The Covid-19 pandemic has wrought significant challenges on both national and regional economic systems, resulting in an exacerbation of poverty and a deceleration of economic growth. These repercussions have led to increased social tensions and discontent.
Tuscany a key player in the Italian and European economies, warrants scrutiny concerning its economic structure post-pandemic, particularly in relation to the role played by industry districts and the ways for it to get out of the crisis and get back on track.
This paper undertakes an analysis of the strengths and weaknesses of the Tuscan economy, with a focus on EMAS-APO industry districts. It examines the region's standing in terms of national and European ERCI competitiveness, placing it at an average level. Additionally, the region ranks in the third decile of the DESI digitalization, indicating a commendable technological dynamism. An equally satisfying spending power in R&D, in percentage of GDP higher than the national one, and ranking 6th in the regions' rank, is flanked by a particularly vigorous innovation capacity of the production sector.
Employing a symmetric branch-by-branch SAM 2019 analysis, derived from a sector-by-product 2014 SAM constructed by IRPET, serves as the foundational step in understanding the interventions and investments necessary for resuming growth.
While affirming Tuscany's competitiveness, digitalization and technological dynamism, the analysis underscores the pivotal role of manufacturing. Notably, the textile, fashion and leather sectors hold significant weight, along with the slightly less impactful paper and pharmaceutical sectors, and the noteworthy role of tourism. The region emerges as innovative, competitive and export-oriented, albeit with certain sectors grappling with environmental challenges.
The IMM based analysis reveals a satisfactory average integration degree of the production system, suggesting that, for an effective, robust and sustainable post-Covid recovery and economic resurgence, focused investments and demand stimulus policy should be directed towards the textile, fashion and leather industries. Additionally, there is a strong indication to channel investments into pharmaceuticals and health care, and on transportation (especially freight transportation), as well as wholesale and retail trade. While weaker, there is a notable suggestion to invest in metallurgy, computer hardware, tourism and paper industries.
Citation: Guido Ferrari, Yanyun Zhao, José Mondéjar Jiménez, Luca Secondi. The economy of Tuscany in the post Covid-19 era: struggling with energy crisis and inflation. What to do to resume the journey?1[J]. National Accounting Review, 2024, 6(1): 1-26. doi: 10.3934/NAR.2024001
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The Covid-19 pandemic has wrought significant challenges on both national and regional economic systems, resulting in an exacerbation of poverty and a deceleration of economic growth. These repercussions have led to increased social tensions and discontent.
Tuscany a key player in the Italian and European economies, warrants scrutiny concerning its economic structure post-pandemic, particularly in relation to the role played by industry districts and the ways for it to get out of the crisis and get back on track.
This paper undertakes an analysis of the strengths and weaknesses of the Tuscan economy, with a focus on EMAS-APO industry districts. It examines the region's standing in terms of national and European ERCI competitiveness, placing it at an average level. Additionally, the region ranks in the third decile of the DESI digitalization, indicating a commendable technological dynamism. An equally satisfying spending power in R&D, in percentage of GDP higher than the national one, and ranking 6th in the regions' rank, is flanked by a particularly vigorous innovation capacity of the production sector.
Employing a symmetric branch-by-branch SAM 2019 analysis, derived from a sector-by-product 2014 SAM constructed by IRPET, serves as the foundational step in understanding the interventions and investments necessary for resuming growth.
While affirming Tuscany's competitiveness, digitalization and technological dynamism, the analysis underscores the pivotal role of manufacturing. Notably, the textile, fashion and leather sectors hold significant weight, along with the slightly less impactful paper and pharmaceutical sectors, and the noteworthy role of tourism. The region emerges as innovative, competitive and export-oriented, albeit with certain sectors grappling with environmental challenges.
The IMM based analysis reveals a satisfactory average integration degree of the production system, suggesting that, for an effective, robust and sustainable post-Covid recovery and economic resurgence, focused investments and demand stimulus policy should be directed towards the textile, fashion and leather industries. Additionally, there is a strong indication to channel investments into pharmaceuticals and health care, and on transportation (especially freight transportation), as well as wholesale and retail trade. While weaker, there is a notable suggestion to invest in metallurgy, computer hardware, tourism and paper industries.
| [1] |
Akkemik KA (2012) Assessing the importance of international tourism for the Turkish economy: A social accounting matrix analysis. Tour Manag 33: 790–801. https://doi.org/10.1016/j.tourman.2011.09.002 doi: 10.1016/j.tourman.2011.09.002
|
| [2] | Bagnasco A (1977) Tre Italie. La problematica territoriale dello sviluppo italiano, Bologna. |
| [3] | Banca d'Italia-Eurosistema (2022) Economie regionali. N. 9 - L'economia della Toscana. Rapporto annuale, giugno. Available from: https://www.bancaditalia.it/pubblicazioni/economie-regionali/2021/2021-0009/index.html. |
| [4] | Becattini G (1979) Dal settore industriale al distretto industriale. Alcune considerazioni sull'unità di indagine dell'economia industriale, Rivista di economia e politica industriale, 55: 7–21. |
| [5] | Becattini G (1989) Piccole e medie imprese e distretti industriali nel recente sviluppo italiano, Note economiche, 3: 397–412. |
| [6] | Bellù LG (2012) Social Accounting Matrix (SAM) for analysing agricultural and rural development policies. Conceptual aspects and examples, 1–19. |
| [7] | Bracalente B (2010) L'Umbria nell'Italia di mezzo verso il 2020: introduzione e sintesi della ricerca, In: Bracalente, B., Caratteri strutturali e scenari di sviluppo regionale. L'Umbria verso il 2020. Milano: Franco Angeli. |
| [8] | Breisinger C, Thomas M, Thurlow J (2009) Social accounting matrices and multiplier analysis: An introduction with exercises. International food policy research institute. http://dx.doi.org/10.2499/9780896297838fsp5 |
| [9] | CDP Think Tank. Focus Territori (2020) L'economia toscana: le 5 eccellenze da cui ripartire. Dicembre. Available from: https://www.cdp.it/sitointernet/page/it/leconomia_toscana_le_5_eccellenze_da_cui_ripartire?contentId = TNK31701. |
| [10] | Civardi MB, Lenti RT (2008) Multiplier decomposition, inequality and poverty in a SAM framewor. |
| [11] | De Anguita PM, Wagner JE (2010) Environmental social accounting matrices: theory and applications (1st ed.), Routledge. https://doi.org/10.4324/9780203854440 |
| [12] | European Commission (2020) European Regional Competitiveness Index 2019. Available from: https://ec.europa.eu/regional_policy/sources/docgener/work/rci2019_scorecards.pdf. |
| [13] | European Commission (2022) Digital Economy and Society Index (DESI). Available from: https://digital-strategy.ec.europa.eu/en/library/digital-economy-and-society-index-desi-2022. |
| [14] | Eurostat (2011) Collaboration in Research and Methodology for Official Statistics. Handbook on Methodology of Modern Business Statistics. Available from: https://ec.europa.eu/eurostat/cros/content/ras-method_en. |
| [15] | Eurostat (2022) Database. Available from: https://ec.europa.eu/eurostat/databrowser/view/tgs00047/default/table?lang = en. |
| [16] | Ferrari G (1999) A la recherche de la matrice perdue, in Comptabilité Nationale. Nouvelles frontières, Edith Archambault et Michel Boë da (éds.). Economica, 297–310. |
| [17] |
Ferrari G, Ma K, Mi Z (2020) SAM-based analysis of China's economic system and measurement of the effects of a VAT rate cut after the tax reform. Natl Account Rev 2: 26–52 https://doi.org/10.3934/NAR.2020002 doi: 10.3934/NAR.2020002
|
| [18] |
Ferrari G, Mondéjar Jiménez J, Secondi L (2018) Tourists' Expenditure in Tuscany and its impact on the regional economic system. J Clean Prod 171: 1437–1445. https://doi.org/10.1016/j.jclepro.2017.10.121 doi: 10.1016/j.jclepro.2017.10.121
|
| [19] |
Ferrari G, Mondéjar Jiménez J, Secondi L (2021) The role of tourism in China's economic system and growth. A Social Accounting Matrix (SAM)-based analysis. Econ Res 35: 252–272. https://doi.org/10.1080/1331677X.2021.1890178 doi: 10.1080/1331677X.2021.1890178
|
| [20] | Fofana I, Lemelin A, Cockburn J (2005) Balancing a Social Accounting Matrix: Theory and application. http://dx.doi.org/10.2139/ssrn.2439868 |
| [21] | Hayes A (2023) The Supply Chain: from raw materials to order fulfilment. |
| [22] | Holland DW, Wyeth P (1993) SAM multipliers: Their decomposition, interpretation and relationship to input-output multipliers, In: Otto, D.M., Microcomputer Based Input-output Modeling, Washington State University. https://doi.org/10.1201/9780429033797 |
| [23] | Holst DR, Neal SH, Chaiwan A (2013) Social and Resource Accounting: Multiplier Analysis, UC Berkeley and Chiang Mai University Training Workshop, 14–18 January, Phnom Penh, Cambodia. |
| [24] |
Keaney M (2021) One world, no longer: the past, the present, and the future of global value chains. Natl Account Rev 3: 1–49. https://doi.org/10.3934/NAR.2021001 doi: 10.3934/NAR.2021001
|
| [25] |
Keuning SJ (1991) Proposal for a Social Accounting Matrix which Fits into the Next System of National Accounts. Econ Syst Res 3: 233–248. https://doi.org/10.1080/09535319100000020 doi: 10.1080/09535319100000020
|
| [26] |
Keuning SJ, de Ruuter A (1988) Guidelines to the construction of a Social Accounting Matrix. Rev Income Wealth 34: 71–100. https://doi.org/10.1111/j.1475-4991.1988.tb00561.x doi: 10.1111/j.1475-4991.1988.tb00561.x
|
| [27] | IRPET (2020a) Economia. Available from: . |
| [28] | IRPET (2020b) Il Sistema produttivo toscano. Firenze, maggio. Available from: http://www.irpet.it/wp-content/uploads/2020/09/irpet-il-sistema-produttivo-toscano.pdf. |
| [29] | IRPET (2021) Note rapide. Firenze, ottobre. Available from: http://www.irpet.it/wp-content/uploads/2021/10/irpet-nota-rapida-n--7-ottobre-2021-rs.pdf. |
| [30] | ISTAT (2011) Le tavole delle risorse e degli impieghi e la loro trasformazione in tavole simmetriche. Nota metodologica. Available from: https://www.istat.it/it/files//2011/01/nota_metodologica.pdf. |
| [31] | ISTAT (2021) Conti economici territoriali. Anni 2018–2020. Available from: https://www.istat.it/it/archivio/265014#: ~: text = Nel%202020%2C%20il%20reddito%20disponibile%20delle%20famiglie%20%C3%A8%20diminuito%20del, Conti%20nazionali. |
| [32] | ISTAT (2022a) National Accounts. Available from: http://dati.istat.it/?lang = en. |
| [33] | ISTAT (2022b) Ricerca e sviluppo. Spesa-reg. Available from: http://dati.istat.it/index.aspx?queryid = 21942. |
| [34] | Lima MC, Cardenete MA, Hewings GJ, et al. (2004) A structural analysis of a regional economy using Social Accounting Matrices: 1990–1999. Investig Reg-J Reg Res 5: 113–138. |
| [35] |
Madsen B, Jensen-Butler C (2005) Spatial accounting methods and the construction of spatial social accounting matrices. Econ Syst Res 17: 187–210. https://doi.org/10.1080/09535310500114994 doi: 10.1080/09535310500114994
|
| [36] |
Madsen B, Zhang J (2010) Towards a new framework for accounting and modelling the regional and local impacts of tourism. Econ Syst Res 22: 313–340. https://doi.org/10.1080/09535314.2010.529067 doi: 10.1080/09535314.2010.529067
|
| [37] | Mainar-Causapé AJ, Ferrari E, McDonald S (2018) Social accounting matrices: basic aspects and main steps for estimation. Publications Office of the European Union: Luxembourg. Available from: file:///C:/Users/zhuan/Downloads/jrc_sams_manual-2018.pdf. |
| [38] |
Morilla CR, Diaz-Salazar GL, Cardenete MA (2007) Economic and environmental efficiency using a social accounting matrix. Ecol Econ 60: 774–786. https://doi.org/10.1016/j.ecolecon.2006.02.012 doi: 10.1016/j.ecolecon.2006.02.012
|
| [39] |
Parikh A, Thorbecke E (1996) Impact of rural industrialization on village life and economy: a social accounting matrix approach. Econ Dev Cult Change 44: 351–377. https://doi.org/10.1086/452218 doi: 10.1086/452218
|
| [40] | Politecnico di Milano (2022) Misurare la digitalizzazione delle regioni italiane: il DESI regionale2021. Osservatorio agenda digitale. Webinar. Available from: https://www.pphc.it/desi-regioni-italia/. |
| [41] | Polo C, Valle E (2009) Estimating tourism impacts using input-output and SAM models in the Balearic Islands, In: Matias, A., Nijkamp, P., Sarmento, M., (Eds.), Advances in Tourism Economics. New Developments, 121–143. |
| [42] |
Pyatt G (1999) Some relationships between T-accounts, input–output tables and social accounting matrices. Econ Syst Res 11: 365–387. https://doi.org/10.1080/09535319900000027 doi: 10.1080/09535319900000027
|
| [43] |
Pyatt G (2001) Some Early Multiplier Models of the Relationship Between Income Distribution and Production Structure. Econ Syst Res 13: 139–164. https://doi.org/10.1080/09537320120052434 doi: 10.1080/09537320120052434
|
| [44] |
Pyatt G, Round JI (1977) Social Accounting Matrices for Development Planning. Rev Income Wealth 23: 339–364. https://doi.org/10.1111/j.1475-4991.1977.tb00022.x doi: 10.1111/j.1475-4991.1977.tb00022.x
|
| [45] | Pyatt G, Round JI (1979) Accounting and Fixed Price Multipliers in a SAM Framework. Econ J 89: 850–873. |
| [46] | Pyatt G, Round JI (1985) Social Accounting Matrices: A Basis for Planning, The World Bank, Washington D C. |
| [47] | Regione Toscana (2022a) Produzioni vegetali. Vitivinicolo. Available from: https://www.regione.toscana.it/vitivinicolo#: ~: text = Il%20settore%20vitivinicolo%20rappresenta%20per, circa%2060.000%20ettari%20di%20vigneti. |
| [48] | Regione Toscana (2022b) Produzioni vegetali. Olio di oliva. Available from: https://www.regione.toscana.it/produzioni-vegetali/olio-di-oliva. |
| [49] | Regione Toscana (2022c) Ambiente/Energia. La Geotermia. Available from: https://www.regione.toscana.it/-/geotermia. |
| [50] | Regione Toscana (2019) Turismo termale e del benessere in Toscana. Available from: https://www.regione.toscana.it/-/turismo-termale-e-del-benessere-in-toscana. |
| [51] | Regione Toscana (2022d) Il livello di digitalizzazione delle imprese toscane: dati 2021. Firenze. Available from: https://www.regione.toscana.it/-/il-livello-di-digitalizzazione-delle-imprese-toscane-dati-2021-benchmarking-nazionale-ed-europeo. |
| [52] | Regione Toscana (2022e) Distretti EMAS della Toscana. Available from: https://www.regione.toscana.it/-/distretti-emas-della-toscana. |
| [53] | Regione Toscana (2022f) Ambiente/Sviluppo sostenibile ed economia circolare. Available from: https://www.regione.toscana.it/-/distretto-conciario-s-croce-sull-arno. |
| [54] | Regione Toscana (2022g) Ambiente/Sviluppo sostenibile ed economia circolare. Available from: https://www.regione.toscana.it/-/distretto-cartario-di-lucca. |
| [55] |
Robinson S, Cattaneo A, and El-Said M (2001) Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods. Econ Syst Res 13: 47–64. https://doi.org/10.1080/09535310120026247 doi: 10.1080/09535310120026247
|
| [56] | Round J (2003) Social accounting matrices and SAM-based multiplier analysis, The impact of economic policies on poverty and income distribution: Evaluation techniques and tools, 14, 261–276. |
| [57] | Santos S (2010) A quantitative approach to the effects of social policy measures. An application to Portugal, using Social Accounting Matrices, (No. 33/2010). EERI Research Paper Series. |
| [58] | Santos S (2012) A SAM (Social Accounting Matrix) approach to the policy decision process, Instituto Superior de Economia e Gestã o - DE Working papers nº 28-2012/DE/UECE. |
| [59] |
Scandizzo PL, Ferrarese C (2015) Social accounting matrix: A new estimation methodology. J Policy Model 37: 14–34. https://doi.org/10.1016/j.jpolmod.2015.01.007 doi: 10.1016/j.jpolmod.2015.01.007
|
| [60] | Stone R (1962) A Social Accounting Matrix for 1960, Chapman and Hall, Cambridge. |
| [61] | Thorbecke E (2017) Social accounting matrices and social accounting analysis, In: Methods of interregional and regional analysis, 281–332, Routledge. |
| [62] |
Van Wyk L, Saayman M, Rossouw R, et al. (2015). Regional economic impacts of events: A comparison of methods. South African J Econ Manag Sci 18: 155–176. https://doi.org/10.4102/sajems.v18i2.593 doi: 10.4102/sajems.v18i2.593
|
| [63] | Van Leeuwen ES, Nijkamp P, Rietveld P (2009) A Meta-analytic Comparison of Regional Output Multipliers at Different Spatial Levels: Economic Impacts of Tourism, In: Matias, Á., Nijkamp, P., Sarmento, M. (eds), Advances in Tourism Economics. New Developments, 13–33. https://doi.org/10.1007/978-3-7908-2124-6_2 |
| [64] |
Xie J (2000) An environmentally extended social accounting matrix. Environ Resour Econ 16: 391–406. https://doi.org/10.1023/A:1008376618447 doi: 10.1023/A:1008376618447
|
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| 1. | Xinzhi Ren, Xianning Liu, A competition un-stirred chemostat model with virus in an aquatic system, 2019, 98, 0003-6811, 2329, 10.1080/00036811.2018.1460811 | |
| 2. | Wendi Wang, Rui Fu, Mengshi Shu, A bacteriophage model based on CRISPR/Cas immune system in a chemostat, 2017, 14, 1551-0018, 1361, 10.3934/mbe.2017070 | |
| 3. | Saptarshi Sinha, Rajdeep K. Grewal, Soumen Roy, 2018, 103, 9780128151839, 103, 10.1016/bs.aambs.2018.01.005 | |
| 4. | Saptarshi Sinha, Rajdeep Kaur Grewal, Soumen Roy, 2020, Chapter 18, 978-1-0716-0388-8, 309, 10.1007/978-1-0716-0389-5_18 | |
| 5. | Sukhitha W. Vidurupola, Analysis of deterministic and stochastic mathematical models with resistant bacteria and bacteria debris for bacteriophage dynamics, 2018, 316, 00963003, 215, 10.1016/j.amc.2017.08.022 | |
| 6. | Daniel A. Korytowski, Hal L. Smith, How nested and monogamous infection networks in host-phage communities come to be, 2015, 8, 1874-1738, 111, 10.1007/s12080-014-0236-6 | |
| 7. | Saroj Kumar Sahani, Sunita Gakkhar, A Mathematical Model for Phage Therapy with Impulsive Phage Dose, 2020, 28, 0971-3514, 75, 10.1007/s12591-016-0303-0 | |
| 8. | Sukhitha W. Vidurupola, Linda J. S. Allen, Impact of Variability in Stochastic Models of Bacteria-Phage Dynamics Applicable to Phage Therapy, 2014, 32, 0736-2994, 427, 10.1080/07362994.2014.889922 | |
| 9. | WENDI WANG, DYNAMICS OF BACTERIA-PHAGE INTERACTIONS WITH IMMUNE RESPONSE IN A CHEMOSTAT, 2017, 25, 0218-3390, 697, 10.1142/S0218339017400010 | |
| 10. | Hayriye Gulbudak, Paul L. Salceanu, Gail S. K. Wolkowicz, A delay model for persistent viral infections in replicating cells, 2021, 82, 0303-6812, 10.1007/s00285-021-01612-3 | |
| 11. | Ei Ei Kyaw, Hongchan Zheng, Jingjing Wang, Htoo Kyaw Hlaing, Stability analysis and persistence of a phage therapy model, 2021, 18, 1551-0018, 5552, 10.3934/mbe.2021280 | |
| 12. | Ei Ei Kyaw, Hongchan Zheng, Jingjing Wang, Stability and Hopf Bifurcation Analysis for a Phage Therapy Model with and without Time Delay, 2023, 12, 2075-1680, 772, 10.3390/axioms12080772 | |
| 13. | Ei Ei Kyaw, Hongchan Zheng, Jingjing Wang, Hopf bifurcation analysis of a phage therapy model, 2023, 18, 2157-5452, 87, 10.2140/camcos.2023.18.87 | |
| 14. | Zainab Dere, N.G. Cogan, Bhargav R. Karamched, Optimal control strategies for mitigating antibiotic resistance: Integrating virus dynamics for enhanced intervention design, 2025, 00255564, 109464, 10.1016/j.mbs.2025.109464 | |
| 15. | Carli Peterson, Darsh Gandhi, Austin Carlson, Aaron Lubkemann, Emma Richardson, John Serralta, Michael S. Allen, Souvik Roy, Christopher M. Kribs, Hristo V. Kojouharov, A SIMPL Model of Phage-Bacteria Interactions Accounting for Mutation and Competition, 2025, 87, 0092-8240, 10.1007/s11538-025-01478-2 |
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Guido Ferrari, Yanyun Zhao, José Mondéjar Jiménez, Luca Secondi. The economy of Tuscany in the post Covid-19 era: struggling with energy crisis and inflation. What to do to resume the journey?1[J]. National Accounting Review, 2024, 6(1): 1-26. doi: 10.3934/NAR.2024001
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