Dissecting Between‐Plant and Within‐Plant Wage Dispersion: Evidence from Germany

• Author: Gabriel Felbermayr,Daniel Baumgarten,Sybille Lehwald
• Date: 01 January 2020
• DOI: http://doi.org/10.1111/irel.12249
• Published date: 01 January 2020

Dissecting Between-Plant and Within-Plant Wage

Dispersion: Evidence from Germany

DANIEL BAUMGARTEN , GABRIEL FELBERMAYR and

SYBILLE LEHWALD

Using rich linked employer–employee data for (West) Germany between 1996

and 2014, we conduct a decomposition analysis based on recentered influence

function (RIF) regressions to analyze the relative contributions of various plant

and worker characteristics to the rise in German wage dispersion. Moreover, we

separately investigate the sources of between-plant and within-plant wage disper-

sion. We find that industry effects and the collective bargaining regime contribute

the most to rising wage inequality. In the case of collective bargaining, both the

decline in collective bargaining coverage and the increase in wage dispersion

among the group of covered plants have played important roles.

Introduction

Wage inequality has been on the rise in most (industrialized) countries in

the last few decades (Acemoglu and Autor 2011). Recent research has pointed

to the growing importance of workplace heterogeneity for this development: a

large fraction of the increase in overall wage inequality is due to increased

wage dispersion between as opposed to within firms or plants. While this trend

JEL codes: J31, J51, C21, F16.

*The authors’affiliations are, respectively, LMU Munich, CESifo, RWI, Munich, Germany. E-mail:

daniel.baumgarten@econ.lmu.de; Kiel Institute for the World Economy, Kiel University, Kiel, Germany. E-

mail: felbermayr@ifw-kiel.de; Federal Ministry for Economic Affairs and Energy, Berlin, Germany. E-mail:

sybille.lehwald@bmwi.bund.de. The authors are very thankful to the editor, Chris Riddell, and an anony-

mous referee for very helpful comments. Furthermore, they thank Thomas Lemieux, Bernd Fitzenberger,

Steffen Mueller, Jakob Munch, Mette Foged, Monika Schnitzer, Anna Gumpert, Martin Watzinger, as well

as participants at the IO and trade seminar at the LMU, the EDGE Jamboree in Copenhagen, DICE Duessel-

dorf, and the Workshop International Economics in Goettingen for valuable comments and discussion. They

further thank the Research Data Center of the German Federal Employment Agency at the Institute for

Employment Research (IAB) for their great support with accessing the data. All remaining errors are their

own. This research project originally builds on a study on behalf of the Bertelsmann Foundation and was

further developed with financial support from Deutsche Forschungsgemeinschaft through CRC TRR 190.

The authors thank these institutions for their support. The views expressed are the authors’and should not

be interpreted as representing the views of the Federal Ministry for Economic Affairs and Energy.

INDUSTRIAL RELATIONS, DOI: 10.1111/irel.12249. Vol. 59, No. 1 (January 2020). 2020 The Authors.

Industrial Relations published by Wiley Periodicals, Inc. on behalf of Regents of the University of

Californi a (RUC)., Inc., 350 Main Street, Malden, MA 02148, USA, and 9600 Garsington Road, Oxford,

OX4 2DQ, UK.

This is an open access article under the terms of the Creative Commons Attribution License, which permits

use, distribution and reproduction in any medium, provided the original work is properly cited.

85

is shared by many countries, the specific factors explaining this increase are

still underexplored.

1

Against this background, the aim of this article is to pin down the role of cen-

tral plant and worker characteristics for the increase in wage inequality, focusing

on (West) Germany. For this purpose, we use detailed linked employer–

employee data, covering the years 1996 to 2014. We adopt a particularly rich

framework and jointly evaluate the contributions of standard worker characteris-

tics (education, age, tenure, occupation, and nationality) and an extensive set of

plant characteristics (plant size, export status, collective bargaining coverage,

existence of a works council, technological status, investment intensity, industry,

and region). As the central contribution of our analysis, we separately analyze

the sources of changes in between-plant and within-plant wage dispersion, thus

shedding light on the (possibly divergent) drivers of these two important sub-

components of wage inequality and informing theoretical analyses.

2

In doing so,

we also provide updated evidence on differences in the sources of upper-tail and

lower-tail (between-plant) wage dispersion. Finally, we analyze the sources of

the recent slowdown in German wage inequality (cf. M€

oller 2016) and compare

the results for West Germany to the ones for East Germany.

Disentangling the role of each single variable for the rise in wage dispersion,

conditional on other variables, is politically relevant. For instance, an increase in

overall inequality due to aging of the workforce is valued differently than a simi-

lar increase driven by a higher wage gap between skill groups . However, quanti-

fying the relative contributions of various factor s to rising inequality in a unified

framework, both through compositional changes and changes in the conditional

wage structure, is an empirical challenge. To this end, we apply a state-of-the-art

decomposition method based on recentered influence function (RIF) regressions

(Firpo, Fortin, and Lemieux 2009). Crucially, compared to sequential decomposi-

tions, this approach has the further advantage of being path independent.

Our main findings are as follows. First, we confirm that wage dispersion

among full-time male workers in Germany has risen strongly and fairly

1

Davis and Haltiwanger (1991), Dunne et al. (2004), and more recently, Barth et al. (2016), Handw-

erker and Spletzer (2016), and Song et al. (2019) provide evidence for the United States; Faggio, Salvanes,

and Van Reenen (2010) and Mueller, Ouimet, and Simintzi (2017) for the UK; Card, Heining, and Kline

(2013) for Germany; and Helpman et al. (2017) for Brazil. In contrast, there are mixed results regarding the

importance of the between-firm component in Sweden (Akerman et al. 2013; H



akanson, Lindqvist, and Vla-

chos 2015).

2

Note that even a (hypothetically) important contribution of plant-level characteristics to overall wage

inequality does not necessarily imply that these factors are also the sources of increased between-plant wage

dispersion. Instead, they could also be associated with higher within-plant wage inequality. By the same

token, individual-level characteristics (and the returns to them) could well be responsible for increased

between-plant wage dispersion, e.g., through increased sorting.

86 / DANIEL BAUMGARTEN,GABRIEL FELBERMAYR,AND SYBILLE LEHWALD

continuously between 1996 and 2010, but slightly declined thereafter. Both the

strong increase and the subsequent slight decrease were driven by the

between-plant as opposed to the within-plant component of wage dispersion.

Second, two employer-level characteristics contributed the most to increas-

ing wage dispersion: industry effects and the collective bargaining regime. The

former matters in terms of the wage structure effect while, in the case of col-

lective bargaining, both the composition and the wage structure play a substan-

tial role. The former reflects the strong decline in collective bargaining

coverage and the latter is due to both an increase in the wage gap between

covered and uncovered plants and a strong increase in wage dispersion within

the group of covered plants. According to the point estimates of the decompo-

sition results, the effects associated with the industry and the collective bar-

gaining regime together account for more than 100 percent of the total

increase in the log wage variance between 1996 and 2010, where one has to

take into account that several other factors are associated with declining wage

dispersion. Both industry effects and collective bargaining have contributed to

rising wage dispersion in very specific ways. They are sources of increasing

between-plant wage dispersion, but they are, if at all, negatively related to

within-plant wage inequality. Moreover, they have affected lower-tail as

opposed to upper-tail (between-plant) wage inequality.

Third, in terms of individual-level characteristics, education is the characteristic

that matters the most where both employment shifts toward more highly skilled

workers and, even more so, changes in the skill-related wage structure, particularly

in the wage gap between highly educated and less educated workers, have played

important roles. These factorscontributedtobothwithin-plant and between-plant

wage dispersion. Interestingly, we find that the skill-related wage structure effect, in

particular, is quantitatively even more important for between-plant than for within-

plant wage inequality, reflecting that a major part of changes in the skill-wage gaps

has arisen from increasing between-plant wage differentials. We attr ibute this finding

to increased assortative matching along the skill dimension.

Fourth, just as interesting as the factors that have contributed the most to

rising wage dispersion are the ones that have not. Plant size, exporting status,

plant technology, and investment per worker are all of little if any quantitative

importance for the increase in wage dispersion. This is remarkable given that

many potential culprits for the increase in wage inequality such as the rise of

superstar firms, globalization, and technological change could be expected to

materialize (at least partly) via these channels. It also underscores that simple,

monocausal explanations for the rise in wage dispersion do not exist and that

the impact of drivers such as international trade may be having indirect (e.g.,

by affecting institutions) rather than direct effects.

Between-Plant and Within-Plant Wage Dispersion /87