An Empirical investigation of asymmetric information in China's automobile insurance market

<?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">MAP20180025976</controlfield>
    <controlfield tag="003">MAP</controlfield>
    <controlfield tag="005">20180830115944.0</controlfield>
    <controlfield tag="008">180808e20180701che|||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">219</subfield>
    </datafield>
    <datafield tag="100" ind1=" " ind2=" ">
      <subfield code="0">MAPA20180012082</subfield>
      <subfield code="a">Qu, Bo</subfield>
    </datafield>
    <datafield tag="245" ind1="1" ind2="3">
      <subfield code="a">An Empirical investigation of asymmetric information in China's automobile insurance market</subfield>
      <subfield code="c">Bo Qu, Li Wei, Ping Wei</subfield>
    </datafield>
    <datafield tag="520" ind1=" " ind2=" ">
      <subfield code="a">In this paper, we investigate the asymmetric information in China's automobile insurance market and propose a set of risk classification approaches to alleviate it. Using a unique data set of automobile insurance coverage in China from 2011 to 2013, we discover that policyholders who purchase more insurance coverage are riskier, which indicates the presence of information asymmetry in China's automobile insurance market. Controlling the attributes of the automobiles does not affect the main results. Moreover, based on data mining technology, we find that the risk classification approach is able to mitigate the problem of asymmetric information in China's automobile insurance market to some extent with little additional cost</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080586294</subfield>
      <subfield code="a">Mercado de seguros</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080617219</subfield>
      <subfield code="a">Industria automovilística</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080603779</subfield>
      <subfield code="a">Seguro de automóviles</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080606695</subfield>
      <subfield code="a">Información asimétrica</subfield>
    </datafield>
    <datafield tag="650" ind1=" " ind2="4">
      <subfield code="0">MAPA20080613327</subfield>
      <subfield code="a">Clasificación de riesgos</subfield>
    </datafield>
    <datafield tag="651" ind1=" " ind2="1">
      <subfield code="0">MAPA20080644178</subfield>
      <subfield code="a">China</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20080000868</subfield>
      <subfield code="a">Wei, Li</subfield>
    </datafield>
    <datafield tag="700" ind1="1" ind2=" ">
      <subfield code="0">MAPA20180012358</subfield>
      <subfield code="a">Wei, Ping</subfield>
    </datafield>
    <datafield tag="773" ind1="0" ind2=" ">
      <subfield code="w">MAP20077100215</subfield>
      <subfield code="t">Geneva papers on risk and insurance : issues and practice</subfield>
      <subfield code="d">Geneva : The Geneva Association, 1976-</subfield>
      <subfield code="x">1018-5895</subfield>
      <subfield code="g">02/07/2018 Volumen 43 Número 3 - julio 2018 , p. 520-538</subfield>
    </datafield>
  </record>
</collection>