Abstract
The use of Composite Indicators (CIs) experienced significant momentum in recent years within academia, policy makers, practitioners, and global institutions. Despite the burst of popularity, an ongoing criticism has been developed around their computation with respect to both weighting and aggregation. In brief, weighting involves the relative importance of the attributes chosen, whereas the latter involves the process that transforms them into a single value. The use of CIs for the analysis of spatial differences in levels of development and well-being shares the above criticism, therefore, encouraging initiatives (e.g. OECD, Better Life Index (BLI)) to somewhat overcome the weighting issue. Nonetheless, initiatives like the BLI come at the price of missing a single overall measure of spatial performance. This study, instead, in the attempt to contribute to the ‘beyond GDP (Gross Domestic Product)’ international trend, builds upon a very recent methodological contribution—the σ–μ efficiency (Greco et al. 2019)—that takes into account both the aforementioned issues by combining the use of Stochastic Multi-attribute Acceptability Analysis with an approach which accounts for both the average performance and its spread according to different weighting vectors. More in detail, the analysis aims to measure spatial socio-economic differences at the regional level by using the wide set of indicators developed within the BES (Benessere Equo e Sostenibile) initiative from the Italian national statistical institute (ISTAT). When applied to the Italian regional case study, the σ–μ efficiency while confirming the overall North-South divide, shows a more nuanced picture with interesting differences with respect to well-established alternative aggregation techniques such as equal weight. Considering three macro areas (North, Centre, and South), the analysis confirms the trend already observed GDP-wise and shows that the divide is moving from the tripartite North–Centre–South division to a bipartite Centre/North–South one, according to a much broader set of socio-economic indicators. Hence, it shows that the divide is much deeper and generalised than the extent to which it is captured by spatial differences in GDP alone. At a more granular level, with particular regard to the Southern macro area, by disentangling the overall performance (μ) from the balance between the considered components (σ) the analysis does show potential for an important contribution in the field of spatial analysis at least to the extent that it allows unveiling complex patterns of uneven socio-economic performance. For instance, both Puglia and Sardegna, sharing the same (higher) frontier, register a better performance than geographically confining regions such as Basilicata, Calabria, and Campania and the other big island Sicilia, respectively. Furthermore, moving from the consideration that such a pattern is not fully confirmed in terms of GDP, the current σ–μ analysis methodologically confirms the existence of trade-offs and synergies between GDP and overall spatial disparities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
While eight out of the 12 indicators were analysed in their recent evolution, the remaining four were estimated for the following three years.
- 2.
See http://www.mef.gov.it/documenti-allegati/2019/def/DEF_2019_Allegato_BES_16_04_19_H_19_30.pdf. Retrieved: 22/08/2019.
- 3.
Currently 11. See http://www.oecdbetterlifeindex.org/. Retrieved: 22/08/2019.
- 4.
For a detailed discussion on this point including the economic rationale the reader is referred to Greco et al. (2019c, p. 946).
- 5.
It is worth noticing how the non-negative coefficient α for the mean μ i′ and the non-positive coefficient −β for the standard deviation σ i′ are coherent with the idea that μ i′ is intended to be maximised and σ i′ is intended to be minimised. Therefore, the greater αμ i′−βσ i′, the better the unit i′ performs with respect to μ i′ and σ i′.
- 6.
The categories are: health, education, working conditions, economic well- being, social relationships, quality of government and institutions, safety, individual wellbeing, heritage, environment, R&D, quality of public services (own translation). Available at https://www.istat.it/it/archivio/224669. Retrieved: 29/08/2019.
- 7.
- 8.
Eurostat, regional gross domestic product by NUTS 2 regions, retrieved: 01/09/2019.
- 9.
For macro-areas composition the reader is addresses to Appendix.
References
Annoni, P., & Kozovska, K. (2010). EU Regional Competitiveness Index 2010. European Commission, Joint Research Centre.
Ascani, A., Crescenzi, R., & Iammarino, S. (2012). Regional Economic Development: A Review. WP1.
Atkinson, A. B. (1970). On the Measurement of Inequality. Journal of Economic Theory, 2(3), 244–263.
Atkinson, A. (2015). Inequality. Harvard University Press.
Bagnasco, A. (Ed.). (1984). Tre Italie. La problematica territoriale dello sviluppo italiano. Bologna: Il Mulino.
Bandura, R. (2005). Measuring Country Performance and State Behavior: A Survey of Composite Indices. New York: Office of Development Studies, United Nations Development Programme (UNDP).
Barro, R. J., & Sala-i-Martin, X. (1992). Convergence. Journal of Political Economy, 100(2), 223–251.
Bubbico, R. L., & Dijkstra, L. (2011). The European Regional Human Development and Human Poverty Indices. Regional Focus, 2, 1–10.
Cannari, L., Magnani, M., & Pellegrini, G. (2009). Quali politiche per il Sud? Il ruolo delle politiche nazionali e regionali nell’ultimo decennio. Banca d’Italia.
Costanza, R., Hart, M., Talberth, J., & Posner, S. (2009). Beyond GDP: The Need for New Measures of Progress. The Pardee Papers.
Dijkstra, L., Annoni, P., & Kozovska, K. (2011). A New Regional Competitiveness Index: Theory, Methods and Findings. European Commission. Directorate-General for Regional Policy.
Dunnell, K. (2009). National Statistician’s Article: Measuring Regional Economic Performance. Economic & Labour Market Review, 3(1), 18–30.
European Commission, 2010, Fifth report on economic, social and territorial cohesion. http://ec.europa.eu/regional_policy/sources/docoffic/offic
Feldman, M., Hadjimichael, T., Lanahan, L., & Kemeny, T. (2016). The Logic of Economic Development: A Definition and Model for Investment. Environment and Planning C: Government and Policy, 34(1), 5–21.
Fujita, N. (2007). Myrdal’s Theory of Cumulative Causation. Evolutionary and Institutional Economics Review, 3(2), 275–284.
Greco, S., Ishizaka, A., Matarazzo, B., & Torrisi, G. (2018). Stochastic Multi-Attribute Acceptability Analysis (SMAA): An Application to the Ranking of Italian Regions. Regional Studies, 52(4), 585–600.
Greco, S., Ishizaka, A., Tasiou, M., & Torrisi, G. (2019a). On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness. Social Indicators Research, 141(1), 61–94.
Greco, S., Ishizaka, A., Resce, G., & Torrisi, G. (2019b). Measuring Well-Being by a Multidimensional Spatial Model in OECD Better Life Index Framework. Socio-Economic Planning Sciences, 70, 1–10.
Greco, S., Ishizaka, A., Tasiou, M. and Torrisi, G. (2019c). Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators. European Journal of Operational Research, 278(3), 942–960.
Haller, A. P. (2012). Concepts of Economic Growth and Development Challenges of Crisis and of Knowledge. Economy Transdisciplinarity Cognition, 15(1), 66.
Hansen, N. M. (1965). Unbalanced growth and regional development. Economic inquiry, 4(1), 3.
Hicks, N. L. (1979). Growth vs Basic Needs: Is There a Trade-Off? World Development, 7(11–12), 985–994.
Huq, M. M., Clunies-Ross, A., & Forsyth, D. (2009). Development Economics. London: McGraw Hill.
Istituto Nazionale di Statistica (ISTAT). (2018). BES 2018: The Equitable and Sustainable Well-Being. Rome: ISTAT.
Kubiszewski, I., Costanza, R., Franco, C., Lawn, P., Talberth, J., Jackson, T., & Aylmer, C. (2013). Beyond GDP: Measuring and Achieving Global Genuine Progress. Ecological Economics, 93, 57–68.
Kuznets, S. (1934). National Income, 1929–1932. In National Income, 1929–1932 (pp. 1–12). NBER.
Lahdelma, R., Hokkanen, J., & Salminen, P. (1998). SMAA-Stochastic Multiobjective Acceptability Analysis. European Journal of Operational Research, 106(1), 137–143.
Martin, R. (2011). Regional Economic Resilience, Hysteresis and Recessionary Shocks. Journal of Economic Geography, 12(1), 1–32.
Oates, W. E. (1972). Fiscal federalism. Books.
OECD Publishing. (2014). How’s Life in Your Region?: Measuring Regional and Local Well-Being for Policy Making. OECD Publishing.
Pike, A., Rodríguez-Pose, A., & Tomaney, J. (2007). What Kind of Local and Regional Development and for Whom? Regional Studies, 41(9), 1253–1269.
Piketty, T. (2014). Capital in the Twenty-First Century. Cambridge, MA: Harvard University Press.
Putnam, R. D. 1993. Making Democracy Work: Civic Traditions in Modern Italy, Princeton 1993 (trad. it. La tradizione civica nelle regioni italiane, Milano.
Sagar, A. D., & Najam, A. (1998). The Human Development Index: A Critical Review. Ecological Economics, 25(3), 249–264.
Seiford, L. M., & Zhu, J. (2003). Context-dependent data envelopment analysis—measuring attractiveness and progress. Omega, 31(5), 397–408.
Stanickova, M., & Melecký, L. (2018). Understanding of Resilience in the Context of Regional Development Using Composite Index Approach: The Case of European Union NUTS-2 Regions. Regional Studies, Regional Science, 5(1), 231–254.
Stiglitz, J., Sen, A. K., & Fitoussi, J. P. (2009). The Measurement of Economic Performance and Social Progress Revisited: Reflections and Overview. OFCE Working Paper. New York, NY: Columbia University.
Thirlwall, A. P. (2006). Growth and Development (8th ed.). Basingstoke: Palgrave Macmillan.
Tiebout, C. M. (1956). A pure theory of local expenditures. Journal of political economy, 64(5), 416–424.
Todaro, M. P., & Smith, S. C. (2015). Economic Development (12th ed.). New York: Pearson.
Torrisi, G., Pike, A., Tomaney, J., & Tselios, V. (2015). (Re-) Exploring the Link Between Decentralization and Regional Disparities in Italy. Regional Studies, Regional Science, 2(1), 123–140.
United Nations. (2018). The Subnational Human Development Index: Moving Beyond Country-Level Averages. Retrieved August 21, 2019, from http://hdr.undp.org/en/content/subnational-human-development-index-moving-beyond-country-level-averages.
Vecchi, G. (2011). In ricchezza e in povertà: il benessere degli italiani dall’unità a oggi. Bologna: Il Mulino.
Veenhoven, R. (1996). Happy Life-Expectancy. Social Indicators Research, 39(1), 1–58.
Yang, L. (2014). An Inventory of Composite Measures of Human Progress. Occasional Paper on Methodology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Rights and permissions
Copyright information
© 2021 The Author(s)
About this chapter
Cite this chapter
Greco, S., Tasiou, M., Torrisi, G. (2021). A Balanced Development? The Novel σ–μ Efficiency of Italian Regions. In: Colombo, S. (eds) Spatial Economics Volume II. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-40094-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-40094-1_2
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-40093-4
Online ISBN: 978-3-030-40094-1
eBook Packages: Economics and FinanceEconomics and Finance (R0)