This document summarizes a presentation given at an OECD seminar on skills distribution, wage inequality, and social inequality. The presentation analyzed data from the OECD Survey of Adult Skills to examine the relationship between the distribution of numeracy skills across countries and wage inequality. It found that countries with a more unequal distribution of numeracy skills, such as the US and France, tended to have higher wage inequality. Differences in how skills and education are rewarded in the labor market, rather than differences in average skill levels, accounted for 30-90% of the variation in wage inequality between countries.

1. New Approaches to Economic Challenges
Seminar, 21 October 2014
SKILLS DISTRIBUTION, WAGE
INEQUALITY AND SOCIAL
INEQUALITY
Dirk Van Damme
OECD Directorate for Education and Skills (EDU)
Marco Paccagnella
OECD Directorate for Education and Skills (EDU)

2. The distribution of human capital matters
• Overall purpose of this seminar is to
provide evidence on how the distribution
of skills (and not only the average) relates
to various outcome measures:
– Wage inequality
– Social inequality (Gini coefficient)
– Economic output (GDP/capita)

3. The distribution of human capital matters
• Analysis of OECD Survey of Adult Skills
(PIAAC) data (2012)
– Focusing on numeracy as a critically
important foundation skill
– Focusing on cross-country variation

4. SKILLS AND WAGE
INEQUALITY
• Marco Paccagnella
• EDU/SBS

5. Motivation
• High levels of inequality are a huge
political concern, especially in the midst of
a prolonged recession
• Human capital is a crucial factor affecting
the growth potential of an economy…
• …but how does it affect inequality?
– Increasing returns to education?
– Skill-biased technical change?
– Which role for economic institutions?

6. This talk
• Joint analysis of the distribution of skills
(numeracy proficiency) and (labour)
earnings
• Estimates of the returns to education and
proficiency along the distribution of wages
• Decomposition of cross-country
differences in wage inequality

7. The data
• PIAAC: unique dataset with comparable
individual-level information on education,
proficiency, and wages
• Drawback: can’t look at the household
level
• Preferred measure of dispersion:
percentile ratios/differences

8. The distribution of proficiency
• Consistent ranking of countries,
irrespective of the indicator and the
domain (literacy/numeracy)
– High dispersion in US, FR, ES, CA
– Low dispersion in JP, KR, SK, CZ
• Bottom-end inequality generally higher
than top-end inequality

9. Dispersion in Numeracy
Inequality indices – Numeracy Skills
Country CV 90th-10th 90th-50th 50th-10th
Australia 0.21 136.59 62.33 74.26
Austria 0.18 121.24 55.95 65.30
Canada 0.21 138.28 62.61 75.67
Czech Republic 0.16 110.94 50.90 60.03
Denmark 0.18 126.10 57.47 68.63
Estonia 0.17 113.92 53.46 60.45
Finland 0.18 127.65 59.21 68.44
France 0.22 141.80 62.39 79.41
Germany 0.20 133.09 59.10 73.99
Ireland 0.21 129.33 59.28 70.05
Italy 0.20 126.26 59.87 66.39
Japan 0.15 110.05 50.89 59.17
Korea 0.17 114.60 51.31 63.29
Netherlands 0.18 125.11 53.97 71.14
Norway 0.19 131.77 57.90 73.88
Poland 0.20 127.86 59.20 68.66
Slovak Republic 0.17 117.16 51.03 66.12
Spain 0.21 129.61 57.08 72.53
Sweden 0.20 132.84 58.74 74.10
United States 0.23 144.84 66.66 78.18
Flanders (Belgium) 0.18 127.84 57.13 70.71
England/N. Ireland (UK) 0.21 137.71 64.38 73.33
OECD Average 0.20 130.99 59.20 71.79

10. Dispersion in Literacy
Inequality indices – Literacy Skills
Country CV 90th-10th 90th-50th 50th-10th
Australia 0.18 122.28 55.04 67.24
Austria 0.16 110.10 50.51 59.60
Canada 0.18 125.56 56.20 69.36
Czech Republic 0.15 102.34 47.06 55.28
Denmark 0.18 116.25 49.88 66.38
Estonia 0.16 111.84 50.99 60.85
Finland 0.18 123.49 55.16 68.33
France 0.19 123.94 54.03 69.91
Germany 0.18 121.56 54.40 67.16
Ireland 0.18 115.71 52.14 63.57
Italy 0.18 113.74 53.68 60.05
Japan 0.13 99.78 44.06 55.72
Korea 0.15 103.81 46.31 57.49
Netherlands 0.17 121.61 51.88 69.73
Norway 0.17 115.30 49.98 65.32
Poland 0.18 120.92 55.11 65.81
Slovak Republic 0.15 99.37 42.89 56.49
Spain 0.19 123.52 55.30 68.22
Sweden 0.18 122.24 52.86 69.38
United States 0.18 126.13 57.14 68.99
entities
Flanders (Belgium) 0.17 119.07 51.11 67.96
England/N. Ireland (UK) 0.18 123.45 57.03 66.41
OECD Average 0.18 119.41 53.29 66.12

11. Distribution of Numeracy Proficiency
0
.01
.002 .004 .006 .008
FRANCE
JAPAN
0 100 200 300 400 500
United States Czech Republic
France Japan
OECD Average

12. Distribution of (log) Hourly Wages
FRANCE
JAPAN

13. Skills and Wage Inequality

14. Top-end Inequality

15. Bottom-end Inequality

16. Within-group Dispersion
• Focus on groups defined by age and
education
• Important to know how homogeneous
such groups are in terms of proficiency
and earnings

17. Skill Inequality Declines with Education

18. Wage Inequality Does Not

19. Skill Dispersion Increases with Age

20. As Does Wage Dispersion

21. The Drivers of Wage Inequality
• Unconditional Quantile Regression: Estimate
the impact of changing the distribution of
explanatory variable on the marginal quantiles
of the outcome variable – wages, in our case
• If the estimated impact of a variable is larger at
the top than at the bottom of the distribution,
then an increase in that variable is associated
with an increase in inequality

22. The Drivers of Wage Inequality
• We focus on the impact of years of
education and numeracy
• At the same time, we control for basic
socio-demographic characteristics
• This method also allow to decompose
cross-country differences in wage
inequality into “quantity” and “price”
components

23. Basic Results

24. Cross-country Heterogeneity

25. Which Kind of Education?

26. Analysis at the Country Level

27. Analysis at the Country Level

28. Analysis at the Country Level

29. Analysis at the Country Level

30. Analysis at the Country Level

31. Analysis at the Country Level

32. Analysis at the Country Level

33. Analysis at the Country Level

34. Analysis at the Country Level

35. Analysis at the Country Level

36. Analysis at the Country Level

37. Analysis at the Country Level

38. Analysis at the Country Level

39. Analysis at the Country Level

40. A Decomposition Exercise
• We take the United States as a reference
country
• We ask how much of the higher level of
inequality in the United States can be
explained by differences in “quantities”
(i.e. in the population distribution of
certain characteristics) vs. “prices” (i.e.
differences in the way such characteristics
are rewarded in the labour market)

41. A Decomposition Exercise
• It is mostly an accounting exercise, which
disregards “general equilibrium effects”
• It involves a number of arbitrary decisions
(but results seem to be robust to those)
• As a consequence, should be taken with an
appropriate degree of caution

42. Results – A Snapshot
Country Raw
Gap
90/10
Composition Effect Wage Structure Effect
Education Numeracy Total Education Numeracy Total
Australia 0.507 -0.022 -0.023 -0.074 0.459 0.060 0.581
Czech R. 0.624 0.035 0.024 0.068 -0.268 0.844 0.556
Denmark 0.701 0.009 -0.009 0.016 0.628 0.377 0.685
France 0.715 0.066 0.000 0.086 0.312 0.232 0.629
Germany 0.218 0.001 -0.018 0.019 0.662 0.191 0.199
Italy 0.441 0.114 -0.013 0.156 0.130 1.076 0.285
Japan 0.247 0.038 -0.030 0.010 -0.235 0.233 0.237
Korea -0.121 0.019 0.001 0.164 0.199 0.519 -0.286
Sweden 0.873 0.036 -0.024 0.020 0.382 0.241 0.852

43. Results
• Composition effects seem to play a minor
role
– Proficiency has the “wrong” sign: most
countries are more proficient than the US
– Education has the “right” sign: most countries
are less educated than the US
• Wage structure effects account for 30 to
90% of the observed gap

44. Tentative Conclusions / Interpretation
• Can only speculate about relative
importance of institutions vs. market
forces
• In any case, policies can play a key role in
shaping the evolution on inequality and its
social impact
• Positive take-home message: investing in
skills could raise earnings without causing
increases in inequality

45. Results – 90/10 difference
Country Gap
90th/10th
Composition Effect Wage Structure Effect
Education Numeracy Total Education Numeracy Total
Australia 0.507 -0.022 -0.023 -0.074 0.459 0.060 0.581
Austria 0.536 0.046 -0.030 0.054 0.381 0.029 0.482
Canada 0.275 0.007 -0.017 -0.022 0.427 0.065 0.297
Czech R. 0.624 0.035 0.024 0.068 -0.268 0.844 0.556
Denmark 0.701 0.009 -0.009 0.016 0.628 0.377 0.685
England/UK 0.392 0.028 -0.032 0.001 0.131 -0.162 0.391
Estonia 0.117 0.020 -0.013 0.009 0.543 0.276 0.109
Finland 0.713 0.041 -0.011 0.061 0.051 0.386 0.652
Belgium 0.684 0.028 0.010 0.111 0.321 0.585 0.572
France 0.715 0.066 0.000 0.086 0.312 0.232 0.629
Germany 0.218 0.001 -0.018 0.019 0.662 0.191 0.199
Ireland 0.381 -0.098 -0.004 -0.088 -0.058 0.168 0.469
Italy 0.441 0.114 -0.013 0.156 0.130 1.076 0.285
Japan 0.247 0.038 -0.030 0.010 -0.235 0.233 0.237
Korea -0.121 0.019 0.001 0.164 0.199 0.519 -0.286
Netherlands 0.475 0.004 -0.034 -0.027 0.602 0.143 0.502
Norway 0.743 -0.008 -0.023 -0.028 0.591 0.240 0.771
Poland 0.285 0.019 -0.001 -0.065 0.027 0.435 0.350
Slovak Rep. 0.263 0.011 -0.007 -0.004 -0.366 0.402 0.268
Spain 0.366 0.072 -0.002 0.084 0.116 0.620 0.282
Sweden 0.873 0.036 -0.024 0.020 0.382 0.241 0.852

46. Results – 90/50 difference
Country Gap
90th/50th
Composition Effect Wage Structure Effect
Education Numeracy Total Education Numeracy Total
Australia 0.269 -0.010 -0.012 -0.030 0.132 -0.375 0.299
Austria 0.308 0.039 -0.006 0.068 -0.051 -0.254 0.239
Canada 0.244 -0.005 0.005 -0.010 0.561 -0.036 0.254
Czech R. 0.394 0.022 0.006 0.015 -0.389 -0.074 0.378
Denmark 0.471 0.017 -0.002 0.018 0.010 -0.190 0.453
England/UK 0.195 -0.002 -0.003 -0.002 0.323 -0.223 0.197
Estonia 0.094 -0.018 0.000 -0.024 0.482 -0.163 0.118
Finland 0.378 0.022 0.004 0.041 -0.105 -0.130 0.337
Belgium 0.390 0.019 0.012 0.073 -0.024 -0.046 0.317
France 0.346 0.051 0.000 0.072 -0.076 -0.334 0.274
Germany 0.276 0.001 0.003 0.040 0.123 -0.124 0.235
Ireland 0.176 -0.017 -0.000 -0.003 0.135 -0.191 0.179
Italy 0.240 0.063 -0.005 0.115 -0.080 0.047 0.124
Japan 0.065 0.015 0.006 0.039 -0.117 -0.120 0.026
Korea -0.080 0.000 0.008 0.116 0.259 0.152 -0.196
Netherlands 0.323 0.005 0.023 0.036 0.045 0.059 0.287
Norway 0.428 -0.003 -0.003 -0.003 0.203 -0.194 0.432
Poland 0.136 -0.002 0.002 -0.129 0.349 -0.074 0.264
Slovak Rep. 0.145 0.006 -0.012 -0.028 -0.320 -0.292 0.173
Spain 0.187 0.011 -0.003 0.037 0.177 0.072 0.149
Sweden 0.446 0.023 -0.018 0.007 0.027 -0.340 0.439

47. Results – 50/10 difference
Country Gap
50th/10th
Composition Effect Wage Structure Effect
Education Numeracy Total Education Numeracy Total
Australia 0.238 -0.011 -0.012 -0.044 0.328 0.435 0.282
Austria 0.229 0.007 -0.024 -0.014 0.432 0.284 0.243
Canada 0.030 0.012 -0.022 -0.013 -0.134 0.100 0.043
Czech R. 0.231 0.012 0.018 0.053 0.120 0.918 0.177
Denmark 0.230 -0.008 -0.007 -0.001 0.617 0.567 0.231
England/UK 0.196 0.031 -0.029 0.003 - 0.192 0.061 0.194
Estonia 0.023 0.039 -0.013 0.033 0.061 0.439 -0.009
Finland 0.335 0.020 -0.015 0.020 0.155 0.516 0.315
Belgium 0.293 0.009 -0.001 0.038 0.346 0.631 0.255
France 0.369 0.014 0.000 0.014 0.395 0.567 0.355
Germany -0.057 -0.000 -0.021 -0.021 0.539 0.315 -0.036
Ireland 0.205 -0.081 -0.004 -0.084 -0.193 0.359 0.289
Italy 0.201 0.051 -0.009 0.041 0.209 1.029 0.161
Japan 0.182 0.023 -0.035 -0.029 -0.119 0.353 0.211
Korea -0.041 0.018 -0.007 0.048 -0.059 0.367 -0.089
Netherlands 0.151 -0.001 -0.057 -0.063 0.557 0.084 0.214
Norway 0.315 -0.004 -0.020 -0.024 0.388 0.434 0.339
Poland 0.149 0.021 -0.003 0.063 0.509 -0.542 0.086
Slovak Rep. 0.118 0.005 0.005 0.023 -0.046 0.695 0.095
Spain 0.180 0.061 0.001 0.047 -0.061 0.549 0.133
Sweden 0.427 0.013 -0.006 0.014 0.355 0.581 0.413

48. SKILLS, SOCIAL
INEQUALITY AND
ECONOMIC OUTPUT
• Dirk Van Damme
• EDU/IMEP

49. Questions
• How is the distribution of numeracy skills
in the adult population related to overall
social inequality as measured by the Gini
coefficient?
– And how are they related to economic output
as measured by GDP per capita
• And how are skills distributions in
particular groups related to overall social
inequality and economic output?

50. Overview of country-level correlations of various
numeracy distribution measures with Gini and GDP/capita
Gini
GDP per
capita
Mean score -.63 .11
Percentage of adults scoring at or below Level 2 .59 -.20
Percentage of adults scoring at Level 4 or 5 -.54 .36
Percentile difference 90th minus 10th percentile .35 .61
Percentile difference 75th minus 25th percentile .40 .59
Percentile difference 90th minus 50th percentile .47 .49
Percentile difference 50th minus 10th percentile .21 .65

51. A higher mean numeracy score is positively
related to higher social equality
Australia
Estonia
Austria
Canada
Czech Republic
Denmark
Finland
Ireland Germany
Gini coefficient
Italy
Japan
Korea
Netherlands
Norway
Poland
Slovak Republic
Spain
Sweden
United States
Flanders (Belgium)
United Kingdom
R² = 0.3888
0.40
0.35
0.30
0.25
0.20
245 250 255 260 265 270 275 280 285 290
Mean numeracy score

52. But a wide skills dispersion is not very strongly
related to higher social inequality…
Australia
Austria
Canada
Estonia
Finland
Czech Rep Denmark
Ireland Germany
Italy
Japan
Korea
Netherlands
Norway
Poland
Slovak Rep
Spain
Sweden
United States
Flanders
UK
R² = 0.1195
Gini
0.40
0.35
0.30
0.25
0.20
105 110 115 120 125 130 135 140 145
Score point difference between percentile 90 and 10 on the numeracy scale

53. …while a wider skills dispersion relates
positively with higher economic output
Australia
Ireland
Austria Canada
GDP per capita
Czech Rep
Denmark
Estonia
Germany
Finland
Italy
Japan
Korea
Netherlands
Norway
Poland
Slovak Rep
Spain
Sweden
United States
Flanders UK
R² = 0.3663
50000
45000
40000
35000
30000
25000
20000
15000
110 115 120 125 130 135 140 145
Score point difference between percentile 90 and 10 on the numeracy scale

54. A higher skills dispersion at the top of the distribution
relates positively to higher social inequality…
Australia
Austria
Canada
Estonia
Gini
Czech Rep Denmark
Finland
Italy
GIreerlmanadny
Japan
Korea
Netherlands
Norway
Poland
Slovak Rep
Spain
Sweden
United States
Flanders
UK
R² = 0.2254
0.40
0.35
0.30
0.25
0.20
50 52 54 56 58 60 62 64 66 68
Score point difference between percentile 90 and 50 on the numeracy scale

55. …as well as to higher economic output
Australia
Ireland
Austria Canada
GDP per capita
Czech Rep
Denmark
Estonia
Germany
Finland
Italy
Japan
Korea
Netherlands
Norway
Poland
Slovak Rep
Spain
Sweden
United States
R² = 0.2413
Flanders UK
50000
45000
40000
35000
30000
25000
20000
15000
50 52 54 56 58 60 62 64 66 68
Score point difference between percentile 90 and 50 on the numeracy scale

56. But the relationship between higher skills dispersion in the
lower half and higher social inequality is much weaker…
United States
Australia
Austria
Canada
Estonia
Finland
Czech Rep Denmark
Ireland Germany
Italy
Japan
Korea
Netherlands
Norway
Poland
Slovak Rep
Spain
Sweden
Flanders
UK
R² = 0.049
Gini
0.40
0.35
0.30
0.25
0.20
58 63 68 73 78
Score point difference between percentile 50 and 10 on the numeracy scale

57. …while there still is a strong relationship with
economic output
United States
Australia
Austria Canada
GDP per capita
Czech Rep
Denmark
Estonia
Finland
Germany
Ireland
Italy
Japan
Korea
Netherlands
Norway
Poland
Slovak Rep
Spain
Sweden
Flanders UK
R² = 0.4133
50000
45000
40000
35000
30000
25000
20000
15000
58 63 68 73 78
Score point difference between percentile 50 and 10 on the numeracy scale

58. More low-skilled adults relates positively to
higher social inequality…
Australia
Estonia
Austria
Canada
Czech Rep
Denmark
Finland
Germany Ireland
Italy
Japan
Korea
Netherlands
Norway
Poland
Slovak Rep
Spain
Sweden
United States
Flanders
UK R² = 0.3479
Gini
0.40
0.35
0.30
0.25
0.20
35 40 45 50 55 60 65 70 75
Percentage adults scoring below Level 2 on the numeracy scale

59. …while more high-skilled adults relates
negatively to social inequality
Australia
Canada
Austria
Estonia
Czech Rep Denmark
Finland
Ireland Germany
Italy
Japan
Korea
Netherlands
Norway
Poland
Slovak Rep
Spain
Sweden
United States
Flanders
UK
R² = 0.2916
Gini
0.40
0.35
0.30
0.25
0.20
4 6 8 10 12 14 16 18 20
Percentage adults scoring Level 4 or 5 on the numeracy scale

60. Country-level correlations of various skills distribution
measures among specific groups and Gini and GDP/capita
Gini
GDP per
capita
Score-point difference 16-24 year-olds minus 55-64
year-olds
-.01 -.36
Score-point difference between adults with tertiary
and lower than upper secondary education
.17 .43
Score-point difference 75th minus 25th percentile
among adults with lower than upper secondary
education
.19 .38
Score-point difference 75th minus 25th percentile
among adults with upper secondary education
.30 .53
Score-point difference 75th minus 25th percentile
among adults with tertiary-type A education
.36 .32
Score-point difference between adults with at least
one parent who attained tertiary education and
adults with neither parent who attained upper
secondary education
.19 .25

61. A higher skills distribution among mid-educated
adults is positively related to economic output
Australia
R² = 0.281
Austria Canada
GDP per capita
Czech Rep
Ireland Germany
Denmark
Estonia
Finland
Italy
Japan
Korea
Netherlands
Norway
Poland
Spain
Slovak Rep
Sweden
United States
Flanders UK
50000
45000
40000
35000
30000
25000
20000
15000
50 52 54 56 58 60 62 64 66 68
Score point difference between percentile 75 and 25 on the numeracy scale - upper
secondary education attainment

62. Tentative conclusions
• A higher mean numeracy level, more high-skilled
and less low-skilled are all related to less
social inequality
• However, the width of the skills distribution
don’t seem to matter a lot for social inequality
• But its shape matters: a wider dispersion in the
upper half is related to higher social inequality
• A wider dispersion of skills seems also to be
related to higher economic output

63. Tentative conclusions
• Improving skills of adults seems to be good for
economic output, even when such policies widen
the total skills distribution
• While this does not seem to harm social equality
a lot, except when the better-skilled distance
themselves from the median
• But leaving behind a large group of low-skilled
adults is also bad for social equality

64. Thank you !
dirk.vandamme@oecd.org
marco.paccagnella@oecd.org
www.oecd.org/edu/ceri
www.oecd.org/site/piaac/
twitter @VanDammeEDU
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