7. Education and Wage Dispersion: New Evidence for Europe · 2008. 1. 20. · 7. Education and Wage Dispersion: New Evidence for Europe1. Santiago Budría and Pedro Telhado Pereira

7. Education and Wage Dispersion: New Evidence for Europe1

Santiago Budría and Pedro Telhado Pereira

1 INTRODUCTION

Academic research has a long tradition of calculating the returns to schooling. By characterizing the payoffs to the educational investment we can obtain useful information for both public and private decisions on how much to invest in education. Returns to schooling are a useful indicator of the economic benefits of education and an incentive for individuals to invest in their own human capital. However, these payoffs have been typically characterized in terms of averages and, somewhat surprising, little attention has been paid to the dispersion around the average.

Recent empirical research has shown that returns to schooling are subject to an important amount of variation across individuals. This has two notable implications. First, the fact that individuals with the same educational level obtain different rewards in the labour market introduces a within-groups dispersion component in the relation between education and inequality. Aggregate wage inequality is due not only to differences between educational groups but arises from differences within these groups as well.

Conventional wisdom asserts that policies aimed at increasing average schooling levels are expected to reduce earnings inequality by increasing the proportion of high-wage earners. A more equitable distribution of education, it is argued, will result in a more equitable distribution of earnings. Although such policies may reduce average differences between differently educated individuals, the final impact on overall inequality is not straightforward. If, as is the case, higher levels of education are associated with higher wage dispersion, educational expansion may result in more inequality within groups of similarly educated individuals. By increasing the weight of these high-variance groups, higher educational levels may instead add to overall wage inequality.

Second, if individuals with the same observable characteristics obtain different returns from similar schooling, then their educational decision is

1

Education and Inequality Across Europe 2

subject to a certain amount of wage risk. Arguably the individuals possess more information than the researcher on unobservable variables that might affect the returns to schooling such as motivation, commitment, ability or school quality. It is likely that part of the observed variation in returns reflects heterogeneity rather than risk as differences in a priori information may introduce a gap between the expected gains of two apparently equal individuals. What is not in doubt, however, is that individuals do not know with certainty their ex post return to schooling. This has clear implications for the demand for education. Most probably this uncertainty characterizing educational investments has a strong influence on individuals’ decisions concerning further schooling as well as their preferences for certain types of qualifications. Notwithstanding this, the risky aspects of education have not been sufficiently explored in the literature to date.

In this chapter we exploit a simple idea. Education, rather than assuring a certain amount of earnings, gives access to a distribution of earnings. We characterize this distribution by using Ordinary Least Squares (OLS) and Quantile Regression (QR) estimation techniques. Estimation by OLS assumes that the marginal impact of education on wages is constant over the wage distribution. In this case, the effect of completing one additional year of schooling can be represented by a shift (to the right) of the conditional wage distribution. Within the QR framework, by contrast, we can measure the wage effects of schooling at different points of the distribution, thus describing changes not only in the location but also in the shape of the distribution. By combining OLS with QR we can assess the impact of education on wage inequality both between and within groups: while OLS returns highlight average differentials between educational groups, differences in QR returns measure wage differentials between individuals who belong to the same educational group but are located at different quantiles of the wage distribution. Moreover, these within-education-group differences can be used to describe the wage risk associated with investment in education.

We use comparable data for Finland, France, Germany, Greece, Italy, Norway, Portugal, Sweden and the UK. We also analyse changes over time, covering a period that ranges from 26 years in the case of Sweden (1974–2000) down to seven years in the case of Portugal (1993–2000). Changes in OLS and QR returns are used to identify changes in conditional wage dispersions.

The rest of the chapter is organized as follows. The next section summarizes the literature on returns to education thereby paying special attention to estimates of the spread around the conventional average return. Section 3 describes the countries, datasets and variables used in the analysis while Section 4 presents the quantile regression model. Section 5 explores the

Education and Wage Dispersion: New Evidence for Europe 3

relationship between education and wage inequality using recent cross-section data. Section 6 uses several waves of the national datasets to investigate the role that education has had in shaping the European wage distribution over the last decades. Section 7 presents conclusions and some policy implications. The appendix following this chapter provides a brief summary of the national data sources and samples on which the empirical analysis is based. 2 OVERVIEW OF THE LITERATURE

Returns to education have been calculated for a large variety of countries and years. Stimulated by the need to draw worldwide conclusions about the benefits of education, a number of studies have undertaken the task of summarizing the existing international evidence. The main message arising from them is that schooling investments have a significant economic payoff. Psacharopoulos (1985, 1994) and Psacharopoulos and Patrinos (2004) collect evidence for about 100 countries over assorted ranges of years. According to their most recent estimates, the OLS return to an additional year of schooling ranges from about 4 to 17 per cent and tends to be higher among low-income countries, the worldwide average being some 10 per cent. Trostel et al. (2002) estimate returns to schooling in 28 countries using data from 1985 to 1995. Their OLS estimates range from around 2 to 19 per cent and produce a worldwide average of 4.8 per cent for men and 5.7 per cent for women. Unlike Psacharopoulos and Patrinos (2004) they find no apparent relation between the rate of return and per capita income. Ashenfelter et al. (1999) use data for 27 countries covering the period 1974 to 1995 and report an average OLS estimate of 6.6 per cent.

Some years ago the EC–TSER funded PURE project (SOE2–CT98–2044) produced several studies on the private returns to education in Europe. In the review by Asplund and Pereira (1999) the average return to an additional year of schooling is found to range between 4.5 and 9.0 per cent, with an average of 6.3 per cent. Harmon et al. (2001) report estimates between 4.1 and 8.6 per cent, with an average of 7.0 per cent. The lowest return typically corresponds to Sweden and the highest to Portugal. Pereira and Martins (2002) and Martins and Pereira (2004) emphasize the importance of using a common equation and comparable data when calculating returns to schooling for 15 European countries. Their estimates range from 4.1 to 12.6 per cent, with an average of 7.9 per cent.

In order to assess the causality between education and earnings, some studies have investigated the robustness of simple OLS estimates by accounting for possible ability and selectivity bias. If more able individuals


are also more likely to attain more education, then one may wonder to what extent the OLS return estimate reflects a return to ability rather than a return to schooling. In case schooling levels are positively correlated with omitted ability and ability is positively correlated with the wage rate, then the OLS return is overestimated (upward biased). Similarly, individuals may self-select into education according to their expected potential returns. If this is the case, then the educated individuals do not represent a random sample, implying that the estimated returns cannot be taken to be representative of the whole population. These issues have been addressed by Ashenfelter et al. (1999), Card (2001), Trostel et al. (2002) and Dearden et al. (2002), among others. The main message mediated by this strand of the literature is that compared to simple OLS estimates the returns to education do not change by more than one-third when ability and selectivity are accounted for.

Traditionally the returns to education have been calculated as averages, the assumption being that each level of education can be assigned a single (average) wage level. This approach has its limitations, though. If individuals having completed the same education earn different returns, average estimates provide an incomplete description of the relationship between wages and education. By focusing on averages, researchers ignore the amount of wage inequality that arises from differences within groups. This seems to be an inappropriate approach since, as documented in Chapter 6 of this volume, the changes in the European wage distribution over recent years have for the most part occurred within groups.

This line of thought has initiated researchers to investigate the uncertainty surrounding investments in education. Harmon et al. (2003) allow the return to education to vary across individuals by use of a random coefficient model. Using UK data they find an average return of 7.8 per cent for men and 6.5 per cent for women and a dispersion of about 4.0 per cent for men and 3.3 per cent for women. Their results imply that after controlling for observable characteristics, almost 5 per cent of men fail to earn a positive return on their educational investment. Maier et al. (2004) report that between 20 and 30 per cent of German male workers earn a negative return from schooling while more than 25 per cent earn a return exceeding 15 per cent. Lauer (2004) uses data for France and Germany to examine differences across educational qualifications regarding the amount of (unexplained) within-group dispersion. For both countries she finds that wage dispersion is lowest among workers with a vocational qualification and highest among workers with a tertiary education.

Introduced by Koenker and Bassett (1978), the QR model has recently been used to describe asymmetries in returns to education. Quantile returns measure the impact of schooling on earnings at different points of the conditional earnings distribution. This allows differences across quantiles to


be used as measures of the amount of within-education-group inequality as they represent the wage differential between individuals with the same amount of education but located at different earnings quantiles. Drawing on quantile regression estimation, Buchinsky (1994, 1998a) for the USA, Machado and Mata (1997, 2005) and Hartog et al. (2001) for Portugal, Abadie (1997) for Spain and Gosling at al. (2000) for the UK, among others, find that returns to education are more dispersed as we move towards higher levels of education.2

Despite these efforts, there is still little comparable evidence for Europe. Major differences between the existing studies arise not only from crucial differences in model specifications but also from the use of different definitions of variables, disparate datasets and differently defined samples of individuals. Pereira and Martins (2002) and Martins and Pereira (2004) fill in this knowledge gap by using comparable data and a common wage equation to calculate OLS and QR returns to schooling in 15 European countries. They find that in most countries returns to education increase when moving up the wage distribution. In other words, individuals in high-paid jobs earn a higher return from their schooling investment than do similarly educated individuals in low-paid jobs. According to their estimates, the average difference between the return at the bottom decile (D1) and the top decile (D9) of the wage distribution is 2.7 per cent in Europe, ranging from –1.9 per cent in Greece to 8.9 per cent in Portugal. This finding has two clear implications. First, to the extent that prospective students are not aware of the characteristics that will put them at some particular point of the wage distribution, Pereira and Martins’ findings suggest that the returns to education are highly unpredictable. Second, if investments in education give a better reward to the earnings-rich, then an educational expansion can be expected to raise within-group wage dispersion.3

Budría and Pereira (2005) differentiate between educational qualifications and find that in most European countries wage dispersion is greater among the tertiary educated than among the less educated. Similar findings are reported by Barth and Lucifora (2006). This result implies that the positive association between education and within-group wage inequality is particularly strong when it comes to tertiary-level education but only moderate at lower educational attainment levels. Budría (2006) uses data from a number of European countries to calculate QR returns to schooling in the private and the public sector. He finds that in a majority of countries the wage risk associated with investments in education is substantially higher in the private sector.

An important lesson from the above studies is that differences within educational groups should be acknowledged. However, an open question still remains: What are the sources of these differences? Chen (2002) decomposes


the earnings risk associated with US college education into a time-invariant individual effect and a transitory component and shows that most of the risk stems from the individual effect. Using data for Germany, Schnabel and Schnabel (2002) conclude that family background plays an important role in determining the individual’s return to education. For an average return of 6 per cent, they find that the differences in returns across individuals differing in their parental background can be more than three percentage points. This adds a significant amount of wage risk to the education investment. Using a QR framework, Arias et al. (2001) study a sample of twins to control for ability. They show that once ability is accounted for, the tendency of education to be better rewarded in high-paid jobs becomes less acute but does not disappear. This finding is interpreted as evidence of within-education-group wage inequality being partly due to differences in ability.

An alternative explanation is overeducation. Overeducated workers earn less than their adequately educated peers but more than their less educated peers (Dolton and Vignoles, 2000; Sloane, 2002). Hence, a situation where a certain proportion of the high skilled workers accept jobs with low skill requirements and low pay would be consistent with increasing returns to education when moving up the conditional wage distribution. Budría and Moro-Egido (2007) use European Community Household Panel (ECHP) data to estimate QR returns to education for overeducated and adequately educated workers. They find that the dispersion in returns within the two groups is similar and conclude that overeducation is not a convincing explanation (cf. Chapter 4 of this volume). Finally, Barth (in Chapter 8 of this volume) uses data from a number of European countries to establish some interesting causalities between supply and demand factors, labour market institutions and within-education-group wage dispersion at the tertiary level.

An important issue is whether the risk associated with schooling is quantitatively similar to the risk associated with other investments. Palacios-Huerta (2003) analyses the risk–return properties of investments in education and compares them to those of financial assets. He finds that for a given amount of risk, the expected payoff from investing in education (and especially in higher education) is typically greater than the payoff from investing in financial assets. In other words, human capital returns dominate financial market returns. Moreover, this occurs for most demographic groups and education and experience levels. 3 COUNTRIES, DATASETS AND VARIABLES

In our analysis we use national data on earnings and education for Finland, France, Germany, Greece, Italy, Norway, Portugal, Sweden and the UK. The


appendix following this chapter provides a brief description of these national datasets, including the years covered, the number of observations used and the country-specific definitions of variables.

Our approach is very similar to that in Martins and Pereira (2004). Yet, relative to their study we introduce three extensions. First, we use more recent data to up-date the available estimates of returns to education in Europe. Second, we differentiate between age groups. Third, we use a large number of waves in the country-specific datasets to investigate the role that education has had in shaping the European wage distribution over the recent years.

We apply the same estimation procedure and define the population group to be the same for all countries under study. The sample is restricted to male wage earners who are employed in the private sector4 (but not in agriculture), aged between 18 and 60 and normally working between 35 and 85 hours a week. Self-employed, individuals whose main activity status is paid apprenticeship or unpaid family worker are excluded from the samples. Women are disregarded because of the extra complication of potential participation selectivity bias. Workers with a monthly wage rate that is less than 10 per cent or more than 10 times the average wage rate are also excluded. When reporting cross-sectional evidence we use the most recent year available for each country.5

Our dependent variable is monthly earnings rather than hourly wages. With this choice we aim to avoid the measurement error that is typically associated with hours worked. Wages are measured before taxes in Finland, France, Germany, Norway and the UK, and after taxes in Greece, Italy, Portugal and Sweden. These differences in the dependent variable should be assessed when making cross-country comparisons. Table 7.1 Descriptive statistics

D9/D1 D9/D5 D5/D1Finland 12.27 18.38 2.88 1.80 1.60France 12.62 21.39 2.78 1.90 1.46Germany 13.29 21.14 2.77 1.73 1.60Greece 11.05 21.92 3.35 1.68 2.00Italy 11.28 21.53 2.56 1.70 1.50Norway 12.76 19.36 2.26 1.69 1.33Portugal 6.80 21.40 2.88 1.95 1.48Sweden 12.69 19.25 1.94 1.52 1.28UK 13.14 18.78 3.41 1.90 1.80

Average years of education

Average years of experience

Wage ratios


Table 7.1 presents descriptive statistics for each country under study. Average schooling years generally exceed ten years. Portugal is an exception with an average of only 6.8 years. Experience levels are about 20 years in all countries. The last three columns of the table report wage ratios at different points (deciles) of the wage distribution. Wages in the top 10 per cent (D9) of the wage distribution are between two and three times as high as are wages in the bottom 10 per cent (D1) of the distribution. The D9/D5 ratio is higher than the D5/D1 ratio in most cases, indicating that wages are more dispersed in the upper than in the lower half of the wage distribution. 4 THE MODEL

The QR model can be written as ln with (ln ) i i i i i iw X e Quant w X Xθ θ θ θβ β= + = (7.1) where Xi is a vector of exogenous explanatory variables and βθ a vector of unknown parameters. Quantθ (lnwi|Xi) denotes the θ th conditional quantile of lnw given X. After defining the check function ρθ (z) = θz if z ≥ 0 or ρθ (z) = (θ –1)z if z < 0, the θ th regression quantile, 0<θ <1, is defined as a solution to the problem

Min (ln ) k i i

Î R iw Xθ θ

βρ β

⎧ ⎫−⎨ ⎬

⎩ ⎭∑

This problem is solved using linear programming methods. Standard errors for the vector of coefficients to be estimated are obtainable by use of the bootstrap method described in Buchinsky (1998b).

Our wage equation includes as explanatory variables years of schooling, years of experience and experience squared:6

2

1 2ln ,i i i iw years exp exp e iθ θ θ θα β δ δ= + + + + θ (7.2) where θ = 0.1, 0.2, …, 0.9 refers to the quantile being analysed.


5 EMPIRICAL RESULTS

In the following, we calculate and present OLS returns as well as QR returns for five representative quantiles along the wage distribution: 0.10, 0.25, 0.50, 0.75 and 0.90 which we denote by 10q, 25q, 50q, 75q and 90q, respectively. We start by reporting results for the total population and thereafter we discriminate between age groups. 5.1 Total Population

In Table 7.2 we report results for the whole sample. The average return to an additional year of schooling ranges from 4.5 per cent in Sweden to 9.2 per cent in Finland, the estimated returns being significant at the 1 per cent level for all countries. However, as the QR estimates show, the impact of schooling on wages is by no means constant over the wage distribution. The coefficients on schooling tend to be higher in the upper part than in the lower part of the conditional distribution. France and Portugal are two illustrative examples. In France the average return of 8.4 per cent masks a return of only 4.1 per cent in the first quantile and 11.4 per cent in the top quantile. In Portugal the average return is 7.0 per cent but the returns at the very bottom and the top of the distribution are 4.6 and 7.7 per cent, respectively. Table 7.2 OLS and OR returns to years of schooling

OLS

returns 10q 25q 50q 75q 90q9.22 8.00 7.90 8.84 9.87 10.67

(0.33) (0.65) (0.32) (0.26) (0.22) (0.50)8.42 4.05 5.97 8.26 10.14 11.36

(0.11) (0.17) (0.13) (0.14) (0.14) (0.18)7.41 4.46 6.44 6.74 7.88 8.19

(0.32) (0.81) (0.55) (0.37) (0.34) (0.45)5.74 6.06 5.63 5.76 5.92 5.88

(0.36) (0.90) (0.44) (0.33) (0.32) (0.42)5.55 4.01 3.78 4.73 6.04 7.45

(0.25) (0.34) (0.40) (0.27) (0.34) (0.53)8.33 5.45 7.23 8.73 9.44 9.91

(0.10) (0.12) (0.11) (0.07) (0.13) (0.21)6.95 4.63 5.81 7.01 7.53 7.74

(0.15) (0.18) (0.21) (0.20) (0.18) (0.23)4.54 2.54 2.92 4.64 5.77 6.76

(0.38) (0.36) (0.38) (0.52) (0.31) (0.52)6.44 4.55 5.73 6.81 7.15 7.29

(0.25) (0.52) (0.40) (0.27) (0.37) (0.42)

QR returns at quantile

Finland

France

Portugal

Sweden

UK

Germany

Greece

Italy

Norway

Note: Standard deviations are in parentheses. All coefficients are significant at the 1 per cent confidence level.


To facilitate the analysis we plot the country-specific QR return profiles (Figure 7.1). Clearly, in most countries the return to education increases along the conditional wage distribution. Individuals in high-paid jobs earn a higher return from their schooling investment than similarly educated individuals in low-paid jobs.7 This result implies that conditional on observable characteristics, wages are more dispersed among higher educated individuals. In other words, education tends to increase within-group wage dispersion: if we give an additional year of education to individuals who are seemingly equal but located at different quantiles of the wage distribution, then their wages will become more dispersed.

Greece, where the returns to education are uniform over conditional quantiles, is an exception to this general pattern. In the Greece case, education has a neutral impact on within-group wage dispersion. It is noteworthy that in none of the surveyed countries is education found to decrease within-group wage dispersion.

In Figure 7.2 we summarize the extent of within-education-group wage inequality in the different countries under study. More specifically, we measure (in percentage points) the difference in returns to schooling between the top and the bottom quantile (the 90q–10q spread) and between the 0.75 and the 0.25 quantile (the 75q–25q spread). Additionally, in order to investigate which part of the wage distribution contributes relatively more to overall wage inequality, each measure is further decomposed into half-spreads (90q–50q and 75q–50q).

0

2

4

6

8

10

12

10q 25q 50q 75q 90q OLS

Finland France Germany Figure 7.1 OR returns to schooling


0

2

4

6

8

10

12

10q 25q 50q 75q 90q OLS

Greece Italy Norway

0

2

4

6

8

10

12

10q 25q 50q 75q 90q OLS

Portugal Sweden UK Figure 7.1 (continued) OR returns to schooling

We find, except for Greece, that the return differential between the two extreme quantiles is quite high: 7.3 percentage points in France, 4.5 in Norway, 4.2 in Sweden, 3.7 in Germany, 3.4 in Italy, 3.1 in Portugal and 2.7 in Finland and the UK, the average being 3.5 percentage points. Moreover, in most countries the 90q–10q spread more than doubles the 90q–50q spread, suggesting that the contribution of education to wage inequality is more substantial at the lower than at the upper tail of the wage distribution. Finland


and Italy are exceptions to this pattern as the wage structure in these two countries is more dispersed above the median.

-1

0

1

2

3

4

5

6

7

8

Finland France Germany Greece Italy Norway Portugal Sweden UK

90q-10q 90q-50q 75q-25q 75q-50q Figure 7.2 Differences in the returns to schooling between selected

quantiles

The 75q–25q spread gives a slightly different ranking of the countries. Norway and Finland, for example, rank 2nd and 8th, respectively, when using the 90q–10q spread but 4th and 5th when relying on the 75q–25q spread. This suggests that the contribution of education to wage dispersion at the tails of the wage distribution is relatively high in Norway and low in Finland.

We also tested whether the differences across quantiles in the returns to schooling are statistically significant (these results are not reported here). For all countries, with the exception of Greece, we could reject the hypothesis of equality of coefficients. Moreover, this result did not change when using hourly wages instead of monthly earnings as the dependent variable in the regression. We therefore conclude that the returns to education in Europe cannot be well described in an average sense due to significant differences in returns across segments of the wage distribution.

5.2 Age Groups

Next we differentiate between three age groups: those aged 18 to 30, 31 to 45 and 46 to 60. For each group we calculate the wage effects of schooling.8 The results are reported in Tables 7.3, 7.4 and 7.5 and plotted in Figure 7.3.


0

5

10

15

10q 25q 50q 75q 90q OLS

Finland

0

5

10

15

10q 25q 50q 75q 90q OLS

France

0

5

10

15

20

10q 25q 50q 75q 90q OLS

Germany

0

5

10

15

10q 25q 50q 75q 90q OLS

Greece

0

5

10

15

10q 25q 50q 75q 90q OLS

Italy

0

5

10

15

10q 25q 50q 75q 90q OLS

Norway

0

5

10

15

10q 25q 50q 75q 90q OLS

Portugal

0

5

10

15

10q 25q 50q 75q 90q OLS

Sweden

0

5

10

15

10q 25q 50q 75q 90q OLS

UK

18-30 31-45 46-60

Figure 7.3 QR wage effects of schooling, by age groups


Table 7.3 OLS and QR returns to schooling, 18–30 age group

OLSreturns 10q 25q 50q 75q 90q10.91 13.58 12.71 10.56 9.64 9.98(0.67) (1.53) (1.16) (0.72) (0.46) (0.91)6.54 2.61 4.25 5.68 8.12 9.60

(0.21) (0.21) (0.18) (0.25) (0.26) (0.40)14.61 17.65 17.09 13.93 8.68 7.94(0.91) (0.98) (0.99) (1.34) (1.06) (2.03)8.09 14.19 8.68 7.24 7.88 7.98

(1.49) (0.36) (1.97) (1.04) (1.55) (1.48)4.90 4.38 3.66 4.97 4.80 5.25

(0.61) (1.33) (0.63) (0.58) (0.56) (1.15)7.23 4.54 6.45 7.21 7.91 9.21

(0.19) (0.27) (0.24) (0.20) (0.25) (0.42)5.34 3.65 4.19 5.21 6.13 6.55

(0.28) (0.34) (0.32) (0.29) (0.26) (0.54)4.12 3.53 3.49 4.19 4.50 5.15

(0.56) (1.23) (0.68) (0.97) (0.88) (1.11)5.34 3.65 4.19 5.21 6.13 6.55

(0.28) (0.34) (0.32) (0.29) (0.26) (0.54)

Portugal

Sweden

UK

Germany

Greece

Italy

Norway


Finland

France

Note: Standard deviations are in parentheses. All coefficients are significant at the 1 per cent confidence level. Table 7.4 OLS and OR returns to schooling, 31–45 age group

OLSreturns 10q 25q 50q 75q 90q

9.61 9.90 8.37 8.74 10.21 10.29(0.54) (1.31 (0.69 (0.51) (0.55) (0.96)8.91 4.61 6.54 9.04 10.69 12.52

(0.20) (0.26) (0.18) (0.19) (0.21) (0.30)5.85 3.09 4.54 6.44 7.50 8.03

(0.47) (0.76) (0.53) (0.67) (0.50) (0.65)5.43 7.02 5.73 5.63 5.22 5.82

(0.55) (1.56) (0.72) (0.37) (0.60) (0.71)5.53 3.48 3.55 4.37 6.02 7.57

(0.38) (0.75) (0.53) (0.50) (0.55) (0.61)9.19 5.52 7.60 9.68 10.87 11.11

(0.18) (0.15) (0.11) (0.17) (0.19) (0.36)6.87 4.69 6.21 6.91 7.55 7.67

(0.22) (0.39) (0.33) (0.29) (0.24) (0.39)4.78 2.55 3.11 4.69 6.66 7.81

(0.71) (0.91) (0.67) (0.89) (0.76) (1.00)7.30 5.62 6.96 7.60 7.90 8.07

(0.20) (0.47) (0.27) (0.20) (0.24) (0.32)


Finland

France

Portugal

Sweden

UK

Germany

Greece

Italy

Norway



Table 7.5 OLS and QR returns to schooling, 46–60 age group

OLSreturns 10q 25q 50q 75q 90q

8.58 6.09 6.21 7.92 8.35 10.95(0.64) (1.71) (0.72) (0.55) (0.72) (1.19)8.99 4.17 6.71 9.12 10.80 11.7

(0.24) (0.41) (0.28) (0.27) (0.23) (0.52)5.01 .64 2.03 5.09 7.77 7.95

(0.79) (1.13) (1.06) (0.65) (1.04) (0.77)3.68 2.67 3.43 3.92 4.91 4.24

(0.69) (2.54) (0.88) (0.67) (0.53) (0.65)6.56 4.46 3.72 5.23 8.16 8.73

(0.67) (0.99) (0.55) (0.89) (0.82) (1.38)8.25 5.79 7.54 9.26 9.19 8.65

(0.21) (0.24) (0.30) (0.22) (0.26) (0.50)7.85 5.50 7.46 7.91 8.34 9.18

(0.26) (0.55) (0.40) (0.31) (0.31) (0.79)4.65 2.18 3.19 4.46 5.37 5.00

(0.65) (0.77) (0.75) (0.83) (0.82) (1.26)6.17 4.12 5.57 6.72 7.21 6.54

(0.22) (0.38) (0.22) (0.30) (0.25) (0.39)

Portugal

Sweden

UK

Germany

Greece

Italy

Norway


Finland

France


Two things are worth noting. First, in most countries the wage effects of schooling are increasing over the wage distribution for all age groups. There are some notable exceptions, though. In Finland, Germany and Greece the estimated coefficients are higher at the bottom quantiles among young workers implying that in this particular age group education contributes to wage compression.

Second, the conditional wage dispersion tends to be higher among older workers. In Finland, Germany, Italy and the UK the highest differential across quantiles is obtained for the 46–60 age group. In France and Portugal the QR profile is similar for all three age groups while in Norway and Sweden the dispersion across quantiles is highest for the 31–45 age group. 6 CHANGES OVER TIME

In this section we investigate how education has shaped the European wage distribution in recent years. We cover a period that ranges from 26 years in the case of Sweden (1974–2000) to seven years in the case of Portugal (1993–2000).


In Figure 7.4 we plot the evolution of OLS returns as well as QR returns for three selected quantiles: 10q, 50q and 90q. Increases over time in the OLS return describe increases in between-education-group wage inequality while increases in the 90q–10q spread correspond to increases in within-education-group wage inequality.9 For the sake of brevity, we do not discuss the reported results country by country. This is done in Budría and Pereira (2005). Instead we focus on isolating common patterns and on pointing out diverging trends. To simplify the analysis, we restrict our interest to the last ten years (or the nearest available approximation) for each country.

Table 7.6 documents the main changes in a coherent and summarized fashion. The first and the second column of the table report changes in OLS returns and 90q–10q spreads, respectively. The last two columns list changes at the two extreme quantiles. As a threshold between relevant and non-relevant changes we choose a one percentage point variation. Table 7.6 Changes in OLS and QR returns to schooling over the last

decade

Δ(OLS) Δ(90q–10q) Δ(90q) Δ(10q)

Finland (1990–2001) -1.44 1.62 -0.84 -2.46France (1993–2001) -0.31 0.57 -0.31 -0.88Germany (1989–99) 0.14 0.25 0.39 0.13Greece (1988–99) 1.68 0.30 1.86 1.56Italy (1991–2000) 0.44 1.26 1.23 -0.03Norway (1991–2000) 1.07 2.73 2.05 -0.68Portugal (1993–2000) -3.37 -0.24 -3.72 -3.47Sweden (1991–2000) 0.06 1.23 0.59 -0.64UK (1994–2003) 0.19 1.67 0.06 -1.61 Note: Standard deviations are in parentheses. All coefficients are significant at the 1 per cent confidence level.

The OLS column reveals that the changes in the average return to education do not display a discernible trend in Europe. Wage inequality between educational groups increased in Greece and Norway, remained roughly constant in France, Germany, Italy, Sweden and the UK, and decreased in Finland and Portugal. Hence, in a majority of the European countries under study the average return to education did not change much over the last decade. This finding is in line with the evidence reported in Asplund and Barth (2005). Reviewing the European literature, they conclude that despite large increases in average schooling levels, the returns to education in European countries have remained stable and at high levels over


0

5

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1985 1990 1995 2000

Finland

0

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1990 1995 2000

France

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1985 1990 1995 2000

Germany

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1975 1980 1985 1990 1995 2000

Greece

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1985 1990 1995 2000

Italy

0

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1980 1985 1990 1995 2000

Norway

0

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1992 1994 1996 1998 2000

Portugal

0

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1975 1980 1985 1990 1995 2000

Sweden

0

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1994 1996 1998 2000 2002 2004

UK

OLS 10q 50q 90q

Figure 7.4 Evolution of the returns to schooling at different quantiles


the last few years. As a candidate explanation they suggest that skill-biased technological change has fostered the demand for skilled labour, thus maintaining educational returns at high levels.

Despite this apparent stability in average returns, one finding stands out prominently when turning to the changes in conditional returns: the dispersion in returns within educational groups tended to increase across Europe. As shown in Table 7.6, the 90q–10q spread rose by more than one percentage point in Finland, Italy, Norway, Sweden and the UK while the increase was sensitively lower in France, Germany and Greece.

Assessing the causes underlying these findings is beyond the scope of this chapter. Nevertheless some candidate explanations may be advanced.10 A first candidate relates to the supply side. Changes in the distribution of skills, experience as well as type and quality of qualifications may have induced larger wage differentials among the educated. For example, the expansion in education over the last few decades may have been parallel to an increasing proportion of low ability individuals accessing higher education. If ability and education are complementary, then we should observe a deterioration of the returns earned by individuals located in the lower part of the wage distribution (i.e. with lower ability) and, hence, an increase in the dispersion of returns within educational groups. A second candidate relies on the demand side. Employers may have responded to the educational expansion by directing efforts to ensure that their new recruits have appropriate skills and not merely formal skills. Discrimination among the highly educated may have benefited individuals with more motivation, higher ability and qualifications that better match labour market requirements, which would contribute to widening wage differentials. There is consensus in the literature on shifts in labour demand having favoured high-skilled labour at the expense of low-skilled labour in most developed countries. Our results seem to suggest that these shifts have benefited not only the highly educated but among the highly educated especially those with more ability and better qualifications.

The results for Finland, France, Italy, Norway, Sweden and the UK seem to be consistent with the supply-side explanation. The last two columns of Table 7.6 show that in these countries the return to schooling decreased more in the lower than in the upper quantiles of the wage distribution. Put differently, the widening wage dispersion among the educated was mainly due to decreasing returns among those in low-paid jobs. In contrast, the demand-side explanation seems to fit better to Germany and Greece. Although the changes in the 90q–10q spread were small in these countries, the observed increase was for the most part due to rising returns among those in high-paid jobs.


7 CONCLUSIONS

In this chapter we have explored the connection between education and wage dispersion in Europe. Drawing on quantile regression techniques we have found that returns to education are typically increasing over the wage distribution. This result suggests that education may contribute to increasing overall wage dispersion: if the wages of the more educated are also more dispersed, then educational expansion may induce increased wage inequality. When differentiating between age groups, the wage dispersion increasing effect of education was found to be larger in the older age groups.

Using data for the last decade or so, we found that wage dispersion within educational groups has tended to increase in Europe. As the increase in average schooling levels can be expected to continue, the consequent changes in the educational composition of the European workforce are likely to result in further wage inequality.

Hartog and Vijverberg (2002) and Hartog et al. (2003) construct education–occupation cells to investigate the labour market response of earnings to risk. They find a positive relation between average wages and dispersion which suggests that the labour market generates a compensation for the risk borne. According to their results, education is likely to have become less attractive for some individuals in Europe, as the increases in within-education-group dispersion have not been accompanied by proportional increases in average returns to education. The impact of increased risk on the overall demand for education is nevertheless an open question. Hartog and Serrano (2007) contribute to this strand of the literature by assessing the impact of risk on the demand for university education in Spain. They find that an average individual will require a two percentage point increase in expected income for a 10 percentage point increase in the residual variance of earnings. Belzil and Hansen (2004), in contrast, report a low degree of risk aversion and conclude that increases in earnings dispersion have little effect on the demand for education. In sum, our current knowledge on these issues is still very limited.

Our findings have several policy implications. By investing in education individuals are expected to improve their labour market position. If wage equality is a political goal, then facilitating access to education of individuals who earn or are more likely to earn lower wages would contribute to less wage inequality. Returns to education are not homogeneous across the population, however. There are individuals who earn low or even negative net returns and for whom expansion of education provision is not particularly beneficial. This raises equity and efficiency concerns. On the one hand, policies aimed at increasing average schooling levels without targeting specific population groups may result in more wage inequality. Hence, wage


equality could be promoted by design of instruments which identify individuals whose expected returns to education are lower, in combination with mechanisms that promote enrolment into further education among these individuals. On the other hand, asymmetries in the returns to education may be the result of labour market institutions as well as differences in skills, ability, school quality and type of qualifications. To the extent that these asymmetries are due to poor integration between schooling systems and labour markets, policies aimed at improving the match between educational qualifications and labour market requirements are of particular importance.

From an individual perspective, the payoffs of schooling are largely unpredictable. This has important policy consequences. The design of schooling systems, the financing of education, and the school-to-work transition may boost or, alternatively, mitigate the risky aspects of education. Presumably students consider the risks associated with their educational decisions and take them into account when comparing alternative choices. As individuals differ in their attitude towards risk, it is likely that some individuals do not pursue further education due to the uncertainty attached to investments in education. Hartog et al. (2002) have explored the impact of parental educational background and income on the children’s attitude towards risk. They find that children whose parents are less educated or poorer exhibit more risk aversion. This implies that policies oriented toward reducing the perceived risk and promoting schooling among risk-averse individuals may have beneficial effects on efficiency and economic equality. Investigating the characteristics of those individuals who earn lower returns would help in the task of promoting education precisely among those who have fewer incentives to invest in it. NOTES

1. We gratefully acknowledge financial support from the European Commission within the so-called EDWIN project (HPSE-CT-2002-00108). We thank EDWIN partners for useful comments.

2. The QR technique has also been used to address two well-known topics: the gender wage gap (Garcia et al., 2001; Fitzenberger and Wunderlich, 2002; Albrecht et al., 2003; Gardeazabal and Ugidos, 2004; Arulampalam et al., 2007; De la Rica et al., 2007) and returns to training (Hartog et al., 2000; Arulampalam et al., 2004). For some additional applications, see Fitzenberger et al. (2001).

3. See Chapter 2 of this volume for a macroeconomic perspective on the causality between education and inequality.

4. Two exceptions are Greece and Portugal for which a distinction between private and public sector servants was not possible for some years.

5. These years are: Finland: 2001, France: 2001, Germany: 1999, Greece: 1999, Italy: 2000, Norway: 2000, Portugal: 2000, Sweden: 2000 and UK: 2003.


6. This choice of the simplest possible regression model allows us to have a common specification for all countries since some of the variables typically included in wage equations (such as tenure and part-time work) were not available for some of the countries.

7. This kind of comparisons should be made with caution, though. Differences between the upper and lower quantiles correspond to differences in the returns earned by individuals who given their schooling attainment are in low-paid and high-paid jobs. Arguably, the position of an individual in the wage distribution is not independent of his or her schooling level. Some individuals would be located at lower quantiles had they attained less education. However, the quantile regression model does not allow us to remove the effect of education on the individual’s position in the wage distribution. It is not clear to what extent the estimates would be affected if this effect could be accounted for.

8. In this context we prefer the term ‘wage effects’ to ‘returns’. The return to an investment is the discounted value of all future income streams at the time of the investment. It can be shown that the coefficient for schooling in a wage regression is a return only under very restrictive assumptions. When this coefficient is estimated for different age groups, it may not reflect differences in the ‘returns’ earned by individuals but rather wage differentials across educational levels at different stages of the lifecycle.

9. Alternatively we could have used the coefficient of the median quantile (50q) to analyse changes in between-education-group wage inequality. The difference is that while the OLS estimate measures the wage impact of schooling at the mean of the conditional wage distribution, the coefficient at 50q measures the impact of schooling at the median of the conditional wage distribution. By choosing the OLS coefficient, we facilitate comparison with previous research.

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APPENDIX: DESCRIPTION OF DATA SOURCES AND ESTIMATING SAMPLES

In Table A1 we briefly describe the datasets and estimating samples used in producing the results presented in this chapter. For further details, see Budría and Pereira (2005). Table A1 National data sets

Country Data sourcePeriod covered

Final number of observations in the last year available Comments

Finland Labour Force Survey (LFS)

1984–2001 5 356 Change in educational categories from 1998 onwards.

France Labour Force Survey (LFS)

1990–2001 21 142

Schooling levels correspond to:1 = no vocational education,2 = basic vocational education, 3 = intermediate education, 4 = tertiary education.

Greece Household Budget Surveys (HBS)

1974–99 1 885 Net wages, no distinction between the public and the private sector.

Italy Survey of Household Income and Wealth (SHIW)

1987–2000 2 116 Net wages.

For Tables 7.1 to 7.6: Norwegian Structure of Earnings Survey (NSES) 2000

21 297 (NSES)

For Table 7.7: Level of Living Surveys (LLS)

974 (LLS)

Portugal Labour Force Survey (LFS)

1993–2000 5 738 Net wages, no distinction between the public and the private sector before 1998.

Sweden Level of Living Survey (LLS)

1974–2000 973 Monthly wages are net but hourly wages are gross.

UK Labour Force Survey (LFS)

1994–2003 14 642

Due to its large sample size, we used the NSES dataset to calculate the returns to education in 2000. To examine changes over time, we used the LLS as it covers a longer period.

Norway 1984–2000

Germany German Socio-Economic Panel (GSOEP)

1984–99 1 895

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7. Education and Wage Dispersion: New Evidence for Europe · 2008. 1. 20. · 7. Education and Wage Dispersion: New Evidence for Europe1. Santiago Budría and Pedro Telhado Pereira