Marital status as a risk factor in life insurance : an empirical study in Taiwan

<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/MARC21/slim http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
  <record>
    <leader>00000cab a2200000   4500</leader>
    <controlfield tag="001">MAP20160024234</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20160809150221.0</controlfield>
    <controlfield tag="008">160808e20160502usa|||p      |0|||b|eng d</controlfield>
    <datafield tag="040" ind1=" " ind2=" ">
      <subfield code="a">MAP</subfield>
      <subfield code="b">spa</subfield>
      <subfield code="d">MAP</subfield>
    </datafield>
    <datafield tag="084" ind1=" " ind2=" ">
      <subfield code="a">6</subfield>
    </datafield>
    <datafield tag="100" ind1="1" ind2=" ">
      <subfield code="0">MAPA20160009927</subfield>
      <subfield code="a">Wang, Hsin Chung</subfield>
    </datafield>
    <datafield tag="245" ind1="1" ind2="0">
      <subfield code="a">Marital status as a risk factor in life insurance</subfield>
      <subfield code="b">: an empirical study in Taiwan</subfield>
      <subfield code="c">Hsin Chung Wang, Jack C. Yue, Yi-Hsuan Tsai</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <subfield code="a">Gender and age are the top two risk factors considered in pricing life insurance products. Although it is believed that mortality rates are also related to other factors (e.g. smoking, overweight, and especially marriage), data availability and marketing often limit the possibility of including them. Many studies have shown that married people (particularly men) benefit from the marriage, and generally have lower mortality rates than unmarried people. However, most of these studies used data from a population sample; their results might not apply to the whole population. This study explores if mortality rates differ by marital status using mortality data (1975-2011) from the Taiwan Ministry of the Interior. In order to deal with the problem of small sample sizes in some marital status groups, it is used graduation methods to reduce fluctuations in mortality rates. A relational approach also uses to model mortality rates by marital status, and then compare the proposed model with some popular stochastic mortality models</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20130004266</subfield>
      <subfield code="a">Yue, Jack C.</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20160009934</subfield>
      <subfield code="a">Tsai, Yi-Hsuan</subfield>
    </datafield>
    <datafield tag="773" ind1="0" ind2=" ">
      <subfield code="w">MAP20077000420</subfield>
      <subfield code="t">Astin bulletin</subfield>
      <subfield code="d">Belgium : ASTIN and AFIR Sections of the International Actuarial Association</subfield>
      <subfield code="x">0515-0361</subfield>
      <subfield code="g">02/05/2016 Volumen 46 Número 2 - mayo 2016 , p. 487-505</subfield>
    </datafield>
  </record>
</collection>