LEADER 05531nam 22009013 450 001 9910598264403321 005 20241230084506.0 035 $a(CKB)4920000000812363 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/83118 035 $a(MiAaPQ)EBC31860546 035 $a(Au-PeEL)EBL31860546 035 $a(oapen)doab82435 035 $a(oapen)doab82290 035 $a(oapen)doab83027 035 $a(oapen)doab92352 035 $a(oapen)doab92240 035 $a(oapen)doab92479 035 $a(oapen)doab83551 035 $a(EXLCZ)994920000000812363 100 $a20241230d2021 uy 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aASA 2021 Statistics and Information Systems for Policy Evaluation $eBOOK of SHORT PAPERS of the on-Site Conference 205 $a1st ed. 210 $aFlorence$cFirenze University Press$d2021 210 1$aFlorence :$cFirenze University Press,$d2021. 210 4$dİ2021. 215 $a1 electronic resource (6 p.) 225 1 $aProceedings e report 311 08$a9788855184618 311 08$a885518461X 330 $aThe paper describes an exercise of classification of a subset of five-digit categories of the 2007 ATECO classification system of economic activities. The analysis is grounded on the hypothesis that economic sectors can be clustered according to the competency level required to human resources recently working in industries or services in Italy. The analysis may be useful to evaluate a possible relationship between economic development and education. The analysis consisted of a mapping and then a clustering of the Ateco categories according to the between-distribution dissimilarity of any possible couple of categories. The basic idea was to highlight the Ateco categories that require either more education than others or more education and working experience (human capital) than others, pinpointing, in particular, the categories that require larger percentages of tertiary education and those residing close to territorial hubs. The competency level was measured with a combination of educational attainment and in-service experience of Italian employees, as defined by Istat, the Italian statistical institute. The employees? educational level was evaluated with the frequency distribution of five (ordinal) classes of education of people employed in 2018 and 2019 in both private and public establishments and offices; the working experience with a logarithmic transform of the average number of in-service years of employees. The analysis highlighted both a sort of input-related classification of the economy and a supply-side classification of the labour market. The results are in line with the theory of the existence of a cluster of creative companies residing close to territorial hubs. 410 0$aProceedings e Report 517 $aChapter Assessment of agricultural productivity change at country level 517 $aChapter Sizing & Allocation in Labour Market 517 $aChapter Reducing inconsistency in AHP by combining Delphi and Nudge theory and network analysis of the judgements 517 $aChapter Prediction of wine sensorial quality 517 $aChapter Determinants of the transition to upper secondary school 517 $aChapter Motivation of basketball players 517 $aChapter Sustainable Innovation 517 $aChapter Gender and Information and Communication Technologies interest 517 $aChapter Patient-generated evidence in Epidermolysis Bullosa 517 $aChapter Unsupervised spatial data mining for the development of future scenarios 517 $aChapter The role of the extra-man play actions in elite water polo matches 517 $aChapter Clustering students according to their proficiency 517 $aChapter Development of an innovative methodology to define patient-designed quality of life 517 $aChapter Media and fake news 517 $aChapter Emergency remote teaching 517 $aChapter The effectiveness of marketing tools in a consumer goods market in Italy during the Great Recession 517 $aChapter Understanding the sensory characteristics of edible insects to promote entomophagy 517 $aChapter Measuring the effectiveness of COVID-19 containment policies in Italian regions 517 $aChapter Linear regression pathmox segmentation tree 517 $aChapter Tourism of Italians in Italy through crisis and development 517 $aChapter The top candidate is an intermediate one 517 $aChapter Big data analysis and labour market 517 $aChapter Unemployment dynamics in Italy 517 $aChapter Total Process Error framework 517 $aASA 2021 statistics and information systems for policy evaluation 606 $aSocial research & statistics$2bicssc 610 $aCategory Mapping 610 $aAteco 2007 610 $aClustering economic categories 610 $aHuman capital 610 $aCreative companies 615 7$aSocial research & statistics 700 $aBertaccini$b Bruno$0434293 701 $aFabbris$b Luigi$0438107 701 $aPetrucci$b Alessandra$0442750 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910598264403321 996 $aASA 2021 Statistics and Information Systems for Policy Evaluation$94305219 997 $aUNINA