In this article we investigate whether inequality in the inter-industry wage premia may be explained by unobserved differences in workers' educational skills. We use the last 2007 wave of EU-SILC data set for Portugal, a nation which can be considered a case-study, due to its traditional high inter-industry wage dispersion. Applying both OLS and Quantile Regression techniques, our results suggest that quality differences across workers still remain an irrelevant matter in the wage premia determination, even after many noteworthy educational reforms adopted in that country since the beginning of the 1990s.Returns to education, inter-industry wage inequality, quantile regression, Portugal

Marco Biagetti

Sergio Scicchitano

English

Research Papers in Economics

     
 
 
  
  
Volume 31, Issue 1 
  
Exploring the inter-industry wage premia in Portugal along the wage 
distribution: evidence from EU-SILC data 
  
 
 
Marco Biagetti  
lian Ministry of Economic Development, Department of 
Economic and Social Cohesion 
Sergio Scicchitano  
University of Rome "La Sapienza" and Italian Ministry of 
Economic Development
Abstract 
In this article we investigate whether inequality in the inter-industry wage premia may be explained by unobserved 
differences in workers' educational skills. We use the last 2007 wave of EU-SILC data set for Portugal, a nation which 
can be considered a case-study, due to its traditional high inter-industry wage dispersion. Applying both OLS and 
Quantile Regression techniques, our results suggest that quality differences across workers still remain an irrelevant 
matter in the wage premia determination, even after many noteworthy educational reforms adopted in that country 
since the beginning of the 1990s.
We would like to thank the associate editor Dan Anderberg, Marco Marini and an anonymous referee for helpful comments. The views 
expressed in this article are those of the authors and, in particular, do not necessarily reflect those of the Ministry of Economic Development. 
The usual disclaimer applies. 
Citation: Marco Biagetti and Sergio Scicchitano, (2011) ''Exploring the inter-industry wage premia in Portugal along the wage distribution: 
evidence from EU-SILC data'', Economics Bulletin, Vol. 31 no.1 pp. 93-99. 
Submitted: Dec 11 2010.   Published: January 04, 2011. 
 
       1
1.  Introduction 
There is a wide consensus that the inter-industry wage inequality across countries 
remains a relevant topic in the current economic literature. As a matter of fact, since the 
seminal article by Krueger and Summers (1988) some empirical analysis have explored 
possible explanations. Particularly, unobserved quality differences across workers have 
also been called to explain the behavior of income inequality among industries, in 
different countries (e.g., Gibbons and Katz, 1992).  
In this context, Portugal is a chiefly suitable country, because its inter-industry 
wage dispersion is found to be high when compared with other European countries 
(Hartog et al. 2001). In such context, Martins (2004), by using  a 1995 Portuguese data 
set, pointed out that - at the beginning of the 1990s - unobservable differences across 
workers were not a critical element in determining industry wage premia in that country.  
Exactly since the early nineties a period of strong economic expansion began in 
Portugal, enhanced by several economic reforms. At the same time obvious changes in 
many aspects of the income distribution occurred (Budria 2010).    
In particular, the improvement in the quality of education and a stronger match 
between achieved educational levels and occupations were the key elements for the 
reforms of the schooling system in that country during the 1990s (OECD 2010). 
Portuguese government defined new curricula and established upper-secondary 
education as a three-year cycle. Moreover, vocational schools were granted a new legal 
framework in 1993. These schools absorb a substantial number of students seeking a 
job-oriented training after completing the 9
th form of basic schooling, representing the 
most important alternative training path leading to a professional qualification offered 
by the official educational system. Particular attention was also given to the tertiary 
level. The new legislation allowed public universities, on a voluntary basis, to acquire 
independent legal status in the form of public foundations governed by private law. The 
new environment should have facilitated stronger educational links to professions, 
regions and labor markets and more effective university-industry links for research and 
innovation. Overall, the reform provided a sound basis to improve the ability of 
Portuguese education to raise higher-level employment skills. 
In this article we demonstrate that actually in Portugal, even after these relevant 
economic reforms, individual skills still remain an irrelevant matter in the wage 
determination, while non-competitive factors continue to play a more important role.    
To address this issue we apply a Quantile Regression (QR) semi-parametric 
approach to the last 2007 wave of the European Union Statistics on Income and Living 
Conditions inquiry (EU-SILC), the new European homogenized panel survey, which - 
to the best of our knowledge - has never been used yet in exploring the unobserved 
quality explanation of industry wage differentials. The QR method is more interesting, 
as well as more suitable, for it allows us to get a particularly precise picture of the 
dynamics of the dependent variable at different points of the distribution, rather than at 
the conditional mean.  
Overall, the article supports the idea that in Portugal there is not only a relevant 
cross-industries dispersion in returns to education, but also a high heterogeneity in wage 
premia at different points of the wage distribution, which OLS modeling of conditional 
average of a dependent variable completely fails to account for. In particular, we find 
evidence that, although recent governmental efforts endeavouring to improve the quality   2
of education across workers, it cannot be called yet to spell out Portuguese inequality in 
the inter-industry wage premia. 
This empirical article is organized as follows. The next section describes the data. 
Section 3 illustrates our econometric specification. Section 4 reports the results. Section 
5 displays the robustness check, while in the last section we present our main 
conclusions. 
 
2. Data 
 
Data are collected from the 2007 EU-SILC survey, available since March 1
st 
2009. It is the new homogenized panel survey that has replaced European Community 
Household Panel (ECHP).  
Our analysis focuses on Portuguese full-time male workers aged between 25 and 
65: women have been disregarded on account of potential selectivity biases. Younger 
males have been also dropped because they are still in the almost exclusively 
educational period of their life, i.e. they are very much more likely enrolled in a 
secondary or tertiary course than performing a work activity whatsoever. In the 2007 
EU-SILC wave 4,665 Portuguese men are interviewed: yet, our dependent variable - the 
hourly (logarithmic) gross wage – is available for 1,735 full-time Portuguese male-
workers aged between 25 and 65 years. These constitute our reference sample. Our 
regressors are schooling years, which has been built up following the usual framework
1, 
the number of years spent in paid work and its squared: the second is regarded as being 
a proxy for individual experience while the third takes account of possible non 
linearities. Sectors surveyed are those considered in EU-SILC, i.e. the 12 macro-sectors 
obtained by joining several NACE 1.1 classification codes (see table 1 for further 
details). 
 
Tab. 1. Summary statistics 
Industries - NACE 1.1 Class.  EU-SILC class.  % 
Fishing, Agriculture, hunting and forestry  a+b  7.17 
Mining and quarrying, Manufacturing, Electricity, gas and water supply  c+d+e  22.96 
Construction  f 19.42 
Wholesale and retail trade; repair of motor vehicles, motorcycles and personal and household good  g  15.83 
Hotels and restaurants  h 4.49 
Transport, storage and communication  i  5.67 
Financial intermediation  j 2.31 
Real estate, renting and business activities  k  4.81 
Public administration and defence, compulsory social security  l  10.03 
Education  m 3.13 
Health and social work  n 1.86 
Other community, social and personal service activities, Private households with employed persons, 
Extra-territorial organizations and bodies  o+p+q 2.31 
Total          100 
                                                 
1 That usual framework refers to making use of the highest ISCED level of education attained by a 
male worker, and for each level assigning the legal minimum number of years typically required to 
achieve it: more precisely, those who reached only an ISCED 1 grade have been given 5 years of 
schooling; 8 years of school have been assigned to those with an ISCED 2 grade; 13 years to those with 
an ISCED 3; 14 to men who attained an ISCED 4 grade and 18 years to those who reached an ISCED 5   3
3. Econometric specification: OLS versus quantile regression 
 
We assume our OLS specification to have the following form: 
 
i i i i i i X X w ε δ γ β α + + + + =
2 S ln                                                                       (1) 
 
As is well known, OLS implicitly assume that the impact of the regressors along 
the conditional distribution of the response variable are irrelevant. But as is already 
equally known covariates may influence it on its whole shape. This case can be studied 
by performing a quantile regression (QR), which has the following functional form 
(Koenker & Basset, 1978): 
 
 
()
() () ⎪
⎭
⎪
⎬
⎫
⎪
⎩
⎪
⎨
⎧
+ + + − − +
+ + + + −
∑
∑
≤
≥
β
θ θ θ θ
θ θ θ θ
β
θ
δ γ β α θ
δ γ β α θ
i i
i i
x w i
i i i i
i i i i
x w i
X X w
X X w
Quant
ln :
2
2
ln :
| S ln | 1
| S ln |
                  (2) 
 
 
The equation (2) is normally written as: 
 
 
( )
2 S ln min i i i i
i R
X X w
k θ θ θ θ θ
β
δ γ β α ρ − − − − ∑
∈
                            (3) 
 
where  () z θ z ρθ =  if  0 ≥ z  or  ( ) ( )z θ z ρθ 1 − =  if z<0. 
 
This problem is solved using linear programming methods. Standard errors for the 
vector of coefficients are obtainable by using a bootstrap procedure. In this study 300 
replications were carried out. 
 
4. Results 
 
In table 2 we show OLS returns to education as well as conditional returns at 5 
representative quantiles in the 12 Portuguese industries. Differences between percentiles 
of the wage distribution computed for 4 different extremes taken by twos (θ90-θ10 and 
θ75-θ25) are also reported.  
Following Martins (2004), we test the relevance of the unobserved worker quality 
hypothesis by comparing the differences in returns across high and low-wage industries 
and evaluating the correlation between OLS returns and both QR coefficients and inter-
quantile differences. The intuition is that for the unobserved ability explanation to be 
relevant, high wage industries should also show a large difference in returns between 
the top of the wage distribution, where high ability workers are expected to be found, 
and its bottom, where low ability employees are likely to be. 
From the results, a high dispersion across industries clearly emerges. The highest 
OLS sectoral coefficient (health and social work) is almost 3 times higher than the   4
lowest (Other community, social and personal service activities, Private households 
with employed persons, Extra-territorial organizations and bodies). 
 
 
 
 
By comparing OLS and QR coefficients, it can be noted that the industry with the 
highest (lowest) value in terms of OLS is not the industry with the highest (lowest) 
spread between the top and the bottom of the distribution, both for the θ90-θ10 and θ75-
θ25 differences.  
We find that the interquantile differences between the extremes of the distribution 
are not bigger for the industries with high OLS returns compared to the industries with 
low OLS returns. With regard to the θ90-θ10 spread, results evidence that the average 
difference between the 6 industries with higher OLS returns and their 6 counterparts 
with lower estimated OLS coefficients is tighter and only equal to 0.007. Using the 
same OLS ranking of industries, such gap is found to be even negative for the θ75-θ25 
spread. For the unobserved heterogeneity to be a critical element for wage premia 
determination, we would expect a really bigger differential between the top of the 
distribution, where the highest ability workers are expected to be, and its bottom.  
 
 
5. Robustness check 
 
Further, we test whether gaps between quantile coefficients estimated in our QR 
are statistically significant. The test has been carried out with respect to the 2 spreads 
considered in the article (θ90-θ10=0 and θ75-θ25=0) and to all quantiles. More 
Tab. 2. Conditional returns to schooling - OLS and QR - and significance tests 
   a+b c+d+e  f  g  H  i  j  k  l  m  n  o+p+q 
OLS  0.047 0.076 0.068 0.047 0.068 0.068  0.061  0.064 0.081 0.098 0.116 0.039 
(0.080) (0.000) (0.000) (0.000) (0.000) (0.000)  (0.007)  (0.000) (0.000) (0.000) (0.000) (0.032)
θ=0.10 0.044 0.032 0.034 0.023 0.021 0.032  0.015  0.027 0.052 0.096 0.081 0.026 
(0.013) (0.003) (0.014) (0.073) (0.232) (0.186)  (0.340)  (0.170) (0.005) (0.000) (0.102) (0.470)
θ=0.25 0.039 0.051 0.045 0.040 0.033 0.068  0.032  0.052 0.085 0.102 0.104 0.060 
(0.001) (0.002) (0.000) (0.023) (0.241) (0.089)  (0.108)  (0.009) (0.000) (0.000) (0.000) (0.066)
θ=0.50 0.031 0.079 0.053 0.041 0.086 0.061  0.071  0.072 0.093 0.098 0.112 0.045 
(0.130) (0.000) (0.000) (0.000) (0.000) (0.015)  (0.000)  (0.000) (0.000) (0.000) (0.000) (0.031)
θ=0.75 0.065 0.093 0.090 0.047 0.089 0.086  0.106  0.065 0.093 0.089 0.126 0.053 
(0.194) (0.000) (0.000) 0.0038 (0.000) (0.000)  (0.001)  (0.023) (0.000) (0.000) (0.000) (0.003)
θ=0.90 0.073 0.089 0.100 0.068 0.071 0.088  0.055  0.059 0.069 0.047 0.146  -0.002 
   (0.318) (0.000) (0.000) (0.001) (0.000) (0.000)  (0.138)  (0.092) (0.000) (0.041) (0.000) (0.936)
θ90-θ10 0.029 0.057 0.066 0.045 0.050 0.056  0.040  0.032 0.017 -0.049 0.065  -0.028 
(0.614) (0.000) (0.031) (0.025) (0.205) (0.036)  (0.372)  (0.295) (0.154) (0.049) (0.161) (0.606)
θ75-θ25 0.025 0.042 0.045 0.007 0.057 0.019  0.074  0.013 0.008 -0.013 0.023  -0.007 
    (0.407) (0.012) (0.008) (0.642) (0.148) (0.209)  (0.022)  (0.570) (0.290) (0.472) (0.525) (0.842)
All θ  eq. (0.682) (0.000) (0.001) (0.146) (0.008) (0.016)  (0.000)  (0.453) (0.001) (0.386) (0.311) (0.288)
 
Note. Data are from cross sectional UDB SILC 2007 – version 1 of March 2009.  P-values in brackets. 
Bootstrap st. errors are obtained with 300 replications   5
specifically, p-values are obtained through a bootstrapped variance-covariance matrix 
that includes between quantile blocks. The results indicate that in most cases the 2 linear 
hypothesis (θ95-θ5=0 and θ75-θ25=0) are found to be not significant. As expected, 
significance decreases when the interquantile spread also decreases. We also find that 
even the joint equality of coefficients at all quantiles is not rejected in many cases. 
Moreover, while the OLS returns are well correlated with almost all the QR 
returns (table 3), both in terms of Pearson and Spearman’s correlations, they are not 
correlated with the difference between the top and the bottom of the distribution. The 
Pearson’s correlation coefficient between OLS and the θ75-θ25 quantile difference is 
even negative.    
 
 
Tab. 3. Correlation between OLS 
and QR coefficients 
   Pearson   Spearman 
θ=0.10 0.782  0.664 
θ=0.25 0.778  0.624 
θ=0.50 0.903  0.885 
θ=0.75 0.812  0.711 
θ=0.90 0.633  0.448 
θ90-θ10 0.074  0.265 
θ75-θ25 -0.048  0.004 
 
 
 
6. Conclusions 
 
In this article we have applied OLS and QR techniques to the last 2007 EU-SILC 
wave in order to clear up a possible puzzling presence of inter-industry inequality wage 
premia for full-time male workers in Portugal.  
We have found high dispersion of OLS returns amongst the 12 considered sectors 
of the Portuguese economy, but a weak correlation between OLS and the magnitude of 
2 quantile benchmarking differences (θ95-θ5 and θ75-θ25). Furthermore, we have 
demonstrated that if sectors are ranked by OLS coefficients and divided in 2 groups 
(high and low returns), the inter-quantile difference is small (and for the difference θ75-
θ25 is even negative). Tests on the 2 linear hypothesis θ95-θ5=0 and θ75-θ25=0 are 
found to be not significant while OLS are well correlated with almost all the QR 
coefficients but not with the difference between the top and the bottom of the 
distribution.  
Our results show that in Portugal, unobserved heterogeneity possibly generated by 
the quality of workers is still irrelevant in the wage determination, despite reforms of 
the educational system during the 1990s, aimed at improving the quality of individual 
skills and favoring a stronger link with the job market. Therefore, the evidence suggests 
that the effect of these reforms on the wage premium of human capital, seems to have 
not come yet, while other factors such as the relative strength of industries in a small 
country may play a more important role. 
   6
References 
 
Budria, S. (2010) Economic Inequality in Portugal: A Picture in the Beginnings of the 
21st century, Journal of Income Distribution, 19 (2), 79-110. 
Gibbons, R. and L. Katz (1992) Does Unmeasured Ability Explain Inter-Industry Wage 
Differentials?, Review of Economic Studies, 59, 515–535. 
Hartog, J., Pereira, P.T., and J.C. Vieira (2000) Inter-industry wage dispersion in 
Portugal: high but falling, Empirica, 27, 353–364. 
Koenker, R. and G. Bassett (1978) Regression quantiles, Econometrica, 46, 33– 50. 
Krueger, A. and L. Summers (1988) Efficiency Wages and the Inter-Industry Wage 
Structure, Econometrica 56, 259–293. 
Martins P. (2004) Industry Wage Dispersion: Evidence from the Wage Distribution, 
Economics Letters, 83(2), 157-163.  
OECD (2010) Economic Policy Reforms: Going for Growth. Paris: OECD. 
  
 

Does Unmeasured Ability Explain Inter-Industry Wage Differentials?,

Economic Inequality in Portugal: A Picture in the Beginnings of the 21st century,

Economic Policy Reforms: Going for Growth.

Efficiency Wages and the Inter-Industry Wage Structure,

Industry Wage Dispersion: Evidence from the Wage Distribution,

Inter-industry wage dispersion in Portugal: high but falling,

Regression quantiles,

Exploring the inter-industry wage premia in Portugal along the wage distribution: evidence from EU-SILC data

http://www.accessecon.com/Pubs/EB/2011/Volume31/EB-11-V31-I1-P11.pdf

Exploring the inter-industry wage premia in Portugal along the wage distribution: evidence from EU-SILC data

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