This section analyses inequality in Spain in terms of hourly wages, wage income, household income, consumption and wealth.Footnote 4 In particular, it examines in depth the economic decisions and economic policy instruments that generally imply that inequality is lower when compared in terms of income and consumption and higher when the focus is on wealth.
Inequality analysis can focus on different variables that are closely interconnected. For example, there are individual differences in hourly wages, in the ability to obtain income other than labour income, in access to public goods or in decisions on household formation, consumption and saving. In general, all these measures are related. For instance, workers’ wage income is a function of their hourly wage and of the number of hours they work over a specific period.
In the remaining of this section, earnings’ inequality across workers is analysed in Sect. 3.1, whereas Sect. 3.2 focuses on inequality across households. In turn, households’ gross income depends first on the members of the household and how their time is organised between paid work, housework and leisure. Moreover, wage income is not the only component of household income, as in addition to income from paid employment there is income from self-employment and there are unemployment benefits, pensions and other transfers that serve as a form of social insurance in situations of hardship. The total sum of all these forms of income is not available for consumption, as a portion must be deducted for payment of taxes, giving net disposable income. Lastly, households themselves decide what portion of their net income they wish to consume, depending among other factors on the level of uncertainty underlying their future income expectations, their stage of life, their available wealth and the public or subsidised goods available to them. All income that households decide not to consume are savings; households’ wealth will vary according to the assets in which they invest their savings, the price paid and the rate of return obtained.
Wage income inequality across workers
On average, the wage income of Spanish household members amounts to 60% of the household’s total annual income, making it a prime candidate for analysis in the study of inequality. The inequality observed in this variable is analysed below, distinguishing between inequality stemming from differences in hourly wages and that stemming from differences in the number of hours worked.
In 2014 hourly wage inequality in Spain was similar to the median of the euro area countries. Drawing on information by Eurostat, in 2014 the hourly wage of workers in the ninth decile of the distribution in Spain was 3.3 times higher than that of those in the first decile. This inequality indicator (the P90/P10 ratio) was close to the median of the EU countries: below countries such as Portugal, Ireland, Germany or the United Kingdom, but above France, Belgium and the Nordic countries (see panel 1 of Fig. 1).
In the case of Spain, hourly wage differences were smaller at the low end of the distribution. As Table 1 shows, in 2014, while the P90/P10 ratio for hourly wages was 3.3, the P50/P10 ratio was just 1.6. At the high end of the distribution there were larger differences; specifically the P90/P50 ratio was 2.1.
There are important differences in demographic characteristics across the hourly wage distribution. A larger proportion of women, young people, workers with low levels of educational attainment and those with limited tenure are concentrated at the low end of the distribution. Specifically, panel 1 of Table 2 shows that in 2014 63% of workers with wages in the first decile of the wage distribution are women, although they account for 48% of all wage-earners. In turn, most of the workers in this first decile (62%) have no education beyond compulsory schooling, compared with 43% for all wage-earners. In addition, workers’ age and tenure are higher in the higher deciles of the wage distribution. This is related to significant hourly wage differences in Spain by gender, age, level of educational attainment and tenure. Figure 2 presents the result of a regression model designed to separate the effect of each such variable on hourly wages. This analysis confirms significant negative wage differentials for women, young people, new hires and workers with lower levels of educational attainment.Footnote 5 However, the evidence available suggests that hourly wage differentials between groups are not particularly high compared with other countries (Simón 2010).
Furthermore, there are interesting facts regarding the contractual conditions of groups of workers with the lowest hourly wage (panel 2 of Table 2). First, there is a higher incidence of part-time work among the groups of workers with the lowest hourly wages. Specifically, the proportion of part-time work is 37% among women, 39% among young people, 32% among workers with lower levels of educational attainment and 41% among new labour market entrants. Those numbers are much higher than the incidence of part time in other sociodemographic groups. As a consequence, when the number of hours worked is considered in the analysis, the differences in wage income between individuals increase.
Second, young workers and new labour market entrants generally interrupt their periods of work more often, as they are more dependent on temporary contracts. There are no substantial differences between men and women or between persons with different levels of educational attainment on the number of days workers are not at work—because they are off work, on unpaid leave or laid off—. However, there are significant differences by age and by tenure. Specifically, according to the Earnings Structure Survey (EES) data, there are up to five days’ difference in time actually worked per month between different age groups and between new labour market entrants and all other workers. All in all, inequality measures are higher when monthly earnings rather than hourly wages are compared. Taking into account the number of hours and days worked in the month, in 2014 the wage income of the ninth decile was 5.6 times higher than that of the first decile whereas, as indicated in the previous section, in terms of hourly wages this ratio was just 3.3 times (see Table 1). This increase in inequality is concentrated at the low end of the distribution. Thus, median wage-earners received 2.5 times more than the first decile in terms of monthly earnings (1.6 in terms of hourly wages), but they continued to receive slightly less than half the level of the ninth decile (2.2 in terms of monthly earnings, 2.1 in terms of hourly wages). Indeed, Spain is one of the countries that shows the largest increase in inequality when wages are analysed monthly rather than hourly, presenting a degree of wage income inequality above the median. In this respect, notable increases in inequality are also observed in other countries, such as Germany, the Netherlands, the United Kingdom and Austria (see panel 2 of Fig. 1), where there is a higher incidence of short-term and short-hour contracts among the groups with the lowest hourly wages.
Income, consumption and wealth inequality across households
As well as income from wages, individuals receive income from self-employment, capital income, unemployment benefits, pensions and other, mainly public, transfers that must be taken into account to analyse inequality. In addition, people do not generally take decisions in isolation but as members of a household, where different members may receive income and share the use of certain goods. Accordingly, total household income inequality must be considered as well as per capitaFootnote 6 income inequality. For this purpose, we analyse below the EFF data for 2014 on gross per capita income and total household income, as the overall income of all household members and its characteristics.
In Spain there is a high correlation between the socioeconomic characteristics of adult household members. Specifically, the correlation between the educational attainment level of the head of household and his/her partner verges on 70% in Spain (see panel 1 of Fig. 3). This explains, for example, why when one household member is unemployed, there is a relatively high probability that the other household member is unemployed (panel 2 of Fig. 3). In consequence, households have a relatively limited ability to insure themselves from negative labour market shocks to one partner through the earnings of the other partner.Footnote 7
As a result of this high correlation, the differences observed in individual wage income inequality do not narrow significantly when total household income is considered. One way to identify how household formation reduces the differences in household members’ individual wage income is by comparing the measures of inequality of individual wage income and household wage income (see the first four columns of Table 3). If we consider a simulation where couples were formed under a random mating assignment, inequality in household income would be substantially lower than inequality in individual income. However, in the case of Spain, the measures of inequality of household wage income compared with individual wage income are only slightly lower: specifically, the P90/P10 ratio for individual wage income is 10.9 and the P90/P10 ratio for household wage income is 10.0.
The bulk of household’s income at the lower end of the per capita household’s per capita income distribution comes from unemployment benefits and employment income (see Panel 1 of Fig. 4). At the worst point of the crisis, around 70% of the income of the first decile of the distribution came from unemployment benefits and wage or self-employment income. Pre-crisis, this figure stood at 50% and included a higher proportion of pensions and other public transfers, owing to the better relative position of wage income and the lower unemployment rate (in any event, unemployment benefits accounted for approximately 10% of total income in this decile).
Compared to other European countries,Footnote 8 in Spain, per capita income inequality is relatively high. As Table 3 shows, the P90/P10 ratio for households’ per capita income was 6.3 in 2014. Given the abovementioned structure of sources of income, this is attributable to the higher incidence of unemployment in Spain, which resulted in a high concentration of households collecting unemployment benefits at the low end of the distribution. In addition, the fact that the unemployment rate remained high, even in the most expansionary periods, meant that inequality in gross income per capita before the crisis was also high in Spain by international standards (panel 1 of Fig. 5).
The limited ability to obtain income at the low end of the distribution is mitigated by the relatively large average household size in Spain. This permits certain economies of scale in household expenditure. According to the literature, young peoples’ decision to leave the family home is generally very closely linked to their job stability and, where there is a high incidence of short-term contracts, this is generally achieved at a relatively late age (Matea 2015; Barceló and Villanueva 2016, 2018). Moreover, the fact that young people are leaving the parental home later conditions the age at which they start to have children and the number of children they have. In consequence, Spain is one of the countries where the age of mothers at first birth is the highest and the fertility rate is the lowest (Adsera 2011). It is also one of the European countries with the lowest percentage of old people living alone or in institutional households. However, this is linked, at least in part, to the lower educational level of the older generations and, therefore, it will foreseeably change in the future in view of the increase in the level of educational attainment observed since the start of the last century.Footnote 9
The late age at which young people leave the parental home and the high pension replacement rate (OECD 2017), among other factors, mean that Spain’s inequality in total household income is relatively lower, when compared with that of other countries (panel 3 of Fig. 5). Indeed, the concentration at the low end of the household income distribution of households with older members that are chiefly supported by pensions may be explained by these household formation behaviour. Panel 2 of Fig. 4 shows that, in 2014, more than 50% of incomes in the first decile came from pensions and other, mainly non-employment, transfers.
Regarding other sources of household’s income, self-employment income and capital income play a smaller role in explaining differences in inequality levels. Self-employment income made up 14% of total income within the 9th decile and 7% within the 1st decile. In turn, capital income amounted to 10% of total income of the 9th decile, and then decreased progressively for the lower deciles, down to 2% in the first decile.Footnote 10 The inequality indicators for market income, which comprises wage income, self-employment income and capital income, are very similar to those observed for wage income. However, when imputed income from home ownership is considered, the inequality between Spanish households becomes slightly narrower, in terms of total and per capita income. Indeed, imputing income from home ownership reduces total income inequality (Goerlich 2016). This is because, according to the EFF, 61% of households in the first income decile own their own homes, so their incomes, which are low without the imputed income, increase considerably when it is added.Footnote 11 By contrast, in the higher deciles, there is a proportionally lower increase in income when the imputed income is added, showing that the distribution of imputed income from home ownership is considerably more uniform than the distribution of all other income overall. In particular, if imputed income from home ownership is added to total household income, inequality measured by the P90/P10 ratio falls from 6.3 to 5.9 in per capita income and from 7.0 to 6.7 in total income in 2014.
Finally, the progressive nature of direct personal taxes reduces inequality. The Spanish tax system’s progressivity stems, in particular, from the existence of a non-taxable allowance and rising marginal tax rates in the personal income tax scale. To analyse the role that direct taxation plays in reducing inequality, the personal income tax paid by each household has been estimated by deducting tax liabilities from gross income to give after-tax income.Footnote 12 As Table 3 shows, net income inequality is lower than gross income inequality, measured by the Gini index or the income ratio p90/p10. Specifically, in accordance with the latter indicator, the ratio is 7.0 in gross terms and 6.1 in net terms. The results are the same when per capita income is analysed.
Personal income tax in Spain is slightly less progressive than the OECD average. One way to compare the degree of progressivity of different personal income tax systems is by analysing the different tax wedges—the difference between gross and net income—arising from personal income tax and social security contributions for different income brackets. According to OECD data, the difference in tax wedge between persons receiving 167% of their country’s average income and those receiving 67% was 7.8 percentage points in Spain in 2016 compared with 8.1 percentage points for the average of the OECD countries. Accordingly, direct personal taxation reduces inequality in terms of gross income per capita by slightly less in Spain than on average in the OECD countries.
The redistributive nature of a tax system may be determined by differences in the parameters of other taxes in addition to income tax. However, the redistributive effects of indirect taxation in Spain (essentially value added tax and excise duties) are low because progressivity is limited (see Bover et al. 2017). This is not exclusive to the Spanish tax system and is because indirect taxation rates are essentially proportional and do not vary according to income. Thus, according to the European Commission, average effective VAT rates vary only slightly by income deciles in most EU countries (see Institute for Fiscal Studies 2011).
Household consumption and wealth
The level of inequality and the way in which it evolves is often discussed in terms of income. However, from the standpoint of utility or well-being, people’s level of consumption may be more relevant. Consumption aggregates the goods an individual enjoys directly. As well as being influenced by individuals’ expected income and the uncertainty surrounding that income, the purchase of these goods is also affected by wealth, which also determines potential access to external financing, as well as the point in the life cycle of the members of the household, and their access to public or subsidised goods (see Attanasio and Pistaferri 2016). In this regard, total consumption and per capita consumption show less inequality between households than net income (as Blundell et al. 2008). For example, the P90/P10 ratio for total consumption is 4.4, compared with 6.2 for total net income (see Table 3). This happens at all levels of the income distribution, irrespective of whether income is measured in total or per capita terms and of the point in the economic cycle.
The smaller inequality in consumption that is observed partly reflects higher mobility of income at the tails of the distribution, despite the strong persistence of differences in the income distribution. The expected course of future income is a fundamental factor in explaining consumption decisions.Footnote 13 For its part, households’ expectations about their future income largely depend on how their income and that of their social reference group have developed in the past.Footnote 14 Thus, in any economy where income mobility is very limited, levels of inequality will be highly persistent. In the case of Spain, using EFF data, it is found that 58% of households with a low relative income in 2011 (measured as the 20% lowest incomes) remained at this level in 2014, whereas the remaining 42% improved their relative position.Footnote 15 Meanwhile, a household’s future income depends both on the current level of income and any shocks affecting it. Table 4 shows estimates of predictive income distributions, using household income data from the panel sample of the EFF, where income dynamics varies depending on the households’ position in the income distribution in the preceding period and the size of the shock they receive at each age.Footnote 16 In this regard, according to the evidence shown in the table, in the face of (very) negative shocks (those in the bottom part of the shock distribution), the situation of households in the upper part of the income distribution tends to deteriorate sharply, to a much greater extent than in the case of households in the lower part of the income distribution. This asymmetry is relevant when analysing how different households form their income expectations and how these expectations affect consumption and spending. Specifically, given the effects of adverse shocks on them, higher income households tend to raise their saving rate. The opposite happens in the case of lower-income households, whose situation improves significantly in the event of (very) positive income shocks. That is, such “unusual” shocks are associated with lower persistence than other shocks and hence have a higher propensity to wipe out the history of past shocks. These dynamics cause significant revenue movements at the tails of the distribution,Footnote 17 which is consistent with the evidence that income inequality exceeds consumption inequality.
The way in which a household’s consumption changes in response to fluctuations in income also depends on its level of wealth and, to a lesser extent, the age of its members. The wealthiest households are better able to maintain their level of consumption in the face of falling income, given that they may have assets or easier access to borrowing. Therefore, for these families, consumption will depend to a lesser extent on income than in other households, at least up to a certain age. Thus, it has been observed that in households headed by a person under 55 years, the availability of more wealth allows for more stable consumption, whereas in households headed by a person over 55, wealth barely plays any role in consumption stabilising. In effect, as Table 5 shows, the change in average household consumption in response to changes in income is smaller in wealthier families whose head of household is aged under 55. Specifically, among households with little relative wealth (at the 5th percentile) and a 30-year-old head of household, a 1% decrease in income causes a 0.5% drop in consumption, whereas this decrease is smaller (0.1%) among wealthier households (at the 95th percentile). However, in households aged over 55 years, the drop in consumption in response to a drop in income of 1% is always around 0.3%, regardless of the level of wealth.
Of course, certain public services, such as health and education, have a significant redistributive effect in consumption. There is empirical evidence using various methodological approaches and applying criteria to impute to households the value of public services provided by general government that shows that these services have a significant redistributive effect (see Goerlich 2016). In the case of education, for instance, this is particularly true for preschool and primary education, as well as for compulsory secondary education, while the effects are less clear in the case of higher education.Footnote 18
In terms of wealth inequality, as shown in Table 3, it greatly exceeds income inequality. Specifically, the Gini index is 0.68, much higher than observed in other variables analysed previously and the wealth ratio P80/P20 is 15.5.Footnote 19 Similarly, the wealthiest 1% own 20% of all wealth and the wealthiest decile holds 52.7% of total wealth.
Income dynamics explain, to some extent, the greater accumulation of wealth by high-income households. It should be borne in mind that wealth accumulates year after year, such that differences in wealth increase over time among those households that maintain their relative income positions. Moreover, as already mentioned, faced with the risk of a negative shock causing a significant reduction in their income, higher-income households tend to increase their savings, which is an additional explanatory factor in the dynamics of wealth inequality.Footnote 20 However, differences in wealth inequality derive not only from different saving habits, but also from differences in the composition of households’ asset portfolios and the evolution of their prices. Ownership of assets is widespread, even among low-income brackets. Specifically, 94.3% of households in the first two income deciles own some kind of asset. However, the composition of these assets varies significantly with household income. Thus, in the first two deciles, 89.5% of total assets are associated with real estate property, whereas this percentage drops to 57.5% in the last decile. Asset holdings by this latter segment are related to self-employed business (15% total assets), and certain financial assets, such as shares and participating interests, which represent 11.2% of total assets. If prices of these assets perform better than those of real estate assets, this would tend to increase wealth inequality, and vice versa. Recent economic literature has emphasised the heterogeneity of access to different assets and their returns to explain the fact that wealth is more concentrated than income (see Gabaix et al. 2016; Fagereng et al. 2016).
In any event, unlike the case of income or consumption, the level of wealth inequality in Spain is lower than that in comparable countries. In comparative terms, again using information from the HFCS, it is observed that, despite the large differences in household incomes by international standards, Spain has a smaller wealth inequality (see Fig. 6), which may be related to the fact that there is a widespread concentration of saving in real-estate assets, even among higher-income households.