The main goal of this paper is to build after-the-crisis scenarios for European regions. The methodology applied to develop these scenarios starts from general reflections on what the crisis has brought (and will bring) about, and on the way these changes are perceived and even anticipated by policy makers. These qualitative reflections are used as the basis for what may be called “quantitative foresights” , built on the discontinuities that will emerge after the crisis. What is called a “reference scenario” is built under the assumption that there will be a perception that structural changes will happen, that an extrapolation of past trends has no meaning at all, but that policies will not act in an effective way. This scenario will be compared to a second one, called the pro-active scenario, in which changes will be perceived and even anticipated; and to the re-active or defensive one, based on the assumptions that changes are not fully perceived by economic actors. The structural changes brought about by the crisis are translated into quantitative assumptions for a macro econometric regional growth model created by the authors, called MASST, an acronym recalling its structural features: a macroeconomic, social, sectoral, territorial model, a methodology as neutral as possible which leaves to the model to produce the tendencies and behavioural paths of regional GDP up to 2025. Policy implications are highlighted at the end of the paper.
We develop a non-parametric clustering model of spatial and/or spatio-temporal phenomena based on Kolmogorov entropy. The methodology will be tested using Federal Financing and Housing Administration (FHFA), formerly known as FHA, an agency of HUD (US Dept of Housing and Urban Development) quarterly HPI (Housing Price Index) data of 350 plus cities in the US. This multivariate data will be also analyzed with Principal Component Analysis (PCA) techniques to identify key regions involved in creating the housing bubble and its spread to the rest of cities. This provides evidence of the differential impact of the housing crisis across US regions and suggests an important role for state level as opposed to federal (centralized) regulations.
This paper tackles two issues: (1) lead and lag relationship among regions and the role of the industry mix effect to this phenomenon are explored; (2) concurrent and lagged effects of the industry mix on the regional economic fluctuations are measured explicitly with the national shock identified from the principal components method. The empirical analysis focuses on five Midwest states. The findings reveal that, the business cycles of Michigan, Ohio, Indiana and Wisconsin coincide with the national cycle while the cycle of Illinois lags the national cycle by 3 to 4 months. This phenomenon turns out to be generated from the differences in industry structure since the manufacturing sector reacts promptly to the national shock while the services sector respond in a few months. As a result, relatively service-oriented Illinois lags other neighboring states. Analysis reveals that the industry mix effects explain more than 60 percent of the variance of the state coincident index and around 40 percent of the variation of state total non-farm employment. In addition, the simulation of VAR model demonstrates that the transmission mechanism and autoregressive property of economic activity expand the time differences in the business cycles among regions caused by the industry mix effects.
The paper provides selective comments on the usefulness of economic multipliers in assessing the impacts of regional policies with particular focus on transportation infrastructure investments. Regional multipliers are extremely simple and very convenient tools for estimating the overall impacts of a regional policy on such things as local income and employment. The issue is whether this simplicity and convenience is being bought at the expense of serious distortions in the accuracy of the calculations. The paper looks at the realism of the assumptions that underlie most multiplier based analysis and at the potential for institutional biases leading to distorted outcomes when deploying regional multipliers. There is a particular, but not exclusive focus on the United States.
The standard method for predicting traffic flows on urban road networks, static traffic assignment, is based on the principle that drivers seek their own least cost routes from their origins to their destinations. This principle corresponds to a network user-equilibrium state in which for every origin-destination pair, all used routes have equal costs and no unused route has a lower cost. Under somewhat mild assumptions, the network equilibrium problem can be formulated as a convex optimization problem with linear constraints, and solved with an iterative algorithm. The precision and speed of such solutions has increased remarkably during the past ten years. Although the total flows on links of the urban road network are uniquely determined in this formulation, route flows and multi-class link flows are not. An additional assumption, called the condition of proportionality, may be imposed to determine these flows uniquely. This condition is the basis for the design of a new algorithm, Traffic Assignment by Paired Alternative Segments (TAPAS). Results of computational experiments for two network representations are presented. The difference in the network representations pertains to restrictions on the use of certain links of the network by trucks in a two-class assignment. The findings illustrate the importance of class-specific network representations and the condition of proportionality in road traffic assignments at the route and link level.
The problem of the spatial monopolist is well known where, facing a spatial distribution of consumers with the same tastes and resources, the firm seeks to optimize. Shopping behavior is invariably assumed to be deterministic but in this paper a version of stochastic behavior is used instead. Specifically, the density parameter of the Poisson distribution is adopted to represent the expected demand of those spatial households. So consumers found at different locations generate different frequencies of shopping trips to the spatial firm. In turn, these location-specific trip frequencies vary with the firm's optimizing price, which is determined in the usual way. Subsequent aggregation over all possible locations allows for the identification of the trip volume and the market demand for each feasible trip frequency. The paper also assesses how consumer heterogeneity, a feature of all advanced societies, will affect the various solutions. Three groups (poor, average, and rich) of consumers with different demand attributes are considered. The spatial monopolist now sets an optimizing price that weights each group's specific demand parameters. The stochastic distribution of shopping trips necessarily shifts to reflect this modification to spatial demand. Numerical comparative statics indicate how market solutions shift for small changes in marginal cost, household demand, transportation cost, and consumer heterogeneity.
In this paper we explore how various locational characteristics interact with the population size of settlements to influence the domestic net migration component of population growth in the United States. More specifically, we hypothesize that the attractiveness of a county to migrants is affected by three factors: (1) size class: the county's inclusion within the boundaries of an urban agglomeration of a particular population range; (2) centrality: the county's relative location within its own agglomeration, that is, whether it contains the high-density core or encompasses lower-density suburban or exurban territory; and (3) hierarchical proximity: the location of the county's own agglomeration with respect to other agglomerations of higher order within the urban hierarchy. This third, situational component constitutes the major focus of the paper. After briefly demonstrating the concept of hierarchical proximity within a hypothetical Christaller-type (marketing principle) central place city system, we present some illustrative empirical findings from the late 1990s. The evidence suggests that two types of situational effects appear to operate, depending on the size classes involved. We conclude that hierarchical proximity influences local-scale growth through a blending of both ‘ urban shadow' (migration-depressing) and ‘ urban synergy' (migration-enhancing) factors.
Our modern world is moving towards a ‘ New Urban World'. More people than ever before are living in urban areas and modern cities are becoming powerhouses of creative ideas, innovative technologies, sustainable developments and socioeconomic wealth in an open and globalizing economy. And most likely this trend will continue. Cities will also play a pivotal role in the future of an urbanized Europe, although they are certainly confronted with grand challenges. Emerging threats to urban environments may, however, be turned into new opportunities. Thus, urban areas may act as spearheads of sustainable economic growth for European countries. This paper will offer a novel contribution to the assessment of the socio-economic performance of 9 selected European cities in the North Sea basin over two time periods. The exploration of the socio-economic benefits of these cities leads to the definition of a ‘ democratic beauty', whereas urban performance is measured along multiple dimensions. We will evaluate their performance and rank their relative efficiency performance using a new variant of Data Envelopment Analysis (DEA), through which we are also able to further discriminate between the class of efficient Decision Making Units by introducing the notion of super-efficiency. The ‘ winners' in this competitive assessment of highly efficiently cites are labeled here as incredible cities. The paper will be concluded with some policy recommendations.
This paper focuses on manufacturing employment growth across the 26 states of Brazil. We employ the Glaeser et al. (1992) approach to identify the role played by knowledge externalities in growth and convergence. To assess robustness of the results, we compare cross-section models, dynamic panel models and pooled-periods fixed-effect models. We find that cross-section models confirm the positive impact of Porter's and Jacobs' competition externalities on growth, whereas dynamic panel models and pooled-periods fixed-effect models are consistent with the predictions of Marshall-Arrow-Romer and Porter regarding the role of specialisation in manufacturing vis-à -vis other employment. The results provide new insights into the rapid growth since 1981 in particularly the North and Centre West of Brazil.
This paper presents a spatial microsimulation modelling approach to the estimation of small area income distributions in two historic cities located in the island countries of Britain and Japan. In particular, it revisits relevant past research comparing the social geography of Edinburgh and Kyoto. First, the paper provides a brief overview of social and spatial inequalities in Britain and Japan and revisits recent debates according to which Japan is the most equitable society in terms of income compared with other industrialised countries and in particular compared with Britain. This is followed by a discussion of two spatial microsimulation models that were developed and used for the estimation of small area income distributions in Edinburgh and Kyoto. The paper discusses the data and modelling approaches underpinning the two models and argues that spatial microsimulation outputs can be used to paint a better picture of the social and economic geography in the two cities and to also provide an excellent basis for further analysis.
Recently, Batabyal and Beladi (2009) have constructed a metric of the expected total monetary damage from the unintentional introduction of invasive species into a country called Home. In this note, we extend this line of inquiry by analyzing the statistical properties of this total monetary damage random variable. By “ statistical properties,” we mean the variance and the covariance. Specifically, we first compute the variance of the total monetary damage at time t. Next, we derive a closed-form expression for the covariance of the total monetary damage at time t and at time t+s where s> 0. Finally, we discuss three practical implications of our findings for the management of invasive species.
The regional scientist, like all researchers in social and economic sciences, finds himself faced with a complex world where he has to find order in the chaos. Regional scientists in this new scientific world can use behavioural approaches to understand regions, not only as rational economic places, but as social places with their history, culture and lifestyle. This paper studies the foundations of a behavioural approach in regional science, to advance our understanding of cultural regions, and the way they are organized and valued by the local people. Lived regions are mirrors of our societies, and before planning any action in space and time, regional scientists have to understand the way people live and work in their places.