Diego Giuliani

Weighting Ripley’s K-Function to Account for the Firm Dimension in the Analysis of Spatial Concentration:

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.

Conditional versus unconditional industrial agglomeration: disentangling spatial dependence and spatial heterogeneity in the analysis of ICT firms’ distribution in Milan

A series of recent papers have introduced some explorative methods based on Ripley’s K-function (Ripley in J R Stat Soc B 39(2):172–212, 1977) analyzing the micro-geographical patterns of firms. Often the spatial heterogeneity of an area is handled by referring to a case–control design, in which spatial clusters occur as over-concentrations of firms belonging to a specific industry as opposed to the distribution of firms in the whole economy. Therefore, positive, or negative, spatial dependence between firms occurs when a specific sector of industry is seen to present a more aggregated pattern (or more dispersed) than is common in the economy as a whole. This approach has led to the development of relative measures of spatial concentration which, as a consequence, are not straightforwardly comparable across different economies. In this article, we explore a parametric approach based on the inhomogeneous K-function (Baddeley et al. in Statistica Nederlandica 54(3):329–350, 2000) that makes it possible to obtain an absolute measure of the industrial agglomeration that is also able to capture spatial heterogeneity. We provide an empirical application of the approach taken with regard to the spatial distribution of high-tech industries in Milan (Italy) in 2001.

Approximate maximum likelihood estimation of the autologistic model

Approximate Maximum Likelihood Estimation (AMLE) is a simple and general method recently proposed for approximating MLEs without evaluating the likelihood function. The only requirement is the ability to simulate the model to be estimated. Thus, the method is quite appealing for spatial models because it does not require evaluation of the normalizing constant, which is often computationally intractable. An AMLE-based algorithm for parameter estimation of the autologistic model is proposed. The impact of the numerical choice of the input parameters of the algorithm is studied by means of extensive simulation experiments, and the outcomes are compared to existing approaches. AMLE is much more precise, in terms of Mean-Square-Error, with respect to Maximum pseudo-likelihood, and comparable to ML-type methods. Although the computing time is non-negligible, the implementation is straightforward and the convergence conditions are weak in most practically relevant cases.

Empirical assessment of the tourism-led growth hypothesis: the case of the Tirol-Südtirol-Trentino Europaregion.

Cointegration tests have become very popular in empirical analyses of the tourism-led growth hypothesis (TLGH). They were first introduced into the literature on tourism economics by Balaguer and Cantavella-Jordá, and then were made popular by many researchers attempting to assess the causal long-run relationship between international tourism and economic growth. The vast majority of these studies analyse countries in which tourism is one of the most important sectors of the national economy and, in most cases, the TLGH is validated. With respect to previous contributions to the literature, this paper examines the TLGH in sub-national trans-frontier economies, taking as its case the three administrative areas forming the region known as ‘Tirol–Südtirol–Trentino Europaregion’. Direct comparisons with the results for across-the-border regions that have similar international tourism markets provide new insights for our understanding of the TLGH.

Incomplete geocoding and spatial sampling: The effects of locational errors on population total estimation

Due to the increasing availability of georeferenced microdata in several fields of research, surveys can benefit greatly from the use of the most recent spatial sampling methods. These methods allow to select spatially balanced samples, which lead to particularly efficient estimates, by incorporating the distances among the exact locations of statistical units into the design. Unfortunately, since locations of units are rarely exact in practice due to imperfections in the geocoding processes, the implementation of spatial sampling designs is actually often limited. This paper aims at demonstrating that spatial sampling designs can be implemented even when spatial information is not completely accurate. In particular, by means of a Montecarlo sampling simulation study about the estimation of water pollution, it is proved that the use of spatial sampling methods still lead to more spatially balanced samples, and more efficient estimates, also when the geocoding of population is not exact.

Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function

ABSTRACT Effects of missing data and locational errors on spatial concentration measures based on Ripley’s K-function. Spatial Economic Analysis. Measures based on Ripley’s K-function are the preferred tools to test the concentration of individual agents in an economic space. In many empirical cases, however, the datasets contain different inaccuracies due to missing data or uncertainty about the location of the agents. Little is known thus far about the effects of these inaccuracies on the K-function. This paper sheds light on the problem through a theoretical analysis supported by Monte Carlo experiments. The results show that patterns of clustering or inhibition may be observed not as genuine phenomena but only as the effect of data imperfections.

A spatial analysis of health and pharmaceutical firm survival

ABSTRACT The presence of knowledge spillovers and shared human capital is at the heart of the Marhall–Arrow–Romer externalities hypothesis. Most of the earlier empirical contributions on knowledge externalities; however, considered data aggregated at a regional level so that conclusions are based on the arbitrary definition of jurisdictional spatial units: this is the essence of the so-called modifiable areal unit problem. A second limitation of these studies is constituted by the fact that, somewhat surprisingly, while concentrating on the effects of agglomeration on firm creation and growth, the literature has, conversely, largely ignored its effects on firm survival. The present paper aims at contributing to the existing literature by answering to some of the open methodological questions reconciling the literature of Cox proportional hazards model with that on point pattern and thus capturing the true nature of spatial information. We also present some empirical results based on Italian firm demography data collected and managed by the Italian National Institute of Statistics (ISTAT).

Design-based estimation in environmental surveys with positional errors

The recent increased availability of information about the micro-geographic positions of population units in environmental surveys has led to important developments in spatial sampling methodologies and, as a result, has improved the estimation accuracy. In real data, however, information about the location of units is often affected by inaccuracy about their exact spatial positions, and these non-sampling errors can affect the estimation procedure. This paper aims to investigate the effects of positional errors on total estimation through a Monte-Carlo simulation study based on real populations of trees. Starting from perfect positioning, we examine two typical types of coarsening that frequently impact two different species of trees. The simulation results show that the exploitation of spatial information to estimate population totals continues to be relevant in the context of environmental surveys, even in the presence of inaccuracies.

Consumers’ willingness to pay for green cars: a discrete choice analysis in Italy

In September 2015, the Volkswagen Group was involved in a massive scandal regarding vehicles’ emissions of substances harmful to the environment. This scandal, known as Dieselgate, caused a commotion in the automotive sector and raised the question of whether or not there exists consumer demand for cleaner cars. This paper aims to investigate consumers’ willingness to pay a premium price for lower CO2 emitting cars. To do so, it adopts a discrete choice methodological approach and an exploratory survey involving 278 potential Italian car buyers. The results provide strong support for the primary hypothesis of the research that consumers are willing to pay more for cleaner vehicles, as expressed by a positive marginal willingness to pay for lower emissions. Potential car buyers indeed appear willing to pay a price premium of about € 2100 for a 20% CO2 reduction per kilometre from the current standard level that car industry has to comply with. In particular, we estimate a willingness to pay approximately € 88 for 1-g of CO2 reduction per kilometre (with a 95% confidence interval ranging between € 54 and € 122).

La sopravvivenza immediata delle start-up italiane del settore manifatturiero sanitario: un’analisi multilevel

L’obiettivo del presente lavoro e quello di fornire nuove evidenze circa le determinanti della probabilita di sopravvivenza di breve periodo delle start-up italiane attive nel settore farmaceutico e nel settore della produzione di dispositivi medico-sanitari. Al fine di valutare l’effetto di caratteristiche specifiche delle singole imprese, e di tener conto delle variabili di contesto osservate e non osservate, la probabilita di sopravvivenza a tre anni viene descritta mediante un modello logistico multilevel. L’analisi si basa sulle osservazioni a livello di popolazione raccolte e gestite dall’ISTAT in conformita con le direttive dell’OCSE e di EUROSTAT sulla demografia d’impresa, in grado di garantire la coerenza delle informazioni raccolte con particolare riferimento alle entrate e alle uscite delle imprese dal mercato. L’elevato numero di effetti random e la conseguente elevata dimensionalita dell’in¬tegrazione richiesta dal processo di stima rendono le tecniche di stima standard poco affidabili. Le stime sono state quindi effettuate mediante il metodo del-l’entropia relativa per l’ottimizzazione di funzioni con rumore (Bee et al., 2015).