The Spatial Dimension of Urbanization in Indonesia

Transcription

1 The Spatial Dimension of Urbanization in Indonesia Andrea Civelli Univ. of Arkansas Arya Gaduh Univ. of Arkansas May 2015 VERY PRELIMINARY - PLEASE DO NOT CITE Abstract We use the newly-released World Bank East Asia and Pacific Urban Expansion (WB-EAP UE) dataset to study the link between urbanization, economic, and social development in Indonesia. We implement two distinct approaches to study this link for the case of Indonesia. First, we combine the dataset with satellite data on luminosity to study the link between urbanization and aggregate, district-level, income. We explicitly embed into the analysis the spatial network structure of the districts and we find that the combined signals from urban expansion and luminosity significantly improve the estimates of regional economic growth relative to those exclusively based on luminosity. Second, we estimate a simple fixed-effects model using a panel dataset from the WB-EAP UE and the detailed village-level dataset to study how the expansion of built-up areas commensurate with those of socio-economic infrastructure, and whether urban expansions are associated with changes in the economic and social environment, and hence the quality of life in newly urbanized areas. We propose a strategy to select the control group for the newly urbanized villages based on spatial connectivity and clustering criteria. We find that the extent of infrastructural development, as well as socio-economic and environmental outcomes associated with urban expansion varies relative to the initial level of urbanization. Keywords: Urban Expansion, Indonesia, Spatial Analysis, Sub-national Economic Activity. JEL Classification: E01, E23, I18, O11, O12, O47, O53, O57. 1 Introduction Urban expansion shapes economic activities, economic growth, and population dynamics at any administrative level. It can foster connectivity between social groups and opportunities for economic development. When urbanization effectively supports higher concentration of people and businesses, it creates tighter markets at the local level which increase productivity and spillover effects within a city or across neighbor villages. At the regional level, healthy and homogeneous urban textures facilitate the flows of goods, resources, and labor across geographic areas and urban University of Arkansas, Walton College of Business, Department of Economics, Business Building 402, Fayetteville, AR andrea.civelli@gmail.com. University of Arkansas, Walton College of Business, Department of Economics, Business Building 402, Fayetteville, AR agaduh@walton.uark.edu. 1

2 agglomeration are associated to wealth and income growth. At the same time, agglomeration may lead to detrimental social outcomes such as crimes, urban poverty, as well as environmental and health issues that may reduce or even completely offsets the economic benefits of urban expansion Understanding and better quantifying the link between urbanization, economic, and social development is, hence, of primary interest in studying economic development. We use the newly-released World Bank East Asia and Pacific Urban Expansion (WB-EAP UE) dataset to study this link. The WB-EAP UE dataset documents the spatial expansion of urban built-up areas between 2000 and 2010 in the East Asia and Pacific region. We implement two distinct approaches to study this link for the case of Indonesia (World Bank Group, 2015). First, we combine the dataset with satellite data on luminosity to study the link between urbanization and aggregate, district-level, income. Night luminosity and urban expansion separately has proven to be useful to estimate GDP at national and supranational level (World Bank Group, 2015; Henderson et al., 2012; Chen and Nordhaus, 2011; Ghosh et al., 2010). We apply the methodology introduced by Henderson et al. (2012) for luminosity, and incorporate the spatial signals from urban expansion to improve estimates of regional economic growth. Second, we construct a panel from the WB-EAP UE and the detailed village-level Village Potential (or Podes) dataset and use a simple fixed-effects model to study how the expansion of built-up areas commensurate with those of socio-economic infrastructure, and whether urban expansions are associated with changes in the economic and social environment, and hence the quality of life in newly urbanized areas. We directly address concerns that infrastructure development could not catch up with urbanization, which will worsen the poor s access to services and their welfare (Richardson, 1987; Henderson, 2002; Moore et al., 2003; Gong et al., 2012; Bakker et al., 2008). We focus on the districts in Java, Bali (and Nusa Tenggara for the first approach), where significant urban expansions occurred in this period. We find that the combined signals from urban expansion and luminosity significantly improves the estimates of regional economic growth. We find that this improvement occurs in large part through the spatial channel, since urbanization shares a main west-east spatial distribution pattern with district income that cannot be identified by luminosity. Luminosity and urban expansion are then jointly exploited to construct a prediction model of regional economic growth. Meanwhile, at the village level, we find that the extent of infrastructural development, as well as socio-economic and environmental outcomes associated with urban expansion varies relative to the initial level of urbanization. Our paper, therefore, contributes to the literature in two ways. First, we provide empirical evidence on the value of urban expansion data in improving estimates of aggregate regional economic activities. Second, we provide a rich and, to our knowledge, rare description of the interactions between urban expansion and various socio-economic and environmental outcomes in a developing country. In the next section, we focus on the district-level analysis. We discuss the methodological approach and the results of the empirical analysis. Section 3 analyzes the village-level correlates of urbanization. We begin with the discussion of the data, empirical strategy, followed by the results. Section 4 concludes. 2 Urbanization, Luminosity and Income at the District Level In this section, we use the urban expansion dataset to supplement the satellite data on luminosity provided by the National Oceanic and Atmospheric Administration (NOAA) to estimate regional differences in income growth across Indonesian districts. We analyze the contribution of urban expansion to the prediction of district-level GDP, with a specific focus on the spatial characteristics of urbanization. We extend the methodology introduced by Henderson et al. (2012) for luminosity 2

3 Legend Urbanization No urbanization Urban 2000 Urban expansion 2010 Figure 1: Urbanization expansion between 2000 and World Bank data. to incorporate both urbanization and luminosity in a signal extraction statistical model for regional economic growth. Given the importance of accurate measurements of economic activities for development and growth studies, refinements to this methodology at different administrative levels have important policy relevance, especially for coutries with weak statistical collection capabilities. Our sample includes the 151 districts on the islands of Java, Bali, and Nusa Tenggara for the moment. Most of the Indonesian urbanization has occurred on Java and Bali, where a pervasive increase in urban coverage is observed between 2000 and Urbanization in Nusa Tenggara has expanded more slowly, even though West Nusa Tenggara presents urbanization rates similar to those of Bali and most districts of Java. Figure 1 simply illustrates the World Bank urban expansion index for the districts of Java and Bali. Urban land coverage in 2000 was around 8.4 percent and there is an average expansion of approximately 1 percent. Section 2.1 begins with a brief introduction of the methodology to characterize the spatial dimension of urbanization, based on the concept of spatial autocorrelation and the related spatial filtering approach described in Griffith (2003). 1 In Section 2.2, we apply this method to the spatial autocorrelation of urbanization and relate the urban index to the spatial features of luminosity and the official GDP statistics. We find that urbanization shares interesting geographic patterns with both lights and income. Section 2.3 will then use the urban expansion index to extend the model of Henderson et al. (2012) by incorporating the urban index as a second signal to predict district-level GDP. We find that urban expansion improves the quality of our estimates of GDP growth above and beyond those of luminosity. Moreover, the spatial component identified in the previous section plays an important role in correctly specifying the contribution of urbanization to the estimation of economic activity. We then use this model to estimate the growth of economic activity of Jakarta s hinterland and to generate a gradient map of GDP estimates at village level. 2.1 Spatial Autocorrelation and Spatial Filtering Urbanization and urban expansion are spatial phenomena that typically occur in clusters. Figure 1 illustrates this notion. These clusters, however, have different size and can spread over the region in many ways. We characterize the spatial dimension of urbanization across districts using a measure of spatial autocorrelation, the Moran I coefficient (MI, henceforth), and the related spatial filtering approach presented in Griffith (2003). Spatial autocorrelation is the correlation among values of a georeferenced variable due to the proximity of the locations at which the variable is observed. It is a property inherited by a georeferenced variable from the spatial structure of the territory or network 1 See Appendix A for a detailed description of the methodology. 3

4 over which the variable is distributed, and it reflects possible linkages and spillover effects across geographic units. It is also well known that spatial autocorrelation would bias the estimates of coefficients and standard deviations in an econometric model if not accounted for. Spatial filtering is a way to deal with spatial autocorrelation as an alternative to other classical spatial econometric specifications, but it also provides interesting insights on how a georeferenced variable is distributed over the space (in this case districts). 2 A key element of this type of analysis is the spatial weight matrix C that represents the baseline connectivity structure among the districts. We construct C based on the rook s contiguity rule: an element of the matrix c i,j = 1 if districts i and j share a border, c i,j = 0 otherwise. Let N be the number of districts in the analysis, C is then a N N matrix with all zeros on the diagonal. Let z be a georeferenced variable and z i the value of this variable in district i, then the MI autocorrelation of z is define as N c i,j (z i z)(z j z) i j MI = c i,j (z i z) 2 (1) i j Positive autocorrelation corresponds to MI > (N 1) 1, so the Moran I is centered on a negative value, close to though not exactly at 0, and the coefficient is not necessarily restricted between [ 1, 1], even though is usually not far off this range. Large positive or negative values of MI, indicate a deviation of z from randomness in distribution in the spatial sense. Independently of the realization of z, the connectivity structure imposes some direct restrictions on MI since it determines ex-ante which pairs of districts can spatially interact. These effects of C on the spatial autocorrelation of a georeferenced variable can be accurately characterized through the use of the N eigenvectors of the adjusted connectivity matrix C i C = (I N L N L N/N)C(I N L N L N/N) (2) where I N is the N N identity matrix and L N is an N 1 vector of 1 s (Griffith, 2003). Each of these N eigenvectors can be represented, as any other attribute variable, on the district map and would have a corresponding unique M I value. The eigenvectors form a set of orthogonal and uncorrelated map patterns whose MI span the entire space of feasible M I values; in other words, the spatial autocorrelation of any georeferenced variable distributed over the spatial structured represented by C can be obtained as linear combination of the spatial autocorrelations of these eigenvectors. Furthermore, this result suggests a simple approach to decompose a georeferenced variable into a spatial and an a-spatial component. The spatial filtering approach offers two advantages when phenomenon exhibits a relevant spatial dimension and elements of spatial analysis are necessary. First, it is based on a simple procedure for the identification of the eigenvectors of C that explain the majority of the latent spatial autocorrelation of a variable. These eigenvectors can then be used to synthetically summarize the spatial characteristics of the variable itself both statistically and visually. Second, applying the filter selection to different georeferenced variables distributed over the same map structure, it is also possible to study the common spatial characteristics of those variables and improve the understanding of their interaction. These common filtered components can be easily added to an econometric model to correctly account for spatial co-movements between variables or to control for factors that have a relevant spatial distribution and are unobservable or missing from a dataset. 2 For an example, among the others, see the study of unemployment differences across German regions in Patuelli et al. (2011). 4

5 More details about spatial autocorrelation, the computation of the set of eigenvectors of C, and a full explanation of the procedure to select the eigenvectors for the spatial filtering are left to Appendix A. 2.2 Spatial Characteristics of Urbanization We turn now to the spatial analysis of urbanization. Table 1 report the MI for the World Bank urban index, luminosity and official district GDP for the two years in the analysis, 2000 and The first observation is that the spatial autocorrelation is positive for the three of them; it is relatively moderate for urbanization and luminosity (around.4.5), but stronger for GDP. Second, the M I does not increase over time; it is rather stable and slightly falls in 2010 for luminosity. This is an interesting finding, suggesting that urbanization in Indonesia did not become more concentrated between 2000 and 2010 on the extensive margin represented by this urbanization index. Concentration and agglomeration might then occur on the intensive margin instead, with an increase in population or built-up density. The outcome of the selection procedure of the eigenvectors of C is summarized in Table 2. The eigenvectors are ordered by decreasing MI of the map pattern associated to them, and the rank position is indicated by the subscript i in the notation E i. The left hand panel of Table 2 reports all the relevant eigenvectors for the urbanization index in each of the two years in our analysis. On the right side, we highlight the eigenvectors in common to the three variables, which will be used for the representation in Figure 3. We find that a dozen of the possible eigenvectors are relevant to characterize the latent spatial autocorrelation of urbanization in Indonesia. As the indexes indicate, the majority of them are associated to medium-high M I s (8 eigenvectors out of the highest 14 are selected). Two eigenvectors correspond to low MI, E 41 and E 42, while the last two represent low and actually negative autocorrelations. The most interesting feature of this approach is that we can provide a visual interpretation of the eigenvectors in a map that identifies trends and clusters of the autocorrelation associated to an eigenvector. Typically, eigenvectors with high M I represent global patterns, medium M I correspond to regional patterns, while small M I identify local spatial components. An example of the differences in these three types of patterns are illustrated in Figure 2. Panel (a) of the figure illustrates the map of E 3, in which we can recognize four main clusters that unfold from West to East: the first one is around Jakarta, this is followed by a second cluster of districts with opposite values, another cluster at the center of Java, and finally a very large cluster of intermediate values that begins from the East side of Java, continues to Bali, and covers the islands of Nusa Tenggara. The map in panel (b) corresponds to smaller, regional clusters; the map in Java gets more fragmented and Nusa Tenggara is now separate from the other islands. Finally, panel (c) illustrates the case of local patterns which become quite evident evident at the two longitudinal extremes of the map; in particular, it is easy to distinguish three clusters on the East side of the map overlapping with West Nusa Tenggara, East Nusa Tenggara, and the group of Southern islands. We next compare the spatial characteristics of urbanization with those of luminosity and district GDP looking at their common eigenvectors. This exercise will be useful for the predictive regressions of GDP in the next section as it provides the spatial filtered components to be used in those 3 GDP data is obtained from the Indonesia Database for Policy and Economic Research (INDO-DAPOER) dataset; we use total constant price GDP excluding Oil and Gas (NA.GDP.EXC.OG.KR). The luminosity data came from the DMSP-OLS satellite dataset to which we apply the standard corrections and transformations. In particular, gas flares are removed and data from different satellites are inter-calibrated following the adjustment procedure proposed by Elvidge et al. (2014). For each of the two years, we further smooth luminosity by taking a three-year average centered around the two reference periods. 5

6 regressions. Table 2 reports the individual eigenvectors, while Figure 3 shows a map for each of these three sets of eigenvectors combined according to a weighting scheme that reflects their relative explanatory power of urbanization. 4 We find that urbanization intersects GDP and luminosity at different levels of autocorrelation. Urbanization and GDP share a quite local spatial pattern (panel a of the figure), with higher fragmentation on the West side of the map. Also the three variables together have in common a very local pattern (panel b), which is characterized by an increase in fragmentation on the East side of the map as well in correspondence of the Nusa Tenggara islands. Interestingly, luminosity and GDP share some spatial autocorrelation independently of urbanization for medium values of MI, and the map in panel (c) expresses a pattern mostly at regional level. Finally, these results hold for both periods. Formally, the filtering approach allows us to differentiate spatial effects over time. In this case, we find a remarkable stability of the spatial structure over time as Table 2 indicates. Table 1: MI for urban index (Urb), luminosity (Lum), and official district GDP (GDP) based on the rook s contiguity C spatial matrix. Urb Lum GDP Table 2: Principal eigenvectors selected for urbanization (Urb) and shared with luminosity (Lum) and official district GDP (GDP). Specific Eigenvectors Common Eigenvectors Urb Urb & GDP Urb & GDP & Lum GDP & Lum 2000 E 1 E 3 E 4 E 8 E 9 E 11 E 13 E 14 E 41 E 42 E 92 E 110 E 4 E 8 E 9 E 11 E 1 E 3 E 13 E 14 E 5 E 7 E E 1 E 3 E 4 E 8 E 9 E 11 E 13 E 14 E 41 E 42 E 92 E 110 E 4 E 8 E 9 E 11 E 1 E 3 E 13 E 14 E 5 E 7 E Urbanization and Local GDP A growing strand of the literature exploited large-scale satellite data to estimate true economic activity mainly at the national level. The most successful papers have focused mainly on luminosity data and their use to estimate GDP at the national and subnational level (Henderson et al., 2012; Chen and Nordhaus, 2011; Doll et al., 2006; Olivia and Gibson, 2015; Bickenbach et al., 2013; Sutton et al., 2007). We extend the model of Henderson et al. (2012) in order to include a second signal in the predictive equation of district GDP. We then add to this regression the spatial component found through the filtering procedure in the previous section, and we analyze the effects of misspecified spatial elements on the estimate of Henderson s model. The relation between urbanization and 4 These weights are obtained as part of the selection procedure of the filtering eigenvectors and it is explained in Appendix A 6

7 (a) E 3 - MI = 1.34 (b) E 13 - MI =.81 (c) E 41 - MI = 0.43 Figure 2: Three examples of map patterns for the relevant eigenvalues of urbanization corresponding to high, medium, and low MI values. 7

8 (a) Urbanization and GDP (b) All three variables (c) Luminosity and GDP Figure 3: The three map patterns shared by urbanization, luminosity, and GDP obtained from the spatial filtering selection procedure of the eigenvectors of C 8

9 economic growth has been already studied in the past mostly at the national level (for some recent work on this topic, see Chen et al., 2014; Brulhart and Sbergami, 2009; Bruckner, 2012), but the spatial dimension and the effects of possible spillovers across regions remains under-explored. Multi-sensor approaches to GDP estimation usually mix luminosity, change in vegetation, and land use changes and apply different statistical strategies. An interesting example is Bruckner (2012), who study local GDP in a Chinese province and find a significant contribution to GDP prediction of land cover data. The full derivation of the model we use is presented in Appendix B; we start here with the GDP predictive equation (equation B.4 in the Appendix), reported again below for convenience x j = ψ 1 z j,1 + ψ 2 z j,2 + e j (3) where x j is the real GDP growth rate in district j officially measured by national accounting, and z j,1 and z j,2 indicate respectively the growth rate of observed lights in district j, and the change in the share of urbanized land in the same district. The goal of the Henderson s methodology is to exploit regression (3) and the moments of the signals to ultimately estimate y j, the true growth rate of the economic activity in district j. It is worth stressing out again that besides z j,1 and z j,2, also GDP x j is treated by the model as one of the signals to be used to estimate the true economic activity; the predictive equation (3) is an important, but only intermediate, step towards the actual final goal of estimating y j. Table 3 illustrates the results of the estimates of (3) for our set of districts. The equation is written in log-linear version as a two-period panel and the model is then estimated using panel fixed effects. In column (1), we replicate the baseline specification of Henderson et al. (2012). The coefficient of luminosity is.6 and it is very significant; it is also twice as big as the equivalent estimate of.3 obtained by Henderson et al. (2012) with data at country level. In column (2), we add urbanization to the regression and we find it is strongly significant as well with a point estimate of Urbanization adds an important predictive power to the simple luminosity, increasing the (within R2-)fit of the model from.46 to.63. The intuition that urbanization could bring an improvement to the estimation model of economic activity is strongly confirmed. We then consider the spatial components in columns (3) and (4). If the variables share a common latent spatial autocorrelation structure, the coefficients of the regression could be biased upwards. We showed in the previous sections that urbanization exhibits a moderate, but not negligible spatial autocorrelation and that it shares a set of eigenvectors with GDP and luminosity, which was characterized mostly as a local spatial factor. This term in column (3), labeled as General Component, is significant too, but more importantly makes the urbanization coefficient fall by.095 to (while the coefficient of luminosity returns to a value similar to that in specification 1). After adding the Specific Component, the factor shared by GDP and urbanization only, the urbanization estimate drops even more to even though the coefficient of this new term is not statistically significant. This result is very important because it shows that going from national to a subnational scale, the spatial component of a signal like urbanization has to be taken into account in the implementation of the model. The correction of the coefficient of urbanization in this case is around 8 10%, but it could be even higher in cases of stronger spatial autocorrelation in the signal. We conclude this section with the second stage of the procedure, which allows us to find the optimal combination of the signals in order to maximizes the accuracy of the estimate of economic activity from lights, urbanization, and real GDP. Economic activity, y j, is expressed as a linear combination of officially measured GDP, x j, and predicted GDP from equation (3), ˆx j = ˆψ 1 z j,1 + ˆψ 2 z j,2 ŷ j = λx j + (1 λ)ˆx j (4) 9

10 for an optimal value of the weight λ which is defined in equation (B.6). The moment conditions necessary to solve for λ are based on the empirical moments of the long-term growth rates between 2000 and 2010 of the three signals. Solving the system of equations (B.7)-(B.14), we find that the optimal weight is λ =.76, which is slightly smaller than the λ in Henderson et al. (2012) for their favorite parameterization for good countries. 5 Table 3: Estimates of the GDP predictive equation 3 at district level. No Spatial Terms With Spatial Terms (1) (2) (3) (4) Luminosity (0.112)*** (0.069)*** (0.076)*** (0.080)*** Urbanization (0.166)*** (0.166)*** (0.170)*** General Component (0.242)** (0.237)** Specific Component (0.268) R 2 - within Obs Notes: */**/*** denotes significance at the 10/5/1 percent level. Each model is estimated in log-linear specification using panel fixed effects; the independent variable is log(gdp). The two spatial components are derived from the spatial filtering in Section 2.2; the General Component is the filter in common to the three variables; the Specific Component is the filter shared by GDP and urbanization. Robust standard errors in parentheses. As an illustrative example of the applicability of our estimates, we conduct two simple exercises. In the first we use the model to estimate the economic activity for 7 districts in the hinterland of Jakarta and we compare these estimates to the official GDP measures in Table 4. The model is very effective in matching the official GDP measures, but it seems to predict lower rates of economic growth in areas where urbanization was already high in 2000, namely the inner Jakarta region. This is a result that definitely deserves a further inspection in the future to optimally employ the predictive features of urban expansion. Second, we use the predictive equation (3) to estimate the GDP growth rate between 2000 and 2010 at village level, for which luminosity and urban expansion data, but not GDP measures, are available. Figure 4 shows the gradient map of this estimate based on model (2) of Table 3, i.e. abstracting from the spatial component at village level. This map illustrates that a large share of the villages does not show any growth based on this estimation method of measuring GDP because they do not have an increase in luminosity or urbanization. The majority of villages whose GDP grows displays a substantial growth over these ten years (up to 80%); however, we can also identify some cases of very high growth around the country that would be impossible to observe otherwise. 5 This λ corresponds to the estimates of ψ 1 and ψ 2 of model (3) in Table 3 and to the following estimate of the parameters of the model: σ 2 y =.23, σ 2 x =.03, σ 2 1 =.16, σ 2 2 =.015, β 1 =.84, β 2 =

11 Table 4: Official measures of GDP and the predicted economic activities in Greater Jakarta. DKI Jakarta West Jakarta Central Jakarta South Jakarta East Jakarta North Jakarta Official GDP Estimated GDP Other Greater Jakarta Districts Tangerang Tangerang Munic. District Bekasi District Bogor District Depok Munic. Bekasi Munic. Bogor Munic. Official GDP Estimated GDP Figure 4: Predicted village GDP growth between 2000 and 2010 based on equation (3) - model (2) estimates. 11

12 3 Socio-Economic and Environmental Correlates of Urbanization This section explores how spatial patterns of urbanization are correlated with village-level socioeconomic infrastructure and outcomes. As with the district analysis, we focus on villages in the islands of Java and Bali, where most of the urban expansion occurred between 2000 and Before we begin our analysis, we show that the spatial dimension of urbanization remains important as we go down to the village level. According to the World Bank East Asia and Pacific Urban Expansion (WB-EAP UE) dataset, the mean urban built-up areas of villages in the Java/Bali sample is 15.5 percentage points (p.p.) in 2010, up from 14.0 percentage points in However, this average increase of 1.5 p.p. masks the large spatial variation in urban expansion across different provinces. Table 5 summarizes the average urban built-up area and population in each of the villages in 2000 and 2010 (2011 for the population), along with the associated expansions in the different provinces in Java/Bali. Two key facts emerge. First, the level of urbanization between the different provinces vary widely. The starkest difference is between DKI Jakarta (where the nation s capital is) and the rest of the provinces. Second, the capital city remains at the center of urban expansion, despite having close to a 100 percent share of urban-built areas in DKI experienced the largest urban expansion in the decade of interest, followed by two of its adjacent provinces, Banten and West Java. Much of the expansion in the latter provinces were driven by the need to support activities in the former, as the share of urban built-up areas in villages in DKI Jakarta edged closer to 100 percent. The expansions in the remaining four are much smaller compared to these three provinces. The largest increases in population also occurred in these three provinces. Table 5: Unweighted Average Share of Urban Cover and Population in Villages, 2000 and 2010 Share of Urban Cover Population ( 000) % Pop Change Change Change (1) (2) (3) (4) (5) (6) (7) DKI Jakarta West Java Central Java Yogyakarta East Java Banten Bali Source: Authors calculation based on the WB-EAP UE, Podes Data for the population data. In the following, we make use of these spatial differences in the level of urbanization to explore how urban expansions are associated with village-level development. The next subsection discusses the data and research design for our village-level analysis, followed by our results on the correlates of urban expansion in these regions. In doing so, we also highlight how the WB-EAP UE dataset complements existing and detailed village-level datasets as well as household-level datasets that are publicly available in Indonesia. 3.1 Data and Empirical Strategy For our analysis, we combine the WB-EAP UE dataset with two waves of the Village Potential (or Podes) dataset. Podes is a triennial census of Indonesian villages collected by Indonesia s statistical 12

13 agency, BPS Indonesia. It collects detailed information from village informants about village characteristics, such as demographics, geography, as well as social and economic infrastructure. For this analysis, we construct a panel dataset of villages using two waves of this survey, namely from 2000 and Since the questionnaire for the survey evolves over time, we were not able to construct a panel for all questions. However, we are able to construct the panel for most of the questions on social and economic infrastructure, and a set of outcome variables related to crime and health. The panel dataset with two periods allows us to estimate a first difference model that removes time-invariant unobservables (Greene, 2012). To wit, we estimate a fixed-effect model for the following specification: Y it = β 0 + β 1.urbMean it + c i + µ it + ε it, t {2000, 2011} where Y it are the outcome variables of interest of village i at time t, urbmean is the share of urban built-up areas in the village, c i is the time-invariant unobservables, µ it is the time-variant unobservables, and ε denotes random errors. With the panel structure, we are able to reduce omitted variable bias by eliminating the time-invariant village-level unobservables c i using a fixed effects model. The outcome variables of interest comprise of binary and continuous variables, and for the binary variables, this amounts to estimating a linear probability model with fixed effects. However, we still face a potential omitted variable bias from time-varying unobservables. To reduce the potential bias further, we leverage the spatial structure of the villages to create a set of treatment and comparison groups that are more similar compared to those in the full sample. The treatment group refers to a set of villages that increased the share of urban built-in areas by at least 5 p.p., while the comparison group are villages that do not experience urban expansion but are within 3 kilometers of the former villages. In addition, we also include a set of control variables for some of the outcomes. This is the specification implemented when we analyze how the association between urban expansion and the set of outcomes vary with the initial level of urbanization described below. Finally, we explore how these associations are heterogeneous with respect to the initial (2000) level of urbanization. We therefore stratify the treatment and comparison villages into those with low, medium, and high level of initial urbanization. Figure 5 shows the distribution of the share of urban built-up areas in the treatment and comparison villages for the full and the within-3-km samples. The distribution for the comparison villages are on the left of that for the treatment villages. We therefore based the cut-offs for our stratification on the distribution in the initial urban cover of the comparison villages. Specifically, villages whose initial urbanization level are in the 4th (3rd) quartile of this distribution is categorized as high (middle) initial urbanization villages. Below-median initial-urbanization villages are placed in the low category. 3.2 Results We group the dependent variables into two broad categories, i.e., socio-economic infrastructures and outcomes. Included in the former are market and financial infrastructures, and social (health and education) infrastructures. Meanwhile, the socio-economic outcomes discussed include infectious diseases, crime, pollution, access to water, and the incidence of slum settlements. Overall, based on the analysis of the extensive margins, infrastructures appeared to keep up with urban expansion. However, our findings suggest that extensive margin analysis may not be adequate: For schools and health facilities where quantity data are available, we find the number of facilities may not have kept up with population growth. At the same time, urban expansion is associated with reduced distance to these critical infrastructures. In terms of outcome, at the extensive margin, urban expansion is associated with an increase in the incidence of a number of health problems, but no significant 13

14 Comparison Treatment Density urbmean2000 Density urbmean2000 (a) All population Comparison Treatment Density urbmean2000 Density urbmean2000 (b) Villages within 3km of an urbanized village Figure 5: Distribution of Mean Share of Urban Built-Up Areas in Villages in Java and Bali,

15 increases in the incidence of (reported) crimes and pollution. Unfortunately, we do not have panel data on the number of victims for the different crimes. We also find that these associations vary with respect to the initial level of urbanization. In the results tables, note that each line represents the coefficient from a single regression of the depedendent variable on urban expansion. In our reporting, we will begin with a discussion of the overall effects, which are reported in Column (1) of the tables. 6 We then discuss the results stratified by the initial level of urbanization. When the outcome variables are not quoted in population terms, we included a specification that controls for population size. For health-related outcomes, we also included a specification that controls for the presence of and distance from the different facilities. Tables 6 to 8 provide the set of results on infrastructure. Column (1) of Table 6 presents the linear probability with fixed effects estimate of the presence of an institution on urban expansion using the full dataset. We find that modern economic infrastructures and institutions are highly correlated with urban expansion: The likelihood that a village has markets, strip malls, or banks is positively associated with the percentage point increase in the share of urban built-up. We do not find a similar increase in institutions that serve the more traditional rural markets such as People s Credit Bank or cooperatives. The likelihood of police presence in the village is also positively associated with urban expansion. In terms of social infrastructure, urban expansion and the likelihoods that a school or health facility is present are positively correlated. Two notable exceptions are primary school and maternity hospital. For primary schools, this is not unexpected given the government push to expand primary education across the countries beginning in the 1970s (Duflo, 2001). We find consistent results when we examine distances to the different infrastructures. Table 7 regresses distance from the various villages to these infrastructures. This distance is zero when the infrastructure is present in the village. The coefficients are negative across the board, suggesting that urban expansion shrinks the distance to economic, security and health infrastructures with increasing share of urban built-up in a village. Above we generally find positive correlations between urban expansion and the presence of social infrastructure. Despite these findings for the extensive margin, the picture is somewhat different when we look at the intensive margins. Table 8 present a fixed-effects estimate of the quantity of each infrastructure per 1000 people on urban expansion. These results suggest that social infrastructure development, in particular those for education, may not have fully caught up with urban expansion and its associated population growth. Aside for higher education, the coefficients are negative. It is not significant for kindergarten. Health infrastructures, on the other hand, appear to be more able to adapt to urban expansion. The coefficients are negative for maternity hospitals (but positive for general hospitals), and for publicly-funded health clinics, Puskesmas (but are positive for private practices). Meanwhile, Tables 9 to 12 explore how urban expansion is associated with a range of policyrelevant outcomes. We will first focus on the first column that is estimated on the full sample. In terms of economic activities, Table 9 shows, at the extensive margin, that urban expansion is associated with increases in small and medium enterprises and food establishments. Meanwhile, at the extensive margin, Tables 10 and 11 do not suggest an increase in the likelihood of incidences of diseases and crimes, except for dengue fever and assault. However, as shown in Table 12, we find that urban expansion is associated with an increased likelihood of a slum (including those at river banks) and water pollution. Although there is an overall increase in access to piped water, urban 6 For the overall effects, we present the results for the full sample (instead of the 3 km sample) to save space. The results for the 3km sample are very similar, and available upon request. 15

16 expansion is also associated with an increased reliance on pumped water which draws on the groundwater and retail purchases. The former, especially in DKI Jakarta, is causing salination of ground water, which is one of the reasons why (poorer) residents often need to purchase water from vendors (Henderson, 2002). Although the likelihoods that the various diseases (other than dengue) occur do not increase with urban expansion, we do find its impact on disease-related deaths increase with urban expansion. Table 13 presents the fixed effects regression of death per 1000 people on urban expansion. We find positive associations across the board for infectious diseases such as dengue, diarrhea, malaria, measles, and pulmonary illnesses. Meanwhile, Table 14 presents the results of a fixed effects regression of the number of households living in the slum or the slum by the river on urban expansion. We find at the intensive margin, urban expansion is associated with an increasing number of people building settlements by the river Village-level Development and the Heterogeneity of the Initial Urbanization Figure 5 shows that urban expansion occurs across villages with a wide range of initial level of urbanization. We therefore explore whether the associations between village-level development, and the outcomes vary with respect to the initial level of urbanization. For this exercise, we reduce the bias from time-varying unobservables by limiting the sample to the subset of villages located within 3 km of villages that experienced at least 5 percent increase in urban cover. We then separately analyze the sample based on whether the villages have a high, medium, or low-level of initial urbanization in 2000 (as defined in Section 3.1). Overall, we find that in most cases, the associations we found reflect those from the high initial urbanization subsample, albeit with some exceptions. This highlights the significance of the spatial dimension of urbanization, and the necessity of spatial information to understand village-level effects of urbanization. We focus first on the even columns in Tables 6 and Tables 7 that show the extensive margin analysis for the economic and social infrastructures. The signs and significance in the overall sample are replicated in the sample with high initial urbanization for all but one i.e., the distance to permanent and semi-permanent markets. The coefficients for modern economic infrastructures tend to be smaller (and are significant less often) for medium-level initial urbanization villages, and much less so for the low-level initial urbanization villages. Interestingly, the coefficients for cooperatives are positive in medium and low initial urbanization villages, and is significant for the low initial urbanization villages. In contrast to the results on economic infrastructures, the coefficients for school outcomes tend to be larger for low initial urbanization villages. Since schools are likely to be the first infrastructures built as villages urbanized, it is not suprising that at the extensive margin, the coefficients are larger in low initial urbanization areas. For health facilities, hospitals are more likely to be associated with urban expansion only in areas with high and medium initial urbanization. However, health facilities with smaller fixed costs e.g., private practices and pharmacies appear to respond to urban expansions irrespective of the initial level of urbanization. In terms of the number of schools per person, the associations vary for junior and secondary schools. These results suggest the failure to catch up in building additional schools, which might result in the intensification of the use of existing schools in high initial urbanization areas. Meanwhile, in terms of health facilities, we find as before that hospitals tend to be associated with urban expansion in high initial urbanization areas. Interestingly, the coefficients on private practices tend to be larger in medium and low initial urbanization areas. In terms of economic outcomes, the even columns of Table 9 suggests that the associations between business activities and urban expansion tend to be stronger in high initial-urbanization areas 16

17 except for food establishments, where the coefficients are similar across different levels of initial urbanization. Meanwhile, in terms of disease incidences, at the extensive margin, urban expansions are associated with increases in the likelihood of dengue irrespective of initial level of urbanization (Table 10). In terms of disease related deaths, however, we find that how the coefficients are associated with the initial level of urbanization vary differently for different diseases (Table 13). However, for dengue fever, we find that the positive association between urban expansion and the incidence of (and deaths from) dengue fever to be very robust, even after controlling for the various health facilities. In terms of outcomes related to the living environment, we find that the associations mostly come from the areas with high initial urbanization (see Tables 12 and 14). This is true in the case of the emergence of slums and pollution. Finally, with regards to water access, we find that increased access to piped water mostly happen in areas with high initial urbanization, suggesting that the networked pipe system does not always grow with urban expansion. Poor households are typically left out of the networked system, and would often have to rely on purchased water (Henderson, 2002; Bakker et al., 2008). This is consistent with our findings that urban expansion is in general associated with greater reliance of retail purchases as well as pumped (groundwater) sources Urban Expansion and Population Growth Before we conclude with the village-level analysis, we comment on how the urban expansion data contains information beyond population expansion. A simple correlation between the measure of urban expansion and log village population from Podes suggests, as expected, a relatively high correlation of 0.50 in the overall sample. To examine whether this urban expansion measure contains additional information above and beyond population growth, we included log population control in the linear probability regressions of Tables 6, 7, and Comparisons of estimates with and without log population controls suggest that population growth does not fully capture changes associated with urbanization as measured by the expansion in the urban built-up. 4 Concluding Remarks Using two distinct analytical approaches, we provide empirical evidence on the link between urbanization and economic development in a developing country. At the regional level, we show that urban expansion provides significant signals that can improve the accuracy and precision of estimates of aggregate economic activities. The within unit fit of the predictive model of GDP increases by more than one third (from.46 to.65). Moreover, we find empirical evidence on how different villages respond to urban expansion. In particular, we find that these responses vary depending on the initial level of urbanization and in particular, they mainly arise from villages with high initial levels of urbanization. We also find variations in the adequacy of development responses to urban expansion, which resulted in a diversity of socio-economic outcomes. Our findings highlight the academic and policy value of spatial information of urbanization. The signal extraction exercise that links urbanization, luminosity and regional economic activities can help improve measurements of sub-regional economic activities in countries whose economic data are not as rich as Indonesia. In order to further improve this methodology, the statistical model should be tested on other regions for which the urbanization index is available; moreover, the actual effectivenesses of urbanization in rural areas must still be explored. Finally, the difference between urbanization and other signals related to changes in land cover and different land uses deserves 7 Except for Table 10, they are reported in Columns (3), (5) and (7). 17

18 more attention. The change in the degree of forestation, in particular, could be a valid substitute of urbanization in the non-urban areas in which agriculture is the main source of income and subsistence. Furthermore, at the micro level, the spatial information dataset can be merged with household level datasets to understand the household-level welfare implications of urbanization. We believe that these will be fruitful directions for future research. 18

19 Table 6: Economic Infrastructure and Urban Expansion, 2000 and 2010: Extensive Margins Initial Urbanization All High Medium Low (1) (2) (3) (4) (5) (6) (7) A. Market and Finance Semi-perm. market (0.086)*** (0.124)*** (0.101) (0.096)* (0.096) (0.133) (0.138) Temp. market (0.095)** (0.122)** (0.134) (0.123)* (0.086) (0.148) (0.158) Strip mall (0.164)*** (0.194)*** (0.189)*** (0.191)*** (0.183) (0.221)*** (0.171)*** Bank (0.118)*** (0.176)*** (0.152)** (0.130)*** (0.117) (0.193)** (0.206) People s Credit Bank (0.177) (0.193) (0.204) (0.200) (0.199) (0.350) (0.326)* Cooperatives (0.205) (0.258) (0.268) (0.286) (0.271) (0.312)** (0.272) B. Security Police post (0.140)*** (0.228)*** (0.184) (0.135)*** (0.141) (0.155) (0.139) Civ. sec. post (0.036)*** (0.042)** (0.043)* (0.038)** (0.037) (0.107) (0.110) C. School Facilities Kindergarden (0.179)*** (0.160)*** (0.086)* (0.223)*** (0.137) (0.317)** (0.156)* Primary Sch (0.007) (0.011) (0.029) (0.002) (0.016) (0.017) (0.018) Junior Sec (0.138)*** (0.133)*** (0.098) (0.175)*** (0.126) (0.272)* (0.123) Senior Sec (0.147)** (0.170) (0.133)** (0.185)** (0.129) (0.145)** (0.097) College (0.166)*** (0.218)*** (0.169)*** (0.181)*** (0.144)*** (0.214)*** (0.152)*** D. Health Facilities Hospital (0.064)*** (0.091)*** (0.078) (0.080)** (0.059) (0.076) (0.076) Maternity hospital (0.156) (0.205) (0.198)** (0.199) (0.162) (0.167)* (0.156)*** Puskesmas (0.084)** (0.123)* (0.117) (0.116) (0.116) (0.181) (0.166) Integrated Health Post (0.024)** (0.038)* (0.042) (0.012)* (0.019) (0.072) (0.074) Polyclinic (0.173)** (0.209)*** (0.141) (0.204)** (0.179) (0.219) (0.170) Doctor Pvt. prac (0.174)*** (0.185)*** (0.139)** (0.210)*** (0.169)** (0.320)** (0.241) Midwife Pvt. prac (0.095)*** (0.091)*** (0.100)*** (0.153)*** (0.165)*** (0.209)*** (0.170)** Pharmacy (0.165)*** (0.191)*** (0.194)*** (0.198)*** (0.180)*** (0.238)*** (0.214)*** Log. Pop. N N Y N Y N Y Observations Notes: */**/*** denotes significance at the 10/5/1 percent level. Each line is a separate estimate of a linear probability with fixed effects model of the presence of different infrastructures on urban expansion with clustered standard errors (at the subdistrict level) in parentheses. 19