Digital Transformation and Subjective Job Insecurity in Germany
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European Sociological Review, 2021, 1–19
doi: 10.1093/esr/jcaa066
Original Article
Digital Transformation and Subjective Job
Downloaded from https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcaa066/6104095 by guest on 24 May 2021
Insecurity in Germany
Katharina Dengler and Stefanie Gundert*
Research Department Panel Labour Market and Social Security, Institute for Employment Research (IAB),
90478 Nuremberg, Germany
*Corresponding author. Email: stefanie.gundert@iab.de
Submitted December 2018; revised October 2020; accepted November 2020
Abstract
The present study examines to what extent employees in Germany are afraid of losing their jobs,
depending on the degree of computerization of their occupations. So far, empirical evidence on the re-
lationship between digital transformation and subjective job insecurity is scarce. We distinguish three
interrelated insecurity measures: cognitive job insecurity, i.e. the individual assessment of job loss
probability, labour market insecurity, i.e. the perceived availability of job alternatives, and affective
job insecurity, i.e. the extent to which individuals are worried about a potential job loss. The analysis
is based on a large-scale panel study from Germany and refers to the period between 2013 and 2016.
Computerization is measured by the occupation-specific substitution potential, i.e. the extent to which
occupational tasks are substitutable by computers or computer-controlled machines. The results from
multivariate panel analysis suggest that the digital transformation has a negative impact on cognitive
job insecurity. We do not find effects on labour market insecurity and affective job insecurity.
Introduction Job insecurity is a prominent research topic in soci-
ology. There are many studies on its individual and soci-
Current debates on digital transformation often revolve
etal consequences (Giesecke, 2009; Kalleberg, 2009;
around the fear that computers could replace human la-
Scherer, 2009; Barbieri, 2016). While stable employ-
bour. A prominent study by Frey and Osborne (2017) sug-
ment provides economic and social resources and there-
gests that approximately 47% of US employees will be at
by fosters social integration, employment insecurity and
risk of automation in the next 10–20 years. However, digit-
job loss can have detrimental effects on individual well-
al transformation can also offer opportunities, e.g. by creat-
being and contribute to social exclusion (Gundert and
ing new jobs. Therefore, employees do not necessarily
Hohendanner, 2014).
perceive their jobs as threatened by technological progress. In recent years, the subjective perception of job inse-
According to recent population surveys, many people are in- curity has been receiving increasing attention. The impli-
deed concerned that robots and artificial intelligence may cations of insecure employment for employees largely
lead to job losses (European Commission, 2017). However, depend on individuals’ subjective assessment of their
most employees do not expect that their own jobs will be situation. Subjective job insecurity negatively affects life
replaced by computers (Kelley, Warhurst and Wishart, satisfaction and physical and mental health (Carr and
2018). The link between technological progress and job in- Chung, 2014; De Witte, Pienaar and De Cuyper, 2016)
security is thus ambiguous.
C The Author(s) 2021. Published by Oxford University Press.
V
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.2 European Sociological Review, 2021, Vol. 00, No. 0
as well as job satisfaction, job performance, motivation, carried out by humans are increasingly taken over by
and turnover intentions (Sverke, Hellgren and Näswall, machines. Such fears are not new (Mokyr, Vickers and
2006; Lee, Huang and Ashford, 2018). Given the far- Ziebarth, 2015); they date back to the technological un-
reaching consequences of subjective job insecurity, a employment approach of Keynes (1933). On the other
large body of research has addressed its determinants hand, although new digital technologies can substitute
(for an overview, see, e.g. Chung and Mau, 2014; Keim, certain jobs, they can also offer new opportunities. First,
Landis and Earnest, 2014; Lee, Huang and Ashford, additional jobs can be created, as the new digital prod-
Downloaded from https://academic.oup.com/esr/advance-article/doi/10.1093/esr/jcaa066/6104095 by guest on 24 May 2021
2018). The extent to which individuals perceive their ucts and services must be built and provided. Second,
jobs as insecure is affected not only by individual, firm through product, process and service innovations, an
and job characteristics but also by social, political, and increase in productivity can lead to price cuts, and
macro-economic context factors. demand for products may increase. In sum, an overall
Technological progress in the course of the digital positive employment effect is possible (Appelbaum and
transformation is currently discussed as another deter- Schettkat, 1995).
minant of subjective job insecurity. As noted by Gallie Thus far, empirical studies have yielded controversial
et al. (2017), empirical evidence on the relationship be- results. For the United States, Acemoglu and Restrepo
tween technology and subjective job insecurity is scarce. (2020) find a decline in employment by analysing the
In the present study, we address this research gap and effects of industrial robots between 1990 and 2007.
determine to what extent computerization contributes Dauth et al. (2017) find no negative effects of industrial
to subjective job insecurity. We examine this question robots on total employment for Germany. There is a
using Germany as an example of an industrialized coun- negative effect in the manufacturing sector, but the de-
try where the impact of digital transformation is inten- crease in employment is offset by additional jobs in the
sively debated (Neufeind, O’Reilly and Ranft, 2018). service sector.
At the same time, due to the then favourable economic Another branch of literature addresses so-called
situation, subjective job insecurity has been relatively automation probabilities of occupations. Frey and
low on average in recent years compared to other Osborne (2017) suggest that approximately 47% of
European countries (European Commission, 2017). US jobs will be susceptible to automation in the next
This makes Germany a good case for investigating 10–20 years. In contrast, studies assuming that single
whether fear of job loss increases in the course of digital tasks rather than entire occupations will be replaced
transformation. provide significantly lower values. For the United
For the analysis, we use the German panel study States, Arntz, Gregory and Zierahn (2017) conclude
‘Labour Market and Social Security’ (PASS) and exam- that only 9% of employees are at risk of automation in
ine the period between 2013 and 2016. The extent to the next two decades. For Germany, Dengler and
which employees are affected by technological progress Matthes (2018a,b) and Arntz, Gregory and Zierahn
is measured by occupation-specific substitution poten- (2016) expect that only 12–25% of jobs are suscep-
tial, an indicator developed by Dengler and Matthes tible to automation.
(2018a). It measures the degree to which occupational It should be borne in mind, however, that automa-
tasks are substitutable by computers or computer- tion risks refer only to technical feasibility. Whether
controlled machines. We analyse whether increasing tasks are actually substituted by computers will also de-
computerization is related to increasing subjective job pend on other factors, such as legal or ethical obstacles.
insecurity. The sociological literature on technological change has
Our study is among the first to examine this relation- long emphasized that the development and implementa-
ship, using an innovative measure of computerization. tion of technology does not follow a path without alter-
Furthermore, the analysis extends the state of research natives (Wajcmann, 2006). Rejecting the paradigm of
by examining individual change in subjective job inse- technological determinism, innovations are seen as the
curity over time with panel analytical methods. result of political choices guided by particular interests
and power relations in social settings. Despite this com-
mon ground, the theories differ in their assumptions
Digital Transformation and Job Insecurity about the social impact of technology (Liker, Haddad
Fears that machines could take people’s jobs are a major and Karlin, 1999). Marxist-oriented labour process
concern in the media, politics and science. On the one studies focus on technology as a capitalist strategy of
hand, it is argued that digital transformation will con- increasing management control, with unambiguously
tribute to making jobs redundant, as jobs that are negative consequences for workers. Early studiesEuropean Sociological Review, 2021, Vol. 00, No. 0 3
illustrate how automation in industrial production led Theoretical Background, State of Research,
to deskilling and the substitution of workers by and Hypotheses
machines (Noble, 2011). Similarly, digital technology is
In accordance with the literature, we differentiate
regarded as a threat to workers’ power by promoting
between cognitive and affective job insecurity, two sep-
workplace surveillance, skill polarization and substitution
arate yet related components of subjective job insecurity
(Kristal, 2013). In contrast, social science and technology
(Borg and Elizur, 1992; Anderson and Pontusson, 2007;
studies assume that interests and power relations are not
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Huang et al., 2010). Cognitive job insecurity refers to
fixed but change over time (Wajcmann, 2006). The impact
the individually expected probability of losing one’s job.
of technology on employment is considered ambiguous.
In contrast, being defined as an emotional reaction to a
Whether digital technology is used to replace or comple-
perceived risk of job loss, affective job insecurity reflects
ment human labour is assumed to be contingent on the or-
the extent to which individuals are worried about the
ganizational and societal context (Shestakofsky, 2017).
possibility of losing their job.
In sum, the sociological and economic literature pro-
Figure 1 provides a schematic summary of common
vide no clear indication of whether digital transform-
theoretical assumptions regarding subjective job insecur-
ation will actually lead to a decrease or increase in
ity and its determinants (e.g. Anderson and Pontusson,
employment. Accordingly, employees do not necessarily
2007; Chung and Mau, 2014; Keim, Landis and
see their own jobs threatened by technological progress.
There is little evidence as to whether digital progress ac- Earnest, 2014; Lee, Huang and Ashford, 2018). First,
tually fosters subjective job insecurity. Job insecurity has the model assumes a positive relationship between cog-
been defined in different ways (for an overview see, Lee, nitive job insecurity (probability of job loss) and affect-
Huang and Ashford, 2018). Many definitions follow ive job insecurity (fear of job loss), which has been
Greenhalgh’s and Rosenblatt’s (1984) understanding of confirmed by empirical evidence (e.g. Anderson and
job insecurity as a subjective perception that refers to Pontusson, 2007; Berglund, Furaker and Vulkan, 2014;
the way individuals assess objective threats to the con- Hipp, 2016).
tinuity of their jobs. Cognitive job insecurity is regarded as a necessary
Overall, subjective job insecurity in Germany has but not sufficient condition for affective job insecurity
been comparatively low recently. While 10% of (Berglund, Furaker and Vulkan, 2014). In addition, the
employed and self-employed workers in Germany extent to which employees who expect to lose their jobs
expected to lose their job within the next 6 months, the are worried about this potential job loss hinges on the
EU average was 16% in 2015 (Eurofound, 2015). expected consequences. According to the model, the
Between the 1990s and mid-2000s, the proportion of expected consequences are largely determined by per-
employees who were worried about losing their jobs ceived income insecurity and labour market insecurity
increased in Germany (Lengfeld and Hirschle, 2009). (Anderson and Pontusson, 2007). Income insecurity
Later, this proportion has decreased, a trend that has derives from the availability of non-wage sources of in-
been attributed to the favourable economic development come, such as financial support from the state or family.
(Lengfeld and Ordemann, 2017). Labour market insecurity refers to perceived chances of
Despite the overall decrease in subjective job insecur- finding an equivalent job in the event of a job loss.
ity, digital transformation may foster fear of job loss Having few financial resources or poor labour market
among employees whose occupations are particularly prospects is assumed to increase individuals’ fear of job
affected by computerization. To our knowledge, only loss. Indeed, empirical studies have shown that both
Gallie et al. (2017) have analysed the relationship be- income and labour market insecurity contribute to
tween computerized technology and subjective job inse- affective job insecurity (Anderson and Pontusson, 2007;
curity so far. Their indicator of technology is a Berglund, Furaker and Vulkan, 2014; for labour market
composite measure incorporating information on insecurity: Hipp, 2016).
whether a job involves the use of computerized or auto- The literature has identified numerous determinants
mated equipment, on the proportions of employees of subjective job insecurity that can be grouped into
working with those technologies and on the importance three broad categories: individual characteristics
and complexity of computer use in an organization. (including socio-demographic and job attributes), the
They find greater insecurity in high-technology organi- labour market context and the organizational
zations. The authors regard it as possible that automa- environment.
tion and the resulting decline in traditional job tasks Evidence on the role of socio-demographic attributes,
have a negative impact on subjective job insecurity. like age, gender, and family context, is inconclusive and,4 European Sociological Review, 2021, Vol. 00, No. 0
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Figure 1. Theoretical framework
Source: Own illustration in accordance with the literature (e.g. Anderson and Pontusson, 2007; Chung and Mau, 2014; Keim, Landis and Earnest, 2014;
Hipp, 2016; Lee, Huang and Ashford, 2018).
10
8 6
Percent
4
2
0
0 .2 .4 .6 .8 1
Degree of computerization
Figure 2. Distribution of the degree of computerization
Source: Own calculations, PASS (2013–2016), BERUFENET (2013–2016), unweighted.
for the sake of brevity, will not be presented here in de- individual labour market resources, notably qualifica-
tail. Regarding other individual characteristics, research tions. With few exceptions (e.g. Lowe, 2018), most
indicates that subjective job insecurity is shaped by studies point to a negative association; i.e. individualsEuropean Sociological Review, 2021, Vol. 00, No. 0 5
with higher qualifications tend to be less insecure than Finally, the organizational context is regarded as an-
individuals with lower qualifications (for cognitive job other important source of employees’ sense of job inse-
insecurity: Anderson and Pontusson, 2007; Fullerton curity. Experienced and even anticipated organizational
and Wallace, 2007; Erlinghagen, 2008; Lübke and change or downsizing can increase subjective job inse-
Erlinghagen, 2014; for affective job insecurity: curity (Keim, Landis and Earnest, 2014; Lee, Huang and
Böckermann, 2004; Hipp, 2016). Past unemployment Ashford, 2018).
experience increases cognitive (Erlinghagen, 2008; In sum, there is a large literature on the determinants
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Lübke and Erlinghagen, 2014; Gallie et al., 2017; Lowe, of subjective job insecurity. However, the role of tech-
2018) and affective job insecurity (Böckermann, 2004; nology has hardly been addressed so far (Gallie et al.,
Lengfeld and Hirschle, 2009; Lübke, 2018). 2017). The basic assumption of the present study is that
Furthermore, there is evidence that individuals with bet- subjective job insecurity is affected by the degree to
ter health status are less likely to perceive their jobs as which occupations can be substituted by computers. The
insecure (Erlinghagen, 2008; Lübke and Erlinghagen, literature provides different explanations as to how soci-
2014). etal and economic phenomena, such as technology-
Evidence regarding occupational position is unclear. induced substitutability, translate into individual percep-
While some authors fail to find a relationship between tions of job insecurity. Organizational communication
occupational position and subjective job insecurity and media reporting could be two potential channels
(Gallie et al., 2017; Lowe, 2018; Lübke, 2018), several through which computerization can influence subjective
studies suggest that (unskilled) blue-collar workers are job insecurity (Mutz, 1992; Garz, 2012; Keim, Landis
more insecure about their jobs than (skilled) white-
and Earnest, 2014). In recent years, digital transform-
collar workers (for cognitive job insecurity: Anderson
ation and its potential employment effects have received
and Pontusson, 2007; Fullerton and Wallace, 2007;
special attention from the media. Therefore, even in the
Berglund, Furaker and Vulkan, 2014; Hipp, 2016; for
absence of personal experience with digital technology
affective job insecurity: (Hipp, 2016; Lengfeld and
at the workplace, employees might increasingly consider
Hirschle, 2009). Subjective job insecurity is also related
losing their jobs as likely. However, while media cover-
to the level of formal job security. A variety of studies
age of societal issues (e.g. unemployment) clearly influ-
show that both cognitive (Campbell et al., 2007;
ences individuals’ awareness of these phenomena as
Erlinghagen, 2008; Berglund, Furaker and Vulkan,
public problems, the link between media reporting and
2014; Lübke and Erlinghagen, 2014; Balz, 2017; Gallie
individuals’ assessment of their personal situation (e.g.
et al., 2017) and affective insecurity (Lengfeld and
risk of job loss) is weaker (Garz, 2012). These findings
Hirschle, 2009; Berglund, Furaker and Vulkan, 2014;
are consistent with the fact that a majority of respond-
Lübke, 2018) are higher among employees with fixed-
ents in a recent British population survey are of the opin-
term contracts than among permanent employees.
Moreover, employees in the public service sector are less ion that digital transformation endangers employment,
insecure than those in the private sector (for cognitive while only a small group expects to be affected them-
job security: Anderson and Pontusson, 2007; selves (Kelley, Warhurst and Wishart, 2018).
Erlinghagen, 2008; Hipp, 2016 ; Lowe, 2018; for affect- Actual organizational change and communication
ive job insecurity: Berglund, Furaker and Vulkan, 2014; are presumably more relevant mechanisms linking com-
Hipp, 2016; Lübke, 2018). Furthermore, cognitive and puterization and subjective job insecurity. It can be
affective job insecurity are lower among individuals argued that occupation-specific substitutability is more
with better economic resources (Böckermann, 2004; likely to affect subjective job insecurity in organizations
Fullerton and Wallace, 2007; Erlinghagen, 2008; where digital technology has been implemented or
Berglund, Furaker and Vulkan, 2014). announced by management. However, perceived inse-
Another strand of research examines the impact of curity is not merely a result of the implementation of
the labour market context, such as unemployment rates. new technologies but also depends on the way compa-
In summary, there is clear evidence that higher un- nies communicate such change. In the context of down-
employment rates are associated with higher cognitive sizing and restructuring, an open communication style
and affective job insecurity (Anderson and Pontusson, and the involvement of employees in organizational
2007; Campbell et al., 2007; Fullerton and Wallace, decision-making can counteract insecurity (Keim,
2007; Lengfeld and Hirschle, 2009; Dixon, Fullerton Landis and Earnest, 2014; Gallie et al., 2017; Lee,
and Robertson, 2013; Lübke and Erlinghagen, 2014; Huang and Ashford, 2018). Likewise, whether employ-
Hipp, 2016; Balz, 2017). ees see their jobs threatened by digital technology6 European Sociological Review, 2021, Vol. 00, No. 0
probably depends on the personnel strategy of compa- for almost all variables—except for firm size, where it is
nies and on how they communicate it to their 7%—and thus very low (Table 1). There is no indication
workforce. that specific groups are systematically missing, as the
Based on these theoretical considerations, we assume distributions and means of the variables in the full sam-
that occupation-specific computerization has an impact ple and the analysis sample are very similar.
on subjective job insecurity. Regarding cognitive job in-
security, we argue that employees whose occupations Dependent variables: measures of insecurity
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become increasingly substitutable by computers increas- In the empirical analysis, we examine the relationship
ingly believe that they are likely to lose their jobs. At the between computerization and three dependent variables:
same time, it seems plausible that employees expect their affective job insecurity, cognitive job insecurity and la-
chances of finding an alternative job in their field to di- bour market insecurity.2 The degree of affective job inse-
minish. Thus, the occupation-specific degree of compu- curity is measured by the question ‘To what extent are
terization is expected to directly increase cognitive job you worried that you could lose your job?’ on a scale
insecurity and labour market insecurity. The job insecur- ranging from 1 (not worried at all) to 4 (very worried).
ity model discussed above (Figure 1) implies that as a Cognitive job insecurity is measured by asking individu-
consequence, employees also become more worried als how much they agree with the statement ‘My own
about losing their jobs, i.e. it assumes an indirect rela- job is at risk’ on a scale ranging from 1 (strongly dis-
tionship between computerization and affective job agree) to 4 (strongly agree). Labour market insecurity is
insecurity. measured by the perceived difficulty of finding a new
In short, in the empirical analysis, we test how job (‘How easy or hard would it currently be for you to
employees’ subjective job insecurity changes over time find a job that is at least as good as the one you have
as the degree of computerization in their occupations now?’) on a scale ranging from 1 (very easy) to 5 (very
changes: hard). Most individuals in the sample do not worry
about losing their job and do not see their job at risk,
Hypothesis 1: Cognitive job insecurity increases with
but the majority expect difficulties in finding a new
increasing computerization.
equivalent job (Table 1).3
Hypothesis 2: Labour market insecurity increases with
increasing computerization.
Hypothesis 3: Affective job insecurity increases with
Degree of computerization
The independent variable of primary interest is an indica-
increasing computerization; this relationship is mediated
tor reflecting the degree of computerization. The data do
by cognitive job insecurity and labour market insecurity.
not provide information on the actual degree of compu-
terization at the organizational level. Computerization is
measured at the occupational level and reflects the state
Data
of technological development in the economy. Thus, it
Sample can be viewed as a factor of the broader labour market
We analyse longitudinal data from four waves (2013– context rather than a characteristic of a specific
2016) of the household panel study ‘Labour market and organization.
social security’ (PASS), a survey designed for research on The degree of computerization is operationalized by
the labour market and poverty in Germany (Trappmann the occupation-specific substitution potential, a measure
et al., 2019). The data are particularly well suited for developed by Dengler and Matthes (2018a).4 It indicates
this study, as it not only includes rich information on the extent to which occupational tasks are replaceable
individuals’ employment situation, socio-economic char- by computers or computer-controlled machines. Dengler
acteristics and household background but also informa- and Matthes (2018a) determine degrees of computeriza-
tion on subjective job and labour market insecurity in tion for occupations in Germany based on the occupa-
the period under study. tional expert database BERUFENET5 of the Federal
The analysis sample is based on employees between Employment Agency, which is similar to the US
25 and 64 years of age whose jobs are subject to social O*NET. BERUFENET contains occupational data for
security contributions.1 After deletion of 3,650 observa- all occupations in Germany.
tions (21%) due to missing data on the dependent or in- Dengler and Matthes (2018a) use these data for
dependent variables, the sample consists of 13,972 the year 2013, in which occupational experts assigned
observations. The share of missing values is below 5% approximately 8,000 tasks to 4,000 occupations.European Sociological Review, 2021, Vol. 00, No. 0 7
Table 1. Full and analysis sample—descriptive statistics
Full sample Analysis sample
Variable Proportions/means Standard deviation Proportions/means Standard deviation
Affective job insecurity
Not worried at all 0.44 0.50 0.45 0.50
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Slightly worried only 0.31 0.46 0.32 0.47
Somewhat worried 0.17 0.38 0.17 0.37
Very worried 0.07 0.26 0.07 0.25
Missing 0.01 0.10
Cognitive job insecurity
Strongly disagree 0.40 0.49 0.41 0.49
Disagree 0.45 0.50 0.46 0.50
Agree 0.09 0.28 0.09 0.29
Strongly agree 0.04 0.18 0.03 0.18
Missing 0.02 0.15
Labour market insecurity
Very easy 0.06 0.23 0.06 0.23
Fairly easy 0.13 0.33 0.13 0.34
Neither easy nor hard 0.22 0.41 0.22 0.42
Fairly hard 0.33 0.47 0.33 0.47
Very hard 0.26 0.44 0.26 0.44
Missing 0.02 0.12
Degree of computerization 0.37 0.27 0.37 0.27
Missing 0.02 0.13
Qualification
No vocational training 0.13 0.34 0.12 0.33
Apprenticeship training/master craftsmen training 0.69 0.46 0.70 0.46
University degree 0.17 0.38 0.18 0.38
Missing 0.00 0.06
Duration of unemployment (in months) 26.03 42.65 25.43 42.47
Missing 0.04 0.20
Severe health restrictions or disability
No 0.78 0.41 0.79 0.41
Yes 0.22 0.41 0.21 0.41
Missing 0.00 0.05
Subjective health status (1 ¼ bad; 5 ¼ very good) 3.35 0.97 3.36 0.96
Missing 0.00 0.04
Age 44.12 10.48 44.13 10.43
Missing 0.00 0.00
Age2/100 20.57 9.17 20.56 9.13
Missing 0.00 0.00
Gender
Women 0.52 0.50 0.51 0.50
Men 0.48 0.50 0.49 0.50
Missing 0.00 0.00
Children younger 18 years in the household
No 0.61 0.49 0.60 0.49
Yes 0.39 0.49 0.40 0.49
Missing 0.00 0.00
Migration background
No 0.75 0.44 0.77 0.42
Yes 0.24 0.42 0.23 0.42
Missing 0.02 0.13
(continued)8 European Sociological Review, 2021, Vol. 00, No. 0
Table 1. (Continued)
Full sample Analysis sample
Variable Proportions/means Standard deviation Proportions/means Standard deviation
German residency
West German residency 0.70 0.46 0.70 0.46
East German residency 0.30 0.46 0.30 0.46
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Missing 0.00 0.00
Equivalent household income (log, in Euro) 7.27 0.48 7.30 0.46
Missing 0.01 0.10
Partner employed
No 0.65 0.48 0.63 0.48
Yes 0.35 0.48 0.37 0.48
Missing 0.00 0.00
Gross hourly wage (log, in Euro) 7.53 0.62 7.57 0.61
Missing 0.02 0.15
Occupational position
Low-skilled manual 0.16 0.37 0.15 0.35
Skilled manual 0.11 0.32 0.12 0.32
Low-skilled non-manual 0.19 0.39 0.18 0.38
Skilled non-manual 0.31 0.46 0.31 0.46
High-skilled non-manual 0.23 0.42 0.25 0.43
Missing 0.00 0.00
Fixed-term contract
No 0.84 0.37 0.86 0.34
Yes 0.14 0.35 0.14 0.34
Missing 0.02 0.13
Public sector
No 0.79 0.40 0.80 0.40
Yes 0.19 0.40 0.20 0.40
Missing 0.01 0.11
Job tenure (in months) 84.24 103.13 89.21 105.94
Missing 0.01 0.10
Part-time employment (20 h)
No 0.85 0.36 0.87 0.34
Yes 0.14 0.35 0.13 0.34
Missing 0.01 0.10
Firm size
Small (European Sociological Review, 2021, Vol. 00, No. 0 9
non-routine tasks and thus are not classified as substitut- Control variables
able. Consequently, the degree of computerization The analysis includes a variety of control variables
assessed for ‘salespersons’ is 67%. derived from previous research. As socio-demographic
The degree of computerization was determined for attributes, we first consider qualifications by distin-
each occupation. However, it should be noted that the guishing three categories: no vocational training, ap-
assessment is solely related to technical feasibility. If a prenticeship (or master craftsmen) training and
task is classified as replaceable, this does not mean that university degree. Unemployment experience is meas-
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it will actually be replaced in the next few years. Where ured by the cumulative duration of previous unemploy-
human labour is regarded as more economic, flexible or ment (in months). Individual health is measured by a
of better quality or where legal or ethical barriers pre- dummy variable indicating severe health restrictions or
vent the use of new technologies, a task is not likely to disabilities as well as information on subjective health
be replaced. status, measured by five categories from 1 (bad) to 5
While Dengler and Matthes (2018a) determined the (very good). Furthermore, we consider age, age squared,
degree of computerization for the year 2013, we also and gender. The models also include dummy variables
calculate it for the years 2014–2016. To account for for migration background, residency in East Germany,
changing task profiles within occupations over time, i.e. and the presence of children below the age of 18 years in
the fact that new tasks may arise and former tasks may the household.
disappear, we use current data on tasks profiles from The economic situation is measured by the logarith-
BERUFENET for 2014, 2015, and 2016 and match mic equivalent household income in Euros,10 a dummy
them with Dengler’s and Matthes’ (2018a) assessment variable indicating whether individuals live with an
of which tasks are routine and which are not. Thus, we employed partner, and logarithmic gross hourly wages
apply their assessment of task substitutability to the in Euros.
years 2014–2016. We can merge the measure of compu- To control for job characteristics, we include dummy
terization to the PASS, because in both data sets, variables for fixed-term contracts and employment in
occupations are coded according to the German classifi- the public sector. Furthermore, we consider job tenure
cation of occupations (Kldb2010).6 After merging, the (in months) and a dummy variable for part-time employ-
sample data include information on the occupation- ment (20 h). We control for firm size by distinguishing
specific degree of computerization for each person in small (30% and 70%) skilled manual, skilled non-manual and high-skilled
and 14% work in an occupation with a high degree of non-manual positions.
computerization (>70%). Table 1 summarizes the distributions of all variables
in the full sample.
Table 2. Distribution across occupations with low, medium,
and high degrees of computerization
Method
Degree of computerization Share of observations (in %)
While the analysis at hand uses panel data to examine
Low (0–30%) 41.7 individual change in subjective job insecurity, several
Medium (>30–70%) 44.0 other individual-level studies are confined to (pooled)
High (>70%) 14.3
cross-sectional data (e.g. Böckermann, 2004; Fullerton
Source: Own calculations, PASS (2013–2016), BERUFENET (2013–2016), and Wallace, 2007; Berglund, Furaker and Vulkan,
weighted. 2014; Gallie et al., 2017; Lowe, 2018). The study by10 European Sociological Review, 2021, Vol. 00, No. 0
Lengfeld and Hirschle (2009) is one of a few using indi- job and labour market insecurity. The regression models
vidual panel data. include the set of control variables described above as
A drawback of studies that do not apply panel data well as a panel wave indicator. In the third step, we re-
analysis is that it remains unclear whether their results gress the degree of computerization on affective job inse-
are biased by unobserved heterogeneity. Panel regression curity. Since we assume this relationship to be mediated
models address unobserved heterogeneity by exploiting by cognitive job insecurity and labour market insecurity,
the fact that panel data provide two sources of variance: these variables are included as additional independent
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variation between individuals and within individuals variables.
over time. In random-effects models, coefficients are
estimated based on both between-person and within-
person variation. Estimation results are unbiased only if
Results
there are no unobserved time-constant variables that Table 3 displays the results for cognitive job insecurity.
are correlated with the independent variables—a strong Model 1a refers to a bivariate analysis without any other
assumption that is often violated. In contrast, in fixed- variables, while Model 1b includes the whole set of
effects models, the estimation of coefficients is based control variables. For time-varying variables, separate
only on within-person variation and therefore not biased between-person (bbetween) and within-person (bwithin)
by unobserved time-constant variables, as these are coefficients are displayed. Another column (boverall)
eliminated. refers to time-invariant variables and variables with par-
Given the ordinal level of measurement of the de- ticularly low within-person variation for which a de-
pendent variables, fixed-effects ordered logit estimation composition of coefficients is not possible or difficult. It
would be the preferred method of analysis. However, should be noted that the coefficients cannot be inter-
the estimation is complicated by the incidental parame- preted in terms of size, but only regarding their sign and
ters problem (Lancaster, 2000), i.e. the fact that a con- significance.
sistent estimation is not possible in short panels. In the Starting with Model 1a (without any control varia-
literature, there is no consensus on the implementation bles), we find that computerization is positively related
of fixed-effects ordered logit models (e.g. Baetschmann, to cognitive job insecurity. This finding remains un-
Staub and Winkelmann, 2014; Muris, 2017). One op- changed when we control for various potential determi-
tion of obtaining fixed-effects estimates for ordered logit nants (Model 1b). The significant and positive between-
models is the so-called hybrid model (Allison, 2009). person coefficients show that employees whose jobs are
Based on random-effects analysis, the hybrid model more easily replaceable by computers are more likely to
includes fixed effects by modelling unobserved hetero- expect a job loss than employees whose jobs are less
geneity as a function of time-invariant characteristics, replaceable. In line with Hypothesis 1, we also find
including time-averaged regressors, with an additive weakly significant positive within-person coefficients;
error term that is assumed to be independent of the i.e. employees whose occupations become increasingly
regressors (Muris, 2017). Thus, the model combines the substitutable over time increasingly expect to lose their
strengths of random-effects and fixed-effects models. jobs. Effect sizes can be illustrated by average marginal
Coefficients of time-varying independent variables are effects (AME) for each category of the dependent vari-
decomposed into within-person and between-person able in the full ordered logit model (1b). They show, for
components. A within-person coefficient indicates how example, that if the degree of computerization increases
individual change in an independent variable is associ- by one percentage point, the probability of choosing the
ated with individual change in the dependent variable. first response category (representing the lowest level of
Within-person estimates are not biased by unobserved cognitive job insecurity) decreases by 13.8%; the prob-
time-constant characteristics. Selection bias—if pre- ability of choosing the fourth response category (repre-
sent—is incorporated in the corresponding between- senting the highest insecurity level) increases by 2.6%
person coefficients. (tables including AME for all models are shown in
Using the Stata command ‘xthybrid’ for hybrid Supplementary Appendix C).
ordered logit models by Schunck and Perales (2017), the Next, we test whether employees expect their chan-
analysis is carried out in three steps. According to the ces of finding a new equivalent job to diminish when the
hypotheses, computerization is expected to directly in- degree of computerization in their occupation increases
crease cognitive job insecurity as well as labour market (Hypothesis 2). Table 4 shows evidence regarding the re-
insecurity. In the first two steps, we therefore estimate lationship between computerization and labour market
the relationship between computerization and cognitive insecurity. The between-person estimates show—both inEuropean Sociological Review, 2021, Vol. 00, No. 0 11
Table 3. Hybrid ordered logit models for cognitive job insecurity
Dependent variable: cognitive job insecurity Model 1a (without Model 1b (with
control variables) control variables)
ßbetween ßwithin ßbetween ßoverall ßwithin
Degree of computerization 0.430*** 0.842* 0.541*** 0.872*
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(0.117) (0.437) (0.120) (0.448)
Qualification (reference: no vocational training)
Apprenticeship training/master craftsmen training 0.111
(0.092)
University degree 0.123
(0.121)
Duration of unemployment (in months) 0.000
(0.001)
Severe health restrictions or disability 0.137 0.062
(0.089) (0.095)
Subjective health status (1 ¼ bad; 5 ¼ very good) 0.317*** 0.081**
(0.043) (0.032)
Age 0.155*** 0.270**
(0.025) (0.107)
Age2/100 0.153*** 0.162
(0.029) (0.100)
Women 0.030
(0.065)
Children younger 18 years in the household 0.251*** 0.050
(0.072) (0.141)
Migration background 0.220***
(0.070)
East German residency 0.331*** 0.561
(0.093) (0.431)
Equivalent household income (log, in Euro) 0.106 0.139
(0.096) (0.091)
Partner employed (reference: no partner/partner not employed) 0.015 0.046
(0.070) (0.105)
Gross hourly wage (log, in Euro) 0.025 0.084
(0.083) (0.149)
Fixed-term contract 1.362*** 0.565***
(0.111) (0.139)
Public sector 0.225*** 0.412
(0.081) (0.335)
Job tenure (in months) 0.001 0.006***
(0.000) (0.001)
Part-time employment (20 h) 0.182* 0.184
(0.106) (0.179)
Firm size [reference: small (12 European Sociological Review, 2021, Vol. 00, No. 0
Table 3. (Continued)
Dependent variable: cognitive job insecurity Model 1a (without Model 1b (with
control variables) control variables)
ßbetween ßwithin ßbetween ßoverall ßwithin
Cut 2 2.864*** 5.229***
(0.075) (0.909)
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Cut 3 4.614*** 7.008***
(0.101) (0.906)
Observations 13,972 13,972
Individuals 5,851 5,851
Note: ***P < 0.01, **P < 0.05, *P < 0.1. Cluster-robust standard errors in parentheses.
Observations: 13,972; individuals: 5,851.
Source: Own calculations, PASS (2013–2016), BERUFENET (2013–2016).
the bivariate analysis (Model 2a) and in the analysis subjective job insecurity are related. Notably, the find-
with the full set of control variables (Model 2b)—that ings presented in Table 5 (Model 3b) point to intra-
employees whose jobs are comparatively easy to substi- individual effects over time and thus go beyond the cur-
tute by computers are, on average, more likely to antici- rent state of research. They imply that employees who
pate bad labour market chances. However, this finding consider the loss of their jobs to be increasingly likely
is not reflected at the individual level. The coefficients and their labour market opportunities to be increasingly
for the within-person estimates are also positive but not poor become increasingly worried about a potential job
significant. The respective AME are very small and not loss. Furthermore, we find that employees are less wor-
significant (see Supplementary Appendix C). Thus, con- ried about a job loss the higher their household income
trary to Hypothesis 2, we find no clear evidence that is, suggesting that lower income insecurity is related to
employees whose jobs become increasingly substitutable lower affective job insecurity.
over time increasingly regard their labour market chan- As shown in Supplementary Appendix D, adding oc-
ces as poor. cupational position to the models does not substantially
According to Hypothesis 3, we expect to find that change our main findings: computerization has a posi-
individuals are increasingly worried about losing their tive effect on cognitive job insecurity, but not on affect-
jobs as computerization of their occupations proceeds. ive job and labour market insecurity. Non-manual
Moreover, we expect the assumed relationship to be workers in skilled and high-skilled positions appear less
mediated by cognitive job and labour market insecurity. pessimistic regarding their risk of job loss and labour
The empirical findings regarding affective job insecurity market chances than workers in low-skilled non-manual
(Table 5) are not in favour of these expectations. In positions (reference group). Affective job insecurity
Model 3a (without control variables), we find a positive seems more pronounced among low-skilled manual
and significant between-person coefficient for computer- workers.
ization. However, this finding no longer holds when we In summary, evidence is mixed. We find that employ-
control for the full set of control variables (Model 3b). ees whose occupations become increasingly substitutable
The within-person coefficients for computerization are by computers over time are increasingly likely to expect
positive but not significant and the AME are very small a job loss. However, the results are only weakly signifi-
and not significant either (Supplementary Appendix C). cant. Additionally, there is no evidence that employees
As there is no significant relationship between compu- are increasingly anticipating poor labour market chan-
terization and affective job insecurity, searching for a ces or becoming increasingly worried about losing their
mediating process is obsolete. job.12
Overall, results regarding the control variables are The reason could be that associations at the between-
plausible and—at least with regard to the between- person level are in part brought about by unobserved
person effects—mostly in line with previous studies. It variables such as coping mechanisms, e.g. individual risk
should be emphasized that the analysis confirms basic aversion or other personality traits (Lee, Huang and
theoretical assumptions about how different aspects of Ashford, 2018). Moreover, there are presumablyEuropean Sociological Review, 2021, Vol. 00, No. 0 13
Table 4. Hybrid ordered logit models for labour market insecurity
Dependent variable: labour market insecurity Model 2a (without Model 2b (with
control variables) control variables)
ßbetween ßwithin ßbetween ßoverall ßwithin
Degree of computerization 1.596*** 0.329 1.706*** 0.365
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(0.154) (0.384) (0.151) (0.392)
Qualification (reference: no vocational training)
Apprenticeship training/master craftsmen training 0.135
(0.101)
University degree 0.202
(0.126)
Duration of unemployment (in months) 0.005***
(0.001)
Severe health restrictions or disability 0.589*** 0.104
(0.106) (0.101)
Subjective health status (1 ¼ bad; 5 ¼ very good) 0.153*** 0.092***
(0.048) (0.031)
Age 0.118*** 0.140
(0.033) (0.101)
Age2/100 0.215*** 0.131
(0.038) (0.101)
Women 0.264***
(0.069)
Children younger 18 years in the household 0.019 0.016
(0.083) (0.133)
Migration background 0.014
(0.077)
East German residency 0.078 0.318
(0.096) (0.495)
Equivalent household income (log, in Euro) 0.461*** 0.186
(0.127) (0.140)
Partner employed (reference: no partner/partner not employed) 0.060 0.008
(0.083) (0.094)
Gross hourly wage (log, in Euro) 0.071 0.397***
(0.099) (0.138)
Fixed-term contract 0.156 0.021
(0.106) (0.126)
Public sector 0.359*** 0.534*
(0.091) (0.306)
Job tenure (in months) 0.002*** 0.001
(0.000) (0.001)
Part-time employment (20 h) 0.356*** 0.218
(0.126) (0.178)
Firm size (reference: small (14 European Sociological Review, 2021, Vol. 00, No. 0
Table 4. (Continued)
Dependent variable: labour market insecurity Model 2a (without Model 2b (with
control variables) control variables)
ßbetween ßwithin ßbetween ßoverall ßwithin
Cut 2 2.115*** 5.284***
(0.086) (1.070)
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Cut 3 0.147* 3.322***
(0.085) (1.063)
Cut 4 2.432*** 0.718
(0.094) (1.063)
Observations 13,972 13,972
Individuals 5,851 5,851
Note: ***P < 0.01, **P < 0.05, *P < 0.1. Cluster-robust standard errors in parentheses.
Observations: 13,972; individuals: 5,851.
Source: Own calculations, PASS (2013-2016), BERUFENET (2013-2016).
differences in characteristics of the organizational con- their jobs as higher. While computerization is also posi-
text, e.g. regarding personnel policy. Such factors, on tively related to labour market insecurity, the results
which the data provide no information, can contribute imply that this association is mainly driven by differen-
to significantly positive between-person estimates. ces between persons rather than by individual change
over time. Put differently, employees whose occupations
are comparatively easily replaceable by computers are,
Conclusion on average, more pessimistic about their labour market
In the current debate on how digital transformation will chances than those whose jobs are less replaceable, but
affect employment, there is often a fear that human la- there is no evidence that computerization enhances la-
bour will increasingly be replaced by computers in the bour market insecurity at the individual level. Finally,
future. However, research on subjective job insecurity in regarding affective job insecurity, there is no significant
the context of technological progress is remarkably evidence of a positive association with computerization.
scarce. The present study addressed this research gap by The present study contributes to the literature on
examining the relationship between occupation-specific subjective job insecurity in several ways. It is innovative
computerization, i.e. the degree to which an occupation because it is one of very few studies using panel analytic-
can be substituted by computers or computer-controlled al methods to examine intra-individual change in sub-
machines, and subjective job insecurity. Adopting a jective job insecurity over time. The results corroborate
panel analytical approach, the analysis aimed at answer- basic theoretical assumptions about how different inse-
ing the question of whether employees whose occupa- curity components are interrelated. By providing
tions become increasingly substitutable by computers evidence that the relationships between cognitive job
over time are becoming increasingly insecure about their insecurity, affective insecurity and labour market inse-
jobs and labour market prospects. We distinguished curity are not merely driven by unobserved heterogen-
three interrelated insecurity measures: cognitive job inse- eity, the analysis goes beyond the current state of
curity, i.e. the perceived probability of losing one’s job, research.
labour market insecurity, i.e. the perceived availability Moreover, our study is among the first to examine
of job alternatives, and affective job insecurity, i.e. the the relationship between digital transformation and sub-
fear of job loss. We assumed employees’ subjective as- jective job insecurity. We use an innovative measure of
sessment of job and labour market insecurity to increase computerization, namely the substitution potential of
with increasing computerization in their occupations. occupations. The findings suggest that employees are
The empirical results are mixed. In summary, there aware of the possible negative employment effects of
is—albeit weak—evidence that computerization fosters computerization. As occupation-specific computeriza-
cognitive job insecurity at the individual level, meaning tion increases, employees consider the possibility of los-
that employees whose occupations become increasingly ing their own jobs as increasingly likely. At the same
replaceable increasingly rate the probability of losing time, however, employees are not increasingly scared byEuropean Sociological Review, 2021, Vol. 00, No. 0 15
Table 5. Hybrid ordered logit models for affective job insecurity
Dependent variable: affective job insecurity Model 3a (without control variables) Model 3b (with control variables)
ßbetween ßwithin ßbetween ßoverall ßwithin
***
Degree of computerization 0.579 0.169 0.151 0.023
(0.138) (0.460) (0.122) (0.417)
2.345*** 1.270***
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Cognitive job insecurity
(0.061) (0.050)
Labour market insecurity 0.561*** 0.298***
(0.040) (0.034)
Qualification (reference: no vocational training)
Apprenticeship training/master craftsmen 0.148
training
(0.092)
University degree 0.125
(0.118)
Duration of unemployment (in months) 0.001*
(0.001)
Severe health restrictions or disability 0.109 0.062
(0.090) (0.095)
Subjective health status (1 ¼ bad; 5 ¼ very good) 0.221*** 0.093***
(0.045) (0.034)
Age 0.206*** 0.039
(0.029) (0.104)
Age2/100 0.239*** 0.186*
(0.033) (0.106)
Women 0.138**
(0.067)
Children younger 18 years in the household 0.101 0.277**
(0.076) (0.141)
Migration background 0.303***
(0.078)
East German residency 0.282*** 0.579
(0.080) (0.499)
Equivalent household income (log, in Euro) 0.478*** 0.219**
(0.117) (0.102)
Partner employed (reference: no partner/partner not 0.024 0.041
employed)
(0.073) (0.121)
Gross hourly wage (log, in Euro) 0.039 0.017
(0.087) (0.140)
Fixed-term contract 0.800*** 1.194***
(0.101) (0.139)
Public sector 0.173* 0.247
(0.094) (0.348)
Job tenure (in months) 0.002*** 0.007***
(0.000) (0.001)
Part-time employment (20 h) 0.152 0.070
(0.116) (0.186)
Firm size (reference: small (16 European Sociological Review, 2021, Vol. 00, No. 0
Table 5. (Continued)
Dependent variable: affective job insecurity Model 3a (without control variables) Model 3b (with control variables)
ßbetween ßwithin ßbetween ßoverall ßwithin
Large (250 employees) 0.107 0.288
(0.108) (0.333)
Regional unemployment rate 0.013 0.053
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(0.014) (0.061)
Cut 1 0.433*** 5.064***
(0.072) (0.978)
Cut 2 1.966*** 7.708***
(0.077) (0.982)
Cut 3 4.230*** 10.407***
(0.094) (0.988)
Observations 13,972 13,972
Individuals 5,851 5,851
Note: ***P < 0.01, **P < 0.05, *P < 0.1. Cluster-robust standard errors in parentheses.
Observations: 13,972; individuals: 5,851.
Source: Own calculations, PASS (2013–2016), BERUFENET (2013–2016).
a potential job loss. Apparently, computerization con- of losing one’s job due to computerization does not
tributes to cognitive job insecurity but not necessarily to seem to pose a major threat to workers. In an economic
affective insecurity. This may be due to several factors. downturn, employees might be more likely to perceive
In addition to individual coping strategies, certain fea- digital innovations as a threat. Comparative studies
tures of the organizational context, in particular human could examine the impact of contextual factors in more
resources strategy and communication style might have detail. Adopting a comparative perspective is important
a protective effect. Fear of job loss is probably not mere- for future research, as digital transformation is a global
ly a result of the implementation of new technologies phenomenon. How individuals perceive the resulting
but also depends on whether companies can convince chances and risks probably depends on country-specific
their employees that new technologies will not displace factors such as the economic situation and labour mar-
workers. For example, instead of laying off employees, ket policies.
companies might adapt their workers’ task profiles to
more technologically advanced work environments. One
limitation of our study is the lack of detailed organiza- Notes
tional information. Therefore, it was not possible to 1 In Germany, jobs with monthly earnings above 450
examine the role of organizational characteristics. Euros (as of 2013) are subject to social security
Notably, there was no information on the extent to contributions including health, nurse care, pension,
which workplaces are actually equipped with new digit- unemployment and accident insurance.
al technology. 2 See Supplementary Appendix A for the associations
To conclude, there is currently no evidence that in between the dependent variables.
Germany, individual fear of job loss has considerably 3 For the interpretation of results, a clear distinction
increased as a consequence of increasing computeriza- of cognitive and affective job insecurity is crucial. It
tion. Nevertheless, as digital transformation proceeds, could be argued that the measure of affective inse-
there will probably be more profound changes in the curity is somewhat imprecise and may possibly
working environment that are likely to affect subjective overlap in part with cognitive job insecurity.
job insecurity. Moreover, digital transformation might However, the measures of job insecurity applied
have a stronger impact in a less favourable economic here bear great similarity to indicators commonly
situation. In the context of a persistently good economy used in research. Future studies could examine the
and falling unemployment rates, subjective job insecur- extent to which the indicators adequately reflect
ity in Germany has declined and has been particularly the theoretical concepts.
low until recently. Under such conditions, the possibility 4 See Supplementary Appendix B for more details.European Sociological Review, 2021, Vol. 00, No. 0 17
5 See http://berufenet.arbeitsagentur.de (last accessed exclusion of a relatively high proportion of obser-
7.1.2021). vations with missing values does not substantially
6 We recode the 4-digit occupations of KldB 1992 alter our findings.
provided in the PASS dataset into 5-digit occupa-
tions of KldB 2010.
7 The degree of computerization can change for indi-
Supplementary Data
viduals within occupations, but also if individuals Supplementary data are available at ESR online.
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move to other occupations. Either way, the funda-
mental interpretation of results would be the same: Acknowledgements
employees who experience an increase of compu-
The authors would like to thank Mark Trappmann and three
terization at the workplace (either due to an in-
anonymous reviewers for their helpful comments and sugges-
crease of substitutable tasks in the same occupation
tions, which contributed to improving this article. Special
or by changing to an occupation with a higher de- thanks to Johannes Ludsteck for his valuable advice and great
gree of computerization) may increasingly feel inse- support. All remaining mistakes are the sole responsibility of
cure. As a robustness check of whether the effect of the authors.
computerization is driven by occupational movers,
we re-estimate the models excluding all persons
who changed their occupations at least once within References
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