On the employment and health impact of the COVID-19 shock on Italian regions: a value chain approach

ABSTRACT

We evaluate the exposure of Italian regions to employment and the health risk associated with the spread of COVID-19. First, we estimate the degree of participation of Italian regions in a plurality of value chains linked to consumption, investment and exports. Second, we investigate the different levels of contagion risk associated with each value chain and the possibility of reducing such risk through remote work. We find that regions are affected differently by lockdown policies because of their highly heterogeneous embeddedness in different value chains, and their diverse sectoral contributions to each of them.

KEYWORDS:

• COVID-19 lockdown
• value chains
• input–output models
• contagion risk
• remote working

JEL:

• R10
• R15

ACKNOWLEDGEMENTS

This paper was prepared as a contribution to the activities of the Data Driven-Economic Impact Group on the COVID-19 Emergency of the Italian Ministry for Technological Innovation and Digitization. The authors thank the editors and two anonymous referees for their useful comments.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Notes

1. According to the OXFORD stringency index (Hale et al., 2020), among the most populated countries in Europe, Italy indeed adopted the most stringent measures (stringency indices: Italy= 91.67 on 20 March 2020 and 93.52 on 4 April 2020; Germany = 76.85 on 22 March; France = 87.96 on 17 March; Spain = 85.19 on 30 March; and the UK = 79.63 on 24 March). Data are publicly available at https://covidtracker.bsg.ox.ac.uk/.

2. From the point of view of IO analysis, COVID-19 may be considered akin to a natural disaster. In this respect, relevant developments in the literature providing a sequential modelling approach for the impact of natural disasters are Okuyama et al. (2004) and Avelino and Hewings (2019). At the regional level, a multi-regional impact assessment model for disaster analysis has been proposed by Koks and Thissen (2016). As to the COVID-19 crisis, a sequential approach in an interregional framework is undertaken by Reissl et al. (2022a, 2022b). Moreover, interregional IO tables have recently been applied to evaluate regional integration in global and domestic value chains. Popularized by applications using inter-country IO tables (Borin & Mancini, 2017; Koopman et al., 2014; Los et al., 2016; Timmer et al., 2014a), this approach has been extended to interregional data (e.g., by Bentivogli et al., 2019) and can also be applied to European NUTS-2 regions using the EUREGIO IO database constructed (Thissen et al., 2018, 2019). Furthermore, Timmer et al. (2014b, 2021) and Los et al. (2015) have exploited global IO relationships to characterize value chains in a similar way to the present paper.

3. Furthermore, Appendix B in the supplemental data online has additional details regarding the methodology and data, complementing the information contained in sections 2 and 3.

4. $F{d}_{z,s}$ is a column vector of dimension $(N\cdot M)\times 1$, where $N$ is the number of regions and $M$ is the number of sectors. $A$ is a square matrix of dimension $(N\cdot M)\times (N\cdot M)$. $(I-A{)}^{-1}F{d}_{z,s}$ is a column vector of dimension $(N\cdot M)\times 1$. As an example, consider households in region s who demand goods and services linked to their need $z$. Parts of these goods and services are directly produced by firms located in $s$, but others will be imported from the other $N-1$ regions. Furthermore, the production of the final goods and services will indirectly activate other production steps which may be geographically dispersed.

5. Under the label of a public administration value chain, we include both public administration expenditures and the consumption of non-profit institutions serving households (NPISHs).

6. For the links between these 12 expenditure functions and the goods and services belonging to each of them, see https://ec.europa.eu/eurostat/statistics-explained/index.php/Glossary:Classification_of_individual_consumption_by_purpose_(COICOP)/.

7. For the documents released by the Italian government (available only in Italian), see, for example, https://www.gazzettaufficiale.it/eli/id/2020/04/11/20A02179/sg. Under these government measures, firms claiming to serve essential value chains, even as indirect suppliers, were permitted to continue to operate. The government had the possibility to intervene ex-post by performing checks to ensure whether the statement of a firm was truthful.

8. Our database does not allow us to disaggregate investment and export demand by level of necessity as we do for domestic consumption demand. However, it is of paramount importance to consider exports in the analysis in order to explain the heterogeneous exposure of Italian regions to economic and health risk.

9. These data were released by the National Statistical Office during spring 2022 and, in general, they are not at disposal in real time for ex ante exposure analyses.

10. The Use matrix for a given economy has rows, corresponding to $W$ goods and services, and $M+K$ columns, where $M$ is the number of sectors and K is the number of components of final demand. The $\mathrm{Make}$ matrix, within the Supply table, has rows and $W$ columns, showing which goods and services each industry in the economy produces.

11. See Appendix B in the supplemental data online for further details.

12. The aggregation of occupations at the sectoral and regional levels, instead, results in a continuous index between 0 and 1 which we use in our IO simulations. For further details, see Appendix B in the supplemental data online.

13. See https://www.oecd.org/sti/ind/inter-country-input-output-tables.htm/.

14. See http://www.coeweb.istat.it/.

15. See https://data.jrc.ec.europa.eu/dataset/84356c3b-104d-4860-8ce3-075d2eab37ab/.

16. The reference year for the IO table is 2015. This is the latest release of the OECD’s ICIO database. For the list of Italian regions, see Table A2 in Appendix A in the supplemental data online. Table A1, also online, lists the sectors.

17. See also Table A3 in Appendix A in the supplemental data online.

18. Table A3 in Appendix A in the supplemental data online reports the weblinks for all the data that we directly employed in the analysis.

19. These data represent the Italian counterpart of O*NET, which contains similar occupation characteristics for the United States. For more details, see https://www.onetonline.org/.

20. In order to obtain reliable information for each sector at the regional scale, we link data from 2016, 2017 and 2018.

21. In other words, the COVID-19 risk in each sector/region combination of the economy depends on the share of employees in occupations characterised by high COVID-19 risk employed by that sector in that region.

22. CP is the Italian National Statistical Office’s classification of occupations. In many respects, it mimics international counterparts such as International Standard Classification of Occupations (ISCO) (https://www.ilo.org/public/english/bureau/stat/isco/isco08/).

23. See section 2 for a formal definition of the two dimensions capturing contagion risk.

24. Since the focus of the paper is on ex-ante exposure measures, the measured impact is in terms of direct and indirect effects. Induced effects due to endogenous consumption behaviour are not considered. For examples of approaches dealing with endogenous changes in consumption, see Persky and Felsenstein (2006, 2008). For a complementary analysis to that presented here, see Reissl et al. (2022a, 2022b), in which firm and consumer behaviour is modelled to sequentially respond to exogenous shocks.

25. For a comprehensive discussion of risks related to global value chains, see Baldwin and Freeman (2021).

26. Supply-side issues mostly appeared in late 2020 and early 2021. The relevant exception, which does not exactly temporally overlap with non-pharmaceutical interventions undertaken by advanced economies, is the lockdown imposed in China in early 2020. Ferraresi and Ghezzi (2020) show that backward integration with China contributes to explaining the early decline in production of Italian regional economies in February 2020 (see also Meier & Pinto, 2020, for evidence on the US economy). However, these effects were small relative to those exerted by the national lockdown imposed in March 2020. Moreover, China’s industrial production rebounded strongly after spring 2020, relaxing supply-side pressures stemming from that economy.

Additional information

Funding

A. Roventini, F. Lamperti, G. Fagiolo, M. Napoletano, M. Guerini and F. Vanni acknowledge the financial support of the H2020 project ‘Growth Welfare Innovation Productivity’ (GROWINPRO) [grant agreement number 822781]. M. Guerini received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement [number 799412 (ACEPOL)].