Stigma effects of layoffs?

Economics Letters 64 (1999) 241–247

Stigma effects of layoffs? Evidence from German micro-data Christian Grund* University of Bonn, Betriebswirtschaftliche Abteilung II, Adenauerallee 24 – 42, D-53113 Bonn, Germany Received 7 July 1998; accepted 24 March 1999

Abstract Based on a signaling model of Gibbons and Katz (1991; Journal of Labor Economics 9, 351–380) this paper compares the development of the wages of dismissed employees to those who lost their job because of plant closings. In contrast to the US and Canada there is no evidence for a stigma effect of layoffs in Germany. Some explanations of the divergent results are pointed out.  1999 Elsevier Science S.A. All rights reserved. Keywords: Stigma effect; Signaling; Layoff; Plant closing; Labor-turnover JEL classification: J63; J65

1. Introduction There are several theories explaining the connection between job changes and future wage gains or losses (for example the theory of human capital, the job search theory and the matching theory) as well as corresponding empirical studies. But only a few authors distinguish between the different causes of displacements such as Gibbons and Katz (1991), Doiron (1995) and Stevens (1997). In these articles laid off employees are distinguished from those who lost their job because of plant closings (closed down employees). The papers analyze a possible stigma effect of dismissals. In the seminal article of Gibbons and Katz (1991) laid off employees are stigmatized, because the dismissal acts as a signal of below average productivity of the laid off workers. Hence, their post-displacement wages will be lower than the corresponding wages of closed down employees 1 . Gibbons and Katz show that this theoretical result can be confirmed empirically. There have been a lot of job losses in Germany during the 1990s. The yearly average of displaced *Tel.: 1 49-228-73-9213; fax: 1 49-228-73-9210. E-mail address: [email protected] (C. Grund) 1 See the original article of Gibbons and Katz (1991) for the detailed model. 0165-1765 / 99 / $ – see front matter PII: S0165-1765( 99 )00091-9

 1999 Elsevier Science S.A. All rights reserved.

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workers amounts to 6%. From 1991 to 1996; each year 1.4–2.4 million employees are affected by dismissals or plant closings especially because of the high dynamic of the East German labor market (Boerner and Schramm, 1998). However, no research exists that distinguishes between layoffs and plant closings for Germany. The aim of this paper is to prove the possible existence of a stigma effect of layoffs for Germany. A short summary of the empirical results of the US and Canadian studies is given in Section 2. In Section 3 these results are compared with the German evidence. The paper closes with a discussion of the results.

2. Empirical results for the US and Canada Gibbons and Katz (1991) test their model with data of the Displaced Worker Survey (DWS). Estimating OLS-regressions, they find that the predicted stigma effect is observable for white collar, but not for blue collar workers. The laid off white collar workers have 5.5% higher wage losses (and 25% longer unemployment spells) than white collars displaced by plant closings. Gibbons and Katz explain this divergence between blue and white collar workers with the fact that trade unions reduce the employers’ discretion over whom to layoff, and blue collars are more likely union members. Doiron (1995) confirms the main results of Gibbons and Katz for the Canadian Survey of Displaced Workers. But she is able to show that union membership does not influence the results. Hence, Doiron suspects that the productivity of blue collars is more difficult to observe. Additionally, Doiron argues that there could be more general regulations at the blue collars’ labor market. Finally, Stevens (1997) distinguishes between layoffs and plant closings using longitudinal data from the Panel Study of Income Dynamics (PSID 1968–1988). The higher short-term wage losses for laid off workers are confirmed, although wage losses are observable already 1 year before plant closings. This implies a weakening of the stigma effect of the layoffs. There is no corresponding study for Germany, even though the distinction between layoffs and plant closings has been made in the German Socio-Economic Panel (GSOEP) since 1991.

3. The German evidence The following study uses data from the German Socio-Economic Panel covering the years 1991–1996. Only full-time male employees between 20 and 60 years who lost their job because of a layoff or a plant closing, and who were re-employed in another firm 1 year later are considered for this analysis. These men can be separated from other job leavers (e.g. because of quits, (pre-) retirements, intrafirm transfers or the ending of time-limited work contracts) by a self assignment to one of these job leaving categories. Additionally, their pre- and post-displacement wages had to be at least 900 DM per month in 1991 prices (all wages are deflated with the German consumer’s price index). The sample consists of 204 laid off and 164 closed down employees. The analysis focuses on the average gross pre- and post-displacement wages. Table 1 shows that differences in the rates of wage changes between layoffs and plant closings are very small. Average real wage gains for East German displaced workers are observable. This implies the question why employees do not quit to get higher wages at another job. This question can easily be solved by a

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Table 1 Average gross pre- and post-displacement monthly wages a Whole sample

Pre-displacement wage Post-displacement wage Change rate Sample size a

East Germany

West Germany

Layoff

Plant closing

Layoff

Plant closing

Layoff

Plant closing

3079

3238

2447

2443

3762

4121

3101

3259

2567

2621

3679

3981

1 0.007 204

1 0.006 164

1 0.049 106

1 0.073 85

2 0.022 98

2 0.034 79

Wages in German Mark (DM) of 1991. Source: GSOEP (waves 8–13), own calculations.

closer look into the data. Generally, high wage increases in East Germany during the first years after the unification can be observed. It must be taken into account that the corresponding stayers and quitters realize even higher average real wage gains in East Germany ( 1 0.09 and 1 0.17, respectively). In contrast, absolute wage losses for West German displaced employees become clear. Nevertheless, these reductions are much smaller than the wage losses of displaced workers in the US and Canada (Gibbons and Katz, 1991, p. 362; Doiron, 1995, p. 904). These results may change if we control for possible differences in labor market relevant characteristics of both groups. The main difference of both groups is the average pre-displacement tenure of laid off workers (6.05 years) and closed down workers (9.89 years). The following OLS-regression with the log of pre-displacement wages (ln wage(t 2 1)), the log of post-displacement wages (ln wage(t)) and the change in log wages (Dln wage) as dependent variables are estimated to control for these characteristics (see the note in Table 2 for the independent variables). Table 2 only shows the estimated coefficients of the layoff-dummy and hence the relative difference in wages (change of wages) to employees displaced by plant closings. The model of Gibbons and Katz predicts that pre-displacement wage differences do not exist. But laid off employees should have lower post-displacement wages. However, this predicted stigma effect of layoffs is not observable in the German data. In contrast to the American studies no significant differences between both groups of displaced employees can be revealed. Only a weak tendency of the stigma effect for East Germans and blue collars is noticable. But these results do not differ significantly from zero, either. Remember that the stigma effect has been shown for white collars, but just not for blue collars in the US and Canada. The result for Germany is robust in respect to other estimations without the unemployment duration variable. Stevens (1997) analyzes a longer term wage development of US displaced workers. To investigate a possible wage reduction before the displacement, 1 more year before and after the displacement is analyzed now. The limitation of only two further years is due to the trade-off between added years and the sample size. Table 3 shows the average gross monthly wages and the yearly change rates of the reduced sample. Real wages of both groups decrease only directly after the displacement. ‘Normal’ increasing rates are observable for the years before and after. In contrast to the model of Gibbons and Katz the wage losses of closed down employees after the displacement are somewhat higher. The results of the regressions (Table 4) confirm that no stigma effect is observable for

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Table 2 Estimated coefficients of the layoff-dummy (absolute T values in parenthesis)a Dependent variable

Whole sample (n 5 368) West Germany (n 5 177) East Germany (n 5 191) Blue collars (n 5 259) White collars (n 5 109)

ln wage(t 2 1)

ln wage(t)

Dln wage

2 0.017 (0.508) 2 0.020 (0.414) 2 0.000 (0.000) 2 0.003 (0.085) 0.016 (0.222)

2 0.025 (0.850) 2 0.005 (0.116) 2 0.030 (0.718) 2 0.037 (1.074) 0.019 (0.301)

2 0.008 (0.249) 0.016 (0.348) 2 0.030 (0.678) 2 0.033 (0.840) 0.003 (0.045)

a

Dependent variables: Dln wage 5 ln(wage(t) / wage(t 2 1)); ln wage(t 2 1) 5 log of gross monthly wages before the displacement; ln wage(t) 5 log of gross monthly wages after the displacement. Independent variables: years of schooling, potential years of experience (age 6 years of schooling) and its square, years of previous tenure, months of unemployment in last 2 years, dummies for East Germany, foreign employees, job in industrial sector, occupational position (11), firm size (5), year of displacement (6) and the layoff dummy (as reason for displacement). For example, the adjusted R 2 on the ln wage(t 2 1) and ln wage(t) regression for the whole sample is 0.58 while it is only 5.3% for the Dln wage regression. Source: GSOEP (waves 8–13), own calculations.

Table 3 Development of wages before (t 2 2 and t 2 1) and after (t and t 1 1) the displacement (average gross monthly wage, in 1991 DM) and yearly change rates a Layoffs Wage(t 2 2) Wage(t 2 1) Wage(t) Wage(t 1 1) Sample size a

3613 3768 3662 3778

1 0.043 2 0.028 1 0.032

Plant closings

Stayer

3835 3985 3735 3833

3767 3905 3989 4081

79

1 0.039 2 0.063 1 0.026 71

1 0.037 1 0.022 1 0.023 8126

Source: GSOEP (waves 8–13), own calculations.

Table 4 Estimated coefficients of the layoff-dummy (absolute T values in parenthesis)a Dependent variable

Coefficient T value R 2adj. a

ln wage(t 2 2)

ln wage(t 2 1)

ln wage(t)

ln wage(t 1 1)

0.013 (0.226) 0.661

0.027 (0.484) 0.593

0.036 (0.827) 0.699

0.035 (0.779) 0.677

ln wage(t 2 2) 5 log of monthly gross wage 2 years before the displacement, etc. The same specification as the regressions of Table 2 is estimated. Source: GSOEP (waves 8–13), own calculations.

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Germany. The coefficients of the layoff-dummy is even positive after the displacement and higher than before. The GSOEP includes some questions concerning the comparison of pre- and post-displacement jobs regarding a number of job-relevant characteristics (e.g. work load, distance to the workplace, fringe benefits) and job satisfaction in general. There is no evidence for a stigma effect of layoffs compared with plant closings in these dimensions as well as shown in Grund (1998). The general results are unambiguous. Nevertheless some cautionary advice is necessary. A sample selection bias is possible, because only 50% of all displaced workers were re-employed 1 year later and responded to the panel questions again. Besides, the sample size is smaller and the data are inferior to the US and Canadian data samples in some aspects.

4. Discussion The main result of this study is that no stigma effect of layoffs compared with plant closings is observable for Germany in contrast to the US and Canada. Therefore, two further questions have to be discussed: firstly, ‘‘how can the German findings be explained?’’ And, secondly, ‘‘why do they differ from the US and Canadian findings?’’. First of all, there are some general arguments against (or a possible equalization of) the discussed stigma effect. Layoffs due to mismatches are possible. In this case, a laid off employee may be more productive in another firm. Layoffs due to employers’ opportunism are also possible, for example to appropriate wage bonds. These two aspects are arguments against a subaverage productivity of laid off employees. On the other hand, plant closings of big firms can strain regional labor markets when many persons with similar qualifications are searching for a new job at the same time. This may imply considerable wage losses for the closed down workers if they are not perfectly mobile. Additionally, advanced notifications of plant closings may lead to early quits of the more productive employees. This would lead to a decreasing average productivity among the closed down employees. Especially, their productivity may no longer be higher than the productivity of laid off workers. There are also some arguments to explain the different results between the American studies and my findings. The previous aspect may already be more important for Germany because employers have to consult threatening plant closings with the works councils timely and extensively (§111 Betriebsverfassungsgesetz – industrial constitution law of 1972). That is why employees are informed about plant closings very early, so especially the more productive employees have the possibility to get a new job in another firm before the plant closing. Hence, this enlarges the possibility for Germany that closed down employees become stigmatized, too. Furthermore, relatively high unemployment benefits in Germany lead to high acceptance wages also for laid off employees in the framework of the job search theory. This may be one of the causes why they will prefer to stay unemployed for an additional period instead of accepting perceptible wage losses. The prevailing German collective bargaining contracts are a reason for the rigidity of wages between the firms. These agreements are in force for non-members of the trade unions, too. Thus, German firms have less discretion on the wages they pay than companies in the US. Finally, there exists a prescription for a social choice of operational displacements with the criteria tenure, age and maintenance in Germany (§1(3) Kuendigungsschutzgesetz). But particularly important

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employees are excepted from this prescription. Hence, the influence on the results seems to be negligible. The arguments presented above may be able to explain the results for Germany and the differences from the US and Canada. The deviations seem to be rooted in the differences on labor market institutions. The answer to the question whether and to what extent laid off as well as closed down employees are both stigmatized in respect to other groups could be an exciting task for further work.

Acknowledgements I am grateful for helpful comments from Silke Becker, Guido Bulmahn, Matthias Kraekel, Gunter Steiner, Frank Westermann and an anonymous referee.

Table A.1 Means and shares of wage relevant variables of laid off and closed down employees, respectively (standard deviation in parenthesis)a Variable

Layoffs

Plant closing

Years of schooling Years of potential experience Years of pre-displacement tenure Months of unemployment during the last 2 years East German Foreign Job in industrial sector Blue collar workers

11.33 (2.36) 19.98 (10.34) 6.05 (7.88) 1.59 (2.69)

11.58 (1.91) 21.20 (9.74) 9.89 (9.23) 1.11 (2.47)

0.52 0.20 0.56 0.69

0.52 0.16 0.63 0.71

Firm size 1 (less than five employees) 2 (5–19 employees) 3 (20–199 employees) 4 (200–1999 employees) 5 (2000 and more employees)

0.06 0.33 0.32 0.22 0.08

0.10 0.29 0.30 0.19 0.13

Sample size

204

164

a

Source: GSOEP (waves 8–13), own calculations.

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Table A.2 Average gross pre- and post-displacement monthly wages of quitters and corresponding stayers a Stayer

Wage(t 2 1) Wage(t) Change rate Sample size a

Quitter

All

West

East

All

West

East

3748 3863 1 0.031 13259

4102 4180 1 0.019 10134

2599 2834 1 0.090 3125

3378 3720 1 0.101 626

3843 4142 1 0.078 416

2445 2874 1 0.174 208

Wages in German Mark (DM) of 1991. Source: GSOEP (waves 8–13), own calculations.

References Boerner, S., Schramm, F., 1998. Fluktuationsneigung in den neunziger Jahren: Eine empirische Untersuchung anhand des ¨ Personalforschung 12 (1998), 79–97. Sozio-oekonomischen Panels. Zeitschrift fur Doiron, D.J., 1995. Lay-offs as signals: the Canadian evidence. Canadian Journal of Economics 28 (1995), 899–913. Gibbons, R., Katz, L.F., 1991. Layoffs and lemons. Journal of Labor Economics 9 (1991), 351–380. ¨ die Bundesrepublik Deutschland Grund, C., 1998, Stigmatisierung von Arbeitnehmern durch Entlassungen? – Befunde fur anhand von Daten des sozio-oekonomischen Panels (SOEP). In: Backes-Gellner, U., Geil, L., Kraekel, M. (Eds.), Quantitative und Qualitative Personalanpassungsstrategien – Personaloekonomische Analysen ihrer Institutionellen Bedingtheit und ihrer Konsequenzen. Mering, Muenchen,1998, pp. 35–55. Stevens, A.H., 1997. Persistent effects of job displacement: the importance of multiple job losses. Journal of Labor Economics 15 (1997), 165–188.