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Earnings Variability and Earnings Instability of Women and Men in Canada: How Do the 1990s Compare to the 1980s? Earnings Variability and Earnings Instability of Women and Men in Canada

CHARLES M. BEACH Queen’s University Kingston, Ontario

ROSS FINNIE Queen’s University and Statistics Canada

DAVID GRAY University of Ottawa Ottawa, Ontario

Cet article utilise les données de la collection LAD pour examiner comment la variabilité des salaires des travailleurs et l’instabilité salariale se sont modifiées au cours des périodes 1982–89 et 1990–97 au Canada. En adoptant la méthodologie de Gottschalk et Moffitt (1994), nous décomposons l’ensemble des variations des salaires des travailleurs en une variation permanente et une composante transitoire (ou instabilité des salaires). Nous constatons que : (1) il y a eu un accroissement de la variabilité des salaires dans leur ensemble, chez les travailleurs canadiens, entre les dates susmentionnées et que l’accroissement est beaucoup plus marqué chez les hommes, en particulier ceux dont l’emploi est intermittent; (2) cet accroissement de la variabilité des salaires est dû en grande partie à l’élargissement des différences entre les salaires «permanents» pour l’ensemble des travailleurs et non aux changements transitoires concernant les salaires individuels; (3) la variabilité des salaires des hommes tend à suivre une courbe en forme de U suivant les âges, tandis que pour les femmes on a un schéma beaucoup plus plat et, pour les deux sexes, l’instabilité des salaires est forte chez les jeunes et décroît avec l’âge. This paper uses LAD panel data to investigate how variability of workers’ earnings and earnings instability for Canada changed between 1982–89 and 1990–97. Following the methodology of Gottschalk and Moffitt (1994), we decompose the total variation of workers’ earnings into permanent variation and a transitory component (or earnings instability). It is found that: (i) there has been an increase in overall earnings variability among Canadian workers between the two sub-periods, with the increase much more marked among men, particularly with non-continuous labour market attachment; (ii) the greatest part of this increase in earnings variability was driven by widening “permanent” earnings differentials across workers and not by transitory movements in individuals’ earnings; and (iii) men’s earnings variability tends to be U-shaped across ages, whereas it is much flatter for women, and for both sexes earnings instability is high for young workers and decreases with age.

CANADIAN PUBLIC P OLICY – ANALYSE DE POLITIQUES,

VOL . XXIX, SUPPLEMENT/ NUM ÉRO SP ÉCIAL 2003

S42 Charles M. Beach, Ross Finnie and David Gray INTRODUCTION

I

n the 1990s, Canada’s labour market was characterized by a number of major changes in relation to the previous decade. A greater integration of the Canadian and United States economies (with increased north-south trade flows), increased globalization writ more large, rapid technological change, shifting modes and organization of production, demographic shifts (such as immigration activity), and structural changes to employment contributed to significant workplace and labour market changes. The Canadian economy also recovered much more slowly from the recession of the early 1990s than from the recession of the early 1980s, as unemployment rates remained at significantly higher levels in the 1990s. Meanwhile, Canadian governments adopted austere fiscal policies that involved reduced expenditures related to income support and other social programs, in efforts to rein in their burgeoning deficits.

Many of these developments might be expected to have an impact on the distribution of labour market earnings across individuals. Indeed, crosssectional analyses have shown that earnings inequality and polarization increased significantly in Canada in the 1990s.1 The advent of longitudinal data also made it possible to move beyond crosssectional variables and comparisons in order to investigate earnings dynamics for individuals over time. Recent work has found, for example, significant declines in the degree of upward earnings mobility observed in the 1990s compared to the 1980s, particularly in the case of men.2 The topic of earnings dynamics has other facets besides mobility per se, and one important set of questions relates to the degree of variability of an individual’s earnings over time. It seems to be generally assumed that earnings variability has increased, but the extent of this phenomenon is unknown. In this context, the focus of this paper is in the spirit of earlier research by Gottschalk and Moffitt (1994), which is based on the distinction between CANADIAN PUBLIC P OLICY – ANALYSE DE POLITIQUES,

permanent earnings variation for an individual that is associated with factors such as human capital investments or other persistent worker attributes and transitory earnings variation (or earnings instability) for a given individual from one year to another. We carry out an analysis, based on Canadian data, whose methodology is borrowed from Gottschalk and Moffitt (1994). The basic empirical approach is to measure the total variation in earnings among individuals, and then to decompose this quantity into its systematic, lifetime, or long-term component versus its transitory, year-to year component, and then measure how the total and the two component parts evolved from the 1980s to the 1990s. We examine these patterns for the aggregate labour force and also along the dimensions of gender and age group. Gottschalk and Moffitt (1994) found that both a growing instability of earnings (i.e., the transitory component) and a widening dispersion of permanent earnings contributed to the widening degree of wage inequality which occurred between the late 1970s and the 1980s, although the latter element was much larger. Using a different methodology, Haider (1998) found that earnings instability increased during the 1970s, and lifetime earnings variation (i.e., the permanent component) increased substantially during the early 1980s among US males. This paper investigates these phenomena for Canada for a more recent period covering the 1980s and 1990s. The topic is of interest for several reasons. First, in light of all of the forces that have changed the functioning of labour markets and the structure of earnings, it is useful to know from a purely descriptive perspective how overall earnings variation has changed over time by applying precise empirical measurement procedures. Second, it is important to know to what extent any such increase in earnings variability is “permanent” and thus related to lifetime income patterns. Such patterns are tied to the distribution of Canadians’ “life chances” to succeed in the labour market, and most observers would prefer that opportunities have not been widening. Third, a finding of increased earnings instability, stemming VOL. XXIX , SUPPLEMENT/ NUM ÉRO SP ÉCIAL 2003

Earnings Variability and Earnings Instability of Women and Men in Canada from factors such as increased job turnover (as found by Green and Riddell 1997), a widening distribution in the wage distribution offered by employers, modified remuneration schemes, or weaker employment-protection regulations, would indicate an increase in economic insecurity of the type measured by Osberg and Sharpe (2000, 2002) and others in the recent literature on income distribution. As Haider states, when individuals lack perfect foresight and are subjected to borrowing and liquidity constraints, “increases in earnings stability reduce welfare because earnings instability hinders the individual’s ability to smooth consumption” (1998, p. 4). The next section of this paper contains a brief survey of the relevant literature. The following section then sets out the analytical framework and introduces the dataset employed. The fourth section outlines the main characteristics of the estimation samples and provides some descriptive findings. The major empirical results then appear in sections five (for men and women) and six (broken down by age group). The major findings are reviewed, and some implications are considered in the concluding section.

SURVEY OF THE LITERATURE The Canadian literature on earnings instability is fairly sparse, largely due to an historical lack of longitudinal data following given individuals over time that are required for this type of analysis. Consequently, the only existing work is based on administrative data files of one sort or another.3 In an early piece, Kennedy (1989) uses a single and very specific cohort of earners drawn from Canadian Pension Plan files to test Gibrat’s “law of proportional effect,” employing a formal modelling approach to essentially test the variability in individuals’ earnings around individual-specific age-earnings profiles. More recently, Morissette and Berubé (1996) employ data merged from the Canada Customs and Revenue Agency’s T-1 tax forms (filed by individuals) and T-4 Supplementary Tax Files

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(submitted by employers) in order to focus on two specific issues: (i) the incidence and duration of spells of low earnings, and (ii) comparisons of inequality measures using earnings averaged over longer periods of time versus annual measures. Their analysis covers the period 1975–94, and only men aged 25–58 are included in the analysis. Baker and Solon (1999) is the closest Canadian work to that undertaken in this paper. They employ the same tax-based dataset as Morissette and Berubé (1996), in this case covering the period from 1976 to 1992 and including only workers having positive earnings for at least nine consecutive years over this period. They use a structured, parametric, time-series econometric methodology that is based on selected cohorts within their estimating sample. This approach allows them to discern total earnings variation and to decompose it into its permanent and transitory parts. Despite its similar orientation and objective, the present paper differs from the Baker-Solon piece in several important ways. First, following the Gottschalk-Moffitt piece described below, the model employed here is non-parametric and relatively simple in its specification of temporal earnings changes. Second, we only require workers to have at least two years of reported earnings over the period to enter the analysis, thus including workers with weaker attachment to the labour force. There are also a number of differences in the sampling processes, such as how workers who change their Social Insurance Numbers (on the order of 4 percent per year) are handled; in our data file, they are tracked across that change, whereas such individuals are lost from the T-4 file. In addition, our data file includes information on self-employment income and postsecondary student status, which is used in the selection of the working samples, as discussed below. Our analysis includes both genders, and our dataset covers a later period, specifically 1982–97. In short, while the underlying statistical approach employed here is much simpler than those employed in the Baker-Solon piece, the estimating samples are broader and more recent.



S44 Charles M. Beach, Ross Finnie and David Gray ANALYTICAL FRAMEWORK AND DATA SAMPLES Analytical Framework The analytical framework developed by Gottschalk and Moffitt (1994, p. 254) and used here involves a variance decomposition methodology that exploits the longitudinal nature of the available data.4 The first step involves the calculation of the measure of global variation that is to be decomposed. Consider the following variables: yit = log earnings for person i in year t, which is the unit of observation Ti = number of years of earnings data observed for person i, i = 1, ..., N and K = ∑ N i=1 T i = N• T, where an over-bar indicates a sample average. T is thus the average number of years of earnings data for the sample of N workers. It follows that  1 yi =    Ti 

∑

Ti

y t =1 it

is average (log) earnings over

the earnings-reported years of worker i, and

 1 y=   K

∑ ∑ N

i =1

Ti

y t =1 it

is the global, or overall,

average level of (log) earnings across all workers in the dataset. The measure of total, or global, earnings variation used is then the unbiased variance estimate:  1   VarTotal =  K − 1

∑ ∑ N

Ti

i=1

t =1

( y it − y ) 2 .

(1)

This expression is the variance over all data points yit, that is, individual i’s observed earnings in a given year t. It reflects both the variation in earnings between workers, which can be interpreted as cross-sectional earnings inequality measured over a certain time period, and the variation in earnings across time for a given worker.


This global variance measure appearing in equation (1) serves as the benchmark for the decomposition into the transitory and the permanent components, whose precise equations appear in Beach, Finnie and Gray (2001). The transitory variation, or the earnings instability component, represents the average across workers of the intertemporal variation in (log) earnings. That latter quantity is an estimate of the year-to-year volatility or instability of the (log) earnings for a given worker. The measure of persistent or permanent earnings variance essentially captures the cross-sectional variation in earnings (which have been averaged over time for each individual) among workers in the sample. It can then be shown that the total variance equals the sum of the transitory variance and the permanent variance, thus providing a convenient decomposition of total variance. When calculating these components, actual reported log earnings is replaced by the life-cycle adjusted (log) earnings, which is generated as actual reported (log) earnings adjusted for the normal effects of age on earnings profiles. The purpose of this exercise is to abstract from earnings mobility associated with a life cycle. Beach and Finnie (1998, 2000) have found that a dominant factor driving individuals’ year-to-year changes in earnings is movements along life-cycle trajectories and, therefore, not related to instability per se. These two components of total earnings variance in a purely cross-sectional context are illustrated in Chart 1. Our empirical analysis consists of examining what has happened to total earnings variance and these two component terms between the periods 1982–89 and 1990–97. The event of an increase between two time periods in the permanent component of earnings variation is illustrated in Chart 2.

The Data File and the Estimation Samples The data file is Statistics Canada Longitudinal Administrative Database (LAD), which is a 10 percent representative sample of all Canadian income-tax

VOL. XXIX , SUPPLEMENT/ NUM ÉRO SP ÉCIAL 2003

Earnings Variability and Earnings Instability of Women and Men in Canada

CHART 1 Illustration of Permanent versus Transitory Variance Components

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filers drawn from Revenue Canada T-1 tax files containing over 1.5 million records per year. The measure of earnings used in the paper is total wage and salary income (henceforth “earnings”) as reported on individuals’ T-1 tax forms.

EARNINGS

Earnings Profile for High-Skill Worker Variance between Earnings Profiles (permanent component) Earnings Profile for Low-Skill Worker Variance about Earnings Profile (transitory component)

AGE

The estimation samples used in this analysis include all paid workers aged 20 to 64 who were not full-time students during the earnings year, who received at least $1,000 (in 1997 constant dollars) of wage and salary income, whose wage and salary income (earnings) exceeded any net self-employment income, and who reported at least two years of above-minimum earnings (as just defined) in the LAD file. The intention of these omissions is to approximate Statistics Canada’s concept of “All Paid Workers” while excluding those with only limited attachment to the labour market.5 The period covered by the study is 1982 to 1997, but we identify separate estimation samples for the two sub-periods in order to make comparisons. The broad estimation sample (or BES) includes any

CHART 2 Illustration of Increase in Permanent Component of Earnings Variance: 1982–89 versus 1990–97 (B) 1990–1997

(A) 1982–1989 EARNINGS

EARNINGS High Profile

High Profile

Low Profile Low Profile

AGE


AGE


S46 Charles M. Beach, Ross Finnie and David Gray worker-year record that satisfies the inclusion criteria that 2 ≤ Ti ≤ 8. The narrow estimation sample (or NES) is a subsample of the BES in which persons report above-minimum earnings in each year of the relevant sub-period (i.e., Ti = 8). There are thus four separate estimation samples for each gender: BES and NES for the periods 1982–89 and 1990–97. The coverage of the LAD and its degree of representativeness of the general population are discussed in the Appendix. The numbers of records in the full LAD file and the effects of the various exclusion criteria for the final BES and NES estimation samples for each of the sample years 1982–97 are listed in Table A1 of Beach, Finnie and Gray (2001). That working paper contains many of the technical details for this project. The most numerous exclusions are for those over age 64, the self-employed (most of whom had very low earnings), and non-continuous participants in the labour market. The resulting broad estimation sample includes 1.069 million observations in 1997, or 50.3 percent of the full LAD file in that year, while the narrow estimation sample includes 595,600 observations, or 55.7 percent of the BES sample and 26.7 percent of the full LAD file. The BES sample varies from 924,000 observations in 1982 to the 1,069,000 observations for the 1997 year just noted. The NES sample has 538,900 records for the first sub-period (1982–89) and 595,600 in the second sub-period (1990–97). By construction, the number of individuals is constant for each year within these two sub-intervals. There is clearly a substantial difference between the BES and NES samples, indicating that about half of the former are non-continuous labour market participants (including those who age out of the sample). The BES sample might thus be expected to show more marked business-cycle variations than the NES sample, as labour force participation rates are quite pro-cyclical. The first sub-period (1982–89) begins near the end of the sharp 1980–81 recession and then includes the (full) subsequent expansion, which lasted through 1989, whereas the second period (1990–97) CANADIAN PUBLIC P OLICY – ANALYSE DE POLITIQUES,

includes the complete 1990–92 recession, but not the entire growth period which followed. Our principal results do not, therefore, represent comparisons between two sub-periods that reflect identical business cycles; they reflect the observed differences between an earlier time period and a more recent one. Near the end of the paper, we check the robustness of our findings by presenting results from two intervals covering more similar business-cycle conditions, and our results turn out to be not sensitive to the choice of intervals. The estimation samples of this paper also involve breakdowns by age crossed with gender. The eight resultant groups are Entry (age 20–24), Younger (age 25–34), Prime (age 25–54), and Older (age 55–64). This allows us to examine earnings variability patterns over different phases of the life cycle. For 1997, the age breakdown in the BES is as follows (in thousands of observations).

SUMMARY TABLE 1 Number of Observations in the Broad Estimating Sample, 1997 (thousands of observations)

Entry (20–24) Younger (25–34) Prime (35–54) Older (55–64)

Women

Men

32.1 (6.4%) 137.2 (27.4%) 285.1 (57.0%) 45.9 (9.2%)

38.8 (6.8%) 152.0 (26.7%) 315.7 (55.5%) 62.2 (10.9%)

The full set of subsample sizes are provided in Table A1 and reflect the demographic shifts and labour market participation trends that occurred over this period. In particular, there is a diminishing number of younger workers and an increase in the number of women. The NES subsamples are smaller, and these patterns also reflect individuals’ movements across age groups over the relevant sample period. In particular, individuals exit the Entry groups and enter the Younger groups as they age, and a similar dynamic operates across the entire age spectrum. VOL. XXIX , SUPPLEMENT/ NUM ÉRO SP ÉCIAL 2003


MEDIAN EARNINGS AND EARNINGS INEQUALITY Before proceeding to the earnings variability decomposition exercise, we summarize the trends in

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earnings inequality that are discerned from our dataset. The median level of real earnings are listed in Table 1. As mentioned above, all annual figures for earnings are expressed in constant 1997 dollars. They indicate that women have lower earnings than

TABLE 1 Median Real Earnings by Sex and Age Group, 1982–1997

Men Year

All

All

Women

Entry

Young

Prime

Older

All

Entry

Young

Prime

Older

18.4 15.8 15.6 15.8 16.0 16.9 18.0 19.9 16.5 14.0 13.0 12.7 13.0 13.0 12.9 15.6

34.3 33.2 33.2 33.0 32.4 32.4 32.7 33.1 32.1 30.3 30.1 29.5 29.7 29.4 29.2 29.8

41.7 41.6 42.3 42.7 42.7 42.9 43.4 43.7 42.7 40.9 41.3 40.7 41.1 40.9 40.5 40.8

37.0 35.1 35.4 35.7 35.7 35.7 35.9 36.1 37.2 33.7 33.5 33.1 33.4 33.5 33.4 34.0

19.3 18.7 18.6 18.7 18.9 19.3 20.0 21.3 21.1 20.4 21.0 21.0 21.4 21.3 21.3 22.2

15.2 13.5 12.8 12.6 12.7 13.1 13.9 15.5 13.2 11.7 11.0 10.4 10.1 9.8 9.5 11.4

21.1 20.7 20.5 20.1 20.0 20.1 20.5 21.1 21.0 20.2 20.6 20.5 20.6 20.3 20.1 20.5

20.4 20.4 20.8 21.1 21.4 21.9 22.6 23.5 23.8 23.4 24.3 24.4 24.9 24.9 24.9 25.2

19.9 18.7 18.5 18.6 18.6 18.4 18.5 18.9 20.6 18.4 18.9 18.9 19.5 19.7 19.7 19.8

21.6 22.0 23.7 24.8 26.1 – – – 20.9 20.3 20.9 21.4 22.8 – – –

37.0 36.6 37.2 37.5 37.2 37.3 38.2 38.4 35.7 34.6 34.8 34.3 34.9 34.7 35.1 35.7

43.5 43.8 44.8 45.5 45.7 46.2 46.6 46.7 45.2 44.1 44.7 44.4 45.3 45.2 44.9 45.0

38.5 39.0 39.9 40.5 40.8 40.7 40.5 38.8 39.8 38.9 39.6 39.1 40.0 39.9 39.6 37.8

22.1 23.2 24.0 24.7 25.3 26.0 26.5 26.6 24.5 25.0 26.1 26.4 27.1 27.3 27.5 27.4

18.0 18.9 19.6 20.1 20.5 – – – 17.6 17.7 18.1 18.0 18.1 – – –

23.9 24.4 24.8 24.9 25.0 25.2 25.6 25.5 24.2 24.1 24.7 24.6 24.8 24.6 24.6 24.7

23.0 23.9 24.7 25.5 26.1 27.0 27.5 28.0 26.4 26.8 27.9 28.1 28.7 28.9 29.0 29.0

22.3 22.6 23.0 23.5 23.7 23.8 23.7 22.6 23.1 23.4 24.0 24.0 24.6 24.5 24.5 22.7

Broad Estimating Sample 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

27.0 26.1 26.1 26.2 26.2 26.6 27.2 28.5 27.8 26.5 26.9 26.7 27.1 27.1 27.0 28.1

35.0 34.1 34.4 34.7 34.6 34.9 35.6 36.8 35.5 33.6 33.8 33.4 33.8 33.8 33.5 34.9

Narrow Estimating Sample 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

30.2 30.9 32.3 33.4 34.2 34.9 35.7 35.7 31.6 31.6 32.7 32.9 34.0 34.4 34.5 34.6

37.8 38.5 40.0 41.1 41.7 42.3 43.1 43.1 39.1 38.8 39.8 40.0 41.2 41.6 41.8 42.0

Note: Figures are in thousands of constant 1997 dollars.



S48 Charles M. Beach, Ross Finnie and David Gray men, but that their relative earnings ratios have been rising over time (from 0.715 to 0.79 in the BES sample and from 0.732 to 0.792 in the NES sample), while median earnings have been declining (in real and relative terms) for younger workers. The earnings gap between men and women has shrunk, as median real earnings for women rose from 71.5 percent of that of men in 1982 to 79 percent in 1997 for the BES, and from 73.2 percent to 79.2 percent for the NES. This trend is consistent with findings of the literature on the male-female pay gap, a survey of which can be found in Benjamin, Gunderson and Riddell (2002, ch. 12). More directly pertinent to our analysis is the fact that the BES means and medians are noticeably lower than the NES figures, and the ratio of the BES earnings to NES earnings has declined between 1982 and 1997 over time (e.g., medians have fallen from 0.87 to 0.81 for women and from 0.93 to 0.83 for men). Median earnings are thus not only higher for full-time, full-year workers, but the earnings of those less attached to the labour force have been declining relative to the full-time, full-year group. This pattern likely reflects the generally rising unemployment rates that occurred over much of this period and the relatively poor performance of the Canadian labour market over much of the 1990s, which may have had a more adverse impact on labour market outcomes for the BES group. Indeed, the BES samples show a decline in median earnings for all men and for each male age group, while the NES sample indicates a rise in median earnings for all men and for prime-age workers, as seen in the accompanying text table. Turning from the median measure to some simple measures of earnings dispersion, the last three columns of Tables 2 (men) and 3 (women) report three measures that are commonly used to measure the dispersion of the distribution of earnings: the variance of the log of earnings, the coefficient of variation, and the difference between the ninetieth and tenth percentiles of the distribution of log earn-


SUMMARY TABLE 2 Percentage Change in Real Median Earnings Between 1982 and 1997, Men and Women

BES (%)

NES (%)

Men All Entry Younger Prime Older

–0.3 –15.2 –13.1 –2.2 –8.1

11.1 n/a –3.5 3.4 –1.8

Women All Entry Younger Prime Older

15.0 –25.0 –2.8 23.5 –0.5

24.0 n/a 3.3 26.1 1.8

ings. They reveal that earnings inequality is much greater in the more heterogeneous BES samples than the corresponding NES samples for both women and men. It is also greater among women than men (except as measured by the coefficient of variation over the 1990s). In terms of evolution over time, earnings inequality has been generally rising for the BES, and especially for men, as shown in Summary Table 3.

SUMMARY TABLE 3 Percentage Change in Earnings Dispersion Measures between 1982 and 1997

Var (ln Y)

C.V.

(%)

(%)

90 th/10th Percentile (%)

Men Women

11.9 3.9

19.8 10.3

5.3 -0.1

NES Men Women

–1.5 –7.9

19.3 7.9

–3.7 –6.6

BES



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TABLE 2 Relative Dispersion Statistics in Real Earnings for Men, 1982–1997 Broad Estimation Sample

Year

1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

10th

Percentile

9,400 7,800 8,000 8,300 8,400 8,900 9,700 10,900 9,700 7,800 7,400 7,300 7,800 7,900 7,800 9,300

90th

Percentile

63,400 62,800 63,400 63,800 64,000 64,500 65,900 67,200 66,600 65,400 66,200 66,100 67,100 67,300 67,500 69,400

Variance of ln(Yit)

Coefficient of Variation

90 Per – 10 Per of ln(Yit)

0.6537 0.7355 0.7303 0.7174 0.7045 0.6812 0.6568 0.6211 0.6635 0.7672 0.8021 0.8096 0.7937 0.7881 0.7995 0.7313

0.6646 0.6919 0.6950 0.6963 0.6985 0.7026 0.7161 0.7043 0.7177 0.7520 0.7563 0.7695 0.7725 0.7812 0.7995 0.7962

1.9088 2.0858 2.0700 2.0395 2.0307 1.9806 1.9160 1.8189 1.9266 2.1264 2.1912 2.2033 2.1521 2.1423 2.1580 2.0099

0.5775 0.5706 0.5554 0.5537 0.5498 0.5613 0.5802 0.5980 0.6271 0.6318 0.6297 0.6342 0.6329 0.6433 0.6604 0.6888

1.5546 1.5189 1.4078 1.3383 1.3003 1.2586 1.2521 1.3347 1.4801 1.5085 1.5252 1.4934 1.4362 1.4226 1.4405 1.4968

Narrow Estimation Sample 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

13,500 14,100 16,100 17,600 18,500 19,600 20,300 18,900 15,500 15,000 15,100 15,700 17,100 17,600 17,500 16,900

63,900 64,400 65,800 67,100 67,900 69,000 71,000 71,800 68,100 67,800 69,400 69,900 71,900 73,000 73,900 75,500

0.4798 0.4585 0.4046 0.3714 0.3571 0.3407 0.3426 0.3978 0.4336 0.4486 0.4562 0.4429 0.4132 0.4093 0.4253 0.4728

Note: The last column contains the difference between the ninetieth percentile log earnings and the tenth percentile log earnings; figures are in constant 1997 dollars.



S50 Charles M. Beach, Ross Finnie and David Gray TABLE 3 Relative Dispersion Statistics in Real Earnings for Women, 1982–1997 Broad Estimating Sample

Year

1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

10th

Percentile

4,600 4,200 4,300 4,300 4,300 4,500 4,800 5,300 5,200 4,600 4,700 4,600 4,800 4,800 4,900 5,500

90th

Percentile

40,000 39,800 40,300 40,500 40,800 41,500 42,300 43,500 44,200 44,100 45,800 46,000 46,600 46,500 46,700 47,700

Variance of ln(Yit)

Coefficient of Variation

90 Per – 10 Per of ln(Yit)

0.7194 0.7629 0.7646 0.7670 0.7686 0.7609 0.7428 0.7104 0.7229 0.7809 0.8034 0.8093 0.8042 0.7989 0.8039 0.7477

0.6850 0.7012 0.7099 0.7186 0.7204 0.7291 0.7300 0.7148 0.7109 0.7343 0.7401 0.7457 0.7457 0.7525 0.7613 0.7554

2.1628 2.2488 2.2377 2.2427 2.2501 2.2216 2.1762 2.1051 2.1401 2.2604 2.2767 2.3026 2.2730 2.2708 2.2545 2.1602

0.6050 0.5808 0.5668 0.5654 0.5607 0.5714 0.5839 0.6018 0.6187 0.6086 0.6091 0.6106 0.6070 0.6147 0.6253 0.6526

1.8308 1.7083 1.6350 1.5960 1.5726 1.5571 1.5539 1.6284 1.7683 1.7026 1.7027 1.6800 1.6455 1.6292 1.6390 1.7105


6,700 7,700 8,500 9,000 9,400 9,800 10,000 9,400 7,900 8,600 9,000 9,300 9,800 10,000 10,000 9,400

41,800 42,500 43,600 44,400 45,300 46,500 47,300 47,900 46,300 47,200 49,400 49,900 50,800 51,000 51,500 52,000

0.5570 0.4974 0.4627 0.4449 0.4326 0.4283 0.4287 0.4856 0.5333 0.4911 0.4903 0.4783 0.4629 0.4571 0.4686 0.5131

Note: See note to Table 2.



Earnings Variability and Earnings Instability of Women and Men in Canada The BES results thus show a greater increase in earnings inequality than the NES figures, suggesting that changes in the degree of labour market attachment are a major part of the observed pattern of earnings inequality increases over the period. This finding might be related to Picot (1997), who notes that the distribution of working hours has played a role in generating changes in the distribution of earnings in the Canadian labour market in the 1990s. Unfortunately, since the LAD file does not contain any information regarding the number of hours worked over the course of each tax year, we cannot probe very deeply into the effects of labour market attachment and length of the work week in generating annual earnings. Summary Table 3 also reveals that for two of the three measures of dispersion, it actually fell slightly within the NES for both genders. These figures also reveal that while earnings dispersion is generally higher among women than is the case among men, this gap is narrowing over this interval.

DECOMPOSITION ANALYSIS: EARNINGS VARIABILITY AND INSTABILITY The first step in our approach to measuring earnings variability in general, and instability in

S51

particular, is the estimation of life-cycle earnings profiles based on log-earnings regressions. The dependent variable is y it , the log earnings for an individual in a given year, and the independent variables consist of a quartic in age for each of the four estimation samples (males BES and NES, females BES and NES). Results are provided in Beach, Finnie and Gray (2001). Estimates of total earnings dispersion (or variability) for the different estimating samples based on the approach explained earlier appear in columns (1) and (4) of Table 4. These calculations differ from the figures presented for earnings variability in the previous section, as the latter entail only comparisons of the variation in raw earnings observed in 1982 to that observed in 1997. From this point on, all of the observations for raw earnings have been adjusted for life-cycle effects. In this exercise all observations between 1982–89 and 1990–97 are included in the calculations, and an average value for the entire earlier period is compared to an average value for the entire later period. Measured earnings variability is higher for women than men, but this differential fell by about half between the two sub-periods. For instance, within the BES, the male-female (arithmetic)

TABLE 4 Decomposition of Earnings Variability, 1982–89 versus 1990–97

1982–1989 Sample

Total Transitory Variance Variance

Men, BES Men, NES Women, BES Women, NES

0.5832 0.3568 0.7231 0.4619

0.2434 0.1174 0.3003 0.1563

1990–1997 Permanent Variance 0.4579 0.2394 0.5355 0.3056

Total Transitory Variance Variance 0.6604 0.4006 0.7342 0.4740

0.2681 0.1142 0.2810 0.1360

Percentage Change Between Periods Permanent Variance 0.5351 0.2864 0.5786 0.3380

Total Transitory Variance Variance 13.25 12.29 1.53 2.61

10.13 –2.75 –6.42 –13.00

Permanent Variance 16.87 19.66 8.06 10.60

Notes: BES = broad estimation sample. NES = narrow estimation sample. The decomposition is exact in the case of the NES, but not in the case of the BES.



S52 Charles M. Beach, Ross Finnie and David Gray difference in the total variation in earnings was 0.723 – 0.583 = 0.14 for the earlier period and 0.734 – 0.660 = 0.074 for the later period. The gender differentials in total variation are listed in the top panel of Summary Table 4, and are produced from the figures listed in columns (1), (4), and (7) of Table 4.

SUMMARY TABLE 4 Female/Male Relative Earnings Dispersion (Variation) Differentials

BES NES

1982–89

1990–97

0.24 0.29

0.11 0.18

BES/NES Relative Earnings Dispersion (Variation) Differentials

Men Women

1982–89

1990–97

0.64 0.57

0.65 0.55

Percentage Change in Total Variance: 1982–89 versus 1990–97

BES NES

Men (%)

Women (%)

+13.2 +12.3

+1.5 +2.6

In the middle panel of Summary Table 4, the total variation within the BES sample and the NES sample is compared. The dispersion within the BES is greater than the dispersion for the more homogeneous NES sample of continually employed workers. Interestingly, the male-female ratios of the earnings dispersions between the BES and the NES are similar in the two periods.


In regards to the evolution between these two periods, the overall variation of earnings increased significantly from the 1982–89 period to the 1990– 97 period for men, rising 13.2 percent in the BES and 12.3 percent in the NES, as shown in the bottom panel of Summary Table 4. In contrast, the increases were much more marginal for women: 1.5 and 2.6 percent, respectively. Was this increased variation, however, due to increased volatility from one year to another around individuals’ given lifecycle profiles (i.e., the transitory element), or due to a widening of the permanent component reflecting persistent earnings differences among workers? Our decomposition technique addresses this question. Calculations of the permanent and transitory differences appear in columns (2), (3), (5), and (6) of Table 4. Expressing these components relative to their sum (which is exactly equal to total variance in the NES sample, but only approximately so in the BES sample) is a straightforward measure of their relative importance.6 The percentages sum to 100 for each of the eight subsamples. The primary results from the decomposition exercise are listed in Summary Table 5. As indicated in the top panel, overall, the transitory component accounts for approximately one-third of the total variation in earnings in each period, while the permanent component accounts for the other two-thirds. That is, persistent differentials in earnings across workers (i.e., the “between” variation) are about twice as important in magnitude as the volatility of earnings about individuals’ given life-cycle profiles (i.e., the “within” variation), with the permanent component being slightly more important for the NES samples than for the more heterogeneous BES samples. The relative size of the transitory component declined across the sample periods, while the permanent component increased. This is true for both men and women, although the pattern is more apparent in the NES samples.



S53

SUMMARY TABLE 5 Relative Variation Components: Transitory versus Permanent

1982–89

Men Women

BES NES BES NES

1990–97

Transitory (%)

Permanent (%)

Transitory (%)

Permanent (%)

34.7 32.9 35.9 33.8

65.3 67.1 64.1 66.2

33.4 28.5 32.7 28.7

66.6 71.5 67.3 71.3

Change in Variation Components (multiplied by 10,000), 1982–89 versus 1990–97

Men Women

BES NES BES NES

Sum

Transitory

Permanent

1,019 438 238 121

247 –32 –193 –203

772 470 431 324

Note: The shares in the upper panel are normalized to equal unity in the case of the BES.

To provide an alternative perspective, columns (2), (3), (5), and (6) of Table 4 and the bottom panel of Summary Table 5 show how the total variation in earnings and its two components changed between the two sub-periods. These figures are in terms of levels, and the units are not meaningful. They are thus multiplied by 10,000 in Summary Table 5 in order to improve the presentation. The signs and the relative magnitudes are of interest. The permanent component rose substantially in all four samples, especially for men, while the transitory variance component decreased in all cases except the BES for men. In three of the four cases, then, increases in the permanent component entirely drove the increases in overall earnings variation between the two time periods, and changes in the transitory component actually acted to decrease overall earnings dispersion. Only in the case of BES men did the transitory component also contribute to the increased overall earnings variation. In short,

widening life-cycle differentials appear to have been largely responsible for the rising earnings dispersion observed from the 1980s to the 1990s.

EARNINGS VARIABILITY DECOMPOSITION BY AGE GROUP Earnings variability results by age group are provided in Table 5 for men and Table 6 for women. The principal findings are listed in the Summary Tables 6 and 7 and in Figures 1 and 2 (for the NES). Each of the columns in Summary Table 6 gives the breakdown of the total variation into the two components, and these relative shares sum to 100. The first point to note from the diagrams is that the profile for the total variation is U-shaped across age groups for men (i.e., as one moves from younger groups to older ones), but flat and then rising for women. Another interesting finding is that, with the



S54 Charles M. Beach, Ross Finnie and David Gray TABLE 5 Decomposition of Earnings Variability, 1982–89 versus 1990–97 (by age group for men)

1982–1989 Sample


1990–1997 Permanent Variance


Percentage Change Between Periods Permanent Variance


Permanent Variance

BES Entry Younger Prime Older

0.6435 0.5634 0.5393 0.7295

0.2752 0.2040 0.1703 0.2739

0.3972 0.4252 0.4635 0.5452

0.6846 0.6294 0.6316 0.8649

0.2874 0.2283 0.1999 0.3241

0.4245 0.4822 0.5534 0.6237

6.39 11.72 17.11 18.55

4.41 11.91 17.35 18.31

6.87 13.40 19.41 14.39

NES Entry Younger Prime Older

0.5222 0.3467 0.3314 0.4558

0.1991 0.1100 0.0834 0.1321

0.3231 0.2367 0.2480 0.3237

0.4929 0.3754 0.3872 0.5843

0.1829 0.1084 0.0882 0.1539

0.3100 0.2670 0.2990 0.4304

–5.61 8.28 16.84 28.19

–8.15 –1.37 5.84 16.44

–4.04 12.82 20.59 32.98


TABLE 6 Decomposition of Earnings Variability, 1982–89 versus 1990–97 (by age group for women)

1982–1989 Sample


1990–1997 Permanent Variance


Percentage Change Between Periods Permanent Variance


Permanent Variance

BES Entry Younger Prime Older

0.6265 0.7477 0.7345 0.7420

0.2816 0.2848 0.2361 0.2189

0.3728 0.5386 0.6029 0.6004

0.6468 0.7392 0.7350 0.8055

0.2854 0.2763 0.2165 0.2474

0.3812 0.5424 0.6340 0.6329

3.24 –1.13 0.72 8.56

1.34 –2.98 –8.33 13.02

2.25 0.71 5.16 5.42

NES Entry Younger Prime Older

0.4503 0.4689 0.4602 0.4907

0.1739 0.1608 0.1168 0.1117

0.2764 0.3081 0.3434 0.3790

0.4596 0.4784 0.4717 0.5483

0.1798 0.1568 0.1037 0.1234

0.2798 0.3216 0.3680 0.4249

2.06 2.03 2.50 11.74

3.35 –2.45 –11.26 10.52

1.22 4.39 7.15 12.13





S55

FIGURE 1 Total and Permanent Earning Variances by Age Group of NES Workers for Men 0.6 90–97 Total

0.5 82–89 Total 0.4

90–97 Permanent

0.3

82–89 Permanent

0.2

0.1 Entry 20–24

Younger 25–34

Prime 35–54

Older 55–64

Age Group

FIGURE 2 Total and Permanent Earning Variances by Age Group of NES Workers for Women 0.6 90–97 Total 0.5

82–89 Total

90–97 Permanent

0.4

82–89 Permanent 0.3

0.2

0.1 Entry 20–24

Younger 25–34

Prime 35–54

Older 55–64

Age Group



S56 Charles M. Beach, Ross Finnie and David Gray SUMMARY TABLE 6 Magnitudes of the Permanent Component of Earnings Variation Relative to the Transitory Component by Age/Sex Group: 1982–89 versus 1990–97

Men

Women

1982–89

1990–97

1982–89

1990–97

Transitory Permanent (%) (%)

Transitory Permanent (%) (%)

Transitory (%)

Permanent (%)

Transitory (%)

Permanent (%)

BES Entry Younger Prime Older All

40.9 32.4 26.9 33.4 34.7

59.1 67.6 73.1 66.6 65.3

40.4 32.1 26.5 34.2 33.4

59.6 67.9 73.5 65.8 66.6

43.0 34.6 28.1 26.7 35.9

57.0 65.4 71.9 73.3 64.1

42.8 33.7 25.5 28.1 32.7

57.2 66.3 74.5 71.9 67.3

NES Entry Younger Prime Older All

38.1 31.7 25.2 29.0 32.9

61.9 68.3 74.8 71.0 67.1

37.1 28.9 22.8 26.3 28.5

62.9 71.1 77.2 73.7 71.5

38.6 34.3 25.4 22.8 33.8

61.4 65.7 74.6 77.2 66.2

39.1 32.8 22.0 22.5 28.7

60.9 67.2 78.0 77.5 71.3

Note: The shares in the upper panel are normalized to equal unity in the case of the BES.

exception of older workers, for each sample period the relative importance of the permanent component generally increases with age (while the transitory component therefore necessarily declines), a pattern that is broadly consistent with the life-cycle human capital model of wage determination and wage growth. Summary Table 7 shows the changes in earnings variability expressed in levels that occurred between these two time periods. The figures are multiplied by 10,000 for the sake of a neater presentation. Total earnings variability generally increased between the two time periods, as indicated by the first column in each panel. Consistent with the overall results by sex presented in the preceding section, the increase was quite large for men, but with the exception of the older women group, only relatively minor for women. These trends combine to generate a narrowing of the total earnings variability gap between men and women. For both genders, the inCANADIAN PUBLIC P OLICY – ANALYSE DE POLITIQUES,

crease in total earnings variability rose for most age groups, however. In the case of males, the total variability increases with age, while in the case of females, the age effect on total variability does not apply for the two younger groups. In the second and third columns of each panel, the figures for the permanent component and the transitory component are listed. Again consistent with the overall findings by sex presented above, the increases in earnings variability were driven primarily by increases in the permanent component rather than the transitory component. This finding applies especially for men, and particularly for the BES group. The dominance of the permanent component in the overall increase in earnings variability generally rises with age, with the notable exception for the older groups within the BES, for whom the transitory component generally played a more important role in the greater overall increases in earnings variation. VOL. XXIX , SUPPLEMENT/ NUM ÉRO SP ÉCIAL 2003


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SUMMARY TABLE 7 Changes in Levels from 1982–89 to 1990–97 in Total Variation and its Components (Permanent and Transitory) by Age/Sex Group (multiplied by 10,000)

Men

Women

Sum

Transitory

Permanent

Sum

Transitory

Permanent

BES Entry Younger Prime Older All

395 813 1,195 1,287 1,019

122 243 296 502 247

273 570 899 785 742

122 –47 115 610 238

38 – 85 –196 285 –193

84 38 311 325 431

NES Entry Younger Prime Older All

–293 287 558 1,285 438

–162 – 16 48 218 –32

–131 303 510 1,067 470

93 95 115 576 121

59 – 40 –131 117 –203

34 135 246 459 324

These findings, based on the breakdown by age group, suggest that the overall results aggregated by sex (presented in the preceding section) above reflect some important composition effects as well as labour market forces. With the demographic effect of the aging of the active workforce, more individuals have become members of the prime-age and older age groups, which is the stage where the increases in total earnings variation were driven by the increases in the permanent component of earnings variation rather than the transitory component. Within the NES groups, that trend applies to all but the entry level workers. These results disaggregated by age group thus provide a more precise view of the underlying “structure” (i.e., the importance of these composition effects) of the patterns discerned for the pooled male sample and the pooled female sample over time.

ROBUSTNESS OF THE EMPIRICAL RESULTS The two intervals over which our variances are calculated, 1982–89 and 1990–97, have the attractive

feature of symmetry, as they both last for eight years, and they span the entire LAD sample period. On the other hand, they do not reflect identical phases of the business cycle. The early period starts at the trough of the recession of the early 1980s and ends near its peak. The later time period starts at the peak of the expansion of the 1980s, covers the recession of the early 1990s, and ends in the fifth year of an expansion. Given that the macroeconomic conditions were somewhat different during the two time periods, it is possible that the trend effect that we attempt to discern between these two time periods may to some extent be confounded with businesscycle effects. To address this possibility, we conduct a supplementary empirical exercise in order to examine the robustness of our variance calculations to a change in the time intervals. The buffer years of 1989 and 1990 are omitted from our estimating sample, so that the two time periods become 1982–88 and 1991– 97. This omission generates two seven-year intervals, thus preserving the symmetry feature.



S58 Charles M. Beach, Ross Finnie and David Gray Both of these time periods commence near the trough of a business cycle and end six years into an expansion phase. Since we expect any sensitivity in the results to our choice of time interval years to manifest itself the most across regions of Canada (as opposed to age/sex groups), we consider a regional decomposition analysis for this robustness exercise. We also expect this sensitivity to be stronger among the broad estimating sample, with its concentration of marginally attached workers, than among the narrow estimation sample. The results from the re-estimation procedure are presented in Beach, Finnie and Gray (2001). A casual inspection reveals that the changes in earnings variability between the earlier and the later periods (expressed in percentage terms) are slightly larger in the shortened intervals compared to the full intervals of 1982–89 and 1990–97. Nevertheless, the signs and the relative magnitudes of the percentage changes over time are quite robust to the change in intervals over which the variance components are calculated. This pattern is consistent with the conjecture that there is a secular trend of increasing earnings variance over time. As the gap between the two time periods widens (from no gap between adjacent intervals of 1982–89 and 1990–97 to two years), the contrast between their measures of dispersion is enhanced, which suggests that the business cycle is not driving our primary results.

CONCLUSION This study has examined earnings variability in Canada using a methodology developed by Gottschalk and Moffitt (1994) applied to the Statistics Canada Longitudinal Administrative Database (LAD) data file over the period 1982 to 1997. The variation in earnings between individuals and over time is broken into two components; the persistent differences in earnings across individuals which remains after taking into account the general shape of age-earnings profiles (i.e., life-cycle effects), and the year-to-year variation in earnings around indiCANADIAN PUBLIC P OLICY – ANALYSE DE POLITIQUES,

viduals’ particular age-adjusted earnings profiles. A person might have consistently above-average or below-average earnings from one year to another relative to other individuals of the same age, which is the permanent component. The person’s earnings will also typically vary around his or her personspecific profile over time, which is the transitory component. The contribution of this paper is to provide estimates of total earnings variation and its permanent and transitory components for two periods of time, 1982–89 and 1990–97 and to see how these variances have shifted over time for male and female wage earners in Canada. Several major results have been found. First, there was an increase in overall earnings variability among Canadian workers between the two sub-periods, but the rise was much more marked for men than for women. The latter finding of an upward trend for men is in accordance with Baker and Solon (1999), whose analysis extends to 1993. Second, the greatest part of this increase in earnings variability (especially for men) was driven by the permanent component, that is, by a widening dispersion of (life-cycle) earnings differentials across workers. The increased volatility of workers’ earnings about their life-cycle earnings profiles did, however, play a secondary role in the overall increase in men’s earnings variability, while for women this effect was very small or even worked, in the case of some age groups, to reduce overall earnings variability. The third principal finding is that earnings variability has generally been higher for women than men, but, as noted above, increased much more markedly for men over the period, particularly when individuals with non-continuous attachment to the labour market are included. Fourth, men’s overall earnings variability tends to be U-shaped across age groups (going from VOL. XXIX , SUPPLEMENT/ NUM ÉRO SP ÉCIAL 2003

Earnings Variability and Earnings Instability of Women and Men in Canada younger to older), whereas it is much flatter for women. For both sexes, the transitory component is, not surprisingly, high for young workers and decreases with age (except for the oldest group of workers), a finding which is also supported (for men) by Baker and Solon (1999). Conversely, the permanent component rises with age for both men and women as earnings profiles diverge and become more stable. For men, total earnings variability increased over this time period for essentially all age groups, but especially for men with non-continuous attachment to the labour market and older workers. Most of these increases were driven by the permanent component. There were only small increases in the variation of women’s earnings for all of the specific age groups except for the older one, but in all cases the permanent component increased, often substantially, while the transitory component increased to a much lesser degree or even declined. One important policy implication that follows from these results stems from the finding that the rising variability of earnings in the Canadian labour market (particularly among men) has been driven primarily by longer run factors that have been generating widening “permanent” earnings differentials across workers, and not by transitory movements in individuals’ earnings from one year to another. Such a dynamic presumably reflects the widening skill differentials or the associated returns to given skill sets that have already been much discussed in the literature, although it has not been established whether these are due to changes in the levels of, or in the returns to, formal education, to labour market experience, to information technology skills, or to adaptability in shifting labour markets. Regardless of the precise source, the results attest to the importance of ensuring that as many workers as possible have the skills required to succeed in the modern labour market. By augmenting and/or diversifying their human capital, the persistent component of their earnings profiles should increase in magnitude, raising their life-cycle earnings and consequently their well-being. It is desirable not only to invest in younger people in the form of for-

S59

mal schooling, but also to provide for the upgrading of older workers’ skills. As indicated in Aghion and Howitt (2001) and elsewhere, in the framework of endogenous economic growth, it is possible that a significant increase in the average level of human capital in the labour force, coupled with the widening returns to education that have been welldocumented in North America, could have the impact of widening earnings inequality. An increase in the relative supply of skilled workers will accelerate the pace of technological change, thus reinforcing the skill premium. Although some analysts and observers might view the resulting increase in inequality as an undesirable side-effect, it seems clear that most individuals would reap substantial private benefits. Furthermore, effective human resource development policies have the global economic effects of improving productivity and growth. To address the inequality issue, Aghion and Howitt suggest the following caveat to the human resource development policy recommendation. “As to the question of what sort of increase is most likely to favor a reduction in inequality, the analysis suggests that general education, which teaches fundamental analytical and problem solving skills, and which fosters creativity and an open attitude to novel intellectual challenges, may reduce inequality more than education in narrow, technology specific skills” (2001, p. 25). In contrast to policy measures addressing persistent worker attributes, policy measures designed to help workers weather shorter term fluctuations in their earnings, such as unemployment insurance and workers’ compensation, do not perhaps merit the increased attention which they would deserve had there actually been a marked increase in earnings volatility, which some observers believe to have been the case. A secondary implication stems from the finding that earnings variability of both the transitory and permanent natures have increased dramatically among older workers between these two time periods.While public policy has experienced



S60 Charles M. Beach, Ross Finnie and David Gray considerable success in addressing poverty among the elderly over the last three decades and is now turning its attention to policy agendas focused on children and youth, older workers who are still active in the labour market should not be ignored. Further research into the earnings dynamics among older workers is warranted. Certain forms of unemployment assistance targeted to older, displaced workers might be considered.

NOTES This research was made possible by the Applied Research Branch of Human Resources Development Canada. The Small Area and Administrative Data Division of Statistics Canada provided access to the LAD data upon which this study is based. Don McDougall and Roger Sceviour provided excellent computing assistance. The paper also benefited from the comments of the participants of a preconference presentation of the material in this study. We thank Gordon Betcherman and Andrew Sharpe for their constructive reviews. 1For evidence based on various datasets, a non-exhaustive list of references includes Burbidge, Magee and Robb (1997); Beach and Slotsve (1996); Finnie (1997); Freeman and Needles (1993); Gray, Mills and Zandvakili (2002); Morissette and Berube (1996); Morissette, Myles and Picot (1994); Picot (1997); and Richardson (1998). 2See,

for example, Finnie and Gray (1998) and Beach and Finnie (1998, 2000). 3The more recently available SLID is also another rela-

tively recently available longitudinal database, but it has not as yet been used to address the issues covered in this paper. Its window spans from 1993 to 1999 only. 4Another

technical reference can be found on pages 403–05 of Johnston’s (1984) textbook. 5The

LAD employs special procedures to deal with individuals who have changed their SINs (social insurance numbers), who have multiple SINs, and other non-standard cases (see Finnie 2000), which comprise on the order of 4 percent of the file in any given year. Fulltime students are identified with an algorithm based on the tuition and education tax credit responses on T-1 forms.


6It

can be demonstrated mathematically that the decomposition is exact when all individuals have observations for all seven years of the interval. This situation is analogous to a “balanced” panel dataset, and it applies to our NES. In the event that some individuals have observations for only some of the seven years that span both of these estimation intervals (i.e., our BES), it can be shown that the decomposition breaks down, meaning that the sum of the two components does not equal the total variation. In all of our calculations, however, the discrepancy was not of large magnitude in relative terms. No adjustment has been made to normalize the values of the two components so that they sum exactly to the value of the total variation, as presented in Tables 4 to 6.

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S62 Charles M. Beach, Ross Finnie and David Gray APPENDIX THE LAD DATABASE The LAD’s coverage and representativeness of the adult population of paid workers is very good since the rate of tax filing is very high in Canada: middle and high-income recipients are required to do so, while lowincome individuals have incentives to file in order to recover income tax and other payroll tax deductions made throughout the course of the tax year. The full set of tax files from which the LAD is constructed are estimated to cover from 91 to 95 percent of the target adult population (Finnie 1997), which compares favourably to other databases, including the census (noting here that individuals do not have the same incentives to complete their census forms). One consideration to keep in mind is that there may have been an increase in the proportion of individuals filing tax forms over the period studied due to the introduction of the Federal Sales Tax Credit in 1986, the Goods and Services Tax (GST) credit in 1990, and various other federal and provincial benefits. While this improved coverage means that the LAD has become increasingly representative of the underlying adult population, it also poses potential problems for comparisons of earlier and later years, since the “new” filers are perhaps more likely to have low earnings in any given year and possibly more unstable earnings from year to year. To investigate this issue, Beach, Finnie and Gray (2001) examine the number of return filers from 1982 to 1997. It increases fairly steadily each year, driven primarily by a secular increase in the labour force. Just before the GST tax credit was implemented, the number of filers sampled in the LAD file grew 3.3 percent (between 1989 and 1990). Just after the tax credit was implemented in 1991, the number of filers rose by 1.8 percent (between 1990 and 1991). There is, therefore, no excessively abrupt jump in the filing rate that one might attribute to this change in the tax code. Moreover, once the exclusions for young, old, full-time students, and self-employed workers are made, the addition of the low-earnings cut-off has only a very minor effect of between 0.5 to 0.7 of a percentage point of the full LAD file set of records. Such a pattern, furthermore, holds for the entire series of annual observations. There have been slight increases in the proportion of LAD filers excluded from our samples due to low earnings (falling below the 1,000 constant 1997 dollar cut-off) from 0.55 of 1 percent in 1982 to 0.68 of 1 percent by 1997, but the changes are very slight. Between 1985 and 1986, this exclusion rate was virtually unchanged (0.60 to 0.61 of 1 percent), while from 1989 to 1990 it actually fell very slightly (from 0.65 to 0.62 of 1 percent), although this may partly reflect the onset of the 1990–92 recession. In short, the intertemporal comparison problem appears to be quite minor.




S63

TABLE A1 Sample Sizes by Sex and Age Group, 1982–1997

Men Year

All

Entry

Women

Younger

Prime

Older

All

Entry

Younger

Prime

Older

170,440 171,885 175,685 177,070 182,520 183,365 187,130 183,095 183,560 180,775 175,695 171,665 167,595 164,045 160,965 152,045

218,840 224,495 229,840 232,970 242,125 246,290 254,525 260,965 266,470 276,505 283,780 290,320 297,310 305,490 312,935 315,725

58,595 66,870 66,460 66,665 67,170 66,415 66,120 65,610 54,805 63,120 61,875 60,985 60,470 60,250 61,055 62,160

392,415 410,120 423,860 434,360 452,975 462,545 474,920 462,740 481,415 499,010 501,655 502,540 503,320 508,350 516,155 500,315

75,965 75,415 74,610 72,265 70,150 65,990 61,330 43,720 55,275 53,120 50,035 47,100 45,210 44,405 44,770 32,115

132,075 136,885 142,085 146,180 153,495 156,850 161,170 159,120 160,675 161,150 157,825 153,715 149,445 146,130 144,295 137,165

152,550 160,660 169,355 177,395 189,655 199,350 211,485 218,495 229,605 242,520 251,410 258,955 265,675 273,965 282,240 285,125

31,825 37,160 37,805 38,520 39,670 40,360 40,930 41,410 35,860 42,220 42,385 42,770 42,990 43,850 44,850 45,905

114,045 113,840 113,280 111,875 109,785 105,945 94,545 82,715 115,790 111,100 105,135 98,510 91,060 83,385 71,505 59,975

149,260 156,000 162,185 167,670 172,620 178,110 183,195 188,565 173,895 182,600 190,915 198,255 205,385 211,620 216,895 221,015

10,500 14,920 19,930 25,570 31,645 37,685 44,005 50,460 8,185 11,905 16,085 21,050 26,390 32,280 38,880 46,290

217,120 217,120 217,120 217,120 217,120 217,120 217,120 217,120 268,355 268,355 268,355 268,355 268,355 268,355 268,355 268,355

38,795 30,675 22,335 14,280 6,645 – – – 25,485 18,550 12,615 7,700 3,450 – – –

76,615 77,370 78,210 78,800 79,170 78,385 70,810 62,945 94,090 91,080 86,905 82,055 76,475 70,280 60,850 51,625

96,080 101,040 105,690 109,915 113,830 117,750 121,685 125,410 143,410 150,895 157,995 164,290 170,195 175,355 179,800 183,220

5,630 8,035 10,885 14,125 17,475 20,985 24,630 28,765 5,370 7,835 10,840 14,315 18,240 22,725 27,705 33,510

Broad Estimating Sample 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997

531,555 545,130 552,960 555,065 567,390 567,310 574,615 557,680 566,055 578,395 576,325 576,120 578,150 581,895 587,235 568,730

83,680 81,880 80,980 78,355 75,575 71,240 66,840 48,005 61,220 57,995 54,975 53,155 52,775 52,110 52,280 38,800


321,740 321,740 321,740 321,740 321,740 321,740 321,740 321,740 327,280 327,280 327,280 327,280 327,280 327,280 327,280 327,280

47,935 36,980 26,345 16,625 7,685 – – – 29,410 21,675 15,145 9,460 4,445 – – –

