Is technological change really skill biased? Evidence from the introduction of ICTs on the textile sector (1980-2000)

This paper investigates the effects of the introduction of information and communication technologies (ICTs) on the skills of a workforce. Using micro-data collected from workers in the textile sector, we analyse whether the introduction of ICTs has modified workers’ tasks, so that higher skills and longer training periods than before are necessary. Our survey has shown that ICTs i) have replaced unskilled labour in some cases and skilled labour in others; ii) have changed workers’ tasks in some cases but not in others; and finally, iii) have brought about an increase in skills for only a small number of occupations. This empirical evidence does not confirm the hypothesis that technological change, and in particular change introduced by ICTs, is necessarily skill biasedTechnological change, skill bias, textile industry

Crossing the hurdle: the determinants of individual scientific performance

An original cross sectional dataset referring to a medium sized Italian university is implemented in order to analyze the determinants of scientific research production at individual level. The dataset includes 942 permanent researchers of various scientific sectors for a three year time span (2008 - 2010). Three different indicators - based on the number of publications or citations - are considered as response variables. The corresponding distributions are highly skewed and display an excess of zero - valued observations. In this setting, the goodness of fit of several Poisson mixture regression models are explored by assuming an extensive set of explanatory variables. As to the personal observable characteristics of the researchers, the results emphasize the age effect and the gender productivity gap, as previously documented by existing studies. Analogously, the analysis confirm that productivity is strongly affected by the publication and citation practices adopted in different scientific disciplines. The empirical evidence on the connection between teaching and research activities suggests that no univocal substitution or complementarity thesis can be claimed: a major teaching load does not affect the odds to be a non-active researcher and does not significantly reduce the number of publications for active researchers. In addition, new evidence emerges on the effect of researchers administrative tasks, which seem to be negatively related with researcher's productivity, and on the composition of departments. Researchers' productivity is apparently enhanced by operating in department filled with more administrative and technical staff, and it is not significantly affected by the composition of the department in terms of senior or junior researchers.Comment: Revised version accepted for publication by Scientometric

Ninety years of publications in Economic History: evidence from the top five field journals (1927-2017)

The growing appeal of the long run perspective among economists and the fiftieth anniversary of the of the publication of the Conrad and Meyer article (1958), which signed the Cliometric Revolution, have attracted a lot of interest on the origin and the development of Economic history. This paper explores the evolution of the field with a new articulated database of all the 6,516 articles published in five journals (Economic History Review, Journal of Economic History, Explorations in Economic History, European Review of Economic History and Cliometrica) from their establishment to 2017. We show that these journals are the most important in the field, with a wide influence also outside it. Our main results are that the Cliometric Revolution took quite a long time to fully display its effects, which became evident only in the 1990s, when personal computer and software packages became available. Finally, as for the last two decades, we find that the process of integration of economic history into economics is, so far, slower than previously suggested and limited to US. On the other hand, the most striking and neglected change is the overall success of Continental European scholars within the field. Are these changes the harbinger of a new divergence between the two shores of the Atlantic with the rise of a new paradigm based on the “Historical economics” approach? It is too early to tell

Fine-grained classification of journal articles by relying on multiple layers of information through similarity network fusion: the case of the Cambridge Journal of Economics

This paper explores the possibility of classifying journal articles by exploiting multiple information sources, instead of relying on only one information source at a time. In particular, the Similarity Network Fusion (SNF) technique is used to merge the different layers of information about articles when they are organized as a multiplex network. The method proposed is tested on a case study consisting of the articles published in the Cambridge Journal of Economics. The information about articles is organized in a two-layer multiplex where the first layer contains similarities among articles based on the full-text of articles, and the second layer contains similarities based on the cited references. The unsupervised similarity network fusion process combines the two layers by building a new single-layer network. Distance correlation and partial distance correlation indexes are then used for estimating the contribution of each layer of information to the determination of the structure of the fused network. A clustering algorithm is lastly applied to the fused network for obtaining a classification of articles. The classification obtained through SNF has been evaluated from an expert point of view, by inspecting whether it can be interpreted and labelled with reference to research programs and methodologies adopted in economics. Moreover, the classification obtained in the fused network is compared with the two classifications obtained when cited references and contents are considered separately. Overall, the classification obtained on the fused network appears to be fine-grained enough to represent the extreme heterogeneity characterizing the contributions published in the Cambridge Journal of Economics

Is there cross-fertilization in macroeconomics? A quantitative exploration of the interactions between DSGE and macro agent-based models

This paper compares Dynamic Stochastic General Equilibrium (DSGE) and Macro Agent-Based Models(MABMs) by adopting mainly a distant reading perspective. A set of 2,299 papers is retrieved from Scopus byusing keywords related to MABM and DSGE domains. The interactions between the two streams of DSGE andMABM literature are explored by considering a social axis (co-authorship network), and an intellectual axis (citedreferences and bibliographic coupling). The analysis gave results that are neither consistent with a unitarystructure of macroeconomics, nor with a simple dichotomic structure of alternative paradigms and separatedacademics communities. Indeed, the co-authorship network shows that DSGE and MABM form fragmentedcommunities still belonging to two different larger MABM and DSGE communities rather neatly separated.Collaboration insists mainly inside the smaller groups and inside each of the two larger DSGE and MABMcommunities. Moreover, the co-authorship network analysis does not show evidence of systematic collaborationbetween MABM and DSGE authors. From an intellectual point of view, data show that DSGE and MABM articlesrefer to two different sets of bibliographic references. When a measure of paper-similarity is adopted, it appearsthat DSGE literature is fragmented in 4 groups while the MABM articles are clustered together in a unique group.Hence, DSGE approach is less monolithic than at the time of the New Synthesis: indeed, a large and a growingliterature has developed at the margins of the core DSGE approach which includes elements of heterogeneousagent modelling, social interactions, experiments, expectations formation, learning etc. The analysis gave noevidence of cross-fertilization between DSGE and MABM literature whilst it rather suggests a totallydissymmetric influence of DSGE over MABM literature, i.e., only MABM modelers look at DSGE but not vice-versa. The paper questions the capacity of the current dominant approach to benefit from cross-fertilization

The secret to job satisfaction is low expectations: How perceived working conditions differ from actual ones

Working conditions exert a major influence on accidents and illnesses at work as well as on job satisfaction and health, yet very little research has examined the determinants of working conditions. By exploiting the Italian Labour Force Survey, this paper provides evidence on the underlying factors affecting working conditions. It provides a behavioural interpretation of the results, which stems from the discrepancy between actual and expected working conditions. Workers declare their perceived working conditions influenced by the difference between the actual and the expected working conditions. Variables concerning personal characteristics, such as gender, education and being employed in the first job, shift expectations about working conditions and accordingly perceived working conditions. On the contrary, variables related to work characteristics, such as working full time, with shifts and in a large place, affect actual and thus perceived working conditions (negatively)

Machine learning of microscopic structure-dynamics relationships in complex molecular systems

In many complex molecular systems, the macroscopic ensemble's properties are controlled by microscopic dynamic events (or fluctuations) that are often difficult to detect via pattern-recognition approaches. Discovering the relationships between local structural environments and the dynamical events originating from them would allow unveiling microscopic level structure-dynamics relationships fundamental to understand the macroscopic behavior of complex systems. Here we show that, by coupling advanced structural (e.g., Smooth Overlap of Atomic Positions, SOAP) with local dynamical descriptors (e.g., Local Environment and Neighbor Shuffling, LENS) in a unique dataset, it is possible to improve both individual SOAP- and LENS-based analyses, obtaining a more complete characterization of the system under study. As representative examples, we use various molecular systems with diverse internal structural dynamics. On the one hand, we demonstrate how the combination of structural and dynamical descriptors facilitates decoupling relevant dynamical fluctuations from noise, overcoming the intrinsic limits of the individual analyses. Furthermore, machine learning approaches also allow extracting from such combined structural/dynamical dataset useful microscopic-level relationships, relating key local dynamical events (e.g., LENS fluctuations) occurring in the systems to the local structural (SOAP) environments they originate from. Given its abstract nature, we believe that such an approach will be useful in revealing hidden microscopic structure-dynamics relationships fundamental to rationalize the behavior of a variety of complex systems, not necessarily limited to the atomistic and molecular scales