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Impact of education and health on poverty reduction: Monetary and non-monetary evidence from Fiji

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  • Gounder, Rukmani
  • Xing, Zhongwei

Abstract

Fiji signed the United Nations 2015 target of halving extreme poverty from its 1990 level, but like many developing countries it is facing challenges in meeting this goal. This paper presents the economic modelling using Fiji's Household Income and Expenditure Survey 2002/03 dataset to examine the economic and social factors crucial for poverty reduction. Two hypotheses are tested: first, we estimate the monetary effects of education at the aggregate and disaggregated returns to education (primary, secondary, tertiary levels) and by income quartiles, and second, test the non-monetary education and health factors as channels of impact promulgated as effects against poverty prevalence. The monetary results indicate that all income quartile households (i.e. lowest to highest) benefit from additional skills obtained through formal education. While those at the lowest income quartile in particular benefit the most from formal education, however it cannot sustainably prevent people with only primary education from falling into poverty. The results for non-monetary models show that education has a positive and significant influence on the tendency of the people to engage in health prevention activities and in acquiring good housing facilities.

Suggested Citation

  • Gounder, Rukmani & Xing, Zhongwei, 2012. "Impact of education and health on poverty reduction: Monetary and non-monetary evidence from Fiji," Economic Modelling, Elsevier, vol. 29(3), pages 787-794.
  • Handle: RePEc:eee:ecmode:v:29:y:2012:i:3:p:787-794
    DOI: 10.1016/j.econmod.2012.01.018
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    Cited by:

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    2. Muhammad Masood Azeem & Amin W. Mugera & Steven Schilizzi & Kadambot H. M. Siddique, 2017. "An Assessment of Vulnerability to Poverty in Punjab, Pakistan: Subjective Choices of Poverty Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 134(1), pages 117-152, October.
    3. Aziza Usmanova & Ahmed Aziz & Dilshodjon Rakhmonov & Walid Osamy, 2022. "Utilities of Artificial Intelligence in Poverty Prediction: A Review," Sustainability, MDPI, vol. 14(21), pages 1-39, October.
    4. Chauhan, Apurv, 2015. "Plates for slates: The impact of a school feeding programme on community representations of schools," International Journal of Educational Development, Elsevier, vol. 41(C), pages 292-300.
    5. Khalid Mehmood & Masood Sarwar Awan & Muhammad Ishfaq & Muhammad Nauman Malik & Tariq Mehmood, 2024. "Personal Earnings in Pakistan: Role of Information Communication Technology (ICT) and Livelihood Diversification," Journal of Economic Impact, Science Impact Publishers, vol. 6(1), pages 100-105.
    6. Li, Qing & Yu, Shuai & Échevin, Damien & Fan, Min, 2022. "Is poverty predictable with machine learning? A study of DHS data from Kyrgyzstan," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
    7. Madhav Prasad Dahal, 2017. "Do Female Education and Ownership of Asset Matter for Poverty Reduction in Nepal?," Journal of Development Innovations, KarmaQuest International, vol. 1(2), pages 58-85, October.
    8. Zheng Wang & Mingwei Yang & Zhiyong Zhang & Yingjuan Li & Chuanhao Wen, 2022. "The Impact of Land Transfer on Vulnerability as Expected Poverty in the Perspective of Farm Household Heterogeneity: An Empirical Study Based on 4608 Farm Households in China," Land, MDPI, vol. 11(11), pages 1-16, November.
    9. repec:kqi:journl:2017-2-4 is not listed on IDEAS
    10. Nazeem ud din & Khalid Zaman & Shagufta Ashraf & Faiza Sajjad & Sundas Saleem & Uzma Raja, 2015. "Quality versus quantity in health care and educational reforms: combating poverty," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(1), pages 267-293, January.
    11. Ismaila Rimi Abubakar, 2022. "Multidimensional Poverty among Nigerian Households: Sustainable Development Implications," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 164(2), pages 993-1014, November.
    12. Claudia Andreea Urean & Viorel Dorin Lacatus & Loredana Mocean, 2016. "Information And Communications Technology As A Poverty Reduction Tool," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 107-113, December.

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    More about this item

    Keywords

    Poverty; Econometrics; Monetary models; Non-monetary models; Fiji;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • R29 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Other

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