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What drives employment–unemployment transitions? Evidence from Italian task-based data

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Abstract

Relying on a unique longitudinal integrated database supplying micro-level information on labor market transitions (concerning the 2011–2017 period) and occupation task characteristics (e.g. routine-task intensity), this paper provides fresh evidence of the determinants of unemployment risk in Italy. We find that workers employed in routine-intensive occupations (measured with the RTI proposed by Acemoglu and Autor in Handb Labor Econ 4B:1043–1171, 2011) display—on average—higher unemployment risks than the rest of the workforce. This result is driven by workers employed in occupations entailing a large proportion of routine cognitive tasks and it is concentrated in high and medium–low skill occupations. In addition, the distribution of unemployment risk and its relation with routine-task intensity varies significantly across sectors—with higher risk in manufacturing and construction—confirming the importance of industry-level economic, technological and institutional heterogeneities. Finally, by exploring the gender dimension, we find that that being in a routine-intensive occupation increases unemployment risk for male workers only.

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Fig. 1

Source: own elaboration on IFLS. Note: horizontal lines indicate the average value for the total economy (blue line for U Narrow and grey line for U Wide)

Fig. 2

Source: own elaboration on INAPP-ICP, ILFS

Fig. 3

Source: own elaboration on INAPP-ICP, ILFS

Fig. 4

Source: own elaboration on INAPP-ICP, ILFS

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Notes

  1. Many studies have empirically investigated the dynamics of employment and income polarization in the western economies. Among others, Autor et al. (2006), Goos and Manning (2007), Spitz-Oener (2006), Mazzolari and Ragusa (2007), Autor and Dorn (2009, 2013), Goos et al. (2009), Acemoglu and Autor (2011), OECD (2017), Vom Lehm (2018), Naticchioni et al. (2014). Another approach has been proposed by authors like Fernandez-Macías and Hurley (2016), and Cirillo (2016), relating employment patterns to industry-level technological trajectories, country-level heterogeneities, institutional and demand factors.

  2. The Programme for the International Assessment of Adult Competencies (PIAAC) is a programme of assessment and analysis of adult skills carried out by the OECD.

  3. As illustrated in the data section, the RTI is shown for each ISCO 5-digit occupation.

  4. This variable is taken from a specific section of the ILFS containing retrospective questions referring to the same month of the previous year.

  5. An additional source of heterogeneity is individual. The potential estimation bias related to such source of heterogeneity is softened since we include in all the adopted specifications a very large amount of individuals-level information.

  6. The O*NET repertoire represents the major source of information regarding the qualitative characteristics of work, working activities and workplaces’ organizational features. An extremely large amount of empirical literature (see Autor et al, 2003 and followers) build upon the O*NET repertoire to study recent trends in the advanced economies’ labor markets.

  7. This is because, in our sample, the number of non-responding individuals is larger when looking at consecutive quarters. Since these lost observations include mostly individuals changing their employment status, by using quarterly transitions we would lose an excessive amount of relevant information.

  8. We rely on a calibration estimator in order to reduce attrition and potential selection bias. The auxiliary variables used in the calibration system refer to the Italian demographic and employment structure. The longitudinal sample was built using the rotation design of the ILFS. To minimize attrition problems caused by the non-random selection of the units included in the longitudinal sample, we apply the calibration method of Deville and Särndal (1992). This approach allows adjusting the sample to actual population values.

  9. The same evidence with specific reference to cognitive (RTCI) and manual (RTMI) tasks is reported in the Appendix (Table 11).

  10. The occupational categories are: (1) Legislators, Managers and Higher Officials; (2) Professionals; (3) Technicians and Associate Professionals; (4) Clerks; (5) Service Workers and Shop and Marker Sale Workers; (6) Craft Workers and Skilled Agricultural and Fishery Workers; (7) Plant and Machine operators and Assemblers; (8) Elementary Occupations.

  11. While being not robust to the different specifications, this last result might be explained by managerial delayering (Bloom & Van Reenen, 2011; Rajan & Wulf, 2006) and by the reforms implemented in the last decade which contributed to the diffusion of non-standard work (i.e. mostly temporary employment) in the Italian public sector increasing unemployment risks particularly for those in the lower organizational layers (Cirillo et al., 2017).

  12. In sectoral estimates standard errors are clustered by 4-digits occupations only.

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Authors and Affiliations

  1. CEFOP-LUISS University, Rome, Italy

    Nicola Cassandro

  2. National Institute for Public Policy Analysis, INAPP, Rome, Italy

    Marco Centra

  3. Sapienza University of Rome, Rome, Italy

    Dario Guarascio

  4. University of Cassino and Southern Lazio, Cassino, Italy

    Piero Esposito

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  1. Nicola Cassandro
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  2. Marco Centra
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  3. Dario Guarascio
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  4. Piero Esposito
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All authors contributed to the study conception, design, material preparation, data collection and analysis.

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Correspondence to Piero Esposito.

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Appendix

Appendix

See Figs. 5, 6, 7 and Tables 8, 9, 10, 11, 12, 13 and 14.

Fig. 5
figure 5

Unemployment risk by age cohort

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Fig. 6
figure 6

Unemployment risk by educational attainment

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Fig. 7
figure 7

Source: own elaboration on INAPP-ICP, ILFS. ISCO 1 digit classification: 1 Legislators, senior officials and managers; 2 Professionals; 3 Technicians and associate professionals; 4 Clerks; 5 Service workers and shop and market sales workers; 6 Skilled agricultural and fishery workers, Craft and related trades workers; 7 Plant and machine operators and assemblers; 8 Elementary occupations. Note: following the Italian version of the ISCO Classification (CP), category 6 aggregates the two sub-groups 6 and 7

Routine Task Indexes by ISCO-1digit occupation.

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Table 8 Growth rates of value added by sector
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Table 9 Transition rates from employment (unemployment) to unemployment (employment): average 2011–2017
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Table 10 The evolution of RTI, RCTI and RMTI between 2011 and 2017
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Table 11 RMTI and RCTI by transition and main characteristics, average 2011–2017
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Table 12 Top 10 occupations by routine intensity
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Table 13 Bottom 10 occupations by routine intensity
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Table 14 Estimation results for the entire sample: specification without sector dummies
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Cassandro, N., Centra, M., Guarascio, D. et al. What drives employment–unemployment transitions? Evidence from Italian task-based data. Econ Polit 38, 1109–1147 (2021). https://doi.org/10.1007/s40888-021-00237-5

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