URBAN-TO-RURAL POPULATION GROWTH LINKAGES: EVIDENCE FROM OECD TL3 REGIONS *

Transcription

1 JOURNAL OF REGIONAL SCIENCE, VOL. 56, NO. 1, 2016, pp URBAN-TO-RURAL POPULATION GROWTH LINKAGES: EVIDENCE FROM OECD TL3 REGIONS * Paolo Veneri Directorate of Public Governance and Territorial Development, Organisation for Economic Co-operation and Development (OECD), 2 rue André-Pascal, Paris Cedex 16, France. paolo.veneri@oecd.org Vicente Ruiz Development Centre, Organisation for Economic Co-operation and Development (OECD), 2 rue André-Pascal, Paris Cedex 16, France. vicente.ruiz@oecd.org ABSTRACT. The objective of this paper is to understand, from an international comparative perspective, how population growth in rural regions is affected by the relationship with their nearby urban centers. By means of a cross-sectional analysis on OECD small regions (Territorial Level 3), the paper distinguishes spread effects positive spillovers arising from urban growth from the net effect of distance to nonrural places. The results show that spread effects outweigh backwash effects, so that rural regions benefit from growth in urban places. A rural region s distance from urban and intermediate regions has a negative effect on its population growth rate. Nevertheless, both the strength of this effect and the growth spillovers decline with distance, and this occurs relatively faster in Europe. The results further suggest that proximity to large urban areas has a higher positive influence than proximity to intermediate areas, but only outside Europe. 1. INTRODUCTION The economic and demographic dynamics of regions are highly differentiated both within and across countries. Economists have long sought to explain the distinctive patterns of convergence/divergence that characterize the overall picture of regional economic performance. On average, urban regions of OECD countries record higher performances than their rural counterparts, in terms of both GDP per capita and population growth rates. Agglomeration economies are among the main factors, which generate advantages for urban regions in terms of productivity and potential for growth, bringing about localized aggregate increasing returns (Duranton and Puga, 2004). Conversely, rural regions are on average more likely to undergo economic decline and depopulation. This seems to be particularly the case of those rural regions located far from important populated centers (Dijstra and Poelman, 2008; Brezzi et al., 2011). However, there is very high variability in the performances of rural regions: some decline, others grow quickly, and yet others even outperform urban regions (OECD, 2011). The aim of this paper is to explain part of the variability in the growth performances of rural regions by focusing on their relationships to urban centers. Our hypothesis is that the population dynamics of rural regions are partly explained by their distance from urban *The authors are grateful to Rudiger Ahrend, Alessandro Alasia, Monica Brezzi, Lewis Dijkstra, Karen Maguire, Joaquim Oliveira Martins, Mark Partridge, Bill Tompson, and three anonymous referees for their valuable comments and suggestions. The views expressed in this paper are those of the authors and do not reflect those of the OECD or its member countries. The authors alone take full responsibility for any errors in the paper. DOI: /jors

2 4 JOURNAL OF REGIONAL SCIENCE, VOL. 56, NO. 1, 2016 places. Distance can approximate the potential for certain types of linkages between urban and rural areas. Such linkages include, but are not limited to, demographic movements (migration), commuting, flows of production factors (e.g., location opportunities for firms), and service provision (Reimer et al., 2011; OECD, 2013). As a consequence, urban and rural areas are sometimes not only connected but can also be seen as a single socioeconomic entity. Local socioeconomic processes generate spatial externalities that spread beyond regional boundaries to affect nearby areas. These spatial interactions do not necessarily coincide with administrative boundaries. They are sometimes called functional regions, and they are important for public policies. Our analysis is framed within the spread-backwash effects literature. It attempts to verify whether growth processes taking place in urban places positively spread to close by rural places (spread effect) or if instead they attract resources at their expense (backwash effect). In principle, relationships with urban centers can have either positive or negative impacts on the population growth rate of rural regions. Thus, the nature of these relationships can be either competitive with zero, negative, or positive outcomes or have a virtuous complementarity. Rural regions close to urban centers are expected to benefit more from such a connection. Indeed, they can exploit the potential of their linkages with diverse and complementary economies and take further advantage of better access to both advanced services and larger and thicker markets. A cross-sectional analysis on OECD small regions 1 is carried out in order to verify the extent to which population growth rates of rural regions are affected by their proximity to urban centers and by the performance of those urban centers. Regions in OECD are classified according to two territorial levels (TL) to facilitate international comparisons. The lowest level (small regions, TL3) is in turn classified according to urban or rural geography as: predominantly urban, intermediate, and predominantly rural regions (OECD, 2009). 2 This paper analyzes both the existence of spread-backwash effects i.e., spillovers of growth from urban to rural regions and the net effect of distance to the closest urban place. The latter effect can be considered to account for the relationships with urban places as an autonomous factor of growth, in addition to the benefit arising from the performance of such urban places. To our knowledge, no international comparative analysis exists in this literature. In addition, one novelty of this paper is represented by its units of analysis, which are small regions of comparable though different size across countries. Differently from the existing literature, which focuses on individual countries and generally adopts an intra-metropolitan perspective, this study makes it possible to investigate whether the spread-backwash effects act beyond the boundaries of a single region, recognizing that the scope of urban rural relationships exceeds the space of daily commuting flows and embraces a wider set of linkages. Our results confirm the hypothesis that in OECD countries spread effects outweigh backwash effects, and that proximity to urban places positively affects population growth in rural regions. These effects are found for a large set of OECD countries, and they prove to be higher in magnitude for European regions, where it is also found that small and medium-sized cities have a stronger role in generating spread effects relative to non-european countries. On the other hand, urban size does not emerge as a major factor of rural growth at the spatial scale considered here. 1 Hereafter, the term small regions or regions refers to the administrative level of OECD TL3. 2 TL3 regions are classified into predominantly urban, intermediate or predominantly rural on the basis of population density by local unit (i.e., counties, municipalities, wards, etc.) and size of cities. Local units (municipalities, counties in the U.S., etc.) are classified as rural if their population density is lower than 300 inhabitants per square kilometer. Subsequently, TL3 regions are classified as rural if the population living in rural local units is higher than 50 percent. For more details, see OECD (2009).

3 VENERI AND RUIZ: URBAN-TO-RURAL POPULATION GROWTH LINKAGES 5 GDP per capita (thousands US$, constant prices, PPP) Time Urban Intermediate Rural Source: OECD Regional Database. FIGURE 1: Average GDP Per Inhabitant in OECD Small Regions, by Type of Region. The paper is organized as follows. Section 2 sets the scene of the analysis by showing the heterogeneity of economic and demographic performances of OECD regions on the basis of their degree of urbanity. Section 3 provides a conceptual framework and reviews the literature that seeks to understand the implications of urban and rural linkages in terms of population growth. Section 4 illustrates the empirical strategy, while Section 5 describes the data used in the analysis. Section 6 sets out the results and their interpretation, and Section 7 concludes. 2. TRENDS IN URBAN AND RURAL REGIONS: STYLIZED FACTS Using data available from the OECD Regional Database, it is possible to consider the different economic performances of regions across OECD countries by type of region (i.e., urban, intermediate, and rural). Over the last decade, no convergence can be found, on average, among urban, rural, and intermediate regions in terms of GDP per capita. Figure 1 shows that the GDP per capita of these three types of regions approximately followed the same trend between 2000 and Differences by type of region in the level of this indicator remained practically unchanged. Urban regions recorded higher values of GDP per capita during the entire period, while predominantly rural regions recorded the lowest average level of the same indicator. This may be due, to a certain 3 Figure 1 includes all of the OECD regions for which data on GDP from 2000 to 2008 are available. Hence, all OECD countries are included except for Canada, Chile, Iceland, Israel, Mexico, Switzerland, and the United States.

4 6 JOURNAL OF REGIONAL SCIENCE, VOL. 56, NO. 1, 2016 Population growth rate between 2000 and Source: OECD Regional Database. Urban Intermediate Rural FIGURE 2: Population Growth Rates in OECD Regions, by Type of Region, extent, to the productivity premium caused by agglomeration economies (Ciccone, 2002; Rosenthal and Strange, 2004; Puga, 2010; Park and Von Rabenau, 2011). It should be also acknowledged that differences in the costs of living between urban and rural areas may partially compensate for the gap in the levels of GDP per capita across types of regions. Overall, the aggregate picture in Figure 1 may hide the fact that opportunities for growth exist in all types of regions and that rural regions are indeed highly represented among the fastest-growing regions in OECD countries (OECD, 2011, p. 35). On considering the patterns of regional demographic performance, additional differences emerge among rural, intermediate, and urban regions. Figure 2 shows the pattern of population growth rates between 2000 and 2008 by type of region. The figure confirms with more detail that rural regions should not be necessarily seen as facing a phase of decline. More specifically, it emerges that, despite the fact that the average population growth rates are slightly lower than those of urban and intermediate regions, the variance of these rates is much higher in rural regions, with peaks and troughs of very high and very low values. Such higher variance is not due to the different average population sizes between types of regions, because the latter is not correlated with the distance from the mean in population growth for each group. 4 The remainder of this paper addresses the higher variability in the population growth rates of rural regions. As noted above, the hypothesis to be tested is that closeness to 4 Results are available upon request.

5 VENERI AND RUIZ: URBAN-TO-RURAL POPULATION GROWTH LINKAGES 7 nonrural places and the economic performance of the latter are important in explaining such variance by exploiting the potentialities of urban rural linkages. 3. CONCEPTUAL FRAMEWORK AND RELATED LITERATURE The relationships between urban and rural areas and their role in shaping regional economic performances have long been studied within the framework of core-periphery models (Parr, 1973). A traditional concept with which this approach is associated is that of spread-backwash effects. This concept was originally introduced in order to understand regional disparities in economic development (Myrdal, 1957), but it has been also used to conceptualize and investigate urban rural interactions (Gaile, 1980; Henry et al., 1997). In the latter context, urban and rural performances are considered to be inter-related, producing a series of positive and negative spatial externalities, which can be classified as spread or backwash effects. A spread effect can thus be defined as the positive effect mainly measured in terms of population or employment growth that the growth in an urban center yields in the nearby rural areas. Conversely, backwash effects occur when the effect of growth processes in urban centers draws employment and population away from rural areas (Henry et al., 1997). The relationships between areas located in physical proximity are usually very complex, so that both spread and backwash effects can occur. The dominance of either effect depends on the specific features of the region and on the nature of the linkages between different places (Feser and Isserman, 2006; Partridge et al., 2008). These linkages are in turn strongly influenced by distance, which can determine the prevalence of one effect over the other. There are various mechanisms at the basis of urban to rural spread effects. They provide a rationale for supporting cities also for the development of nearby rural areas. Among these mechanisms are: the creation of employment opportunities for rural dwellers that commute to urban centers; the provision of location opportunities for firms in cheaper places but still close to urban markets; and residential opportunities in accessible rural places connected to urban amenities (Chen and Partridge, 2013). Also backwash effects can emerge for different reasons. First, if distance to an urban centers is too long, rural workers may decide to migrate to a city for work and consumption reasons. There can also be migration to urban areas if the general provision of public services is too low in rural regions. Connected with service provision, public investment (e.g., infrastructure) can be relatively more concentrated where demand is higher, as in growing densely populated areas. Rural to urban migration can be selective to residents that are younger, with higher levels of education and skills, and this process may accelerate ageing in rural areas. As regards firms, the more innovative ones tend to sort in urban areas in order to benefit from agglomeration economies and thicker labor markets. Overall, when rural areas are distant from urban centers, small in terms of economic size, with an old demographic structure and a large redundant labor force (e.g., high unemployment rates), backwash effects are more likely to occur. The next sections will provide evidence in support of these hypotheses. The dependent variable for our analysis is the rate of population growth between 2000 and The choice of this dependent variable is consistent with much of the relative literature (Glaeser et al., 1995; Cheshire and Magrini, 2006; Cirilli and Veneri, 2011; Faggian et al., 2012), and the reasons for this choice are manifold. First, demographic flows are a key type of linkage between urban and rural areas. The decline of many remote rural areas is perceived mostly in terms of depopulation, which is a central challenge to address and a major policy issue. Second, changes in population or employment can be conceived as reflecting, at least in the medium term, changes in productivity levels

6 8 JOURNAL OF REGIONAL SCIENCE, VOL. 56, NO. 1, 2016 (Glaeser et al., 1995) and in people s expectations of a higher level of well-being compared with that available in their place of origin (Faggian et al., 2012). Given the idea that people vote with their feet, quality of life and amenities are more likely to be captured by population changes than by changes in production or employment. Third, compared with countries, regions can be seen as much more open economies sharing a common pool of mobile production factors (e.g., labor, capital, etc.). Given this degree of openness, movements of factors can be viewed as indicators more appropriate more stable and more easily available at regional level than changes in output. Moreover, at relatively small spatial scales, there is the risk that changes in output may be biased by an imperfect match between residence and job locations. Previous empirical analyses conducted to understand how growth in urban areas affects that of rural areas have mainly focused on population changes. Moreover, these studies have focused on single case-study regions or regions belonging to a single country. Barkley et al. (1996) analyzed spread-backwash relationships by examining changes in population densities within eight functional economic regions in the United States. They found spread effects for rural areas close to urban places, but backwash effects for the most remote rural territories on the fringes of the functional regions. Similar results have been obtained by other studies, especially for the United States and Canada (Kahn et al., 2001; Partridge, Olfert, and Alasia, 2007; Ganning et al., 2013). In a more recent work on China, Chen and Partridge (2013) found that medium-sized cities yield spread effects, while backwash effects are observed from the largest urban agglomerations. Schmitt et al. (2006) analyzed French functional economic regions and found that spread effects measured in terms of population and employment growth are more likely to operate from urban cores to hinterlands than vice versa, especially when population growth is taken into account. However, they also found a negative association between increases in urban export jobs and rural service jobs in the functional regions where rural areas are growing and urban ones are declining. This can be explained by a negative effect of growing employment opportunities in urban areas for rural dwellers, who may find it convenient to relocate to urban centers. Overall, the existing literature, which mostly focuses on the United States or on a few other individual countries, provides evidence that connecting rural places to urban centers can be a viable policy option for rural development thanks to urban to rural spread effects. However, several conditions must be in place in rural areas: among them are proximity to urban centers and the capacity to provide quality public services and amenities (Henry et al., 1997). Identification of spread effects should take account of other factors affecting rural development, which include skilled human resources, the capacity to design and implement local strategies, and a good industrial mix characterized by numerous small- and medium-sized firms (Polèse and Shearmur, 2006). It has also been found that different types of regional amenities are associated with the attractiveness of regions, both in the United States and Europe (Partridge, 2010; Rodríguez-Pose and Ketterer, 2012). Finally, recent evidence shows that decisions to migrate across rural and urban areas are also made to enlarge a household s set of opportunities (diversification; Arzaghi and Rupasingha, 2013). The literature on urban rural migration shows that characteristics of the local industrial base are also significant factors in explaining the spread-backwash effects between urban and rural areas. The type of sectoral specialization can affect the strength of growth spillovers, since some sectors can have larger within-core impacts than others. In this respect, transport, services provided by utilities, and the processing of natural resources have been found among those economic activities with the highest spillover effects (Hughes and Holland, 1994). Moreover, high shares of employment in agriculture have been found to be associated with higher losses of population, since a reduction in

7 VENERI AND RUIZ: URBAN-TO-RURAL POPULATION GROWTH LINKAGES 9 agricultural activities or an increase in labor productivity can give rise to outmigration from the regions (Brezzi and Piacentini, 2010). Manufacturing activities also exhibit a distinctive pattern of spatial diffusion. Glaeser and Kohlhase (2004) show that these activities tend to locate in places of medium or low density. However, they are unlikely to locate in the lowest density areas, which are too far away from urban centers. Glaeser and Kohlhase thus confirm the finding that distance to an urban center is a key condition for spread effects to prevail over backwash effects in the development of rural areas. 4. EMPIRICAL STRATEGY In order to estimate how population growth in rural regions is affected by the relationships with their urban counterparts, we followed the approach adopted by Partridge, Bollman, Olfert, and Alasia (2007), who use a partial-adjustment model where changes in the population reflect long-run transitions to equilibrium. The model starts from the assumption that regions initially find themselves in a steady state, with a given level of population density. Population density is considered to reflect the actual preferences of the representative household. Well-being is assumed to be a function of site-specific amenities and characteristics of households and firms in terms of their utility and profitability, respectively. All of these characteristics can be affected by the proximity to urban centers. The level of population density after t years is a combination between the actual and the equilibrium population density. Thus, population density in rural regions can be modeled as follows: (1) PD it PD i0 = X i0 PD i0, where is a measure of the speed of the transition to the new equilibrium density, PD is the population density in the ith region in time t, X is the set of characteristics of the regions that explains the variation in the population density, and is the vector of coefficients associated with such characteristics. If the difference between the actual and the equilibrium population density the left-hand-side of Equation (1) is log-transformed, then that difference can be approximated with the population growth rate. Hence, it is possible to specify a simple linear equation that can be estimated through a cross-section of OECD small regions. The baseline specification for a given rural region i, located in the country j is the following: (2) %po p i,(t 0) = + dens i,t0 + ECON i,t0 + GEOG i,t0 + SOC DEMO i,t0 + J + ε i,t 0, where the vector of population growth rates in rural regions (between 2000 and 2008) is regressed on a set of variables that include the natural logarithm of population density at the beginning of the period. The rest of the variables can be grouped into three dimensions or vectors. The first vector of the right-hand side of Equation (2) (ECON) includes basic economic characteristics of rural regions at the beginning of the period, such as GDP and the sectoral specialization. The idea is that high specialization in the secondary sector can be associated with lower growth, given both the above-mentioned potential importance of having a diversified productive structure and the fact that employment in manufacturing has been shrinking in the 2000s in almost all OECD countries. GDP controls for the economic size of rural areas. The second dimension (GEOG) concerns the relationships with the closest urban region, including its distance and GDP growth rate. A dummy variable distinguishing an urban from an intermediate region was also included in this group. A

8 10 JOURNAL OF REGIONAL SCIENCE, VOL. 56, NO. 1, 2016 third group of variables consists in sociodemographic controls (SOC_DEMO), such as the unemployment rate and the demographic structure. The unemployment rate controls for the stagnation of the labor market in the region, which is expected to favor backwash effects (out-migration). As regards demographic structure, an initial older population can be a key factor in explaining the extent of population growth potential in the time span of this analysis and it provides a measure of the general economic and residential opportunities in the region. Finally, country dummies are included to account for common unobserved factors within countries, such as institutional characteristics, fiscal policies, and general macroeconomic conditions. The variables concerning the relationships with urban and intermediate regions make it possible to estimate the spread/backwash effects following the growth processes taking place in these regions, as well as the net effect of distance. More precisely, population and GDP growth rates in urban or intermediate regions allow the presence of spillover effects to be tested. This can also aid understanding of whether there is a competing or complementary growth relationship between urban and rural regions that are close each other. The distance from the two types of regions has also been included in quadratic form so as to allow for potential nonlinearities. In this regard, it is expected that distance has a negative effect on growth, but a decreasing one for higher values (positive quadratic term). The interaction of distance with population and GDP growth in the closest urban or intermediate region was included in our model in order to verify whether and how the spatial spillover effect changes with distance. All variables are further described in Section 5. A cross-sectional model was chosen for several reasons. First, as stated by Rappaport (2004), cross-sectional regressions of local population growth on local characteristics are effective means to identify the partial correlates of changes in local productivity and quality of life. A fixed-effect approach would risk capturing all the cross-sectional variation in the model, since regional conditions at the beginning of the period often have persistent effects on population growth. The persistence of the determinants of population growth also substantially reduces endogeneity biases. In addition, since one of the key effects to be estimated is that of the physical distance between rural and urban regions, which is constant over time, a panel approach would not be appropriate. Finally, data availability for a number of countries is limited in time, so that a more longitudinal analysis would have to be compensated with an even lower number of observations, thus undermining the internationally comparative perspective on the implications of urban rural linkages for rural development, which is the key novelty of this work. The proposed model was set within a spatial framework. Units of analysis have a specific geographic location and are sometimes located in close proximity. The potential spatial heterogeneity, meaning the variability of the relationship under study across space, needs to be taken into account. For example, distance from urban centers may have one role in certain areas (i.e., countries) and a different one in others. This heterogeneity may also apply in different macroregions within a country; hence, controlling for the country to which the region belongs might not be enough. In the context of our analysis, removing unobserved spatial patterns was a primary task also because spatial unobservables may be sources of endogeneity (Basile et al., 2014). A simple Moran s I test on the OLS residuals did not reject the presence of spatial autocorrelation in the residuals, making it necessary to use a specification that accounted for the resulting potential biases. 5 Spatial heterogeneity is likely to be more important than spatial dependence, since the 5 The Moran test was conducted using both four and three nearest-neighbor spatial matrixes. Results were consistent with the matrix adopted and are available upon request.

9 VENERI AND RUIZ: URBAN-TO-RURAL POPULATION GROWTH LINKAGES 11 presence of spillovers is directly modeled through the inclusion of distance variables and growth rates of nearby urban centers. In order to account for spatial heterogeneity, we opted for a semiparametric specification, where the nonparametric component is a smooth interaction of latitude and longitude. As suggested by McMillen (2012), nonparametric estimations are much more accurate than spatial autoregressive (SAR) models using a contiguity matrix, especially because the latter can be easily misspecified. A penalized spline geo-additive model (PS-GAM) in a semiparametric form was used, as indicated in (3): (3) y i = X i i + f(no i, e i) + ε i, where y i is the dependent variable (population growth in the ith rural region), X ij are the independent variables of Equation (2), and i their associated coefficients, respectively; the term f(no i, e i ) is the nonparametric part of the model, which smoothes the data over space to remove spatial autocorrelation. It consists in a smooth function based on thin plate regression splines accounting for the interaction between latitude (northing, no i ) and longitude (easting, e i ). Full details on this method are given in Wood (2003). ε i is an independent and identically distributed error term. The smoothing parameter estimation problem was solved by using the generalized cross-validation criterion as in Wood (2008). Spatial heterogeneity might also come from the different definitions of the units of analysis (regions) across countries. Despite our analysis relies on OECD TL3 regions, which definition allows for international comparisons, the different sizes of regions across and within countries can introduce a bias when running the empirical model. Nevertheless, the use of the spatial nonparametric approach (PS-GAM) together with the inclusion of country fixed effects allows the different sources of spatial heterogeneity to be accounted for. Moreover, in order to further address the heterogeneity issues, the model is also estimated for different macroareas: Europe, North America, and South America. 5. DATA The analysis was carried out using a cross-section of OECD small regions (TL3), the smallest official territorial definition at which the OECD provides data. This choice maximizes the comparability of units across different countries, which is a key objective of this work. Country-specific rural/urban definitions have the advantage to be more accurate, but they would be less suitable for international comparisons. OECD small regions (TL3) generally correspond to the administrative tier below the national government and the regional level, and they cover the entire territory of countries. In most countries (e.g., Belgium, France, Italy, Korea, Netherlands, Spain, Turkey), TL3 corresponds to provinces. In some countries they correspond to a definition close to that of a functional or statistical region (e.g., economic areas in the United States; census divisions in Canada; development regions in Greece; groups of municipalities in Germany, Mexico, Portugal, and the United Kingdom). The classification of TL3 regions according to their urban rural character is mainly based on population density and is validated by each country s National Institute of Statistics (OECD, 2009). To be acknowledged is that TL3 regions can be substantially different from other national definitions of metropolitan statistical areas and nonmetropolitan territories. In addition, TL3 do not always represent distinct labor markets, since metropolitan regions, such as, for example, Paris in France or Toronto in Canada, are made up of several TL3 regions (departments and census subdivisions, respectively). While this problem should not strongly affect the analysis carried out here, which focuses on nonurban regions, it might affect the magnitude of the coefficients of distance-related variables;

10 12 JOURNAL OF REGIONAL SCIENCE, VOL. 56, NO. 1, 2016 thus, results should be interpreted cautiously. Moreover, it should be acknowledged that in order to allow for international comparisons, the accuracy of urban/rural OECD classifications presents some limits. For example, the OECD classification of the TL3 region covering Boston and Minneapolis as intermediate rather than urban could be questioned. TL3 regions represent in any case a compromise necessary to develop a standard for a territorial classification of the developed world and they have already been extensively used in empirical regional analyzes of OECD countries. In this paper, the main units of analysis are those regions classified as predominantly rural. The primary data source was the OECD Regional Database 6 (except for the information on the distance across regions, which was computed by the authors). The base year was 2000, and the population growth rate covered the period between 2000 and Due to limited data availability, the number of observations considered depended on the control variables included in the analysis. The most extended model included 206 regions, in which observations from 14 OECD countries were included, representing both Europe and Asia, but excluding North and South America. 7 On the other hand, a reduced form of the model covered 25 countries, including American countries and Australia. 8 The inclusion of GDP as an explanatory variable accounted for most of the variation in the number of countries included in the empirical specifications. Table 1 summarizes all the variables that were used in the empirical analysis. The first set of variables related to the basic characteristics of the observed rural regions, such as their sectoral composition, which was measured through the share of employment in manufacturing, water, and energy production (C, D, and E NACE groups, respectively). The second set of variables took account of the information regarding the closest urban region for each observation. More precisely, this information included the distance (in kilometers) from each rural region to the closest urban or intermediate region (geographical centroids were considered to compute distance), as well as the GDP growth rate of the corresponding urban or intermediate region. A dummy variable indicating whether the closest region is an intermediate or an urban one was also included. Finally, several further controls were included: the elderly dependency rate to account for the demographic structure, the initial population density to account for agglomeration and congestion effects, the unemployment rate, and country dummies. 6. RESULTS Estimation results of the PS-GAM model are reported in Table 2. OLS results are also included in the table and are consistent with those of the PS-GAM specifications. The second and third columns report a model specification where the distance variable also appears in quadratic form, so as to allow for nonlinearities in the effect of distance, as in Partridge, Bollman, Olfert, and Alasia (2007). The PS-GAM(2) specification includes the growth rate of the closest urban region in terms of population instead of GDP. The first and third PS-GAM specifications instead include the interaction terms between distance to the closest urban and intermediate region and its growth of population and GDP, respectively. All specifications account between 50 percent and 70 percent of total variance Countries whose regions are included in the analysis are: Austria, Belgium, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Norway, the Slovak Republic, Spain, and Sweden. 8 In addition to the countries listed in footnote 3, the reduced specification includes the following countries: Australia, Canada, Chile, Iceland, Korea, Mexico, Switzerland, Turkey, the United Kingdom, and the United States.

11 VENERI AND RUIZ: URBAN-TO-RURAL POPULATION GROWTH LINKAGES 13 TABLE 1: Variables Used for the Empirical Analysis Variable Description Source Population growth in rural regions Distance (km) Ln population density (km 2 ) Elderly dependency rate, 2000, rural regions GDP (ln) real prices (US$ PPP), 2000, rural regions Share of employment in industrial activities, 2000, rural regions Unemployment rate, 2000, rural regions Growth rate of real GDP, , urban and intermediate regions Growth rate of real population, , urban and intermediate regions Dummy, urban region = 1 Interaction between GDP growth in urban region and distance Interaction between population growth in urban region and distance GDP per capita (ln) (US$ PPP), 2000 urban and intermediate regions Total population (ln), 2000, urban and intermediate regions Population growth between 2000 OECD regional database and 2008 in rural regions (%) Distance in kilometers from each Authors computation rural region (centroid) to the closest urban or intermediate region Natural logarithm of total OECD regional database regional population divided by regional total area in km 2 Population older than 64 divided OECD regional database by population aged years old Natural logarithm of GDP in real OECD regional database prices, US$, purchasing power parity Share of employment in OECD regional database industrial sectors (C, D, E following NACE classification) over total employment Unemployment rate (%) OECD regional database Growth rate of real GDP in urban or intermediate regions between 2000 and 2008 (%) Growth rate of population in urban or intermediate regions between 2000 and 2008 (%) Dummy variable: 1 if the closest region is urban; 0 if the closest region is intermediate Distance to the closest urban or intermediate region times its GDP growth rate between 2000 and 2008 Distance to the closest urban or intermediate region times its population growth rate between 2000 and 2008 Natural logarithm of GDP in real prices, US$, purchasing power parity in the closest urban or intermediate region, 2000 Natural logarithm of total population in the closest urban or intermediate region, 2000 Note: All variables refer to the year 2000, except when specified otherwise. Authors elaborations on OECD regional database Authors elaborations on OECD regional database OECD regional database Authors elaborations on OECD regional database Authors elaborations on OECD regional database OECD regional database OECD regional database

12 14 JOURNAL OF REGIONAL SCIENCE, VOL. 56, NO. 1, 2016 TABLE 2: Estimation Results, Standard Errors in Brackets. Dependent Variable: Growth Rate of Population in Rural Regions ( ) OLS PS-GAM(1) PS-GAM(2) PS-GAM(3) PS-GAM(4) a PS-GAM(5) PS-GAM(6) (Intercept) ** Ln population density (km 2 ) *** *** Distance (km) 3.0E-04 *** 2.91E-04 * 3.84E-04 *** 1.48E-04 * 2.99E-04 *** 1.01E-04 * 2.59E E E E E E E E-05 Square of distance (km) 2.5E-07 ** 1.24E E E E-07 ** 1.31E-07 * Growth rate of GDP, , urban and interm. 1.01E E E E E E Growth rate of pop., , urban * *** *** *** and interm Dummy, urban region = * GDP per cap. (ln) (US$ PPP), 2000, * * * urban & interm GDP real prices (ln) (US$ PPP), 2000, rural regions Elderly dependency rate, 2000, rural regions Share of empl. in industrial activities, 2000, rural reg. Unemployment rate, 2000, rural regions (%) Interaction: pop. growth (urban & interm.) and distance Interaction: GDP growth(urban and interm) * distance * * * ** *** *** *** *** *** *** *** *** ** ** ** ** *** *** *** *** *** *** *** (Continued)

13 VENERI AND RUIZ: URBAN-TO-RURAL POPULATION GROWTH LINKAGES 15 TABLE 2: Continued OLS PS-GAM(1) PS-GAM(2) PS-GAM(3) PS-GAM(4) PS-GAM(5) PS-GAM(6) Total population (ln), 2000, urban and interm s(lat, long) F: 15.8 *** 15.9 *** *** ** *** 24.1 *** No. obs R 2 (adj.) Country dummies Yes Yes Yes Yes Yes Yes Yes Turning point of distance effect (km) Notes: a Distance is considered from the urban centers only. * P < 0.1; ** P < 0.05; *** P < Source: Authors elaboration based on data from the OECD Regional Database.

14 16 JOURNAL OF REGIONAL SCIENCE, VOL. 56, NO. 1, 2016 Population growth in rural regions ( ), component plus residual Distance from the closest urban or intermediate region (km) Source: Authors elaboration on OECD data. FIGURE 3: Population Growth in Rural Regions and Distance to the Closest Urban or Intermediate Region, Partial Residual Plot. The analysis confirms the existence of positive population growth spillovers from urban or intermediate regions to rural ones. Even controlling for key features of urban regions, such as their size and levels of GDP per capita, the simple fact of being close to a growing urban place in terms of population, but not GDP is on average advantageous for rural regions. However, this effect decays with distance, since the interaction term between population growth in urban regions and distance is negative and significant, consistently with the findings of other works in the literature (among others, Schmitt et al., 2006; Partridge, Bollman, Olfert, and Alasia, 2007). The level of GDP per capita in the closest urban regions is positively associated with rural population growth, suggesting that relatively higher-income urban places yield on average higher spread effects. On the whole, results suggest that urban-led spread effects are a dominant feature of the developed world and they do not apply to North America only. The fears that urban-led growth generates backwash effects might be overstated. They might have applied in the past, perhaps during the phases of development where many workers from agriculture and resource industries in rural areas moved to cities, but they do not seem to be supported by evidence from developed countries today. Once urban growth spillovers and the other controls variables have been accounted for, distance still has a negative and statistically significant coefficient, as shown in Figure 3. This finding is robust to all the different specifications reported in Table 2. According to the specification with the largest geographical coverage (PS-GAM(5)), a one

15 VENERI AND RUIZ: URBAN-TO-RURAL POPULATION GROWTH LINKAGES 17 standard deviation increase to the mean in the distance variable is associated with a decrease in population growth rate of 0.12 percentage points. The positive coefficient of the quadratic term of the distance variable suggests that the positive effect of proximity to urban places decreases more than proportionally as distance increases. However, such decreasing effect is relatively slow. As reported at the bottom of Table 2, the effect of distance ceases to be positive after a level of remoteness from urban regions ranging from 320 to 590 km, depending on the specification considered. 9 Physical proximity accounts for a broad set of potential linkages between urban and rural areas. Being remote and distant from cities tends to reduce the attractiveness of rural regions as residence locations for people working in urban locations, since long distances decrease the possibility to commute (Partridge et al., 2010). However, in most specifications population growth in rural regions is not significantly sensitive to whether the closest nonrural region is predominantly urban or intermediate. Thus, the net effect of distance is dominant over the simple fact of being closer to one or the other type of region. In the fourth specification (PS-SAR(4)), the closest urban region was constrained to being a predominantly urban region only, so that all the intermediate regions were excluded from the model in terms of both growth spillovers and distance variables. However, the dummy indicating whether the closest region was urban (instead of intermediate) was kept in order to control for the type of the actual closest region. All the previous results were confirmed, suggesting that urban scale does not play a major role and that, when the closest region is intermediate, the strongest urban rural linkages are more likely to occur with the latter than with a more distant urban region. The issue of urban scale measured in terms of total population of the closest urban or intermediate regions is also accounted for more directly in the last two specifications of Table 2. The relative coefficients are not statistically different from zero, confirming that urban scale does not affect rural growth, differently from what was found though at an intra-metropolitan scale by Partridge, Bollman, Olfert, and Alasia (2007) and Henry et al. (1997), who identified positive and negative effects, respectively. Our results also differ from those for developing countries, as shown by a recent study on China, which found that medium-sized cities yield spread effects while the largest ones yield backwash effects (Chen and Partridge, 2013). On the whole, the nonsignificant effects of urban size might suggest that the key features of urban-led rural growth in the developed world are labor market linkages and access to basic urban services. Differently from what found from the previous research for North American regions (Partridge et al., 2008), access to high-level services from the top tiers of the urban hierarchies turns out to be less important. The remaining nonspatial variables in the model help to explain the variation of the population growth rates in rural areas. First, a higher elderly dependency rate is associated with lower population growth. This was expected, because an older demographic structure can be a cause in itself of lower population growth, and because a region with a smaller pool of working-age people may mask less possibility to commute. The initial level of GDP in a rural region is associated with higher growth, suggesting that larger economic size may benefit from scale economies, yielding a comparative advantage in attracting migrants (Kahn et al., 2001). A lower initial unemployment rate is also associated with higher population growth. High unemployment rates may induce working-age 9 The thresholds at which the effect of distance turns positive has been computed based on the linear and quadratic distance coefficients. Such thresholds have been reported only when both coefficient were statistically significant at 90 percent.

16 18 JOURNAL OF REGIONAL SCIENCE, VOL. 56, NO. 1, 2016 people to migrate as a way to react to a business cycle shock. In addition, unemployment rates may proxy for a lack of matching in the labor market, delineating a relative scarcity of labor with the skills able to boost the region s success in the period under analysis (Glaeser et al., 1995). Finally, the share of jobs in manufacturing, water, and energy production is negatively associated with population growth, suggesting that local labor demand conditions are important, at least at the spatial scale considered in this analysis. This result differs from that found for Canada by Partridge, Bollman, Olfert, and Alasia (2007), who considered smaller spatial units. The last two columns of Table 2 report a reduced specification that allowed the largest number of observations (and countries) to be included in the analysis. On the whole, the results confirm all those of previous specifications. The only difference consists in the statistical significance of the coefficient relative to population density in rural areas, which in these specifications is likely to capture the size of rural economies, previously approximated with total GDP. Robustness Checks and Spatial Dependence Robustness checks were conducted by accounting for spatial autocorrelations of residuals by modeling spatial dependence instead of spatial heterogeneity. This meant modeling the dependence of the values of each observation on the values of the neighboring observations at nearby locations through the use of a spatial weighting matrix (LeSage and Pace, 2009). To this end, a robust LM test on the OLS residuals from the first specification reported in Table 2 suggested the use of a SAR model, which consists in adding the spatial lag of the dependent variable and estimating all coefficients through maximum likelihood. 10 The spatial lags are computed by using a spatial matrix whose values are 1 for the four nearest-neighbors of the ith region and 0 otherwise. 11 Table 3 presents the results of the robustness checks, which are almost perfectly consistent with those obtained by applying the PS-GAM model (Table 2). A positive coefficient of the lagged dependent variable (rho) indicates a positive correlation between population growth rates of nearby rural regions. In principle, this result suggests that relationships of complementarities prevail over relationships of (zero-sum) competition among rural regions, at least when population growth is taken into account. That said, this interpretation should be treated with special caution, since, as pointed out by McMillen (2010) and more recently by Gibbons and Overman (2012), SAR models may suffer from identification issues if the data-generating process is not properly specified. In any case, results from Table 3 suggest that the rho coefficient relative to the lag of the dependent variable is never statistically significant, except for the last specification, which is the one with the widest geographical coverage. Macroregional Differences Our empirical strategy took account of spatial heterogeneity and allowed results to be interpreted as general relationships applying to the whole sample of OECD regions. However, the findings presented above can be usefully supplemented by considering whether 10 Results of the LM robust Moran tests on the OLS residuals are available upon request. 11 A k-nearest-neighbor matrix was considered more adequate for the analysis than a distance-based matrix since: (a) rural regions do not necessarily border on other rural regions; (b) the size of the territorial units differs across the countries studied. As a robustness check, estimations and tests were carried out also considering 3, 5, and 6 nearest-neighbors. The results did not change significantly. All of the matrices were obviously standardized so that the sum of each row was equal to 1.