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Rice Price, Job Misery, Hunger Incidence: Need to Track Few More Statistical Indicators for the Poor

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  • Mapa, Dennis S.
  • Castillo, Kristelle
  • Francisco, Krizia

Abstract

Reducing hunger incidence in the country is still the major policy challenge confronting our leaders today. Statistics on hunger produced by both government and private institutions show a very slow reduction in hunger incidence over the last five years. Official data from Philippines Statistics Authority (PSA) show the percentage of Filipinos experiencing extreme poverty (hunger) decreased only slightly from 10.9 percent of the population in 2009 to 10.4 percent in 2012 and increasing marginally to 10.7 percent during the 1st semester of 2013. The results of the 8th National Nutrition Survey (NNS) of 2013 conducted by the Food Nutrition and Research Institute (FNRI) show the same small reduction in the proportion of children aged 0-5 years who are underweight (indirect measure of hunger) from 20.7 percent in 2008 to 19.8 percent in 2013. Self-rated hunger incidence data from the Social Weather Stations (SWS) also reveal a similar bleak picture, where hunger incidence in households averaging at 19.5 percent in 2013 from 19.1 percent in 2009, slowing down slightly to an average of 18.3 percent in 2014. This slow reduction in hunger incidence is a puzzle considering the country’s respectable economic growth performance, with Real Gross Domestic Product (GDP) growing at an annual average of 6.3 percent during the period 2010-2014. This paper looks at the factors that influence the dynamic nature of hunger incidence in the Philippines using the data from the SWS quarterly surveys on hunger. Variables identified as potential determinants of hunger incidence are, among others, changes in the price of rice and job misery index (sum of the employment and unemployment rates). A Vector AutoRegressive (VAR) model is used to determine the effect of a shock to the possible determinants on total hunger. Results show that a shock (increase) in the price of rice at the current quarter tends to increase hunger incidence in the succeeding quarter. A shock (increase) in job misery index at the current quarter also increases the hunger incidence in the next quarter. Further analysis using the time-varying parameter (TVP) model shows a higher effect of changes in the price of rice to hunger incidence after the global rice crisis in 2008. This shows that hunger incidence is becoming very sensitive to changes in the price of rice.

Suggested Citation

  • Mapa, Dennis S. & Castillo, Kristelle & Francisco, Krizia, 2015. "Rice Price, Job Misery, Hunger Incidence: Need to Track Few More Statistical Indicators for the Poor," MPRA Paper 61990, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:61990
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    File URL: https://mpra.ub.uni-muenchen.de/61990/1/MPRA_paper_61990.pdf
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    References listed on IDEAS

    as
    1. Kim, Chang-Jin & Nelson, Charles R, 1989. "The Time-Varying-Parameter Model for Modeling Changing Conditional Variance: The Case of the Lucas Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(4), pages 433-440, October.
    2. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, April.
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    Cited by:

    1. Beja, Edsel Jr., 2019. "Consumer Expectations Survey and Quarterly Social Weather Survey: Evidence of Convergent Validity and Causality," MPRA Paper 101074, University Library of Munich, Germany.

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    More about this item

    Keywords

    Hunger Incidence; Vector AutoRegressive (VAR) model; State Space; Time-Varying Parameters (TVP) model;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • I38 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Government Programs; Provision and Effects of Welfare Programs

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