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