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

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

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.

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

Convolutive superposition for multicarrier cognitive radio systems

Recently, we proposed a spectrum-sharing paradigm for single-carrier cognitive radio (CR) networks, where a secondary user (SU) is able to maintain or even improve the performance of a primary user (PU) transmission, while also obtaining a low-data rate channel for its own communication. According to such a scheme, a simple multiplication is used to superimpose one SU symbol on a block of multiple PU symbols.The scope of this paper is to extend such a paradigm to a multicarrier CR network, where the PU employs an orthogonal frequency-division multiplexing (OFDM) modulation scheme. To improve its achievable data rate, besides transmitting over the subcarriers unused by the PU, the SU is also allowed to transmit multiple block-precoded symbols in parallel over the OFDM subcarriers used by the primary system. Specifically, the SU convolves its block-precoded symbols with the received PU data in the time-domain, which gives rise to the term convolutive superposition. An information-theoretic analysis of the proposed scheme is developed, which considers different amounts of network state information at the secondary transmitter, as well as different precoding strategies for the SU. Extensive simulations illustrate the merits of our analysis and designs, in comparison with conventional CR schemes, by considering as performance indicators the ergodic capacity of the considered systems.Comment: 29 pages, 8 figure

Family Succession and Firm Performance: Evidence from Italian Family Firms

This article contributes to the growing empirical literature on family firms by studying the impact of the founder–chief executive officer (CEO) succession in a sample of Italian firms. We contrast firms that continue to be managed within the family by the heirs to the founders with firms in which the management is passed on to outsiders. Family successions, that is, successions by the founder’s heirs, are further analyzed by assessing the impact of the sectoral intensity of competition on the post-succession performance. This analysis also addresses the endogeneity in the timing of the CEO succession by controlling for a pure mean-reversion effect in the firm’s performance. We find that the maintenance of management within the family has a negative impact on the firm’s performance, and this effect is largely borne by the good performers, especially in the more competitive sectors. These results indicate that there is no inherent superiority of the family-firm structure and emphasize the importance of conducting an analysis of governance in a variety of institutional settings.Family successions; Family firms; Founder-run firms

Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks

Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedded malicious code to the victim users, facilitating the use of social engineering techniques to infect their machines. Research showed that machine-learning algorithms provide effective detection mechanisms against such threats, but the existence of an arms race in adversarial settings has recently challenged such systems. In this work, we focus on malware embedded in PDF files as a representative case of such an arms race. We start by providing a comprehensive taxonomy of the different approaches used to generate PDF malware, and of the corresponding learning-based detection systems. We then categorize threats specifically targeted against learning-based PDF malware detectors, using a well-established framework in the field of adversarial machine learning. This framework allows us to categorize known vulnerabilities of learning-based PDF malware detectors and to identify novel attacks that may threaten such systems, along with the potential defense mechanisms that can mitigate the impact of such threats. We conclude the paper by discussing how such findings highlight promising research directions towards tackling the more general challenge of designing robust malware detectors in adversarial settings