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Analytical conceptsCurrent dollars versus constant dollars Current dollars versus constant dollars“Current dollars” are what we usually mean when we refer to a currency in the current time period. The term “constant dollars” refers to dollars of several years expressed in terms of their value (“purchasing power”) in a single year, called the base year. This type of adjustment is done to eliminate the impact of widespread price changes. Current dollars are converted to constant dollars using an index of price movements. The most widely used index for household or family incomes, provided that no specific uses of the income are identified, is the Consumer Price Index (CPI), which reflects average spending patterns by consumers in Canada. The following table shows the annual rates of the Consumer Price Index. To convert current dollars of any year to constant dollars, divide them by the index of that year and multiply them by the index of the base year you have chosen (remember that the numerator contains the index value of the year you want to move to). For example, using this index, $10,000 in 1997 would be $10,548 in 2000 constant dollars ($10,000 × 113.5/107.6 = $10,548). Table C

1980  52.4  1988  84.8  1996  105.9 
1981  58.9  1989  89.0  1997  107.6 
1982  65.3  1990  93.5  1998  108.6 
1983  69.1  1991  99.5  1999  110.5 
1984  72.1  1992  100.0  2000  113.5 
1985  75.0  1993  101.8  2001  116.4 
1986  78.1  1994  102.0  2002  119.0 
1987  81.5  1995  104.2 
An earner is a person who received income from employment (wages and salaries) and/or selfemployment during the reference year. The term income recipient is generally used for someone who received a positive (or negative) amount of income of any given type.
The mean or average income is computed as the total or “aggregate” income divided by the number of units in the population. It offers a convenient way of tracking aggregate income while adjusting for changes in the size of the population.
There are two drawbacks to using average income for analysis. First, since everyone’s income is counted, the mean is sensitive to extreme values: unusually high income values will have a large impact on the estimate of the mean income, while unusually low ones, i.e. highly negative values, will drive it down. (See also Recipients versus nonrecipients and Negative values.) Secondly, it does not give any insight into the allocation of income across members of the population. For this, measures such as percentiles or Gini coefficients may be used.
For every table showing average incomes, it must be kept in mind whether nonrecipients of that type of income are included or excluded from the population. In the case of total family income, the difference of including or excluding units with zero income is small since there are very few such families. However, if one is interested in the average amount of individual selfemployment earnings, the value will be quite different if one includes those persons who were not selfemployed. Zero values are included in all tables focusing on the three main income concepts (market, total and aftertax income), government transfers and taxes. Zero values are excluded in table T402.
Negative income amounts can arise in two ways: net losses from selfemployment (expenses exceed receipts), or net investment losses (losses exceed gains). As with zero values, negative values can have a large impact on results. In general, the published income tables treat negative values no differently than positive values, but there are a few exceptions: for the calculation of both Gini coefficients and the low income gap, negative values are converted to zeroes; and in the derivation of the major income earner of a family or household, the absolute value is used instead (see “Major income earner” under “Family definitions”).
Income percentiles like quintiles and deciles are a convenient way of categorizing units of a given population from lowest income to highest income for the purposes of drawing conclusions about the relative situation of people at either end or in the middle of the scale. Rather than using fixed income ranges, as in a typical distribution of income, it is the fraction of each population group that is fixed.
First, all the units of the population, whether individuals or families, are ranked from lowest to highest by the value of their income of a specified type, such as aftertax income. Then the ranked population is divided into five groups of equal numbers of units, called quintiles. Analogously, dividing the population ranked by income into ten groups, each comprising the same number of units, produces deciles.
Most analyses should be carried out on the people of different percentiles within one population distribution. Care should be taken in making comparisons between percentiles that resulted from different distributions, because any difference in either the population or the income concept used to rank units could have a large effect. It is probable that both the income ranges represented by each percentile and the people making up each percentile will be different.
The median income is the value for which half of the units in the population has lower incomes and half has higher incomes. To derive the median value of income, units are ranked from lowest to highest according to their income and then separated into two equalsized groups. The value that separates these groups is the median income (50th percentile).
Because the median corresponds exactly to the midpoint of the income distribution, it is not, contrary to the mean, affected by extreme income values. This is a useful feature of the median, as it allows one to abstract from unusually high values held by relatively few people.
Since income distributions are typically skewed to the left – that is, concentrated at the low end of the scale – median income is usually lower than mean income.
The implicit rate of either transfers or taxes, as the case may be, is a way of showing the relative importance of transfers received or taxes paid for different families or individuals. This concept is similar, but not identical, to the effective rate of taxation. For a given individual or family, the effective rate is the amount of transfers/taxes expressed as a percentage of their income, usually market income, total income, or aftertax income. The implicit rate for a given population is the average (or aggregate) amount of transfers/taxes expressed as a percentage of their average (or aggregate) income.
When comparing family incomes to study such things as income adequacy or socioeconomic status, one often wants to take the family size into account. Basically stated, the income amount itself is not sufficient to understand a family’s financial wellbeing without knowing how many people are sharing it. Two approaches have been used to help with the analysis of family income. One is to produce data by detailed family types, so that within a given family type, differences in family size are not significant. In fact, many income measures have been crossed by detailed family types in the published tables.
The other way to take into account family size is to adjust the income amount, for the purposes of analysis only. The major challenge of this approach is to select an appropriate adjustment factor. While there is no single best method, it is still better to apply some kind of adjustment factor rather than no adjustment at all.
The simplest method is to use per capita income, that is, to divide the family income by the family size. A limitation of per capita income, however, is that it tends to underestimate economic wellbeing for larger families as compared to smaller families. This is due to the fact that it assumes equal living costs for each member of the family, but some costs, primarily those related to shelter, decrease proportionately with family size (they may also be lower for children than for adults). For example, the shelter costs for an adult married couple with no children are arguably not much more than those for an adult living alone.
To take such economies of scale into account, it is common to use an “equivalence scale” to adjust family incomes. Instead of implicitly assuming equal costs for additional family members as the per capita approach does, the equivalence scale is a set of decreasing factors assigned to the first member, the second member, and so on. Dividing the income value by the sum of the factors assigned to each member derives the adjusted income amount for the family.
There is no single equivalence scale in use in Canada. The one used in the published income tables and in concepts such as the Low Income Measure (LIM) has, however, achieved a high degree of acceptance. In this equivalence scale, the factors are as follows:
The Gini coefficient measures the degree of inequality in an income distribution. Gini coefficients are published for a variety of income concepts such as market income, total income and aftertax income, and are used to compare the uniformity of income allocation between different income concepts across different populations or within the same population over time.
Values of the Gini coefficient can range from 0 to 1. A value of zero indicates income is equally divided among the population with all units receiving exactly the same amount of income. At the opposite extreme, a Gini coefficient of 1 denotes a perfectly unequal distribution where one unit possesses all of the income in the economy. A decrease in the value of the Gini coefficient can, by and large, be interpreted as reflecting a decrease in inequality, and vice versa. As a rough rule of thumb when using data from SLID or SCF at the Canada level, a difference of 0.01 or more between two Gini coefficients is considered statistically significant.
