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This study explores the downsizing propensity of family and non-family firms by considering their territorial embeddedness during both periods of economic stability and financial crisis. By drawing on a panel dataset of Spanish manufacturing firms for the period 2002–2015, we show that, all things being equal, family firms have a lower propensity to downsizing than non-family firms. When considering the effect of territorial embeddedness, we found that territorially embedded family firms have an even lower propensity to downsizing than their non-family counterparts. Furthermore, the concern of territorially embedded family firms for their employees’ welfare was particularly pronounced during the years of the global financial crisis. This result is explained by the existence of socially proximate relationships with the firms’ immediate surroundings, based on similarity and a sense of belonging, which push deeply rooted family firms to treat their employees as salient stakeholders during hard times. Overall, our study stresses the importance of local roots in moderating the relationship between family firms and downsizing.

The Covid-19 pandemic caused disruptive effects for individuals, firms, and societies. In this paper, we offer insights on the major issues and challenges firms are facing in the Covid-19 pandemic, as well as their concerns for Corporate Social Responsibility (CSR) themes. To do so, we investigate large Italian firms' discussion on Twitter in the first nine months of the pandemic. We downloaded all Twitter posts from 1st of March, 2020, to 17th of November, 2020 by the accounts of the largest Italian firms, i.e. those with 250 or more employees. We then built the bipartite network of accounts and hashtags and, using an entropy-based null model as a benchmark, we projected the information contained in the network into the accounts layers, identifying a network of accounts in which a link indicates a non trivial similarity in terms of their usage of hashtags. We find that the conversation is focused around 13 communities, 10 of which include Covid-19 themes. The core of the network is formed of 5 communities, which deal with environmental sustainability, digital innovation and safety. Firms' ownership type does not seem to influence the conversation. 10 communities out of 13 mention hashtags related to CSR, with the environmental and social dimensions as the prevalent ones. Interestingly enough, the social dimension seems more relevant in the communities dealing with digital innovation and safety. However, the relevance of CSR hashtags is very small at the single message level, but with some peculiarities arising in specific communities. Overall, our paper highlights the role of network methods on Twitter data as a tool which can support managers and policy makers to design their strategies and decision making, capturing firms' emerging issues and relevant themes., 30 pages, 4 figures

The present research is aimed at analysing the role of the State towards the first British and Italian railway companies before the nationalisations. The article reviews the British and Italian railway legislation and financial reports, retrieved from historical archives. The results highlight two very different roles of the public institutions: while one State acted as a regulator, the other one mainly acted as an entrepreneur.

Literature on the formation of intention toward entrepreneurship in adolescents has focused on either parental (vertical) transmission of social capital or network effects from peers or neighbours (horizontal). Considering the simultaneous effect of parents, peers, and neighbours, we suggest that such three levels identify a mechanism whereby the individual perception of their importance interacts with their objective characteristics. With a unique dataset for second-year high-school adolescents in the Italian city of Palermo, and employing Logit and 3SLS methods, we find evidence for a strong parental effect and for secondary peer (peers) effects on student intention. We also detect clear endogenous effects from the neighbourhood and the overall context. Moreover, entrepreneurship is confirmed to be perceived, even by high-school students, as a buffer for possible unemployment and social mobility.

Regions, independent of their geographic level of aggregation, are known to be interrelated partly due to their relative locations. Similar economic performance among regions can be attributed to proximity. Consequently, a proper understanding, and accounting, of spatial liaisons is needed in order to effectively forecast regional economic variables. Several spatial econometric techniques are available in the literature, which deal with the spatial autocorrelation (SAC) in geographically referenced data. The experiments carried out in this article are concerned with the analysis of the SAC observed for unemployment rates in 439 NUTS-3 German districts. The authors employ a semiparametric approach-spatial filtering-in order to uncover spatial patterns that are consistently significant over time. The authors first provide a brief overview of the spatial filtering method and illustrate the data set. Subsequently, they describe the empirical application carried out: that is, the spatial filtering analysis of regional unemployment rates in Germany. Furthermore, the authors exploit the resulting spatial filter as an explanatory variable in a panel modeling framework. Additional explanatory variables, such as average daily wages, are used in concurrence with the spatial filter. Their experiments show that the computed spatial filters account for most of the residual SAC in the data. © 2011 SAGE Publications.

As observed in many advanced economies experiencing an increase of self-employment rates since the late 1970s, a flourishing small- and medium-size enterprise sector is traditionally associated with positive economic development and growth. In the regional context, areas benefiting from an established entrepreneurial culture are in general more successful and innovative, as well as better equipped to sustain structural changes and to lessen unemployment. It is therefore important to investigate the reasons why individuals choose self-employment, and why they do it despite lower protection, higher risks, and possibly more effort than what is required in a comparable wage employment position. Existing research identifies better prospects of entrepreneurial earnings as compared to wages as a major stimulus towards self-employment. However, besides pecuniary motivations, other factors may be considered when it comes to the occupational choice. These include displacement, uncertainty, (the threat of) unemployment, and (dis)-satisfaction. Building on a job quits model, we propose a representation of transition behaviour from wage to self-employment which includes subjective evaluations of pecuniary and nonpecuniary satisfaction on the previous job. Individual microdata are drawn from the Swiss Household Panel (SHP), and cover the time period 1999–2008. Additionally, we focus on the dynamics of job satisfaction in order to highlight the role played by shocks in subjective evaluations, and introduce their interaction with levels to control for threshold effects.

In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. Neural networks are modern statistical tools based on learning algorithms that are able to process large amounts of data. Neural networks are enjoying increasing interest in several fields because of their effectiveness in handling complex data sets when the functional relationship between dependent and independent variables is not specified explicitly. The present paper compares two NN methodologies. First, it uses NNs to forecast regional employment in both the former West and East Germany. Each model implemented computes single estimates of employment growth rates for each German district, with a 2-year forecasting range. Next, additional forecasts are computed, by combining the NN methodology with shift-share analysis (SSA). Since SSA aims to identify variations observed among the labour districts, its results are used as further explanatory variables in the NN models. The data set used in our experiments consists of a panel of 439 German (NUTS 3) districts. Because of differences in the size and time horizons of the data, the forecasts for West and East Germany are computed separately. The out-of-sample forecasting ability of the models is evaluated by means of several appropriate statistical indicators.

Research and development (R&D), in the field of nanomaterials is expected to be a major driver of innovation and economic growth. Consequently, it is of great interest to understand which factors facilitate the creation of new technological knowledge. The existing literature has typically addressed this question by employing a knowledge production function based on firm-, regional- or even country-level data. Estimating the effects for the entire national system of innovation, however, assumes poolability of regional data. We apply our reasoning to Germany, which has well-known regional disparities, in particular between the East and the West. Based on analyses at the NUTS 3 regional level, we find different knowledge production functions for the East and the West. Moreover, we investigate how our results are affected by the adoption of alternative aggregation levels. Overall, our findings suggest that a careful evaluation of poolability and aggregation is required before estimating knowledge production functions at the regional level.

Intention toward any occupational choice can be widely categorized as a rational choice process combined with a subjective attitude function. There is extensive literature dealing with the formation of intention toward entrepreneurship in adolescents, in particular as a result of either parental (vertical) transmission of social capital or network effects from peers or neighbours (the latter two being two different levels of horizontal transmission varying in proximity in terms of bonding and bridging). We contribute to this literature by considering the joint effect of all these three levels simultaneously, in order to avoid an underspecification of the model due to omission of important cultural factors. We hypothesize that such three levels identify a mechanism where the individual perception of their importance interacts with their objective characteristics. With data for second-year high-school students, and employing empirical triangulation through Logit and 3SLS methods, we find evidence for a strong parental effect and of secondary peer effects on student intention. We also detect clear endogenous effects from the neighbourhood and the overall cultural context. Moreover, entrepreneurship is confirmed to be perceived, even by students, as a buffer for unemployment and social mobility.

Forecasting economic values in administrative units provides very important information for political, institutional and economic agents for their respective planning processes. A crucial stage is the choice of the econometric method to obtain these future values taking into account the diversity and complexity of real economy. Two aspects may be considered when choosing an econometric specification. Firstly, disparities in economic development and welfare within countries (i.e., at the regional level) are often bigger than between countries (Elhorst 1995; Taylor and Bradley 1997; Ertur and Le Gallo 2003; Patuelli 2007; see, for example, the cases of Germany and Spain), and they often show typical geographical/spatial structures. Secondly, with regard to regional unemployment disparities, policy makers need, in order to correctly target their actions and policies, to understand two aspects of such disparities: (a) the determinants of ‘equilibrium’ unemployment and its variation; and, (b) the region-specific and the cross-regional dynamics of unemployment. On the one hand, the need for an explicit consideration of the existence of spatial interdependence in econometric models, which is consistent with regional science theories asserting the importance of spatial linkages in local economic processes, led to what is nowadays quite a large literature of empirical papers. On the other hand, the temporal perspective of the problem has attracted less attention in spatial models, but should be considered jointly.

"Die geographische Verteilung und die Persistenz (das Beharrungsvermögen von Schocks) von regionalen bzw. lokalen Arbeitslosenquoten in heterogenen Volkswirtschaften (wie bspw. Deutschland) war in den vergangenen Jahren Gegenstand zahlreicher theoretischer und empirischer Studien. Besonderer Augenmerk lag auf den dynamischen Anpassungsprozessen der Arbeitslosigkeit und am durchschnittlichen Grad der Abhängigkeit der gegenwärtigen Arbeitslosigkeit (alternativ auch des GDP) von ihrer Verteilung in der Vergangenheit. Hier präsentieren wir einen neuen ökonometrischen Ansatz zur Analyse der Persistenz der Arbeitslosigkeit, welcher der regionalen Heterogenität und der räumlichen Autokorrelation in den Niveaus und der Dynamik Rechnung trägt. Zunächst diskutieren wir die Verwendung eines räumlichen Filters als Substitut für Fixe Effekte in einem Panel-Schätzansatz. Der räumliche Filter dient als Proxy für regionsspezifische Information (z.B. bzgl. der Ausstattung an natürlichen Ressourcen oder bzgl. der Größe des Marktes im Einzugsgebiet), die üblicherweise in fixen Effekten aufgefangen werden; analoges gilt für die Filtermodellierung der heterogenen Dynamik. Das vorgestellte Verfahren bietet den Vorteil, dass der räumliche Filter -durch parameter-sparsames Auffangen der Information, die räumliche Korrelation erzeugt- die Zahl der Freiheitsgrade erhöht, gleichzeitig auf zeitkonstante räumliche Korrelation in den Residuen kontrolliert und Einsicht gibt in die räumlichen Muster der regionalen Anpassungsprozesse. Es werden verschiedene Untersuchungen bzgl. der regionalen Muster in den heterogenen autoregressiven Parametern dargestellt, die mit Arbeitslosigkeitsdaten für die deutschen Kreise geschätzt werden. In der regionalen Arbeitslosigkeit finden wir einen allgemein hohen, dennoch recht heterogenen Grad an Persistenz." (Autorenreferat) "The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analysing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region-specific information (e.g., the endowment of natural resources, or the size of the "home market") that is usually incorporated in the fixed effects parameters. The same argument applies for the spatial filter modelling of the heterogenous dynamics. The advantages of our proposed procedure are that the spatial filter, by incorporating region-specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time-stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes. We present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive parameters estimated for unemployment data for German NUTS-3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates." (author's abstract)

Nanomaterials are seen as a key technology for the 21st Century, and much is expected of them in terms of innovation and economic growth. They could open the way to many radically new applications, which would form the basis of innovative products. In this context, it seems all the more important for regions to put their own innovation systems in place, and to ensure that they offer a suitable location for such activities in order to benefit from the expected growth. Many regions have already done so by establishing ‘science parks’ and ‘nanoclusters’. As nanomaterials are still in their infancy, both public research institutes and private businesses could play a vital role in the process. This paper investigates what conditions and configurations allow a regional innovation system to be competitive in a cutting-edge technology like nanomaterials. We analyse European Patent Office data at the German district level (NUTS-3) on applications for nanomaterial patents, in order to chart the effects of localised research and development (R&D) in the public and private sector. We estimate two negative binomial models in a knowledge production function framework and include a spatial filtering approach to adjust for spatial effects. Our results indicate that there is a significant positive effect of both public and private R&D on the production of nanomaterial patents. Moreover, we find a positive interaction between them which hints at the importance of their co-location for realising the full potential of an emerging technology like nanomaterials.

In this paper we empirically assess the evolution for the EU regions of both employment and unemployment before and after the Global Crisis. After a review of the literature on the theories and key determinants of regional unemployment, we shall overview the main findings concerning the labour market impact of the Global Crisis. The empirical analysis will initially be carried out at the national level including all EU countries; subsequently, we shall focus on the EU regions (at the NUTS-2 level), in order to detect possible changes in the dispersion of regional unemployment rates after the crisis. Our econometric investigations aim to assess the effect, on labour market performance, of previous developments in regional labour markets time series, as well as the importance of structural characteristics of the labour markets, in terms of the sectoral specialization of the regional economies. In fact, the local industry mix may have played a crucial role in shaping labour market performance in response to the crisis. In addition, we consider further characteristics of the regional labour markets, by including indicators of the level of precarization of labour and of the share of long-term unemployed, as indicators of the efficiency of the local labour markets. From a methodological viewpoint, we exploit eigenvector decomposition-based spatial filtering techniques, which allow us to greatly reduce unobserved variable bias – a significant problem in cross-sectional models – by including indicators of latent unobserved spatial patterns. Finally, we render a geographical description of the heterogeneity influence of past labour market performance over the crisis period, showing that the past performance has a differentiated impact on recent labour market developments.

Immigrant self-employment rates vary considerably across regions in Switzerland. Business ownership seems to provide an alternative to wage labour, where immigrants have to face structural barriers such as the limited knowledge of the local language, or difficulties in fruitfully making use of their own human capital. Despite the historically high unemployment rates with respect to natives, immigrants in Switzerland are less entrepreneurial. It is therefore important to uncover the determinants that may facilitate the transition from the status of immigrant to the one of economic agent. Among others factors, concentration in ethnic enclaves, as well as accumulated labour market experience and time elapsed since immigration, have been associated to higher business ownership rates. In this paper we use a cross- section of 2,490 Swiss municipalities in order to investigate the role played by the ethnic concentration of immigrants, as well as cultural factors, in determining self- employment rates.

In any economic analysis, regions or municipalities should not be regarded as isolated spatial units, but rather as highly interrelated small open economies. These spatial interrelations must be considered also when the aim is to forecast economic variables. For example, policy makers need accurate forecasts of the unemployment evolution in order to design short- or long-run local welfare policies. These predictions should then consider the spatial interrelations and dynamics of regional unemployment. In addition, a number of papers have demonstrated the improvement in the reliability of long-run forecasts when spatial dependence is accounted for. We estimate a heterogeneous-coefficients dynamic panel model employing a spatial filter in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment, as well as a spatial vector-autoregressive (SVAR) model. We compare the short-run forecasting performance of these methods, and in particular, we carry out a sensitivity analysis in order to investigate if different number and size of the administrative regions influence their relative forecasting performance. We compute short-run unemployment forecasts in two countries with different administrative territorial divisions and data frequency: Switzerland (26 regions, monthly data for 34 years) and Spain (47 regions, quarterly data for 32 years).

The geographical distribution and persistence of regional/local unemployment rates in heterogeneous economies (such as Germany) have been, in recent years, the subject of various theoretical and empirical studies. Several researchers have shown an interest in analysing the dynamic adjustment processes of unemployment and the average degree of dependence of the current unemployment rates or gross domestic product from the ones observed in the past. In this paper, we present a new econometric approach to the study of regional unemployment persistence, in order to account for spatial heterogeneity and/or spatial autocorrelation in both the levels and the dynamics of unemployment. First, we propose an econometric procedure suggesting the use of spatial filtering techniques as a substitute for fixed effects in a panel estimation framework. The spatial filter computed here is a proxy for spatially distributed region-specific information (e.g., the endowment of natural resources, or the size of the ‘home market’) that is usually incorporated in the fixed effects parameters. The advantages of our proposed procedure are that the spatial filter, by incorporating region-specific information that generates spatial autocorrelation, frees up degrees of freedom, simultaneously corrects for time-stable spatial autocorrelation in the residuals, and provides insights about the spatial patterns in regional adjustment processes.We present several experiments in order to investigate the spatial pattern of the heterogeneous autoregressive parameters estimated for unemployment data for German NUTS-3 regions. We find widely heterogeneous but generally high persistence in regional unemployment rates.

In this paper, a set of neural network (NN) models is developed to compute short-term forecasts of regional employment patterns in Germany. NNs are modern statistical tools based on learning algorithms that are able to process large amounts of data. NNs are enjoying increasing interest in several fields, because of their effectiveness in handling complex data sets when the functional relationship between dependent and independent variables is not explicitly specified. The present paper compares two NN methodologies. First, it uses NNs to forecast regional employment in both the former West and East Germany. Each model implemented computes single estimates of employment growth rates for each German district, with a 2-year forecasting range. Next, additional forecasts are computed, by combining the NN methodology with Shift-Share Analysis (SSA). Since SSA aims to identify variations observed among the labour districts, its results are used as further explanatory variables in the NN models. The data set used in our experiments consists of a panel of 439 German districts. Because of differences in the size and time horizons of the data, the forecasts for West and East Germany are computed separately. The out-of-sample forecasting ability of the models is evaluated by means of several appropriate statistical indicators.

Economic forecasting is a well-recognized field of study in economists. However, the ability of economists to make forecast has often been questioned. Recently, Lahiry (2011) summarizes this idea, saying that ‘There is skepticism not only by laymen but by most academic economists regarding the true capability of macroeconomic forecasters. The conventional wisdom is that economic forecasters are mere charlatans.’ Despite such negative opinions residing in part of the professional and academic domains of economics, businesses and policymakers need predictions, especially when the world economy is experiencing the Great Recession. In this sense, Kennedy (2003, p. 364) asserted that ‘even if forecasts are poor, there are none better, and perhaps a poor forecast is better than none at all.’ The accuracy of economic forecasting depends on many factors, such as the quality of the data, the adequacy of the chosen method, and computational limitations. When it comes to predictions for (small) geographical/georeferenced entities, the scarcity of disaggregated data has limited the development of methods and the consequent interest in this particular empirical exercise for a long time. In contrast, forecasts at the regional/local level are a necessary complement to forecasts at the national level, allowing, for example, policymakers to allocate public expenditure between regions more efficiently. This idea reinforces the need for econometric techniques for obtaining local predictions, and in particular those where the information on each spatial unit is considered together with the one on its neighbouring units, as the latter ones are most likely to influence the former’s socioeconomic outcomes with their policies due to spatial proximity (e.g., think of interregional commuting) (see, e.g., Baltagi et al., 2012, 2014). Moreover, emerging empirical evidence suggests that, depending on the data set-up available (e.g., in terms of the cross-sectional and time dimensions) different methodological approaches may be desirable (Mayor and Patuelli, 2012, 2014). The aim of this special issue is to provide an outlet for some recent advances in regional forecasting, showing, at the same time, future research directions. Both methodological and applied contributions have been selected, varying methodologically from traditional, timeseries-based panel econometrics to spatial econometrics. The special issue contains nine

This paper is on balancing reactivity and deliberation in cooperative multi-robot systems. We outline which are the challenging issues that has to be addressed to get an effective solution. Then, a balancing method, based on introducing a concept of "Map Focus", is proposed. The framework is also enhanced with "Cooperative Behaviors", useful for robotic cooperation for accomplishing tasks in dynamic environments. We provide the details of the implementation and of the experimental results.

EBIC contrast in correspondence of the etch-pit rows from room temperature microhardness indentations and from polishing scratches are examined with and without successive mechanical and/or thermal treatments. According to the observations clean dislocations show no contrast, contaminated dislocations show contrast which may be treated by assuming suitable boundary conditions consistent with the existence of space charge cylinders around the dislocations. Contrast bands observed in correspondence of etch-pit rows after suitable treatments are related to point defects introduced together with the dislocations. Relative contrast of screw and edge character dislocations is deduced from the different visibility of half-loop arms. EBIC-Kontrast der Atzgrubenreihen von Zimmertemperatur-Mikroharteeindrucken und von Polierkratzern werden mit und ohne aufeinanderfolgende mechanische und/oder thermische Nachbehandlungen untersucht. Nach den Beobachtungen zeigen reine Versetzungen keinen Kontrast, kontaminierte Versetzungen zeigen Kontrast, der mit der Annahme geeigneter Randbedingungen konsistent mit der Existenz von Raumladungszylindern um die Versetzung behandelt werden kann. Kontrastbander, die in den Atzgrubenreihen nach geeigneten Behandlungen beobachtet werden, lassen sich Punktdefekten zuordnen, die zusammen mit den Versetzungen eingefuhrt werden. Der relative Kontrast der Versetzungen mit Schrauben- und Stufencharakter wird aus der unterschiedlichen Sichtbarkeit von Halbschleifenarmen abgeleitet.

This study analyses the effect of population ageing in the ten federal states (Lander) of West Germany during the period ranging from 1970 to 2014. To that aim, we estimate a Bayesian VAR model including wages, labour force participation rates, unemployment rates and the share of population above 65 years. Structural regional labour market shocks are identified employing a combination of zero and sign restrictions. Our findings suggest that shocks to population ageing on impact, increase the participation rate, the unemployment rate and decrease wages. However, the above effects on participation and unemployment are short-lasting and after a few periods their response is inverted. Our variance decomposition analysis reveals that the most relevant channel of impact on the economy implied by ageing is that of wages.

Literature on the formation of intention toward entrepreneurship in adolescents has focused on either parental (vertical) transmission of social capital or network effects from peers or neighbours (horizontal). Considering the simultaneous effect of parents, peers, and neighbours, we suggest that such three levels identify a mechanism whereby the individual perception of their importance interacts with their objective characteristics. With a unique dataset for second-year high-school adolescents in the Italian city of Palermo, and employing Logit and 3SLS methods, we find evidence for a strong parental effect and for secondary peer (peers) effects on student intention. We also detect clear endogenous effects from the neighbourhood and the overall context. Moreover, entrepreneurship is confirmed to be perceived, even by high-school students, as a buffer for possible unemployment and social mobility.