Jag känner mig så ensam

Det känns som att jag vandrar emot
ett enormt svart hål
och ingen står bakom mig.

av Wilma98 (ris och ros)

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Anonym användare (9fb35f6b49)

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Inte riktigt en dikt, men älskar den :)

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Anonym användare (9e69596bc8)

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The literature on father absence is frequently criticized for its use of cross-sectional data and methods that 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 find negative effects of father absence on offspring well-being, although the magnitude of these effects is smaller than what is found using traditional cross-sectional designs. The evidence is strongest and most consistent for outcomes such as high school graduation, children’s social-emotional adjustment, and adult mental health.

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Anonym användare (9e69596bc8)

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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 course1 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 relationships, and labor market outcomes. 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 well-being is due entirely to these factors. This critique is well founded because family disruption is not a random event and because the characteristics that cause father absence are likely to affect child well-being through other pathways. Similarly, parents’ expectations about how their children will respond to father absence may affect their decision to end their relationship. 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 2000) 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, and labor force success.

In the next section, entitled “Strategies for Estimating Causal Effects with Observational Data,” we describe these strategies, their strengths and weaknesses, and how they have been applied to the study of father absence. In the section entitled “Evidence for the Causal Effect of Family Structure on Child Outcomes,” we examine the findings from these studies in each of the four domains of well-being. 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.

Go to: STRATEGIES FOR ESTIMATING CAUSAL EFFECTS WITH OBSERVATIONAL DATA Identifying causal effects with observational data is a challenging endeavor for several reasons, including the threat of omitted variable bias, the fact that multiple---and often reciprocal---causal 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 adolescence), differences in postdivorce living arrangements (whether the mother lives alone or remarries), and differences by gender, race, and social class. Studies based on these models typically find that divorces that occur during early childhood and adolescence are associated with worse outcomes than divorces that occur during middle childhood, that remarriage has mixed effects on child outcomes, and that boys respond more negatively than girls for outcomes such as behavior problems (see, for example, Amato 2001, Sigle-Rushton & McLanahan 2004).

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 1990s, 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.g., income, parents’ education, and age), as well as measures of parental relationships (e.g., conflict) and mental health (e.g., depression). Unfortunately, controlling for multiple background characteristics does not eliminate the possibility that an unmeasured variable is causing both family structure and child well-being. Nor does it address the fact that multiple causal pathways may be at work, with children’s characteristics and parents’ relationships reciprocally influencing each other. Adding control variables to the model can also create new problems if the control variables are endogenous to father absence. (See Ribar 2004 for a more detailed discussion of cross-sectional models.)

Lagged Dependent Variable Model A second approach to estimating the causal effect of father absence is the lagged dependent variable (LDV) model, which uses the standard OLS model described above but adds a control for child well-being prior to parents’ divorce or separation. This approach requires longitudinal data that measure child well-being at two points in time---one observation before and one after the separation. The assumption behind this strategy is that the pre-separation measure of child well-being controls for unmeasured variables that affect parents’ separation as well as future child well-being.

Although this approach attempts to reduce omitted variable bias, it also has several limitations. 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, if pre-separation well-being is measured with error, the variable will not fully control for omitted variables. Third, lagged measures of well-being do not control for circumstances that change between the two points in time and might influence both separation and well-being, such as a parent’s job loss. Another challenge to LDV studies is that divorce/separation is a process that begins several years before the divorce/separation is final. 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. Alternatively, children’s immediate response to divorce may be more negative than their long-term response, leading to an overestimate 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.g., high school graduation, early childbearing), and the LDV strategy is not appropriate for these outcomes. (See Johnson 2005 for a more detailed technical discussion of the LDV approach in studying family transitions.)

These advantages and limitations are evident in Cherlin et al.’s (1991) classic study employing this method. Drawing on longitudinal data from Great Britain and the United States, the authors estimated how the dissolution of families that were intact at the initial survey (age 7 in Great Britain and 7--11 in the United States) impacted children’s behavior problems as well as their reading and math test scores at follow-up (age 11 in Great Britain and 11--16 in the United States). 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 outcome, 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. For example, the model can estimate age-specific effects, whether effects persist or dissipate over time, and whether they interact with other characteristics such as gender or race/ethnicity. The model also allows the researcher to conduct a placebo test---to 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, 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 time---after 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 intercept), this finding would indicate the presence of selection bias. [See Singer & Willett (2003) for a more detailed technical discussion of GCMs and Halaby (2004) for a more detailed discussion of the assumptions and trade-offs among the various approaches to modeling panel data.]

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