Employment trajectories in Germany: do firm characteristics and regional disparities matter?

Dütsch, Matthias; Struck, Olaf

doi:10.1007/s12651-014-0156-3

Journal for Labour Market Research

Table 2 Individual, firm-specific, and region-specific factors influencing employment trajectories (three-level logistic random intercept models)

From: Employment trajectories in Germany: do firm characteristics and regional disparities matter?Erwerbsverläufe in Deutschland: Zur Bedeutung betrieblicher Charakteristika und regionaler Disparitäten

Independent variables	1999					2002
	Exit from job	Interfirm career path			Unemployment	Exit from job	Interfirm career path			Unemployment
	Exit from job	Upward mobility	Lateral mobility	Downward mobility	Unemployment	Exit from job	Upward mobility	Lateral mobility	Downward mobility	Unemployment
Individual factors
Sex (1 = female)	0.432^∗∗∗	−0.408^∗∗∗	−0.264^∗∗∗	−0.139^∗∗	0.245^∗∗∗	0.568^∗∗∗	−0.253^∗∗∗	−0.158^∗∗∗	0.228	0.184^∗∗∗
Nationality (1 = foreign)	0.023	−0.105^∗	−0.502^∗∗∗	−0.125^∗	0.358^∗∗∗	0.001	−0.221^∗∗∗	−0.639^∗∗∗	−0.181	0.291^∗∗∗
Age: Reference: 25–34 years
35–44 years (1 = yes)	−0.354^∗∗∗	−0.601^∗∗∗	−0.253^∗∗∗	−0.274^∗∗∗	0.106^∗∗∗	−0.346^∗∗∗	−0.572^∗∗∗	0.008	−1.154^∗∗∗	0.021
45–52 years (1 = yes)	−0.326^∗∗∗	−0.858^∗∗∗	−0.341^∗∗∗	−0.484^∗∗∗	0.297^∗∗∗	−0.263^∗∗∗	−0.898^∗∗∗	0.346^∗∗∗	−1.320	0.121^∗∗∗
Highest education level: Ref.: Secondary school and vocational training
No vocational training (1 = yes)	0.111^∗∗∗	0.326^∗∗∗	−0.141^∗∗	−0.388^∗∗∗	0.085^∗∗	0.169^∗∗∗	0.043	−0.116^∗	−0.277	0.062^∗
Advanced secondary school and vocational training (1 = yes)	0.021	0.379^∗∗∗	−0.012	0.055	−0.207^∗∗∗	0.042	0.289^∗∗∗	0.013	−0.423	−0.223^∗∗∗
University degree (1 = yes)	−0.303^∗∗∗	0.136^∗∗	0.007	−0.343^∗∗∗	−0.367^∗∗∗	−0.520^∗∗∗	−0.362^∗∗∗	−0.461^∗∗∗	−1.895^∗∗∗	−0.320^∗∗∗
Job Position: Ref.: Skilled blue-collar
Unskilled blue-collar (1 = yes)	0.318^∗∗∗	−0.133^∗∗	0.088^∗	0.279^∗∗∗	0.665^∗∗∗	0.309^∗∗∗	0.082	0.092^∗	0.473^∗∗	0.574^∗∗∗
Master craftsman (1 = yes)	−0.274^∗∗∗	−0.499^∗∗∗	0.331^∗∗∗	−0.503^∗∗∗	−0.321^∗∗	−0.387^∗∗∗	0.442^∗∗∗	−0.142	−1.497^∗	−0.595^∗∗∗
White-collar (1 = yes)	−0.225^∗∗∗	−0.149^∗∗∗	−0.001	−0.483^∗∗∗	−0.314^∗∗∗	−0.139^∗∗∗	0.271^∗∗∗	0.341^∗∗∗	−0.880^∗∗∗	−0.365^∗∗∗
Cohorts and previous employment state: Ref.: Permanently employed
First employment (1 = yes)	0.968^∗∗∗	1.794^∗∗∗	0.173	0.456	1.318^∗∗∗	0.803^∗∗∗	0.813^∗∗∗	−0.152	3.067^∗∗∗	1.181^∗∗∗
Entrance at most 1 year ago (1 = yes) × Share of employment	2.211^∗∗∗	3.966^∗∗∗	1.733^∗∗∗	3.324^∗∗∗	2.181^∗∗∗	2.168^∗∗∗	3.727^∗∗∗	1.379^∗∗∗	3.184^∗∗∗	2.110^∗∗∗
Entrance at most 1 year ago (1 = yes) × Share of unemployment	3.516^∗∗∗	4.151^∗∗∗	1.378^∗∗∗	3.740^∗∗∗	4.779^∗∗∗	3.244^∗∗∗	3.607^∗∗∗	1.055^∗∗∗	3.058^∗∗∗	4.330^∗∗∗
Entrance at most 1 year ago (1 = yes) × Share of nonemployment	2.742^∗∗∗	3.638^∗∗∗	1.030^∗∗∗	3.197^∗∗∗	2.752^∗∗∗	3.068^∗∗∗	3.784^∗∗∗	0.520^∗∗∗	3.279^∗∗∗	2.483^∗∗∗
Entrance 1–5 years ago (1 = yes) × Share of employment	1.646^∗∗∗	3.925^∗∗∗	1.710^∗∗∗	2.800^∗∗∗	1.107^∗∗∗	1.533^∗∗∗	3.564^∗∗∗	1.488^∗∗∗	2.748^∗∗∗	1.016^∗∗∗
Entrance 1–5 years ago (1 = yes) × Share of unemployment	1.461^∗∗∗	2.275^∗∗∗	0.737^∗∗∗	1.554^∗∗∗	2.691^∗∗∗	1.477^∗∗∗	1.626^∗∗∗	−0.542^∗∗	1.365^∗∗∗	2.476^∗∗∗
Entrance 1–5 years ago (1 = yes) × Share of nonemployment	1.097^∗∗∗	2.420^∗∗∗	0.504^∗∗∗	1.165^∗∗∗	1.585^∗∗∗	1.103^∗∗∗	1.944^∗∗∗	−0.407^∗∗	1.388^∗∗∗	1.449^∗∗∗
Entrance more than 5 years ago (1 = yes) × Share of employment	3.851^∗∗∗	6.100^∗∗∗	2.832^∗∗∗	4.274^∗∗∗	1.094^∗∗∗	3.837^∗∗∗	5.523^∗∗∗	2.908^∗∗∗	4.879^∗∗∗	0.968^∗∗∗
Entrance more than 5 years ago (1 = yes) × Share of unemployment	0.139	−0.548	−0.256	−0.538	1.095^∗∗∗	−0.078	−6.138^∗	−2.968^∗∗∗	−2.000	1.013^∗∗∗
Entrance more than 5 years ago (1 = yes) × Share of nonemployment	0.509^∗∗∗	−4.672^∗∗∗	−1.159^∗	−5.993^∗∗	0.004	0.242^∗∗	−5.702^∗∗∗	−1.421^∗∗	−6.997^∗∗∗	0.400^∗∗
Firm-specific factors
Age distribution (Blocked promotion-opportunities: 1 = yes)¹	0.159^∗∗∗	0.025	−0.199^∗	0.095^∗	0.135^∗∗∗	0.105^∗∗∗	−0.035	0.409^∗∗∗	0.481	0.080^∗∗
Firm size: Ref.: Small firm
Small–medium-sized firm (1 = yes)	−0.165^∗∗	−0.188	0.377^∗	0.173	−0.408^∗∗∗	−0.381^∗∗∗	−0.104	−0.008	0.759	−0.511^∗∗∗
Medium-sized firm (1 = yes)	−0.274^∗∗∗	−0.296^∗∗	0.430^∗	0.184	−0.633^∗∗∗	−0.425^∗∗∗	−0.166	0.138	1.229	−0.828^∗∗∗
Larger firm (1 = yes)	−0.268^∗∗∗	−0.137	0.326^∗	0.195	−0.758^∗∗∗	−0.557^∗∗∗	−0.530^∗∗∗	−0.191	−0.168	−1.110^∗∗∗
Atypical employment (Share of fixed-term employees)	0.989^∗∗∗	0.307	1.035^∗∗∗	0.383	1.362^∗∗∗	0.962^∗∗∗	0.049	−0.033	1.303	1.561^∗∗∗
Investments in further training (1 = yes)	−0.212^∗∗∗	−0.007	−0.325^∗∗∗	−0.241^∗	−0.178^∗∗	−0.316^∗∗∗	0.132	−0.486^∗∗∗	−0.946^∗	−0.314^∗∗∗
Technological state of machinery and equipment (1 = state-of-the-art equipment)	−0.145^∗∗∗	0.037	−0.225^∗∗	−0.327^∗∗∗	−0.368^∗∗∗	−0.006	0.260^∗∗∗	0.086^∗	−.0683	−0.201^∗∗∗
Co-determination (Works council: 1 = yes)	−0.212^∗∗∗	−0.526^∗∗∗	−0.627^∗∗∗	−0.126	0.030	−0.070	−0.253^∗∗∗	−0.227^∗∗∗	0.274	−0.030
Sector: Ref.: Manufacturing industry
Agriculture, forestry, and mining (1 = yes)	−0.347^∗∗∗	−1.361^∗∗∗	−0.536^∗∗	−0.201	0.674^∗∗∗	0.621^∗∗∗	1.401^∗∗∗	0.675^∗∗∗	1.631^∗	0.039
Construction (1 = yes)	0.548^∗∗∗	−0.200^∗∗	−0.029	0.260^∗∗	0.938^∗∗∗	0.739^∗∗∗	0.171	0.095	1.045	0.792^∗∗∗
Trade (1 = yes)	0.213^∗∗∗	0.149^∗	0.115	0.514^∗∗∗	0.113	0.123	0.102	0.039	0.956	0.135
Services for firms (1 = yes)	0.117	0.330^∗∗∗	−0.279	0.011	0.133	0.186	0.120	0.649^∗∗∗	0.894	−0.027
Other services (1 = yes)	0.134^∗∗∗	−0.037	0.232^∗∗	−0.037	−0.106	−0.025	−0.105	−0.169^∗∗∗	0.388	−0.219^∗∗∗
Region-specific factors
Types of region: Ref.: Densely populated agglomerations
Agglomerations with outstanding centers (1 = yes)	0.293^∗∗∗	0.708^∗∗∗	0.637^∗∗∗	−0.017	0.126	0.209^∗∗	0.758^∗∗∗	0.457^∗∗∗	1.102^∗∗	0.156
Urbanized areas of higher density (1 = yes)	0.397^∗∗∗	0.773^∗∗∗	0.748^∗∗∗	0.130	−0.055	−0.001	0.114	0.454^∗∗∗	−0.397	−0.162
Urbanized areas of medium density and large regional centers (1 = yes)	0.176^∗∗∗	0.062	0.277^∗	0.009	0.141	0.113	0.558^∗∗∗	0.362^∗∗∗	−0.446	−0.085
Urbanized areas of medium density without large regional centers (1 = yes)	0.086	−0.057	0.513^∗∗	0.186	−0.040	0.084	0.873^∗∗∗	0.054	−1.235	−0.313^∗
Rural areas of higher-density (1 = yes)	0.138^∗	−0.330^∗∗	0.321^∗	0.242	0.218	0.032	0.219	0.128	0.616	−0.000
Rural areas of lower-density (1 = yes)	0.110	0.043	0.0103	0.068	0.167	0.208^∗	0.203	0.213^∗	−0.951	0.224^∗
Productivity (GDP per capita)	0.040^∗∗∗	0.021^∗∗	0.027^∗∗∗	0.017^∗	−0.035^∗∗∗	−0.013^∗	−0.012	−0.015^∗∗∗	−0.294^∗∗	−0.007
Unemployment rate	−0.017^∗∗∗	−0.014	−0.021^∗	−0.019	0.014	−0.008^∗	−0.036^∗∗∗	−0.020^∗∗∗	−0.748^∗∗∗	0.030^∗∗∗
Accumulation of human capital (Share of students)	−0.002	−0.003	0.008^∗∗	0.005	0.004	0.001	0.009^∗∗∗	0.010^∗∗∗	0.154^∗∗∗	−0.003
Constant	−3.357^∗∗∗	−6.706^∗∗∗	−6.002^∗∗∗	−6.987^∗∗∗	−4.643^∗∗∗	−2.998^∗∗∗	−7.077^∗∗∗	−4.783^∗∗∗	−6.297^∗∗∗	−4.189^∗∗∗
Episodes (persons)	370,779	370,779	370,779	370,779	370,779	363,339	363,339	363,339	363,339	363,339
Episodes (firms)	1,836	1,836	1,836	1,836	1,836	2,140	2,140	2,140	2,140	2,140
Episodes (regions)	97	97	97	97	97	97	97	97	97	97
Residual variance (firms)	0.208	0.109	0.775	0.591	0.531	0.183	0.274	0.024	1.248	0.738
Residual variance (regions)	0.075	0.067	0.054	0.048	0.009	0.044	0.054	0.010	0.090	0.052
Log likelihood (final values)	−80,269	−20,597	−19,151	−15,144	−30,915	−74,991	−14,466	−18,377	−12,833	-31,670

Notes: The dependent variables are coded as dummy variables. In the columns 1 and 6 the value 1 represents a job exit; the value 0 denotes that the employee remains in the firm. In the columns 2–5 and 7–10 the value 1 indicates one of the destination states, respectively, whereas the value 0 subsumes all of the other employment states. In each regression the whole sample is used. Regression coefficients are reported after three-level logistic random intercept estimations.
^∗ p<0.05; ^∗∗ p<0.01; ^∗∗∗ p<0.001
¹ “1” indicates that an employee is positioned ahead of the median age in the internal age distribution.
Source: Linked Employer–Employee Data (LIAB); own calculations

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