Change language
English Deutsch
Change currency
€ EUR £ GBP $ USD
Licensed Unlicensed Requires Authentication Published by De Gruyter Oldenbourg January 14, 2021

The Effect of Rapid Structural Change on Workers

  • Eva Weigt EMAIL logo
From the journal Jahrbücher für Nationalökonomie und Statistik
https://doi.org/10.1515/jbnst-2019-0067

Abstract

This paper deals with the question how workers’ labour market and non-monetary outcomes are impacted by a negative sector-specific labour demand shock. This issue is analysed in a setting of rapid structural change that happened in Eastern Germany after the fall of the Berlin Wall in 1989. The sector-specific labour demand shock can be assumed to be exogenous to other worker characteristics as it was not anticipated and as career planning was highly restricted in the GDR. Using survey data from the German Socio-Economic Panel (SOEP), I find considerable and partly persistent losses in labour market outcomes of workers from declining compared to booming industries. Life satisfaction of workers from declining industries is decreased in the short run whereas the probability to move to the West and to identify with a left-wing political party is increased merely in the longer run.

Keywords: structural change; labour demand shock; East Germany; SOEP
JEL Classification: J23; J31; J62
Corresponding author: Eva Weigt, Halle Institute for Economic Research (IWH), Kleine Märkerstraße 8, D-06108 Halle (Saale), Germany, E-mail:

Acknowledgements

I would like to thank Steffen Mueller for indispensable help and advice when developing this paper. I am also grateful for insightful talks with Udo Ludwig about the transformation processes in Eastern Germany in the early 1990s. Finally, I would like to thank Richard Bräuer, Daniel Fackler, Sebastian Findeisen, Gerhard Heimpold, Lisa Hölscher, Sabrina Jeworrek, Claus Schnabel and participants of the Research Seminar at the University of Magdeburg, the 16. IWH/IAB-Workshop on Labour Market Policy: Structural Change on the Labour market suggestions as well as two anonymous referees for helpful comments and suggestions.

Appendix

Table A1:

Description of outcome variables.

Monthly labour earnings unconditional on employment Gross labour earnings in the month before the survey; includes 0 earnings (e.g., for individuals who do not work); earnings are in DM deflated to prices in 1995; the fixed exchange rate from DM to Euro is: 1 EUR = 1,95583 DM; 1 DM = 0,51129 EUR.
Weekly working hours unconditional on employment Hours worked per week according to the labour contract; includes 0 working hours (e.g., for individuals who do not work).
Share of workers who are non-employed Captures whether a person is employed or not in the specific year.
Log hourly wage Wages in logs and conditional on employment; calculated by dividing gross monthly labour earnings by monthly working hours (= weekly working hours*4.3).
Monthly labour earnings conditional on employment Gross labour earnings in the month before the survey; earnings are in DM deflated to prices in 1995.
Weekly working hours conditional on employment Hours worked per week according to the labour contract.
Life satisfaction Answer to the question “How happy are you at present with your life as a whole?” with answers ranging from 0 = “not satisfied at all” to 10 = “completely satisfied”.
Monthly labour earnings unconditional on employment Gross labour earnings in the month before the survey of workers who are actually employed; earnings are in DM deflated to prices in 1995.
Living in West Germany Dummy that is 1 for individuals who are from the original East German sample, but are identified to currently live in West Germany and 0 for those living in East Germany.
Party identification Dummy that is 1 for individuals identifying with a political party), 0 for individuals not identifying with a political party; information included in the SOEP for East Germans only since 1992
Identification with right-wing political party Dummy that is 1 for individuals identifying with a right-wing political party, i.e. the Republikaner as the most right-wing political party in Germany in the period of observation, 0 for those identifying with other types of political parties
Identification with left-wing political party Dummy that is 1 for individuals identifying with a left-wing political party, i.e. the Partei des Demokratischen Sozialismus as the most left-wing political party in Germany in the period of observation, 0 for those identifying with other types of political parties
Table A2:

Development of the sample size over time.

Survey year No. of realised interviews Cumulated no. of drop-outs
1990 1145 0
1991 1110 35
1992 1116 29
1993 1055 90
1994 1004 141
1995 978 167
1996 949 196
1997 920 225
1998l 874 271
1999 851 294

  1. SOEP waves 1990 to 1999; the sample restrictions described on p. 12 above are applied.

Table A3:

Transition of workers between sectors between 1990 and 1993.

Agriculture, forestry, fishery Mining and quarrying Manufacturing Electricity, gas and water supply Construction Wholesale, retail trade; repair of motor vehicles Hotels and restaurants Transport, storage, communication Finance, insurance Other business services Public admin., defence, social insurance Other public services, private households Not employed No. of workers in 1990
1993
Agriculture, forestry, fishery 1990 28.5% 1.0% 7.0% 0.0% 18.0% 10.0% 0.5% 2.5% 3.0% 3.5% 8.0% 2.5% 15.5% 200
Mining and quarrying 0.0% 43.5% 0.0% 2.2% 13.0% 10.9% 0.0% 4.3% 0.0% 2.2% 4.3% 6.5% 13.0% 46
Manufacturing 1.1% 0.4% 42.8% 0.6% 6.8% 9.5% 0.2% 2.3% 1.7% 2.1% 4.0% 4.9% 23.6% 474
Electricity, gas and water supply 0.0% 4.8% 2.4% 54.8% 9.5% 2.4% 0.0% 0.0% 0.0% 4.8% 9.5% 2.4% 9.5% 42
Construction 0.8% 0.0% 6.7% 0.0% 58.0% 5.9% 0.0% 2.5% 0.8% 5.9% 6.7% 3.4% 9.2% 119
Hotels and restaurants 0.0% 0.0% 9.1% 0.0% 9.1% 18.2% 45.5% 0.0% 0.0% 9.1% 0.0% 0.0% 9.1% 11
Transport, storage, communication 0.7% 0.0% 3.0% 0.0% 0.0% 5.2% 0.7% 65.9% 0.7% 2.2% 3.0% 2.2% 16.3% 135
Finance, insurance 0.0% 0.0% 8.3% 0.0% 0.0% 0.0% 0.0% 0.0% 66.7% 0.0% 8.3% 8.3% 8.3% 12
Other business services 0.0% 0.0% 8.8% 2.9% 14.7% 17.6% 0.0% 0.0% 0.0% 29.4% 11.8% 0.0% 14.7% 34
No. of workers in 1993 64 26 235 28 153 93 8 110 24 41 58 40 193 1073

  1. Own calculations based on the SOEP waves 1990 and 1993; the sample restrictions described on p. 12 above are applied; furthermore, calculations only include workers with valid sector information in 1993 or without valid sector information in that year as they are not employed; percentage shares are calculated by dividing the number of workers in each cell by the number of workers employed in each sector in 1990.

Table A4:

Effect of sectoral change in labour demand on labour market outcomes unconditional on employment – explanatory dummy variable.

Monthly labour earnings Weekly working hours Probability to be non-employed
Coefficient Std. err. Coefficient Std. err. Coefficient Std. err.
γ1991 171.91*** 6.997 −8.240*** 1.933 0.097*** 0.021
γ1992 394.78*** 39.849 −11.930*** 1.125 0.178*** 0.034
γ1993 668.73*** 52.184 −12.655*** 0.669 0.180*** 0.024
γ1994 796.72*** 71.103 −12.963*** 1.425 0.184*** 0.022
γ1995 907.84*** 49.249 −12.280*** 0.923 0.163*** 0.013
γ1996 1029.70*** 61.952 −12.196*** 1.083 0.154*** 0.011
γ1997 1109.45*** 62.911 −13.531*** 1.125 0.177*** 0.013
γ1998 1121.61*** 69.166 −14.086*** 1.412 0.182*** 0.020
γ1999 1219.71*** 70.638 −11.794*** 1.356 0.131*** 0.021
δ1991 95.556*** 12.075 1.889 2.181 −0.030 0.023
δ1992 199.57** 60.909 3.111* 1.366 −0.079* 0.042
δ1993 180.42** 69.522 3.496** 1.280 −0.090** 0.028
δ1994 281.14*** 81.393 2.970* 1.434 −0.085*** 0.023
δ1995 317.98*** 70.105 2.751 1.575 −0.080*** 0.020
δ1996 92.363 57.653 −2.413 1.301 0.004 0.016
δ1997 40.926 83.986 −2.489* 1.155 0.014 0.020
δ1998 81.632 64.946 −0.640 1.898 −0.009 0.027
δ1999 69.962 55.811 −2.357 1.601 0.016 0.019
age2 −0.463 0.105 0.002 0.007 0.000 0.000
age3 −0.080*** 0.028 −0.002*** 0.000 0.000*** 0.000
age4 −0.002 0.001 −0.000*** 0.000 0.000*** 0.000
Constant 397.47*** 35.877 41.679*** 0.681 −0.005 0.017
R2 0.166 0.080 0.045
No. of observations 10,002 10,002 10,002
No. of groups 1145 1,145 1,145

  1. Own calculations based on the SOEP waves 1990 to 1999; the sample restrictions described on p. 12 above are applied; fixed effects estimates; monthly earnings and hourly wages are in DM and deflated to prices in 1995; standard errors are adjusted for clustering at the sector level; γ k captures the development of the outcome variables for the reference group (workers from declining industries), δ k the difference between workers from the reference group and those from booming industries in each year;*denotes significance at the 10% level, **at the 5% level and ***at the 1% level.

Table A5:

Effect of sectoral change in labour demand on labour market outcomes conditional on employment – explanatory dummy variable.

Gross labour earnings Log hourly wage Weekly working hours
Coefficient Std. err. Coefficient Std. err. Coefficient Std. err.
γ1991 205.42*** 8.791 0.308*** 0.016 −1.842*** 0.054
γ1992 500.57*** 17.324 0.577*** 0.036 −2.252*** 0.134
γ1993 809.28*** 39.410 0.761*** 0.041 −2.358*** 0.154
γ1994 989.11*** 49.113 0.867*** 0.041 −2.522*** 0.140
γ1995 1101.69*** 43.466 0.936*** 0.037 −2.983*** 0.203
γ1996 1227.22*** 57.190 0.977*** 0.036 −3.002*** 0.253
γ1997 1338.68*** 48.702 1.036*** 0.022 −3.031*** 0.311
γ1998 1392.48*** 56.027 1.058*** 0.031 −3.254*** 0.275
γ1999 1424.88*** 54.783 1.050*** 0.021 −3.095*** 0.351
δ1991 96.030*** 12.468 0.105*** 0.022 0.388 0.221
δ1992 180.18** 55.737 0.136** 0.055 0.544 0.431
δ1993 105.30 61.384 0.069 0.048 −0.236 0.168
δ1994 185.37*** 53.657 0.100** 0.043 −0.034 0.233
δ1995 162.31** 59.512 0.103** 0.039 −0.652* 0.309
δ1996 126.84* 60.934 0.075 0.046 −0.547** 0.221
δ1997 132.40** 54.021 0.051 0.032 −0.432* 0.222
δ1998 106.49* 52.604 0.050 0.035 −0.709** 0.264
δ1999 120.30* 63.443 0.075** 0.028 −0.956* 0.452
age2 −0.095 0.228 −0.000 0.000 0.001 0.004
age3 −0.018 0.015 0.000 0.000 −0.000*** 0.000
age4 −0.000 0.000 −0.000 0.000 −0.000*** 0.000
Constant 0.000*** 0.000 1.232*** 0.018 0.000*** 0.000
R2 0.028 0.059 0.054
No. of observations 7637 7637 7637
No. of groups 1145 1145 1145

  1. Own calculations based on the SOEP waves 1990 to 1999; the sample restrictions described on p. 12 above are applied; fixed effects estimates; monthly earnings and hourly wages are in DM and deflated to prices in 1995; standard errors are adjusted for clustering at the sector level; γ k captures the development of the outcome variables for the reference group (workers from declining industries), δ k the difference between workers from the reference group and those from booming industries in each year; *denotes significance at the 10% level, **at the 5% level and ***at the 1% level.

Table A6:

Effect of sectoral change in labour demand on life satisfaction and mobility – explanatory dummy variable.

Life satisfaction Living in West Germany
Coefficient Std. err. Coefficient Std. err.
γ 1991 −0.678*** 0.075 0.004 0.002
γ 1992 −0.668*** 0.026 0.024*** 0.006
γ 1993 −0.563*** 0.091 0.034*** 0.007
γ 1994 −0.601*** 0.071 0.036*** 0.008
γ 1995 −0.338*** 0.026 0.045*** 0.007
γ 1996 −0.404*** 0.049 0.047*** 0.007
γ 1997 −0.524*** 0.028 0.049*** 0.008
γ 1998 −0.489*** 0.038 0.052*** 0.008
γ 1999 −0.348*** 0.038 0.058*** 0.009
δ 1991 0.305** 0.126 −0.000 0.002
δ 1992 0.337*** 0.031 −0.011 0.008
δ 1993 0.180 0.125 −0.015 0.008
δ 1994 0.216** 0.085 −0.016 0.009
δ 1995 0.082** 0.030 −0.025** 0.009
δ 1996 −0.016 0.084 −0.027*** 0.008
δ 1997 −0.025 0.095 −0.028** 0.008
δ 1998 0.034 0.118 −0.032*** 0.009
δ 1999 −0.145 0.159 −0.036*** 0.009
age2 −0.003** 0.001 0.000** 0.000
age3 0.000*** 0.000 0.000 0.000
age4 0.000*** 0.000 0.000*** 0.000
Constant 6.792*** 0.034 0.000 0.005
R2 0.003 0.012
No. of observations 9967 10,002
No. of groups 1145 1145

  1. Own calculations based on the SOEP waves 1990 to 1999; the sample restrictions described on p. 12 above are applied; fixed effects estimates; standard errors are adjusted for clustering at the sector level; γ k captures the development of the outcome variables for the reference group (workers from declining industries), δ k the difference between workers from the reference group and those from booming industries in each year; *denotes significance at the 10% level, **at the 5% level and ***at the 1% level.

Table A7:

Effect of sectoral change in labour demand on political orientations–explanatory dummy variable.

Identifying with a political party Identifying with right-wing political party Identifying with left-wing political party
Coefficient Std. err. Coefficient Std. err. Coefficient Std. err.
γ1993 −0.048 0.029 0.043*** 0.008 0.022** 0.008
γ1994 −0.064** 0.025 0.025** 0.009 0.045*** 0.010
γ1995 0.035 0.019 0.019** 0.007 0.049*** 0.003
γ1996 −0.019 0.022 0.018** 0.007 0.079*** 0.006
γ1997 −0.037 0.027 0.033*** 0.009 0.075*** 0.007
γ1998 −0.032 0.022 0.024** 0.009 0.074*** 0.014
γ1999 −0.000 0.025 0.021* 0.009 0.073*** 0.017
δ1993 −0.036 0.032 −0.004 0.020 −0.012 0.008
δ1994 0.023 0.029 0.013 0.021 −0.065** 0.028
δ1995 −0.124*** 0.021 0.009 0.016 −0.041 0.023
δ1996 −0.051 0.028 −0.006 0.013 −0.029 0.053
δ1997 −0.036 0.037 0.015 0.029 −0.091** 0.031
δ1998 −0.109** 0.033 −0.005 0.012 −0.075 0.042
δ1999 −0.000 0.026 −0.009 0.014 −0.055** 0.019
age2 −0.000 0.000 0.000 0.000 −0.001 0.000
age3 0.000** 0.000 0.000 0.000 0.000 0.000
age4 0.000 0.000 0.000 0.000 0.000* 0.000
Constant 0.000*** 0.000 0.000 0.000 0.000*** 0.000
R2 0.007 0.035 0.011
No. of observations 7730 2529 2529
No. of groups 1143 713 713

  1. Fixed effects estimates; own calculations based on the SOEP waves 1992 to 1999; the sample restrictions described on p. 12 above are applied; standard errors are adjusted for clustering at the sector level; γ k captures the development of the outcome variables for the reference group (workers from declining industries), δ k the difference between workers from the reference group and those from booming industries in each year; *denotes significance at the 10% level, **at the 5% level and ***at the 1% level.

Table A8:

Effect of sectoral change in labour demand on labour market outcomes unconditional on employment – continuous explanatory variable.

Monthly labour earnings Weekly working hours Probability to be non-employed
Coefficient Std. err. Coefficient Std. err. Coefficient Std. err.
γ1991 204.05*** 16.503 −6.892*** 0.833 0.077*** 0.011
γ1992 457.99*** 44.914 −10.333*** 0.757 0.144*** 0.022
γ1993 714.87*** 57.119 −11.372*** 0.846 0.152*** 0.021
γ1994 866.50*** 80.078 −12.033*** 1.187 0.165*** 0.028
γ1995 989.32*** 78.672 −11.528*** 1.109 0.143*** 0.020
γ1996 1012.07*** 85.292 −13.653*** 1.114 0.180*** 0.027
γ1997 1082.27*** 68.999 −14.4339*** 0.877 0.182*** 0.013
γ1998 1101.53*** 89.424 −14.282*** 1.162 0.180*** 0.021
γ1999 1199.47*** 84.589 −12.893*** 1.102 0.138*** 0.021
δ1991 62.322*** 18.981 2.959 2.298 −0.044* 0.023
δ1992 122.89* 65.645 3.547*** 1.083 −0.076** 0.031
δ1993 111.56 77.129 2.985** 0.848 −0.062* 0.030
δ1994 177.26* 88.832 2.878 1.641 −0.055 0.031
δ1995 174.77 99.119 2.261 1.283 −0.046 0.027
δ1996 46.554 76.419 −1.153 1.531 0.019 0.028
δ1997 11.471 73.756 −0.601 1.356 0.000 0.018
δ1998 43.117 73.779 0.898 1.332 −0.020 0.018
δ1999 29.544 61.905 −0.918 1.157 0.001 0.015
age2 −0.479*** 0.000 0.004 0.007 0.000 0.000
age3 −0.074** 0.000 −0.001*** 0.000 0.000*** 0.000
age4 −0.002 0.001 −0.000*** 0.000 0.000** 0.000
Constant 399.38*** 40.853 41.434*** 0.583 −0.009 0.016
R2 0.161 0.082 0.049
No. of observations 11,366 11,366 11,366
No. of groups 1296 1296 1296

  1. Fixed effects estimates based on SOEP waves 1990 to 1999; the sample restrictions described on p. 12 above are applied; fixed effects estimates; the explanatory variable ranges from −0.76 (employment change for “Agriculture, forestry, fishery”) to 1.49 (employment change for “Finance, insurance” from 1989 to 1993); monthly earnings and hourly wages are in DM and deflated to prices in 1995; standard errors are adjusted for clustering at the sector level; *denotes significance at the 10% level, **at the 5% level and ***at the 1% level.

Table A9:

Effect of sectoral change in labour demand on labour market outcomes conditional on employment – continuous explanatory variable.

Monthly labour earnings Log hourly wage Weekly working hours
Coefficient Std. err. Coefficient Std. err. Coefficient Std. err.
γ1991 236.67*** 17.465 0.344*** 0.019 −1.616*** 0.115
γ1992 555.22*** 44.625 0.619*** 0.035 −2.036*** 0.155
γ1993 836.44*** 43.908 0.789*** 0.023 −2.466*** 0.091
γ1994 1039.12*** 52.370 0.904*** 0.022 −2.675*** 0.168
γ1995 1140.30*** 56.602 0.968*** 0.024 −3.329*** 0.189
γ1996 1262.51*** 54.090 1.008*** 0.020 −3.285*** 0.149
γ1997 1364.63*** 59.236 1.051*** 0.015 −3.376*** 0.234
γ1998 1401.26*** 64.288 1.067*** 0.020 −3.689*** 0.249
γ1999 1439.72*** 67.219 1.072*** 0.020 −3.730*** 0.279
δ1991 56.178** 23.553 0.059* 0.030 0.325 0.196
δ1992 96.995 66.669 0.069 0.064 0.367 0.250
δ1993 70.426 55.417 0.048 0.046 −0.203 0.160
δ1994 122.53* 56.189 0.068 0.042 −0.179 0.200
δ1995 99.922 66.468 0.054 0.046 −0.508 0.289
δ1996 104.52* 50.743 0.058 0.035 −0.490** 0.202
δ1997 94.789 60.043 0.033 0.021 −0.607** 0.210
δ1998 74.104 58.136 0.027 0.029 −0.662** 0.250
δ1999 78.035 74.377 0.052** 0.020 −1.137** 0.390
age2 −0.152 0.220 −0.000 0.000 0.000 0.003
age3 −0.000 0.013 0.000 0.000 −0.000 0.000
age4 −0.000 0.001 −0.000 0.000 −0.00 0.000
Constant 427.38*** 29.943 1.226*** 0.016 41.569*** 0.234
R2 0.493 0.551 0.051
No. of observations 8601 8601 8601
No. of groups 1296 1296 1296

  1. Fixed effects estimates based on SOEP waves 1990 to 1999; the sample restrictions described on p. 12 above are applied; fixed effects estimates; the explanatory variable ranges from −0.76 (employment change for “Agriculture, forestry, fishery”) to 1.49 (employment change for “Finance, insurance” from 1989 to 1993); monthly earnings and hourly wages are in DM and deflated to prices in 1995; standard errors are adjusted for clustering at the sector level; *denotes significance at the 10% level, **at the 5% level and ***at the 1% level.

Table A10:

Effect of sectoral change in labour demand on life satisfaction and mobility – continuous explanatory variable.

Life satisfaction Living in West Germany
Coefficient Std. err. Coefficient Std. err.
γ 1991 −0.566*** 0.077 0.005** 0.002
γ 1992 −0.563*** 0.050 0.017*** 0.017
γ 1993 −0.497*** 0.063 0.026*** 0.026
γ 1994 −0.538*** 0.054 0.029*** 0.029
γ 1995 −0.340*** 0.037 0.034*** 0.034
γ 1996 −0.476*** 0.076 0.035*** 0.035
γ 1997 −0.582*** 0.078 0.037*** 0.037
γ 1998 −0.581*** 0.090 0.035*** 0.035
γ 1999 −0.456*** 0.101 0.042*** 0.041
δ 1991 0.268** 0.097 0.000 0.002
δ 1992 0.205*** 0.057 −0.013 0.007
δ 1993 0.160 0.112 −0.014 0.008
δ 1994 0.148 0.082 −0.013 0.010
δ 1995 0.026 0.035 −0.018* 0.009
δ 1996 −0.061 0.111 −0.020** 0.007
δ 1997 −0.029 0.104 −0.017* 0.009
δ 1998 −0.109 0.122 −0.022** 0.010
δ 1999 −0.109 0.153 −0.020* 0.011
age2 −0.003*** 0.001 0.000** 0.000
age3 0.000*** 0.000 0.000 0.000
age4 0.000*** 0.000 −0.000*** 0.000
Constant 6.855*** 0.077 0.001 0.008
R2 0.003 0.010
No. of observations 1326 11,366
No. of groups 1296 1296

  1. Fixed effects estimates based on SOEP waves 1990 to 1999; the sample restrictions described on p. 12 above are applied; fixed effects estimates; the explanatory variable ranges from −0.76 (employment change for “Agriculture, forestry, fishery”) to 1.49 (employment change for “Finance, insurance” from 1989 to 1993); standard errors are adjusted for clustering at the sector level; *denotes significance at the 10% level, **at the 5% level and ***at the 1% level.

Table A11:

Effect of sectoral change in labour demand on political orientations – continuous explanatory variable.

Identifying with a political party Identifying with right-wing political party Identifying with left-wing political party
Coefficient Std. err. Coefficient Std. err. Coefficient Std. err.
γ1993 −0.048 0.028 0.032** 0.012 0.019*** 0.005
γ1994 −0.036 0.023 0.025** 0.011 0.013 0.011
γ1995 −0.004 0.026 0.015 0.011 0.032*** 0.008
γ1996 −0.026 0.026 0.014 0.008 0.058** 0.021
γ1997 −0.040 0.028 0.032* 0.015 0.032** 0.013
γ1998 −0.066** 0.028 0.022* 0.011 0.033** 0.013
γ1999 0.012 0.019 0.015 0.012 0.040** 0.013
δ1993 0.016 0.033 −0.018 0.018 −0.004 0.009
δ1994 0.050* 0.025 −0.001 0.021 −0.059** 0.022
δ1995 −0.064** 0.027 −0.008 0.018 −0.028 0.019
δ1996 −0.021 0.033 −0.005 0.013 −0.030 0.044
δ1997 −0.004 0.038 −0.004 0.031 −0.065** 0.027
δ1998 −0.054 0.039 −0.007 0.011 −0.072* 0.033
δ1999 0.024 0.029 −0.010 0.013 −0.046*** 0.011
age2 −0.000 0.000 −0.000 0.000 −0.000 0.000
age3 0.000*** 0.000 0.000 0.000 0.000 0.000
age4 0.000 0.000 0.000 0.000 0.000 0.000
Constant 0.356*** 0.017 0.005 0.007 0.085*** 0.016
R2 0.029 0.005 0.012
No. of observations 8793 2845 2845
No. of groups 1294 804 804

  1. Fixed effects estimates based on SOEP waves 1992 to 1999; the sample restrictions described on p. 12 above are applied; fixed effects estimates; the explanatory variable ranges from −0.76 (employment change for “Agriculture, forestry, fishery”) to 1.49 (employment change for “Finance, insurance” from 1989 to 1993); standard errors are adjusted for clustering at the sector level; *denotes significance at the 10% level, **at the 5% level and ***at the 1% level.

References

Acemoglu, D. and Restrepo, P. (2018). Artificial intellicenge, automation and work, NBER Working Paper 24196, Massachusetts.10.3386/w24196Search in Google Scholar

Akerlof, G.A., Rose, A.K., Yellen, J.L., and Hessenius, H. (1991). East Germany in from the cold: the economic aftermath of the currency union. Brookings Pap. Econ. Activ. 1: 1–105, https://doi.org/10.2307/2534638.Search in Google Scholar

Arntz, M., Gregory, T., and Zierahn, U. (2019). Digitalization and the future of work: macroeconomic consequences, IZA Discussion Paper 12428: Bonn.10.2139/ssrn.3411981Search in Google Scholar

Arzheimer, K. (2015). The AfD: finally a successful right-wing populist Eurosceptic party for Germany? W. Eur. Polit. 38: 535–556, https://doi.org/10.1080/01402382.2015.1004230.Search in Google Scholar

Autor, D.H., Dorn, D., and Hanson, G. (2013). The China syndrome: local labor market effects of import competition in the United States. Am. Econ. Rev. 103: 2121–2168, https://doi.org/10.1257/aer.103.6.2121.Search in Google Scholar

Autor, D.H., Dorn, D., Hanson, G., and Song, Jae. (2014). Trade adjustment: worker-level evidence. Q. J. Econ. 129: 1799–1860, https://doi.org/10.1093/qje/qju026.Search in Google Scholar

Autor, D.H., Dorn, D., Hanson, G., and Majlesi, K. (2020). Importing political polarization? The electoral consequences of rising trade exposure. Am. Econ. Rev. 110: 3139–3183, https://doi.org/10.1257/aer.20170011.Search in Google Scholar

Balcells Ventura, L. (2006). Trade openness and preferences for redistribution: a cross-national assessment of the compensation hypothesis. Bus. Polit. 8: 1–50, https://doi.org/10.2202/1469-3569.1131.Search in Google Scholar

Black, S.E., Devereux, P.J., and Salvanes, K.G. (2015). Losing heart? The effect of job displacement on health. Ind. Labor Relat. Rev. 68: 833–861, https://doi.org/10.1177/0019793915586381.Search in Google Scholar

Braakmann, N. (2018). Company closures and the erosion of the political centre: evidence from Germany. Br. J. Ind. Relat. 56: 835–858, https://doi.org/10.1111/bjir.12292.Search in Google Scholar

Brunner, E., Ross, S.L., and Washington, E. (2011). Economics and policy preferences: causal evidence of the impact of economic conditions on support for redistribution and other ballot proposals. Rev. Econ. Stat. 93: 888–906, https://doi.org/10.1162/rest_a_00088.Search in Google Scholar

Burda, M. and Hunt, J. (2001). From reunification to economic integration: productivity and the labor market in Eastern Germany. Brookings Pap. Econ. Activ. 2: 1–71, https://doi.org/10.1353/eca.2001.0016.Search in Google Scholar

Cameron, D.R. (1978). The expansion of the public economy: a comparative analysis. Am. Polit. Sci. Rev. 72: 1243–1261, https://doi.org/10.2307/1954537.Search in Google Scholar

Colantone, I. and Stanig, P. (2018). The trade origins of economic nationalism: import competition and voting behavior in Western Europe. Am. J. Polit. Sci. 62: 936–953, https://doi.org/10.1111/ajps.12358.Search in Google Scholar

Couch, K.A. and Placzek, D.W. (2010). Earnings losses of displaced workers revisited. Am. Econ. Rev. 100: 572–589, https://doi.org/10.1257/aer.100.1.572.Search in Google Scholar

Dauth, W., Findeisen, S., and Suedekum, J. (2017a). Trade and manufacturing jobs in Germany. Am. Econ. Rev. 107: 337–342, https://doi.org/10.1257/aer.p20171025.Search in Google Scholar

Dauth, W., Findeisen, S., Suedekom, J., and Wößner, N. (2017b). German robots – the impact of industrial robots on workers, IAB-Discussion Paper 30/2017, Nürnberg.Search in Google Scholar

Dippel, C., Gold, R., and Heblich, S. (2015). Globalization and its (dis-)content: trade shocks and voting behavior, NBER Working Paper Series 21812, Massachusetts.10.3386/w21812Search in Google Scholar

Edin, P.A., Evans, T., Graetz, G., Hernnäs, S., and Michaels, G. (2019). Individual consequences of occupational decline, CEP Discussion Paper 1629, London.10.2139/ssrn.3415780Search in Google Scholar

El-Sahli, Z. and Upward, R. (2017). Off the waterfront: the long-run impact of technological change on dockworkers. Br. J. Ind. Relat. 55: 225–273, https://doi.org/10.1111/bjir.12224.Search in Google Scholar

Erikson, R., Henning, M., and Otto, A. (2016). Industrial and geographical mobility of workers during industry decline: the Swedish and German shipbuilding industries 1970–2000. Geoforum 75: 87–98.10.1016/j.geoforum.2016.06.020Search in Google Scholar

Fackler, D., Müller, S., and Stegmaier, J. (2017). Explaining wage losses after job displacement: employer size and lost firm rents, IWH Discussion Papers 32, Halle (Saale).Search in Google Scholar

Fackler, D. and Rippe, L. (2017). Losing work, moving away? Regional mobility after job loss. Lab. Travail. 31: 457–479, https://doi.org/10.1111/labr.12106.Search in Google Scholar

Fackler, D., Stegmaier, J., and Weigt, E. (2019). Does extended unemployment benefit duration ameliorate the negative employment effects of job loss. Lab. Econ. 59: 123–138, https://doi.org/10.1016/j.labeco.2019.03.001.Search in Google Scholar

Fieldhouse, E. and Hollywood, E. (1999). Life after mining: hidden unemployment and changing patterns of economic activity amongst miners in England and Wales, 1981–1991. Work Employ. Soc. 13: 483–502, https://doi.org/10.1017/s0950017099000331.Search in Google Scholar

Frey, C.B. and Osborne, M.A. (2017). The future of employment: how susceptible are jobs to computerisation? Technol. Forecast. Soc. Change 114: 254–280, https://doi.org/10.1016/j.techfore.2016.08.019.Search in Google Scholar

Fuchs-Schündeln, N. and Masella, P. (2016). Long-lasting effects of socialist education. Rev. Econ. Stat. 98: 428–441, https://doi.org/10.1162/rest_a_00583.Search in Google Scholar

Grünert, H. (1997). Starrheit und Mobilität im Beschäftigungssystem der DDR, Der Hallesche Graureiher 5, Forschungsberichte des Instituts für Soziologie. Martin-Luther-Universität Halle-Wittenberg.Search in Google Scholar

Heske, G. (2005). Bruttoinlandsprodukt, Verbrauch und Erwerbstätigkeit in Ostdeutschland 1970–2000: neue Ergebnisse einer volkswirtschaftlichen Gesamtrechnung. Hist. Soc. Res. 17: 1–336.Search in Google Scholar

Hunt, J. (2000). Why do people still live in East Germany? DIW Discussion Papers 201, Berlin.10.3386/w7564Search in Google Scholar

Hunt, J. (2001). Post-unification wage growth in East Germany. Rev. Econ. Stat. 83: 190–195, https://doi.org/10.1162/rest.2001.83.1.190.Search in Google Scholar

Hunt, J. (2002). The transition in East Germany: when is a ten-point fall in the gender wage gap bad news? J. Labor Econ. 20: 148–169, https://doi.org/10.1086/323935.Search in Google Scholar

Hunt, J. (2006). Staunching emigration from East Germany: age and the determinants of migration. J. Eur. Econ. Assoc. 4: 1014–1037, https://doi.org/10.1162/jeea.2006.4.5.1014.Search in Google Scholar

Jacobson, L.S., LaLonde, R.J., and Sullivan, D.G. (1993). Earnings losses of displaced workers. Am. Econ. Rev. 83: 685–709.10.17848/wp92-11Search in Google Scholar

Kambourov, G. and Manovskii, I. (2009). Occupational specificity of human capital. Int. Econ. Rev. 50: 63–115, https://doi.org/10.1111/j.1468-2354.2008.00524.x.Search in Google Scholar

Kassenboehmer, S.C. and Haisken-DeNew, J.P. (2009). You’re fired! The causal negative effect of entry unemployment on life satisfaction. Econ. J. 119: 448–462, https://doi.org/10.1111/j.1468-0297.2008.02246.x.Search in Google Scholar

Kirchgässner, G. (2017). Voting and popularity, the Oxford handbook of public choice 2. Oxford: Oxford University Press.Search in Google Scholar

Kroh, M., Kühne, S., and Siegers, R. (2017). Documentation of sample sizes and panel attrition in the German socio-economic Panel (SOEP) (1984 until 2015), SOEP Survey Papers 408, Berlin.Search in Google Scholar

Lachowska, M., Mas, A., and Woodbury, S.A. (2018). Sources of displaced workers’ long-term earnings losses. Am. Econ. Rev. 110: 3231–3266.10.1257/aer.20180652Search in Google Scholar

Liepmann, H. (2018). The impact of a negative labor demand shock on fertility – evidence from the fall of the Berlin Wall. Lab. Econ. 54: 210–224, https://doi.org/10.1016/j.labeco.2018.07.003.Search in Google Scholar

Liepmann, H. and Müller, D. (2018). A proposed data set for analyzing the labor market trajectories of East Germans around reunification, FDZ Methodenreport 03/2018, Nürnberg.Search in Google Scholar

Ludwig, U. (2010). Aus zwei Volkswirtschaften mach eine – Strukturelle Brüche und Disparitäten im Aufholprozess der ostdeutschen Transformationswirtschaft. In: Institut für Wirtschaftsforschung (Ed.), 20 Jahre deutsche Einheit: Von der Transformation zu europäischen Integration. Tagungsband, IWH-Sonderheft 3/2010, pp. 115–133, Halle (Saale).Search in Google Scholar

Lutz, B., Nickel, H.M., Schmidt, R., and Sorge, A. (Eds.). (1996). Arbeit, Arbeitsmarkt und Betriebe. Opladen: Leske und Budrich. München.10.1007/978-3-322-91437-8Search in Google Scholar

Möller, J. and Schmillen, A. (2012). Distribution and determinants of lifetime unemployment. Lab. Econ. 19: 30–47.10.1016/j.labeco.2011.06.013Search in Google Scholar

Müller, S., Stegmaier, J., and Yi, M. (2017a). Transferability of skills across sectors and heterogeneous displacement costs. Am. Econ. Rev. 107: 32–336.10.1257/aer.p20171024Search in Google Scholar

Müller, S., Stegmaier, J., and Yi, M. (2017b). Industry mix, local labor markets, and the incidence of trade shocks. mimeo. Available at: https://www2.gwu.edu/∼iiep/waits/documents/yi_industrymix.pdf.Search in Google Scholar

Neal, D. (1995). Industry-specific human capital: evidence from displaced workers. J. Labor Econ. 13: 653–677, https://doi.org/10.1086/298388.Search in Google Scholar

OECD. (2019). The future of work: OECD employment outlook 2019. Paris: OECD Publishing.Search in Google Scholar

Schmieder, J.F., von Wachter, T. and Bender, S. (2010). The long-term impact of job displacement in Germany during the 1982 recession on earnings, income, and employment, IAB Discussion Paper 1/2010, Nürnberg.Search in Google Scholar

Schmieder, J.F., von Wachter, T., and Heining, J. (2018). The costs of job displacement over the business cycle and its sources: evidence from Germany. mimeo. Available at: http://www.econ.ucla.edu/tvwachter/papers/Jobloss_wp_2019_11_10.pdf.10.3386/w30162Search in Google Scholar

Schneider, J. (2017). Einigkeit und Recht und Freiheit. 25 Jahre deutsche Wiedervereinigung (1990-2015). Eine ordnungstheoretische Analyse. Stuttgart: Franz Steiner Verlag.10.25162/9783515118484Search in Google Scholar

Schupp, J. and Wagner, G.G. (1990). Die DDR-Stichprobe des Soziooekonomischen Panels (SOEP): Konzept und Durchführung der “Basiserhebung 1990” in der DDR, DIW Discussion Papers 15. Berlin.Search in Google Scholar

Statistisches Bundesamt (1994). Erwerbstätige 1950 bis 1989, Sonderreihe mit Beiträgen für das Gebiet der ehemaligen DDR 14, Wiesbaden.Search in Google Scholar

Sullivan, D. (2010). Empirical evidence on occupation and industry specific human capital. Lab. Econ. 17: 567–580, https://doi.org/10.1016/j.labeco.2009.11.003.Search in Google Scholar

Sullivan, D. and von Wachter, T. (2009). Job displacement and mortality: an analysis using administrative data. Q. J. Econ. 124: 1265–1306, https://doi.org/10.1162/qjec.2009.124.3.1265.Search in Google Scholar

Syverson, C. (2011). What determines productivity? J. Econ. Lit. 49: 326–365, https://doi.org/10.1257/jel.49.2.326.Search in Google Scholar

Upward, R. and Wright, P. (2019). Don’t look down: new evidence on job loss in a flexible labour market. Economica 86: 166–200, https://doi.org/10.1111/ecca.12254.Search in Google Scholar

Wagner, G.G., Frick, J.R., and Schupp, J. (2007). The German socio-economic panel study (SOEP): scope, evolution and enhancements. Schmollers Jahrb. 127: 139–169.10.2139/ssrn.1028709Search in Google Scholar

Winship, C., and Radbill, L. (1994). Sampling weights and regression analysis. Socio. Methods Res. 23: 230–257.10.1177/0049124194023002004Search in Google Scholar
Received: 2019-11-01
Accepted: 2020-10-20
Published Online: 2021-01-14
Published in Print: 2021-04-27

© 2020 Walter de Gruyter GmbH, Berlin/Boston

From the journal
Jahrbücher für Nationalökonomie und Statistik
Jahrbücher für Nationalökonomie und Statistik
Volume 241 Issue 2

Journal and Issue

Articles in the same Issue

Frontmatter
Original Articles
Measuring Tolerant Behavior
Returns to Scale as an Established Scaling Indicator: Always a Good Advisor?
The Nonlinear Dynamics of Corporate Bond Spreads: Regime-Dependent Effects of their Determinants
The Effect of Rapid Structural Change on Workers
Data Observer
BAuA-Working Time Survey (BAuA-WTS; BAuA-Arbeitszeitbefragung)
Downloaded on 30.11.2023 from https://www.degruyter.com/document/doi/10.1515/jbnst-2019-0067/pdf
Scroll to top button