PPA 500: Summaries of Literature Review Articles for Teen Birthrate Study
Deborah Franklin
PPA 500
1. Astone, Nan Marie and Sara S. McLanahan. "Family Structure, Residential Mobility, and School Dropout: A Research Note."
Astone and McLanahan examined the hypothesis that the mobility of a student’s nonintact family had an impact on her/his dropping out of high school. They sought an understanding of the reasons for the poorer school performance of adolescents living in single-parent and stepparent households compared to students in households with both parents. Building on others’ research findings that nonintact families move more than intact families and that moving lowers school performance, the authors argued that the residential mobility of nonintact families is an important factor in a student’s decision to drop out of high school.
The authors used data from the 1980, 1982, 1984, and 1986 High School and Beyond Study of students in 1,000 high schools. Their sample of 10,434 young men and women consisted of individuals who were sophomores in 1980 and participated in all four surveys. Their sample included white, black, Mexican and Puerto Rican students.
The data was analyzed in two steps. First, using a multinomial logit model, Astone and McLanahan considered the relationship between family structure and residential mobility. The dependent variable was categorical with three categories: one move, two moves, three or more moves that resulted in a change of schools in the five-year time span between 5th and 10th grade. The independent variables for this regression were family structure, region of residence, urban or rural residence, race and ethnicity, and family socioeconomic status as measured by a composite variable.
The results of this regression supported the authors’ assumption that nonintact families move more than intact families. Race and ethnicity and socioeconomic were not significant factors. Family structure was a significant factor. Children in single-parent and stepparent families were more likely to change schools more than children in intact families. Compared to children in intact families, children in stepparent families were more likely to have moved at least twice.
The second step of the analysis focused on the relationship between family structure and the risk of dropping out of high school. A single-equation logistic model was utilized. The dependent variable was high school success (never left school). The regression was estimated twice, once with independent variables for residential mobility and once without them. The independent variables included in both regressions were family structure, region of residence, urban or rural residence, gender, race and ethnicity, number of siblings, and socioeconomic status.
The authors reported the results of both regressions. The results of regression without the variables for residential were as expected: children in intact families were less likely to have dropped out of high school than children in single-parent and step-parent families. When the variables for residential mobility were included in the regression, residential mobility accounted for 29% of the variation in school success between children in intact families and children in stepparent families. This variation was statistically significant. Statistically insignificant was the 18% difference in school success between children in single-parent families and children in intact families explained by residential mobility.
As expected, the authors found evidence that nonintact families are more likely move than intact families. Does that increased mobility account for the differences in school success between children from intact and from nonintact families? The answer is not clear. Residential mobility accounts for some of the difference in school success, but the difference is only significant for children in stepparent families. The authors call for further and more rigorous research to examine the relationships between family structure, residential mobility and school success.
While the authors did not discuss the other factors that had a significant relationship to school success, they did present the results of their regression estimations. In addition to family structure and residential mobility, gender, race and ethnicity, number of siblings, and socioeconomic status were significant. These results suggest the variables that I might include in the regression equation for high school dropout rates.
2. Evans, William N., Wallace E. Oates, and Robert Schwab. "Measuring Peer Group Effects: A Study of Teenage Behavior." Journal of Political Economy, October 1992, 100 (5): 966-91.
Evans and his co-authors examined the effects of peer groups on teen pregnancy and high school dropout rates. The theory underlying their study is the theory of local finance. The production function for local goods and services includes inputs such as labor and capital and the characteristics of the local population. When households choose a neighborhood to live in, they are also choosing their children’s peer group, specifically their schoolmates.
In studies of both teen pregnancy and high school dropout rates, peer group effects are often cited as important factors. Evans and his co-authors were concerned that peer group effects were overstated in studies using OLS regressions because peer group becomes an endogenous variable when the selection of a neighborhood also results in the selection of the peer group.
The authors sought evidence of the overstatement of peer group effects by using a total of four models. In separate studies of the effect of peer groups on teen pregnancy and on dropout rates, they used two equation models, a single-equation model and a simultaneous equation model, and compared the results. For both studies, they used data from the NLSY. Their sample of 1,453 cases included both black and white women who were under age 20 and lived in a standard metropolitan statistical area (SMSA).
First, the authors looked at peer group effects on teen pregnancy. The dependent variable in both the single-equation model and the simultaneous-equation model was a dichotomous variable, pregnancy before age 20. The independent variables shared by both teen pregnancy models consisted of individual and family characteristics, publicly provided goods and services, and peer group effects. Among these variables were race, family structure, family income, religion, the availability of family planning clinics, AFDC payments, and the percent of disadvantaged students in the respondent’s school. The last variable was on the sole proxy for peer group effects.
The single-equation probit model treated peer group effects (disadvantaged students) as an exogenous variable. When this model was estimated, the coefficient for disadvantaged students was positive and significant. The more disadvantaged students in a school, the more pregnancies among the students. The authors note two other results. Family characteristic variables were more important than peer group effects and taking a sex education class was negatively related to pregnancy.
The simultaneous-equation model treated peer group effects as an endogenous variable. In addition to the shared variables listed above, this equation included exogenous variables that were determinants of the peer group variable (disadvantaged students) but not determinants of pregnancy. These additional variables included the SMSA unemployment rate, median family income, poverty rate, and percent of adults with college degrees. When the simultaneous-equation model was estimated, the coefficient for peer group effects was not significant and was negatively related to pregnancy, the opposite of the results of the single-equation estimation. According to the authors’ interpretation, the single-equation model wrongly attributes to peer group effects the impact of unobservable family characteristics.
The authors then looked at the peer group effects on the probability of being a high school dropout. They used the same data, sample, models, and explanatory variables in this study as in the study of teen pregnancy. The dependent variable was a dichotomous variable, high school not completed by age 20. Once again, in the single-equation model the peer group variable was treated as an exogenous variable. When the single-equation model was estimated, the coefficient for disadvantaged students was positively and significantly related to being a dropout. When the peer group effects variable was treated as an endogenous variable in the simultaneous-equation model estimation, its coefficient was negatively related to dropout and it was not significant. The authors interpreted these results as meaning that unobservable factors that determine peer group are more important than the peer group effects.
In the conclusion, the authors caution that their study does not mean peer group effects are unimportant. For one reason, there may be better definitions of peer group than the one they used, the schoolmates of respondents. The important finding in their study is the effect of endogeneity. When individuals have a choice about the groups they belong to, any study of peer group effects should account for that endogeneity.
I had some concerns about this study and I learned something about the well-known underreporting of pregnancy and abortion in the NLSY. The first concern I had was the use of only one variable as a proxy for peer group, the percentage of disadvantaged students in the respondent’s school. That seems to be a very broad definition of peer group. High school populations really consist of a number of very different peer groups. None of the richness of this variety is reflected in the choice of variables. I also question whether the unemployment rate, median family income, and poverty rates of a respondent’s SMSA are truly exogenous to teen pregnancy. These variables are used in the simultaneous-equation model as determinants of the percent of disadvantaged students but not determinants of teen pregnancy. A number of studies, including my own, contradict this assumption. As to the NLSY and the underreporting of pregnancy and abortion that is known to exist, the authors explained that one possibly reason for this underreporting is the presence of at least one parent when the respondent is being interviewed. Teens are not likely to answer questions truthfully if the answers are going to get them in trouble with their parents!
3. Hoffman, Saul D., E. Michael Foster, and Frank F. Furstenberg, Jr., "Reevaluating the Costs of Teenage Childbearing," Demography February 1993: 1-13.
Hoffman, Foster, and Furstenberg reexamined the costs of teenage motherhood. They consider whether the consequences of teen birth are overstated. Recognizing that teen mothers often come from economically and socially disadvantaged backgrounds, the authors sought to separate the effects of teen parenthood from the effects of their background by comparing teen mothers to their sisters.
Hoffman and his co-authors used data from the Panel Study of Income Dynamics (PSID). The sample for this study included 428 pairs of sisters who were aged 2-14 in 1968 at the beginning of the survey. The women in the sample were aged 21-33 at the time of the study.
In their analysis, they used a fixed-effect model; and they also presented a cross-sectional analysis for comparison. For continuous variables, such as income, they used ordinary regression to estimate the effects of teen birth. For dichotomous variables, such a high school graduation, they used logistic regression.
They acknowledged several problems with their fixed-effect model, including the question of whether women with sisters could be compared to women without sisters and the assumption that the home environment was the same for both sisters with each sister receiving the same resources. They also noted that if a teen mother had less potential than her sister did, the model would overstate the effects of teen birth.
Their fixed-effect model resulted in weaker estimates than the comparable cross-section model, evidence that previous studies had overestimated the consequences of teen birth. However, the fixed-effect model results indicated teen birth does negatively effect economic and social well being. Teen mothers completed fewer years of education. Only 54% graduated from high school, but an estimated 71% would have graduated if they had delayed childbearing until they were 20 years old. The model also estimated teen birth almost doubles the probability the teen will be poor and decreases by more than half the probability she will be middle class. Interestingly, they also found family background had an effect on fertility: half of the sisters in the study shared the same fertility status.
As some other researchers have recently found, the authors concluded that the negative effects of teenage birth were overstated. However, they also found that when family background is taken into account, the effects of teen birth are reduced but not eliminated. Important and significant effects remain for high school graduation and economic well being. As to the public policy issues related to teen birth and the debate about whether teen birth is a cause or a symptom of poverty, the authors support continued efforts to reduce teen childbearing.
Applying this study to this research project, I sought variables to represent family background in my regression model, including family income and poverty status. This study also suggests the existence of an endogenous relationship between teen birth and high school achievement. As high school achievement is one of the explanatory variables in my teen birthrate regression model, that relationship will have to be taken into consideration.
4. Jencks, Christopher and Kathryn Edin, "Do Poor Women Have a Right to Bear Children?" The American Prospect Winter 1995:43-52.
Jencks and Edin broaden the discussion of teenage childbearing. They challenge the assumptions about the effects on teenage childbearing on both mother and child and question the approach the welfare reform. First, the authors challenge the assumption that parents could support their children if they just wait until after their teen years to have children, get married, and stay in school. Then, they consider the policy implications of this assumption, especially welfare reform.
Can all women who want them afford to have children? Jencks and Edin claim that we like to think that all women can, if only they delay childbirth until they are adult, married, and high school graduates. The authors argue otherwise and call such beliefs "fairy tales."
The first fairy tale they debunk is that if women wait until they are out of their teen years to have a child, they will be able to raise their child without government support. The basis for this fairy tale is that the correlation between teen childbearing and the subsequent need for government assistance is mistaken for causation. The authors point out that women who have children as teens differ in many ways from those who do not. They come from poorer families, have more trouble in school, and are more likely to have dropped out of school (before getting pregnant). In other words, their chances of being economically self-sufficient are small no matter how old they are when they had their first child.
If single mothers marry, they will not need welfare. This is another fairy tale. Women who have a child outside of marriage are more likely to need welfare than married mothers. But, having a husband is not the answer. The husband must make enough money to help support the family at a level above the poverty level. Otherwise the women is better off on welfare. After estimating the wage a man would have to earn, the number of men making that wage, and the number of women who want children, the authors state that there are acceptable potential husbands for only two-thirds of the women who want children. Women who marry men who earn low wages or work sporadically will still need welfare, as will women whose ex-husbands do not pay child support.
The belief that high school graduation equals a good job is the third fairy tale. Even if women graduate from high school, they are unlikely to be able to support their children on their earnings. In 1989, less than half of the 25-34 year old working women made $15,000, the wage needed for a single mother to support two children. One study found that women who had poor academic skills but still graduated from high school made only $1 to $1.25 more an hour than high school dropouts.
Even if they are older than 19, married (or willing to be if they can find someone acceptable) and a high school graduate, some women who want children can not afford to have them. If this is true, as the authors claim, what should the government’s response be? Does government declare that these women can not have children? If not, then is government going to provide assistance to these women and their children? Jencks and Edin argue that these are the key questions in the welfare reform debate.
There are opponents of welfare that want to prevent the poor from having children. The authors refer to them as "baby blockers." Some baby blockers support such welfare reforms as eliminating food stamps, Medicaid, and welfare payments for single mothers as a means to discourage unwed motherhood. The authors do not believe that even such drastic reforms will prevent women from having children they can not afford to raise without assistance. Policy makers have a limited number of choices: provide no assistance and accept the dire consequences for children, provide limited assistance such as food and medical care and again accept dire consequences for children, or provide for basic needs with the intent to ameliorate some of the dire consequences for children.
5. King, Randall H., Steven C. Myers, and Dennis M. Byrne, "The Demand for Abortion by Unmarried Teenagers," American Journal of Economics and Sociology, April 1992: 223-235.
King, Myers, and Byrne studied the demand for abortion to determine to what extent economic factors influenced teens’ decisions to give birth or abort. They developed a model based on the economic theory of demand. Economic choice is influenced by the direct price and the opportunity costs of the choice. In this study, the direct price of abortion was not available, so only opportunity costs were considered. The opportunity cost of abortion is directly related to the direct and indirect costs of giving birth and raising a child. According to the theory of demand, teens with higher opportunity costs of birth would be more likely to choose abortion than teens with lower opportunity costs.
The authors used data from the National Longitudinal Study of Youths (NLSY) and studied 16-19 year old, never married teens in their first pregnancy. All individual characteristics were from the year preceding the pregnancy to control for the direction of causality. The dependent variable in their probit function was the decision to abort or give birth. The dependent variable was a dummy variable, with the value of zero for birth and one for abortion. The independent variables included predicted hourly wage rates, local unemployment rates, family income, poverty status, high school and college enrollment status, age, ethnicity, and frequency of religious attendance. Wage rates, unemployment rates, and school enrollment status were measures of the opportunity costs. The authors chose to use the frequency of religious attendance to proxy a teen’s religious commitment rather than dummy variables for particular religions for two reasons. One, other studies have shown that a particular religious affiliation in itself is not a significant factor in teen’s pregnancy decisions. Two, they considered the level of religious commitment to be a more appropriate measure of taste for abortion.
The authors report that their regression results were consistent with their expectations and that all independent variables, except high school enrollment, were statistically significant. The authors conclude that high unemployment rates, low wages, and few job opportunities lower the opportunity costs of giving birth and are associated with a lower probability of abortion. Comparing teens who gave birth to teens who opted to abort, the study found teens who aborted lived in communities with lower unemployment rates, higher family incomes, and fewer families in poverty. Teens who aborted were also more likely to be enrolled in college, but equally likely to be enrolled in high school. Black teenagers gave birth more frequently than whites, while Hispanics had birth and abortion rates similar to whites. Teens with lower predicted wages and teens living in poverty were less likely to abort. Higher family income was associated with higher probability of abortion. More frequent religious attendance was associated with lower abortion rates. White teens were most likely to abort, even when all other variables were held constant. The authors conclude economic factors are important in teens’ decision to abort or give birth and point to the noteworthiness of opportunity costs. Teens in better economic circumstances are more likely to opt for abortion. The authors predict that the teen abortion rate will increase in the future as wages for teens continue to increase.
The authors raised concerns about the underreporting of abortion in the NLSY survey data that I believe make their results less reliable. The data from the 1979 NLSY survey was obtained through personal interviews, which is a factor in the underreporting of abortion. The 1984 survey introduced confidential survey forms. The authors assumed that the confidential forms increased the accuracy of abortion reporting, an assumption born out by comparing survey data with other sources and by the doubling of the number of abortions. Their results should be considered with the underreporting of abortion and the changes in abortion reporting in mind.
Applying this study to my paper, I included variables for race/ethnicity, high school dropout rates, family income, and family poverty in my regression model.
6. Leibowitz, Arlene, Marvin Eisen, and Winston Chow. "An Economic Model of Teenage Pregnancy Decision Making," Demography February 1986: 67-77.
Leibowitz, Eisen, and Chow sought an understanding of how pregnant teens make decisions. Their study examined the factors related to teens’ decision making in the face of a pregnancy. Their premise was that economic factors influence teens’ choices to abort, to give birth without marrying, or to marry before giving birth.
Leibowitz and her co-authors based their theory on the economic model of fertility. This model, used primarily to study the fertility of married women after their families are completed, assumes that a rational decision-maker considers the costs and benefits of bearing an additional child. Usually, one of the difficulties with this model is that it looks at childbearing decisions after the fact, making it difficult to determine causality. Because the Leibowitz study measured variables before the childbearing decision was made, determining causality was not a problem. By eliminating this problem, Leibowitz and her co-authors were able to investigate the impact of welfare on teens’ pregnancy outcomes.
Leibowitz and her co-authors utilized a conditional logit function. The dependent variable was the pregnancy outcome, i.e. abortion, unwed birth, or marriage before birth. They used cross sectional data. Their sample consisted of 386 pregnant teens, aged 13-19, in Ventura County, California. The teens, all experiencing their first pregnancy, were interviewed twice, once in their first trimester and then six months after either an abortion or birth.
The model assumed that the pregnant teens weighted both the tangible and intangible benefits and costs of their choices. In the regression model, dummy variables proxied for the intangible benefits and costs. The variables in the model included proxies for the teen’s value of time, public and self-support, ethnicity, and religion. The value of time was measured by school enrollment status, with time being more valuable for teens enrolled in school. If the teen was self-supporting, rather than being supported by her family or friends, the self-support dummy variable equaled one. The public support dummy variable equaled one if the teen’s family received welfare or if Medi-Cal paid for the teen’s medical care. There were dummy variables for Mexican-American and for Catholic. The variable for Catholic was not significant and was dropped from the final regression equation.
Interesting findings in this study include age-differences in the teens’ decision making. Women aged 16 and 17 were more likely to give birth than 18 or 19 year olds, perhaps because the older group could more easily obtain abortions. Teens who reported higher grades in high school were more likely to choose abortion than teens reporting lower grades. Teens who had already dropped out of high school or otherwise completed their schooling were more likely to give birth than teens who were still enrolled in school. Mexican-American teens were more likely than white teens to give birth without marrying and to get married before giving birth. Just as the authors had hypothesized, teens whose families were on AFDC or had Medi-Cal coverage were more likely to give birth and stay single. The authors concluded that the effect of public aid can be detected: teens receiving public support are more likely to carry their pregnancy to term and to stay single.
The authors acknowledge problems with their economic model of decision-making and with their sample. Their economic model of decision-making requires the pregnant teen to think rationally about the expected costs versus the expected benefits of having a child. It assumes some knowledge of future earnings and the actual costs of raising a child, as well as a rationality and maturity which may be lacking in teens. Also, their sample consists of pregnant teenagers who visited a free clinic in Ventura County, California. The clinic served women who "perceived their pregnancy as a problem" and was "the primary intake point for nearly all women who received abortions" in the county. The sample is very biased towards pregnant teens who are not planning to give birth. While the authors admit their sample is biased, they believe it is representative of teens who might be considering abortion. However, I believe this bias makes suspect their conclusions about why some teens choose to give birth. The sample is also fairly small; just 297 teens were in the final sample.
Applying this study to my research paper, I included variables for Mexican-American cultural heritage, high school dropout rates, and female high school achievement in the regression model.
7. Lundberg, Shelly and Robert D. Plotnick. "Adolescent Premarital Childbearing: Do Economic Incentives Matter?" Journal of Labor Economics, 1995, vol. 13, no. 2: 177-200.
Lundberg and Plotnick focused on the impact of state’s family planning, abortion, and welfare policies on teenage childbearing. They hypothesized these public policies affected teens’ decisions by changing the benefits and costs associated with pregnancy and childbirth. Their economic approach posited that teens’ choices would be influenced by both long-run costs and short-run costs and that states’ policies altered these costs.
They utilized data from the National Longitudinal Study of Youth (NLSY). Their sample population was 1,718 white and black women, aged 14-16. Their model was a three-stage nested logit model. The three stages were three sequential decision points: the decision to become pregnant, the decision to abort or carry the pregnancy to term, and the decision to marry or not before giving birth. The authors included public policy variables in each stage of the regression. The four policy variables for the first stage model were the short-run costs of family planning services. Three variables measured the availability and ease of access to family planning services. The fourth variable was a dummy variable for state laws and regulations restricting contraception. The policy variables for the second stage, the decision to abort or carry to term, were measures of the short-run costs of abortion. These policy variables consisted of three dummy variables related to state funding for abortion services (funding for all abortions, funding for all medically necessary abortions, funding for abortions only if the pregnancy was a result of rape or incest), an index for the restrictiveness of the state abortion laws, and an indicator of the percentage of counties within the state with abortion providers. The public policy variable in the third stage was the amount of AFDC benefits for teens who gave birth but did not marry.
In addition to the public policy variables, the three stages of the model included family background variables. Both the white and black teen models included variables for the teens’ family structure, whether their mothers worked, their mothers’ education, the number of siblings, and religiosity. For the white teen model, dummy variables for Baptist and Catholic and for foreign language spoken in the home were added. For the black teen model those variables were not included, but a dummy variable for being born in the south was.
The models for black teens yielded an unexpected result: no policy variable had a significant effect. However, the public policy factors were significant in the models for white teens. For whites, the higher the AFDC payment, the more likely the pregnant teen was to give birth without marrying. White teens were more likely to choose abortion if the state funded abortion services than if the state did not. States with more conservative policies regarding abortion and contraception and with lower AFDC benefits were associated with higher premarital birthrates. Also associated with higher birthrates were mother-only households. Teens with mothers who worked and had higher levels of education were less likely to become teenage mothers. In their conclusion, the authors noted mixed results. Economic incentives that affect the cost of becoming a teenage mother mattered to white teens, but did not seem to make a significant difference for black teens. For policy makers, the authors hold out this hope, "For those interested in developing public policies to reduce premarital childbearing, evidence that economic incentives matter is good news." (page196) However, they also caution that a state’s public policies on family planning may not be causal factors in teens’ pregnancy decisions but reflections of the values and mores of the state’s residents.
Because the authors looked at pregnancy rates as well as birthrates, they had to take into account abortion rates. They found pregnancy and abortion were underreported. Black teens were estimated to underreport abortions by as much as 80% in the surveys. White teens underreported about one-third of their abortions. This underreporting could have led to biased estimates, but the authors do not believe that it did. However, it did make interpreting any results about black teens problematic and was cited as a possible reason for the inconclusive results. In my opinion, the data was questionable enough to discount any findings about black teens and to require some skepticism in interpreting the results for white teens.
Applying this analysis to my research paper, I included a variables for working mothers with children under 18, for female-headed households, and for the availability of family planning services.
8. Plotnick, Robert D. "The Effects of Attitudes on Teenage Premarital Pregnancy and Its Resolution," American Sociological Review December 1992:800-811.
Plotnick drew on problem behavior theory to investigate the effects of teens’ attitude on premarital childbearing. Problem behavior theory posits that a person’s behaviors are functions of that person’s perceived environment and personality system. The personality system has three components: personal belief structure, motivational-instigational structure, and personal control structure. Plotnick hypothesized that these components of the personality system would have different effects on teens’ decisions to become pregnant and on the resolution of the pregnancy.
Plotnick used data from the NLSY and limited his sample to white girls age 14 to 16 in 1979 who reported that they had never been married or had a child. His sample consisted of 1,142. He used a two-stage nested logit model to examine the effects of teens’ attitudes and family background. Premarital pregnancy is the dependent variable in the first stage. The resolution of the pregnancy, abortion, married before birth, or birth out of wedlock, is the dependent variable in the second stage.
Several of the explanatory variables are components of the personality system. Self-esteem and locus of control are part of the personal belief structure. Egalitarian attitudes about gender roles, favorable attitudes about school, and high educational aspiration are part of the motivational-instigational structure. Religiosity is part of the personal control structure. Plotnick referred to these variables as attitudes.
Acknowledging that family characteristics also are significant, Plotnick included a number of variables for family background in this model. The continuous variables are mother’s years of education and number of siblings. The are dummy variables for different religions, birth in the south, living in the south, the presence of a working woman in the household, and family structure.
Plotnick estimated the two-stage logit model two different ways. The complete model included the variables for attitude and for family background. The other model included only family background variables. Results were reported separately for the complete model and the family background only model.
Plotnick’s estimates for the complete model indicated that attitudes have an impact on premarital pregnancy and its resolution. The attitude variables were significantly related to premarital pregnancy: locus of control, attitudes about gender roles, attitudes toward school, educational, and occasional attendance at church (Catholic). Attitudes about gender roles and occasional attendance at church (Catholic) were positively related to premarital pregnancy, the other attitude variables are negatively related. Five family background variables were significantly related to premarital pregnancy. The dummy variable for living with a mother and a stepfather was positively related to premarital pregnancy. Negatively related were mother’s education and the dummy variables for Protestant (non-Baptist), Catholic, and Jewish/Other.
The attitude variables were also significantly related to the resolution of pregnancy. Interestingly, educational expectations was significantly and positively related to both abortion and marriage before birth. Poor church attendance (Catholic) and occasional church attendance (Catholic) were also positively and significantly related to both abortion and marriage before birth. Plotnick notes that the relationships between religiosity and resolution of pregnancy are puzzling and merit further study.
When attitude variables were left out of the regression model, as is the case for the family background only model, seven family background variables were significantly related to premarital pregnancy. The dummy variables for living with mother and stepfather, for other family structure, and for a working woman in the home were significantly and positively related. Significantly and negatively related were mother’s education and dummy variables for Protestant (non-Baptist), Catholic, and Jewish/Other.
Plotnick’s findings are for white teens. While he selected only white teens to eliminate the problem of underreported pregnancy and abortion in the NLSY, by excluding blacks, Asians and Hispanics he limits the interpretation of his findings. In a multi-ethnic state such as California, this is a serious limitation.
9. Yamaguchi, Kazuo and Denise Kandel, "Drug Use and Other Determinants of Premarital Pregnancy and Its Outcome: A Dynamic Analysis of Competing Life Events," Journal of Marriage and the Family May 1987:257-270.
Yamaguchi and Kandel considered to what extent the use of marijuana and other illicit drugs predicts premarital pregnancy and its outcome, abortion or birth before or after marriage. They hypothesized that drug using teens, characterized as nonconformists and risk takers, were more likely to become pregnant before marriage and more likely to abort than to give birth. This hypothesis was partially based on their knowledge of the positive relationship between drug use and sexual activity.
The sample population for their study was 706 young women, with an average age of 24.3, who had participated in a survey as 10th and 11th graders in 1970-71. A follow up survey was conducted in 1980-81. The original random sample, from which the smaller follow up sample was taken, was representative of high school students in New York. The method for collecting data in both surveys was personal household interviews.
The authors used two methods to examine the relationship between drug use and premarital pregnancy and its outcome. These two methods, an event history analysis and a logistic regression, were utilized because the authors considered the outcomes of premarital pregnancy to be determined by a two-step process. The first step is the occurrence of the pregnancy. An event history analysis was used to estimate the probability of a premarital pregnancy. This analysis method estimates coefficients that are similar to regression coefficients. The second step is the outcome of the pregnancy. A logistic regression, which is similar to an ordinary least squares regression, was used to predict the likelihood of a specific outcome. The independent variables were the same for both analytical methods and were a combination of time-constant and time-varying variables. The time-constant variables were social, behavioral, and family characteristics: race, father’s education, family psychiatric problems, grade average, school nonattendance, depression, parental control of dating, and extent of peer activity. The data for the time-constant variables for the event history analysis used to predict the probability of pregnancy were from the first survey. Data for the time-constant variables in the logit regression to predict pregnancy outcomes were those reported at the time of the premarital pregnancy. The time-varying variables were marijuana use, other illicit drug use, cohabitation, school enrollment status, and age.
Current and former illicit drug use (other than marijuana), co-habitation, and being a high school dropout were strong predictors of premarital pregnancy. Also predicting a higher rates of pregnancy were being black, poor grades, and high levels of peer activity in high school. A history of family psychiatric problems increased the rate of pregnancy. Being over 22, compared to under 18, predicted a lower premarital pregnancy rate.
Race, school enrollment status, and illicit drug use had strong effects on pregnancy outcome. Race had the strongest effect. The odds of a black woman having a premarital birth were 16.4 times as large as for nonblacks. The odds of premarital birth were 5.3 times as large for high school dropouts than for nondropouts. Childbirth was preferred over abortion by women who had dropped out of high school. Women who were post high school students preferred abortion. Illicit drug use had a strong, positive effect on abortion. Some covariates had a strong impact on both premarital pregnancy and its outcomes: illicit drug use, being black, high levels of peer activity in high school, and being a high school dropout. The authors conclude that teens who use illicit drugs are at considerable risk for premarital pregnancy and, once pregnant, they are more likely to choose abortion than childbirth.
Two aspects of the data collection raise concerns. In the first survey in 1970-71, only 10th and 11th graders were interviewed. This sample excluded any teens who had dropped out of high school before the 10th grade. The teens considered to be most at risk for premarital pregnancy, young dropouts, were left out of the sample, resulting in a biased sample. Also, the face-to-face interview process has been consistently shown to result in underreporting of pregnancy and abortion. Any analysis of Yamaguch’s and Kandel’s findings should take these concerns into account.
10. Zimmerman, Shirley L., and Constance T. Gage, "A Potential Case of Social Bankruptcy: States’ AFDC Payments and Their Teen Birth Rates," Policy Studies Journal 1997:109-123.
Zimmerman and Gage analyzed the relationship between states’ AFCD payment levels and their teen birthrates as a means to examine the connection between welfare and teen childbearing. Unlike most other studies of teen birthrates, which are based on one theory, their study considered several perspectives and incorporated several theories. The rational choice theory that people make choice that are rewarding to them assumes that AFDC benefits are incentives for teens to give birth and remain single. The expectancy theory that people act in certain ways if the effect of their action will help them achieve their goals suggests that teens who are overwhelmed by their circumstances are not capable of seeking out alternatives to early and unwed childbearing. The cultural model holds that subgroups have values, attitudes and expectations outside the mainstream. Teens living in a subgroup of unsuccessful people will have lowered expectations and socially undesirable values. Family structure and economic hardship models focus on individual characteristics, and studies in these modes have found independent effects for family structure and income. The social capital theory is concerned with the norms, habits, and attitudes that develop in families, social groups, and communities. According to the social capital theory, frequent moving has undesirable effects.
To analyze the effects of AFDC payments on teen birthrates, Zimmerman and Gage utilized a pooled time series covering a 30-year time span at five different time points: 1960, 1970, 1980l, 1985, and 1990. The authors estimated three different regression equations using SPSS. States’ teen birthrate was the dependent variable for all three regressions. The first regression was an ordinary least squares (OLS) regression, referred to as the random effects model. The independent variables were states’ racial composition (percent of whites), level of AFDC payments, and their rates of divorce, poverty, unemployment, and population change. The second regression included all of these variables and a dummy variable for each observation year, to control for effects of the observation year. The third regression included all the variables from the other two regressions and a state dummy variable to control for unobserved state heterogeneity. The state dummy variable controlled for social, cultural, and political factors that could be associated with states’ teen birthrates.
Two trends, steady changes over time, were noted. The standard deviations for states’ poverty rates reflected a growing gap between states with the highest and lowest poverty rates. There was a narrowing gap in between the states with the highest and lowest divorce rates. Interestingly, Mississippi had the lowest AFDC benefits every year, except 1990, and had the highest teenage birthrate each year.
For the first regression model, the random effects model, the estimated coefficients for poverty rates and rates of population change were significant and positive. The coefficient for AFDC was significant and negative. The coefficients for divorce rates, unemployment rates, and percent of whites were not significant. The results for the second model, with the dummy variables to control for the effects of the observation years, were notably different. All the coefficients were significant. AFDC was negatively related to teen birthrates, as was race. Poverty rates, divorce rates, rates of population change, and unemployment rates were positively related to teen birthrates. The third model, which included dummy variables for observation years and states, also yielded different results. The coefficient for AFDC was not significant, though it was still negatively related to teen birthrates. Only poverty rates and rates of population change were significant, and both were positively related to teen birthrates.
The authors concluded that there was no evidence of a positive relationship between higher AFDC payments and teen birthrates. The regression results challenge the assumption, based on the rational choice theory, that incentive effects of welfare apply to teen birthrates. The authors suggest that what is important in low AFDC states is the lack of alternatives that might lower teen birthrates. Their findings raise questions about whether public policy should be focused on reducing poverty, not AFDC payments, and ameliorating the effects of population mobility to lower the teen birthrate.
Taking their suggestion that their results warrant further investigation, I will add a variable for the rate of population change to my original multivariate regression equation and perform tests (e.g. chow tests) to see if that variable improves the regression.
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