본문

KDI연구

KDI연구원들이 각 분야의 전문보고서를 제공합니다.

국토인프라

단행본

Community Development and Human Reproductive Behavior

페이스북
커버이미지
  • 저자 홍사원(洪思媛)
  • 발행일 1979/06/01
  • 시리즈 번호 26
원문보기
요약 1. Interaction among Individual and Community Variables
Interaction among the independent variables existed for all
dependent variables (fertility and fertility limitation on both the
individual and community levels). The effects, however, were
small enough to be excluded from the analyses.

2. Community-level Analysis
1)Fertility Differentials
(1) More than two-thirds of the variance in community
fertility levels was explained by the independent
variables employed. The aggregate variables explained
more than the structural variables for both fertility
measures : 3.8 times more for the number of children
ever born and 2.1 times more for the live
birth-woman ratio
(2) The average age at marriage(for the live
birth-woman ratio) and the average age(for the
number of children ever born) were the most
powerful predictor variables
(3) Other than these two functionally related
demographic variables, the availability of electricity
and the proportion of migration were the two
variables with the most predictive power for
differential fertility levels among villages.

2)Fertility Limitation Differentials
(1) For the Fertility Limitation Differentials, different
results were found. A little less variance was
explained than in the case of fertility. both aggregate
and structural variables explained more of the
variance in "ever use" than in "current use."
(2) The structural variables explained more of the
variance in "current use" than the aggregate
variables(both in absolute and in relative size),
whereas the aggregate variables explained more than
the structural variables for "ever use."
(3) The variables with the most predictive power were
village rules (structural variable) and the proportion
of membership holders(aggregate variable).

3)Summary of Community-level Analysis
Structural variables explained more for "current"
reproductive differentials. The aggregate variables explained
more for every dependent variable except "current use." But
the relative size of the variance explained by the structural
variables was always greater for both current
measures(fertility and fertility limitation behavior)

3. Individual-level Analysis
1)Fertility Differentials
(1) About half of the total variance in fertility was
explained by employing of the variables. Almost 55
times more variance in fertility behavior was
explained by the individual variables than by the
structural variables. The aggregate variables explained
2.6 times more than the structural variables.
(2) Most of the predictive power was attributable to a
single variable : age. After age was controlled, only
6% of the variance was explained by the variables
(3) When the women were grouped into five groups, the
effects of the individual variables were smaller for
the oldest age group. T도 aggregate and structural
variables, in contrast, had relatively more predictive
power. The magnitude of their effects, however, was
still small.
(4) Community-level variables, both structural and
aggregate, were closely related to the standard
deviations of fertility behavior for each village.

2)Fertility limitation Differentials
(1) Individual-level characteristics explained most of the
variation in individual fertility limitation
measures(with a little less explanatory power than for
fertility). Little of the variance was explained by the
community variables.
(2) The individual variables explained more variance in
"ever use"than in "current use," whereas the
aggregate and structural variables explained more in
"current use."
(3) The number of sons was the most critical factor in
differentiating fertility limitation behavior. Forty-nine
percent of the variance for "ever use" and 17% for
"current use" were explained by this variable alone.
(4) After controlling for the number of sons, the
variables directly related to family planning(FP)
(contact with FP workers, knowledge of places for
FP or abortion advice, sources of FP information,
reference group's FP approval) were greater in
predictive power, while most of the explanatory
power was found among other individual variables.

3)Summary of Individual-level Analysis
For individual-level reproductive behavior individual
characteristics explained most of the variance, especially age
(for fertility) and the number of sons(for fertility limitation),
although there were some variations.
같은 주제 자료 이 내용과 같은 주제를 다루고 있는 자료입니다.

같은 주제의 자료가 없습니다.


※문의사항 미디어운영팀 고정원 전문연구원 044-550-4260 cwkoh@kdi.re.kr

가입하신 이동통신사의 요금제에 따라
데이터 요금이 과다하게 부가될 수 있습니다.

파일을 다운로드하시겠습니까?
KDI 연구 카테고리
상세검색