Category: Sexual abuse

The Impact of Childhood Sexual Abuse on the Educational Attainment and Adult Earnings of Canadian Women

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The Impact of Childhood Sexual Abuse on the by Christopher J. Bruce, Ph.D. & Daniel V. Gordon, Ph.D.

This article first appeared in the autumn 2007 issue of the Expert Witness.

Introduction

One of the most complex issues facing the courts in any sexual abuse case is the determination of the impact that the harm has had on the plaintiff’s earning capacity. Not only is very little statistical evidence available on this issue, but the data that do exist have often proved to be unreliable.

Recently, this lack of a reputable source has been resolved with the publication of a Statistics Canada survey in which a representative sample of Canadians was asked about their experiences of victimization. From this survey, we were able to obtain information about a large set of (adult) women who had suffered sexual abuse as children.

The purpose of this paper is to report our findings concerning the consequences of that abuse on the educational attainment and earnings of the victims. Our surprising result is that, among most such victims, abuse does not have a statistically significant effect on adult education and earnings. Furthermore, in the only group for which abuse
was found to have a significant effect – women who were first abused between the ages of seven and thirteen
– education and income were higher than among women who had not been abused.

The remainder of the paper is divided into three sections: a review of the existing literature; the development of an economic model concerning the effect of abuse; and a summary of our statistical findings.

Existing Literature

The academic literature concerning the effects of childhood sexual abuse is composed of two streams. In the first, researchers have attempted to develop theories of childhood social-psychological development that can be used to understand the pathways by which abuse affects its victims. In the second, researchers have used statistical tests to identify correlations between abuse and its predicted outcomes, such as depression, alcoholism, and school completion rates.

Theoretical

Many theories concerning the impact of sexual abuse are founded on the ‘core-symptom’ model, in which a
core problem or event is presumed to have led to one or more symptoms. The most common of these models characterize sexual abuse as a trauma, leading researchers to predict that abuse will result in post traumatic stress disorder.

An alternative interpretation suggests that, rather than
having a single effect, sexual abuse might produce multifaceted effects. Finkelhor and Browne (1985), for example, argued that sexual abuse had four primary effects: traumatic sexualization, stigmatization (i.e. low esteem and self-destructive behaviour), betrayal, and powerlessness.

A third approach, ‘developmental models,’ proposes that sexual abuse may affect victims by interfering with development in areas such as social- and self-functioning. In this view, therefore, the age at which the child is abused may be a critical determinant of the long-term effects of that abuse. For example, Celano (1992) suggested that the impact of sexual abuse may differ among Piaget’s three stages of childhood moral development (Piaget, 1965): preschool (ages 0-6), latency (7-13), and adolescence (14-19). She hypothesised that whereas preschoolers may not recognise that abuse is morally reprehensible, and adolescents may consider their moral culpability to be ameliorated by failure to provide informed consent, children in the latency period may consider themselves (at least in part) to be responsible for many of the types of abuse. Accordingly, she predicts that abuse will be most harmful if it occurs in the latency period.

Statistical

Statistical studies can roughly be divided among those that investigate the effect of sexual abuse on: psychological factors, (such as depression, self-esteem, and sexuality); social outcomes, (such as alcoholism and delinquency); and economic outcomes, (specifically, schooling and adult income).

This literature is virtually unanimous in its finding that childhood sexual abuse has statistically significant effects on the victim’s psychological well-being. The outcomes that are most commonly found include: low self-esteem, post-traumatic stress disorder, depression, affective and personality disorders, and anti-social behaviour.

The sociological literature has provided evidence concerning the effect that abuse has on criminal activity. Studies that aggregate across a number of different types of childhood abuse
– neglect, physical abuse, and sexual abuse, for example
– commonly find that criminal activity is correlated with
this aggregate; although some find no statistically significant correlation. Importantly, however, when ‘abuse’ is separated into its components, it is only ‘neglect’ that is found to be correlated with criminal activity. Neither physical nor sexual abuse appears to be a significant determinant in this formulation.

Very mixed results have been obtained when researchers investigate the impact of abuse on schooling and income. Slade and Wissow, (2006) found that individuals who had been maltreated as children had lower high school GPAs than the control group, but no greater problems with teachers, with completion of homework, or with school absences. And some studies have found evidence that victims of childhood sexual abuse performed better in school than those who had not been abused (e.g. Eckenrode, et al. 1993 and Buckle, et al. 2005).

Economic Model

In our model, we assume that individuals divide their time among three activities: solitary leisure activities, such as reading, watching television, and playing video games; social leisure activities, such as team sports, club memberships, and interacting with friends; and “market” activities, such as investing in education and working in the labour market. The value of each activity increases as additional time and effort is devoted to it. And the “cost” of time spent in any one activity is the value that is foregone from the other activities (the “opportunity cost” concept so familiar to economics students).

We hypothesize that the effect of sexual abuse is to reduce the benefits that individuals obtain from each of the three activities. Normally, one would expect that this would lead to a reduction in each of them. However, assume that the negative effect on the time and effort devoted to two of the activities was greater than it was on the remaining one. In that case, it is possible that the victim might “substitute” away from the more seriously-affected activities towards the less-seriously affected one. As a result, the latter might even increase.

For example, if abuse had a much more significant (negative) impact on the individual’s ability to socialize than on
her ability to undertake schoolwork or to compete in the labour market, abuse might lead to a lesser reduction in the latter activities than expected, and could even lead to an increase in those activities. In common parlance, the individual might be said to have compensated for the harm to her socialization skills by ‘throwing herself’ into academic and work-related activities. In such a case, abused individuals might be observed to complete more years of education than the non-abused, and might earn higher incomes; but this would come at the expense of a significant withdrawal from normal social activities. We propose to test for this effect in the analysis reported in the next section.

Statistical Estimates

We obtained information concerning 6,528 adult Canadian
women, (drawn from the 1999 General Social Survey), of whom 607 reported that they had been sexually abused as children. Using the data from this survey, we conducted two statistical tests.

In the first of these, we estimated the effect of a series
of variables on educational achievement. The variables that are usually found to be important – such as parents’ education, individual’s place of birth, and whether the individual belonged to a “visible minority” – all proved
to be significant in our data. In addition, we found that if the individual had first been sexually abused between the ages of seven and thirteen, she obtained more education than did individuals who had not been abused or who had first been abused before seven or after thirteen.

In our second test, we estimated the effect of a number of background variables on adult earnings. As expected, we found that variables such as the individual’s education and whether she belonged to a visible minority had significant effects on income. And, again, we found that individuals who had been abused between the ages of seven and thirteen had statistically higher levels of income than did those who had not been abused or who had first been abused before seven or after thirteen. Importantly, this effect is in addition to the increase in income that would have arisen from the effect of abuse on education.

To summarise, we found: first, that abuse before the age of seven or after the age of thirteen had no statistically discernable effect on either the victim’s education or her adult income. Second, abuse between the ages of seven and thirteen increased average educational levels and increased average incomes, both directly and indirectly (through the effect on education).

Conclusion

We have found that, on average, sexual abuse is not associated with lower educational levels or lower adult incomes among victims. This does not mean that all victims have higher income levels than those who have not been victimised: some victims will be above-average and some below. When the court is dealing with a particular plaintiff, it should always rely on factors that are specific to that individual. Nevertheless, our results suggest that it cannot be concluded, without such specific information, that the individual’s adult income will be adversely affected by sexual abuse.

References

Buckle, S., S. Lancaster, M. Powell, and D. Higgins (2005) “The Relationship Between Child Sexual Abuse and Academic Achievement in a Sample of Adolescent Psychiatric Inpatients,” 29 Child Abuse and Neglect, 1031-1047.

Celano, M. (1992) “A Developmental Model of Victims’ Internal Attributions of Responsibility for Sexual Abuse,” 7 Journal of Interpersonal Violence, 57-69.

Eckenrode, J., M. Laird, and J. Doris (1993) “School Performance and Disciplinary Problems Among Abused and Neglected Children,” 29 Developmental Psychology,
53-62.

Finkelhor, D., and A. Browne (1985) “The Traumatic Impact of Child Sexual Abuse: A Conceptualization,” 55 American Journal of Orthopsychiatry, 530-541.

Piaget, J. (1965) The Moral Judgement of the Child, (New York: Free Press).

Slade, E., and L. Wissow (2006) “The Influence of Childhood Maltreatment on Adolescents’ Academic Performance,”
Economics of Education Review, (in press).

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Christopher Bruce is the President of Economica and a Professor of Economics at the University of Calgary. He is also the author of Assessment of Personal Injury Damages (Butterworths, 2004).

Daniel V. Gordon, Ph.D., is a professor of economics at the University of Calgary, where he specialises in the use of statistics for economic analysis.

Evaluation of Harm to a Class of Individuals

Posted on By economica

by Kelly Rathje

This article was originally published in the Winter 2000 issue of the Expert Witness.

When we are asked to estimate a claimant’s potential future income (without- or with-accident) we rely on two types of data – data specific to the individual, such as the claimant’s tax returns, and statistical data concerning individuals “similar” to the plaintiff, such as information drawn from the Census.

When the plaintiff is part of a common class of victims, however, it is possible to rely on more sophisticated statistical techniques to assess the impact of the injurious act. Such classes of plaintiffs might include, for example, victims of chemical or radiation poisoning in a factory or residential area or victims of sexual or physical abuse at a school.

In these cases, economists can rely on a technique known as econometric modelling (see the accompanying article from this newsletter) to determine whether the average income of the class of victims differs significantly from the average income of a similar group chosen at random from the population.

The difference may be determined by specifying characteristics, common to both groups, and examining how these factors influence income. Any difference in income not attributable to the specified characteristics could be attributed to the incident, and thus the loss of income due to the incident may be determined.

To use this method, an economist would need to gather data, do some comparative statistical analysis, and then apply the econometric model. These steps are outlined below.

Data

The data for the claimant’s group is most commonly compiled from information provided by the individuals within that group. The comparison group, which is to represent a random sample from the population, can often be obtained from broad data sources such as the census.

Using these sources, the economist would create two types of variables. The first of these are “numerical” variables; that is variables that can be measured using numerical scales. For example, if the economist is trying to identify the determinants of income, numerical variables might include age, years of education, and work experience.

The second set of variables, “dummy” variables, are variables that cannot be measured numerically. For example, these might include place of residence or sex of the individual. For example, if the economist wished to test the hypothesis that people in the Maritimes earned less than individuals in the rest of Canada (ROC), a variable might be created that divided the group between Maritimes and ROC.

Comparisons

Before any formal estimation is done, economists usually look at the raw data to see if any trends or relationships are present. Using the characteristics indicated above (age, place of residence, years of education, and current income), trends of interest to economists might be employment rates, average numbers of years of educational attainment, and average income levels for each group.

Econometric modelling

Using the characteristics outlined, an (econometric) equation is created to examine the factors that influence income. The equation, in its simplest form, might be as follows:

I = C + b1[age] + b2[maritimes] + b3[claimants]

What this equation predicts is that income, I, will be determined by the individual’s age, place of residence, membership either in or out of the “claimant” group, and a fixed factor, C. In this equation, “age” is a numerical variable – it might take values such as 25 or 47 years old, for example.

“Maritimes” and “claimants” are dummy variables. In this case, “Maritimes” takes the value 1 if the individual lives in the Maritimes and 0 if he or she lives in the ROC; and “claimants” takes the value 1 if the individual is one of the plaintiffs and 0 if he or she was chosen from the random sample of other individuals in the population.

Once the data set has been collected, and the form of the equation has been identified, statistical techniques are applied to the data to estimate the “best” values of b1, b2, and b3.

The data might suggest, for example, that the most likely relationship among the variables is:

I = 25,000 + 500[age]- 4,500[maritimes] – 20,000[claimants]

This indicates that for each year an individual ages, income increases by $500, on average; and that if the individual lives in the Maritimes, income will be, on average, $4,500 less than if that individual lives in the ROC. The above estimation also indicates that, on average, the claimant group will earn $20,000 less than average individuals in the population, all else being equal. For example, a 37-year-old, who lives in the Maritimes, and is not part of the claimant’s group would earn $39,000 (= 25,000 + 500[37] – 4,500[1] – 20,000[0]); and a 37-year-old, who lives in the Maritimes, and is a part of the claimant’s group would earn $19,000 (= 25,000 + 500[37] – 4,500[1] – 20,000[1]);

Now suppose the economist also has information on the employment status of each individual in both groups. The next step that may be undertaken is to estimate what an individual’s income would be given the above characteristics, but limiting the observations to employed individuals only. That is, the economist might control for employment status by including only observations at which the income is greater than zero. This would indicate how much of the difference in income, found in the first estimation, could be attributed to employment status. The resulting equation might be, for example:

I = 21,000 + 200[age] – 4,500[maritimes] – 12,000[claimants]

Given that I > 0

Recall from above, when considering both employed and unemployed individuals together, the equation indicated that the claimant’s group earned approximately $20,000 less than the random population. Now, controlling for employment, they are found to earn $12,000 less. This implies that $8,000 of the earnings gap between the plaintiff group and the general population can be explained by the higher unemployment rate of the former group.

Now suppose there is additional information regarding the education levels of the groups. The next logical step would be to add educational attainment as one of the explanatory variables. Thus, the equation would include the number of years of education, place of residence, age, and “claimant” status. This specification adds another explanatory factor to help predict income levels. Still controlling for employment status, the resulting equation might be:

I = 20,000 + 100[age]- 4,000[maritimes] + 2,000 [education] – 7,000[claimants]

Given that I > 0

This equation, given the known characteristics in this example, has the most explanatory power. It indicates to the economist that controlling for all the known variables, there still exists a difference in income of $7,000 between the claimants and an individual chosen at random from the general population, given that both individuals have the same characteristics.

Note, however, that this does not mean that the effect of the tortious act is, on average, $7,000 per year per claimant. First, remember that when no allowance was made for employment status or education, the average difference between the annual incomes of the claimants and members of the general population was $20,000. What the last equation predicts is that if we compare two individuals who have the same education and the same employment status, we will find that the “claimant” earns, on average, $7,000 less than the non-claimant. However, the effect of the tortious act may have been to increase the unemployment rates of the claimants and reduce their educational attainments (particularly if they were injured while they were minors). In that case, the $7,000 would represent the lower bound on the estimated impact of the injury.

Second, part of the income differential between claimants and non-claimants may be the result of factors that have not been taken into account in the equations. For example, assume that the claimants had all been harmed by the release of a toxic chemical. It might be that individuals who are susceptible to that chemical share some genetic factor that also reduces their abilities to earn income. If that genetic factor is not taken into account, the statistician may attribute the lower incomes of members of that group to the chemical when, in fact, that group would have earned lower incomes in any event.

Another drawback is that this method determines average incomes for the group, and thus, average differentials for the group. That is, the income differentials between the claimant group and the random group apply to the overall group, and not necessarily to each claimant. When the claimants are considered individually, the economist may find that some of the claimants are earning more than the average income predicted by the model; some are earning less income than the average income predicted by the model; and some are earning the same income the model predicted. However, on average, the group still has a reduction in earnings, when compared to individuals chosen at random from the population, with the otherwise same characteristics (other than the incident).

We were recently asked to determine whether there was economic evidence to support a claim that a group of individuals experienced a loss of income as a result of a common incident. We followed much of the same steps and methodology described here in determining: (i) whether an income differential existed; and (ii) the extent to which each of the known factors influence income. This methodology allowed a quantitative measure of the loss of income to be predicted, given the information provided by the group, and compared to a random sample of the population.

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Kelly Rathje is a consultant with Economica and has a Master of Arts degree (in economics) from the University of Calgary.

Recent Canadian Court Decisions Concerning the Impacts of Child Sexual Abuse on Earnings

Posted on By economica

by Christopher Bruce and Matthew Foss

This article was originally published in the winter 1999 issue of the Expert Witness.

We reviewed approximately 35 Canadian tort cases involving sexual assault that had been decided in the last decade. In this section, we review the findings of those cases under three damage headings: loss of earnings, loss of opportunity to marry, and punitive damages.

No loss of earnings

In approximately one quarter of the cases we reviewed, the courts denied the plaintiff’s claim for loss of earnings (after sexual assault had been proven). In some, – for example, B(KL) v. B(KE), M(LN) v. Green, and H(JL) v. H(DH) – the courts concluded that the non-pecuniary damages were “adequate” and, hence, pecuniary damages were not necessary. In others, for example C(P) v. C(RJ) and A(T) v. K(R), it was concluded that the plaintiff had not offered sufficient evidence on which to base an award. Finally, in both Gray v. Reeves and S(T) v. P(JW), the court could find no difference between the income the plaintiff would have expected to earn had he or she not been assaulted and the income he/she was actually earning.

These cases strike us as being consistent with the academic literature, which found that many victims of sexual abuse had not suffered long-term effects, particularly on earnings. Hence, it is not unexpected that some plaintiffs would be denied damages on this ground, particularly if they had not provided strong evidence of long-term (significant) harm.

Loss of earnings

In approximately three quarters of the cases which proceeded to trial, the plaintiff was able to obtain damages for loss of earnings. This is a higher percentage than the literature would lead one to expect. However, it must be remembered that the cases that reach trial are not a random sample of all possible cases. Presumably, few individuals who suffered no loss of income would press a case to that point.

The most common basis for the calculation of loss of income was that the plaintiff had suffered a reduction in the level of educational attainment, or had suffered a delay in reaching his or her ultimate educational level. In A(C) v C(JW), for example, it was found that one of the plaintiffs would have become an automobile mechanic and would only do so now, if at all, after a significant delay. In C(PA) v. T(JC), it was found that the effect of the abuse had been to prevent the plaintiff from graduating from high school. In P(J) v. Sinclair, the plaintiff’s actual earnings were compared to those she would have made had she completed two years of post-secondary education. And in P(S) v. K(F), the court concluded that it was reasonable to assume that the plaintiff would have completed high school but would not now do so.

Alternatively, in many cases, the courts accepted the argument that the plaintiff had achieved the same level of education as she or he would have in the absence of the abuse, but that the plaintiff would now suffer from higher unemployment, increased part-time work, or reduced overtime work. In B(J) v. M(F), the abuse was so severe that the court concluded the plaintiff would now be unable to work at all. In C(H) v. C(GC), the court accepted the argument that the effect of the abuse would force the plaintiff to reduce working hours from full-time to part-time. And in J(A) v. D(W), the court found that the plaintiff had missed a considerable amount of time from work during the pre-trial period.

In many of the cases in which damages for loss of income were awarded, the court implicitly recognised the argument that the victim’s low income might be due not only to the sexual abuse, but also to a dysfunctional family background. In A(C) v. C(JW), for example, there were four plaintiffs. The court noted that four out of five of plaintiff LK’s siblings had criminal records, were drug abusers, or otherwise had exhibited “lack of vocational success.” It appears that the court reduced LK’s damages on this basis. In the same case, on the other hand, the court noted that all three of plaintiff CH’s brothers had been successful and it awarded substantially more to CH than it had to LK. Similarly, in K(W) v. Pornbacher, because the plaintiff had a pre-existing prognosis of attention deficit disorder, the court reduced the damages it would otherwise have awarded. And in T(KA) v. B(JH), the court reduced the past loss to take account of “other contingencies” that might have affected the plaintiff’s earning capacity.

A common thread running through many of the cases in which victims were awarded damages for loss of income was the expectation by the court that the victim would soon “recover” from the effects of the abuse. The incomes of individuals who had suffered abuse 10 or 20 years prior to the trial were expected to “catch up,” within a few years of the court’s decision, to the incomes of those who had never been abused. In D(PA) v. H(AE), for example, the court awarded damages for only two years future loss. In V(JL) v. H(P), the court accepted the contention that the plaintiff would fully catch up within 5 years. And in P(J) v. Sinclair, the court assumed that the loss would continue for only 10 years into the future.

Loss of opportunity to marry

In two cases, the plaintiff argued that the effects of the sexual abuse had impaired her ability to marry. In Gray v. Reeves, the court concluded that the plaintiff had failed to provide adequate evidence concerning this claim, and refused to award damages. In LMN v. M(MJ), however, the court accepted the claim. Unfortunately, the court awarded a single sum to cover both loss of income and loss of marital prospect. Hence, it is not possible to determine what the value of the award was for loss of marital prospect.

Punitive damages

We were able to identify six cases in which punitive damages were discussed. In three, M(TD) v. G(KS), N(JL) v. G(KS), and Glendale v. Drozdzik, the court refused to award punitive damages. In the latter, the British Columbia Court of Appeal quoted approvingly from Huff v. Price, to the effect that:

The award of punitive damages should not try to do again what has already been done by the compensatory damages, including the aggravated damages. … And, of course, if a criminal penalty has been imposed then that should be taken into consideration. (p. 300)

Nevertheless, the court in Glendale added that:

…the rule would be too absolute if it were that punitive damages cannot be awarded if there has been a criminal penalty. Sometimes the criminal penalty might be as little as a conditional discharge…

In none of the three cases in which punitive damages were awarded were the defendants jailed for their assaults on the plaintiffs. In B(JD) v. M(F), the defendant was charged criminally for offences against other children, but not for those against B(JD). In C(H) v. C(GC), no criminal charges were laid against either of two defendants. And in M(M) v. F(R), the defendant received only three years probation on the criminal charge. All three, therefore, appear to be consistent with the view that punitive damages should, generally, only be awarded when there have been no criminal penalties, or those penalties have not been severe.

Summary

To summarise, the courts have looked for evidence that the plaintiff’s past and future earnings were impeded by the sexual abuse. This involved examining factors such as the plaintiff’s likely education without the sexual abuse, work history, and foreseeable career path in the future. Other factors that were given weight in the decisions included the accomplishments of siblings and the environment that the plaintiff grew up in (excluding the sexual abuse). The courts have not, in general, been overly generous to plaintiffs in their awards for lost earnings.

References

A (C.) v. C. (J.W.), 1997, 36 C.C.L.T. (2d) 224, 35 B.C.L.R. (3d) 234 (BCSC)

A.(D.A.) v. B. (D.K.), 1995, 27 C.C.L.T. (2d) 256 (Ontario Court of Justice (General Division))

A (T.) v. K. (R.), 1995, 15 B.C.L.R. (3d) 274, [1996] 3 W.W.R. 720 (BCSC)

B. (J.D.) v. M. (F.) 1998, Docket: Bracebridge 233/96, (Ontario Court of Justice (General Division))

B. (K.L.) v. B. (K.E.), 1991, 7 C.C.L.T. (2d) 105, 71 Man. R. (2d) 265 (Man. Q.B.)

C. (H.) v. C. (G.C.) 1998, Docket: 101497/96, 101496/96, (Ontario Court of Justice (General Division))

C. (P.) v. C. (R.J.), 1994, 114 D.L.R. (4th) 151, (Ontario Court of Justice (General Division))

C. (P.A.) v. T. (J.C.), 1998, Docket: Courtenay S3229, (BCSC)

D. (P.A.) v. H. (A.E.), 1998, 49 B.C.L.R. (3d) 340, [1999] 2 W.W.R. 139, (BCSC)

Glendale v. Drozdzik, 1993, 77 B.C.L.R. (2d) 106, 101 D.L.R. (4th) 101, (B.C.C.A.)

Gray v. Reeves, 1992, 64 B.C.L.R. (2d) 275, 10 C.C.L.T. (2d) 32, [1992] 3 W.W.R. 393, 89 D.L.R. (4th) 315, (BCSC)

H. (J.L.) v. H. (D.H.), 1999, Docket: F/C/259/96 (NBQB)

H. (S.) v. L. (R.G.), 1993, 85 B.C.L.R. (2d) 232, [1994] 2 W.W.R. 276 (BCSC)

Huff v. Price, 1990, 51 B.C.L.R. (2d) 282 (B.C.C.A.)

J. (A.) v. D. (W.) 1999, 136 Man. R. (2d) 84 (Man. QB)

K. (W.) v. Pornbacher, 1997, 32 B.C.L.R. (3d) 360, 27 C.C.E.L. (2d) 315, 34 C.C.L.T. (2d) 174, [1998] 3 W.W.R. 149 (BCSC)

LMN v. M. (M.J.), 1998, Docket: New Westminster SO-41750 (BCSC)

M. (L N) v. Green Estate, 1996, Docket: Doc. Vancouver C932295, (BCSC)

M. (M.) v. F. (R.), 1996, 22 B.C.L.R. (3d) 18, [1996] 8 W.W.R. 704, (BCSC)

M. (T.D.) v. G. (K.S.) 1997, Docket: Vancouver C961248, (BCSC)

N. (J.L.) v. L. (A.M.),.[1989] 1 W.W.R. 438, 47 C.C.L.T. 65, 56 Man. R. (2d) 161 (Man. Q.B.)

P. (J.) v. Sinclair, 1999, Docket: Victoria 93/3581, (BCSC)

P. (S.) v. K. (F.), [1997] 3 W.W.R. 161, 1996, 150 Sask. R. 173, 32 C.C.L.T. (2d) 250, (Sask. QB)

P. v. F., 1996, 24 B.C.L.R. (3d) 105 (BCSC)

S. (J.E.) v. M. (P.D.) Estate, 1998, Docket: Victoria 97 2335, (BCSC)

S. (L.) v. R. (L.), 1996, Docket: Vancouver C942578 (BCSC)

S. (P.) v. S. (J.), 1996, Docket: Milton C12210/93, (Ontario Supreme Court General Division)

S. (T.) v. P. (J.W.), 1999, Docket: Victoria 98/1477, (BCSC)

T. (K.A.) v. B. (J.H.), 1998, 51 B.C.L.R. (3d) 259, (BCSC)

T. (L.) v. T. (R.W.), 1997, 36 C.C.L.T. (2d) 207, 36 B.C.L.R. (3d) 165, (BCSC)

V. (J.L.) v. H. (P.), 1997, 31 B.C.L.R. (3d) 155 (BCSC)

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Christopher Bruce is the President of Economica and a Professor of Economics at the University of Calgary. He is also the author of Assessment of Personal Injury Damages (Butterworths, 2004).

Matthew Foss is an M.A. student in the Department of Economics, University of Calgary. This is a continuation of his article “The Calculation of Damages in Sexual Abuse Cases” which appeared in the previous issue of this newsletter.

The Calculation of Damages in Sexual Abuse Cases

Posted on By economica

by Matthew Foss

This article was originally published in the autumn 1999 issue of the Expert Witness.

In the last decade, there has been a dramatic increase in the number of sexual assault victims who have sued their abusers in tort. The purpose of this article is to offer a brief review of the academic literature concerning the impact of abuse on the victim’s psychological well-being, education, and earning capacity. A second article, to be published in the next issue of this newsletter will discuss the response of the courts to these lawsuits.

Caveat

Any survey of the sexual abuse literature must begin with a caveat concerning the reliability of the research – that being that much of this research is unreliable due to the use of naïve or biased techniques.

One type of research investigates samples of adult patients in therapy. Since participants in these surveys are already in treatment, there is little doubt that the studies will find that the victims of abuse have disorders. In most cases, adequate control groups are not used to compare the results with, nor are measures taken to find the proportion of sexual abuse victims that sought out clinical treatment. Therefore, most of the results from these studies are questionable at best.

The other major type of research is biased in the other direction. Samples from the general population are taken. Although this gives a slightly more representative sample, in that it has a built-in control group, those that might have been most seriously affected by sexual abuse, the patients at mental institutions, are excluded.

Also, many of these samples use data that were not collected using consistent definitions of sexual abuse. Koverola et al argued that making a distinction based on the severity of the type of sexual abuse is arbitrary, and is likely meaningless. The magnitude to which intercourse is more damaging than manual penetration is difficult to determine, if it exists.

Moreover, the data are often based on subjective recall by survivors, with no objective methods to validate responses. Wachtel and Scott assert that many studies do not differentiate between the different forms of child sexual abuse, nor have they used a standardized outcome measure for cognitive or psychological functioning.

Wachtel and Scott further argue that researchers have an incentive to exaggerate the consequences of the sexual abuse. Their reason for this is to induce more support. If the effects are seen to be minor, then the need for response is also small

Impacts on Psychological Well-Being

Finkelhor and Browne reviewed the child sexual abuse literature. They found that at least eight non-clinical studies had reported that women within the general population, with a history of child sexual abuse, had identifiable mental health problems. They also found that only one study that attempted to find mental health impairment, in a non-clinical sample, had failed to find it.

According to the authors, among the more commonly found problems were: depression, self destructive behavior, anger and hostility, poor self esteem, feelings of isolation, difficulty trusting others, marital and relationship problems, and a tendency towards revictimization. Moreover, Finkelhor and Browne found that child sexual abuse was frequently cited as a background to substance abuse, prostitution, multiple personality disorders, and borderline disorder.

They further found that five empirical studies had shown that child sexual abuse was associated with increased likelihood of subsequent victimization Moreover, sexual abuse victims were more likely to be in abusive relationships. Their hypothesis was that sexual abuse makes victims more vulnerable to abusive individuals, or perhaps unable to anticipate dangerous sexual situations.

Conte, Berliner, and Schuerman employed a sample of 369 child sexual abuse victims that were assessed at the time of disclosure, and seen at a sexual abuse center. Two measures were used to assess the subjects, a symptom checklist was completed by the health care worker and a child behavior profile was completed by the non-offending parent. The victims ranged in age from four to seventeen years of age. The definitions of sexual abuse and their reported frequencies, within the sample, included: oral sex, 18% of the population; vaginal-penile intercourse, 19% of the population; and fondling, 62% of the population.

The results, as measured by the symptom checklist completed by the health care workers, showed that 18.7% of the sample had signs of depression, 32.8% exhibited low self esteem, and 15.4% had signs of academic problems. The authors found that victims of child sexual abuse differed from a comparison group, in a statistically significant manner, in a number of areas. These areas included: a lower self-esteem, more aggressive behavior, more fearful, and more difficulty in concentrating.

Wachtel and Scott concluded that there were three types of effects. The first of these are direct impacts, such as anxiety and low self-esteem. Second, there may be manifestations of these direct impacts, like school problems or sleep disorders. Finally, symptoms may arise from unsuccessful or dysfunctional attempts to cope. Among these are delinquency and prostitution.

Sauzier, Salt, and Calhoun used a sample of children entering the Family Crisis Program. Examining the preschool children in their study, they found that compared to a control group of “normal” children, their sample exhibited significantly higher overall pathology, and more specific difficulties. Most importantly, cognitive ability was significantly lower for the sample group than for the ‘normal’ group. However, there were not significant differences with respect to antisocial behavior, intellectual deficit, and hyperactivity.

Looking at a pre-adolescence group, the seven to thirteen year olds, a similar trend was evident. The abuse victims exhibited more psychopathology than did the normal group. However, in this age group, both academic disability and learning disability were not statistically different from the treatment group.

Sauzier, Salt, and Calhoun reached several postulates as a result of their study. First, children that suffered physical injuries during the sexual abuse were at greater risk for exhibiting behavioral problems. Moreover, those suffering aggression would be likely to be hostile and have fears about the aggressiveness of others. They observed that the age of onset and the duration of abuse were not related to negative effects on the child.

Suprisingly, they found that children who were sexually abused by stepfathers exhibited lower self-esteem than those children that were abused by their biological fathers. Their explanation for this result was that children with stepparents were more likely to have suffered from disrupted family environments. Angry reactions on the part of the mother after learning of the abuse were related to lower self-esteem, as should be expected.

Nash, Zivney, and Hulsey investigated a sample of 102 randomly selected sexual abuse cases that were being treated at a clinic in Dallas. The results of their study showed that children who were abused by more than one perpetrator were the most likely to suffer impairments. Other factors that made the abuse more likely to generate severe outcomes included: the earlier the abuse started, the greater the number of incidents, and the frequency not the duration of the abuse.

Crucially the authors found that the prognosis was worse when the family had been disrupted. They concluded that a child facing sexual abuse might be a product of a very neglectful household. Given this factor, it may be very difficult to determine where the effects of the neglect end and the effects of the sexual abuse begin.

Wachtel and Scott argue that it is important to examine the environment within which child sexual abuse occurs. Factors that often are associated with child sexual abuse are physical abuse, neglect, and parental alcoholism. It may be that the apparent symptoms of the abuse would have arisen even if the abuse had not occurred, because of the presence of these other negative influences on the child’s well-being. Furthermore, even if these other factors did not themselves “cause” psychological harm, they may have inhibited the victim’s ability to cope with abuse.

Wachtel and Scott also argue that if we accept that child sexual abuse is a complex situation that includes other factors beyond the sexual abuse, then we need to re-examine the question that we are asking. Instead of asking does child sexual abuse result in negative outcomes for victims, or what are the impacts of child sexual abuse, the questions need to be refined. Questions such as what effects are specific to child sexual abuse, and what are the incremental effects of child sexual abuse when neglect or physical abuse are also present, need to be examined.

Finkelhor and Browne argued that most studies were better at establishing the fact that sexual abuse constitutes a risk factor for later long-term effects than at determining the magnitude of the risk. They argued that less than one third of victims of child sexual abuse show serious psychopathology, although the remaining two thirds are not symptom free.

In an attempt to determine what the more significant factors were that made a victim more likely to suffer more serious outcomes, Finkelhor and Browne considered several possible factors. They cited Russell’s finding that 59% of victims suffering completed or attempted intercourse, or oral sex said that they were extremely traumatized. They also argued that not all studies established a difference between the impacts of abuse by a relative compared to a non-relative. Their speculation was that this distinction might not model the closeness of the relationship. It may be possible that a friend of the family may have more of a bond with the child than a distant relative.

They did find that the use of force was an important traumatic factor. Victims that suffered physical coercion had increased trauma. The duration of the abuse was associated with increased trauma in only three of eight studies that examined this relationship. Perhaps this is due to the fact that duration does not always capture frequency. For example, it is possible that an abusive situation might last for a period of years with only two or three incidents. Compare this to a situation where the abuse takes place over a week but involves a dozen or more instances of sexual abuse

The Effects on Education and Standard of Living

Reyome studied the school performance of sexually abused and neglected children. These were compared with non-abused children drawn from two groups -from families on public assistance and from lower middle-class families. Information was gathered on the cognitive achievement of all the subjects.

When school-based measures were used, the sexually abused children were more likely to have received lower grades than the control groups. Spelling and math achievement exhibited the largest differences. Moreover, almost half of the sexually abused students had repeated a grade, compared to less than one-third of the matched public assistance, and one-sixth of the matched lower middle class students.

Macmillan argued that the consequences of abuse included both the amount of education a victim hopes to attain, and the amount of time and energy that is given to schoolwork. This lowered investment in education was expected to both lower grades and interfere with the level of education that a victim would attain. Moreover, he argued that since educational attainment is a key determinant of occupational status, victimization would have the effect of lowering occupational status.

To test his model, Macmillan made use of two data sources: the U.S. National Youth Survey, a longitudinal study involving 1725 youths aged 11-17 that took place over a ten year period; and the Canadian General Social Survey (1993).

Using the National Youth Survey, Macmillan reported that adolescent victimization has a negative impact on earnings. Using three measures of violent victimization, he found that earnings per hour were one dollar lower for those that were victims. However, he did not find, when looking only at sexual assault, that there was a statistically significant difference over non-victims.

When Macmillan used the GSS data, he found that sexual assault victims suffered an income deficit of about $6000 per year. Again, the data supported his model showing that an additional year of education was associated with an increase in income of $1500 per year.

Macmillan further tested to see whether there was an impact of age of the victim at the time of abuse. His findings were that when the victim was in adolescence during the victimization, annual income was decreased by $6000 using CGSS-93 data. This is compared to an annual decrease of $3700 when the victim was eighteen or nineteen at the time of the violence. Macmillan argued that this again was consistent with the life course model. The greatest damage to earnings is done during adolescence, when the victimization has more impact on the socio-economic life course.

Summary

Briefly, I believe that the following conclusions can be drawn from a review of the academic literature:

  • Not all victims of sexual abuse suffer long-term, observable psychological harm. Indeed, as many as two-thirds of victims show no significant effects.
  • Abuse may be more harmful if it is prolonged or violent.
  • Victims suffer from depression, anger, hostility, marital problems, and self-destructive behavior.
  • Victims of sexual abuse are at increased risk for further sexual assault, such as rape; and show a high risk for substance abuse and prostitution.
  • In many cases, it is not clear whether it was the sexual abuse itself, or the unhealthy psychological climate in which the child lived that led to the perceived psychological damage. Many victims of sexual abuse live in dysfunctional families, often with long histories of substance abuse and marital discord.
  • Evidence of the long-term effects of abuse on educational attainment and labour market earnings is not strong. Some studies have found significant effects, but the number of statistically reliable studies is extremely small.

References

Asher, S.J. “The Effect of Childhood Sexual Abuse: A Review of the Issues and Evidence” In Walker, L. E. A. (Ed) Handbook on Sexual Abuse of Children, (New York: Springer Publishing Company, 1988) pp. 1-17

Bagley, C. and K. King Child Sexual Abuse: The search for healing, (London: Tavistock/Routledge, 1990)

Bell, D. and K. Belicki “A Community-Based Study of Well-Being in Adults Reporting Childhood Abuse” Child Abuse and Neglect Volume 22 No. 7 (1998) 681-684

Cantwell, H.B., “Sexual Abuse of Children in Denver, 1979, Child Abuse and Neglect, 5, (1981), 75-85.

Chandy J. M., R. Wm. Blum., M.D Resnick, “Gender-specific Outcomes for Sexually Abused Adolescents” Child Abuse and Neglect, Volume 20 No.12, (1996), 1219-1231

Conte J. R. and L. Berliner “The Impact of Sexual Abuse on Children: Empirical Findings” In Walker, L. E. A. (Ed) Handbook on Sexual Abuse of Children, (New York: Springer Publishing Company, 1988) pp. 72-93

Conte, J. R., L. Berliner, and J. Schuerman, Impact of Sexual Abuse on Children, unpublished: University of Chicago School of Social Service Administration, (1986).

Finkelhor, D. and A. Browne “Assessing the Long-Term Impact of Child Sexual Abuse: A Review and Conceptualization” In Walker, L. E. A. (Ed) Handbook on Sexual Abuse of Children, (New York: Springer Publishing Company, 1988) pp. 55-71

Koverola, C. et al “Relationship of Child Sexual Abuse to Depression” Child Abuse and Neglect Volume 17, (1993) 393-400

Macmillan, Ross “Adolescent Victimization and Income Deficits in Adulthood: Rethinking the Costs of Criminal Violence from a Life Course Perspective”, Working Paper Department of Sociology University of Minnesota, (1999)

Nash, M. R. , O. A. Zivney, and T. Husley “Characteristics of Sexual Abuse Associated With Greater Psychological Impairment Among Children” Child Abuse and Neglect Volume 17 (1993) 401-408

Reyome, N.D. “Comparison of the School Performance of Sexually Abused, Neglected and Non-Maltreated Children”, Child Study Journal, Volume 23 No. 1 (1993), 17-38

Russell, D.E., “The Incidents and Prevalence of Intrafamilial and Extrafamilial Sexual Abuse of Female Children” Child Abuse and Neglect, 7 (1983), 133-146.

Schwartz, B.G. et al Child Sexual Abuse (Newbury Park: Sage Publications, 1990) pp. 75-108

Wachtel, A. and B. Scott “The Impact of Child Sexual Abuse in Developmental Perspective” In Child Sexual Abuse Critical Perspectives in Prevention, Intervention, and Treatment (Eds) Bagley, C.R. and R.J. Thomlison (Wall & Emerson Inc: Toronto, 1991) pp 79-120

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Matthew Foss was an MA student in the Department of Economics at the University of Calgary