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Earnings inequality and workers’ skills in Italy
Structural Change and Economic Dynamics 51 (2019) 215–224
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Earnings inequality and workers’ skills in Italy Maurizio Franzini, Michele Raitano ∗ Sapienza University of Rome, Via del Castro Laurenziano 9, 00161, Rome, Italy
a r t i c l e
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Article history: Received 27 June 2019 Received in revised form 27 August 2019 Accepted 15 September 2019 Available online 17 September 2019 JEL classification: I24 D31 J31 Keywords: Earnings inequality Education Skills Theil decomposition Italy
a b s t r a c t The increasing trend of earnings inequality observed in many countries is usually ascribed to a higher premium to skills, commonly proxied by education. Focusing on Italy, a country characterized by a steep rise in earnings inequality since the ‘90 s, we aim at verifying whether this trend is attributable to education. Making use of administrative data about private employees, we carry out Theil decompositions and estimate wage equations to investigate how much of this trend is linked with education and other observable worker’s and firm’s characteristics. We find that the rise in earnings inequality is explained by the “within education” component, rejecting the idea that it is due to a higher premium for the high-skilled. Furthermore, controlling for workers’ and firms’ characteristics in wage regressions – also including workers’ literacy and numeracy recorded in OCED-PIAAC – we find that level and trend of earnings inequality are not explained by these characteristics. © 2019 Elsevier B.V. All rights reserved.
1. Introduction In the past decades inequality in disposable income, which is considered the best proxy of inequality in living conditions and economic wellbeing (Canberra Group, 2011), has increased in almost all advanced countries. In several countries, this process has been concomitant with a similar trend in labour income inequality (OECD, 2011; Salverda et al., 2009, 2014; Atkinson, 2015; Bourguignon, 2017). Most existing studies consider the rise in inequality as a consequence of processes that take place in the labour market. It is generally claimed in mainstream studies that structural phenomena such as skill biased technological change, digitalization and globalization, through their impact on labour supply and demand, have widened the productivity gap between differently skilled workers (e.g., Bound and Johnson, 1992; Katz and Murphy, 1992; Acemoglu and Autor, 2011; Autor et al., 2013).1 However, other studies have focused on further drivers of the rise in inequality (Franzini and Pianta, 2015; Piketty, 2014; Baccaro and Howell,
∗ Corresponding author. E-mail address: [email protected] (M. Raitano). 1 However, studies based on employer-employee linked datasets have recently pointed out that a large part of the increase in wage inequality is between - rather than within - firms and plants (e.g., Card et al., 2013). https://doi.org/10.1016/j.strueco.2019.09.004 0954-349X/© 2019 Elsevier B.V. All rights reserved.
2017; Denk and Cournède, 2015; Causa et al., 2016 and 2018): e.g., the increased power of capital over labour, the strengthening of a sort of “oligarch capitalism”, the financial deregulation and the “financialization” of current economies, structural and institutional reforms, especially the financial deregulation, a retrenchment in redistributive policies and the reforms weakening labour market institutions. Nevertheless, skill-biased technological change (SBTC) and globalization have been considered by mainstream studies the most important causes of the rise in inequality until recent years. According to this interpretation, in order to be productive ICT technologies have to be coupled with high skilled workers, whose demand would therefore increase. Conversely, new technologies, along with the competition of low skilled workers in less developed countries fostered by the globalization process (that also fostered the chances of offshoring jobs in such countries), would push down the demand for low skilled workers in advanced countries, thus compressing their wages. In a similar vein, more recently, various authors (e.g., Autor et al., 2006; Goos and Manning, 2007; Acemoglu and Autor, 2011; Cortes et al., 2014) have pointed out that technological progress and digitalization mostly displaced medium skilled workers performing routine-jobs (e.g., clerical and administrative jobs) which are neither complementary to ICT (like non-routine cognitive tasks performed by high-skilled workers) nor neutral to it (like non-routine manual tasks performed by low-skilled workers). As a consequence, the labour market would have become polarized,
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i.e., most high-skilled workers and some low-skilled workers, but not medium-skilled workers, would enjoy higher wages and better occupational opportunities (Das and Hilgenstock, 2018). According to these views, the trend of wage inequality is explained by increasing skill premia, hence different skills held by individuals are considered the main driver of inequality. Consequently, investment in human capital – usually proxied by education – is strongly recommended as the best policy not only for fostering GDP growth but also for reducing earnings and disposable income inequality. Contrary to this view, some empirical studies have come to the conclusion that standard human capital proxies, such as experience and education, explain at most a third of the variance in wages, while residual or within-group wage inequality – i.e., wage dispersion among workers with the same education – is much greater and increasing in many countries (Lemieux, 2006). This is not to deny that the inequality between differently educated individuals – i.e., “mean” skill premia – is crucial to explain wage gaps; the point is that a large part of total earnings inequality occurs within the group of equally educated workers. Therefore, while yielding on average a high and (at least in some countries) increasing return, investment in education is exposed to a large variance and is quite risky, as suggested by the high and increasing inequality in the earnings of similarly educated workers. Despite this evidence, the literature has focused on the wage gap between workers with different observable characteristics (especially education). A recurrent justification for this focus has been that also wage disparities within educational groups are related to returns to skills, since individual detailed abilities are usually unobservable in empirical analyses (Card and Lemieux, 1996; McCall, 2000). However, as pointed out by Atkinson (2007) and Bourguignon (2017), a single explanation of multifaceted trends in inequality across countries based on skill premia looks questionable. Therefore, mechanisms behind the within education inequality should be investigated much more than it is actually done in theoretical and empirical studies. In this paper we aim at shedding light into the black box of within education inequality focusing on Italy, a country characterized by a steep rise in earnings inequality in the last decades. To this aim, we shall try to answer three linked questions: i) how much of wage inequality can be imputed to education?; ii) has the importance of education as a driver of wage inequality increased over the last years?; iii) are there other factors impacting on wage inequality that capture unobservable components of workers’ human capital and abilities? If education had been predominant in Italy as a driver of increasing earnings inequality, we should observe a rise both in the wage of high skilled compared to low skilled workers and in the share of total inequality explained by education. Moreover, being education an imperfect proxy of workers’ skills, total inequality should be wholly explained by including in the analysis further workers’ and firms’ characteristics that might be associated with individual unobservable skills. These expectations are not borne out by our analyses which instead lead to the conclusion that earnings inequality is largely explained by the within education component and by the inequality which remains unexplained even after several further workers’ and firms’ characteristics have been taken into account. We use an innovative dataset – called AD-SILC – built by merging the 2005–2012 annual waves of the Italian component of the European Union Statistics of Income and Living Conditions (EU-SILC, the Italian component is named IT-SILC), with administrative social security records – managed by the Italian National Social Security Institute (INPS) – that track private employees in the period
1990–2013.2 Social security files report, on an annual basis, gross earnings, weeks of work and some worker’s and firm’s characteristics (e.g., the broad occupation, the region of work, the industry), but do not record worker’s education. On the contrary, IT-SILC waves record education, but cover a limited time span. Merging longitudinal administrative record of all individuals interviewed in IT-SILC 2005–2012 with IT-SILC information allows us to enrich social security records with the missing information about education. To answer our research questions, we thus use the AD-SILC dataset and estimate the role of education in explaining earnings inequality over the 1990–2013 period carrying out a Theil decomposition of total inequality by educational groups. Moreover, we inquire the role of education on earnings inequality also through a regression based approach, observing the trend of residual inequality once further worker’s and firm’s characteristics (e.g., worker’s experience, firm’s industry and size) which might be associated with unobservable components of human capital are taken into account. Furthermore, because IT-SILC only records the highest attained degree without providing further details on the educational path (e.g., the field of study or proxies of the quality of education), we also use cross-sectional OECD-PIAAC (Programme for the International Assessment of Adult Competences) data. Such data provide details on the educational path and individual’s scores in literacy and numeracy tests, which are considered good proxies of workers’ skills and competences; they also allow us to compare the size of residual inequality once these proxies of unobservable abilities are taken into account. More in detail, the paper is organized as follows. Section 2 presents the data and the empirical methodology. Section 3 shows the trends of the main indicators of earnings distribution in Italy in the period 1990–2013 also distinguishing workers by education. Section 4 presents our main results about the limited explanatory power of education and other observable variables as regards the trends of earnings inequality since the ‘90 s in Italy, while Section 5 shows our results about residual inequality after taking PIAAC data into account. Section 6 concludes, discussing the policy implications of our findings. 2. Dataset and empirical methods 2.1. Data We use the AD-SILC dataset (where the “AD” stands for “administrative), that merges the 2005–2012 waves of the IT-SILC survey (i.e., the Italian component of the EU-SILC) with the administrative longitudinal social security records collected by the Italian National Social Security Institute (INPS), that record employment and earnings histories of all individuals working in Italy from the moment they entered the labour market up to the end of 2013. Social security records offer a comprehensive picture of the career of all individuals working in Italy, as they report, on a yearly basis and for each working relationship: working weeks, the type of working relationship (e.g., as an employee in the public or in the private sector or as a self-employed) and gross earnings, which include overtime, personal income taxes and social insurance contributions paid by the workers as well as allowances for maternity, sickness and temporary job suspension (through the so called Cassa Integrazione Guadagni). INPS data thus allow us to perfectly reconstruct year by year the effective labour market experience (in weeks) after the entry in activity and weekly wages (computed by dividing the total income earned in the longest employment relationship during a year by the corresponding number of worked
2 Individual fiscal codes have been used as the matching key for merging the two datasets.
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weeks). For employees in the private sector, INPS data also record the contractual arrangement (full-time versus part-time, while the information about fixed-term or open-ended contracts is recorded only from 1998), the region of work and (from 1990) firm’s size and industry, coded at the 2-digit NACE level. On the other hand, INPS archives do not record workers’ education, that is a crucial variable for our purposes. This information is instead available in IT-SILC. Therefore, the AD-SILC dataset enriches retrospectively the IT-SILC cross-sectional waves with longitudinal working histories taken from the INPS archives of the individuals sampled in IT-SILC. In this paper we focus on employees in the private sector because earnings of public employees have some flaws in INPS archives,3 while self-employed earnings are plagued by underreporting and by top and bottom coding in administrative archives. Note, however, that most studies on earnings inequality focus on private employees (e.g., Card et al., 2013; Devicienti et al., 2018); moreover using administrative rather than survey data allows to greatly reduce measurement errors.4 In our analysis we take account of three earnings variables (all in Euros, converted to 2014 constant prices using the harmonised index of consumer prices):
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sitional changes in the labour force over the observed period due to changing enrolment rates in higher education by younger workers. In conclusion, we focus on the gross earnings of a sample of 69,911 individuals (705,966 observations) in the age class 25–54 who have been employed in the private sector at least once in the 1990–2013 period. Individuals are classified in three groups according to the highest attained degree: at most lower secondary educated, upper secondary educated, and tertiary graduates. In a further exercise, to reduce the omitted variable bias due to the unobservability of individual abilities over education in IT-SILC data, we make also use of the cross-sectional OECD-PIAAC (Programme for the International Assessment of Adult Competencies) data that record employees’ gross monthly wages and details of both the educational path (highest educational attainment and field of study) and the individuals’ scores in literacy and numeracy tests, which may be taken as a good proxy of workers’ competences and abilities (Pellizzari and Fichen, 2013).6 The sample we use includes 1520 individuals (excluding non-Italian citizens) who were working as employees at the time of the interview and reported a positive monthly gross wage. 2.2. Methods
1 annual earnings, that is the most suitable variable to capture the influence of labour market outcomes on workers’ living standard. Annual earnings depend on unitary wages – i.e., hourly wages, not recorded in INPS archives – and hours of work in the year that depend on the number of hours usually worked in a week (i.e., on overtime and part-time arrangements) and on the number of working weeks (i.e., on periods spent out of work in a year, which is also affected by temporary arrangements). 2 weekly wage, not influenced by the variable number of weeks of work in a year; 3 weekly wage of “strong workers”, i.e., working the whole year (52 weeks) with a full-time arrangement. This is the most reliable proxy of hourly wage, usually considered by economists as the best measure of workers’ productivity and as the outcome of the contractual bargaining. In all our analyses we only consider individuals with a positive wage in the year, i.e., we do not include those with “zero” earnings in a certain year (e.g., the long-term unemployed). The retrospective AD-SILC dataset is representative of the population living in Italy in 2005–2012. Its cross-sections are used to obtain reliable estimates for aggregates whose sampling distribution (e.g., by age) is similar to that of the population. For this reason, in this paper we do not consider non-Italian citizens (they are under-represented in the retrospective panel because of their greater mobility in and out of Italy) and the active population aged over 55 years (as the dataset refers to individuals interviewed in 2005–2012, the sample representativeness of older workers in the early years of observation in AD-SILC is not adequate).5 We do not even consider those aged less than 25 to depurate earnings data from possibly limited wages earned by students and from compo-
3 Data for public employees are available in AD-SILC for the period 1996-2011 only; moreover, the coverage of public employees in the social security administrative archives that we had at disposal is not complete due to delays in the digitalization of some information (mostly about workers of the education sector). 4 Being our sample based on the IT-SILC samples, it is not perfectly representative of top earners because of either under-reporting by very rich respondents or lower survey participation rates by very rich households, or both (Burkhauser et al., 2018). 5 Furthermore, the increase in the retirement age introduced with the pension reform process started during the ‘90s modified the size and the composition of the labour force aged over55. Therefore, excluding these individuals also allows us to overcome compositional issues related to the participation of the elderly in the labour market.
To better understand the role of education as a driver of earnings inequality, we split the workers in three subgroups according to their highest educational attainment and assess – through a decomposition by subgroups exercise – the relative size of the inequality that emerges within and between the three educational groups. Thus, we can measure the share of total inequality due to differences in education – henceforth, the “between inequality” – and the share of inequality not due to education, referring to individuals with the same education – henceforth, the “within inequality”. To this aim, we use the Theil index of inequality, that, differently from the Gini index, is perfectly decomposable among subgroups; in fact, it can be expressed as the sum of between and within inequality. The between groups inequality is computed through a counterfactual distribution where all individuals with the same education earn the mean wage of that group, so identifying mean distances among groups (the main focus of the economic literature); the within groups inequality is instead the weighted average of the inequality within each group.7 To capture trend of residual inequality we follow Card et al. (2013) and Devicienti et al. (2018), and also compute the root mean square deviation (RMSE) of a set of OLS regressions, run year by year, of annual earnings on a set of covariates and compare the estimated trend of the RMSE with the value of the unconditional standard deviation of annual earnings. In more detail we first run a base model simply controlling for basic socio-demographic characteristics (gender, age, age squared and region of work). Then we add education and, finally, we run a full model where we add further individuals’ and firms’ characteristics that may be associated with workers’ education and unobservable abilities, i.e., labour market experience since the entry in activity (also squared), a dummy on part-timers, the broad occupational group (blue-collar, white-collar, manager), a cubic polynomial on firm’s size and dummies on the 2-digit NACE industry.8
6 The PIAAC survey was carried out in Italy between September 2011 and March 2012. 7 In the Theil decomposition, inequality within each group is weighted by the relative income got by each group (Cowell, 1995). 8 In the full model we include all the variables about the employee and the firm available in INPS archives in the whole period 1990-2013. In detail, we include 119 covariates, e.g., a dummy for females and for those working on a part-time basis; a square polynomial on age and experience, a cubic polynomial on firm’s size, three
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We run the same type of exercise also on the cross-sectional PIAAC dataset, comparing the value of the unconditional standard deviation of monthly wages with the RMSE of three OLS specifications: a base model where we only control for gender and dummies about the 5-year age class; a model where we add to the base model the three educational groups and the field of study of those who attained at least an upper secondary degree (general, social sciences, STEM, health); a full model where we also include PIAAC individual scores in literacy and numeracy tests (also squared). 3. The trend of earnings inequality in Italy Over the period 1990–2013 we find different trends of mean earnings according to the notion of wage considered (Fig. 1). While both annual earnings and weekly wages have stagnated in real terms since the beginning of the ‘90 s, our proxy of hourly wages for “strong workers” (i.e., those employed the whole year with a full-time arrangement) shows a reasonable increase from 2003 onwards. However, Italy has been characterized over the last decades by a large increase in the share of (often involuntary) parttime jobs: our data show that the share of private employees whose longest working span in the year was spent in part-time arrangements increased from 12.2% in 2000 to 22.8% in 2010; at the same time the share of employees working the whole year with a fulltime arrangement has reduced overtime from 53.6% in 2005 to 46.8% in 2013. Therefore, it has to be stressed that our proxy of hourly wages is not representative of all workers but refers to a declining share of relatively advantaged employees. More in general, earnings distribution in Italy has been characterized by increasing inequalities within workers. Despite stagnating mean annual earnings (Fig. 1), different trends characterize the various percentiles and the values of the 10th and the 25th percentiles fell considerably in the period 1990–2013 (Fig. 2).9 Consistent with these trends, the Gini index (of both annual and weekly wages) steeply increased over the observed period, with a tendency of the increase to flatten in more recent years.10 As expected, the Gini is higher for annual rather than weekly wages; in fact, the distribution of weeks of work amplifies earnings inequality because those who earn lower wages are at greater risk of unemployment in a year (Fig. 3). A different trend emerges when looking at weekly wages of the “strong workers”: the Gini index is quite constant since 2003, thus suggesting that the increase in inequality from 2003 onwards is mainly due to the spreading of part-time arrangements. When we distinguish workers by education, a return for higher educated employees clearly shows up; indeed, mean annual earn-
dummies on the broad occupation and on education, twenty dummies on the region of work, and eighty-three dummies on the NACE industry. 9 Trends of bottom percentiles might rather underestimate trends of earnings inequality since in our analyses we are not including the so-called “para-subordinate” workers – i.e., dependent self-employed, those working as self-employed in legal terms but “economically dependent” on a single client. Parasubordinate arrangements were often used until recent years as a cheap type of contract for replacing low-paid employees (Raitano, 2018). 10 As remarked, earnings inequality is computed by taking into account only the subsample of individuals working during a certain year, thus without considering outflows from the labour force, e.g., due to the economic crisis that started in 2008. As a consequence, if a recession had the effect of removing the least paid workers from the workforce, earnings inequality among the remaining ones could paradoxically be reduced. To take into account the whole effect of the crisis on earnings inequality in Italy, Raitano (2019) computed the Gini of annual earnings of private employees including in computations those workers who were active in a certain year and then became unemployed (with zero earnings) in the following years. The findings are impressive: earnings inequality increased by 21% in Italy in the period 2008-2013 when involuntary unemployed are taken into account, while earnings inequality within employees (i.e., excluding “zero earners”) rose by 2.5% from 2008 to 2013 (Fig. 3).
ings of tertiary graduates exceeds by around 40% and 80% earnings of those with at most a lower secondary or an upper secondary degree, respectively (Fig. 4). Interestingly, this “rough” measure of the skill premium has increased since the beginning of the 2000s when we focus on annual earnings, while it has remained rather constant (or even reduced) when we focus on our proxy of hourly wages of “strong workers”. This diverging trend thus signals that the relative increase in the return to education related to annual earnings is due to a lower probability for tertiary graduates to be hired through short-term temporary and part-time arrangements. Apart from this rough measure of return to education, it is interesting to focus on a measure of the risk of the individual investment in education. To this aim, we computed some indicators of a possible overlapping of the annual earnings distribution of differently educated workers (Fig. 5). These indicators show that – despite the higher mean return – a large share of tertiary workers did not enjoy any monetary benefit in a given year. Indeed, we find that in 2013 the earnings of 38.5% of tertiary graduates were less than the mean earning of upper secondary graduates, and 25.1% of tertiary graduates earned less than the average employee with at most a lower secondary degree. However, the shares of tertiary graduates at risk of earning less of those with lower degrees have reduced overtime. Finally, we computed the Gini index of annual earnings of differently educated employees (Fig. 6). The main finding is that inequality within tertiary graduates is the highest – it is also higher than the Gini computed on total workers – even if it has steadily reduced since 2003. Conversely, the Gini of annual earnings within both low and middle educated workers has steadily increased in the observation period, thus engendering a sort of convergence of earnings inequality within the three educational groups.
4. The trend of within education and residual inequality The relative weight of the within and between educational groups inequality is assessed through a decomposition of the Theil index by educational attainments. If the relative importance of education in explaining individual wage inequality had increased, we should find that the increase in total inequality is mostly associated with an increase in the between component, which, as already clarified, captures the influence on total inequality of the gaps in the mean earnings of differently educated workers. Actually, focusing on annual earnings, we find that total and within education inequality followed the same trend during 1990–2013 period, while between inequality stayed rather constant, with a very slight increase from 2003 onwards (Fig. 7). On the whole, the share of total inequality explained by the between component is rather limited – between 7 and 10% according to the wage dimension (Fig. 8) – and more important, the trend of the share of the between inequality was constant over the observed period as concerns annual earnings, and even reduced, as concerns weekly wages and weekly wages of full-time and always employed workers.11 As already pointed out, educational attainment might be a poor proxy of individuals’ skills; indeed, a large heterogeneity between workers with the same education, might emerge, due to, e.g., the field of study, the type of upper secondary or tertiary degree, the
11 The mean education increased over the 1990-2013 period in Italy. Among private employees aged 25-54 the share of tertiary graduates rose from 5.3% in 1990 to 11.9% in 2013, while the share of those with at most a lower secondary degree reduced from 60.5% to 35.3% over the same period. Despite this major structural change, further analyses – available upon request – show that the outcome of the decomposition exercise does not change if we keep constant at the 1990 values the distribution of workers by educational attainment.
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Fig. 1. Trend of mean wages for private employees. Index number: 1990 = 100. Source: elaborations on AD-SILC data.
Fig. 2. Trend of percentiles of annual earnings. Index number: 1990 = 100. Source: elaborations on AD-SILC data.
quality of the attained education or, more in general, further skills or abilities unobservable in an empirical setting. Our decompositions, despite their impressiveness, are then not enough to argue that skills and human capital are not a major driver of the trend of earnings inequality in Italy. The AD-SILC dataset only includes information on educational attainment, that may be considered as a poor proxy of human capital. The dataset does not include further proxies of human capital and skills (e.g., the quality of the attained education, the specific worker’s competences, her motivation and effort), that are unobservable in our empirical setting. Nevertheless, we might suppose that these unobservable skills are (or tend to become, over time) observable by the employers and therefore can be rewarded also in terms of better hiring chances, contractual arrangements, occupation and of promotion. Accordingly, we might argue that, among workers with the same education, those with better skills should be hired in more rewarding industries and firms, with better contractual arrange-
ments (i.e., full-time instead than part-time), they should achieve a better occupational status (i.e., manager or white-collar rather than a blue-collar) and should experience fewer unemployment spells, thus accumulating a higher experience since their entry in the labour market. Therefore, these further dimensions of the working status and career might capture, at least partially, unobservable worker’s skills. In other terms, workers unobservable skills could affect these dimensions of the working career which are correlated with wages and, in turn, have an influence on wage inequality. Higher returns to human capital could then manifest themselves as an improvement in such dimensions and the explanation of rising inequality based upon human capital and skill endowments could be vindicated. To evaluate such hypothesis, following, Card et al. (2013), we carried out OLS regressions of annual earnings and computed the standard deviation of the residuals (the RMSE) as an index of residual inequality in order to verify how much of individual wage gaps
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Fig. 3. Trend of Gini of wages. Source: elaborations on AD-SILC data.
Fig. 4. Ratio between mean earnings of differently educated workers. Source: elaborations on AD-SILC data.
not explained by observed characteristics might be associated with workers’ skills other than education (see Fig. 9, where, to allow comparisons, we also show the unconditional standard deviation of annual earnings). As discussed in Section 2.2, estimated models in Fig. 9 include an increasing number of covariates: in the first “base” model we only control for basic demographic characteristics (gender, age and region of work), then we add education and in the final “full” model we also add dozens of covariates summarizing occupation, contractual arrangement, industry and firm’s size and experience since the entry in the labour market. As expected, in comparison with the unconditional standard deviation of annual earnings, the higher
the number of covariates, the lower the standard deviation of the residuals. However, the reduction in the RMSE is rather limited even in the “full model” where we include around 120 covariates, as clarified in Section 2.2. More important, and consistent with the trend of the between education inequality shown previously, the trend of RMSE is increasing over the whole observation period; furthermore, it is very similar to the trend of the unconditional standard deviation of annual earnings, thus pointing out that also these further covariates that might mask unobservable components of workers’ skills and human capital do not explain the increasing trend of earnings inequality in Italy since the beginning of the ‘90 s.
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Fig. 5. Overlapping between annual earnings distributions of differently educated workers. Source: elaborations on AD-SILC data.
Fig. 6. Gini of annual earnings, by education. Source: elaborations on AD-SILC data.
In conclusion, this evidence does not lend any support to the idea that the size and the dynamics of earnings inequality mainly depends on workers’ skills, as proxied by educational attainments and by other workers’ and firms’ characteristics that might capture unobservable components of workers’ human capital.
5. Wage inequality and workers’ abilities: evidence from OECD-PIAAC Inequality within equally educated people can be due to several factors, some of which are very difficult to observe in empirical analysis. The list of the factors that may affect within inequality
should include at least the following items: i) chance and luck;12 ii) individual innate abilities; iii) detailed characteristics of the acquired education (e.g., the type of attended programmes and the field of study); iv) the quality of the attained education;13 v)
12 A clear example of how luck impacts on inequality refers to the long-term effect on earnings related to the economic conditions at the time of entry in the labour market. Actually, those who start working during a recession are permanently harmed (Oreopoulos et al., 2012). 13 Actually, the concept of quality of education is very elusive. In particular it is not clear in what sense some schools or universities can be considered the best. Do they best meet the specific needs of the firms? Or do they favor the future needs of adaptability of the economy (perhaps favoring an accumulation of general cognitive skills)? Or does a wage premium for those who attended top universities depend on an effective increase in individual productivity? Or does it only depend on a signal
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Fig. 7. Trend of within and between inequality by education. Annual earnings. Source: elaborations on AD-SILC data.
Fig. 8. Theil decomposition by education: share of the between education inequality. Source: elaborations on AD-SILC data.
hard skills other than those acquired at school; vi) individual soft skills (non-cognitive abilities);14 vii) social connections and ties (Granovetter, 2005). With the exception of chance and luck, these factors can be distinguished in two groups: those related to cognitive abilities and skills of workers, with effects on individual productivity (factors ii-v), and those not related to skills and productivity, like social connections and, maybe, also soft skills, whose relationship with productivity is unclear.
associated with the “good name” of the University? In addition, there is no guarantee that, in the absence of appropriate changes in the labour demand, a growing supply of “well-educated” workers would engender a general increase in wages, thus reducing wage inequality. 14 Soft skills are factors shaping social and relational competences of each individual – such as risk aversion, willingness to exert effort, extroversion, willingness to work in team, the sense of discipline or leadership – which labour market success seems to increasingly depend on (Bowles et al., 2001; Goldthorpe and Jackson, 2008).
Individual characteristics may greatly influence individual productivity, but they are difficult to measure. However, the Programme for the International Assessment of Adult Competences (PIAAC) carried out by OECD is well suited to this purpose as it records, among the others, information on employees’ gross wages, variables recording some details of the educational path (i.e., the field of study) and, mostly, scores on individual competences in literacy and numeracy, which might be considered good proxies of workers’ unobservable skills, besides education. Unfortunately, PIAAC survey was carried out in a single crosssection and does not allow us to test whether the trend of inequality is explained by changing returns to workers’ literacy and numeracy.15 However, PIAAC data are very useful to measure the explanatory power of wage gaps associated with these further proxies of workers skills.
15 Unfortunately, since fiscal codes are not recorded, PIAAC data cannot be merged with the longitudinal administrative social security records managed by INPS.
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Fig. 9. RMSE of OLS estimates of annual earningsa . a The following covariates are included in the various model: dummies on gender and region of work and age and age squared in the “base model”; dummies on educational attainments are added in the “base + education” model; dummies on part-time jobs, on occupation, on Nace 2-digit industries plus experience and experience square and a cubic polynomial on firm’s size are added in the “full” model. Source: elaborations on AD-SILC data.
Table 1 RMSE of OLS regressions of log monthly wages in Italya : Employees aged 25–54. Unconditional S.D.
1,771.9
RMSE M1
M2
M3
1,700.1
1,599.3
1,582.8
a
The following covariates are included in the various model: dummies on gender and age class in model M1; dummies on educational attainments and the field of study are added in model M2; scores in literacy and numeracy tests and their squared are added in model M3. Source: elaborations on OECD-PIAAC data.
To this aim, as discussed in Section 2, consistent with the analysis carried out in Section 4, making use of the cross-sectional PIAAC dataset about Italy, we regressed through OLS gross monthly wages of employees in Italy on an increasing set of covariates and compared the RMSE of these specifications with the unconditional standard deviation of monthly wages (Table 1). Despite all additional covariates exert a significant influence on earnings, the inclusion of dummies about education and the field of study (model M2) only slightly reduced the RMSE, as already seen in Section 4. More important, the RMSE very slightly reduces when we add scores in numeracy and literacy to the covariates, to capture a possible influence on earnings of workers’ abilities and competences over education. These findings suggest that literacy and numeracy tests capture competences linked to the individual educational path, thus their inclusion does not substantially improve the explanatory power of our models. As stated by Egger and Grossmann (2004), differences in cognitive skills seem to be of little value in the explanation of wage inequality within education groups. 6. Conclusions In this paper we have argued that, contrary to a widely held opinion, increasing wage inequality in Italy over the last two decades only to a very limited extent depends on higher skill-premia. We came to this conclusion considering not only the return to education (the usual proxy for human capital) and the share of wage inequality due to differences in education but also the influ-
ence of several variables able to capture non-observable skills. The trend of the standard deviation of residuals of wage regressions with dozen of covariates provides further support to our claim. Our findings also provide information on inequality within equally educated workers. In particular, wages are widely dispersed across tertiary graduates, pointing to a high riskiness of the investment in education. Indeed, the share of wage inequality not explained by individuals’ skills and their productive abilities is sizeable and we know very little about its determinants. One important point to clarify is how much of such inequality originates within firms and how much of it is firm-specific, i.e., between rather than within firms. This initial step could orientate further research in order to better evaluate the role played by – among others – such factors as market structure, collective bargaining and wage-setting processes, propensity to technological and organizational innovation and social connections. A further aspect to be investigated in future research concerns the influence on within inequality of institutional factors, in particular of the deregulation of the labour market that started in Italy in the mid-1990s and led to an increasing share of precarious workers, also among tertiary graduates. Moreover, if detailed occupations were recorded (i.e., at 3-digit ISCO; note that INPS archives only distinguish blue-collar, white-collar and manager), it would be very interesting to carry out an analysis similar to the one presented in this paper by focusing on detailed occupations rather than educational attainments as a main driver of earnings inequality. To identify what the unexplained wage inequality depends on is important from several points of view. First, we could learn more about the actual working of labour markets and uncover significant differences across countries, regions or industries. Second, we could gain a better understanding of how acceptable inequalities are and of their economic and social consequences. Human capital is usually considered acceptable as a cause of inequality if the access to education is open to everybody. Its limited importance as a driver of wage inequality suggests, hence, that wage inequality could be much less acceptable than usually believed. This would be, in particular, the case if institutional
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arrangements (i.e., wage-setting processes) discriminate among otherwise ‘equal’ workers or, more seriously, if family origins and social connections play a major role in that inequality. Besides impinging on the acceptability of inequality, such determinants could have effects on its consequences. For example, social connections could lead to an allocation of workers that prevents the achievement of efficiency and slows down economic growth. Third, knowing more about unexplained wage inequality could help design better policies to counter inequality. The assumption that human capital is the main driver of wage inequality leads to a straightforward policy recommendation: invest more in human capital and bridge the education gap. Of course, allowing workers from more disadvantaged backgrounds to have access to a higher education is more than desirable. However, the point is that if wage inequality is largely due to other factors, such investment could result in failure, eventually fuelling over-education which is a widespread phenomenon in several countries. In conclusion, knowing better about the mechanisms generating wage inequality is an essential step to advance our knowledge not only of inequality but also of a significant number of economic and social problems and to identify the most effective policies to address them. References Acemoglu, D., Autor, D., 2011. Skills, tasks and technologies: implications for employment and earnings. In: Ashenfelter, O., Card, D. (Eds.), Handbook of Labor Economics, Volume 4. Elsevier. Atkinson, A.B., 2007. The distribution of earnings in OECD countries. Int. Labour Rev. 146 (1-2), 41–60. Atkinson, A.B., 2015. Inequality. What Can Be Done? Cambridge University Press. Autor, D., Dorn, D., Hanson, G., 2013. The china syndrome: local labor market effects of import competition in the United States. Am. Econ. Rev. 103, 2121–2168. Autor, D., Katz, L., Kearney, M., 2006. The polarization of U.S. Labor market. Am. Econ. Rev. 96 (2), 184–194. Baccaro, L., Howell, C., 2017. Trajectories of Neoliberal Transformation: European Industrial Relations Since the 1970s. Cambridge University Press. Bound, J., Johnson, G., 1992. Changes in the structure of wages in the 1980s: an evaluation of alternative explanations. Am. Econ. Rev. 82, 371–392. Bourguignon, F., 2017. World Changes in Inequality: An Overview of Facts, Causes, Consequences and Policies. BIS Working Papers, n. 654. Bowles, S., Gintis, H., Osborne-Groves, M., 2001. The determinants of earnings: a behavioral approach. J. Econ. Lit. 39, 1137–1176. Burkhauser, R., Hérault, N., Jenkins, S., Wilkins, R., 2018. Survey under-coverage of top incomes and estimation of inequality: what is the role of the UK’s SPI adjustment? Fisc. Stud. 39 (2), 213–240.
Canberra Group, 2011. Handbook on Household Income Statistics. United Nations, Geneva. Card, D., Heining, J., Kline, P., 2013. Workplace heterogeneity and the rise of west german wage inequality. Q. J. Econ. 128 (3), 967–1015. Card, D., Lemieux, T., 1996. Wage dispersion, returns to skills, and black-white wage differentials. J. Econom. 74, 319–361. Causa, O., Hermansen, M., Ruiz, N., 2016. The Distributional Impact of Structural Reforms. OECD Economics Department Working Papers, No. 1342. Causa, O., Vindics, A., Akgun, O., 2018. An Empirical Investigation on the Drivers of Income Redistribution Across OECD Countries. OECD Economics Department Working Papers, No. 1488. Cortes, M., Jaimovich, N., Nekarda, C., Siu, H., 2014. The Who and How of Disappearing Routine Jobs. https://voxeu.org/. Cowell, F., 1995. Measuring Inequality. Prentice Hall Publisher, Harvester. Das, M., Hilgenstock, B., 2018. The Exposure to Routinization: Labor Market Implications for Developed and Developing Economies. IMF Working Paper, 18-135. Denk, O., Cournède, B., 2015. Finance and Income Inequality in OECD Countries. OECD Economics Department Working Papers, No. 1224. Devicienti, F., Fanfani, B., Maida, A., 2018. Collective Bargaining and the Evolution of Wage Inequality in Italy. WorkINPS paper, n. 12. Egger, H., Grossmann, V., 2004. Non Cognitive Abilities and Within-Group Wage Inequality. IZA Discussion Paper, n. 1024. Franzini, M., Pianta, M., 2015. Explaining Inequality. Routledge, London. Goldthorpe, J., Jackson, M., 2008. Education-based meritocracy: the barriers to its realization. In: Lareau, A., Conley, D. (Eds.), Social Class. How Does It Work? Russell Sage Foundation Press. Goos, M., Manning, A., 2007. Lousy and lovely jobs: the rising polarization of work in Britain. Rev. Econ. Stat. 89 (1), 118–133. Granovetter, M., 2005. The impact of social structure on economic outcomes. J. Econ. Perspect. 19, 33–50. Katz, L., Murphy, K., 1992. Changes in Relative Wages, 1963-87: Supply and Demand Factors. Quarter. J. Econ. 107, 35–78. Lemieux, T., 2006. Increasing residual wage inequality: composition effects, noisy data, or rising demand for skill? Am. Econ. Rev. 96 (3), 461–498. McCall, L., 2000. Explaining levels of within-group wage inequality in U.S. Labor markets. Demography 37 (4), 415–430. OECD, 2011. Divided We Stand. Why Inequality Keeps Rising. Oecd, Parigi. Oreopoulos, P., von Wachter, T., Heisz, A., 2012. The short- and long-term career effects of graduating in a recession. Am. Econ. J. Appl. Econ. 4 (1), 1–29. Pellizzari, M., Fichen, A., 2013. A New Measure of Skills Mismatch: Theory and Evidence from the Survey of Adult Skills (PIAAC). OECD Social, Employment and Migration Working Papers, n. 153. Piketty, T., 2014. Capital in the Twenty-First Century. Harvard University Press. Raitano, M., 2018. Italy: para-subordinate workers and their social protection. In: OECD (Ed.), The Future of Social Protection: What Works for non-Standard Workers? OECD Publishing, Paris. Raitano, M., 2019. “Trends and structural determinants of income inequality: an overview. In: European Commission (Ed.), Addressing Inequalities: A Seminar of Workshops. Publications Office of the European Union, Luxembourg. Salverda, W., Nolan, B., Checchi, D., Marx, I., McKnight, A., Tóth, I., van de Werfhorst, H. (Eds.), 2014. Changing Inequalities in Rich Countries. Analytical and Comparative Perspectives. Oxford University Press. Salverda, W., Nolan, B., Smeeding, T. (Eds.), 2009. The Oxford Handbook of Economic Inequality. Oxford University Press.
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Structural Change and Economic Dynamics journal homepage: www.elsevier.com/locate/sced
Earnings inequality and workers’ skills in Italy Maurizio Franzini, Michele Raitano ∗ Sapienza University of Rome, Via del Castro Laurenziano 9, 00161, Rome, Italy
a r t i c l e
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Article history: Received 27 June 2019 Received in revised form 27 August 2019 Accepted 15 September 2019 Available online 17 September 2019 JEL classification: I24 D31 J31 Keywords: Earnings inequality Education Skills Theil decomposition Italy
a b s t r a c t The increasing trend of earnings inequality observed in many countries is usually ascribed to a higher premium to skills, commonly proxied by education. Focusing on Italy, a country characterized by a steep rise in earnings inequality since the ‘90 s, we aim at verifying whether this trend is attributable to education. Making use of administrative data about private employees, we carry out Theil decompositions and estimate wage equations to investigate how much of this trend is linked with education and other observable worker’s and firm’s characteristics. We find that the rise in earnings inequality is explained by the “within education” component, rejecting the idea that it is due to a higher premium for the high-skilled. Furthermore, controlling for workers’ and firms’ characteristics in wage regressions – also including workers’ literacy and numeracy recorded in OCED-PIAAC – we find that level and trend of earnings inequality are not explained by these characteristics. © 2019 Elsevier B.V. All rights reserved.
1. Introduction In the past decades inequality in disposable income, which is considered the best proxy of inequality in living conditions and economic wellbeing (Canberra Group, 2011), has increased in almost all advanced countries. In several countries, this process has been concomitant with a similar trend in labour income inequality (OECD, 2011; Salverda et al., 2009, 2014; Atkinson, 2015; Bourguignon, 2017). Most existing studies consider the rise in inequality as a consequence of processes that take place in the labour market. It is generally claimed in mainstream studies that structural phenomena such as skill biased technological change, digitalization and globalization, through their impact on labour supply and demand, have widened the productivity gap between differently skilled workers (e.g., Bound and Johnson, 1992; Katz and Murphy, 1992; Acemoglu and Autor, 2011; Autor et al., 2013).1 However, other studies have focused on further drivers of the rise in inequality (Franzini and Pianta, 2015; Piketty, 2014; Baccaro and Howell,
∗ Corresponding author. E-mail address: [email protected] (M. Raitano). 1 However, studies based on employer-employee linked datasets have recently pointed out that a large part of the increase in wage inequality is between - rather than within - firms and plants (e.g., Card et al., 2013). https://doi.org/10.1016/j.strueco.2019.09.004 0954-349X/© 2019 Elsevier B.V. All rights reserved.
2017; Denk and Cournède, 2015; Causa et al., 2016 and 2018): e.g., the increased power of capital over labour, the strengthening of a sort of “oligarch capitalism”, the financial deregulation and the “financialization” of current economies, structural and institutional reforms, especially the financial deregulation, a retrenchment in redistributive policies and the reforms weakening labour market institutions. Nevertheless, skill-biased technological change (SBTC) and globalization have been considered by mainstream studies the most important causes of the rise in inequality until recent years. According to this interpretation, in order to be productive ICT technologies have to be coupled with high skilled workers, whose demand would therefore increase. Conversely, new technologies, along with the competition of low skilled workers in less developed countries fostered by the globalization process (that also fostered the chances of offshoring jobs in such countries), would push down the demand for low skilled workers in advanced countries, thus compressing their wages. In a similar vein, more recently, various authors (e.g., Autor et al., 2006; Goos and Manning, 2007; Acemoglu and Autor, 2011; Cortes et al., 2014) have pointed out that technological progress and digitalization mostly displaced medium skilled workers performing routine-jobs (e.g., clerical and administrative jobs) which are neither complementary to ICT (like non-routine cognitive tasks performed by high-skilled workers) nor neutral to it (like non-routine manual tasks performed by low-skilled workers). As a consequence, the labour market would have become polarized,
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i.e., most high-skilled workers and some low-skilled workers, but not medium-skilled workers, would enjoy higher wages and better occupational opportunities (Das and Hilgenstock, 2018). According to these views, the trend of wage inequality is explained by increasing skill premia, hence different skills held by individuals are considered the main driver of inequality. Consequently, investment in human capital – usually proxied by education – is strongly recommended as the best policy not only for fostering GDP growth but also for reducing earnings and disposable income inequality. Contrary to this view, some empirical studies have come to the conclusion that standard human capital proxies, such as experience and education, explain at most a third of the variance in wages, while residual or within-group wage inequality – i.e., wage dispersion among workers with the same education – is much greater and increasing in many countries (Lemieux, 2006). This is not to deny that the inequality between differently educated individuals – i.e., “mean” skill premia – is crucial to explain wage gaps; the point is that a large part of total earnings inequality occurs within the group of equally educated workers. Therefore, while yielding on average a high and (at least in some countries) increasing return, investment in education is exposed to a large variance and is quite risky, as suggested by the high and increasing inequality in the earnings of similarly educated workers. Despite this evidence, the literature has focused on the wage gap between workers with different observable characteristics (especially education). A recurrent justification for this focus has been that also wage disparities within educational groups are related to returns to skills, since individual detailed abilities are usually unobservable in empirical analyses (Card and Lemieux, 1996; McCall, 2000). However, as pointed out by Atkinson (2007) and Bourguignon (2017), a single explanation of multifaceted trends in inequality across countries based on skill premia looks questionable. Therefore, mechanisms behind the within education inequality should be investigated much more than it is actually done in theoretical and empirical studies. In this paper we aim at shedding light into the black box of within education inequality focusing on Italy, a country characterized by a steep rise in earnings inequality in the last decades. To this aim, we shall try to answer three linked questions: i) how much of wage inequality can be imputed to education?; ii) has the importance of education as a driver of wage inequality increased over the last years?; iii) are there other factors impacting on wage inequality that capture unobservable components of workers’ human capital and abilities? If education had been predominant in Italy as a driver of increasing earnings inequality, we should observe a rise both in the wage of high skilled compared to low skilled workers and in the share of total inequality explained by education. Moreover, being education an imperfect proxy of workers’ skills, total inequality should be wholly explained by including in the analysis further workers’ and firms’ characteristics that might be associated with individual unobservable skills. These expectations are not borne out by our analyses which instead lead to the conclusion that earnings inequality is largely explained by the within education component and by the inequality which remains unexplained even after several further workers’ and firms’ characteristics have been taken into account. We use an innovative dataset – called AD-SILC – built by merging the 2005–2012 annual waves of the Italian component of the European Union Statistics of Income and Living Conditions (EU-SILC, the Italian component is named IT-SILC), with administrative social security records – managed by the Italian National Social Security Institute (INPS) – that track private employees in the period
1990–2013.2 Social security files report, on an annual basis, gross earnings, weeks of work and some worker’s and firm’s characteristics (e.g., the broad occupation, the region of work, the industry), but do not record worker’s education. On the contrary, IT-SILC waves record education, but cover a limited time span. Merging longitudinal administrative record of all individuals interviewed in IT-SILC 2005–2012 with IT-SILC information allows us to enrich social security records with the missing information about education. To answer our research questions, we thus use the AD-SILC dataset and estimate the role of education in explaining earnings inequality over the 1990–2013 period carrying out a Theil decomposition of total inequality by educational groups. Moreover, we inquire the role of education on earnings inequality also through a regression based approach, observing the trend of residual inequality once further worker’s and firm’s characteristics (e.g., worker’s experience, firm’s industry and size) which might be associated with unobservable components of human capital are taken into account. Furthermore, because IT-SILC only records the highest attained degree without providing further details on the educational path (e.g., the field of study or proxies of the quality of education), we also use cross-sectional OECD-PIAAC (Programme for the International Assessment of Adult Competences) data. Such data provide details on the educational path and individual’s scores in literacy and numeracy tests, which are considered good proxies of workers’ skills and competences; they also allow us to compare the size of residual inequality once these proxies of unobservable abilities are taken into account. More in detail, the paper is organized as follows. Section 2 presents the data and the empirical methodology. Section 3 shows the trends of the main indicators of earnings distribution in Italy in the period 1990–2013 also distinguishing workers by education. Section 4 presents our main results about the limited explanatory power of education and other observable variables as regards the trends of earnings inequality since the ‘90 s in Italy, while Section 5 shows our results about residual inequality after taking PIAAC data into account. Section 6 concludes, discussing the policy implications of our findings. 2. Dataset and empirical methods 2.1. Data We use the AD-SILC dataset (where the “AD” stands for “administrative), that merges the 2005–2012 waves of the IT-SILC survey (i.e., the Italian component of the EU-SILC) with the administrative longitudinal social security records collected by the Italian National Social Security Institute (INPS), that record employment and earnings histories of all individuals working in Italy from the moment they entered the labour market up to the end of 2013. Social security records offer a comprehensive picture of the career of all individuals working in Italy, as they report, on a yearly basis and for each working relationship: working weeks, the type of working relationship (e.g., as an employee in the public or in the private sector or as a self-employed) and gross earnings, which include overtime, personal income taxes and social insurance contributions paid by the workers as well as allowances for maternity, sickness and temporary job suspension (through the so called Cassa Integrazione Guadagni). INPS data thus allow us to perfectly reconstruct year by year the effective labour market experience (in weeks) after the entry in activity and weekly wages (computed by dividing the total income earned in the longest employment relationship during a year by the corresponding number of worked
2 Individual fiscal codes have been used as the matching key for merging the two datasets.
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weeks). For employees in the private sector, INPS data also record the contractual arrangement (full-time versus part-time, while the information about fixed-term or open-ended contracts is recorded only from 1998), the region of work and (from 1990) firm’s size and industry, coded at the 2-digit NACE level. On the other hand, INPS archives do not record workers’ education, that is a crucial variable for our purposes. This information is instead available in IT-SILC. Therefore, the AD-SILC dataset enriches retrospectively the IT-SILC cross-sectional waves with longitudinal working histories taken from the INPS archives of the individuals sampled in IT-SILC. In this paper we focus on employees in the private sector because earnings of public employees have some flaws in INPS archives,3 while self-employed earnings are plagued by underreporting and by top and bottom coding in administrative archives. Note, however, that most studies on earnings inequality focus on private employees (e.g., Card et al., 2013; Devicienti et al., 2018); moreover using administrative rather than survey data allows to greatly reduce measurement errors.4 In our analysis we take account of three earnings variables (all in Euros, converted to 2014 constant prices using the harmonised index of consumer prices):
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sitional changes in the labour force over the observed period due to changing enrolment rates in higher education by younger workers. In conclusion, we focus on the gross earnings of a sample of 69,911 individuals (705,966 observations) in the age class 25–54 who have been employed in the private sector at least once in the 1990–2013 period. Individuals are classified in three groups according to the highest attained degree: at most lower secondary educated, upper secondary educated, and tertiary graduates. In a further exercise, to reduce the omitted variable bias due to the unobservability of individual abilities over education in IT-SILC data, we make also use of the cross-sectional OECD-PIAAC (Programme for the International Assessment of Adult Competencies) data that record employees’ gross monthly wages and details of both the educational path (highest educational attainment and field of study) and the individuals’ scores in literacy and numeracy tests, which may be taken as a good proxy of workers’ competences and abilities (Pellizzari and Fichen, 2013).6 The sample we use includes 1520 individuals (excluding non-Italian citizens) who were working as employees at the time of the interview and reported a positive monthly gross wage. 2.2. Methods
1 annual earnings, that is the most suitable variable to capture the influence of labour market outcomes on workers’ living standard. Annual earnings depend on unitary wages – i.e., hourly wages, not recorded in INPS archives – and hours of work in the year that depend on the number of hours usually worked in a week (i.e., on overtime and part-time arrangements) and on the number of working weeks (i.e., on periods spent out of work in a year, which is also affected by temporary arrangements). 2 weekly wage, not influenced by the variable number of weeks of work in a year; 3 weekly wage of “strong workers”, i.e., working the whole year (52 weeks) with a full-time arrangement. This is the most reliable proxy of hourly wage, usually considered by economists as the best measure of workers’ productivity and as the outcome of the contractual bargaining. In all our analyses we only consider individuals with a positive wage in the year, i.e., we do not include those with “zero” earnings in a certain year (e.g., the long-term unemployed). The retrospective AD-SILC dataset is representative of the population living in Italy in 2005–2012. Its cross-sections are used to obtain reliable estimates for aggregates whose sampling distribution (e.g., by age) is similar to that of the population. For this reason, in this paper we do not consider non-Italian citizens (they are under-represented in the retrospective panel because of their greater mobility in and out of Italy) and the active population aged over 55 years (as the dataset refers to individuals interviewed in 2005–2012, the sample representativeness of older workers in the early years of observation in AD-SILC is not adequate).5 We do not even consider those aged less than 25 to depurate earnings data from possibly limited wages earned by students and from compo-
3 Data for public employees are available in AD-SILC for the period 1996-2011 only; moreover, the coverage of public employees in the social security administrative archives that we had at disposal is not complete due to delays in the digitalization of some information (mostly about workers of the education sector). 4 Being our sample based on the IT-SILC samples, it is not perfectly representative of top earners because of either under-reporting by very rich respondents or lower survey participation rates by very rich households, or both (Burkhauser et al., 2018). 5 Furthermore, the increase in the retirement age introduced with the pension reform process started during the ‘90s modified the size and the composition of the labour force aged over55. Therefore, excluding these individuals also allows us to overcome compositional issues related to the participation of the elderly in the labour market.
To better understand the role of education as a driver of earnings inequality, we split the workers in three subgroups according to their highest educational attainment and assess – through a decomposition by subgroups exercise – the relative size of the inequality that emerges within and between the three educational groups. Thus, we can measure the share of total inequality due to differences in education – henceforth, the “between inequality” – and the share of inequality not due to education, referring to individuals with the same education – henceforth, the “within inequality”. To this aim, we use the Theil index of inequality, that, differently from the Gini index, is perfectly decomposable among subgroups; in fact, it can be expressed as the sum of between and within inequality. The between groups inequality is computed through a counterfactual distribution where all individuals with the same education earn the mean wage of that group, so identifying mean distances among groups (the main focus of the economic literature); the within groups inequality is instead the weighted average of the inequality within each group.7 To capture trend of residual inequality we follow Card et al. (2013) and Devicienti et al. (2018), and also compute the root mean square deviation (RMSE) of a set of OLS regressions, run year by year, of annual earnings on a set of covariates and compare the estimated trend of the RMSE with the value of the unconditional standard deviation of annual earnings. In more detail we first run a base model simply controlling for basic socio-demographic characteristics (gender, age, age squared and region of work). Then we add education and, finally, we run a full model where we add further individuals’ and firms’ characteristics that may be associated with workers’ education and unobservable abilities, i.e., labour market experience since the entry in activity (also squared), a dummy on part-timers, the broad occupational group (blue-collar, white-collar, manager), a cubic polynomial on firm’s size and dummies on the 2-digit NACE industry.8
6 The PIAAC survey was carried out in Italy between September 2011 and March 2012. 7 In the Theil decomposition, inequality within each group is weighted by the relative income got by each group (Cowell, 1995). 8 In the full model we include all the variables about the employee and the firm available in INPS archives in the whole period 1990-2013. In detail, we include 119 covariates, e.g., a dummy for females and for those working on a part-time basis; a square polynomial on age and experience, a cubic polynomial on firm’s size, three
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We run the same type of exercise also on the cross-sectional PIAAC dataset, comparing the value of the unconditional standard deviation of monthly wages with the RMSE of three OLS specifications: a base model where we only control for gender and dummies about the 5-year age class; a model where we add to the base model the three educational groups and the field of study of those who attained at least an upper secondary degree (general, social sciences, STEM, health); a full model where we also include PIAAC individual scores in literacy and numeracy tests (also squared). 3. The trend of earnings inequality in Italy Over the period 1990–2013 we find different trends of mean earnings according to the notion of wage considered (Fig. 1). While both annual earnings and weekly wages have stagnated in real terms since the beginning of the ‘90 s, our proxy of hourly wages for “strong workers” (i.e., those employed the whole year with a full-time arrangement) shows a reasonable increase from 2003 onwards. However, Italy has been characterized over the last decades by a large increase in the share of (often involuntary) parttime jobs: our data show that the share of private employees whose longest working span in the year was spent in part-time arrangements increased from 12.2% in 2000 to 22.8% in 2010; at the same time the share of employees working the whole year with a fulltime arrangement has reduced overtime from 53.6% in 2005 to 46.8% in 2013. Therefore, it has to be stressed that our proxy of hourly wages is not representative of all workers but refers to a declining share of relatively advantaged employees. More in general, earnings distribution in Italy has been characterized by increasing inequalities within workers. Despite stagnating mean annual earnings (Fig. 1), different trends characterize the various percentiles and the values of the 10th and the 25th percentiles fell considerably in the period 1990–2013 (Fig. 2).9 Consistent with these trends, the Gini index (of both annual and weekly wages) steeply increased over the observed period, with a tendency of the increase to flatten in more recent years.10 As expected, the Gini is higher for annual rather than weekly wages; in fact, the distribution of weeks of work amplifies earnings inequality because those who earn lower wages are at greater risk of unemployment in a year (Fig. 3). A different trend emerges when looking at weekly wages of the “strong workers”: the Gini index is quite constant since 2003, thus suggesting that the increase in inequality from 2003 onwards is mainly due to the spreading of part-time arrangements. When we distinguish workers by education, a return for higher educated employees clearly shows up; indeed, mean annual earn-
dummies on the broad occupation and on education, twenty dummies on the region of work, and eighty-three dummies on the NACE industry. 9 Trends of bottom percentiles might rather underestimate trends of earnings inequality since in our analyses we are not including the so-called “para-subordinate” workers – i.e., dependent self-employed, those working as self-employed in legal terms but “economically dependent” on a single client. Parasubordinate arrangements were often used until recent years as a cheap type of contract for replacing low-paid employees (Raitano, 2018). 10 As remarked, earnings inequality is computed by taking into account only the subsample of individuals working during a certain year, thus without considering outflows from the labour force, e.g., due to the economic crisis that started in 2008. As a consequence, if a recession had the effect of removing the least paid workers from the workforce, earnings inequality among the remaining ones could paradoxically be reduced. To take into account the whole effect of the crisis on earnings inequality in Italy, Raitano (2019) computed the Gini of annual earnings of private employees including in computations those workers who were active in a certain year and then became unemployed (with zero earnings) in the following years. The findings are impressive: earnings inequality increased by 21% in Italy in the period 2008-2013 when involuntary unemployed are taken into account, while earnings inequality within employees (i.e., excluding “zero earners”) rose by 2.5% from 2008 to 2013 (Fig. 3).
ings of tertiary graduates exceeds by around 40% and 80% earnings of those with at most a lower secondary or an upper secondary degree, respectively (Fig. 4). Interestingly, this “rough” measure of the skill premium has increased since the beginning of the 2000s when we focus on annual earnings, while it has remained rather constant (or even reduced) when we focus on our proxy of hourly wages of “strong workers”. This diverging trend thus signals that the relative increase in the return to education related to annual earnings is due to a lower probability for tertiary graduates to be hired through short-term temporary and part-time arrangements. Apart from this rough measure of return to education, it is interesting to focus on a measure of the risk of the individual investment in education. To this aim, we computed some indicators of a possible overlapping of the annual earnings distribution of differently educated workers (Fig. 5). These indicators show that – despite the higher mean return – a large share of tertiary workers did not enjoy any monetary benefit in a given year. Indeed, we find that in 2013 the earnings of 38.5% of tertiary graduates were less than the mean earning of upper secondary graduates, and 25.1% of tertiary graduates earned less than the average employee with at most a lower secondary degree. However, the shares of tertiary graduates at risk of earning less of those with lower degrees have reduced overtime. Finally, we computed the Gini index of annual earnings of differently educated employees (Fig. 6). The main finding is that inequality within tertiary graduates is the highest – it is also higher than the Gini computed on total workers – even if it has steadily reduced since 2003. Conversely, the Gini of annual earnings within both low and middle educated workers has steadily increased in the observation period, thus engendering a sort of convergence of earnings inequality within the three educational groups.
4. The trend of within education and residual inequality The relative weight of the within and between educational groups inequality is assessed through a decomposition of the Theil index by educational attainments. If the relative importance of education in explaining individual wage inequality had increased, we should find that the increase in total inequality is mostly associated with an increase in the between component, which, as already clarified, captures the influence on total inequality of the gaps in the mean earnings of differently educated workers. Actually, focusing on annual earnings, we find that total and within education inequality followed the same trend during 1990–2013 period, while between inequality stayed rather constant, with a very slight increase from 2003 onwards (Fig. 7). On the whole, the share of total inequality explained by the between component is rather limited – between 7 and 10% according to the wage dimension (Fig. 8) – and more important, the trend of the share of the between inequality was constant over the observed period as concerns annual earnings, and even reduced, as concerns weekly wages and weekly wages of full-time and always employed workers.11 As already pointed out, educational attainment might be a poor proxy of individuals’ skills; indeed, a large heterogeneity between workers with the same education, might emerge, due to, e.g., the field of study, the type of upper secondary or tertiary degree, the
11 The mean education increased over the 1990-2013 period in Italy. Among private employees aged 25-54 the share of tertiary graduates rose from 5.3% in 1990 to 11.9% in 2013, while the share of those with at most a lower secondary degree reduced from 60.5% to 35.3% over the same period. Despite this major structural change, further analyses – available upon request – show that the outcome of the decomposition exercise does not change if we keep constant at the 1990 values the distribution of workers by educational attainment.
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Fig. 1. Trend of mean wages for private employees. Index number: 1990 = 100. Source: elaborations on AD-SILC data.
Fig. 2. Trend of percentiles of annual earnings. Index number: 1990 = 100. Source: elaborations on AD-SILC data.
quality of the attained education or, more in general, further skills or abilities unobservable in an empirical setting. Our decompositions, despite their impressiveness, are then not enough to argue that skills and human capital are not a major driver of the trend of earnings inequality in Italy. The AD-SILC dataset only includes information on educational attainment, that may be considered as a poor proxy of human capital. The dataset does not include further proxies of human capital and skills (e.g., the quality of the attained education, the specific worker’s competences, her motivation and effort), that are unobservable in our empirical setting. Nevertheless, we might suppose that these unobservable skills are (or tend to become, over time) observable by the employers and therefore can be rewarded also in terms of better hiring chances, contractual arrangements, occupation and of promotion. Accordingly, we might argue that, among workers with the same education, those with better skills should be hired in more rewarding industries and firms, with better contractual arrange-
ments (i.e., full-time instead than part-time), they should achieve a better occupational status (i.e., manager or white-collar rather than a blue-collar) and should experience fewer unemployment spells, thus accumulating a higher experience since their entry in the labour market. Therefore, these further dimensions of the working status and career might capture, at least partially, unobservable worker’s skills. In other terms, workers unobservable skills could affect these dimensions of the working career which are correlated with wages and, in turn, have an influence on wage inequality. Higher returns to human capital could then manifest themselves as an improvement in such dimensions and the explanation of rising inequality based upon human capital and skill endowments could be vindicated. To evaluate such hypothesis, following, Card et al. (2013), we carried out OLS regressions of annual earnings and computed the standard deviation of the residuals (the RMSE) as an index of residual inequality in order to verify how much of individual wage gaps
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Fig. 3. Trend of Gini of wages. Source: elaborations on AD-SILC data.
Fig. 4. Ratio between mean earnings of differently educated workers. Source: elaborations on AD-SILC data.
not explained by observed characteristics might be associated with workers’ skills other than education (see Fig. 9, where, to allow comparisons, we also show the unconditional standard deviation of annual earnings). As discussed in Section 2.2, estimated models in Fig. 9 include an increasing number of covariates: in the first “base” model we only control for basic demographic characteristics (gender, age and region of work), then we add education and in the final “full” model we also add dozens of covariates summarizing occupation, contractual arrangement, industry and firm’s size and experience since the entry in the labour market. As expected, in comparison with the unconditional standard deviation of annual earnings, the higher
the number of covariates, the lower the standard deviation of the residuals. However, the reduction in the RMSE is rather limited even in the “full model” where we include around 120 covariates, as clarified in Section 2.2. More important, and consistent with the trend of the between education inequality shown previously, the trend of RMSE is increasing over the whole observation period; furthermore, it is very similar to the trend of the unconditional standard deviation of annual earnings, thus pointing out that also these further covariates that might mask unobservable components of workers’ skills and human capital do not explain the increasing trend of earnings inequality in Italy since the beginning of the ‘90 s.
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Fig. 5. Overlapping between annual earnings distributions of differently educated workers. Source: elaborations on AD-SILC data.
Fig. 6. Gini of annual earnings, by education. Source: elaborations on AD-SILC data.
In conclusion, this evidence does not lend any support to the idea that the size and the dynamics of earnings inequality mainly depends on workers’ skills, as proxied by educational attainments and by other workers’ and firms’ characteristics that might capture unobservable components of workers’ human capital.
5. Wage inequality and workers’ abilities: evidence from OECD-PIAAC Inequality within equally educated people can be due to several factors, some of which are very difficult to observe in empirical analysis. The list of the factors that may affect within inequality
should include at least the following items: i) chance and luck;12 ii) individual innate abilities; iii) detailed characteristics of the acquired education (e.g., the type of attended programmes and the field of study); iv) the quality of the attained education;13 v)
12 A clear example of how luck impacts on inequality refers to the long-term effect on earnings related to the economic conditions at the time of entry in the labour market. Actually, those who start working during a recession are permanently harmed (Oreopoulos et al., 2012). 13 Actually, the concept of quality of education is very elusive. In particular it is not clear in what sense some schools or universities can be considered the best. Do they best meet the specific needs of the firms? Or do they favor the future needs of adaptability of the economy (perhaps favoring an accumulation of general cognitive skills)? Or does a wage premium for those who attended top universities depend on an effective increase in individual productivity? Or does it only depend on a signal
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Fig. 7. Trend of within and between inequality by education. Annual earnings. Source: elaborations on AD-SILC data.
Fig. 8. Theil decomposition by education: share of the between education inequality. Source: elaborations on AD-SILC data.
hard skills other than those acquired at school; vi) individual soft skills (non-cognitive abilities);14 vii) social connections and ties (Granovetter, 2005). With the exception of chance and luck, these factors can be distinguished in two groups: those related to cognitive abilities and skills of workers, with effects on individual productivity (factors ii-v), and those not related to skills and productivity, like social connections and, maybe, also soft skills, whose relationship with productivity is unclear.
associated with the “good name” of the University? In addition, there is no guarantee that, in the absence of appropriate changes in the labour demand, a growing supply of “well-educated” workers would engender a general increase in wages, thus reducing wage inequality. 14 Soft skills are factors shaping social and relational competences of each individual – such as risk aversion, willingness to exert effort, extroversion, willingness to work in team, the sense of discipline or leadership – which labour market success seems to increasingly depend on (Bowles et al., 2001; Goldthorpe and Jackson, 2008).
Individual characteristics may greatly influence individual productivity, but they are difficult to measure. However, the Programme for the International Assessment of Adult Competences (PIAAC) carried out by OECD is well suited to this purpose as it records, among the others, information on employees’ gross wages, variables recording some details of the educational path (i.e., the field of study) and, mostly, scores on individual competences in literacy and numeracy, which might be considered good proxies of workers’ unobservable skills, besides education. Unfortunately, PIAAC survey was carried out in a single crosssection and does not allow us to test whether the trend of inequality is explained by changing returns to workers’ literacy and numeracy.15 However, PIAAC data are very useful to measure the explanatory power of wage gaps associated with these further proxies of workers skills.
15 Unfortunately, since fiscal codes are not recorded, PIAAC data cannot be merged with the longitudinal administrative social security records managed by INPS.
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Fig. 9. RMSE of OLS estimates of annual earningsa . a The following covariates are included in the various model: dummies on gender and region of work and age and age squared in the “base model”; dummies on educational attainments are added in the “base + education” model; dummies on part-time jobs, on occupation, on Nace 2-digit industries plus experience and experience square and a cubic polynomial on firm’s size are added in the “full” model. Source: elaborations on AD-SILC data.
Table 1 RMSE of OLS regressions of log monthly wages in Italya : Employees aged 25–54. Unconditional S.D.
1,771.9
RMSE M1
M2
M3
1,700.1
1,599.3
1,582.8
a
The following covariates are included in the various model: dummies on gender and age class in model M1; dummies on educational attainments and the field of study are added in model M2; scores in literacy and numeracy tests and their squared are added in model M3. Source: elaborations on OECD-PIAAC data.
To this aim, as discussed in Section 2, consistent with the analysis carried out in Section 4, making use of the cross-sectional PIAAC dataset about Italy, we regressed through OLS gross monthly wages of employees in Italy on an increasing set of covariates and compared the RMSE of these specifications with the unconditional standard deviation of monthly wages (Table 1). Despite all additional covariates exert a significant influence on earnings, the inclusion of dummies about education and the field of study (model M2) only slightly reduced the RMSE, as already seen in Section 4. More important, the RMSE very slightly reduces when we add scores in numeracy and literacy to the covariates, to capture a possible influence on earnings of workers’ abilities and competences over education. These findings suggest that literacy and numeracy tests capture competences linked to the individual educational path, thus their inclusion does not substantially improve the explanatory power of our models. As stated by Egger and Grossmann (2004), differences in cognitive skills seem to be of little value in the explanation of wage inequality within education groups. 6. Conclusions In this paper we have argued that, contrary to a widely held opinion, increasing wage inequality in Italy over the last two decades only to a very limited extent depends on higher skill-premia. We came to this conclusion considering not only the return to education (the usual proxy for human capital) and the share of wage inequality due to differences in education but also the influ-
ence of several variables able to capture non-observable skills. The trend of the standard deviation of residuals of wage regressions with dozen of covariates provides further support to our claim. Our findings also provide information on inequality within equally educated workers. In particular, wages are widely dispersed across tertiary graduates, pointing to a high riskiness of the investment in education. Indeed, the share of wage inequality not explained by individuals’ skills and their productive abilities is sizeable and we know very little about its determinants. One important point to clarify is how much of such inequality originates within firms and how much of it is firm-specific, i.e., between rather than within firms. This initial step could orientate further research in order to better evaluate the role played by – among others – such factors as market structure, collective bargaining and wage-setting processes, propensity to technological and organizational innovation and social connections. A further aspect to be investigated in future research concerns the influence on within inequality of institutional factors, in particular of the deregulation of the labour market that started in Italy in the mid-1990s and led to an increasing share of precarious workers, also among tertiary graduates. Moreover, if detailed occupations were recorded (i.e., at 3-digit ISCO; note that INPS archives only distinguish blue-collar, white-collar and manager), it would be very interesting to carry out an analysis similar to the one presented in this paper by focusing on detailed occupations rather than educational attainments as a main driver of earnings inequality. To identify what the unexplained wage inequality depends on is important from several points of view. First, we could learn more about the actual working of labour markets and uncover significant differences across countries, regions or industries. Second, we could gain a better understanding of how acceptable inequalities are and of their economic and social consequences. Human capital is usually considered acceptable as a cause of inequality if the access to education is open to everybody. Its limited importance as a driver of wage inequality suggests, hence, that wage inequality could be much less acceptable than usually believed. This would be, in particular, the case if institutional
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arrangements (i.e., wage-setting processes) discriminate among otherwise ‘equal’ workers or, more seriously, if family origins and social connections play a major role in that inequality. Besides impinging on the acceptability of inequality, such determinants could have effects on its consequences. For example, social connections could lead to an allocation of workers that prevents the achievement of efficiency and slows down economic growth. Third, knowing more about unexplained wage inequality could help design better policies to counter inequality. The assumption that human capital is the main driver of wage inequality leads to a straightforward policy recommendation: invest more in human capital and bridge the education gap. Of course, allowing workers from more disadvantaged backgrounds to have access to a higher education is more than desirable. However, the point is that if wage inequality is largely due to other factors, such investment could result in failure, eventually fuelling over-education which is a widespread phenomenon in several countries. In conclusion, knowing better about the mechanisms generating wage inequality is an essential step to advance our knowledge not only of inequality but also of a significant number of economic and social problems and to identify the most effective policies to address them. References Acemoglu, D., Autor, D., 2011. Skills, tasks and technologies: implications for employment and earnings. In: Ashenfelter, O., Card, D. (Eds.), Handbook of Labor Economics, Volume 4. Elsevier. Atkinson, A.B., 2007. The distribution of earnings in OECD countries. Int. Labour Rev. 146 (1-2), 41–60. Atkinson, A.B., 2015. Inequality. What Can Be Done? Cambridge University Press. Autor, D., Dorn, D., Hanson, G., 2013. The china syndrome: local labor market effects of import competition in the United States. Am. Econ. Rev. 103, 2121–2168. Autor, D., Katz, L., Kearney, M., 2006. The polarization of U.S. Labor market. Am. Econ. Rev. 96 (2), 184–194. Baccaro, L., Howell, C., 2017. Trajectories of Neoliberal Transformation: European Industrial Relations Since the 1970s. Cambridge University Press. Bound, J., Johnson, G., 1992. Changes in the structure of wages in the 1980s: an evaluation of alternative explanations. Am. Econ. Rev. 82, 371–392. Bourguignon, F., 2017. World Changes in Inequality: An Overview of Facts, Causes, Consequences and Policies. BIS Working Papers, n. 654. Bowles, S., Gintis, H., Osborne-Groves, M., 2001. The determinants of earnings: a behavioral approach. J. Econ. Lit. 39, 1137–1176. Burkhauser, R., Hérault, N., Jenkins, S., Wilkins, R., 2018. Survey under-coverage of top incomes and estimation of inequality: what is the role of the UK’s SPI adjustment? Fisc. Stud. 39 (2), 213–240.
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