inequality 0.2 0.2 0.2 0.2 0.2 0.2 E age 15-24 46 38 45 39 47 44 26 17 2115 2169 0.79 0.73 age 25-34 43 46 43 50 45 58 28 35 2663 2759 1.00 0.93 age 35-49 59 75 60 81 67 93 32 35 3001 3372 1.12 1.13 age 50-64 61 90 62 96 69 112 14 13 2981 3613 1.11 1.21 within-group inequality 51 60 52 68 59 84 between-group inequality 10 14 9 14 9 13 F manufacturing 57 72 58 78 64 94 57 52 2653 2925 0.99 0.98 constructions 43 46 41 49 43 58 13 10 2722 2778 1.02 0.93 services 77 84 79 91 88 108 30 38 2694 3099 1.01 1.04 within-group inequality 61 74 62 81 68 97 between-group inequality 0 1 0 1 0 1
Notes: See box 3 for the decomposition formulae. Due to rounding, the sum of within-group and between-group inequality may not exactly add up to total inequality.
Illustrative example. Consider year 1985 and the gender decomposition (panel B in Table 8). Column 2 shows that total inequality (i.e. over all persons, in section A) is 61according to the index GE(0). When we disaggregate according to gender, we calculate that inequality in the same year and for the same index is 58 for men and 51 for women. The share of men and women in 1985 is 70 per cent and 30 per cent, respectively (column 5). Averaging inequality for men and for women, using these shares as weights, we obtain the within- group inequality value of 56. To compute the between-group inequality, we first eliminate inequality within men (i.e. assign to each man the men’s mean earnings of reported in column 6) and inequality within women (i.e. assign to each woman the women’s mean earnings reported in column 6). The between-group value of 5 obtained in 1985 is the inequality that still remains between men and women. Finally, column 7 reports the ratio of the group’s mean earnings to the population mean.
Conclusions
In this paper we have studied the Italian earnings distribution over the time period 1985- 1996, using administrative data from the Italian institute for social security (INPS). We have documented a slight, but not negligible, increase in earnings inequality, according to a battery of commonly used distributional indicators. Even though changes in computed inequality indices from one year to the next are small, their bootstrapped standard errors are small too, increasing our confidence in the statistical validity of the conclusions reached.
The gap between the richest tenth and the poorest tenth broadened, but by less than what happened to the distance between the richest tenth of the distribution and the median. This could be explained by observing that the poorest tenth managed to reduce over time the distance between their position and that of the person in the median position. Concomitantly, the fact that the share of total earnings accruing to the bottom tenth of the employee population has increased, implies that we cannot speak of an unambiguous increase in inequality. We can therefore predicate a rise in earnings inequality only according to a - possibly large - subset of the available inequality measures.
Decomposition of inequality indices by various population subgroups have followed, aimed at shedding light on the causes of the observed distributional changes. For all the population partitions used, inequality is mainly explained by its within-group component, which, in all cases, is increasing too.
Male’s earnings are more unequally distributed than female’s, but the two groups are getting more similar to each other over time in terms of group’s share and mean earnings. Men’s inequality, though, is still growing faster than women’s. When focusing on low-pay, we find that the female’s poorest tenth displayed an impressive growth of 30% over the time period investigated, while the corresponding figure for men is a much more modest 6%. Not surprisingly, much of the reduction in the distance between the poorest tenth and median earnings in the whole distribution can be explained by this growth in the least well-paid jobs for females.
Among occupations, white collars have the most unequal earnings distribution, with inequality growing fastest too, during the 1980s and 1990s. Inequality seems to have lowered for apprentices, but no unambiguous statement could be made about managers and blue collars. The between-group component was able to explain up to 36% of the observed inequality value. Moreover, this component has exhibited an impressive growth during the period 1985-1996, more than doubling its level. This may be seen as implying that the skills correlated with our occupation classification - e.g. education, which is not observable in our dataset - have increasingly attracted differential rate of returns in the labour market.
The classic wage-age profile is found in our data, with monthly wages increasing as the employees grow older. The same pattern is revealed with respect to wage variability. Relative to the population mean, the mean of the youngest age group fell while for more senior employees the situation is reversed, suggesting that structural changes in the Italian labor market may have pushed the return to seniority and experience upwards. When we examine trends in inequality within each age subgroup, we witness to a widening of relative earnings differences in all but the youngest age group (due to the improved situation of very low wages
in that group). Differences between age groups were able to account for between 13 and 19 percent of observed total inequality, and have exacerbated during the 1990s.
Modifications of the Italian industrial structure also contributed to the increase in earnings inequality. The share of manufacturing and constructions declined during the 1980s and 1990s, while the share of employees in the services sector expanded. Manufacturing had the lowest relative mean earnings in 1985 and lost some additional grounds in 1996. Even more conspicuous was the reduction of relative mean earnings experienced by constructions, while the relative mean in services increased. The service sector was also found to be the one with higher inequality levels in both 1985 and 1996, followed by manufacturing and constructions. However, in terms of inequality growth, earnings differentials opened up faster in manufacturing, followed by constructions and services. Virtually all observed inequality was accounted for by the within-group component, with the between-group component that slightly increased its importance over time.
Our decomposition by geographical areas pointed to a substantial uniformity in the distributions of monthly wages in the north, centre and south. It was noted that this result might be spurious, and mainly arising from the (mis)practice of southern firms to underreport the number of days worked by their employees. When focusing on annual earnings for year- round workers, differences between the geographical areas of the countries re-emerge as expected, with the earnings distribution of the north lying almost everywhere to the right of those of the centre and the south. Differences are even more pronounced when considering annual earnings for all workers (year-round or not), with the greater concentration of low remuneration in the south of the country than in the north increasing correspondingly. As this latter measure of earnings conflates both wage variability and quantity of labor variability, the larger gap between the north and the south it produces is not surprising, simply reflecting the different labor market conditions and overall job precariousness in the two areas.
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