ANNEX A.1 Tables

Table A1.1: Detailed OLS results. Dependent variable: logarithmic earnings.

Education entered years completed

1989 1992 1995 1999 2002

Female –0.21423 –0.137898 –0.114109 –0.118123 –0.101109 Experience 0.02729 0.024385 0.020623 0.020405 0.014123 Experience² –0.00039 –0.000326 –0.000241 –0.000268 –0.000171 Education 0.06504 0.080326 0.086729 0.108630 0.113805 2-digit industry dummies Yes Yes Yes Yes Yes

Region dummies Yes Yes Yes Yes Yes

City-town-village dummies Yes Yes Yes Yes Yes Constant 8.22843 7.614628 7.642688 9.130891 9.913645 Observations 145198 131745 153112 164706 137713

R² 0.43 0.44 0.41 0.42 0.42

All variables are signifi cant at 0.01.

Standard error estimates are robust to heteroskedasticity and fi rm-level clustering.

Own regression estimates based on NLC Wage Surveys, employees between 16 and 65 years of age.

Left-hand side variable: log of defl ated after-tax earnings. Right-hand side variables: fe- male dummy; potential labour market experience (diff erence of age and modal age at highest completed education) and its square; education (computed years completed:

8 if 8 grades or less; 11 if vocational school, 12 if secondary school with a baccalaure- ate, 16 if college or more). All regressions contain 2-digit industry dummies, 7 region dummies, and 4 city-size dummies (Budapest, other city, small town, and village).

Table A.1.2: Detailed OLS results. Dependent variable: logarithmic earnings.

Education entered in three categories (reference: 8 grades or less)

1989 1992 1995 1999 2002

Female –0.221833 –0.15396 –0.13249 –0.14249 –0.12743 Experience 0.027930 0.02491 0.02059 0.02162 0.01687 Experience² –0.000427 –0.00036 –0.00026 –0.00031 –0.00025 Vocational sch. 0.117344 0.12931 0.10610 0.12195 0.09701 Secondary sch. 0.237784 0.30011 0.29741 0.35363 0.31321 College 0.571678 0.66290 0.69716 0.87188 0.91789 2-digit industry dummies Yes Yes Yes Yes Yes

Region dummies Yes Yes Yes Yes Yes

City-town-village dummies Yes Yes Yes Yes Yes Constant 8.770922 8.30731 8.40765 10.10147 10.95878 Observations 145198 131745 153112 164706 137713

R² 0.44 0.45 0.43 0.45 0.44

All variables are signifi cant at 0.01.

Standard error estimates are robust to heteroskedasticity and fi rm-level clustering.

Own regression estimates based on NLC Wage Surveys, employees between 16 and 65 years of age.

Left-hand side variable: log of defl ated after-tax earnings. Right-hand side variables:

female dummy; potential labor market experience (diff erence of age and modal age at highest completed education) and its square; categories of highest completed education level (reference: 8 grades of primary school or less). All regressions contain 2-digit industry dummies, 7 region dummies, and 4 city-size dummies (Budapest, other city, small town, and village).

Decomposition

Table A1.3: Employment rate and educational attainment of men aged 25–64 in Hungary and in the EU (15), 2001

Educational attainment

Emplyment rate Ratio in population Average of EU

(15) countries Hungary Average of EU

(15) coutries Hungrary Less than upper secondary education 0.71 0.57 0.38 0.63 Upper secondary education^* 0.82 0.75 0.39 0.22 At least college 0.88 0.84 0.23 0.15 Total (unweighted) 0.79 0.65 1.000 1.000 Unweighted diff erence: 0.14.

* For Hungary Classifi cation2 is used: upper secondary education includes gymnasiums and vocational secondary schools.

Sources: Employment rate of EU countries: OECD Employment Outlook 2003. Statis- tical Annex Table D., Educational attainment of the population: OECD Education at a Glance. OECD 2003.

Hungarian data are estimations based on data of 2001 Census.

Let denote employment rate by F, employment rate of those whose educa-FF tional attainment isi by i ⁱ, and ratio of population whose highest educa- tional attainment is i byi ysⁱ. Let use EUUés MMindexes for the average of the EU countries and Hungary respectively. We can decompose the diff erence in two ways:

F_EU F

F – F_U FF_MM= ∑_i ƒƒƒ (_MMⁱ ss – s_EUⁱ_U s_MMⁱ ) + ∑_iss_EUⁱ_U(ƒƒƒ – ƒ_EUⁱ_U ƒƒ )_MMⁱ

= ∑_i ƒƒƒ (_EUⁱ_U ss – s_EUⁱ_U s_MMⁱ ) + ∑_iss_MMⁱ (ƒƒƒ – ƒ_EUⁱ_U ƒƒ ) ._MMⁱ

In the fi rst case – see the fi rst line of the equation – using the Hungarian employment rates of educational categories and the EU ditribution of the population by educational categories for weighting. In the second case – see the second line of equation – using the EU employment rates of edu- cational categories and the Hungarian distribution of the population by educational categories for weighting.

A.2

Th e earnings function we have used:

W=

W α0+α1R+α20+α3U+UU α4E+α5E²+α6R×E+α7O×E+α8U×UU E+α9SEX ,X where W is the natural logarithm of the monthly (before tax) wage, E stands for (potential) labour market experience, inserting variable SEX (male = 1, female = 0) is intended to control for potential disadvantage of women.

Interaction terms R×E, O×E, U×E are inserted to see whether the im- pact of under- or over-education on wage is independent of labour market experience or not. From our point of view the following partial derivatives are relevant:

∂W = α₁ + α6E,

∂R

∂∂∂W = α₂ + α7₇₇E,

∂O∂W =α₃+α8E,

We will focus on α∂U 1,α2,α3.

Th e wage regressions were run on nine annual samples of Employment Offi ce’s wage survey for the period 1994 to 2002. Th ey were estimated by ols with robust standard errors that might produce biased coeffi cients due to endogeneity, simultaneity or selectivity bias. Detailed estimation results are found in Galasi (2004b).

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