The Analysis and the Measurement of Poverty: An Interval-Based Composite Indicator Approach

Drago, Carlo (2021): The Analysis and the Measurement of Poverty: An Interval-Based Composite Indicator Approach.

Abstract

The analysis and measurement of poverty is a crucial and unsolved issue in the field of social science. This work aims to measure poverty as a multidimensional notion using a new composite indicator. However, subjective choices as different weighting schemes on the indicator's construction could affect their interpretation and policy. It is necessary to consider the possible weighting configurations randomly to overcome this problem, and it is proposed in this work as interval-based composite indicators based on the results. This work aims to obtain robust and reliable measures based on a relevant conceptual model of poverty we have identified, considering various factors as weightings. Methodologically speaking, it is proposed an original procedure for measuring poverty in which it is computed a different composite indicator for each simulated weighting scheme of the identified factors. The weighting scheme in the Monte-Carlo simulation randomly creates an interval-based composite indicator based on the results. The different intervals are compared using different criteria (upper bound, center, and lower bound), and various rankings help analyze extreme scenarios and policy hypotheses. Critical situations are identified in Sicilia, Calabria, Campania and Puglia. The results demonstrate a relevant and consistent indicator measurement and the shadow sector's relevant impact on the final measures

• Item Type: MPRA Paper
• Language: Italian
• Keywords: Poverty, composite indicators, interval data, interval-based composite indicators, symbolic data
• Subjects: C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C15 - Statistical Simulation Methods: General; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C43 - Index Numbers and Aggregation; I - Health, Education, and Welfare > I3 - Welfare, Well-Being, and Poverty; I - Health, Education, and Welfare > I3 - Welfare, Well-Being, and Poverty > I32 - Measurement and Analysis of Poverty

References

D'Ambrosio, C. (Ed.). (2018). Handbook of Research on Economic and Social Well-being. Edward Elgar Publishing.

Sindzingre, A. (2007). The multidimensionality of poverty: An institutionalist perspective. In The many dimensions of poverty (pp. 52-74). Palgrave Macmillan, London.

Mauro V, Biggeri M, Maggino F. Measuring and monitoring poverty and well-being: A new approach for the synthesis of multidimensionality. Social Indicators Research. 2018 Jan;135(1):75-89.

Alkire S, Foster J. Counting and multidimensional poverty measurement. Journal of public economics. 2011 Aug 1;95(7-8):476-87.

Maggino F. La misurazione dei fenomeni sociali attraverso indicatori statistici. Aspetti metodologici. Working Paper, Università degli Studi di Firenze, www. eprints. unifi. it; 2009.

Nardo M, Saisana M, Saltelli A, Tarantola S, Hoffman H, Giovannini E. Handbook on constructing composite indicators: methodology and user guide. Organisation for Economic Cooperation and Development (OECD). Statistics Working Paper JT00188147, OECD, France. 2005.

Hansen K, Kneale D. Does how you measure income make a difference to measuring poverty? Evidence from the UK. Social indicators research. 2013 Feb;110(3):1119-40.

Marlier E, Atkinson AB. Indicators of poverty and social exclusion in a global context. Journal of Policy Analysis and Management. 2010 Mar;29(2):285-304.

Atkinson AB, Marlier E. Human development and indicators of poverty and social exclusion as part of the policy process. Indian Journal of Human Development. 2011 Jul;5(2):293-320.

Baker J, Schuler N. Analyzing urban poverty: a summary of methods and approaches. The World Bank; 2004 Sep 2.

Saisana M, Saltelli A, Tarantola S. Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2005 Mar;168(2):307-23.

Aiello F, Attanasio M. Some issues in constructing composite indicators. In VIII international meeting on quantitative methods for applied sciences, Certosa di Pontignano 2006 Sep (pp. 11-13).

Grabiński, T. Wielowymiarowa analiza porównawcza w badaniach dynamiki zjawisk ekonomicznych [Multivariate comparative analysis in research over the dynamics of economic phenomena], Zeszyty Naukowe Akademii Ekonomicznej w Krakowie, Special series: Monografie No. 61, ISSN: 0209-1674 (1984).

Bąk, A. Modelowanie symulacyjne wybranych algorytmów wielowymiarowej analizy porównawczej w języku C++ [Simulation modeling of selected algorithms of multivariate comparative analysis with C++ language], Wrocław: Wydawnictwo Akademii Ekonomicznej we Wrocławiu, ISBN: 8370114016 (1999)

Dehnel, G., Walesiak, M. (2019), A comparative analysis of economic efficiency of medium-sized manufacturing enterprises in districts of Wielkopolska province using the hybrid approach with metric and interval-valued data, STATISTICS IN TRANSITION new series", June, Vol. 20, No. 2, pp. 49-67. https://doi.org/10.21307/stattrans-2019-014

Walesiak, M., Dehnel, G. (2020), The Measurement of Social Cohesion at Province Level in Poland Using Metric and Interval-Valued Data, "Sustainability", 12(18), 7664, 1-19. https://doi.org/10.3390/su12187664

Anand S, Sen A. Concepts or human development and poverty! A multidimensional perspective. United Nations Development Programme, Poverty and human development: Human development papers. 1997:1-20.

Sen A. Poverty and famines: an essay on entitlement and deprivation. Oxford university press; 1982.

Sen A. The standard of living. Cambridge University Press; 1988 Dec 8.

Sen A. Inequality reexamined. Oxford University Press; 1992.

Asselin LM. Composite indicator of multidimensional poverty. Multidimensional Poverty Theory. 2002 Jun

Alkire S, Roche JM, Ballon P, Foster J, Santos ME, Seth S. Multidimensional poverty measurement and analysis. Oxford University Press, USA; 2015.

Lok-Dessallien R. Review of poverty concepts and indicators. UNDP Soc Dev Poverty Elimin Div Poverty Reduct Ser from http://www. undp. orgpovertypublicationspovReview pdf. 1999;21.

Abdu M, Delamonica E. Multidimensional child poverty: From complex weighting to simple representation. Social Indicators Research. 2018 Apr;136(3):881-905.

Kim SG. What have we called as "poverty"? A multidimensional and longitudinal perspective. Social Indicators Research. 2016 Oct;129(1):229-76.

Cerioli A, Zani S. rA fuzzy approach to the measurement of poverty, s in Income and Wealth Distribution, Inequality and Poverty, ed. by C. Dagum, and M. Zenga, pp. 272t284. Springer&Verlag, Berlin. 1990.

Lemmi AA, Betti G, editors. Fuzzy set approach to multidimensional poverty measurement. Springer Science & Business Media; 2006 Dec 6.

Costa M, De Angelis L. The multidimensional measurement of poverty: a fuzzy set approach. Statistica. 2008 Dec 31;68(3/4):303-19.

Mussard S, Noel PA. Multidimensional decomposition of poverty: a fuzzy set approach. 2005.

Kakwani, N., & Silber, J. (Eds.). Quantitative approaches to multidimensional poverty measurement. Springer. (2008)

Bourguignon, F., Chakravarty, S.R. The Measurement of Multidimensional Poverty. The Journal of Economic Inequality 1, 25–49 (2003). https://doi.org/10.1023/A:1023913831342

De Muro P, Mazziotta M, Pareto A. Composite indices of development and poverty: An application to MDGs. Social indicators research. 2011 Oct 1;104(1):1-8.

Nyiwul L, Selim TH. Poverty As Social Deprivation: A Survey. Review of Social Economy. 2006 Feb 1;64(2).

Becker W, Saisana M, Paruolo P, Vandecasteele I. Weights and importance in composite indicators: Closing the gap. Ecological indicators. 2017 Sep 1;80:12-22.

Paruolo P, Saisana M, Saltelli A. Ratings and rankings: voodoo or science?. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2013 Jun;176(3):609-34.

Drago, C. (2017) Interval Based Composite Indicators. FEEM Working Paper No. 42.2017. Available at SSRN: https://ssrn.com/abstract=3038751 or http://dx.doi.org/10.2139/ssrn.3038751

Drago C. Il monitoraggio della domanda di reddito di cittadinanza in tempo reale facendo uso di Big Data: un’analisi basata su indicatori ad intervallo. V Convegno Nazionale dell’Associazione Italiana per gli Studi sulla Qualità della Vita-Fiesole (FI). 2018 Dec:13-5.

Gatto, A., & Drago, C. (2020). Measuring and modeling energy resilience. Ecological Economics, 172, 106527

Drago C. Gatto A. (2018) A robust approach to composite in dicators exploiting interval data: The Interval-Valued Global Gender Gap Index (IGGGI) Ipazia 4th Workshop on gender: Culture and gender issues 9 March 2018

Billard L, Diday E. From the statistics of data to the statistics of knowledge: symbolic data analysis. Journal of the American Statistical Association. 2003 Jun 1;98(462):470-87.

Billard L. Some analyses of interval data. Journal of computing and information technology. 2008 Dec 30;16(4):225-

Barclay M, Dixon-Woods M, Lyratzopoulos G. The problem with composite indicators. BMJ quality & safety. 2019 Apr 1;28(4):338-44.

Młodak A. On the construction of an aggregated measure of the development of interval data. Computational Statistics. 2014 Oct;29(5):895-929.

Fura B, Wojnar J, Kasprzyk B. Ranking and classification of EU countries regarding their levels of implementation of the Europe 2020 strategy. Journal of cleaner production. 2017 Nov 1;165:968-79.

Schang L, Hynninen Y, Morton A, Salo A. Developing robust composite measures of healthcare quality–Ranking intervals and dominance relations for Scottish Health Boards. Social Science & Medicine. 2016 Aug 1;162:59-67

Sunaga T. Theory of interval algebra and its application to numerical analysis. RAAG memoirs. 1958;2(29-46):209..

Moore RE. Methods and applications of interval analysis. Society for Industrial and Applied Mathematics; 1979 Jan 1.

Gioia F, Lauro CN. Basic statistical methods for interval data. Statistica applicata. 2005;17(1):75-104.

Lauro CN, Palumbo F. Principal component analysis of interval data: a symbolic data analysis approach. Computational statistics. 2000 Mar;15(1):73-87.

Saltelli A. (2016) Sensitivity Analysis: an Introduction. Presentation Summer School on Sensitivity Analysis SAMO 2016, Villa Orlandi, Anacapri, July 4-8, 2016

Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S. Global sensitivity analysis: the primer. John Wiley & Sons; 2008 Feb 28.

Mballo C, Diday E. Decision trees on interval valued variables. The electronic journal of symbolic data analysis. 2005;3(1):8-18.

Song P, Liang J, Qian Y. A two-grade approach to ranking interval data. Knowledge-Based Systems. 2012 Mar 1;27:234-44.

Grzegorzewski, P. (2018, September). Measures of Dispersion for Interval Data. In International Conference Series on Soft Methods in Probability and Statistics (pp. 91-98). Springer, Cham.

Kamanou, G., Ward, M., & Havinga, I. Chapter VI. Statistical Issues in Measuring Poverty from Non-Household Survey Sources, 2005

Kuc-Czarnecka, M., Piano, S. L., & Saltelli, A. (2020). Quantitative storytelling in the making of a composite indicator. Social Indicators Research, 1-28.

Hellwig, Z. (1972). Procedure of Evaluating High-Level Manpower Data and Typology of Countries by Means of the Taxonomic Method, [in:] Gostkowski Z. (ed.), Towards a system of Human Resources Indicators for Less Developed Countries, Papers Prepared for UNESCO Research Project, Ossolineum, The Polish Academy of Sciences Press, Wrocław, 115-134

Walesiak, M. (2018), The choice of normalization method and rankings of the set of objects based on composite indicator values, Statistics in Transition - new series, December, Vol. 19, No. 4, 693–710. https://doi.org/21307/stattrans-2018-036

Mazziotta, M., Pareto, A. (2016). On a generalized non-compensatory composite index for measuring socio-economic phenomena. Social indicators research, 127(3), 983-1003, https://doi.org/10.1007/s11205-015-0998-2

Mazziotta, M., Pareto, A. (2018). Measuring well-being over time: the adjusted Mazziotta–Pareto index versus other non-compensatory indices. Social Indicators Research, 136(3), 967-976, https://doi.org/10.1007/s11205-017-1577-5

Smith A. (2005) Lurking in the shadows – Italy's other economy, The Florentine June 9, 2005

Bovi, M, and Castellucci L. "What do we know about the size of the underground economy in Italy beyond the" common wisdom"? Some empirically tested propositions." (1999).

Stranges M. Poverty and social exclusion in the Italian regions: an attempt of measurement through a simple index. Dynamiques de Pauvretés Et Vulnérabilités en Démographie Et en Sciences Sociales: Actes de la Chaire Quetelet 2007. 2011:59.

Giuliano G, Raciti P, Tenaglia S. An Application of Multidimensional Poverty Indicator to Survey Data, 2020

Gatto A. Drago C. When Renewable Energy, Empowerment, and Entrepreneurship connect: Measuring Energy Policy Effectiveness in 230 Countries. Energy Research and Social Science Forthcoming (2021)

Drago, C., Decomposition of the Interval Based Composite Indicators by Means of Biclustering (September 2019). CLADAG 2019 12-th Scientific Meeting Classification and Data Analysis Group Cassino, September 11 – 13, 2019