A machine learning approach to economic complexity based on matrix completion

Gnecco, Giorgio; Nutarelli, Federico; Riccaboni, Massimo

doi:10.1038/s41598-022-13206-0

Cited by 9 publications

(21 citation statements)

References 13 publications

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“…A final remark can be made about the trade-off between the prediction capability and the biasedness of the MC method (see also the discussion on this issue presented in the Supplementary Material of Gnecco et al 2022). According to Foucart et al (2017) and Ma and Chen (2019), biasedness in MC depends, for instance, on how unobserved entries are selected (in our case, only entries associated with selected skills are obscured, and for the professions for which this occurs, all the entries associated with such skills are actually obscured).…”

Section: Declarationsmentioning

confidence: 99%

Can Machines Learn Creativity Needs? An Approach Based on Matrix Completion

2022

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Technological progress has been recently associated with a crowding-out of cognitive-skill intensive jobs in favour of jobs requiring soft skills, such as ones related to social intelligence, flexibility and creativity. The nature of soft skills makes them hardly replaceable by machine work and among subsets of soft skills, creativity is one of the hardest to define and codify. Therefore, creativity-intensive occupations have been shielded from automation. Given this framework, our study contributes to a nascent field on interdisciplinary research to predict the impact of artificial intelligence on work activities and future jobs using machine learning. In our work, we focus on creativity, starting from its possible definitions, then we get significant insights on creativity patterns and dynamics in the Italian labour market, using a machine learning approach. We make use of the INAPP-ISTAT Survey on Occupations (ICP), where we identify 25 skills associated with creativity. Then, we apply matrix completion—a machine learning technique which is often used by recommender systems—to predict the average importance levels of various creative skills for each profession, showing its excellent prediction capability for the specific problem. We also find that matrix completion typically underestimates the average importance levels of soft skills associated with creativity, especially in the case of professions belonging to the major group of legislators, senior officials and managers, as well as intellectual professionals. Conversely, overestimates are typically obtained for other professions, which may be associated with a higher risk of being automated.

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Section: Declarationsmentioning

confidence: 99%

Can Machines Learn Creativity Needs? An Approach Based on Matrix Completion

2022

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“…For each instance, the best value of was found by minimizing a suitable error on the validation set, whereas the final performance was evaluated on the test set. Further details on the MC optimization problem (1) and on the Soft Impute algorithm can be found in Metulini et al (2022) and in the Supplementary Material of Gnecco, Nutarelli, and Riccaboni (2022).…”

Section: Matrix Completionmentioning

confidence: 99%

The emergence of social soft skill needs in the post COVID-19 era

2023

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Social soft skills are crucial for workers to perform their tasks, yet it is hard to train people on them and to readapt their skill set when needed. In the present work, we analyze the possible effects of the COVID-19 pandemic on social soft skills in the context of Italian occupations related to 88 economic sectors and 14 age groups. We leverage detailed information coming from ICP (i.e. the Italian equivalent of O*Net), provided by the Italian National Institute for the Analysis of Public Policy, from the microdata for research on the continuous detection of labor force, provided by the Italian National Institute of Statistics (ISTAT), and from ISTAT data on the Italian population. Based on these data, we simulate the impact of COVID-19 on workplace characteristics and working styles that were more severely affected by the lockdown measures and the sanitary dispositions during the pandemic (e.g. physical proximity, face-to-face discussions, working remotely). We then apply matrix completion—a machine-learning technique often used in the context of recommender systems—to predict the average variation in the social soft skills importance levels required for each occupation when working conditions change, as some changes might be persistent in the near future. Professions, sectors, and age groups showing negative average variations are exposed to a deficit in their social soft-skills endowment, which might ultimately lead to lower productivity.

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“…Again, introducing a new IS may raise uncertainties that cause system users to doubt its usefulness. Notably, the uncertainties may be driven by the perceived complexity of the new system or doubts about its effectiveness (Gnecco et al, 2022; Al‐Okaily, 2021). Based on PDT, doubts about system usefulness are eliminated when system users are involved in the design process (Gnecco et al, 2022; Al‐Okaily, 2021; Choe, 1998).…”

Section: Literature Reviewmentioning

confidence: 99%

Do accountants' participation in accounting information systems design enhance perceived accounting information quality: Evidence from Ghana

Aboagye‐Otchere

Nartey

Enusah

et al. 2023

E J Info Sys Dev Countries

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This study examines how accountants' participation in accounting information system (AIS) design can improve their perceptions of the quality of the information provided by the system. An empirical survey of 333 chartered accountants in Ghana was used and the research model was tested using covariance‐based structural equation modeling. The results indicate that accountants' participation in AIS design is positively associated with perceived accounting information quality. Additionally, autonomous motivation was found to mediate the relationship between accountants' participation and perceived information quality. In contrast, accountants' perceived usefulness of accounting information does not mediate the relationship between participation in AIS design and perceived accounting information quality. This suggests that accounting information quality is enhanced when accountants participate in AIS design hence play a critical role in information system design. However, accounting information quality is not enhanced through perceived usefulness of accounting information.

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A machine learning approach to economic complexity based on matrix completion

Cited by 9 publications

References 13 publications

Can Machines Learn Creativity Needs? An Approach Based on Matrix Completion

Can Machines Learn Creativity Needs? An Approach Based on Matrix Completion

The emergence of social soft skill needs in the post COVID-19 era

Do accountants' participation in accounting information systems design enhance perceived accounting information quality: Evidence from Ghana

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