In his presidential address to the Regional Science Association over thirty years ago, William Alonso presented the case for "Five Bell Shapes in Development" and argued that "the developed countries will enter fully in to the realm of the right-hand tail of these curves" (p. 16) and that this transition might result in several surprises. He proposed, therefore, that we should study the right tail of these "curves" as well as interactions among them. Much of what Alonso suggested has come to pass, although his prognostications were not always exact. And although he touched on several issues of relevance to regional scientists, the discipline has been slow to move away from a growth-centered paradigm. The strength of regional science—the capacity to consider economic, demographic, and geographical aspects of an issue simultaneously—has yet to be focused on some of the "right-hand" challenges that have arisen, population loss, for example. In this article, we provide a review of regional science research within the context of Alonso’s five bells and hypothesize how Alonso’s propositions might differ in today’s world. We then focus more specifically on one particular area: population loss. Using these examples allows us to highlight how regional science might contribute to the conceptualization of "right-hand tail" development challenges, especially where theory, issues of spatial scale, and interregional dependencies are concerned.
This article discusses the interaction between demographic aging, population decline, and various aspects of the local development challenges facing public authorities. In particular, this article examines some of the financial issues arising from population aging and decline and the ways in which new approaches to public finance are being used in support of European Union regional and urban policy. In this context, it is argued that a comprehensive portfolio investment approach has the potential to significantly improve policy effectiveness.
The disequilibrium and equilibrium models of migration disagree on how local amenities and labor market dynamics influence regional in-migration. Research into migration motives and decision-making show that migration for some individuals is mainly driven by proximity to the labor market, while migration for others is mainly amenity driven. As this is an ongoing process, it should result in a spatial sorting based on migration motives. This means that global models explaining in-migration underestimate the influence of both factors through averaging out of the coefficients across these diverse regions. In this article, we compare a local and a global model explaining in-migration through residential quality and labor market proximity. We find significant differences in the influence of the explanatory variables between regions. Demonstrating this spatial heterogeneity shows that the impacts of factors underpinning migration vary across regions. This result highlights the importance of the regional context in anticipating and designing regional policy concerning population dynamics.
To address the development of rural basic public services (RBPS) among contiguous destitute areas of China, we develop a comprehensive RBPS evaluation methodology to examine RBPS development level of 728 poverty-stricken counties, using geographical information system (GIS) to describe their multiscale and multidimensional spatiotemporal change during 2010–2012; besides, we also try to reveal how RBPS interacts with county economy (CE) by integrating Tapio model and weighted Voronoi circle-layer structure. Our results show that (1) at a multiscale of area–province–county, in spite of the overall low level, RBPS is steadily growing during 2010–2012, along with a positive spatial autocorrelation and an obvious nonequilibrium that is high in east China but low in west; however, the RBPS gaps among the whole counties are gradually narrowing, shifting their development grades from a mostly relative shortage or relatively severe shortage in 2010 to a main state of relatively richness or relatively equilibrium in 2012, (2) from a multidimensional view, the RBPS gaps among most dimensions of different areas are gradually narrowing, except for the dimension of social public safety service that shows a significant regional differentiation among different areas. RBPS in Tibetan areas is the most unequalled and falls into the most obvious heterogeneity, and (3) there exist weak correlations between county-level original RBPS and original CE for each year and each circle layer, while significantly positive correlation is found only between mean RBPS and mean CE for four circle-layer subsets of counties, respectively; overall, RBPS development level lags behind that of CE as a main result of the weak decoupling between them. This study may provide a good understanding of the status, regional differences, and evolution of RBPS in poverty-stricken rural China, and serves as a scientific reference regarding decision-making in both promoting intrarural antipoverty harmonious development and constructing the new countryside of China.
While population growth has been consistently tied to decreasing racial segregation at the metropolitan level in the United States, little work has been done to relate small-scale changes in population size to integration. We address this question through a novel technique that tracks population changes by race and ethnicity for comparable geographies in both 2000 and 2010. Using the Theil index, we analyze the fifty most populous metropolitan statistical areas in 2010 for changes in multigroup segregation. We classify local areas by their net population change between 2000 and 2010 using a unique unit of analysis based on aggregating census blocks. We find strong evidence that growing parts of rapidly growing metropolitan areas of the United States are crucial to understanding regional differences in segregation that have emerged in past decades. Multigroup segregation declined the most in growing parts of growing metropolitan areas. Comparatively, growing parts of shrinking or stagnant metropolitan areas were less diverse and had smaller declines in segregation. We also find that local areas with shrinking populations had disproportionately high minority representation in 2000 before population loss took place. We conclude that the regional context of population growth or decline has important consequences for the residential mixing of racial groups.
Regional disparities have been measured mainly using a variety of concentration statistics applied to spatial data. This article modifies a factor decomposition of the Theil index of inequality allowing to assess which part of regional income inequalities could be due to neighborhood features. The proposal is illustrated for the case of the Spanish peninsular provinces during the period 1981–2011. It is shown that the comparison of inequality among neighborhood and specific provincial factors open a new manner of analysis that can shed light on the terms of policy recommendations, specially on those identified as place-based oriented, by introducing a new perspective about what is the relevant place on which the policy has to be set in order to achieve regional inequalities reductions.
The aim of this article is to demonstrate how a particular modeling framework, based on extended input–output analysis, can be used to obtain a clearer understanding of the impact of regional decline of the effects of high, and rising, unemployment; of falling industrial final demand; of welfare payments; and of declining population. The activity–commodity framework used here provides a systematic way of adding demographic variables to the familiar Leontief interindustry model and the extended inverse derived from it provides a rich source of information about the interaction of demographic and economic change, expressed as demographic–economic and economic–demographic multipliers. Drawing on the author’s research in the 1980s and 1990s, this article considers two empirical examples to show the framework’s analytical value: a simple extended model is used to assess the distributional effects of welfare payments in a declining region; and a more elaborate version is linked to a set of regional labor market accounts, summarizing intercensal change in population and employment. This model is used to produce a comprehensive assessment of the effects of population and employment change in two UK regions, one a growing region (East Anglia) and the other a region in decline (Merseyside). In a final section, the benefits and limitations of the extended input–output modeling framework are discussed in comparison with some of the alternative modeling frameworks that are currently available.
Creativity has in recent years received much attention from the research community, in relation to both technological innovation and knowledge spillovers. In the same vein, the concept of a creative class and of a creative city has gained a rising popularity. The present study aims to investigate the impacts of the urban "ambiance" on the spatial dispersion of heterogeneous types of creative people over different urban agglomerations. To that end, creative people are classified according to their profession or job class into Bohemians, creative core, and creative professionals. This article, then, seeks to relate the presence of each of these groups to the cultural ambiance of a given locality beside other moderator variables. Next, an econometric model is constructed and applied to explain the spatial distribution of creative professions in the Netherlands. Our study first maps out the spatial spread of these three creative classes in the Netherlands. Next, the shares of these creative classes are related to cultural, ecological, ethnic, and geographic characteristics of Dutch municipalities. Our results show that Bohemians and people belonging to the creative core exhibit a specific spatial pattern: they appear to be overrepresented in municipalities with a relative overconcentration of culture, nature, and ethnic diversity and with a short distance to job places.
In developed countries with declining population growth, sustainable rural economic growth is a problematic issue that is made more difficult by severe international cost-saving competition. Well-organized spatial and economic systems may play a key role in solving this specific problem. These systems can be achieved by spatial reorganization and agglomeration economies in less congested rural areas. However, rural areas typically have lower levels of social welfare partly as a result of the limited variety of goods and services, which further reduces centripetal forces on population and economic activity. Accordingly, in rural areas, it may be important to organize a spatial structure that sustains the distribution of a variety of goods and services in insufficient economies of scale and scope by coordinating a common local central place as an interregional spatial framework. This article examines a location model for forming an intermediate hierarchical center to maintain both efficiency and equity for economic agents in rural areas.
Economic growth convergence, one of the classical assumption in regional economic growth, has been perplexing. There are many empirical studies trying to test if there is regional convergence in China. In this article, we bring new information of the finer spatial scale to the existing literature by using neoclassical convergence analysis, cross-sectional specifications, panel data models, and spatial econometric techniques to test the convergence hypothesis across 2,286 cities and counties in China. Empirical findings from cross-sectional data and spatial panel data show that significant absolute β and conditional β convergence are present in gross domestic product per capita after controlling for investment return rate, human capital, savings rate, population growth, technology advancement, capital depreciation rate, and initial technology level. We also find spatial agglomeration in urban and county economic growth is strong, and spatial effects are significant. Urban and county economic growth convergence rates for 1992–2010 show a gradually accelerated development trend. We present significant evidence that levels of investment, human capital, and initial technology impose significant facilitating effects on city and county economic growth, while savings and population growth have significant negative effects. And city and county economic growth differ in terms of convergence levels and influential factors.
Rural leaders can point to low housing costs as a reason that their area should be competitive for business attraction. To what extent do rural housing costs offset transportation and other locational disadvantages in cost structures? The United States lacks information to systematically answer the question. We adapt a strategy employed by The Economist in exploring purchasing power parity: the Big Mac index. We gather information on Big Mac prices with a random sample of restaurants across the contiguous United States. We find that core metro counties exhibit slightly higher Big Mac prices than other counties, but that differences across the balance of the rural–urban continuum code are not significant, implying that costs in a metroadjacent county are not different than areas that are much more rural. We show that some groups of states exhibit lower prices, especially in the southeast. Furthermore, we test for the presence of spatial monopoly and find that distance to other MacDonald’s restaurants has some influence on price. Stores at a greater distance from their competitors tend to charge more, ceteris paribus. We also show our results are consistent with other localized estimates of living costs. Our general findings could help rural decision makers determine whether their area truly holds cost advantages for firms looking to relocate.
In 1970, Tobler produced a movie simulating population growth in the City of Detroit. He argued that his model did not need to include terms for faraway places like Singapore, while still being relative accurate in his forecast by invoking what he called the first law of geography. In spatial optimization, like the general warehouse location problem (GWLP), it is assumed that all possible linkages need to be included, as arbitrarily dropping potential variables may prevent optimal solutions from being identified. In this article, it is demonstrated that it may be possible to meet such exacting standards in spatial optimization, while at the same time being guided by Tobler’s argument for being simple and frugal. This article gives a demonstration of how this might be achieved using the GWLP as an example. A new model form is proposed which distinguishes between "near" (low cost) allocations and "far" (high cost) allocations and uses both explicit and implicit variables for capacity allocations. Computational experience in using this new model is given which shows that optimal solutions can be identified and verified while eliminating a substantial number of allocation/transport variables and constraints. This article ends with a challenge for the reformulation and redevelopment of other spatial optimization problems in regional science.
Agglomeration spillovers are a major driver of policy creating science parks across the world. However, agglomeration benefits may be offset by competition arising out of spatial proximity of firms. Analysis of Taiwanese firms’ total factor productivity distribution shows that, depending on location choice, the impact of agglomeration and selection is heterogeneous across firm types. Spatial analysis is applied to evaluate the regional innovation policy of establishing science parks. A sectoral analysis of productivity distributions reveals that there is a positive relationship between technology intensity of the production process and firm productivity levels when firms are located in science parks.
There is evidence from the literature that firms enjoy higher productivity levels, when the workforce employed is culturally more diverse. It is an open question whether this gain is utilized to shift the supply curve and set lower prices in order to achieve a higher demand and possibly higher revenues. This knowledge gap is not addressed in the existing literature and forms the departure of our research. We introduce a reduced-form model, inspired by the study of Melitz and Ottaviano on heterogeneous firms, and add labor productivity by using the approach of Ottaviano and Peri on cultural diversity. In our empirical study, we employ German data, while the field of research is conducted for single plants, and industry-specific effects are taken into account. Our analysis shows significant positive effects of the cultural diversity of the high-skilled workforce on the market size of single establishments. We conclude that emerging productivity gains are not just paid as dividend or factor rewards but are also used to set lower prices in order to achieve higher demand.
Framework Programs (FPs) of the European Union (EU) finance collaboration among research units located in different parts of Europe and as such they mediate the flow of a significant amount of knowledge across distantly located European regions. Contrary to expectations, no evidence has been found in the literature on the supposed positive regional innovation impact of FP participation. We assume in this article that the overall missing impact of EU FP participation on regional patenting masks an important spatial regime effect. Our results are supportive of this assumption. While FP research subsidies act as a substitute for funding from other sources in regions of old EU member states, innovation in lagging regions in Central and Eastern Europe tends to rely more on the external knowledge transferred via FP-funded research networks to compensate for their less developed local knowledge infrastructures. Our findings are important, as they suggest that, in combination with other policies, strengthening research excellence and international scientific networking in relatively lagging regions could be a viable option to increase regional innovativeness.
This article presents a theoretical growth model that accounts for technological interdependence among regions in a Mankiw-Romer-Weil world. The reasoning behind the theoretical work is that technological ideas cannot be fully appropriated by investors and these ideas may diffuse and increase the productivity of other firms. We link the diffusion of ideas to spatial proximity and allow ideas to flow to nearby regional economies. Through the magic of solving for the reduced form of the theoretical model and the magic of spatial autoregressive processes, the simple dependence on a small number of neighboring regions leads to a reduced form theoretical model and an associated empirical model where changes in a single region can potentially impact all other regions. This implies that conventional regression interpretations of the parameter estimates would be wrong. The proper way to interpret the model has to rely on matrices of partial derivatives of the dependent variable with respect to changes in the Mankiw-Romer-Weil variables, using scalar summary measures for reporting the estimates of the marginal impacts from the model. The summary impact measure estimates indicate that technological interdependence among the European regions works through physical rather than human capital externalities.
Traditional "Marshallian" theories predict a linear relationship between internal migration and regional wage differentials. Using panel data on gross place-to-place migration flows in the United States, we estimate a semiparametric version of the modified gravity model and find evidence of a nonlinear effect of wage differentials in line with alternative theories of interregional migration, including the "option value of waiting" theory, liquidity constraints, and wealth-conditioned immobility. Traditionally, the migration decision process is believed to be mainly composed of two criteria: "whether to move" and "where to move." However, the empirical evidence of nonlinearity found in this study supports the potential presence of another important decision criterion, "when to move" on interregional migration.
Several types of proximity affect knowledge flows with different strength. Insufficient attention has been paid to the interrelations between such forms of proximity at the same time, each one assumed to facilitate the flow of goods, ideas, and spillovers on its own but not in relation to one another. Moreover, if decreasing returns have been conceptually attributed to proximity effects on the intensity of scientific cooperation and learning processes, empirical evidence about nonlinearities in different proximity effects has never been demonstrated. This article aims to fill these gaps. Results on all Nomenclature of Territorial Units for Statistics (NUTS2) regions of the 27 Countries of the European Union (EU27) point toward the existence and relevance of synergic effects between different types of proximity. In particular, while social proximity has a positive impact on scientific cooperation, with decreasing magnitude as spatial distance increases, results on cognitive and technological proximity suggest that some form of complementarity seems to exist with spatial distance. In fact, when spatial distance increases, in order to cooperate, regions must also be cognitively and technologically close.
To investigate how a pedestrian chooses a particular route in an urban center, this study analyzes the effects of individual and built environment characteristics on the route choice using binary logistic regression of 524 survey responses. Conducted in a strategic area, the survey, as often is the case, collects data that are skewed and face the separation issue—the same outcome always occurs for a particular value of a predictor—according to which estimates by the conventional maximum likelihood (ML) method are inflated. Thus, one mechanical and one statistical alternative are employed: (1) exclusion of a variable that causes separation and (2) estimation by Firth’s penalized method. The two alternatives produce comparable results of the significance testing, that is, p values, but their coefficient estimates considerably differ inasmuch as the mechanical approach used for the ML logistic regression forcefully omits the important variable and subsequently biases the estimates of other predictors. Compared to these ML estimates, empirical findings from the Firth logistic regression are presented in a way that corrects for the ML bias.
While there is a wealth of empirical research examining the potential relations and effects of foreign workers, immigration and cultural diversity on wages, employment, economic growth, and—in recent years—innovation, very little of this research has provided a convincing empirical demonstration of the mechanisms through which foreign workers would affect innovation. Most accounts hypothesize that foreign workers provide a different perspective that contributes to a diversity of ideas in the firm, while some also add the idea that foreign workers might help a firm build international networks. Nonetheless, these mechanisms have for the most part remained entirely theoretical, with few attempts being made at uncovering the intermediary relationships. This article contributes to filling this gap by focusing on the second of these mechanisms, asking whether firms that employ foreign workers also have broader international networks and whether this may, in turn, promote innovation through access to new knowledge. This article builds on survey data from approximately 500 firms in Norway, with more than ten employees, covering all sectors and regions. We find evidence that firms with highly educated foreign workers collaborate more frequently with international partners and that there is a positive relation between having a variety of international partners and the probability of product innovation and new-to-market product innovation.
In this article, we test whether economic growth depends on human capital development mainly operating through an upgrading of human capital stock in the area where the universities are located. We specify a growth model where a qualitative measure of human capital development, university efficiency, is considered in conjunction with a customary quantitative measure of human capital development, number of graduates. The model is estimated on panel data over the period 2003 to 2011. The evidence suggests that both indicators of human capital development have a positive and significant impact on gross domestic product per capita. Results also show that knowledge spillovers occur between areas through the geographical proximity to the efficient universities, suggesting that the geography of production is affected. Results hold when robustness checks are performed.
The production of scientific and technical knowledge is mostly concentrated in specific locations. Knowledge flows very easily within regions; however, scientific and technical knowledge does flow also between different regions. The aim of this article is to analyze knowledge flows between agglomerations of innovative inputs, and their effects on the innovative performance of regions. We estimate a regional knowledge production function and we test, through appropriate spatial econometric estimation techniques, the effect of both geographical and relational autocorrelation (as measured by participation to joint research networks in the European Union [EU] Sixth Framework Programme [FP6]). We adopt two selection criteria in order to define different relational "geographies" (hence spatial weights matrices), and we model the unobservable structure and link value of knowledge flows within these joint research networks. Our results confirm established evidence that knowledge flows within interregional research networks along a top-down nonsymmetrical and hierarchical structure. However, the EU enlargement - and a modified structure of incentive for collaboration activity of European institutions- changed the direction of knowledge flows toward a top-down dynamics of knowledge diffusion from coordinator to participants for "EASTWARD" research networks (whose coordinator is in the west and most participants in the east); while the contrary (a hierarchical bottom-up dynamic of knowledge transfer) is true for WESTWARD networks (whose coordinator is in the east and most participants in the west). FP6 is therefore a platform for knowledge barter exchange for EU15, while works as a mere one-way channel for knowledge diffusion from EU15 toward Central and Eastern European countries.
The Regional Economics Applications Laboratory (REAL) celebrated its twenty-fifth anniversary in 2014. Since then, REAL has become one of the leading research centers of regional science worldwide. In this article, we describe the scholarly network involving REAL’s alumni working in academia in Brazil. We analyze the patterns of research collaboration among around fifty Brazilian researchers whose main activities are related to academic institutions in Brazil. The Brazilian REAL network has shown to be an interesting case study that reflects the pattern of evolving collaboration networks in scientifically emerging economies. The expansion of the REAL scientific collaboration network in Brazil emerges as a relevant mechanism for both a qualitative leap in national scientific production in regional science and for the dissemination of knowledge in peripheral regions of the country. Conducted under the leadership of Geoffrey J. D. Hewings, it has helped to develop regional science in the country still further. This case study serves as an example of knowledge diffusion and of the role played by key researchers in the evolution of this network, providing a contribution to the science of research and innovation.
Social and interregional inequality patterns across US states from 1929–2012 are analyzed using exploratory space–time methods. The results suggest complex spatial dynamics for both inequality series that were not captured by the stylized model of Alonso. Interpersonal income inequalities of states displayed a U-shaped pattern ending the period at levels that exceeded the alarmingly high patterns that existed in the 1920s. Social inequality is characterized by greater mobility than that found for state per capita incomes. Spatial dependence is also distinct between the two series, with per capita incomes exhibiting strong global spatial autocorrelation, while state interpersonal income inequality does not. Local hot and cold spots are found for the per capita income series, while local spatial outliers are found for state interpersonal inequality. Mobility in both inequality series is found to be influenced by the local spatial context of a state.
Using data from the 2009 National Household Travel Survey, we quantify the effects of settlement patterns on individual driving habits and the resulting automotive carbon dioxide (CO2) emissions. We employ CO2 emissions to capture this impact accurately, as it reflects both vehicle miles traveled and any spatial differences in vehicle fuel efficiency choices. While previous studies have compared automotive travel in urban and suburban areas, our approach characterizes emissions across the entire US rural–urban gradient, focusing on the effects of population density. Rather than using categorical measures of contextual density (city, suburb, town, etc.), we use a geographical information system to calculate continuous measures of contextual density, that is, density at different proximities to households. These measures of contextual density allow us to model travel effects induced by the gravitational pull of the population densities of urban cores. Further, our methodological approach frames location choice as an endogenous treatment effect; that is, residential locations are not randomly assigned across our sample and significantly alter driving behavior. We find that individuals living in urban cores generate the lowest per capita automotive CO2 emissions, due to close proximities of population concentrations. Rather than attracting individuals who would likely have low CO2 emissions anyway, urban location apparently mitigates the emissions of people who would otherwise tend to have high automotive CO2 emissions. We find larger elasticities with respect to density than previous studies and also find that the attractive forces of population densities affect driving patterns at distances up to sixty-one kilometers outside of urban areas.
Using the world input–output tables available from the World Input–Output Database project, we quantify production line positions of thirty-five industries for forty countries and the rest of the world region over 1995 to 2011. In contrast to the previous related literature, we do not focus only on the output supply chain but also consider sectors’ input demand chains. This distinction is important because both these chains jointly constitute the entire production process, and the output sales structure of each sector is generally different from the structure of its inputs purchases. We use the output upstreamness (OU) measure of Antràs et al. and our proposed input downstreamness (ID) measure to quantify industry relative position, respectively, along the global output supply chain and the global input demand chain. Focusing on time variation, we find that potential input–output data uncertainties do not affect the observed patterns of the average OU and ID changes for the vast majority of countries and sectors. Further, for most countries the increase in OUs/IDs over time is found to be driven by a rise in cross-border intermediates sales/purchases.
Transmission constraints often limit the flow of electricity in a regional transmission network leading to strong interaction effects across different geographically distributed points within the system. In modern wholesale electricity markets, these transmission constraints lead to spatial patterns within the nodal electricity spot prices. This study exploits these spatial patterns to better predict spot prices within a wholesale electricity market. More specifically, we use the latest spatial panel data econometric models to compare within-sample and out-of-sample forecasts against nonspatial panel data models. The spatial panel data approach is explained by demonstrating a simple network optimization model. We find that a dynamic, spatial panel data model provides the best predictions within a forecasting error context. Our results may suggest that the spatial autocorrelation between node prices extends beyond the current market-defined zonal boundaries, which calls into question whether the zonal boundaries accurately reflect the congestion boundaries within the system.
Building on work funded by the European Spatial Planning Observatory Network 2013 Program, the article analyzes the regional development of the "creative workforce" among its active population against regional economic growth measured by changes in per capita gross domestic product over the period 2001 to 2008. The analysis establishes regional typologies in this relationship according to the "sense" and evolution of this association, allowing a critical evaluation of processes and policies that may explain the large degree of spatial variation encountered, and addresses the issue of causal relationships between these two dimensions, suggesting the need to rethink development policies based on "creative capital."
The labor market effects of the recent financial and economic crisis are rather heterogeneous across countries and regions. Such differences in labor market performance among industrialized countries are an issue of ongoing research. The objective of this article is to analyze labor market disparities among European regions and to provide evidence on the factors behind these differences. Whereas previous research focused on the effects of national labor market institutions, we also take structural characteristics of regions into account and investigate differences in labor demand responsiveness and their potential determinants. The data set covers the Nomenclature des unités territoriales statistiques 2 regions in the EU15 for the period 1980 to 2008. We employ an error correction model that is combined with spatial residual correlation. Our findings point to substantially distinct wage and output elasticities of employment among European countries and regions. Moreover, the rate of adjustment to disequilibrium is subject to significant variation across units of observation. There is robust evidence that labor market institutions affect the adjustment speed of regional labor markets and the wage elasticity of employment. Moreover, the findings suggest that some characteristics of regional labor markets matter as well. However, corresponding results are less robust compared with the evidence on labor market institutions.
European Union (EU) is very attractive for foreign direct investment (FDI) in services and policy makers should know the reasons explaining investment location choices of foreign firms in order to attract them. This article explores FDI location determinants in service functions in the EU-28. Studies dealing with such an issue stay generally focused on the location of service functions in the manufacturing sector. Our assumption is that the location determinants of service functions may differ according to sectors. So, we propose to study whether the location pattern of some service functions is sector independent, whereas it is sector dependent for other service functions. Using a database developed by Ernst and Young, we estimate mixed logit models on foreign firms’ location choices. Our contribution is to consider simultaneously three sectors and eight service functions for 271 European regions, during the period 1997 to 2011. Our fundamental findings are that service functions location choices are different according to sectors and that location determinants vary according to the service function considered. The only variables significant for all service functions are agglomeration variables. However, our contribution is to distinguish between different types of agglomeration (regional, sectorial, functional, and group agglomeration) and to show that some agglomeration variables act differently according to service functions.
We develop a large-scale high-resolution time series of nested multiregion input–output (MRIO) tables, encompassing a range of technical advances that are relevant for MRIO applications worldwide. First, our database is the first ever hierarchically nested system of subnational and international MRIO tables on three independent counts: (a) it features global country-level coverage, (b) it is available as a long annual time series, and (c) it is complemented with matching information on element uncertainty. Second, it is at the time of writing the largest existing MRIO system in the world, and in its creation a number of challenges related to computer storage and run time had to be overcome. The MRIO tables feature complete interregional trade at this level of detail, in combination with detailed regional–international trade with 185 countries. Our experiences with constructing such a large and detailed framework contribute knowledge needed by practitioners wishing to assemble similar databases for other countries, in that our build pipeline can readily be adopted for the integration of subnational MRIO databases, for example, for the United States, China, Australia, Spain, and Germany. We demonstrate our approach by constructing a time series of MRIO tables for the example of the Chinese economy between 1997 and 2011, distinguishing each of the 30 provinces and 135 industry sectors for each province, and linking each province with 185 world countries.
Although the phase of euphoria seems to be over, policy makers and regional agencies have maintained their interest in cluster policy. Modern cluster theory provides reasons for positive external effects that may accrue from interaction in a group of proximate enterprises operating in common and related fields. Although there has been some progress in locating clusters, in most cases only limited knowledge on the geographical extent of regional clusters has been established. In the present article, we present a hybrid approach to cluster identification. Dominant buyer–supplier relationships are derived by qualitative input–output analysis from national input–output tables, and potential regional clusters are identified by spatial scanning. This procedure is employed to identify clusters of German research and development-intensive industries. A sensitivity analysis reveals good robustness properties of the hybrid approach with respect to variations in the quantitative cluster composition.
Formal modeling of local population growth has usually tended to focus on identifying patterns that are presumed to hold universally. However, as Glaeser, Ponzetto, and Tobio highlighted, these laws are reliable for long-term dynamics; but in some moments or for some places, the balance between the different factors may change, giving rise to different specific behaviors. In this article, we study local population growth in Spain with no intention of searching for universal patterns. Rather, we are interested in identifying how relevant the temporal and spatial heterogeneity may be, that is, to assess the even and uneven effects that population growth determinants can exert across time and space. The geographically weighted regression (GWR) approach applied in this article for two different decades, 1991–2001 and 2001–2011, captures the spatial heterogeneity. Results on the spatially differentiated population growth factors are compared with the global ordinary least squares (OLS) estimators for both decades. Essential factors in urban and regional economics such as size (initial population) or distance (either to the big cities or to the coast) can have different effects on population growth across both space and time, corresponding to the global estimated effects for some areas but diverging from these in others. Using GWR estimation procedures, we can identify changes in the sign or in the intensity of a factor’s effect across space, such that some factors could enhance population growth in one place but reduce it in another. Only after all spatially differentiated local effects have been analyzed and taken into consideration can appropriate national or regional policies be designed à la carte to promote, retain, or deter population growth.
Subsidies for research and development (R&D) are an important tool of public R&D policy, which motivates extensive scientific analyses and evaluations. This article adds to this literature by arguing that the effects of R&D subsidies go beyond the extension of organizations’ monetary resources invested into R&D. It is argued that collaboration induced by subsidized joint R&D projects yield significant effects that are missed in traditional analyses. An empirical study on the level of German labor market regions substantiates this claim, showing that collaborative R&D subsidies impact regions’ innovation growth when providing access to related variety and embedding regions into central positions in cross-regional knowledge networks.
Building on previous literature that provides extensive evidence concerning flows of knowledge generated by interfirm agreements, in this article, we aim to analyze how the occurrence of such collaborations is driven by multidimensional proximity among participants and by their position within firms’ networks. More specifically, we assess how the likelihood that two firms set up a partnership is influenced by their bilateral geographical, technological, organizational, institutional, and social proximity and by their position within networks. Our analysis is based on agreements in the form of joint ventures or strategic alliances, announced over the period 2005–2012, in which at least one partner is localized in Italy. We consider the full range of economic activities, which allows us to offer a general scenario and to investigate specifically the role of technological relatedness across different sectors. The econometric analysis, based on the logistic framework for rare events, provided three noteworthy results. First, all five dimensions of proximity jointly exert a positive and relevant effect in determining the probability of interfirm knowledge exchanges, signaling that they complement each other rather than function as alternative channels. Second, the highest impact on probability is due to technological proximity, followed by organizational, geographical, and institutional proximities, while social proximity has a limited effect. Third, we find evidence concerning the positive role played by networks, through preferential attachment effects, in enhancing the probability of interfirm agreements.
Two modern approaches to development policy have recently evolved and disputed with each other, namely, the space-neutral and the place-based approaches. Perhaps the most notable conceptual development common in these modern approaches is a strong awareness of the key role of geography in policies targeting aggregate economic growth. Thus, it became clear in the new policy thinking that the impact of countries’ structural policies largely depends not only on the specific instruments (e.g., human capital development, infrastructure investments, and small- and medium-sized enterprises support) but also on the concrete patterns in which these instruments are deployed geographically. It is suggested in this article that macroeconomic models that integrate geography could usefully help policy makers in their choice among different complex geography-instrument mixes. I survey the most important modeling challenges raised by the two modern economic development approaches. To illustrate how economic models can respond to these challenges, I briefly introduce the Geographic Macro and Regional (GMR)–Europe model. To complete the illustration with a practical example, an impact analysis of a space-neutral–place-based policy mix implemented in regions of the European Union (EU) is presented. It is found that promoting research excellence in leading agglomerations combined with human capital development in the rest of the regions in Europe could result in a sustained positive gross domestic product impact of EU Framework Programs at the aggregate EU level. Nevertheless, it is also important to emphasize that the aggregate impact masks marked regional differences.
This article examines the role of academic and private R&D spending in the frame of a knowledge production function estimated across 3,109 US counties. We distinguish the role of local, face-to-face, knowledge spillovers that are determined by geographical proximity from distant spillovers captured by a matrix of patent creation–citation flows. The advantage of the latter matrix is its capacity to capture the direction of the spillovers. We control for the spatial heterogeneity between metropolitan and nonmetropolitan counties as well as between states. Our empirical results show that spillovers due to private knowledge contribute to higher returns in metropolitan counties than in nonmetropolitan regions. On the other hand, knowledge created in the academia leads to spillovers displaying spatially homogeneous returns. Our results imply that future innovation policies need to grasp more fully the role of distant knowledge spillovers, especially those generated in the academia, and recognize better the presence of heterogeneity in the sources and location of knowledge creation.
The article aims to explore internal migration flows, test for economic convergence, and assess the effects of internal migration (net and gross) on convergence and growth in terms of a neoclassical model in Croatia in the period 2000 to 2011. Croatia is a country with significant and persistent regional economic disparities, migration, and turbulent economic and political changes. The main findings of panel data analysis with fixed effects show that (i) contrary to the expectations based on neoclassical theory, the Croatian counties have been facing absolute and conditional economic divergence; (ii) in- and out-migration works symmetrically; (iii) net migration mainly appears to be a force that accelerates divergence, just opposite to gross in- and out-migration; (iv) although the estimated parameters of net and gross migration have expected signs, their effect size lies in the range from statistically significant but minor to insignificant; and (v) migrant characteristics and behavior matter when the effect size is considered.
This article contributes to the recent literature in spatial econometrics that focuses on space–time data modeling implementing a multilocation time-series statistical framework to analyze a regional system. Drawing on the global vector autoregression approach introduced in Pesaran, Schuermann, and Weiner, a multiregional spatial vector autoregressive (MultiREG-SpVAR) model is formulated and then applied to study the spatiotemporal transmission of macroeconomic shocks across the regions in Spain. The empirical application analyzes the extent to which a region’s economic output growth is influenced by the growth of its neighbors (push-in or inward growth effect), and also investigates the relevance of spillovers derived from temporary region specific output growth shocks (push-out or outward growth effect). Our results identify some regions that perform as "growth generators" within the Spanish regional system since growth shocks from these regions spillover to a large number of regions of the country, playing a key role in the transmission of regional business cycles. The policy implications of our results suggest that national and/or regional governments should stimulate economic activity in these leading regions in order to enhance the economic recovery process of the whole Spanish economy.
When regional disparities follow a cyclical short-run pattern, convergence analysis results can be sizably distorted. To tackle this issue, we propose a method based on the extraction of the trend from regional income time series that eschews misleading results when the nature of the cyclical pattern changes over time. Using real per capita personal income data for forty-eight conterminous US states and the distribution dynamics approach, we identify the following three distinct consecutive phases: strong convergence (1930–1970), substantial persistence (1971–1980), and divergence (1981–2010).
This article deals with the implementation of a new version of a macroeconometric regional growth model called MAcroeconomic, Sectoral, Social, Territorial Model (MASST). The new version presents interesting novelties with respect to the past, since it is able to embrace the two main supranational regulations with which the European Union binds decision-making processes in national economies, that is, public budget limitations and austerity measures on the one hand, and competitiveness/growth measures on the other hand. The novelties lie both in the technical way the economic crisis and its measures are formalized in a regional growth model and in the potentialities that the model achieves, namely: (i) measuring the costs of short-term austerity rules vis-à-vis long-term growth aims and their interactions and feedbacks; (ii) pointing out the regional heterogeneity in the effects generated by macroeconomic trends and conditions.
We develop a spatial model representing three cities of different size and connected by a road. We study two versions of a two-stage game where firms first decide where to locate and then set quantities or prices. We show that, in the case of quantity competition, maximal dispersion or agglomeration arises. Also, multiple equilibria are possible. In the case of price competition, maximal dispersion or partial dispersion arises. An asymmetric spatial equilibrium is possible even if the model is completely symmetric ex ante. A number of results are also derived when comparing the Cournot and the Bertrand locational equilibria in terms of profits, consumer surplus, and total welfare, and with respect to the welfare-maximizing locations.
This article reviews a growing literature investigating how "immigrant" diversity relates to urban economic performance. As distinct from the labor-supply focus of much of the economics of immigration, this article reviews work that examines how growing heterogeneity in the composition of the workforce may beneficially or harmfully affect the production of goods, services, and ideas, especially in regional economies. Taking stock of existing research, the article argues that the low-hanging fruit in this field has now been picked and lays out a set of open issues that need to be taken up in future studies in order to fulfill the promise of this work.
Higher Education Institutions (HEIs) and their relations to regional development have raised political expectations and scientific interest since the middle of the twentieth century. The high number of scientific surveys conducted calls for a meta-analysis that integrates multidisciplinary case study results. This secondary case survey analysis offers new insights into the scientific knowledge base on HEI-region relations. Knowledge gaps as well as uncertain, single and cross case verified knowledge could be identified. Whereas the unilateral HEIs’ impacts on the region have been broadly analyzed, there still is little knowledge on how to improve the HEIs-region relations. The article discusses the role of HEIs, which—being initially perceived as mere location factor—evolved to an active actor in the regional innovation and governance system. The article furthermore offers recommendations for policy makers and practitioners on how to use the knowledge gained for the further development of the HEI-region relations but also how to deal with knowledge gaps and context-specific research results.
The aim of the article is to examine, for the first time to our knowledge, the relationship between the intraregional spatial inequality and the regional income level in the European Union (EU)-27 regions, in the context of the inverted-U hypothesis. Although this hypothesis might exist not only at the intranational but also at the intraregional level, the empirical consideration of the latter has been largely ignored. This is quite surprising because intraregional inequality is a very important issue in regional science literature. The results of the article establish a strong empirical relationship that contradicts the conventional wisdom. In other words, the results imply a U-shaped rather than an inverted U-shaped curve, raising doubts about the adequacy and interpretative power of this hypothesis. Moreover, the acknowledgment of the importance of intraregional inequality in the EU, the empirical evidence of spatial effects in intraregional spatial inequality, and the possibility of a trade-off between the regional income level and intraregional inequalities constitute other important contributions of this article, deriving major economic and political implications. The evidence presented in this article can be used as a building block for future theoretical and empirical work.
Spatial data are often aggregated into spatial units and differences between spatial units can complicate the analysis of the data. One solution to this problem is spatial unit conversion, also called areal interpolation. Of the many areal interpolation methods proposed thus far, few method are based on spatial econometrics: a subset of econometrics which is concerned with the role of spatial autocorrelation (a general property of spatial data that implies that data in nearby locations are similar) in the regional economic model response. In this article, an areal interpolation method that considers both the spatial autocorrelation and the pycnophylactic property (a most basic premise of areal interpolation that the sum of the data given in a specific area must be constant) is proposed by combining a spatial econometric model and a linear regression-based areal interpolation method. Parameters of the proposed method are estimated using the expectation-maximization algorithm. The performance of the proposed method was examined through empirical analysis using real data and ratios on aging populations. The results indicate the importance of considering both the pycnophylactic property and the spatial autocorrelation in areal interpolation. The results also show the applicability of spatial econometrics to areal interpolation problems.
Nonrecursive structural equation modeling is applied to cross-sectional data from a survey conducted in Seoul, Korea, in 2009 and from geographic information systems, in an effort to model the reciprocal relationship between attitudes toward travel modes and land use in a neighborhood. Then, this study examines how the direction of the relationship differentiates the effects that the two factors have on trip frequencies. The direction is found to be contingent on trip purpose, that is, the attitudes–land use relationship is recursive for commuting trips, reciprocal for leisure trips, and insignificant for shopping trips. If the attitudes are omitted altogether, the estimated land use effect decreases considerably for commuting trips and slightly for leisure trips, which suggests the degree to which residential self-selection is concerned with these purposes of trips.
The emergence of new technologies together with the process of globalization and global outsourcing raise a question mark over the potential advantages of locating in an industrial district. In this context, there is a clear need for new studies to investigate whether the district effect still exists in these new circumstances. We propose that the increased availability of social capital, knowledge, and innovation would justify the firms in industrial districts obtaining competitive advantages and, therefore, greater levels of performance over the rest of the companies in an industry, allowing us to explore why the district effect is maintained in the current circumstances. The development of this study, in the footwear industry in Spain, has allowed us to analyze the existence of significant differences between industrial district firms and firms outside industrial districts. The results obtained in our study show that agglomerated firms, that is, firms located within industrial districts, achieve a greater performance than firms outside the industrial districts. With this study, we contribute to a deeper analysis of the competitive differences arising from the district effect. On one hand, we analyze if the competitive advantages of companies located in the districts will reveal differences in the obtained performance—growth, profitability, innovation performance, and general performance. We also analyze three key competitive factors in the competitive dynamics of industrial districts, with particular attention to social capital. In this sense, we look separately at the three dimensions of social capital—structural, relational, and cognitive.
The location of commercial spaces in multistory buildings affects their values. We developed a three-level mixed-effects spatial hedonic model in which the nested structure of individual commercial units in multistory buildings is explicitly taken into account in the price determination process. The proposed modeling framework is intuitive and flexible from both substantive and technical perspectives, as evidenced by its diverse extensions to accommodate both the spatial autocorrelation and the complex heteroscedasticity inherent in property values. A unique data set based on the assessed values of commercial properties and officetels, collected by the National Tax Service of Korea in 2012, is merged with structural and location information on buildings for a case study. We found both the floor level and building effects are important factors when valuing commercial units in multistory buildings. We show that model fit is significantly affected if the spatial autocorrelation at the individual level is not included in hedonic price models.
Economic and social cohesion at a regional level is one of the main objectives of the European Union (EU). The European regional development policy aims to promote a harmonious, balanced, and sustainable development through inclusive growth. Yet, while economic cohesion, proxied by gross domestic product (GDP) per capita, has attracted significant attention with most studies finding little regional convergence since 1985, social cohesion has been virtually ignored. This article tries to cover this gap by asking the question of whether regional convergence in social welfare, measured by Amartya Sen’s welfare index, has taken place across regions of the EU-15. Using panel data models with or without spatial interaction effects, we find that the absence of convergence in GDP per capita is not matched in terms of social welfare. Welfare levels have converged significantly across European regions and this convergence has been built on a series of structural and institutional factors, among which female participation in the labor force is the most relevant.
In this article, we apply recent advances in quasi-experimental estimation methods to analyze the effectiveness of Germany’s large-scale regional policy instrument, the joint Federal Government/State Programme "Gemeinschaftsaufgabe Verbesserung der regionalen Wirtschaftsstruktur" (GRW), which is a means to foster labor-productivity growth in lagging regions. In particular, adopting binary and generalized propensity-score matching methods, our results indicate that the GRW can be generally considered effective. However, we find evidence for a nonlinear relationship between GRW funding and regional growth associated with a maximum subsidy level beyond which financial support does not generate further labor-productivity growth. In other words, there is a "purchase limit" on regional growth. Although the matching approach is very appealing due to its methodological rigor and didactical clarity, throughout the empirical application, we faced difficulties in balancing the set of covariates among treated and comparison regions, given that two sets of the regions differ strongly with respect to their underlying structural characteristics. Such imperfect balancing may limit the practical applicability of matching techniques in regional data settings. Overall, however, the matching approach can still be considered of great value for regional policy analysis and should be the subject of future research efforts in the field of empirical regional science.
Most regional economic databases (e.g., US Economic Census and County Business Patterns [CBP]) have some employment records suppressed and then represented as ranges, in order to guarantee the confidentiality of the data. This article incorporates the implicit temporal relationships between annual employment data over several years into an optimization model designed to estimate suppressed records. This model minimizes (1) the sum of the deviations between the estimates and target values within the corresponding ranges and (2) the sum of the deviations between the estimates and an employment trend curve endogenously determined through absolute-value regression. The 1999–2006 CBP data for Arizona are used to test the model. Two decision-theoretic criteria (Pareto frontier and concordance–discordance analysis) are used to analyze the results, pointing to a specific set of parameters yielding the best estimates.
In this applied study, we use US county data to examine patterns in fine particulates (also called fine particulate matter or PM2.5) ambient concentrations as a measure of air pollution within the framework of the environmental Kuznets’ curve (EKC). We pay particular attention to the role of social capital and notions of ruralness. Consistent with expectations, we find that peak of the EKC ranges between US$24,000 and US$25,500 for PM2.5 concentrations depending on the estimator used. Also consistent with expectations, higher levels of social capital places downward pressure on PM2.5 concentrations, but that effect is weaker in more rural areas. The implication is that the promotion of economic growth may harm the environment at lower levels of income but will improve the environment as income continues to grow.
This article, based on the inaugural Andrew Isserman lecture, explores whether regional science has lived up to its founder’s aspirations to create an interdisciplinary and international field to tackle key societal problems with reasoning, evidence, and sound policy recommendations. I distinguish methods-driven research from problem-driven research and illustrate the pitfalls of the former with the emergence and use of economic base multipliers from export base theory. Then, beginning with Walter Isard’s bold vision in the first issue of the International Regional Science Review, I follow the evolution of the Review under Andrew Isserman’s three decades of editorship, exploring the difference between methods-driven and descriptive research articles and those addressed to regional problem solving. Editor Isserman actively sought out scholars and special issue editors with an interest in policy and a willingness to work across disciplines and borders. He raised funding for themed conferences that would yield exciting new articles, a practice his coeditors and successors have continued. In his own research, despite his love of methods and facility with them, Isserman often chose to work on important regional problems such as whether the Appalachian program had produced real personal income gains, how the Soviet Union should pursue regional development under perestroika, and in recent years, rural poverty and agriculture and biotechnology. From work on deindustrialization and military industrial conversion, I argue that exposure to the intricacies of real-world policy making strengthens both theory and empirical research.
Regional migration and growth are increasingly associated with high-quality in situ natural amenities. However, most of the previous US research has focused on the natural amenities of the Mountain West or the South. The Great Lakes, with their abundant fresh water and natural amenities, would also appear well positioned to provide the foundation for this type of economic growth. Yet, while some parts of the western Great Lakes region are prime examples of amenity-led growth, other areas in the eastern Great Lakes may not have capitalized on their natural amenities, perhaps because of their strong industrial legacy. Using a unique county-level data set for the Great Lakes region (including Indiana, Illinois, Michigan, Minnesota, New York, Ohio, Pennsylvania, and Wisconsin), we test whether growth in the region is associated with proximity to lake amenities and whether there are offsetting industrial legacy or pollution effects. We also examine whether amenities have additional attraction value for those with high levels of human capital. Consistent with theory that suggests that natural amenities are normal or superior goods, we find that coastal areas in the region are positively associated with increases in shares of college graduates. However, we find little evidence that lake amenities contribute to broader household migration, especially after 2000. Based on these results, there may be opportunities to leverage Great Lake amenities to support economic growth in terms of attracting individuals with high levels of human capital who are most likely to make quality of life migration decisions.
This article examines the relationship between migration and transportation improvements. More precisely, will transportation improvements between central business districts (CBDs) and rural areas make migration trends more favorable in rural areas? Due to household utility geographical differentials, there are interregional migration. Thus, it is resaonable to believe that transportation improvements that increase the access of rural population to the labor and the service market of CBDs will influence interregional migration in rural areas positively. I will examine whether this is true for Iceland, a thinly populated area with two CBDs. A macro panel data set from Iceland will be used. It represents several essential varaibles of the house market for seventy-nine municipalities in Iceland during the period from 1986 to 2006. Furthermore, I will investigate whether there are any gender aspects regarding the matter.
In this application, income and job growth in the eastern United States are explained using a partial adjustment model with regime switching potential and spatial spillover, or "Smooth Transition" spatial process models (STAR). This relatively new class of spatial regression models provides a parametric approach to testing hypotheses about the influence external economies have on "core-periphery" structures characterizing the Dixit-Stiglitz-Krugman type models describing regional economies. Growth is endogenized as a locally contagious process and specific to regimes, which are also endogenously determined by access to urban economies. The estimation approach relaxes distributional assumptions, using instruments to estimate the STAR process models with special attention given to instrument selection and definitions of spatial neighbors. In the empirical analysis, the authors focus on the specification of partial adjustment models by employing a common factor test to discern serial correlation from partial adjustment processes. Finally, an ex post qualitative analysis is conducted using phase diagrams to study convergence and stability properties of the regime-specific equilibrium solutions. A sensitivity analysis suggests that steady state employment levels in counties with Appalachian development highway networks is higher than counties without this infrastructure, but income levels are not different.
This article checks for the robustness of the estimate of the impact of market access (MA) on the regional variability of human capital, derived from the New Economic Geography literature. The hypothesis is that the estimate of the coefficient of the measure of MA is actually capturing the effect of regional differences in the industrial mix and the spatial dependence in the distribution of human capital. Results for the Spanish provinces indicate that the estimated impact of MA vanishes and becomes nonsignificant once these two elements are included in the empirical analysis.
This article seeks to examine the effects of the aging population in Illinois with inclusion of the household’s heterogeneity across migration status and investment in human capital. By adopting a stylized Mincer wage regression, the article shows that there are significant gaps in returns to education between migration statuses in Illinois; further, there exist significant relationships between a resident’s demographics and the probability of in- and out-migration to/from Illinois. Using a two-sector Overlapping Generations (OLG) model incorporated with the household’s heterogeneity over migration status, this article projects the economic growth of Illinois in the future. This article also shows that the effects of the government’s immigration policy that aims at replacing low-productive international immigrants with native and relatively high-productive unemployed individuals who have been unemployed, are very limited in terms of per capita income, welfare, and aggregate productivity. On the contrary, a tax and transfer policy inducing international immigrants to invest more in their education works relatively better under the demographic changes facing Illinois over the next three decades.
This article presents a computable general equilibrium model for the region of Sardinia (Italy) with the purpose of investigating the macroeconomic impact of research and development (R&D) policies. The model incorporates induced technical change obtained through knowledge accumulation and external knowledge spillovers. It turns out that the cost of R&D policies may change according to wage setting in the region. Indeed, the likely size of the optimal subsidy that is required to reach a given target growth is lower when wages are bargained locally compared to the case where wages are bargained nationally. Furthermore, the capacity of such a policy to generate knowledge spillovers from international and interregional trade is quite modest. Indeed, the capacity of the regional system to internalize innovations embedded in imported goods is partially offset by an increase in internal efficiency that lowers the spillover intensity through a reduction in the share of imports.
This article concentrates on a crucial technical aspect of regional entrepreneurship research: how do we measure the most innovative of entrepreneurs, the entrepreneurs most likely to create regional growth? Innovation is a crucial component of entrepreneurship; yet, the frequent use of entrepreneurship proxies that do not consider innovation motivated us to propose and develop an indicator of innovative entrepreneurship that is useful for studies of regions, counties, states, and metropolitan areas as well. We posit that a novel combination of start-ups in innovative industries and self-employed in innovative industries yields entrepreneurship indicators that incorporate three widely recognized functions of entrepreneurship, including innovation. We detect sharp contrasts between our innovative entrepreneurship indicators and widely used entrepreneurship proxies. Our analysis demonstrates that innovative entrepreneurship is a useful empirical concept and that ignoring innovation in entrepreneurship likely has produced misleading research results and policy implications about regional entrepreneurship, its determinants, and its role in regional economic growth.
In many cases, only an unbalanced panel data set with some observations missing at random is available. This note derives a Cliff and Ord test for spatially autocorrelated disturbances for such data. In a small Monte Carlo simulation exercise, the performance of the proposed test is similar to its balanced counterpart. In almost all simulation experiments, the test is properly sized. Naturally, the lower the power of the test, the higher the share of missing data.
The average propagation length (APL) has been proposed as a measure of the fragmentation and sophistication of an economy. For a one-sector economy, we show that the APL is strictly proportional to the macro multiplier of that economy. The same holds for strong intra-industry linkages. Hence, for comparing economies and comparing single industries, the concept of the APL is of no value. For pure interindustry linkages, however, we find that the length of the supply chain between two different industries is negatively related to the strength of the multiplier between those two industries, be it weakly. Hence, the APL should only be used to compare pure interindustry linkages.
Various spatial data analyses have been used for the identification of functional regions. Functional regions are identified by grouping many areal units into fewer clusters to classify the areal units in terms of similar properties, as well as to constrain the spatial contiguity of the areal units in each cluster. This article proposes a spatial optimization model, called the p-functional regions problem, to solve a regionalization problem by considering geographic flows. The magnitude of geographic flows, such as journey-to-work, is widely considered a good indicator of functional relationships between areas so that regionalization models incorporating various criteria, such as the maximum intraregion flows or the total inflows from other units, may be used to identify the p regions. We also propose an analytical target reduction approach to enhance the model tractability in generating optimal solutions to large problems and to demonstrate the effectiveness of the optimization model using journey-to-work data from Seoul (South Korea) and South Carolina (the United States).
The significant role that Walter Isard played in regional analyses is well known at least among those of us conducting regional studies, as recently reviewed by Boyce. In this short article, we examine several aspects of Isard’s input–output accounts and models and raise two important points about his accounting framework and the underlying theory.
The objective of this article is to show how the techniques of regional science and peace science can be applied for the economic development of poor countries. This article also describes techniques for integrating regional science and peace science.
We provide the first theoretical analysis of the effects of alternate forms of taxation on economic growth in a dynamic model with multiple regions. The regions are heterogeneous, but, in each region, consumers have constant relative risk aversion preferences, there is no growth in the stock of human capital, and there are three kinds of manufacturing activities involving the production of blueprints for inputs or machines, the inputs or machines themselves, and a single consumption good. Our analysis generates four salient findings. First, we define the multiregion equilibrium. Second, we characterize the multiregion equilibrium and show that in this equilibrium, each region grows at a constant rate starting at time t = 0. Third, we show that except in knife-edge cases, output in each region grows at a different long-run rate. Finally, we determine the effects of asset, profit, and investment taxes on the economic growth rates of the regions under study.
The spatial concentration of firms has long been a central issue in economics under both the theoretical and the applied point of view mainly due to the important policy implications. A popular approach to its measurement, which does not suffer from the problem of the arbitrariness of the regional boundaries, makes use of micro data and looks at the firms as if they were dimensionless points distributed in the economic space. However, in practical circumstances the points (firms) observed in the economic space are far from being dimensionless and are conversely characterized by different dimension in terms of the number of employees, the product, the capital, and so on. In the literature, the works that originally introduce such an approach disregard the aspect of the different firm dimension and ignore the fact that a high degree of spatial concentration may result from the case of both many small points clustering in definite portions of space and only few large points clustering together (e.g., few large firms). We refer to this phenomenon as clustering of firms and clustering of economic activities. The present article aims at tackling this problem by adapting the popular K-function to account for the point dimension using the framework of marked point process theory.
The aim of this article is to explore the structure of cities as a function of labor differentiation, gains to trade, a fixed cost for constructing the transportation network, a variable cost of commodity transport, and the commuting costs of consumers. Firms use different types of labor to produce different outputs. Locations of all agents are endogenous as are prices and quantities. This is among the first articles to apply smooth economy techniques to urban economics. Existence of equilibrium and its determinacy properties depend crucially on the relative numbers of outputs, types of labor, and firms. More differentiated labor implies more equilibria. We provide tight lower bounds on labor differentiation for existence of equilibrium. If these sufficient conditions are satisfied, then generically there is a continuum of equilibria for given parameter values. Finally, an equilibrium allocation is not necessarily Pareto optimal in this model.
A contingent valuation survey (willingness-to-pay study) was conducted in 2004 to measure household demand for typhoid vaccines in a rural township in China with approximately 54,000 people living in 141 villages. The results showed that travel distance to vaccination sites and vaccination price affected the private demand for typhoid fever vaccinations. The number and location of vaccination outposts are thus important decision variables for a new vaccination campaign that is under consideration. This article develops and applies an optimization model for planning vaccination programs. The model determines what price to charge, how many vaccination outposts to use, where to locate them, and what capacities they need. The model uses demand information from the contingent valuation survey and cost information from similar vaccination campaigns in China and assumes that costs must be covered by user fees. The model determined that the number of outposts to use for maximizing coverage was fourteen, which would make average one-way travel distance for users about 0.6 km. The optimal vaccination price was USD 1.25, and about 87 percent of the population would be vaccinated. A suboptimal solution of the model showed that only 6 outposts instead of 14 would probably vaccinate about 83 percent of the population, and the price could be reduced to USD 0.83. The model is easy to use and solve and can be applied to different size regions.
This article studies the consequences of debt policies on the spatial distribution of output in a two-country model. It departs from the usual setup of local public finance by relaxing the assumption of balanced budget. Further, to single out the pure effect of debt, the article eliminates effects coming from tax and expenditure policies by assuming them exogenous and identical between countries except for the timing of taxation. Expected taxation rather than current tax levels motivates migration. Starting from an initial spatial configuration, be it Core–Periphery or symmetric equilibrium, the analysis identifies the critical thresholds of divergence or convergence of debt ratios which break the initial configuration. The article also shows that a high-debt country or a fast debt reducing country is a weaker player in the tax competition game. Finally, tax harmonization does not necessarily reduce migration flows.
The housing search process, a topic of interest to both practitioners and researchers, starts with an alternative formation and screening practice. Due to the limitation of cognitive capacity, household members at this level evaluate potential alternatives based on many factors, such as lifestyle, preferences, and so on, to form a manageable choice set. This article attempts to provide a detailed study of this screening and filtering practice to develop a modeling framework that can replicate the choice set formation process. In order to show the potential of the method, one prospective decision criteria—the average desired commute to work distance—is considered the potential attribute that the household evaluates for feasible housing alternatives. It is postulated that alternatives will only be included in the choice set if the average work distance satisfies the household distance threshold. This article explores the viability of using proportional hazard models in the housing search process. Some of the specifications of hazard-based models that are typically used on temporal data are examined on average work distance. Several household sociodemographic attributes from eight waves of the Seattle Metropolitan Area’s Puget Sound Transportation Panel (PSTP) are utilized for model estimation, along with built environment variables, characteristics of the supply side of the market, and several other economic indicators. The approach presented in this article provides a remedy for the large choice set problem typically faced in discrete choice modeling.
This article summarizes Walter Isard’s important contributions to environmental economics and ecological economics. The former is the traditional field that incorporates some limited aspects of the environment into neoclassical economic theory, while the latter is a more comprehensive integration of economic and ecological principles in a set of eclectic frameworks. Walter’s contributions include pioneering research, primarily in a spatial context, and the mentoring of prominent researchers in these fields during the period of 1965–75. We examine these contributions in the context of the state of the art of knowledge in these fields at the time and today. We also discuss Walter’s influence in Europe in these areas. We conclude with a discussion of some promising new directions, with an emphasis on regional science dimensions.
Toward the end of his life, a shift occurred in Walter Isard’s thinking about how graduate study in regional science should proceed. This shift and its implications for the discipline itself have led me to problematize Walter’s sense of the scientific in regional science. In this article, I offer a highly stylized characterization of what Walter thought regional science should be about at various points of his life and relate the evolution of his thinking to recent work in the philosophy of science. I shall argue that Walter’s view of what made regional science a science did not change much, nor did his view of what in general regional scientists needed to study. I shall also argue that his view of what constituted adequate scholarship did change considerably, as did his views of what regional science should encompass in the way of theory and methods and what future progress in the field will entail.
As African Americans are poorer than non-African Americans, increasing racial integration might lead to increasing poverty integration. Alternatively, if racial segregation pushed higher- and lower-income African Americans to reside together, increasing racial integration may lead higher-income African Americans to sort into higher-income non-African American neighborhoods, decreasing poverty integration. Using consistently bounded census tract data for thirty-six large metropolitan areas (MAs) from 1970 to 2009, a fixed effect model measures the relationship of a census tract’s end of the decade proportions of the metropolitan population by race and poverty status group between 1980 and 2009 to the proportions of each race and poverty group resident in a census tract at the start of the decade. The article finds that racial integration occurs mostly within own poverty groups and poverty integration occurs mostly within own racial groups, making these integration processes largely independent. Poverty and racial segregation were slightly decreased, however, because the nonpoor racially integrated with the poor in a manner consistent with gentrification and status caste exchange theory.
Elhorst provides Matlab routines to estimate spatial panel data models at his website. This article extends these routines to include the bias correction procedure proposed by Lee and Yu if the spatial panel data model contains spatial and/or time-period fixed effects, the direct and indirect effects estimates of the explanatory variables proposed by LeSage and Pace, and a selection framework to determine which spatial panel data model best describes the data. To demonstrate these routines in an empirical setting, a demand model for cigarettes is estimated based on panel data from forty-six US states over the period 1963–1992.
The potential for spatial dependence in models of voter turnout, although plausible from a theoretical perspective, has not been adequately addressed in the literature. Using recent advances in Bayesian computation, the authors formulate and estimate the previously unutilized spatial Durbin error model and apply this model to the question of whether spillovers and unobserved spatial dependence in voter turnout matters from an empirical perspective. Formal Bayesian model comparison techniques are employed to compare the normal linear model, the spatially lagged X model (SLX), the spatial Durbin model, and the spatial Durbin error model. The results overwhelmingly support the spatial Durbin error model as the appropriate empirical model.
How do internal resources, relational resources, and relational mechanisms determine knowledge transfer among crucial partners in clusters? We analyze a sample of Spanish footwear manufacturers to distinguish the relative impact of internal resources, intra-cluster relationships, and governance (including power asymmetries) on knowledge transfer between leading firms and their partners. Internal resources appear to be highly beneficial for knowledge transfer among partners, while intra-cluster relationships diminish such transmissions. The governance structure of the partnership also appears to have an important influence on knowledge transfer between leading organizations and their suppliers. These results suggest valuable implications for practitioners, researchers, and policy makers at both the firm and meso levels.
The vector assignment p-median problem (VAPMP) and the ordered p-median problem (OMP) are important extensions of the classic p-median problem. The VAPMP extends the p-median problem by allowing assignment of a demand to multiple facilities, and a wide variety of multi-assignment and backup location problems are special cases of this problem. The OMP optimizes a weighted sum of service distances according to their relative ranks among all demands. The OMP is well known as it represents a generalization of both the p-median and the p-center problems. In this article, a new model is developed which extends both the VAPMP and OMP problems. In addition, beyond median, center, and vector assignment, this new model can resolve problems where the system objective involves maximizing distance. The new model also gives rise to meaningful special-case problems, such as a "reliable p-center" problem. Different integer linear programming (ILP) formulations of the new problem are presented and tested. It is demonstrated that an efficient formulation for a special case of the VAOMP problem can solve medium sized problems optimally in a reasonable amount of time.
The notion of face-to-face contacts has recently become very popular as a reason why firms still locate in proximity to others after the "death of distance." Controlled laboratory experiments provide direct and reliable evidence on the importance of face-to-face contacts. It is the purpose of this article to survey and to organize new and developing string of literature with a special focus on its importance for regional economics. However, the article might also serve to alert more experimentalists to the importance of their work for current regional science, of which they seem not to be aware.
This article applies the novel concept of global panel cointegration to analyze the role played by trade and foreign direct investment (FDI) activity in driving regional total factor productivity (TFP). Using West German state-level data for the period 1976–2008, the approach allows us to identify the magnitude of direct trade and FDI effects as well as spatial spillovers from these variables. The author finds that the inclusion of spatial lags significantly improves the fit of the empirical model and allows us to strongly reject the null of no cointegration among the variables in the full spatial specification. For the long-run cointegration equation, the empirical results hint at export- and FDI-led growth. Additionally, outward FDI activity shows to have positive spatial spillover effects among German regions, while the spatial patterns of import and inward FDI activity indicate substitution effects of interregional input–output linkages in favor of international ones over the sample period 1976–2008. In the short run, TFP growth is predominantly affected by changes in exports, inward and outward FDI stocks, where the latter variable also provokes positive spillovers. The author uses alternative spatial weighting matrices based on geographical information as well as interregional goods transport flows to check the robustness of the obtained spillover effects in the long and short run. Here, the results turn out to be similar for the different empirical specifications employed throughout the analysis. Finally, summing over the four variables to get a direct and indirect net effect of internationalization activity, the author finds that the direct effect is always positive, while the indirect net effect is positive in the short run but slightly negative in the long-run equation.