When supply meets demand: Wage inequality in Portugal

Centeno, Mário; Novo, Álvaro A. Article When supply meets demand: Wage inequality in Portugal IZA Journal of European Labor Studies Provided in Cooperation with: IZA – Institute of Labor Economics Suggested Citation: Centeno, Mário; Novo, Álvaro A. (2014) : When supply meets demand: Wage inequality in Portugal, IZA Journal of European Labor Studies, ISSN 2193-9012, Springer, Heidelberg, Vol. 3, Iss. 23, pp. 1-20, https://doi.org/10.1186/2193-9012-3-23 This Version is available at: https://hdl.handle.net/10419/125585 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Centeno and Novo IZA Journal of European Labor Studies ORIGINAL ARTICLE OpenAccess When supply meets demand: wage inequality in Portugal Mário Centeno and Álvaro A Novo* *Correspondence: [email protected]om Research Department, Banco de Portugal, Av. Almirante Reis, 71 1150 Lisboa, Portugal Abstract Wage inequality in Portugal has increased over the last thirty years, with two distinct periods. The period from 1984 to the mid-90s witnessed strong increases in both upper- and lower-tail inequality. A shortage of skills combined with skill-biased technological changes were at the core of this evolution. Since the mid-90s, lower-tail inequality has decreased, while upper-tail inequality has increased, but at a slower rate. A larger supply of skills and polarization of labor demand contribute to this evolution. Other developed economies share similar trends, but the Portuguese experience shows clearly the role of supply and demand factors in shaping the wage distribution. JEL codes: J3; D3; O3 Keywords: Inequality; Polarization; Supply; Demand 1Introduction The debate over the level and trends in wage inequality in developed economies has evolved around a continental divide. In the 80s and 90s, inequality was high and increasing in Anglo-Saxon countries (Autor et al. 2008; Card and Lemieux 2001), while it remained stable in Continental Europe. Recently, this view has been challenged, with a reassessment of inequality developments in some European countries, in particular in Germany (Dustmann et al. 2009). The more recent evidence points to a significant increase in inequality in Europe (OECD 2011). We contribute to this debate and stress the key role of supply and demand factors to explain wage inequality in Portugal. Using a supply, demand, and institutions framework, we study the determinants of inequality in the period from 1984 to 2009. Portugal is frequently depicted as a highly regulated labor market in which institutions promote rigid wage and employment adjustments. We challenge this view and show that demand factors – skill-biased technological change and polarization – and supply factors – the large increase in skill supply – have shaped the changes in inequality. We uncover a low elasticity of substitution between high- and low-skill workers that also helps in explaining the large skill wage premium. Demand and supply conditions have changed quite substantially in Portugal, making it an interesting case to trace the sources for wage inequality in labor market forces. In addition, the institutional framework of the Portuguese labor market – collective bargaining, minimum wage, and employment protection – were already in place before 1984. Using data from Quadros de Pessoal – a comprehensive employer-employee matched dataset © Centeno and Novo; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 2014, 3:23 http://www.izajoels.com/content/3/1/23 2014 Centeno and Novo IZA Journal of European Labor Studies Page 2 of 20 of private sector employment – we find a continuous increase in overall inequality from 1984 to 2009. Throughout the 80s and until the mid-90s, wage inequality increased steeply at both ends of the wage distribution. The supply of skills remained quite low during the first period, but a large shift occurred during the 90s, with the share of college graduates almost tripling, from 5% to 14%. Wage inequality fell at the lower-tail and slowed down at the upper-tail. A counterfactual analysis suggests that the main contribution to the increase in inequality in the initial period was the result of demand factors, generating positive price effects, while mild composition effects also pushed inequality up. In the second period, there are negative price effects (contributing to a reduction in inequality), offset by positive composition effects (that pushed inequality up). A simple demand and supply model explains about 80% of the wage premium variation (Katz and Murphy 1992). The educational supply shifts were reinforced by an increase in female participation and immigration, which are groups with educational attainment above the average. In addition, the demand for skills was fueled by European Community accession (1986), euro membership (1998) and large FDI flows. The extraordinary increase in the supply of skills generated divergent paths for the college wage gap by experience group. This shift has compressed the college wage premium for less experienced individuals since the early 90s. For more experienced workers (20 or more years of work), the college wage premium increased steadily, and has only flattened out recently. The different trends in college supply by experience group explain 82% of the increase in the gap of the college wage premium between younger and older workers. Wage inequality in Portugal seems to be the result of a race between education and technology. In the first part of the sample, technology wins over education, while more recently education picks up with the pace of the demand for skills. The Portuguese experience resembles the evolution of inequality in the U.S. Both have a low elasticity of substitution between high and low skills, implying a strong reaction of the wage skill premium to relative supply conditions. They also share negative price effects in the more recent period associated with the reduction in lower-tail inequality (minimum wage developments). Previous studies of wage inequality in Portugal include Cardoso (1998), Machado and Mata (2001), Martins and Pereira (2004), Cardoso (2004) and Machado and Mata (2005). These studies cover only the first half of our sample. Machado and Mata (2005) extend the analysis with counterfactual price and composition effects, using a quantile regression decomposition method. For the overlapping period, the wage premium estimates reported in these studies are in line with the ones we obtain. The ability of simple supply and demand models to explain wage inequality in Portugal and the similarities with the results obtained for the U.S. provide relevant lessons for the design of policies – such as the minimum wage and education – to tackle the growing wage inequality in Europe. The scope for general redistributive policies should be evaluated in conjunction with the role played by two important market factors: the supply of education and the demand for skills. 2Data Quadros de Pessoal (QP) is an administrative dataset collected on an annual basis (reported to March of each year until 1993 and to October since then) by the Portuguese 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 3 of 20 Ministry of Employment. Coverage is mandatory for firms with at least one salaried worker, except for civil servants, entities that employ non-permanent rural workers, and domestic workers. This dataset has been used to study different aspects of the Portuguese labor market, among which wage inequality in Machado and Mata (2001) and Cardoso (2004). We use data from 1984 to 2009, with the exception of the years for which the data were not collected, 1990 and 2001.1Forthepurposeofthisstudy,wecollectthemonthlywage, hours worked, age, education, and occupation of workers. In 2009, the data covered nearly 3 million employees. QP registers different wage components and hours worked. We use the base wage measure, which corresponds to the monthly wage of regular working hours. Additionally, we consider a total wage measure that includes, besides the base wage, subsidies paid on a monthly basis (e.g. seniority), bonuses, and overtime pay. Details about the sample construction are deferred to the Appendix. 3 The Portuguese wage structure Figure 1 and Table 1 display basic features of the Portuguese economy wage structure for male and female workers from 1984 to 2009. Figure 1 illustrates the non-monotonic widening of the wage distribution over this long period for both male and female workers. The 90th percentile increases by approximately 35 log points (×100) relative to the median for both males and females. The main difference between male and female inequality changes in the period occur below the median, with higher wage growth for females. The Portuguese labor market was characterized by significant demographic and educational changes in this period. Table 2 takes a closer look at the evolution of inequality within groups of age and education. We use the 50th to 10th and 90th to 50th percentiles log wage gaps and employment shares to illustrate price and quantity trends. There is an impressive shift in the supply of skills. The share of low-skill individuals (those with 6 or less years of schooling) decreased by 28 percentage points, while the share of high-skill workers rose from 3% in 1984 to 14% in 2009. The age composition of the Figure 1 Log wage distribution percentile changes, 1984-2009 (M/F). Source: Quadros de Pessoal data for 1984 and 2009, full-time workers aged 16 to 65 with 0 to 39 years of potential experience. Full-time workers are those who worked 35+hours per week and earned at least the minimum wage. Calculations were deflated using the consumer price index. 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 4 of 20 Table 1 Summary statistics 1984 1995 2009 Minimum wage Log real minimum wage (1982 prices) 3.875 3.992 4.181 Percentage of minimum wage earners 4.246 8.533 14.429 College wage premium (Log points) All workers 0.675 0.896 0.833 1-9 years of experience 0.619 0.802 0.656 10-19 years of experience 0.779 1.061 0.933 20-29 years of experience 0.844 1.137 1.204 30+ years of experience 0.900 1.123 1.294 Male Location measures 10th Percentile 3.973 4.066 4.184 Median 4.306 4.487 4.549 90th Percentile 4.872 5.236 5.450 Average 4.379 4.596 4.719 Dispersion measures Standard deviation of log wages 0.368 0.491 0.528 Standard errors of log wage residuals 0.278 0.366 0.402 Female Location measures 10th Percentile 3.888 3.992 4.181 Median 4.093 4.230 4.395 90th Percentile 4.630 5.001 5.265 Average 4.189 4.378 4.575 Dispersion measures Standard deviation of log wages 0.309 0.428 0.463 Standard errors of log wage residuals 0.231 0.317 0.330 Notes: See note to Figure 1. The log wage residuals are obtained with log wage regressions that control for five education categories, eight age categories, and all possible interactions between these two variables. College wage premium computed as described in the Appendix. working population also changed during this period. The share of young workers (under 35) decreased from 50% in 1984 to 42% in 2009, and among young workers, the share of high-skill increased from 3% to close to 18%. Similarly, in Germany and the U.S., wage dispersion fans out with age and education (Dustmann et al. 2009). Table 2 highlights the fact that upper-tail wage dispersion rose within education and age groups, suggesting that workforce composition cannot fully account for the rise in inequality. However, the increase in within-group inequality was not generalized to all groups, suggesting a differentiated price effect. Within-group, lower-tail inequality increases in the first period and decreases in the second period, pointing to a price effect of opposite sign in each of the periods. 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 5 of 20 Table 2 Within-group wage dispersion and worker share, by age and education Within-group Worker share wage dispersion 1984 1995 2009 1984 1995 2009 Low skill Age <36 50/10 0.209 0.217 0.111 0.287 0.158 0.085 90/50 0.395 0.422 0.490 Age 36-45 50/10 0.355 0.341 0.199 0.165 0.132 0.119 90/50 0.458 0.616 0.594 Age >45 50/10 0.353 0.434 0.260 0.197 0.153 0.168 90/50 0.496 0.666 0.763 All 50/10 0.310 0.358 0.200 0.649 0.442 0.372 90/50 0.455 0.639 0.667 Medium skill Age <36 50/10 0.420 0.391 0.239 0.202 0.340 0.257 90/50 0.591 0.771 0.704 Age 36-45 50/10 0.600 0.785 0.525 0.072 0.101 0.135 90/50 0.518 0.780 1.161 Age >45 50/10 0.644 1.003 0.741 0.048 0.065 0.095 90/50 0.736 0.784 1.356 All 50/10 0.561 0.528 0.377 0.321 0.506 0.486 90/50 0.741 1.100 1.149 High skill Age <36 50/10 0.731 0.983 0.786 0.014 0.028 0.085 90/50 0.571 0.920 0.867 Age 36-45 50/10 0.815 1.220 1.115 0.009 0.014 0.038 90/50 0.562 0.877 1.117 Age >45 50/10 1.287 1.308 1.865 0.007 0.010 0.019 90/50 0.608 1.000 1.228 All 50/10 0.865 1.194 0.984 0.030 0.051 0.142 90/50 0.725 1.082 1.344 Notes: The tabulated statistics are for both genders. See note to Figure 1. Low skill - 6 or less years of schooling; Medium skill - 7 to 12 years of schooling; High skill - college degree. The demography changed faster between 1995 and 2009 than in the earlier period (Table 2). This led to the decomposition of the real wage percentile changes in these two periods (Figure 2). The trends in inequality are quite different for the two genders in the two periods. The first period witnessed a strong increase in male upper-tail inequality and a more modest one in the lower-tail. For females, lower-tail inequality remained constant, whereas there was a larger increase in upper-tail. In the second period, the increase in lower-tail inequality for females is negligible, while for males there is a reduction in inequality. In the upper-tail, the increase in inequality is similar for both genders but more modest in comparison with the previous period. Given the significant change in composition, particularly on the educational side, part of the inequality evolution might be explained by these fundamentals. To account for it, we compute residual inequality measures. Table 1 presents the standard deviation of 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 6 of 20 Figure 2 Log wage distribution percentile changes, Males (left panel), Females (right panel); 1984-1995 and 1995-2009. See also notes to Figure 1. log-wages and the standard errors of the residuals obtained from OLS log-wage regressions estimated separately for each year, controlling for five education levels, eight age categories, and all possible interactions between these two variables (see the Appendix for more details). The trends in residual inequality mimic the ones obtained with overall inequality; an increase in the first period (1984-1995), from 0.278 to 0.366, 8.8 log points, followed by a smaller increase of 3.6 log points in the 1995-2009 period. The overall inequality increased by 12.3 and 3.7 log points in each period. The similar evolution of overall and residual inequality confirms the evidence in Table 2, where a significant fraction of the increase in wage inequality occurred within age and education groups. The results reported used the base wage. However, institutions and market forces may have distinct effects on the different components of the total wage. We checked if the trends in wage inequality were robust to different wage definitions by using measures of total wages (base wages +other regular monthly payments +overtime pay), hourly base wages, and hourly total wages. The conclusions did not change with the inclusion of other wage components or the explicit consideration of hours. For the remainder of the paper, we stick with base wages, which are more prone to be influenced by institutional arrangements. Therefore, the estimated measures can be interpreted as upper bounds on the role of non-market forces on the evolution of inequality. This is preliminary evidence of the role of the supply and demand shifts in shaping the evolution of Portuguese wage distribution during these 30 years. It will be explored in a more structured way in the next sections. 3.1 The role of composition and prices How can we decompose the changes in inequality between the impact of the workforcecompositionandtheprices?Weobservedanincreaseininequalitywithinnarrowly defined age and education groups, which hints at significant price effects, but with a sign and magnitude that may have changed over time. In a partial equilibrium framework, we explore the role of composition and price effects by constructing a counterfactual wage distribution using the kernel re-weighting method developed by DiNardo et al. (1996). A caveat common in this literature is the partial equilibrium nature of the decomposition proposed, as it assumes that prices and quantities (characteristics) are independent. In the current setting, with large changes in the composition of qualifications and experience (age), this assumption of independence is likely to be violated. 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 7 of 20 Figure 3 Actual and counterfactual 90/50 and 50/10 overall wage inequality, Quadros de Pessoal 1984-2009. The series labeled “Observed ratio” present the actual log difference between the percentiles in the data. The series labeled “Year: f(w|skills)” correspond to the log difference of the percentiles of a reweighted (counterfactual) distribution of year “Year” where the weights are proportional to the distribution of skills (age, schooling, and interactions) in each year, depicted in the x-axis and the distribution of skills in year “Year”. See the Appendix for additional details. Figure 3 plots observed and counterfactual overall inequality. The three counterfactual curves plotted hold prices constant at their 1984, 1995, or 2009 levels, while labor force composition is allowed to evolve as observed over the full sample, 1984 to 2009. Thus, in these figures, a vertical difference between the curves identifies the price effect at each year, i.e., the composition is held constant at that year’s level while the prices change across counterfactual curves. Movements along each counterfactual curve identify composition effects. Table 3 complements this information with the rates of change for particular years. For each wage gap, the first row shows the observed change. The following rows show the counterfactual change that would have prevailed if the workforce composition had been the same in 1984, 1995 or 2009, i.e., the price effects. There are two distinct periods of inequality growth. Inequality grew at a faster pace from 1984 to 1995 than in the subsequent period, 1995-2009. In the upper-tail (90/50), overall male wage inequality grew by 18.3 log points in the first sub-period, and after 1995 it grew by 15.2 log points. If the workforce composition had remained constant at 1984 levels, the 90/50 wage gap would have increased by 12.8 log points between 1984 and 1995 and decreased by 5.1 log points between 1995 and 2009. The change of prices from 1984 to 1995 explains a substantial part of the observed increase in inequality, but the same is not true afterwards; the price changes from 1995 to 2009 yielded reductions or tiny increases in inequality. A tentative explanation for this change of pattern between periods 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 8 of 20 Table 3 Observed and composition-constant changes in overall and residual inequality, log points ×100 Overall inequality Residual inequality 1984-1995 1995-2009 1984-2009 1984-1995 1995-2009 1984-2009 90/50 Males Observed 18.3 15.2 33.4 8.9 10.1 19.0 1984 composition 12.8 -5.1 7.7 8.8 2.6 11.4 1995 composition 12.0 -7.1 4.9 8.0 2.8 10.8 2009 composition 19.9 0.8 20.8 10.2 4.5 14.7 Females Observed 23.4 9.9 33.3 8.4 4.2 12.6 1984 composition 10.9 -24.7 -13.9 5.3 -10.1 -4.8 1995 composition 17.3 -28.1 -10.8 6.6 -11.3 -4.7 2009 composition 25.3 1.8 27.1 12.2 -5.6 6.6 50/10 Males Observed 8.9 -5.6 3.3 6.9 -1.9 5.0 1984 composition 5.0 -14.6 -9.6 5.7 -12.1 -6.3 1995 composition 6.4 -16.1 -9.7 5.5 -10.7 -5.1 2009 composition 8.7 -17.4 -8.7 8.0 -10.7 -2.7 Females Observed 3.3 -2.5 0.8 5.5 2.7 8.2 1984 composition -1.5 -12.6 -14.1 1.3 -10.5 -9.2 1995 composition -3.4 -15.3 -18.7 3.5 -11.0 -7.5 2009 composition 2.1 -28.7 -26.6 9.6 -14.5 -5.0 Notes: Tabulated statistics present observed and composition-constant overall and residual inequality from Quadros de Pessoal, 1984-2009. Residual inequality is obtained from a regression of log hourly wages on a full set of experience dummies, dummies for 5 discrete schooling categories, and a full set of interactions among the schooling dummies and a quartic in experience. All models are estimated separately by gender. See the Appendix for further details in the compositional adjustment method. In relation to Figure 3, the values reported in this table are obtained by subtracting pairs of counterfactual curves vertically. For instance, for overall male upper-tail inequality, the price effect induced by wage changes from 1995 to 2009 with 1984 composition, -5.1 log points, is obtained by subtracting the values in 1984 of the 2009 counterfactual curve from the 1995 counterfactual curve. rests on the substantive shift in the supply of skills that occurred in the second period. One might speculate that demand factors explain the positive and substantial price effect of the early period, while supply increases counterbalanced the increase in demand to yield rather paltry (or negative) price effects in the more recent period. All these effects are evident in the upper left panel of Figure 3. The counterfactual curves for 1995 and 2009 are closer, resulting in smaller price effects (vertical distances). The counterfactual series (moving along the xaxis) are steeper after the mid-90s, implying that the contribution of compositional changes to inequality grew over time. In the lower-tail (50/10), the first period is characterized by an increase in overall inequality of 8.9 log points for males, while between 1995 and 2009 this increase is almost wiped out by a reduction of 5.6 log points. The change in prices from 1984 to 1995 explains again a substantial part of the increase in inequality. In the second period, the price change would seem to have resulted in even larger reductions in lower-tail male inequality. The composition effect cancels out part of the price effect. Although not as 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 15 of 20 Table 6 Regression models for the 90/50 ad 50/10 wage ratios Male Female 90/50 50/10 90/50 50/10 College/Noncollege relative supply -0.065 -0.185 -0.267 0.006 0.037 0.048 0.072 0.033 Log real minimum wage -0.030 -0.265 -0.331 -0.457 0.110 0.141 0.210 0.098 Natural unemployment rate -0.011 -0.016 -0.010 -0.008 0.003 0.004 0.005 0.003 Time 0.020 0.018 0.035 0.007 0.003 0.003 0.005 0.002 Constant 0.528 0.901 1.085 1.924 0.387 0.499 0.743 0.346 No. of observations 24 24 24 24 R20.992 0.828 0.969 0.934 Notes: See notes of Table 4. Standard errors in italic. with a continuous increase in the upper half and a reduction in the lower half of the distribution. This polarization is observed in overall inequality, residual inequality and in educational wage gaps, a result also obtained for a large number of countries in Goos et al. (2009). What can account for this differentiated evolution at both ends of the wage distribution in the two periods? The short answer to this question has two parts. The Portuguese economy went from a skill-biased technological trend in the 80s and 90s, matched with a lack of skill supply, to a polarization of labor demand, matched with an increase in college graduates. We follow Goos and Manning (2007) and look for shifts in the employment structure consistent with the “polarization of work”, where the increased demand for skills of higher-educated workers is matched with a reduced demand for intermediate-skill workers, while the demand in occupations with low levels of education remained stable. These shifts in the demand for skills can be the result of multiple developments. Goos et al. (2009) identify three possible origins for this polarization of work: (i) the “routinization” hypothesis of Levy et al. (2003); (ii) the effects of globalization that characterize the process of an international division of labor, in which global outsourcing plays a relevant role; and (iii) a link between employment demand and inequality, as more unequal societies create a demand for low-skill services (Manning 2004). The Portuguese economy was particularly sensitive (and exposed) to some of these processes. It faced rapid integration into the European economy after joining the European Community in 1986, a much richer economic area, which implied a substantial increase of its degree of openness to technological shocks. In the more recent period, demand has increased for low-skill services, mostly related to retail, tourism and real estate (together with a mild boom in construction). In Portugal, both demand and supply have evolved 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 16 of 20 in the same direction since 1995, while in other developed economies there has been a reduction in the pace of skill growth in the more recent period. The polarization of work is a demand-side phenomenon characterized by rising relative demand for high- and low-skill occupations. Its implications are testable: equilibrium wages and quantities should both increase, resulting in a positive covariation. We apply a methodology similar to the one in Goos and Manning (2007) to our data, using the educational level to proxy for the occupational skill level. Figure 7 presents the change in the shares in total employment from 1984 to 1994 and from 1996 to 2009 by occupation skill percentile, using the education level as proxy for the skill intensity.2This exercise uses 996 occupations for the first period and 348 for the subsequent period, which correspond to the 4-digit occupations in the National Classification of Occupations. There are strikingly different patterns in the recomposition of employment between periods. In the first period, there was a significant reduction in the share of employment in occupations with lower skills and an increase in occupations with higher skills. In sharp contrast, in the post-1995 period, employment growth seems to have polarized. There is a sizeable increase in the share of employment for occupations with higher skills, a reduction in intermediate-skill jobs and also an increase in the share of low-skill jobs. In the polarization period, the professions with intermediate skills (average schooling between 8 and 10 years) that decreased the most include, for instance, “Sales personnel”, “Electro-mechanical workers”, “Clerical workers”, and “Non-qualified workers in manufacturing”. In the same period, the professions that increased the most include both low- and high-qualified workers, for instance,“Cooks”,“Truckdrivers”,and“Janitors” (with average schooling of 5 years) and “Nurses”, “Engineers”, “Information and technology analysts” (with average schooling of 14 or more years). This shows that the Portuguese economy is integrated in the world economy and faced the three main origins of polarization identified above. Declining demand for routine occupations, e.g. clerical jobs, and increasing demand for jobs characterized by abstract reasoning and manual tasks, e.g. IT and cooks. This result is in line with Fonseca et al. (2013). Figure 7 Change in occupation employment share by occupational skill percentile. The figure plots log changes in employment shares by 1984 and 1996 occupational skill percentile rank using a locally weighted smoothing regression (with bandwidth 0.8). We measure the occupational skill by the employment-weighted percentile rank of the occupation’s mean years of education. 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 17 of 20 This trend in employment growth was matched by similar changes in the wage distribution, as shown in Figure 8. The real wage evolved monotonically with skills during the first period, with negative real wage growth up to the 40th percentile of the wage distribution and positive gains thereafter, particularly above the 3rd quartile. In the post-1995 period, wage growth follows a U-shaped pattern, with the lowest gains observed between the 30th and 60th wage percentiles. This means that labor market prices and quantities covary positively in each of these two periods – a demand dominated evolution. This positive covariation can be assessed in a more formal way by regressing the (log) employment share estimated in Figure 7 on the changes in log wages by the wage percentile of Figure 8. We estimate a regression separately for each period using data from the 4th to the 97th percentiles of the wage and skill distributions. We obtain a coefficient of 0.391 (t-ratio of 27.54) for the 1984-1995 period and 1.471 (t-ratio of 7.97) for the 1995-2009 period. The monotone increase in wage inequality in the 80s and 90s and the “polarized” growth of wage inequality in the more recent period are paralleled by conformable changes in employment by skill. 5Conclusion Supply and demand market forces resulted in an increase in wage inequality, in particular at the top of the wage distribution. The upper-tail inequality increase was bigger before the mid-90s than afterwards. We interpret this slowdown not as a reduction in demand pressure, but as the result of an extraordinary increase in the supply of skills. Two results are particularly important for this conclusion to be drawn. First, in the post-1995 period, we observe a polarization of work – a demand phenomenon, characterized by relative employment and wage gains for low- and high-skill workers. Second, more skilled and younger cohorts experience a reduction in the college wage gap, a consequence of the increase in the supply of skills concentrated in this younger group. Lower-tail inequality increased in the pre-1995 period (especially in the 1987-1995 period) and declined (or at best remained stable) subsequently. The behavior of lowertail wage changes is mainly explained by negative demand shocks during the first period and by the polarization of work and the minimum wage more recently, which benefited low-wage jobs (against intermediate-skill jobs) and helped in reducing inequality. Figure 8 Change in real wages, by wage percentile. See notes to Figure 1. 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 18 of 20 We see these results as evidence that market forces play a key role in understanding the changes in the wage distribution in Portugal. This is in accordance with the available evidence for other countries, such as Germany, the U.K., and the U.S. The role of the institutional setting to contain inequality developments is of major relevance to the debate in European countries that share a similar regulatory environment. We show that the minimum wage can contain lower-tail inequality – though, we ignore the impact on employment – and that increasing educational achievement is the main supply factor to curb wage inequality. Endnotes 1We also excluded the first two years of the data, 1982 and 1983, due to potential under-reporting of firms and the quality of some variables. 2In 1995, the occupational codes changed, and the mapping of pre- and post-1995 classifications is not perfect. For this reason, we use them separately and exclude 1995 due to transition problems evident in the occupational coding. Appendix Sample selection The selection criteria applied to our samples consisted in keeping all wage spells corresponding to full-time workers earning at least the minimum wage. These restrictions were imposed to make our results more comparable with those reported in key papers in the field, such as Autor et al. (2006). Education and age The education variable distinguishes five groups. This number of categories aimed at capturing the changes in mandatory schooling that were faced by workers in our sample. The five categories are: less than 4 years of schooling, between 4 and 6 years of schooling, 7to9yearsofschooling,10to12yearsofschooling,andacollegedegree.Whenever necessary these categories are aggregated in broader groups. The age variable considers eight age groups: less than 25 years old; 25-29; 30-34; 35-39; 40-44; 45-49; 50-54; and more than 54 years old. Relative supply measures We calculate the quantities supplied of college and noncollege graduates using the QP samples. We construct a labor quantity sample measured in efficiency units for all workers with 0 to 39 years of potential experience. These workers are split into 400 gender × education ×potential experience cells. Experience groups are single-year categories of 0 to 39 years; education groups are the same as above. The quantity data are merged with price data containing mean real wage by year, gender, potential experience, and education. To compute the efficiency units, we use mean real wage by year, gender, education, and age. Education wage differentials The data are sorted into gender-education-potential experience groups based on a breakdown of the data into two gender, five education, and four potential experience categories (0-9, 10-19, 20-29, and 30 or more years). Log monthly base wages of full-time workers are regressed in each year separately by gender on the dummy variables for four education 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 19 of 20 categories, a quartic in experience and interactions of the experience quartic with the education dummies. The composition adjusted mean log wage for each of the forty groups in a given year is the predicted average log wage from these regressions for each relevant experience group. Mean log wages for broader groups in each year represent weighted averages of the relevant cell means, using a fixed set of weights, equal to the mean share of total employment by each group over 1984 through 2009. Computing counterfactuals The observed wage density at time t,f(w|t), can be decomposed into the product of the density of observable wages conditional on observable attributes xat time t,g(w|x,T=t), and the density of the same attributes, h(x|T=t). Formally, f(w|T=t)=g(w|x,T=t)h(x|T=t)dx (3) and similarly for time T=t. In order to compute the counterfactual wage distribution in year tthat would have prevailed if the workforce attributes were the same as in year t, one needs to re-weight the “price” function, g(w|x,T=t), by the ratio of the “composition” functions, h(x|T=t) h(x|T=t). As shown by DiNardo et al. (1996), this ratio can be easily calculated by noting that h(x|T=t) h(x|T=t)=Pr(T=t|x) Pr(T=t|x)×(1−Pr(T=t)) Pr(T=t). Notice that the reweighting function can be computed by using a dichotomous variable model – logit or probit – in the pooled data for years tand t. In our case, the set of conditioning variables includes dummy variables for five levels of schooling and eight age groups, and all possible interaction terms between education and age dummies. The same principle can be applied to decompose residual inequality; the price function g(w|x,T=t)is replaced with the residual price function g(|x,T=t).Theresiduals,, are obtained from a regression of log wages on the same set of attributes listed above. The Katz and Murphy framework In Katz and Murphy (1992) aggregate production depends only on the quantities of skilled and unskilled workers. We take as skilled workers those with a college degree, and unskilled workers are those without a college degree. The CES function stipulates an aggregate elasticity of substitution between the two types of labor. Aggregate output can be written as: Qt=αt(atNct)ρ+(1−αt)( btNnt)ρ1 ρ,(4) where Nct and Nnt are the quantities employed of college and noncollege equivalents, at and btare the college and noncollege labor augmenting technological change, αtis a technology parameter, and ρis the production parameter. Skill-biased technological changes imply an increase in at btor αt. The aggregate elasticity of substitution can be computed as σ=1 (1−ρ). Under the assumption that college and noncollege equivalents are paid their marginal productivity, we can use the expression for aggregate output to solve for the college wage differentials: ln wct wnt =1 σDt−ln Nct Nnt ,(5) 2014, 3:23 http://www.izajoels.com/content/3/1/23 Centeno and Novo IZA Journal of European Labor Studies Page 20 of 20 where Dtindexes relative demand shifts favoring college graduates. The greater σis, the smaller the impact of shifts in relative supplies on relative wages, and the greater must be the fluctuations in demand shifts to explain the time series variation of relative wages for given time series variation of relative quantities. Competing interests The IZA Journal of European Labor Studies is committed to the IZA Guiding Principles of Research Integrity. The authors declare that they have observed these principles. Acknowledgments We acknowledge the financial support provided by FCT’s grant PTDC/EGE-ECO/112177/2009. We thank José Ferreira Machado and Ana Rute Cardoso for helpful discussions. We are grateful to Lucena Vieira for the outstanding data handling of the Quadros de Pessoal dataset. Opinions expressed herein do not necessarily reflect the views of the Banco de Portugal. Any errors are of our responsibility. Responsible editor: Sara de la Rica Received: 17 March 2014 Accepted: 28 August 2014 Published: References Abraham KG, Haltiwanger JC (1995) Real wages and the business cycle. J Econ Lit 33(3):1215–1264 Autor DH, Levy F, Murnane RJ (2003) The skill content of recent technological change: An empirical exploration. The Q J Econ 118(4):1279–1333 Autor DH, Donohue III JJ, Schwab SJ (2006) The cost of wrongful-discharge laws. Rev Econ Stat 88:211–231 Autor DH, Katz LF, Kearney MS (2008) Trends in U.S. wage inequality: Revising the revisionists. Rev Econ Stat 90(2):300–323 Card D, Lemieux T (2001) Can falling supply explain the rising return to college for younger men? 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