call me on sunday: the impact of permanent availability on...

Call me on Sunday: The impact of permanent availability onemployee well-being∗

Elena Shvartsman†

University of Basel and IZA

Susanne Steffes‡

ZEW and University of Cologne

This version: April 2018[ work in progress – please do not cite or circulate without permission ]

Abstract

This paper presents preliminary results on the effects of leisure interruptions by meansof ICT on employee well-being. First evidence suggests that ICT use during non-workinghours impairs employee well-being with respect to the perceived work-to-family conflict.This relationship also holds for within individual comparisons. However, once individualfixed effects are accounted for, the estimated effects drop in size suggesting that there is aself-selection of individuals who are less sensitive to leisure interruptions into jobs associatedwith business-related ICT use during non-working hours.

JEL-Classification: J28, M50, M54, O33Keywords: work-related well-being, ICT use, leisure interruptions

∗Data from the Linked Personnel Panel (LPP) were kindly provided by the Institute for Employment Research(IAB). All remaining errors are our own.†Corresponding author: Elena Shvartsman, Faculty of Business and Economics, University of Basel, Peter

Merian-Weg 6, P.O. Box, 4002 Basel, Switzerland, email: [email protected]‡Centre for European Economic Research (ZEW), L 7, 1, P.O. Box 103443, 68034 Mannheim, Germany, email:

[email protected]

1 Introduction

A recent article in the New York Times about Amazon’s corporate culture has received far-

ranging attention (Kantor and Streitfeld, 2015). One of the issues raised in the article was the

fluid transition between work-life and leisure; amongst others the article claims “at Amazon,

workers are encouraged to (. . .), toil long and late (emails arrive past midnight, followed by text

messages asking why they were not answered), and held to standards that the company boasts

are ‘unreasonably high’.” (Kantor and Streitfeld, 2015). While it is plausible to assume that

not every worker faces such harsh challenges on a daily basis, the advances in information and

communication technology (ICT) have indisputably contributed to blurred boundaries between

work-life and leisure by facilitating the so called “technology-assisted supplemental work” (Fen-

ner and Renn, 2004).

The effects of this development on employee well-being pose an interesting question, since

lower employee well-being, in terms of, for instance, mental health, job satisfaction, or work-life

balance, may have far reaching consequences. Hence, the aim of this study is to analyse potential

effects of leisure interruptions by means of ICT on work-related and general well-being.

The provision of employees with devices that permit their reachability during non-working

hours could be considered a human resource management (HRM) practice. While this specific

practice has not received attention in the economic literature yet, there is some evidence on

the relationship between other HRM practices and employee well-being. Generally speaking,

this evidence suggests that practices associated with self-determination, such as job control

or autonomy, may enhance employee well-being, while the opposite is true for practices asso-

ciated with employer-exerted control, for instance, over working hours (e.g., Shvartsman and

Beckmann, 2015). Furthermore, the relationship between working hours and well-being out-

comes has received substantial attention in the literature (Bell et al., 2012; Robone et al., 2011;

Wooden et al., 2009),1 with evidence suggesting that in particular a working hours mismatch,

i.e., working more than desired, may impair individual well-being. The evidence on job control

and working hours is particularly interesting, because it is a priori not clear whether ICT use

during non-working hours prolongs working hours and makes them more irregular or whether it

may on the contrary increase worker autonomy by allowing to assume work-related obligations

outside of the regular working schedule and thereby to better align work and private lives.

1A review on the relationship between working hours and health can be found in Bassanini and Caroli (2015).

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Finally, the specific relationship between the usage of modern communication technology

during non-working hours and employee well-being has been addressed by several other academic

fields, for instance, management, organizational psychology, and sociology. In this context,

Boswell and Olson-Buchanan (2007) analyse determinants of communication technology use

in terms of work-related attitudes and work-life conflict, while Schieman and Young (2013)

consider the opposite direction and analyse how work-related communication affects the work-

life conflict and psychological problems. These studies have, however, several limitations. For

instance, they mainly draw on non-representative populations, such as employees of a specific

law firm (Cavazotte et al., 2014). More worrisome is, however, that these studies do not address

issues concerning endogeneity, for instance, due to individual self-selection into occupations

or companies and the omission of potential confounding factors. The results of these studies

therefore do not allow for causal interpretations.

The main purpose of this paper is to fill this gap by providing a quantitative analysis of the

associated research question. By applying appropriate econometric techniques that supposedly

tackle endogeneity issues, our study intends to offer results that allow to derive meaningful man-

agement implications. Furthermore, our analysis draws on a representative employee-employer

linked data, which should allow for a high degree of external validity. On the contrary to previous

studies, we also intend to take into account potential beneficial effects of work-related ICT-use

during leisure as it may present an autonomy-increasing resource for some employees. Finally,

the employed data offer a rich set of control variables, which should minimize any associated

omitted variable bias.

The remainder of this paper is structured as follows. In Section 2, we present the theoretical

considerations, which underlie this research. In Section 3, we present the data and the key

variables of this analysis. Section 4 continues with the empirical strategy. In Section 5, we

present the first results, while Section 6 provides a brief overview of the potential future avenues

for this study.

2 Theoretical Background

The extent to which individuals can exert control over ICT use during non-working hours may

play a crucial role in its resulting effects on well-being. In this context, the Job Demand-Control

(JDC) Model (Karasek, 1979) offers an appealing framework, which basic implication is that

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individuals feel burdened when the faced job demands are too high. At the same time, a greater

job control latitude can mitigate the burdening effects of job demands. According to the JDC

model, the effects of ICT use during non-working hours on well-being are a priori ambiguous.

The general usage of communication devices could increase employee job control and autonomy

by providing flexibility on the time and location of work. More so, as the usage of these devices

does not necessarily increase working hours but potentially only smooths their allocation over the

24 hours cycle, for instance, by permitting to leave ones working place despite ongoing business.

However, if control is excreted by the employers or colleagues by contacting their employees by

means of these devices in their non-working hours, these leisure interruptions could decrease the

perceived job control and thereby impair employee well-being.

Concepts from sociology and psychology suggest that a segmentation between working and

non-working hours is important in terms of recreation from work (Derks and Bakker, 2014).

With respect to this consideration, an employer or colleagues induced leisure interruption should

impair individual well-being.

However, in this context, one should also differentiate whether individuals self-select into

jobs associated with such leisure interruptions. According to the Boundary Theory of Ashforth

et al. (2000), the extent to which individuals identify with their work determines how strongly

they engage with their workplace. With respect to this theory, only individuals with a strong

work-commitment will a priori chose to blur their work and private life. Given this deliberate

choice, any negative effects of leisure interruptions on worker well-being should be mitigated.

3 Data and Variables

For this analysis, we use data from the German Linked Personnel Panel (LPP), which is provided

by the Institute for Employment Research (IAB). The LPP is a novel panel data set attached to

the IAB Establishment Panel, but limited to private sector establishments operating in manu-

facturing and services with at least 50 employees subject to social security.2 The LPP contains

establishment level information from the Establishment Panel, a survey of these establishments’

HR representatives, and is linked to a survey of a random draw of these establishments’ employ-

ees. So far, three waves of the LPP are available. The establishment surveys were conducted

in 2012, 2014, and 2016, whereas the corresponding employee surveys were conducted sub-

2For further information on the IAB Establishment Panel, see Ellguth et al. (2014). The LPP is described infull detail in Kampkötter et al. (2016).

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sequently in 2013 (starting in December 2012), 2015, and 2017. In each wave, the raw data

contain interviews of HR representatives from approximately 800 establishments and 7,000 em-

ployees. Around 70% of the interviewed employees agreed for their data to be merged with the

establishment surveys.

In order to capture employee well-being with respect to their work-life balance, we consider

a short version of the work-to-family conflict index according to Netemeyer et al. (1996). This

index consists of three items covering the extent to which individuals feel that their job demands,

job stress, and work-related time expenditure impair their private and family lives.3 In order

to transform our outcome variable to a metric scale and also to facilitate interpretation, we

standardise it into a variable with mean 0 and standard deviation 1.4

Our explanatory variable also stems from the employee survey and is the response to the

question “How often do you receive business calls or reply to emails during your leisure time?”,

with answer possibilities “never” (1), “sometimes per year” (2), “sometimes per month” (3),

“sometimes per week” (4), and “daily” (5). Figure 1 plots the frequency of leisure interruptions

by means of ICT in the raw data sample. We observe that while the majority of respondents

reports to never work in such a way, about 3% of the sample do so on a daily basis. For the

following analysis, we generate a dummy variable, where we consider “daily” and “weekly” as

frequent interruptions and group them together, while the other categories serve as reference.5

[Insert Figure 1 about here]

4 Empirical Strategy

The aim of this analysis is to identify the effect of work-related leisure interruptions induced by

ICT use on employee well-being. In order to provide an indication for the associated correlations,

we run a simple OLS regression, where we regress an individual’s i self-assessed work-to-family

conflict in t, denoted by yit on his ICT use in t, denoted by ICTit and several confounding

3The work-life balance index constitutes three items, which refer to the extent an individual considers thefollowing items to apply on a 1 (“does not apply at all”) to 5 (“completely applies”) scale: (i) the interferenceof job demands with private and family life, (ii) the impairment of private and family life due to work-relatedexpenditure of time, and (iii) the impairment of family life due to job stress.

4We follow the double standardization approach as in, e.g., Bresnahan et al. (2002) or Bloom et al. (2011). Thatis, we first standardise the individual items, which accounts for potentially different distributions of the items’responses. Thereafter, we standardise the sum of these standardised items, in order to facilitate interpretation.Hence, the point estimates can be interpreted as standard deviations from the sample’s mean.

5However, we also consider a specification, where the category “monthly” forms part of the treatment group.

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factors, summarized by the vector X:

yit = γICTit +Xitβ + ηt + εit. (1)

In equation (1), the variable ICTit takes on value 1 if an individual reports daily or weekly

leisure interruptions by means of ICT, and 0 otherwise. ηt is a time fixed effect captured by

a time dummy variable, and εit denotes an idiosyncratic error term with zero mean and finite

variance.

The vector X conveys the fact that an individual’s work-to-family conflict may also depend

on various factors that are not related to the use of ICT. Therefore, Xit includes the individual’s

age and its squared value, the respondent’s gender, dummies indicating whether he is a foreign

or dual citizen (German and foreign), six dummies for his highest schooling degree, information

on his marital status and whether he has children. We also control for employment related

confounders, more specifically, the individual’s annual net wage (in logs), whether he has a

fixed-term contract, the amount of his actual working hours, whether he is employed in part-

time, whether he is at least sometimes working from home, whether he receives bonuses, his

working hours regime and whether he works in shifts, and his occupational status by including

dummies for whether the individual is a white-collar worker and how many employees are under

his supervision. We also control for an individual’s assessments of his work’s interdependence

with his colleagues, eight dummies for collegiality (helping or receiving help from colleagues),

job autonomy, multitasking, and perceived job-related time pressure. Next, we also include

establishment characteristics, such as the size of the firm the individual is employed in, the

firm’s sector, region, and the employee’s perception of the company culture. Finally, we account

for an individual’s big five personality traits, his risk tolerance, and his perceived job security.

However, the challenge in identifying the effect of ICT use on well-being is to account for

potential sources of endogeneity. First, an increased use of ICT during leisure may reflect an in-

creased work-load, which by itself potentially deteriorates individual well-being. We address this

issue by including actual working hours into the set of our control variables. Second, workers may

be heterogenous with respect to, for instance, their abilities to deal with leisure interruptions

and hence, differ in to what extent such interruptions affect their well-being. If one assumed that

such abilities were at least temporary constant and depended on time-invariant, personal charac-

teristics, then the inclusion of individual fixed effects that eliminate time-invariant heterogeneity

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should alleviate this endogeneity problem.

Hence, we specify the following fixed effects model:

yit = γICTit +Xitβ + αi + ηt + εit. (2)

In equation (2), αi is the individual-specific time-invariant effect. Hence, our specification relies

on within individual variation and essentially measures the average within individual effect of a

change in ICT use during non-working hours on well-being.

Yet, there also may be self-selection of workers into occupations/establishments associated

with a more frequent ICT use during leisure. That is, certain individuals may be more willing

to engage in occupations or to work with firms which are known for technology assisted supple-

mental work, so that we would only observe such individuals in treatment and hence estimate an

average within effect of changes in ICT-induced leisure interruptions only for these individuals.

However, if individuals indeed self-selected into jobs, and we therefore only observed those in-

dividuals, who are, for instance, less sensitive to leisure interruptions, then the estimated effect

would be biased towards zero. Finally, we do not observe individuals for whom the detrimental

effects of such work policies were so severe that they dropped out of our sample, for instance,

because of health issues. Again, the aforementioned argument applies, i.e., if the particularly

burdened individuals dropped out of our sample, our overall effect should be biased downwards.

In all specifications, we cluster the standard errors at the establishment level. Furthermore,

we limit our sample to employees who did not change their establishment between observations.

Finally, we omit unskilled individuals from our sample.

5 First Results

Table 1 summarizes our main results with respect to the effect of daily or weekly leisure inter-

ruptions by means of ICT use on the self-assessed work-to-family conflict. In this Table, column

(1) depicts results from an unconditional correlation, column (2) presents results according to

equation (1), i.e., the OLS regression accounting for the vector of control variables X, and finally

the last column, (3), refers to the results according to equation (2), which account for individual

fixed effects. For reasons of visualization, the point estimates for ICT use during leisure are

also graphically depicted in Figure 2, where the first bar (blue) displays the estimated coeffi-

cient of the unconditional correlation, the second bar (pink) of the conditional OLS regression,

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and finally the last bar (green) displays the point estimate for the fixed effects regression. The

simple correlation is statistically significant. Once we account for potential confounding factors,

it appears that individuals who face ICT use during non-working hours daily or weekly report

a statistically higher work-to-family conflict than comparable peers, where the effect amounts

to roughly 30% of a standard deviation. Finally, we present the within individual comparison,

which reveals that the previously obtained effect significantly drops in size and also loses stat-

istical power. This result speaks in favour of a strong self-selection of individuals into respective

jobs.


Figure 3 continues with results from a similar specification as in Figure 2, albeit, the treat-

ment group is now constituted by individuals who reply to emails or business calls at least

monthly during leisure. While the results remain qualitatively very similar, it is visible that the

confidence intervals are more narrow, implying that the positive effect of leisure interruptions by

means of ICT is somewhat stronger, if one also accounts for monthly users. Since this difference

may stem from a mere increase in variation, we remain cautious with interpretations. However,

this result could also indicate that once one accounts for rather infrequent users, the share of

self-selected individuals into such occupations is reduced and hence we observe a more unbiased

effect.


We proceed by approaching the question, whether autonomy can mitigate potentially negat-

ive effects of ICT-induced leisure interruptions or whether, for individuals who enjoy a sufficient

level of autonomy, such work arrangements may also present a resource by providing more

flexibility on the working schedule. We therefore interact the perceived level of job autonomy

with ICT-induced leisure interruptions. The results of this specification are presented in Figure

4. Although the point estimate of the interaction term exhibits the expected sign, i.e., negat-

ive, meaning that a greater perceived level of job autonomy mitigates potential work-to-family

conflicts induced by ICT-driven leisure interruptions, these estimates are not statistically signi-

ficant. Hence, we cannot conclude that individuals with a greater level of job autonomy are less

burdened by ICT-induced leisure interruptions.


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Finally, we pursue the question of whether leisure interruptions as such increase the per-

ceived work-to-family conflicts or whether these leisure interruptions merely reflect an increased

workload. We therefore run a specification where we split our treatment variable by whether the

individuals simultaneously report an increase in actual working hours. We also restrict this spe-

cification to full-time employees, i.e., those reporting at least 35 contractual working hours per

week. These results are displayed in Figure 5. Interestingly, we see that for the within individual

comparison, the positive effect of leisure interruptions on the perceived work-to-family conflict

only sustains for individuals who report a simultaneous working time increase. Albeit, we cannot

infer from these results whether an increased working load led to more leisure interruptions or

the increased leisure interruptions resulted in more reported actual working hours.


6 Outlook in Lieu of a Conclusion

The preliminary results presented in the previous section suggest that leisure interruptions may

impair employee well-being. However, once we account for individual fixed effects, the estimated

coefficients drop in size. This suggests that there is a self-selection of individuals who are less

sensitive to leisure interruptions into jobs associated with business-related ICT use during non-

working hours.

The presented study is work in progress. First, in the future, we intend to analyse the

effects of ICT-induced leisure interruptions on further well-being outcomes, such as overall job

satisfaction or a mental health index. Second, we intend to provide causal effects of ICT use

during non-working hours on employee well-being. As previously stated, the identification of such

effects is subject to several challenges, such as the individual self-selection into jobs associated

with high ICT use or the disentanglement of mere leisure interruptions by ICT use from a

simultaneous increase in working hours and ICT use during leisure. In the future, we therefore

intend to further refine our estimation strategy, for instance, by employing an IV strategy.

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References

Ashforth, Blake E., Glen E. Kreiner, and Mel Fugate, “All in a day’s work: Boundaries and

micro role transitions,” Academy of Management Review, 2000, 25 (3), 472–491.

Bassanini, Andrea and Eve Caroli, “Is work bad for health? The role of constraint versus choice,”

Annals of Economics and Statistics, 2015, (119/120), 13–37.

Bell, David, Steffen Otterbach, and Alfonso Sousa-Poza, “Work hours constraints and health,”

Annals of Economics and Statistics, 2012, (105/106), 35–54.

Bloom, Nick, Tobias Kretschmer, and John Van Reenen, “Are family-friendly workplace practices

a valuable firm resource?,” Strategic Management Journal, 2011, 32 (4), 343–367.

Boswell, Wendy R. and Julie B. Olson-Buchanan, “The use of communication technologies after

hours: The role of work attitudes and work-life conflict,” Journal of Management, 2007, 33 (4), 592–

610.

Bresnahan, Timothy F., Erik Brynjolfsson, and Lorin M. Hitt, “Information technology, work-

place organization and the demand for skilled labor: Firm-level evidence,” The Quarterly Journal of

Economics, 2002, 117 (1), 339–376.

Cavazotte, Flávia, Ana Heloisa Lemos, and Kaspar Villadsen, “Corporate smart phones: Pro-

fessionals’ conscious engagement in escalating work connectivity,” New Technology, Work and Employ-

ment, 2014, 29 (1), 72–87.

Derks, Daantje and Arnold B. Bakker, “Smartphone use, work–home interference, and burnout: A

diary study on the role of recovery,” Applied Psychology, 2014, 63 (3), 411–440.

Ellguth, Peter, Susanne Kohaut, and Iris Möller, “The IAB Establishment Panel – Methodological

essentials and data quality,” Journal for Labour Market Research, 2014, 47 (1-2), 27–41.

Fenner, Grant H. and Robert W. Renn, “Technology-assisted supplemental work: Construct defin-

ition and a research framework,” Human Resource Management, 2004, 43 (2-3), 179–200.

Kampkötter, Patrick, Jens Mohrenweiser, Dirk Sliwka, Susanne Steffes, and Stefanie

Wolter, “Measuring the use of human resources practices and employee attitudes: The linked person-

nel panel,” Evidence-based HRM: A Global Forum for Empirical Scholarship, 2016, 4 (2), 94–115.

Kantor, Jodi and David Streitfeld, “Inside Amazon: Wrestling Big Ideas in a Bruising Workplace,”

The New York Times, 15th August 2015.

Karasek, Robert, “Job demands, job decision latitude, and mental strain: Implications for job re-

design,” Administrative Science Quarterly, 1979, 24 (2), 285–308.

Netemeyer, Richard G., James S. Boles, and Robert McMurrian, “Development and validation

of work–family conflict and family–work conflict scales,” Journal of Applied Psychology, 1996, 81 (4),

400.

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Robone, Silvana, Andrew M. Jones, and Nigel Rice, “Contractual conditions, working conditions

and their impact on health and well-being,” European Journal of Health Economics, 2011, 12 (5),

429–444.

Schieman, Scott and Marisa C. Young, “Are communications about work outside regular working

hours associated with work-to-family conflict, psychological distress and sleep problems?,” Work &

Stress, 2013, 27 (3), 244–261.

Shvartsman, Elena and Michael Beckmann, “Stressed by your job: Does personnel policy matter?,”

Schmollers Jahrbuch, 2015, 135 (4), 429–463.

Wooden, Mark, Diana Warren, and Robert Drago, “Working time mismatch and subjective well-

being,” British Journal of Industrial Relations, 2009, 47 (1), 147–179.

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Tables and Figures

Figure 1: Frequency of leisure interruptions by means of ICT

Source: Linked Personnel Panel, 2013/2015/2017, own calculations.

Figure 2: ICT use and work-to-family conflict


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Table 1: Work-to-Family Conflict and Availability

Dependent variable Work-to-Family Conflict (standardised)

(1) (2) (3)OLS OLS FE

Coef. SE Coef. SE Coef. SE

Interruptionsdaily/weekly

0.595∗∗∗ 0.031 0.309∗∗∗ 0.030 0.113∗ 0.059

Female −0.091∗∗∗ 0.028Nationality dual −0.088 0.070Nationality foreign 0.055 0.074Age 0.024∗∗∗ 0.007Age2 (×100) −0.029∗∗∗ 0.008Married 0.003 0.028 0.165∗∗ 0.081Children 0.146∗∗∗ 0.024 0.031 0.054Working from home 0.161∗∗∗ 0.033 0.184∗∗∗ 0.049Net wage (ln) 0.038 0.035 −0.167 0.105Fixed-term −0.031 0.054 −0.115 0.086Actual WH 0.028∗∗∗ 0.002 0.008∗∗ 0.004Part-time employment 0.234∗∗∗ 0.042 −0.068 0.086Shift work 0.304∗∗∗ 0.025 0.110 0.080Flexible regime 0.092∗∗∗ 0.025 0.003 0.038Nr. supervised empl. −0.000 0.000 0.002∗∗∗ 0.000Blue collor worker −0.008 0.026 −0.073 0.062Company culture (std) −0.145∗∗∗ 0.011 −0.149∗∗∗ 0.022Estab. size (×106) −0.024 1.91Bonus 0.020 0.020 −0.028 0.038Interdepend. 1 (std) 0.019∗ 0.010 0.053∗∗∗ 0.016Interdepend. 2 (std) 0.055∗∗∗ 0.010 0.024 0.017Job autonomy (std) −0.063∗∗∗ 0.011 −0.041∗∗ 0.017Multitasking (std) 0.008 0.010 −0.008 0.018Job worries (std) 0.076∗∗∗ 0.010 0.063∗∗∗ 0.015Risk (std) 0.015 0.010 −2.573 1.694Time pressure (std) 0.235∗∗∗ 0.010 0.135∗∗∗ 0.018Extraversion (std) −0.024∗∗ 0.011 −0.233 0.807Conscientiousness (std) −0.005 0.011 −0.785 1.352Neurotism (std) 0.145∗∗∗ 0.010 1.139 0.780Openness (std) 0.006 0.011 1.523 1.135Agreeableness (std) −0.010 0.011 −0.472 1.630

Collegiality NO YES YES(dummies)Industry FE NO YES NORegion FE NO YES NOSchooling FE NO YES NOWave FE NO YES YES

Observations 8,882 8,882 8,914R2 / R2-within 0.042 0.292 0.089

Notes: ∗/∗∗/∗∗∗ denotes statistical significance at the 10/5/1% level.Source: Linked Personnel Panel (LPP), 2012(3)/2014(5)/2016(17), own calculations.

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Figure 3: ICT use and work-to-family conflict (robustness)


Figure 4: ICT use, work-to-family conflict and autonomy


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Figure 5: ICT use, work-to-family conflict and working time increase


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call me on sunday: the impact of permanent availability on...

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