Does education raise productivity and wages equally? The moderating role of age and gender

Kampelmann, Stephan; Rycx, François; Saks, Yves; Tojerow, Ilan

doi:10.1186/s40172-017-0061-4

Table 4 Estimates according to workers’ gender, three educational categories

From: Does education raise productivity and wages equally? The moderating role of age and gender

	GMM-SYS			LP
	Value added per hour worked (ln)	Wage cost per hour worked (ln)	Value added-wage cost gap^c	Value added per hour worked (ln)
	(1)	(2)	(3)	(4)
Lagged dependent variable (ln)	0.661^*** (0.056)	0.453^*** (0.135)	0.621^*** (0.045)	0.791^*** (0.043)
Shares of workers^a
Male low-educated (ME12)	Reference	Reference	Reference	Reference
Female low-educated (FE12)	−0.029 (0.060)	−0.060 (0.049)	0.007 (0.046)	−0.034 (0.026)
Male middle-educated (ME34)	0.058^** (0.025)	0.031^** (0.016)	0.029 (0.024)	0.009 (0.013)
Female middle-educated (FE34)	0.014 (0.060)	−0.035 (0.041)	0.019 (0.043)	0.025 (0.020)
Male high-educated (ME567)	0.101^* (0.054)	0.150^*** (0.050)	0.009 (0.045)	0.119^*** (0.032)
Female high-educated (FE567)	0.151^* (0.077)	0.082 (0.069)	0.125^** (0.051)	0.128^*** (0.039)
Hansen over-identification test, p value	0.319	0.138	0.737
Arellano-Bond test for AR(2), p value	0.129	0.338	0.215
Number of observations	6714	6714	6714	6691
Number of firms	1844	1844	1844	1844
Chi-squared statistic for equality of regression coefficients, H₀
FE12 = ME34	2.36	4.10^**	0.25	2.71^*
FE12 = FE34	0.68	0.43	0.08	3.11^*
FE12 = ME567	3.77^*	11.30^***	0.00	12.33^***
FE12 = FE567	6.09^**	3.44^*	4.96^**	10.76^***
ME34 = FE34	0.55	2.88^*	0.06	0.58
ME34 = ME567	0.70	6.19^**	0.23	14.68^***
ME34 = FE567	1.42	0.54	3.68^*	9.29^***
FE34 = ME567	1.60	9.45^***	0.05	10.60^***
FE34 = FE567	4.35^**	2.57	4.51^**	5.38^**
ME567 = FE567	0.31	1.11	3.17^*	0.05
Interpretation^b
a) Among male workers	ME12 < ME567 ME12 < ME34 but ME34 = ME567	ME12 < ME567 ME12 < ME34 ME34 < ME567	ME12 = ME567 ME12 = ME34 ME34 = ME567	ME12 < ME567 ME34 < ME567 but ME12 = ME34
a) Among male workers	⇒ ME12 significantly less productive than ME34 and ME567	⇒ Education increases wage costs significantly	⇒ Education has no significant impact on profits	⇒ ME567 significantly more productive than ME12 and ME34
a) b) Among female workers	FE12 < FE567 FE34 < FE567 but FE12 = FE34	FE12 < FE567 but FE12 = FE34 FE34 = FE567	FE12 < FE567 FE34 < FE567 but FE12 = FE34	FE12 < FE567 FE34 < FE567 FE12 < FE34
a) b) Among female workers	⇒ FE567 significantly more productive than FE12 and FE34	⇒ FE567 significantly more costly than FE12	⇒ FE567 significantly more profitable than FE12 and FE34	⇒ Education increases productivity significantly

Notes: Standard errors, that are robust to heteroskedasticity and autocorrelation, are reported between parentheses. Regressions also control for: % of workers with 10 years of tenure or more; % workers younger than 30 and older than 49 years, respectively; % women; % part-time workers; % blue-collar workers; % workers with fixed term employment contract; % apprentices; % temporary agency workers; ln of firm size; ln of capital stock per worker; level of collective wage bargaining; region where the firm is located (2 dummies); industries (8 dummies), and years dummies (11). AR (2) refers to second-order autocorrelation in first-differenced errors. Models (1) to (3) include first and second lags of explanatory variables (except time dummies) as instruments
^***p < 0.01, ^**p < 0.05, ^*p < 0.1
^a Low-educated workers (E12) have at most a degree of lower secondary school. Middle-educated workers (E34) have at most a degree from upper (general, technical or professional) secondary school. High-educated workers (E567) have a tertiary educational attainment (i.e. at least a Bachelor’s or equivalent degree)
^b‘<’ (‘=’) indicates if regression coefficients are (not) statistically different at the 10% level
^cValue added-wage cost gap = ln(value added per hour) − ln(wage cost per hour)

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