AN ESTIMATION OF DISPOSABLE PERSONAL INCOME OF THE SPANISH MUNICIPALITIES IN 1997

Since 1992, Lawrence R. Klein Institute –Autónoma University of Madrid- estimates the disposable income of the Spanish municipalities, recently published in the ‘Anuario Comercial de España’ –Spanish Trade Yearbook- as scaled levels. Municipal personal income has been considered as one of the most important economic indicators, very used in a wide range of studies concerned with regional convergence, welfare analysis, marketing targets, etc. This kind of estimation can be carried out by both direct and indirect methodology. The first proceeding requires a huge information database generally difficult to obtain and not always precise, which main defect is that it cannot reflect the underground economy of Spanish municipalities. That is why direct methodology always has needed the help of indirect proceedings. These last ones find out the statistical relation of the personal disposable income and a group of socio-economic indicators for all the geographic units considered, municipalities, provinces, regions, countries, etc. In this paper, the authors present some of the indirect methods used to estimate the disposable income of Spanish municipalities. Especially the Klein estimation combines some multivariate analysis –panel data, factor and cross-section regression analysis- with a big database of almost 200 socio-economic indicators. The final estimation of the 8.099 municipalities disposable income allows us to acquire a better knowing of Spanish micro-territorial development.

Time-trend in spatial dependence: Specification strategy in the first-order spatial autoregressive model

The purpose of this article is to analyze if spatial dependence is a synchronic effect in the first-order spatial autoregressive model, SAR(1). Spatial dependence can be not only contemporary but also time-lagged in many socio-economic phenomena. In this paper, we use three Moran-based space-time autocorrelation statistics to evaluate the simultaneity of this spatial effect. A simulation study shed some light upon these issues, demonstrating the capacity of these tests to identify the structure (only instant, only time-lagged or both instant and time-lagged) of spatial dependence in most cases.Space-time dependence; Spatial autoregressive models; Moran’s I

Multigranular scale speech recognition: tehnological and cognitive view

We propose a Multigranular Automatic Speech Recognizer. The hypothesis is that speech signal contains information distributed on more different time scales. Many works from various scientific fields ranging from neurobiology to speech technologies, seem to concord on this assumption. In a broad sense, it seems that speech recognition in human is optimal because of a partial parallelization process according to which the left-to-right stream of speech is captured in a multilevel grid in which several linguistic analyses take place contemporarily. Our investigation aims, in this view, to apply these new ideas to the project of more robust and efficient recognizers

The impact of objective and subjective measures of air quality and noise on house prices: a multilevel approach for downtown Madrid

Air quality is one of the major concerns in big cities. It is therefore of interest to evaluate properly air pollution. Specifically, this paper aims at measuring how air quality is incorporated in transaction prices in downtown Madrid. For that purpose, we use multilevel models since our sample is hierarchically organized into 3 levels. Our first-level consists of 5,080 house prices. The second level consists of 759 census tracts while the third level consists of 43 neighbourhoods. We have variables available for each level, individual characteristics for the first level and various socio-economic data for the other levels. The outline of the paper is as follows. First, we combine a set of noise and air pollutants measured at a number of monitoring stations available for each census tract. Second, we apply kriging to match the monitoring station records to the census data. Third, we estimate hedonic models in order to measure the marginal willingness to pay for air quality in downtown Madrid. While the conventional approach to estimate hedonic models is to use ordinary least squares, we exploit the hierarchical nature of our data and estimated multilevel models instead. These allow for a more reliable statistical inference.

Evolution of the influence of geography on the location of production in Spain (1930-2005)

In this paper, we investigate the relative importance of geographic features on the location of production in Spain. Specifically, we want to quantify how much of the spatial pattern of GDP can be attributed to only exoge-nous first nature elements (physical and political geography) and how much can be derived from endogenous second nature factors (man-made agglomeration economies). In order to disentangle both effects empiri-cally, and to learn how they are interrelated, we control for second nature. We use a methodology based on an analysis of variance (ANOVA), which is applied to a panel of 47 Spanish provinces in the period 1930-2005. We demonstrate that results can be biased if spatial autocorrelation and spatial heterogeneity, as well as multicollinearity and endogeneity, are not prop-erly taken into account. In the Spanish case, we detect strong spatial het-erogeneity in the form of two main clusters. As expected, gross second na-ture forces are more important than net natural advantages, though their effects range from about 55% in the hinterland to 80% in the coast.Agglomeration, Geography, Spatial Heterogeneity, Endogeneity, Spanish Regions

ECOLOGICAL INFERENCE AND SPATIAL HETEROGENEITY - A NEW APPROACH BASED ON ENTROPY ECONOMETRICS

In this paper, we compare the results obtained by the application of three alternative methods of ecological inference. The data is on per capita household disposable income in the 50 provinces and 78 municipalities of Asturias, Spain. The first method is based on Ordinary Least Squares regression model, which assumes constancy or homogeneity. The second method is based on a spatial autocorrelation model, which assumes heterogeneity in two spatial regimes. The third method is based on a varying-coefficients model, which assumes total heterogeneity. The second model is estimated by Maximum Likelihood, whereas the latter is estimated by using Generalized Maximum or Cross Entropy.

Spatial interaction models applied to the design of retail trade areas

Intermetropolitan trade areas are geographical zones defined by consumer movements over space -retail flows- from their municipalities of residence towards a head town, to purchase special goods: clothing and footwear, furniture, food, etc. These market areas own an economic sense that do not have other more commonly used territorial divisions, such as towns, provinces or regions. Since 1992, the Lawrence R. Klein Institute -Autonoma University of Madrid-actualises the Spanish Retail Trade Atlas and determines regional trade areas and sub-areas, using spatial gravity models and survey. The authors' experience in this Project allows them to analyse the different procedures suggested for modelling the consumer store-choice process and from this, estimating the market share of a retail outlet or a town. Store choice models can be classified into two main groups. First, the descriptive-determinist approach includes a group of techniques that rely on observation or normative assumptions. It is well-known the procedure devised by Applebaum (1961) for constructing primary trade areas from customers spotted on a location map or the classical central place theory, based on the nearest-centre hypothesis. 'Reilly's law of retail gravitation' (1931) considers not only distance but also attractiveness of alternative shopping opportunities. Secondly, the explicative-stochastic approach uses information revealed by past behaviour to understand the dynamics of retail competition and how consumers choose among alternative shopping opportunities. Huff was the first to use a utility function and introduced the spatial interaction models to explain consumer behaviour. They argued that consumers rate alternatives on the basis of their evaluation of the total utility of the store and not merely on its location. Huff's model is a particular case of the discrete-choice models known as multinomial logit (McFadden, 1974). Both models satisfies the so-called 'Independece of Irrelevant Alternatives' (IIA) property, that is, the ratio of the probabilities of an individual selecting two alternatives is unaffected by the addition of a third alternative. While this may be reasonably representative of certain aspatial choice situations, it is very unlikely to occur in spatial choice because of the fixed locations of spatial alternatives. The competing destinations model, derived from purely spatial considerations, provides a way of overcoming some problems with the logit and nested logit models that arise from the transference of essentially aspatial theory to the spatial realm. This work focuses on market area delimitation models and presents the estimation process developed by the L.R. Klein Institute in determining intermetropolitan trade areas. It is also applied a competing destinations model to the trade area of Madrid, a very peculiar one because of its magnitude and the important shopping concentration around the capital. Finally, we want to highlight the main applications derived from the knowledge and actualisation of the consumer retail flows. These applications take into account not only retailing but also another economic activities relating with market attraction areas.

Urban growth and territorial dynamics in Spain (1985-2001): A spatial econometrics analysis

The study of the territorial/regional development in Spain has nowadays a relatively long tradition, but from the point of view of cities development the number of studies and documents decreases drastically. This paper tries to improve the knowledge of the Spanish urban system. The aim of this paper is twofold: firstly, to determine the factors that explain the urban growth of Spanish cities; secondly, to observe the cities situation in terms of ¡§winners¡¨ and ¡§losers¡¨ after the long period of integration of Spain in the EU. A spatial conditional ƒÒ- convergence equation is specified and the Durbin-Wu-Haussman exogeneity test is used to check on the existence of simultaneity between urban growth and the control variables. The classic problems of spread and backwash are studied by including a spatial autoregressive term and spatial regimes ¡Vconvergence clubs- in the growth model.Urban growth, Spanish cities, conditional ƒÒ-convergence, endogeneity, Durbin-Wu-Haussman, spatial autocorrelation, spatial heterogeneity, convergence clubs

Building an Environmental Quality Index for a big city: a spatial interpolation approach with DP2

The elaboration of Environmental Quality Indexes (EQI) for big cities is one of the main topics in regional and environmental economics. One of the usual methodological paths consists of generating a single measure as a linear combination of several air contaminants applying Principal Component Analysis (PCA). Then, as a final step, a spatial interpolation is carried out to determine the level of contamination across the city in order to point out the so-called ‘hot points’. In this article, we propose an alternative approach to build an EQI introducing some methodological and practical novelties. From the point of view of the selection of the variables, first we will consider noise -joint to air pollution- as a relevant environmental variable. We also propose to add ‘subjective’ data -available at the census tracts level- to the group of ‘objective’ environmental variables, which are only available at a number of environmental monitoring stations. This combination leads to a mixed environmental index (MEQI), which is more complete and adequate in a socioeconomic context. From the point of view of the computation process, we use kriging to match the monitoring stations registers to the Census data. We follow an inverse process as usual, since it leads to better estimates. In a first step, we krige the environmental variables to the complete surface and finally, we elaborate the environmental index. At last, in order to build the final synthetic index, we do not use Principal Components Analysis -as it is usual in this kind of exercises- but a better one, the Pena Distance method (DP2).Environmental index, Air pollution, Noise, Subjecive expectations, Kriging, Distance indicators