• Journal of Korean Society of Forest Science
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• v.96 no.6
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• pp.705-713
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• 2007

The purpose of this study was to understand the organization and the forestry household income applied the result of 2005 Forestry Census and the Economic survey of Forest household in Korea. As a result of the analysis, there are 97,108 households of a forester in korea 2005. Full time forestry household occupied 7,925 families (8.2%), part time forestry household occupied 88,183 families (91.8%). Therefore, the most household of a forester is part time forestry household in korea. The average people of the household of a forester family is 2.7 people. The age of forest manager are sixties nearly, over sixty years old forest manager occupied 51,505 people (53%). The forest management period of forestry households is more than 6 months in 39,229 families (40.4%), less than 1 month in 7,216 families (7.4%). The average area of forestry households owning forest is 4.6ha. According to the type of business, logging families possesses 19.5 ha. The mean income of forestry households is \27,148,000. One of them, forestry income is \6,529,000, which occupied 24.1%. Furthermore, forestry households less than \5,000,000 forest product sales performance occupied 51,633 families, which is 64%. Forest operating costs is \6,566,000 mean per family. In addition, the number of a forestry household resident villages is 11,829 villages, where is 32.8% of whole villages in korea. There are 2,979 villages where reside forestry household in gyeongsangbuk-do.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.2
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• pp.196-213
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• 2019

This paper attempts to analyze the contribution of different socioeconomic factors such as income, age, gender, household composition, education and employment status etc. to the difference between the Capital and Non-Capital Regions in the net wealth inequality of household in Korea. To this end, a two-stage Oaxaca-Blinder type decomposition is employed regarding the regional gap in the inequality of net wealth based upon the Recentered Influence Function of the Gini index for 'the 2018 Household Finance and Living Conditions Survey.' Despite the shortcomings of the survey data on wealth, the findings reveal that regional differences in income, marriage status (divorce), job type (agriculture, forestry and fishery related, and technical and assembly), family type (multi-cultural) variables deepen the regional gap in the net-wealth inequality, but employment status (full-time), job type (administrative and specialized, and service sales), household size variables mitigate the gap, and that regional differences in life cycles play an offsetting role.