6 Underlying trends and macroeconomic effects

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In order to assess the empirical relationship between the alternative variance measures on the one hand and the underlying trends and major macroeconomic effects on the other, we estimate a series of multiple regressions of the time series of observations on the variance measures. As in the graphical presentation of Figures 4 to 9, the data points reflect both the underlying trends and the aggregate labour market changes in Canada from 1982 to 2000. There are a total of 15 time-series observations for each of the variance measures, starting with the 1982-to-1986 sample window and ending with the 1996-to-2000 sample window. Following Haider's (2001) parsimonious specifications, the macroeconomic effects are represented by the aggregate unemployment rate and the real gross domestic product (GDP) growth rate. For each of the five-year windows, the unemployment rate regressor assumes the average annual value over the five-year window (expressed as a percentage). The real GDP growth variable is calculated by first taking the fourth quarter GDP value in year t divided by that in year t-1 subtracting one, then computing the mean of the five such annual growth rates over the five years within a window (expressed as a decimal). The three variance measures are treated as separate dependent variables in the regression analysis for men and women as a whole and for each of the eight age-sex groups under analysis. The general form of the regression equation is estimated as

where Y_t is one of the three variance measures, T is a linear time trend, GR_t is the average annual GDP growth rate, UR_t is the average annual unemployment rate and ε_t is a regression error term. The net trend effect is picked up by the β₁ coefficient. The inclusion of the time trend also has the effect of de-trending either of the remaining independent variables.

Because of the way that the variables are calculated in terms of rolling, overlapping windows, the error terms in the regressions are likely to be highly correlated. To address this issue, we specify an error structure that follows a fourth-order moving average process. Although for many of the regression equations some of the four moving average estimated coefficients turn out to be insignificant, we include them in all specifications. The equations were estimated by maximum likelihood techniques (the AUTO command in the SHAZAM regression program).

6.1 Net trend effects

Estimates of the β₁ trend coefficient from the above equation appear in Table 3, first for women and men as a whole (Panel A) and then broken down by age group (Panel B).⁶ Figures in parentheses are the trend effects expressed in percentage terms, relative to the sample means of the dependent variables. Basically, the net trend effects for women and men as a whole replicate the graphical shifts in the initial Figures 1 to 3: long-run earnings inequality has risen over the 1982-to-2000 period; earnings instability has declined; and, since the former trend dominates in magnitude the latter, total earnings variance—our closest measure to observed cross-sectional earnings inequality—has also risen, though at a slower rate than the rise in long-run earnings inequality. This

pattern holds for both men and women. But the rise in long-run inequality is about twice as strong for men than it is for women, and the decrease in earnings instability is about four to five times stronger for women than for men. As a result, the increase in total earnings variance was highly significant and much more marked for men, and only marginally significant and much weaker for women workers.

Across the four age groups, the increasing trend in long-run earnings inequality rises markedly with age for both men and women, though much more strongly for male workers. Trends in earnings instability, however, are mixed across age groups. For men, the strong increasing trend in long-run inequality again dominates the relatively weak and mixed trends in earnings instability, so that the net trend in total earnings variance is also strongly positive and increasing with age. For women, the trends in earnings instability are often stronger (in percentage terms) than those in long-run inequality, so that the mixed trend pattern in total earnings variance generally reflects that for earnings instability. The net trend effects pretty well reflect the general shifts in the age profile of the variance measures illustrated in Figures 4 to 9.

Finally, Panel C provides a complementary set of net trend effects for men and women as a whole, based on a pooled regression. In this case, the four age groups (of 15 observations each) were pooled into one regression (of 60 observations) with the set of regressors specified above, plus 3 age-group dummy variable controls so that more degrees of freedom are gained. The common trend coefficients are listed in Panel C. Since the pooled regressions are estimated by ordinary least squares, the coefficient estimates are generally unbiased, but their standard errors are incorrect, so indicators of statistical significance are not included. As can be seen, the pooled trend coefficients for long-run inequality and for total variance are quite similar to the aggregate trend coefficients in Panel A. The earnings instability trend coefficients, however, have switched sign to become positive, though they are still quite small. Evidently, any underlying trends in earnings instability are not robustly or reliably estimated, while those for long-run inequality and for total earnings variance are.

6.2 Macroeconomic effects

Macroeconomic effects are captured by two variables: the (aggregate) unemployment rate and real GDP growth rate. The regression results for the former appear in Table 4 and those for the latter are in Table 5. Each cell in these tables contains three figures. The first is the actual regression coefficient (ˆβ₃ or ˆβ₂ ). The figure in parentheses is the percentage change of the relevant effect ( ˆβ₃ or ˆβ₂ divided by the mean of the dependent variable). For example, in the top-left cell of the first table, the number 2.71 indicates that the estimated effect of a one percentage point increase in the unemployment rate is to raise the degree of long-run earnings inequality for men in the labour market over the 1982-to-2000 period by 2.71%. The figure in square brackets is the (partial) elasticity corresponding to the estimated regression coefficient (i.e., ˆβ₃ or ˆβ₂ multiplied by the ratio of the mean of the relevant regressor to the mean of the corresponding dependent variable). Thus, again in the top-left cell of Table 4, the estimated effect of a 1% rise in the aggregate unemployment rate is a 0.26% increase in long-run earnings inequality for men in the labour market.

The unemployment rate is an indicator of labour market tightness. Reduced unemployment rates and thus tighter labour markets—according to conventional economic theory—would be expected to disproportionately benefit the earnings of low-skilled lower-wage workers, so that earnings inequality should attenuate and earnings instability be reduced; higher unemployment rates should have the opposite effect. We would therefore expect positive unemployment rate effects on all three variance measures. Since male workers are traditionally more concentrated in primary and manufacturing/construction/transportation sectors, which have greater cyclicality than service sector employment where women are more concentrated, one would also expect stronger counter- cyclicalityin the unemployment-rate effects for men than for women.

The results presented in Table 4 turn out to be very much consistent with this expectation. There are positive unemployment rate effects for all samples, for both men and women as a whole (panel A) and for all ages (panel B), for long-run earnings inequality and for total earnings variance. These results, at least for men and women as a whole, appear to be robustly estimated. These effects are indeed also stronger for men than they are for women. Since the two variance components sum to the total variance, the sum of the unemployment rate effects—as measured by the regression coefficients—is the same as that estimated for total variance across each row of the table. The coefficient effects on long-run earnings inequality are about twice as strong as on earnings instability, so that for men the former effect accounts for about two thirds of the effect on total earnings variance. Higher unemployment is thus also found to increase earnings instability for men, as one would expect from conventional theory. For women workers, however, the unemployment- rate effect on earnings instability shows a weaker and more mixed pattern. Indeed, for women as a whole, the estimated effect turns out to be negative, although for the pooled estimates in Panel C it is quite small. Finally, the unemployment rate effect is U-shaped across age groups for both long- run inequality and total earnings variance for men. It is smallest among younger and prime-age workers, who typically have the strongest labour market attachment among all age/sex groups, and it is largest for entry and older workers, who often include workers with more intermittent labour market attachment and who typically experience the highest rates of unemployment. Again, the pattern across ages for women is more uneven or mixed.

The GDP growth rate variable is an indicator of growing earnings prosperity and increased employment experience in the labour market; hence it picks up a different facet of the business cycle. Greater (real) GDP growth rates and hence faster growing economies, according to conventional economic theory, would be expected to have a negative effect on earnings variance measures through three related, but conceptually distinct, routes or channels, given that we are controlling for aggregate unemployment rates. The first channel operates through the labour force participation rate and, hence, the employment rate: higher economic growth and real wage rates generally increase participation rates through an upward-sloping labour supply, likely more so for women than for men and more strong among lower-skilled workers that are less permanently attached to the labour market. The second channel operates through hours worked: again an upward-sloping labour supply effect induces longer hours worked (conditional on being employed), and again is likely stronger for women than men, and among lower-skilled workers with less than regular normal-hours work. The third channel is the so-called trickle-down effect on hourly wages: higher growth and tighter labour markets are likely to bid up disproportionately the wages of relatively low-skilled workers, particularly in more cyclically sensitive sectors, such as primary and manufacturing/ construction/transportation, where men are more concentrated.⁷

These conventional expectations for the impact of real GDP growth rate effects are only partially validated by the regression results presented in Table 5. The findings for women in the labour market across all three earnings variance measures are in line with these expectations, but for men our priors are supported only with respect to earnings instability (i.e., improved economic growth, not surprisingly, reduces the degree of earnings instability in the labour market). Again, the coefficients on long-run earnings inequality are generally larger (in absolute terms) than those on earnings instability: in the case of women, by a factor of eight. The implied elasticities and percentage changes are also, right across the board, much weaker or smaller than those found in the previous table for unemployment rate effects. Interestingly, women are found to have stronger GDP growth-rate effects on long-run inequality and total earnings variance than men, while men have stronger growth-rate effects (and in the direction expected) on earnings instability than women. Looking at patterns across ages, one notes that, for both men and women, the growth-rate effect (algebraically) increases with age for long-run earnings inequality and for total earnings variance— except for the case of older women. For earnings instability, the growth rate effect generally manifests a U-shaped pattern across age groups for men and a declining pattern across ages for women. Interestingly, unemployment rate effects come through quite consistently with conventional theory and they operate most strongly through long-run earnings inequality (and hence total earnings variance), whereas GDP growth rate effects operate more consistently through the earnings instability component.

A summary of cyclical regression effects from Tables 4 and 5 is presented in Table 6. The entry 'C' designates counter-cyclical findings (i.e., poor economic times result in higher earnings variances), while entry 'P' indicates pro-cyclical effects (i.e., good economic times result in higher variances). As found by Haider (2001), counter-cyclical effects clearly dominate, with greater economic growth and lower unemployment generally reducing earnings variances. The exception of a pro-cyclical effect of economic growth on long-run earnings inequality for men, however, stands out.

The inconsistency of the growth rate effects with conventional economic explanations for male long-run earnings inequality (and hence total variance) poses a puzzle. This finding is consistent across the alternative estimation methods, and it was also found in Beach, Finnie and Gray (2005) using a somewhat different methodology that included a regional dimension to pick up macroeconomic effects. It would appear that alternative phenomena are occurring, but they are not picked up by conventional explanations. An alternative paradigm or explanation offered in Beach, Finnie and Gray (2005) is based on economic restructuring and changing demographics. According to this proposition, high growth areas of the country have attracted substantial in-migration of young workers, whose earnings levels tend to be relatively low and have indeed fallen significantly compared with the previous generation of youth, and of immigrants, whose earnings have also fallen significantly relative to non-immigrants over the last 20 years. Indeed, overall levels of Canadian immigration shifted up in the mid-to-late 1980s and continued at a much higher level in the 1990s than in the 1960s and 1970s. The 1990s also saw a marked decrease in the rate of growth—indeed, a downsizing—of the public sector, a decline in the overall unionization rate in the private sector, and steps toward deregulation in selective and formerly protected industries, such as airlines and telecommunications.

More generally, two phenomena: growing globalization, out-sourcing and international trade; and, the advent of skill-biased technological change based on chip-based recent information technology have been argued to have had huge effects on economic restructuring and reorganization of the workplace (Katz and Autor 1999, Verma and Taras 2005). The Canada–United States Free Trade Agreement took effect in 1989 and the North American Free Trade Agreement took effect in January 1994. The results, as Courchene and Telmer (1998) and others have argued, have been a massive reorganization of Canadian trade patterns away from an east-west axis to a north-south axis and a corresponding increase in the competitiveness of output markets and, hence, increased cost awareness, restructuring of workplace arrangements and a greater use of out-sourcing and non- standard work arrangements (Bartel et al. 2005). If these 'new economy' changes have generally been implemented in the more high-growth and more manufacturing-oriented sectors of the economy, this could explain the widening degree of earnings inequality, particularly for male workers, contrary to the conventional view of the impact of growth. More research is obviously needed to evaluate and test between the conventional and new-economy explanations of how economic growth is affecting earnings inequalityin the current labour market.

 

⁶ Note that an artifact of the construction of the dependent variables is that there is likely to be a significant time trend.

⁷ Unfortunately, since the analysis uses administrative data, we cannot observe amount of working time, so we cannot separate out these distinct channels in our regression estimates.