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  <controlfield tag="001">IPP-00000266221</controlfield>
  <controlfield tag="003">IPP</controlfield>
  <controlfield tag="005">20180809121701.0</controlfield>
  <controlfield tag="008">180809s2011    xx     d | ||r |||||eng||</controlfield>
  <datafield tag="041" ind1="#" ind2="#">
   <subfield code="a">eng</subfield>
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  <datafield tag="100" ind1="1" ind2="#">
   <subfield code="a">Mapa, Dennis S.</subfield>
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  <datafield tag="245" ind1="1" ind2="0">
   <subfield code="a">Determinants of poverty in elderly-headed households in the Philippines</subfield>
  </datafield>
  <datafield tag="264" ind1="#" ind2="1">
   <subfield code="c">2011</subfield>
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  <datafield tag="520" ind1="#" ind2="#">
   <subfield code="a">This paper looks at the impact of population dynamics on poverty in elderly-headed households in the Philippines using data from the Family Income and Expenditure Survey (FIES) from 2000 to 2006. The population of the elderly, or those 60 years and above, has increased from 3.2 million in 1990 to 4.6 million in 2000. This group is growing at a rate of 3.6% per annum and estimated to reach 7 million in 2010. Data from the FIES shows that the percentage of the elderly who are poor is increasing since 2003. Moreover, the percentage of elderly-headed households has been on the rise since 1997. An ecenometric model based on the logistic regression shows that the presence of a young dependent (aged 14 yaers old or below) increases the probability that the elderly-headed household will become poor by about 9 percentage points, controlling for other factors such as income of household, education, age, and gender of the household head, income transfer from abroad and regional-specific characteristics. The results of the econometric model suggest that the high proportion of young dependents create negative effects on the welfare of the elderly-headed household by increasing the probality of thar household by increasing the probabilty of that household being poor. From the point of viwe policy, addressing the alarming poverty incidence in the country must include measures that will manage the country's bourgeoning population and bring down the fertility rate to a level that is conducive to higher income growth.</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="0">
   <subfield code="a">Poverty</subfield>
  </datafield>
  <datafield tag="650" ind1="2" ind2="0">
   <subfield code="a">Age-structured populations</subfield>
  </datafield>
  <datafield tag="650" ind1="2" ind2="0">
   <subfield code="a">Rural elderly</subfield>
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  <datafield tag="700" ind1="1" ind2="#">
   <subfield code="a">Bersales, Lisa Grace S.</subfield>
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   <subfield code="a">Albis, Manuel Leonard F.</subfield>
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  <datafield tag="700" ind1="1" ind2="#">
   <subfield code="a">Daquis, John Carlo P.</subfield>
  </datafield>
  <datafield tag="773" ind1="0" ind2="#">
   <subfield code="t">Transactions of the National Academy of Science and Technology</subfield>
   <subfield code="g">Vol. 33, no. 1 (Jul. 2011), 218</subfield>
  </datafield>
  <datafield tag="852" ind1="#" ind2="#">
   <subfield code="a">UPD</subfield>
   <subfield code="b">DMLP</subfield>
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  <datafield tag="942" ind1="#" ind2="#">
   <subfield code="a">Article</subfield>
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   <subfield code="a">FI</subfield>
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