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Multidimensional Measures of Poverty and Well-being Based on Latent Variable Models

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Quantitative Approaches to Multidimensional Poverty Measurement

Abstract

Development is a multidimensional concept incorporating diverse social, economic, cultural and political dimensions and economic growth, though necessary, is not sufficient in itself to bring about development in this broad sense. According to Nobel Prize Laureate Amartya Sen (for example, Sen, 1985, 1999), the basic purpose of development is to enlarge people’s choices so that they can lead the life they want to. In this approach, the choices are termed ‘capabilities’ and the actual levels of achievement attained in the various dimensions are called ‘functionings’. Thus human development is the enhancement of the set of choices or capabilities of individuals whereas functionings are a set of ‘beings’ and ‘doings’ which are the results of a given choice. The concept of human development proposed by Mahbub ul Haq, in the first Human Development Report in 1990 (see UNDP, 1990), largely inspired by Sen’s various works, represents a major step ahead in the concretization of this extended meaning of development and in the effort to bring people’s lives to the centre of thinking and analysis. Since then, human development and human deprivation have been the object of extensive theoretical and empirical research. They have been studied from various angles: conceptual, methodological, operational and policy making. As it is not possible to directly observe and measure human development in its broad sense or the lack of it, they are generally constructed as composite indices based on several variables (indicators).

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References

  • Anderson, T.W. (1984) An Introduction to Multivariate Statistical Analysis. New York: John Wiley & Sons.

    Google Scholar 

  • Balestrino, A. and N. Sciclone (2000) ‘Should we use Functionings instead of Income to Measure Well-being? Theory, and Some Evidence from Italy’. Mimeo, University of Pisa.

    Google Scholar 

  • Ballon, P. and J. Krishnakumar (2005) ‘Estimating Basic Capabilities: A Latent Variable Approach Applied to Bolivian Data’. Paper presented at the 5th International Conference on the Capability Approach: Knowledge and Public Action, held in Paris, France.

    Google Scholar 

  • Bartholomew, D.J. and M. Knott (1999) Latent Variable Models and Factor Analysis. London: Edward Arnold.

    Google Scholar 

  • Biswas B. and F. Caliendo (2002) ‘A Multivariate Analysis of the Human Development Index’, Indian Economic Journal, 49 (4).

    Google Scholar 

  • Bollen, K.A. (1989) Structural Equations with Latent Variables. New York: John Wiley & Sons.

    Book  Google Scholar 

  • Browne, M.W. (1984) ‘Asymptotically Distribution-free Methods for the Analysis of Covariance Structures’ British Journal of Mathematical and Statistical Psychology, 37, 62–83.

    Article  Google Scholar 

  • Browne, M.W. and G. Arminger (1995) ‘Specification and Estimation of Mean and Covariance Structural Models’, in G. Arminger, C.C. Clogg and M.E. Sobel (eds) Handbook of Statistical Modelling for the Social and Behavioral Sciences Newbury Park, CA: Plenum Press, pp. 311–59.

    Google Scholar 

  • Cerioli, A. and S. Zani (1990) ‘A Fuzzy Approach to the Measurement of Poverty’, in Dagum and Zenga (eds), Income and Wealth Distribution, Inequality and Poverty. Berlin: Springer Verlag.

    Google Scholar 

  • Cheli, B. and A. Lemmi (1995) ‘A “Totally” Fuzzy and Relative Approach to the Multidimensional Analysis of Poverty’, Economic Notes, 24, 115–34.

    Google Scholar 

  • Chiappero Martinetti, E. (2000) ‘A Multidimensional Assessment of Well-Being based on Sen’s Functionings Approach’, Rivista Internazionale di Scienze Sociali, 2, 207–39.

    Google Scholar 

  • Gouriéroux, C., A. Monfort and A. Trognon (1984) ‘Pseudo-maximum Likelihood Methods: Theory’, Econometrica, 52, 681–700.

    Article  Google Scholar 

  • Di Tommaso, M.L. (2006), ‘Measuring the Well-being of Children using a Capability Approach: An Application to Indian Data’, Working Paper CHILD no. 05/2006, University of Turin.

    Google Scholar 

  • Hotelling, H. (1933) ‘Analysis of a Complex of Statistical Variables into Principal Components’, Journal of Educational Psychology, 24, 417–41.

    Article  Google Scholar 

  • Jöreskog, K. (1973) ‘A General Method for Estimatimg a Linear Structural Equation System’, in A. S. Goldberger and O.D. Duncan (eds), Structural Equation Models in the Social Sciences. New York: Seminar Press.

    Google Scholar 

  • Jöreskog, K. (2002) ‘Structural Equation Modelling with Ordinal Variables using LISREL’, http://www.ssicentral.com/lisrel/ordinal.htm.

    Google Scholar 

  • Jöreskog, K. and A. Goldberger (1975) ‘Estimation of a Model with Multiple Indicators and Multiple Causes of a Single Latent Variable’, Journal of the American Statistical Association, 70 (351).

    Google Scholar 

  • Klasen, S. (2000) ‘Measuring Poverty and Deprivation in South Africa’, Review of Income and Wealth, 46, 33–58.

    Article  Google Scholar 

  • Krishnakumar, J. (2004) ‘Going Beyond Functionings to Capabilities: an Econometric Model to Explain and Estimate Capabilities’. Paper presented at the 4th International Conference on the Capability Approach: Enhancing Human Security, held in Pavia, Italy.

    Google Scholar 

  • Krishnakumar, J. (2007) ‘Going Beyond Functionings to Capabilities: An Econometric Model to Explain and Estimate Capabilities’, Journal of Human Development, 8 (1), 39–63.

    Article  Google Scholar 

  • Krishnakumar, J. and A.L. Nagar (2006) ‘On Statistical Properties of Multidimensional Indicators based on Principal Components, Factor Analysis, MIMIC and SEM’. Working paper.

    Google Scholar 

  • Kuklys, W. (2005) Amartya Sen’s Capability Approach: Theoretical Insights and Empirical Applications. Berlin: Springer.

    Google Scholar 

  • Lelli, S. (2001) ‘Factor Analysis vs. Fuzzy Sets Theory: Assessing the influence of different techniques on Sen’s Functioning Approach’, Center for Economic Studies, K.U. Leuven.

    Google Scholar 

  • Maasoumi, E. and G. Nickelsburg (1988) ‘Multidimensional Measures of Well-being and an Analysis of Inequality in the Michigan Data’, Journal of Business and Economic Statistics, 6 (3), 327–34.

    Google Scholar 

  • McGillivray, M. (2005) ‘Measuring Non-Economic Well-being Achievement’, Review of Income and Wealth, 51 (2), 337–64.

    Article  Google Scholar 

  • Morris, Morris D. (1979) Measuring the Condition of the World’s Poor: The Physical Quality of Life Index. New York: Pergamon.

    Google Scholar 

  • Muthen, B. (1983) ‘Latent Variable Structural Equation modelling with Categorical Data’, Journal of Econometrics, 22, 43–65.

    Article  Google Scholar 

  • Muthen, B. (1984) ‘A General Structural Equation Model with Dichotomous, Ordered Categorical and Continuous Latent Indicators’, Psychometrika, 49, 115–32.

    Article  Google Scholar 

  • Muthen, B. (1989) ‘Latent Variable Modelling in Heterogenous Populations’, Psychometrika, 54, 557–85.

    Article  Google Scholar 

  • Muthen, B. (2002) ‘Beyond SEM: General Latent Variable Modelling’, Behaviormetrika, 29 (1), 81–117.

    Article  Google Scholar 

  • Nagar, A.L. and S. Basu (2001) ‘Weighting Socio-Economic Indicators of Human Development (A Latent Variable Approach)’. New Delhi: National Institute of Public Finance and Policy.

    Google Scholar 

  • Noorbaksh, F. (2003) ‘Human Development and Regional Disparities in India’, Discussion Paper. Helsinki: UNWIDER.

    Google Scholar 

  • Qizilbash, M. (2002) ‘A Note on the Measurement of Poverty and Vulnerability in the South African Context’, Journal of International Development, 14, 757–72.

    Article  Google Scholar 

  • Rahman, T., R.C. Mittelhammer and P. Wandschneider (2003) ‘Measuring the Quality of Life across Countries: A Sensitivity Analysis of Well-being Indices’, WIDER International Conference on Inequality, Poverty and Human Well-being, Helsinki, Finland.

    Google Scholar 

  • Ram, R. (1982) ‘Composite Indices of Physical Quality of Life, Basic Needs Fulfilment, and Income: A Principal Component Representation’, Journal of Development Economics, 11, 227–47.

    Article  Google Scholar 

  • Schokkaert, E. and L. van Ootegem (1990) ‘Sen’s Concept of the Living Standard Applied to the Belgian Unemployed’, Recherches Economiques de Louvain, 56, 429–50.

    Google Scholar 

  • Sen, A.K. (1985) Commodities and Capabilities. Amsterdam: North-Holland.

    Google Scholar 

  • Sen, A.K. (1993) Capability and Well-being, in M. Nussbaum and A.K. Sen (eds), pp. 30–53.

    Google Scholar 

  • Sen, A.K. (1999) Development as Freedom. Oxford: Oxford University Press.

    Google Scholar 

  • Slottje, D. J. (1991) ‘Measuring the Quality of Life across Countries’, The Review of Economics and Statistics, 73 (4), 684–93.

    Article  Google Scholar 

  • Skrondal, A. and S. Rabe-Hesketh (2004) Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Boca Raton, USA: Chapman & Hall/CRC.

    Book  Google Scholar 

  • UNDP (annual, from 1990 ) Human Development Report. Oxford: Oxford University Press.

    Google Scholar 

  • Wagle, U. (2005) ‘Multidimensional Poverty Measurement with Economic Well-being, Capability and Social Inclusion: A Case from Kathmandu, Nepal’, Journal of Human Development, 6 (3), 301–28.

    Article  Google Scholar 

  • White, H. (1982) ‘Maximum Likelihood Estimation of Misspecified Models’, Econometrica, 50, 1–26.

    Article  Google Scholar 

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Authors
  1. Jaya Krishnakumar
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Editor information

Editors and Affiliations

  1. International Poverty Centre, University of Sydney, Brazil

    Nanak Kakwani (Former Director) (Former Director)

  2. Bar-Ilan University, Israel

    Jacques Silber

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© 2008 Palgrave Macmillan, a division of Macmillan Publishers Limited

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Krishnakumar, J. (2008). Multidimensional Measures of Poverty and Well-being Based on Latent Variable Models. In: Kakwani, N., Silber, J. (eds) Quantitative Approaches to Multidimensional Poverty Measurement. Palgrave Macmillan, London. https://doi.org/10.1057/9780230582354_7

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