Technological and Organizational

Change and the Careers of Workers*

Michele Battisti†, Christian Dustmann, and Uta Schönberg

‡

This version: December 2017

Abstract: This paper addresses what happens to workers who hold jobs that

disappear due to technological and organizational change (T&O). We show that

although T&O reduces firm demand for mainly routine-task based jobs, affected

workers face no higher probability of non-employment or lower wage growth than

unaffected workers. Rather, firms that adopt T&O play an important active role in

curtailing its potentially harmful effects by offering affected workers re-training

opportunities to upgrade to more abstract jobs, with the exception of older workers.

Overall, our findings paint a more positive picture of T&O’s welfare effects than is

commonly suggested.

* We gratefully acknowledge funding by Norface’s Welfare State Future program.

† Ifo Institute for Economic Research at the University of Munich and CESifo Research Network.

Contact information: battisti@ifo.de (Michele Battisti).

‡ University College London (UCL) and Centre for Research and Analysis of Migration

(CReAM). Contact information: c.dustmann@ucl.ac.uk (Christian Dustmann),

u.schoenberg@ucl.ac.uk (Uta Schömberg).

1

Technological and organizational change (T&O) is seen as the main reason for the

decline in the employment share of occupations with routine task content.1 The vast

majority of the existing literature analyzing the consequences of T&O focuses on its

impact on firms and their demand for routine tasks. But what are the consequences

of T&O for the workers who held routine jobs (i.e., worked in occupations with a

predominantly routine task content) before changes were implemented (hereafter,

routine jobholders)? Although hardly any work investigates this question, this

aspect is of critical importance. If routine jobholders lose employment permanently

or for a prolonged period of time, or if they relocate to inferior and lower paying

jobs, T&O may have severe negative welfare implications. This scenario dominates

the popular public debate—at least in the US context.2 But if routine jobholders

retrain and are able to move to jobs with more abstract skill content, either in the

same firm or in other firms, T&O offers tangible benefits. Identifying which of

these two scenarios applies is crucial not only for determining T&O’s welfare

effects on workers but also to provide more information in a political debate which

is strongly influenced by populists who foment anxieties about T&O-induced job

destruction specifically and globalization more generally.

To assess which of the aforementioned scenarios dominates we match a

survey panel data set on German firms which covers 15 years and contains detailed

information on T&O with registry data providing the entire work histories of all

workers ever employed in these firms. This allows us to follow workers over time

irrespective of whether they stay in the firm that carried out T&O or not. We begin

our investigation by showing that in firms that implement T&O the employment

share of routine jobs declines and that of abstract jobs increases relative to firms that

1 See, for example, Autor, Levy, and Murnane, 2003; Goos and Manning, 2007; the recent survey by

Acemoglu and Autor, 2011, and the recent theoretical contribution of Feng and Graetz, 2015 2 See, for example, the Economist (http://www.economist.com/news/specialreport/21700758will-

smarter-machines-cause-mass-unemployment-automation-and-anxiety) and MIT Technology Review

(https://www.technologyreview.com/s/515926/how-technology-is-destroying-jobs/).

2

do not implement T&O. This result holds even within industries and local labor

markets. Firm-level T&O is also associated with a rise in the firm level employment

share of medium aged workers (30–49) and a decline in that of workers over 50.

The results of our placebo tests suggest that these shifts in firm workforce

composition are caused by T&O rather than by different pre-existing trends in the

demand for specific worker types.

In our main empirical analysis, we shift our focus from firms to workers to

investigate how workers—particularly, routine jobholders and older workers—

adjust to T&O measured at the firm level. A possible confounder in our analysis

could be that companies engaged in T&O may be high-wage firms with low

turnover rates employing high-wage workers who are more likely to upgrade to

abstract jobs than transition into under- or non-employment. In order to address this

concern, we control for worker and firm wage fixed effects in our regressions. We

further use event study methodology to corroborate our results. We find that

although T&O reduces the firm’s employment share of routine jobs, those holding

routine jobs at T&O implementation on average suffer neither employment losses

nor reduced wage growth. Rather, these workers are more likely to move to more

abstract jobs. We provide indirect evidence that these upward movements are

facilitated by an increase in firm-provided training opportunities, which can actively

dampen the potential harmful effects of T&O on routine jobholders.

Although those holding routine jobs at T&O implementation do not lose out

on average, those aged 55 and above do not succeed in upgrading to abstract jobs

either within or outside the firm. Rather, if affected by T&O, they are more likely to

permanently transition into non-employment. In fact, T&O increases such

transitions not only for older routine jobholders but for all older workers, including

those in abstract jobs and those with a university degree. Not only are these findings

consistent with the assumption that older workers lack the workplace skills

necessary to deal with T&O, but they also suggest that when workers are close to

3

exiting the labor market and therefore do not have much time to enjoy the return to

training, neither they nor the company find it worthwhile to invest in new skills.

We next ask whether there are particular characteristics of firms that

facilitate the re-training of workers after T&O. One facilitating factor may be the

degree to which firms have training personnel at hand, or have experience with

training workers in-house. We test this hypothesis by investigating whether firms

that train more young workers within Germany’s apprenticeship training system are

more likely to upgrade the skills of their other workers when they carry out T&O.

We find strong support for this hypothesis: routine jobholders are more likely to

upgrade to abstract jobs following T&O if the share of apprentices in the firm prior

to T&O implementation is higher. An additional reason for firms to retrain or

further train workers may be the presence of unions that support training activities.

Although their influence has dwindled over the past two decades, unions continue to

be more powerful in Germany than in countries such as the US and the UK.

Moreover, compared to other countries with powerful unions such as France and

Italy, unions in Germany play a more active role in advising and supporting firms

and workers in all matters of apprenticeship and further training.3 Our findings

indeed suggest that unions may mitigate T&O’s effects on routine jobholders:

movements from routine to abstract jobs in response to T&O occur more frequently

in firms that recognize a union (and are thus bound by collectively negotiated

wages, but also benefit from services—such as training support—that unions offer)

than in firms that do not. These findings highlight that firms’ in-house training

capacity and institutions such as unions may alleviate some of the negative welfare

implications of T&O, by facilitating upward movements from routine to abstract

task-based jobs.

3 See for example Bahnmüller (2009) and Seitz (1997) for an overview. Nearly all unions in

Germany, including the three largest IG Metall, Verdi, and IG Bergbau, Chemie und Energie,

provide further training programs at often highly subsidized rates. Dustmann and Schönberg (2012)

also show that unions increase apprenticeship training in Germany.

4

Our paper contributes to three main strands of the literature—research on the

link between routine-biased T&O and job polarization, on T&O adjustment

mechanisms of both workers and firms, and on age-biased T&O.4 Three recent

papers aim to establish direct evidence for the polarization hypothesis by exploiting

T&O variation either across industries and countries (Michaels, Natraj, and Van

Reenen, 2013) or across local labor markets (Autor and Dorn, 2013; Akerman,

Gaarder, and Mogstad 2015).5 One innovative strength of our analysis is that our 15

years of linked survey-registry data allows us to exploit T&O variation across firms

(rather than industries or local labor markets) and detailed worker characteristics to

identify a direct link between T&O and firms’ demand for certain tasks. Our results

not only corroborate the key findings of these three studies, but also demonstrate

that T&O substitutes for routine jobholders within both industries and local labor

markets. Several older papers draw on firm-level T&O data as we do, but because

they tend to have no longitudinal information on worker skill composition, they are

unable to relate T&O to changes in the firm’s workforce composition (see for

example Bresnahan, Brynjolfsson, and Hitt (2002)). Caroli and van Reenen (2001)

use longitudinal data on firm skill composition, but are prevented from directly

testing the polarization hypothesis since they cannot observe workers’ education

and the types of task they perform. Altogether, none of these papers tracks the

careers of workers affected by T&O, which is the focus of our paper.

In investigating what happens to routine jobholders after their jobs are

eliminated, our analysis is closely related to that of Cortes (2016), who uses PSID

4 A related recent literature investigates the labor market effects of a specific technology —

industrial robots—exploiting variation in robot exposure across industries (Graetz and Michaels,

2015, 2017) or local labor markets (see Acemoglu and Restrepo, 2017 for the US and Dauth et al.,

2017 for Germany) as opposed to variation in T&O across firms, as we do. In contrast to our paper

and the literature summarized below, this literature emphasizes the effects of industrial robots on

overall (rather than skill- or age-specific) employment and wages.

5 Gaggl and Wright (2017) use a discontinuity design where firms below a size threshold qualify

for ICT subsidies to investigate firms’ labor demand responses, and find evidence for

complementarities between ICT and non-routine cognitive-intensive work.

5

data to explore the effects of routine-biased technical change on the patterns of

occupational transition out of routine occupations and the wage changes of

individual workers.6 In contrast to Cortes (2016), our approach does not rely on

structural assumptions to infer the effects of T&O on task transitions. Rather, we

exploit direct information on T&O at the firm level to investigate routine

jobholder’s movements downward (to non-employment and manual jobs) and

upward (to abstract jobs) in response to T&O. By drawing on detailed firm data, we

can also shed light on whether firms invest in training when introducing T&O, and

which types of firms are particularly successful at upgrading workers from routine

to abstract jobs. We are, to the best of our knowledge, the first to document that

upward movement to abstract jobs within and outside the firm facilitated by firm

training activities is a dominant T&O adjustment mechanism of routine jobholders.7

Our paper likewise adds to the literature on age-biased technological change

by analyzing the T&O’s impact on changes in a firm’s employment share of older

workers and identifying these workers’ heterogeneous adjustment mechanisms to

T&O. Existing work typically concentrates on T&O’s impact on workers close to

retirement. Bartel and Sicherman (1993), for example, use industry level data to

show that unexpected implementation of T&O induces workers to retire earlier.8.

Conversely, Aubert, Caroli, and Roger (2006) demonstrate that firms using

innovative workplace practices employ fewer older workers.9 We go beyond these

papers by analyzing in detail how different types of older workers respond to T&O.

6 Autor and Dorn (2009) also touch at this question, by showing that routine occupations “are

getting old”, and that young college workers—but not older college workers or non-college

workers—reallocate to high-skill non-routine employment.

7 Several previous papers also establish a positive correlation between T&O and firm training

activities (Bresnahan, Brynjolfsson, and Hitt, 2002; Behaghel, Caroli, and Roger, 2014; Lynch and

Black, 1998; Behaghel, Caroli, and Walkowiak, 2012; Behaghel and Greenan, 2012).

8 See also Peracchi and Welch, 1994, for earlier evidence on older workers’ labor force

participation and Hægeland, Rønningen, and Salvanes, 2007 for more recent work.

9 Beckmann (2007) uses cross-sectional IAB Establishment Panel data from 1995 to assess the

effect of self-reported technological state and IT investment on age specific labor demands, while

Rønningen (2007) uses Norwegian panel data to investigate the relation between the introduction of

6

1 Background, Data, and Descriptives

1.1 Background

We conduct our analysis for Germany, which, similar to the U.S., UK, and other

OECD countries, witnessed an erosion of jobs in the middle of the wage distribution

in the 1990s and 2000s (see Spitz-Oener, 2006; Dustmann, Ludsteck, and

Schönberg, 2009; Black and Spitz-Oener 2010). Such employment polarization is

commonly attributed to routine-biased technological change, which lowers firm

demand for routine tasks typically located in the middle of the wage distribution,

while increasing demand for the abstract tasks that dominate its upper end (Autor,

Levy, and Murnane 2003; Goos and Manning, 2007).10

We illustrate this

polarization in Figure 1a by plotting decadal changes in employment shares of

occupations ranked according to their median wage in 1990. Figure 1a shows that

employment shares of occupations in the middle of the wage distribution decreased,

particularly through the 1990s.

Consistent with the polarization hypothesis and in line with findings for the

U.S. (e.g., Acemoglu and Autor, 2011; Autor and Dorn, 2013), the share of workers

employed in routine occupations declined in Germany by about 10 percentage

points between 1990 and 2010 (from about 42% to 32%), whilst the share of

workers in abstract occupations increased by roughly the same amount and the share

workers in manual occupations remained roughly constant (Figure 1b).11

In this

paper, we first explore whether and to what extent firm-level T&O is indeed

new or improved products, processes, and innovation activity at the firm level on (levels of) wage

bill shares for workers in different age groups. Likewise, Behaghel, Caroli, and Roger (2014) use

cross-sectional information on ICT and innovative work practices to study changes in the wage bill

shares of older workers in France. All these papers tend to find that the employment of older workers

is negatively affected by technological innovation.

10

Barany and Siegel (2014) emphasize that at least part of the observed polarization may stem

from structural changes rather than responses to technological change per se.

11

See Section 1.2.3 for the classification of occupations into manual, routine and abstract.

7

responsible for the decline in middle wage routine jobs. We then investigate how

T&O affects workers’ careers.

1.2 Data and Descriptives

Our empirical analysis combines three main data sources: the IAB Establishment

Panel (IABEP), registry records for all firms and workers covered by social

security, and the Qualification and Career Survey. The IABEP, administered from

1993 to 2012, is an annual representative survey of up to 16,000 firms that provides

detailed information on organizational change, innovation, and training activities.

We match these data with registry records of the work histories of all workers ever

employed at a surveyed firm and are thus able to trace out workers’ careers even

after they have left the company. To assess occupational task content we rely on the

Qualification and Career Survey.

1.2.1 The IAB Establishment Panel

The IABEP survey was first administered in 1993 to 4,265 West German firms, and

extended to East German firms in 1996. By 2010, the number of surveyed firms had

increased to over 16,000. From this database, we select all West German firms that

participated in the IABEP in any of the years for which information on

organizational change is available. Furthermore, we require that these firms

employed at least 10 employees, and were not active in agriculture. In addition to

data on such variables as IT investments, product and process innovation and

training activities, the survey contains a series of questions on different types of

technological and organizational changes occurring in the company over the

previous 3 years. Questions on organizational change were asked first in 1995 and

then at 3-year intervals (i.e., 1998, 2001, 2004, 2007, and 2010). We focus on four

questions asked consistently in all these waves to which the firm answers either yes

or no: whether it has shifted responsibilities and delegated decisions; whether it has

introduced team work or working groups with their own responsibilities; whether it

8

has introduced profit centers (that is, units or departments that carry out their own

cost and result calculations); and whether it has internally restructured or merged

departments or areas of activities.

Our baseline measure of T&O intensity simply adds up the number of

organizational changes over the 3-year period. Each change not only implies

important alterations in how the firm operates but commonly requires changes in the

technology being used (e.g., the introduction of a profit center typically implies the

adoption of a new computer system). More generally, following Bresnahan,

Brynjolfsson, and Hitt (2002), we view our organizational change measures as part

of a clustered three-way complementarity between IT investments, reorganization,

and product innovation. In such a complementary relation, rapidly falling IT prices

motivate firm investment in IT, which in turn changes the organization’s optimal

structure, and both the IT investments and reorganization ultimately help firms offer

better services and products.

We present relevant summary statistics for our T&O measures in Figure 2.

First, by pooling over all 3-year periods between 1992 and 2010 and weighting by

baseline employment to make results representative across workers, Figure 2a

shows that nearly 45% workers work in firms that did not implement any measure

of T&O in the previous 3 years, while about 12% of workers are employed in firms

that adopted at least three changes. According to Figure 2b, departmental mergers

were the most common type of T&O. More than 40% of the firms implemented one

over the previous 3-year period. For comparison, about 28% of the firms shifted

responsibilities across departments, 18% introduced team work, and 17%

introduced a profit center.

In Panel A of Table 1, we provide evidence that, as hypothesized for

example by Bresnahan, Brynjolfsson, and Hitt (2002), our T&O measures are

indeed complementary to IT investment and product innovation. Relative to firms

that do not carry out any T&O, firms that adopt at least three such measures over a

9

3-year period are more likely to undertake IT investments, and invest more than

twice as much in IT per employee. Firms that heavily reorganize are also nearly

three times more likely to implement product or process innovations. Overall, this

evidence suggests that our T&O measure is indeed capturing fundamental changes

in how firms are operating. In Panel B of Table 1, we show that companies that

carried out at least one T&O change over a three years period are larger, pay higher

wages, and exhibit a higher revenue per worker at baseline (i.e., at the beginning of

the 3-year measurement period) than those that implemented no changes. The

differences between firms that introduced one or two changes and those that

implemented at least three, however, are small. As the panel shows, at baseline,

restructuring firms tend to have a higher fixed firm effect and employ workers with

a higher worker fixed effect than non-restructuring firms.12

Restructuring firms also

employ a larger share of workers in abstract jobs. These differences, however, are

relatively small and largely disappear when comparing firms that introduced one to

two versus three to four organizational changes.

1.2.2 Registry Data

Our second data source is the registry of social security records in Germany over the

1975 to 2010 period, which, in addition to unique worker and establishment

identifiers, contains detailed information on factors such as workers’ wages,

education, age, occupation, industry, and place of work. From this database, we

select all individuals employed at least once in an IABEP firm (as of June 30 in a

given year) together with all their employment spells before joining and after

leaving the firm, thereby tracing out their pre- and post-T&O careers. As is typical

in registry data, wages are censored at the social security limit, so, following

Dustmann, Ludsteck, and Schönberg (2009), Card, Heining, and Kline (2013), and

12 We employ the full registry data to compute a fixed firm and fixed worker effect for each firm

and worker in our sample estimated over rolling 7-year windows prior to T&O adoption, building on

Abowd, Kramarz, and Margolis (1999) and Card, Heining, and Kline (2013) (as detailed in the next

section).

10

others, we impute the censored wages by assuming that wages are drawn from a

normal distribution with a variance that varies by age, gender, and education (please

see our Appendix for details). As detailed below, we use the occupation variable in

the registry data, combined with detailed information on task usage from the

Qualification and Career Survey to classify workers into those that hold manual,

routine, or abstract jobs.

We also distinguish three educational groups: low-skilled workers without

any postsecondary education (9.6% in our sample), medium-skilled workers who

completed an apprenticeship or equivalent (75.2%), and high-skilled workers who

completed a university or college degree (15.2%). We further differentiate between

three main age groups: individuals aged below 30, between 30 and 49, and above

50. In the firm level regressions, we restrict the sample to regular employees not in

training, aged between 16 and 65; in the worker level regressions, we additionally

exclude workers aged over 62 at the beginning of the observation period (who are

thus over 65 at the time of observation).

We use the full registry data to estimate wage regressions that include both a

fixed firm effect and a fixed worker effect (as in Abowd, Kramarz, and Margolis,

1999, and Card, Heining, and Kline, 2013), in addition to controls for age, age

squared, and year fixed effects. We estimate these regressions in 7-year rolling

windows, starting in 1985–1992. Then, for each worker and firm in our sample, we

merge in the pre-estimated worker and establishment fixed effects that refer to a 7-

year period before T&O implementation.

1.2.3 The Qualification and Career Survey

To categorize the occupations in the registry data, we use detailed information from

the 1991/1992 wave of the German BIBB/IAB Qualification and Career Survey

(BIBB) on 19 activities performed at work, which we classify into routine, abstract,

and manual tasks (see Appendix Table A1). Then, following Antonczyk,

Fitzenberger, and Leuschner (2009), for each survey individual i , we proxy the time

11

spent on each task type as the number of tasks performed of type j divided by the

total number of tasks performed:

Task𝑖𝑗 =Number of tasks of type j performed by individual i

Total number of tasks performed by i

Thus, if an individual carries out 6 tasks in total, 3 of which are routine tasks, the

routine index is 0.5. We then aggregate the individual task indices at the 3-digit

occupational level, use the maximum mean task index to classify the occupation as

routine, manual, or abstract, and finally merge this information with the registry

data, again at the 3-digit occupational level.

Table 2 summarizes differences between the three task types along several

analytically relevant dimensions. The table shows that routine, abstract, and manual

occupations across all years represent around 37.1, 45.2, and 17.7 percent,

respectively, of employment in our sample. The observation that the own-task index

on the main diagonal is substantially larger than cross-task index off the diagonal in

Panel A highlights that our classification of occupations into manual, routine, and

abstract works well. Panel B indicates that abstract occupations enjoy a 33% wage

premium over routine occupations, while wages in routine and manual occupations

are roughly similar. There is a strong correlation between occupational task content

and worker educational attainment, with a 30% share of highly educated workers in

abstract occupations, but only 3% and 2.7% in routine and manual occupations,

respectively. These figures suggest that high-skilledworkers work mainly in abstract

occupations, while medium-skilled workers are more likely to work in routine and

manual occupations, although the share of medium-skilled workers in abstract

occupations can still be as high as 66%. Age, in contrast, is roughly evenly

distributed across occupational categories.

2 Empirical Specification and Identification Strategy

We first explain how we estimate the extent to which a firm’s T&O affects its

employment shares of task and age based worker groups (e.g., workers in routine

12

vs. abstract jobs, younger vs. older workers). We then describe how we trace out the

effect of T&O on the careers of the workers whose job it affects. This methodology

will allow us to address questions such as: does a firm’s introduction of T&O result

in routine jobholders or older workers exiting into non-employment, or does it result

in them transitioning to abstract jobs?

2.1 Estimation at the Firm Level

Consider the following relationship between the employment share of workers of

type g, 𝑦𝑗𝑡𝑔

, (e.g., workers in routine jobs or older workers) and technological and

organizational capital (𝑇𝑂𝑗𝑡) in firm j at time t:

𝑦𝑗𝑡𝑔

= 𝛾𝑔𝑇𝑂𝑗𝑡 + 𝜏𝑡𝑔

+ 𝑓𝑗

𝑔+ 𝑒𝑗𝑡

𝑔

where 𝜏𝑡𝑔

denotes worker type-specific year fixed effects, and 𝑓𝑗𝑔

designates firm

fixed effects that are allowed to vary across worker types. We estimate this

regression in first differences, relating 3-year changes in the employment share of

type g workers to changes in technological or organizational capital (Δ𝑇𝑂𝑗𝑡):

Δy𝑗𝑡𝑔

= 𝛾𝑔Δ𝑇𝑂𝑗𝑡 + ∆𝜏𝑡𝑔

+ Δ𝑒𝑗𝑡𝑔

(1)

We measure Δ𝑇𝑂𝑗𝑡 as the number of organizational changes implemented by a firm

over a 3-year period, between t-3 and t. By first differencing, we eliminate any

worker type-specific firm fixed effect that may affect the firm’s demand for

organizational capital, on the one hand, and for routine, abstract, or older

jobholders, on the other. Hence, in equation (1), a positive (negative) coefficient

𝛾𝑔 > 0 (𝛾𝑔 < 0) is suggestive of complementarity (substitutability) between

technological and organizational capital and group g workers.

We also adopt an additional tighter specification that controls for worker

type and industry specific fixed effects at the one-digit level, 𝐼𝑗𝑔

, and commuting

zone fixed effects (𝑅𝑗𝑔

), as well as firm size (in logs) at baseline (FS𝑗𝑡−3). This

results in the following first-difference equation:

13

Δy𝑗𝑡𝑔

= 𝛾𝑔Δ𝑇𝑂𝑗𝑡 + ∆𝜏𝑡𝑔

+ 𝐼𝑗𝑔

+ 𝑅𝑗𝑔

+ 𝛽𝑔FSjt−3 + ∆𝑢𝑗𝑡𝑔

(2)

This specification allows firms in certain industries (e.g. those particularly affected

by increased trade) or larger firms to be systematically more likely to implement

T&O, while simultaneously exhibiting differential trends in their demand for certain

worker groups. At the same time, using commuting zone fixed effects to compare

workforce composition shifts in firms that do and do not implement T&O within the

same local labor market addresses concerns of reverse causality whereby firms

experiencing an increased supply of workers who typically perform abstract

(routine) tasks are more (less) likely to adopt T&O. When estimating both these

equations, we weight firm level observations by employment at baseline and cluster

our standard errors at the firm level.

We additionally present results of placebo tests to demonstrate that any

shifts in the employment shares of certain worker types are indeed likely to be

caused by T&O. Although we focus throughout the paper on T&O’s effects on

employment share changes rather than wage bill share changes (which some view as

a more direct measure of firm demand for certain worker types), results using wage

bill shares are very similar to those reported below. 13

2.2 Estimation at the Worker Level

To investigate T&O’s effects on the careers of the workers it affects, we estimate

specifications of the following type, where j’ denotes the firm at which worker i was

employed in t-3 (the baseline firm), g’ designates the group to which worker i

belonged at baseline (e.g., employed in a routine job) and y𝑖𝑗′𝑡𝑔′

measures the

13 Michaels, Natraj, and Van Reenen (2013), Caroli and van Reenen (2001), and Behaghel, Caroli,

and Roger (2014) focus on wage bill share specifications. Caroli and van Reenen (2001), for

example, discuss how wage bill share equations emerge naturally from a setup in which the firm’s

cost function is assumed to be a restricted translog and firms minimize costs under given factor

prices and fixed capital stock so that the only variable inputs are different types of labor (i.e.,

workers with different types of skills).

14

outcome at time 𝑡 (e.g., employment) of worker 𝑖 who was employed in firm 𝑗′ and

belonged to group 𝑔′ in the baseline period 𝑡 − 3:

y𝑖𝑗′𝑡𝑔′

= 𝛾𝑔′∆𝑇𝑂𝑗′𝑡 + 𝐼𝑗′𝑔′

+ 𝑅𝑗′𝑔′

+ 𝜏𝑡𝑔′

+ 𝛿1𝑔′

FSj′t−3 + 𝑥′𝑖𝑡−3𝛿2𝑔′

+ 𝛿3𝑔′

𝑓𝑗′ +

𝛿4𝑔′

𝜃𝑖𝑡−3 + 𝑒𝑖𝑗′𝑡𝑔′

, (3)

Analogous to our firm level regressions, ∆𝑇𝑂𝑗′𝑡 denotes the number of

organizational changes implemented over a 3-year period (t-3 to t) by the firm at

which individual i was employed at baseline (in t-3). In addition to worker type-

specific year fixed effects 𝜏𝑡𝑔′

, we control for the same set of baseline firm

characteristics as in the firm-level regressions (i.e., industry and commuting zone

fixed effects 𝐼𝑗′𝑔′

and 𝑅𝑗𝑔′

, and firm size in t-3, FSj′t−3). We also condition on a set of

individual characteristics (𝑥′𝑖𝑡−3) at baseline (gender, foreign citizenship, and age).

To account for the possibility that restructuring firms are high-wage firms that offer

differential career prospects to their employees irrespective of organizational

change, we additionally control for (baseline) firm wage fixed effects (𝑓𝑗′), pre-

estimated jointly with worker fixed effects on the full registry wage data or the t-9

to t-3 period (see Section 2.2.2). Likewise, to allow for the possibility that these

restructuring firms employ high-wage workers unlikely to transit into non-

employment and prone to upgrade to higher paying jobs, we further condition on

worker wage fixed effects (𝜃𝑖𝑡−3) , also pre-estimated jointly with the firm fixed

effects on the full registry data for the t-9 to t-3 period.

To paint a detailed picture of how T&O affects worker careers, we consider

the following main outcome variables (y𝑖𝑗′𝑡𝑔′

): the individual is working; the

individual is working in the same occupational group; the individual is working in a

manual, routine, or abstract occupation; the individual is working in a different

occupational group but in the same firm; or the individual has left the firm. In our

baseline specifications, we measure these outcomes up to 3 years after the

15

implementation of T&O. We also adopt a more long term perspective and analyze

the impact on workers’ career outcomes over a 6-year period. We cluster standard

errors at the baseline firm level.

3 Results

3.1 Effects of T&O on Jobs

3.1.1 T&O and Workforce Composition

In Table 3, we present results based on equations (1) and (2) to determine whether

and to what extent firm T&O affects workforce composition in terms of tasks,

education, and age. In the first set of columns (columns (1)), we control only for

year fixed effects (as in equation (1)); in the second (columns (2)), we add controls

for firm size at baseline, commuting zones, and industry (as in equation (2)). As

Panel A shows, T&O is associated with a reduction in the employment share of

routine jobs, with each organizational change over the previous 3 years reducing

this share by between 0.227 and 0.363 percentage points. Given the average 1.2

percentage point overall reduction in the routine employment share over a 3-year

period (last row of Panel A), this response is substantial, as the estimated reduction

in column (2) implies that each additional organizational change can account for

about 19% of the overall decline in routine employment over that period. Given that

firms may adopt up to four organizational changes over a 3-year period, multiple

changes have an even larger impact on routine employment shares. Panel A also

underscores that the reduction in the routine employment share is accompanied by

an increase in the abstract employment share of roughly the same magnitude, while

the manual employment share remains largely unchanged.

Panel B reports the outcomes of performing the same analyses with workers

classified according to education rather than main task type. The results in the first

set of columns, which are conditional only on year fixed effects, indicate that, in

16

line with Michaels, Natraj, and Van Reenen (2013), T&O significantly reduces the

employment share of medium-skilled workers, while significantly increasing that of

abstract workers and leaving that of low-skilled workers unchanged. However, these

estimates become smaller and statistically insignificant once we control for firm

size at baseline, and industry and commuting zone fixed effects (second set of

columns). We should expect a weaker association between T&O and changes in the

employment shares of medium- and highly-skilled workers (relative to changes in

those of routine and abstract jobholders), due to the particularities of the German

education system where about 45% workers who have completed apprenticeship

training (our medium-skilled category) are employed in abstract occupations that

would require college training in the U.S. and UK (see also Panel B of Table 2).

Panel C of Table 3 clearly reveals an age bias of T&O by showing the

relation between T&O and changes in the employment shares of younger (aged

below 30), medium-aged (aged between 30 and 49), and older workers (aged 50 and

above). That is, although T&O does not alter the employment share of younger

workers, it sharply increases that of medium-aged workers while sharply lowering

that of older workers.

Lastly, we perform a placebo test in which we regress changes in the task-

age specific employment shares on both contemporaneous and future T&O with the

sample restricted to firms who participated in the IABEP over two consecutive 3-

year periods (Table 3 Panel D). The estimated effects of contemporaneous T&O for

this restricted sample are similar to those for the full sample (Panel A). Future T&O,

in contrast, has little impact on firm workforce composition, suggesting that our

baseline results are not simply picking up differential firm specific trends or reverse

causality.

3.1.2 T&O, Firm Growth, and Reshuffling

A reduction in the routine employment share in response to T&O does not

necessarily imply the destruction of routine jobs in restructuring firms; if such firms

17

grow in size, for example, their total employment of routine jobholders may well

increase even though their routine employment share declines. A reduction in the

latter, accompanied by an increase in the abstract employment share, could also be

driven by routine jobholders switching to abstract occupations within the firm

(internal reshuffling). In Table 4, therefore, we provide initial evidence on the

relation between T&O and firm growth, hiring, internal reshuffling, and worker

separations.

As the table shows, conditional on year, commuting zone, industry fixed

effects, and baseline firm size, each additional adopted organizational change is

associated with a slight (statistically insignificant) increase in mean firm wages and

firm size, a slight (statistically insignificant) decrease in separation rate (the number

of workers leaving the firm over the 3-year period divided by employment at

baseline), and a modest (statistically significant) increase in the external hiring rate

(the number of workers joining the firm over the 3-year period divided by

employment at baseline). Overall, there is therefore little evidence that firms that

implement T&O grow differentially from firms that do not. On the other hand, the

estimates on internal reshuffling (number of continuing employees who changed

task over the 3-year period divided by baseline employment) provide clear evidence

that T&O is associated with more internal task reshuffling.

3.2 Effects of T&O on Jobholders

Our analysis so far revealed that T&O reduces firm demand for routine-based jobs.

But how are the workers who held routine jobs before T&O implementation

affected by T&O? Do they exit into under- or non-employment or do they

successfully upgrade to abstract occupations? If the latter holds true, does this

upgrading take place within the firm and how do these transition rates differ by

workers’ age? We provide the first overview of T&O’s effects on worker career

paths over the 3-year period using regressions based on equation (3) that relate

18

organizational changes in the baseline firm between t-3 and t to the career outcomes

of the workers at time t employed in these firms at baseline (in t-3). All regressions

control for year fixed effects, commuting zone, and the industry fixed effects of the

baseline (t-3) firm, baseline firm size, gender, foreign status, baseline age, a pre-

estimated firm wage fixed effect for the baseline firm and a pre-estimated worker

wage fixed effect, the latter two jointly estimated over a 7-year period prior to T&O

adoption.

3.2.1 Transitions by Task Usage at Baseline

For routine jobholders at baseline (Table 5, Panel A, row (2)), the results show that,

in line with our findings that T&O reduces the firm’s routine employment share

(Table 3, Panel A), T&O also decreases the probability that routine jobholders will

continue to be employed in routine occupations (column (2)). Yet T&O appears to

neither affect the probability that these workers will transit into non-employment

(column (1)) nor increase the probability that they will separate from the firm

(column (4)). Rather, and in line with the evidence in Table 4, T&O increases the

probability of a routine jobholder being assigned to a different (manual or abstract)

occupational group within the same firm (column (3)). This effect is not only highly

statistically significant but also large in magnitude since the overall probability of

switching tasks within the same firm over a 3-year period of 1.61%. Moreover,

conditional on workers being employed in both periods, T&O is associated with

increased wage growth of those workers holding routine jobs at baseline (column

(5))14

. Overall, the estimates provide little support for the contention that T&O

harms the careers of routine jobholders either in absolute terms or relative to manual

or abstract jobholders, in large part because it increases the probability of their

undertaking different tasks within the same firm.

14 In the wage growth regression, we do not condition on worker and firm wage fixed effects.

19

3.2.2 Transitions by Age at Baseline

To measure transitions by age, we repeat the analysis in Panel B of Table 5, now

distinguishing between three age groups: younger worker (aged below 30), medium

aged workers (aged between 30 and 49), and older workers (aged 50 and above). In

line with our conclusion that T&O decreases the employment share of older workers

(Table 3, Panel C), we find that T&O reduces the probability that these workers will

continue to be employed in the same occupational group. The results further suggest

that most older workers who move away from their baseline occupation in response

to T&O transit into non-employment. More specifically, each additional

organizational change lowers the probability of an older worker remaining

employed between t-3 and t by 0.649 percentage points, an effect roughly equal to

that of one additional organizational change on the probability of older workers

leaving the task performed at baseline. T&O also increases the probability that older

workers will move to a different occupational group within the same firm; however,

this effect is much smaller than that for routine jobholders. For medium aged

workers, in contrast, T&O not only improves employment probabilities but strongly

increases the probability of occupational switching within the firm, while reducing

the probability of exit.

To better understand which age groups among older workers are

particularly affected by T&O, we further distinguish between those aged 50–54, 55–

59, and >59 (Table 5, Panel C). Our estimates indicate that T&O has little impact

on the employment probabilities of workers in the 50–54 age group, but rather

increases their probability of moving to a different occupational group within the

same firm. This finding contrasts sharply with T&O greatly increasing the

transitions into non-employment and separation of workers over 54 while inducing

no increase in their transitions into other occupational groups within the same firm.

The above observations highlight that, although occupational switching within the

firm helps to cushion T&O’s possible adverse effects on routine jobholders, it only

20

benefits workers younger than 55. Those over 54 not only respond to T&O

primarily by separating from the firm and moving into non-employment, but

(according to our longitudinal data) most such movements are permanent and likely

to represent transitions into early retirement.

3.2.3 Transitions of Routine Jobholders by Age

To explore such worker transitions in more detail, we focus in Table 6 on workers

employed in routine occupations at baseline. A remarkable 75% (0.183/0.242) of

routine jobholders affected by T&O respond to T&O by upgrading to an abstract

occupation, about two-thirds of them (0.122/0.183) within the same firm (Panel A).

T&O’s effect on transitions into non-employment or manual work, in contrast, is

statistically insignificant. This pattern holds for both 3-year and 6-year transitions.

Splitting up these routine jobholders into the same three age groups as before (Table

6, Panel B) further reveals that T&O increases upward movement from routine to

abstract occupations for all age groups, but particularly for workers below 50.

Whereas for workers under 30 upgrades occur to a similar extent both within and

between firms, for workers aged 50 and above, almost all upgrades into abstract

occupations take place within the firm. In this older age group, T&O’s effect on

upgrading from routine to abstract tasks varies widely by age (Table 6, Panel C),

with nearly all upward movement driven by workers under 55. Routine jobholders

aged 55 and above, in contrast, are more likely to permanently move into non-

employment following T&O.

3.2.4 Firm Training Activities

All our results so far imply that firms can play an important role in actively

curtailing T&O’s harmful effects by offering upgrading opportunities to routine

jobholders. This interpretation is further supported by the results in Table 7,

documenting that T&O is associated with a rise in firms’ internal and external

training activities (conditional on year, commuting zone, and industry fixed effects

21

and (log) baseline firm size). Not all workers benefit equally, however. Training

activities increase for medium- and high-skilled workers much more than for low-

skilled workers. Unfortunately, our data do not allow us to distinguish firm training

activities by age or task.

3.2.5 Labor Market Transitions of Older Workers

The findings in both Tables 5 and 6 underscore that T&O results in routine

jobholders 55 and older transitioning predominantly into non-employment while

benefiting little from a larger number of firm training opportunities. These

observations suggest that for firms and workers to undertake investment in new

skill acquisition, the period over which this investment amortizes must be

sufficiently long, which makes it not worthwhile for routine jobholders aged 55 and

over who are near retirement age. Older workers might also find it more difficult to

learn the new skills required after T&O implementation.

For a better understanding of how older workers more generally respond to

T&O, we next explore whether T&O also increases non-employment rates for older

abstract jobholders or whether a university education shields older workers from

T&O’s possible adverse effects. According to Table 8, T&O increases the

probability that workers over 55 will separate from the firm and move to non-

employment regardless of whether they were previously employed in a manual,

routine, or abstract occupation. These effects are as strong for high-skilled workers

as for low- or medium-skilled workers. Hence, neither employment in a

(previously) abstract occupation nor a university education appears to cushion

T&O’s adverse career effects on older workers. Rather, our results support the

notion that older workers do not generally possess the workplace skills necessary to

deal with such changes, and investing in new skill acquisition seems not

worthwhile, for firms or workers, when workers are close to retirement age.

22

3.2.6 Robustness Checks

Our estimates in Tables 5, 6 and 8 condition on (pre-estimated) firm and worker

wage fixed effects (𝑓𝑗′ and 𝜃𝑖𝑡−3 in equation (3)), in addition to firm and worker

characteristics at baseline such as the firm’s industry affiliation and size and the

worker’s age. The former two control variables eliminate two important sources of

potential biases: First, restructuring firms may be high wage firms offering more

upgrading opportunities to their employees irrespective of T&O. Second,

restructuring firms may employ high wage workers who are likely to move up to

higher paying abstract jobs irrespective of T&O.

To provide additional support for the hypothesis that the associations

between T&O and routine jobholders’ upward movement from routine to abstract

occupations and T&O and older workers’ movements into non-employment reflect

a causal relation, we perform an event study as a robustness check. We summarize

these findings in Figure 3. The figure contrasts firms that do not introduce T&O

between t-3 and t but implement at least two changes between t and t+3 (treated

firms) with those that carry out no changes over the entire t-3 to t+3 period (control

firms). Figure 3A plots the number of organizational changes implemented by the

treatment and control firms in each 3-year period, between t-6 and t+6. By

construction, neither treatment nor control firms implement any T&O between t-3

and t (the pretreatment period). In the treatment period (between t and t+3),

treatment firms implement 2.6 changes on average, compared to no changes in

control firms. Treatment firms also introduce more organizational changes than

control firms in the t+3 to t+6 post-treatment period (1.3 vs 0.4 on average).

Figure 3B shows that upgrading from routine to abstract occupations in

treatment relative to control firms predominantly occurs in the t to t+3 treatment

period, during which treatment firms adopt at least two organizational changes

while control firms do not adopt any. Figure 3C illustrates that the share differences

between treatment and control firms for transition into non-employment by older

23

workers are small in the t-6 vs. t-3 and t-3 to t pretreatment periods, but open up in

the treatment t vs. t+3 period, which supports the hypothesis that transitions into

abstract occupations and non-employment are indeed caused by T&O.

3.3 Task Upgrading, Education and Labor Market Institutions

Overall, our findings paint a more positive picture of T&O’s welfare effects than

usually discussed. Facilitated by firms’ training opportunities, routine jobholders—

aged below 55—affected by T&O respond to T&O by moving from routine to

abstract occupations, rather than to non-employment or to inferior jobs. An

important question is whether this upgrading is facilitated by particular institutional

and firm characteristics.

Facilities inside the firm that support retraining of workers may be a first

factor that promotes upgrading following T&O. For example, firms that routinely

train young workers may find it easier and less costly to provide the necessary

support for more experienced workers to upgrade to an occupation with more

abstract skill content. Germany has a well-established youth training scheme, often

referred to as the “apprenticeship system” that educates nearly 65% of school

leavers and equips them with both practical skills through hands-on training inside a

firm and academic skills through classroom-based learning in vocational schools.

To qualify as a training firm, firms need to fulfill certain conditions, such as

employing qualified training personal (see Dustmann and Schönberg, 2012, for

details). We might therefore expect that firms that train a larger share of young

workers through the apprenticeship scheme are also more likely to retrain adult

workers when they carry out T&O.

To investigate this hypothesis, we exploit variation in the employment share

of apprentices in the firm at baseline (i.e., the number of apprentices divided by all

employees in year t-3). We regress workers’ career outcomes at time t on T&O

implemented by the baseline firm between t-3 and t, the share of apprentices at

24

baseline (at t-3), and an interaction of both variables, in addition to our usual control

variables. The estimates in Panel A of Table 9 show that a higher share of

apprentices in the firm at baseline indeed significantly increases the probability that

a routine jobholder succeeds in moving up to an abstract occupation following T&O

(column (2)). The estimates imply that T&O has no impact on upgrading from

routine to abstract jobs in firms that did not train any apprentices at baseline (25%

of firms), but increases upward movements to abstract jobs by 0.14 percentage

points in firms with an apprenticeship share at the median (2.89%; 6.304 ×

0.0289 − 0.041) and by 0.66 percentage points in firms with an apprenticeship

share at the 90th

percentile (11.1%; 6.304 × 0.111 − 0.041). Estimates in columns

(3) and (4) further highlight that firms’ involvement in apprenticeship training

predominantly affects upgrades within firms rather than between firms. A larger

apprenticeship share in the firm at baseline also increases transitions to abstract jobs

in response to T&O for workers aged 55 and above, but much less so than for

workers aged below 55 (compare columns (2) and (6) in panel A). Overall, these

findings suggest that firms experienced in the training of young workers are more

likely to retrain adult workers when T&O renders their existing skills obsolete.

The presence of formal worker representation in the firm may be a second

factor that facilitates upgrading of workers following T&O, in particular if unions

actively support the skill acquisition of workers throughout their career. Compared

to other countries with powerful unions such as Italy and France, unions in

Germany play a more active role in advising and supporting firms and workers in all

matters of apprenticeship and further training.15

However, unions have no direct

control over layoffs and hiring in the firm. Unlike in the UK and US (as well as in

Italy and France), union agreements in Germany are binding only for firms that are

members of an employer association. In our sample, 52% of firms are members of

15 See for example https://www.igmetall.de/besser-mit-bildung-tarifvertraege-sichern-

weiterbildung-12923.htm; https://www.igbce.de/themen/bildung/weiterbildung/analyse-

instrumente/14072; https://www.igbau.de/Bildung_Berufsbildung.html.

25

an employer association and 84.5% of workers are covered by union agreements

through their firms’ membership. To test whether unions promote upgrading

following T&O, we estimate similar regressions as in Panel A, but now interact the

firm’s membership in an employer association (“union recognition”) with T&O.

The findings in Panel B of Table 9 suggest that unions may indeed play an

important role in facilitating within-firm (but not between-firm) upward movements

from routine to abstract occupations following T&O: such upgrading takes place

only in firms that recognize a union.

A third factor that may influence firms’ decisions to upgrade rather than

layoff workers in response to T&O are firing costs: if firing costs are exceedingly

high, firms may find it optimal to train rather than fire workers when skill

requirements change. According to OECD indicators of employment protection

legislation,16

firing costs in Germany are similar to those in France, Italy or

Portugal, and considerably higher than in the US or UK for instance. At the same

time, German firms can partially circumvent high firing costs by employing workers

under fixed term contracts. According to the German Federal Statistical Office, in

2009 8.6% of employees over 25 were on fixed term contracts (a share similar to the

7.8% in our sample), with workers under 34 being more strongly affected than

workers over 55 (17.9% vs 4.5%).17

The results in Panel C of Table 9 show that a

higher share of employees on fixed term contracts in the firm at baseline has little

impact on the probability that routine workers transition to abstract jobs in response

to T&O (column (2)). However, a higher share of workers on fixed term contracts

increases the probability that a routine worker aged above 55 remains employed

with the firm following T&O (column (6)). Whereas each additional change of

T&O reduces the employment probability of older workers by 2.18 percentage

points in firms that did not employ any worker on fixed term contracts at baseline,

16 See http://www.oecd.org/els/emp/oecdindicatorsofemploymentprotection.htm

17

See https://www.destatis.de/DE/ZahlenFakten/Indikatoren/QualitaetArbeit/Dimension4/

/4_2_BefristetBeschaeftigte.html

26

T&O has hardly any effect on the employment prospects of older workers in firms

that employed 19% (the 90th

percentile in our sample) of its workforce on fixed term

contracts at baseline (12.357 × 0.190 − 2.182 = 0.16 percentage points).18

These

findings suggest that restructuring firms employing a large share of workers on

fixed term contracts respond to changing skill requirements by laying off workers

on fixed term contracts, rather than workers on permanent contracts close to

retirement. Workers on fixed term contracts may therefore partially shield older

workers (who are typically on permanent contracts) from T&O’s possible harmful

effects.

4 Discussion and Conclusions

Based on 15 years of detailed T&O data combined with entire work histories for

those workers who were ever employed in surveyed firms, we first show that, in line

with the routine-biased change hypothesis, T&O engaged firms reduce their

employment share of workers in routine jobs relative to unengaged firms. Yet,

workers holding routine jobs at T&O implementation do not on average suffer

employment losses or reduced wage growth. Rather, they succeed in moving up to

more abstract jobs, often facilitated by firm training opportunities.

Despite this lack of harm on average, however, we also demonstrate a

decline in employment prospects of routine jobholders over 55 years of age. They

typically fail to upgrade successfully to abstract jobs and instead withdraw

permanently from the labor market. Interestingly, these adjustment mechanisms

hold not only for older workers in routine jobs but also for older workers in abstract

jobs, including those with a university degree. Thus, tertiary education acquired

before labor market entry is insufficient to shield workers from T&O’s possible

harmful effects throughout their entire careers.

18 The positive coefficient on the interaction between the number of organizational changes and

the share of workers on fixed term contracts for all routine workers in column (1) is almost entirely

driven by workers aged 50 and above.

27

Although the (relatively small) group of older workers seem to suffer from

T&O, our findings first and foremost highlight that changing skill requirements

caused by technological innovations need not result in a welfare loss even for those

workers whose jobs disappear and whose skills become partially obsolete. Rather,

firms may accomplish the necessary skill upgrading by training workers who lack

the required skills, as opposed to replacing them with workers who already possess

those skills.

Our findings for Germany contrast with the common view based on US

research that new technologies and accompanying organizational restructuring lead

to non-employment or a deterioration in job quality for a large fraction of the

workforce. For example, Cortes, Jaimovich, and Siu (2017) conclude, based on CPS

data, that those groups that are most affected by a decline in employment in routine

jobs are increasingly likely to be non-employed. Similarly, Autor and Dorn (2013)

argue that computerization has led to job degradation for a large group of workers.19

In a similar vein, Acemoglu and Restrepo (2017) report substantial job losses

caused by industrial robot usage across commuting zones. One reason for why we

draw different conclusions could be differences in methodology and data—unlike

existing studies in the US, we measure technological innovations at the firm level

and are able to follow workers across firms, industries, and commuting zones.

However, different conclusions may be due to fundamental differences between the

German and the US labor markets. Being characterized by consensus-based

industrial relations, with the majority of workers trained within firm-based

apprenticeship schemes, and comparatively strict employment regulations, the

German labor market may simply have responded differently to the challenges of

technological change. Labor market institutions may therefore affect the way

technological change impacts on workers and their welfare. We find some evidence

19 See https://opinionator.blogs.nytimes.com/2013/08/24/how-technology-wrecks-the-middle-

class/.

28

in line with this hypothesis: the upgrading of routine workers to abstract tasks in

response to organizational restructuring takes place predominantly in firms that

recognize a union, and is increasing in the share of apprentices a firm trains. The

wide-spread apprenticeship system and unions’ involvement in training activities

may therefore promote upward movements from routine to abstract jobs following

T&O, and thus help lessen T&O’s possibly harmful career effects.

Our findings—even if specific only to Germany because of its particular

labor market institutions—not only highlight that there are possibilities, through

firm training, to cushion T&O’s adverse effects on the workforce, likely without

compromising competitiveness. They also have significant implications beyond the

labor market. For example, if indeed economic factors play an important role in

explaining the rise of populism witnessed recently,20

then institutions that are able

to shield vulnerable workers from the possibly negative consequences of

technological progress (and globalization more generally) may also have far-

reaching political consequences.

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Panel A: Organizational changes and alternative measures of technological change

0 1 or 2 3 or 4

(i) percentage of firms 44.32% 43.29% 12.39%

(ii) # investments in IT (sum of yearly dummies) 1.31 1.73 1.79

p-value 0.00 0.00

(iii) investment in IT per employee (=1 in firms with no change) 1.63 2.38

p-value 0.00 0.00

(iv) dummy for product or process innovations (3 year period) 0.11 0.20 0.29

p-value 0.00 0.00

0 1 or 2 3 or 4

(i) firm size 44.16 89.92 134.27

p-value 0.00 0.00

(ii) firm daily wage (in logs) 4.40 4.49 4.49

p-value 0.00 0.00

(iii) fixed firm effect (relative to nonrestructuring firms) 2.68% 2.45%

p-value 0.00 0.00

(iv) fixed worker effect (relative to nonrestructuring firms) 2.93% 1.33%

p-value 0.00 0.02

(v) baseline task usage, in percentages

abstract 39.95% 46.06% 43.89%

p-value 0.00 0.00

routine 39.59% 38.08% 40.24%

p-value 0.14 0.55

manual 20.46% 15.86% 15.87%

p-value 0.00 0.00

(vi) baseline age structure, in percentages

<30 19.83% 19.64% 21.04%

p-value 0.49 0.00

31-50 57.36% 58.04% 57.38%

p-value 0.01 0.96

>50 22.81% 22.32% 21.58%

p-value 0.04 0.00

Sources: Registry data and the IAB Establishment Panel, 1993–2010. We calculate task shares based on a categorization constructed

using the 1991/1992 wave of the German BIBB/IAB Qualification and Career Survey.

Panel B: Number of organizational changes and firm characteristics (at baseline)

Number of changes introduced over past 3 years

Table 1: T&O: Descriptives

Number of changes introduced over past 3 years

Notes: The table reports correlations between the number of organizational changes (T&O) and alternative measures of technological

change (Panel A) and firm characteristics measured at baseline, before organizational change takes place (Panel B). To construct the

number of organizational changes, we sum information from four binary variables for whether firms transfer responsibilities to

subordinates, introduce team work or self-responsible working groups, introduce profit centers, and/or pool or restructure internal

departments. The first row of panel A reports the share of firms by level of organizational change. The average number of organizational

changes across all years is 1.01 (1.81 conditional on at least one change). In panel A, the value of IT investments per

employee—calculated from total investment in euros, share of IT investment in total investment, and employment levels—is normalized

to one for firms that implement no organizational change. In panel B, fixed firm effects and fixed worker effects are pre-estimated from

social security data using the previous seven calendar years for each point in time. All statistics are weighted by employment to make

them representative of workers (larger firm = larger weight) and use firm panel weights to correct for oversampling of larger firms and

certain industries in the IAB Establishment Panel. In all panels, the p-values are derived from pseudo-regressions in which standard

errors are clustered at the firm level.

routine manual abstract

employment shares 37.11% 17.70% 45.19%

Panel A

mean routine task index 0.594 0.247 0.209

mean manual task index 0.194 0.569 0.091

mean abstract task index 0.212 0.184 0.701

Panel B

mean wage (percentage difference relative to routine) 2.63% 33.41%

Percentage low-edu 18.99% 6.34% 3.09%

Percentage medium-edu 77.99% 90.99% 66.83%

Percentage high-edu 3.03% 2.68% 30.08%

Percentage ages <30 17.91% 18.25% 15.68%

Percentage aged 30-49 57.04% 56.90% 59.87%

Percentage aged 50+ 21.89% 21.48% 21.79%

Table 2: Differences between Manual, Routine, and Abstract Tasks

Notes: The first row of the table displays the share of workers in each task in our sample. Panel A shows the task indices,

continuous measures of the prevalence of each task (see Section 2.4), across workers in each of the three task groups. The

first row of panel B compares average wages in the three task groups, using imputed log wages derived as detailed in the

Appendix (cf. Dustmann, Ludsteck, and Schönberg, 2009; Card, Heining, and Kline, 2013). The other rows of panel B display

education and age employment shares in the three task groups (e.g., the share of workers with intermediate education among

routine workers). All statistics are weighted by employment to make them representative of workers (larger firm = larger weight)

and use firm panel weights to correct for oversampling of larger firms and certain industries in the IAB Establishment Sample.

Sources: Registry data and the IAB Establishment Panel, 1993–2010. We calculate task shares based on a categorization

constructed using the 1991/1992 wave of the German BIBB/IAB Qualification and Career Survey.

Panel A: Tasks

(1) (2) (1) (2) (1) (2)

number of changes 0.015 0.029 -0.363*** -0.227** 0.347** 0.198

(0.054) (0.060) (0.122) (0.111) (0.151) (0.145)

year fixed effects yes yes yes yes yes yes

firm size, commuting area, industry no yes no yes no yes

mean change in employment share

Panel B: Education

(1) (2) (1) (2) (1) (2)

number of changes -0.043 -0.008 -0.249* -0.131 0.291** 0.139

(0.057) (0.041) (0.144) (0.120) (0.128) (0.105)

year fixed effects yes yes yes yes yes yes

firm size, commuting area, industry no yes no yes no yes

mean change in employment share

Panel C: Age

(1) (2) (1) (2) (1) (2)

number of changes -0.033 -0.043 0.370*** 0.308** -0.337** -0.265*

(0.065) (0.073) (0.115) (0.135) (0.131) (0.148)

year fixed effects yes yes yes yes yes yes

firm size, commuting area, industry no yes no yes no yes

mean change in employment share

number of observations 26,908 24,939 26,908 24,939 26,908 24,939

Panel D: Placebo from Tasks Regressions

(1) (2) (1) (2) (1) (2)

contemporenuous number of changes -0.026 -0.029 -0.352*** -0.227** 0.378*** 0.257**

(0.043) (0.046) (0.082) (0.089) (0.090) (0.103)

future number of changes 0.071* 0.089* -0.042 0.027 -0.029 -0.116

(0.041) (0.047) (0.102) (0.105) (0.110) (0.117)

year fixed effects yes yes yes yes yes yes

firm size, commuting area, industry no yes no yes no yes

number of observations 12,450 11,893 12,450 11,893 12,450 11,893

Notes: The table reports estimates for the effects of T&O on the task (Panel A), education (Panel B), and age (Panel C) structure of the

firm. Panel D investigates the same groups (tasks) as in Panel A, but includes in the regressions the number of future organizational

changes (implemented between t and t+3) in addition to the number of contemporaneous organizational changes (carried out between t-

3 and t). The dependent variable in each panel and column is the change in the firm level employment share of the specific group (e.g.,

routine workers) between t-3 and t. The main variable of interest, “number of changes,” which measures the number of organizational

changes between t-3 and t, ranges between zero and four. Employment shares vary from 0 to 100; hence, the coefficients reported in the

table refer to the effect of one additional organizational change on the percentage point change in the respective employment share. In

the first set of column, we control only for year fixed effects (as in equation (1) in the text); in the second, we add in controls (as in

equation (2) in the text) for firm size (measured as log employment at baseline), commuting zones (a regional control derived by dividing

West Germany into 50 regions based on commuting patterns), and industry (one digit controls). The unit of observation is the firm/year,

and all regressions are weighted by (baseline) employment. Standard errors are clustered at the firm level; the significance levels are

*10%, **5%, ***1%. In the last rows of each panel, the table shows the mean changes in employment shares of the specific group. To

compute these, we again weight by (baseline) employment and additionally use firm panel weights to correct for oversampling of larger

firms and certain industries in the IAB Establishment Panel.

Sources: Registry data and the IAB Establishment Panel, 1993–2010. We calculate task shares based on a categorization constructed

using the 1991/1992 wave of the German BIBB/IAB Qualification and Career Survey.

Manual Tasks Routine Tasks Abstract Tasks

Age <30 Age 30-49 Age 50-65

-0.308% -1.222% 1.530%

-0.944% 0.014% 0.930%

1.707%1.547%-3.253%

Table 3: T&O and the Firm's Task, Education, and Age Structure

Manual Tasks Routine Tasks Abstract Tasks

Low Edu Medium Edu High Edu

Wage growthEmployment

growth

External

separationsExternal hiring Internal reshuffling

number of changes 0.074 0.473 -0.199 0.274** 0.131**

(0.123) (0.328) (0.316) (0.117) (0.053)

number of observations 25821 25866 25866 25866 25866

mean 7.637% -8.284% 11.605% 19.889% 1.198%

Table 4: T&O, Wage and Employment Growth, Churning, and Internal Reshuffling

Notes: Notes: The table reports estimates for the effects of T&O on firm wage growth (the log difference in mean wages in the firm

between t-3 and t); employment growth (employment in t minus firm employment in t-3, divided by firm employment in t-3); external

hiring (the number of workers who joined the firm between t-3 and t, divided by firm employment in t-3); external separations (the

number of workers who left the firm over the 3-year period between t-3 and t, divided by employment at t-3); and internal reshuffling

(the number of individuals who have changed task within the firm over this 3-year period, divided by baseline employment). The main

variable of interest, “number of changes,” which measures the number of organizational changes between t-3 and t, ranges between

zero and four. All regressions control (as in equation (2) in the text) for firm size (measured as log employment at baseline),

commuting zones (a regional control derived by dividing West Germany into 50 regions based on commuting patterns), and industry

(one digit controls). The unit of observation is the firm/year, and all regressions are weighted by (baseline) employment. Standard

errors are clustered at the firm level; the significance levels are *10%, **5%, ***1%. The last row of the table shows the mean of each

dependent variable in our sample. To compute these, we again weight by (baseline) employment and additionally use firm panel

weights to correct for oversampling of larger firms and certain industries in the IAB Establishment Panel.

Sources: Registry data and IAB Establishment Panel, 1993–2010. We calculate tasks for the internal reshuffling regression based on

a categorization constructed using the 1991/1992 wave of the German BIBB/IAB Qualification and Career Survey.

WorkingWorking at the

same task

Working at same

firm, different task

Working at the

same firmWage growth

(1) (2) (3) (4) (5)

Panel A: By tasks

(1) Manual

number of organizational changes 0.034 -0.026 0.120 0.709** 0.140*

(0.135) (0.185) (0.116) (0.335) (0.075)

mean of dependent variable 83.238% 77.021% 1.974% 67.792% 6.000%

(2) Routine

number of organizational changes -0.023 -0.242* 0.195*** 0.528 0.141*

(0.123) (0.144) (0.072) (0.367) (0.082)

mean of dependent variable 82.210% 77.277% 1.608% 65.665% 6.806%

(3) Abstract

number of organizational changes -0.078 -0.093 0.043** 0.648 -0.062

(0.100) (0.108) (0.021) (0.406) (0.084)

mean of dependent variable 83.190% 81.119% 0.521% 65.534% 9.807%

Panel B: By age

(1) <30

number of organizational changes 0.037 -0.175 0.205** 0.540 0.122

(0.074) (0.129) (0.088) (0.337) (0.111)

mean of dependent variable 81.800% 74.933% 1.512% 58.074% 13.308%

(2) 30-49

number of organizational changes 0.134* 0.029 0.141** 0.872** 0.067

(0.074) (0.097) (0.055) (0.366) (0.079)

mean of dependent variable 89.875% 85.922% 1.292% 71.929% 7.345%

(5) >49

number of organizational changes -0.649* -0.699** 0.051* -0.205 0.028

(0.333) (0.330) (0.030) (0.397) (0.063)

mean of dependent variable 65.394% 63.888% 0.724% 56.897% 3.977%

Panel C: Detailed older age groups

(1) 50-54

number of organizational changes -0.231 -0.312 0.087** 0.277 0.028

(0.425) (0.424) (0.041) (0.538) (0.067)

mean of dependent variable 80.008% 77.942% 0.935% 68.466% 4.316%

(2) 55-59

number of organizational changes -1.362*** -1.371*** 0.003 -0.966*** 0.046

(0.353) (0.345) (0.020) (0.338) (0.069)

mean of dependent variable 53.123% 52.165% 0.535% 47.664% 3.366%

(3) >59

number of organizational changes -0.592** -0.591** 0.001 -0.386* -0.103

(0.235) (0.233) (0.014) (0.210) (0.144)

mean of dependent variable 24.714% 24.412% 0.189% 22.530% 2.754%

Table 5: T&O and Career Outcomes, by Tasks and Age at Baseline

Notes: The table reports estimates of the impact of T&O on the career paths over a three-year period of workers who were employed in one of the

firms in the IAB Establishment Panel at baseline (in t-3), and were at most 62 at baseline; see equation (3) in the text. Panel A distinguishes

between workers who performed manual, routine, or abstract tasks at baseline; Panels B and C distinguish between workers of different age

groups (under 30, between 30 and 49, 50 and above at baseline in Panel B and between 50 and 54, between 55 and 59 and between 59 and 62 at

baseline in Panel C). The main variable of interest, “number of changes,” which measures the number of organizational changes in the baseline

firm between t-3 and t, ranges between zero and four. We consider the following outcome variables: whether the worker is still employed at any

firm in Germany at time t (“working”); whether the worker is employed in the same task at time t as at baseline (“working at the same task”);

whether the worker is employed at a different task, but same firm, at time t (“working at a different task but at the same firm”); whether the worker

is employed at the same firm irrespective of task at time t as at baseline (“working at the same firm”); and “wage growth”, computed from imputed

wages to deal with censoring (see Appendix), between t-3 and t. Whereas regressions in the first four columns refer to all workers who were

employed in one of the firms in the IAB Establishment Panel at baseline—irrespective of whether or not they are employed at time t—the wage

growth regressions in the last column are restricted to workers who are employed at time t. In the first four columns, the dependent variables take

the values of either 0 or 100 so that the coefficients reported in the table refer to the impact of one additional organizational change on the

percentage point change of the particular event. All regressions control for age and age squared at baseline, foreign status, year fixed effects, firm

size (measured as log employment at baseline), commuting zones (a regional control derived by dividing West Germany into 50 regions based on

commuting patterns), and industry (one digit controls). With the exception of the wage growth regressions, they also include pre-estimated fixed

firm effects and fixed worker effects calculated using the seven years prior to the baseline year (cf. Card, Heining, and Kline, 2013). The unit of

observation is the worker/year, and the sample includes 8,154,560 observations in the first four columns and 6,695,055 in the last (wage growth)

column. Standard errors are clustered at the firm level; the significance levels are *10%, **5%, ***1%. The table also reports the means of the

dependent variables. We compute these using the firm panel weights to correct for oversampling of larger firms and certain industries in the IAB

Establishment Panel.

Sources: Registry data and IAB Establishment Panel, 1993–2010. We calculate tasks for the internal reshuffling regression based on a

categorization constructed using the 1991/1992 wave of the German BIBB/IAB Qualification and Career Survey.

Working Manual task Routine taskAbstract

task

Abstract

task, same

firm

Abstract

task,

different firm

Panel A: Routine, all

after 3 years -0.023 0.038 -0.242* 0.183*** 0.122*** 0.060**

number of organizational changes (0.123) (0.062) (0.144) (0.047) (0.038) (0.025)

after 6 years 0.102 0.157* -0.479** 0.220** 0.046 0.174***

number of organizational changes (0.169) (0.082) (0.206) (0.088) (0.084) (0.053)

Panel B: By age (after 3 years)

<30 0.083 0.086 -0.233 0.231*** 0.110* 0.121***

number of organizational changes (0.090) (0.092) (0.151) (0.077) (0.058) (0.043)

30-49 0.074 0.034 -0.145 0.185*** 0.133*** 0.052**

number of organizational changes (0.099) (0.070) (0.136) (0.052) (0.043) (0.026)

>49 -0.576 -0.018 -0.654 0.097*** 0.081*** 0.016

number of organizational changes (0.404) (0.029) (0.399) (0.032) (0.027) (0.017)

Panel C: Detailed age groups for workers older than 49 at baseline (after 3 years)

50-54 -0.142 -0.028 -0.263 0.149*** 0.120*** 0.029

number of organizational changes (0.535) (0.038) (0.534) (0.044) (0.035) (0.026)

55-59 -1.314*** 0.004 -1.341*** 0.024 0.028 -0.004

number of organizational changes (0.386) (0.022) (0.373) (0.023) (0.021) (0.010)

>59 -0.514** -0.041*** -0.485** 0.012 0.019 -0.007

number of organizational changes (0.225) (0.015) (0.220) (0.022) (0.018) (0.012)

Sources: Registry data and IAB Establishment Panel, 1993–2010. We calculate tasks based on a categorization constructed using the

1991/1992 wave of the German BIBB/IAB Qualification and Career Survey.

Notes: The table reports estimates for the effects of T&O on career outcomes over a 3- and 6-year period of workers who were

employed at a firm in the IAB Establishment Panel, performed routine tasks, and were at most 62 at baseline (in t-3); see equation (3) in

the text. Panel A displays results pooled for all routine workers; Panels B and C further distinguish between different age groups (under

30, between 30 and 49, 50 and above at baseline in Panel B and between 50 and 54, between 55 and 59 and between 59 and 62 at

baseline in Panel C). The main variable of interest in both the 3- and the 6-year regressions, “number of changes,” which measures the

number of organizational changes in the baseline firm between t-3 and t, ranges between zero and four. We consider the following

outcome variables: whether the individual is employed (at any firm in Germany) at time t in the 3-year regressions and at time t+3 in the

6-year regressions (“working”); whether the individual is employed in a manual, routine or abstract occupation at time t in the 3-year

regressions or t+3 in the 6-year regressions (“manual task”, “routine task”, “abstract task”); and whether the worker is employed in an

abstract occupation within the same or a different firm at time t or t+3 (“abstract task, same firm” and “abstract task, different firm”). The

dependent variables take the values of either 0 or 100 so that the coefficients reported in the table refer to the impact of one additional

organizational change on the percentage point change of the particular event. All regressions control for age and age squared at

baseline, foreign status, year fixed effects, firm size (measured as log employment at baseline), commuting zones (a regional control

derived by dividing West Germany into 50 regions based on commuting patterns), industry (one digit controls), pre-estimated fixed firm

effects and fixed worker effects calculated using the seven years prior to the baseline year (cf. Card, Heining, and Kline, 2013). The unit

of observation is the worker/year, and the full sample (all routine workers at baseline) includes 8,154,560 observations. Standard errors

are clustered at the firm level; the significance levels are *10%, **5%, ***1%.

Table 6: T&O and Career Outcomes for Routine Workers

(1) (2) (3) (4)

All low-skilled medium-skilled high-skilled

mean employment share in training 17.61% 7.54% 18.41% 22.84%

number of organizational changes 1.278*** 0.842* 2.513*** 2.960***

(0.321) (0.454) (0.388) (0.475)

number of observations 17,199 12,788 15,212 12,786

Table 7: T&O and Training

Notes: The table reports estimates for the effects of T&O on firms’ training activities. The main variable of interest, “number of

organizational changes”, which measures the number of organizational changes over three years between t-3 and t, ranges

between zero and four. The dependent variables are the shares of workers (all workers in column (1) and low-, medium-, and

high-skilled workers in columns (2) to (4)) who received further training between t-3 and t. These shares vary from 0 to 100;

hence, the coefficients reported in the table refer to the effect of one additional organizational change on the percentage point

change in the respective training share. All regressions control (as in equation (2) in the text) for year fixed effects, firm size

(measured as log employment at baseline), commuting zones (a regional control derived by dividing West Germany into 50

regions based on commuting patterns), and industry (one digit controls). The unit of observation is the firm/year, and all

regressions are weighted by (baseline) employment. Standard errors are clustered at the firm level; the significance levels are

*10%, **5%, ***1%.

Sources: Registry data and IAB Establishment Panel, 1993–2010. We calculate tasks based on a categorization constructed

using the 1991/1992 wave of the German BIBB/IAB Qualification and Career Survey.

(1) (2) (3) (4) (5) (6)

Working

Panel A: All

-1.259*** 0.031 -0.891*** -0.167 -0.704*** -0.388**

(0.314) (0.069) (0.298) (0.104) (0.132) (0.196)

Manual

-0.917** 0.160* -0.701 -0.933** -0.029 0.046**

(0.442) (0.089) (0.443) (0.429) (0.044) (0.022)

Routine

-1.247*** 0.052 -1.055*** -0.002 -1.265*** 0.021

(0.338) (0.067) (0.355) (0.020) (0.325) (0.021)

Abstract

-1.281*** -0.033 -0.734** -0.006 -0.003 -1.272***

(0.327) (0.096) (0.298) (0.006) (0.010) (0.327)

Low

-1.320*** 0.113 -0.915** -0.020 -0.868*** -0.432

(0.383) (0.083) (0.374) (0.127) (0.283) (0.280)

Medium

-1.267*** 0.060 -0.953*** -0.251** -0.748*** -0.267

(0.325) (0.058) (0.318) (0.125) (0.139) (0.183)

High

-1.256*** -0.112 -0.674 0.030 0.005 -1.291***

(0.397) (0.169) (0.421) (0.032) (0.062) (0.393)

number of organizational

changes

Panel B: By tasks at baseline

Panel C: By education at baseline

Notes: The table reports estimates for the effects of T&O on the career outcomes over a 3-year period of older workers who were

employed at a firm in the IAB firm panel and between 55 and 62 at baseline; see equation (3) in the text. Panel A displays results pooled

for older workers. Panels B and C break down the analysis by task usage at baseline (manual, routine, abstract) and education (low,

medium, high). The main variable of interest in both the 3- and the 6-year regressions, “number of changes,” which measures the number

of organizational changes in the baseline firm between t-3 and t, ranges between zero and four. We consider the following outcome

variables: whether the individual is employed (at any firm in Germany) at time t (column (1)); wage growth (column (2); defined only for

workers who are employed at t); whether the worker is employed at the same firm as at baseline at time t (column (3)); and whether the

individual performs manual, routine or abstract tasks at time t (columns (4) to (6)). The dependent variables take the values of either 0 or

100 so that the reported coefficients refer to the impact of one additional organizational change on the percentage point change of the

particular event (columns (1) and (3) to (6)) or the percent increase in wages (column (2)). All regressions control for age and age

squared at baseline, foreign status, year fixed effects, firm size (measured as log employment at baseline), commuting zones (a regional

control derived by dividing West Germany into 50 regions based on commuting patterns), and industry (one digit controls). With the

exception of the wage growth regressions, they also include pre-estimated fixed firm effects and fixed worker effects calculated using the

seven years prior to the baseline year (cf. Card, Heining, and Kline, 2013). The unit of observation is the worker/year, and the full sample

(all workers between 55 and 62 at baseline) includes 8,154,560 observations. Standard errors are clustered at the firm level; significance

levels: *10%, **5%, ***1%.

Sources: Registry data and IAB Establishment Panel, 1993–2010. We calculate tasks for the internal reshuffling regression based on a

categorization constructed using the 1991/1992 wave of the German BIBB/IAB Qualification and Career Survey.

number of organizational

changes

Table 8: T&O and Career Outcomes for Workers Aged 55 and Over

Wage growthWorking at the

same firmManual Routine Abstract

number of organizational

changes

number of organizational

changes

number of organizational

changes

number of organizational

changes

number of organizational

changes

(1) (2) (3) (4) (5) (6) (7) (8)

working abstract abstract, abstract, working abstract abstract, abstract,

same firm other firm same firm other firm

Panel A: by apprenticeship training

number of changes 0.012 -0.041 -0.042 0.001 -1.377*** -0.03 -0.015 -0.015

(0.169) (0.094) (0.082) (0.009) (0.445) (0.030) (0.026) (0.014)

number of changes X share apprentices -1.274 6.304*** 4.641** 1.663* 3.206 1.418** 1.121* 0.297

(2.328) (2.427) (2.172) (1.001) (6.119) (0.682) (0.631) (0.280)

mean apprenticeship share

Panel B: by firm's union status

number of changes 0.439 -0.081 -0.103* 0.022 -0.326 -0.121 -0.043 -0.077

(0.343) (0.091) (0.059) (0.068) (0.966) (0.076) (0.051) (0.053)

number of changes X union recognition -0.474 0.280*** 0.245*** 0.035 -1.162 0.149* 0.077 0.072

(0.352) (0.100) (0.069) (0.071) (1.025) (0.079) (0.056) (0.053)

mean union recognition rate

Panel C: by share employees on fixed term contracts

number of changes -0.265 0.140** 0.100*** 0.040 -2.182*** 0.015 0.015 0.000

(0.176) (0.058) (0.046) (0.026) (0.482) (0.024) (0.020) (0.011)

number of changes X fixed term contracts 5.210*** 0.418 0.694 -0.276 12.357*** 0.288 0.332 -0.044

(1.638) (0.631) (0.568) (0.223) (2.743) (0.325) (0.286) (0.113)

mean share on fixed term contracts

Table 9: T&O, Career Outcomes of Routine Workers, and Labor Market Institutions

Notes: The table investigates how the apprenticeship share at baseline (Panel A), union recognition (firm’s membership in an employer association) at baseline (Panel B), and the share of employees on

fixed term contracts at baseline (Panel C) affect T&O’s effects on career outcomes of workers who were employed in one of the firms in the IAB firm panel and performed routine tasks at baseline. Columns

(1) to (4) display results pooled for all routine workers. Columns (5) to (8) refer to routine workers aged 55-62 at baseline. The main variable of interest, “number of changes”, which measures the number of

organizational changes in the baseline firm between t-3 and t, ranges between zero and four. This variable is interacted with the share of apprentices in the firm at baseline (Panel A), whether the firm

recognized a union at baseline (Panel B), and the share of employees on fixed term contracts at baseline (Panel C). We consider the following four outcome variables: whether the individual is employed (at

any firm in Germany) at time t (columns (1) and (5)); whether the individual is employed in an abstract occupation at time t in any firm (columns (2) and (6)); whether the individual is employed in an abstract

occupation at time t at the baseline firm (columns (3) and (7)) or at an outside firm (columns (4) and (8)) at time t. All regressions control, in addition to the apprenticeship share at baseline in Panel A, union

recognition in Panel B, and the share of employees on fixed term contracts at baseline in Panel C, for age and age squared at baseline, foreign status, year fixed effects, firm size (log employment at

baseline), commuting zones (a regional control derived by dividing West Germany into 50 regions based on commuting patterns), industry (one digit controls), pre-estimated fixed firm effects and fixed

worker effects calculated using the seven years prior to the baseline year (cf. Card, Heining, and Kline, 2013). The unit of observation is the worker/year. Standard errors are clustered at the firm level; the

significance levels are *10%, **5%, ***1%.

Sources: Registry data and IAB Establishment Panel, 1993–2010. We calculate tasks for the internal reshuffling regression based on a categorization constructed using the 1991/1992 wave of the German

BIBB/IAB Qualification and Career Survey.

0.044

0.845

0.078

all routine workers routine workers, 55 and older

Figure 1a: Changes in Employment Shares by Occupational Skill Percentile

Figure 1b: Task Shares Over Time

Notes: The figure plots log changes in employment shares by 1990 occupational skill percentile rank using a locally weighted smoothing

regression (bandwidth 0.8 with 100 observations), where skill percentiles are measured as the employment-weighted percentile rank of an

occupation’s median log wage in 1990 (cf. Acemoglu and Autor, 2011). The sample is composed of male workers aged 18-65 in full time

employment and representative of the corresponding worker population in West Germany.

Source: 2% random sample of the Registry Data (SIAB7510), 1980–2010.

Source: Registry data and IAB Establishment Panel, 1993–2012.

Notes: This figure plots the share of workers in manual-, routine-, and abstract dominated occupations between 1993 and 2012. The unit of

observation is the firm/year, and observations are weighted by (baseline) employment and the firm panel weights, to make sure that they are

representative of the worker population in West Germany.

0 0.4314

1 0.2722

2 0.1694

3 0.0881

4 0.0389

Notes: The figures provide some descriptive evidence about the frequency and type of T&O. Panel A

displays the share of firms that implement no, 1, 2, 3, or 4 organizational changes over a 3-year period. Panel

B shows the share of firms that over a 3-year period merge or restructure departments internally; transfer

responsibilities to subordinates; introduce team work or self-responsible working groups; or introduce profit

centers (i.e., units or departments that carry out their own cost and result calculations). The unit of

observation is the firm/year, and observations are weighted by (baseline) employment and the firm panel

weights, to make sure that they are representative of the worker population in West Germany.

Source: IAB Establishment Panel, 1995-2010.

Panel A: Frequency of T&O

Panel B: Types of T&O

Figure 2: T&O: Descriptives

0

0.1

0.2

0.3

0.4

0.5

0 1 2 3 4

Shar

e o

f es

tab

lish

men

ts

Number of organizational changes in a 3-year period

010

20

30

40

Perc

enta

ge o

f E

stab

lish

men

ts

Merging of Departments Shifting of Responsibilities

Introduction of Teamwork Departments with Own Costs

Figure 3: Event-Study Analysis

Notes: The figures display the results of an event-study analysis focused on two outcome variables: upgrades from routine to abstract

jobs (Panel B) and transitions from employment to non-employment of workers age 55 and over at baseline (Panel C). “Treatment” firms

do not implement any T&O between t-3 and t, but carry out at least two organizational changes between t and t+3 (the “treatment

period”). “Control” firms introduce T&O neither between t-3 and t nor between t and t+3. No restrictions on the number of organizational

changes implemented by either treatment or control firms between t-6 and t-3 or between t+3 and t+6 are imposed. Panel A contrasts the

number of organizational changes that treatment and control firms carry out in each period. Panels B and C then plot the differences in

the firm level shares of workers upgrading from routine to abstract tasks (either within or across firms, Panel B) and of older workers who

are remain employed at the end of the period (Panel C) between treatment and control firms in each period, where differences between

treatment and control firms are normalized to 0 in the t-3 vs t pre-treatment period. Firm-level shares vary from 0 to 100. The sample

consists of 520 treatment firms and 2626 control firms, which we follow over time. The unit of observation is the firm/year, and

observations are weighted by (baseline) employment. The vertical bars in panels B and C indicate 95% confidence intervals.

Sources: Registry data and the IAB Establishment Panel, 1993–2010. We calculate task shares based on a categorization constructed

using the 1991/1992 wave of the German BIBB/IAB Qualification and Career Survey.

C. Employment-to-non-employment transitions of workers aged 55 and older

B. Routine to abstract transitions

A. Average number of organizational changes, treatment vs. control groups

Appendix: Imputing Censored Wages

To impute top-coded wages, we first define age-education cells based on five age

groups (with 10-year intervals) and three education groups (no postsecondary

education, vocational degree, and college or university degree). Within each of

these cells, following Dustmann, Ludsteck, and Schönberg (2009) and Card,

Heining, and Kline (2013), we estimate Tobit wage equations separately by year

while controlling for age, firm size (a quadratic and a dummy for firm size greater

than 10), the focal worker’s mean wage and mean censoring indicator (each

computed over time but excluding observations from the current time period), and

firm mean wage, mean censoring indicator, mean years of worker schooling, and

mean university degree indicator (each computed at the current time period by

excluding the focal worker observations). For workers observed in one time period

only, the mean wage and mean censoring indicators are set to sample means and a

dummy variable is included. A wage observation censored at value c is then

imputed by the value 𝑋�̂� + �̂�Φ−1[𝑘 + 𝑢(1 − 𝑘)], where Φ is the standard normal CDF,

u is drawn from a uniform distribution, 𝑘 = Φ[(𝑐 − 𝑋�̂�)/�̂�], and �̂� and �̂� are estimates

for the coefficients and standard deviation of the error term from the tobit

regression.

Repairing machines

Driving vehicles

Hosting , serving (e.g. wait tables), accommodating customers

Securing, watching over, keeping guard

Nursing, personal care of others

Attending, feeding, equipping machinery

Fabricating, manufacturing materials, preparing (e.g., food)

Building, constructing and installing appliances

Filing, sorting, labeling

Billing, computing and bookkeeping[*]

Writing and correspondence[**]

Buying, selling, managing payments, assisting customers[***]

Planning, designing, sketching

Executing or interpreting laws, rules, or regulations

Analysis and research

Computing and programming

Educating, training, teaching, consulting

Publicizing, presenting, disseminating

Hiring, management and control, organizing or coordinating

Billing, computing and bookkeeping[*]

Writing and correspondence[**]

Buying, selling, managing payments, assisting customers[***]

Source: 1991/1992 wave of the German BIBB/IAB Qualification and Career Survey.

Manual tasks

Routine tasks

Abstract tasks

Table A1: Classification of Activities into Tasks using BIBB

Notes: This table categorizes the activities in the 1991/1992 wave of the German BIBB/IAB Qualification and

Career Survey into routine, manual, and abstract tasks. Three task sets—billing, calculating, and bookkeeping;

writing and correspondence; and buying or selling—are considered routine if the work process is predefined to the

last detail and the tasks are highly repetitive or regularly accomplished using a calculator or accounting machine; a

photocopier, filing cards, computer lists, microfilm reader; or a cash register or counter, respectively. Otherwise,

these activities are classified as abstract.