ACTLA Presentation – Without- and With-accident income: Total Compensation

ACTLA Lunch & Learn

Without- and With-accident income: Total Compensation

Prepared by:

Kelly A. Rathje, MA

Without- and With-accident Income: Total Compensation

In the sections below, I provide notes that I used to discuss the different sources of earnings data experts can rely on to estimate an individual’s earning stream, and other sources, usually benefits, that make up an individual’s total compensation.

1 Sources of Earnings Data

When doing a loss of income calculation, we need to estimate both the without- and with-accident income streams. That is, we need to determine what the individual would have earned, had the accident not occurred, and what that individual will earn now, despite the injuries.

1.1 Average of past income

We can look at an average of the individual’s past income, both before and after the accident. If they have a well-established income stream prior to the accident, then assuming that they would have continued to earn the same level of income that had been achieved prior to the accident is usually a reasonable representation of what they will earn in the future. Similarly, if they have an established income stream after the accident, it is a good indicator of what they are capable of earning, despite their injuries.

However, there is a downside to relying on a average of past income, as it  may not account for future promotional wage growth. Also, it may reflect earnings during a boom time in the economy or a slower time (recession) during the economy. For example, if in the oil industry, relying on earnings during the 2010-2015 time frame, although we have 6 years, it may overstate the without-accident earnings since this was during a boom in the oil industry. Similarly, relying on average earnings for the 2015-2020 time frame may understate due to the significant decline in the oil industry.

1.2 Census data

Another source of earnings data is the Canadian Census. This source is the single most reliable source of earnings data in Canada. It provides earnings data for specific occupations, age categories, gender, and education.

Census data if very useful when an individual has just completed their education and are started a career; or only has been working for a few years in an occupation; or has changed occupations during their work. It is also useful when an individual does not have a well-established career (income) path, as in the case with younger individuals. In the with-accident scenarios, it is often the case that the injured party needs to retrain, so to estimate the earnings stream we can look at census data for individuals within the same occupation, and the same level of education.

The census data can also provide a baseline comparison when considering an individual’s average of their past income. It can help us determine if the individual’s earnings are consistent with what statistical averages would suggest, or if they are above- or below-average.

1.3 Salary grids/Collective bargaining agreements

Certain occupations have their salaries and/or wage rates specified by a collective agreement. Two examples of such occupations are teachers and nurses.

The collective agreements and salary grids are a useful source of earnings data as it exactly sets out how an individual’s earnings will progress. For example, the teacher’s salaries are specified for the first year of teaching through the tenth year of teaching – at which time they reach the top of the salary grid. Since teachers do not tend to receive overtime pay or shift differentials, if the school division the individual is going to work for is known, then we know exactly how the salary will progress with experience.

Another example of an occupation with a salary grid is nursing. As you gain experience, the wage rate increases such that after nine years of experience, the top of the salary grid is reached. However, with jobs like nursing, the salary grid reflects the base wage rate. A nurse’s wage if often higher than the base rate due to things like overtime, shift premiums, and education premiums. However, the base wage rate is still useful when estimating the income of a nurse.

First, if the individual has an established income stream as a nurse, the base wage rate, and the reported employment income, can be used to determine “straight-time equivalent hours” (STE). The STE hours are the number of hours a person would have to work, at their base hourly wage, in order to earn the same income they earned after receiving shift premiums, overtime hours, education premiums, lump-sum pay-outs, and so forth. The STE hours, and the base wage rate can then be used to estimate a future income stream.

Second, if there is not an established career, the base wage rates can be used to estimate a wage inflation index, and this index, specific to the nursing occupation, can be applied against the census data (which is reported in 2015 dollars), to determine an income stream, in combination with the census data.

2 Other sources that make up total compensation

In addition to money paid to an employee, an individual may also receive benefits as part of their total compensation for employment. These sources should also be taken into consideration when calculating the income streams for a loss of income assessment. These are usually included under the heading “fringe benefits”.

2.1 Insurance

Extended health, disability, dental, life. This is a benefits package that an employer provides to an employee, and is usually valued at approximately 2.5 to 3.5 percent of earnings, and we usually include the value of insurance as a percentage of income.

2.2 Car allowance

The employer may provide compensation for vehicle use, or provide a company car. This is usually a taxable benefit, and the value of the benefit is already included in the reported T4 earnings (box 34 of the T4 slip).

2.3 RRSP matching

This benefit arises when the employer makes contributions to an RRSP, matching the employee’s contribution to a certain percentage. This is also usually a taxable benefit, and already included in the reported T4 earnings (box 40 of the T4 slip).

2.4 Stock/security options

 This benefit is offered as an offer to purchase shares of the company, at some specified price. The individual may then choose to sell these shares, once they’ve vested, in order to realize a profit on the sold shares. If this option has been exercised in the past, then the net value of the sale is usually a taxable benefit and already reported in the T4 earnings (box 38 on the T4 slip). In this particular case, the cost of the stock option needs to be deducted in order to determine the net value of the sale. The cost is reported in box 41 of the T4 slip. For example, suppose the option to purchase 10 shares is offered at $5 per share. Once it vests, it is sold at a market value of $10 per share. The $100 (= $10 × 10 shares) would be reported as T4 earnings, but the cost of the option $50 (= $5 × 10 shares), which is reported in box 41, would need to be deducted.

There are also cases in which the stock options have no value. For example, suppose the option to purchase 10 shares is offered at $5 per share. Once it vests, the market value of the share is only $2.50 per share. In this case, the cost to purchase the option ($50) is more than what would be received for selling it ($25), so the option has no value.

Stock options do not seem to be that common, and when estimating the value of potential future stock options, we usually include the value of a percentage of income.

2.5 Employer-funded pensions

There are two types of employer-funded pensions. A defined-contribution and define-benefit.

Defined contribution

A defined contribution plan is simply whatever is contributed on the individual’s behalf is what is received out. If the employer contributes 10 percent of earnings, then the value of the employer-funded pension benefit in each year is 10 percent. This is included as a percentage of income.

Defined benefit

A defined benefit pension plan is when both the employer and employee make contributions to the plan, and upon retirement, a formula is used to calculate the pension benefit. Examples of a defined benefit pension plan are the Local Authorities Pension Plan (LAPP), the Alberta Teacher’s Retirement Fund (ATRF), police forces, and so forth.

The value of the defined benefit pension plan can be captured in two ways. First, the value of the employer’s contribution can be included as a percentage of income. Thus, the employee will receive the employer contribution as part of their loss of income. The amount that the employer would have contributed on their behalf can be invested in a private pension plan. Upon retirement, the reduced with-accident pension plus the top-up from the private plan investment, will equal the without-accident pension, thus ending the loss.

A second method to capture the value of the employer-funded pension is to actually calculate the without- and with-accident pension streams upon retirement, and compare the two to determine the loss of income.

2.6 CPP pension

It is assumed that the premiums an employee pays into the Canada Pension Plan (CPP), are roughly equivalent to what that individual will receive as a retirement pension. It seems logical that if an individual is earning less than they would have, had they not been injured, they will not be contributing as much to the pension plan. As a result, the with-accident retirement benefit will be less than in the without-accident scenario. This may not be the case.

For example, if the individual is receiving CPP disability, and will continue to receive disability until age 65, the CPP retirement benefit may not be impacted as all of the years on disability are ignored when calculating the benefit.

Second, if the individual would have earned, and will earn, an income at least as much as the yearly maximum pensionable earnings, then the contributions (and resulting retirement pension) will be the same in both the without- and with-accident scenario.

However, if there is a difference between the without- and with-accident CPP contributions, then it is reasonable to include a percentage of income to reflect the difference in premiums. As an example, suppose in the without-accident scenario, an individual would have earned $100,000, and after being injured, they earn $45,000. The CPP contribution in the without-accident scenario would be $3,166 (the maximum) but in the with-accident scenario would be $2,262. This is a difference of approximately $921 per year. In this particular case, it could be reasonable to include a 3 percent contingency in the without-accident scenario and a 5 percent contingency in the with-accident scenario to reflect the difference.

For a PDF version of this presentation, click here.

Summer 2019 issue of the Expert Witness newsletter (volume 23, issue 1)

Contents:

In this issue of The Expert Witness, we present two articles:

From the Desk of Christopher Bruce: Farewell

Christopher Bruce publishes his final Expert Witness newsletter. In this article, Chris writes about his achievements and the founding of Economica.

Selecting the Productivity Factor

In this article, the economists discuss the real rate of growth earnings, methods of predicting the real rate of growth earnings, and how to select a forecast.

We would also like to share; A Word from the Consultants of Economica.

A Pdf. version of the Newsletter can be found here.

A Word from the Consultants of Economica

We would like to say thank you to Dr. Christopher Bruce. You have been a great mentor for us throughout our careers with Economica. You have provided direction, leadership, advice, and have groomed us to be one of the leading firms in the industry. Your knowledge, guidance, and support throughout the years have been a major contributor to our success, and we truly appreciate everything we have learned from you.

We are thankful for the opportunity you have given us, and we will strive to maintain the level of professionalism, integrity, and service that Economica is known for, and continue to be one of the leading firms in this industry.

Thank you and enjoy your retirement Chris.

 

Selecting the Productivity Factor

One of the most important determinants of the plaintiff’s future earnings is the rate at which those earnings will grow. There are two broad determinants of this rate. First, each individual benefits from increases that arise from gains in experience, promotions, and job changes. Second, as the economy grows, the earnings of all individuals rise with it – the source of the popular aphorism “a rising tide lifts all boats.” The purpose of this article is to summarise the most recent research concerning the latter rate, which economists call the real rate of growth of earnings, and which the courts often refer to as the productivity factor.

We divide our discussion into three parts: In the first, we define what we mean by real rate of growth of earnings. In the second, we provide two types of statistical evidence concerning that rate. Finally, we argue that the most reliable projections of that rate are obtained from agencies that specialise in making such projections. We conclude that those projections indicate that real earnings will grow at approximately 1.25 to 1.50 percent per year in the long run.

1. Definition: Real rate of growth of earnings

Assume that it has been observed that economy-wide earnings have increased at five percent per year. This “observed” rate is referred to as the nominal rate of growth of earnings. Economists divide this rate into two factors: those due to increases in the average level of prices, the rate of price inflation, and those due to increases in the purchasing power of wages, the real rate of growth of earnings.

For example, if the rate of price inflation has been two percent per year, the first two percent of a five percent nominal increase will be needed just to allow individuals to buy the same set of goods that they had been able to purchase before the price increase. The remaining approximately three percent will be available to purchase additional goods. That three percent is called the real rate of growth of earnings.

As there is a strong consensus in the financial community that the long-run rate of price inflation will be approximately two percent, the forecast of wage growth can focus on the real rate of growth. [The financial community widely believes that the rate of inflation will be two percent because (a) that is the rate that the Bank of Canada has targeted since 1996; and (b) the Bank has managed to maintain the actual rate of inflation near its target since the latter was introduced.]

 

2. Methods of predicting the real rate of growth of earnings

In the long run, if workers are to be able to purchase more goods with their earnings (that is, if real wages are to rise), they must produce more goods. Hence, it is commonly argued that long-run increases in average real earnings must approximate long-run increases in average output per worker. As the latter is often called the rate of growth of productivity, the terms “real rate of growth of earnings” and “rate of growth of productivity” are often used interchangeably in the courts. Although this conflation could be misleading in the short run, when deviations between the two are common, if we are concerned with lifetime changes in a plaintiff’s earnings, projections of productivity growth can substitute for projections of real wage growth.

In this section, we provide two types of data concerning the growth of both real earnings and productivity. In the first, projections assume that past growth rates will continue into the future. In the second, models of the growth of the economy are used to derive predictions concerning growth of wages and productivity.

2.1 Historical data

In Table 1, we compare Alberta wage and price inflation, from 2001/2002 through 2017/2018. It is seen from this table that over the 2012-2018 time frame, which coincided with a considerable economic downturn in the Alberta economy (2014-2016), price inflation was higher than wage inflation. However, a longer-term perspective finds that wage inflation averaged approximately 0.78 percent higher than price inflation over the ten-year period 2008-2018; and approximately 1.0 percent higher than price inflation over the seventeen-year period 2001-2018.

 

If it is assumed that the experience of the last two decades or so is indicative of what will happen in the next few decades, then the data in Table 1 suggest that the real rate of growth of wages will be approximately 1.0 percent per year.

The data reported in Table 2, obtained from Statistics Canada, suggest that Canadian labour productivity has increased at an average annual rate of approximately 1.23 percent over the past 37 years (from 1982 through 2018), and 0.88 percent over the last five years (2014-2018).

Again, a forecast of 1.0 to 1.25 percent seems to be supported by the data.

 

2.2 Forecasting Agencies

We have identified five reputable, independent agencies that provide public projections of either real wages or labour productivity. We summarise their long-run projections in Table 3, below.

Table 3 suggests that reputable forecasting agencies are predicting that real wages will grow at approximately 1.25 to 1.50 percent per year over the next two or three decades.

3. Selecting a forecast

Our experience is that most financial experts have relied on historical figures, such as those we reported in Tables 1 and 2, to project the rate of growth of real wages/productivity. For two reasons, we caution against acceptance of this approach.

First, there is no theoretical basis for assuming that what has happened in the past will continue into the future. For example, advances in computer technology are introducing changes to the economy that may differ in significant ways from those that have occurred in the past; the wave of “baby boomers” is about to retire from the labour force; and interest rates have fallen to historical lows.

Second, with very few exceptions, the financial experts who testify in personal injury cases have not devoted significant amounts of time to the analysis of long-run changes in labour productivity. Given a choice between the testimony of individuals whose primary expertise is in the preparation of personal injury reports and that of individuals who devote their professional lives to the forecasting of long-term trends in the economy, it seems to us clear that it is the latter that should be preferred.

Accordingly, we recommend that the courts rely on the forecasts of the five agencies identified in Table 3, and on others with similar expertise, when determining the “productivity factor” to be employed in personal injury and fatal accident actions.

 

 

 

Selecting the Discount Rate (2017)

by Christopher J. Bruce, Derek W. Aldridge, Kelly Rathje, Laura Weir

When calculating the lump sum award that is to replace a stream of losses in the future, it is first necessary to determine the rate of interest, or discount rate, at which the award will be invested. In Canada, this rate is set equal to the real rate of interest, that is, to the nominal (or “observed”) interest rate net of the rate of inflation.1

Whereas most provinces mandate the discount rate that is to be used when calculating the present value of future losses, Alberta has left the determination of that rate to the courts. Accordingly, the testimony of financial experts on this matter has become an important element of most personal injury actions.

Over the last forty years, Economica has made important contributions to the debate concerning the choice of a discount rate. These contributions have come in the form of chapters in our textbook, Assessment of Personal Injury Damages (now in its fifth edition), articles in this newsletter, and submissions to reviews of the mandated rates in Ontario, Saskatchewan, and British Columbia.

In this article, we argue that whereas virtually all financial experts (including ourselves) have implicitly applied what we will call here the active management approach to the determination of the discount rate, it can be argued that an alternative technique, which we will call the annuity approach, is often more appropriate.

In Section I of this article, we describe these two approaches and investigate their relative merits. In Section II, we employ the principles developed in the first section, to examine how numerical measures of the discount rate might be obtained when discounting two types of future costs: medical expenses and losses of earnings. Finally, in Section III, we summarise our findings.

In that Section, we argue that:

  • if the plaintiff chooses to self-manage the investment of his or her award, the appropriate discount rate (net of inflation) is 2.5 percent; whereas
  • if the plaintiff chooses to purchase a life annuity, or have the defendant purchase a structured settlement, the appropriate discount rate (net of inflation) is zero percent. We argue that it is to the advantage of plaintiffs to make this choice in most cases in which their losses are expected to continue into ages of high mortality (usually after age 75 or so).

I. Two Approaches to Selecting the Discount Rate

There are two broad approaches to the determination of the discount rate, the annuity approach and the active management approach. In the former, it is assumed that plaintiffs will use their lump sum awards to purchase annuities. In the latter, it is assumed that they will invest their awards in a portfolio of stocks, bonds, mutual funds, and other financial assets.

In this section, we define the two approaches and investigate their relative merits. We conclude by identifying the circumstances in which each approach might be preferred to the other.

1. The Two Approaches Defined

The Annuity Approach

If the plaintiff has been awarded a lump sum award to replace a stream of losses from the date of trial until some specified termination date – most often the plaintiff’s projected date of retirement or date of death – he or she will be able to replace the future losses by purchasing an annuity, usually from a life insurance company. This purchase can take the form of either a life annuity or, under the auspices of the court, a structured settlement. In either case, the plaintiff will receive a specified stream of benefits until the termination date.

The purchase price of the annuity will be determined by three main factors: the value of the annual payments, the number of years to the termination date (which will, in part, be determined by the life expectancy of the plaintiff), and the rate of interest at which the insurance company is able to invest the funds received from the plaintiff (or defendant, in the case of a structured settlement).

It is this rate of interest that is known as the discount rate. In the case of an annuity, the discount rate is determined primarily by the requirement (arising both from regulation and accepted accounting practices) that the stream of payments the insurance company has contracted to make is matched by the stream of income that the company will receive from its investment. That is, at the time the annuity contract is signed, the insurance company will invest a sufficient amount, in secure financial instruments, that the income generated from that investment will be sufficient to fund the stream of payments the company has contracted to pay.

What this implies is that for each promised future payment, the insurance company will, implicitly make a separate investment that will generate sufficient returns that it will be able to cover the contracted payment at the appropriate date. For example, if it has contracted to pay $50,000 per year for ten years, it will make ten separate investments, each of which has a maturity value of $50,000.

The discount rate applicable to the payment that must be made one year from now is the interest rate currently available on one-year investments (such as one-year bonds); the rate applicable to the payment to be made two years from now is the interest rate currently available on two-year investments; etc. Thus, there could, in principle, be as many discount rates as there are time periods in the plaintiff’s stream of losses. (In practice, however, investments for more than ten or fifteen years tend to have the same interest rate, so a thirty-year annuity might require ten discount rates.)

Note, first, that there is not “a” discount rate. Rather, there is one rate for each year over which the stream of payments is to be made into the future.

More importantly, note also that it is not necessary to “predict” the discount rate(s). As the investments are to be purchased today (i.e. at the date of settlement), it is the interest rates that are available today that are to be used – and these rates are readily available.

Structured settlement: If it is assumed that a structured settlement is to be purchased, the argument concerning choice of a discount rate is similar to that for a life annuity. Again, the insurance company will place the lump sum received from the defendant in a series of investments, each of which will mature on the date that the payment is due. As the insurance company can be expected, once again, to purchase secure investments, the rates of return that are currently available on such investments can be used to determine the discount rate(s).

The Active Management Approach

Alternatively, the plaintiff might use his or her award to purchase a mixed portfolio of financial assets – for example, stocks, bonds, and mutual funds – selling and buying components within that portfolio as changes occur in financial markets. Because the individual is continuously selling old investments and purchasing new ones, the returns on those investments will reflect rising (and falling) rates that are available in the financial markets.

The complication that this approach introduces is that the rates of return that will be available at the times the plaintiff reinvests his or her funds are not known at the time that the court award is made. These rates must be predicted – in contrast to the rates employed in the annuity approach, which are known at the time the award is made.

2. Comparison of the Two Approaches

As the plaintiff’s award is intended to replace an ongoing loss, it is important that the income the plaintiff receives from investment of that award is sufficient, in each period, to provide the desired compensation. In turn, this requires that the rate of return on that investment be as predictable as possible. The less predictable is the rate of return, the less certain can the courts be that the award will be sufficient for its purposes.

The predictability of the rates of return obtained under the annuity and active management approaches differs with respect to three characteristics: volatility of the rate of return on the invested funds, uncertainty concerning the plaintiff’s life expectancy, and protection against unanticipated increases in the rate of inflation. In this section, we compare the two investment approaches with respect to each of these characteristics in turn.

Volatility

The volatility of a class of investments refers to the variability in the rate of return earned on those investments over time. According to one source:

… volatility refers to the amount of uncertainty or risk about the size of changes in a security’s value. A higher volatility means that a security’s value can potentially be spread out over a larger range of values. This means that the price of the security can change dramatically over a short time period in either direction. A lower volatility means that a security’s value does not fluctuate dramatically, but changes in value at a steady pace over a period of time. [investopedia.com, emphasis added]

The more volatile is the price of a security, the more likely it is that the rate of return on that security will deviate from its long run average. In some periods the return will rise above the average and investors will experience a windfall; but in other periods, the return will fall below average and investors will experience a shortfall.

In the very long run, high returns and low returns may average out, and the rate of return obtained will trend towards the long run value. However, many plaintiffs do not invest for a period long enough that they can be confident that the rate of return on investment of their awards will settle on the long run average. This will particularly be true if plaintiffs are unlucky enough to make a major investment shortly before markets enter a sharp downturn such as was experienced in 2008, (or lucky enough to invest shortly before an upturn, such as in 2010).

To avoid the uncertainty that may result if the plaintiff’s award is invested in volatile financial instruments, it is often recommended that they concentrate their investments on secure, non-volatile stocks, bonds, and mutual funds. The Canadian courts have confirmed this recommendation. For example, in its seminal decision in Lewis v. Todd (1980 CarswellOnt 617), the Supreme Court of Canada approved of an expert witness’s use of “high grade investments [of] long duration.” [para. 17]

Investments in life annuities offer the lowest volatility possible: essentially, the rate of return is guaranteed as long as the insurer, and its re-insurers, remains viable.

Investments in an actively managed portfolio experience two forms of volatility that are not found with annuities. First, all but the most conservative, high grade investments experience variations in returns from year to year.

Second, even if a sophisticated investor could avoid most year-to-year variations in the rate of return, no investor can protect him- or herself against variations that occur due to long-term changes in the economy. For example, assume that it had been anticipated that the plaintiff would be able to obtain a two percent rate of return on investment of his/her award, because the economy was expected to grow at that rate. If broad economic fundamentals should change, such that long run growth fell to one percent per year, it is unlikely that the individual investor would be able to maintain a two percent return on investments.

To conclude, if the goal is to minimize volatility in the returns on the plaintiff’s investments, life annuities and structured settlements are superior to active management, especially in the long run. For short periods of time, perhaps five or ten years, an actively managed “portfolio of high grade investments” may offer almost as much security as an annuity.

Life Expectancy

Assume that a plaintiff will require medical expenses of $50,000 per year for the rest of his life. In a personal injury action, his award will be calculated to ensure that if he invests that amount in a fund composed of secure investments, it will provide $50,000 per year for the lifetime of the average Canadian of his age and sex. For example, if he is a 50-year-old Canadian male his life expectancy is approximately 31 years, to age 81. Thus, his award will be calculated to ensure that he can remove $50,000 per year until his age 81, at which point the award have been drawn down (approximately) to zero.

This puts the plaintiff in a quandary: that the life expectancy of 50-year-old males is 31 years implies that (approximately) half of 50 year old males will live longer than 31 years (and half less than that). Thus, if the plaintiff spends $50,000 per year on medical expenses there is a 50 percent chance that his investment fund will be exhausted before he dies.

Alternatively, if he spends less than $50,000 per year, to leave money in the fund for the possibility that he will live beyond age 81, he will have insufficient funds in every year to pay for his required expenses. Even if it happens that the plaintiff lives less than 31 years, he will have been inadequately compensated for his necessary expenses, because he will have been taking the (reasonable) precaution of spending less than $50,000 per year to create a buffer for the possibility he will live longer than average.

In short, if plaintiffs invest their awards in actively managed investment funds, it is virtually certain that their awards will be insufficient to compensate them fully.

Furthermore, it can easily be shown that this outcome also arises when the amount to be replaced is a loss of income – although the shortfall will be less in this case than in the case of most medical expenses, because the impact of mortality is much lower when the loss continues only to retirement ages (when mortality rates are still low) than when it continues to the end of life.

If the plaintiff’s award is placed in a life annuity or structured settlement, however, payment of the desired annual compensation will be guaranteed from the date of settlement to the end date of the compensation period.

In short, whereas a life annuity will pay the plaintiff an amount equal to his or her loss in every year, an award invested in a portfolio of funds will, in most cases, undercompensate the plaintiff. This under-compensation will often be less when the award is intended to compensate for a loss of earned income than when it is to compensate for long term costs of care. Thus, on this ground, life annuities are slightly preferred to mixed portfolios of investments when there has been a loss of earnings; but annuities are definitely preferred when there is a long-term requirement for payment of medical expenses.

Unanticipated Inflation

A drawback to the annuity approach is that the stream of income that it provides may prove to be inadequate if inflation rates rise unexpectedly. For example, if an annuity provided for $10,000 per year, increasing each year at two percent (to allow for anticipated inflation), it would pay $12,190 in year ten. But if inflation proves to be four percent per year, the plaintiff will require $14,800 in year ten to buy what $10,000 would have bought in year one. The annuity will pay $12,190 when $14,800 is required.

It is often possible to buy annuities whose annual payouts increase with the actual rate of inflation. However, as the risk facing the sellers of annuities is quite high in this case, the price of these annuities may be higher than many buyers are willing to pay.

An alternative method of protecting against the effect of unanticipated inflation is to invest in an actively managed portfolio of assets. Under this approach, the individual is assumed to buy and sell financial assets on a continuing basis, replacing low-earning assets with higher-earning ones as market conditions change. If inflation increases, so will the returns on investments, particularly bonds, allowing the plaintiff to maintain a real rate of return (i.e. a rate net of inflation) that is consistent over time.

On this ground, if the rate of inflation cannot be predicted easily, the active management approach may be preferred to the annuity approach. However, central banks around the world have become convinced that one of their primary functions is to maintain a steady, low rate of inflation. The Bank of Canada, for example, has successfully targeted a rate of two percent since the early 1990s. This policy has been so well received that virtually all financial analysts expect this rate to be maintained well into the future.

As there is no reason to expect that the future rate of inflation will deviate significantly from the rate that has been experienced for the last twenty years, there is little reason to base the selection of the investment approach on the need to protect against unanticipated changes in the rate of inflation.

We conclude, therefore, that the ability of the active management approach to provide protection against unanticipated inflation does not offer a compelling reason to choose that approach in preference to the life annuity approach.

3. Summary

We summarise this section by investigating the merits of using the two investment approaches to replace (i) costs of medical care and (ii) losses of earnings.

Costs of Medical Care

For two reasons, if the plaintiff’s award is intended to provide compensation for medical expenses, particularly expenses that extend well into the future, we recommend that the award be invested in a life annuity (or structured settlement). First, as medical expenses are often required for the plaintiff’s entire life, it is important that the award is able to provide benefits should the plaintiff live beyond the average life expectancy. Whereas this can be achieved easily using a life annuity, it cannot be done through the active management approach.

Second, as the requirement for medical expenses often extends many decades into the future, the returns on awards invested in actively managed funds may be subjected to significant volatility, hence placing the risk of inadequate compensation on the plaintiff. The returns on a life annuity, however, are guaranteed by the insurer, thereby removing the risk of volatility from the plaintiff.

The contrary argument, for using the active management approach to the funding of future medical expenses, is that this approach allows for protection against unanticipated inflationary changes. We have argued, however, that such changes are not expected to be so large as to counter the arguments for use of life annuities. Furthermore, if the courts decide that inflation is likely to become an important factor, they can require that plaintiffs purchase inflation-protected life annuities.

We conclude that, in most cases, it should be assumed that when the plaintiff’s award is to provide for medical expenses, it will not be invested in actively managed funds but will, instead, be used to purchase life annuities. The exception occurs when medical expenses are required for only a short period of time.

Loss of Earnings

When the purpose of the plaintiff’s award is to replace a future stream of lost earnings, the argument in favour of life annuities is weaker than it was with respect to medical expenses. The reason for this is that earnings losses will generally end at an age at which the annual rate of mortality is still quite low.

For example, as we argued above, if a 50-year-old man has a life expectancy of 81, there is (approximately) a fifty percent chance that he will live beyond that age and will exhaust any award for medical expenses. Assume, however, that that individual had planned to retire at age 60, bringing any loss of earnings to an end at that age. As the probability of dying before age 60 is very small, the difference between an award that allowed for that probability and one that did not would also be small. Thus, any “error” that arose from using the active management approach might be compensated by other factors.

If we assume again that the risk of unexpected changes in inflation is small, then the primary difference between the annuity approach and the active management approach (with respect to losses of earnings) will arise with respect to volatility. On this basis alone, the annuity approach will be preferred as it offers less risk that an unanticipated fall in interest rates will leave the plaintiff’s award inadequate.

However, it is possible that this uncertainty concerning the rate of return on investments might be offset if the active management approach provided higher average rates of return. For example, if those rates were two or three percentage points higher than those offered by the sellers of life annuities, plaintiffs might prefer to manage their own funds rather than rely on an annuity.

For this reason, we suggest that the active management approach be employed only if it is clear that the plaintiff does not wish to invest his or her award in an annuity (as, in this case, the plaintiff has signaled that the rate of return on actively managed assets is high enough to compensate for the increased risk).

II. Evidence Concerning the Value of the Discount Rate

1. The Annuity Approach

If it is assumed that the plaintiff will purchase a life annuity, the appropriate discount rate will be the rate(s) of return that life insurance companies use when pricing those annuities. In this section, we argue that these rates will approximate the rates of interest that are available on Government of Canada bonds of the appropriate durations.

In Table 1, we summarise those rates for five-year, ten-year, long-term, and real rate of return bonds and for GICs of one-year, three-year, and five-year terms. In this table, the term “long-term bond” applies to government bonds with maturation dates of fifteen years or more. “Real rate of return bonds” are bonds whose rate of return is specified as a fixed value (the real rate of return) plus the actual rate of inflation. Thus, for example, if the fixed value is 1.0 percent and the rate of inflation proves to be 2.5 percent, the bond will pay (approximately) 3.5 percent.2

Table 1 reports both the nominal (observed) and real (net of inflation) rates of return on five- and ten-year bonds, long-term bonds, and GICs. In each case, the real rate has been calculated by reducing the nominal rate by the expected rate of inflation, two percent.3 As the interest rate on real rate of return bonds is reported as a real rate, we report only the real rate of return on those bonds.

In Table 1 it can be seen, first, that the real rates of return on government bonds increase as the duration of those bonds increase; thus confirming that there is not a single discount rate but rather a different rate for each length of investment.

Second, it is also seen that the real interest rates on secure bonds have not recently risen above 0.5 percent for investments of any duration; and have risen above 0.0 percent only on real rate of return bonds.

Our contention is that these rates can be used as indicators of the rates at which life insurance companies will invest the funds they receive for life annuities and structured settlements. We can test this contention by comparing the interest rates employed to determine the prices of structured settlements against the rates reported in Table 1.

This we have done by obtaining quotes for several alternative structured settlements. From these we have been able to determine the interest rates that were employed to obtain those quotes. In Table 2 we report six such structured settlements, for males receiving $1,000 per month ($12,000 per year).4

Three scenarios represent payments that end at age 60 and three represent payments that continue to the date of the plaintiff’s death. (Those that end at age 60 are assumed to be typical of awards for loss of earnings; and those that continue for life are assumed to be typical of awards for medical expenses.) The assumed ages for the plaintiffs, at the date of trial, are, respectively, 20, 35, and 60. Furthermore, in each case we report quotes for both the situation in which the annual payment is to increase by two percent per year and for that in which it will increase by the actual rate of inflation.

Column (4) of Table 2 reports the quotes we received, assuming that the annual payment was to increase by the actual rate of inflation; while column (6) reports the quotes assuming that the annual payment was to increase by two percent per year. Columns (5) and (7) then report our calculation of the implied interest rates that were used to obtain the costs of the various annuities.

For example, the first figure in column (4) indicates that it would cost $489,176 to purchase an annuity that paid a male plaintiff $12,000 per year, indexed for inflation, for the next 40 years (i.e. from age 20 to age 60). The first figure in column (5) then indicates that the insurance company that quoted this amount had implicitly assumed that its investments would earn an average real rate of interest, (i.e. nominal interest net of inflation), of -0.27 percent over the 40-year period in question. Similar costs and real interest rates are reported for the other eleven scenarios.

Notably, in every case in which the payments were fully indexed for future inflation (column 5), the implied real rate of interest was negative – between -1.24 percent and -0.27 percent. It is only when the payments did not provide full protection against inflation – column 7, in which increases were limited to two percent per year – that insurers offered a positive real interest rate. Even then, rates were less than one percent.

We would note that the implied discount rates of the annuities presented in Table 2 are consistent with the implied discount rates of annuities offered  by private insurance firms such as Sun Life Financial and RBC Insurance. For example, the Sun Life Financial annuity calculator indicates that as of April 2017, a $1,000,000 annuity for a 50-year old female will provide an annual income of approximately $41,819 per year (with no inflation adjustment). This implies a discount rate of approximately 0.13 percent. The annuity calculator provided by RBC Insurance indicates that as of April 2017, a $1,000,000 annuity will provide a 55-year old male with annual payments of approximately $50,931 (with no inflation adjustment), for an implied discount rate of 0.15 percent.5

It is informative to compare the rates employed in the calculation of structured settlements (and private annuities) with the rates reported for government bonds, in Table 1. The two annuities with the shortest durations – ten years, from age 50 to 60 – had implied discount rates of -1.24 and -1.02 percent, both very similar to the figure of -1.23 percent reported in Table 1 for five-year bonds in 2016. Similarly, the two annuities with the longest durations – from age 20 for life – had implied discount rates of -0.57 percent and +0.65 percent, with an average very close to the figure of -0.08 percent reported in Table 1 for long-term government bonds.

We conclude from Tables 1 and 2 that, in cases in which the plaintiff purchases a life annuity or structured settlement – particularly one that is fully indexed for inflation – the discount rate can be estimated with some accuracy from the real rates of return currently available on Government of Canada bonds of appropriate durations.

2. Active Management Approach

In the active management approach, it is assumed that plaintiffs will re-allocate funds within their investment portfolios as conditions in financial markets change. Because these changes will be made in the future, the active management approach requires that estimates of future rates of return be calculated.

In this section, we first identify the type of financial instrument in which we assume the plaintiff will invest. We then contrast two methods of forecasting the rates of return on those instruments. Finally, we provide estimates of those rates of return.

Selection of the Appropriate Financial Instrument

The courts have been clear that, as the lump-sum award is intended to replace the plaintiff’s lost earnings, the investments in the plaintiff’s portfolio must not expose the plaintiff to unreasonable risk. For example, in its seminal decision in Lewis v. Todd (1980 CarswellOnt 617), the Supreme Court of Canada approved of the expert’s use of “high grade investments [of] long duration” [para. 17].

As the rates of return on investments in the stock market have historically been very volatile, it is usually recommended that plaintiffs do not restrict their investments to equities. Table 3, for example, reports the value of the Toronto Stock Exchange composite index for July of each year since 2000. It can be seen there that rates of return have been highly volatile, indicating that the rate available to an individual whose investments tracked the market would have depended importantly on the year in which those investments were made. For example, whereas the nominal return on investment in such a portfolio would have averaged 2.2 percent per year between 2000 and 2015, a similar investment would have averaged 6.2 percent per year between 2002 and 2015.

In light of this issue, two approaches might meet the court’s requirement that plaintiffs invest in high grade investments: it could be assumed that plaintiffs will purchase long-term Government of Canada bonds; or that they will invest their awards in financial instruments that offer higher yields than government bonds, but with greater risk – for example, in a mixed portfolio of “blue chip” stocks, corporate bonds, and mutual funds. In the discussion that follows, we will consider both.

Forecasting the Returns on Government Bonds

Two methods have commonly been used to forecast the rate of interest that will be available on government bonds. The first of these, the historical approach assumes that future rates will equal those that were observed in the past. The second, the efficient market approach, assumes that the rates that are currently available in the market reflect the rates that investors believe will prevail in the long run. We explain here why we prefer the efficient market approach.

The historical approach: A fundamental problem with the historical approach is that real interest rates have varied significantly over the last sixty years. As can be seen from Table 4, real rates were as low as 1.50 percent in two decades (1951-1960 and 1971-1980) and as high as 4.70 percent in two others (1981-2000). From this record, it would be possible to find support for almost any long-run rate between 2.0 and 5.0 percent.

More importantly, as indicated in Figure 1, real rates of return have declined virtually continuously for the past twenty years, from approximately 5.5 percent to -0.5 percent. Even if it was to be argued that real rates of interest will return to, say, 3.0 percent over the next twenty years, most plaintiffs will experience rates of return well below that over most of the period in which their award is invested.

A third problem with the use of historical rates is that there is no theory to support it. Adherents simply assume that because real rates took some value in the past, rates will return to that value in the future. Furthermore, they make this assumption in the face of the long run decline in real interest rates reported in Figure 1. If the markets expected the real rate of interest to return to “long-run” levels soon, sophisticated investors would not continue to purchase financial instruments that paid long-run rates as low as -0.08 percent (Table 1).

Finally, the evidence is not just that the real interest rate has declined significantly; this decline is consistent with theoretical predictions. Importantly, as central banks have adopted a policy of maintaining inflation within a narrow band of rates (in Canada, between 1.0 and 3.0 percent), uncertainty about the rate of inflation has been minimized. This reduction in risk has led to an increase in demand for bonds, and an associated reduction in real interest rates.

The Congressional Budget Office of the United States also predicts that interest rates will be lower in the future than in the past, resulting in part from slower growth rates of both the labour force and of productivity, thereby reducing the rate of return on capital; and in part from a shift of income to high-income households who tend to have high savings, thereby increasing the supply of money to the bond market.

The efficient market approach: The second source of information concerning future real rates of interest is the money market. When an investment firm that believes that inflation will average two percent per year purchases twenty-year Government of Canada bonds paying three percent, it is revealing that it expects the real rate of interest on those bonds will average approximately one percent over those twenty years. Thus, if the rate of inflation that investors were forecasting was known, that forecast could be used to deflate the nominal rates of interest observed in the market to obtain the implicit, underlying forecasts of real rates.

A strong case can be made for using an expected inflation rate of two percent. The reason for this is that in the last decade the Bank of Canada has not only made this its target rate of inflation, it has been successful in keeping the actual (long-run) rate of inflation very close to that target (which, in turn, has led most financial institutions to predict that future inflation will average two percent).6

Furthermore, in choosing to target a low rate of inflation, the Bank has been following a view that has achieved widespread acceptance in the economics community – that is, that control of inflation, at a low level, should be one of central banks’ primary roles.

On this basis, at the end of 2016 the real rate of interest on long-term government of Canada bonds appeared to be as little as 0.00 percent. (See the figures for long-term bond rates in Table 1.)

An alternative approach is to rely on information concerning bonds whose rate of return is denominated in terms of real interest rates – called real return bonds, or RRBs. By observing the rates of return at which these bonds sell, the risk free real rate of return that investors believe will prevail over the long run can easily be determined. That is, even if plaintiffs do not purchase RRBs, the real rate of interest that is observed on those bonds provides an unbiased indicator of the rate of interest that is expected by sophisticated investors. In Table 1, it is seen that the return on these bonds has recently fallen to as little as 0.41 percent.7

Forecasting Returns on a Mixed Portfolio

Forecasting the returns on a conservative, mixed portfolio is complicated by the fact that there is no common agreement about what the components of such a portfolio should be. Hence, not only is it difficult to obtain the current rates of return on conservative investments, there is also very little information about how such returns have varied over the past. Both issues complicate the forecasting process.

An approach that we suggest might mitigate this problem would be to rely on the rates of return that have been available on conservative portfolios offered by Canadian banks. We have been able to obtain information about four of these: the RBC Select Very Conservative Portfolio, CIBC Managed Income Portfolio, TD Comfort Conservative Income Portfolio, and ScotiaBank Selected Income Portfolio-Series A. Although these funds differ from one another in their details, they all have investment objectives similar to those stated for the RBC portfolio:

To provide income and the potential for modest capital growth by investing primarily in funds managed by RBC Global Asset Management, emphasizing mutual funds that invest in fixed-income securities with some exposure to mutual funds that invest in equity securities. The portfolio invests in a mix of Canadian, U.S. and international funds.

To achieve this goal, RBC invests primarily in bond funds. The result, seen in the first columns of Table 5 below, is that since 2011 this fund has consistently earned a nominal rate of return between 2.5 and 5.0 percent – with one deviation, to 6.74 percent, in 2014 – suggesting a real rate of return over that period of approximately 1.0 to 3.5 percent. Table 5 reports similar results for the other three portfolios (again, with 2014 being the only year that each of them achieved a nominal return that exceeded 5.00 percent).

The volatility in the rates of return on all four portfolios reported in Table 5 is considerably less than that on investments in the Toronto Stock Exchange, as reported in Table 3.

But that does not necessarily mean that plaintiffs would be advised to invest in a conservative mixed portfolio. Although the returns on such portfolios may be higher than that on life annuities, the returns on the latter are fixed once they are purchased, and hence have lower (zero) volatility than the returns on all other investments. The question remains: do the higher rates of return on mixed portfolios compensate the plaintiff for the higher volatility of their returns? This is a question that cannot be answered by financial experts, but only by the courts or government regulators.

What Table 5 does suggest, however, is that if plaintiffs had purchased mixed conservative portfolios in the last five years they would have achieved average nominal returns of between 3.5 and 4.5 percent per annum – or approximately 2.0 to 3.0 percent in real terms. This suggests that 2.5 percent represents a conservative estimate of the real rate available to plaintiffs seeking conservative investments.

III. Summary

In personal injury and fatal accident actions, the plaintiffs are assumed to invest their awards in such a way as to provide streams of returns that will replace their future annual losses. Two factors may intervene to hinder plaintiffs’ ability to achieve this goal. First, they may live longer than average. Second, the rate of return on investments may fall below the level that was anticipated when calculating their awards. In both cases, the award will be exhausted before the plaintiff’s death.

One approach plaintiffs can employ to avoid these problems is to invest their awards in life annuities or structured settlements, as these instruments guarantee a specified annual payment for life, and as the rates of return available on them are fixed.

The drawback to annuities is that the interest rates that insurance companies use to price their products are much lower than the rates of return that have been available on conservative mixed portfolios of financial assets. We showed in Section II that, whereas the implicit interest rates on life annuities are similar to the rates available on long-term Government of Canada bonds, or approximately 0.0 to 0.5 percent, the interest rates available on conservative portfolios of assets have been approximately 2.0 to 3.0 percent.

If a loss will not continue into the years beyond which mortality rates begin to rise substantially, the advantage of buying a life annuity may be relatively small compared to investing in a portfolio of assets. In that case, it may be appropriate to assume that that the discount rate can be estimated from the return on a portfolio of assets.

If the loss will continue into years of high mortality, however, the benefits of a life annuity (protection against exhaustion of the award) may exceed the costs (a lower rate of interest).

As it is only the plaintiff who can determine whether the benefits of a life annuity exceed the costs, it seems appropriate that the discount rate be chosen based on the plaintiff’s decision whether to self-manage the investment of his or her award or to use that award to purchase a life annuity (or structured settlement).

  • If the plaintiff chooses to self-manage his or her award, we recommend that the discount rate be set at 2.5 percent.
  • If the plaintiff chooses a life annuity or structured settlement, we recommend that the discount rate be set at zero percent.
  • We anticipate that plaintiffs will make the latter choice in virtually all cases in which their losses will continue into years of high mortality.

 

The Structure of a Cost of Care Report

by Christopher J. Bruce, Kelly A. Rathje

As economists, we are often asked to calculate the present value of future costs of care. As these calculations are based on the reports of cost of care experts (CCEs), we have become uniquely familiar with the structure and content of those reports.

In this article, we provide a review of the format and contents of cost of care reports, drawn from our experience using those reports as inputs into our own calculations. We anticipate that this review will be of greatest use to:

  1. members of the bar: as a checklist against which to evaluate the cost of care reports that have been provided for them and their opponents;
  2. individuals who have recently begun preparing cost of care reports: to provide them with an understanding of how those reports will be used; and
  3. experienced cost of care experts: as an analysis of some of the complexities that can arise in personal injury actions. For these experts, most of our suggestions will be familiar; but we hope that we raise sufficient questions to make this report of interest to them also.

Incremental Costs

One of the most difficult questions facing the cost of care expert (CCE) is that of distinguishing between those costs that would have arisen had the plaintiff not been injured and those that have arisen as a result of the plaintiff’s injuries. This issue is particularly important when the item required with-accident differs only in quality or type from a similar item that would have been purchased without-accident, for example when the plaintiff now requires a different type of automobile than she would have purchased had she not been injured.

A number of issues arise with respect to incremental costs:

1) When the item required by the plaintiff costs more than the equivalent item for a non-injured person, it is important to be very clear about what is being assumed about the characteristics of the item that would have been purchased by the non-injured person. For example, assume that it has been recommended that a paraplegic purchase a van that costs $45,000 per year. As the incremental cost is the difference between that $45,000 and the cost of the car the plaintiff would have purchased if he had not been injured, it is important that the CCE be able to defend any assumption that has been made about the cost of the latter. Would the plaintiff have owned a Honda that cost $20,000, or a Lexis that cost $50,000? On what basis has that conclusion been reached?

2) Following from the preceding point, it is also important to alert the reader to the possibility that the item that has been recommended by the CCE may be of a different quality than the item the plaintiff would have purchased had she not been injured. Would a $45,000 van, for example, “replace” a $50,000 Lexis? And would the quality of accommodation and food in, say, a nursing home replace the equivalent items in the plaintiff’s own home?

3) An important example of quality differentials arises with respect to the care of injured children. Assume that a child’s injuries are sufficiently severe that the CCE has recommended that professional child care be provided for her – for example, 24-hour attendants. Assume also that the child has a stay-at-home mother; that is, one who would have provided 24-hour care before the child began school. Can it be argued that, as the child would have received 24-hour care in the absence of the accident, the accident has not caused any increment in costs? The answer to this question depends on whether the type and extent of care (i.e. the “quality” of care) that the child now needs exceeds that which would normally have been provided by her parents. For example, during the ten hours that the child normally sleeps, incremental care might be recommended because she will wake more often than normal, or because specialized medical care will be required during those hours. If so, it would be useful if this was specified in the cost of care report. Similar specifications may also be necessary with respect to time that a non-injured child would have spent at school or in day-care.

4) When estimating what the plaintiff would have spent on a category of items if he had not been injured, a distinction must be made between the expenditures that he is currently incurring and those that he would have incurred if he had not been injured. For example, if the accident has reduced the plaintiff’s income, it is quite possible that he will now be living in an apartment with a lower monthly rent than he would have incurred had he not been injured. It is the difference between the rent of the apartment the CCE has recommended (with-accident) and the rent of the apartment he would have lived in (without-accident) that is the incremental cost due to the accident.

5) If the item owned by an injured plaintiff has a longer or shorter life expectancy than the equivalent item owned by a non-injured person, the CCE should identify what that difference is. For example, the van required for a paraplegic might have a five-year use life, whereas the car that the plaintiff would have driven if she had not been injured might have had a ten-year life. In such a case, it would not be appropriate to calculate the incremental cost by deducting the purchase price of the car the plaintiff would have bought from the purchase price of the van, as two vans will have to be purchased for each car.

Variations over Time

The requirements for many items will vary over the plaintiff’s lifetime. It is important to identify when such changes will occur and what their effect will be on annual costs:

1) The plaintiff would have incurred some of the costs of care at various times in his lifetime even if he had not been injured. For example, at age 80, the plaintiff may have hired a housekeeper. Thus, if the CCE had recommended housekeeping services from the date of the accident, for life, the cost that would have been incurred after 80 must be deducted from the recommended expense. In such cases, it would be useful if the CCE was to indicate the age at which the plaintiff would have incurred the stated expense (in the absence of the accident), and what the difference is between the cost of the recommended level of housekeeping and the level that would have been purchased had the accident not occurred.

2) It is important to be clear about variations in expenses over the course of a year – such as due to school holidays and vacations – and over a lifetime – such as because the plaintiff would have started elementary school, entered university, had children, or retired.

3) If the plaintiff will have to undergo surgery in the future, the CCE should indicate how long the recovery period will be and how much the extra costs will be during that period. Also, it will be useful to the economist to know whether the plaintiff will be able to return to work before the end of the recovery period.

Ranges of Estimates

We often find that CCEs provide a range of estimates for the cost of a recommended item. For example, it might be reported that the cost of personal care attendants will vary from $14,500 per year to $21,400. When the CCE wishes to report such a range, we recommend that a reason be provided why a single number would not be appropriate, and the source was of each of the costs in the range be identified. For example, if there is more than one cost, that might be because:

  • the CCE received more than one quote, from more than one provider (and if so, why was the lowest quote not chosen?);
  • there were different costs for different qualities of the product;
  • different costs were appropriate to different potential medical outcomes;
  • costs varied among cities in which the plaintiff might live; etc.

Housekeepers and Personal Care Attendants

The CCE report should clearly specify the sources for the hourly costs of individuals who provide housekeeping services, such as housecleaners, yard workers, and maintenance workers. Otherwise the cost of care report may be subject to criticism from experts, such as economists, who are familiar with data concerning the wage rates of these individuals.

Once it has been determined how many hours are required for each type of personal care attendant (for example, nurses and LPNs), there are two basic approaches to estimating the cost of those individuals. The first of these, the agency approach, is to obtain the cost of hiring an agency that will be responsible for all of the specified activities. The second, the hourly wage approach, is to obtain an hourly wage for each type of attendant and then multiply those wages by the specified hours for each type.

Although the latter approach often appears attractive, in the sense that it yields a lower estimate of costs than does the former, there are many reasons to be cautious about use of this approach. First, unless the plaintiff is capable of handling his/her own affairs, the hourly wage approach often assumes that there is a family member who will work without compensation to hire and supervise attendants. But, even if such individuals are available currently, there is no assurance they will be available over the entire course of the plaintiff’s disability. Furthermore, in those years in which family members are available to assist the plaintiff, the courts will generally allow them to claim for the costs of their time, invalidating the assumption that their time is “free”.

Second, the hourly wage approach may not take into account that substitutes will have to be provided for attendants who are ill, wish to take vacations, or who quit without warning.

Third, allowance has to be made for the possibility that attendants will not provide adequate care. This requires that the family or plaintiff have some expertise in both the investigation of the backgrounds of potential hires and in the provision of supervision of existing employees.

Finally, the hourly wage approach would have to provide an allowance for the hiring of additional personnel when emergencies arose. Whereas most agencies will be able to call on their own nurses, and will have close contacts with doctors, ambulances, and hospitals; the plaintiff and his/her family will generally have no expertise in hiring these experts.

Other Factors

We also make the following recommendations concerning the cost of care report:

1) The report should indicate whether GST is included or excluded in the costs recommended.

2) As some items will be GST-exempt while others will not, it is important to distinguish between the two.

3) Indicate whether the costs identified for a particular item are for a different year than the one in which the report was written. For example, if a report was written in 2015, the costs may have been collected from 2014 price lists, or may have been forecasted for a settlement date in 2016. [We generally assume that a report written in 2015 uses “2015 prices”.]

4) It may be advisable, particularly in contentious cases, to have a physician read the CCE’s report and approve the medical expenses, in writing.

Presentation

In addition to the recommendations we have made above, concerning the content of the cost of care report, we also have a number of (minor) suggestions concerning the format, or presentation, of that report:

1) We appreciate it when the CCE provides a summary table in which the annual cost of each item is clearly set out, along with the number of years each cost is to be repeated. We acknowledge that at times, a unit cost, such as medication costs per unit, plus the number of units required over a specific time period is provided. This is just as useful to us, as from this information we can easily determine an annual cost1.

2) If an expenditure is to be made less often than annually – for example, replacement of a car or wheelchair once every five years – it is not necessary for our purposes that the CCE averages the costs over the life of the item. Provide only the replacement cost and the number of years between replacements.

3) On items like cars, houses, and wheelchairs, the cost of care report should, however, provide the annual costs of repair, maintenance, and operation. For a car, for example, provide estimated costs of repairs, of oil changes, and gasoline and tires.

4) The report should be clear about time ranges. For example, it is confusing to say that an item will cost $600 per year from age 25 to 35 and $450 from age 35 to 45, as that leaves it unclear what the cost will be at age 35. We would recommend, instead, that the report say something like: the item will cost $600 per year from 25 to 35 and $450 per year from 36 to 45.

5) There is no need to “round” numbers up or down as economists use spreadsheets for their calculations.

A Sample Economist’s Report

As the economist’s report is always written after the cost of care expert’s report, we suspect that many CCEs will not have seen very many economist reports. As it may help the CCE to understand what types of information are required for an economist report, and in what format that information should be provided, in the table presented below we provide a sample copy of a cost of care calculation for a hypothetical seven-year old male plaintiff.

In the footnote to the table, we have also provided an example of a typical assumption we would make when a range of replacement times has been provided by the CCE.

The columns in the table have the following interpretations:

Tax creditable expense: “Y” in this column indicates that the item can be claimed as a medical expense for income tax purposes.

Cost including tax: the cost of the item, including all taxes.

Replacement life: This column reports the frequency of expenditure. “0” in this column means that the item is purchased only once (there is no frequency); “1” means that the item is purchased every year; “2” means that it is purchase once every two years, etc. For example, under “Mobility” the “Ankle/foot orthotic (adult)” is to be replaced once every three years; the “Adapted bike,” however, is to be purchased only twice, at ages seven and fourteen.

Starting age: The age at which the item is first to be purchased. As the hypothetical plaintiff is seven years old, most purchases begin at seven. However, it is seen, for example, that many items in this table are not to be incurred until the plaintiff is eighteen.

Ending age: Many of the items are to be purchased only over a portion of the plaintiff’s life. Often, as in this case, the costs are different when the plaintiff is a child than when he/she is an adult, and costs may change again when the plaintiff retires or enters a senior’s facility.

Present value of costs: This is the lump sum that would have to be invested today to provide the plaintiff with sufficient funds to replace the stream of future costs in each row. For example, a “Before/after school, weekend care” expenditure of $56,060.76 per year from age seven to thirteen will cost $309,468. This figure varies according to the annual cost, the duration of the expenditure, the discount rate, and the plaintiff’s life expectancy.

 Multiplier: Assume that the cost of care expert has recommended an expenditure of $1,000 per year for the next ten years, and that the present (lump-sum) value of this cost has been calculated to be $8,300. If that expenditure was to be doubled, to $2,000 per year over the same time span, the present value of the cost would also double, to $16,600. Alternatively, we can represent this by saying that for every $1.00 of annual costs (over this ten-year span), the present value of future costs will be $8.30. Any lump-sum cost can be obtained by multiplying the annual cost by 8.30. The latter figure is called the “multiplier.” It can be used by the court to recalculate the present value of future costs if the court should conclude that the annual costs are different from those recommended by the cost of care expert.

     For example, if the court was to rule that annual before/after school costs were $40,000 (instead of the $56,060.76 reported in the table, the present value would be $40,000 multiplied by the reported multiplier, 5.52, yielding $220,800. [Note: there is a separate multiplier for each starting/ending age combination. Multipliers also differ if a different discount rate or life expectancy is used.]

Sample Cost of Care Calculation for Seven-year-old Plaintiff

 * The cost of care expert recommended that the wheelchair be replaced approximately every three to five years. It was also indicated that once the child’s chair had reached its maximum capacity, it would have to be replaced with an adult chair. For the purposes of our calculations, we have assumed that the chair will require replacement every four years until the plaintiff is eighteen.

We would like to thank Stephen Kuyltjes of Rehab Works, Calgary; Sharon Kaczkowski of Kaczkowski Occupational Therapy, Calgary; and Everett Dillman of International Business Planners, El Paso, Texas, all of whom were kind enough to comment on earlier drafts of this article. We are responsible for any remaining errors or omissions.

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).

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

 

The Dependency Rate as a Percentage of After-tax Income: Canada 2008

by Christopher J. Bruce and Kelly A. Rathje

In fatal accident litigation, the plaintiffs are entitled to claim an amount that is sufficient to allow them to maintain the same standard of living as they had enjoyed when the deceased had been alive. In practice, this requires that the court calculate the percentage of the deceased’s  after tax income that would have benefited the survivors directly. In Canada, this percentage is called the dependency rate.

Although most experts conclude that the dependency rate of one member of a couple is approximately 70 percent of the deceased spouse’s (after tax) income; there has recently been some confusion over whether the dependency rate might increase or decrease as family income increases. In particular, some experts have argued that the survivor’s dependency decreases as the deceased’s income increases. For example, whereas the widow of a man with low income might need, say, 80 percent of his income in order to be left in the same financial state as she would have had he lived, the widow of a wealthy man might need only 50 percent.

The purpose of this article is to employ a reliable source concerning  after tax income, expenditure patterns, and savings – the Canadian Survey of Household Spending (SHS) – to investigate this claim. Based on the SHS, we show that the survivor’s dependency rate, in a husband/wife family, does not deviate significantly from 72 percent, regardless of the family’s level of income.

The article is divided into three sections. In the first, we argue that the Canadian data are reliable. Second, we calculate the dependency rate for a surviving wife at each of the five income quintiles. There we will show that that rate does not differ significantly from 72 percent at any of these quintiles. Finally, we comment on the treatment of savings in the calculation of the dependency rate.

We also include an Appendix in which we calculate a dependency rate by category for each of the 17 categories of expenditure in the SHS. [Note: in this article, we do not comment on the question of whether some portion of the survivor’s incomes – the portion they now “save” because they do not “have to” spend it on the deceased – should be set off against the survivor’s loss. The arguments we make here apply equally to both the set-off, or cross-dependency, and sole-dependency approaches.]

I. Survey of Household Spending

The most reliable source of family expenditure data in Canada is Statistics Canada’s Survey of Household Spending (SHS), in which approximately 15,000 families are interviewed. The most recent such survey (for which appropriate data are available) was conducted in 2008. The primary source of information concerning this survey is Statistics Canada’s Spending Patterns in Canada, 2008 (Catalogue No. 62-202-XWE).

The 2008 SHS breaks down gross family income into 18 components: 15 major categories on current expenditures, two categories that reflect future expenditure – “insurance and pension contributions” and “money flows” (where the latter is a measure of net savings) – and one for income taxes. Summary information is provided concerning: number of families in the sample, average family size, number of adults, children, and age of head.

Table 1 provides an example of the type of information that can be drawn from the 2008 SHS. The first column in this table reports the average annual expenditures on each of 17 categories (other than taxes). The second column reports the percentages of total (after tax) income that were devoted to each of these categories.

There are a number of reasons for believing that the SHS data are reliable. First, Statistics Canada makes an effort to collect information from the family head. Second, the data for recurring expenses, such as food and personal care, are collected using a detailed daily diary. Third, all other data are collected through personal interviews taking two to three hours. Finally, Statistics Canada has confirmed that the average incomes reported by respondents to the SHS are consistent with those collected from other sources (such as income tax data) 1.

II. Dependency Rates by Income Quintile

The Appendix to this paper calculates the dependency rate for each of the 17 categories reported in Table 1. This rate is the percentage of the pre-accident expenditures on that category that the surviving spouse will need in order to maintain his or her pre-accident standard of living.

These dependency rates are reported in the second column of Table 2. The rate for each category has been multiplied by the percentage of current consumption devoted to that category, taken from the first column of Table 1, in order to obtain the figures reported in the third column. The latter represent the percentages of pre-accident,  after tax income that the surviving wife will need in order to maintain her pre-accident standard of living.

For example, the first row of Table 2 reports that the average Canadian family spent 12.06 percent of its after tax income on food, and that a widow will need 51 percent of this figure to maintain her pre-accident standard of living. Hence, she now needs 6.15 percent (= 0.51 × 12.06) of the family’s pre-accident income in order to purchase the food that she would have purchased had her husband not been killed.

When similar calculations are made for each of the 17 categories reported in Table 2, and the resulting figures are summed, it is found that the wife will require 72.83 percent of after tax income to maintain her pre-accident standard of living.

Using the same methodology employed to obtain the dependency rate for the average family, we also calculate dependency rates for families in each of the five income quintiles. In Table 3 (shown on the next two pages due to size constraints), we report the findings for each of these calculations, plus data concerning: the incomes of each of these groups and the distribution of their expenses among the 17 expenditure categories.

It is seen there that, before taxes, household incomes vary from a low of $19,179 for the first quintile to a high of $171,237 for the fifth; that income taxes range from 3.44 percent to 24.89 percent of total income; and that savings (as measured by the “money flow” category) range from minus 15.57 percent to plus 17.61 percent of  after tax income.

The most compelling finding in Table 3 is that dependency rates do not vary significantly with gross income, with figures ranging from a low of 72.52 percent for the fourth quintile to a high of 74.18 percent for the first quintile 2. Although this finding may, at first, seem counterintuitive, three factors help to explain it.

First, it is seen in Table 3 that the distribution of expenditures among categories does not vary appreciably among income groups. For example, even in the category with the greatest difference among income groups, shelter, families in the fifth quintile spend only nine percentage points more than do families in the second quintile (28.11 percent versus 18.93 percent). In no other category does percentage expenditure decrease or increase by as much as seven points between the second and fifth quintiles.

Second, because the percentages of expenditures on the 17 categories have to add to 100 percent, every increase in the fraction of income spent on one category must be offset by a decrease in the fraction spent on another. Thus, as long as the dependency rates of the categories that increase are similar to those of the categories that decrease, the average dependency rate across categories will not change.

Finally, our finding that dependency rates do not vary significantly with income depends in part on the assumption that the survivor’s dependency on savings will be the same as her dependency on current consumption – that is, on the assumption that, to maintain her standard of living, the survivor will need the same percentage of the family’s retirement income as she needed of its current income.

If, however, the survivor could only be “made whole” if she was allocated a higher (or lower) percentage of retirement income than current income, then dependency rates would increase (or decrease) as income rose – because high income families devote a higher percentage of their incomes to savings. We discuss this issue in greater detail in Section III.

III. Dependency on Savings

Assume that a husband and wife have family income of $80,000 per year, after taxes, of which they devote $70,000 to current expenditures (that is, to expenditures on food, clothing, shelter, etc.) and $10,000 to savings. Assume also that the wife’s dependency on current expenditures is 70 percent – that is, that she benefits from $49,000 (= 0.70 × $70,000) worth of goods and services each year (during the years in which her husband is working). If her husband is killed, she will require replacement of that $49,000 if her standard of living is to be maintained.

In addition, her husband’s death will deprive her of the benefit she would have received from the (ultimate) expenditure of the $10,000 per year that the couple was saving. In Section II, we assumed that the couple would have spent that money in a manner that was similar to the way in which they were spending their income on current expenditures. Therefore, we would have applied a dependency rate of 70 percent to the $10,000 to determine the loss to the wife.

It appears to us that there are two arguments against use of the latter assumption. First, it may be that, as retired couples have lower incomes than working-age couples, their expenditure patterns will also differ, resulting in different dependency rates. However, as we have found that dependency rates do not vary significantly across income levels (see Section II), this argument is not likely to have a significant effect on the results in Table 3.

Second, it is possible that couples may intend to leave a large portion of their savings either to charity or to their children.

To the extent that charitable donations and bequests are a “public good,” the surviving spouse may need as much as 100 percent of planned donations if she is to maintain her standard of living. For example, if the couple had planned to give $100,000 to their daughter, the surviving wife will not be left “equally well off” if the death of her husband leaves her able to give some amount less than $100,0003.

Assume, for example, that within the highest quintile, couples plan to spend 60 percent of their savings on the purchase of goods and services (when retired), and 40 percent on donations and bequests. If it is assumed that the wife’s dependency on current expenditures is 70 percent and her “dependency” on donations and bequests is 100 percent, her total dependency on savings will be 82 percent (= 0.60 × 70% + 0.40 × 100%), instead of the 73.91 percent we applied to savings in Table 3. In that case, however, her total dependency on after-tax income would increase by less than 1.5 percentage points.

Furthermore, this argument has almost no effect on the dependency rates for couples in the first four quintiles as their savings rates are either very low or negative (implying very small donations and bequests). Thus, once again, adjustment of the assumption concerning dependency on savings has no significant effect on the general conclusion that dependency rates do not vary appreciably with income.

APPENDIX: Dependency Rate by Expenditure Category

The purpose of this Appendix is to calculate the dependency rates for each of the seventeen expenditure categories identified in the Survey of Household Spending.

a) Food: Two steps must be taken in order to determine the dependency with respect to expenditures on food. First, it is necessary to identify the relative consumption levels among family members of different ages and sex. Second, allowance must be made for the fact that economies of scale from bulk buying are lost when one member is removed from the family.

With respect to the first of these calculations, our research indicates that the relative consumption of food, among family members of different ages, can be approximated by the figures in our Table A.1.

For example, if a family is composed of a husband and wife, for every 1.0 “units” of food consumed by the husband, the wife consumes 0.8 units. In this case, the couple consumed 1.8 units of food, of which 44.4 percent (0.8 ÷ 1.8) was devoted to the wife. It is this figure that has been used in the construction of Table 2.

Based on the above, and on the general finding that food costs approximately 10 percent more for a single person than for each member of a married couple due to loss of economies of scale, we conclude that in a family of two adults the dependency would be 51 percent when it is the husband who has died and 61 percent when it is the wife. In a family of four, the dependency would be approximately 76 percent if the husband should die and 83 percent if the wife should die.

b) Shelter: The shelter category consists primarily of payments for rent, mortgage, repairs and maintenance, and utilities, none of which could be expected to be reduced appreciably following the death of a spouse. For this reason, we recommend that the dependency be set at 96 percent. This is the figure that has been entered the second row of Table 2.

c) Household operation: This category consists, principally, of expenses for telephone, child care, domestic services, pet care, household cleaning supplies, paper supplies (e.g., toilet paper and garbage bags), and gardening supplies. Of these, only expenses on telephone and paper products can be expected to vary appreciably with family size. Accordingly, we set the dependency rate at 90 percent for the childless family.

d) Household furnishings: As there is no element of this category on which expenditures would be reduced by the death of a spouse, the dependency is 100 percent.

e) Clothing: The most reliable source of data concerning the division of clothing expenditures among family members is Statistics Canada’s Family Expenditure Survey, 1986. Relying upon that source, we have calculated that a family of two adults and two children (aged five to nine) would require approximately 0.6 adult male units for the boy’s clothing, 0.8 for the girl’s clothing, 1.65 for the wife’s clothing, and 1.00 for the husband’s. Thus, the dependency would be approximately (3.03 ÷ 4.05 =) 75 percent if the husband should die and (2.40 ÷ 4.05 =) 59 percent if the wife should die. In a family of two adults, the equivalent dependencies would be 62 and 38 percent, respectively.

f) Transportation: Approximately 90 percent of transportation is devoted to the purchase, maintenance, and operation of cars and trucks. Thus, the most important determinant of the dependency in this respect will be the number of vehicles owned by the family. If both adults drive but own only one car, the death of one of them can be expected to have little effect on vehicle costs; that is, the dependency would be relatively high.  However, if the family owned more than one vehicle, including one that was used primarily by the deceased, the dependency may be as low as 50 or 60 percent.

For the purposes of illustration, we have assumed in the construction of Table 2 that the family had two cars, giving it a dependency with respect to vehicles of approximately 60 percent. The remaining 10 percent of the transportation budget is devoted to public transportation (including air fares).

Assuming that these expenditures are divided evenly among family members, the total dependency with respect to transportation is 62 percent (= [0.9 × 0.6] + [0.1 × 0.75]) for a four-person family and 59 percent (= [0.9 × 0.6] + [0.1 × 0.50]) for a two person family.

g) Health care: Approximately 30 percent of this expenditure is devoted to health insurance. As premiums generally do not double when family size is increased from one to two, we assume for purposes of illustration that the dependency with respect to health insurance premiums is 60 percent for a two-person family. The remaining 70 percent of the average family’s medical budget is devoted primarily to eye care, dental care, and drug purchases. Lacking any firm data on the distribution of these expenses within the family we shall, for purposes of illustration, assume that they are divided equally. Thus dependency for a two-person family is 53 percent (= [0.30 × 0.6] + [0.70 × 0.5]).

h) Personal care: Personal care includes expenditures on such items as haircuts, hair and makeup preparations, soaps, deodorants, and shaving preparations. The recommended budget developed by the Social Planning Council of Toronto shows that adult females spend approximately 63 percent more than adult males on these expenditures. Hence, if it is the husband who has died, the wife’s dependency is approximately 61 percent.

i) Recreation: Approximately 50 percent of the average family’s recreation budget is devoted to expenditures that may not vary with the size of the family, such as purchases of recreational vehicles and home entertainment equipment. The remaining 50 percent is devoted to admissions to events, purchases of home recreational equipment (such as games and crafts), and purchases of sport and athletic equipment. Assuming that the latter expenses are shared equally among family members, the dependency with respect to recreation proves to be 75 percent (= [0.5 × 1.0] + [0.5 × 0.5]) for a two-person family.

j) Reading: The approximate division of reading is: 35 percent on newspapers, 20 percent on magazines, and 45 percent on books. Assuming that newspaper expenses do not vary by size of family and that one-third of book and magazine purchases are specific to one of the adult members of the family, the dependency with respect to reading proves to be approximately 80 percent (= [0.35 × 1.0] + [0.65 × 0.67]).

k) Education: In the absence of any information concerning the plaintiff family, and recognizing that less than 20 percent of the education expenses listed by Statistics Canada are devoted specifically to young children, the only assumption that can be made with respect to this category is that expenses are divided equally between the two adults if there are no older children in the family. That is, for purposes of Table 2, the dependency is 50 percent.

l) Tobacco and alcohol: As with education, in the absence of specific information about the family and assuming that there are no older children in the family, the dependency for tobacco and alcohol must be set at 50 percent.

m) Games of chance: In the absence of other information, we assume that the couple divides these expenditures equally. That is, the dependency rate with respect to this category is 50 percent.

n) Miscellaneous: Of the expenses listed under Miscellaneous, approximately 70 percent reflect items that would not vary significantly with family size, such as interest on personal loans, purchases of lottery tickets, bank charges, lawyers’ fees, and funeral expenses. Assuming that the dependency with respect to these items is 90 percent and with respect to the remaining items is 50 percent, the total dependency with respect to the miscellaneous category is 78 percent (= [0.7 × 0.9] + [0.3 × 0.5]).

o) Personal insurance payments and pension contributions: Approximately 70 percent of the expenditures in this category are for pension fund payments (primarily the mandatory, government-operated Canada Pension Plan), 15 percent for life insurance premiums, and 15 percent for employment insurance premiums. Thus, the value of the dependency will be determined primarily by the labour force attachments of the adult members of the family and by the number and ages of children.

Consider, first, the life insurance premiums. In a two-adult family, life insurance is normally taken out on the life of the main income earner, with the second family member being the beneficiary.  If either family member dies, the need for such insurance is reduced significantly. That is, the dependency is (approximately) zero.

In a family with children, however, it may be the children who are made the beneficiaries.  Therefore, regardless of which parent has died, the remaining parent can be expected to continue his or her payments to a life insurance scheme.  Indeed, that parent may even increase life insurance coverage to take account of the fact that a further death would leave the children with no parents. In such a case, a 100 percent dependency would appear reasonable.

The value of the dependency with respect to employment insurance contributions will be determined by the employment status of the adult members of the family.  If the deceased was employed and the survivor is not, no contributions will now have to be made to employment insurance.  Therefore, the dependency is zero.  On the other hand, if the deceased was not employed and the survivor is, contributions will be unaffected.  That is, the dependency is 100 percent. And if both adults were fully employed, the dependency will be 50 percent.

Finally, when the family loses the deceased’s contributions to a pension plan, it loses the future consumption it would have enjoyed from that pension. As it is only the spouse, and not the children, who would have benefited from this pension, it is the surviving spouse’s dependency on the couple’s retirement level income that will be relevant.

Applying the technique described in Section II, above, we find that if both members of a couple are over 65, the surviving spouse will have a dependency rate of approximately 73 percent (whether it is the wife or the husband that has died). Hence, if both spouses had been fully employed, the total dependency on the personal insurance and pension contributions category becomes 73.6 percent (= [0.15 × 1.0] + [0.15 × 0.5] + [0.70 × 0.73]) when there are children and 58.6 percent (= [0.15 × 0] + [0.15 × 0.5] + [0.70 × 0.73]) when there are not.

p) Gifts of money and contributions: This category consists of gifts to individuals outside of the family-spending unit – for example to parents and children living in separate households – and of charitable donations. We believe it can be argued that if the wellbeing of the survivors is to be maintained at the pre-accident level, these contributions must also be maintained at the pre-accident level. That is, the dependency with respect to this category is 100 percent.

q) Money flows – assets, loans and other debts: The purpose of this category is to measure households’ net contributions to (or withdrawals from) savings. Its primary components are changes in bank balances, purchases of stocks and bonds, contributions to registered retirement savings plans, and changes in money owed by (or to) the household. To the extent that any money put in to savings will be spent later, the dependency on this category will be the same as the dependency on expenditures that were made while the family members were working, or approximately 74 percent, (see Section II). However, if a significant portion of the household’s financial assets are passed to the couple’s children, through their estate, the dependency on savings approaches 100 percent (as for “gifts and contributions”). For the purposes of the sample calculations reported in Section II, we have assumed that the couple spends all of their savings during their lifetimes. Accordingly, we employ a dependency rate equal to the dependency on current consumption, or approximately 74 percent.

Footnotes:

  1. Personal interview with Danielle Zietsma, Senior Economist, Survey of Household Spending, Statistics Canada, May 31, 2013. [back to text of article]
  1. We repeated the exercise in Table 3 using data for the situation in which it is the wife that had died. The dependency rates for the five quintiles did not change appreciably. They became 74.07%, 72.63%, 71.95%, 71.57%, and 71.56%, from lowest to highest quintile.[back to text of article]
  1. In Ratansi v. Abery (1994), 97 B.C.L.R. (2d) 74 (S.C.) the deceased parents had contributed a substantial portion of their income to their mosque. The court found that it was not “….appropriate or accurate to describe the monies contributed to that institution as ‘income not available for family expenditure’.” Accordingly, the dependency of the surviving children on this portion of their parents’ income was found to be 100 percent.[back to text of article]

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).

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

The Discount Rate Simplified

by Christopher Bruce, Laura Weir, Derek Aldridge, and Kelly Rathje

In every personal injury or fatal accident case in which the plaintiff’s loss continues into the future, it is necessary to calculate the rate of interest at which the damages will be invested. This interest rate is commonly called the discount rate, and it is calculated as the nominal (or observed) rate of interest net of the expected rate of price inflation.

As Alberta has no mandated discount rate, the determination of that rate is left to the courts. In this article, we propose to offer a simple technique for identifying this rate.

We proceed in two steps. First, we discuss the criteria that we believe must be met when selecting the discount rate. Second, we apply these criteria to the relevant data, to make that selection. In a separate article following this one, Derek Aldridge and Christopher Bruce contrast the rates that we propose with those that are available on structured settlements.

1. Criteria

The first step in selecting a discount rate is to recognise that the plaintiff is expected to invest his or her award in such a way that the stream of income generated from that award will exactly reflect the stream of losses that the plaintiff has suffered. If the plaintiff has lost $50,000 per year for twenty years, investment of the lump-sum award should produce $50,000 per year, with the principal being exhausted by the end of the twentieth year.

As this stream of investment income is intended to replace a significant portion of the plaintiff’s lifetime earnings, the courts have ruled that the lump-sum should be invested in low-risk financial instruments. Hence:

The discount rate must be based on an investment portfolio that is of low risk.

Although this requirement does not mean that the plaintiff must put all of his or her award into government bonds or guaranteed investment certificates (the lowest-risk investments available), we argue that the interest rate available on those investments provides the most reliable indicator of the rate of return required by the courts.

The plaintiff may well include in his/her portfolio non-government or non-guaranteed investments, such as corporate bonds, mutual funds, and blue chip stocks; but, that the returns on such investments are higher than those obtained from government bonds results primarily from the higher level of risk associated with them – as was seen with devastating results in the post-2008 stock market crash.

The difference between the rate of interest on a government bond or a GIC and, say, a corporate bond is a measure of the compensation that investors demand for accepting a higher degree of risk on the latter investment than on the former. Once that level of “compensation” is deducted, the net, risk-free, interest rate is approximately the same on both. Hence:

The rates of return on Government of Canada bonds and GICs represent reliable indicators of the rate of interest sought by the courts.

Once it has been decided that it is government bond and GIC rates that are to be used, it is necessary to select from among the various options that are available to the plaintiff. Financial advisors recommend that, in order to reduce risk, investors should purchase a mix of bond durations. In that way, if interest rates should rise, investors can sell their short-term bonds and purchase newly-issued bonds at the higher rates; and if interest rates should fall, although investors will have to accept reduced interest rates on any new investments, they will still experience relatively high rates on their long-term (locked-in) investments. Hence:

Plaintiffs should purchase a mix of short-, medium-, and long-term investments.

If the duration of the plaintiff’s loss is less than ten years, the plaintiff will minimize risk by purchasing investments that have durations that mature on the dates on which the losses are incurred. For example, a one-year bond might be purchased to replace the loss one year in the future, a two-year bond to replace the loss two years from now, etc. Hence:

For losses that will occur in the next ten years, the relevant interest rate for any year is the rate of interest on a Government of Canada bond (or GIC) that has a term equal to that number of years.

But if the plaintiff’s loss extends for more than ten years, it will be wise to adopt an investment strategy in which bonds are purchased for shorter terms than the duration of the loss, and then re-invested periodically. To replace a loss twenty years from now, for example, the plaintiff might purchase five-year bonds today and re-invest the returns every five years until the funds were needed. If a similar practice is followed for every duration of loss, the risk that interest rates will rise or fall, relative to what is expected at the time of the initial investment, will be minimised.

Such a strategy, of rolling over short-term investments in order to generate a long-term return, means that the effective discount rate over the term of the investment will be determined not only by the rates that are available today but also by rates that will become available in the future. Thus, the court must predict what those future rates will be.

Contrary to what many experts argue, this prediction can be made simply and with confidence: the most reliable prediction of the rate of interest that will prevail in the long-run is that it will equal the rate of return currently available on long-run bonds. For example, if the current rate on 15-year government bonds is 3.0 percent, the best prediction of the rate of return that will prevail over the next fifteen years is 3.0 percent.

The argument for basing the prediction on this rate can most easily be understood by showing that the contrary cannot be true. For example, it might be argued that “as interest rates are unusually low today, it can be expected that they will eventually rise above current rates.” If this argument is correct, then individuals who wished to invest their funds for long periods of time (for example, individuals who are saving for their retirement) would not purchase long-term bonds today – they would purchase short-term bonds while waiting for interest rates to rise, and then purchase bonds at the new, higher rates once the interest rate had risen.

But if investors behaved this way, the demand for long-term bonds would decrease; and when demand for a bond decreases, its interest rate rises. (Issuers have to raise the rate of return in order to attract investors.) That is, if investors predict that the long-term interest rate will exceed the rate currently available on long-term bonds, they will act in such a way as to drive up the interest rate on long-term bonds. A bond rate that is less than the expected rate cannot be maintained.

Similarly, if investors believed that interest rates were about to fall, they would sell their short-term bonds and purchase long-term. But this would decrease the demand for short-term funds, driving up short-term interest rates, and increase the demand for long-term funds, driving down long-term interest rates.

In short, if the rate of interest that is currently available on long-term bonds is different from the rate that investors expect will prevail in the future, the long-term bond rate will change “towards” the rate that investors predict. As a result, the interest rate available on long-term bonds will always adjust until it equals the rate that investors predict will prevail in the long run. And, as investors have a strong incentive to make correct predictions about the bond market, it is likely that their predictions are the best that are available. Hence, we conclude that:

The best predictor of the rate of interest that will prevail in the long-run is the rate of interest that is currently offered on long-term bonds.

Finally, as we noted in the introduction to this article, the discount rate is found by netting out the forecasted rate of price inflation from the observed nominal rate of interest. Hence, before the discount rate can be determined:

The long-run rate of price inflation must be forecast.

Fortunately, there is a clear consensus that the long-run rate of inflation in Canada will be two percent. This consensus has developed because, since the early 1990s, the Bank of Canada has not only set two percent as its long-run target, it has both met that target and expressed satisfaction with the results of its policy.

That participants in the “money markets” have come to accept that the Bank will achieve this goal over the long-run is seen in two surveys of business leaders that have been conducted annually since 1994. Consistently, respondents have reported that they expect the long-run rate to be 2.0 percent. Indeed, not only has the average, expected rate been 2.0 percent in most years that the surveys were conducted, the variation of responses “around” 2.0 percent has decreased continuously. Hence:

There is a strong consensus that, in the long run, the rate of inflation will average 2.0 percent in Canada. Hence, the discount rate can be found by reducing the forecasted nominal rate by 2.0 percent.

2. Data

In Figure 1 and Table 1 we report the annual rates of return that have been available since 1995 on five Government of Canada bonds: 2-, 5-, and 10-year bonds, long-term bonds (an average of bonds with a maturity date longer than 10 years), and “real rate of return” bonds (bonds whose rates of return are stated net of inflation). It is seen there that both nominal and real interest rates on Government of Canada bonds have decreased almost continuously since the Bank of Canada introduced its policy of targeting a two percent rate of inflation. Whereas real interest rates were between 4.5 and 7.5 percent in 1995, they have fallen below one percent on most bonds, and even below zero percent on some, in recent years.

 

 

What these figures indicate is that investments in government bonds are unlikely to provide real rates of return above zero percent over the next five years; that bonds of five to ten year durations are unlikely to produce rates in excess of 1.0 percent; and that the market expects long-term real interest rates on government bonds to be less than two percent.

Nevertheless, in recognition of the fact that current rates are at a historical low, we have left our assumed rates at the same values we have employed for the past five years. Those rates, which we report in Table 2, are: 1.8 percent per year on funds invested for three years or less; rising in equal increments to 3.0 percent per year on funds invested for more than fifteen years.

 

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).

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

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

Laura Weir is a consultant with Economica and has a Bachelor of Arts in economics (with a minor in actuarial science) and a Master of Arts degree from the University of Calgary.

The Discount Rate Revisited (Spring 2008)

by Laura Weir, Derek Aldridge, Kelly Rathje, and Christopher Bruce

This article first appeared in the spring 2008 issue of the Expert Witness.

Our readers will recall that every year or two we review our standard discount rate assumptions and publish our findings. It is time to repeat this exercise.

In the Summer 2006 issue of the Expert Witness, we reported that real rates of interest (that is, the rates of return net of inflation) had increased slightly from those outlined in our Summer 2005 article. We responded by increasing our forecast of the short-term (one to six years) real rates of interest. Our forecasted interest rates for the medium to long-term (seven to 15 years or more) remained unchanged – although these rates were slightly higher than the observed real rates of return on Government of Canada bonds, long-term real rate of return bonds, and the long-term rate mandated in Ontario. (Higher rates lead to lower present values, so our estimates can be considered conservative.)

In our 2006 article we specified our assumptions for real interest rates for periods ranging from one-year to 15 years or more. Our assumptions were based on the observed rates of interest on Government of Canada bonds of various terms. We now have more rates to observe and we see that these rates have continued their long-term downward trend. Real rates of interest for five bond series over the last 14 years are depicted in the figure below (with the rates for 2008 estimated using an average of the January through June interest rates).

Figure 1

From the figure we see that real rates have decreased slightly from their 2006 and 2007 levels. However, the decrease in real interest rates is not sufficient to warrant a change in our discount rate assumptions. In particular, we note that the average real rates for the past 12 and 24 months are very similar to the corresponding averages at the time we wrote the previous article. One exception is the long-term rate, whose 24-month average (2.23 percent) is a third of a percent less than the corresponding 24-month average at the time of our previous article.

Although we do not show the comparable interest rates on guaranteed investment certificates (GICs), we have examined them and they are consistently lower than the rates of return on bonds. For example, the rate currently offered for 5-year GICs is approximately three percent, corresponding to a real rate of only one percent.

Our discount rate assumptions, unchanged from our 2006 article, are shown in the table below.

Table 1

Over the years, our approach to forecasting an appropriate discount rate has been criticized by other economists who prefer to rely on historical interest rates in making their forecasts. Below, we address some of these critiques and provide support for our approach.

Our approach, often called the “conservative investment” approach (which assumes a plaintiff will use his damage award to purchase a financial instrument with an appropriate term to maturity and hold that instrument to maturity), has been criticized by other economists who argue for a “market-based” approach (that assumes a plaintiff will buy and sell bonds as interest rates vary instead of holding the bond to maturity). One of us (Bruce) addressed this issue in an article written for the Spring 2007 issue of the Expert Witness entitled “Forecasting the long-term interest rate on Government of Canada bonds: “market-based” versus “conservative” investment“. We summarize his conclusions here as this issue continues to arise.

Some economists suggest that our approach ignores the price changes resulting from changes in the interest rate within the bond market, arguing for the market-based approach that assumes the plaintiff can earn a higher rate of return by actively buying and selling bonds as interest rates change. As a simple example, suppose a plaintiff will incur a loss of income of $100,000, 20 years from now. The conservative approach assumes that he will purchase a 20-year bond, paying five percent in interest per year, for $37,689 and redeem it at maturity for $100,000 to fund his loss in that year.

Assume, however, that the interest rate decreases to four percent one year after purchase. The market-based approach suggests that at four percent, the plaintiff could sell his bond (that has 19 years left to maturity) for $47,464 (= $100,000/1.0419) and earn $9,775 (= $47,464 – $37,689) in profit, for an effective rate of return of 25.94 percent in one year. However, this is actually not a profit because the plaintiff still has to purchase a 19-year bond (at a cost of $47,464) to fund his $100,000 loss of income 19 years from now. Thus, there is no real benefit to actively trading bonds as the interest rate changes.

In addition to the fact that the effective rates of return under the market-based approach are illusory, effective rates of interest are extremely variable. For example, a publication by the Canadian Institute of Actuaries entitled Report on Canadian Economic Statistics 1924-2005 indicates that the 10-year average (1996-2005) effective real rate of return on long-term Government of Canada bonds was 7.36 percent. However, the standard deviation was 9.01 percent, suggesting an average effective real rate of return that could fluctuate between -1.65 percent and 16.37 percent. This suggests that the plaintiff will almost certainly earn a rate of return different from the average long-term rate. Further, while a “profit” can be made by selling a bond when the interest rate decreases, a “loss” would occur if the interest rate increased (say) to six percent, where the 19-year bond would now only cost $33,051, for a net loss of $4,638 (or an effective rate of return of -12.31 percent).

Finally, if we were to rely on an average of past effective rates of interest then what period should we rely on? For example, the Canadian Institute of Actuaries report noted above indicates that the real effective rate of return on Government of Canada long-term bonds averaged -1.31 percent for the period 1956-1980, +8.74 percent for the period 1981-2005, and +6.79 percent for the period 2001-2005. There would be no justification for relying on any one of the above periods over the others, or for averaging these periods together, in attempting to obtain a forecast of the rate of return in the future.

We use the observed rates on government bonds as an indicator of the rates that are anticipated by large institutional investors, with billions of dollars at stake. While one might find that a forecaster is suggesting that (say) 3½ percent is the appropriate real long-term rate, this prediction is contradicted by the fact that the Government of Canada is presently able to sell its long-term bonds which offer a real return of less than three percent. (If expert institutional investors anticipated that real rates on secure investments will average, say 3½ percent over the next ten years, then they would not buy bonds that pay only 2½ percent, and the Government of Canada would be forced to adjust its bond rates.)

Other economists suggest that it would be simpler to assume that a plaintiff will hold a long-term security and then liquidate portions of this security to fund his/her losses in each year. This is simply another version of the market-based approach and, as discussed, there is a great amount of risk inherent in this strategy. Under our approach, if a plaintiff purchases a 5-year government bond with a value at maturity of $10,000, then in five years he is virtually guaranteed to receive $10,000 after redeeming his bond. However, if he were to buy a 20-year bond with the idea that he would liquidate portions of it to fund losses in each year, then he would be at the mercy of the bond prices available in each year. That is, he would be selling portions of his bond (as opposed to redeeming bonds for the guaranteed maturity value) and so, would be relying on the price of bonds attainable at the date he needed to fund his losses. As our discussion regarding the “conservative” versus “market-based” approaches illustrates, a plaintiff trying to fund his losses during periods of high interest rates would likely be selling portions of his bond at prices lower than his original purchase price and so, may not be able to fund his losses in each future year. If there is pressure on interest rates to increase in the next few years, as many economists feel is the case, then it is likely that plaintiffs investing awards from trials occurring in the next year or two would find themselves in this situation. We do not believe it is reasonable to impose this level of risk on a plaintiff.

Over the last ten years our prediction concerning the long-term interest rate has gradually declined from 4¼ percent to three percent. This decline has been in step with the observed rates, which can be seen in the above chart. Other economists have commented on our changes, with the implication that these changes demonstrate a weakness in our methodology. Our response is that the long-term rate has been changing over the past ten years, and it is important to reflect these changes in our calculations. To do otherwise would result in us using interest rates that are inconsistent with the rates that are actually available to plaintiffs.

Even if one finds that over the past few decades, long-term real interest rates have averaged 3½ percent, that rate is not now available to plaintiffs. Today’s plaintiff seeking secure investments simply cannot obtain a guaranteed long-term rate as high as the rates that were available 10 or 20 years ago. Even if the long-term rate rises to 3½ percent in five years, it does not follow that today’s plaintiff will be able to earn a long-term rate of 3½ percent, since he will be limited to the lower rates for the first five years.

Finally, many economists argue that plaintiffs should invest in equities, as well as bonds, and argue that this would result in a portfolio that is less volatile than investing in bonds alone. We find it difficult to justify the assertion that a portfolio that includes equities would be less volatile, given that the value at maturity of Government of Canada bonds is virtually guaranteed. Remember, the purpose behind the plaintiff’s investment of an award is to fund his losses in each future year and this is much different from investing for (say) retirement. The plaintiff must be able to fund his future losses in each year, whereas retirement can be delayed (or retirement plans changed) if there are insufficient funds. A plaintiff who invests in a series of bonds that provide the amount needed to fund his loss in each year, will receive the necessary amount with little to no risk of default. The same can not be said of equities, which carry a very real risk of default. The inclusion of equities can only increase the risk that a plaintiff will not be able to fund their future losses in each year.

We will re-examine our assumptions next year, and expect that some minor adjustments in our shorter-term rates may be warranted, depending on the movement of rates between now and then. As noted, minor changes in our assumptions regarding short-term interest rates will typically lead to negligible changes to our present value estimates. The assumed longer-term rates have a greater influence on our calculations, and if the rate on long-term bonds remains significantly below three percent (as it has since 2004), it may be appropriate to adjust our long-term rates as well.

leaf

Laura Weir, Derek Aldridge, Kelly Rathje, and Christopher Bruce are consultants with Economica.

Fatal Accident Calculations Under the New Legislation

by Kelly Rathje

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

Recent changes to the Insurance Act in Alberta (amendment R.S.A. 2000, c. 1-3 defined in section 626.1) may affect the treatment of survivor pension benefits in fatal accident calculations. Prior to the legislative change, survivor pension benefits were treated as a collateral benefit – in the sense that they represented insurance proceeds paid for by the deceased’s CPP contributions – and these benefits were not included when estimating the family’s dependency loss. Any deduction for the survivor’s benefit would have been equivalent to reducing a loss of income-dependency award because the survivor had received some life-insurance proceeds.

Under the new legislation, however, the forms of payment to be deducted from the award include:

(d) benefits under a prescribed income continuation or replacement plan or scheme…

Thus, under the new legislation, it may be argued that for fatal accidents occurring on or after January 26, 2004, any survivor benefits should now be deducted from the loss of dependency award as these represent “income continuation or replacement”. However, note that the Act does not specifically address CPP survivor’s benefits, though it does state that CPP disability pensions are to be deducted from an injured plaintiff’s losses. It may be argued that the same reasoning applies in the case of a fatal accident, and the survivor’s pensions will be found to be deductible.

Note that this may also imply that any private pension benefits that are received by a surviving spouse may also need to be included in the dependency loss calculations. For example, if the deceased was a teacher or nurse, presumably the surviving spouse would receive any private pension contributions in the form of a lump-sum payment or monthly survivor pension benefits.

In light of the legislation change, we propose that since survivor benefits are now to be deducted from the dependency losses, they must also be factored into the without-accident income path. That is, in any given year there would have been a possibility that the deceased would have died and the survivors would have received benefits, (had the accident under litigation not occurred). In the past, we would not have considered these benefits to be “income” as they would have been treated as collateral benefits.

Allowing for these changes to the legislation requires that we take a two-step approach to estimating the deceased family’s loss of dependency on income.

In the first step, we undertake the following calculations to estimate the family’s loss of dependency.

  • We estimate the employment and retirement incomes that the deceased would have earned over his life, had the accident not occurred (his “without-accident” income path), and the probability that the family will experience a loss of dependency on that income.
  • We then estimate the survivor benefits that dependents would have received had the deceased died, and the probability that these benefits would have been received.
  • We multiply each year’s loss by the probability of each event occurring in the years following the accident, and add the resulting figures to estimate a stream of losses.
  • Finally, we calculate the present discounted value of the stream of losses.

In the second step, we calculate the present discounted value of the survivor benefits the family is now receiving. The dependency loss is then the difference between the figures calculated in the two steps – the expected value of the loss of dependency and the present value of the survivor benefits.

For the loss of dependency calculations, contingencies that reflect the probabilities that the couple might have eventually separated or that the surviving spouse may remarry, are also usually included. These contingencies have the effect of reducing the dependency loss. If the couple had separated, then presumably the surviving spouse would not have benefited from the deceased’s income, and if the surviving spouse remarries, then presumably he/she will no longer be dependent on the deceased’s income. However, when estimating the probability that the surviving spouse would have received survivor benefits regardless of the accident, we do not include remarriage contingencies. Had the deceased died regardless of the accident, the surviving spouse would have received survivor benefits as long as the couple had not separated by that time. Whether or not the spouse subsequently remarried would not have altered his/her eligibility for survivor benefits. Therefore, remarriage has no effect on the without-accident survivor benefits and does not need to be included in the calculations.

Potential issues

Collateral benefit

The argument that survivor benefits should be deducted from the loss of dependency award is based on the assumption that they represent “income continuation or replacement,” as specified in the new legislation. There is, however, an argument that survivor pensions should be treated as “proceeds from insurance,” not as “income continuation” benefits. If they fall in the former category, they may be considered to be a collateral benefit, which would not be deducted.

For example, suppose the surviving spouse is receiving a pension from a private plan. It may be argued that this pension is a collateral benefit – in the sense that it represents insurance proceeds paid for by the deceased’s acceptance of a reduced direct pension. Presumably the deceased had a choice between accepting a pension with a survivor’s benefit and a higher pension with no survivor’s benefit. Both pensions would be actuarially equivalent. The deceased’s choice of the “survivor’s benefit” option is effectively the same as if she had chosen the option of a higher pension with no survivor’s benefit, and used the additional income (while she was alive) to buy life insurance. Had she done so, it is our understanding that the life insurance proceeds would be considered to be a collateral benefit, and not deducted from any dependency losses. That is, any deduction for the survivor’s benefit would be essentially the same as reducing a loss of income-dependency award because the survivor has received some life-insurance proceeds. The courts do not allow the latter, as we understand the law.

Conservative estimate of survivor benefits without-accident

In our calculations, we assume that the survivor benefits actually received by the family are a reasonable reflection of the benefits they would have received had the deceased not died in the action under litigation. This is likely a conservative estimate that will understate the losses since the longer the deceased would have contributed to a pension plan, the higher the benefits would have been.

leaf

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

Estimating non-discriminatory lifetime earnings for young females

by Christopher Bruce and Kelly Rathje

This article first appeared in the spring 2006 issue of the Expert Witness.

A number of judicial decisions have suggested that estimates of the lifetime earnings of young females should be based on data for males. Two arguments have been made in support of this view.

The first argument is that, as the differential between male and female earnings has been falling, incorporating the historical differential will understate the future earnings that young females will achieve once they become established in their careers. The second argument is that it is inappropriate for the courts to institutionalise current wage differentials that are based on discrimination.

The first of these arguments was alluded to in two BC judgments: ([B.I.Z.] v. Sams, [1997] B.C.J. No. 793; and Terracciano v. Etheridge and Fujii, [1997] B.C.S.C. B943125). The latter judgement involved a woman who was aged 16 when injuries from an automobile incident left her a paraplegic. In this judgement, Madam Justice Saunders indicated her preference for using earnings statistics of males to calculate Ms. Terracciano’s without-incident income:

[81] Indeed, it may be as inappropriately discriminatory to discount an award solely on statistics framed on gender as it would be to discount an award on considerations of race or ethnic origin. I am doubtful of the propriety, today, of this Court basing an award of damages on a class characteristic such as gender, instead of individual characteristics or considerations related to behaviour: Toneguzzo-Norvell (Guardian ad litem of) v. Burnaby Hospital, [1994] 1 S.C.R. 114.

The second argument was considered explicitly in the Alberta decision, MacCabe v. Westlock (RCSSD #110 et al [action: 9303 05787]). There, for example, the court accepted the use of income statistics for males to estimate the without-incident potential earnings of a young woman who had been paralysed while still a high school student. The court found that Ms. MacCabe had a without-accident income potential that was well above average. Importantly, the court argued:

[para468] Clearly the evidence establishes that the exceptional individual characteristics of the Plaintiff are such that her abilities would have commanded the equivalent salary of her male counterparts. She would have established a strong attachment to her career. The use of male wage tables is justified. In any event, I am of the view that any award which I grant to the Plaintiff should not and cannot be solely determined by her gender.

[para469] It is entirely inappropriate that any assessment I make continues to reflect historic wage inequities. I cannot agree more with Chief Justice McEachern . . . in Tucker, supra, that the courts must ensure as much as possible that the appropriate weight be given to societal trends in the labour market in order that the future loss of income properly reflects future circumstances. Where I differ is that I will not sanction the “reality” of pay inequity. The societal trend is and must embrace pay equity given our fundamental right to equality which is entrenched in the constitution. . . .

However, these decisions were silent about the possibility that women might earn less than men even after discriminatory practices had been removed. Importantly, for example, the Court of Appeal in MacCabe (9803-0617AC) rejected the trial court’s finding that male contingencies (such as for non-participation, unemployment, and so forth) should be used when estimating Ms. MacCabe’s losses.

[105] In general, tort law and in particular, the quantification of damages necessitates an individual approach. This is where I find the learned trial judge erred. In attempting to rectify potential inequities in the methods for quantifying damages, the learned trial judge neglected to focus on the evidence and the individual actually before her. While principles of equality should inform tort law, the learned trial judge’s application of equitable principles resulted in her ignoring some of the relevant material facts.

[106] In this case, based on the evidence, it was not reasonable to calculate MacCabe’s damages based on male contingencies. MacCabe stated she wanted to have children and would have preferred to stay at home with them for some period. This meant she would not have worked a pattern typical for male physiotherapists. There was no evidence to indicate it was more likely that MacCabe would not have had children and chosen not to take time off from full time paid employment as a physiotherapist. Thus, it would be inappropriate to apply male contingencies to her when there was no evidentiary basis that she would have worked a typical male pattern.

The appellate court in MacCabe recognised that, even in a world in which men and women experienced equality of opportunity, incomes might differ between the sexes if they made different choices. Most importantly, women might choose to take more time off to care for children; might choose to work fewer hours per week or weeks per year; and might retire earlier than men.

If this is true, one approach to predicting the earnings of young women might be to take current earnings data for men and adjust them downward for these differences in contingencies. In this article, we consider the impacts that each of four such contingencies might have on the male/female earnings differential.

Participation rates

The percentage of any group that is either working or available for work at any time is known as the “participation rate.” Primarily because of the impact of child-rearing, women have lower participation rates than do men at virtually every age. Hence, if everything else is equal, women’s earnings in an average year will be lower than men’s in proportion to the differences in participation rates.

Statistics indicate that the best predictor of whether a particular woman will be in the labour force in the future is whether she was in the labour force in the past. In particular, married women who had been working prior to the birth of their first child have a strong tendency to return to work within one to two years of the birth of that child. Nonetheless, there is a large number of women who delay re-entry until their youngest child is of school age. [1]

Also, many authors have detected a strong positive correlation between education level and female participation. That is, the most highly educated women tend to have the strongest attachment to the labour force. Table 1, for example, indicates that 86.4 percent of 25-54 year-old, university-educated women in Alberta participated in the labour force in 2002, compared with 80.7 percent of 25-54 year-olds who had high school education. (Participation rates drop significantly after age 54 due to retirement.)

Table 1

Note that the figures reported in Table 1 reflect the current labour force participation of women. It seems likely, however, that women who are currently in their 20s and 30s will maintain their high participation rates as they age. Therefore, the figures for the older age categories, in Table 1, might be adjusted upwards when projecting the future behaviour of plaintiffs who have not yet reached the age of majority.

Given the figures in Table 1, and recognising that male participation rates are approximately 95 percent for all age and education groups (between 24 and 54), the earnings for females might be estimated by reducing male earnings by the difference between 95 percent and the relevant female labour force participation rate. For example, given the information in Table 1, the earnings for females with post-secondary education could be estimated by reducing the earnings of comparable males by approximately 10 percent. Similarly, earnings for those with high school education might be obtained by reducing male earnings by approximately 15 percent.

Part-time employment

Even when they are in the labour force, women report lower annual incomes than men because they are more likely to work part-time. As Table 2 indicates, in the age group 25-54, approximately 20 to 25 percent of women work part-time, whereas only 2 to 5 percent of men do so. That is, on average, the number of women working part-time is approximately 20 percentage points higher than the number of men. If part-time is interpreted to mean “half time,” this implies that women in the labour force work 10 percent fewer hours than men. Everything else being equal, therefore, this difference suggests that male earnings should be reduced by a further 10 percent, in order to obtain an estimate of female earnings.

Table 2

Hours Worked

There is also evidence to suggest that, even among individuals who work “full-time,” women work fewer hours per week than do men. Table 3 indicates, for example, that women’s “usual hours per week” are about 80 percent of those of men. Furthermore, Sweetman [2] reports that this ratio varies only slightly by education level: in the age group 40-44, for example, the ratio of female to male hours worked is 81 percent for those with high school and 85 percent for those with a bachelor’s degree.

Table 3

It would be double-counting, however, to reduce male earnings by both 10 percent for part-time work and 15 to 20 percent for hours worked, as the latter differential includes the effect of the former. Rather, it appears that, among those individuals who work full time, women work 5 to 10 percent fewer hours than do men. It is this contingency that should be applied to male earnings.

Retirement

It is seen in Table 4 that women retire approximately two years earlier than men, on average. Hence, any estimate of female lifetime earnings will have to take this difference into account.

Table 4

Summary

Even if there was no discrimination in the labour market – that is, even if women received the same hourly wages as men – on average, women’s annual earnings would still be lower than men’s. Among those with high school education, the differential would be as much as 35 percentage points, due to differences in labour force participation rates (15%), propensity to work part-time (10%), and hours worked per week (10%). Among university graduates, the differential would be approximately 25 percentage points.

Accordingly, if it is concluded that women’s attachment to the labour market will not change in the future, women’s average annual earnings cannot be expected to rise above 65 percent (high school graduates) to 75 percent (bachelor’s degrees) of men’s earnings.

Only if women increase the percentage of their time that they devote to the labour market will that 25 to 35 percent differential begin to fall.

Application

How should these statistics be applied? We believe that two broad cases can be distinguished. In the first, the plaintiff was old enough at the time of her injury that it is possible to determine both the occupation she would have entered and the strength of her attachment to the labour market. (This was the situation in MacCabe, for example.) In those cases, information specific to the plaintiff should be used to predict her (non-injury) earning capacity.

In the second case, the plaintiff was young enough that neither her career nor her labour market attachment can be predicted. In such cases, we believe that information about the plaintiff’s family background is sufficient to allow the court to identify approximately what her educational attainment would have been. Census data concerning incomes by education can then be used to predict the plaintiff’s earning capacity.

But, is it census data for females, or for males, that should be used for this purpose? We have argued that, if the court believes either that labour force discrimination will largely disappear over the next few decades, or that the effects of discrimination should not be institutionalised in damage awards, it is male data that should form the basis of the award. However, the information we have presented in this paper suggest that, even in the absence of discrimination, women will earn less than men because of differences in attachment to the labour force. For that reason, we would propose that, for young females, the forecast of earnings capacity should be based on male data; but that those data should be adjusted downwards as we discussed above.

Footnotes

1. C.J. Bruce, Assessment of Personal Injury Damages (Butterworths: Toronto, Vancouver), fourth edition, 2004, page 167. [back to text of article]

2. Arthur Sweetman, 2002. “Working Smarter: Education and Productivity,” The Review of Economic Performance and Social Progress, in: Andrew Sharpe, Executive Director & France St-Hilaire, Vice-President, Research & Keith Banting, Di (ed.), The Review of Economic Performance and Social Progress 2002: Towards a Social Understanding of Productivity, volume 2 Centre for the Study of Living Standards. [back to text of article]

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).

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

The Discount Rate Revisited

by Christopher Bruce, Derek Aldridge, Kelly Rathje, and Hugh Finnigan

This article first appeared in the summer 2005 issue of the Expert Witness.

In the Autumn 2000 issue of this newsletter, we conducted an extensive review of the various methods of measuring the real rate of interest, or discount rate, and presented evidence concerning the movement of those measures over the period 1995-2000. (That article is available on our website, at economica.ca/ew53p1.htm.)

That survey was subsequently updated in our Winter 2001/02 and Spring 2003 issues. What we found in both of those updates was that interest rates had begun to fall relative to the historically high levels that had persisted over most of the 1990s.

At the time of our Spring 2003 article, we concluded that the best estimate of the long-run discount rate was 3¼ percent. But we also argued that, as interest rates on short-term bonds and GICs were lower than those on longer term investments, it would be appropriate to employ an interest rate of 2¼ percent on the first five years of any investment.

The primary purpose of this article is to examine nine additional quarters (27 months) of data to determine whether the trend we observed in our earlier articles has continued, or whether a revision in our recommended interest rate is appropriate. We also report on recent changes to Ontario’s mandated discount rate; and we revisit the question of whether forecasts of future interest rates should be based on information about past interest rates or on information about current, long-run interest rates.

Revised Data

Instead of providing tables of data comparable to those presented in our earlier articles, we now present a chart that shows the trend in interest rates since 1995. The complete data set can be found on our web site (see www.economica.ca/ew102p1.htm). Figure 1 shows the trend in real interest rates on government bonds of various term lengths, as well as the rate of return on real rate of return bonds.

Figure 1

Note: We deduct two percentage points from the “nominal” or observed interest rates to reflect inflation anticipated by investors. As the Bank of Canada has managed to keep the core rate of inflation within a small band around this target since the early 1990s, and as it has been the stated intention of not only the Bank of Canada but most other central banks (most notably that of the European Union) to keep the inflation rate at that level, there is now virtual unanimity among investors that two percent will be the long run rate of inflation in Canada. Accordingly, it can be concluded that investors have been acting as if the real rate of interest is the observed, nominal rate less two percent.

The data in Figure 1 indicate that real rates of interest have continued the downward trend that began in 1996/97. Whereas we concluded two years ago that long-term real interest rates were approximately 3¼ percent and short-term rates approximately 2¼ percent; it now appears that real rates have fallen substantially below those levels. In particular, note that the rate of return on 10-year Government of Canada bonds, net of the 2 percent expected rate of inflation, is now below 2 percent and has not exceeded 3¼ percent (our earlier prediction of the “long-term” rate) since early 2002. Even the real rate on long-term (30-year) Government of Canada bonds has fallen below 2½ percent in the most recent quarter. And the rate of return on real rate of return bonds is now below 2 percent and has not been above 3 percent since mid-2003.

Although we do not show the comparable interest rates on guaranteed investment certificates (GICs), we have examined them and they are consistently lower that the rates of return on bonds. For example, the real rates on 1-year GICs have been consistently negative since late-2001, meaning that investments in one-year GICs are not keeping pace with inflation. The real rate on 5-year GICs has been below one percent for all but two of the last eight quarters and has not exceeded two percent since early 2002.

Forecasting the Real Rate of Interest

For some time now, Economica has been arguing that current interest rates, net of the two percent expected rate of inflation, provide the most reliable basis on which to predict future interest rates. Some other economic experts in Western Canada disagree with us, and base their forecasts on information about historical interest rates. We consider their position to be unjustified. We explain why here:

The continued dramatic fall in interest rates.

First, as a glance at Figure 1 will indicate, the real rate of interest in the last decade has fallen continuously and dramatically: the long-term rate has fallen from about 7 percent to below 2½, while the short-term rate has fallen from about 6 percent to below 1 percent. Clearly, any prediction that was based on an average of the figures in this period (or any other period extending back to the early 1980s) would seriously overstate the rates that will be available to a plaintiff investing his or her award today.

It is important to note that although rates are low by historic standards, today’s investors who seek the security of investments comparable to government bonds and GICs cannot avoid these low rates. The fact that average interest rates over the last 30 years were much higher does not help today’s plaintiff-investor. Even if one believed that long-term real rates will rebound to (say) 3.5 percent in the next 5-10 years, the best that today’s investor will be able to do is place his funds in 5-10 year investments earning 1.5-2 percent and then hopefully reinvest at the higher 3.5 percent rate. This of course will not yield nearly the same result as if he had been able to invest at 3.5 percent right from the start.

Economic theory.

Second, the fall in real interest rates is consistent with macroeconomic theory. Specifically, many macroeconomists are arguing that the relatively high real interest rates that were observed in the 1980s resulted from the high volatility in expectations concerning the nominal rate of inflation. That is, when inflation is unpredictable, investors who place their funds in long-term bonds face a considerable amount of uncertainty. If inflation proves to be higher than expected, the real rate of return that they realise may be very low or even negative. To compensate for this uncertainty, investors demand a relatively high expected rate of return. Conversely, when inflation becomes predictable, as has happened in the last five to ten years, investors face much less uncertainty and are willing to accept lower real rates of return. Accordingly, there is sound reason to believe that, as long as the Bank of Canada maintains its current course[*] (which it is expected to do), both nominal inflation and real interest rates will remain at the low levels that have been observed recently.

Furthermore, most economists believe that real interest rates will be higher in a period in which governments run large deficits – and, therefore, have to borrow heavily – than in those in which revenues exceed expenditures. As is well known, although the Canadian government ran sizeable deficits in the late 1980s and 1990s, it has now adopted a goal of achieving a balanced (or, even, surplus) budget. Again, this leads us to believe that real interest rates will be lower in the future than they were in the 1980s and 1990s.

The actions of large, institutional investors.

Most of the bonds reported in Figure 1 are purchased by large, institutional investors, such as pension funds. That these investors are willing to purchase, say, ten-year bonds paying a real rate of approximately two percent, or real rate of return bonds that are paying below two percent (as of the second quarter of 2005) indicates that they do not anticipate that they can obtain better rates of return on other secure investments. That is, regardless of whether private investors actually purchase the government bonds identified in Figure 1, the returns on those bonds indicate that the institutions that base millions of dollars of investment on their predictions of the financial markets are forecasting that real rates of return will remain low in the foreseeable future. (If expert institutional investors anticipated that rates on secure investments will average, say 3½ percent over the next ten years, then they would not buy bonds that pay only two percent, and the Government of Canada would be forced to adjust its bond rates.)

A future increase in rates will have little effect on most plaintiffs.

Even if real interest rates were to increase substantially in the next ten years or so, (against expectations), that would have relatively little effect on the investments of many plaintiffs, for two reasons. First, it is the rates of return that are available today that will dictate the average rate that plaintiffs will be able to obtain over at least the first 5-10 years of the period of their loss. As noted above, if rates increase in the future, the best that today’s plaintiff can do is place his funds in 5-10 year investments at today’s low rates and then later reinvest at the (hopefully) higher future rates. Even if rates return to their historical average, this investment approach will still yield an average rate of return that is below the historical average. Second, since most plaintiffs will need to begin consuming their award immediately (to replace their lost income and fund their costs of care), the most substantial portion of the interest that they will earn on the investment of their awards will occur in the first half of their period of loss, before they have drawn down much of their capital (that is, during the period in which they are funding most of their annual losses from interest income). If the period of loss is 20 years or less (like most plaintiffs), then the interest they earn in the first ten years will have a much greater impact on their investments than the interest they earn in the next ten years. Thus a return to higher interest rates after 10 years or so will have only a small impact on these plaintiffs. For plaintiffs with a period of loss that is only ten years or less, future increases in interest rates will have almost no effect on their investments.

Note that one implication of relying heavily on current interest rates is that it requires that we change our discount rate assumption more frequently than if we simply relied on historical averages. However, making changes to our discount rate assumption does not imply a weakness in our methodology. To do otherwise would result in us using interest rates that we know are inconsistent with the rates that are actually available to plaintiffs. Even if one believed that over the long-term, real interest rates will average (say) 3 or 3½ percent, it does not necessarily follow that our discount rate assumptions should remain fixed. This is because (as we explained above), the rates of return over the next ten or so years will have a substantial impact on the investment results of most plaintiffs. Thus, it is important to account for the rates that are available to plaintiffs now, as well as the rates that will be available (on average) over the long-term.

Ontario’s mandated discount rate

Following a detailed review, Ontario (in year 2000) revised its regulations concerning its mandated discount rate. The new methodology that was chosen yields a discount rate of 1.5 percent for the first fifteen years of any award and 2.5 percent for all years beyond that point. Specifically, Ontario’s revised regulation 53.09 states:

53.09 (1) The discount rate to be used in determining the amount of an award in respect of future pecuniary damages, to the extent that it reflects the difference between estimated investment and price inflation rates, is,

(a) for the 15-year period that follows the start of the trial, the average of the value for the last Wednesday in each month of the real rate of interest on long-term Government of Canada real return bonds (Series V121808, formerly Series B113911), as published in the Bank of Canada Weekly Financial Statistics for the 12 months ending on August 31 in the year before the year in which the trial begins, less 1 per cent and rounded to the nearest ¼ per cent; and

(b) for any later period covered by the award, 2.5 per cent per year. O. Reg. 488/99, s. 2; O. Reg. 263/03, s. 4 (1).

The average month-end rate of return on real rate of interest bonds from September 2003 through August 2004 was 2.58 percent. Deducting one percentage point and rounding to the nearest quarter percent yields a mandated discount rate of 1.5 percent for trials that occur in 2005. Given the rates on real rate of interest bonds since September 2005, we can also be almost certain that Ontario’s mandated discount rate (for the first 15 years of loss) will fall to 1.0 percent for trials that occur in 2006.

As discussed in an earlier article (see “Ontario’s Mandated Discount Rate – Rule 53.09(1)” that appeared in the Autumn 2000 issue of the Expert Witness), we have a concern with Ontario’s policy of deducting one percentage point from the one-year average of the rate on real rate of return bonds. What is perhaps more interesting is their finding that 2.5 percent reflects the long-term real rate of interest. This rate is lower than the long-term rate we have been using, though it is consistent with the current rate on long-term Government of Canada bonds.

Conclusion

Our review of the empirical and theoretical sources suggests that it would be appropriate to adjust our discount rate assumption. We also propose to make a change in how we apply our discount rate assumptions in our calculations. Whereas we previously assumed that the real rate of return on all of a plaintiff’s investments would be 2.25 percent for the first five years and 3.25 percent thereafter, we now assume that for income required in the first few years, an investment will be made at short-term rates, while for income needed in the more distant future, investments will be made that will earn the predicted long-term rates. Below we will further explain the approach we will use for our calculations, and then we will outline the discount rate assumptions we will use.

To illustrate the effect of this approach, note that a child plaintiff who will not experience a loss of income for ten years will manage to earn a relatively high rate of return because he will be able to invest in “long-term” investments, and he will be more likely to benefit from possible future increases in interest rates. On the other hand, an older plaintiff who will experience a loss of income over the next five years only, will not be able to benefit from long-term investments or from possible increases in interest rates. She will face the low rates available on short-term investments.

Consider the following example of a plaintiff who will incur a loss of income of $10,000 seven years from now. If she is compensated for the future loss today, she could use her award to purchase a government bond with a seven-year term, and not touch the award until it is needed in seven years, when the bond matures. We will see in Table 1 (below) that such a bond has been paying about 2.2 percent, net of inflation over the last two years. This plaintiff will not need to invest at the lower rates offered by shorter-term assets, but she will also not benefit from the rates offered on longer-term bonds, nor will she benefit from a possible future increase in interest rates. To invest her award in secure assets she faces two options: she can make successive short-term investments in hopes that the rates will increase in the near future, or she can make a longer-term (seven-year) investment and accept the interest rate that is available to her. Given her investment-obligations as a plaintiff, we would expect that that latter option would be more appropriate. Even if she chose the former option we would not expect her overall return to improve, unless we knew that rates would increase in the near future. (And if it was known that rates will increase in the near future, then this increase would already be reflected in the current rate of return offered on seven-year bonds.)

For our calculations we will choose an average rate of return that will be earned on investments that are held until each future year of the period of loss. For example, we assume that the portion of the plaintiff’s award that is held for seven years to compensate her for her loss in year 7 will earn an average rate of return of 2.2 percent. Similarly, we assume that the portion of the plaintiff’s award that is held for 15 years to compensate her for her loss in year 15 will earn an average rate of return of 3.0 percent. And so forth.

To obtain the discount rates for use in our calculations, we propose to take an average of the most recent two years of quarterly interest rates (using two years of monthly rates would yield the same results), for the Government of Canada benchmark bond yields for 2-, 3-, 5-, 7-, 10-year, and long-term bonds. After deducting two percentage points for anticipated inflation and rounding to the nearest tenth of a percent, this will give us our assumed real interest rates for various future terms. We assume the one-year rate is the same as the reported two-year rate, and for the “in-between” years we simply extrapolate. We assume that the long-term rate applies to year 15 and beyond. The rates that result from this approach are shown in Table 1.

Table 1

Note that we believe that the rates proposed in Table 1 (above) remain conservative (that is, they may understate future losses) since they reflect interest rates that have been available over the past two years, instead of only the rates that are available now (even though only the rates available now can be assured to today’s investor).

Footnotes

* Since approximately December 1993, the Bank of Canada has successfully maintained a policy of keeping inflation at a two percent target (the midpoint of its 1-3 percent target range). [back to text of article]

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).

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

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

From 2003 through 2005, Hugh Finnigan was a consulting economist at Economica, with a Master of Arts degree from the University of Calgary.

Selecting the Discount Rate – An Update

by Christopher Bruce, Derek Aldridge, Kelly Rathje, and Scott Beesley

This article first appeared in the spring 2003 issue of the Expert Witness.

In the Autumn 2000 issue of this newsletter, we conducted an extensive review of the various methods of measuring the real rate of interest, or discount rate, and presented evidence concerning the movement of those measures over the period 1995-2000.

That survey was subsequently updated in our Winter 2001/2002 issue (Vol. 6, No. 4). What we found was that interest rates had begun to fall, relative to the historically high levels that had persisted over most of the 1990s.

At that time, we concluded that the best estimate of the long-run discount rate was 3½ percent. But we also argued that, as interest rates on short-term bonds and GICs were lower than those on longer term investments, it would be appropriate to employ an interest rate of 2½ percent on the first five years of any investment.

The purpose of this article will be to provide five additional quarters (15 months) of data to determine whether the trend we observed at the beginning of 2002 has continued, or whether a revision in our recommended interest rate is appropriate.

Revised data

Tables 1 and 2 provide updates of the information contained in the equivalent tables of the Winter 2001/2002 article. In particular, we have added data for all four quarters of 2002 plus the first quarter of 2003.

Table 1 reports the “raw” data from which some of the real interest rate figures in Table 2 have been calculated. The first column reports the “core rate of inflation” – a measure of the rate of inflation that removes the effects of change in those components of the price index that often move erratically, such as food, energy, and taxes. It is often argued that this measure offers a more reliable predictor of future changes in prices than does the “standard” measure of price inflation. (See the Autumn 2000 article for a detailed description of the core rate of inflation.)

The next three columns in Table 1 report the rates of return on Government of Canada 5-year and 10-year bonds and on 5-year Guaranteed Investment Certificates (GICs). The former represent the minimum rates of return that investors can expect on safe investments. The rate of return on GICs, on the other hand, represents the interest rate available on a mixed, low-risk portfolio of stocks and bonds.

Table 1

Table 2 reports seven measures of the real rate of interest – that is, the rate of interest net of the expected rate of inflation. The first of these is the market-determined rate of return on “real rate of return bonds” – bonds whose value is denominated in terms of the real rate of interest. These bonds are of particular importance because they are purchased by sophisticated investors and because they tend to held for long periods of time.

The second, fourth, and sixth columns report the 5- and 10-year government bond interest rates and 5-year GIC rates net of the core inflation measure.

Finally, columns three, five, and seven report the government bond and GIC rates net of the Bank of Canada’s target rate of inflation of 2 percent. As the Bank has managed to keep the core rate of inflation within a small band around this target for the last eight years, it is widely believed that 2 percent is the rate that is expected by most investors. That is, investors are believed to act as if the real rate of interest is the observed, nominal rate less 2 percent.

Table 2

Interpretation of the data

The data in Table 2 indicate that real rates of interest have continued the downward trend that began in 1996/1997. Whereas we concluded a year ago that long-term interest rates were approximately 3½ percent and short-term rates approximately 2½ percent; it appears that those rates have now fallen to 3 percent and 2¼ percent, respectively.

Note that the latter rate is close to the rates reported in the Bank of Canada’s Monetary Policy Report of April 2003 (Chart 19, p. 24).

In addition, 3 percent is the rate at which the Bank of Canada recently issued a new set of 33-year real rate of interest bonds. As we argued in the Autumn 2000 issue of the Expert Witness, the rate of return on these bonds is a particularly reliable estimate of the expected real interest rate as they are purchased primarily by large institutional investors (like pension funds) that have made considerable investments in the prediction of future rates of interest and inflation.

For this reason, we believe that it would be appropriate to revise our existing 2½ and 3½ percent two-part forecast of real interest rates. Based primarily on the observed rate on 5-year Government of Canada bonds, we propose to use a rate of 2¼ percent for the first five years of all calculations. For all subsequent years we propose to use a rate of 3¼ percent – though we note that a rate as low as 3 percent could be supported based on the most recent observed rates on 10-year Government of Canada bonds and based on the Bank of Canada’s current issue of real rate of return bonds. Our long-term rate is perhaps slightly conservative, but we will re-examine this issue next year and decide then if changes are warranted.

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).

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

Scott Beesley is a consultant with Economica and has a Master of Arts degree (in economics) from the University of British Columbia.

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

Retirement trends in Canada

by Kelly Rathje

This article first appeared in the spring 2003 issue of the Expert Witness.

The value of an individual’s potential earning capacity depends in part on how long she will be in the labour force. That is, for the calculation of lost earnings, it is necessary to make assumptions concerning the age at which the individual would have retired (and will now retire).

The focus of this article is to compare retirement ages of Canadians over the five year period 1991-95 to retirement ages of Canadians over the five year period 1996-2000.* The findings show that for most educational and industry categories, Canadians are retiring earlier than they did even five years ago.

In the early 1980s, the median retirement age was close to age 65. However, retirement ages decreased steadily from 1986 to approximately 1993. Notably, in 1987, the Government of Canada reduced the age (from age 65 to age 60) at which Canada Pension Plan benefits could be collected (albeit with a reduced pension amount). I compare the retirement patterns of Canadians over the 1991-95 period to the retirement patterns of Canadians over the 1996-2000 year period to see if the trend for earlier retirement has continued. In Table 1, the distribution of retirement ages in Canada over the two five-year periods (1991-95 and 1996-2000) is summarized.

As shown in Table 1, in the five year period 1991-95, the highest percentage of individuals retired in the 60 to 64 year age category. In the five year period 1996-2000, 60 to 64 still remained the most popular age group for retirement, however the percentage of individuals retiring within the age category had dropped by 6 percentage points (37 percent to 31 percent). The percentage of individuals retiring “earlier”, in the 50 to 54 age category and 55 to 59 age category both increased – the former having the largest increase (9 percent to 15 percent, or 6 percentage points). This suggests that although many Canadians are still choosing to retire at a “normal” retirement age (60 to 64), there is a shift to earlier ages.

Table 1

Although the percentage of people retiring in their 60s decreased within the time period considered, the percentage of individuals retiring at age 70 or above remained unchanged.

Table 2 compares median retirement ages in Canada over the periods 1991-95 and 1996-2000. (The “median” retirement age is defined such that 50 percent of individuals retire at ages above that age and 50 percent below it.)

As shown in Table 2, the overall median retirement age has decreased by one year, from approximately age 62 (1991-95) to 61 (1996-2000). The median retirement ages of private employees also decreased by one year (from age 63 to age 62), however the retirement ages of public employees decreased by two years (from 60 to 58). Self-employed individuals experienced no change in retirement trends from 1991-95 to 1996-2000. The earlier retirement ages of public employees are likely due to the generous pension plans available to most public employees, which often offer incentives to retire at earlier ages.

Table 2

Table 3 summarises retirement ages by industry. As shown in the table, many industry categories also experienced decreases in median retirement ages between 1991-95 and 1996-2000. However, self-employed individuals did not alter their retirement patterns in most industries and, if anything, the retirement age of self-employed individuals may be increasing.

Table 3

Considering the “employees” category for the goods-producing occupations, retirement ages remained constant for all industries except for utilities and construction. These two experienced a decline in retirement ages of 2 years. For service-producing industries, all declined except management, which remained constant at 65. Educational services showed the largest decline in retirement ages over the two five year periods – from age 61 to age 57, a drop of four years. This may have resulted from restructuring within the education sector that led to the offering of early retirement packages to many teachers. If so, there may be a reversal of this decline in the future (as fewer early retirement incentives are offered).

Self-employed individuals again show higher retirement ages than employees. Within the goods-producing industries, most show an increase in the age of retirement (there was a decline in manufacturing), which goes against the overall trend for retirement patterns of Canadians. The service-producing industries, however, showed a one year decrease in retirement ages for the trade, management, and other industries; constant retirement ages for transportation and professional industries; and a one year increase in retirement ages for health care and accommodation industries. The largest increase was in the finance, insurance, and real estate industries, which saw a four-year increase in retirement ages over 1991-95.

The above tables show retirement trends by industry, and the statistics combine both male and female statistics, and also do not consider specific levels of education. The statistics for males and females, by education levels are summarized in Table 4.

Table 4

Over 1991-95, men and women overall had similar retirement patterns. That is, there was only one year difference in the retirement ages at each education level. In 1996-2000, the male retirement patterns saw very little change from 1991-95, with most education levels having unchanged retirement ages. Only males at the high school diploma level experienced a decline in retirement (age 61 to 60).

Females, on the other hand, have followed a decline at each level by at least one year. Females with a university education resulted in the lowest median retirement age (57) over 1996-2000. Also note that at the high school diploma level, males and females experienced the same retirement age (60).

I note, however, that the female retirement trends may be underestimated. Retirement trends are based on historical retirement, and may not reflect the trends of future generations. That is, young women in the labour force now may experience different retirement patterns than women who were of retirement age in the last five years. The current generation of women are obtaining higher levels of education, and are participating in the labour force more, as compared to those women of retirement age now. This suggests that their labour force attachment may be greater than the attachment of the older cohorts. Therefore, it may be theorized that young women now in the labour force will retire later than women who faced the retirement decision in recent years.

Also, there is evidence to suggest that in the future, there may be pressure for Canadians, both male and female, to delay retirement. For example, in a paper entitled “Future Age of Retirement”,** Brown argued that as the baby boom generation moves into the retirement ages, they will attempt to liquidate assets in order to buy goods and services. This will reduce the value of the assets due to the number of retirees attempting to do this. Also, the smaller “baby bust” generation will be the source of labour within the economy. The production in the economy may slow due to the smaller labour force, resulting in price inflation. This may force some potential retirees to postpone retirement since the value of their assets will have decreased. Thus, Brown’s prediction for retirement in Canada is that the median retirement age will fluctuate between age 60 and 61 over the next 47 years. That is, the overall median age of retirement and the trend of decreasing retirement ages may not continue on into the future.

Conclusion

Based on the above information, it seems that the median retirement age in Canada has fallen from age 62 to 61 and, depending on educational attainment and gender, the average retirement of an individual may be as low as 57, or as high as 65. Self-employed individuals continued to retire at approximately age 65.

For the purposes of loss of income calculations, it seems reasonable to consider the education level of the individual, since there are statistics available for both males and females. In addition, if a career path has been established, one should also consider retirement patterns of the specific industry. For minors, the overall or educational statistics would be appropriate.

Footnotes

* The source of the data provided in this article is the Statistics Canada publication Perspectives on Labour and Income, Summer 2002 Vol. 14, No. 2, and Summer 1997 Vol. 9, No. 2. [back to text of article]

** Brown, Robert L. “Future Age of Retirement” Canadian Investment Review, Fall 2002, pages 32-37. [back to text of article]

leaf

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

Male Versus Female Earnings – Is the Gender Wage Gap Converging?

by Kelly Rathje

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

When we estimate the potential income of a young female (without- or with-accident) who does not have a well-established career path, we rely on census data and usually present earnings for both males and females. As is well-known, men have, on average, earned more than women. A number of reasons have been offered for this, including: labour force discrimination, different occupational choices, differences in labour force participation trends, and so forth. However, it is also well-known that the average income earned by women has been increasing relative to that earned by men. In 1967, women’s earnings were approximately 58 percent of men’s earnings. By 1997, women’s earnings were approximately 73 percent of men’s earnings. But will this trend continue, and will the gender wage gap continue to close in the future?

A recent paper by Michael Shannon and Michael Kidd addresses the question of the size of the gender wage gap in the future.* Using recent Canadian data, they project future trends, based on current trends in educational attainment and labour force participation. They then use these predicted trends to estimate the wage gap from 2001-2031 using a statistical model. They find that the wage gap will continue to close, however a wage gap of approximately 22 percent will still exist in 2031.

In this article, I first examine current and projected trends in educational attainment and labour force participation – two factors influencing earnings. Then, I present Shannon and Kidd’s results regarding the projected gender wage gap. Finally, I consider the implications of their results for the estimation of the potential incomes of young females.

Educational attainment

One factor that influences earnings is educational attainment. In recent years, female educational attainment has increased relative to that of males. To incorporate recent trends in educational attainment, Shannon and Kidd create an age-education pattern for both males and females. In 2000, it is found that individuals in the 25-29 year age category are better educated than individuals in the 55-59 year age group, and that this trend will continue into the future. For example, in 2000 approximately 2 percent of individuals (either males and females) in the 25-29 year age category have less than a high school education, compared to approximately 23 percent in the 60-64 year age category. As the individuals in the 25-29 year category age, the pattern of educational attainment is carried through into future years. The number of individuals in the 55-59 year age category in 2030 (individuals who were in the 25-29 year age category in 2000) that have less than a high school education will decline to approximately 2 percent, and we see higher education levels for all age groups in the future.**

In addition, female university enrollment has increased. In fact, women now account for the majority of university students, and females are entering fields that were typically male-dominated (such as engineering, applied sciences, and mathematics).

For the purposes of their calculations, Shannon and Kidd make the conservative assumption that educational enrollments will remain constant into the future. In 2000, approximately 22 percent of women (aged 25-64) had a high school diploma, 32 percent had a post-secondary diploma, 14 percent had a bachelor’s degree, and 5 percent had a graduate degree. By 2031 it is predicted that approximately 17 percent of women will have a high school diploma, 35 percent will have a post-secondary diploma, 18 percent will have a bachelor’s degree, and 8 percent will have a graduate degree.

Male educational attainment, as a comparison, is predicted to remain relatively unchanged over the 30 year period considered. In 2000, approximately 19 percent of males had a high school diploma, 33 percent had a post-secondary diploma, 13 percent had a bachelor’s degree, and 8 percent had a graduate degree. By 2031 it is predicted that approximately 20 percent of men will have a high school diploma, 36 percent will have a post-secondary diploma, 14 percent will have a bachelor’s degree, and 7 percent will have a graduate degree.

These results indicate that women are “catching up” to males in the percentage that obtain higher levels of education. Since higher education tends to lead to higher wages, the increased educational attainment of women, and the constant attainment of males, contributes to a closing of the gender wage gap.

Labour force participation

Another factor that influences women’s earnings is that they tend to take time away from the labour force (either to withdraw entirely or to reduce hours to part-time status) for a period of time – as is common for women who choose to have families. Thus, women, on average, bring less experience to their jobs, which means they tend to have lower incomes at any given age.

Labour force participation rates have shown steady growth over the last three decades, and many experts anticipate that they will continue to rise. Moreover, relying upon historical participation rates by age cohort may be misleading as many women are delaying the onset of pregnancy. In 1986, on average, women were approximately 25 years old when their first child was born. By 1996, women were approximately 27 years old when their first child was born. During the early years between finishing school and starting a family, women are tending to work full-time in their careers. It is in the early years of one’s career that substantial wage growth usually occurs. By delaying starting a family, women can be more flexible in career decisions such as traveling, relocation, overtime, etc. Thus, women may benefit from the higher wage increases earlier on in their careers. Also, they may be able to exit the labour force at a time that will have less impact on their careers, and their earning potential.

Shannon and Kidd predict that women will have increased their number of years of work experience by 2031. A summary of the actual (1994 and 2001) and estimated (2001 and 2031) years of work experience is outlined in Table 1.

Figure 1

Two important predictions are made in Table 1. First the number of years of experience obtained by males at each age group will not change significantly over the next 30 years. Whereas males 45-49 had worked an average of 25.6 years in 1994, for example, they are predicted to have worked 25.5 years in 2031. Similarly, the work experience of 55-59 year-old males is predicted to change by only 0.2 years – from 36.6 to 36.8 years over the same time period.

Second, whereas the work experience of young females is predicted to remain relatively unchanged, older women are predicted to obtain more years of lifetime employment. For example, while the work experience of 35-39 year-old females is predicted to change by 0.1 years between 1994 and 2031 – from 12.5 to 12.4 – the work experience of 55-59 year-olds is predicted to increase by 2.3 years – from 23.9 to 26.2. And the experience of 60-64 year-olds is predicted to increase by 5.1 years.

Shannon and Kidd concluded that these changes will produce only a slight narrowing of the wage gap between men and women – and then only in older age groups. But their results did not allow for changes in number of hours worked in a lifetime. It is also possible that some wage gains could be obtained by women if they were to work more full-time hours, and less part-time, and if they were to increase their full-time hours. In 1997, for example, women working full-time, worked 39 hours per week on average, whereas men worked 43 hours.

Shannon and Kidd incorporate the trends summarized above to determine the future wage gap. Their results are shown in Table 2 below.

Figure 2

There, it is projected that the gender wage gap will decline in the future. On average, it is projected that the difference between incomes for males and females in the 25-44 year age category will fall to approximately 17 percent by 2031. That is, full-time, full-year wages for females within the 25-44 year age category are projected to be approximately 83 percent of their male counterparts. By comparison, women in the 45-64 year age group will earn approximately 71 percent of their male counterparts’ incomes.

Conclusions & implications

Shannon and Kidd’s results imply that the gender wage gap will continue to close, but a gap of approximately 22 percent will still exist in 2031. Increasing female labour force participation and educational attainment, coupled with the relative stability of the male labour force participation and education attainment contribute to the wage gap closure.

In comparison to the wage gap closure from 1967 to 1997 (42 percent to 27 percent, or 15 percentage points), the results for the next three decades suggest that convergence of the gender wage gap will slow from 2001 to 2031 (29 percent to 22 percent, or 7 percentage points). The authors’ findings also suggest that changes in the wage gap for older individuals (within the 45-64 year age group) will produce the greatest convergence (43 percent to 29 percent, or 14 percentage points).

Part of the projected wage gap in 2031 is due to the differences in the labour market characteristics addressed by Shannon and Kidd. Since women tend, on average, to work fewer years over their work-life; work fewer hours per week; and are more likely to withdraw from the labour force or reduce their hours to part-time for the purposes of raising a family, their wages will, on average, be less than those of their male counterparts. However, these characteristics historically have accounted for only half of the wage gap. The portion of the wage gap that cannot be explained by labour market characteristics is generally attributed to discrimination and to differences in preferences between men and women. For example, women tend to be the primary caregivers. Thus, they may choose to work in lower-paying jobs that have more flexibility regarding sick days and hours worked, or within positions that are easily entered and exited. These are factors which also contribute to the wage gap, but are not easily captured using traditional statistical methods, such as those used by Shannon and Kidd.

What do these findings imply for using male earnings when predicting the potential income for young females? It seems reasonable to conclude that the findings suggest that historical average income figures for women underestimate the future potential income of an average young woman today. This is because historical income figures reflect women who (on average) had a much different labour force experience than today’s average young woman will experience, and that young women in the future will experience. It seems that the “reality” for today’s average woman lies somewhere between historical figures for males and females. It appears that even young women who will follow a “traditional” average female career path will earn more than the average women represented by historical data since today’s females are acquiring higher education levels and displaying a greater labour force attachment by participating full-time in the labour force longer.

Thus, it may be appropriate to use average earnings for males to predict the future potential income of an average young female, and then to apply contingencies to reflect the possibility of labour force absences and part-time employment. I emphasize, however, that this approach still carries difficulties. For example, women tend to enter different careers than men, even when they are working full-time. That is, there is still a tendency for occupations to be “male-dominated” or “female-dominated”, and the female-dominated occupations tend to pay less, even considering the same level of educational attainment between men and women. Thus, using male earnings data for any given level of education (considering all occupations) may overstate the potential life-time earnings of a young female.

Footnotes

* Shannon, Michael and Michael Kidd, 2001, “Projecting the Trend in Canadian Gender Wage Gap 2001-2031”, Canadian Public Policy. Vol. XXVII, No. 4, 447-467. [back to text of article]

** Shannon and Kidd also consider a scenario in which enrolment increases in the future at the same rate it had increased in the prior 12 years. For the purposes of this article, I focus on the more conservative scenario in which they assume that there is a one-time jump in enrolment from 1994-2000, and then enrolment remains constant over the 2001-2031 period. [back to text of article]

leaf

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

Selecting the Discount Rate – An Update

by Christopher Bruce, Derek Aldridge, Scott Beesley, and Kelly Rathje

This article was originally published in the Winter 2001/02 issue of the Expert Witness.

In the Autumn 2000 issue of this newsletter, we conducted an extensive review of the various methods of measuring the real rate of interest, or discount rate, and presented evidence concerning the movement of those measures over the period 1995-2000.

At that time, we concluded that our best estimate of the long-run discount rate was 4.0 percent. But we added the caveat that, as interest rates in 2000 had deviated significantly from the average of the preceding years, it would be important to maintain a close watch on those rates – to determine whether 2000 was an aberration or whether it represented the beginning of a new trend.

In particular, we concluded that article with the statement:

If bond rates do not rise relative to the rate of inflation in the near future, we will be revising our real rate of interest forecast downward.

The purpose of this article will be to provide five additional quarters (15 months) of data to determine whether such a revision is appropriate.

Revised data

Tables 1 and 2 provide updates of the information contained in Tables 1 and 2 of the Autumn 2000 article. Four changes have been made to the latter tables. First, we have added data for the fourth quarter of 2000 and for all four quarters of 2001. Second, in some cases, the relevant statistical authorities have revised their estimates of the figures we reported previously. In those cases, we have provided the revised figures.

Third, we have added information concerning interest rates on five-year Government of Canada bonds. Finally, in the interest of space, we have omitted the estimates of the real rate of interest that relied on information concerning the “standard” inflation rate.

Table 1

Table 1 reports the “raw” data from which some of the real interest rate figures in Table 2 have been calculated. The first column reports the “core rate of inflation” – a measure of the rate of inflation that removes the effects of change in those components of the price index that often move erratically – such as food, energy, and taxes. It is often argued that this measure offers a more reliable predictor of future changes in prices than does the “standard” measure of price inflation.

The next three columns in Table 1 report the rates of return on Government of Canada 5-year and 10-year bonds and on 5-year Guaranteed Investment Certificates (GICs). The former represent the minimum rates of return that investors can expect on safe investments. The rate of return on GICs, on the other hand, represents the interest rate available on a mixed, low-risk portfolio of stocks and bonds.

Table 2 reports seven measures of the real rate of interest – that is, the rate of interest net of the expected rate of inflation. The first of these is the market-determined rate of return on “real rate of return bonds” – bonds whose value is denominated in terms of the real rate of interest. These bonds are of particular importance because they are purchased by sophisticated investors and because they tend to be held for long periods of time.

The second, fourth, and sixth columns report the 5- and 10-year government bond interest rates and 5-year GIC rates net of the core inflation measure.

Finally, columns three, five, and seven report the government bond and GIC rates net of the Bank of Canada’s target rate of inflation of 2 percent. As the Bank has managed to keep the core rate of inflation within a small band around this target for the last six years, it is widely believed that 2 percent is the rate that is expected by most investors. That is, investors are believed to act as if the real rate of interest is the observed, nominal rate less 2 percent.

Table 2

Interpretation of the data

The data in Table 2 indicate that real rates of interest are lowest on the shortest-term investments, GICs and 5-year bonds, and highest on the longest-term investments, 10-year bonds and real rate of interest bonds. This suggests to us that investors believe that the current slowdown in the economy, which has induced central banks to lower interest rates very significantly, may continue for two or three years but will not continue in the long term.

For this reason, we believe that it would be appropriate to adopt a two part forecast of real interest rates. Based primarily on the observed rate on 5-year Government of Canada bonds, we propose to use a rate of 2.50 percent for the first five years of all calculations. Based primarily on the observed rate on 10-year Government of Canada bonds, we propose to use a rate of 3.50 percent for all subsequent years. Note that the latter rate is close to the average real rate of return on GICs over the period 1964 to 1998, (3.58 percent), reported in Bruce, Assessment of Personal Injury Damages, Third Edition, at page 231.

Once again, however, in recognition of the uncertainty facing our economy, we will revisit this question at the end of this year.

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).

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

Scott Beesley is a consultant with Economica and has a Master of Arts degree (in economics) from the University of British Columbia.

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

Evaluation of Harm to a Class of Individuals

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.

leaf

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

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).

Selecting the Discount Rate

by Christopher Bruce, Derek Aldridge, Scott Beesley, & Kelly Rathje

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

One of the most important determinants of the lump sum award for future losses is the discount rate, or real rate of interest. Simply put, this is the rate of interest at which the plaintiff is assumed to invest the award, after the effects of price inflation have been removed.

For example, assume that the court has found that if the plaintiff was to incur a loss today, the value of that loss would be $10,000. But, because the loss will occur one year from now, and the rate of inflation between today and one year from now will be 2 percent, the loss will actually be $10,200.

The court must determine how much the plaintiff will have to invest today in order to ensure that he or she will have $10,200 available one year from now. The discount rate is the interest rate that is used to make this calculation. The purpose of this article is to determine the current value of the discount rate.

We proceed in four steps. First, we distinguish between “nominal” interest rates and “real” interest rates and explain why the latter are generally used in preference to the former. Second, we review a number of alternative methods of measuring the interest rate. Third, we review a number of methods of estimating the expected rate of inflation. Finally, we report the values of these alternative measures for the years 1997-2000 and we conclude with a recommendation concerning the appropriate value to be used today.

Real versus nominal interest rates

There are two methods of calculating the present value of a future loss. The first is to “discount” the loss by the “nominal” rate of interest – that is, by the rate of interest that is observed at financial institutions. The second is to remove the inflationary estimate from the projected loss, to obtain what is called a “real” loss, and then discount that loss by the “real” rate of interest – that is, the nominal rate after the rate of inflation has been removed. The two methods yield identical results.

For example, assume that the nominal rate of interest is 6 percent. The first method of determining the award is to divide $10,200 by 1.06, (that is, by 1 plus the interest rate). That number is found to be $9,623. It can easily be confirmed that if 6 percent of $9,623 is added to $9,623 one obtains $10,200. That is, if the plaintiff was to invest an award of $9,623 at 6 percent, he or she would have $10,200 at the end of one year.

In the second method, one first “removes” inflation, here 2 percent, from both the future loss and the nominal interest rate. In both cases, this is done by dividing by 1.02, (that is, by 1 plus the inflation rate). Thus, as intuition would suggest, the real level of damages is found to be $10,200/1.02 = $10,000. The real interest rate is found to be 1.06/1.02 = 1.0392, or 3.92 percent. (Note that, in the same way that 1.06 is 1 plus the nominal interest rate, 1.0392 is 1 plus the real interest rate.) When $10,000 is divided by 1 plus the real interest rate, 1.0392, one obtains $9,623, exactly the same answer that was obtained using the nominal method.

Economists generally prefer to use the real loss/real interest rate approach when calculating lump sum awards for future losses. The primary reason for this is that real interest rates tend to be much more stable and, therefore, much more easily predicted, than either inflation rates or nominal interest rates.

Alternative measures of the interest rate

Because plaintiffs often have to rely on the investment of their awards to provide a significant portion of their future incomes, it is important that they place their awards in relatively risk-free investments. For this reason, the discount rate is usually based on the rate of return on either long-term government bonds or secure private sector investments. Once a nominal rate has been determined for one of these investments, it is then necessary to determine an expected rate of inflation (over the duration of the investment) in order to calculate the real rate of return.

In this section, we will consider three types of secure investments. In the following section, we will discuss three methods of estimating the expected inflation rate.

Real return bonds The first investment vehicle is Government of Canada real return bonds. These are long-term, secure bonds whose rate of return is denominated in terms of a real interest rate. (That is, the government guarantees that the investor will receive a specified (real) interest rate plus the actual rate of inflation.) There are a number of advantages to using the rate of return on these bonds.

First, when that rate is used, it is not necessary to make a separate projection of the rate of inflation.

Second, these bonds are guaranteed by the government of Canada.

Third, the estimate of the real rate of interest that is obtained by observing the prices at which these bonds are traded in the financial markets provides an objective measure of the real rate of interest that is forecast by sophisticated investors. Note, we are not suggesting that plaintiffs should, or will, invest their awards in real return bonds. Rather, we are arguing that the observed returns on these bonds provides an excellent, objective measure of the expected real rate of return – as these bonds are purchased primarily by individuals who are close observers of money markets and who have a great deal of money at stake when selecting their investments. (Generally, it is pension fund administrators who purchase real return bonds.)

Recently, Ontario revised its Rules of Court concerning the selection of the discount rate. Whereas the previous rule required that the courts use a fixed rate of 2.5 percent, the new rule bases the rate on current observations of the interest rate on real return bonds. For further analysis of Ontario’s new rule, see the accompanying article “Ontario’s Mandated Discount Rate – Rule 53.09(1).”

Guaranteed investment certificates A second approach to the determination of the real discount rate is to identify a measure of the rate of return on a “safe portfolio” of investments (i.e. the kind of portfolio in which a plaintiff could be expected to invest) and to deduct from that rate the expected rate of inflation. We have long recommended that the rate of return on five year guaranteed investment certificates, GICs, be used for this purpose.

Again, as we commented with respect to real return bonds, we are not suggesting that the plaintiff should use his or her award to purchase GICs. Rather, as the types of investments contained in GICs are similar to those that one would expect a prudent investor to purchase, the rate of return on GICs provides an objective measure of the rate of return that plaintiffs can expect to obtain. (Furthermore, as the quoted rate on GICs is net of investment management fees, there is no need to make a separate calculation of the management fee.)

Long-term Government of Canada bonds The rate of return on long-term government bonds can be used as a benchmark against which to measure the returns on other investments. As these bonds are widely held by private citizens (unlike real return bonds) and as they are among the most secure investments available, it would be expected that plaintiffs would never earn a nominal rate of return less than that obtainable from Government of Canada bonds. (If the plaintiff’s investments began to obtain a lower rate of return, the plaintiff could always, easily, transfer his or her investments to government bonds.) Hence, any suggested discount rate must pass the test that it is not lower than the rate obtainable on government bonds. Conversely, we would suggest that the discount rate used should also not significantly exceed the government bond rate, as that would imply that plaintiffs should place their awards in unacceptably risky investments.

Estimating the rate of inflation

The real rate of interest is calculated by removing the effects of price inflation from the nominal rate of interest. As the interest rate is to apply to investments that will continue for many years into the future, the relevant rate of inflation is the average rate that is expected to apply over that future. We will discuss three methods of forecasting this rate.

Current rate of inflation One simple method is to assume that the current rate of inflation will continue into the future. Use of this rate is based on the observation that investors appear to adjust their expectations of the future when current conditions change. Often it is assumed that this shift of expectations occurs with a short “lag,” of six months to two years. But, in periods in which the rate of inflation is not changing quickly, only small errors will be produced if the current rate is used.

Core rate of inflation In Canada, the reported rate of inflation is measured as the change in the price level of a representative “basket” of goods over a 12 month period. For example, the rate of inflation reported for January 2001 will be the percentage change in prices between January 2000 and January 2001.

What this means is that if there is a large, one-time increase in prices in January 2000, measured inflation will be relatively high in each month from January 2000 to December 2000 and then will fall significantly in subsequent months. The reason for this is that the increased price level produced by the January 2000 price increase will continue to be in effect in every future month. Hence, in every month between January 2000 and December 2000, prices will be higher than in the corresponding month a year earlier. Inflation in those months will be correspondingly high.

For example, assume that the CPI had been 100 in every month during 1999, had risen to 110 in January 2000, and stayed at that level for the rest of the year. Then, in every month during 2000 the CPI would be 110, in comparison with 100 in the same month the year before. Hence, in every month in 2000 the rate of inflation would be reported as 10 percent – even though there had not been a price increase since January.

But, when calculating the January 2001 inflation rate, the price level for that month will be compared to a price level (January 2000) that already contains the one-time increase of January 2000. Hence, the measured rate of inflation in January 2001 (i.e. between January 2000 and January 2001) will drop back to the long-run maintainable rate.

In our example, if the CPI remains at 110 in January 2001, inflation between January 2000 and January 2001 will be 0 percent. The one time increase in January 2000 will have had only a temporary impact on the rate of inflation.

What this observation implies is that if we wish to use the current rate of inflation to forecast the long-run rate of inflation, we must first remove the effect of one-time price increases. The Bank of Canada attempts to provide such a measure of long-run price inflation with what it calls its core rate of inflation. In particular, this measure removes movements in the costs of food and energy and movements in prices due to the effects of indirect taxes.

For example, the core rate of inflation would not include the effects of the doubling of oil prices during 2000. Why? Because, although a doubling of prices from $15 a barrel to $30 (and higher) was not completely unexpected, very few observers expect to see prices rise much higher. Hence, even if prices remain at their current level, within 12 months of the initial increases, inflation (the change in the level of prices) will fall. (The increase in oil prices is an example of the one-time increase we discussed above.)

And, of course, if prices should fall back to their pre-2000 levels, short-term inflation will fall even more – perhaps into negative numbers – for the next 12 months. But no one will expect those low levels of inflation to continue any more than they expect the current high levels to continue.

The implication, then, is that the core rate of inflation may be a better indicator of the long-run, expected rate of inflation than is the measure that is usually reported in the press. For this reason, in the tables below, we report both the core rate and the published rate.

The Bank of Canada’s target rate For the last decade, the Bank of Canada’s monetary policy has been directed at producing a rate of inflation of 2 percent (plus or minus 1 percent). As anyone who can remember the 1970s and 1980s can attest, the Bank has been singularly successful in reaching this goal.

Indeed, it has been so successful, that we believe that it can be argued that most investors have come to believe that the long-run rate of inflation will be (approximately) 2 percent. (The Bank itself reports that most financial analysts are predicting inflation rates of approximately 2 percent. See Bank of Canada Monetary Policy Report, November 2000, p. 32.) For this reason, when determining the real interest rate, in the tables below, we report calculations employing an inflation rate of 2 percent.

The data

We present two tables. Table 1 reports quarterly values of the two nominal interest rates – 10-year Government of Canada bonds and GICs – and two of the measures of expected inflation – the standard version and core inflation – for 1997, 1998, 1999, and the first three quarters of 2000. (We do not report the Bank of Canada target rate of inflation, as it did not change over this period.)

Table 1

Table 2 reports the real rates of interest obtained, first, from the real return bonds and, second, from adjusting the two nominal interest rates by each of the three measures of expected inflation. This produces seven measures of the real rate of interest.

Table 2

What these figures suggest is, first, that the interest rate on real return bonds has been remarkably constant over the last three and a half years, rarely deviating very far from the 4.0 to 4.1 percent range until 2000, when it fell to approximately 3.7 percent.

Second, it is seen that the real rate of interest on 10-year government bonds has also fluctuated around 4.0 percent, but with far larger deviations than was seen with respect to the rate on real return bonds. Some of the wider of these deviations can easily be explained, however.

Note, for example, that the low real rates produced in 1998 and 1999 when the 2 percent inflation factor is used may have resulted because a long period of below-2 percent inflation had caused financial markets to believe that the Bank had lowered its target rate. (The conventional measure of inflation exceeded 2.0 percent only once between the first quarter of 1996 and the third quarter of 1999, when it was reported to be 2.1 percent in the first quarter of 1997.) If the markets had come to expect inflation rates of 1.5 percent in 1998 and 1999, for example, most of the real rates in those years would have been close to 4.0 percent.

The relatively high rates found in 2000 when long-run bond rates are discounted by core inflation, and the relatively low rates found in that year when they are discounted by the standard measure of inflation, could both be “explained” if it was found that financial markets had begun to accept the Bank of Canada’s statement that it was targeting a long-run inflation rate of 2 percent.

The consistently low rates found on GICs, however, are disconcerting. Over the entire period reported in Table 2, and for a number of years prior to that, the rates of return on GICs were significantly lower than those on government bonds. This suggests that plaintiffs would be extremely unwise to invest in GICs for the foreseeable future.

We conclude, therefore, that current estimates of the discount rate should be based on the rates observed on real return bonds and on long-term Government of Canada bonds. Arguably, these rates fluctuated around 4.0 percent for most of the last four years. They have, however, fallen slightly during 2000.

This raises the question of whether 2000 is an aberration, or whether the recent decline in real rates is the beginning of a long-term trend. Some evidence that the decline is expected to be short-lived comes from the Alberta government’s Budget 2000 documents. There, it is reported that nine respected forecasting agencies predicted an average interest rate on Government of Canada 10-year bonds of approximately 6.21 percent (over the years 2000-2003). As it is unlikely that those agencies would have forecast an inflation rate in excess of 2 percent, implicitly they have forecast a real rate of interest of approximately 4.1 percent.

In this light, we believe that a rate of 4.0 percent is the best, current estimate of long-run real interest rates. However, Economica will be monitoring movements in the interest rates on real return and 10-year Government bonds closely. If bond rates do not rise relative to the rate of inflation in the near future, we will be revising our real rate of interest forecast downward.

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).

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

Scott Beesley is a consultant with Economica and has a Master of Arts degree (in economics) from the University of British Columbia.

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

Rates of Return to Advanced Education in Alberta

by Kelly Rathje

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

Conventional wisdom appears to suggest that, if young Canadians wish to be competitive in today’s economy, they should concentrate on the relatively technical disciplines, such as engineering and business. I have tested this “wisdom” using information about post-secondary education in Alberta.

In my study, I assume that post-secondary education represents an investment that the individual makes in him- or herself. As with any investment, the investor expects to receive a return on that investment. By calculating the implicit “rates of return” on investment in various types and levels of post-secondary education, and then comparing those rates with the interest rate one could expect to receive on a secure financial investment, I hope to answer the question: “does higher education pay off”?

To understand what is meant by a rate of return on “investment” in education, consider the following simple example. Assume that, at age 20, you were told that if you spent $10,000 on a particular investment, you would be paid $500 per year for the rest of your life. It is easy to see that that investment was equivalent to spending $10,000 on a bond that paid 5% interest (for the rest of your life). Similarly, imagine that if you were to spend $10,000 on education at age 20, that education would result in an increase in your income of $500 per year (for the rest of your life). The purchase of that education could be thought of as yielding an annual rate of return of 5%.

I calculate the rates of return on various types of educational investments and compare those rates of return to the interest rates that one can obtain on secure financial instruments. For the latter purpose, I use a real interest rate of 4.25 percent, (the usual discount rate in personal injury assessments). If the rate of return on a particular level of education is greater than 4.25 percent, then I consider that level of education to be a worthwhile investment for the individual.

When thinking of education as an investment, it is first necessary to identify the “costs” of that investment and the benefits. The cost component of my analysis is composed of tuition fees, the costs of books and supplies, and, most importantly, the income that is “given-up” by choosing to attend school rather than enter the labour force. The benefit component is measured by the increase in income from having one level of education rather than another. For example, the benefit of having a bachelor’s degree over a high school diploma would be measured by the difference in the earnings stream, after the completion of the bachelor’s degree, over the earnings stream of a high school diploma holder.

Before the rates of return were calculated, I examined average incomes by level of education. From my results, the average incomes for males are greater at all levels of education (high school to Ph.D. degrees) than for females. High school resulted in the lowest income, followed by trade school, then college. For the different university degrees available, average income increases with education. The Ph.D. graduates earned the highest incomes, for both males and females.

These results were not a surprise, there have been many studies and articles written about the relationship between education and income. Post-secondary education results in increased average incomes, and the higher the level of education, the higher the average income. Also, the male incomes are higher than the female incomes, another result that is not surprising.

Turning to the rates of return, I found that overall, the highest return on investment (that is the highest benefits relative to the costs) results from university education. (See Table 1.) Among university graduates, bachelor’s degrees resulted in the highest rate of return. (See Table 2.)

Table 1: Private Rates of Return from Post-secondary Education: Alberta

Table 1

Table 2: Private Rates of Return from University Education: Alberta

Table 2

Overall, the graduate degrees do not offer rates of return on investment that are as high as the rates earned on undergraduate degrees, either for males or females. Many programs at the graduate degree level did not meet my 4.25 percent benchmark, implying that the cost of obtaining this level of education is not justified by the return on investment.

The highest returns for males result from the science and technical programs such as commerce, engineering, and science. (See Table 2.) Females, by comparison, receive the highest returns from the commerce, nursing and health programs – again the more technical programs. The same result occurred at the master’s level, where the science and technical programs offered the highest returns for males. For females, commerce offered the highest return. The next highest resulted from the education and social sciences programs – liberal arts programs, rather than the more technical programs. At the Ph.D. level, the humanities program for males and the fine arts program for females result in the highest return on investment. Thus, at the Ph.D. level, the highest returns result from the liberal arts programs, not the science and technical programs that fared well at the undergraduate and master’s levels.

Females generally receive a higher return on investment than males, even though the resulting incomes are lower than for males. The lower average incomes for females means that the amount of income “given up” while attending post-secondary institutes is less for females than for males, making the costs for females lower. Since costs are lower, it does not take as long to recoup the investment.

A surprising result is that the return to college and trade education falls just short of my 4.25 percent benchmark. This implies that an individual considering investing in this level of post-secondary education would receive a higher return on their money by allocating the funds to an alternative investment, or attending university. Although the average incomes do increase with any type of post-secondary education, from an investment perspective, the returns resulting from trade and college education are not as high as from a university education.

My results indicate that individuals planning to “invest” in post-secondary education would receive the highest return from an undergraduate degree, especially the science and technical programs. The increase in income will more than cover the cost of attaining the degree, meaning the investment will “pay-off”. Diploma and certificate programs offered by trades and colleges do increase average incomes, but it is questionable whether or not these “pay off” from an investment perspective based on my assumptions. The average income these graduates receive is higher than income received by high school graduates, but the increase may not cover the entire cost of acquiring the education.

leaf

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