過去的研究顯示未成年生育對未成年母親及其小孩的健康會有不利的影響，因此未成年生育一直是公共衛生關注的問題。台灣過去未成年生育相關因子的研究著重在個人層次的研究，但個人層次只能回答在不同危險和保護因子之下未成年生育的機率，如要回答生育率的變化譬如未成年生育率的時間趨勢和各地區別的差異，可能要仰賴生態層次的研究加以解釋。根據國外的文獻未成年生育率地區之間的差異或時間上的變化與社會經濟環境因素有關如地區人民的收入、教育狀態、青少年結婚率、離婚率、都市化程度、人口密度、人口種族的組成、勞動力狀況、避孕和流產服務的可利用率。本生態研究要探討縣市的社會經濟指標與未成年生育率的關係，找出影響各地區未成年生育率和生育率時間上改變的重要因素，以做為相關政策上參考。
本研究為以台灣地區23縣市為樣本的生態研究，分別收集1977、1982、1987、1992、1997、2002年依變項未成年生育率(0/00)，自變項人口密度(人/平方公里)、家庭收入(千元/年)、未成年母親的高中教育達成率(%)、15歲以上女性高中教育達成率(%)、15歲以上人口勞動參與率(%)、女性勞動參與率(%)、失業率(%)等社會經濟指標，aggregate資料的收集分別來自台閩地區人口統計、家庭收支調查和人力資源統計年報。資料以MS Excel鍵檔後，以SAS統計軟體進行統計分析，統計方法包括以Pearson correlation（r）探討未成年生育率和社會經濟指標的相關性及每隔五年的改變變化，線性複迴歸分析社會經濟指標對未成年生育率的預測性及每隔五年的改變變化，必要時再以主成分分析解決複迴歸分析時自變項彼此相關性過高所產生共線性的問題。
結果顯示，各地區未成年生育率與社會經濟指標有關，人口密度、每戶家庭每年平均收入、15歲以上女性高中教育達成率與未成年生育率有顯著的負相關，2002年相關係數分別為-0.74、-0.6、-0.8，而且相關性2002年較1977年強（r=-0.59、-0.5、-0.8），15歲以上勞動力參與率、15歲以上女性人口的勞動力參與率與生育率在1987(r=0.49、 0.43)和1992(r =0.51、 0.53)年有顯著的正相關，1997和2002年則沒有顯著的相關性，失業率和未成年生育率則一直沒有顯著的相關性。我們發現1977-2002年ln人口密度和未成年生育率呈現很強的線性負相關，相關係數分別為分別為-0.76、-0.71、-0.89、-0.91、-0.91、-0.92。將ln人口密度與其他社會經濟指標放入複迴歸分析中，結果發現社會經濟指標對同年未成年生育率有很高的預測力(R-squares =0.59、 0.76、 0.88、 0.88、 0.84、0.85) 對五年後的未成年生育率也有很高的預測力(R-squares =0.74、0.84、 0.90、 0.88、 0.85)。由於社會經濟指標彼此間高度相關，因此進一步進行主成分迴歸分析發現與未成年生育率有顯著相關之主成分，大都與社會經濟發展程度有關。每隔五年(1987-1982、1982-1987、 1987-1992、1992-1997、1997-2002) 經濟指標的改變（或改變的分率）與同時段或下一時段未成年生育率的改變都顯現弱度相關。
都市化是影響未成年生育的重要因素，都市化地區未成年生育率較低，可能因都市化地區是較競爭的經濟環境，年輕人要維持經濟的競爭優勢必須避免早期生育，都市化程度低也可能代表地區資源分配不夠如經濟資源、教育資源、醫療資源等，而使得未成年生育率高，因此如果要降低未成年生育率就必須從地區的經濟資源、教育資源、醫療資源等結構性問題做改進，對於都市化地區透過何種因素影響生育的選擇仍需進一步研究探討。

Studies have shown that teenage childbearing has adverse effects on both the health of adolescent mothers and their children. Consequently, adolescent fertility has been a public health issue. Studies on the risk and protective factors for adolescent childbearing in Taiwan have been at the individual level factors. However, the individual level studies can only address the probability of teenage childbearing for an individual. If causes for the variations of adolescent fertility rate across regions and over time were to be addressed, then we should rely on ecological studies. According to literature from other countries, the variations were related to socioeconomic environment such per capita income, education, marriage rates among the adolescent, divorce rates, urbanization, population density, racial/ethnic composition, labor force participation, contraception and accessibility of abortion clinics. We are lack of similar studies in Taiwan. This ecological study aims to investigate the relationship between various socioeconomic indicators and adolescent fertility rates, and identify important factors that affect the rates in different counties and cities and their trends. The results could be used in making related policies.
This is an ecological study where aggregation units are 23 counties and cities in Taiwan. It covers the years of 1977, 1982, 1987, 1992, 1997, and 2002. Contextual (or aggregate) variables include adolescent fertility rate as the dependent variable in regression analysis, population density, average annual family income, proportion of adolescent mothers with high school diploma, proportion of female population age 15 and older with high school diploma, proportion of population age 15 and older in paid-labor force, proportion of female population age 15 and older in paid-labor force, and unemployment rate as the independent variables. The aggregate data were taken from Taiwan-Fukien Demographic Fact Book Republic of China, Report on the Survey of Family Income and Expenditure in Taiwan Area of Republic of China and Year Book of Manpower Statistics Taiwan Area of Republic of China. Date were stored with MS Excel, and analyzed with Statistical Analysis Software (SAS). Pearson (or zero-order) correlation coefficients (r) were calculated for the bivariate association between the contextual variables. Multiple linear regression was performed to determine the relation between the dependent and independent variables. If necessary, principal component regression was employed to tackle the multicollinearity problem which exists when the independent variables are highly correlated.
Our results show that adolescent fertility rate has significantly inverse relationship with population density, average annual family income, and proportion of female population age 15 and older with high school diploma in bivariate analysis. The inverse relation was stronger in 2002 (r=-0.74，-0.6，and -0.8, respectively) than in 1977(r=-0.59，-0.5，and -0.8, respectively). Both proportion of population age 15 and older in paid-labor force, and proportion of female population age 15 and older in paid-labor force were positively and significantly associated with the adolescent fertility rate in 1987 (r=0.49，and 0.43, respectively) and 1992 (r =0.51，and 0.53, respectively), but the bivariate associations were not statistically significant for 1997 and 2002. Unemployment rates were not significantly with the adolescent fertility rates for all the studied years. It was found that the population density after natural logarithm transformation had very strong inverse linear relationship with the adolescent fertility rate for all the studied years (r=-0.76, -0.71, -0.89, -0.91, -0.91, and -0.92, respectively) and the transformed observations were later used in multiple linear regression analysis. The socioeconomic indicators in any given year could predict the adolescent fertility rate in the same year or 5 years later with high power as indicated by the R-squares (0.59, 0.76, 0.88, 0.88, 0.84, and 0.85, respectively for the same years; 0.74, 0.84, 0.90, 0.88, and 0.85, respectively for 5 years later). Due to multicollinearity between the socioeconomic variables, principle component regression was then performed. Several significant principal components related to degrees of socioeconomic development were identified. Changes (or proportion changes) in the socioeconomic indicators during each 5-year period (1987-1982, 1982-1987, 1987-1992, 1992-1997, and 1997-2002), on the other hand, were weakly associated with the changes (or proportion changes) in adolescent fertility rates over the same 5 year period and the next 5 year period .
Urbanization appears to be a very important predictor of adolescent fertility rate. The more urbanized is a city or a county, the lower is its adolescent fertility rate. A more urbanized city means a more economically competitive environment. To maintain competitiveness or have edges over competitors, youngsters should avoid early childbearing. Lesser degree of urbanization could also mean lesser regional resources such as economic resources, educational resources, medical resources and etc. which could result in higher adolescent fertility rate. In order to lower adolescent fertility rate, we must start to solve the structural problems.

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