The Portuguese economy has shown a rather striking evolution in the last ten years. In the late 1990s, Portugal posted impressive results in both the real and monetary areas. Its GDP per capita grew faster than the EU average and met the Maastricht criteria for EMU in good times. However, from 1999 the economy started to slow down and in early 2002 entered
recession. In this section, we will examine changes in income, earnings and capital income inequality that took place over the expansion years.
Before presenting the results, it must be noted that when comparing distributions (either across countries or across time) it is important to assess the statistical significance of the observed differences. As Moran (2006, p. 226) puts it, “given that statistics such as the Gini index are widely used to make normative evaluations of a nation’s capacity for distributive justice, statistical inference can become an important mechanism for evaluating which movements in the index are likely to represent ‘real’ and substantively important distributional change and which might be better attributed to error or random variation in the samples”. With this in mind, the analysis that follows builds up on bootstrap methodology. Specifically, we present the results of a set of non-parametric tests based on the bootstrapped means of the difference between the inequality statistics in 2001 and 1994. To make the statistical inference more robust, we use three alternative bootstrap methods: the standard normal, the percentile and the bias-corrected method. These methods are described in Appendix B.
10.1 Changes in the income distribution
Table 13 reports the evolution of the concentration and skewness statistics of the income distribution. The overall picture is one of decreasing inequality. While in 1994 the Gini index, the coefficient of variation and the top10%/bottom90% ratio were, respectively, 0.44, 0.91 and 3.91, in 2001 these statistics were 0.40, 0.85 and 3.76. The bottom half of Table 13 assesses the significance of these changes. The first row (‘Sample’) summarizes the total variation between 1994 and 2001 as measured by the difference between the sample moments. The next two rows report the mean (‘Bootstrapped mean’) and the standard deviation (‘Bootstrapped standard error’) of the 2001-1994 differential across bootstrap replications. The last three rows test whether the total change is statistically different from zero using, alternatively, the standard normal, the percentile, and the bias-corrected method. The results show that income inequality, as measured by the Gini index, was significantly higher in 1994 than in 2001. In turn, the total changes in the coefficient of variation, the top10%/bottom90% ratio and the skewness coefficients fail to be statistically significant.
Table 13. Changes in the concentration and skewness of the income distribution Gini CV Top 10%/ Bottom90% Location of Mean% Mean/ Median Skewness 1994 0.436 0.91 3.91 63.6 1.27 2.88 1995 0.421 0.85 3.71 62.4 1.25 2.47 1996 0.409 0.84 3.63 62.4 1.24 2.66 1997 0.409 0.82 3.64 62.5 1.24 2.01 1998 0.410 0.84 3.70 63.4 1.24 2.43 1999 0.403 0.85 3.71 63.9 1.24 2.97 2000 0.397 0.80 3.57 62.2 1.24 2.14 2001 0.400 0.85 3.76 64.4 1.26 3.00 ∆(2001-1994) Sample -0.04 -0.06 -0.15 0.80 -0.01 0.12 Bootstrapped mean -0.03 -0.06 -0.15 0.72 -0.01 0.13
Bootstrapped standard error 0.01 0.05 0.20 1.34 0.03 0.77
Normal test ***
Percentile test ***
Bias-Corrected test ***
***
denotes significant at the 1% confidence level. Bootstrap tests based on 500 random replications of sample size equal to the number of households in the dataset (4,614). Source: Portuguese Surveys of the 1994-2001 European Community Household Panel.
Next, in Table 14 we look at the evolution of the shares earned by households in different parts of the distribution. For illustrative purposes, the shares of the different quantiles are depicted in Fig. 10, where each series has been normalized by its value in 1994.
Table 14. Changes in the income distribution – shares of the sample totals (%)
The Poor Quintiles The Rich
Year 1 1-5 5-10 1st 2nd 3rd 4th 5th 10-5 5-1 1 1994 0.017 0.377 0.835 3.87 9.27 15.65 23.53 47.67 11.59 12.88 5.91 1995 0.029 0.412 0.903 4.12 9.78 16.00 23.68 46.48 11.51 12.40 5.35 1996 0.027 0.500 0.983 4.50 10.17 16.11 23.52 45.69 11.17 12.36 5.26 1997 0.033 0.512 0.988 4.54 10.08 16.17 23.51 45.70 11.34 12.75 4.81 1998 0.026 0.472 0.949 4.41 10.36 16.17 23.20 45.86 11.33 12.23 5.58 1999 0.031 0.561 1.053 4.81 10.63 16.00 22.86 45.70 11.04 12.54 5.65 2000 0.040 0.553 1.100 4.89 10.58 16.22 23.31 45.00 10.95 12.58 4.92 2001 0.050 0.600 1.120 5.09 10.32 15.86 22.68 45.88 10.88 12.45 6.20 ∆(2001-1994) 0.033 0.223 0.285 1.22 1.05 0.21 -0.85 -1.79 -0.71 -0.43 0.29
Source: Portuguese Surveys of the 1994-2001 European Community Household Panel.
We find that households in the lower tail of the income distribution improved their economic position. Specifically, the share of total income earned by the income-poorest more than doubled, going from 0.02% in 1994 up to 0.05% in 2001. Similarly, the income share earned by households in the first quantile was about 1.3 times larger in 2001 than in 1994 (5.09% against 3.87%). At the same time, income receipts among the income-rich tended to diminish. The share of income earned by the households located in the top quantile decreased from 47.7% to 45.9%. This change is qualitatively similar among the households that belong to the
top 10% of the income distribution, with the exception of those in the top 1%. Among this group, we cannot detect any discernible trend
0.90 0.95 1.00 1.05 1.10 1.15 1.20 1.25 1.30 1.35 1.40 1994 1995 1996 1997 1998 1999 2000 2001 0-20 20-40 40-60 60-80 80-100
Fig. 10 Shares of the sample totals, by income quintiles. Series normalized by the 1994 values. Source: Portuguese Surveys of the 1994-2001 European Community Household Panel.
In Panel 1 of Fig. 11 we plot the Lorenz curves of the income distributions in 1994 and 2001. A frequently used criterion to asses the welfare implications of a given distribution is the concept of Lorenz dominance (Atkinson, 1970): distribution A Lorenz dominates distribution B if the Lorenz curve of distribution A is always above the Lorenz curve of distribution B9. This is the case in Fig. 11. Specifically, we find that the shares of income earned by families in the lowest 20%, 40%, 60% and 80% of the income distribution were, respectively, 1.22, 2.27, 2.48 and 1.78 percentage points higher in 2001 than in 1994. As the bootstrap results show, such differences are statistically significant at the 1% confidence level, indicating that the income distribution in 2001 Lorenz dominates that in 1994.
9 Lorenz dominance is a powerful concept. If the Lorenz curve for a distribution f lies everywhere above that for another distribution g, then any aggregate inequality measure M, such as the Gini coefficient, that satisfies
Panel 1: Lorenz Curves Panel 2: Bootstrap tests Income ∆(2001-1994) 0-20 0-40 0-60 0-80 Sample 1.22 2.27 2.48 1.78 Bootstrapped mean 1.24 2.28 2.48 1.79 Bootstrapped standard error 0.18 0.47 0.79 0.11 Normal test *** *** *** *** Percentile test *** *** *** *** Bias-Corrected test *** *** *** ***
Fig. 11 Changes in the income Lorenz curve. *** denotes significant at the 1% confidence level. Bootstrap tests based on 500 random replications of sample size equal to the number of households in the dataset (4,614). Source: Portuguese Surveys of the 1994-2001 European Community Household Panel.
10.2 Changes in the earnings distribution
According to the data, earnings inequality tended to decrease over the sample period. As Table 15 shows, the Gini index, the coefficient of variation and the top10%/bottom90% ratio decreased, respectively, from 0.56, 1.14 and 4.93 in 1994 to 0.53, 1.06 and 4.64 in 2001. Still, only the variation of the Gini coefficient appears to be statistically significant. As regards the skewness of the distribution, the results show small and non-significant changes over the sample period.
Table 15. Changes in the concentration and skewness of the earnings distribution
Gini CV Top 10%/ Bottom 90% Location of Mean% Mean/ Median Skewness 1994 0.558 1.14 4.93 60.9 1.27 2.47 1995 0.547 1.08 4.66 59.3 1.27 2.06 1996 0.540 1.06 4.59 58.5 1.29 1.91 1997 0.541 1.06 4.63 58.5 1.26 1.73 1998 0.534 1.07 4.62 59.4 1.21 2.15 1999 0.534 1.07 4.62 58.6 1.25 2.08 2000 0.525 1.04 4.48 58.0 1.21 2.00 2001 0.530 1.06 4.64 61.00 1.23 2.07 ∆(2001-1994) Sample -0.03 -0.08 -0.29 0.10 -0.04 -0.40 Bootstrapped mean -0.03 0.08 -0.29 0.14 -0.04 -0.41
Bootstrapped standard error 0.01 0.05 0.29 1.47 0.05 0.39
Normal test ***
Percentile test **
Bias-Corrected test **
**
denotes significant at the 5% confidence level, ***
denotes significant at the 1% confidence level. Bootstrap tests based on 500 random replications of sample size equal to the number of households in the dataset (4,614). Source: Portuguese Surveys of the
Table 16 reports the evolution of the shares earned by households in different parts of the distribution. The normalized figures are depicted in Fig. 12. The trends are similar to those documented for the income variable: the poor tended to improve their economic position. Specifically, the share of earnings obtained by families in the bottom 30% of the earnings distribution rose from 0.45% to 1.84%, while the share of earnings obtained by families in the 30-40 decile rose from 3.87% to 4.87%. Again, this trend was parallel to decreasing shares earned by the rich. Specifically, the top quantile accounted for 52.48% of the total earnings in 2001, against a 54.84% in 1994. Similarly, the share obtained by the earnings-richest fell from 6.76% to 6.15%.
Table 16. Changes in the earnings distribution – shares of the sample totals (%)
The Poor Quintiles The Rich
Year 0-30 30-40 3rd 4th 5th 10-5 5-1 1 1994 0.446 3.874 15.35 25.49 54.84 13.38 15.24 6.76 1995 0.450 4.160 15.66 26.08 53.65 13.33 14.71 6.12 1996 0.714 4.442 15.73 26.00 53.12 13.14 14.98 5.82 1997 0.744 4.382 15.97 25.53 53.37 13.38 15.13 5.76 1998 1.051 4.594 16.22 25.31 52.82 12.75 15.00 6.16 1999 1.116 4.571 15.94 25.57 52.80 12.76 15.13 6.10 2000 1.306 4.740 16.35 25.79 51.81 12.55 14.38 6.38 2001 1.840 4.870 15.84 24.96 52.48 12.47 15.43 6.15 ∆(2001-1994) 1.394 0.996 0.49 -0.53 -2.36 -0.91 0.19 -0.61
Source: Portuguese Surveys of the 1994-2001 European Community Household Panel.
0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1994 1995 1996 1997 1998 1999 2000 2001 0-40 40-60 60-80 80-100
Fig. 12 Shares of the sample totals, by earnings quintiles. Series normalized by the 1994 values. Source: Portuguese Surveys of the 1994-2001 European Community Household Panel.
earnings distribution were, respectively, 2.39, 2.88 and 2.35 percentage points higher in 2001 than in 1994, and such differences are statistically significant (Panel 2 of Fig. 13).
Panel 1: Lorenz Curves Panel 2: Bootstrap tests
Earnings ∆(2001-1994)
0-40 0-60 0-80 Sample 2.39 2.88 2.35 Bootstrapped mean 2.40 2.85 2.37 Bootstrapped standard error 0.61 0.91 1.23 Normal test *** *** * Percentile test *** *** * Bias-Corrected test *** *** **
Fig. 13 Changes in the earnings Lorenz curve. * denotes significant at the 10% confidence level, ** denotes significant at the 5% confidence level, *** denotes significant at the 1% confidence level.Bootstrap tests based on 500 random replications of sample size equal to the number of households in the dataset (4,614). Source: Portuguese Surveys of the 1994-2001 European Community Household Panel.
10.3 Changes in the capital income distribution
While the concentration of income and earnings tended to decrease over the sample period, capital income became increasingly compressed. Table 17 illustrates this trend.
Table 17. Changes in the concentration and the skewness of the capital income distribution
Gini CV Top 10%/ Bottom 90% Location of Mean% Mean/ Median 1994 0.963 7.17 515.25 92.1 16.43 1995 0.957 5.63 381.46 91.7 10.26 1996 0.963 8.58 428.12 92.5 28.35 1997 0.965 7.47 548.47 92.8 17.52 1998 0.968 7.96 483.69 93.4 21.81 1999 0.972 8.19 701.43 94.0 18.73 2000 0.967 6.89 615.58 93.8 15.29 2001 0.970 11.98 706.52 94.2 35.17 ∆(2001-1994) Sample 0.01 4.81 191.27 2.10 18.74 Bootstrapped mean 0.01 4.81 191.26 2.09 18.74
Bootstrapped standard error 0.01 2.95 258.08 0.75 10.11
Normal test * *** *
Percentile test ***
Bias-Corrected test * ** **
*
denotes significant at the 10% confidence level, **
denotes significant at the 5% confidence level, ***
denotes significant at the 1% confidence level. Bootstrap tests based on 500 random replications of sample size equal to the number of households in the dataset (4,614). The mean-to-median ratio is not reported: in all years the modal value of capital income is zero and, thus, this statistic is not well defined. Source: Portuguese Surveys of the 1994-2001 European Community
We find that the Gini index, the coefficient of variation and the top10%/bottom 90% ratio rose from, respectively, 0.96, 7.17 and 515.25 in 1994 to 0.97, 11.98 and 706.52 in 2001. The statistical significance of these changes is however limited. In turn, changes in the skewness of the distribution turn out to be significant, with the location of the mean and the skewness coefficient being significantly higher in 2001 than in 1994.
Table 18 tracks the evolution of the capital income distribution over the sample years. Two things are worth noting. First, the share of capital income earned by the bottom 90% of the distribution did not change by much. This is due to an overhelming majority of households reporting zero capital income in all surveyed years. Second, changes in the top tail of the distribution were heterogeneous. On the one hand, the share earned by those households in the top 10-5% and 5-1% of the distribution show a decreasing trend, from 7.1% in 1994 to 2.8% in 2001 in the former case and from 38.1% to 31.1% in the later case. On the other hand, the share obtained by the households in the top 1% of the capital income distribution exhibits an upward trend, going from 53.1% to 64.8%. The evolution of these shares is depicted in Fig. 14.
Table 18. Changes in the capital income distribution Shares of the sample totals (%)
The Poor The Rich
Year 0-90 10-5 5-1 1 1994 1.712 7.14 38.06 53.08 1995 2.304 9.67 39.51 48.51 1996 2.057 8.02 36.21 53.71 1997 1.614 6.57 35.77 56.05 1998 1.826 4.23 37.43 56.52 1999 1.262 3.51 33.72 61.51 2000 1.429 4.17 40.74 53.66 2001 1.250 2.84 31.08 64.83 ∆(2001-1994) -0.462 -4.30 -6.98 11.75
Source: Portuguese Surveys of the 1994-2001 European Community Household Panel.
Finally, in Fig. 15 we depict the 1994 and 2001 Lorenz curves. Even though differences in the ordinates of the curve fail to be significant at the 90th and the 99th percentiles, they are significant at the 1% confidence level in the 95th percentile10. All in all, this evidence suggests that over the sample period changes in the capital income distribution were non- trivial and leaded towards higher concentration.
0.2 0.4 0.6 0.8 1.0 1.2 1.4 1994 1995 1996 1997 1998 1999 2000 2001 0-90 90-95 95-99 99-100
Fig. 14 Shares of the sample totals, by selected intervals. Series normalized by the 1994 values. Source: Portuguese Surveys of the 1994-2001 European Community Household Panel.
Panel 1: Lorenz Curves Panel 2: Bootstrap tests
Capital income ∆(2001-1994)
0-90 0-95 0-99 Sample -0.01 -0.05 -0.12 Bootstrapped mean -0.01 -0.05 -0.11 Bootstrapped standard error 0.01 0.02 0.09 Normal test *** Percentile test *** Bias-Corrected test ***
Fig. 15 Changes in the capital income Lorenz curve. *** denotes significant at the 1% confidence level. Bootstrap tests based on 500 random replications of sample size equal to the number of households in the dataset (4,614).
Source: Portuguese Surveys of the 1994-2001 European Community Household Panel.
11. Conclusions
Understanding and explaining economic inequality within a country and over time crucially depends on the information that we have at our disposal. In this paper, we gathered detailed and up-to-date information to characterize economic inequality along a variety of dimensions. This was done for Portugal, a country placed high in the international rankings of inequality.
Data on the income, earnings and capital income earned by the Portuguese households was collected to describe the range, shape, concentration and skewness of the resulting distributions. The simultaneous analysis of these distributions allowed us to differentiate between the income-, earnings- and capital income-rich and the income-, earnings- and capital income-poor. These groups were found to differ significantly along a variety of socioeconomic dimensions, including age, employment status, education and marital status. These socioeconomic factors were explored further to describe their connection with economic inequality.
The data was taken from the European Community Household Panel dataset (ECHP), a standardized survey carried out in the European Union from 1994 to 2001. This dataset presented two main advantages. The first one was comparability. This was an appealing feature, insofar as most international comparisons of inequality are based on different datasets and sampling periods, diverging sample of individuals, and differently defined variables. The calculations reported in this paper can be easily extended to any of the other 14 countries included in the ECHP11. This task, which is beyond the present paper, would allow for straight comparisons between European countries. As a second advantage, the ECHP monitors the same group of families and individuals over time. This allowed us to analyze the dynamics of economic mobility in Portugal. Furthermore, the paper documented changes in inequality from 1994 to 2001, a period of economic expansion in Portugal. During these years, income and earnings inequality tended to decrease, while capital income inequality tended to increase. Using bootstrap techniques, we found statistical evidence that these changes were significant. This conclusion is fairly robust to the choice of the bootstrap test.
As a limitation, we did not provide explanations for the observed patterns. Establishing the causal relation between demographic characteristics, the unequal opportunities that individuals face, the functioning of labour markets, the role of institutions, and the scope of the public system of transfers, on the one hand, and overall inequality and its evolution over time, on the other hand, is a task for further research. Similarly, a theory of inequality that is consistent with most of the facts reported in this paper is still in the waits.