The Adverse Effects of “Healthy” Shift Plans: Evidence ... · While the design of shift work...

The Adverse Effects of “Healthy” Shift Plans: Evidence from a Large German Automobile Plant

Bernd Frick1, Robert Simmons2, and Friedrich Stein3

Abstract Using a balanced panel of some 400 organizational units in a large automobile plant we analyze changes in employee behavior (i.e. in absenteeism) following an organizational innovation intended to improve worker health and well-being. During the period under consideration (January 2009 thru December 2011) the firm replaced its traditional shift schedule that was considered “unhealthy” by a (presumably) “worker-friendly” system. Our estimations show that this organiza-tional innovation was accompanied by an increase – rather than a decrease – in absenteeism. Keywords: Shiftwork, Absenteeism, Organizational Change JEL-Code: M54, J22; L62

1 Department of Management, University of Paderborn, Warburger Strasse 100, 33098 Paderborn, E-

mail: [email protected] and Institute for Labor and Personnel Economics, MobileLife Cam-pus, Volkswagen AG, Hermann-Muench-Strasse 1,38440 Wolfsburg, Germany.

2 Department of Economics, The Management School, Lancaster University, Lancaster LA1 4YX, UK. E-mail: [email protected]

3 Institute for Labor and Personnel Economics, MobileLIfe Campus, Volkswagen AG, Hermann-Muench-Strasse 1, 38440 Wolfsburg, Germany. E-Mail: [email protected]

mailto:[email protected]



1. Introduction

Shift work is common in many industries around the world. Capital-intensive produc-

tion in e.g. automobile and steel plants on the one hand and the uno actu provision of

services in e.g. hospitals, emergency rescue services, police and fire departments re-

quire around the clock presence of workers. In Germany, for example, 42% of all

manufacturing firms use some sort of shift work and about 15% of the workforce in

that sector work on rotating shifts (see Jirjahn 2008: 146). However, although wide-

spread the impact of different shift schedules/shift systems on worker well-being and

individual health outcomes has not yet been studied extensively by economists (ex-

ceptions include Pocock et al. 1972, Backes-Gellner et al. 1999 and Brachet et al.

2012). The topic has until recently remained a domain for occupational medicine (see

section 2 below).

In this paper we use monthly data on absenteeism in some 400 organizational units in

a large German automobile plant over a period of 36 months to analyze the impact of

changes in the shift schedule on workers’ health. Our study design has a number of

advantages: First, we have complete information for all the units over an extended

period of time and, second, we have a quasi-experimental design allowing us to identi-

fy the negative effects of a move towards a shift schedule that was initially considered

as beneficial especially by medical experts. The presumably “healthy” shift system was

repealed after a few months because workers started to express their discontent with

the new schedule very soon after its implementation. Given these two events we iden-

tify considerable “hidden (health) costs” of the new shift system that was rejected by

particular groups of workers.

The remainder of the paper is structured as follows: Section 2 provides a review of the

relevant literature while section 3 describes the two different shift systems used in the

respective plant of the automobile company. In section 4 we present the data and

some descriptive evidence while section 5 includes our econometric evidence docu-

menting the changes in worker behavior following the implementation of a new shift

system that specialists from occupational medicine strongly favor above its predeces-

sor as it is considered to foster worker health and well-being. Moreover, we also pre-

sent in this section the results of a detailed analysis of the health effects of the change

in the shift system for particular subgroups of workers. Finally, section 6 concludes.

2. Literature Review

A broad consensus seems to exist on the negative consequences of (rotating) shift

work: First, people working on rotating shifts report more sleeping problems, poorer

physical health and poorer psychological well-being than non-shift workers (e.g. An-

gersbach et al. 1980, Knauth et al. 1980, Koller 1983, Martens et al. 1999, Costa 1996,

2003, Akerstedt 2003, Nakata et al. 2004). Second, shift work has been found to be

detrimental to family and social life (e.g. Jansen et al. 2004) and to lead to higher (vol-

untary) employee turnover (e.g. Askildsen et al. 2003). Moreover, accident risks at

work have been found to be significantly higher during night hours (e.g. Hänicke et al.

1998) and the retiring age of shift workers is younger than that of non-shift workers

(e.g. Shen and Dicker 2008).

Less consensus seems to exist on the positive and neutral effects of shift work. It ap-

pears, however, that shift work has no significant impact on work attitudes (e.g. Blau

and Lunz 1999). Moreover, if chosen voluntarily, working night shifts has no negative

effects on cognitive and psychomotor performance either (e.g. Petru et al. 2005). One

of the few positive effects of shift work is that for many workers with low daytime

earnings an opportunity exists to self-select into shift work and supplement their earn-

ings (e.g. Kostiuk 1990).

An important issue that has not yet been convincingly addressed in the literature is the

impact of shift work in general and different shift systems/schedules in particular on

worker behavior (such as e.g. absenteeism). Due to differences in the data and the

estimation techniques used, the available studies fail to reach consensus: A number of

papers show that shift work is associated with higher absenteeism (e.g. Chaudhury and

Ng 1992, Drago and Wooden 1992, Dionne and Dostie 2007) while others document

that shift work has no impact on individuals’ number of absence spells per year (e.g.

Böckerman and Ilmakunnas 2008).

While the design of shift work schedules has been found to be particularly important

for employee health and well-being in some studies, others fail to find clear evidence

in favor of a particular shift system: On the one hand, Oexman et al. (2002) find that

rotating hours changing once a week and backward rotating hours can cause consider-

able shift work coping problems. On the other hand, Bambra et al. (2008) document

that switching from backward to forward shift rotation and from slow to fast rotation

can have positive effects on a number of health outcomes. However, based on a sys-

tematic review of more than 20 empirical studies looking at the relationship between

rotating shift work and sickness absence Merkus et al. (2012) fail to find unanimous

results. Thus, irrespective of its widespread use, the empirical evidence on the influ-

ence of the structure of shiftwork (e.g. type of rotation and length of cycles) on various

measures of worker performance remains limited and inconclusive (examples are e.g.

Brachet, David and Drechsler (2012) and Backes-Gellner et al. (1999)).

Another strand of literature that needs to be mentioned in this context is the genre of

nano-econometrics or ‘insider’ econometrics that has grown in personnel economics

following the seminal work on steel plants in the United States by Ichniowski, Shaw

and Prennushi (1997) and on windshield installers in a large US firm by Lazear (2000).

This approach emphasizes rigorous econometric analysis of panel data generated with-

in one company or a few companies. Recently, these two studies have led to a rapid

and considerable increase not only in the quantity but also the quality of empirical

work on the productivity effects of different human resource management instru-

ments and practices (or bundles thereof). Bandiera at al. (2005, 2009, 2010), for ex-

ample, study the response of fruit-pickers employed by a single farm in the UK to a

change in the remuneration system. Hamilton et al. (2003) investigate the productivity

effects of introducing teamwork in a US garment factory. Knez and Simester (2001)

analyze the performance of ground crews at airports served by Continental Airlines

following the introduction of a company-wide tournament to improve service quality

and Banker, Lee, Potter and Srinivasan (2001) look at the changes in the behavior of

sales employees in ten outlets of a large US retail firm following the implementation of

a performance-based compensation plan (for surveys of the literature see e.g. Bloom

and Van Reenen 2010, Ichniowski and Shaw 2009, Lazear and Shaw 2007, as well as

Shaw 2009).

3. Shift Systems

All the organizational units in our study are located in the body shop, the paint shop or

the assembly in the same plant of a large German vehicle manufacturer. Irrespective

of the shift system in use, work for the different shift teams starts at 6:30 am, 2:30 pm

and 10:30 pm. At the beginning of our observation period, all units worked under a

shift system that required 6 weeks of weekly rotation from morning shift (M) to day

shift (D) followed by three weeks on night shift (N). Thus, the shift system is discontin-

uous with work days ranging from Monday to Friday with weekends off (see figure 1).

Figure 1 Initial Shift System (January 2009 until December 2010)

Week 1 2 3 4 5 6 7 8 9 Shift M D M D M D N N N

This system was heavily criticized by employee representatives and, therefore, re-

placed by a system considered to be more worker-friendly particularly by occupational

medicine experts. The new system started on Jan. 1st, 2011 and abandoned the exten-

sive continuous night shifts. Moreover, a forward rotating long cycle system of 5 days

was implemented. Under the new system workers were required to work a five day

week (Monday to Friday) starting in the morning (at 6:30), then switch to the day shift

for week two (starting at 2:30 pm) before working 5 days on the night shift in the third

week. In the week following the night shift, the cycle starts again (see figure 2; note

that Friday night shifts finish Saturday morning).

Figure 2

New Forward Rotating Shift System (January 2011 until August 2011)

Week 1 2 Days Mo Tu We Th Fri Sa So Mo Tu We Th Fri Sa So Shift M M M M M - - D D D D D - -

Week 3 4 Days Mo Tu We Th Fri Sa So Mo Tu We Th Fri Sa So Shift N N N N N - - M M M M M - -

A few months later, the decision was repealed and the shift plan modified again. This

time the rotation direction of the system was changed. The new shift system was im-

plemented following the company’s summer break on Sept. 1st, 2011 and included a

weekly backward rotating long cycle (5 days) system (see figure 3). The system re-

mained in practice beyond the end of the observation period (December 2011).

Figure 3

Backward Rotating Shift System (September 2011 until December 2011)

Week 1 2 Days Mo Tu We Th Fri Sa So Mo Tu We Th Fri Sa So Shift M M M M M - - N N N N N - -

Week 3 4 Days Mo Tu We Th Fri Sa So Mo Tu We Th Fri Sa So Shift D D D D D - - M M M M M - -

Although forward rotating systems are considered to provide more recovery time be-

tween different shift spells (e.g. Härmä et al. 2006: 71) backward rotation was pre-

ferred by the workers’ representatives because of its impact on the rather long break

on weekends. However, over a complete 3 week shift cycle, both systems (forward vs.

backward) provide the same amount of leisure time at weekends. What is different,

however, is the distribution of the slots of leisure, resulting in one rather short week-

end during the forward rotating cycle (moving from night shift to morning shift is asso-

ciated with 48 hours of leisure time during which workers have to recover from their

shifts. Therefore, the time available for leisure and social activities is limited on that

particular weekend). Summarizing, the difference in the distribution in recovery time

at weekends should be considered the main reason for the second adjustment of the

shift system (lack of acceptance by workers induced management to return to a back-

ward rotating system while simultaneously avoiding the problems associated with a

discontinuous system).

4. Data and Descriptive Evidence

In order to analyze the impact of a change in the shift schedule on absenteeism we use

a balanced panel including monthly data on absenteeism from 409 organizational units

in one particular plant of a large German automobile company over an extended peri-

od of time (January 2009 thru December 2011) during which no other changes in e.g.

the production process occurred that could have affected worker absenteeism. Our

study design has a number of advantages: First, the required information is completely

available for all units over a period of 36 consecutive months and, second, the set-up

resembles a quasi-experimental design allowing us to identify the effects on worker

absenteeism of a move towards a shift schedule that is considered as beneficial by all

experts. An explanation for the “adverse effects” that we find can be seen in the fact

that the new – and presumably “healthy” – shift system was rejected by the majority

of the workers (and, consequently, the works council). Our focus on finely tuned data

from within a large company enables us to analyze the impact of different shift sys-

tems on worker absenteeism with a precision that would be lacking in broader estab-

lishment-based surveys.

Our initial data set included monthly information on 1,031 organizational units per-

forming a variety of different tasks in the production process (body shop, paint shop,

assembly, quality management as well as supporting activities). Construction of a bal-

anced panel resulted in a data set including 451 organizational units (information on

the remaining units was incomplete because of structural changes in the organization

of approximately half of the units (e.g. elimination of some units, creation of new

units, mergers of existing units)). Moreover, before estimating our models we per-

formed a series of plausibility checks that lead to the elimination of units with massive

outliers (e.g. the number of employees in a particular unit increased by more than

100% in two months and declined similarly only a month later). Finally, due to the

company’s data protection regulations we had to exclude units with less than 5 em-

ployees, leading to a further reduction in sample size to 409 organizational units. For

these units we have the necessary information on the monthly absence rate as well as

the number of employees. In total, the units in the sample employ some 7,500 work-

ers.

Table 1 Summary Statistics

Variable Mean Std. Dev. Min. Max.

Employees 17.99 7.69 5 49

Absence Rate 6.25 5.38 0.00 56.00

Shift Regime 1 0.67 - 0 1



Number of organizational units: 409 Number of unit-month-observations: 14,724

Fortunately, the limited number of explanatory variables and the resulting lack of con-

trols is not a serious problem because personnel turnover is unusually low at this com-

pany (less than 5% per year) implying that the composition of the teams in the units

remains fairly stable over the observation period. Furthermore, the reduction of the

data set through elimination of units with incomplete data does not bias the results

since the characteristics of the excluded units resemble those of the units that are in-

cluded. The data was obtained from the firm’s central human resource reporting sys-

tem. Monthly absenteeism is measured in percent of regular hours of work. Since we

have 409 organizational units in the sample that we observe over a 36 month period,

our data set consists of 14,724 unit-month-observations.

Figure 4 displays the development of the average absence rate over the observation

period. As expected, average absenteeism is higher during the winter months. Fur-

thermore, the dips in absenteeism during the summer months and especially in July

are not surprising since the plant shuts down production for three weeks during the

summer.

Figure 4 Absence Rates by Month

A comparison of average absence rates under the three different shift systems (see

figure 5) reveals a particularly low level of absenteeism for 2009. This is most likely due

to the uncertainty resulting from the aftermath of the economic crisis. In general, ab-

0

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- - - Shift plan changes

sence rates are lower during economic downturns because a tight labor market (due

to high unemployment) offers limited alternatives to workers losing their jobs (i.e. the

opportunity costs of losing the job increase). Another explanation is that during the

economic crisis capacity utilization was lower and, therefore, workers were exposed to

less stress.

Moreover, the comparison seems to suggest that absenteeism was higher when the

forward rotating shift system was in use (solid black line). In our econometric analysis

we analyze whether this difference is statistically significant and whether the second

change in the shift system was associated with a statistically significant reduction in

absenteeism (not surprisingly, the seasonal pattern of absenteeism is similar to the

one in figure 4).

Figure 5 Monthly Absence Rates by Shift System

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Old System (2009)

Old System (2010)

Forward Rotation (2011)

Backward Rotation (2011)

A final observation that warrants some discussion in this context is the difference in

the seasonal pattern of the yearly absence rate during the summer period. While for

the years 2009 and 2010 the lowest absence rates were recorded for July, in 2010 Au-

gust was the month with the lowest absence rates. This strange phenomenon is easy

to explain: As already mentioned above the plant shuts down for a summer break

which, in turn, coincides with the school holidays in the federal state where the plant

is located.

5. Estimation and Results

To test for the impact of the two changes in the shift system on absence rates we first

estimate a fixed effects model with clustered standard errors. In the model we include

the observed absence rate (in % of monthly working hours) as the dependent variable

(see Table 2). Moreover, in order to account for the specific nature of our dependent

variable we also estimate a generalized linear model (GLM). Month dummies to con-

trol for seasonal effects are included. What we are most interested in is – of course –

the coefficients of the two regime dummies representing the different shift systems

(the initial regime serves as our reference category). It appears from Table 2 that all

variables included in the estimation of the model are statistically significant. Most im-

portant are the coefficient of the two regime dummies: The first change in the shift

system (from 6 weeks of morning and evening shifts plus 3 weeks of night shift) from a

backward rotating long cycle to a forward rotating long cycle (regime 2) had a statisti-

cally significant and positive effect on absence rates, suggesting that the introduction

of the new (and presumably “healthy”) shift system led to an increase in the absence

rate by about 0.58%. The second change (from the forward rotating to a similar back-

ward rotating system, e.g. regime 3) was also associated with a higher absenteeism

compared to the initial level.4

4 The effects also hold when the reference category is changed.

Table 2 Fixed Effects Estimation of Shift Systems on Absence Rate

Variable Coeff. Rob. Std. Err. T

Employees 0.070 0.032 2.32**

Shift Regime 2 0.584 0.169 3.45***

Shift Regime 3 0.360 0.214 1.68*

Month Dummies Included Constant 4.97 0.58 8.52***

R2 (overall) * 100 2.9 *** p< 0.001; ** p < 0.05; + not significant

However, the latter effect is statistically significant at the ten percent level only.

Moreover, the coefficients of the two regime dummies are statistically significant, sug-

gesting that the change towards the “healthy” system was associated with a larger

increase in absenteeism than the change towards a “less healthy” system. The results

are corroborated by our GLM regression (see Table 3) with the only relevant difference

being that now the coefficient for the second change in the shift system loses its statis-

tical significance.

Table 3 GLM Regression of Shift Systems on Absence Rate


Employees 0.062 0.020 3.07**

Shift Regime 2 0.576 0.171 3.36***

Shift Regime 3 0.351 0.217 1.62+

Month Dummies Included

Constant 5.21 0.43 11.93***

*** p< 0.001; ** p < 0.05; + not significant

Analysis by Subgroups

In order to better understand the reasons for the increase in absenteeism following

the changes in the shift system we have estimated our models separately for 14 differ-

ent areas that differ in terms of the tasks performed by workers in the respective units

(like e.g. sheet metal shop, body shop, paint shop, assembly, logistics, quality control,

etc.).5

Figure 6

Absence Rates for the Different Subgroup Sample

These additional analyses helped us to identify two areas as the main drivers of the

increase in absence rates. Both areas include tasks/jobs with relatively low levels of

physical stress and strain. It is, therefore, not surprising that in these two areas we find

a high percentage of employees with physical impairments and chronic health prob-

lems. We conjecture that due to the composition of the jobs/tasks in the different are-

as and the different levels of physical stress and strain in these areas, separate estima-

tions are warranted. Figure 5 above indicates that the monthly absence rates are sig-

5 Due to data protection reasons we cannot disclose the areas with particularly high/low percentages

of health impaired workers.

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Jan May Sep Jan May Sep Jan May Sep

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Total

Two units with high percentage of health-impaired employees

Twelve units with low percentage of health-impaired employees

nificantly higher over all periods in the two “sickness prone areas” compared to the

average absence rates per month for the entire sample (not surprisingly, the seasonal

absence patters are identical across the different areas).6

The results of the separate estimations are presented in Table 4 and indicate that the

increase in absenteeism following the changes in the shift plan to be significantly high-

er for the two areas with a high percentage of health-impaired workers compared to

the twelve areas with a low percentage of workers suffering from chronic health prob-

lems. However, for both areas the impact of the first shift plan change is highly signifi-

cant and is associated with a 0.99% and 0.44% increase in absenteeism respectively. In

both areas, the second change in the shift system led to a reduction in monthly absen-

teeism (the respective coefficients are statistically insignificant).7

Table 4

Separate Estimations – Units with High Percentage of Health-Impaired Employees vs. Units with Low Percentage of Health-Impaired Workers

Variable High Percentage of Health-Impaired Workers

Low Percentage of Health-Impaired Workers

Employees 0.147*** 0.035+

Shift Regime 2 0.995*** 0.446**

Shift Regime 3 0.506+ 0.313+


Constant 4.46*** 6.26***

Number of observations 3,312 11,412

*** p< 0.001; ** p < 0.05; + not significant

6 Additional estimation with interaction terms (area with physically less/more demanding jobs times

new shift systems) resulted in insignificant coefficients, suggesting the absence of area-specific ef-fects (see Table A1 in the appendix).

7 Again, GLM regressions yielded comparable results. These are available from the authors on request.

As a further robustness check we split our sample at the median of the absence rate

and repeated the estimations with the two 50% subsamples. Moreover, we also divid-

ed the dataset in two equally large samples at the median of the size of the units (see

Tables A2 and A3 in the appendix). The results reveal interesting differences in the

basic pattern that need to be analyzed in more detail: First, the change from regime 1

to regime 2 was associated with a particularly large increase in monthly absenteeism

in those units where the absence rate was already high before the implementation of

the new system.8 Second, the increase in absenteeism from regime 1 to regime 2 was

particularly large in the larger units.

6. Summary and Conclusions

Our main result is that the change from a shift system considered as “unhealthy” (as it

is characterized by three continuous weeks of night shifts) to a more “healthy” (for-

ward rotating) schedule is associated with a statistically significant increase (instead of

a decrease) in monthly absence rates. This increase, however, declines following a

second modification of the shift system: Changing the direction of rotation (from for-

ward to backward, i.e. from a system considered “healthy” to one that is considered

“unhealthy” by (almost all) medical experts) is associated with a decrease in monthly

absence rates. Moreover, the increase is found to be particularly large in two areas

with particularly high percentage of health-impaired employees.

Hence, the introduction of a presumably „healthy“ shift system is associated with an

0.58% increase in employee absenteeism. The most plausible explanation for this sur-

prising change in employee behavior is lack of acceptance of this type of organizational

8 The findings are corroborated by the results of various quantile and interquantile regression esti-

mates (see Tables A4 and A5 in the Apendix).

change. We estimate the “hidden costs” due to the increase in absenteeism at about

€1.7million (about €220,000 per month).9 Since the organizational units included in

our sample comprise only 30% of workforce the total costs are more than three times

as high (nearly €5.1 million). We conjecture that management can avoid these costs

by, first, communicating its goals and strategies and, second, by seeking the consent

and support of workers.

9 Calculated as hours lost due to additional absenteeism times gross hourly wage costs per workers.

Appendix

Table A1 Fixed Effects Estimation – Including Dummies for Areas with Physically Less Demanding

Jobs x New Shift System


Employees 0.075 0.032 2.30**

Jobs_Shift2 0.458 0.343 1.33+

Jobs_Shift3 0.163 0.474 0.35+

Shift Regime 2 0.481 0.191 -3.65***

Shift Regime 3 0.323 0.197 2.44**


Constant 5.68 0.620 9.16***

R2 (overall) * 100 3.09

Table A2 Fixed Effects Estimation - Robustness Check of Absence Rate (I)

Median Split Absence Rate

Variable Median Split Low Absence Rate

Median Split High Absence Rate

Full Sample (n=409 units)

Employees 0.067* 0.094+ 0.070**

Shift Regime 2 0.283* 0.907*** 0.584***

Shift Regime 3 0.635** 0.113+ 0.360*


Constant 3.52*** 7.59*** 4.97***

R2 (overall) * 100 3.1 1.6 2.9

Table A3 Fixed Effects Estimation - Robustness Check of Absence Rate (II)

Median Split Unit Size

Variable Median Split Small Units

Median Split Large Units

Full Sample (n=409 units)

Employees 0.174*** 0.045+ 0.070**

Shift Regime 2 0.410+ 0.704*** 0.584***

Shift Regime 3 0.357+ 0.292+ 0.360*


Constant 4.52*** 6.34*** 4.97***

R2 (overall) * 100 2.3 3.8 2.9

Table A4 Quantile Regression of Absence Rates

Variable 10% 25% 50% 75% 90%

Employees 0.084*** 0.123*** 0.108*** 0.042*** -0.016+

Shift Regime 2 0.069+ 0.211** 0.574*** 0.810*** 1.193***

Shift Regime 3 0.000+ 0.082+ 0.308+ 0.731*** 0.587*


Constant -0.83*** 0.11+ 3.28*** 8.25*** 13.53***

Pseudo R2 (overall) * 100 4.4 4.5 2.8 1.4 1.7

Table A5 Interquantile Regression of Absence Rates

Variable 10/25 25/50 50/75 75/90

Employees 0.038*** -0.015** -0.066*** -0.058***

Shift Regime 2 0.142* 0.363*** 0.235+ 0.383+

Shift Regime 3 0.082+ 0.225+ 0.423+ -0.144+


Constant 0.90*** 3.17*** 4.96*** 5.27***

Pseudo R2 (overall) * 100 (lower) 4.4 4.5 2.8 1.4

Pseudo R2 (overall) * 100(upper) 4.5 2.8 1.4 1.7

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