Skip to main content

Advertisement

Log in

Multi-Poverty in Cameroon: A Structural Equation Modeling Approach

  • Published:
Social Indicators Research Aims and scope Submit manuscript

Abstract

The primary objective of this study is to capture multi-poverty with values for welfare dimensions rather than the typical approach of a composite welfare indicator. The method used to explain, measure and calculate the scores for five dimensions of welfare is Structural Equation Modeling. Poverty analysis methods applied on these scores show that each type of poverty has specific determinants, although some determinants are common to several dimensions of poverty. Similarly, each region is affected by particular types of poverty while no form of poverty is unique to a single region. We thus propose to target multi-poverty via dimensional scores to formulate policy. A comparison with previous approaches shows that dimensional scores are more appropriate for identifying the specific needs of the population in the fight against poverty.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Abbreviations

CFA:

Confirmatory factor analysis

CFI:

Comparative fit index

CHSD:

Cameroon household survey data

CWI:

Composite welfare indicator

GESP:

Growth and employment strategy document

GFI:

Goodness-of-fit index

MM:

Measurement model

NISC:

National Institute of Statistic of Cameroon

RMSEA:

Root mean square error of approximation

RMSR:

Root mean square residual

SEM:

Structural equation modeling

UNDP:

United Nations Development Programme

WLS:

Weighted least squares

References

  • Afschin, G. (2008). Mutual dependency between capabilities and functionings in Amartya Sen’s capability approach. Social Choice and Welfare, 31, 345–350.

    Article  Google Scholar 

  • Anderson, T. W., & Rubin, H. (1956). Statistical inference in factor analysis. Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, 5, 111–150.

    Google Scholar 

  • Asselin, L. M. (2009). Analysis of multidimensional poverty: Theory and case studies. London, New York: Springer.

    Book  Google Scholar 

  • Baker, J., & Grosh, M. (1994). Poverty reduction through geographic targeting: How well does it work? World Development, 22(1), 983–995.

    Article  Google Scholar 

  • Bartlett, M. S. (1937). The statistical concept of mental factors. British Journal of Psychology, 28, 97–104.

    Google Scholar 

  • Basu, K. (1987). Achievements, capabilities and the concept of well-being. Social Choice and Welfare, 4, 69–76.

    Article  Google Scholar 

  • Bentler, P., & Yuan, K. (1999). Structural equation modeling with small samples: Test statistics. Multivariate Behavioral Research, 34(2), 181–197.

    Article  Google Scholar 

  • Bollen, K. A., & Maydeu-Olivares, A. (2007). A polychoric instrumental variable estimator for structural equation modeling with categorical variables. Psychometrika, 72(3), 309–326.

    Article  Google Scholar 

  • Bourguignon, F., & Chakravarty, S. R. (2003). The measurement of multidimensional poverty. Journal of Economic Inequality, 1, 25–49.

    Article  Google Scholar 

  • Chakravarty, S. R., Mukherjee, D., & Ranade, R. R. (1998). On the family of subgroup and factor decomposable measures of multidimensional poverty. Research on Economic inequality, 8, 175–194.

    Google Scholar 

  • DiStefano, C., Zhu, M., & Mîndrià, D. (2009). Understanding and using factor scores: Considerations for the applied researcher. Practical Assessment, Research and Evaluation, 14(20), 1–10.

    Google Scholar 

  • Duclos, J. Y., & Abdelkrim, A. (2006). Poverty and equity: Measurement, policy, and estimation with DAD. New York: Springer, Jacques Silber.

    Google Scholar 

  • Duclos, J. Y., Sahn, D., & Younga, S. D. (2006). Robust multidimensional poverty comparisons. The Economic journal, 116, 943–968.

    Article  Google Scholar 

  • Fan, X., & Sivo, S. A. (2007). Sensitivity of fits indices to model misspecification and model types. Multivariate Behavioral Research, 42(3), 509–529.

    Article  Google Scholar 

  • Filmer, D., & Pritchett, L. (2001). Estimating wealth effects without expenditure data: An application to educational enrolment in states of India. Demography, 38(3), 115–132.

    Google Scholar 

  • Foster, J., Greer, J., & Thorbecke, E. (1984). A class of decomposable poverty measures. Econometrica, 52(3), 761–777.

    Article  Google Scholar 

  • Green, B. F. (1976). On the factor scores controversy. Psychometrika, 41(2), 263–266.

    Article  Google Scholar 

  • Halleröd, B. (1995). The truly poor: Indirect and direct consensual measurement of poverty in Sweden. Journal of European Social Policy, 2(5), 111–129.

    Article  Google Scholar 

  • Hox, J. J., & Bechger, T. M. (2003). An introduction to structural equation modelling. Family Science Review, 11, 354–373.

    Google Scholar 

  • Jöreskog, K. G. (2005). Structural equation modeling with ordinal variables using Lisrel. Lisrel homepage. http://www.ssicentral.com/lisrel/corner.htm. Accessed February 20, 2010.

  • Jöreskog, K. G., and Sörbom D. (2004). Lisrel 8.7. Scientific Software International. Inc.

  • Jöreskog, K. G., Sörbom, D., & Yang, W. (2006). Latent variables scores and observational residuals. Scientific Software International: Lincolnwood IL.

    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., & Ballon, P. (2008). Estimating basic capabilities: A structural equation model applied to Bolivia. World Development, 36(6), 992–1010.

    Article  Google Scholar 

  • Maasoumi, E. (1999). Multidimensional approaches to welfare analysis. In Jacques Silber (Ed.), Handbook of income inequality analysis (pp. 325–379). Dordrecht, Boston: Kluwer.

    Google Scholar 

  • Mack, J., & Lansley, S. (1985). Poor Britain. London: Allen and Unwin.

    Google Scholar 

  • Makdissi, P., & Quentin, W. (2004). Measuring poverty reduction and targeting performance under multiple government programs. Review of Development Economics, 8(4), 573–582.

    Article  Google Scholar 

  • McDonald, R. P., & Burr, E. J. (1967). A comparison of four methods of constructing factors scores. Psychometrika, 32(4), 381–401.

    Article  Google Scholar 

  • McDonald, R. P., & Ringo, H. M. (2002). Principles and practice in reporting structural equation analysis. Psychological Methods, 7(1), 64–82.

    Article  Google Scholar 

  • Ningaye, P., Ndanyou, L., & Saakou, G. M. (2011). Multidimensional poverty in Cameroun: Determinants and spatial distribution. African Economic Research Consortium Research Paper, 211.

  • NISC. (2010). Trends, Profile and determinants of Poverty in Cameroon in 2007. NISC. http: www.statistics.cameroon.org. Accessed 20 December 2011.

  • Njong, M. A., & Baye, F. M. (2010). Asset growth, asset distribution and changes in multidimensional poverty in Cameroon. African Journal of Economic Policy, 17(1), 85–104.

    Google Scholar 

  • Olson, H., Ulf, F., Tron, T. S., & Howell, R. D. (2000). The performance of ML, GLS and WLS estimation in structural equation modeling under conditions of misspecification and nonnormality. Structural Equation Modeling, 7(4), 557–595.

    Article  Google Scholar 

  • Parka, A., Sangui, W., & Guobao, W. (2002). Regional poverty targeting in China. Journal of Public Economics, 86, 123–153.

    Article  Google Scholar 

  • Pui-Wa, L. (2009). Evaluating estimation methods for ordinal data in structural equation modeling. Qual quant, 43, 495–507.

    Article  Google Scholar 

  • Razafindrakoto, M., & Roubaud, F. (2005). Les multiples facettes de la pauvreté dans un pays en développement: le cas de la capitale malgache. Economie et Statistique, 383(383–385), 131–155.

    Article  Google Scholar 

  • Robeyns, I. (2005). The capability approach: A theoretical survey. Journal of Human Development, 6(1), 93–114.

    Article  Google Scholar 

  • Sahn, D. E., & Stifel, D. (2003). Exploring alternative measures of welfare in the absence of expenditure data. Review of Income and wealth, 49(4), 463–489.

    Article  Google Scholar 

  • Sen, A. K. (1983). Poor relatively speaking. Oxford Economic Papers, 35(2), 153–169.

    Google Scholar 

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

    Google Scholar 

  • Sen, A. K. (1987). On ethics and economics. Oxford: Basil Blackwell.

    Google Scholar 

  • Sivo, S. A., Fan, X., Witta, E. L., & Willse, J. T. (2006). The search for optimal cutoff properties: Fit index criteria in structural equation modeling. The Journal of Experimental Education, 74(3), 267–288.

    Article  Google Scholar 

  • Skrondal, A., & Rabe-Hesketh, S. (2005). Structural equation modeling: Categorical variables. Entry for Encyclopedia of Statistics in Behavioral Science (pp. 713–731). Wiley: Chichester.

  • Stanley, M. A., Larry, J. R., Alstine, V. J., Bennett, N., Lind, S., & Stiwell, D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105(3), 430–445.

    Article  Google Scholar 

  • Stéfan, L., & Verger, D. (1997). Pauvreté d’existence, monétaire ou subjective sont distinctes. Économie et Statistique, 308-309-310, 113–142.

    Google Scholar 

  • Sudhir, A., & Sen, A. K. (1997). Concepts of human development and poverty: A multidimensional perspective. Human Development Papers. New York, Oxford: Oxford University Press.

    Google Scholar 

  • Susmilch, C. E., & Weldon, J. T. (1975). Factor scores for constructing linear composites: Do different techniques make a difference? Sociological Methods & Research, 4(2), 166–188.

    Article  Google Scholar 

  • Thompson, B. (1993). Calculating of standardized, noncentered factor scores: An alternative to conventional factor scores. Perceptual and Motor Skills, 77, 1128–1130.

    Article  Google Scholar 

  • Thurstone, L. L. (1935). The vectors of mind. Chicago: University of Chicago Press.

    Google Scholar 

  • Townsend, P. (1979). Poverty in the United-Kingdom. Harmondsworth: Penguin Books.

    Google Scholar 

  • UNDP. (1997). Human development report 1997. New York, Oxford: Oxford University Press.

    Google Scholar 

  • UNDP. (2010). Human development report 2010: 20th anniversary edition. Washington: UNDP.

    Google Scholar 

  • Vero, J., & Patrick, W. (1997). Reexaming the measurement of poverty: How do young people in the stage of being integrate in the labor force manage. Economie et Statistique, 308-309-310, 143–158.

    Google Scholar 

  • Weston, R., & Gore, P. A. (2006). A brief guide to structural equation modeling. The Counseling Psychologist, 34(5), 719–751.

    Article  Google Scholar 

Download references

Acknowledgments

This work was carried out with financial and scientific support from the Poverty and Economic Policy (PEP) Research Network, which is financed by the Australian Agency for International Development (AusAID) and the Government of Canada through the International Development Research Centre (IDRC) and the Canadian International Development Agency (CIDA).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paul Ningaye.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ningaye, P., Alexi, T.Y. & Virginie, T.F. Multi-Poverty in Cameroon: A Structural Equation Modeling Approach. Soc Indic Res 113, 159–181 (2013). https://doi.org/10.1007/s11205-012-0087-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11205-012-0087-8

Keywords

Navigation