QUALIFICATION REQUIREMENTS AND EDUCATIONAL ATTAINMENT

IN PORTUGAL, 1985-1997

Maria Clementina Santos* M. Mendes de Oliveira*

This paper uses data from staff logs (Quadros de Pessoal) to analyse the evolution of over-and undereducation for the period 1985-1997 in Portugal. In 1997, nearly 50 percent of workers have more education than their jobs require. Furthermore, from 1985, a year prior to the accession to the European Union, to 1997 it is found that the incidence of overeducation has duplicated whilst that of undereducation had only a modest decrease. Effects of over-and undereducation upon workers’ earnings are also evaluated. Results suggest that there are losses of productivity and earnings due to the mismatch between educational attainment and qualifications required in the labor market. Overeducated workers, holding a job for which their qualifications exceed those that are standard among fellow co-workers, would benefit from changing to a job where their levels of schooling were fully utilised. On the other hand, undereducated workers (that is, those with educational credentials below what is commom in their occupation) would stand to gain if they were to catch up with the level of schooling of fellow co-workers. These findings justify some skepticism upon the ability of the portuguese economy, after access to European Community, in absorbing the increasing number of more educated people supplied by the educational system. Keywords: Overeducation, Skills, Human Capital, Job Matching

* Faculdade de Economia, University of Oporto, and CETE – Centro de Estudos de Economia Industrial, do Trabalho e da Empresa. Financial support from the Fundação para a Ciência e a Tecnologia is gratefully acknowledged. We thank also an anonymous referee for useful comments and suggestions and the Portuguese Ministry of Labor and Social Security for the data employed in this study.

1. Introduction The existence of a mismatch in the labour market between skills required by available jobs and school-provided workers’ qualifications has been an issue for decades. Concern began earlier (e.g., Freeman (1976) or Thurow (1975)); but it was in the early 1980’s that the concepts of overeducation and undereducation took root in the economics of education literature, after seminal work by Rumberger (1981) and Duncan and Hoffman (1981). Twenty years of research in the field have consistently established that overeducation and undereducation are pervasive in the market; and, in what relates to earnings, that workers whose educational qualifications exceed those required by the jobs they hold (overeducated workers, for short) tend to earn higher returns to their years of schooling than co-workers who are not overeducated, but lower returns than they would be able to get at a job where their schooling credentials were fully utilized. Symmetrically, undereducated workers receive lower returns than their co-workers with the required (and, thus, higher) level of education, but higher returns than employees who have the same educational attainment and work in jobs that require just their level of education. These findings have been reported by a number of studies for economies on different stages of development, with widely different labour-market institutional frameworks or education systems, and employing strikingly different means of measuring overeducation and undereducation. A representative survey across countries might include reference to Duncan and Hoffman (1981), Rumberger (1987), Sicherman (1991) or Cohn and Khan (1995) for the United States, Hartog and Oosterbeek (1988) or Groot (1993) for the Netherlands, Groot and Maassen van den Brink (1997) for the United Kingdom, Alba-Ramírez (1993) for Spain, Daly et al. (2000) for Germany and the United States, or Kiker et al. (1997) for Portugal. Some authors employed workers’ self-evaluation of educational requirements to measure overeducation/undereducation, as is the case with the seminal studies for the U.S. and the Netherlands, whereas others relied upon independent evaluations of functional requirements and job-required qualifications (Rumberger(1981), Burris (1983)), and others gave preference to data based criteria (Verdugo and Verdugo (1989), Kiker et al. (1997)). Spurred by accession to the European Union in 1986, the Portuguese economy experienced a trend of strong increase of the educational level and vocational training of the work force, at the same time that deep changes in the economic structure were called for. In the presence of considerable rigidities of the labour market, adjustment was sluggish and pockets of severe mismatch between educational qualifications and required job skills developed. In this paper, we plan to address these issues, using Portuguese data for the period 1985-1997. We will review the evolution of over- and undereducation measures over the period and evaluate their effects upon workers’ earnings. In Section 2, we discuss measurement issues and describe the data set. The incidence and evolution of over- and undereducation in Portugal in recent years are

1

presented in Section 3 and their effects on earnings in Section 4. Concluding remarks are in Section 5. 2. Data and measurement Data used in this study come from staff logs (Quadros de Pessoal) collected annually by the Portuguese Ministry of Employment from all business firms with more than one employee. Available is information on workers’ characteristics such as gender, age, education, occupation, qualification level, years with the firm, hours worked and earnings, an on job attributes such as type of industry, geographic location and plant size. The variables listed in Appendix Table A were constructed with this information. After exclusion of observations with incomplete or inconsistent data and of a number of categories of individuals for whom reported earnings may impart a bias upon correct evaluation of labour income1, results to be reported below pertain to a random sample of 84873 workers surveyed in 1985 (25865 females and 59008 males), 78042 workers in 1991 (27183 females and 50859 males) and 83612 workers in 1997 (34671 females and 48941 males). Sample means are given in Appendix Table B. In their pioneering study, Duncan and Hoffman (1981) employed the worker’s own evaluation of educational standards necessary to perform his or her job to ascertain whether there was overschooling, adequate schooling or underschooling. The measurement being open to criticism on the grounds of subjectivity, biased recall and possibly conflicting evaluation by workers holding identical jobs and schooling levels, subsequent research attempted to improve upon the method. An alternative measure rested upon the evaluation of required skills by independent job analysts and their correspondence to school-provided abilities. Examples of this methodology can be found in the use of the general educational development (GED) information contained in the Dictionary of Occupational Titles of the U.S. Department of Labor (Rumberger, 1981) and in Dutch job qualification scales (Hartog and Oosterbeek, 1988). In this setting, conversion of GED scores or qualification levels into equivalent years of schooling has proved to be a difficult task. Rather than relying upon the worker's own evaluation or upon exogenously designed criteria, Verdugo and Verdugo (1989) proposed a different indicator of overeducation/undereducation based on the distribution of actual educational attainments in each occupation defined at the 3-digit level. Their index classifies as undereducated a worker whose schooling deviates (negatively) from the observed occupation average by more than one standard deviation, and as overeducated those with (positive) deviation from the mean in excess of one standard deviation. In previous work (Kiker et al., 1997), we experimented with a variant of the procedure, replacing Verdugo and Verdugo's mean-centred bracket with a different measure of central location, the distribution mode. The mode is less sensitive to the

1 Excluded were individuals who were simultaneously owners and executives, self-employed, part-time and unpaid family workers, individuals under 14 years of age, farmers and farm labourers.

2

presence of outliers in the data and provides a more accurate measure of the extent of surplus or deficit schooling than can be garnered from the mean. Both arguments can best be illustrated by means of a simple numerical example. Consider a group of 10 workers with the same 3-digit occupation code, of which 8 are college graduates (16 years of schooling); the ninth is a high-school graduate (12 years) and let x denote years of schooling of the tenth worker. According to the mode criterion, the ninth worker will be classified as undereducated with 4 years of deficit schooling, no matter what the education level of the last worker turns out to be. In contrast, the mean-centred criterion will classify the ninth worker as undereducated or adequately educated depending on whether the tenth worker has at least 8 (x ≥ 8) or less than 8 years (x < 8) of schooling. Furthermore, if the ninth worker is deemed undereducated, the amount of the assigned education deficit may vary from 0.1 years to around 2.3 years as the last worker's attainment increases from 8 to 16 years of schooling. Particularly striking are the results when x = 16, that is, when all but one worker are college graduates. The mode-based criterion plausibly takes 16 years to be the required schooling level and measures the extent of the schooling deficit of the undereducated worker as 4 years, whilst the mean-centred evaluation produces a 14.3-16.9 range for years of required schooling and a value of 2.3 for the amount of undereducation.2 If one is to resort to observed data to establish incidence of overeducation/undereducation and measure the extent of surplus or deficit schooling, the arguments presented above clearly suggest that a mode-based criterion will produce better results than the mean-based alternative. Furthermore, we aim in this paper at the evolution of over- and undereducation. In this setting, methods of measurement that rest upon administrative sources, which are only infrequently updated, fail to accommodate changes in technological conditions and workplace organization. On the contrary, data based criteria, at least, have the potential for self-correction. In fact, if a technological change takes place that has the effect of making higher education the required level thereafter, firms will retrain the existing work force, adjust hiring standards upwards and replace each vacant or newly created position with a better-educated employee. Over time, this will lead to a change in the mean and modal values of education and, thus, to a more correct evaluation of the extent of over- and undereducation. In this paper, we classified as adequately educated workers with completed schooling equal to the modal value of employees holding identical jobs, as defined by the occupational codes recorded in the Quadros de Pessoal, at the 3-digit level in the 1985 and 1991 waves and at the comparable 4-digit level in 1997 data.3 Appendix Tables C and D 2 Verdugo and Verdugo (1989) refrained from using a quantitative measure of over- and undereducation and relied on their mean-centred bracket merely to define dummy variables as to whether a worker was over- or underschooled. 3 A new National Classification of Occupations (Ministério do Emprego e da Segurança Social, 1994) was adopted in 1994. This review of an earlier classification, dating from 1980 (Ministério do Trabalho, 1980), was conducted on the basis of the International Standard Classification of Occupations (ISCO-88) published

3

provide detailed information on the number of workers, mean and modal values of schooling and percentage of workers deemed adequately educated (that is, with the modal value of schooling) for each of the most representative occupational codes.4 If the quantification of over- and undereducation is still an unsolved matter in the literature, the consensus appears to be wider on what the attempted target is: the possible mismatch between the worker’s educational investment and the qualifications actually required by the job he happens to hold. It can be argued however that, individual heterogeneity being the norm, different educational attainments among workers with similar jobs are more a reflection of different innate ability, productivity, social background, quality of the schooling acquired or of other unobserved trait rather than evidence of a mismatch between the individual’s and the job’s attributes. It might be the case that workers with higher education levels than are average or standard among their co-workers were less able, less productive or have lower-quality education than what is common among workers with the number of years of schooling they possess and thus be forced to take jobs further down the occupation ladder5. Symmetrically, undereducated workers might be viewed as overachievers who were able to get jobs normally slotted for people with higher education credentials than they hold. This view is hardly reconciled with overwhelming evidence to the effect that overeducated workers are paid a premium over, and undereducated workers earn less, than their (adequately schooled) co-workers. If they were equally productive as fellow co-workers, despite different schooling levels, the persistence and generality of pay differentials could hardly be explained. 2. Incidence of overeducation and undereducation A rapid overview of the evolution of educational attainment in Portugal in recent years can be garnered from Figure 1.

by the International Labour Office. In both pre-1994 and post-1994 data we retained the level corresponding to the so-called base groups of occupations. 4 Full results, relating to some 225 occupations in both the 1985 and 1991 waves and 350 in the 1997 wave, are available from the first author on request. 5 Productivity losses associated with overeducation were reported by Tsang (1987). Recent research (Büchel, 2002), however, concluded that overeducated employees tend to be more productive than their adequately schooled colleagues.

4

Figure 1

0%

10%

20%

30%

40%

50%

60%

< 4 yrs 4 yrs 6 yrs 9 yrs 11-12 yrs University

Years of schooling

Percentage distribution of workers by years of schooling, 1985, 1991, 1997

198519911997

In the Portuguese educational system, elementary schooling takes 4 years; as of 1985, close to 57% of all workers in our sample had just 4 years of education, a figure that declined markedly over the 13 years of the analysis, to less than 39% by 1997. A fringe of workers however, mainly elder people, was illiterate or failed to complete elementary school; they represented 9% of our sample in 1985 and 2.5% in 1997. Secondary school currently requires 8 (formerly, 7) additional years, with different degrees being granted at the end of the 6th, 9th and 12th years of schooling. Mandatory schooling has been set at 9 years for children born after 1980; as a result, the percentage of workers with 9 years of education more than doubled from 1985 (7%) to 1997 (15%), when the effects of the legal change took hold. Completion of secondary school climbed from 12% in 1985 to 15.5% in 1997. Just 1.9% of our workers in 1985, 2.3% in 1991 and 5.6% in 1997 held university degrees (requiring from 14 to 18 years of education, depending on age cohort, type of college degree and course of studies undertaken). The incidence or overeducation and undereducation by gender for each of the years 1985, 1991 and 1997 is shown in Table 1.

5

Table 1 Percentage Distribution of Workers by Educational Status

Females Males Worker’s status 1985 1991 1997 1985 1991 1997 Overeducated 16 24 34 19 25 34 Adequately educated 62 58 49 63 61 50 Undereducated 22 18 17 18 14 16 Total 100 100 100 100 100 100 Sample size 25865 27183 34671 59008 50859 48941

Notes: Workers with the modal number of years of schooling for their occupation are classified as adequately educated. Overeducated are those who completed more years of schooling (less, for undereducated) than is the mode among workers with the same base group occupation code.

Given the very low level of educational attainment in Portugal (an average close to 7 years in the 1997 sample), it is not surprising that overeducation is more prevalent than undereducation. In the 1985-1997 period, overeducation is clearly on the rise, whilst undereducation exhibits a slightly decreasing trend. Using a similar criterion, Cohn et al. (2000) also report a greater incidence of overschooling than of underschooling for both the United States and Hong Kong, but no discernible trend in either territory in regard to the evolution of the two measures from 1986 to 1991. In a recent meta-analysis, Groot and Maassen van den Brink (2000) report average rates of incidence of 21.5% for overeducation and 13.9% for undereducation, in European countries covered in the 25 studies they surveyed. Tables 2 (for female workers) and 3 (for males) contain information on the distribution of over- and undereducation by years of potential experience, computed by subtracting from the worker’s age at the time of the surveys the sum (number of years of schooling required for degree held + 6). Particularly striking are the figures on the first line, which refer to entrants in the labour market (5 years or less of potential experience). From them, it can be garnered that in recent years the overwhelming majority (65% of female workers and 73% of males) of entrants take jobs for which their educational qualifications are higher than those common among co-workers. On the other hand, only 2% of new entrants are found to possess lower educational level than is standard in their jobs. These findings are in line with the increase in school enrolments observed in the last two decades, in the wake of legislative action to raise mandatory schooling to 9 years. Among more experienced workers, the percentages of incidence of over- and undereducation take opposite directions, descending for the former and ascending for the latter as new brackets come into light. Experience being correlated with age, it should be expected that older cohorts would reveal higher prevalence of deficit schooling and that; as they leave the market, undereducation becomes less and less significant, as in fact happens with females from 1985 to 1997; for males, the picture is less clear. But it is somewhat disturbing to find that the rate of incidence of overeducation increases from 1985 to 1997

6

for all the brackets of potential experience. The figures strongly suggest that younger and more educated cohorts have been largely unable to find jobs in which their educational attainments would be fully utilised, casting doubt about the ability of the changing economic structure to take advantage of better endowments of human capital.

Table 2 Percentage Distribution of Workers by Years of Potential Experience, Females

Overeducated workers Undereducated workers Number of years of

potential experience 1985 1991 1997 1985 1991 1997 0-5 48 58 65 5 5 2 6-10 40 59 58 10 5 7 11-15 28 45 50 13 9 11 16-20 13 32 42 16 14 14 21-25 7 18 33 20 16 17 26-30 7 10 22 22 19 22 31-35 7 7 10 30 22 26 36-40 7 5 8 36 27 27 41 or more 4 4 6 52 42 33

Table 3 Percentage Distribution of Workers by Years of Potential Experience, Males

Overeducated workers Undereducated workers Number of years of

potential experience 1985 1991 1997 1985 1991 1997 0-5 71 76 73 4 4 2 6-10 54 63 65 6 7 6 11-15 39 51 52 9 9 10 16-20 23 38 45 12 10 12 21-25 16 29 35 14 11 15 26-30 12 19 25 15 13 18 31-35 10 13 15 18 15 23 36-40 8 11 11 23 16 26 41 or more 5 6 7 37 26 28

Tables 4 and 5 display information on the distribution by years of tenure with the same employer. By construction, the first bracket (5 years of tenure or less) includes virtually all of the new entrants in the labour market; it includes too workers with longer working lives who held jobs with other firms before moving to their current (at the time of the surveys) positions. Apparently, newcomers to the market accept positions for which they are overqualified but gradually move into jobs where their education credentials are

7

put to better use. At the same time, extensive pockets of undereducation remain among individuals with long tenures. However, the 1985 to 1997 trend is still one of rising extent of overeducation, whereas the rate of adequate allocation is diminishing for most brackets. Again, this lends support to the view that, despite the modernisation spurred by UE accession in the last 15 years, the economy has been unable to absorb the higher-skilled individuals that the educational system annually provides to the market. Lower productivity growth and considerable social waste are to be expected.

Table 4 Percentage Distribution of Workers by Years of Tenure, Females

Overeducated workers Undereducated workers Number of years of

tenure 1985 1991 1997 1985 1991 1997 0-5 23 33 43 15 11 11 6-10 17 28 35 20 15 15 11-15 10 20 30 25 21 19 16-20 8 12 21 28 25 29 21-25 9 8 11 32 30 35 26-30 16 7 6 40 33 39 31-35 11 7 5 42 47 39 36-40 15 5 4 43 61 49 41 or more 17 10 3 46 50 61

Table 5 Percentage Distribution of Workers by Years of Tenure, Males

Overeducated workers Undereducated workers Number of years of

tenure 1985 1991 1997 1985 1991 1997 0-5 26 32 43 12 10 11 6-10 22 31 34 15 12 14 11-15 14 25 27 21 15 18 16-20 11 18 25 25 17 21 21-25 10 14 13 26 20 33 26-30 10 12 9 28 22 36 31-35 12 12 7 31 23 41 36-40 9 15 8 35 26 38 41 or more 12 13 9 33 35 43

8

4. Effects on earnings The general form of the regressions reported below is

ln Y = α + S δ + H β + Z γ + u where Y denotes labour earnings, α is a constant, S is a (row) vector of variables related to the overeducation/undereducation issue, δ is a conformable (column) vector of coefficients, H is a vector of human-capital and hours-worked variables (TENURE and its square, EXPER and its square, LNHOURS), β is the vector of coefficients of the variables in H, Z is a vector of dummy variables to control for plant size, industry and geographical location, γ is the associated vector of coefficients, and u is a random term. Following the usual pattern in the overeducation/undereducation literature, observed years of completed schooling, EDUC for shortness, are decomposed in three parts:

EDUC = ADSCHOOL + OVERSCH − UNDERSCH . After evaluation of the number of years of schooling required for adequate job performance (ADSCHOOL), if the individual's observed educational attainment is different the excess is assigned to OVERSCH or, alternatively, the deficit is attributed to UNDERSCH. Thus, instead of a single explanatory variable for schooling, all three components belong in vector S. Other than these, in S are also included interaction terms defined by the product of the three main variables and those purporting to measure firm-specific and market-related general human capital, TENURE and EXPER. The earnings equation can be rewritten as

ln Y = α + φa ADSCHOOL + φo OVERSCH + φu UNDERSCH + + δat ADTEN + δae ADEXP + δot OVTEN + δoe OVEXP + δut UNDTEN + δue UNDEXP +

+ H β + Z γ + u, as posited in earlier research (de Oliveira et al, 2000). Under the selected specification, marginal returns to a year of required schooling are measured by the partial derivative

∂∂

ln Y ADSCHOOL

= φa + δat TENURE + δae EXPER.

Similarly, marginal returns to surplus schooling (for overeducated workers) and to deficit schooling (for undereducated workers) are given by

∂∂

ln Y OVERSCH

= φo + δot TENURE + δoe EXPER

and

∂∂

ln Y UNDERSCH

= φu + δut TENURE + δue EXPER,

9

respectively. In this way, we allow for different profiles of the returns to overeducation and undereducation in relation to tenure and experience. Inclusion of the six interaction terms allows for substitutability or complementarity of different forms of the human capital endowment (educational, general or firm-specific, chiefly). Overeducated workers may substitute education for the lack of previous job experience, accepting jobs requiring less education than they actually possess in order to acquire the necessary experience and thus improve their chances of finding a better match. Similarly, undereducated workers may substitute experience for the lack of education and thus acquire a mix of human capital endowments adequate enough for effective job performance. Supposedly, deficit schooling might be compensated by a larger stock of firm-specific capital or of general market-market experience, whereas surplus schooling might be viewed as a substitute for the lack of firm seniority or of work experience. It is generally expected, and evidence has held that view, that signs are positive for φa and φo and negative for φu, with φo < φa. Table 6 conveys information on the estimates obtained with the sample of female workers for each of the years 1985, 1991 and 1997; Table 7 refers to the sample of males.6 In both samples, disregarding possible interaction effects of tenure and experience, estimates of the marginal returns to schooling adequate for job performance are 4.7%, 5.4% and close to 7.5% for women in 1985, 1991 and 1997, respectively, and 5.1%, 7.5% and 8.6% for men. Computed instead at the mean values of tenure and experience (from Appendix Table B), marginal returns climb from 6.1 to 7.0 and 9.1% for females and from 6.9 to 8.6 and 10.4 for males, for each of the years 1985, 1991 and 1997, respectively. All four sets of estimates suggest an increase in the returns to education from the mid 1980's to the late 1990's, a finding that may be surprising in light of the observed increase of the average level of education of the Portuguese workforce. Nevertheless, our results are fully corroborated by recent studies of the Portuguese labour market using different waves of the Quadros de Pessoal data base: both Cardoso (1998) and Machado and Mata (2001) report increasing returns, over the period 1983-1992 for the former and for the years 1982 and 1994 in the latter case.7 Marginal returns to a year of overeducation fall in the range 3.7-5.0%, whilst those for undereducation are between –3.0 and –7.1%. Groot and Maassen van den Brink (2000) 6 Separate earnings regressions by gender are standard in the literature. The possible existence of gender discrimination in the labour market would of itself recommend the approach. Evidence of possible discrimination in the Portuguese labour market can be found in a number of studies. Kiker and Santos (1991) report a gender differential of 19 to 22% with 1985 data. Using quantile regressions and the same data base (the Quadros de Pessoal), Machado and Mata (2001) find that the distribution of women's wages is to the left of men's by as much as 8.6% (in 1982) or 10.7% (in 1994) for the lowest decile, 13.2% (1982) or 14.3% (1994) at the median and 19.9% (1982) or 17.2% (1994) at the 9th decile. Note, however, that our calculation of the modal value of years of schooling for each occupation was made irrespective of gender, neither available evidence nor logical reasoning having led us to believe that the adequate schooling for each occupation depended upon the worker's being a woman or a man. 7 The increase in the "average" marginal returns, however, may be masking different evolution by schooling level. See Machado and Mata (2001) for evidence regarding an increase in the dispersion of the rates of return to education.

10

report an average (across studies) rate of return to required education of 7.6% (standard deviation: 2.4) for Europe, with rates of 2.1 (standard deviation: 4.6) for over- and –1.2 (standard deviation: 7.0) for undereducation8. If differential effects of tenure and experience are allowed for, returns to education are shown to improve more clearly with the former than with the latter.

Table 6. Selected Coefficient Estimates, Females Variable Coefficient estimates 1985 1991 1997 ADSCHOOL 0.0474 0.0539 0.0745 OVERSCH 0.0374 0.0457 0.0418 UNDERSCH -0.0301 -0.0464 -0.0642 ADTEN 0.0012 0.0014 0.0019 ADEXP 0.0002 0.0002 0.0002 OVTEN 0.0013 0.0021 0.0015 OVEXP 0.0001 (*) -0.0005 -0.0008 UNDTEN -0.0006 -0.0005 -0.0005 UNDEXP -0.0000 (*) 0.0003 0.0004 TENURE 0.0130 0.0101 0.0080 TEN2 -0.0003 -0.0002 -0.0002 EXPER 0.0123 0.0133 0.0166 EXPER2 -0.0002 -0.0002 -0.0003 LNHOURS -0.1534 -0.1208 0.2889 R2 0.633 0.577 0.580 Sample size 25865 27183 34671

Notes: Dependent variable is ln Y. Besides those reported in the table, included as regressors were a constant, 3 plant-size dummies, 4 region dummies and 9 industry dummies. All coefficients are statistically significant at the 0.05 level or better, except for those noted (*). Heteroskedasticity-consistent estimates of the standard errors (White, 1980) were used.

Returns to schooling in excess of that required by the job are positive, but lower than those obtained in a job for which educational attainment is fully adequate. Returns to overeducation improve with the accumulation of firm-specific capital, as evinced from the positive and statistically significant estimates for δot; the evidence is not as clear in what concerns δoe. The results run counter the view of overeducated workers as being in a transient stage towards a better match. Tenure, in particular, is fairly well rewarded, which we take to be strongly suggestive of sizeable investments in firm-specific capital by both employee and employer. In the same direction, Büchel (2002) reports both higher chances 8 In fact, our numbers are not strictly comparable to those in Groot and Maassen van den Brink (2000). The rates of return we chose to compare with theirs apply to individuals with 0 years of tenure and experience. Actual returns, in our study, should include additional terms for tenure and experience, as can be garnered from the expressions for the partial derivatives shown above.

11

of being selected for company-related training and longer firm tenures of overeducated workers in Germany.

Table 7. Selected Coefficient Estimates, Males Variable Coefficient estimates 1985 1991 1997 ADSCHOOL 0.0508 0.0748 0.0856 OVERSCH 0.0388 0.0495 0.0344 UNDERSCH -0.0390 -0.0616 -0.0712 ADTEN 0.0011 0.0006 0.0013 ADEXP 0.0004 0.0003 0.0005 OVTEN 0.0010 0.0011 0.0018 OVEXP 0.0003 0.0001 (*) -0.0001 (*) UNDTEN -0.0006 -0.0001 (*) -0.0002 (*) UNDEXP 0.0000 0.0002 (*) -0.0001 (*) TENURE 0.0163 0.0184 0.0178 TEN2 -0.0003 -0.0003 -0.0003 EXPER 0.0146 0.0178 0.0192 EXPER2 -0.0003 -0.0003 -0.0003 LNHOURS 0.2673 0.1727 0.7517 R2 0.554 0.492 0.547 Sample size 59008 50859 48941

Notes: Dependent variable is ln Y. Besides those reported in the table, included as regressors were a constant, 3 plant-size dummies, 4 region dummies and 9 industry dummies. All coefficients are statistically significant at the 0.05 level or better, except for those noted (*). Heteroskedasticity-consistent estimates of the standard errors (White, 1980) were used.

Deficit schooling is penalized. The coefficient estimates of UNDERSCH and of the interaction term UNDTEN consistently display a negative sign and are generally significant. Marginal returns to the schooling investment of undereducated workers appear to deteriorate with prolonged tenure with the same employer, a result that cannot be reconciled with a view in which firm-experience compensates for the lack of adequate education. A possible explanation for the behaviour of the coefficients of the interaction terms lies in technological change. If a process is taking place by which undereducated workers are gradually being replaced by more qualified individuals, the obsolescence and devaluation of the educational endowments of the former will show up in the form of negative signs for δut. Employers will tend to shift their investments in training and firm-specific capital to overeducated workers whose educational background provides a better match with the job skills now rendered necessary by technological change and marginal returns to surplus schooling would be positively correlated with tenure, leading to a positive sign for δot. The results on Tables 6 and 7 are clearly supportive of this hypothesis.

12

The estimates of the coefficients in β and γ (the latter not shown, for the sake of brevity) are standard. Tenure-earnings and experience-earnings profiles reveal the classic concave shape. It is also interesting to look at the 1985 to 1997 evolution of the estimates. As was pointed out before, marginal returns to adequate schooling of both female and male workers appear to have increased from 1985, prior to accession to the European Union, to the 1990’s. The premium for an extra year of unrequired schooling, though lower than one could get at a job where that endowment were fully utilized, seems to be around 3 or 4%, but the numbers suggest that it may be declining as the relative supply of better-educated workers increases. The penalty associated with undereducation, on its turn, appears to have gained in severity, rising from the 3 to 4% range of the mid 1980’s to 6 or 7% by 1997. The coefficients estimated for ADTEN and OVTEN generally display an ascending trend, which we take to reflect the increased importance attributed by employers to retaining and rewarding better-educated workers. 5. Concluding remarks In the presence of rigidities in the labour market, the possible existence of a severe mismatch between skills required by available jobs and school-provided workers’ qualifications has recently drawn a considerable amount of attention. Undoubtedly, over- and undereducation bring about significant costs for the individual, but social costs are also of concern. In fact, previous evidence for other countries suggests that increased educational qualifications may not automatically lead to an increase in skills and income, giving rise to sizeable social waste. In this paper, we addressed these issues, using Portuguese data for the period 1985-1997. We should stress at the outset that use of the term overeducation in connection with a country where average educational attainment is utterly poor, by comparison to trends prevailing in Europe and in the United States, has the potential for being seriously misleading. In fact, if we were to single out a most important result in our analyses it would undoubtedly be the fact that returns to investment in formal education remain clearly attractive, in spite of the increase in the average schooling of the Portuguese workforce over the last decades. Returns appear to have increased from the mid 1980’s to the 1990’s and compare favourably to what is standard in more advanced economies. With that cautionary note in mind, we reviewed the evolution of over- and undereducation measures over the period and found that there is strong evidence of a persistent and growing disadjustment between school-provided skills and those required by existing jobs in the Portuguese economy. Apparently, the rhythm at which the educational system began to produce better-educated workers in the last 15 years outpaced modernisation of the economic structure in the wake of the 1986 accession to the European Union and sizeable pockets of overeducation (underutilization of skills) emerged.

13

Effects of over- and undereducation upon workers’ earnings were also evaluated. Consistently with numerous studies in a variety of European and American countries, we found adverse effects of misallocation of educational resources. Overeducated workers, holding a job for which their qualifications exceed those that are standard among fellow co-workers, would benefit from changing to a job where their levels of schooling were fully utilised. On the other hand, undereducated workers (that is, those with educational credentials below what is common in their occupation) would stand to gain if they were to catch up with the level of schooling of fellow co-workers. Given that our figures for 1997 assert that one out of every two Portuguese workers is either over- or undereducated, by comparison to people holding similar jobs, the productivity and income losses associated with so large a mismatch may reach a significant extent. It may be argued that the operation of technological progress, bringing about an upgrading of required skills, would produce a picture similar to the one we observed. As younger and more educated cohorts join the labour market and gradually permeate occupations predominantly held by older and less educated people, our statistical measurement tends to classify recent entrants as overeducated. In time, so the argument runs, more educated workers become the majority in an occupation, overeducation declines and eventually vanishes. And, indeed, our results are generally consistent with this scenario. There is a downside to it, however. Our research suggests too that employers prize overeducated workers above their (supposedly) adequately schooled counterparts, both in current pay and in opportunities for future advance. In cruder words, a 30% figure for overeducation may in fact be covering a 70% figure for undereducation. Either way, a mismatch exists. A more dynamic and modern economy would surely make room for a better use of the educational skills of workers now deemed overeducated. But it is largely beyond the scope of the paper the proposal of policy measures to overcome the shortcomings of labour demand in Portugal. To the contrary, in what concerns the supply side our findings confirm, if need be, the benefits of investment in education. Furthermore, they convey the advantage of a massive effort to upgrade over a short span the market skills of a sizeable fraction of Portuguese workers, both those now perceived to be undereducated and those who will soon join their ranks as more ambitious educational achievements become the rule. Perceived shortcomings of the preceding analysis strongly suggest that a panel study of workers would be more adequate to settle some issues. Indeed, a panel study would better control for unobserved individual heterogeneity and allow a firmer grasp on the effects of technological progress and the increase in the relative supply of better-educated workers, taking place simultaneously in the labour market. We plan to address these issues in future work.

14

REFERENCES

Alba-Ramírez, A. (1993). Mismatch in the Spanish labor market: Overeducation? Journal

of Human Resources 28, 259-278. Büchel, F. (2002). The effects of overeducation on productivity in Germany – the firms'

viewpoint. Economics of Education Review 21, 263-275. Burris, Val (1983). The social and political consequences of overeducation. American

Sociological Review 48, 454-467. Cardoso, A.R. (1998). Earnings Inequality in Portugal: High and Rising? Review of Income

and Wealth 44, 325-343. Cohn, E., Johnson, E. and Ng, Y.C. (2000). The incidence of overschooling and

underschooling and its effect on earnings in the United States and Hong Kong. Research in Labor Economics 19, 29-61.

Cohn, E. and Khan, S.P. (1995). The wage effect of overschooling revisited. Labour

Economics 2, 67-76. Daly, M.C., Büchel, F. and Duncan, G.J. (2000). Premiums and penalties for surplus and

deficit education: evidence from the United States and Germany. Economics of Education Review 19, 169-178.

de Oliveira, M. M, Santos, M.C. and Kiker, B.F. (2000). The role of human capital and

technological change in overeducation. Economics of Education Review 19, 199-206. Duncan, G.J. and Hoffman, S. (1981). The incidence and wage effects of overeducation.

Economics of Education Review 1, 75-86. Freeman, R.B. (1976). The Overeducated American. Academic Press, New York. Groot, W. (1993). Overeducation and the returns to enterprise-related schooling. Economics

of Education Review 12, 299-309. Groot, W. and Maassen van den Brink, H.M. (1997). Allocation and the returns to over-

education in the UK. Education Economics 5, 169-183. Groot, W. and Maassen van den Brink, H.M. (2000). Overeducation in the labor market: a

meta-analysis. Economics of Education Review 19, 159-168. Hartog, J. and Oosterbeek, H. (1988). Education, allocation and earnings in the

Netherlands: Overschooling? Economics of Education Review 7, 185-194.

15

Kiker, B.F. and Santos, M.C. (1991). Human Capital and Earnings in Portugal. Economics of Education Review 10, 187-203.

Kiker, B.F., Santos, M.C. and de Oliveira, M.M. (1997). Overeducation and

undereducation: Evidence for Portugal. Economics of Education Review 16, 111-125. Machado, J.A.F. and Mata, J. (2001). Earning Functions in Portugal 1982-1994: Evidence

from Quantile Regressions. Empirical Economics 26, 115-134. Ministério do Emprego e da Segurança Social (1994). Classificação Nacional das

Profissões, Versão 1994. Instituto do Emprego e Formação Profissional, Lisboa. Ministério do Trabalho (1980). Classificação Nacional das Profissões, Versão 1980.

Secretaria de Estado do Emprego, Lisbon. Rumberger, R.W. (1981). Overeducation in the USA Labor Market. Praeger, New York. Rumberger, R.W. (1987). The impact of surplus schooling on productivity and earnings.

Journal of Human Resources 22, 24-50. Sicherman, N. (1991). Overeducation in the labor market. Journal of Labor Economics 9,

101-122. Thurow, L.C. (1975). Generating Inequality. Basic Books, New York. Tsang, M.C. (1987). The impact of underutilization of education on productivity: A case

study of the US Bell companies. Economics of Education Review 6, 239-254. Verdugo, R. and Verdugo, N.T. (1989). The impact of surplus schooling on earnings: Some

additional findings. Journal of Human Resources 24, 629-643. White, H. (1980). A Heteroskedasticity-Consistent Covariance Matrix Estimator and a

Direct Test for Heteroskedasticity. Econometrica 48, 817-838.

16

Appendix Table A. Definition of variables

Variable Description ln Y Natural logarithm of total monthly earnings in escudos EDUC Number of years of education completed ADSCHOOL Number of years of adequate schooling OVERSCH Number of years of overeducation UNDERSCH Number of years of undereducation TENURE Number of years of tenure in the current job TENURE2 TENURE squared EXPER Number of years of presumed work experience in firms other than the

current one (age-education-tenure -6) EXPER2 EXPER squared ADTEN Interaction term ADSCHOOL×TENURE ADEXP Interaction term ADSCHOOL×EXPER OVTEN Interaction term OVERSCH×TENURE OVEXP Interaction term OVERSCH×EXPER UNDTEN Interaction term UNDERSCH×TENURE UNDEXP Interaction term UNDERSCH×EXPER LNHOURS Logarithm of total monthly hours worked PLANT4 Dummy variable, 1 if number of employees in the plant is ≤4 PLANT99 Dummy variable, 1 if number of employees in the plant is >4 and ≤99 PLANT499 Dummy variable, 1 if number of employees in the plant is >99 and ≤499 PLANTBIG Dummy variable, 1 if number of employees in the plant is ≥500 NORTH Dummy variable, 1 if job in the Northern region CENTER Dummy variable, 1 if job in the Central region LISBON Dummy variable, 1 if job in the Lisbon-and-Tagus-Valley region ALENT Dummy variable, 1 if job in the Alentejo region ALGAR Dummy variable, 1 if job in the Algarve region TEXTILE Dummy variable, 1 if plant in the textile and garments industry PRIMSECT Dummy variable, 1 if plant in the forestries, fisheries or mining industry MANUF Dummy variable, 1 if plant in the manufacturing industry UTIL Dummy variable, 1 if plant is a public utility CONSTRU Dummy variable, 1 if plant in the construction and building industry TRADE Dummy variable, 1 if plant in the wholesale or retail trade industry RESTHOT Dummy variable, 1 if plant in the hotels and restaurants industry TRANSP Dummy variable, 1 if plant in the transportation industry FINANCE Dummy variable, 1 if plant in the finance, insurance and real estate

industry SERVICE Dummy variable, 1 if plant in the services industry

17

Appendix Table B. Sample means of variables

Variable Females Males 1985 1991 1997 1985 1991 1997 ln Y 10.181 11.025 11.440 10.449 11.323 11.694 ADSCHOOL 5.941 6.019 6.691 5.462 5.409 6.341 OVERSCH 0.604 0.867 1.230 0.807 1.044 1.239 UNDERSCH 0.796 0.724 0.675 0.699 0.582 0.684 TENURE 9.242 9.255 7.125 10.355 10.309 7.618 TENURE2 142.768 164.478 116.681 187.750 203.582 135.798 EXPER 12.805 14.165 13.984 16.382 17.009 16.434 EXPER2 257.158 301.371 301.162 384.762 405.934 394.734 ADTEN 54.485 58.346 49.021 59.792 58.040 51.397 ADEXP 68.885 78.126 83.567 82.168 86.060 95.367 OVTEN 4.413 5.148 4.937 6.295 8.315 5.388 OVEXP 4.369 7.676 11.434 7.918 11.015 13.220 UNDTEN 9.214 9.775 7.557 10.026 8.171 8.545 UNDEXP 12.442 11.668 11.104 12.962 10.936 12.637 LNHOURS 5.189 5.171 5.156 5.206 5.189 5.163 PLANT4 0.058 0.074 0.120 0.044 0.054 0.090 PLANT99 0.443 0.487 0.552 0.473 0.507 0.593 PLANT499 0.310 0.283 0.228 0.275 0.267 0.227 PLANTBIG 0.189 0.156 0.100 0.208 0.172 0.091 NORTH 0.422 0.351 0.408 0.352 0.348 0.392 CENTER 0.114 0.128 0.152 0.148 0.132 0.153 LISBON 0.418 0.477 0.372 0.441 0.469 0.385 ALENT 0.020 0.029 0.028 0.034 0.036 0.036 ALGAR 0.026 0.014 0.040 0.026 0.015 0.033 TEXTILE 0.314 0.301 0.220 0.082 0.093 0.060 PRIMSECT 0.002 0.001 0.003 0.012 0.010 0.015 MANUF 0.266 0.220 0.241 0.367 0.315 0.332 UTIL 0.006 0.005 0.001 0.021 0.018 0.001 CONSTRU 0.012 0.015 0.017 0.112 0.138 0.183 TRADE 0.162 0.157 0.183 0.158 0.152 0.162 RESTHOT 0.059 0.066 0.090 0.030 0.031 0.046 TRANSP 0.059 0.071 0.031 0.106 0.140 0.046 FINANCE 0.060 0.057 0.101 0.074 0.063 0.107 SERVICE 0.066 0.111 0.104 0.041 0.042 0.031 Sample size 25865 27183 34671 59008 50859 48941

18

Appendix Table C. Schooling indicators for selected occupations, 1985 and 1991

1985 1991

Occup. code

No. of obs.

Mean (yrs.)

Mode (yrs.)

Ad. ed. (%)

Overed(%)

No. of obs.

Mean (yrs.)

Mode (yrs.)

Ad. ed. (%)

Overed(%)

032 515 9,6 12 56,3 0,6 403 9,9 12 52,6 3,2219 1364 11,2 12 37,6 29,0 1586 11,4 12 33,9 31,5300 1309 9,3 12 43,5 4,4 1076 9,4 12 38,4 7,4331 617 7,7 4 34,2 65,8 742 7,9 4 29,7 70,1333 2641 9,6 12 41,5 2,2 1299 10,5 12 51,5 5,6360 544 4,6 4 78,1 19,9 559 5,1 4 69,1 29,3370 778 4,5 4 85,4 14,6 850 4,8 4 78,5 21,5380 1028 7,7 4 29,0 70,8 957 8,5 12 31,5 0,6391 2063 5,0 4 67,8 26,0 1897 5,3 4 61,2 35,3393 7341 9,0 12 39,6 1,7 5870 9,1 12 40,8 1,8399 635 5,5 4 49,8 45,0 606 5,3 4 56,9 37,5432 1119 7,5 4 36,3 63,3 990 8,2 12 30,8 2,8451 3860 5,5 4 63,7 34,9 3630 6,1 4 51,4 47,1531 671 4,0 4 76,5 7,8 894 4,2 4 76,2 15,1532 1745 4,3 4 77,4 14,9 1736 4,8 4 69,2 26,7552 1653 3,8 4 63,0 10,6 1762 4,2 4 68,9 15,5589 714 4,5 4 61,8 23,4 662 5,4 4 53,2 40,0700 2774 5,2 4 67,6 25,0 3288 5,7 4 59,2 36,2752 1602 3,9 4 74,7 7,5 1113 4,1 4 73,1 11,4753 497 4,2 4 76,9 14,3 502 4,5 4 72,7 18,9754 776 3,9 4 76,7 5,3 682 4,1 4 80,1 10,0756 907 4,0 4 74,3 9,0 1047 4,4 4 72,1 19,5759 784 4,0 4 73,3 11,5 712 4,3 4 70,7 19,5776 519 4,0 4 81,7 7,9 405 4,4 4 77,8 17,3795 3533 4,4 4 79,5 16,3 3880 4,8 4 64,9 32,6802 1218 4,2 4 75,5 15,0 685 4,8 4 62,8 34,2811 480 4,0 4 85,4 5,4 682 4,2 4 81,8 11,6832 1685 5,1 4 68,0 27,7 1145 5,4 4 59,0 38,1839 927 4,7 4 68,0 19,2 556 5,3 4 59,5 35,4843 818 4,6 4 78,6 20,4 624 5,0 4 67,5 31,3855 1262 6,1 4 53,7 44,1 1127 6,3 4 51,5 47,1874 1563 4,5 4 74,4 18,2 1173 4,8 4 69,2 27,3951 1770 3,6 4 70,1 4,8 1932 4,0 4 75,4 10,6954 773 3,8 4 76,7 6,3 718 4,3 4 76,9 14,1971 1194 4,2 4 68,8 15,7 854 4,6 4 68,7 23,7974 447 3,7 4 69,6 7,4 547 4,1 4 74,2 12,8976 624 3,8 4 68,6 9,5 430 4,5 4 69,1 20,7985 3062 4,2 4 91,7 6,8 3468 4,5 4 83,2 15,7999 7776 4,6 4 58,5 22,0 7661 5,5 4 53,9 35,7

Notes: See Ministério do Trabalho (1980) for description of the occupation codes. The numbers under the heading "Ad. ed." refer to the percentage of workers with the modal value of schooling; "Overed" refers to the percentage of workers with schooling above the modal value.

19

20

Appendix Table D. Schooling indicators for selected occupations, 1997

1997 Occupation

code Number of

observations Mean (yrs.)

Mode (yrs.)

Ad. ed. (%)

Overed (%)

1231 550 12,2 16 33,3 0,03119 528 9,4 9 28,8 40,33415 2271 9,5 12 36,3 9,63431 1422 10,5 12 37,8 15,84122 7510 10,1 12 46,0 6,14131 1393 6,6 4 39,2 58,94132 892 6,8 4 38,1 59,54142 836 7,8 9 34,5 21,35122 1137 4,9 4 64,5 30,65123 2780 6,2 4 43,8 54,35220 5034 7,2 4 30,9 68,47122 2362 4,5 4 71,3 22,37123 944 5,1 4 64,7 30,77124 978 4,8 4 65,0 31,67137 583 6,1 4 40,0 58,57214 1388 5,3 4 55,0 43,37222 724 5,6 4 51,8 46,47223 672 5,7 4 50,3 48,17241 1206 7,0 6 34,3 37,77411 813 5,0 4 58,2 35,27412 706 5,0 4 58,9 36,77422 704 4,8 4 60,7 34,07436 4630 5,2 4 51,0 47,17442 2125 5,0 4 53,6 43,78261 525 4,4 4 74,1 18,38262 866 4,9 4 61,3 34,88264 892 4,9 4 61,7 34,98281 511 7,9 9 33,9 23,18282 746 7,7 6 35,7 45,88322 606 5,4 4 59,9 40,18324 1436 5,0 4 66,8 33,29132 2548 4,8 4 64,3 29,19151 1099 6,2 4 43,9 53,69152 1286 6,6 4 39,7 57,49312 1943 4,8 4 60,0 30,89322 3737 5,5 4 49,4 45,89333 525 5,9 4 43,6 51,1

Notes: See Ministério do Emprego e da Segurança Social (1994) for description of the occupation codes. The numbers under the heading "Ad. ed." refer to the percentage of workers with the modal value of schooling; "Overed" refers to the percentage of workers with schooling above the modal value.