The Effect of Incarceration on Future Earnings

by Christopher J. Bruce, Derek W. Aldridge

In personal injury and fatal accident claims, the courts are often required to determine what effect a criminal record would have had on the earnings of the plaintiff. We suspect that most individuals would expect that criminals will have lower wages and higher unemployment rates than the average citizen.

In this paper, we summarise the most reliable research that has been conducted into this question. We divide this summary into four lines of investigation: the impact of incarceration on earnings and employment; the effect of an increase in the duration of incarceration; the determinants of a successful transition from jail to civilian life; and the determinants of the probability of recidivism.

Incarceration

Although the raw data indicate that those who have been incarcerated have lower earnings and higher unemployment rates than those who have no criminal record, research indicates that this is an example of “correlation, not causation.” (For further information on the misuse of statistics in legal proceedings, see Bruce, 2004.) That is, careful analysis of the data have shown that individuals with low earnings and high unemployment rates are more likely to commit (and be convicted of committing) crimes than are those with high earnings and low unemployment rates. Hence, the primary reason that earnings are low among those who have been incarcerated is that those individuals are drawn from a population of individuals who have low earnings, not because the incarceration changed their employment prospects.

Grogger (1995), for example, found that neither arrests nor jail terms had long term effects on the earnings or employment of young men. And Kling (1999) concluded that, for most individuals, incarceration only reduced earnings by 0 to 3 percent, five to eight years after release. The only group for whom incarceration had a significant effect on post-release earnings was white collar criminals (such as accountants and stockbrokers). Similarly, Richey (2015) found that the effect of conviction on earnings was small or zero. Western (2002), however, found that although conviction had no effect on employability, it reduced the rate of growth of earnings by approximately 33 percent. (Note, however, that as rates of growth are often approximately two or three percent per year, a 33 percent reduction implies a reduction in rate of growth of approximately one percent or less.)

Duration of incarceration

A number of recent articles have attempted to determine whether an increase in the length of a jail sentence, holding the severity of the crime constant, has an effect on post-incarceration employment and earnings. One study found that there “…. is no substantial evidence of a negative effect of incarceration length on employment or earnings.” (Kling, 2006) Another found that the length of prison sentence for drug offences had no significant effect on earnings; but that length of sentence had a very significant effect with respect to incarceration for fraud and embezzlement. (Lott, 1992)

Transition from prison into the workplace

A small number of studies have investigated the factors that influence the success of transition out of prison. Typical of these is Visher and Travis (2003) in which the authors found that men with close ties to families and friends made the most successful transitions into the workplace, particularly if they lived with their wives and children. Families appeared to be especially important if they provided emotional support and housing assistance.

Recidivism

Numerous studies – e.g. Gendreau et. al. (1996), Jones (2005), and Motiuk and Vuong (2005) – have concluded that those who have been released from prison are more likely to reoffend if they have experienced high levels of unemployment or job instability, lack a skill or trade, or are drug users. They are also more likely to reoffend the younger they are.

The finding that it is younger individuals who are most likely to re-offend implies that most offenders have left the criminal population by the time they are in their late 20s. One Canadian study (Correctional Service of Canada, 1993), found that, at age 32, the average age at which respondents had committed their last offence was 23. A subsequent study (Ouimet and LeBlanc, 1996), based on interviews with 238 young men who had previously been young offenders, found that whereas more than half had been criminally active between the ages of 18 to 25, only 18 percent had been criminally active after 25.

Summary

The scientific literature suggests that incarceration has a relatively small effect on lifetime earnings. Although those who have been incarcerated earn lower incomes than those who have not been incarcerated, it is primarily because they are drawn from a group that tends to have relatively low earnings, not because the incarceration “causes” low earnings. Further, the data appear to indicate that the likelihood of being convicted and sent to jail decreases as an individual ages. Hence, those 30 and 40 year olds who were incarcerated in their early 20s are not likely to become repeat offenders.

It appears, therefore, that once a plaintiff reaches the age of approximately 25, the best predictors of his future earnings are standard factors like earnings history, education, and occupation. Whether or not that individual has been incarcerated will not add a significant amount of information to the factors that are used to forecast earnings of non-incarcerated individuals.

Sources

  • Bruce, Christopher, (2004) “The Reliability of Statistical Evidence Concerning the Impact of Disability;” Expert Witness, 9(4), http://www.economica.ca/ew09_4p1.htm.
  • Correctional Service of Canada, (1993) “Recidivists tend to be…;” Forum on Corrections Research, 5(3).
  • Gentreau, P. et. al. (1979) “Norms and recidivism for first incarcerates: Implications for programming;” Canadian Journal of Criminology, 1-26.
  • Grogger, J. (1995) “The effect of arrests on the employment and earnings of young men;” Quarterly Journal of Economics, 51-71.
  • Jones, D. (2005) “Offender employment: A research summary;” Forum on Corrections Research, 17(1), 13-20.
  • Kling, J. (1999) “The effect of prison sentence length on the subsequent employment and earnings of criminal defendants;” Woodrow Wilson School Discussion Papers in Economics.
  • Kling, J (2006) “Incarceration length, employment, and earnings;” American Economic Review, 863-876.
  • Lott, J (1992) “Do we punish high income criminals too heavily?;” Economic Inquiry, 583-608.
  • Motiuk, L, and B. Vuong (2005), “Offender employment: What the research tells us;” Forum on Corrections Research, 17(1), 21-24.
  • Ouimet, M., and M. LeBlanc (1996) “Life events in the course of the adult criminal career;” Criminal Behavior and Mental Health, 6(1), 75-97.
  • Richey, J. (2015) “Shackled labor markets: Bounding the causal effects of criminal convictions in the U.S.;” International Review of Law and Economics, 41, 17-24.
  • Visher, C., and J. Travis (2003) “Transitions from prison to community: Understanding individual pathways;” Annual Review of Sociology, 29, 89-113.
  • Western, B. (2002) “The impact of incarceration on wage mobility and inequality;” American Sociological Association, 4, 526-546.

leaf

Christopher Bruce is the President of Economica and a Professor of Economics at the University of Calgary.

Derek Aldridge is a consultant with Economica and has a Master of Arts degree (in economics) from the University of Victoria.

Are Data from the 2011 Census Reliable?

by Christopher J. Bruce

When estimating future earnings in personal injury and fatal accident cases, financial experts often rely on information provided by the Canadian Census. Of particular importance are data concerning incomes by age, sex, occupation, and education. For example, if a 24 year-old male plaintiff would have been a journeyman carpenter, his potential earnings might be based on average incomes for Canadians with that certification, in the age groups 25-29, 30-34, 35-44, etc.

In the past, these data would have been drawn from a section of the Census known as the “long form.” This portion of the Census survey, which contained much more detailed information than on was on the rest of the Census, was given to only one household out of five. (The remainder of the Census survey asks only basic questions about such demographic factors as age, sex, language, and area of residence.)

For the 2011 Census, however, the government decided to replace the long-form questions with a “National Household Survey (NHS).” Although the 2011 NHS asked the same questions as had the 2006 Census long form, whereas the long form had been mandatory, the NHS was voluntary. The result, as had been expected, was that the percentage of households answering this portion of the survey fell significantly, from 93.8% in 2006 to 77.2% in 2011.This created three statistical problems concerning the reliability of the data (variability in small community data, sample error, and non-response bias). As Statistics Canada had anticipated these problems, however, it took steps to mitigate them, steps that have maintained the reliability of the data that are of value to the courts. Wayne R. Smith, Chief Statistician of Canada, recently wrote an article in which he discussed these steps. [“The 2011 National Household Survey – the complete statistical story,” http://www.statcan.gc.ca/eng/blog-blogue/cs-sc/2011NHSstory. June 4, 2015.] In this article, I summarise Dr. Smith’s discussion.

Variability in small community data

As the sample size of any survey becomes smaller, the data become less and less reliable, due to an increase in variance. In response, Statistics Canada routinely withholds data concerning the smallest communities. In 2011, they withheld the results from 1,100 such communities, up from 160 in the 2006 Census. That is, all of the data reported in 2011 meet the normal statistical requirements for reliability.

Sample error

As the overall size of a sample decreases, there is an increase in what is known as the “sampling error;” that is, from the problem that the average characteristics of the sample differ from the average of the total population. Because Statistics Canada expected a smaller percentage of households to answer the voluntary NHS than had answered the mandatory long form, they anticipated that the total size of the “sample” (the households answering the survey) would be lower in 2011 than in 2006.

To deal with this problem, Statistics Canada increased the number of households who were asked to answer the long portion of the 2011 Census. Whereas one household in five were asked to answer the 2006 long form, one household in three were asked to answer the NHS. The result was that, even though a smaller percentage of households responded to the NHS than had responded to the 2006 long form, the number of households answering the NHS was higher than in 2006, (2,657,461 versus 2,443,507, representing 6,719,688 versus 2006’s 6,136,517).

Although this approach does not correct for all errors, those errors become less and less important as the data are aggregated. Thus, for example, the data for the average income of all carpenters in Alberta are more reliable than for the average income of carpenters in Calgary.

Non-response bias

The most worrisome problem that arises when a survey is made voluntary is that the households who choose to respond to that survey may differ significantly from those who refuse to do so. For example, if those carpenters with relatively high incomes are more likely to respond to the NHS than are those with low incomes, the average incomes reported by the NHS will be biased upwards.

Statistics Canada could not control, ex ante, for the possibility that this would happen. However, they were able, ex post, to investigate whether the respondents to the NHS were representative of the overall groups from which they were drawn – that is, they were able to determine whether the respondents “looked” different from the average.

To make this determination, Statistics Canada was assisted by the fact that they had a considerable amount of information about the respondents to the NHS before those individuals answered the NHS survey. Most importantly, they also had their responses to the short questions on the Census that are mandatory for all Canadians. In addition, they were also able to link the NHS respondents to those individuals’ tax files, immigrant landing data, and the Indian Register.

Using sophisticated statistical techniques they were able to determine that the average respondent to the NHS had very similar characteristics to the average Canadian with respect to age, sex, language, area of residence, income tax, immigration status, and aboriginal status. This finding leads Statistics Canada to conclude that the NHS respondents were, in most cases, representative of the larger population from which they were drawn. And when Statistics Canada was unable to conclude that the individuals who replied to a specific sub-class of questions were representative of the population, the resulting data were not released, or they were released with an accompanying cautionary note.

Summary

To summarise: Although the long-form portion of the 2011 Census was made voluntary, there is sound reason to believe that the data that are of greatest relevance to the calculation of lost earnings can be relied upon.

  1. The information in this article is drawn from a blog written by Wayne R. Smith, Chief Statistician of Canada, entitled “The 2011 National Household Survey – the complete statistical story,” June 4, 2015. This blog can be found at: http://www.statcan.gc.ca/eng/blog-blogue/cs-sc/2011NHSstory.

 

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).

Economica’s Privacy Policy

by Christopher Bruce

On January 1, 2004 the federal Personal Information Protection and Electronic Documents Act was extended to every organization that collects, uses or discloses personal information in the course of a commercial activity within a province. On the same date Alberta’s Personal Information Protection Act came into effect. The purpose of the (Alberta) Act is to “govern the collection, use and disclosure of personal information by organizations” (see www.psp.gov.ab.ca/faq.html). In light of these events, we outline Economica’s privacy policy:

  • We do not reveal any information concerning the specifics of any case, including the names and personal circumstances of the litigants, to any party other than the law firm that has retained us – unless that firm has specifically requested that we do so. We will not, for example, provide copies of our reports to the plaintiff or defendant, to any other expert who has been retained in the litigation at hand, or to any other law firm that is involved in the litigation without a specific request by the firm that has retained us.
  • All of the documents that we receive concerning the specifics of a case are kept in secure areas and/or in secure computer files. All such documents will be maintained in such a manner that they are not accessible to casual observation by visitors to our offices.
  • We will not discuss a case with any party other than the firm that has retained us, without previously having received the permission of that firm. We will not, for example, request personal information from the plaintiff, the plaintiff’s family, or the plaintiff’s employer, or from other experts without first informing the firm that retained us.
  • When speaking with third parties, such as employers, we will not reveal the name or circumstances of the plaintiff unless it is necessary to do so. If, for example, it is possible to obtain details concerning the plaintiff’s pension plan from his/her employer without revealing the plaintiff’s name, we will do so.
  • When disposing of confidential files concerning any litigant, we will have those files shredded by a professional firm.

leaf

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).

Why have automobile insurance premiums been rising?

by Christopher Bruce

According to the most recent Statistics Canada data, automobile insurance premiums in Alberta increased by 68.6 percent between 2001 and 2003 (29.9 percent per year), at a time when the consumer price index increased by only 8.0 percent (3.9 percent per year). Similarly, over the 10-year period 1994-2003, premiums increased by 97.8 percent (7.9 percent per year) while the consumer price index increased by only 26.4 percent (2.6 percent per year). (See Figure 1.)

Although the government has reacted to this increase by introducing wide-ranging legislative changes, no satisfactory explanation has been given for why premiums should have risen so dramatically. The purpose of this article is (i) to review eleven of the explanations that have been given for rising premiums and (ii) to investigate those explanations to determine whether they are consistent with the evidence.

Figure 1

1. Number of accidents

Everything else being equal, an increase in the number of accidents per driver must increase insurance companies’ average payouts and, therefore, their average premiums. However, statistics indicate that the number of accidents has not increased significantly in the last decade. Between 1994 and 2001, for example, the number of collisions increased only from 431.4 to 460.5 per 10,000 vehicles, an average rate of increase of less than one percent per year. This cannot explain the sizeable premium increases of the last few years.

2. Severity of accidents

Even if the number of accidents had declined, the costs of claims per driver might have increased if the average severity of accidents had risen. If fewer people had been injured than in the past, but each injury had been much more serious than previously, total costs of claims might have risen.

With respect to severity, it is known that whereas the number of collisions involving injuries or fatalities increased only slightly between 1994 and 2001 – from 72 to 83 per 10,000 registered vehicles – and the number of collisions involving property damage remained almost constant – at about 360 per 10,000 registered vehicles – the number of “bodily injury” claims almost doubled – from 65 to 112 per 10,000 registered vehicles – over the same period. As bodily injury claims are generally much more expensive than other types, this trend suggests that the average cost of claims should have risen over the 1994-2001 period. Indeed, the Insurance Bureau reports that the average cost of injury claims rose by 44.6 percent (4.2 percent per year) between 1993 and 2002.

What these statistics do not explain, however, is why automobile insurance premiums increased so dramatically in 2002 and 2003. The statistics indicate that whereas the dramatic rise in premiums has been a recent phenomenon, the number and severity of bodily injury claims per vehicle has increased steadily for almost ten years. This evidence suggests that the recent rise in premiums is not closely connected with the increase in severity of accidents.

3. Damages

Even if the number and severity of accidents had remained constant, it is possible that the average cost of accidents could have risen if the courts had become more liberal in their awards of damages to accident victims. With respect to serious personal injury and fatal accident claims, the evidence on this question is clear, however – in the last 20 years there has been virtually no change in the manner in which the courts assess damages. Although there are no definitive statistics on this issue, the principles of damage assessment, in major injury cases, have not changed in Alberta since the mid-1980s. If damages for major injury cases have increased, it is not because there has been a change in the attitude of the courts; it is because Albertans’ incomes have been rising – necessitating larger awards to compensate victims for their losses of income.

It is possible that damage awards for minor injuries have increased substantially. However, it is noteworthy that insurance companies, who have argued that this is one of the major causes of increased premiums, have not released any data to back this claim. It seems reasonable to draw an adverse inference from this failure. Surely if the data supported the insurance industry’s arguments, they would have made those data public.

4. Fraud

Insurance companies commonly argue that consumer fraud is a major source of inflationary pressure on insurance rates. There are two major problems with this argument. First, although insurance fraud undoubtedly occurs, insurance companies have been unable to provide any statistically reliable evidence to show that fraudulent claims amount to more than a small percentage of payouts.

Second, and more importantly, even if fraud was a major problem, no evidence has been put forward to suggest that fraudulent claims have increased substantially in the last two years. For an increase in fraud to explain a significant portion of the 69 percent increase in premiums that has been observed, fraudulent claims would have to have increased dramatically. There is no evidence at all that this has occurred.

5. Medical costs

A recent study by the Insurance Research Council (a U.S.-based agency) found that “escalating medical costs are the key factor behind” the growth in automobile insurance claims in the past five years. It seems unlikely that this source could account for a significant portion of the recent rise in premiums in Alberta, however, as a substantial portion of medical costs resulting from automobile accidents are covered by Alberta Health Care. Since 1996, those costs have been covered under an annual levy that has increased at a relatively steady rate, of approximately 12 percent per year. For this source to explain a significant portion of the 69 percent increase in premiums seen in the last two years, there would have to have been a dramatic increase in the annual levy, an increase that has not been observed.

6. Legal costs

An additional component of the cost of insurance is the fees charged by lawyers and other experts. Although a substantial portion of victims’ legal fees are paid by the victims out of their damages – and, therefore, do not contribute to insurance companies’ costs – insurers have to hire their own lawyers and may sometimes have to pay a portion of the victims’ legal fees. Nevertheless, any argument that these costs have contributed to the substantial increase that has been observed in automobile insurance premiums founders on a lack of evidence that these fees have increased substantially in the last few years. It is one thing to argue that legal fees may, or may not, be “too high,” it is another thing altogether to argue that they have risen as a percentage of insurance costs.

7. Return on investment

To a certain extent the costs of operating an insurance company are offset by the company’s ability to invest the premiums it has received until drivers make their claims. The higher is the interest on those investments, the less does the company have to charge in the form of premiums. Some commentators have argued recently that the observed increase in premium costs has resulted from the decline in the average rate of return on investments.

This is not a compelling argument, however, as this decline cannot explain more than a small portion of the dramatic increases in premiums. If insurance companies hold premiums for half a year on average (that is, if premiums are collected at the beginning of the year and then spent at a constant rate over the year), and if the rate of return on investments is, say, 8 percent, then the interest that is collected will (on an annual basis) equal 4 percent of premiums. If the rate of return then declines to 5 percent, the effective return on the investment of premiums will fall to 2.5 percent, a drop of only 1.5 percent. As this is roughly the order of magnitude of recent declines in rates of return, this factor cannot explain a significant percentage of the recent increases in premiums.

8. Administrative costs

Approximately 25 to 30 percent of an insurance company’s costs are for administration – salaries of salespeople and adjusters, rent, cost of supplies, advertising expenses, etc. There is no evidence to suggest that these costs have risen significantly in the last few years.

9. Re-insurance

Insurance companies have argued that one of the most important sources of increased costs in the last two years has been the increase in premiums that they have had to pay to re-insurance companies since September 11, 2001. This argument is implausible. Figure 1 illustrates the increases in both automobile and homeowners’ insurance premiums in Alberta in recent years. If re-insurers had raised their rates in response to the perceived increase in terrorism, they would have raised those rates by at least as much for homeowners’ insurance as for automobile insurance. But it is seen clearly in Figure 1 that homeowners’ insurance premiums rose by far less than did automobile insurance premiums. This provides compelling evidence that increases in re-insurance premiums have not been a major source of the reported increase in automobile insurance premiums.

10. Collusion

Some critics of the insurance industry have argued that the recent increases in automobile insurance premiums have resulted from collusive behaviour among insurance companies. This argument is suspect for two reasons. First, it is difficult to explain why insurance companies would have raised premiums for automobile insurance and not for homeowners insurance. Second, there are more than 100 automobile insurance companies operating in Alberta. Over a century of experience suggests that it is extremely difficult even for an industry of only three or four firms to maintain a collusive stance. It is unlikely that 100 firms could do so.

11. Statistical interpretation

There is some concern that the dramatic increases that have been observed in automobile insurance premiums in the last few years have resulted from the way that statistics are collected and reported rather than from “real,” underlying factors. Two arguments have been made in this respect.

First, the Insurance Bureau argues that the manner in which Statistics Canada collects information about automobile insurance premiums produces misleading results. Nevertheless, the Bureau’s own data (published in the December 2003 issue of their newsletter Perspective) indicate that automobile insurance premiums in Alberta rose by approximately 63 percent (5 percent per year) between 1993 and 2003 and by approximately 30 percent (14 percent per year) between 2001 and 2003. Although these numbers are much lower than those produced by Statistics Canada, they are still substantially higher than the overall consumer price inflation figures for those periods. (Also, whereas Statistics Canada’s data measure changes in the price of a fixed “basket” of insurance policies, the IBC data measure changes in the costs of actual insurance policies that have been purchased. Thus, if, as premiums rise, consumers purchase less comprehensive policies, the IBC data will underestimate the rate of increase of given policies.)

Second, it is well known to observers of the automobile insurance industry that premiums move in a cyclical manner. When premiums are relatively low, insurers’ profits fall and many firms leave the market. This reduces competition and allows premiums to rise. But as that happens, profits also rise, attracting new firms, and driving down premiums again. Typically, this cycle takes approximately 10 years. The data in Figure 1 show, for example, that there were significant increases in premiums in the early 1980s, early 1990s, and early 2000s; and stagnation of premiums in the mid-1980s and mid-1990s.

This observation suggests that the recent, dramatic increases are simply part of a larger, cyclical movement in automobile premiums. Even if this is true, however, the average increase over the last 10 years – even when calculated on the basis of the IBC figures (5 percent per year) – has been more than double the average rate of consumer price inflation. Clearly, cyclical and statistical factors alone cannot account for this substantial increase.

Table 1

Conclusion

The information that has been reviewed in this paper suggests that two factors are primarily responsible for the pattern of premium changes that have been observed in Alberta in the last decade. First, the dramatic increases in the last two years represent a “natural” upturn in a long term cycle in premiums. Past patterns suggest that these increases will be followed by stagnation of premiums for the next six or seven years.

Second, there is some evidence to suggest that the average severity of personal injury claims has been rising. As I find no evidence that this increase has been due to fraud, to an increase in the number of accidents, or to changes in the criteria employed by the courts to calculate damages, it appears that the most plausible explanation is that the losses suffered by plaintiffs have been increasing in value.

leaf

A large number of individuals were kind enough to provide me with assistance in the preparation of this article. I would particularly like to thank Don Higa, Jim Rivait, Walter Kubitz, Derek Aldridge, and Harris Hanson.

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).

Policies to deal with rising premiums

by Christopher Bruce

In the first article in this newsletter, I analyzed the arguments concerning eleven possible sources of increased automobile insurance premiums. The purpose of this article will be to review each of those sources to determine whether there are changes in government policy that might reduce premium costs.

1. Number of accidents – Experience Rating.

The Alberta government has already taken one of the boldest and most innovative steps possible towards reducing the number of automobile accidents. This is their proposal to introduce experience rating – the direct tying of premium values to drivers’ accident-causing behaviour – to the automobile insurance sector.

Experience rating has two highly desirable characteristics. First, the individual driver has complete control over his or her premiums. If individuals drive cautiously, avoiding accidents and driving violations, their premiums will decline to the lowest available rate. Furthermore, individual drivers’ rates will not be relatively high just because they happen to belong to a group, like young males, that has a relatively high accident rate.

Second, there is a substantial body of statistical evidence to show that when insurance premiums are related to experience, accident rates fall. When individuals know that they can reduce their premiums significantly by driving more carefully, they do so.

Under the government’s proposal, the impact of a serious driving offence or an at-fault accident will be much greater than it is currently, under what is known as “actuarial” rating of premiums. For example, under the proposed system, a typical Edmonton driver who has recently begun to drive (i.e. who has no “experience”) will pay a premium of about $2,000. After four years of no accidents or driving offences, that driver will pay only $700 – a saving that will continue for each and every year into the future as long as the driver has no accidents or convictions. One would expect an annual saving of $1,300 to be of sufficient size that it would induce most individuals to take additional precautions against unsafe driving.

Furthermore, the proposed system would move the driver four steps up “the premium ladder” each time he or she had an at-fault accident. So the driver with four years of accident free driving would be bumped from the reduced $700 premium to the $2,000 base premium, losing the entire $1,300 “bonus”.

And the incentive to avoid driving convictions is even stronger. A single impaired driving conviction would increase premiums by 200 percent.

These proposals are highly desirable. The government deserves far more credit than it has received for recommending them. Nevertheless, the government needs to reconsider two aspects of its plan.

First, it is mistaken in its proposal that a government agency should set the base premium rate. For example, in the case discussed above, the $2,000 base premium for Edmonton drivers was not one chosen by the insurance companies, but by the government. This is unreasonable for two reasons. First, the government cannot know what the true cost is to the insurance companies of providing insurance. As a result, the base rate that it will choose is almost certain to be either too high or too low.

If it is too high, insurers will make excess profits at the expense of Alberta drivers. If it is too low, insurance companies will make losses and some of them will refuse to provide coverage to Albertans.

Second, when the government sets premiums, the competitive incentive for insurance companies to find ways to lower rates is lost. If insurance companies are forced to charge the same rates regardless of how efficient they are, there is less incentive for them to seek ways of being efficient. It is these competitive pressures that keep premiums from rising more than they have.

There is a simple solution to these problems: the government should set only the percentage increases and decreases that are to result from various “experiences” and leave the insurers to set the base rates from which those increases and decreases are to be calculated. This will get the government out of the business of setting rates, while leaving intact the strong incentives created by a system of experience rating.

The second problem with the new legislation is that it does not deal with the adverse incentives that it gives to insurance companies. Specifically, experience rating results in a situation in which insurers know they will make substantial profits on some classes of customers and lose money on others. Thus, it gives them a strong incentive to refuse to insure the money-losing group. In the scheme proposed by the Renner committee, that group will be composed primarily of young males.

Insurers will lose money on this group because the number of accidents drivers have had in the past is only loosely related to the number that they can be expected to have in the future. What decades of statistics tell us is that a nineteen-year-old male with a perfect, three-year driving record is more likely to have an accident in the next year than is a forty-year-old male with the same driving record. And a nineteen-year-old who had an accident last year is more likely to have an accident next year than is a forty-year-old with the same experience. This means that insurers will expect to pay out more claims to nineteen-year-old drivers than to forty-year-old drivers.

Assume, for example, that ten percent of nineteen-year-old drivers who have had a clean record for three years will have an accident in the next year; whereas only five percent of forty-year-olds with a similar record will have an accident next year. If the average accident costs the insurance company $10,000, it will expect to pay out an average of $1,000 for each nineteen-year-old and $500 for each forty-year-old.

If the government forces insurers to charge the two groups the same premium, they will have to charge something between $500 and $1,000 just to cover their expected claims costs. For example, if the two groups were the same size, the premium would be $750 (the average across the two). But this means that they will expect to make a $250 profit on the average driver in the older group and a $250 loss on the average driver in the younger group.

As insurance companies are profit-driven, we can expect that they will respond by doing their best to attract older drivers – and to turn away younger drivers. The stakes are high. Those companies that find themselves with a relatively high percentage of young drivers will lose money and will soon be forced out of the market. Companies will use every loophole at their disposal to attract as many drivers in the older age groups as possible.

For example, companies might offer to sell automobile insurance through employers, in much the same way they currently sell long-term disability and dental plans. As employees are predominantly in the 25-64 year age group, and as high risk drivers are predominantly in the 16-24 and 65+ age groups, such a practice would allow firms to “skim” off the low-claim drivers.

The government will need to introduce strict controls to ensure that companies are not seriously disadvantaged if they write insurance for groups whose average claims exceed average costs.

2. Severity of accidents – Improved policing.

Reductions in severity are most likely to come from improvements in the design of automobiles; and in the use of safety devices such as seat belts and air bags. Nevertheless, provincial governments can reduce severity by enforcing highway speed limits more strictly – particularly on segments of roads that are known to have high accident rates.

Recent scientific evidence, published in journals such as Accident Analysis and Prevention, Injury Prevention, and the Canadian Medical Association Journal, concludes that the two changes that offer the greatest promise for reducing the incidence and severity of accidents are: first, raising the legal drinking age; and, second, banning the use of hand-held cellular telephones by drivers of moving vehicles.

3. Damages – Restrictions on tort.

Many of the proposals that have circulated in the last year or so have had to do with the placement of restrictions on tort damages. In general, these proposals are based on the assumption that victims are currently being “overcompensated;” hence, a reduction in damages will not cause a hardship to victims. The two most commonly-made proposals are that individuals should not be able to claim from two insurers for the same loss – the “double compensation” issue – and that loss of income should be calculated net of income taxes – because victims do not have to pay taxes on their awards, they will be adequately compensated if damages are based on after-tax income.

Typically, double compensation occurs when the victim is compensated for loss of income both by the defendant and by the victim’s own long-term disability insurance. Under the new legislation in Alberta, victims will be allowed to collect from only one of these parties. This proposal seems reasonable except that it is usually suggested that the victim be required to collect from his or her own insurance company. Effectively this requires that the victim be made to pay for damages caused by a negligent driver – and it allows the negligent driver to evade responsibility for his/her actions. Neither of these outcomes seems defensible. Furthermore, if disability insurers are able to re-write their policies in such a way as to avoid paying damages when their clients are able to collect from negligent drivers, the legislation will affect only disability insurance premiums (which will decrease), not automobile insurance premiums.

The second proposal for reducing tort damages – that victims be compensated only for after-tax losses – also seems reasonable. As plaintiffs do not pay taxes on court-awarded damages, the payment of “gross” income overcompensates them. The primary argument against this proposal is that plaintiffs currently rely on this “overcompensation” to help them pay for their legal fees (which are only partially paid by the defendant). If plaintiffs have to pay for their legal fees out of after-tax income, their awards net of legal fees will leave them undercompensated.

A third element of the new legislation sets limits on the award of “non-pecuniary” damages. This is not based on the assertion that victims are being overcompensated by the courts. Rather, it is based on the assertion that victims of “minor” injuries are exaggerating their injuries and, therefore, defrauding the system. The issue of fraud is discussed in the next section.

4. Fraud.

Setting limits on damages is an entirely inappropriate method of dealing with fraudulent claims, primarily because it punishes the innocent. If fraud is an important factor in the determination of automobile insurance premiums, there are two appropriate responses. Insurers can increase their vigilance; and, in cases of egregious behaviour, they can ask the government to lay criminal charges. As both of these responses are already available to the insurance industry, the government does not need to take further steps.

5. Medical costs.

It is clear that public policy towards medical costs is unlikely to be influenced significantly by government concern over automobile insurance premiums. In this area, drivers and insurers can only hope that government health policy incidentally acts to reduce personal injury claims costs.

6. Legal costs – no fault.

It is often argued that legal costs could be minimized if a form of no fault insurance was introduced. Whatever the advantages of no fault might be, there are three important problems with it that must be dealt with before such a proposal can be considered seriously.

First, because more parties can make claims in a no fault system than in a fault system (“at fault” drivers can make claims in no fault systems but not in fault systems), there is very little chance that no fault will reduce premiums. Indeed, the evidence shows that no fault jurisdictions have premiums very similar to those in fault jurisdictions.

Second, the purported source of savings in a no fault system is that accident victims are denied access to the court system; hence legal bills are reduced. But, the courts serve an important function – they allow parties to appeal decisions made by their insurance companies that they feel are unfair. It is possible that an appeal system can be introduced to no-fault insurance, but there is some evidence to suggest that if such a system really is fair, it will cost as much as do the courts. In short, any savings in administrative costs tend to come at the expense of justice.

Third, there is consistent, strong evidence to suggest that there are more accidents in no fault jurisdictions than in fault jurisdictions because drivers in the former do not have to take responsibility for their actions. (Recent statistical studies conclude that when no fault insurance is introduced, the accident rate rises by approximately 6 percent.) Indeed, not only are the drivers who are at fault for their own injuries not made to pay higher premiums, they are fully compensated by the insurance system for any costs they incur.

7. Return on investment.

If insurance companies have been harmed by falling rates of return on their investments, there is nothing the government can do to help, short of making short-term loans at below-market rates.

8. Administrative costs – Public insurance.

It has often been suggested that administrative costs could be reduced if the private insurance system was replaced by a government-run monopoly. This suggestion ignores the fact that monopolies have been found, almost universally, (i) to be less responsive to their customers than are competitive firms; and (ii) to be less efficient than are firms that have to face competitive pressures. (Some proponents suggest that automobile insurance is less expensive in Saskatchewan and Manitoba than in Alberta because it is provided by monopoly in the former two provinces. However, this ignores the many subsidies that those insurers receive from their governments and also ignores the fact that British Columbia’s premiums are not significantly different from Alberta’s.)

9. Re-insurance.

Following the terrorist attacks of September 11, 2001, re-insurance companies have raised their premiums significantly. As the terrorist attacks should have only a negligible effect on automobile insurance claims, the re-insurers’ actions are unjustifiable. It might be appropriate for the Government of Alberta to provide re-insurance coverage to firms working within Alberta until re-insurance rates return to a level that is consistent with the risk that is being faced.

10. Collusion.

Unless some evidence is presented to suggest that automobile insurers are colluding, no action needs to be taken on this issue.

11. Statistics.

If Statistics Canada has overestimated the rate of increase of automobile insurance premiums and if premiums do follow a regular cycle, which is currently at its peak, then there is little or no rationale for the Alberta government to do anything about premiums. Alberta might cooperate with the Insurance Bureau and Statistics Canada in reassessing the method by which premium inflation is measured; but, otherwise, Alberta merely needs to wait a year or two and that inflation rate will fall significantly by itself. The drastic changes proposed by the government are completely out of line with the (non) seriousness of the situation.

Summary

This article has concluded that the government would be justified in adopting the following policy changes:

  • Introduce experience rating, as proposed by the Renner committee, but without government control over the base premium rate.
  • Increase police surveillance of moving traffic violations, particularly in areas identified as being of high risk.
  • Raise the legal drinking age.
  • Ban the use of hand-held cellular telephones by drivers of moving vehicles.
  • Introduce a regulation that losses of income be made on an after-tax basis.
  • Provide re-insurance to the automobile insurance industry until rates return to a level that can be justified based on expected claims.
  • Cooperate with Statistics Canada and the Insurance Bureau of Canada in investigating the manner in which the inflation rate of automobile insurance premiums is measured.

leaf

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).

Management fees

by Derek Aldridge

This article first appeared in the winter 2002 issue of the Expert Witness.

We occasionally encounter cases in which it is argued that the injured plaintiff requires an additional award to pay for the services of a financial manager (that is a “management fee” award). I have recently been involved in a couple of cases in which I had to consider this issue in detail, and in this article I will share some of my thoughts on the matter.

Typically (in my experience) these cases involve seriously injured plaintiffs (often children) with large loss of income and cost of care claims. It is anticipated that the plaintiff will be unable to manage his own financial affairs, and will therefore need the assistance of a financial advisor. The advisor (most banks and related financial institutions provide these services) will invest the plaintiff’s money, ensure that his bills are paid, prepare his taxes, and so forth. The annual cost of these services is mainly based on a percentage of the funds under management each year (though the actual cost schedules are often complex). Because the plaintiff will need to spend part of his award on a financial manager, he therefore needs additional funds to cover these costs (that is, a management fee award). The difficulty arises when we consider whether or not the plaintiff will receive a higher rate of return on his investments, due to the expertise of the financial manager. That is, it may be the case that the financial manager’s fee will be at least partially offset by the increased return on investment. (For example, if I am paying a financial manager $5,000 per year, I expect that the return to my investments will be at least $5,000 per year greater than if I did not use a financial manager.)

However, this is not a simple issue. When we determine a reasonable real discount rate to use in our calculations, we assume that plaintiffs will invest their money in simple low-risk investments such as government bonds. It is our understanding that this is their only obligation – they need to do better than keeping their money in a safe deposit box, but they do not need to pursue an “active” (and more risky) investment strategy. However, when the plaintiff uses a financial advisor, what sort of service should we expect that the plaintiff will request? If the plaintiff requests that the manager act very conservatively and invest the money in a similar manner as is expected of a plaintiff-investor, then there will be no increased return to offset the cost of the financial manager. A management fee award will be needed. Alternatively, if the plaintiff is obligated to make full use of the financial advisor, then presumably the advisor will do better than the conservative government bond strategy assumed for a plaintiff-investor, and there will be a higher return to offset the costs. It may be the case that the total net return (after management fees) is higher than the return that can be earned by simply investing conservatively in government bonds. In order to properly estimate the awards in this case, we would need to estimate the expected long-run real rate of return that the manager will earn, re-estimate all our future loss calculations, and then estimate the management fee. Note that in any province with a mandated discount rate, the issue is even more complex, since the economist does not have the option to simply change the discount rate based on the anticipated investment strategy of the plaintiff.

Suppose it is the case that a plaintiff can use a financial manager and earn a net real return that is greater than the “normal” real rate of return earned by a plaintiff-investor. Why then would we not expect that all plaintiffs should use investment advisors, in order to best mitigate their losses? In a province with a mandated discount rate, if a higher net return can be earned using an investment advisor, then why does the mandated rate not reflect this?

These are complicated issues. In my view the preferred approach in most cases is to separate the plaintiff’s need for “financial assistance” from the actual management of her funds. “Financial assistance” would include the day-to-day services needed by a plaintiff who cannot manage her own financial affairs – such as bill-paying, handling spending money, paying taxes, and so forth. These services could presumably be handled by an accountant or a lawyer. The services would not include actual investment management – it would be anticipated that the person assisting with the plaintiff’s financial affairs would arrange for conservative investment of the plaintiff’s award in the usual low-risk vehicles. If it could be determined that this level of financial assistance would cost (say) $5,000 per year, than that cost could simply be incorporated as a normal cost of care, without introducing the difficult and contentious issue of financial management.

leaf

Derek Aldridge is a consultant with Economica and has a Master of Arts degree (in economics) from the University of Victoria.

What is Econometrics?

by Kelly Rathje & Christopher Bruce

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

Commonly, economic experts will testify that a particular characteristic of the plaintiff, such as his years of education or his marital status, is “correlated” with one of the factors that is of interest to the court, such as future income or retirement age. The branch of economics that seeks to determine whether such correlations exist is called econometrics. In this article, we explain briefly how econometric techniques work.

Assume that we are interested in determining whether the annual incomes that individuals earn are correlated with, or determined by, years of education. Assume also that 70 individuals have been observed and that for each individual, we know their number of years of education and annual income.

We have plotted the observations for these individuals in Figure 1. For example, individual A has 15 years of education and an annual income of $45,000.

Figure 1

When income levels are plotted against years of education, one would expect that the observations would be scattered, as seen in Figure 1. What the econometrician wishes to do is determine whether these scattered points form a “pattern.” One simple pattern that is often tested is that of a straight line. In this case, the formula for a straight line is:

I = a + b1(E)

where I is income; a is a constant; b1 measures the amount that education influences income; and E is years of education.

What the econometrician tries to do is to find the line which minimises the distances between the observations and the points on that line. The straight line which appears to meet this criterion with respect to the observations in Figure 1 has been drawn there. The formula for this line is

I = 6,850 + 2,000(E) (1)

This formula says that if the individual has 12 years of education, his income is predicted to be $30,850.

I = 6,850 + 2,000(12) = 30,850

It can be seen from Figure 1 that, in general, the observations lie fairly close to the line. For this reason, we would conclude that the hypothesis that education affects income is supported. Furthermore, because the “sign” on the 2,000 component of the equation is positive, we would also conclude that education has a positive effect on income. (In this case, each extra year of education appears to lead to 2,000 extra dollars of annual income.)

Equation (1), which investigates the effect which only one variable has on another, is not typical of the equations that are normally of interest to economists. Typically, for example, we would assume that there is a large number of factors, in addition to education, that will affect income. In that case, econometricians extend their equations to include numerous variables.

For example, suppose the economist has additional information about the age of each individual in the data set. This variable can also be added to the equation to help “explain” income. The equation would become:

I = a +b1(E) + b2(A),

where A is “age.” The resulting estimated equation might be something like:

I = 5,000 + 1,900(E) + 200(A) (2)

This model now indicates that for every extra year of education an individual has, they will earn an extra $1,900, on average, and for each additional year in age, there is an increase of $200. In other words, if an individual has a high school diploma, and is 34 years old, then the equation indicates on average, they will earn $34,600 (= 5,000 + [1,900 x 12] + [200 x 34]). Similarly, if an individual holds a bachelor’s degree (16 years of education), and is 34 years old, then the equation indicates that, on average, they will earn $42,200 (= 5,000 + [1,900 x 16] + [200 x 34]).

The variables used as examples to this point – income, education, and age – all share the characteristic that they can easily be measured numerically. Other variables which might influence the wage rate are less easily converted to numerical equivalents, however. Assume, for example, that our hypothesis was that incomes were higher in rural areas than in cities, or that men were paid higher incomes than women, all else being equal.

As econometric analysis is a statistical technique, it requires that the economist enter all of his or her information as numbers. The way that econometricians deal with this problem is to construct what are called “dummy variables.”

In this procedure, one of the observations is arbitrarily chosen to be the “reference variable” and it is given the value of 0 whenever it appears. The other observation is then given the value of 1. For example, if “female” was the reference category, then the dummy variable would be given the value 0 whenever the observed individual was female and would be given the value 1 whenever the individual was male.

Assume that this has been done and equation (2) has been re-estimated with a male/female dummy variable included. The new equation might look like:

I = 3,000 + 1,900(E) + 200(A) + 4,000(M) (3)

where M is 1 if the individual is male and 0 if she is female. The interpretation that is given to the value that appears in front of M in this equation is that income is $4,000 higher when the worker is a male than when the worker is female.

Alternatively, because the dummy variable takes on the value 0 when the worker is female, the relevant regression equation for females is simply equation (3) excluding the dummy variable:

I(female) = 3,000 + 1,900(E) + 200(A)

And because the dummy variable takes on the value 1 when the worker is male, the relevant equation for males becomes:

I(male) = 3,000 + 1,900(E) + 200(A) + 4,000(1)

= 7,000 + 1,900(E) + 200(A)

The income model is one example of how econometrics is used, and how it is useful to determine trends and relationships between variables. Other uses may include forecasting prices, inflation rates, or interest rates. Econometrics provides the methodology to economists to make quantitative predications using statistical data.

leaf

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

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).