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Pandemic crisis and firm survival: evidence from the Italian manufacturing industry

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Abstract

The paper studies the characteristics that affected firms’ ability to react to the economic crisis induced by the COVID-19 pandemic. We use firm-level data on Italian manufacturing companies to examine the role played by external relations and the characteristics of the local environment. In line with recent works, the study finds that micro-firms, highly indebted and less productive firms have a higher probability of exiting the market. Results also support the view that digital presence and financial soundness have favoured the survival of enterprises. Beyond firm-specific factors, we highlight that a firm’s resilience capability is related to its external linkages, namely the participation in a group or to a network contact. As for local ecosystem factors, while firms in industrial districts have a higher default probability, our results suggest that an innovative local environment can slightly mitigate the probability of permanently closing during the crisis.

Plain English Summary. Surviving the pandemic: Resilience through Unity?

The paper investigates firm survival in Italy at the onset and during the COVID-19 pandemic. In addition to individual characteristics, we focus on the role played by external relations and the characteristics of the local environment. Building on previous research on a firm’s resilience to shocks, the analysis considers traditional idiosyncratic determinants related to the internal organization (size, age, group membership, capitalization, profitability, productivity, debt maturity structure, …). and contextual, ecosystem factors. The study supports the view that specific individual factors (size, low debt and short-term exposure, productivity, presence of a website) have helped enterprises to waive the crisis. Besides, belonging to a group or a network contract is both associated with a lower probability of exiting the market in the period 2020–2022. Firms in traditional industries –fashion and furniture—have had a higher default probability than firms in other manufacturing industries. The location in industrial clusters increases the default probability with a moderating effect exerted by a specialization of the local labour system in the medium to high-tech industries and/or knowledge-intensive services. Finally, it is also worth noting that the region-specific length of lockdown policies is not significantly associated with firm survival.

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Notes

  1. In this respect, it is worth mentioning that recent empirical studies providing evidence on the positive impact of digital development on firms’ survival are based on the World Bank’s Enterprise Surveys where the online presence is proxied by having own company’s website or social media page (i.e., Muzi et al., 2023; Wagner, 2021).

  2. First with the Cura Italia Legislative Decree (Decreto Legislativo Cura Italia) and then with the Liquidity Legislative Decree (Decreto Legislativo Liquidità), the intervention to support liquidity to firms was significantly strengthened in Italy by providing, among other things, eligibility without assessment of creditworthiness, the expansion of the group of beneficiaries, the increase in guarantee coverage (up to 100% for operations up to 30 thousand euros), the increase in the guaranteed amount per individual company and the expansion of the type of eligible operations. These measures exponentially increased the applications received by the Guarantee Fund for SMEs, preventing the flow of credit to businesses from drastically decreasing, as happened in previous economic crises.

  3. In Italy, business crisis is substantially identified with firm undercapitalization (i.e., the firm displaying a level of equity below the legal limit). Orlando and Rodano (2020) suggest that undercapitalization often anticipates firm’s exit: around 60 percent of involved firms go out of business within 3 years. According to their predictions, the number of firms that could be involved in early warning procedures may be almost twice as large as that foreseeable based on accounting data from 2018.

  4. We consider as “Default” firms whose legal status in the database AIDA-BvD is reported as “Ceased”, “Ceased– closure due to bankruptcy”, “Ceased in liquidation”, “In liquidation” or “Active enterprise in a state of insolvency”.

  5. In this case, the trade-off is between the loss of accuracy if one selects a procedure of outlier detection that remove “good” observations, and the bias of the estimates if the procedure keeps “bad” ones (anomalous observations).

  6. The IQR is a measure of data dispersion that represents the range of values that extends from the first quartile (25th percentile) to the third quartile (75th percentile) of the data distribution.

  7. We recognize that using the Interquartile Range (IQR) is a rather crude technique for the determination of outliers that are defined as observations that appear to deviate markedly from the rest of the sample (Grubbs, 1969). Nevertheless, as highlighted by Barbato et al. (2011), the main appeals of this method are simplicity and low sensitivity to distortion due to outliers (robustness), since only the central part of the distribution is relied upon to compute IQR. Barbato et al. (2011) also distinguish between mild and extreme outliers and since the major weakness of the conventional IQR method is its tendency to discard legitimate values in large data sets, they suggest using modified IQR methods. The latter may allow for conserving the most attractive features and introducing some variations to allow for discarding only very extreme values. In the same vein, we followed a rather unconventional but more conservative sieve criterion. The upper and lower limits are based on the 1st/99th percentiles instead of the conventional 25th/75th percentiles. This range was aimed at avoiding removing too many observations and thus excluding from the analysis only the very extreme outliers, as suggested by Barbato et al. (2011). The use of similar techniques is also recommended for data that present asymmetric distributions (Hubert and Van der Veeken, 2008).

  8. The procedure of data quality led to a slightly different distribution of firms relative to the reference population with micro-firms under-represented and small firms over-represented in the cleansed sample relative to the initial database while the share of the other size classes remained unchanged (Table A1 in the Appendix).

  9. The indicator is defined, for each LLS, as the ratio between the number of employees of the local units active in medium-tech industries (21 and 26, Nace Rev. 2), high-tech industries (Divisions 20, 27– 30, Nace Rev. 2) and knowledge-intensive sectors (Divisions 50, 51, 58– 63, 64– 66, 69– 75, 78, 80, 84– 93 excluding Division 84) and the total number of employees of the local units (Istat, 2022b). The taxomomy refers to Eurostat (2020).

  10. ATECO 2007 is the classification of economic activities used by the Italian Institute of Statistics (ISTAT). It correspond to the “Nomenclature statistique des activités économiques dans la Communauté européenne” (NACE), the statistical classification adopted in the European Union. The latest classification is NACE Rev. 2, which was implemented from 2007.

  11. Network contract is an industrial policy instrument introduced in the midst of the 2009 financial crisis by the Italian government within emergency legislation, Decree-Law No. 33/2009.

  12. The high-technology and medium high-technology industries are sets of economic sectors with the higher level of R&D intensity compared to the other categories, namely medium low-technology, and low-technology industries (European Commission, 2012). Services are mainly aggregated into knowledge-intensive services (KIS) and less knowledge-intensive services (LKIS) based on the share of tertiary educated persons at NACE 2-digit level (Eurostat, 2020).

  13. Firms that expected the restrictions to last for more than four months were more likely to implement strong responses to reduce fixed costs such as dismissing employees or cancelling investment projects than firms that expected a quick return to normalcy.

  14. Both Muzi et al., (2023) and Wagner (2021) found marginal effects that they consider to be large on average: from – 0.046 to – 0.051 and from – 0.048 to – 0.028, respectively.

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Acknowledgements

The authors declare that funds and grants were received during the preparation of this manuscript.

Eleonora Cutrini acknowledges partial research support from the European Union—NextGenerationEU under the Italian Ministry of University and Research (MUR) National Innovation Ecosystem grant ECS00000041—VITALITY – CUP D83C22000710005. Federico Ninivaggi acknowledges financial support from the European Union—European Social Fund, under the program "Innovative Doctorate" – PhD Scholarships for the innovation of the regional system- 2020 edition, Marche Region (Italy). This article was presented at the XXI Annual Workshop of the Italian Society for Industrial Economics and Industrial Policy (SIEPI) in Naples (June 15-16, 2023) and the 64° Congress of the Italian Economic Association in L’Aquila (October 19-21, 2023). We are grateful to the participants for their comments and suggestions on an earlier draft.

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    Eleonora Cutrini & Federico Ninivaggi

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Cutrini, E., Ninivaggi, F. Pandemic crisis and firm survival: evidence from the Italian manufacturing industry. J. Ind. Bus. Econ. (2024). https://doi.org/10.1007/s40812-024-00309-0

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