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The distributional consequences of the recent economic crisis are still broadly unknown. While it is possible to speculate which groups are likely to be hardest-hit, detailed distributional studies are still largely backward-looking due to a lack of real-time microdata. This paper studies the distributional and fiscal implications of output changes in Germany 2008–2009, using data available prior to the economic downturn. We first estimate labor demand on 12 years of detailed, administrative matched employer-employee data. The distributional analysis is then conducted by transposing predicted employment effects of actual output shocks to household-level microdata. A scenario in which labor demand adjustments occur at the intensive margin (hour changes), close to the German experience, shows less severe effects on the income distribution compared to a situation where adjustments take place through massive layoffs. Adjustments at the intensive margin are also preferable from a fiscal point of view. In this context, we discuss the cushioning effect of the tax-benefit system and the conditions under which German-style work-sharing policies can be successful in other countries. KeywordsLabor demand–Output shock–Tax-benefit system–Crisis–Income distribution
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Distributional Consequences of Labor-demand
Shocks: The 2008-09 Recession in Germany
Olivier Bargain
Herwig Immervoll
Andreas Peichl
Sebastian Siegloch
CESIFO WORKING PAPER NO. 3403
CATEGORY 4: LABOUR MARKETS
M
ARCH 2011
An electronic version of the paper may be downloaded
from the SSRN website: www.SSRN.com
from the RePEc website: www.RePEc.org
from the CESifo website: Twww.CESifo-group.org/wpT
CESifo Working Paper No. 3403
Distributional Consequences of Labor-demand
Shocks: The 2008-09 Recession in Germany
Abstract
The distributional consequences of the recent economic crisis are still broadly unknown.
While it is possible to speculate which groups are likely to be hardest-hit, detailed
distributional studies are still largely backward-looking due to a lack of real-time microdata.
This paper studies the distributional and fiscal implications of output changes in Germany
2008-09, using data available prior to the economic downturn. We first estimate labor demand
on 12 years of detailed, administrative matched employer-employee data. The distributional
analysis is then conducted by transposing predicted employment effects of actual output
shocks to household-level microdata. A scenario in which labor demand adjustments occur at
the intensive margin (hour changes), close to the German experience, shows less severe
effects on income distribution compared to a situation where adjustments take place through
massive layoffs. Adjustments at the intensive margin are also preferable from a fiscal point of
view. In this context we discuss the cushioning effect of the tax-benefit system and the
conditions under which German-style work-sharing policies can be successful in other
countries.
JEL-Code: D580, J230, H240, H600.
Keywords: labor demand, output shock, tax-benefit system, crisis, income distribution.
Olivier Bargain
University College Dublin
Belfield, Dublin 4 / Ireland
olivier.bargain@ucd.ie
Herwig Immervoll
ISER / University of Essex
Colchester, Essex / UK
h.immervoll@econ.cam.ac.uk
Andreas Peichl
IZA
Bonn / Germany
peichl@iza.org
Sebastian Siegloch
IZA
Bonn / Germany
siegloch@iza.org
23rd March 2011
Bargain is affiliated to UC Dublin, IZA, ESRI, the Geary Institute and CHILD. Immervoll is
affiliated to the OECD, IZA and ISER. Peichl is affiliated to IZA, the University of Cologne,
ISER and CESifo. Siegloch is affiliated to IZA and the University of Cologne. We would like
to thank Tim Callan, Daniel Hamermesh, John Martin and Paul Swaim for valuable
comments. Usual disclaimers apply.
1 Introduction
The 2008-09 economic downturn has led to a broad discussion, both in the public and
academic arena, on the likely distributional and scal consequences of the crisis and on
which policy might be most ective at mitigating the adverse labor market and welfare
consequences of the downturn. In fact, policy orts to minimize welfare losses were
seriously hampered by how little was known about the distribution of changes in employ-
ment and incomes and about the capacity of existing redistribution systems to soften the
negative impacts of job and earnings losses. In this context the German experience is
particularly interesting. While Germany has su¤ered a substantial drop in GDP (around
5 percent on average— an even larger slump than in the United States), employment levels
and unemployment rates were unusually resilient as most of labor adjustments occurred
at the intensive margin (working hours). This is in contrast to many other Western
countries, which experienced far greater levels of lay. While many analysts and policy
makers have focused on Germany’s employment ects and its work-sharing policies (see,
e.g., Hijzen & Venn (2011), Cahuc & Carcillo (2011)), much less is known about precise
distributional and scal consequences of alternative labor market adjustments.
We investigate this question, focusing on the German situation for the years 2008-
09. While it is possible to speculate about which groups are likely to be hardest-hit,
detailed distributional studies are usually not available until the crisis is long over and
decisions have already been made. For that reason we develop a straightforward approach
to gauge the distributional and scal implications of large output changes at an early
stage, i.e., without having the appropriate microdata. We rst estimate labor demand
on 12 years of high-quality, micro-level administrative employer-employee data (LIAB).
The estimates are used to predict labor-demand ects of the output shocks observed
during the downturn at a disaggregated level (by industry and for labor inputs detailed
by age, skill and contract type). Interestingly, we are able to transpose these labor
market changes to household-level microdata commonly used for distributional analyses
(the German So cio-Economic Panel, SOEP).
1
Using this combined approach we can analyze the rst-round consequences of the re-
cession for income changes at the household level. We suggest two contrasting scenarios
1
To the best of our knowledge, this is the rst empirical study linking output changes to distributional
and scal consequences using a detailed micro model of labor-demand responses. The approach is concep-
tually related to the literature on linking micro and macro models (see, e.g., Bourguignon et al. (2003)
or Peichl (2009) for a survey, and Bourguignon et al. (2008), Hérault (2010), Ahmed & O’Donoghue
(2010), Ferreira et al. (2008) and Robilliard et al. (2008) for distributional and crisis-related analyses).
In particular, our method is closer to the top-down”approach which aims to approximate the ect of
macro changes on income distribution. Further di¤erences with approaches are discussed in the following
sections.
1
when translating labor-demand reactions to earnings losses at the household level. The
rst polar case (intensive scenario) allows only for adjustments of employees’ working
hours rather than sta¤ levels. Although being stylized, this scenario comes close to the
observed German situation and also to that of other countries where much of the reduc-
tions in total working hours occurred at the intensive margin (e.g., Austria, Belgium, the
Czech Republic, the Slovak Republic). The second polar case (extensive scenario) shows
what happens if the same overall adjustment in total working hours occurs exclusively
via lays and hires— a scenario more in line with the situation experienced in the United
States, Greece, Ireland, Spain or the UK (OECD (2010)).
2
Our results show that low-skilled and non-standard workers faced above-average risks
of earnings losses. An examination of the resulting income losses shows, however, that
automatic stabilization by the tax-bene…t system is ective in cushioning a signicant
share of the gross-income losses. Moreover, we nd that the margin of adjustments does
indeed matter. Given the likely pattern of job losses among di¤erent groups of workers,
adjustments at the extensive margin result in a sizable widening of the income distri-
bution, increasing inequality and a rise in the number of poor people by more than 10
percent. In the intensive scenario poverty headcounts rise by under 4 percent, while most
inequality measures are predicted to change little. Importantly, adjustments at the in-
tensive margin are also preferable from a scal point of view at least in the short-term.
We discuss the limits of our analysis, notably the fact that the hour-adjustment would be
even more favorable in countries with less generous unemployment insurance. However, it
is less ective if the economic structure encourages temporary work or does not provide
incentives for rms to retain workers.
The remainder of the paper is structured as follows. Section 2 briey summarizes
the labor market changes in Germany during the crisis and contrasts them with the US
experience. In Section 3 we lay out our empirical approach, present the data and the
estimation of the labor-demand model. In Section 4we predict the rst-round ect of
output shocks on the demand for di¤erent labor inputs, compare them to observed labor
market trends, and analyze the distributional consequences of labor market adjustments
at the household level. Finally, we derive and discuss the scal consequences of working-
hour reductions versus layo¤s. Section 5 concludes.
2
Our demand model, speci…ed on total hours (rather than employment levels), captures the actual
total labor demand adjustment (comprising both margins) reasonably well. In fact, we show that the
German labor market performance was very much in line with past reaction to output changes as far as
changes in total hours worked are concerned.
2
2 The German Labor Market during the Crisis
The German labor market performance has received considerable attention since the onset
of the 2008-09 economic crisis. Figure 1 illustrates the unique adjustment patterns in
Germany by contrasting the evolution of output and employment against those observed
in the United States.
Figure 1: Labor market adjustments: Germany vs. US
92
93
94
95
96
97
98
99
100
101
102
92 93 94 95 96 97 98 99 100 101
GDP relative to peak
Employment / total hours relative to peak
q0 (peak)
q6
q7
q6
DEU
USA
solid lines: employment
dashed lines: total hours worked
Source: OECD National Accounts database and Eurostat labor market statistics. Notes: Q0 is the
quarter when GDP peaked (2007Q4 for US and 2008Q1 for Germany), and each data point refers to
consecutive quarters since then.
During the recent economic crisis, Germany su¤ered particularly sizable output losses
of almost 7 percent since GDP peaked in 2008Q1. Yet employment levels, as shown by
the black solid line, remained practically unchanged, suggesting an unusually low Okun’s
coe¢ cient value. Nonetheless, Figure 1 shows that the crisis did have a signi…cant ect
on the German labor market. Up until 2009Q2, hours worked per employee (as well
as total working hours in the economy) had declined by 4 percent (black dashed line).
3
Hence, on aggregate, the adjustments materialized exclusively at the intensive margin (the
di¤erence between the solid and the dashed lines). In contrast to the German situation,
US employment dropped by almost 5 percent despite a smaller drop in GDP (grey solid
line). Most of the adjustment happened along the extensive margin, whereas working-
hour reductions along the intensive margin accounted for only around one third of the
drop in total hours worked (grey dashed line).
The speci…c adjustment witnessed in Figure 1 is partly the result of possibilities and
constraints induced by labor market conditions and institutions (see, e.g., Möller (2010),
Eichhorst et al. (2010), OECD (2010)). In the German context the government-supported
short-time working scheme (Kurzarbeit) has tended to receive most of the attention. Yet
while a substantial share (around 25 percent) of working-time reductions during the crisis
to date can indeed be directly attributed to this programme, other factors were more
important on aggregate. The greatest reductions, accounting for more than one third of
recorded changes in total hours worked, were due to opening clauses in collective agree-
ments, which allowed temporary reductions in weekly working hours (and earnings), or to
so-called pacts for employment and competitiveness”between employers and employees
(Bellmann et al. (2008)). In addition, working-time accounts or time banks”, as well as
substantially reduced overtime, account for around 20 percent each (Bach & Spitznagel
(2009)).
In our analysis we set up a framework which is general enough to comprise both
the intensive and extensive margin. This allows us to simulate two polar scenarios of
adjustment which come close to the contrasted situations depicted in Figure 1. This will
be described in the following section.
3 Empirical Approach
To study the short-term ects of a large output shock on employment and income, we
derive the likely patterns of demand-side adjustments using own labor-demand estima-
tions. We assume a right-to-manage”setting, with employment and hours chosen by the
rm. Wages are xed in the short term and labor inputs are the only margins of adjust-
ment for rms (capital is constant). The labor-demand model is estimated on matched
employer-employee data for Germany. In a second step the demand-side model is linked
to household-level data, and tax/bene…t simulations are conducted in order to derive the
distributional consequences. In our approach the macro level output shocks are not de-
rived from a stylized CGE-type of model but correspond directly to the observed changes
4
per industry for the years 2008-09.
3
We ignore longer-term changes in prices and wages,
which is justi…ed in the German case, since wage adjustments were not a primary chan-
nel for reducing labor costs during the downturn (Collective Agreement Archive (2009),
Bellmann & Gerner (2011)). Instead we focus on short-term labor-demand adjustments,
which are the most immediate driver of household income losses during a labor market
downturn. Before proceeding with the distributional analysis in Section 4, this section
presents details on data sources and labor-demand estimations.
3.1 Data
The demand model relies on a high-quality linked employer-employee dataset (LIAB)
from the Institute for Employment Research (IAB) in Nuremberg, Germany, (see Alda
et al. (2005) for more information on the dataset and von Wachter & Bender (2006)
for a recent application). The rm component of the LIAB is the IAB Establishment
Panel (Kölling (2000)). The term establishment”refers to the fact that the observation
unit is the individual plant, not the rm. The Establishment Panel is a representative,
strati…ed, random sample including establishments with at least one worker for whom
social contributions were paid. Information on employment levels and changes, sta¤
qualications, investment as well as industry liation and output are used.
The employee data correspond to the employment statistics of the German Federal
Employment Agency (Bundesagentur r Arbeit) and are drawn from administrative re-
cords comprising all employees paying social security taxes or receiving unemployment
bene…ts (see, e.g., Bender et al. (2000)). The dataset covers about 80 percent of German
employees in the private sector. The entire public sector is excluded, as civil servants are
not observed in the social security data. Information recorded in the data include employ-
eeshistories on daily wages, age, seniority, schooling, training, occupation, employment
type (full-time, part-time or irregular employment), industry and region.
Data from the employee history are linked with the establishment sample year-by-year
using a plant identi…er. Since the unied sample for East and West Germany exists only
since 1996, we focus on the period 1996-2007. We select establishments with at least 10
employees, in order to b e able to identify substitution patterns between di¤erent types of
workers. In total our resulting sample consists of 37; 958 establishment-year observations.
The number of establishment-years is 19; 520 in manufacturing (51 percent of the total),
5; 035 in construction (13 percent), 1; 847 in transport and communications (5 percent),
10; 956 in services (29 percent) and 600 in nancial services (2 percent).
3
The method we suggest is rather general. It can also be applied as a tool for ex ante policy response
analyses if one uses projections of output changes (instead of actual ones) in order to analyze forward-
looking counterfactual scenarios.
5
For the distributional analysis we use the German Socio-Economic Panel (SOEP), a
representative survey of the entire German population with around 25; 000 sample indi-
viduals living in more than 10; 000 households per cross-section (see Wagner et al. (2007)).
For the present paper we utilize information on labor market status, gross wage, job type,
bene…ts, industry, working time, household composition, age, education levels and housing
costs. We use the 2008 wave, which contains labor market information for the year 2007,
in particular hours worked and wages.
4
In order to make the information consistent with
the distributional analysis using policy parameters as of January 1, 2009, we use a static
ageing technique, which allows us to control for changes in global structural variables as
well as income adjustments that di¤erentiate by income components (see Gupta & Kapur
(2000)). We restrict the sample to the same industries as in the LIAB, but include the
unemployed. This yields 5; 532 households and 9; 218 individuals.
To calculate net incomes and scal ects, we link the data to the tax and benet
simulation model of the Institute for the Study of Labor, IZAMOD (see Peichl et al.
(2010)). IZAMOD contains a tax-bene…t calculator comprising all relevant features
of the German tax and bene…t system, such as income taxation and social insurance
contribution rules, as well as unemployment, housing and child bene…ts.
5
We make use
of the population weights available in SOEP. The results are therefore representative of
the German population. Using the simulated tax and benet payments, we can compute
disposable income for each household.
3.2 Labor-demand Model
We estimate a structural labor-demand model on the LIAB data. For our purposes it is
essential to adopt a micro rather than a macro approach for mainly two reasons. Firstly,
the explicit goal of our contribution is to assess the consequences of output changes on
the demand for narrowly de…ned groups of workers. This implies that we have to account
for substitution patterns between di¤erent labor inputs at the rm level. Secondly, macro
models of labor demand produce unbiased results only under quite restrictive assumptions
with regard to employment adjustments (see Bresson et al. (1992)).
Following standard practice, we adopt the dual approach by assuming a constant
output, specifying a cost function and using Shephard’s lemma to derive the labor-demand
functions (Hamermesh (1986, 1993) and Bond & Van Reenen (2007)). We opt for a
Generalized Leontief speci…cation as proposed by Diewert (1971), which is a linear second-
4
As explained in the introduction, it is precisely the lack of rapid microdata production that justi…es
our approach.
5
Note that IZAMOD also has a behavioral module allowing for the simulation of labor supply
reactions, which is not used in this application.
6
order approximation to any arbitrary cost function. Importantly, it does not restrict
the substitution elasticities of input factors. We follow the speci…cation of Diewert &
Wales (1987) and take a short-term perspective, assuming capital to be xed (or perfectly
separable from labor inputs). We also allow for non-constant returns to scale, which is
important in the context of our study, since the output elasticities are not restricted to
equal unity.
For a given rm there are i = 1; :::; I labor inputs corresponding to the cells we dene
below. We ignore rm and time indices to clarify notations. We write C, the short-term
labor costs of a rm, as follows:
C =
P
i
i
w
i
+
P
i
P
j
ij
w
0:5
j
w
0:5
j
Y +
P
i
(
Y Y i
w
i
)Y
2
; (1)
with Y the rm-speci…c output and w
i
the wage of labor group iThe symmetry condition
ij
=
ji
, 8i; j, is the only restriction imposed on the coe¢ cients. Derentiating C with
respect to wages w
i
yields the factor demands X
i
, and dividing by Y gives the input-output
ratio:
X
i
=Y =
ii
+
P
j6=i
ij
w
j
w
i
0:5
+
i
1
Y
+
Y Y i
Y; (2)
which is the basis of our labor-demand estimation. Since we are analyzing the comparative-
static ect of output shocks, our main measure of interest is the output elasticity of input
(labor) demand, which is written as:
iY
=
@X
i
@Y
Y
X
i
= 1
i
X
i
(3)
3.3 Estimation
The detailed administrative data allow us to distinguish I = 12 labor inputs p er industry.
We di¤erentiate between two skill/education levels, three age groups and two categories
of employment contract. Skilled workers hold a university, polytechnical or college de-
gree or have completed vocational training. Age groups are dened as 15-29 (young),
30-54 (middle-age) and 55-64 (old). We di¤erentiate between full-time workers and a
non-standardemployment type category comprising both part-timers and irregular em-
ployment (short-term employment, temporary workers and those in marginal employment
referred to as Mini/Midijob in Germany). We estimate input-output ratios separately for
7
the ve industries (manufacturing, construction, trade and communications, services and
nancial sector), which gives 5 12 = 60 di¤erent cells for the distributional analysis.
There is clearly no complete congruence, and possibly a trade-o¤, between the de…ni-
tion of labor inputs used for the purpose of labor-demand estimation on one hand and a
disaggregated cell de…nition for precise distributional analyses on the other. We feel that
the choice made here presents a reasonable balance. In particular, skill and age/experience
groups constitute derent types of productive factors for rms and also correspond to
groups exposed to di¤erent risks of unemployment or working-time adjustments during a
labor market downturn. One may wish more disaggregation for the distributional analysis
(e.g., gender, migrants) but this would be more d cult to justify in terms of labor-input
di¤erentiation. The output variable used for estimating the model is de…ned as busi-
ness volume excluding intermediate inputs. For the nancial sector we instead measure
output”as balance sheet total (banking) and total premiums paid (insurances).
We specify our labor-demand model with respect to total working hours— exploiting
establishment level working-time information. This setup therefore captures changes in
both employment (heads) and work intensity (hours) and implicitly assumes perfect sub-
stitutability between the two adjustment margins. To the best of our knowledge, an hours
speci…cation at the micro level is unique. Most of the related studies estimating demand
systems rely on the textbook head-count speci…cation. A few other papers specify their
model in terms of hours by appending working-hours measures to the data (see Hamer-
mesh (1993)), but due to a lack of rm-level information, such working-hours measures
normally rely on semi-aggregate averages (in most cases at the industry level) at a given
point in time (see Freier & Steiner (2010) for a recent example). Our micro approach is
set up as follows: we rst extract average full-time working hours at the establishment
level directly from LIAB data. At this point we only have information on average full-time
hours for a specic establishment in a certain year. We then extract mean working hours
for each cell from the SOEP data. After which, we retrieve SOEP information on average
full-time hours by industry and year, mirroring the available LIAB data. In a fourth step
we calculate ratio of SOEP cell-speci…c working hours and SOEP industry-year full-time
averages, which we nally apply to the LIAB data to construct a nely grained working
hours distribution across our labor-demand cells in each establishment and year.
For the estimation we add two linear terms to the equations (2). We include time
dummies to capture time trends as well as potential policy or business-cycle ects, and
add disturbance terms "
i
for the i = 1; ::; 12 inputs in each industry. The disturbance
vector f"
1
; :::; "
12
g is assumed to be multivariate and normally distributed, with mean
vector zero and constant covariance matrix . The system of 12 equations per industry is
estimated using the Seemingly Unrelated Regression (SUR) prop osed by Zellner (1962).
8
SUR rst employs equation-by-equation OLS to obtain the covariance matrix of the error
terms, . A Feasible Generalized Least Squares estimation on the full system, condi-
tional on , is then conducted. Thus, SUR allows error terms to be contemporaneously
correlated across regressions and is more cient than separate OLS estimations.
It is useful to check the predictive power of the model. In Figure 2 we plot yearly
relative changes in total hours worked as reported in the LIAB data against changes as
predicted by the model for each industry over the period 1996-2007.
6
Predicted changes
in working hours are derived by multiplying the industries’ output elasticities by the
industry-speci…c aggregate output change. With the exception of the nancial sector, the
graphs show the predictions to be rather accurate. This is reassuring with regards to
the estimated model and provides condence that using employment reaction to output
changes over the entire period results in good approximations of employment changes in
speci…c time periods.
Table 1 presents output elasticities of labor demand. For readability we present average
elasticities for broader input groups in this table. Complete results for all 60 cells are
reported in Table 8 in the appendix. All group elasticities are positive— as predicted by
theory. The average output elasticity across all cells is 0:64, which is well in line with other
studies determining employment reactions to output shocks (normally output elasticities
lie in [0:5; 0:9], see e.g., Brechling & OBrien (1967), Fay & Med (1985) or Card (1986)).
The results suggest that across all sectors unskilled employees are hired more quickly in
a boom and red faster during a recession. Output elasticities of young and, especially,
older workers are also above average. As expected, those on non-standard employment
contracts are more likely to be ected by output changes than regular (“full-time”)
employees.
6
Note th at we could not use any observations for the nancial sector for the years 2006 and 2007
because the LIAB output measure for these industries changed as of 2006.
9
Figure 2: Predictive power
-20 0 20 40
1995 2000 2005 2010
year
manufacturing
-20-10 0 10 20 30
1995 2000 2005 2010
year
construction
-40 -20 0 20 40
1995 2000 2005 2010
year
traffic & trade
-20 0 20 40 60 80
1995 2000 2005 2010
year
service
-100 0 100200300400
1995 2000 2005 2010
year
financial sector
-20 0 20 40
1995 2000 2005 2010
year
total
in %
Changes in total hours worked
observed predicted
Source: Observed hours form the LIAB, predicted hours calculated using LIAB output data and
estimated elasticities.
Table 1: Output elasticities
Group Man Con Tra Ser Fin Total
Skilled 0.57 0.45 0.79 0.62 0.94 0.59
Unskilled 1.05 0.5 1.02 0.99 1.02 0.96
Young 0.74 0.55 0.02 0.72 0.87 0.68
Middle-age 0.62 0.41 0.92 0.61 0.96 0.61
Old 0.75 0.61 1.04 0.99 0.94 0.82
Full-time 0.65 0.43 0.80 0.63 0.95 0.63
Non-standard 0.68 0.93 1.23 0.97 0.92 0.83
Total 0.65 0.46 0.83 0.67 0.94 0.64
Source: Own calculations using the LIAB. Notes: All numbers are averages weighted by the number of
total hours in the respective cells. Man = Manufacturing, Con = Construction, Tra = Transp ort &
Communications, Ser = Services, Fin = Financial Services.
10
4 Employment and Distributional ects
We now model the impact of the crisis, rst on employment using the labor-demand
model, then on household income distribution by feeding the predicted employment ef-
fects into the SOEP data. Our reference period for the output shock (and subsequent
employment/distributional changes) is the period 2008-09, which corresponds to the re-
cent downturn period in Germany.
4.1 Output Shocks and Predicted Employment E¤ects
Results are summarized in Table 2. The top panel reports changes in cial output
aggregates and employment by industry over the crisis period. Output, as measured
by value added for each industry from German national accounts, dropped in all of the
shown industries. Overall, the German economy shrunk by 5 percent over this period.
In the selected sample of industries, value added declined by even more (8 percent).
7
In
particular, the decline in manufacturing output, a slump of 18 percent, is noteworthy.
Employment changes are shown in headcounts (employment levels) as well as total
hours worked, accounting for adjustments along both the extensive and intensive margin.
It is evident that the output shock did result in sizable labor-demand ects overall. Yet
there is a considerable di¤erence between the margins of adjustment. While changes in
employment levels are minimal, total hours worked dropped substantially over a relatively
short period of time, with a very large drop of about 10 percent in the manufacturing
sector.
The bottom panel of Table 2 shows changes in total hours worked across industries and
for di¤erent groups of workers as predicted by the labor-demand model. For the prediction
we multiplied reported industry output changes with the corresponding output elasticities
of labor demand in each of the 60 cells. As we have chosen a total hours”specication,
our predictions are conceptually comparable to the cial changes in total working hours
shown in the top part of the table. Our predictions capture the overall changes well—
both quantitatively and qualitatively. This match is reassuring in terms of the external
validity of the estimated model and validates our implicit assumption that past elasticities
provide a good approximation of present labor market responses. The correspondence
between predicted and observed working-hours changes is also an important nding: it
suggests that, despite its magnitude, the downturn in Germany has not resulted in a
structural break of rm behavior. Only in the transport and communications sector do we
overestimate the labor-demand reaction (possibly explained in part by stimulus spending
7
The di¤erence is mostly due to the public sector, where value added actually increased during the
crisis period.
11
Table 2: Output shocks and actual vs. predicted hours adjustments
Man Con Tra Ser Fin Total
cial statistics
Output (value added, price adjusted)
2008 496.4 78.8 130.5 949.6 76.1 1731.3
2009 406.2 77.7 119.2 917.6 74.9 1595.6
% change -18 -1 -9 -3 -2 -8
Employment levels (in 1000 workers)
2008 7352 1741 2079 12420 1045 24637
2009 7163 1746 2067 12415 1042 24433
% change -3 0 -1 0 0 -1
Total hours worked (in millions)
2008 10383 2680 3015 15827 1483 33387
2009 9352 2630 2915 15401 1457 31754
% change -10 -2 -3 -3 -2 -5
Predictions
Total hours worked (% change)
Total -12 -1 -7 -2 -2 -7
Skilled -10 -1 -7 -2 -2 -6
Unskilled -19 -1 -9 -3 -2 -11
Young -14 -1 0 -2 -1 -8
Middle-age -11 -1 -8 -2 -2 -7
Old -14 -1 -9 -3 -2 -9
Full-time -12 -1 -7 -2 -2 -7
Non-standard -12 -1 -11 -3 -1 -8
Sources: Value added from the German National Accou nts (constant prices, chain-linked index, 2000 =
100). cial employment statistics from the Institute for Employment Research. Predictions are based
on the LIAB. Notes: Man = Manufacturing, Con = Construction, Tra = Transport & Communications,
Ser = Services, Fin = Financial Services.
bene…ting this sector). Moreover, the table suggests that di¤erent types of workers are
ected di¤erently, with old, unskilled and non-standard workers su¤ering the most.
4.2 Cell Identi…cation and Shock Scenarios
We now feed the predicted employment shocks for each cell into the SOEP, a representative
micro dataset often used for distributional analyses. The SOEP is informationally rich
and allows us to di¤erentiate by skill, age, employment group and industry, just as we did
in the linked employer-employee data. Table 3 provides an overview of selected worker
characteristics for both the LIAB and SOEP datasets. The table reveals that although
general socio-demographic characteristics such as gender or nationality di¤er, the two
12
datasets compare well as far as the dimensions of our cells are concerned. In particular,
the age and employment-type distributions are almost identical.
Table 3: Worker characteristics, wave 2007
General LIAB SOEP
Observations (persons) 1,828,126 9,218
Share of women 38.3 44.4
Share of foreigners 5.4 16.0
Share of working in East 20.6 16.2
Skill distribution
Share of skilled 85.9 91.0
Share of unskilled 14.1 9.0
Age distribution
Share of young 17.9 18.4
Share of medium-aged 67 68.2
Share of old 15.1 13.4
Mean age 41.8 41.6
Job distribution
Share of full-timers 73.4 72.9
Share of part-timers 26.6 27.1
Source: Own calculations using the LIAB and the SOEP.
The labor-demand model is speci…ed in terms of total hours and hence accounts for
adjustments at both the extensive and intensive margin. Yet the model cannot predict
which margin is used by a particular rm or sector. Thus, we must suggest concrete scen-
arios of labor market adjustments to translate total hour changes into income changes at
the cell level. Since actual labor-input adjustments during the 2008-09 crisis were mainly
along the intensive margin in Germany, we rst suggest a scenario where adjustments
exclusively materialize as a change in worked hours (e.g., a switch from full-time to part-
time employment). We simply change working hours proportionally in line with the total
change in labor demand at the cell level, holding employment levels constant.
In a second polar case we suggest a scenario where the same total hours adjustments
only occur at the extensive margin through lays. That is, adjustments consist in changes
in employment rates at the cell level. If the predicted change in labor demand for a given
cell is X%, we randomly draw X% of workers within the SOEP cell and make them
unemployed. This second scenario is closer to the adjustment pattern seen in countries
where lays were more important than changes in average hours worked.
We feel that these two scenarios provide interesting counterfactuals for the distribu-
13
tional and scal impact of the labor market downturn, which highlight the role of the
adjustment margin in shaping distributional outcomes and correspond reasonably well to
the adjustment patterns observed in Germany and the United States. It is likely, however,
that the di¤erences between the distributional ects of our stylized scenarios provide up-
per bound estimates. First, adjustments will generally take place along both the intensive
and the extensive margins. On a more technical level, we abstract from the facts that
working-hours reductions will not be uniform within each cell and that unemployment
risks within cells will not b e evenly distributed. However, in the context of our distri-
butional analysis, the random draw will have no noticeable impact as cell de…nitions are
already disaggregated.
8
4.3 Distributional and Fiscal Impacts
The distributional analysis is based on SOEP data before and after the two scenarios of
labor-demand adjustment. We denote by Base the pre-crisis (baseline) situation, by
Intensive”the post-crisis scenario resulting from adjustments along the intensive margin
only, and by Extensive”the post-crisis scenario resulting from extensive-margin adjust-
ments. Income distribution measures are based on household total income equivalized
using the modi…ed OECD scale. Capturing the household context (family size and
composition) is of course a principal reason for performing the distributional analysis on
SOEP-type data rather than using the worker-based LIAB directly.
Income and hours changes. We examine the distributions of both gross and net
incomes in order to capture the cushioning ect of the tax-bene…t system. We assume
policy parameters as of January 1, 2009.
9
Table 4 shows large working-hours changes for
workers in the manufacturing industry mirroring the predicted labor-input adjustments
reported in Table 2.
10
Gross earnings follow changes in total working hours. They are
8
For instance, in the case of th e extensive scenario, any non-random modeling attempt would, in fact,
run into di¢ culties, as it would have to utilize characteristics (such as age, education) that are similar
to the ones used to distinguish cells. Also note that some intermediary scenarios based on more realistic
combinations of the intensive and extensive margins could be suggested but would require additional
assumptions. We keep this work for future research.
9
It is important to note that net income calculations do not account for bene…ts (Kurzarbeitergeld)
paid through th e short-time working programme (Kurzarbeit), as our data do not allow us to identify the
like ly recipients of these bene …ts. This is relevant when considering the distributional ects reported for
the intensive”scenario below. While this provides a lower-b oun d for the incomes of many of the workers
ected by reduced working hours, recall that the large majority of working-hour reductions in 2009 (75
percent) were not on account of Kurzarbeit.
10
Note that because the sampling frames for the SOEP and LIAB data are di¤erent and predictions
from the demand model have been app lied cell-by-cell to the SOEP, total working-hour changes by
industry do not match exactly.
14
not the same, however, since working hours are shown at the individual level, whereas
incomes are measured on an equivalized”household basis and, hence, are also ected
by the incomes of other family members. This is also why incomes can change for the
non-employed and why relative changes in (household) earnings can exceed changes in
(individual) working-hours reductions. Across industries it is, in particular, unskilled
workers who are found to su¤er the greatest earnings losses. The net incomes of young
individuals also decline sharply. Average losses are even larger than for the older age-
group, despite the earlier nding in Table 2 that older workers are somewhat more likely
to face job loss or working-time reductions than young workers. One reason is that older
workers are more likely to be living with a partner whose income partly shields them from
a drop in household incomes.
It is striking that the net income ects are more sizable in the intensive scenario.
This is because hours in the intensive scenario are equally reduced for everybody who is
working in a speci…c cell. Hence, every worker in this cell su¤ers an equal, but relatively
small, income losses. Tax burdens also decline for these workers, which is why income
losses are smaller on a net basis than before taxes. In the extensive scenario certain
workers are laid resulting in a sharp drop of their gross income. On top of reduced
tax burdens, a considerable part of the earnings loss tends to be set by an entitlement
to unemployment benets. Consequently, the income cushioning ect of the tax-bene…t
system is larger than under the intensive scenario, and the di¤erence between net and
gross income changes is more sizable as a result. Note that these ects also operate
for non-employed individuals, who can be sharing a household with job losers entitled to
unemployment bene…ts.
Comparing changes in gross and net income gives some indication of the ectiveness
of social safety nets at absorbing some of the income loss. The income of low-skilled
workers is likely to be relatively close to the level of minimum-income bene…ts. Safety-net
bene…ts, therefore, absorb a large part of their earnings losses on average resulting in
large derences between gross and net earnings changes. Reecting the 400/800 euro
ceiling on monthly earnings in the German Mini/Midijob programme, the wages of many
workers in the non-standard category are also especially low. However, these jobs are
particularly attractive for second earners. Because of their higher-earning partners, they
are then less likely to receive means-tested bene…ts when losing all or part of their own
earnings.
Income distribution. Table 5 presents changes of incomes and working hours by
decile groups. Interestingly, relative net income losses in the intensive”scenario are very
similar from deciles 4 to 10. Perhaps even more strikingly, the lowest two decile groups
15
Table 4: Relative change in earnings and hours for working-age individuals and family
members (by group, in %)
Intensive Extensive
Gross Net Hours Gross Net Hours
Skilled -3.6 -2.4 -3.3 -3.6 -2.2 -3.3
Unskilled -6.6 -3.4 -6.3 -6.6 -2.7 -6.3
Young -3.6 -2.7 -3.6 -3.6 -2.4 -3.6
Middle-age -3.8 -2.2 -3.0 -3.9 -2.0 -3.1
Old -3.5 -1.8 -3.3 -3.6 -1.7 -3.4
Full-time -3.6 -2.6 -3.4 -3.6 -2.3 -3.4
Non-standard -4.7 -2.7 -4.6 -4.7 -2.4 -4.6
Non-employed -4.3 -1.7 . -4.2 -1.2 .
Manufacturing -9.4 -7.0 -11.2 -9.2 -6.2 -11.2
Construction -1.3 -0.9 -0.7 -1.3 -0.8 -0.8
Transport-Comm -6.3 -4.2 -7.0 -6.4 -3.8 -7.1
Services -2.8 -1.8 -2.2 -3.0 -1.8 -2.3
Financial -2.4 -1.8 -1.5 -2.4 -1.5 -1.5
Total -3.7 -2.5 -3.5 -3.7 -2.2 -3.5
Source: Own calculations using the SOEP and IZAMOD. Notes: Incomes are equivalized (modi…ed
OECD scale), working hours are shown on an individual basis.
experience the smallest net income changes— showing the ectiveness of the benet sys-
tem. A somewhat similar picture emerges if labor-demand adjustments take place entirely
through lays. Once again, net income losses tend to be less severe than in the intensive
scenario. This is not the case, however, for the rst two decile groups. The reason is that
those at the bottom of the income distribution tend to be entitled to means-tested benets,
which ensure that net incomes at the very bottom change very little in both the intensive
and extensive scenarios. As a result, whether or not those ected by earnings losses
are entitled to unemployment benets makes little di¤erence, and net income changes
for the two scenarios are more similar for the bottom two deciles than for middle-class
households.
Distributional measures. Table 6 reports a range of global distribution meas-
ures (Gini, General Entropy, inter-decile ratio), as well as absolute and relative poverty
headcount (Foster-Greer-Thorbecke: FGT0) and poverty intensity (FGT1, FGT2). As
customary the poverty line is de…ned as the 60 percent of median income. Consistent
with the results by income deciles, overall inequality is reduced in the intensive” scen-
ario. The income distribution is compressed, as parts of the working population su¤er
income losses, while the net incomes of the non-employed change less. In the extensive”
16
Table 5: Relative change in earnings and hours by income decile (in %)
Intensive Extensive
Gross Net Hours Gross Net Hours
1 -3.7 -0.3 -3.2 -4.1 -0.6 -3.1
2 -3.8 -0.6 -3.8 -3.8 -0.7 -3.7
3 -3.9 -2.0 -3.8 -3.7 -1.2 -3.6
4 -3.8 -2.7 -3.5 -3.7 -1.7 -3.5
5 -3.8 -2.9 -3.5 -4.0 -2.2 -3.7
6 -4.3 -3.0 -3.9 -4.2 -2.5 -3.8
7 -3.6 -2.6 -3.5 -3.7 -2.5 -3.7
8 -3.7 -2.8 -3.4 -3.6 -2.6 -3.4
9 -3.4 -2.5 -3.1 -3.2 -2.3 -3.1
10 -3.8 -2.5 -3.3 -3.9 -2.5 -3.4
Total -3.7 -2.5 -3.5 -3.7 -2.2 -3.5
Source: Own calculations using the SOEP and IZAMOD. Notes: Incomes are equivalized (modi…ed
OECD scale), working hours are shown on an individual basis. Decile groups are for the selected sample
only (working-age individuals and household members) and are based on the pre-crisis”baseline.
scenario, however, inequality rises, as some workers are laid while others are not af-
fected by the crisis at all. Because the incidence of job losses is particularly high for some
groups who tend to have low incomes even prior to unemployment (e.g., young and low-
skilled workers), this additional unemployment yields a further dispersion of the income
distribution. The di¤erence between the inequality measures in the two scenarios illus-
trates that facilitating working-hours adjustments can play an important role in limiting
the growth of income disparities during a downturn.
This can also be seen when looking at poverty measures. In the intensive scenario
the share of the poor as indicated by the headcount ratio using a constant poverty line
(FGT0) increases only slightly, while we see a substantial rise of more than 10 percent in
the extensive case. Other poverty indicators arrive at quantitatively similar results. But
interestingly, with a variable poverty line (FGT0v), the number of poor in the intens-
ive scenario actually goes down, since median income (and hence the poverty threshold)
drops more strongly than incomes at the very bottom of the distribution. These res-
ults underline the importance of evaluating relative poverty measures alongside absolute
changes in income levels— especially when assessing the distributional consequences of
rapid economic changes.
Fiscal ects. Finally, we shed some light on the role of the margin of adjustment
for government budgets. Table 7 shows the scal ects of the two scenarios relative to
the baseline case, i.e., the German tax benet system as of January 1, 2009, without any
17
Table 6: Inequality and poverty measures and relative change
Base Intensive Extensive
Net Net (in%) Net (in%)
Gini 0.324 0.323 -0.385 0.330 1.637
GE0 0.176 0.174 -1.193 0.181 2.972
GE1 0.197 0.197 -0.161 0.203 3.079
P9010 4.251 4.175 -1.807 4.307 1.304
FGT0 0.205 0.213 3.588 0.229 11.653
FGT1 0.048 0.050 2.142 0.054 12.388
FGT2 0.019 0.020 4.289 0.023 19.085
FGT0v 0.205 0.195 -5.067 0.214 4.516
Source: Own calculations using the SOEP and IZAMOD. Notes: Measures are based on equivalized
disposable incomes (modi…ed OECD s cale) and refer to the selected sample only (working-age
individuals and household members). Th e poverty line is set at 60 percent of median income (of the
total population) and is either cons tant, using the baseline median (FGT0, FGT1, FGT2), or varies,
using the median of each scenario (FGT0v).
crisis-related employment changes. As one would expect, both scenarios result in a highly
negative ect on the government budget. Tax revenue and social insurance contributions
(SIC) decrease, as labor earnings drop for those employees ected by the crisis. It is
interesting to note the di¤erences between the two scenarios in terms of taxes and SIC. In
the intensive case the proportional reduction in combination with the progressive income
taxation and regressive SIC yields higher relative tax revenue reductions. In the extensive
scenario employment reductions are highest in the middle part of the income distribution
(cf. Table 5), where SIC payments are higher than tax liabilities. As the highly progressive
German income tax is concentrated at the top (with the top 10 p ercent paying more than
55 percent of the income tax revenue), the reduction in tax revenue is relatively lower
than the decrease in SIC. Due to higher bene…t expenditures, the scal consequences of
the extensive scenario are, however, substantially more severe (benet payments increase
by 6 percent). In total, the government’s budget decreases by 7 percent in this case.
This yields an eventual shortfall which is approximately 3 billion euros higher than in the
intensive scenario, given our considered population sample.
11
11
In a back-of-the-envelope calculation one could argue that the German short-term working scheme
was an cient investment for the initial phase of the crisis— costing a similar amount (3 billion euros per
year)— encouraging reductions in total working hours and thus keeping many employees in the workforce.
18
Table 7: Fiscal ect
Intensive Extensive
Changes in billion euros in % in billion euros in %
Tax revenue -5.6 -4.2 -3.0 -2.3
Social insurance contributions -5.4 -3.2 -6.3 -3.8
Bene…t payments -1.0 1.1 -5.3 5.9
Total budget ect -11.9 -5.7 -14.6 -7.0
Source: Own calculations using the SOEP and IZAMOD. Notes: Percentage changes refer to each
category (ex: tax revenue goes down by 4.2 percent in intensive scenario)
Discussion. A principal result of the analysis is that sharing earnings losses in a
downturn among larger groups of workers can produce less inequality— and lower imme-
diate scal costs than widespread lays. In general, the distributional advantages of
achieving capacity adjustments through working-hours reductions, rather than layo¤s, are
greater in countries with lower automatic stabilizers, i.e., with less generous unemploy-
ment benets and lower average tax burdens than Germany. In this respect, our results
conrm the claim that the distributional distortions of the US labor market— experiencing
massive layo¤s while having a much less redistributive tax and bene…t system— have been
particularly severe.
Yet the question remains whether countries should adopt a strategy of working-time
reductions to minimize layo¤s. The answer depends largely on the nature of the labor mar-
ket downturn and on the specic initial conditions (such as the structure of the economy
or labor market institutions) in each country. In Germany conditions for working-hours
reductions were, in many respects, ideal. First, the greatest output losses were su¤ered in
the export-oriented manufacturing industry. Firms in this sector had both the motivation
and the nancial resources to retain valuable skilled workers during a temporary period
of severely reduced output demand. Second, the output shock was indeed temporary: ex-
ternal demand for German manufacturing goods recovered; and the jobs of workers with
reduced hours therefore remained viable after the downturn. Third, and as discussed in
Section 2, policy developments prior to the downturn (especially working-time accounts
and specic provisions in collective agreements), as well as policy responses to the crisis
(e.g., the short-time working scheme, Kurzarbeit), had strongly facilitated working-hours
adjustments.
On the other hand, this type of measure, which protects existing jobs, tends to re-
inforce employer incentives to hoard highly educated or experienced workers, while less
attractive jobs may be cut more quickly (Hijzen & Venn (2011), Cahuc & Carcillo (2011)).
In other words, working-hours adjustments may in fact worsen the relative position of
poorly protected low-skilled and non-standard workers, who were shown to be particu-
19
larly likely to su¤er earnings losses in a downturn. This is likely to be a concern in highly
segmented labor markets, e.g., in Spain (but also in Germany, where non-standard forms
of employment have become more common).
The speci…city of the output shock also has to be borne in mind, when assessing the
merits of exporting certain national policies to other countries. If the sectoral incidence
of output shock is di¤erent, labor hoarding might be much less bene…cial for rms, and
hence less widespread as a result. If, for instance, rms in the ected sector are severely
credit-constrained, they may have little choice but to lay workers. More importantly,
lower output demand may not be temporary as recessions are frequently accompanied by
structural changes. Policies that actively encourage rms to delay lays in these cases
can be an obstacle to a necessary restructuring process and, hence, hold back economic
recovery.
5 Conclusion
In this paper we analyze the distributional and scal impact of the 2008-09 crisis in
Germany. We base our analysis on a disaggregated labor-demand model, which is justi…ed
by the fact that labor-demand changes are the principal driving factor of household income
losses in the early phase of a labor market downturn. The predicted adjustments are then
combined with detailed household microdata to translate changes in individual earnings
into income changes at the household level. Thus, the method can b e used before detailed
income data become available and can therefore aid timely policy responses to output
shocks.
The choice of Germany is interesting, since, on the one hand, it su¤ered from a severe
output drop, which translated into a substantial labor market downturn— like many other
Western countries. However, on the other hand, the adjustments occurred almost exclus-
ively at the intensive margin, with employment levels and unemployment rates remaining
unusually stable. This re‡ects in part Germany’s policy measures before and during
the crisis— facilitating labor-cost adjustments via working-hours reductions rather than
lays.
Our labor-demand model is exible enough to capture the real-world demand reactions
following the German recession well. At the same time, the approach enables us to assess
the distributional and scal consequences— in particular with respect to the margin of
adjustment. More precisely, we propose two polar cases to assess the importance of the
di¤erent margins. The rst scenario, close to the German experience, assumes that all
employment adjustments take place via such working-hour reductions. The second one
20
better re‡ects the situation in countries such as the United States, Greece and Spain,
where adjustments of employment levels were far greater.
Our results show that low-skilled and non-standard workers faced above-average risks
of earnings losses, in particular if they worked in the manufacturing sector where output
reductions were very large. When examining the resulting income losses, it transpires,
however, that automatic stabilization by the tax-bene…t system is ective in cushioning a
signi…cant share of the gross-income losses— especially among low-income groups (cf. also
Dolls et al. (2010)). As far as the margin of adjustment is concerned, we show that while
promoting working-hour adjustments through work-sharing and other measures cannot
prevent signi…cant income losses, it can be highly ective in avoiding very large increases
in income poverty and scal costs. In those two dimensions the German policy responses
to the crisis were successful.
Nevertheless, the conditions for working-hours reductions in Germany were ideal, as
the output drop mostly occurred in the export-oriented sectors, where motivation to
hoard skilled labor was high and rms had the necessary nancial resources to do so.
We, therefore, argue that whether the German policy can be successful in other countries
crucially depends on initial conditions (especially the structure of the economy and labor
market institutions) as well as the specicities of the output shocks.
From a methodological point of view, we use recent historical data to make inference
about the ects of the current labor market downturn. The demand model provides
an interesting average” approximation of short-term ects of output shocks. Yet in-
stitutional changes over recent years may have ected the demand for di¤erent groups
of workers in complicated ways, and the policies put in place during the crisis had their
own specic ects. Hence, an important, but challenging, improvement would consist in
explicitly modeling policy institutions (such as Kurzarbeit) in the labor-demand estima-
tion. Another obvious limitation is that the adopted short-term horizon goes along with
the assumption of constant wage levels. Although it would be worthwhile to model wage
variations by interacting labor demand and supply iteratively in order to attain equilib-
rium (see e.g., Peichl & Siegloch (2010)), we have argued that this assumption is not too
restrictive in the context of our study, as wage reductions were not a primary response to
the labor market downturn in Germany.
21
A Appendix
Table 8: Output elasticities per cell
Cell values Man Con Tra Ser Fin
Sk/You /FT 0.67 0.42 -0.09 0.63 0.88
Sk/You /PT 0.96 -0.29 0.78 0.94 0.76
Sk/Mid/FT 0.53 0.45 0.85 0.52 0.96
Sk/Mid/PT 0.50 2.10 1.21 0.97 0.95
Sk/Old/FT 0.77 0.40 0.99 0.98 0.93
Sk/Old/PT 0.62 0.29 2.22 1.00 0.97
USk/You/FT 0.95 1.17 -0.20 0.99 1.10
USk/You/P T 0.99 0.89 0.95 0.96 0.95
USk/Mid/FT 1.15 -0.35 1.30 0.99 1.04
USk/Mid/PT 0.41 -0.32 1.26 0.99 1.00
USk/Old/FT 0.89 3.09 0.74 1.04 1.00
USk/Old/PT 0.25 0.36 -0.33 0.99 0.96
Source: Own calculations using the LIAB. Notes: (U)Sk = (Un)skilled, You=Young, Mid= Middle-age,
FT = full-time, PT = Part-timer and irregular employees.
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... The literature on the distributional impact of the Covid crisis for Germany is scarce. In addition to the few papers mentioned above, Bruckmeier et al. (2021) extend the approach of Bargain et al. (2012) and combine macro-and micro-modelling to nowcast the macroeconomic effect of COVID-19 in Germany in 2020. The key difference of their approach to our analysis is in the selection of individuals who experience a shock. ...
... The STW scheme has been an essential tool for containing the extent of job destruction in the ongoing COVID-19 crisis in Germany. This scheme has already been in place for many years; for example, it played an important role in cushioning the impact of the financial crisis of 2008-2009, see Bargain et al. (2012). ...
... To be precise, the approach ofBruckmeier et al. (2021) combines five different tools: (i) a VAR model of output shocks at the industry-level, (ii) a structural labour demand model, (iii) estimated information on employment changes by industry and worker type (by interacting the estimated industry-level output shocks with output elasticities from the labour demand model), (iv) a maximum entropy-based approach to feed these predicted shocks to household micro-data, and (v) a microsimulation model to assess the distributional consequences. Elements (i) and (iv) are new compared to the approach ofBargain et al. (2012). In our approach, we do not need steps (i) and (ii) as we use administrative data on the use of the STW scheme which directly gives us the shocks from step (iii). ...
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In this paper, we investigate the impact of the COVID-19 pandemic on German household income in 2020 using a micro-level approach. We combine a microsimulation model with novel labour market transition techniques to simulate the COVID-19 shock on the German labour market. We find the consequences of the labour market shock to be highly regressive with a strong impact on the poorest households. However, this effect is nearly entirely offset by automatic stabilisers and discretionary policy measures. We explore the cushioning effect of these policies in detail, showing that short-time working schemes and especially the one-off payments for children are effective in cushioning the income loss of the poor.
... All working households across the income distribution suffer from income losses, with the highest income losses experienced in the first decile. In contrast to the 2008/09 recession, which primarily affected the manufacturing sector (Bargain et al. 2012), the shutdown of economic activities in 2020 also severely impacted the service sector, in particular the hotel and restaurant industries and the travel industry. The tax benefit system acts as an important automatic stabilizer as expected losses in disposable income are significantly reduced for affected working households. ...
... These papers suggest similar basic patterns and mechanisms behind the distributional effects, which are a) a decline of the overall negative effects on the income distribution during the crisis, b) the importance of short-time work schemes as the main insurance mechanism, and c) a progressive total income effect with income gains in the lower tail of the income distribution and a reduction in the Gini coefficient and the poverty rate due to non-employment policy measures. Our results also emphasize the importance of (access to) unemployment insurance and the short-time work program as an automatic stabilizer (Bargain et al. 2012;Dolls et al. 2012). Further studies show that although political interventions in all European countries have helped cushioning the income losses caused by the crisis, the extent of the cushioning effect varies greatly (Almeida et al. 2021;Figari and Fiori 2020). ...
... 11 Bellmann et al. (1999) differentiate between white-and blue-collar workers and three skill groups and find output elasticities between 0.6 and 0.8 for Germany. Using a similar input scheme, Bargain et al. (2012) find an average output elasticity of 0.69. ...
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The highly dynamic nature of the COVID-19 crisis poses an unprecedented challenge to policy makers around the world to take appropriate income-stabilizing countermeasures. To properly design such policy measures, it is important to quantify their effects in real-time. However, data on the relevant outcomes at the micro level is usually only available with considerable time lags. In this paper, we propose a novel method to assess the distributional consequences of macroeconomic shocks and policy responses in real-time and provide the first application to Germany in the context of the COVID-19 pandemic. Specifically, our approach combines different economic models estimated on firm- and household-level data: a VAR-model for output expectations, a structural labor demand model, and a tax-benefit microsimulation model. Our findings show that as of September 2020 the COVID-19 shock translates into a noticeable reduction in gross labor income across the entire income distribution. However, the tax benefit system and discretionary policy responses to the crisis act as important income stabilizers, since the effect on the distribution of disposable household incomes turns progressive: the bottom two deciles actually gain income, the middle deciles are hardly affected, and only the upper deciles lose income. Supplementary information: The online version contains supplementary material available at 10.1007/s10888-021-09489-4.
... Accounting for these macroeconomic feedback effects would require linking our microdata to a macroeconometric simulation model (Peichl 2009). Second, we do not simulate changes in government behavior or individual behavioral 10 See Immervoll et al. (2006), Bargain et al. (2012) and Dolls et al. (2012) for further applications of the reweighting approach. Similar imputations from the LFS to EUROMOD input data have been conducted by Navicke et al. (2014) and Salgado et al. (2014). ...
... In EUROMOD, the baseline household weights supplied with the national crosssectional databases have been calculated to adjust for sample design and/or differential non-response. In our empirical analysis, we follow the approach taken by Immervoll et al. (2006), Bargain et al. (2012) and Dolls et al. (2012) and employ reweighting techniques to simulate a sample of repeated cross sections for each EA member state over the period 2000-2013. We impute various labor force characteristics from the LFS microdata based on 18 age-gender-education strata. ...
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This is the first paper that assesses the importance of different stabilization channels of an unemployment insurance system for the euro area (EA). We provide insights into the potential added value of common unemployment insurance as a fiscal risk sharing device which crucially hinges on its ability to provide interregional smoothing. Running counterfactual simulations based on microdata for the period 2000–2013, we find that 10% of the income fluctuations due to transitions into and out of unemployment would have been cushioned through interregional smoothing at EA-level. Smoothing gains are unevenly distributed across countries, ranging from \({-5}\)% in Malta to 22% in Latvia. Our results suggest that the interregional smoothing potential is as important as intertemporal smoothing through debt. We find that four member states would have been either a permanent net contributor or net recipient. Contingent benefits could limit the degree of cross-country redistribution, but might reduce desired insurance effects. We also study heterogeneous effects within countries and discuss moral hazard issues at the level of individuals, the administration and economic policy.
... The two nesting options are given in Figure 3. For the own-price demand elasticity for low-skilled 34 A recent example for a flexible-form labour demand estimation with several dimensions of labour heterogeneity (skill, age and type of employment contract) is Bargain et al. (2011a). for the left and right panel of Figure 3 respectively. ...
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This chapter reviews options of labor market modeling in a computable general equilibrium framework. On the labor supply side, two principal modeling options are distinguished and discussed: aggregated, representative households and microsimulation based on individual household data. On the labor demand side, we focus on the substitution possibilities between different types of labor in production. With respect to labor market coordination, we discuss several wage-forming mechanisms and involuntary unemployment.
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Chapter
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The current economic crisis has had spatially differentiated impacts in both the US and Europe. A growing collection of literature investigates the determinants of the magnitude of this recession across regions. This paper aims to contribute to this literature by analysing variations in unemployment rate across European regions over the period 2004–11. In particular, we focus on two apparently neglected issues, namely labour market rigidity and the sensitivity of the local economy to the national or European business cycle. The rigidity of the labour market is increasingly viewed as a possible reason for differences among countries in terms of the performance of labour markets, although existing empirical evidence remains inconclusive. In this respect, our aim is to assess the relevance of national labour legislation, as measured by the OECD Employment Protection Legislation (EPL) Index for the performance of regional labour markets by taking advantage of the natural experiment provided by the crisis. Furthermore, for the first time in economic literature, we employ quarterly data on unemployment rate for all European regions over the period 2000–11 to construct an index of co–movement regarding regional cycles with national or European cycles, to be used as an important determinant of variation in the unemployment rate. After controlling for a number of covariates, as well as for spatial dependence in various forms, our results indicate that EPL did not affect unemployment growth over the entire period. However, we did find that regions with a low long–term unemployment rate in countries with high EPL performed better in the pre-crisis period (2004–07), whereas no effect was found during the crisis. We also found that synchronisation of the regional cycle with the national one was significant for explaining regional labour market performance. Taken together, these results denote the limited relevance of national policies and legislation for accommodating spatially differentiated shocks, whereas policies aimed at modifying local cycles may prove to be more effective.
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This paper examines the impact of the economic crisis and the policy reaction on inequality and relative poverty in four European countries, namely France, Germany, the UK and Ireland. The period examined, 2008 to 2013, was one of great economic turmoil, yet it is unclear whether changes in inequality and poverty rates over this time period were mainly driven by changes in market income distributions or by tax-benefit policy reforms. We disentangle these effects by producing counterfactual (”no reform”) scenarios using tax-benefit microsimulation and representative household surveys for each country. For the first stage of the Great Recession, we find that the policy reaction contributed to stabilizing or even decreasing inequality and relative poverty in the UK, France and especially in Ireland. Market income changes nonetheless pushed up inequality and relative poverty in France. Relative poverty increased in Germany due to policy responses combined with market income changes. Subsequent policy reforms, in the later stage of the crisis, had markedly different cross-country effects, decreasing overall poverty in France, increasing it in Ireland and giving mixed effects for different sub-groups in Germany and the UK. This article is protected by copyright. All rights reserved
Article
The German experience of the financial crisis was very different from that of most other European countries. Germany was hit by a very strong shock that was relatively concentrated in the exporting, manufacturing industries. In addition, the German labour market was very resilient during the crisis due to earlier labour market reforms and policy instruments facilitating labour hoarding. As a consequence, the public finances were only moderately affected and not many policy reforms had to be enacted. This paper presents the German experience of the financial crisis. We start by presenting the macroeconomic situation and how the crisis unfolded in Germany, before focusing on the situation of the public finances. Finally, we analyse the policy responses to the financial crisis.
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The present paper provides the most comprehensive assessment to date of the impact of short-time work (STW) schemes during the 2008-09 crisis. The analysis covers 19 OECD countries, 11 of which operated a short-time work scheme before the crisis, five countries introduced a new scheme during the crisis period and three countries never had a short-time work scheme. In order to identify the causal effects of short-time work, a difference-in-differences approach is adopted that exploits the variation in labour-adjustment patterns and the intensity with which STW schemes are used across countries and time. The estimates support the conclusion that STW schemes had an economically important impact on preserving jobs during the economic downturn, with the largest impacts of STW on employment in Germany and Japan among the 16 countries considered. However, the positive impact of STW was limited to workers with permanent contracts, thereby further increasing labour market segmentation between workers in regular jobs and workers in temporary and part-time jobs. The estimated jobs impact is smaller than the potential number of jobs saved as implied by the full-time equivalent number of participants in short-time work, suggesting that STW schemes end up supporting some jobs that would have been maintained in the absence of the subsidy. However, the estimated deadweight is less than that usually estimated for other job subsidy measures. As the OECD area is only just starting to emerge from the crisis, it is still too early to assess the impact of STW schemes in the longer term. Indeed, the main concerns about STW schemes relate to their potentially adverse impacts on the vigour of employment growth during the recovery and economic restructuring in the longer run.