CERQUA, AUGUSTO and LETTA, MARCO (2020): Local economies amidst the COVID-19 crisis in Italy: a tale of diverging trajectories.
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Abstract
Impact evaluations of the microeconomic effects of the COVID-19 upheavals are essential but nonetheless highly challenging. Data scarcity and identification issues due to the ubiquitous nature of the exogenous shock account for the current dearth of counterfactual studies. To fill this gap, we combine up-to-date quarterly local labor markets (LLMs) data, collected from the Business Register kept by the Italian Chamber of Commerce, with the machine learning control method for counterfactual building. This allows us to shed light on the pandemic impact on the local economic dynamics of one of the hardest-hit countries, Italy. We document that the shock has already caused a moderate drop in employment and firm exit and an abrupt decrease in firm entry at the country level. More importantly, these effects have been dramatically uneven across the Italian territory and spatially uncorrelated with the epidemiological pattern of the first wave. We then use the estimated individual treatment effects to investigate the main predictors of such unbalanced patterns, finding that the heterogeneity of impacts is primarily associated with interactions among the exposure of economic activities to high social aggregation risks and pre-existing labor market fragilities. These results call for immediate place- and sector-based policy responses.
Item Type: | MPRA Paper |
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Original Title: | Local economies amidst the COVID-19 crisis in Italy: a tale of diverging trajectories |
Language: | English |
Keywords: | impact evaluation; counterfactual approach; machine learning; local labor markets; firms; COVID-19; Italy |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods D - Microeconomics > D2 - Production and Organizations > D22 - Firm Behavior: Empirical Analysis E - Macroeconomics and Monetary Economics > E2 - Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy > E24 - Employment ; Unemployment ; Wages ; Intergenerational Income Distribution ; Aggregate Human Capital ; Aggregate Labor Productivity R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R12 - Size and Spatial Distributions of Regional Economic Activity |
Item ID: | 104404 |
Depositing User: | Dr Augusto Cerqua |
Date Deposited: | 04 Dec 2020 03:16 |
Last Modified: | 04 Dec 2020 03:16 |
References: | Abadie A, Diamond A, & Hainmueller, J (2010). Synthetic control methods for comparative case studies: Estimating the effect of California’s tobacco control program. Journal of the American Statistical Association, 105(490): 493–505. Abrell J, Kosch M, & Rausch S (2019). How effective was the UK carbon tax? A machine learning approach to policy evaluation. A Machine Learning Approach to Policy Evaluation (April 15, 2019). CER-ETH–Center of Economic Research at ETH Zurich Working Paper, 19, 317. Adams-Prassl A, Boneva T, Golin M, & Rauh C (2020). Inequality in the impact of the coronavirus shock: Evidence from real time surveys. Journal of Public Economics, 189: 1–33. Andini M, Ciani E, de Blasio G, D’Ignazio A, & Salvestrini V (2018). Targeting with machine learning: An application to a tax rebate program in Italy. Journal of Economic Behavior & Organization, 156: 86–102. Ascani A, Faggian A, & Montresor S (2020). The geography of COVID-19 and the structure of local economies: The case of Italy. Journal of Regional Science, first published online: 20 November 2020. Athey S, & Imbens G (2016). Recursive partitioning for heterogeneous causal effects. Proceedings of the National Academy of Sciences, 113(27): 7353–7360. Athey S, Bayati M, Doudchenko N, Imbens G, & Khosravi K (2018). Matrix completion methods for causal panel data models. National Bureau of Economic Research, No. w25132. Athey S, Bayati M, Imbens G, & Qu Z (2019). Ensemble methods for causal effects in panel data settings. AEA Papers and Proceedings, 109: 65–70. Bailey D., Clark J, Colombelli A, Corradini C, De Propris L, Derudder B, ... & Kemeny T (2020). Regions in a time of pandemic. Regional Studies, 54(9): 1163–1174. Baker SR, Farrokhnia RA, Meyer S, Pagel M, & Yannelis C (2020). How does household spending respond to an epidemic? consumption during the 2020 covid-19 pandemic. The Review of Asset Pricing Studies, 10(4): 834–862. Barbieri T, Basso G, & Scicchitano S (2020). Italian Workers at Risk During the Covid-19 Epidemic. GLO Discussion Paper Series No. 513. Bartik AW, Bertrand M, Cullen Z, Glaeser EL, Luca M, & Stanton C (2020). The impact of COVID-19 on small business outcomes and expectations. Proceedings of the National Academy of Sciences, 117(30): 17656–17666. Belloni A, Chernozhukov V, Fernández‐Val I, & Hansen C (2017). Program evaluation and causal inference with high‐dimensional data. Econometrica, 85(1): 233–298. Benatia D (2020). Reaching new lows? The pandemic’s consequences for electricity markets. USAEE Working Paper No. 20–454. Benatia D, de VILLEMEUR EB (2019). Strategic reneging in sequential imperfect markets. CREST Working Papers 2019-19. Benedetti FC, Sedláček P, & Sterk V (2020). EU start-up calculator: impact of COVID-19 on aggregate employment. EUR 30372 EN, Publications Office of the European Union, Luxembourg, 2020. Bick A, & Blandin A (2020). Real-time labor market estimates during the 2020 coronavirus outbreak. SSRN Electronic Journal No. 3692425. Bijnens G, Karimov S, Konings J (2019). Wage indexation and jobs. A machine learning approach. VIVES Discussion Paper No. 82. Blundell R, Costa Dias M, Joyce R, & Xu X (2020). COVID‐19 and Inequalities. Fiscal Studies, 41(2): 291–319. Buchheim L, Krolage C, & Link S (2020). Sudden Stop: When Did Firms Anticipate the Potential Consequences of COVID-19? CESifo Working Paper No. 8429. Burlig F, Knittel CR, Rapson D, Reguant M, & Wolfram C (2020). Machine learning from schools about energy efficiency. Journal of the Association of Environmental and Resource Economists, 7(6): 1181–1217. Cainelli G, Iacobucci D, & Micozzi A (2013). Determinants of territorial differences in entrepreneurial rates. An empirical analysis of Italian local systems. 53rd Congress of the European Regional Science Association (ERSA), Palermo, Italy. Cajner T, Crane LD, Decker RA, Grigsby J, Hamins-Puertolas A, Hurst E, ... & Yildirmaz A (2020). The US labor market during the beginning of the pandemic recession. National Bureau of Economic Research No. w27159. Carvalho VM, Hansen S, Ortiz A, Garcia JR, Rodrigo T, Rodriguez Mora S, & Ruiz de Aguirre P (2020). Tracking the COVID-19 crisis with high-resolution transaction data. CEPR Discussion Papers No. 14642. Casarico A, & Lattanzio S (2020). The heterogeneous effects of COVID-19 on labor market flows: Evidence from administrative data. Covid Economics, 52: 152–174. Caselli M, Fracasso A, & Scicchitano S (2020). From the lockdown to the new normal: An analysis of the limitations to individual mobility in Italy following the Covid-19 crisis. GSSI Discussion Paper Series in Regional Science & Economic Geography No.7/2020. Cerqua A, Di Stefano R, Letta M, & Miccoli S (2020). Local mortality estimates during the COVID-19 pandemic in Italy. GSSI Discussion Paper Series in Regional Science & Economic Geography No.6/2020. Chetty R, Friedman JN, Hendren N, & Stepner M (2020). The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data. National Bureau of Economic Research No. w27431. Chudik A, Mohaddes K, Pesaran MH, Raissi M, & Rebucci A (2020). A counterfactual economic analysis of Covid-19 using a threshold augmented multi-country model. National Bureau of Economic Research No. w27855. Fini R, & Sobrero M (2020). Why Italy needs an entrepreneurial renaissance after COVID-19, in Bellettini G. and Goldstein A. The Italian economy after Covid-19. Short term costs and long-term adjustments, Bononia University Press, Bologna. Forsythe E, Kahn LB, Lange F, & Wiczer D (2020). Labor demand in the time of COVID-19: Evidence from vacancy postings and UI claims. Journal of Public Economics, 189: 1–7. Giupponi G, & Landais C (2020). Subsidizing Labor Hoarding in Recessions: The Employment & Welfare Effects of Short Time Work. Mimeo, May 2020 version. Gourinchas PO, Kalemli-Özcan Ṣ, Penciakova V, & Sander N (2020). Covid-19 and SME Failures. National Bureau of Economic Research No. w27877. Hastie T, Tibshirani R, & Friedman J (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media. Kleinberg J, Ludwig J, Mullainathan S, & Obermeyer Z (2015). Prediction policy problems. American Economic Review, 105(5): 491–495. Lantz B (2019). Machine learning with R: expert techniques for predictive modeling. Packt Publishing Ltd. Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, & Yu B (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44): 22071–22080. Rossi N, & Mingardi A (2020). Italy and COVID-19: Winning the war, losing the peace? Economic Affairs, 40(2): 148–154. Sedláček P (2020). Lost generations of firms and aggregate labor market dynamics. Journal of Monetary Economics, 111: 16–31. Souza M (2019). Predictive counterfactuals for treatment effect heterogeneity in event studies with staggered adoption. SSRN Electronic Journal No. 3484635. Varian HR (2016). Causal inference in economics and marketing. Proceedings of the National Academy of Sciences, 113(27): 7310–7315. Von Gaudecker HM, Holler R, Janys L, Siflinger B, & Zimpelmann C (2020). Labour supply in the early stages of the CoViD-19 Pandemic: Empirical Evidence on hours, home office, and expectations. IZA Discussion Paper Series No. 13158. Wager S, & Athey S (2018). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association, 113(523): 1228–1242. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/104404 |