Relief Policy and the Sustainability of COVID-19 Pandemic: Empirical Evidence from the Italian Manufacturing Industry
Abstract
:1. Introduction
Literature Review and Hypotheses
- H1
- higher productivity;
- H2
- lower profitability;
- H3
- higher default probability.
2. Materials and Method
3. Results
3.1. Discussion: COVID-19, Relief Policy and Firms Dynamics
3.2. Practical Implications
4. Discussion: Firms Characteristics and the Institutional Environment
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Variable | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) |
---|---|---|---|---|---|---|---|---|
Labor cost (per employee) | 1 | |||||||
Credit rating score | 0.105 | 1 | ||||||
(0.000) | ||||||||
Financial debt ratio | −0.050 | −0.429 | 1 | |||||
(0.000) | (0.000) | |||||||
Trade credit ratio | −0.071 | −0.277 | 0.033 | 1 | ||||
(0.000) | (0.000) | (0.000) | ||||||
Size | 0.549 | 0.099 | −0.080 | −0.129 | 1 | |||
(0.000) | (0.000) | (0.000) | (0.000) | |||||
Seniority | 0.274 | 0.280 | −0.011 | −0.227 | 0.443 | 1 | ||
(0.000) | (0.000) | (0.001) | (0.000) | (0.000) | ||||
COVID−19 (% infected) | 0.027 | 0.084 | −0.011 | −0.042 | 0.089 | 0.060 | 1 | |
(0.000) | (0.000) | (0.001) | (0.000) | (0.000) | (0.000) | |||
Institutional quality index | 0.268 | 0.111 | 0.055 | −0.055 | 0.181 | 0.163 | 0.198 | 1 |
(0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | (0.000) |
VARIABLE | TFP (Revenues) ψ t | TFP (Added Value) ψ t | ROA ψ t | Probability of Default ψ t |
---|---|---|---|---|
COVID−19 t (Impact) | 0.0110 *** | −0.0498 *** | −0.2345 *** | 0.3864 *** |
(0.00233) | (0.00198) | (0.00597) | (0.01087) | |
Constant | 0.7951 *** | 1.3046 *** | 1.6213 *** | −0.0517 *** |
(0.00162) | (0.00138) | (0.00415) | (0.00755) |
VARIABLE | TFP (Revenues) ψ t | TFP (Added Value) ψ t | ROA ψ t | Probability of Default ψ t |
---|---|---|---|---|
Code 11 | 0.0420 *** | −0.0270 | −0.137 *** | 0.136 *** |
(0.0146) | (0.0274) | (0.0466) | (0.0385) | |
Code 12 | 0.0130 | −0.117 | 0.319 | 0.111 |
(0.0489) | (0.269) | (0.480) | (0.307) | |
Code 13 | 0.0711 *** | −0.0579 *** | −0.0487 | 0.0113 |
(0.0107) | (0.0120) | (0.0310) | (0.0284) | |
Code 14 | 0.115 *** | −0.114 *** | 0.0188 | 0.156 *** |
(0.0123) | (0.0129) | (0.0277) | (0.0270) | |
Code 15 | 0.0724 *** | −0.0706 *** | 0.0721 ** | 0.0862 *** |
(0.0115) | (0.0123) | (0.0286) | (0.0281) | |
Code 16 | −0.0484 *** | 0.00147 | −0.0136 | 0.0430 |
(0.00763) | (0.0106) | (0.0267) | (0.0265) | |
Code 17 | 0.0215 ** | 0.0409 *** | 0.112 *** | −0.167 *** |
(0.00845) | (0.0119) | (0.0331) | (0.0325) | |
Code 18 | 0.0416 *** | −0.0373 *** | 0.0207 | 0.0745 ** |
(0.00990) | (0.0120) | (0.0299) | (0.0292) | |
Code 19 | 0.156 *** | −0.0392 | 0.0934 | −0.0705 |
(0.0414) | (0.0728) | (0.0965) | (0.121) | |
Code 20 | 0.00514 | 0.0799 *** | 0.255 *** | −0.152 *** |
(0.00895) | (0.0135) | (0.0297) | (0.0297) | |
Code 21 | 0.0488 | 0.161 *** | 0.466 *** | −0.0649 |
(0.0300) | (0.0375) | (0.0838) | (0.0802) | |
Code 22 | −0.00404 | 0.0695 *** | 0.177 *** | −0.171 *** |
(0.00680) | (0.00905) | (0.0230) | (0.0230) | |
Code 23 | −0.00522 | −0.00764 | −0.0699 *** | 0.00971 |
(0.00749) | (0.0114) | (0.0257) | (0.0249) | |
Code 24 | 0.0610 *** | 0.0371 *** | −0.0302 | −0.01000 |
(0.0120) | (0.0132) | (0.0370) | (0.0340) | |
Code 25 | 0.0493 *** | 0.0475 *** | 0.0844 *** | −0.0688 *** |
(0.00567) | (0.00737) | (0.0178) | (0.0178) | |
Code 26 | 0.000784 | 0.103 *** | 0.249 *** | −0.0920 *** |
(0.0129) | (0.0121) | (0.0305) | (0.0312) | |
Code 27 | −0.0182 * | 0.0556 *** | 0.142 *** | −0.0493 * |
(0.00977) | (0.0114) | (0.0278) | (0.0273) | |
Code 28 | −0.0264 *** | 0.0584 *** | 0.159 *** | −0.0195 |
(0.00641) | (0.00824) | (0.0201) | (0.0204) | |
Code 29 | −0.0129 | 0.0380 ** | 0.0881 ** | −0.0352 |
(0.0156) | (0.0171) | (0.0429) | (0.0417) | |
Code 30 | 0.0425 * | 0.0665 *** | 0.165 *** | 0.0948 ** |
(0.0242) | (0.0200) | (0.0468) | (0.0440) | |
Code 31 | −0.0675 *** | −0.0534 *** | −0.0420 | 0.140 *** |
(0.00732) | (0.0111) | (0.0267) | (0.0258) | |
Code 32 | −0.00862 | −0.0120 | 0.143 *** | −0.0673 ** |
(0.00966) | (0.0126) | (0.0295) | (0.0303) | |
Code 33 | 0.110 *** | 0.102 *** | 0.268 *** | −0.0615 ** |
(0.0104) | (0.0100) | (0.0238) | (0.0265) |
References
- Ahmad, W.; Kutan, A.M.; Chahal, R.J.K.; Kattumuri, R. COVID-19 and firm-level dynamics in the USA, UK, Europe, and Japan. Int. Rev. Financ. Anal. 2021, 78, 101888. [Google Scholar] [CrossRef]
- Saengtabtim, K.; Leelawat, N.; Tang, J.; Suppasri, A.; Imamura, F. Consequences of COVID-19 on Health, Economy, and Tourism in Asia: A Systematic Review. Sustainability 2022, 14, 4624. [Google Scholar] [CrossRef]
- Liu, K. COVID-19 and the Chinese economy: Impacts, policy responses and implications. Int. Rev. Appl. Econ. 2021, 35, 308–330. [Google Scholar] [CrossRef]
- Liu, Z.; Hu, B. China’s economy under COVID-19: Short-term shocks and long-term changes. Mod. Econ. 2020, 11, 908–919. [Google Scholar] [CrossRef] [Green Version]
- Aruga, K.; Islam, M.; Jannat, A. Effects of the State of Emergency during the COVID-19 Pandemic on Tokyo Vegetable Markets. Sustainability 2022, 14, 9719. [Google Scholar] [CrossRef]
- Philippon, T. Efficient programs to support businesses during and after lockdowns. Rev. Corp. Financ. Stud. 2021, 10, 188–203. [Google Scholar] [CrossRef]
- Hoshi, T.; Kawaguchi, D.; Ueda, K. Zombies, Again? The COVID-19 Business Support Programs in Japan. J. Bank. Financ. 2022, 106421. [Google Scholar] [CrossRef]
- Storm, S. Lessons for the Age of Consequences: COVID-19 and the Macroeconomy. Rev. Political Econ. 2021, 1–40. [Google Scholar] [CrossRef]
- Cetrulo, A. Is remote working here to stay? Lessons and ideas for a post-pandemic future. Riv. Quadrimestrale Dell’inapp Anno XI 2021, 2, 36–49. [Google Scholar] [CrossRef]
- Gallo, G.; Raitano, M. SOS incomes: Simulated effects of COVID-19 and emergency benefits on individual and household income distribution in Italy. J. Eur. Soc. Policy 2022. [Google Scholar] [CrossRef]
- Carta, F.; De Philippis, M. The impact of the COVID-19 shock on labour income inequality: Evidence from Italy. Bank of Italy Occasional Paper No. 606. 2021. [Google Scholar] [CrossRef]
- Tokarchuk, O.; Gabriele, R.; Neglia, G. Teleworking during the COVID-19 crisis in Italy: Evidence and tentative interpretations. Sustainability 2021, 13, 2147. [Google Scholar] [CrossRef]
- Di Mauro, F.; Syverson, C. The COVID crisis and productivity growth. VOX CEPR Policy Portal 2020, 16. [Google Scholar]
- Furceri, D.; Celik, S.K.; Jalles, J.T.; Koloskova, K. Recessions and total factor productivity: Evidence from sectoral data. Econ. Model. 2021, 94, 130–138. [Google Scholar] [CrossRef] [PubMed]
- Svarstad, E.; Kostøl, F.B. Unions, collective agreements and productivity: A firm-level analysis using Norwegian matched employer–employee panel data. Br. J. Ind. Relat. 2022; ahead of print. [Google Scholar]
- Bosio, E.; Djankov, S.; Jolevski, F.; Ramalho, R. Survival of Firms during Economic Crisis; Policy Research Working Paper nr. 9239; World Bank: Washington, DC, USA, 2020. [Google Scholar]
- Petrin, A.; Poi, B.P.; Levinsohn, J. Production function estimation in Stata using inputs to control for unobservables. Stata J. 2004, 4, 113–123. [Google Scholar] [CrossRef] [Green Version]
- Abbassi, W.; Kumari, V.; Pandey, D.K. What makes firms vulnerable to the Russia–Ukraine crisis? J. Risk Financ. 2022. [Google Scholar] [CrossRef]
- Kumar, S.; Zbib, L. Firm performance during the COVID-19 crisis: Does managerial ability matter? Financ. Res. Lett. 2022, 47, 102720. [Google Scholar] [CrossRef]
- Switzer, L.N.; Tu, Q.; Wang, J. Corporate governance and default risk in financial firms over the post-financial crisis period: International evidence. J. Int. Financ. Mark. Inst. Money 2018, 52, 196–210. [Google Scholar] [CrossRef]
- Yin, J.; Han, B.; Wong, H.Y. COVID-19 and credit risk: A long memory perspective. Insur. Math. Econ. 2022, 104, 15–34. [Google Scholar] [CrossRef]
- Costa, S.; De Santis, S.; Monducci, R. Reacting to the COVID-19 crisis: State, strategies and perspectives of Italian firms. Riv. Di Stat. Uff./Rev. Off. Stat. 2022, 73–107. [Google Scholar]
- Fang, J.; Gozgor, G.; Nolt, J.H. Globalisation, economic uncertainty and labour market regulations: Implications for the COVID-19 crisis. World Econ. 2022, 45, 2165–2187. [Google Scholar] [CrossRef]
- Kassem, S. Labour realities at Amazon and COVID-19: Obstacles and collective possibilities for its warehouse workers and MTurk workers. Glob. Political Econ. 2022, 1, 59–79. [Google Scholar] [CrossRef]
- Agostino, M.; Trivieri, F. Does trade credit play a signalling role? Some evidence from SMEs microdata. Small Bus. Econ. 2014, 42, 131–151. [Google Scholar] [CrossRef]
- Falavigna, G.; Ippoliti, R. Financial constraints and the sustainability of dividend payout policy. Sustainability 2021, 13, 6334. [Google Scholar] [CrossRef]
- Falavigna, G.; Ippoliti, R. Financial constraints, investments and environmental strategies: An empirical analysis of judicial barriers. Bus. Strategy Environ. 2022, 31, 2002–2018. [Google Scholar] [CrossRef]
- Nifo, A.; Vecchione, G. Do institutions play a role in skilled migration? The case of Italy. Reg. Stud. 2014, 48, 1628–1649. [Google Scholar] [CrossRef]
- Levinsohn, J.; Petrin, A. Estimating production functions using inputes to control for unobservables. Rev. Econ. Stud. 2003, 70, 317–342. [Google Scholar] [CrossRef]
- Carletti, E.; Oliviero, T.; Pagano, M.; Pelizzon, L.; Subrahmanyam, M.G. The COVID-19 shock and equity shortfall: Firm-level evidence from Italy. Rev. Corp. Financ. Stud. 2020, 9, 534–568. [Google Scholar] [CrossRef]
- Del Rio-Chanona, R.M.; Mealy, P.; Pichler, A.; Lafond, F.; Farmer, J.D. Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective. Oxf. Rev. Econ. Policy 2020, 36 (Suppl. 1), S94–S137. [Google Scholar] [CrossRef]
- DeAngelo, H.; DeAngelo, L.; Stulz, R.M. Dividend policy and the earned/contributed capital mix: A test of the life-cycle theory. J. Financ. Econ. 2006, 81, 227–254. [Google Scholar] [CrossRef]
- Denis, D.J.; Osobov, I. Why do firms pay dividends? International evidence on the determinants of dividend policy. J. Financ. Econ. 2008, 89, 62–82. [Google Scholar] [CrossRef]
- Falavigna, G.; Ippoliti, R. The impact of institutional performance on payment dynamics: Evidence from the Italian manufacturing industry. J. Bus. Econ. Manag. 2020, 21, 1285–1306. [Google Scholar] [CrossRef]
- Ippoliti, R.; Sanders, A. The demand for civil justice in Germany: An empirical investigation. Int. Rev. Appl. Econ. 2022, 36, 67–84. [Google Scholar] [CrossRef]
Type | Variable | Explanation |
---|---|---|
Dynamics under investigation | TFP (added value) ψ t | Composite indicator of firms’ ability to create added value |
TFP (revenues) ψ t | Composite indicator of firms’ ability to create revenues | |
ROA ψ t | Profit t/Total assets t | |
Probability of default ψ t | Incidence index (‰) of expected insolvency t | |
Explanatory variable | Labor cost (per employee) t | Total labor cost / number of employee t |
Control Variables | Credit rating score t−1 | Count variable that ranges between 1 (worst score) and 7 (excellent score) t−1 |
Financial debt ratio ψ t−1 | Total short-term financial debts t−1/Total assets t−1 | |
Trade credit ratio ψ t−1 | Total short-term operating debts t−1/Total assets t−1 | |
Size ψ t−1 | Total assets t−1 | |
Seniority ψ t−1 | Year t−1–Year founding | |
COVID-19 (% infected) t | Incidence index (%) of individuals positive to COVID-19 t | |
Institutional quality index t−1 | Composite indicator that ranges between 0 and 1 t−1 | |
Fixed Effects | NACE code—2 digit (FE) | Dummy variable according to 2 digits (24 classes) |
Macro area (FE) | Dummy variable according to NUTS1 (5 classes) | |
Macro area (FE) | Dummy variable according to NUTS1 (5 classes) |
NACE (2 Digits) | Description | Labor Cost (Per Employee) | TFP (Revenues) | TFP (Added Value) | ROA | Probability of Default |
---|---|---|---|---|---|---|
10 | Manufacture of food products | 0.09% | 0.68% | −0.79% | −134.10% | 103.49% |
11 | Manufacture of beverages | −1.11% | 1.71% | −1.48% | −43.80% | 62.23% |
12 | Manufacture of tobacco products | −0.32% | 37.68% | 77.41% | −49.81% | −5.28% |
13 | Manufacture of textiles | −2.13% | 2.06% | −4.12% | −170.75% | 123.47% |
14 | Manufacture of wearing apparel | −2.39% | 1.67% | −6.91% | −87.20% | 147.54% |
15 | Manufacture of leather and related products | −2.28% | 2.88% | −10.43% | −134.57% | 231.55% |
16 | Manufacture of wood and of products of wood and cork | −1.24% | 2.20% | −2.93% | 100.80% | 63.77% |
17 | Manufacture of paper and paper products | −1.51% | 2.05% | 2.12% | −38.57% | 27.25% |
18 | Printing and reproduction of recorded media | −2.46% | 1.03% | −4.55% | −114.09% | 102.03% |
19 | Manufacture of coke and refined petroleum products | 0.05% | 3.07% | 1.89% | −55.18% | 32.92% |
20 | Manufacture of chemicals and chemical products | −0.63% | 2.35% | 8.15% | 9.46% | 89.30% |
21 | Manufacture of basic pharmaceutical products and pharmaceutical preparations | −0.60% | 5.00% | 24.99% | −3.75% | 18.55% |
22 | Manufacture of rubber and plastic products | 5.59% | 2.86% | 2.15% | −37.92% | 41.21% |
23 | Manufacture of other non−metallic mineral products | −1.22% | 3.10% | 3.30% | −4.60% | 67.92% |
24 | Manufacture of basic metals | −1.04% | 2.83% | −1.42% | −13.58% | 41.75% |
25 | Manufacture of fabricated metal products, except machinery and equipment | −0.81% | 1.83% | −1.71% | −47.42% | 106.91% |
26 | Manufacture of computer, electronic and optical products | 0.63% | 6.32% | 4.88% | −48.09% | 89.85% |
27 | Manufacture of electrical equipment | −0.78% | 3.47% | 2.24% | −4.01% | 71.81% |
28 | Manufacture of machinery and equipment | −0.55% | 5.11% | 5.04% | −30.95% | 115.82% |
29 | Manufacture of motor vehicles, trailers and semi−trailers | 0.85% | 7.11% | −0.34% | −198.94% | 72.27% |
30 | Manufacture of other transport equipment | −1.16% | 58.93% | 6.51% | −152.70% | 69.29% |
31 | Manufacture of furniture | −1.08% | 1.89% | −3.63% | −65.21% | 133.23% |
32 | Other manufacturing | −1.90% | 2.94% | −0.67% | −5.72% | 117.93% |
33 | Repair and installation of machinery and equipment | −0.36% | 2.61% | −0.43% | −2.44% | 121.69% |
Manufacturing industry | −0.57% | 3.20% | −0.23% | −51.21% | 103.68% |
Variable | TFP (Revenues) ψ t | TFP (Added Value) ψ t | ROA ψ t | Probability of Default ψ t |
---|---|---|---|---|
Labor cost (per employee) ψ t | −0.0785 *** | −0.0593 *** | 0.0738 *** | −0.112 *** |
(0.00796) | (0.00877) | (0.0114) | (0.0134) | |
Credit rating score t−1 | 0.00397 ** | 0.0929 *** | 0.280 *** | −1.018 *** |
(0.00165) | (0.00185) | (0.00398) | (0.00492) | |
Financial debt ratio ψ t−1 | −0.0105 *** | 0.00108 | 0.0124 *** | 0.120 *** |
(0.00106) | (0.00124) | (0.00309) | (0.00358) | |
Trade credit ratio ψ t − 1 | −0.0965 *** | 0.0304 *** | 0.189 *** | 0.0708 *** |
(0.00363) | (0.00380) | (0.00810) | (0.00863) | |
Size ψ t − 1 | −0.108 *** | 0.153 *** | −0.0342 *** | −0.00634 |
(0.00193) | (0.00195) | (0.00424) | (0.00452) | |
Seniority ψ t − 1 | −0.00927 *** | −0.0538 *** | −0.162 *** | −0.0845 *** |
(0.00218) | (0.00225) | (0.00543) | (0.00585) | |
COVID−19 (% infected) t | −0.00122 | −0.0918 *** | −0.153 *** | 0.122 *** |
(0.00126) | (0.00236) | (0.00601) | (0.00767) | |
Institutional quality index t−1 | −0.0696 *** | −0.0335 * | 0.149 *** | −0.297 *** |
(0.0167) | (0.0191) | (0.0467) | (0.0481) | |
Constant | 1.748 *** | 0.0372 | 0.717 *** | 6.445 *** |
(0.0224) | (0.0253) | (0.0455) | (0.0507) | |
Macro area (FE) | Yes | Yes | Yes | Yes |
NACE code—2 digits (FE) | Yes | Yes | Yes | Yes |
Wald chi2 (32) | 10,092.85 | 14,078.39 | 8659.22 | 83,042.39 |
Prob > Chi2 | 0.0000 | 0.0000 | 0.0000 | 0.0000 |
R−squared (overall) | 0.267 | 0.282 | 0.181 | 0.702 |
Observations | 68,741 | 67,898 | 61,775 | 66,184 |
Number of firms | 41,155 | 40,905 | 39,715 | 38,415 |
Test | Statistic | Prob > F |
---|---|---|
Wilks’ lambda | 0.9839 | 0.0000 |
Pillai’s trace | 0.0161 | 0.0000 |
Lawley-Hotelling trace | 0.0163 | 0.0000 |
Roy’s largest root | 0.0163 | 0.0000 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Falavigna, G.; Ippoliti, R. Relief Policy and the Sustainability of COVID-19 Pandemic: Empirical Evidence from the Italian Manufacturing Industry. Sustainability 2022, 14, 15437. https://doi.org/10.3390/su142215437
Falavigna G, Ippoliti R. Relief Policy and the Sustainability of COVID-19 Pandemic: Empirical Evidence from the Italian Manufacturing Industry. Sustainability. 2022; 14(22):15437. https://doi.org/10.3390/su142215437
Chicago/Turabian StyleFalavigna, Greta, and Roberto Ippoliti. 2022. "Relief Policy and the Sustainability of COVID-19 Pandemic: Empirical Evidence from the Italian Manufacturing Industry" Sustainability 14, no. 22: 15437. https://doi.org/10.3390/su142215437