Finite Sample Properties of Moran's I Test for Spatial Autocorrelation in Probit and Tobit Models - Empirical Evidence

In this paper, we investigate the finite sample properties of Moran’s I test statistic for spatial autocorrelation in limited dependent variable models suggested by Kelejian and Prucha (2001). We analyze the socio- economic determinants of the availability of dialysis equipment in 5,507 Brazilian municipalities in 2009 by means of a probit and tobit specifica- tion. We assess the extent to which evidence of spatial autocorrelation can be remedied by the inclusion of spatial fixed effects. We find spa- tial autocorrelation in both model specifications. For the probit model, a spatial fixed effects approach removes evidence of spatial autocorrelation. However, this is not the case for the tobit specification. We further fill a void in the theoretical literature by investigating the finite sample prop- erties of these test statistics in a series of Monte Carlo simulations, using data sets ranging from 49 to 15,625 observations. We find that the tests are unbiased and have considerable power for even medium-sized sample sizes. Under the null hypothesis of no spatial autocorrelation, their em- pirical distribution cannot be distinguished from the asymptotic normal distribution, empirically confirming the theoretical results of Kelejian and Prucha (2001), although the sample size required to achieve this result is larger in the tobit case than in the probit case.

Measuring Spatial Dynamics in Metropolitan Areas

This paper introduces a new approach to measuring neighborhood change. Instead of the traditional method of identifying “neighborhoods†a priori and then studying how resident attributes change over time, our approach looks at the neighborhood more intrinsically as a unit that has both a geographic footprint and a socioeconomic composition. Therefore, change is identified when both as- pects of a neighborhood transform from one period to the next. Our approach is based on a spatial clustering algorithm that identifies neighborhoods at two points in time for one city. We also develop indicators of spatial change at both the macro (city) level as well as local (neighborhood) scale. We illustrate these methods in an application to an extensive database of time-consistent census tracts for 359 of the largest metropolitan areas in the US for the period 1990-2000.

A spatial regression approach to FDI in Vietnam: province-level evidence

Foreign direct investment (FDI) flows into Vietnam have increased significantly in recent years and are distributed unequally between provinces. This paper aims to investigate the locational determinants of FDI in 62 Vietnamese provinces and whether spatial dependence is a significant factor that both researchers and policy-makers should take into account. We report that province-specific percapita income, secondary education enrolment, labor costs, openness to trade, and domestic investment affect FDI directly within the province itself and have indirect effects on FDI in neighboring provinces. The direct and indirect effects coexist with spill over effects and spatial dependence between provinces. Our findings indicate that FDI in Vietnam reflects a combination of complex vertical and export platform motivations on the part of foreign investors; and an agglomeration dynamics that may perpetuate the existing regional disparities in the distribution of FDI capital between provinces

Introducing SpatialGridBuilder: A new system for creating geo-coded datasets

Researchers in the conflict research community have become increasingly aware that we can no longer depend on state-aggregated data. Numerous factors at the substate level affect the nature of human interactions, so if we really want to understand conflict, we need to find more appropriate units of analysis. However, while many conflict researchers have realized this, actually taking the next step and performing data analysis on spatial data grids has remained a rather elusive goal for many because of the difficulty of learning the new techniques to perform such analyses. This paper introduces SpatialGridBuilder, a new, freely available, open-source system with the goal of empowering conflict researchers with no background in GIS methods to start their own spatial analyses. SpatialGridBuilder allows the researcher to: (a) create entirely new spatial datasets, based on the needs of their own research; (b) import their own spatial data; (c) easily add a range of important variables to the datasets, including commonly used conflict variables, plus new variables that have not been presented before; and (d) visualize graphical renderings of this data. Having done this, SpatialGridBuilder will then export the dataset for the researcher to analyse using conventional statistical methods. This article introduces the new program, and demonstrates how it can be used to set up such a statistical analysis. It also shows how different results can be achieved by building grids of different resolutions, thereby encouraging researchers to choose grid resolutions appropriate to their research questions and data. The article also introduces a novel means of determining infrastructure complexity, using Google maps

Testing for spatial autocorrelation: the regressors that make the power disappear

We show that for any sample size, any size of the test, and any weights matrix outside a small class of exceptions, there exists a positive measure set of regression spaces such that the power of the Cli-Ord test vanishes as the autocorrelation increases in a spatial error model. This result extends to the tests that dene the Gaussian power envelope of all invariant tests for residual spatial autocorrelation. In most cases, the regression spaces such that the problem occurs depend on the size of the test, but there also exist regression spaces such that the power vanishes regardless of the size. A characterization of such particularly hostile regression spaces is provided

Relationship between spatial proximity and travel-to-work distance : the effect of the compact city

In this paper, an assessment is made of the relationship between selected aspects of spatial proximity (density, diversity, minimum commuting distance, jobs-housing balance and job accessibility) and reported commuting distances in Flanders (Belgium). Results show that correlations may depend on the considered trip end. For example, a high residential density, a high degree of spatial diversity and a high level of job accessibility are all associated with a short commute by residents, while a high job density is associated with a long commute by employees. A jobs-housing balance close to one is associated with a short commute, both by residents and by employees. In general, it appears that the alleged sustainability benefits of the compact city model are still valid in a context of continuously expanding commuting trip lengths

Testing for Network and Spatial Autocorrelation

Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties for testing for the presence of spatial autocorrelation in continuous variables. In this paper we propose two contributions to the literature on tests for autocorrelation. First, we propose a new test for autocorrelation in categorical variables. While some methods currently exist for assessing spatial autocorrelation in categorical variables, the most popular method is unwieldy, somewhat ad hoc, and fails to provide grounds for a single omnibus test. Second, we discuss the importance of testing for autocorrelation in data sampled from the nodes of a network, motivated by social network applications. We demonstrate that our proposed statistic for categorical variables can both be used in the spatial and network setting

Magnetic state of plutonium ion in metallic Pu and its compounds

By LDA+U method with spin-orbit coupling (LDA+U+SO) the magnetic state and electronic structure have been investigated for plutonium in \delta and \alpha phases and for Pu compounds: PuN, PuCoGa5, PuRh2, PuSi2, PuTe, and PuSb. For metallic plutonium in both phases in agreement with experiment a nonmagnetic ground state was found with Pu ions in f^6 configuration with zero values of spin, orbital, and total moments. This result is determined by a strong spin-orbit coupling in 5f shell that gives in LDA calculation a pronounced splitting of 5f states on f^{5/2} and f^{7/2} subbands. A Fermi level is in a pseudogap between them, so that f^{5/2} subshell is already nearly completely filled with six electrons before Coulomb correlation effects were taken into account. The competition between spin-orbit coupling and exchange (Hund) interaction (favoring magnetic ground state) in 5f shell is so delicately balanced, that a small increase (less than 15%) of exchange interaction parameter value from J_H=0.48eV obtained in constrain LDA calculation would result in a magnetic ground state with nonzero spin and orbital moment values. For Pu compounds investigated in the present work, predominantly f^6 configuration with nonzero magnetic moments was found in PuCoGa5, PuSi2, and PuTe, while PuN, PuRh2, and PuSb have f^5 configuration with sizeable magnetic moment values. Whereas pure jj coupling scheme was found to be valid for metallic plutonium, intermediate coupling scheme is needed to describe 5f shell in Pu compounds. The results of our calculations show that both spin-orbit coupling and exchange interaction terms in the Hamiltonian must be treated in a general matrix form for Pu and its compounds.Comment: 20 pages, LaTeX; changed discussion on reference pape

Regional clusters of innovative activity in Europe: Are social capital and geographical proximity the key determinants?

Finding proper policy instruments to promote productivity growth features prominently on the Lisbon agenda and is central in many national as well as European policy debates. In view of the increased mobility of high-skilled workers in Europe, ongoing globalization and increased interregional and international co-operation, location patterns of innovative activity may be subject to drastic changes. A proper understanding of location patterns of innovative outputs can enhance the effectiveness and efficiency of national and European innovation policies. Building on the literature on the knowledge production function the aim of this paper is to explain the observed differences in the production of innovative output across European regions. Our main research question is whether geographical proximity and social capital are important vehicles of knowledge transmission for the production of innovative output in Europe. Several other variables are used to control for structural differences across European regions. We find support for the hypothesis that both social capital and geographical proximity are important factors in explaining the differences in the production of innovative output across European regions

Spatial weights : constructing weight-compatible exchange matrices from proximity matrices

Exchange matrices represent spatial weights as symmetric probability distributions on pairs of regions, whose margins yield regional weights, generally well-specified and known in most contexts. This contribution proposes a mechanism for constructing exchange matrices, derived from quite general symmetric proximity matrices, in such a way that the margin of the exchange matrix coincides with the regional weights. Exchange matrices generate in turn diffusive squared Euclidean dissimilarities, measuring spatial remoteness between pairs of regions. Unweighted and weighted spatial frameworks are reviewed and compared, regarding in particular their impact on permutation and normal tests of spatial autocorrelation. Applications include tests of spatial autocorrelation with diagonal weights, factorial visualization of the network of regions, multivariate generalizations of Moran's I, as well as "landscape clustering", aimed at creating regional aggregates both spatially contiguous and endowed with similar features