether existing findings for the US and other selected OECD countries also carry-over to countries in LAC. Second, the paper will allow for the testing of the robustness of results to different institutional fragmentation and institutional coordination measures. Third, it will allow for an assessment of whether existing metropolitan coordination measures are effective in helping to produce better economic outcomes. Finally, contrary to existing literature, most of the analysis will be supported by spatial data that are readily available at the global scale and for developing countries. This will allow the paper to contribute to the development of a methodology that can be easily replicated for other regions of the world. 6 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   Table 1. Measures of institutional fragmentation. Representation Fragmentation Index (metro level) Authors I. Size of region Total number of local governments Hendrick and Shi (2015); Hill (1974) Differences in population and area of Barlow (1991) municipalities II. Political fragmentation Total local governments per capita Hendrick and Shi (2015); Hill (1974) Total number of governments per 10,000 Oakerson (1987); Post and Stein (2000) Number of administrative units per 50,000 Ahrend et al. (2014) persons Proportion of unincorporated population Foster (1993) Government per 100,000 persons Hawkins (1971); Ahrend et al. (2014); Schneider (1989) Cities > 10,000 persons per 1 million MSA Morgan and Mareschal population (1999) Number of suburban units with more than Bollens (1986). 10,000 persons, per 100,000 persons in the MSA. Percent of Metro residents in suburbs with Bollens (1986). more than 10,000 people III. Spatial fragmentation Total local governments per square mile Hendrick and Shi (2015) IV. Range of local HH Index of percent of different types of local Hendrick and Shi (2015) governments government V. Suburban domination Percent of population not in central city Hendrick and Shi (2015) (or central city Ratio of population in the city core to that in Ahrend et al. (2014) domination) the periphery Central-city population share Morgan and Mareschal (1999) Percent of metropolitan population held within Savitch et al. (1993) the borders of a central city Central-city area growth Morgan and Mareschal (1999) Central-city elasticity Rusk (1993); Blair et al. (1996) J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 7   Table 2. Measures of metropolitan coordination. Representation Fragmentation Index (metro level) Authors I. Governance Governance Body Ahrend et al. (2014) Age of the metropolitan area Nelson and Foster (1999) Number of municipalities with the same political party Pradenas (2006) II. Land use plan Percent of municipalities covered by integrated transport Kim et al. (2014) and mobility systems between municipalities and central city III. Coordination Percent of special purpose to general purpose Hendrick and Shi (2015) for special governments purpose Number of general purpose units Goodman (1980) Number of special purpose units Goodman (1980) IV. Functional Percent of people working in the central city Feria and Susino (2005) region Number of commuting from the municipalities to the De Esteban (2009). central city Percent of student population that go to the central city Pradenas (2006) Percent resident-job in central city Pradenas (2006) Number of telephone calls per month from the Pradenas (2006) municipality to the central city must be four times greater. 3 The Model To estimate the relationship between institutional fragmentation and economic performance of cities, measured through city productivity, we follow the two-step empirical approach devised by Ahrend et al. (2014). The authors warn about the importance of accounting for individual sorting of highly skilled individuals into cities when estimating productivity differentials across urban areas (Combes et al., 2011). This is necessary in order to account for the tendency of more talented individuals to co- locate in cities that may lead to confounding agglomeration benefits with productivity increases from a more skilled workforce. Thus, in the first step, we use data from the Defense Meteorological Satellite Programs – Operational Linescan System (DSMP-OLS) nighttime lights (NTL) imagery to identify urban areas as well as survey-based micro-data for the period 2000-2014 to estimate productivity differentials across urban areas, net of individual and employment characteristics observables. The estimation on this first stage is then: ∑ ∑ ∑ , (1) 8 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   where is the real wage for individual i at time t; are municipalities fixed effects; is a vector of demographic characteristics, indexed by , that include indicators of education; is a vector of job characteristic, indexed by , that includes industry code and indicators of formality and job benefits; and is an error term. The coefficient captures the productivity differential across cities, after controlling by individual and employment characteristics. In the second stage, we use the estimated productivity differentials, , as the dependent variable in the following expression: , (2) where is a vector of variables for institutional fragmentation; is a vector of variables for metropolitan coordination; is a vector of control variables, included the intercept; and is the error term. 4 Data 4.1 Study region In this work, we analyze Latin American and Caribbean metropolitan areas with more than 500,000 people in 2010 (Figure 1). J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 9   Figure 1. Location of identified metropolitan areas with more than 500,000 people in 2010 in Latin America and the Caribbean. The blue areas are the urban extent of the larger conurbation within each metropolitan area. 4.2 Metropolitan areas delineation from DMSP-OLS images We use data from the Defense Meteorological Satellite Programs – Operational Linescan System (DSMP-OLS) nighttime lights (NTL) imagery to identify urban areas and metropolitan conurbations. The NTL data are based on nighttime imagery recorded by the Defense Meteorological Satellite Programs - Operational Linescan System (DMSP-OLS), and reports the recorded intensity of Earth’s 10 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   surface lights. Nighttime lights products have high correlation to human activities (Hsu et al., 2015), and have been previously used for regional and global analysis of population modeling (Anderson et al., 2010; Lo, 2001), economic performance (Cao et al., 2016; Forbes, 2013), and urbanization (Cheng et al., 2016; Pandey et al., 2013; Sutton et al., 2006; Zhang and Seto, 2011; N. Zhou et al., 2015; Zhou et al., 2015). There are two different nighttime light products from DMSP-OLS that can be used to delineate urban areas: the stable or ordinary product (NTL), and the radiance-calibrated (NTL RC) product. We decided to use the latter, since it is aimed to correct the saturation issue in bright areas such as city centers, where the NTL might be brighter, but the recorded digital number (DN) values are truncated at 63; and the RC product gives better correlations with socioeconomic variables than the stable products (Hsu et al., 2015; Ma et al., 2014). Another known issue of the DMSP-OLS products is the “overglow” effect: dim lighting detected from light in surrounding areas of cities because of the scattering of lights in the atmosphere (Wu et al., 2014). A novel deblurring process was applied to address the issue of over glow in the radiance-calibrated products. This process involves the use of two sequential filters, a standard deconvolution and the frequency of illumination maxima, to withdraw the light from the surroundings back and restacking it vertically on its source pixels at city centers (Abrahams et al., 2016). Deblurred DSMP-OLS RC annual composites for the years 1996, 2000 and 2010 were previously inter-calibrated and corrected for a multi-temporal analysis of urban form and city productivity in Latin America (Duque et al., 2017). In that work, the three nighttime images were used to delineate urban extents in each year for most of the Latin American and Caribbean cities that had more than 50,000 people in 2010. We used those delineated urban extents for the year 2010 to identify the metropolitan areas in the region. We consider the presence of a metropolitan area when more than one municipality or equivalent administrative unit intersects a single urban extent with more than 500,000 people in 2010. We use the administrative unit boundaries from the World Bank Latin American and the Caribbean Spatial Framework Database (Branson et al., 2016) for this purpose. Metropolitan area boundaries were obtained by aggregation of all of the administrative units that intersected the same urban extent. We verified each obtained metropolitan area with ancillary information from official sources to include those municipalities that are part of the official metropolitan area denomination but were not intersected by the urban extent. Figure 2 presents some J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 11   examples of identified functional areas: Mexico City (Mexico), Rio de Janeiro (Brazil), and Buenos Aires (Argentina). Figure 2. Examples of functional areas. Urban extents extracted from 2010 nighttime images (in red), over the GHSL Built-up layer for 2014 (Freire and Pesaresi, 2015), with administrative boundaries (light purple). From left to right: Mexico City (Mexico), Rio de Janeiro (Brazil), and Buenos Aires (Argentina).  4.3 Estimated productivity differentials, As presented in section 3, the first step to estimate the relationship between institutional fragmentation and economic performance of cities consists of extracting the productivity differentials between functional areas by extracting first the sorting effect that causes that more skilled workforce have a tendency to live in larger cities (Ahrend et al., 2014; Combes et al., 2011). The vector of coefficients in equation 1, which becomes the dependent variable in the second stage (equation 2), was provided by Quintero and Roberts (2017) who studied the spatial variations in productivity premiums in 16 LAC countries. In their study, the authors use micro data on real hourly wage in the main occupation. As independent variables the authors use: (1) a vector of observable characteristics per worker (age, gender, marital status, years of education completed, and hours worked in the main occupation); (2) a vector of job characteristics that each worker occupies (sector, formal/informal status, and type of company –large private, small private, and public-); and (3) municipality fixed effects, which is our dependent variable, , in equation 2. All the collected data cover the period 2000-2014. We report the results with two types of that we are calling and : controls for the effects of sorting including all employed wage-workers aged from 14 to 65; controls for the effects of sorting including male wage-workers employed in the private sector and aged from 20 to 55. 12 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   4.4 Measuring institutional fragmentation and metropolitan coordination Based on the literature desk review and the available data, we construct a database with a series of variables to characterize the functional areas included in this study, in terms of institutional fragmentation and metropolitan coordination. We use the administrative boundaries of local governments that conform the metropolitan areas and distributed population data to calculate institutional fragmentation measures using geoprocessing tools in ArcGIS. Administrative boundaries were obtained from OpenStreetMap3 (April 25th 2017) and the World Bank LAC Spatial Database (Branson et al., 2016). We projected the administrative boundaries and the urban extents to the UTM coordinate system to calculate areas in square kilometers. Population counts at the administrative unit and urban extent levels were estimated using the Global Human Settlement Layer (GHS) distributed population grids produced by the Joint Research Centre (JRC) of the European Union (Freire and Pesaresi, 2015; Pesaresi et al., 2016). These layers show population counts for each pixel at 250 meters of spatial resolution, and were produced for the years 1975, 1990, 2000 and 2015. We used the 2000 layer to account for the population in that same year, and the 2015 layer as proxy for the population in year 2010. Metropolitan coordination variables were obtained through a number of official information sources to account for the presence of a metropolitan governance body and public services single purpose districts (see Table A1). Tables 3 and 4 present the list of available variables for institutional fragmentation and metropolitan coordination respectively. The descriptive statistics are presented in Table A2. 4.5 Control variables In order to isolate the predictive power of the variables describing urban form and to reduce omitted- variable bias, we include in the model a number of control variables including city size, locational variables, natural and urban amenities, as well as country fixed effects. A number of different data sources are used to compute control variables. Population data in gridded format for 2010 were                                                              3 http://www.openstreetmap.org/copyright J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 13   obtained from the GHS layers. Natural amenities were calculated using several GIS layers: Water bodies were used to calculate dummy variables for location near the sea (coast). We used the 250 meters resolution raster MODIS Water Mask (Carroll et al., 2009) for this purpose. Finally, we used the Lloyd’s lists of maritime and fluvial ports to account for the presence of ports. Table 5 presents the control variables. 14 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   Table 3. Institutional fragmentation variables (vector F). Dimension Variable Description Data source for calculation I. Size of region no_adminunits Number of administrative units_2010 OpenStreetMap boundaries data (20170420) and the World Bank LAC Spatial Database (integrated in a new vector layer: administrative unit boundaries) II. Political no_au_100th_2010 Number of administrative units per 100,000 Administrative unit boundaries and population count at pixel fragmentation inhabitants_2010 level from GHS (GHS_POP_ PW42015_GLOBE_R2015A_ 54009_250_v1_0 at 250 meters of spatial resolution). III. Central city cc_pop_2010 Central-city population share_2010 Administrative unit boundaries and population count at pixel domination level from GHS (GHS_POP_GPW42015_GLOBE_R2015A_ 54009_250_v1_0) Table 4. Metropolitan coordination variables (vector C). Dimension Variable Description Data source for calculation I. Governance gov_body Presence of a governance body See Table A1 II. Land use plan its_cov Percent of municipalities covered by integrated See Table A1 and mobility transport systems between municipalities and central city (metro, bus) III. Coordination spd_water Existence of a single-purpose districts for water See Table A1 for single spd_energy Existence of a single-purpose districts for energy See Table A1 purpose districts spd_waste Existence of a single-purpose districts for waste See Table A1 collection spd_sum spd_water + spd_energy + spd_waste See Table A1 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 15   Table 5. Control variables (vector X). Dimension Variable Description Data source for calculation I. Size pop_2010 Sum of population count within the urban extent Urban extents 2010 and population count at pixel level from GHS for 2015 (GHS_POP_ GPW42015_GLOBE_R2015A_54009_250_v1_0) density pop_2010/ areac_ue2010_km2 Urban extents 2010 and population count at pixel level from GHS for 2015 (GHS_POP_ GPW42015_GLOBE_R2015A_54009_250_v1_0) and Urban extents 2010 II. Location pop_radio300km Inhabitants in other FUAs within a 300 km radius of a Urban extents 2010 and population count at pixel city/1,000,000 level from GHS for 2015 (GHS_POP_ GPW42015_GLOBE_R2015A_54009_250_v1_0) III. Natural Amenities coast_2010 Dummy for location at the coast MODIS Water Mask (Carroll et al., 2009) and urban extents from deblurred and corrected DMSP- OLS NTL RC 2010 data. VI. Urban Amenities port Dummy for port Lloyd’s List (http://directories.lloydslist.com) 16 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   5 Empirical results Table 6 and Table 7 present the estimates of the relationship between metropolitan fragmentation/coordination and city productivity premium. The results are pretty similar in both cases. Following Ahrend et al. (2014) we report in the first column the positive and significant impact of population on productivity premium, which implies that productivity is higher in larger cities. In this regard Ahrend et al. (2014) reported estimated elasticities that range from 0.016 (for the United Kingdom) to 0.063 (for the United States). In our study, we obtained an estimated elasticity of 0.08 for LAC cities. We also report the coefficients associated to the logged population density (i.e., elasticity of productivity with respect to population) and surface (i.e., elasticity of productivity with respect to area). The results show that an increase in population, while holding the area constant, and an increase in area, while holding population density constant, both have a positive and statistically significant impact on productivity. Finally, the difference between these two coefficients indicates that an increase in area, while holding the total population constant, generates elasticities from 0.04 to 0.07. This range is 0.02 higher than the elasticity range reported by Ahrend et al. (2014), 0.02 to 0.05. The block of indicators for institutional fragmentation is reported in column (3) of Table 6 and Table 7. The results show opposite effects from fragmentation: on the one hand, the negative and statistically significant coefficient for the logged number of administrative units indicates that the presence of multiple local governments affects economic performance because of factors such as higher transactions costs, barriers to the diffusion of growth promoting policies, and other coordination failures (which is consistent with the centralist view). But, on the other hand, the positive and statistically significant coefficient for political fragmentation (no_au_100th_2010) indicates that the presence of multiple local governments may lead to more responsive government to public needs (Nelson and Foster, 1999). Also, “smaller [local governments] make participation easier, make citizens feel more empowered and interested in their communities, and bring neighbors together” (Oliver, 2010, 65). Finally, the negative and statistically significant coefficient for cc_pop2010_ue, indicates that an increase in central city domination may affect economic performance. J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 17   The fourth column in Table 6 and Table 7 includes the block of variables for metropolitan coordination. None of the coordination variables appears significant, which does not provide evidence in favor of the convenience of the regionalist model. It is important to note that these results are conditioned to the variables used to measure the degree of metropolitan coordination. Finally, column five shows that the above conclusions remain the same after including a series of control variables. 18 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   Table 6. Estimates of the relationship between metropolitan fragmentation/coordination and city productivity premium (OLS). Y = City productivity premium ( )  Variable (1) (2) (3) (4) (5) ln(pop 2010) 0.087*** (0.0274) ln(density) 0.070** 0.223*** 0.230*** 0.199*** (0.0289) (0.0591) (0.0595) (0.0521) ln(area_km2) 0.112*** 0.285*** 0.281*** 0.270*** (0.0310) (0.0644) (0.0647) (0.0577) ln(no_ adminunits) -0.202*** -0.198*** -0.180*** (0.0666) (0.0671) (0.0589) no_au_100th_2010 0.197** 0.196** 0.168** (0.0776) (0.0777) (0.0701) cc_pop2010_ue -0.369*** -0.329** -0.214* (0.1305) (0.1362) (0.1222) gov_body -0.043 -0.050 (0.0502) (0.0451) its_cov 0.000 0.001 (0.0006) (0.0005) spd_sum 0.036 0.038 (0.0272) (0.0242) pop_radio300km 0.007*** (0.0017) coast_2010 0.062 (0.0681) Port -0.082 (0.0603) Constant 0.049 -0.073 -1.974** -2.067** -1.909*** (0.3812) (0.3829) (0.7885) (0.7918) (0.6988) Country dummies Y Y Y Y Y Observations 73 73 73 73 73 Adjusted R-squared 0.630 0.641 0.692 0.692 0.768 Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 19   Table 7. Estimates of the relationship between metropolitan fragmentation/coordination and city productivity premium (OLS). Y = City productivity premium ( )  Variable (1) (2) (3) (4) (5) ln(pop 2010) 0.086*** (0.0305) ln(density) 0.066** 0.229*** 0.236*** 0.202*** (0.0322) (0.0649) (0.0656) (0.0568) ln(area_km2) 0.115*** 0.301*** 0.295*** 0.276*** (0.0345) (0.0707) (0.0713) (0.0629) ln(no_ adminunits) -0.220*** -0.214*** -0.191*** (0.0733) (0.0739) (0.0642) no_au_100th_2010 0.204** 0.203** 0.171** (0.0853) (0.0856) (0.0764) cc_pop2010_ue -0.470*** -0.423*** -0.284** (0.1435) (0.1501) (0.1331) gov_body -0.046 -0.056 (0.0553) (0.0491) its_cov 0.000 0.001 (0.0007) (0.0006) spd_sum 0.035 0.036 (0.0299) (0.0263) pop_radio300km 0.007*** (0.0019) coast_2010 0.062 (0.0742) Port -0.119* (0.0657) Constant -0.025 -0.164 -2.142** -2.233** -1.995** (0.4251) (0.4264) (0.8666) (0.8724) (0.7613) Country dummies Y Y Y Y Y Observations 73 73 73 73 73 Adjusted R-squared 0.685 0.694 0.745 0.743 0.811 Note: Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 6. Conclusions This paper studies the impact of metropolitan fragmentation/coordination on economic performance of 73 metropolitan areas in the Latin America and Caribbean region. This contribution offers complementary evidence on the relationship between institutional fragmentation/coordination and economic performance, since most of the available literature in this topic is concentrated on developed countries. 20 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   Following the latest contributions in the literature, we implemented a two-step econometric approach in which we control for individual sorting of highly skilled individuals into cities. We also take advantage of recent developments in remote sensing science and free geospatial libraries to delineate urban extents and identify metropolitan areas in an automatic and highly standardized way, which guarantees comparability across LAC cities. The available literature has not arrived to a definitive answer on this topic, and there is evidence in favor of the three potential models: polycentric, centralist and regionalist. Our results show that there may exist an optimal level of fragmentation in which the benefits of more responsive government are in balance with the higher costs associated to the presence of multiple local governments within the same functional area. This may indicate that in LAC cities the right model is somewhere in between the polycentric and the centralist governance structures. We found no evidence in favor of the regionalist view, since our results show that the presence of a governance body or integrated public services does not necessarily foster increased productivity. In line with previous contributions we found that economic performance increases with city size. While evidence for OECD countries indicates that doubling city size may increase economic performance between 2% and 5% (Ahrend et al., 2014); for LAC cities, we found an impact that ranges from 4% to 7%. 7. References Abrahams, A; Oram, C, Lozano-Gracia, N; (2018). Deblurring DSMP Nighttime Lights: A new method using Gaussian filters and frequencies of illumination. Remote Sensing of Environment. June 2018, 210, pp242-258. Anderson, S. J., Tuttle, B. T., Powell, R. L., & Sutton, P. C. (2010). Characterizing relationships between population density and nighttime imagery for Denver, Colorado: issues of scale and representation. International Journal of Remote Sensing, 31(21), 5733-5746. J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 21   Ahrend, R., Farchy, E., Kaplanis, I., & Lembcke, A. C. (2014). What makes cities more productive? Evidence on the role of urban governance from five OECD countries. OECD Regional Development Working Papers, 2014(5), 0_1. Barlow, I. M. (1991). Metropolitan Government. London; New York: Routledge. Blair, J. P., Staley, S. R., & Zhang, Z. (1996). The central city elasticity hypothesis: A critical appraisal of Rusk's theory of urban development. Journal of the American Planning Association, 62(3), 345-353. Bollens, S. A. (1986). A political-ecological analysis of income inequality in the metropolitan area. Urban Affairs Quarterly, 22(2), 221-241. Branson, J., Campbell-Sutton, A., Hornby, G. M., Hornby, D. D., & Hill, C. (2016). A geospatial database for Latin America and the Caribbean (draft version 1). Geodata, University of Southampton. Cadena, A., Remes, J., Manyika, J., Dobbs, R., Roxburgh, C., Elstrodt, H. P., ... & Restrepo, A. (2011). Building globally competitive cities: The key to Latin American growth. McKinsey Global Institute. Cao, Z., Wu, Z., Kuang, Y., Huang, N., & Wang, M. (2016). Coupling an intercalibration of radiance- calibrated nighttime light images and land use/cover data for modeling and analyzing the distribution of GDP in Guangdong, China. Sustainability, 8(2), 108. Carroll, M. L., Townshend, J. R., DiMiceli, C. M., Noojipady, P., & Sohlberg, R. A. (2009a). A new global raster water mask at 250 m resolution. International Journal of Digital Earth, 2(4), 291- 308. 22 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   Cheng, Y., Zhao, L., Wan, W., Li, L., Yu, T., & Gu, X. (2016). Extracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information. Journal of Geographical Sciences, 26(3), 325-338. Cheshire, P. C., & Gordon, I. R. (1996). Territorial competition and the predictability of collective (in) action. International Journal of Urban and Regional Research, 20(3), 383-399. Combes, P. P., Duranton, G., & Gobillon, L. (2010). The identification of agglomeration economies. Journal of Economic Geography, 11(2), 253-266.   Duque, J. C., Lozano-Gracia, Patino, J. E., and Restrepo, P. 2017. Urban form and productivity: in what shape are Latin American cities? LCR Flagship Report on Cities and Productivity. World Bank (forthcoming). De Esteban, A. (2009). Área metropolitana. Diccionario Crítico de Ciencias Sociales. Dobbs, R., Smit, S., Remes, J., Manyika, J., Roxburgh, C., & Restrepo, A. (2011). Urban world: Mapping the economic power of cities. McKinsey Global Institute. Feiock, R. C. (2009). Metropolitan governance and institutional collective action. Urban Affairs Review, 44(3), 356-377. Feria, J. M. & Susino, J. (2005). Movilidad por razón de trabajo en Andalucía. Dimensiones básicas y organización espacial. Instituto de Estadística de Andalucía. Fischer, M. M. (1980). Regional taxonomy: A comparison of some hierarchic and non-hierarchic strategies. Regional Science and Urban Economics, 10(4), 503-537. Forbes, D. J. (2013). Multi-scale analysis of the relationship between economic statistics and DMSP- OLS night light images. GIScience & remote sensing, 50(5), 483-499. J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 23   Foster, K. A. (1993). Exploring the links between political structure and metropolitan growth. Political Geography, 12(6), 523-547. Freire, S., & Pesaresi, M. (2015). GHS population grid, derived from GPW4, multitemporal (1975, 1990, 2000, 2015). European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/jrc-ghsl-ghs_pop_gpw4_globe_r2015a. Goodman, J. S. (1980). The Dynamics of Urban Growth and Politics. New York: Macmillan. Grassmueck, G., & Shields, M. (2010). Does government fragmentation enhance or hinder metropolitan economic growth?. Papers in Regional Science, 89(3), 641-657. Hawkins, B. W. (1971). The environmental base of urban government reforms. Politics and urban policies, 44-47. Hendrick, R., & Shi, Y. (2015). Macro-level determinants of local government interaction: How metropolitan regions in the United States compare. Urban Affairs Review, 51(3), 414-438. Hill, R. C. (1974). Separate and unequal: governmental inequality in the metropolis. American Political Science Review, 68(4), 1557-1568. Hsu, F. C., Baugh, K. E., Ghosh, T., Zhizhin, M., & Elvidge, C. D. (2015). DMSP-OLS radiance calibrated nighttime lights time series with intercalibration. Remote Sensing, 7(2), 1855-1876. Kim, S. J., Schumann, A., & Ahrend, R. (2014). What Governance for Metropolitan Areas? OECD Regional Development Working Papers. Kim, Yoonhee, and Bontje Zangerling, eds (2016). Mexico Urbanization Review: Managing Spatial Growth for Productive and Livable Cities in Mexico. Directions in Development. Washington, DC: World Bank. doi:10.1596/978-1-4648-0916-3. License: Creative Commons Attribution CC BY 3.0 IGO 24 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   Lo, C. P. (2001). Modeling the population of China using DMSP operational linescan system nighttime data. Photogrammetric engineering and remote sensing, 67(9), 1037-1047. Ma, L., Wu, J., Li, W., Peng, J., & Liu, H. (2014). Evaluating saturation correction methods for DMSP/OLS nighttime light data: A case study from China’s cities. Remote Sensing, 6(10), 9853- 9872. Morgan, D. R., & Mareschal, P. (1999). Central-city/suburban inequality and metropolitan political fragmentation. Urban Affairs Review, 34(4), 578-595. Nelson, A. C., & Foster, K. A. (1999). Metropolitan governance structure and income growth. Journal of Urban Affairs, 21(3), 309-324. Oakerson, R. J. (1987). Local public economies: Provision, production, and governance. Intergovernmental Perspective, 13(3/4), 20-25. Oliver, J. E. (2001). Democracy in suburbia. Princeton University Press. Ostrom, E. (2010). Beyond markets and states: polycentric governance of complex economic systems. Transnational Corporations Review, 2(2), 1-12. Pandey, B., Joshi, P. K., & Seto, K. C. (2013). Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data. International Journal of Applied Earth Observation and Geoinformation, 23, 49-61. Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Freire, S., Halkia, M., ... & Syrris, V. (2016). Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014. Publ. Off. Eur. Union. J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 25   Post, S. S., & Stein, R. M. (2000). State economies, metropolitan governance, and urban-suburban economic dependence. Urban Affairs Review, 36(1), 46-60. Pradenas, J. (2006). Delimitación Funcional Del Área Metropolitana De Santiago. Un Territorio En Busca De Gobierno. Universidad de Chile. Quintero and Roberts (2017) Rusk, D. (1993). Cities without suburbs. Washington, DC: Woodrow Wilson Center Press. Savitch, H. V., Collins, D., Sanders, D., & Markham, J. P. (1993). Ties that bind: Central cities, suburbs, and the new metropolitan region. Economic Development Quarterly, 7(4), 341-357. Schneider, M. (1989). The competitive city: The political economy of suburbia. University of Pittsburgh Pre. Sutton, P. C., Cova, T. J., & Elvidge, C. D. (2006). Mapping “Exurbia” in the conterminous United States using nighttime satellite imagery. Geocarto International, 21(2), 39-45. Stansel, D. (2005). Local decentralization and local economic growth: A cross-sectional examination of US metropolitan areas. Journal of Urban Economics, 57(1), 55-72. UN-Habitat (2012). The State of Latin American and Caribbean Cities 2012 Towards a new urban transition. Nairobi, Kenia. Wu, J., Ma, L., Li, W., Peng, J., & Liu, H. (2014). Dynamics of urban density in China: Estimations based on DMSP/OLS nighttime light data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10), 4266-4275. 26 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   Zhang, Q., & Seto, K. C. (2011). Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sensing of Environment, 115(9), 2320- 2329. Zhou, Y., Smith, S. J., Zhao, K., Imhoff, M., Thomson, A., Bond-Lamberty, B., ... & Elvidge, C. D. (2015). A global map of urban extent from nightlights. Environmental Research Letters, 10(5), 054011. Zhou, N., Hubacek, K., & Roberts, M. (2015). Analysis of spatial patterns of urban growth across South Asia using DMSP-OLS nighttime lights data. Applied Geography, 63, 292-303. J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 27   Appendix Table A1. Data sources. ISO Central City gov_body spd_Water ARG Salta Ministerio del Interior Aguas del Norte ARG Buenos Aires Región Metropolitana de Buenos Aires y del Conurbano Bonaerense Aguas Bonaerenses ARG Cordoba Gobierno de la Provincia de Córdoba Aguas Cordobesas ARG Mendoza Mendoza Gobierno ARG Rosario Gobierno de Rosario Gobierno de Santa Fe ARG Tucuman Observatorio de Fenómenos Urbanos y Territoriales Ministerio del Interior BOL Cochabamba Gaceta Oficial del Estado Plurinacional de Bolivia Servicio municipal de agua potable y alcantarillado sanitario (SEMAPA) BOL La Paz Ministerio de Autonomías Empresa Pública Social del Agua y Saneamiento S.A. (EPSAS) BOL Santa Cruz Autoridad de Agua Potable y Saneamiento Basico BRA Aracaju Deso - Companhia de Saneamento de Sergipe BRA Belém Procuraduría General del Estado de Pará Agência Reguladora Municipal de Água e Esgoto de Belém BRA Belo Horizonte Agencia Região Metropolitana de Belo Horizonte Companhia de Saneamento de Minas Gerais – COPASA BRA Brasilia Companhia de Saneamento Ambiental do Distrito Federal BRA Campinas Gobierno de Sao Paulo Sociedade de Abastecimento de Agua S/A - Sanasa BRA Cuiaba Portal Transparencia Departamento de Água e Esgoto de Várzea Grande -DAE. BRA Curitiba Coordenação da Região Metropolitana de Curitiba (COMEC) Companhia de Saneamento do Paraná -Sanepar- BRA Florianopolis Gobierno de Estado de Amazonas Companhia Catarinense de Águas e Saneamento -Casan- BRA Fortaleza Secretaria do Desenvolvimiento Local e Regional Companhia de Água e Esgoto do Ceará -CAGECE- BRA Joao Pessoa Estado do Paraiba Companhia de Água e Esgotos da Paraíba -CAGEPA- BRA Joinville Aguas de Joinville BRA Londrina Empresa Paulista de Planejamento Metropolitano (EMPLASA) Companhia de Saneamento do Paraná -Sanepar- BRA Maceio Empresa Paulista de Planejamento Metropolitano (EMPLASA) Compañía de Saneamiento de Alagoas BRA Manaus Gobierno de Estado de Amazonas Grupo Aguas do Brasil BRA Natal Companhia de Saneamento de Minas Gerais – COPASA BRA Porto Alegre Associação dos Municípios da Região Metropolitana de Porto Alegre - Granpal Companhia Riograndense de Saneamento -Corsan- BRA Recife Consejo de Recife Companhia Pernambucana de Saneamento -Compesa- BRA Ribeirao Preto GS Inima Brasil BRA Rio de Janeiro Asamblea Legislativa de Río de Janeiro Companhia Estadual de Águas e Esgotos -CEDAE- BRA Salvador Bahia Companhia de Águas e Esgotos de Rondônia -Caerd- BRA Santos Agência Metropolitana da Baixada Santista (AGEM) Companhia de Saneamento Básico do Estado de São Paulo S.A. -Sabesp- BRA Sao Jose Dos Campos Consejo Metropolitano de San José de los Campos Portal Saneamiento Basico BRA Sao Luis Companhia de Saneamento Ambiental do Maranhão -CAEMA- BRA Sao Paulo Empresa Paulista de Planejamento Metropolitano (EMPLASA) Companhia Estadual de Águas e Esgotos do Rio de Janeiro (CEDAE) BRA Sorocaba Empresa Paulista de Planejamento Metropolitano (EMPLASA) Prefectura de Sorocaba BRA Teresina Instituto Brasileiro de Geografia e Estatística Águas e Esgotos do Piauí S.A. -Agespisa- BRA Vitoria Instituto Jones dos Santos Neves (IJSN) Companhia Espírito Santense de Saneamento -Cesan- Continue next page 28 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   Table A1. Data sources (cont.) ISO Central City gov_body spd_Water CHL Santiago Biblioteca del Congreso Nacional de Chile Aguas Andinas COL Barranquilla Área Metropolitana de Barranquilla Triple A S.A. COL Bogotá Superintendencia de Servicios Públicos Domiciliarios COL Bucaramanga Área Metropolitana de Bucaramanga Superintendencia de Servicios Públicos Domiciliarios COL Cali Aguas de Palmira S.A. COL Cucuta Área Metropolitana de Cúcuta Plan Departamental de Agua de Norte de Santander COL Medellín Área Metropolitana Valle de Aburrá Empresas Publicas de Medellín (EPM) Aguas COL Pereira Área Metropolitana Centro Occidente Superintendencia de Servicios Públicos Domiciliarios CRI San José Ministerio de Viviedas y Acentamientos Urbanos Instituto Costarricense de Acueductos y Alcantarillados DOM Santo Domingo Coordoporación de Acueducto y Alcantarillado (CAASD) ECU Guayaquil Interagua GTM Guatemala Municipalidad de Guatemala GTM Quetzaltenango Empresa Municipal de Agua y Alcantarillado "Virgen Guadalupe del Sur" (EMAPAVIGS) MEX Chihuahua Junta Municipal de Agua y Saneamiento de Chihuahua MEX Cuernavaca Sistema de conservación, agua potable y saneamiento de agua de Temixco, Morelos (SACPSATM) MEX Tuxtla Gutiérrez Sistema Municipal de Agua Potable y Alcantarillado (SMAPA) MEX Aguascalientes Compañía de servicios públicos de agua en Aguascalientes (CAASA) MEX Cancún Aguakan S.A. de C.V. MEX Ciudad de México Gobierno del Estado de México Comisión Nacional del Agua (CONAGUA) MEX Guadalajara Gobierno de Jalisco Sistema Intermunicipal de los Servicios de Agua Potable y Alcantarillado (SIAPA) MEX Mérida Junta de Agua Potable y Alcantarillado de Yucatán MEX Monterrey Servicios de Agua y Drenaje de Monterrey -SADAM- MEX Morelia Comité de Agua Potable y Alcantarillado del Municipio de Tarímbaro - Comapat- MEX Puebla Gobierno del Estado de Puebla Conseciones Integrales Puebla MEX Querétaro Comisión Estatal de Aguas Querétaro MEX Saltillo Aguas de Saltillo MEX San Luis Potosí Gobierno Estado de San Luis Potosí Compañía de servicios públicos de Agua -INTERAPAS- MEX Tampico Comisión Municipal de Agua Potable y Alcantarillado -COMAPA- MEX Toluca Gobierno del Estado de México Secretaria de Desarrollo Metropolitano Valle de Toluca MEX Torreon Sistema Municipal de Agua y Saneamiento -SIMAS - MEX Veracruz Comisión del Agua del Estado de Veracruz (CAEV) PAN Ciudad de Pánama Autoridad Nacional de Servicios Públicos -ASEP- PER Arequipa Servicio de Agua Potable y Alcantarillado de Arequipa S.A. (SEDAPAR) PER Lima Municipalidad distrital de Pucusana Servicio de Agua Potable y Alcantarillado de Lima S.A. (SEDAPAL) PER Trujillo Plan de Desarrollo Territorial de Trujillo (PLANDET) Servicio de Agua Potable y Alcantarillado de la Libertad -SEDALIB S.A- PRY Asunción Empresa de Servicios Sanitarios del Paraguay S.A. (ESSAP) SLV San Salvador Oficina de la Administración de El Salvador (OPAMSS) Administración Nacional de Acueducto y Alcantarillado (ANDA) Continue next page J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 29   Table A1. Data sources (cont.) ISO Central City spd_Energy spd_Waste_c ARG Salta Empresa Distribuidora de Electricidad de Salta S.A. (EDESA) Ministerio del Interior ARG Buenos Aires Compañía Administradora del Mercado Mayorista Eléctrico (CAMMESA) Coordinación Ecológica Área Metropolitana Sociedad del Estado (CEAMSE) ARG Cordoba Empresa Provincial de Energía de Córdoba Logística Urbana S.A. (LUSA) ARG Mendoza Empresa Mendocina de Energia -Emesa- Limpieza Metropolitana S.A. E.S.P. (LIME) ARG Rosario Empresa Provincial de la Energía de Santa Fe Limp AR Rosario S.A. ARG Tucuman Ministerio del Interior Ministerio del Interior BOL Cochabamba Empresa de Luz y Fuerza Electrica Cochabamba (ELFEC) Servicio Municipal de Agua Potable y Alcantarillado COCHABAMBA BOL La Paz Distribuidora de Electricidad La Paz (DELAPAZ) Empresas pública social de Agua y Saneamiento EPSAS BOL Santa Cruz Compañía Eléctrica Central Bulo Bulo S.A. Autoridad de Fiscalización y Control Social de Agua Potable y Saneamiento Básico AAPS BRA Aracaju Grupo Energisa Prefectura de Rio de Janeiro BRA Belém BELEM BIOENERGIA BRASIL Prefectura Municipal de Belém BRA Belo Horizonte Compañía Energética de Minas Gerais Agencia Região Metropolitana de Belo Horizonte -RMBH- BRA Brasilia Companhia Energética de Brasília Gobierno de Brasilia BRA Campinas Companhia Paulista de Força e Luz MB Ingeniería y Medio Ambiente BRA Cuiaba Centrais Elétricas Matogrossenses (CEMAT) Prefectura Cuiabá BRA Curitiba Companhia Paranaense de Energia Ares do Paraná BRA Florianopolis Centrais Elétricas de Santa Catarina S.A. (Celesc) Prefectura de Florianópolis BRA Fortaleza Compañía de energía sostenible- Ener Brasil Grupo Taborda BRA Joao Pessoa Energisa Marquise Ambiental BRA Joinville Centrais Elétricas de Santa Catarina S.A. (Celesc) Ambiental BRA Londrina Compañia Paranaense de Energía Colecta e destinacao do resÍduos BIOACCESS BRA Maceio Eletrobras Consorcio público de saneamiento básico da bacia hidrográfica do Rio Dos Santos BRA Manaus Eletrobras Amazonas Energia Manaus Limpia BRA Natal Biomassa BR Banco de Brasil BRA Porto Alegre Energia Proyectos e Investigación Prefectura de Porto Alegre BRA Recife Grupo privado electrico do Brasil- Neoenergia Grupo recoleción de resíduos sólidos (RELIMA SOLVÍ) BRA Ribeirao Preto Companhia Paulista de Força e Luz Koleta Ambiental S.A. BRA Rio de Janeiro Enel Green Power Brasil Compañía Municipal de Limpieza Urbana BRA Salvador Bahia Companhia de Eletricidade do Estado da Bahia Koleta Ambiental S.A. BRA Santos Companhia Paulista de Força e Luz Total Waste Managment AMBIENTAL BRASIL BRA Sao Jose Dos Campos EDP Energias do Brasil Urbanizadora Municipal -URBAM- BRA Sao Luis Novus Energia Sao Luis Coleta de Óleo de Fritura Indama BRA Sao Paulo Enel distribuicao S.A Resíduos e gestão ambiental Utresa BRA Sorocaba Votorantim Energia Prefectura de Sorocaba BRA Teresina Eletrobras Distribuição Piauí Prefectura Municipal de Teresina BRA Vitoria Interconexión Eléctrica S.A. E.S.P. - ISA - Vitoria Ambiental Continue next page 30 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 31   Table A1. Data sources (cont.) ISO Central City spd_Energy spd_Waste_c CHL Santiago Enel Distribución Chile S.A. Sistema Nacional de Información Ambiental de Chile (SINIA) COL Barranquilla Comisión de Regulación de Energía y Gas Edumas COL Bogotá Grupo Energía de Bogotá ASEO Internacional S.A. COL Bucaramanga Comisión de Regulación de Energía y Gas Proactiva COL Cali Celsia S.A. E.S.P. Proactiva COL Cucuta Centrales Eléctricas del Norte de Santander S.A E.S.P (CENS) Grupo Sala COL Medellín Empresas Publicas de Medellín (EPM) Emvarias COL Pereira Superintendencia de Servicios Públicos Domiciliarios Asopereira CRI San José Instituto Costarricense de Electricidad (ICE) Empresas Berthier EBI de Costa Rica S.A. DOM Santo Domingo Comisión Nacional de Energia Ecoservis Dominicana ECU Guayaquil Empresa Energia Publica Puerto Limpio GTM Guatemala Empresa Eléctrica de Guatemala S.A. (EEGSA) Info Ciudad GTM Quetzaltenango Empresa Electrica Municizal de Quetzaltenango Naciones Unidas MEX Chihuahua Comisión Federal de Electricidad Promotora Ambiental MEX Cuernavaca Comisión Federal de Electricidad Ayuntamiento Cuernavaca MEX Tuxtla Gutiérrez Comisión Federal de Electricidad Limpia y Aseo Público Municipal Tuxtla MEX Aguascalientes Comisión Federal de Electricidad Aguascalientes Gobierno de Estado MEX Cancún Comisión Federal de Electricidad Solución integral de residuos solidos cancún (SIRESOL) MEX Ciudad de México Comisión Federal de Electricidad Aseca S.A. MEX Guadalajara Comisión Federal de Electricidad Dirección General de Servicios Urbanos MEX Mérida Comisión Federal de Electricidad Ayuntamiento de Merida MEX Monterrey Comisión Federal de Electricidad General Ambiental MEX Morelia Comisión Federal de Electricidad Biosistem Mexico S.A de C.V. MEX Puebla Comisión Federal de Electricidad Biosistem Mexico S.A de C.V. MEX Querétaro Comisión Federal de Electricidad Gen Industrial S.A. de C.V. MEX Saltillo Comisión Federal de Electricidad Gobierno Saltillo MEX San Luis Potosí Comisión Federal de Electricidad Promotora Ambiental MEX Tampico Comisión Federal de Electricidad Desechos Basuras y Servicios SA MEX Toluca Comisión Federal de Electricidad General Ambiental MEX Torreon Comisión Federal de Electricidad Recicladora Siderúrgica de la Laguna S.A. de C.V. (RESILASA) MEX Veracruz Comisión Federal de Electricidad Secretaría de Medio Ambiente y Recursos Naturales (SEMARNAT) PAN Ciudad de Pánama Empresa de Transmisión Eléctrica S.A. Panama Waste Management (PWM) PER Arequipa Sociedad Electrica del Sur Oeste S.A. (SEAL) Relima Solvi PER Lima Responsabilidad Social y Desarrollo Sostenible Municipalidad metropolitana de Lima PER Trujillo Distriluz S.A. PROMAS Servicios Ambientales PRY Asunción Administracion Nacional de Electricidad (ANDE) Dirección Nacional de Contrataciones Públicas SLV San Salvador AES El Salvador Energia Ministerio de Medio Ambiente y Recursos Naturales Continue next page 32 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   Table A1. Data sources (cont.) ISO Central City its_cov port ARG Salta Sociedad Anónima del Estado de Transporte Automotor - SAETA Lloyd's List ARG Buenos Aires Ferrocarriles Metropolitanos Area Metropolitana de Buenos Aires Lloyd's List ARG Cordoba Transporte automotor municipal sociedad del estado Lloyd's List ARG Mendoza Mendoza Gobierno Lloyd's List ARG Rosario Instituto Nacional de Estadística y Censos de la República Argentina (INDEC) Lloyd's List ARG Tucuman Ministerio del Interior Lloyd's List BOL Cochabamba Banco Interamericano de Desarrollo Lloyd's List BOL La Paz La Paz Bus Lloyd's List BOL Santa Cruz Centro de Estudios para el Desarrollo Urbano y Regional (CEDURE) Lloyd's List BRA Aracaju Grupo Parvi Lloyd's List BRA Belém Instituto de Pesquisa Econômica Aplicada (IPEA) Lloyd's List BRA Belo Horizonte Compañía Brasilera de Trenes Urbanos Lloyd's List BRA Brasilia Secretaría de Estado de Distrito Federal Lloyd's List BRA Campinas Empresa Metropolitana de Transportes Urbanos de São Paulo Lloyd's List BRA Cuiaba Republica Federativad Brasil Lloyd's List BRA Curitiba Urbanização de Curitiba URBS Lloyd's List BRA Florianopolis Consórcio Fênix Lloyd's List BRA Fortaleza Omnibus do Fortaleza Fortalbus Lloyd's List BRA Joao Pessoa Companhia Brasileira de Trens Urbanos Lloyd's List BRA Joinville Gidion Transporte e Turismo Ltda y Transtusa Lloyd's List BRA Londrina Encontro Nacional da Anpege Lloyd's List BRA Maceio Companhia Brasileira de Trens Urbanos Lloyd's List BRA Manaus Departamento Nacional de Infraestrutura de Transportes Lloyd's List BRA Natal Compañía Ferroviaria del Nordeste, CFN Lloyd's List BRA Porto Alegre A Fundação Estadual de Planejamento Metropolitano e Regional - Metroplan Lloyd's List BRA Recife Companhia Brasileira de Trens Urbanos CBTU Lloyd's List BRA Ribeirao Preto Red Ferroviaria Federal Sociedad Anónima Lloyd's List BRA Rio de Janeiro Metrorio Lloyd's List BRA Salvador Bahia Empresa Metropolitana de Transportes Urbanos Lloyd's List BRA Santos Empresa Metropolitana de Transportes Urbanos Lloyd's List BRA Sao Jose Dos Campos Universidade de Taubaté - UNITAU Lloyd's List BRA Sao Luis Secretaria Municipal de Trânsito e Transporte Sao Luis Lloyd's List BRA Sao Paulo Compañía Brasilera de Trenes Urbanos Lloyd's List BRA Sorocaba Empresa Metropolitana de Transportes Urbanos Lloyd's List BRA Teresina Mobilidade Urbana Sustentável (Mobilize) Lloyd's List BRA Vitoria Instituto de Pesquisa Econômica Aplicada (IPEA) Lloyd's List Continue next page J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 33   34 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   Table A1. Data sources (cont.) ISO Central City its_cov port CHL Santiago Empresa de los Ferrocarriles del Estado (EFE) Lloyd's List COL Barranquilla Departamento Nacional de Planeación Lloyd's List COL Bogotá Sistema Integrado de Transporte Bogotá Lloyd's List COL Bucaramanga Metrolinea Lloyd's List COL Cali Metrocali Lloyd's List COL Cucuta Área Metropolitana de Cúcuta Lloyd's List COL Medellín Metro de Medellín Lloyd's List COL Pereira Área Metropolitana Centro Occidente Lloyd's List CRI San José Lloyd's List Instituto Costarricense de Ferrocarriles (Incofer) DOM Santo Domingo Metro Santo Domingo Lloyd's List ECU Guayaquil Metrovia Lloyd's List GTM Guatemala Municipalidad de Guatemala Lloyd's List GTM Quetzaltenango Sistema de Registro Fiscal de Vehiculos Lloyd's List MEX Chihuahua Gobierno de Chihuahua Lloyd's List MEX Cuernavaca Secretaría de Movilidad y Transporte Lloyd's List MEX Tuxtla Gutiérrez Lloyd's List MEX Aguascalientes Gobierno de Aguascalientes Lloyd's List MEX Cancún Marítima Isla Mujeres S.A. Lloyd's List MEX Ciudad de México Ferrocariles Suburbanos Lloyd's List MEX Guadalajara Sistema de Tren Eléctrico urbano (SITEUR) Lloyd's List MEX Mérida Yucatan-Sistema Integral de Transporte Urbano Lloyd's List MEX Monterrey Transmetro Monterrey Lloyd's List MEX Morelia Lloyd's List MEX Puebla Rutapuebla Lloyd's List MEX Querétaro Lloyd's List MEX Saltillo Gobierno de Coahuila Lloyd's List MEX San Luis Potosí Secretaría de comunicaciones y transporte Lloyd's List MEX Tampico Turutadirecta Lloyd's List MEX Toluca Lloyd's List MEX Torreon El Siglo de Torreón Lloyd's List MEX Veracruz Gobierno de Veracruz Lloyd's List PAN Ciudad de Pánama Metro de Panamá Lloyd's List PER Arequipa Municipalidad Provincial de Arequipa Lloyd's List PER Lima Protransporte Lloyd's List PER Trujillo Transporte Metropolitano de Trujillo Lloyd's List PRY Asunción Ministerio de Obras Publicas y Comunicación Lloyd's List SLV San Salvador SUBES El Salvador Lloyd's List J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity 35   Table A2. Descriptive statistics. Variable n p25 Median p75 Mean Std. Dev. Min Max 73 0.84 1.02 1.28 1.05 0.30 0.12 1.65 73 0.91 1.12 1.43 1.16 0.36 0.17 1.88 pop_2010 73 902,390.50 1,573,563.00 2,922,544.00 2,699,436.00 3,698,464.00 203,131.10 21,200,000.00 density 73 276.17 506.37 905.83 819.88 902.17 18.08 4217.76 no_ adminunits 73 4.00 7.00 15.00 12.26 12.41 2.00 76.00 no_au_100th_2010 73 0.30 0.49 0.77 0.66 0.59 0.02 3.29 cc_pop2010_ue 73 0.31 0.50 0.63 0.48 0.20 0.07 0.87 gov_body 73 0.00 1.00 1.00 0.53 0.50 0.00 1.00 its_cov 73 0.00 57.89 100.00 50.51 44.11 0.00 100.00 sdp_sum 73 1.00 1.00 2.00 1.34 0.85 0.00 3.00 pop_radio300km 73 1.10 5.00 12.97 9.64 11.72 0.00 43.55 coast_2010 73 0.00 0.00 1.00 0.40 0.49 0.00 1.00 port 73 0.00 0.00 1.00 0.40 0.49 0.00 1.00 Table A3. Correlation matrix Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (1) 1 (2) 0.9635* 1 pop_2010 (3) 0.2702* 0.2535* 1 density (4) -0.2407* -0.2453* 0.5129* 1 no_ adminunits (5) 0.3112* 0.3383* 0.6523* 0.1673 1 no_au_100th_2010 (6) 0.099 0.1064 -0.2613* -0.2811* 0.3111* 1 cc_pop2010_ue (7) -0.0931 -0.1423 0.024 -0.0375 -0.2978* -0.3172* 1 gov_body (8) 0.2411* 0.2446* 0.2475* 0.1643 0.3095* 0.0848 -0.0765 1 36 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity   its_cov (9) 0.4804* 0.4951* 0.0562 -0.0561 0.138 0.0683 -0.1966 0.2482* 1 sdp_sum (10) 0.1662 0.075 -0.0893 -0.1575 0.0531 0.1306 -0.0905 0.0533 0.1031 1 pop_radio300km (11) 0.227 0.2252 0.0914 0.0192 0.1659 0.0825 -0.1059 0.0813 -0.0544 0.0352 1 coast_2010 (12) 0.1255 0.1678 0.0871 0.1957 -0.0285 -0.1038 -0.0021 -0.0277 0.1957 -0.1629 -0.0777 1 port (13) 0.0384 0.0513 -0.0698 0.133 -0.1035 0.0913 -0.0067 0.0284 0.1455 -0.0308 -0.1197 0.7712* 1