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Downsizing and firm performance: Evidence from German firm data

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This article uses a unique data set to study the short-term effects of downsizing on operational and financial performance of large German firms. In general, productivity and profitability after downsizing are—at the best—comparable to their pre-downsizing levels. During the downsizing event, the performance even drops. Moreover, we make a distinction between firms downsizing because of a business downturn and firms downsizing to increase efficiency. Especially downsizing for the latter firms appears to be unsuccessful.
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Downsizing and firm performance:
Evidence from German firm data
1
Version: January 2015
Tim Goesaert
2
, Matthias Heinz
3
and Stijn
Vanormelingen
4
1
We thank Klaus Desmet, Kristof De Witte, Guido Friebel, Joep Konings, Michael Kosfeld, Nicky Rogge, Ilke Van
Beveren, Thierry Verdier and seminar participants in Frankfurt and Leuven for their comments.
2
KU Leuven, tim.goesaert@kuleuven.be
3
Goethe University Frankfurt, heinz@econ.uni-frankfurt.de
4
KU Leuven, stijn.vanormelingen@kuleuven.be
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Downsizing and firm performance:
Evidence from German firm data
Abstract: This paper uses a unique dataset to study the short term effects of downsizing on
operational and financial performance of large German firms. In general, productivity and
profitability after downsizing are – at the best – comparable to their pre-downsizing levels. During
the downsizing event, the performance even drops. Moreover we make a distinction between firms
downsizing because of a business downturn and firms downsizing to increase efficiency. Especially
downsizing for the latter firms appears to be unsuccessful.
JEL codes: G34, L19, L25, D24
1 Introduction
Early 2005, the Deutsche Bank reported at a press conference the highest profits for years.
Moreover, they announced a reorganization of the company including 6,400 layoffs in order to
achieve higher profitability in the future. In the same period, Opel announced a record loss and
decided to shed 12,000 jobs to decrease their production capacities and rescue the company. Both
CEOs stated that there was no alternative to their decision. The two firms face a different financial
situation, but share one common goal: to increase their performance through downsizing. How
effective is their plan? Do firms end up with better productivity and profitability after shedding
jobs?
Economic theory states several advantages and disadvantages to downsizing. On the one
hand, it can be expected that productivity will increase through a form of Schumpeterian creative
destruction (Schumpeter, 1942): redundant workers get eliminated, less productive workers
replaced with more productive ones and labor disciplines become more strengthened. On the other
hand, employment cutbacks may disrupt relationship networks in a company, destroy firm-specific
human capital and social contracts between employers and employees. This could potentially
undermine the morale of the workforce (Baumol, Blinder and Wolff, 2003; Dong and Xu, 2008;
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Drzensky and Heinz, forthcoming). Despite economic theory on the matter, the intense public
debate on downsizing
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and the importance of those questions for firms, workers, political decision
makers and the society as a whole, it is an open question whether firms really improve their
operational or financial performance after downsizing. Many empirical studies have tried to shed
light on this question, but the results are often contradictory.
The main reason for this inconsistency is the level of information that is needed to make
clear statements. First, one needs to have access to a detailed, preferably micro-level dataset,
containing various performance indicators. As firm-level datasets are becoming more widespread,
this first requirement has become less troublesome. The second, more complicated, issue lies in
identifying the downsizing firms. One approach, which is often used, is to define downsizing as a
drop in total annual employment. The main advantage of this procedure is its simplicity:
employment figures are readily available from company accounts. However, whether this change
in the number of workers truly reflects downsizing or other activities, such as mergers or spin-offs,
remains unclear. A second approach is to use announcements issued by the firms or reported in
the media or press on downsizing. This is the more informative option of the two. However, access
to these sources can be limited. There are other caveats: it could be that firms use statements on
layoffs as a signal to the capital market or to put pressure on various stakeholders, such as
governments and unions, but do not shed any jobs at all. This implies that careful monitoring and
examination of these announcements is necessary. One issue regarding both identification
strategies is that, quite often, no detailed information on the timing of the downsizing process is
available.
We contribute to the existing literature in two ways. First, we employ a micro-level dataset
of the 500 largest German firms. We calculate various indicators of a firm's operational and
5
According to Friebel and Heinz (2014), roughly two articles per day report on downsizing in Germany in Die
Welt, one of the leading national newspapers.
4
financial performance using firm-level company accounts from the commercial database
Amadeus.
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Second, by collecting roughly 50,000 articles from German newspapers we are able to
identify downsizing firms with great precision. The articles contain information on the timing of
the downsizing events and the number of jobs getting lost. In addition, they provide details
regarding the reason behind the downsizing decision. We are, to the best of our knowledge, the
first to analyze the short-term performance of downsizing firms in such a detailed and systematic
way. Overall, we find little evidence of an improvement in firm productivity and profitability. If
anything, these performance indicators seem to drop during the downsizing event, and certainly
do not surpass their pre-downsizing levels after the downsizing event. Differentiating on the reason
behind the downsizing decision, we obtain one subset of firms that have responded to a business
downturn and a second subsample of firms that have reduced their workforce to increase staff
efficiency. We note some differences between these two categories. Those firms that have tried to
increase their efficiency witness a drop in especially – the first year after downsizing, while the
drop in productivity as well as profitability during downsizing is found only for the firms
experiencing a business downturn. The status quo found for these firms after downsizing may
suggest that downsizing succeeded in preventing productivity declines, although profitability does
not fully recover.
The structure of the paper is as follows. We continue with a short literature overview on
other empirical work related to the effect of job cuts. Section 3 contains the identification
procedure of our data and the description of performance measures used in this study. Section 4
shows the empirical specification and the basic results. We present several robustness checks in
Section 5. The last section discusses our findings.
6
This database is published by Bureau Van Dijk.
5
2 Literature Overview
The effect of downsizing and layoffs is far from undocumented, but the scope of the research
is quite heterogeneous: attention has been given to all the stakeholders in the debate (see e.g.
Hallock, Strain and Webber, 2012, for an overview of the literature). A number of papers have
looked at the effect of downsizing on the displaced workers. These tend to have a higher likelihood
of future unemployment, experience significant long-term earnings losses and have a higher
incidence of health and family problems (Huttunen, Moen, and Salvanes, 2011; LaLonde and
Jacobson, 1993; Rege, Telle and Votruba, 2011; Schmieder, Wachter and Bender, 2009; Sullivan
and Von Wachter, 2009). Other studies focus on the performance of the downsizing firms.
This literature can broadly be classified into two separate categories.
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A first line of papers
has looked at the impact of up- or downsizing on productivity and profitability defined at the
aggregate industry-level. Baumol, Blinder and Wolff (2003) find that changes in the average
establishment or firm size has no effects on industry productivity. However, they report a positive
effect of downsizing on profitability. A second line of studies take on a more micro-oriented
approach and analyze the performance defined at the plant- or firm-level. Using census data of
manufacturing plants, Baily, Bartelsman and Haltiwanger (2001) provide evidence that US plants
which decreased employment exhibit significant greater procyclicality of productivity than other,
upsizing, firms. In other work, Baily, Bartelsman and Haltiwanger (1996) find that productivity
tends to decline in plants that are downsizing.
8
Friebel, McCullough and Padilla Angulo (2014)
study the impact of downsizing on firms in a single industry, the US railway sector. They show that
downsizing per se does not yield performance benefits. However, downsizing has a positive impact
7
A third, but smaller field, looks at case studies that investigate the effects of downsizing, like e.g. Dial and
Murphy (1995) for General Dynamics.
8
Baily, Bartelsman and Haltiwanger (1996) recognize the identification problem as well: “Identifying who did
and did not downsize and whether they were successful cannot be done with any precision on the basis of the
characteristics of the plants that are reported in the census data.”
6
on performance when accompanied by particular changes in the output mix.
9
Using Chinese plant-
level data across industries, Dong and Xu (2008) find that private firms that downsize end up with
lower total factor productivity, lower wages and unchanged profits. A synthesis on the research is
given in Datta, Guthrie, Basuil and Pandey (2010). The authors document additional
contradictions: some studies find a positive impact of downsizing on profitability (Chen, Mehrotra,
Sivakumar and Yu, 2001; Espahbodi, John and Vasudevan, 2000; Palmon, Sun and Tang, 1997);
other studies find no or even negative effects (Cascio, Young and Morris, 1997; De Meuse,
Vanderheiden and Bergmann, 1994; Guthrie and Datta, 2010).
To conclude, we note that a number of studies have analyzed how dismissed workers and
firms are affected by downsizing. While these studies show a clear negative impact on displaced
workers, there are contradicting findings on the effect of downsizing on firm performance. We
state that this can, in part, be explained by measurement error and differences in the identification
strategy. The approach we propose in this paper tries to shed more clarity on the identification
problem.
3 Data Description
3.1 Identifying Downsizing Firms
Our identification strategy is based on the examination of German newspaper articles
reporting on downsizing events between 2001 and 2007. In order to optimize this procedure, we
9
Related to this, there are also studies that investigate how single sectors or industries have managed to
increase their productivity. For example, Disney, Haskel and Heden (2003) analyze the productivity growth in
establishments in the UK manufacturing sector between 1980 and 1992. Their main finding is that productivity
growth comes mainly from more productive plants that enter the market, displacing less productive, exiting
plants. Similar results for the US retail trade sector in the 1990s were found by Foster, Haltiwanger and Krizan
(2006). Schmitz (2005) links the increase in productivity of US and Canadian iron ore producers in the early
1980s to changes in work practices.
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restrict our attention to the 500 largest German firms in terms of 2002 turnover, as these companies
receive coverage in the media. This selection is performed using firm-level company accounts from
the Amadeus database, a commercial dataset from Bureau van Dijk containing company accounts
of European firms. Although inclusion criteria for Amadeus can vary among countries, we feel that
this will not affect our selection greatly: German regulations are more flexible for small and medium
firms, leaving a better coverage level for larger firms.
10
From this sample, we exclude former state
enterprises that have been privatized (six firms, e.g. Deutsche Telekom, Deutsche Post), public
utility/state lottery companies (ten firms), investment/private equity companies (five firms) and
the Ruhrkohle.
11
In a next step, we check for the presence of downsizing events in these firms. This
identification is based on German newspaper articles reporting on these events, made available by
the media database LexisNexis.
12
We primarily employ the dataset from Friebel and Heinz (2014),
who collected downsizing events for use in a media content study. Using the same algorithm, we
expand their dataset to ensure that we correctly identify all downsizing firms.
13
A summary of this
procedure is available in Appendix A.1.
In total, including the articles used in Friebel and Heinz (2014), around 50,000 press articles
were checked. This strategy enables us to state that we only fail to identify a downsizing event if
there is absolutely no coverage in almost all German national and local newspapers, magazines and
agency reports or if all media misreport on downsizing within a specific firm. A further advantage
of our media content analysis is that we know which firms exit the market. Two main reasons
emerged: 13 firms went bankrupt and 42 firms were acquired by another firm and integrated in the
10
Private companies are legally not required to file any form of accounts. For publicly traded companies this is
not the case (Bureau Van Dijk, 2011)
11
The Ruhrkohle (RAG) is a highly subsidized holding company that owns most of the German coal mines in the
Ruhr area, founded in 1969 with the aim of closing the mines step by step. Besides the aforementioned firms
we had to exclude one company (Brau und Brunnen), which was reported two times due to a data error. No
irregularities were found for the other firms.
12
LexisNexis offers a large selection of German periodicals, such as journals or specialized magazines.
13
Note that we had to omit 15 firms as it was not clear whether they had really downsized.
8
company.
14
We omit these firms from the analysis. In addition, we are able to collect detailed
information on the downsizing events, i.e. the number of jobs getting lost in Germany and abroad
and the duration of the downsizing process. In the following, we define a downsizing firm as a
company that sheds at least 3% of its jobs in Germany in one downsizing event at a given point in
time.
15
We define the downsizing period as the full year(s) in which the companies shed some of
these jobs, starting from the day of announcement.
One additional control is performed. Firm performance is analyzed using unconsolidated
company accounts. The identification of downsizing events is based on the name of these firms,
but may also involve affiliates of these firms. Consequently, these are not represented in the
unconsolidated accounts. To what extent does this bias the identification strategy? We address this
concern by comparing, where possible, the job cuts mentioned in the articles with the effective
drop in employment, as stated in the unconsolidated company accounts from Amadeus, between
the start and end of the downsizing event. We find that there is a correlation of .88 between both
lists, which strengthen us in our belief that we are able to accurately identify downsizing firms and
the timing of the event. A limited number of downsizing firms reveal positive employment growth,
mainly because of the acquisition of new plants. These firms are dropped from the sample. To sum
up, after all these adjustments, we obtain a dataset of 380 companies, out of which 131 firms have
shed jobs in Germany between 2001 and 2007 and 249 did not.
16
3.2 Identifying Downsizing Reasons
14
Firm exits are observed both in downsizing as in non-downsizing firms.
15
Some firms were mentioned in the articles after shedding only a very small number of jobs. As we cannot be
sure whether the media will consistently report on these cases, we set the threshold value for a downsizing
event at 3%. The majority of studies define this threshold for a downsizing event at either 5% or at 3% (Guthrie
and Datta, 2010).
16
We also detected some firms with negative employment growth, based on Amadeus company accounts, that
have downsized after having acquired another firm. We keep these observations in our sample. We see no
change in our main qualitative results when this selection is excluded.
9
In a second step we identify the reasons firms list to motivate the decision to downsize.
Again, this will be based on an analysis of the media coverage of all downsizing firms. Our
classification follows the American Management Association (Cappelli, 2000; Greenberg, 1990).
We distinguish between business downturns, improved staff utilization and a miscellaneous
category. Business downturn refers to downsizing associated with a shortfall in demand. It includes
downsizing as a reaction to lower demand because of a slowdown of the economy, a weak demand
in the whole industry or the loss of a major customer.
17
Improved staff utilization is defined as a
reduction in jobs driven by the desire for operating efficiencies within the firm. It implies changes
in the output mix, the introduction of new production technologies or changes in the composition
of the labor force.
18
In addition, many of these firms mention that they dropped hierarchical levels
or merged locations and subsidiaries.
19
Firms that are classified in the miscellaneous category
downsized for other reasons, e.g. they relocated to other countries, shed jobs for more than one
reason or provided no additional details on their decision.
In Appendix A.2, we provide a full list of keywords that were used to identify the various
downsizing reasons in the press articles. In total, 80 firms downsize due to a shortfall in demand,
44 firms shed jobs to improve operating efficiency and 5 firms are classified in the miscellaneous
category.
20
In the remainder of the paper, we will mainly focus on firms that have downsized due
to a business downturn and the desire to improve staff utilization.
17
Firms justify this type of downsizing with expressions as overcapacities (i.e. Überkapazitäten), economic
slowdown (Konjunkturflaute), decline in turnover (Umsatzeinbruch), loss of major customers (Verlust von
Großkunden) or industry crisis (Branchenkrise).
18
Firms document this decision with expressions as group reorganization (Konzernumbau), increase in
efficiency (Effizienzsteigerungen) or administrative simplification (Verwaltungsaufwand senken).
19
Similar classifications have been used by Grosfeld and Roland (1995) and Friebel, McCullough and Padilla
Angulo (2014). Often, these categories are labeled in the economic literature as defensive versus offensive
downsizing (Cappelli, 2000). The former is in response to poor economic results and is predominantly
associated with a shortfall in demand; the latter is implemented to increase firm performance and is often the
consequence of a well-prepared management strategy.
20
According to a survey of the American Management Association from 1990, 55% of firms in the U.S. that
downsized reported a business downturn as their reason for downsizing and 24% wanted to improve their staff
utilization (Greenberg, 1990). Interestingly, these proportions are similar to ours.
10
Table 11 in Appendix A.3 provides an overview of the different industries that have referred
to a business downturn as a motivation for downsizing. We additionally present some specific
details about the economic situation in these industries that caused the firms to downsize. For
example, the largest group (15 firms) is the construction industry: after the reunification boom in
the German construction industry at the beginning of the 1990s, the industry relapsed into a long
recession which caused many firms to downsize. As a result, the number of employees in the
industry declined from 1.41 million (1995) to 0.71 million (2007) (Statistisches Bundesamt, 2012).
Table 12 provides more details on the firms that have shed jobs to improve their operating
efficiency: 19 firms dismantled hierarchies or improved administrative processes, 19 others merged
subsidiaries or reorganized the horizontal organizational structure of the firm in other ways and 13
firms reorganized the production process.
To ensure that the identification of the downsizing reasons is correct, we perform an
additional test. As in Friebel and Heinz (2014), we ran an experiment with 14 undergraduate
students from different fields of studies in the FLEX laboratory in Frankfurt.
21
Each of them
received a fixed payment of 10 euros for a job that took them on average less than one hour. We
confronted the students with 30 articles reporting on ten randomly chosen downsizing firms.
22
For
each of the firms, we presented three articles from three different German newspapers covering
the same downsizing event. Students were then confronted with our definition of the downsizing
reasons and were asked to identify the motivation behind the decision. This resulted in a
congruence of 93.5% between the classifications given by the students and our own, excluding the
“do not know” and “no statement possible” answers. Including them we still had a congruence of
90%.
21
Participants were recruited using the online recruiting system ORSEE® (Greiner 2004) and had no further
information on the research project.
22
The companies are Alcatel SEL, Armstrong DL, Balda, Deutsche Börse, Dyckerhoff, E-Plus International, E.on,
Heidelberger Druckmaschinen, Nordex and MVV. These articles were also used in our analysis the downsizing
reasons of the selected companies.
11
3.3 Analyzing Firm Performance
We relate the downsizing event to a number of firm level performance indicators. First, we
focus on indicators of operational efficiency, namely total factor productivity, labor productivity
and capital productivity. There exist a number of techniques to estimate total factor productivity,
most notably non-parametric methods such as Data Envelopment Analysis, Free Disposable Hull
and Index Numbers as well as parametric methods such as the Stochastic Frontier and various
methods designed to estimate production functions (f.e. Olley and Pakes, 1996). Van Biesebroeck
(2007) provides an overview of the most widely applied methods. For the main results, firm level
total factor productivity is computed using the index numbers method. This method allows for a
flexible and heterogeneous production technology and produces robust results when measurement
error is small. This can be expected for datasets in developed countries with narrowly defined
industries (Van Biesebroeck, 2007).
23
We do however perform a robustness check where we use
another methodology to estimate total factor productivity, namely non-parametric order-m
frontiers.
We calculate total factor productivity using the company accounts in Amadeus, which
provides a good coverage level for large, German firms. The multilateral index of TFP is based on
the methodology developed by Good, Nadiri and Sickles (1996). This index provides a consistent
comparison of firm productivity within a panel structure. Transitivity between any two firm-year
observations is guaranteed through the use of a single reference point, defined as the average firm
23
The main advantage of this method compared to parametric methods is that it allows for heterogeneous
production technologies for the different firms. Recall that the firms in our sample are taken from a wide range
of industries. If we would want to use parametric methods and estimate production functions to infer firm level
productivity, the most widely applied methodology are the semi-parametric estimators (for example Olley and
Pakes, 1996 and Levinsohn and Petrin, 2003). To implement these methods, we would need to include a
substantial number of firms in our estimation sample to estimate industry specific Cobb-Douglas production
functions. Although this is feasible, we would have to take the assumption that the large (downsizing) firms in
our dataset use the same production technology as the small(ler) firms in the industry.
12
(see Caves, Christensen and Diewert, 1982). We calculate this hypothetical firm by expanding our
sample with a selection of very large firms from the Amadeus database.
24
This allows us to calculate
the index for each two digit industry more accurately. Total factor productivity for firm i at time t
is then defined as:
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, labor and capital. These are then summed, using the
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of the firm and the reference point as weights.
25
We measure output as
deflated value added, labor as total number of employees and capital as the historical value of
deflated tangible fixed assets. All variables are retrieved from the Amadeus BvD company accounts.
Furthermore we define labor productivity as deflated value added per employee and capital
productivity as the ratio of deflated value added over deflated capital stock.
26
24
Following the definition in Amadeus, very large firms have at least 100 million euros operating revenue, 200
million euros total assets, 1,000 employees or are listed firms. As a robustness check, we expanded this
selection by introducing large German firms as well. These firms have at least 10 million euros operating
revenue, 20 million euros total assets or 150 employees. This yielded the same main qualitative results:
correlation between both measures is about .99.
25
We assume constant returns to scale and define the expenditure share for capital as 1 minus the expenditure
share of labor.
26
The deflators for the output and capital variables are obtained from the EU-KLEMS database and are defined
for most NACE 2-digit industries. We use the value added deflators and the gross fixed capital formation price
13
We also assess the impact of downsizing on profitability, measured by the EBITDA and
profit margin. The former has the advantage that it is less affected by financial or fiscal optimization
policies. The latter is defined as profit/loss after tax, includes extraordinary income and costs and
has the advantage to be more inclusive. These profitability measures are retrieved directly from
the company accounts. Finally, we relate downsizing as well with the average wage cost in the firm.
3.4 Descriptive Statistics
Table 1 and Table 2 conclude this section with a brief overview of our sample. After
accounting for missing values in the company accounts we are left with 285 firms in our sample,
of which 92 are identified as downsizing firms, 31 firms have downsized to increase their staff
efficiency and 59 firms downsized to face a business downturn. Roughly 50% of all firms in our
sample are manufacturing firms. It is clear that the sample is constructed using the 500 largest
German companies: the average firm employs on average 3,700 employees, has sales of 1.5 billion
euros and a profit margin of 5.74%.
Table 1 Descriptive Statistics: Full Sample
Variable Mean Std. dev. Number
Employees 3,716.78
14,365.20
1322
Tangible fixed assets 298,384.70
764,764.70
1322
Sales 1,468,914
5,377,367
1319
Turnover 1,551,934
5,567,148
1322
Value added 438,822.70
1,621,187
1322
EBITDA-margin 9.02
14.95
1311
Profit Margin 5.74
13.84
1218
Summary statistics are for all firm/year observations. Tangible fixed assets, sales, turnover and value added in
thousands of euros.
indices. Deflators are calculated for 32 industries. Detailed information is available at
http://www.euklems.net/index.html
14
Table 2 Descriptive Statistics: Types of Firms in the Sample
Variable Number
Manufacturing 140
Downsizing
Companies 92
Business Downturn 59
Staff Efficiency 31
Manufacturing firms have a two-digit classification (revision 1) between 15 and 37
4 Empirical Strategy and Results
4.1 Empirical Strategy
The effect of downsizing on firm i, active in sector k at time t is tested using the following
equation:
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An important part of our empirical strategy deals with the heterogeneity between downsizing
and non-downsizing firms. The decision to lay off workers may be influenced by some initial
characteristics of the firm that could, in turn, be correlated with its future performance. This seems
especially to be the case for the category of firms which listed a business downturn as main
motivation. We control for this selection bias by looking at the within-firm variation, introducing
firm fixed effects !
in all specifications.
27
These effectively control for all unobserved time-
invariant firm characteristics. The selection decision may be equally driven by time-varying firm
characteristics. However, as we restrict our sample to the observations that are maximum three
years before the firm started downsizing, we relate any change in firm performance to the more
recent characteristics of the downsizing firms. This limits the impact of time-varying variables on
the selection bias. We focus on the short-term effect of downsizing by limiting ourselves to the
27
A similar strategy to remove selection bias is performed in Guadalupe, Kuzmina and Thomas (2012), on the
case of innovation decisions and foreign ownership.
15
firm observations that are maximum three years after the downsizing ended. Other specifications
where we relax or strengthen these timing restrictions will be presented in the next section.
To measure the impact of downsizing on firm performance, we include two dummy variables
in the estimation equation. The DURING-variable captures the firm performance during the
downsizing event. It is a dummy equal to 1 during the downsizing period and set to 0 in all years
before and after the event. The AFTER-dummy picks up the firm performance after the
downsizing event. It is set to 1 in all years after the downsizing period and 0 in the years before
and during. As we look at the within-firm variation, we exclude the downsizing companies for
which these dummies do not change.
28
We include year dummies to control for -
, which represents year-specific shocks, common
to all firms and sectors. The sector specific trend %*'#
,
controls for idiosyncrasies in the
performance evolution of sector /. Note that this estimation strategy is in fact a generalization of
a Differences-in-Differences strategy (Duflo, 2002), cf. Konings and Vandenbussche (2008) among
others for a similar approach. More precisely, we compare the change in the performance of
downsizing firms during and after downsizing with the change in performance of a control group
of firms over the same period. The DURING- and AFTER-dummy capture any changes in firm
outcomes, the firm fixed effects make sure we only exploit within-firm performance differences,
accounting for initial characteristics, and the year fixed effects capture any changes common to all
firms in Germany. In all specifications we report standard errors that are robust against
heteroskedasticity and intra-group correlation.
4.2 Results
28
For example, when only information on one phase of the downsizing process is available. This lowers the
number of downsizing firms in our sample, but does not change our main results.
16
Table 3 presents the results of the impact of downsizing on the various productivity
measures. The first three columns report results for the complete sample of downsizing firms. For
the full sample of downsizing firms, we find no clear evidence of an effect on productivity after
the downsizing event: the coefficient on the AFTER-dummy is estimated to be negative but fails
to be significant for all three measures of productivity. However, we do see a significant drop in
productivity during the downsizing event, in terms of TFP, labor productivity as well as capital
productivity. More precisely, the productivity measures drop by respectively 12%, 9.1% and
14.9%.
29
Note that the drop in capital productivity is as well larger than the drop in labor
productivity reflecting the lower adaptability of capital compared to labor.
We use the extra information on the motivation for firms behind the downsizing and make
a distinction between firms that adjust their labor stock because of a business downturn and firms
that seek to improve their operational efficiency by downsizing. Results are reported in columns 4
to 6 and columns 7 to 9 respectively. The impact of downsizing appears to differ between the two
subsamples. Firms that have listed a business downturn as their main motivation experience a drop
in productivity during downsizing
30
, but have similar productivity levels after the downsizing event
as before its start. This might be an indication that the downsizing was effective. If the downturn
is persistent, productivity would be as well persistently reduced in the absence of restructuring.
31
Firms that try to improve their operational efficiency through downsizing fail to do so in the
short run. On the contrary, we can even note a statistically significant negative sign on the AFTER-
dummy for TFP. A possible explanation for the absence of productivity improvements is that the
effect of downsizing may only be visible in the long run, due to e.g. technological reasons.
29
To be correct, this is only an approximation, the precise drop in TFP is equal to 0
121
234 and
likewise for the other coefficients.
30
The p-value of the coefficient for capital productivity is equal to 0.107 although the point estimate is the
largest in absolute value.
31
We find some evidence for persistence in the business downturn. Applying the same framework but with
turnover as dependent variable, turnover of ``business downturn'' firms is lower during and 1 year after
downsizing.
17
However, behavioral motives may also play a role. Psychological and behavioral economic studies
indicate that downsizing undermines the morale and motivation of those who stay in firms after
layoffs if the reasons are unclear (Baumol, Blinder and Wolff, 2003; De Meuse and Marks, 2003).
Drzensky and Heinz (forthcoming) find proof for this so-called “survivor syndrome” using a
laboratory experiment. Interestingly, survivors reduce their performance considerably after the
decision to lay off a worker only if it concerns a voluntary decision of the principal. When the
layoff occurs exogenously, the effect on the motivation of the workers vanishes. Our results are in
line with Drzensky and Heinz (forthcoming) as a business downturn is most likely to be perceived
as an exogenous factor leading to downsizing. Layoffs to improve efficiency may not be understood
and supported by the employees and could, at least in the short run, destroy workforce morale and
undermine firm productivity.
Table 3 Basic Results: Productivity
Full Sample Business Downturn Improve Efficiency
[1] [2] [3] [4] [5] [6] [7] [8] [9]
TFP LabProd CapProd TFP LabProd CapProd TFP LabProd CapProd
During
-0.120** -0.0913* -0.149* -0.130* -0.128** -0.205 -0.116 -0.0829 -0.106
[0.0554] [0.0473] [0.0762] [0.0767] [0.0630] [0.127] [0.0773] [0.0648] [0.0773]
After
-0.112 -0.0853 -0.120 -0.0908 -0.130 -0.111 -0.159* -0.0694 -0.162
[0.0688] [0.0643] [0.113] [0.097] [0.0882] [0.188] [0.0809] [0.0747] [0.109]
N 1059 1136 1136 948 1025 1030 913 973 962
Heteroskedasticity robust clustered standard errors in parentheses. *, **, *** reports significance at the 10%,
5%, 1% level. All specifications include firm fixed effects, year dummies and a sector specific time trend.
18
Table 4 Basic Results: Profitability
Full Sample Business Downturn Improve Efficiency
[1] [2] [3] [4] [5] [6] [7] [8] [9]
EBITDA ProfMarg Wage EBITDA ProfMarg Wage EBITDA ProfMarg Wage
During
-
1.934*
-2.394***
0.0200 -4.173***
-3.933***
-0.00302 0.123 -
1.274*
0.0356
[1.027]
[0.691]
[0.0215]
[1.076]
[1.023]
[0.0345]
[1.584]
[0.732]
[0.0289]
After
-1.747 -1.038 0.0282 -3.395**
-1.865 0.01 -0.652 -0.794 0.0335
[1.132]
[0.853]
[0.0328]
[1.583]
[1.301]
[0.0535]
[1.339]
[0.894]
[0.0340]
N 1129 1048 1136 1021 943 1028 958 900 969
Heteroskedasticity robust clustered standard errors in parentheses. *, **, *** reports significance at the 10%,
5%, 1% level. All specifications include firm fixed effects, year dummies and a sector specific time trend.
In Table 4 we turn to the effect of downsizing on financial performance and wages. This
provides us with additional information on the changes taking place within the downsizing firms.
Firm profitability may increase if the downsizing resulted in higher efficiency, keeping wages under
control or, in the case of a negative effect on productivity, if there are substantial cost reductions.
Profits may decline if, for instance, firms fail to increase productivity and experience an increase of
labor compensations at the same time. Again, the first three columns show the results for the full
sample. The third column of Table 4 looks at the effect of downsizing on the average wage. Firms
may dismiss their least productive workers which may raise the average wage or may adjust the skill
composition of the labor force impacting as well the average wage in the firm. However, wages in
downsizing firms appear to remain unchanged during and after the downsizing event as the
coefficients on the downsizing dummies are insignificant at any conventional confidence level.
Columns 1 and 2 show the results for the EBITDA and profit margin (where profit is measured
by profits after taxes and extraordinary costs).
We find a negative effect on profitability during downsizing and no significant effect after
downsizing, both in terms of the EBITDA and the profit margin. More precisely, the EBITDA
margin goes down by 1.9% points while the profit margin drops by 2.4% points during the
19
downsizing event. As part of the restructuring costs are expected to be included in the extraordinary
costs, the impact on the profit margin is larger during the downsizing event compared to the impact
on the EBITDA margin. The results are in line with our priors as we found a negative effect of
downsizing on productivity during downsizing and no effect on the wages, leading to negative
pressure on the profitability of firms.
For the firms that have experienced a business downturn, we observe – not surprisingly – a
substantial drop in profitability, both in terms of EBITDA and profit margin during the downsizing
event. After downsizing, the profitability appears to recover somewhat but remains lower than
before downsizing, especially for the EBITDA margin. The subsample of firms that wish to
improve operational efficiency experience no change in performance during the downsizing event
in terms of the profit margin or wages. The profit margin however, drops significantly by 1.27%
points during the downsizing event
Overall, our results show that there is a negative contemporaneous effect of downsizing on
productivity and profitability, especially for the firms restructuring because of a business downturn.
Firms that downsized to increase their efficiency did not achieve their goal. On the contrary they
even report a drop in total factor productivity in the years after the downsizing. The firms that
reacted to a business downturn appear to recover in terms of productivity after downsizing, but
still report lower profitability. These results are consistent with Dong and Xu (2008) who report a
deterioration in total factor productivity for downsizing firms in China. However, in their sample,
the wages of employees drop as well, leaving profitability unaffected.
5 Robustness Checks
20
We perform a number of robustness checks, related to the measurement of total factor
productivity, the dynamics of the performance indicators after the downsizing event and finally we
control for possible autocorrelation in the error term.
5.1 Non-parametric Order- m Efficiency Scores
As a robustness check we compute firm specific efficiency using non-parametric frontier
methods and relate these efficiency scores with the downsizing event. More precisely we apply the
free disposable hull (FDH) approach (Deprins, Simar and Tulkens, 1984), where input-oriented
efficiency is estimated by comparing each firm with all other firms in the data that produce at least
as much value added. The input-oriented efficiency score for firm i is than computed as:
56
78 9:;5<6
=8
57< >?@A9
BC
D
EAFG
HI8J
E
H
7
H
KK
where x is a vector of inputs, namely labor and capital, y is value added and 6
=8
57< is an
estimate for
=
7< LMN7<O . P
represents the set of firms producing more value
added than firm i. The input efficiency score takes values between zero and one, where a score of
one implies maximum efficiency. To solve for the problem that these efficiency scores are sensitive
to outliers, we follow Cazals, Florens and Simar (2002) and compute partial frontier or more
precisely robust order-m efficiency scores. The basic idea is to benchmark a firm with the expected
best performing firm in a sample of m peers rather than benchmarking it with the best performing
peer in the full sample. In practice, the computation of the order-m efficiency score for a particular
firm follows four steps (Daraio and Simar, 2005):
1. From P
, draw a sample of size m with replacement
2. Compute the pseudo FDH efficiency 5Q

R
using this artificial reference sample
21
3. Redo steps 1 and 2 B times
4. Calculate the order-m efficiency score as the average of the pseudo FDH efficiency score,
56

C
ST5Q

R
C
R
These order-m efficiency scores may exceed the value of one as a firm may not be available
as its own peer. Increasing B, improves accuracy but comes at the expense of higher computing
time. The choice for m is less obvious. The smaller m, the larger the share of super-efficient firms
– firms with efficiency scores larger than one – and the larger m, the more the results coincide with
the non-robust full frontier results.
To estimate the impact of downsizing on efficiency of the firm, we follow Daraio and Simar
(2005). They argue against the use of a so-called two-stage approach to estimate the impact of an
external variable, z, on the efficiency of the production process. In this approach, the efficiency
scores would be obtained in a first stage following a procedure outlined above. In the second stage
these firm level efficiency scores are then regressed on the downsizing variables similar to the main
empirical framework. Instead, they suggest to compute conditional efficiency scores
56
78 <U9:;5<6
=8
57< 8U>?@ A9
;<V
W
XY8<Z
W
[<\]@
EAFG
HI8J
E
H
7
H
KK
and to compare these with the unconditional ones to infer the impact of the external variable,
namely the downsizing event. Note that the downsizing variable is categorical and in practice the
conditional efficiency scores are obtained by using only firms in the same subgroup, defined by the
downsizing dummy, as a benchmark. To analyze the influence of downsizing on the production
process, we compare the average ratio 56

78 <U^56

78 for each category defined by the
downsizing variable (De Witte and Kortelainen, 2013). A higher value for the ratio for the group
of downsizing firms means that downsizing has a negative effect on efficiency as conditioning on
downsizing increases the efficiency score of these firms.
22
For the choices of B and m, we follow Daraio and Simar (2007) suggesting to set B equal to
200. We set m to be the same for all subsamples defined by the downsizing status and pick the
value at which the decrease in the super-efficient units becomes small. More precisely, we set m
equal to 30 but check the robustness of the results for different values of the parameter. To mimic
the firm fixed effects specification in the main results, we divide the firms into four categories,
namely firms that never downsize and downsizing firms before, during and after downsizing. This
allows us to look at the change in the efficiency scores within the group of downsizing companies.
The results are plotted in Figure 1. More precisely the average ratio of the conditional over
unconditional input efficiency scores, 56

78 <U^56

78 , together with the 10% confidence
intervals are displayed. To obtain standard errors for the efficiency scores, we apply a bootstrap.
More precisely, we replicate the estimation procedure 500 times where we draw each time with
replacement the complete time series of ' firms, with ' the number of firms in the original
dataset.
32
We find that the ratio of efficiency scores is larger for downsizing companies compared
to non-downsizing companies, which indicates that downsizing companies are less efficient,
although only the difference between the non-downsizing companies and the AFTER-downsizing
group is statistically significant (p-value = 0.016). Moreover, during and especially after the
downsizing event, the efficiency is lower compared to the period before the downsizing event, but
only the difference between BEFORE and AFTER is marginally significant.
32
Daraio and Simar (2007) state that a naive bootstrap as described above, would not yield a consistent
approximation of the desired sampling distribution for full frontier analysis due to its boundary estimation
nature (Tauchmann, 2012). However, for relatively small values of _, the boundary nature vanishes and one
can use the naive bootstrap. (Tauchmann, 2012). We did however check the robustness of our findings using a
bootstrap procedure where we draw in each bootstrap sample
`
' firms. The standard errors of the efficiency
scores are estimated to be slightly larger, but the differences in efficiency scores between the AFTER-
downsizing and non-downsizing group as well as the difference between BEFORE and AFTER for firms that try
to improve their efficiency remain highly significant.
23
Figure 2 shows the results when we make a distinction between the different motivations for
the downsizing event. Consistent with the main results, especially downsizing to improve efficiency
appears to have a negative impact on the measured efficiency after the restructuring. The difference
between AFTER and BEFORE is highly statistically significant (p-value = 0.002) for this category
of companies while the difference is not statistically significant for the “business downturn” firms.
All in all, the results are consistent with the main results in that if anything – downsizing
companies witness a decrease in efficiency after downsizing and this drop is most outspoken for
the group of firms that listed efficiency reasons as motivation.
33
33
Note that the identification strategy used here is somewhat different from the main results. Here we
basically look at the change in productivity for the downsizing firms, so this boils down to a basic “difference
approach”.
24
Figure 1 Non-parametric order-m Efficiency Scores
Figure 2 Non-parametric Order-m efficiency Scores: Different Motivations for Downsizing
25
5.2 Short-term Dynamics
In addition, we explore the short-term dynamics of the post-downsizing outcomes. Is the
change in performance temporary and are we able detect a recovery? We consider two new
dummies to replace the AFTER-dummy: one variable to denote all firm/year observations that
are one year after the downsizing event; one indicator to signal all observations that are two or
three years after the downsizing event. Table 5 and Table 6 summarize our results. The results for
the full sample, show that the efficiency of downsizing firms drops during and one year after the
downsizing event, but they appear to recover afterwards and attain again the efficiency levels of
before the downsizing event, 2 years after the restructuring. Making a distinction between the
reasons for downsizing in columns 4 to 9, shows that firms that listed a reduction in demand as
the main reason for downsizing, witnessed a drop in productivity during the downsizing event, but
that already one year after the downsizing event the efficiency level is not significantly lower any
more compared to the pre-downsizing period. The drop in post-downsizing productivity, for the
firms that have listed increased efficiency as main motivation, only appears in the first year after
the downsizing event. The effect in later years is not statistically significantly different from zero,
which may suggest that the decrease in efficiency had a temporal nature. What is important
however is that there are, even after 2-3 years, no signs of productivity rising to a higher level
compared than in the pre-downsizing period although this was listed as the main motivation for
the restructuring.
The results on profitability in Table 6 show that the profitability of firms experiencing a drop
in demand decreases the most during the downsizing event and recovers already the first year after
the downsizing event. Surprisingly, the coefficient on the EBITDA margin is again significantly
negative two and three years after the downsizing event. We cannot check however whether this is
a transitory effect due to the relatively short time span of our data set. The firms that listed an
increase in efficiency as a motivation witnessed a drop in the profit margin during restructuring.
26
Moreover, there were no signs at all that profitability improved after the restructuring – compared
to the pre-downsizing period – even not after two years.
Table 5 Productivity: Short Term Dynamics
Full Sample Business Downturn Improve Efficiency
[1] [2] [3] [4] [5] [6] [7] [8] [9]
TFP LabProd CapProd TFP LabProd CapProd TFP LabProd CapProd
During -0.131** -0.114** -0.158** -0.146** -0.144** -0.212* -0.119 -0.105* -0.108
[0.0537] [0.0455] [0.0776] [0.0711] [0.0612] [0.128] [0.0773] [0.0623] [0.0771]
After 1 year -0.128* -0.088 -0.204* -0.101 -0.0692 -0.195 -0.186** -0.184** -0.226**
[0.0657] [0.0596] [0.120] [0.0956] [0.0815] [0.192] [0.0766] [0.0814] [0.105]
After 2+ years
-0.0557 -0.0341 0.0472 0.0172 -0.0889 0.165 -0.149 -0.00147 -0.115
[0.0948] [0.0830] [0.137] [0.146] [0.116] [0.224] [0.0961] [0.100] [0.144]
N 1064
1144
1144
954
1033
1037
912
973
961
Heteroskedasticity robust clustered standard errors in parentheses. *, **, *** reports significance at the 10%,
5%, 1% level. All specifications include firm fixed effects, year dummies and a sector specific time trend.
Table 6 Profitability: Short Term Dynamics
Full Sample Business Downturn Improve Efficiency
[1] [2] [3] [4] [5] [6] [7] [8] [9]
EBITDA ProfMarg Wage EBITDA ProfMarg Wage EBITDA ProfMarg Wage
During -1.880* -2.395*** 0.0197 -4.085*** -3.912*** -0.00203 0.14 -1.272* 0.0342
[1.026] [0.693] [0.0215] [1.077] [1.030] [0.0344] [1.592] [0.735] [0.0290]
After 1 year -1.262 -0.823 0.025 -2.789 -1.416 0.0108 -0.289 -1.097 0.0261
[1.258] [0.999] [0.0291] [1.745] [1.534] [0.0473] [1.506] [0.746] [0.0333]
After 2+ years
-2.036* -0.85 0.0288 -3.727** -1.581 0.00904 -0.938 -0.506 0.0333
[1.154] [1.078] [0.0422] [1.665] [1.583] [0.0714] [1.355] [1.370] [0.0403]
N 1137 1057 1144 1028 952 1035 958 900 969
Heteroskedasticity robust clustered standard errors in parentheses. *, **, *** reports significance at the 10%,
5%, 1% level. All specifications include firm fixed effects, year dummies and a sector specific time trend.
27
5.3 Definition of Downsizing
Next, we address the sensitivity of our estimates to the definition of downsizing we have
used throughout the paper. Currently, a firm is considered to downsize if it sheds at least 3% of its
jobs in Germany. We use this threshold as we cannot be sure whether the media reports
consequently on downsizing cases involving only a limited number of employees. We refine our
selection of downsizing firms by setting the threshold at 10%. This drops the number of
downsizing firms from 92 to 56, which may affect the significance of our results. However, in
setting a higher threshold, it could be the case that the effects of downsizing will be more
outspoken. We present the results in Table 7 and Table 8. The results remain qualitatively the same.
Considering all downsizing firms in our sample, we note a drop in productivity as well as
profitability during the downsizing period. Firms that try to increase their efficiency seem to do all
but improve their productivity. Firms that respond to a business downturn face their biggest drop
in both productivity and profitability during the downsizing event.
Table 7 Productivity: Change Definition of Downsizing
Full Sample Business Downturn Improve Efficiency
[1] [2] [3] [4] [5] [6] [7] [8] [9]
TFP LabProd CapProd TFP LabProd CapProd TFP LabProd CapProd
During
-0.132** -0.122** -0.136 -0.170** -0.162** -0.213 -0.113 -0.114* -0.0868
[0.0594] [0.0488] [0.0849] [0.0787] [0.0665] [0.145] [0.0838] [0.0668] [0.0810]
After
-0.101 -0.076 -0.0855 -0.0727 -0.0959 -0.0626 -0.183* -0.0956 -0.187
[0.0779] [0.0658] [0.129] [0.105] [0.0904] [0.203] [0.0934] [0.0816] [0.126]
N 1026
1103
1103
929
1006
1011
899
959
948
Heteroskedasticity robust clustered standard errors in parentheses. *, **, *** reports significance at the 10%,
5%, 1% level. All specifications include firm fixed effects, year dummies and a sector specific time trend.
28
Table 8 Profitability: Change Definition of Downsizing
Full Sample Business Downturn Improve Efficiency
[1] [2] [3] [4] [5] [6] [7] [8] [9]
EBITDA ProfMarg Wage EBITDA ProfMarg Wage EBITDA ProfMarg Wage
During
-1.771 -1.735** 0.0118 -4.396*** -3.803*** -0.0173 0.276 -0.781 0.0308
[1.183] [0.780] [0.0248] [1.382] [1.171] [0.0404] [1.699] [0.798] [0.0313]
After
-2.119 -0.351 0.0253 -4.301** -1.443 0.00176 -0.369 0.18 0.04
[1.443] [0.990] [0.0375] [2.067] [1.319] [0.0582] [1.593] [1.327] [0.0391]
N 1098 1019 1103 1003 925 1009 945 889 953
Heteroskedasticity robust clustered standard errors in parentheses. *, **, *** reports significance at the 10%,
5%, 1% level. All specifications include firm fixed effects, year dummies and a sector specific time trend.
5.4 Serial Correlation
In a final robustness check we target the possible inconsistency of the estimated standard
errors due to positive serial correlation. As Bertrand, Duflo and Mullainathan (2004) show, failing
to account for serially correlated outcomes, such as firm productivity or health outcomes, in
Differences-in-Differences studies may lead to overestimated significance levels and an
underestimation of standard errors. Due to the similar nature of our outcome variables and
methodology with regards to the examples above, we implement a correction proposed by
Bertrand, Duflo and Mullainathan (2004): collapsing the time series information into three stages,
a pre-, during- and post-period, succeeds largely in eliminating the serial correlation.
34
However,
we require an additional adjustment. Ignoring the time series information by averaging the different
outcomes in each stage works only for treatments that start at the same time. This is different in
our context of downsizing firms: the start and ending of the downsizing event is defined for each
34
Tests on our regression residuals reveal significant positive autocorrelation in a number of cases. Bertrand,
Duflo and Mullainathan (2004) show that parametric AR(k) correction fairs poorly in correcting the standard
errors; adjusting the Variance-Covariance matrix behaves well when a large number of groups are considered.
29
firm individually. Following Bertrand, Duflo and Mullainathan (2004), we first regress our different
outcome variables on firm and year dummies, and additionally on industry-specific trends. The
year fixed effects and time trends capture all common shocks between the downsizing firms and
the control group; the firm dummies effectively capture all outcome variation across firms. Next,
we group the corresponding residuals of all downsizing firms in 3 groups - before, during and after
the downsizing event- and calculate by firm the average outcome in each period. Finally, we regress
these averaged performance indicators on a DURING- and AFTER-dummy.
35
The results are
presented in Table 9 and Table 10. Our main conclusions remain unchanged.
Table 9 Productivity: Account for Autocorrelation in the Outcome Variables
Full Sample Business Downturn Improve Efficiency
[1] [2] [3] [4] [5] [6] [7] [8] [9]
TFP LabProd CapProd TFP LabProd CapProd TFP LabProd CapProd
During
-0.0766** -0.0454 -0.0541 -0.0961* -0.0859* -0.0619 -0.0647 -0.0182 -0.0654
[0.0324] [0.0313] [0.0541] [0.0523] [0.0457] [0.0907] [0.0439] [0.0406] [0.0511]
After
-0.0395 -0.0482 -0.0299 -0.0284 -0.0836 -0.0169 -0.0879*** -0.0353 -0.0758
[0.0377] [0.0411] [0.0636] [0.0624] [0.0596] [0.102] [0.0323] [0.0362] [0.0505]
N 123 133 141 68 78 87 55 55 54
Heteroskedasticity robust standard errors in parentheses. *, **, *** reports significance at the 10%, 5%, 1%
level. Estimates are obtained in two stages. First, outcome variables are regressed on firm and year dummies as
well as on an industry specific time trend. The residuals are subsequently regressed on a during and after
downsizing dummy.
35
Note that, due to a decrease in time periods, we only report robust standard errors.
30
Table 10 Profitability: Account for Autocorrelation in the Outcome Variables
Full Sample Business Downturn Improve Efficiency
[1] [2] [3] [4] [5] [6] [7] [8] [9]
EBITDA ProfMarg Wage EBITDA ProfMarg Wage EBITDA ProfMarg Wage
During
-1.420** -1.815*** 0.00757 -2.538*** -2.574*** -0.0195 -0.235 -1.142*** 0.0362*
[0.587] [0.507] [0.0173] [0.819] [0.836] [0.0274] [0.792] [0.375] [0.0196]
After
-0.882 -0.694 0.0176 -1.49 -1.097 0.00845 -0.461 -0.561 0.0168
[0.753] [0.492] [0.0216] [1.074] [0.757] [0.0328] [0.758] [0.374] [0.0175]
N 140 128 138 86 76 83 54 52 55
Heteroskedasticity robust standard errors in parentheses. *, **, *** reports significance at the 10%, 5%, 1%
level. Estimates are obtained in two stages. First, outcome variables are regressed on firm and year dummies as
well as on an industry specific time trend. The residuals are subsequently regressed on a during and after
downsizing dummy.
31
6 Conclusion
This paper studies the short-term performance of downsizing firms. We present a unique
dataset, obtained by examining 50,000 newspaper articles reporting on the 500 largest German
firms. The main advantage of our method is that it greatly reduces the possibility of a
misclassification. In addition, it allows us to obtain further details on the start and duration of the
downsizing event. Finally, this strategy helps us to shed more clarity on the reason behind the
downsizing event. Following the classification used by the American Management Association, we
are able to identify two main subsamples: firms that have downsized in response of a business
downturn and firms that reduced their workforce in order to increase staff efficiency.
The operational and financial performance measures are retrieved and calculated from the
Amadeus database, made available by Bureau van Dijk. We focus on various indicators of firm
performance such as labor, capital and total factor productivity as well as average wage costs and
the EBITDA and profit margin and we apply a Difference-in-Difference approach to identify the
impact of downsizing on these indicators. Combining both subsamples, we find that productivity
as well as profitability drop during downsizing and do not surpass their before-restructuring levels
afterwards. Differentiating on the reason behind the downsizing decision, some differences
emerge. Firms downsizing due to a business downturn witness a contemporaneous drop in
productivity, while firms that tried to increase their efficiency witness a drop in productivity in
especially the first year after downsizing. This could be explained by behavioral motives as,
contrary to downsizing in response to a business downturn, layoffs to improve efficiency may not
be understood and supported by all employees. This could, in the short run, destroy employee
morale and undermine firm productivity.
Our results are robust against different ways to define the downsizing events, serial
correlation and a non-parametric approach to identify the impact of downsizing on efficiency.
32
References
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36
Appendix
A.1 Identifying Downsizing Firms
Friebel and Heinz (2014) identified downsizing events by consulting the media database
LexisNexis. In a first step they compiled a list of German synonyms for the word downsizing,
based on careful reading of the articles. This involved both single words as composed terms. In a
second step, they checked the list of synonyms by conducting two experiments with paid students
from different fields of studies. A first group was asked to write down their own list of synonyms
for downsizing. A second group was confronted with a list of words of which some were from the
list of synonyms, some words that, depending on the context, would indicate a downsizing event
and some that had nothing to do with downsizing. The students were asked to indicate to what
extent these words would describe downsizing.
36
After defining the list of synonyms, they identified
all articles from one of the leading German national newspapers, Die Welt, in which one or several
of the synonyms appeared between December 2000 and September 2008. Next, they checked in
detail all articles in which those firms were mentioned. All articles that reported on downsizing of
these firms were included. This resulted in a dataset of 5,394 articles on a total of 424 companies.
For most of these firms, the total number of jobs shed was mentioned in articles of Die Welt. To
be sure that this number was correct, the authors checked the coverage in other prestigious German
newspapers (e.g. Frankfurter Allgemeine Zeitung, Handelsblatt), in agency reports (e.g. Reuters) and with
information from the company (e.g. annual reports, press communiqués).
Using the data from Friebel and Heinz (2014), we identified 108 out of 477 companies in
our dataset as downsizing firms. As it is still possible that Die Welt did not report on some of the
remaining companies, we extend our search to other German media. We identified all articles and
reports in LexisNexis containing one of the downsizing synonyms identified by Friebel and Heinz
36
For an overview of the details of the experiments, see Friebel and Heinz (2014).
37
(2014) and the name of the remaining 369 companies. For five firms with more than 1 billion euros
sales per year we found further evidence of downsizing. We then read all articles in the Handelsblatt
and the Frankfurter Allgemeine Zeitung in which one of these firms were mentioned between 2001
and 2007. We limited ourselves to these two newspapers as the media coverage for larger firms is
quite extensive. For the firms with less than 1 billion euros sales, we read the reports in all
newspapers, agency reports and magazines that are available in LexisNexis. Note that LexisNexis
contains reports from 10,000 different sources. Our identification strategy enables us to state that
a misclassification of downsizing is only possible if there was absolutely no coverage on a
downsizing event in the German media or incorrect reports in all German media outlets. Our own
search enabled us to additionally identify 52 downsizing firms, in addition to the 108 companies
already identified by Friebel and Heinz (2014). 15 firms were omitted as it was unclear whether
they really had shed jobs.
38
A.2 Identifying Downsizing Reasons
A.2.1 Panel A: Mentioned reasons for downsizing that are classified as business downturn
Überkapazitäten, Abbau von Kapazitäten, Kapazitäten verringern
Auslastung der Standorte, Auslastungsprobleme, nicht ausgelastete Standorte
Schrumpfkur(s), gesund schrumpfen, schrumpfende Leistung
Produktionskürzungen, Produktion kürzen, Reduktion der/reduzierte Produktion
Schleppende/schlechte Konjunktur, Konjunkturflaute, konjunkturelle Lage
Geringer/schwacher Auftragseingang, Abnahme der/mangels Aufträge
Halbierung des Auftragsvolumens, Auftragsflaute
Schwierige Geschäftslage/schwaches Geschäft
Absatz-/Umsatzeinbruch, Umsatzrückgang, Umsatzeinbußen
Nachfrageeinbruch, Rückgang/Einbruch der Nachfrage, schwache Nachfrage
Verlust von Großkunden, Auslaufen von Großauftrag
Konzentration auf/ Aus für [NAME OF PRODUCT] Produktion, Straffung der Produktpalette
Einstellung von [PRODUCT], Kürzung der Produktpalette
Rückläufiger Markttrend, Markteinbruch, desolater/schwacher/schrumpfender Markt
Abwärtstrend der Branche, Branchenkrise, Krise der/in/am [INDUSTRY], Branche leidet
Branchenweiter Stellen-/Personalabbau, [INDUSTRY]flaute, [INDUSTRY]krise
Sinkende Investitionen/Investitionszurückhaltung [OF THE CONSUMERS]
Schlechtes Marktumfeld
Wegfall von Großaufträgen von der Deutschen Bahn
Schwache Verfassung der Bauwirtschaft, sinkende Bauinvestitionen
Anhaltende Reiseflaute, Flaute im Reisegeschäft, Buchungsrückgänge
Aus der Zuckermarktderegulierung resultierende Produktionseinschränkungen
39
A.2.2 Panel B: Mentioned reasons for downsizing that are classified as improved staff
utilization
Umstrukturierung, Restrukturierung, Neuausrichtung, Reorganisation
Konzernumbau, Verwaltungsumbau
Sparprogramm, Sparkurs, Sanierung, Kosten senken, Kostensenkung
Kostennachteile, Kostensenkung, Kostensenkungsmaßnahmen
Doppelstrukturen abbauen, ineffiziente Strukturen abbauen
Verbesserung interner Prozesse, Strukturen straffen, schlankere Strukturen
Betriebsabläufe gestrafft, schnellere Entscheidungswege, Managementebene soll wegfallen
Hierarchie- und Produktionsstrukturen vereinfachen, erhöhte Umsetzungsgeschwindigkeit
Ertragskraft steigern, Effizienz-/Produktionssteigerung, Wettbewerbsfähigkeit steigern
Zahl der Führungsgesellschaften schrumpft, [COMPANY] will sich neu ordnen
Zusammenlegung von [LOCATIONS, SUBSIDIARIES]
40
A.3 Overview of Downsizing Reasons
Table 11 Industry Overview of Downsizing Related to Business Downturn
Industry
Obs
.
Specific reasons for downsizing
Construction of buildings
(and suppliers)
15 After the reunification boom in the construction industry, the number of employees
declined from 1.410 mio in 1995 to 0.71 mio in 2006. (Destatis, 2011)
Manufacture of motor
vehicles (cars, trucks and
suppliers)
12 Some of the German automobile manufacturers had to reduce their capacities due to
decreasing market shares (e.g. Opel) or the lack of follow-up orders (e.g. Karmann, a
contract manufacturer). This led the suppliers to reduce their capacities as well.
Retail trade 6 The weak consumption in Germany forced some retailers to downsize.
Manufacture of
seminconductors
6 The demand for semiconductors is highly cyclical; after a boom in the late 1990s the
demand collapsed in the early 2000s. Moreover, important German customers (Siemens
mobile/BenQ) went bankrupt.
Manufacture of
machines
5 After 9/11, the American market for machines declined. German export-oriented
manufacturers of machines reduced their capacities.
Manufacture of
computer
4 Weak demand in Germany and new competitors from Asia forced (especially smaller)
manufacturers of computers to reduce their production capacities.
Airline industry, tourism 4 After 9/11, airlines and tourism providers in Germany reduced their capacities.
Manufacture of printing
machines
3 After the breakthrough of the internet, newspaper sales declined worldwide. In the
following the demand for printing machines declined as well.
Manufacture of tobacco
products (and machines
for tobacco producers)
3 Reduced tobacco consumption in Germany (and in Europe) forces tobacco producers to
reduce their production capacities.
Manufacturing of
telecommunication
equipment
3 In the late 1990s, telecommunication equipment firms installed new mobile and
internet networks in Europe; excess capacities in the market for the production of
telecommunication equipment followed. In 2001/02 the market collapsed.
Manufacture of
chemicals
3 New competitors from Asia expanded their production capacities of some basic
chemical products, forcing some German competitors to reduce their capacities.
Newspaper publisher 2 After the breakthrough of the Internet, newspaper sales in Germany declined. In
addition, advertising expenditures collapsed.
Manufacture of white
goods
2 The weak German market for white goods and new competitors from Asia encouraged
two household appliances manufactures to reduce their capacities
Manufacture of office
machines
2 According to the statement of one of the two downsizing firms, the market for office
machines in Germany declines by 15% after 9/11.
IT service provider 2 After the burst of the internet bubble, two IT service providers started to downsize.
Manufacture of wind
turbines
2 After a new law heavily subsidizes the installation of wind turbines in Germany, the
newly installed wind energy increased from 793 MW in 1998 to 3247 MW in 2002. Until
2004, the installed wind energy dropped to 2037 MW in 2004. (Bundesverband
Windenergie e.V.)
Manufacture of steel 2 In 2001-2002, the steel industry got into a short crisis; two steel manufacturers in
Germany reduced their capacities.
Manufacture of
beverages
2 Per capita beer consumption in Germany decreased from 118.3 liter in 2001 to 109.5
liter in 2008. (Destatis)
Others 14
41
Table 12 Overview of Downsizing Related to Improved Staff Utilization
Observations Main type of internal reorganization (rough classification)
19 Internal hierarchies are dismantled or administrative processes are improved
19 Merger of subsidiaries or reorganizing of the organizational structure
13 Reorganization of the production process
4 Other reasons; multiple reasons
... There is no consensus in the strategic management literature regarding whether the implementation of retrenchment strategies enhance or diminish performance. From the perspective of the resource-based view, a retrenchment strategy is a strategic move of the organization for performance enhancement (i.e., Abebe, 2009;Goesaert et al., 2015;Barbero et al., 2017). Conversely, the proponents of agency theory argue that retrenchment strategy will reduce firm performance due to agency cost (Barbero et al., 2020;Casillas et al., 2019). ...
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