The producer service sector in Italy: Long-term growth and its local determinants

This paper analyses the local determinants of producer service growth in Italy, focusing on agglomeration economies, and taking into account the particular features of this sector with respect to manufacturing. Using an OECD classification, we estimate a dynamic specification allowing for transitory dynamics around the long-run employment path derived from a model in which both demand and supply factors are considered. Compared with the prevailing modelling approach, the spatial scope of externalities is extended to include possible interactions across different urban areas. Our main findings are the following. Long-run employment growth is positively affected by Marshall-Arrow-Romer externalities, with a minor role played by urbanization externalities, a result similar to that obtained by more recent research on the Italian manufacturing sector and its industrial districts. Among the remaining supply factors, human capital exerts a positive influence on the long-run employment level in producer services industry; among demand factors, the size of the local market appears to be important, given the still incomplete tradability of service output. Significant interactions across urban areas are shown to occur; in particular, positive knowledge externalities on local productivity appear to be induced by location in urban areas contiguous to cities specializing in producer services.agglomeration economies, human capital, producer services

On vector autoregressive modeling in space and time

Despite the fact that it provides a potentially useful analytical tool, allowing for the joint modeling of dynamic interdependencies within a group of connected areas, until lately the VAR approach had received little attention in regional science and spatial economic analysis. This paper aims to contribute in this field by dealing with the issues of parameter identification and estimation and of structural impulse response analysis. In particular, there is a discussion of the adaptation of the recursive identification scheme (which represents one of the more common approaches in the time series VAR literature) to a space-time environment. Parameter estimation is subsequently based on the Full Information Maximum Likelihood (FIML) method, a standard approach in structural VAR analysis. As a convenient tool to summarize the information conveyed by regional dynamic multipliers with a specific emphasis on the scope of spatial spillover effects, a synthetic space-time impulse response function (STIR) is introduced, portraying average effects as a function of displacement in time and space. Asymptotic confidence bands for the STIR estimates are also derived from bootstrap estimates of the standard errors. Finally, to provide a basic illustration of the methodology, the paper presents an application of a simple bivariate fiscal model fitted to data for Italian NUTS 2 regions.structural VAR model, spatial econometrics, identification, space-time impulse response analysis

Foreign trade, home linkages and the spatial transmission of economic fluctuations in Italy

During the recent global recession both the export-oriented northern Italian regions and those in the far less open South experienced a sharp decline in economic activity. One of the possible explanations is the existence of strong domestic linkages propagating foreign demand shocks from North to South. To assess the scope of the spatial transmission of global and local disturbances across Italian regions, in this paper we specify and estimate a bivariate structural spatial VAR model featuring GDP and foreign exports as endogenous variables. A standard gravity equation approach is implemented to model unobserved domestic regional trade flows, while regional sales on foreign markets are related to global trade fluctuations and local shocks to competitiveness, broken down into a national and an idiosyncratic component. In line with expectations, strong domestic linkages are uncovered on the basis of model estimation results. The latter show that even less export-oriented Italian regions, although broadly unaffected on impact, may eventually experience a sharp output decline following a fall in global trade of the size observed in the recent recession.panel VAR model, trade linkages, spatial econometrics

Dynamic macroeconomic effects of public capital: evidence from regional Italian data

This paper assesses the effects of public capital in Italy on the main macroeconomic aggregates: GDP, private capital and labour. A cointegrated vector autoregressive (VAR) model, in line with recent advancements in the field, allows us to take into account the complex nexus of direct and indirect links between the variables. We find a persistent increase in GDP in response to a positive shock to public capital; this result is mainly attributable to a strong stimulus exerted by public infrastructures on private capital (crowding in). The positive effects of public capital are quite pervasive across Italy, albeit to differing extents. In particular, a higher elasticity of GDP to public capital is estimated for the South, whereas marginal productivity turns out to be higher in the Centre-North. This suggests that public capital has a lower economic return in the South, bearing out the existence of a potential conflict between equity and efficiency goals. Finally, we indirectly document the existence of positive spillover effects at the regional level, allowing individual regions to benefit from the endowment of public capital in the rest of the country.public capital, crowding in effects, Italian regional divides, VAR models

Explaining labor productivity differentials on Italian regions

Labor productivity convergence is a key factor in the catching up process of less developed regions. For the regional economies as a whole labor productivity differentials can be traced back to three distinct determinants: - composition effects due to the peculiar structure of the regional economy; a lower than average productivity level could, for instance, be due to the fact that a greater share or the regional labor force is employed in sectors that are denoted by lower productivity at the aggregate level; - different regional endowments, within each given industry, of physical and human capital per worker; - differing levels of total factor productivity (TFP). The study aims at explaining substantial and persistent regional differentials in labor productivity in Italy providing: 1. an assessment of the role played by the three factors above outlined in the variuos regions; 2. an empirical evaluation of the role played by some of the relevant factors suggested in the related literature (e.g., public and social capital, R&D expenditure, international openness, financial markets development, agglomeration and diversification economies, geographic factors), in explaining regional TFP differentials. The empirical analysis makes use of a particularly rich data set including annual regional accounts and capital stock data for 17 industries covering the period 1970-1994. Estimates of human capital broken down by region and industry are produced by the authors pooling information from the Labor force survey and Bank of Italy’s Survey of households income and wealth. The analysis of structural composition effects is carried out by means of the shift-share technique proposed by Esteban (2000), while a cointegrated panel model is used to estimate total factor productivity by region and sector. In an attempt to assess the relevance of spatial externalities in explaining regional TFP levels the final regression analysis makes use of spatial econometric techniques.

Agglomeration within and between regions: Two econometric based indicators

We propose two indexes to measure the agglomeration forces acting within and between different regions. Unlike the existing measures of agglomeration, our model-based indexes allow for simultaneous treatment of both aspects. Local plant diffusion in a given industry is modelled as a spatial error components process (SEC). Maximum likelihood inference on model parameters is dealt with, including the problem of data censoring. The statistical properties of standard agglomeration indexes in the data environment provided by our SEC model are then treated. Finally, our methodology is applied to Italian census data for both manufacturing and service industries.agglomeration, spatial autocorrelation, spatial error components model

Quality upgrading of Italian manufactures: evidence from firms’ prices and strategies

Even before the global crisis, the Italian economy was in difficulties internationally, but slow growth and a declining share of world trade were accompanied by a perceptible process of manufacturing transformation. This paper, using data from the Bank of Italy’s survey of manufacturers, measures a crucial aspect of the transformation, namely quality upgrading, from 2000 to 2006. The gauge of upgrading, not used in earlier literature, is the portion of price changes representing the return to value creation, both tangible (new products and improvement of existing ones) and intangible (branding policies). We find evidence of upgrading capable of explaining a quarter of the firms’ average annual price increases (about 0.5 out of 2 percentage points), with roughly equal effects from the tangible and the intangible components. The analysis also shows that strategies of product upgrading helped foster job creation and sales growth.

Mapping local productivity advantages in Italy: industrial districts, cities or both?

We compare the magnitude of local productivity advantages associated with two different spatial concentration patterns in Italy – urban areas and industrial districts. The former have high population density and host a wide range of economic activities, while the latter are marked by a high concentration of small firms producing relatively homogenous goods. Using data from a large sample of Italian manufacturing firms observed over the 1995-2006 period, we detect local productivity advantages for both urban areas and industrial districts. However, firms located in urban areas reap a larger productivity premium than those operating within districts. The advantages of industrial districts have declined over time; those of urban areas have remained stable. Differences in the composition of firm employees between white- and blue-collars explain a small fraction of the urban productivity premium. The quantile regressions show how more productive firms gain larger benefits by locating in urban areas. Our analysis raises the question of whether Italian industrial districts are less fit than urban areas to prosper in a world characterized by advancing globalization and the growing use of ICT.urban areas, industrial districts, agglomeration economies, productivity, white- and blue-collars, Italian economy

MAPPING LOCAL PRODUCTIVITY ADVANTAGES IN ITALY: INDUSTRIAL DISTRICTS, CITIES OR BOTH?

In 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.

Differential regional effects of monetary policy: a geographical SVAR approach

The study of possible asymmetric effects of monetary policy at a spatially disaggregated scale has recently received renewed attention in the literature due to the introduction of EMU. To quantify the differences in monetary policy transmission different econometric approaches have been proposed. At the macro level both structural simultaneous equations models and structural vector autoregressions (SVAR) to address the issue. The current paper mainly builds on the SVAR approach and extends it by incorporating geographical information in model's specification, making use of the techniques commonly employed in spatial econometrics. While, to capture spatial interactions it would be necessary to adopt a VAR specification modelling jointly the given set of regions, this is generally not feasible using standard VAR models due to the shortage of degrees of freedom. In the proposed specification, information on spatial proximity is used to derive parameter constraints that make the joint estimation feasible for panels of moderate or large dimension, requiring time series of length comparable to that necessary for standard VAR estimatio Having introduced the model's specification, with specific reference to the issue of parameter identification, the paper deals with parameter estimation, that, in this, case is complicated by the complex simultaneos dependence structure. Finally, to test the model's empirical performance, the paper presents an application to the analysis of the differential monetary policy effects on the US states. Based on the estimation results, geographical heterogeneity in the impulse response function found out in previous studies appears to be confirmed.