Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Valter Di Giacinto (ARET, L'Aquila), Matteo Gomellini (ARET, Roma), Giacinto Micucci (ARET, Ancona), Marcello Pagnini (ARET, Bologna) Terms of use: Documents in AbstractIn this paper we compare the magnitude of local productivity advantages associated to two different spatial concentration patterns in Italy, i.e. urban areas (UA) and industrial districts (ID). UA typically display a huge concentration of population and host a wide range of economic activities, while ID are located outside UA and exhibit a strong concentration of small firms producing relatively homogenous goods. We use a very large sample of Italian manufacturing firms observed over the 1995-2006 period and resort to a wide set of econometric techniques in order to test the robustness of main empirical findings. We detect local productivity advantages for both UA and ID. However, firms located in UA attain a larger Total Factor Productivity (TFP) premium than those operating within ID. Besides, it turns out that the advantages of ID have declined over time, while those of UA remained stable. Differences in the white-blue collars composition of the local labor force appear to explain only a minor fraction of the estimated spatial TFP differentials. Production workers (blue collars) turn out to be more productive in ID, while non-production workers (white collars) are more efficiently employed in UA. By analyzing the quantiles of the sample TFP distribution, we document how higher average TFP levels within UA do not seem to be mainly driven by a selection effect pushing less efficient firms out of the market. Rather, a firm sorting effect appears to stand out, suggesting that more productive firms gain strong benefits from locating in UA. On the whole, our analysis raises the question whether Italian ID are less fit than UA to prosper in a changing world, characterized by increased globalization and by the growing use of information technologies.

The study of possible asymmetric effects of monetary policy at a spatially disaggregated scale has received increasing attention in the literature. Different econometric approaches have been proposed to quantify the differences in monetary policy transmission, such as large-scale simultaneous equations models or structural vector autoregressive (SVAR) models. This article builds on the SVAR approach and extends it by incorporating geographical information, using spatial econometric techniques. The author employs information on spatial proximity to derive parameter constraints, enabling joint estimation for medium- and large-sized panels. Moreover, the use of spatial a priori information makes it possible to identify and estimate contemporaneous spatial spillover effects. Specific attention is paid to parameter identification when introducing the model. Subsequently, the author discusses parameter estimation, which is complicated by the simultaneous spatial dependence structure and the need to impose complex parameter constraints. An empirical application is provided with respect to U.S. states.

This article aims at explaining substantial and persistent regional labour productivity differentials in Italy. First, the role played by the diversity of local economic structures is quantitatively assessed. Within-industry productivity levels are then related to regional endowments of physical and human capital per worker and to total factor productivity. Subsequently, an empirical evaluation of the influence exercised by a selected set of explanatory factors on regional total factor productivity (TFP) levels is performed. In this context, spatial econometrics techniques are employed to obtain inferences that are robust to the presence of spatial autocorrelation in the data. Copyright (c) 2006 the author(s). Journal compilation (c) 2006 RSAI.

The STARMA (space-time autoregressive moving average) model class was introduced in the mid-1970s as a spatio-temporal extension to the ARMA time series model class. To enhance the model's ability in dealing with spatial dependence and heterogeneity of observations, the article extends the STARMA model specification by augmenting the set of explanatory variables with simultaneous spatial lags of the observed process and unobservable shocks and by letting model parameters vary with location in space. Having introduced the extended specification, the spacetime impulse response function is subsequently presented as a useful tool in addressing structural issues. The article then deals with maximum likelihood estimation and hypothesis testing; in particular, Lagrange multiplier tests are proposed for spatial heterogeneity in the intercept, conditional variance and ARMA coefficients. The article closes with an application to the analysis of the series of the regional unemployment rate in Italy, aimed at evaluating the extent of the spatial propagation of regional specific shocks to unemployment and the degree of spatial heterogeneity in the process parameters.

Two main hypotheses are usually put forward to explain the productivity advantages of larger cities: agglomeration economies and firm selection. Combes et al. (2012) propose an empirical approach to disentangle these two effects and find no impact of selection on local productivity differences. We theoretically show that selection effects do emerge when heterogeneous trade costs and the different spatial scale at which agglomeration and selection may work are properly taken into account. Our empirical findings confirm that agglomeration effects play a major role. However, they also show a substantial increase in the importance of the selection effect. K E Y W O R D S agglomeration economies, firm selection, market size, openness to trade J E L C L A S S I F I C AT I O N : c52, r12, d241 the elasticity of productivity with respect to city population range between 0.02 and 0.10, and the evidence is confirmed for several countries and sectors. In an analysis on Italian manufacturing firms, Di Giacinto et al. (2014) detect local productivity advantages for both types of agglomerated areas they take into consideration: urban areas, which typically display a huge concentration of population and host a wide range of economic activities, and industrial districts, which exhibit a strong concentration of small firms producing roughly the same products. The authors also find that advantages are much larger for urban areas. 949 J Regional

No abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Valter Di Giacinto (ARET, L'Aquila), Matteo Gomellini (ARET, Roma), Giacinto Micucci (ARET, Ancona), Marcello Pagnini (ARET, Bologna) Terms of use: Documents in AbstractIn this paper we compare the magnitude of local productivity advantages associated to two different spatial concentration patterns in Italy, i.e. urban areas (UA) and industrial districts (ID). UA typically display a huge concentration of population and host a wide range of economic activities, while ID are located outside UA and exhibit a strong concentration of small firms producing relatively homogenous goods. We use a very large sample of Italian manufacturing firms observed over the 1995-2006 period and resort to a wide set of econometric techniques in order to test the robustness of main empirical findings. We detect local productivity advantages for both UA and ID. However, firms located in UA attain a larger Total Factor Productivity (TFP) premium than those operating within ID. Besides, it turns out that the advantages of ID have declined over time, while those of UA remained stable. Differences in the white-blue collars composition of the local labor force appear to explain only a minor fraction of the estimated spatial TFP differentials. Production workers (blue collars) turn out to be more productive in ID, while non-production workers (white collars) are more efficiently employed in UA. By analyzing the quantiles of the sample TFP distribution, we document how higher average TFP levels within UA do not seem to be mainly driven by a selection effect pushing less efficient firms out of the market. Rather, a firm sorting effect appears to stand out, suggesting that more productive firms gain strong benefits from locating in UA. On the whole, our analysis raises the question whether Italian ID are less fit than UA to prosper in a changing world, characterized by increased globalization and by the growing use of information technologies.

Structural VAR model, Spatial econometrics, Identification, Space-time impulse response analysis, C32, C33, R10,