Moreover, research shows that even after one controls for a range of family background differences, children who grow up living in an intact household with both biological parents present seem to do better, on average, on a wide range of social indicators than do children who grow up in a single-parent household (McLanahan and Sandefur, ) ), that have documented the impact divorce may have on children. Sun (cited in ValderValk et aI., ) found children of divorced parents may have a lower sense of psychological well-being than children who grew up with intact families. Research also confirms that children of divorced parents may experience emotional problems such as INTRODUCTION. A long tradition of sociological research has examined the effects of divorce and father absence on offspring’s economic and social-emotional well-being throughout the life course 1 Overall, this work has documented a negative association between living apart from a biological father and multiple domains of offspring well-being, including education, mental health, family
The impact of family structure on the health of children: Effects of divorce
Try out PMC Labs and tell us what you think. Learn More. notecnirp ahanalcm. llenroc hcatarual. yelekreb redienhcsjd. The literature on father absence is frequently criticized for its use of cross-sectional data and methods divorce and effects on children research paper fail to take account of possible omitted variable bias and reverse causality. We review studies that have responded to this critique by employing a variety of innovative research designs to identify the causal effect of father absence, including studies using lagged dependent variable models, growth curve models, individual fixed effects models, sibling fixed effects models, natural experiments, and propensity score matching models.
Our assessment is that studies using more rigorous designs continue to divorce and effects on children research paper negative effects of father absence on divorce and effects on children research paper well-being, although the magnitude of these effects is smaller than what is found using traditional cross-sectional designs. These findings are of interest to family sociologists and family demographers because of what they tell us about family structures and family processes; they are also of interest to scholars of inequality and mobility because of what they tell us about the intergenerational transmission of disadvantage.
The literature on father absence has been criticized for its use of cross-sectional data and methods that fail to account for reverse causality, for omitted variable bias, or for heterogeneity across time and subgroups.
Indeed, some researchers have argued that the negative association between father absence and child divorce and effects on children research paper is due entirely to these factors. This critique is well founded because family disruption is not a random event and because the characteristics that divorce and effects on children research paper father absence are likely to affect child well-being through other pathways.
Finally, there is good evidence that father absence effects play out over time and differ across subgroups. Unless these factors are taken into account, the so-called effects of father absence identified in these studies are likely to be biased. Researchers have responded to concerns about omitted variable bias and reverse causation by employing a variety of innovative research designs to identify the causal effect of father absence, including designs that use longitudinal data to examine child well-being before and after parents separate, designs that compare siblings who differ in their exposure to separation, designs that use natural experiments or instrumental variables to identify exogenous sources of variation in father absence, and designs that use matching techniques that compare families that are very similar except for father absence.
In this article, we review the studies that use one or more of these designs. We limit ourselves to articles that have been published in peer-reviewed academic journals, but we impose no restrictions with regard to publication date note that few articles were published before or with regard to the disciplinary affiliation of the journal.
Although most articles make use of data from the United States, we also include work based on data from Great Britain, Canada, South Africa, Germany, Sweden, Australia, Indonesia, and Norway. Using these inclusion rules, we identified 47 articles that make use of one or more of these methods of causal inference to examine the effects of father absence on outcomes in one of four domains: educational attainment, mental health, relationship formation and stability, divorce and effects on children research paper, and labor force success.
Our goal is to see if, on balance, these studies tell a consistent story about the causal effects of father absence and whether this story varies across different domains and across the particular methods of causal inference that are employed within each domain. We also note where the evidence base is large and where it is thin. We conclude by suggesting promising avenues for future research.
Identifying causal effects with observational data is a challenging endeavor for several reasons, including the threat of omitted variable bias, the fact that multipleand often reciprocalcausal effects are at work, the fact that the causal treatment condition such as divorce may unfold over a period of time or there may be multiple treatment conditions, and the fact that the effects of the treatment may change over time and across subgroups. Traditional approaches to estimating the effect of father absence on offspring well-being have relied primarily on ordinary least squares OLS or logistic regression models that treat offspring well-being as a function of father absence plus a set of control variables.
These models are attractive because the data requirements are minimal they can be estimated with cross-sectional data and because they can accommodate complex specifications of the father absence effect, such as differences in the timing of father absence early childhood versus adolescencedifferences in postdivorce living arrangements whether the mother lives alone or remarriesand differences by gender, race, and social class.
Interpreting these OLS coefficients as causal effects requires the researcher to assume that the father absence coefficient is uncorrelated with the error term in the regression equation. This assumption will be violated if a third omitted variable influences both father absence and child well-being or if child well-being has a causal effect on father absence that is not accounted for in the model.
There are good reasons for believing that both of these factors might be at work and so the assumption might not hold. Until the late s, divorce and effects on children research paper, researchers who were interested in estimating the effect of father absence on child well-being typically tried to improve the estimation of causal effects by adding more and more control variables to their OLS models, including measures of family resources e.
Unfortunately, controlling for multiple background characteristics does not eliminate the possibility that an unmeasured variable is causing both family structure and child well-being.
Adding control variables to the model can also create new problems if the control variables are endogenous to father absence. See Ribar for a divorce and effects on children research paper detailed discussion of cross-sectional models. This approach requires longitudinal data that measure child well-being at two points in timeone observation before and one after the separation. Although this approach attempts to reduce omitted variable bias, it also has several limitations, divorce and effects on children research paper.
First, the model is limited with respect to the window of time when father absence effects can be examined. Specifically, the model cannot examine the effect of absences that occur prior to the earliest measure of child well-being, which means LDV models cannot be used to estimate the effect of a nonmarital birth or any family structure in which a child has lived since birth.
Second, divorce and effects on children research paper, if pre-separation well-being is measured with error, the variable will not fully control for omitted variables. In this case, the pre-divorce measure of child well-being may be picking up part of the effect of the divorce, leading to an underestimate of the negative effect of divorce.
Both of these limitations highlight the fact that the LDV approach is highly sensitive to the timing of when child well-being is measured before and after the divorce. In addition, many of the outcomes that we care most about occur only once e. See Johnson for a more detailed technical discussion of the LDV approach in studying family transitions. These advantages and limitations are evident in Cherlin et al. In OLS regression models with controls, the authors found that divorce increased behavior problems and lowered cognitive test scores for children in Great Britain and for boys in the United States.
However, these relationships were substantially attenuated for boys and somewhat attenuated for girls once the authors adjusted for child outcomes and parental conflict measured at the initial interview prior to divorce. By using data that contained repeated measurements of the same divorce and effects on children research paper, these researchers argue that they were able to reduce omitted variable bias and derive more accurate estimates of the casual effect of family dissolution.
This approach also limited the external validity of the study, however, because the researchers could examine only separations that occurred after age 7, when the first measures of child well-being were collected. A third strategy for estimating causal effects when researchers have measures of child well-being at more than two points in time is the growth curve model GCM.
This approach allows researchers to estimate two parameters for the effect of father absence on child well-being: one that measures the difference in initial well-being among children who experience different family patterns going forward, and another that measures the difference in the rate of growth or decline in well-being among these groups of children. Researchers have typically attributed the difference in initial well-being to factors that affect selection into father absence and the difference in growth in well-being to the causal effect of father absence.
The GCM is extremely flexible with respect to its ability to specify father absence effects and is therefore well suited to uncovering how effects unfold over time or across subgroups. The model also allows the researcher to conduct a placebo testto test whether father absence at time 2 affects child well-being prior to divorce time 1. If future divorce affects pre-divorce well-being, this finding would suggest that an unmeasured variable is causing both the divorce and poor child outcomes.
The GCM also has limitations. First, divorce and effects on children research paper, it requires a minimum of three observations of well-being for each individual in the sample. Second, as was true of the LDV model, it can examine the effect of divorces that occur only within a particular window of timeafter the first and before the last measure of child well-being. Also, like the OLS model, the GCM does not eliminate the possibility that unmeasured variables are causing both differences in family patterns and differences in trajectories of child well-being, including growth or decline in well-being.
For example, an unmeasured variable that causes the initial gap in well-being could also be causing the difference in growth rates. We are more confident in the results of the GCMs if they show no significant differences in pre-divorce intercepts but significant differences in growth rates.
We are also more confident in studies that include placebo or falsification tests, such as using differences in future divorce to predict initial differences in well-being. If later family disruption is significantly associated with differences in pre-divorce well-being the interceptthis finding divorce and effects on children research paper indicate the presence of selection bias. These authors used GCMs to examine the relationship between the proportion of time children spent in different family structures between ages 6 and 12 and scores on the Peabody Individual Achievement Test PIAT cognitive ability test and the Behavioral Problems Index.
They focused on several family types: intact biological-parent families married or cohabitingsocial-father families married or cohabitingand single-parent families.
They found no differences in the initial well-being of the children in these different family structures, suggesting that controls for observable factors had successfully dealt with problems of selection.
The combination of insignificant differences in intercepts and significant differences in slopes increases our confidence in these results. However, it remains possible that time-varying unobserved characteristics were driving both time spent in different family structures and changes in child behavior and achievement.
A fourth strategy for estimating causal effects is the individual fixed effects IFE model, in which child-specific fixed effects remove all time-constant differences among children. This model is similar to the LDV and GCM in that it uses longitudinal data with repeated measures of family structure and child well-being.
It is different in that instead of including pre-separation well-being as a control variable, it estimates the effects of father absence using only the associations between within-child changes in family structure and within-child changes in well-being, plus other exogenous covariates and an error term. The IFE model is equivalent to either including a distinct dummy variable indicator for each child, that absorbs all unobserved, time-constant differences among children, or to differencing out within-child averages from each dependent and independent variable.
In both of these specifications, only within-child variation is used to estimate the effects of father absence. The advantage of this model is that unmeasured variables in the error term that do not change over time are swept out of the analysis and therefore do not bias the coefficient for father absence.
See Ribar for a discussion of fixed effects models. The IFE model also has limitations. As with LDVs and GCMs, IFE models cannot be estimated for outcomes that occur only once, such as high school graduation or a teen birth, or for outcomes that can be measured only in adulthood, such as earnings. Also, as with LDVs and GCMs, the IFE model does not control for unobserved confounders that change over time and jointly influence change in father presence and change in child well-being, divorce and effects on children research paper.
Unlike the other approaches, the IFE model estimates the effect of father absence by comparing before-after experiences for only those children within the treatment group, rather than comparing children in the treatment and control groups.
Finally, and perhaps most importantly, the IFE model is very sensitive to measurement error because estimates of the effect of a change in father absence rely heavily on within-individual changes.
A good illustration of the IFE approach is a study by Cooper et al. Using an OLS model, they found that the number of partnership transitions was associated with lower verbal ability, more externalizing behavior, and more attention problems, but not more internalizing behavior, divorce and effects on children research paper.
These relationships held for both coresidential and dating transitions and were more pronounced for boys than girls, divorce and effects on children research paper. To address potential problems of omitted variable bias, the authors estimated a fixed effects model and found that residential transitions, but not dating transitions, reduced verbal ability among all children and increased behavior problems among boys. The fact that the IFE estimates were consistent with the OLS estimates increases divorce and effects on children research paper confidence in the OLS results.
A fifth strategy for dealing with omitted variable bias is the sibling fixed effects SFE model. This model is similar to the previous model in that unmeasured family-level variables that are fixed i.
In this case, the group is the family rather than the individual, and the difference that is being compared is the difference between siblings with different family experiences rather than the change in individual exposure to different family experiences.
The literature on father absence contains two types of SFE models. One approach compares biological siblings who experience father absence at different ages.
For example, a sibling who is age 5 at the time of a divorce or separation will experience 12 years of father absence by age 17, whereas a sibling who is age 10 when the separation occurs will experience 7 years of father absence by age A second approach compares half-siblings in the same family, where one sibling is living with two biological parents and the other is living with a biological parent and a stepparent or social father.
Both of these strategies sweep out all unmeasured family-level variables that differ between families and could potentially bias the estimate of the effect of divorce. Both approaches also have limitations. The first approach assumes that the effect of divorce does not vary by the age or temperament of the child and that there is a dose-response effect of father absence with more years of absence leading to proportionately worse outcomes, whereas the second approach assumes that the benefits of the presence of both a biological mother and father are similar for children living with and without stepsiblings, divorce and effects on children research paper.
Moreover, if siblings differ in their ability to cope with divorce, and if parents take this difference into account in making their decision about when to divorce, this approach will lead to an underestimate of the effect of a change in family structure.
The major limitation of the second approach is that it assumes that the benefits of living with two biological parents are similar for children living in blended families and children living in traditional two-parent families.
A final limitation of the SFE model is that estimates cannot be generalized to families with only one child. Gennetian examined how children in two-biological-parent families, stepfather families, and single-mother families fared on the PIAT cognitive test as well as how children living with step- or half-siblings compared to those with only full siblings. In simple comparisons, the data revealed a significant disadvantage in PIAT scores for children in single-mother families, stepfather families, and blended families relative to those in two-biological-parent families.
These analyses found very little evidence that children living in single-mother, stepfather, or blended families were disadvantaged on PIAT scores relative to children in nonblended two-biological-parent families, although they did indicate that number of years in a single-mother family had a small negative effect on PIAT scores. Finally, Gennetian further tested the logic of the sibling approach by comparing the well-being of half-siblings, one of whom was living with both biological parents and the other of whom was living with a biological parent and a stepparent.
Impact of Divorce on Children
, time: 6:0210 Side Effects Of Divorce On Children
The paper reveals that the effects of divorce on children often are emotional, psychological, economical and religious spheres among others and recommends ways in which these effects can be Evaluating the Literature. When evaluating the scientific research on the effects of divorce on children and parents, it is important to consider all of the factors affecting the outcome, including family dynamics, children's temperaments and ages at the time of divorce, and family socioeconomic status, as well as any behavioral or academic concerns present prior to divorce ), that have documented the impact divorce may have on children. Sun (cited in ValderValk et aI., ) found children of divorced parents may have a lower sense of psychological well-being than children who grew up with intact families. Research also confirms that children of divorced parents may experience emotional problems such as
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