Escape from parents’ basement? Post COVID-19 scenarios for the future of youth employment in Italy

Titolo Rivista QUADERNI DI ECONOMIA DEL LAVORO
Autori/Curatori Giulia Parola
Anno di pubblicazione 2021 Fascicolo 2020/111 Lingua Inglese
Numero pagine 21 P. 51-71 Dimensione file 257 KB
DOI 10.3280/QUA2020-111003
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As one of the worst-affected European countries by COVID-19 experiences a slow return to normality, all eyes are on what lies ahead. The labor market implications generated by weeks of drastic lockdown might be far-reaching, and uncertainty about the future of jobs in Italy increases. In this time of significant changes, fleshing out a range of possible future developments could help mitigate part of the uncertainty by guiding decisions at an institutional level. This research employs an intuitive logics approach (IL) to scenario development, which is particularly suited to support decision-making (Kosow and Gaßner, 2008) by deriving the implications of different courses of action. Following the IL method, this study appoints 17 experts to qualify the driving forces of youth employoment in Italy according to their level of uncertainty and impact. The results of this paper are four plausible scenarios derived from the intersection of the two highly uncertain and impactful driving forces most likely to be affected by COVID-19: the state of the economy and a skills mismatch between labor demand and supply. Although all four scenarios foresee a negative impact of the crisis on the labor market, this work shows how the government, its agencies, and supranational institutions might mitigate adverse effects by designing and implementing youth skills interventions. This research contributes to the efforts of the academic community in response to the current emergency by improving our understanding of policy options in the Italian labor market context.

Mentre uno dei paesi europei più colpiti dal COVID-19 vive un lento ritorno alla normalità, tutti gli occhi sono puntati su ciò che ci aspetta. Le implicazioni generate da settimane di drastico lockdown sul mercato del lavoro potrebbero essere di vasta portata e l’incertezza sul futuro dell’occupazione in Italia aumenta. In questo periodo di cambiamenti significativi, l’elaborazione di possibili sviluppi futuri può contribuire a mitigare parte dell’incertezza guidando le decisioni a livello istituzionale. Questa ricerca utilizza un approccio per la costruzione di scenari chiamato intuitive logics (IL). Questo metodo è particolarmente adatto a supportare il processo decisionale (Kosow e Gaßner, 2008) poichè aiuta a derivare le implicazioni di diverse linee d’azione. Seguendo il metodo IL, questo studio seleziona 17 esperti per qualificare le forze trainanti dell’occupazione giovanile in Italia in base al loro livello d’incertezza e d’impatto. I risultati di questa ricerca sono quattro scenari plausibili derivati dall'intersezione delle due forze trainanti altamente incerte e incisive che più probabilmente saranno influenzate dal COVID-19: lo stato dell’economia e lo squilibrio delle competenze tra domanda e offerta di lavoro. Sebbene tutti e quattro gli scenari prevedano un impatto avverso della crisi sul mercato del lavoro, quest’articolo mostra come il governo, le sue agenzie e le istituzioni sovranazionali possano mitigare gli effetti negativi pianificando e attuando interventi mirati alle competenze dei giovani. Questa ricerca contribuisce agli sforzi della comunità accademica in risposta all’attuale emergenza migliorando la nostra comprensione delle opzioni politiche nel contesto del mercato del lavoro italiano.

Keywords:Coronavirus, COVID-19, Italia, mercato del lavoro, NEETs, (dis)occupazione giovanile.

  1. Amara R. (1981). The futures field: Searching for definitions and boundaries. The Futurist, 15(1): 25-29.
  2. Annunziata M. (2018, April 14). Twenty years and nothing to show for it: Italy’s broken economic model. Forbes. -- Retrieved April 30, 2019 from https://www.forbes.com/sites/marcoannunziata/2018/04/14/twenty-years-and-nothing-to-show-for-it-italys-broken-economic-model/#42306e3c11a3.
  3. Kosow H. and Gaßner R. (2008). Methods of future and scenario analysis: overview, assessment, and selection criteria. Bonn: Deutsches Institut für Entwicklungspolitik.
  4. Baldwin R. (2019, January 31). Globalisation, automation and the history of work: Looking back to understand the future. VOX CEPR Policy Portal. Retrieved May 20, 2020, -- from https://voxeu.org/content/globalisation-automation-and-history-work-looking-back-understand-future.
  5. Blix M. (2017). The effects of digitalisation on labour market polarisation and tax revenue. CESifo Forum, 18(4): 09-14.
  6. Bonollo B. (2013). The rising NEET phenomenon in Italy: an empirical study of the SHIW sample (Unpublished master's thesis). Università Ca' Foscari Venezia. -- Retrieved July 6, 2019, from http://dspace.unive.it/bitstream/handle/10579/3671/840255-1174639.pdf?sequence=2.
  7. Bradfield R. and El-Sayed H. (2009). Four scenarios for the future of the pharmaceutical industry. Technology Analysis and Strategic Management, 21(2): 195-212.
  8. Cadeo R. (2017, January 9). Badanti, una categoria in crescita costante [Caregivers, a group in constant growth]. Il Sole 24 Ore. Retrieved June 6, 2019, -- from https://www.ilsole24ore.com/art/badanti-categoria-crescita-costante-AD68URSC.
  9. Celi G. and Testa G. (2016). The effects of globalisation on regional migration in Italy. Working Paper No. 85, University of Sussex, Sussex Centre for Migration Research.
  10. CERVED (2020, March). L’impatto del COVID-19 sui settori e sul territorio [The impact of COVID-19 on the sectors and on the territory]. -- Retrieved May 20, 2020 from https://know.cerved.com/wp-content/uploads/2020/03/Cerved-Industry-Forecast_COVID19-.pdf.
  11. CGIa (2016, November 16). Il lavoro nero sottrae 37 Mld di tasse al fisco [Illegal work costs taxpayers 37 billion]. -- Retrieved May 2, 2019 from http://www.cgiamestre.com/il-lavoro-nero-sottrae-37-mld-di-tasse-al-fisco/.
  12. Derbyshire J. (2017). Potential surprise theory as a theoretical foundation for scenario planning. Technological Forecasting and Social Change, 124, 77-87.
  13. Derbyshire J. and Wright G. (2017). Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation. International Journal of Forecasting, 33(1), 254-266.
  14. EUROSTAT (2020). Statistics on young people neither in employment nor in education or training. Luxembourg: EUROSTAT.
  15. Ghignoni E., Naddeo P., Pappadà G., Parello C.P. (2009). Quantitative and qualitative analysis of labour market integration of young people. Quaderni di Economia del Lavoro, 90: 55-128.
  16. Ghignoni E. (2016). The ‘great escape’ from Italian Universities: Do labour market recruitment channels matter? Quaderni di economia del lavoro, 106(2): 49-75.
  17. Goharinezhad S., Maleki M., Baradaran H. R., and Ravaghi H. (2016). Futures of elderly care in Iran: A protocol with scenario approach. Medical Journal of the Islamic Republic of Iran, 30(1): 834-842.
  18. ILO (2020). ILO Monitor: COVID-19 and the world of work. Second edition. Geneva: ILO Publishing.
  19. ISTAT (2018). Mobilitá interna e migrazioni internazionali della popolazione residente [Internal mobility and international migration of the resident population]. Rome: ISTAT.
  20. Jungermann H., and Thuring M. (1987). The use of mental models for generating scenarios. In: Wright G. and Ayton P., eds., Judgmental Forecasting, pp. 245-266. London: Wiley.
  21. Keserü I., Coosemans T. and Macharis C. (2019). Building scenarios for the future of transport in Europe: The Mobility4EU approach. In: Muller B. and Meyer G., eds., Towards user-centric transport in Europe: Challenges, solutions and collaborations, pp. 15-30. (Lecture Notes in Mobility). Cham: Springer International Publishing.
  22. Licursi S. (2018). Graduation after time and peer tutoring. Italian Journal of Sociology of Education, 10(2): 90-109.
  23. Loi D., Patrizio M. and Samek M. (2017). Young women's unemployment in EU. Briefing for the European Parliament Committee on Women’s Rights and Gender Equality of the European Parliament Legal Affairs.
  24. Mackay R.B. and Stoyanova V. (2017). Scenario planning with a sociological eye: Augmenting the intuitive logics approach to understanding the Future of Scotland and the UK. Technological Forecasting and Social Change, 124, 88-100.
  25. Malaska P., Malmivirta M., Meristö T. and Hansén S.-O. (1984). Scenarios in Europe - Who uses them and why?. Long Range Planning, 17(5), 45-49.
  26. Martins P.P.P., Boaventura J.M.G., Fischmann A.A., Costa B.K. and Spers R.G. (2012). Scenarios for the Brazilian road freight transport industry. Foresight, 14(3), 207-224.
  27. Mascherini M. and Ledermaier S. (2016). Exploring the diversity of NEETs. Report for the European Foundation for the Improvement of Living and Working Conditions (Eurofound). Luxembourg: Publications Office of the European Union.
  28. Montanari M., Pinelli D. and Torre R. (2015). From tertiary education to work in Italy: a difficult transition. ECFIN Country Focus: economic analysis from the European Commission’s Directorate-General for Economic and Financial Affairs, 12(5).
  29. Nuti V. (2020, April 5). App anticontagio e test rapidi, il “Piano sanitario” del ministro Speranza [Contact-tracing app and rapid tests, the “Health Plan” of Minister Speranza]. Il Sole 24 Ore. -- Retrieved June 29, 2020, from https://www.ilsole24ore.com/art/app-anticontagio-e-test-rapidi-piano-sanitario-ministro-speranza-ADmf0JI?refresh_ce=1.
  30. OECD (2015). OECD Skills Outlook 2015: youth, skills and employability. Paris: OECD Publishing.
  31. OECD (2019). The future of work: How does Italy compare? OECD Employment Outlook 2019. Paris: OECD Publishing.
  32. Ozguler V.C. (2012). Globalization and youth employment. Quaderni di Economia del Lavoro, 97: 39-58.
  33. Pastore F. (2017). Why so slow? The school-to-work transition in Italy. IZA Institute of Labour Economics Discussion Papers, No. 10767. Bonn: SpringerOpen.
  34. Phadnis S., Caplice C., Singh M. and Sheffi Y. (2014). Axiomatic foundation and a structured process for developing firm-specific intuitive logics scenarios. Technological Forecasting and Social Change, 88: 122-139.
  35. Quintano C., Mazzocchi P., Rocca A. (2018). The determinants of Italian NEETs and the effects of the economic crisis. Genus, 74(1).
  36. Quintini G. and Martin S. (2014). Same same but different: school-to-work transitions in emerging and advanced economies. OECD Social, Employment and Migration Working Papers, No. 154. Paris: OECD Publishing.
  37. Ramírez R. and Selin C. (2014). Plausibility and probability in scenario planning. Foresight, 16(1): 54-74.
  38. Robson K. (2008). Becoming NEET in Europe: A comparison of predictors and laterlife outcomes. Global Network on Inequality Mini-Conference, New York City, USA, February 22.
  39. Schwartz P. (1991). The art of the long view: Planning for the future in an uncertain world. New York: Currency Doubleday.
  40. Shinozaki T. (2012). Not by education alone: how young adults employment status is determined by employment environments and family backgrounds. Social Science Japan Journal, 15(1): 31-52.
  41. Skills Panorama (2016). Italy: mismatch priority occupations. -- Available at https://skillspanorama.cedefop.europa.eu/en/analytical_highlights/italy-mismatch-priority-occupations#_summary.
  42. Vainauskienė V. and Vaitkienė R. (2014). Foresight of brand vulnerability: The case of the Lithuanian market of cosmetic products. Procedia - Social and Behavioral Sciences, 156, 501-505.

Giulia Parola, Escape from parents’ basement? Post COVID-19 scenarios for the future of youth employment in Italy in "QUADERNI DI ECONOMIA DEL LAVORO" 111/2020, pp 51-71, DOI: 10.3280/QUA2020-111003