The recent economic literature is animating a lively debate on the effects of digitalization on employment, ending up claiming that routinary jobs are more exposed to being substituted by machines (Cetrulo et al. 2020). In this framework, the existence and the magnitude of the substitution effect depend on the tasks involved in each job: in particular, routine tasks that are easier to codify are more likely to be automated, while non-routinary tasks that comprise a larger use of soft skills might be less exposed to this risk (Cirillo et al. 2021). Among subsets of soft skills, creativity is one of the hardest to be encoded, thus highly creative occupations tend to suffer less from the introduction of machines and digital systems. In this paper, we exploit similarities in the Italian occupational structure and implement a recently developed machine learning technique to predict the importance levels of creative skills employed in each occupation and to identify the creativity needs of occupations. We find that professions belonging to the major group of legislators, senior officials and managers, as well as intellectual professionals have a greater surplus of creative soft skills. Nevertheless, creativity patterns and trends are extremely heterogeneous and idiosyncratic and this means that there is no clear separation among technical and intellectual skills with respect to creative soft skills. Our study contributes to a nascent field on interdisciplinary research to predict the impact of AI on work activities and future jobs using machine learning (see also Poulakias 2021). Our results suggest that creativity gaps in the labor market are so peculiar that training might be tailored specifically for every occupation, to reduce its risk of being automated. In Italy, the policy response to the challenges posed by digitalization has been the extension of employment protection and short-time work to small firms that were not previously covered (Basso et al. 2022). Looking at the future, especially after recovering from the current Covid-19 pandemic, policy makers should take into account the possibility of better targeting the policies to sectors, occupations and firms as their patterns of sets of soft skills, creativity in particular, are highly heterogeneous between and within sectors. Skills demand in the country is increasing and changing rapidly, yet Italy is struggling to make the transition to a new digitalized economy where soft skills play a central role (OECD 2017). Making progresses will require that the country fosters both the demand and the supply of skills: for example, improving the quality of schooling that will, in time, translate into high-qualified workers, or encouraging and facilitating the transition to digital technologies for firms, so they can compete globally. In general, a wider strategy and a coordinated approach to skills policies is desirable to get Italy back on a growing path.
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We provided evidence for the early divergence of Italian groups dating back to the Late Glacial and for Neolithic and distinct Bronze Age migrations having further differentiated their gene pools. We inferred adaptive evolution at insulin-related loci in people from Italian regions with a temperate climate, while possible adaptations to pathogens and ultraviolet radiation were observed in Mediterranean Italians. Some of these adaptive events may also have secondarily modulated population disease or longevity predisposition.
According to the DIND test, RIMS2 and PCLO genes involved in insulin exocytosis (Additional file 1: Supplementary Results) were found to be subjected to positive selection in both N_ITA and S_ITA clusters (Additional file 1: Figure S5, Table S3). A gene network belonging to the Mucin type O-glycan biosynthesis pathway and formed by loci encoding for mucins, a family of glycosylated proteins that constitute the main protective barrier on mucosal surfaces and cellular membranes by preventing pathogens binding by steric hindrance [61], was instead supposed to have adaptively evolved only in S_ITA (Additional file 1: Figure S6, Table S3, Supplementary Results).
According to the nSL test, a gene network ascribable to the insulin secretion pathway, but made up of different loci with respect to those pointed out by DIND scores, characterized the N_ITA cluster (Additional file 1: Figure S5, Table S4). Among these genes, ADCY2, ADCY3, ADCY9, and GNAS are known to play a role in the regulation of lipolysis at the level of the adipose tissue, thermogenesis, and glucagon signaling (Additional file 1: Supplementary Results), with especially adenylate cyclase (ADCY) genes showing the largest number of connections in the network and participating to the longevity regulating pathway as well. Moreover, variants at two loci belonging to such a network and encoding for components of calcium voltage-gated channels (i.e., CACNA1C and CACNA1D) were previously reported to be involved in the development of type II diabetes (T2D) (Additional file 1: Supplementary Results). nSL results obtained for S_ITA corroborated those based on the DIND statistics as regards a gene network belonging to the mucin type O-glycan biosynthesis pathway (Additional file 1: Figure S6, Table S4) and further indicated genes from the basal cell carcinoma pathway as subjected to positive selection (Additional file 1: Figure S7, Table S4). Interestingly, most of these latter loci encode for frizzled G protein-coupled receptors (FZD) and Wnt glycoproteins that play a role in melanogenesis and participate in the mTOR signaling pathway as well (Additional file 1: Supplementary Results).
Events of balancing selection at two different gene networks were inferred for both the Italian population clusters (Additional file 1: Table S5). In detail, a network was found to be composed by several ADCY genes, some of which (i.e., ADCY2 and ADCY9) were the same putatively subjected also to positive selection in N_ITA, as well as by mitogen-activated protein kinase (MAPK) genes (i.e., MAPK1/ERK, MAPK8/JNK, and MAPK11/P38). These loci are known to play a role especially in the longevity regulating and FoxO signaling pathways (Additional file 1: Supplementary Results). The second network was instead made up of glycerol phosphate acyltransferase (GPAT/AGPAT/MBOAT), diacylglycerol kinase (DGKA), and lipid phosphatase (LPIN/PLPP) genes regulating the metabolism of glycerolipids, along with phospholipase (e.g., PLA2G/PLB) loci involved especially in the arachidonic acid metabolism (Additional file 1: Supplementary Results). Two gene networks were found to characterize exclusively the N_ITA group, being composed of aldehyde dehydrogenase (ALDH) genes important for glycolysis/gluconeogenesis and of protein kinase C (PRKC) and phospholipase (PLCG/PLCB) loci participating to the AGE-RAGE signaling in diabetic complications, glucagon signaling, and insulin resistance pathways (Additional file 1: Table S5). Finally, a further network turned out to be subjected to balancing selection in the S_ITA cluster, including PLA2G genes involved in the metabolism of arachidonic acid and MAPK1 and GRM1 loci that play a role in the FoxO signaling pathway (Additional file 1: Table S5, Supplementary Results).
In detail, selective events able to modulate insulin exocytosis from pancreatic beta cells were supposed to have occurred in the common ancestors of N_ITA and S_ITA clusters (Additional file 1: Figure S5, Table S3, Supplementary Results). Events of positive selection presumably more recent were instead found to characterize exclusively people from N_ITA, being distributed among ten genes that play a role at different levels of the signaling cascade leading to insulin secretion and that regulate key processes contributing to glucose homeostasis (Additional file 1: Figure S5, Table S4, Supplementary Results). Interestingly, the most pervasive signature of selection was observed at ADCY genes (especially ADCY3), which are fundamental for controlling thermogenesis [45] and adiposity [46, 47] and have been proven to modulate susceptibility to T2D and obesity (Additional file 1: Supplementary Results). In line with these findings, analyses testing for balancing selection pointed to adaptive events specific of the N_ITA cluster and mediated by ALDH genes involved in glycolysis and gluconeogenesis or by PRKC and PLCG/PLCB loci playing a role in pathological mechanisms underlying insulin resistance and the onset of diabetic complications (Additional file 1: Table S5).
Adaptive events evolved by ancestors of N_ITA/S_ITA clusters and their health implications for present-day Italians. The putative selective pressures having plausibly prompted local adaptations are displayed on the left, while biological processes subjected to natural selection are reported on the map along with their impact on present-day disease susceptibility. Distribution of biological adaptations having the potential to modulate the longevity phenotype (e.g., involving the mTOR signaling, arachidonic acid metabolism, and FoxO signaling pathways) in the overall Italian population, but especially in people from Southern Italy, is represented by the arrow on the right. Putative selective pressures, biological processes, and distribution of adaptations potentially modulating longevity are color-coded as follows: N_ITA, blue; S_ITA, red be457b7860
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