What Is the Average Number of Hours Worked Per Year

This paper studies the link between working hours and productivity using daily information on working hours and performance of a sample of call centre agents. We exploit variation in the number of hours worked by the same employee across days and weeks due to central scheduling, enabling us to estimate the effect of working hours on productivity. We find that as the number of hours worked increases, the average handling time for a call increases, meaning that agents become less productive. This result suggests that fatigue can play an important role, even in jobs with mostly part-time workers.

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Research Centre for Education and the Labour Market | ROA

ROA-RM-2017/4

Working hours and productivity

Marion Collewet

Jan Sauermann

ROA Research Memorandum

Researchcentrum voor Onderwijs en Arbeidsmarkt | ROA

Research Centre for Education and the Labour Market | ROA

Working hours and productivity

Marion Collewet

Jan Sauermann

ROA-RM-2017/4*

April 2017

* The ROA Research Memorandum Series was created in order to make research results available for discussion,

before those results are submitted for publication in journals.

Research Centre for Education and the Labour Market

Maastricht University

P.O. Box 616, 6200 MD Maastricht, The Netherlands

T +31 43 3883647 F +31 43 3884914

secretary-roa-sbe@maastrichtuniversity.nl

www.roa.nl

Abstract

Working hours and productivity**

This paper studies the link between working hours and productivity using daily

information on working hours and performance of a sample of call centre agents. We

exploit variation in the number of hours worked by the same employee across days and

weeks due to central scheduling, enabling us to estimate the effect of working hours

on productivity. We ﬁnd that as the number of hours worked increases, the average

handling time for a call increases, meaning that agents become less productive. This

result suggests that fatigue can play an important role, even in jobs with mostly part-

time workers.

JEL classiﬁcation: J23, J22, M12, M54

Keywords: working hours, productivity, output, labour demand

Marion Collewet

Centre for Operations Research and

Econometrics (CORE)

Université Catholique de Louvain

Voie du Roman Pays, 34 - L1.03.01

B-1348 Louvain-la-Neuve

Belgium

marion.collewet@uclouvain.be

and ROA, Maastricht University

Jan Sauermann

Swedish Institute for Social Research

Stockholm University

Universitetsvägen 10 F (oors 8 and 9)

SE-106 91 Stockholm

Sweden

jan.sauermann@soﬁ.su.se

and ROA, Maastricht University and IZA

** The authors would like to thank Jordi Blanes-I-Vidal, Alexandra de Gendre, Andries De Grip, Annemarie Künn-

Nelen, John Pencavel, two anonymous referees, and seminar participants at the Paris School of Economics, and

audience at EALE 2016 for valuable comments and suggestions. Jan Sauermann gratefully acknowledges ﬁnancial

support from the Jan Wallanders och Tom Hedelius Stiftelse for ﬁnancial support (Grant number I2011-0345:1).

1 Introduction

Hours worked vary substantially between countries, but also within countries,

e.g. due to the prevalence of part-time work and working hours regulations or

agreements (Bick et al., 2016; OECD, 2016). Understanding how the num-

ber of hours worked aﬀects labour productivity is an important element of

understanding labour demand, and has important implications for the regu-

lation of working hours and ﬁrm management. Still, a lot remains unknown

about the eﬀect of working hours on labour productivity. In theory, there

could be two opposite eﬀects. On the one hand, longer hours can lead to

higher productivity if a worker faces ﬁxed set-up costs and ﬁxed unproduc-

tive time during the day, or if longer hours lead to better utilisation of capital

goods (Feldstein, 1967). On the other hand, worker fatigue could set in af-

ter a number of hours worked, so that the marginal eﬀect on productivity

of an extra hour per worker starts decreasing (Pencavel, 2015). If neither

of these eﬀects apply, or if both cancel each other out, it could also be the

case that marginal productivity does not change with working time, so that

output is proportional to the number of hours worked. Identifying the eﬀect

of working time on productivity is not straightforward for two main reasons.

First, unobservable characteristics of industries, ﬁrms, jobs and individuals

are likely to inﬂuence both working time and productivity, so that the cor-

relation between the two variables is likely to be a biased estimate of the

eﬀect of working time on productivity. Second, external shocks could in-

ﬂuence both working time and productivity, which again leads to a biased

estimation of the eﬀect.

In this paper, we study the inﬂuence of the daily number of hours worked

on workers' productivity using panel data from a call centre in the Nether-

lands from mid-2008 to the ﬁrst week of 2010 (cf. De Grip and Sauermann,

2012; De Grip et al., 2016). For each of the 332 workers in our sample, the

data contain detailed information on the number of daily working hours, and

workers' individual performance, as measured by the average handling time

of calls. The panel structure of our data set allows us to correct for time-

invariant unobserved characteristics of individuals that may inﬂuence both

working time and productivity. Moreover, the exact number of hours worked

by a worker on a given day is determined by central planning. Expected cus-

tomer demand determines the scheduling process, and schedules are hardly

related to individual preferences. This enables us to obtain estimates of the

eﬀect of working time on productivity.

Estimating a model controlling for individual ﬁxed eﬀects and several

types of time ﬁxed eﬀects, we ﬁnd that an increase in working hours by

1 percent leads to an increase in output by only 0.9 percent, measured as

the number of calls answered. This ﬁnding suggests that fatigue sets in

as working time increases. The corresponding decrease in productivity is

mild in this sample where most employees work part-time, but it suggests

that fatigue eﬀects would be much stronger if agents would work full-time.

We ﬁnd evidence of more strongly decreasing returns to hours for workers

with shorter tenure, a result that is not driven by worker attrition. Using

additional data on service quality, we ﬁnd that longer working hours are

associated with a moderate increase of call quality in working hours, a result

that partly oﬀsets the negative eﬀect on the number of calls answered.

This paper contributes to a rich literature that studies the link between

working time and productivity. Studies estimating production functions

based on industry-level data ﬁnd mixed evidence for the returns to work-

ing hours. Whereas some studies ﬁnd increasing returns to hours (Feldstein,

1967; Craine, 1973; Leslie, 1984), which could be the result of not taking

capacity utilisation rates into account (e.g. Tatom, 1980), or be due to ag-

gregation bias (e.g. DeBeaumont and Singell, 1999), other studies conclude

that output is roughly proportional to hours worked per worker (Hart and

McGregor, 1988; Anxo and Bigsten, 1989; Ilmakunnas, 1994). The majority

of studies, however, ﬁnd evidence of decreasing returns to hours (e.g. Leslie

and Wise, 1980; Tatom, 1980; DeBeaumont and Singell, 1999; Shepard and

Clifton, 2000). Typically, studies using aggregate data deal with the endo-

geneity of working time by using panel data and including industry ﬁxed

eﬀects, and by instrumenting for working time using lagged values or ranks.

The validity of such instruments, however, can be questioned, and the mea-

surement of working time and output at these aggregate levels is likely to be

subject to error.

Studies using ﬁrm-level data, or data from workers in individual ﬁrms

or in speciﬁc sectors are typically better at dealing with the endogeneity of

working hours. A few studies use panels of ﬁrms to estimate the link between

working time and ﬁrm or establishment productivity (Cr´epon et al., 2004;

Schank, 2005; Kramarz et al., 2008; Gianella and Lagarde, 2011). They

tend to ﬁnd that output is roughly proportional to the number of hours

worked.1 Due to the data structure, these studies are able to control for the

endogeneity of working time caused by time-invariant ﬁrm characteristics.

However, shocks that would aﬀect both working time and productivity could

still form a potential source of bias.

Studies using data about individual workers in a ﬁrm, or about work-

ers in comparable ﬁrms date back to the early 20th century, when studies

descriptively analysed the relationship between working hours and output,

or compared output before and after a change in working hours (Goldmark,

1912; Vernon, 1921; Kossoris, 1947).2More recent studies, however, exploit

exogenous sources of variation in working hours to address the relationship

between hours and worker level productivity. An early example is the study

of citrus harvesters by Crocker and Horst (1981), who use the size of the

grove worked on as a source of variation in working time. Brachet et al.

(2012) conduct a diﬀerence-in-diﬀerences analysis to compare performance

of paramedics working on short and long shifts. Using data from munition

plants in Britain during the First World War, Pencavel (2015) uses vari-

ation in working time coming from the demand for shells to estimate the

eﬀect of working time on productivity. Dolton et al. (2016) use data from

the Hawthorne experiments (conducted between 1924 and 1932) to exploit

1Cr´epon et al. (2004) and Kramarz et al. (2008) ﬁnd positive eﬀects on productivity

of participating in a working time reduction scheme for French ﬁrms, but this eﬀect is

due to the reorganisation of work that took place as a consequence of the working time

reduction. Work allocation as a mechanism to explain the link between part-time work and

productivity has been studied by K¨unn-Nelen et al. (2013), Specchia and Vandenberghe

(2013), Garnero et al. (2014), and Devicienti et al. (2015).

2See Nyland (1989) for an overview of these and other studies.

the fact that workers were subjected to diﬀerent working times in diﬀerent

periods. While Crocker and Horst (1981) ﬁnd that output is proportional

to hours worked, Brachet et al. (2012), Pencavel (2015), and Dolton et al.

(2016) ﬁnd evidence of decreasing returns to hours. A contrasting result is

found by Lu and Lu (2016), who exploit changes in mandatory overtime laws

for nurses. They ﬁnd that the introduction of overtime laws actually reduced

the quality provided by nurses, an eﬀect that can be explained by changes in

staﬃng policies of permanent and contractual (temporary) nurses.3We con-

tribute to this literature by exploiting exogenous variation in working hours,

which is due to the call centre's central scheduling.

Most of the studies that are able to exploit exogenous variation in working

time to identify the eﬀect of working hours on productivity have concentrated

on either manual workers from the ﬁrst half of the 20th century (Pencavel,

2015; Dolton et al., 2016)4, or on the health sector using more recent data

(Brachet et al., 2012; Lu and Lu, 2016). In this paper, we provide evidence

about call agents in a call centre. Our results can have informative value for

a broader range of medium-skilled level jobs in the service sector, and are

relevant for policies such as working time regulation.

The remainder of the paper is structured as follows. In the next section,

we outline our conceptual framework. Section 3 presents the empirical model

we estimate and our identiﬁcation strategy. Section 4 describes the data we

use. Section 5 presents our main estimation results. In Section 6, we conduct

a number of robustness checks, and we formulate conclusions in Section 7.

3In addition to these studies, there are more studies for the health sector, typically

ﬁnding decreasing returns to working hours. These studies, however, are either based on

indirect performance measures (psychometric tests, simulations of work tasks, self-report

questionnaires; for a review, see Kodz et al., 2003), or on correlations and before-after

comparisons (Rogers et al., 2004; Hart and Krall, 2007; McClay, 2008). There is also a

related literature that has analysed the link between long working hours and health (e.g.

van der Hulst, 2003) and between long hours and occupational injuries (e.g. Vegso et al.,

2007; Lee and Lee, 2016). These studies suggest that working long hours is detrimental

for health and therefore may have negative eﬀects on productivity.

4The relation between working time and productivity might have changed as the nature

of jobs evolved. Pencavel concludes his paper stating that "it would be valuable if the

analysis here could be repeated on contemporary data that contain information on workers'

output and their working hours" (p. 2074).

2 Conceptual framework

2.1 Model

Typically, studies of the relation between working time and productivity

estimate a model of the type:

Y= f( H, X ) +  (1)

where Y is a measure of output, Ha measure of hours worked, Xis a

set of variables which are also relevant for output (the capital stock being

a typical candidate), and  is the error term. Very often, the relationship

between log output and log hours is estimated assuming a Cobb-Douglas

production function. Because we focus on productivity at the level of the

individual worker, for whom capital use is constant (namely one workstation),

the Cobb-Douglas function can be estimated as

ln( Y ) = α· ln( H ) + γ· X+  (2)

In our setting, call centre management uses a speciﬁc measure to evaluate

the performance of its agents: average handling time (AH T ), which is based

on the time taken by an individual agent to answer each call during a given

day or a given week. If output Yis deﬁned as the number of calls made on

a given day, it can be expressed as

Y= H

AHT (3)

Inserting Equation (3) into (2) gives:

ln( AH T ) = (1 − α)· ln( H)− γ· X−  (4)

Average handling time is a negative measure of productivity, since indi-

viduals who take more time to answer calls are less productive (cf. De Grip

and Sauermann, 2012). To facilitate direct interpretation of our estimation

results, we multiply Equation (4) by −1, which allows us to rewrite it to

ln  1

AHT  = ( α−1) · ln(H ) + γ· X + (5)

and to use 1/AHT as a measure of productivity: an increase in 1/AH T can

therefore be interpreted as an increase in productivity.

In this model, a coeﬃcient on ln (H ) equal to zero indicates that work-

ers' productivity does not vary with working hours, which is equivalent to

constant returns to hours. A positive coeﬃcient corresponds to increasing

returns to hours, and a negative one to decreasing returns. The magnitude

of the coeﬃcient on log hours is also directly interpretable: as hours increase

by 1 percent, output increases by αpercent. If we deﬁne the coeﬃcient on

ln( H ) to be β= α− 1, output increases by β + 1 percent as hours increase

by 1 percent.

We estimate the model both at the day and at the week level to address

potential heterogeneity in the returns to working hours at the day and week

level: at the week level, workers have time to recover from one day to another,

so that returns to hours might be more positive than at the day level (cf.

Pencavel, 2016). Moreover, comparing returns to hours at the day and at the

week level can tell us whether productivity can be increased by distributing

weekly working hours diﬀerently across days.

2.2 Mechanisms

Before turning to estimating the model, we want to pay closer attention to

the mechanisms that link working hours to labour productivity. Why exactly

would we expect labour productivity to rise or fall as working hours increase?

Potential reasons for increasing returns to hours are ﬁxed unproductive

time and better capital utilisation rates. In our setting, unproductive time

cannot play a role because we study eﬀective working time (excluding breaks,

see Section 4). Capital utilisation rates are not relevant either because we

focus on the level of the individual worker, who always uses exactly one

workstation consisting of a phone and a computer. One possible mechanism

that could lead productivity to increase with working time is what Vernon

(1921) calls "practice-eﬃciency", i.e. the fact that one gets better at a task

as one "warms up".5It is an open question whether what he describes for

physical work may also apply to call centre agents.

The main reason why productivity would decrease as working hours in-

crease is worker fatigue.6The origins of the literature on fatigue and the

link between working hours and labour productivity lie in the study of the

manufacturing industry in the early 20th century (Goldmark, 1912; Vernon,

1921), with physically demanding jobs and very long hours. But fatigue is

still relevant in today's service economy with shorter hours. Indeed, Ny-

land (1989) emphasizes that the fall in working hours over the course of the

twentieth century has been accompanied by an intensiﬁcation of work due

to the rise of scientiﬁc management, so that the optimal number of working

hours has most probably dropped below levels that were recommended at the

beginning of the twentieth century. In fact, the majority of studies on the

link between working hours and productivity still ﬁnds decreasing returns

to hours. Moreover, physical demands are not the only demands of a job.

Goldmark (1912) already emphasises the important role played by speed and

complexity of a task.7Bakker et al. (2003) ﬁnd that job demands are related

to exhaustion and repetitive strain injury in call centre agents. The agents

we study were exposed to a constant ﬂow of incoming calls and had to solve

problems that were potentially diﬀerent for every single call. The manage-

5"The complex chain of central nervous system, neuro-muscular mechanisms, nerves

and muscles involved takes time to work up to its maximum eﬃciency, and to get into

thorough running order. In fact, this eﬃciency is unattainable except by practice, hence

I have termed it practice-eﬃciency." (Vernon, 1921, p. 13).

6Vernon (1921) cites the deﬁnition by the British Association Committee on Fatigue

from the Economic Standpoint: it sees fatigue as the "diminution of the capacity for work

which follows excess of work or lack of rest, and which is recognised on the subjective side

by a characteristic malaise." (p.1). More recently, in the occupational health literature,

fatigue is deﬁned by van Dijk and Swaen (2003) as "the change in the psychophysiological

control mechanism that regulates task behaviour, resulting from preceding mental and/or

physical eﬀorts which have become burdensome to such an extent that the individual is

no longer able to adequately meet the demands that the job requires of his or her mental

functioning; or that the individual is able to meet these demands only at the cost of

increasing mental eﬀort and the surmounting of mental resistance."

7Interestingly, she takes telephone operators as an example, and reports that a medical

commission deemed a 7-hours workday too long for "telephone girls". Of course, it has to

be said that the work of telephone operators was much more physically demanding than

the work of call centre agents today.

ment of the call centre seemed well aware of the intensity and diﬃculty of the

task agents had to perform: without having actually measured the relation

between working hours and productivity, they expected a full-time contract

to be sub-optimal in terms of productivity and preferred to oﬀer part-time

contracts to their employees for that reason.

3 Empirical strategy

Using panel data, our empirical model can be written as

ln  1

AHTit  = φ+β· ln ( H it ) + γ·X it + δ· T t + µ i +  it (6)

where the subscript i stands for an individual agent, and t represents the

time period (day or week) examined. In this section, we ﬁrst discuss how we

control for potential confounders, and second how we exploit scheduling as

a source of exogenous variation in working hours.

3.1 Controlling for potential confounders

As mentioned above, there are several factors that can result in a biased esti-

mate of the eﬀect of hours on productivity ( b

β). First, the estimate could be

biased due to characteristics of individuals that inﬂuence both their working

time and their productivity, such as preferences or ability. As far as these

individual characteristics are time-invariant, they can be controlled for by

the individual ﬁxed eﬀect µi in our model. Taking account of this unob-

served heterogeneity takes away an important part of the potential bias in

the coeﬃcient on ln (Hit ).

Second, the timing of work might be important as well. If variation

in types or amounts of customer calls varies over time, and this variation

results in changes of agents' productivity, it is important to control for when

individuals work.8In estimations at the day level, we control for day of the

8Note that, although we have precise information on when and how long agents are

working, the data only contain information on performance on the daily level, but not by

the hour.

week dummies (as part of Tt ) and for hours dummies for the time during

which an individual works on a given day (as part of X it ); in estimations

at the week level, we control for 168 dummies that represent each hour of

the week. There might also be shocks on speciﬁc days or weeks, e.g. if

there are technical problems that both induce agents to stay longer hours

and aﬀect their productivity because the shocks inﬂuence the time taken to

help customers or the pressure experienced by agents. To control for this

possibility, our model includes day ﬁxed eﬀects in regressions based on daily

data, or week ﬁxed eﬀects in regressions based on weekly data.

Third, tenure is a potential confounder. Diﬀerences in tenure between

individuals at the beginning of the observation period are captured by the

individual ﬁxed eﬀect. However, as individuals become more experienced,

their productivity is likely to increase, and their working time might also

evolve. If this is the case, some of the eﬀect of tenure will unduly be attributed

to working time. We therefore include a general time trend in the model.

Further, in Section 6, we run additional regressions including individual-

speciﬁc time trends, i.e. interactions of the individual ﬁxed eﬀect with the

time trend. This allows us to provide evidence against the hypothesis that

call centre management could give diﬀerent contracts or schedules to workers

who learn faster.

Fourth, attrition may also be a problem. There is relatively high turnover

in our sample, resulting in that individuals who stay for relatively long in

the sample may be overrepresented in our data. If more productive individ-

uals stay longer, and if they are therefore overrepresented, we will tend to

overestimate returns to hours. We address this problem by estimating our

model for a balanced sub-sample in Section 6. This analysis shows that the

bias caused by attrition is only limited.

Finally, the team in which an individual works may also be a confounder,

in the sense that a team with more positive characteristics (more cohesive,

better team leader, etc.) may both induce an agent to be willing to work

longer hours and make him or her more productive. This would lead to an

underestimation of the fatigue eﬀect, i.e. an overestimation of the returns to

hours. To rule this out, we include team ﬁxed eﬀects in our model.

3.2 Exogeneity of working hours due to central schedul-

ing

In our data, variation in an agent's working time from day to day or from

week to week arises because contractual weekly working time does not need

to be exactly enforced every week. Rather, average working time over a

period of three months should be equal to contractual working time. An

agent's working time for a given day is deﬁned by the planning department,

on the basis of an algorithm that forecasts customer demand, i.e. the number

of incoming calls. A ﬁrst version of an agent's schedule for a given day is

communicated ﬁve weeks in advance, but it can be revised up to the last

moment. We also know that earlier performance of agents does not inﬂuence

future schedules. In principle, this ensures that the exact number of hours

worked by an individual on a given day is not related to shocks aﬀecting this

individual, so that Hit is not correlated with it , the idiosyncratic error term.

There are two potential ways in which individual preferences could in the-

ory still inﬂuence working time on a given day or a given week. First, agents

are allowed to state preferences about their schedule, both general prefer-

ences and preferences for speciﬁc days or weeks, without having a guarantee

that these preferences can be respected. While general preferences stated

by an agent about his or her schedule are captured by the individual ﬁxed

eﬀect, preferences concerning a speciﬁc day or week are not. Second, in case

of unexpectedly high or low demand at the last minute, agents are asked to

stay longer or leave earlier on a voluntary basis, with a corresponding ad-

justment in their pay. In theory, these two factors leave some room for the

agents to work more on days on which they expect to be more productive,

leading to an overestimation of the returns to hours. However, these poten-

tial sources of bias do not appear to play an important role in practice. In

Section 6, we present estimations including scheduled hours (or deviations

from schedules) as an additional control variable. This enables us to check

whether, conditional on the actual number of hours worked, schedules (or

deviations from schedules) are still related to productivity, which would hint

at self-selection by agents. We show that there is only very limited evidence

of self-selection, and that taking it into account barely changes our results.

We conclude that, conditional on our set of ﬁxed eﬀects, scheduling can be

considered an exogenous source of variation in working hours.

4 Data

4.1 Description of the context

We use rich company data on call agents employed in a call centre located

in the Netherlands. This customer service centre handles calls from current

and prospective customers of a mobile telecommunication company. The call

centre comprises ﬁve departments, which are segmented by customer groups.

To focus on workers which have comparable performance, we limit the sample

to the largest department, for customers with ﬁxed contracts.

In this department, all call agents have the same task, answering cus-

tomer calls. Customers call the call centre in case of problems, complaints or

questions and are routed to available agents. Typically, at a given moment,

the number of incoming calls exceeds the number of available agents, gen-

erating non-zero waiting time for customers. Agents who have completed a

call faster are automatically linked to the next customer waiting. This means

that fast agents do not have longer breaks and do not need to wait for the

next customers. Therefore, agents have little slack time when on the job.

In other words, the number of hours that agents spend answering calls, our

main measure of working time, is very close to the number of hours during

which agents are available for calls.

Agents are incentivised to perform, and in particular to handle calls fast.

Agents are organised in 19 teams, each of which is led by a team leader.

The main task of team leaders is to monitor and evaluate the agents of his

or her team. For this purpose, team leaders receive weekly scorecards with

detailed information of their agents' performance (including average handling

time), and regularly listen to the calls of their agents. Agents receive a ﬁxed

wage per hour.9 Following a performance appraisal of the team leader, the

agent receives a grade from 1 (worst) to 5 (best), which determines both

the size of an annual wage increase and an annual bonus. This grade is

determined by both the performance over the year, but also by other, more

subjective factors, such as the behaviour towards co-workers. The maximum

wage increase depends on the ﬁrm performance, and does not exceed 8% for

agents with the highest grade.10 Otherwise, there are no explicit incentives

based on agents' performance, such as piece rates or bonuses upon their daily

or weekly performance.

Our data comprise daily performance information for each agent work-

ing in the call centre. In total, our analysis includes 332 agents on 33,123

agent-working days, over an observation period from week 36/2008 to week

1/2010. Descriptive statistics for the estimation sample are provided in Ta-

ble 1. Besides information on performance, the data also contain information

on the working hours and on some basic characteristics of the agents. The

majority of the agents is female, only 35% of all agents are men. Agents are

on average 29 years old, and the average tenure over all observations is about

140 weeks.11

4.2 Measuring performance

The main measure of performance used by the call centre is average handling

time (AHT). It is deﬁned as the time taken for talking to the customer, plus

9Barzel (1973) studies how working hours are determined by the interplay of supply

and demand when the remuneration of an extra hour of work varies together with the

product of that extra hour. In our setting, workers are paid a constant hourly wage rate,

and therefore face the linear budget constraint which is usually assumed in the standard

model of labour supply. Therefore, our ﬁndings about the relation between hours and

labour productivity are mainly relevant for the demand side of the labour market.

10We observe the results of the annual performance appraisals only for a subsample of

agents. In these data, only very few agents receive the worst and the best grade (1: 1.3%;

5: 0.7%), while most agents receive a 2 (29.9%), 3 (54.6%), or 4 (13.6%), respectively.

The criteria for performance appraisals are agents' performance scorecards throughout

the year, as well as the behaviour of the agent towards co-workers and team leader.

11The ﬁrm's administrative data includes a small number of outliers. We therefore kept

only observations with positive values for hours and performance, and excluded from the

sample half a percentile at the top of the performance and the working hours distribution.

time taken for logging the information in the ﬁrm's database. On average,

agents take 322 seconds, or 5.35 minutes, to handle a call. Figure 1 shows

the density of average handling time and shows that the distribution is right-

skewed. Earlier studies using performance data of call agents have used

similar measures to estimate the impact of training and learning on-the-

job on performance (Liu and Batt, 2007; De Grip and Sauermann, 2012;

Breuer et al., 2013; De Grip et al., 2016). Panel (a) of Figure 2 depicts the

descriptive relationship between our measure of productivity (1/AHT) and

daily working time. Longer working hours seem to be associated with slightly

lower productivity, i.e. slightly longer average handling times.

While it is relatively straightforward to measure how long agents take for

an average call, it is more diﬃcult to measure the quality of these calls. We

use two diﬀerent measures of quality. The ﬁrst is the share of repeat calls.

The system tracks when customers are calling again within seven days after

the last call. Based on this information, the system calculates the share of

customers who call back within 7 days, relative to all customers an agent had

contact with. The higher the share of repeat calls, the lower service quality.

Zero repeat calls indicates that none of the customers an agent had contact

with was calling back within seven days. A value of 1 would indicate that

every customer would have called back.12

A second variable, which is used to assess the quality provided by agents,

is front line completion. This variable is based on the inbound and outbound

calls. Since the main task of agents is to receive inbound calls, and outbound

calls should only be necessary if an agent could not complete a case, a lower

front line completion rate is an indicator of lower quality. Completion is

deﬁned as the diﬀerence between an agent's number of inbound calls and

outbound calls, divided by the number of inbound calls. The measure is 1

if an agent does no outbound calls at all. Thus, a value of 1 reﬂects high

problem-solving ability of the agent.

12One disadvantage of this measure is that customers might have called back for a

diﬀerent reason, which might be unrelated to the original call. In this case, the "repeat"

call would be wrongly attributed to an agent.

4.3 Measuring working time

Working time is calculated as the number of calls handled by an individual

agent, times the agent's average handling time. It thus measures the time

during which an agent is directly working on his or her main task, answering

customer calls. By deﬁnition, this measure does not comprise any non-call

related time, such as breaks, slack, or training hours. We choose this measure

because it serves as a precise measure of eﬀective working time. If we used

a measure which includes slack time and breaks, part of what would be

counted as working time would actually be recovery time. This would lead

to an underestimation of the fatigue eﬀect. Moreover, slack time or time

for breaks is most probably not proportional to the time spent answering

calls. Rules about breaks, for instance, prescribe diﬀerent break schemes for

diﬀerent discrete values of time spent at the workplace. Therefore, the share

of time spent in breaks is not constant across values of time spent at the

workplace. This could also lead to bias in the estimated fatigue eﬀect if we

used a measure of working time which includes breaks.13

To analyse how important our choice of the deﬁnition of working hours is,

we also consider two alternative measures of working time in our estimations

in the next section. The ﬁrst is a measure of hours during which the agent is

available to answer calls at his or her desk. The second is a measure of the

time spent by the agent at the workplace, and includes training, the unpaid

lunch break, etc. They are not, however, precise enough to yield reliable

information about slack time and time spent in breaks.

Table 1 also provides summary statistics of these alternative measures of

working time. Both measures are very strongly correlated with the measure

we chose, in particular the time that agents are available for calls (ρ = 0.95).

Most agents working in the call centre work part time. On average, agents

spend 4 days a week and 6 hours per day at their workplace. Their eﬀective

working time, however, only averages 4.6 hours per day, and 17.7 hours per

week.

13It would be very interesting to study the role played by slack time and breaks, and

whether they function as recovery time for workers (cf. Pencavel, 2016). However, the

data we have does not allow us to do so.

Table 2 shows that there is suﬃcient within-agent variation in the number

of hours worked per day and per week. Agents work days of about 5 eﬀective

working hours about half (45.8 percent) of the time. However, during the

rest of the time, they work very diﬀerent numbers of hours per day, ranging

from about 0 to about 8 hours. At the week level, the number of observations

peaks at around 24 hours a week. About 70 percent of agents in the sample

have at least one working week of that length, but there is still quite some

variation in the length of the working week across observations and agents.

In addition to the information on working hours, the data also contain

information on the total number of scheduled hours for each individual in

each week.

5 Estimation results

In this section, we present the results of estimating the relation between

working hours and productivity. We ﬁrst address our main measure of pro-

ductivity, ln (1/AH T ), and then the two measures of the quality of calls. As

mentioned above, we conduct the estimations both at the daily and at the

weekly level.

5.1 Working hours and productivity

Table 3 presents estimation results of a regression of productivity on working

hours at the daily level, including diﬀerent control variables. Regardless of

the speciﬁcation, productivity appears to decrease slightly as working hours

increase. As the number of controls included in the model increases, the esti-

mated relationship between working time and productivity tends to become

less negative. In particular, controlling for the timing of shifts (day of the

week and hour dummies) leads to a lower estimate of the fatigue eﬀect. This

is because shifts at atypical times, such as nights and weekends, tend to be

both shorter and more productive. There are two likely explanations for this.

First, agents have less calls to handle, creating slack time to rest between

calls. Second, the call centre is open only for very speciﬁc types of calls

during the night and during the weekend, which could be shorter by nature.

Consequently, fatigue is overestimated when timing is not controlled for.14

Our preferred speciﬁcation is shown in Column (7). The interpretation of

the coeﬃcient on ln (H ), β , is that output increases by β+1 percent as hours

increase by 1 percent (see Subsection 2.1). Therefore, our estimation results

suggest that if working hours increase by 1 percent, the number of calls han-

dled will only increase by 0.9 percent. This suggests that, as agents work

longer hours, fatigue makes agents work slower. The log-log speciﬁcation ap-

pears to ﬁt the data. The R2 statistic is higher for this speciﬁcation than for

a linear or a quadratic one. Panel (b) of Figure 2, which plots the coeﬃcients

obtained from a regression of productivity on a set of dummies for 15-minute

working time intervals, and therefore does not impose any functional form on

the data, also hints at a negative logarithmic relationship between working

time and productivity.15 In Column (8), we include individual-level charac-

teristics. The estimates show that, although agents perform less well with

increasing age, tenure signiﬁcantly improves productivity (cf. De Grip et al.,

2016). The performance of male and female call agents does not, however,

diﬀer signiﬁcantly.

Table 4 presents regressions of productivity on working time at the week

level. The magnitude of the negative eﬀect appears less important at the

week level, compared to the results at the day level. This could be explained

by the fact that people have the time to rest from one day to another. The

idea that agents have time to recover from one day to the other is further

supported by the fact that we ﬁnd no relationship between productivity on a

14If we exclude observations, for which the shifts starts before 09:00 or ends after

21:00, we obtain very similar results, with a coeﬃcient on log working hours of -0.105***.

Likewise, if we exclude observations from Saturdays and/or Sundays, the estimates are

almost identical to the ones shown in Column (7) of Table 3 (-0.113***).

15To allow for potential non-linearities in the eﬀect of working hours on performance,

we also estimated the regression shown in Column (7) of Table 3, allowing for structural

breaks above/below which the eﬀects of working hours could be stronger/weaker. We do

not ﬁnd any signiﬁcant structural breaks in our estimations. The results are available

upon request.

given day and the number of hours worked on previous days.16 Column (2)

in Table 4 indeed suggests that it is worth spreading hours worked over more

days. In that speciﬁcation, total hours worked in a week are decomposed

into number of days worked and average number of hours worked per day

in that week. The returns to days per week seem to be slightly higher than

the returns to hours per day. The diﬀerence between both coeﬃcients is,

however, not statistically signiﬁcant.

Table 5 presents estimation results using the two alternative measures

of working time we have: (1) time available for calls and (2) time present

at the workplace. As hypothesised above, the estimated fatigue eﬀects are

smaller if slack time is included in the measure of working time. They even

disappear when breaks are included. When using a measure of working time

which includes breaks, one would be inclined to conclude that output is

proportional to working time and that there are no fatigue eﬀects. However,

the evidence presented in Tables 3 and 4 on the relationship between eﬀective

working time and productivity shows that this would be misleading.

5.2 Working hours and call quality

Table 6 presents the estimation results of a regression of diﬀerent quality

indicators on working time and a series of controls, at both the day and

the week level. Call quality, as measured by repeat calls and front line

completion, slightly increases in the number of working hours. Column (1)

shows that one additional working hour is related to a decrease in the share

of repeat calls by 0.005, which translates to a decrease by 6% of a standard

deviation. Since high shares of repeat calls are equivalent to low performance,

agents with longer hours improve on this quality measure. Column (2) shows

that one additional working hour is related to an increase in the front line

completion rate by 0.018, or 25% of a standard deviation. The eﬀects on call

quality measured at the weekly level are of similar size.

16These results are available from the authors. Pencavel (2016) does ﬁnd evidence

that long working hours negatively aﬀect productivity in subsequent weeks for munition

workers. This does not seem to be the case for call centre agents.

Although the eﬀect size is small, the results show that agents are providing

better calls if working longer hours. We can only speculate about the reason

for this observed increase in quality with working hours. First, this may

be due to a learning eﬀect if many of the problems for which customers

are speciﬁc for a given day and agents learn how to resolve them during

the course of that day. Second, this may be the result of what Vernon

(1921) calls "practice-eﬃciency", if call agents are better able to tune in

to customers' demands as they spend more time at their desk, get used

to answering calls and leave other thoughts behind. For both arguments

to be true, however, the mechanisms should enable the agents to provide a

better solution to consumers' problems but not enable them to help customers

quicker, otherwise we would observe a positive eﬀect of working hours on

average handling time. A third possibility is that there is a trade-oﬀ between

speed and quality and that agents provide better quality because they slow

down has they have been working for a while.17

Taken together, the results show that, as agents work longer hours, they

become slower, meaning that they score worse on the quantity of output,

whereas quality slightly increases.

6 Robustness checks

In Section 3, we discussed potential sources of endogeneity in our estimations.

In this section, we check whether these potential problems indeed play a role

in our data.

6.1 Tenure and attrition

One potential confounder in the relationship between working time and pro-

ductivity is tenure. The results so far controlled for individual ﬁxed eﬀects

that should account for diﬀerences in tenure between individuals at the start

17An alternative explanation for the positive relation between working hours and call

quality would be that in times of high customer demand, customers would be less likely

to call back. Because results for both repeat calls and front line completion conﬁrm our

interpretation, we do not think that this is a likely explanation.

of the observation period. If, however, more productive agents have lower

turnover than less productive agents, high-productivity agents could be over-

represented in the sample. In this case, we may overestimate the returns to

hours, or underestimate the eﬀects of fatigue.

To account for potential diﬀerences in how fast individuals learn on the

job, we estimate a model in which we control for an individual-speciﬁc time

trend (i.e. an interaction of the individual ﬁxed eﬀect with the time trend).

Table 7 presents the estimation results, both at the day and at the week

level. The individual-speciﬁc time trends typically have positive coeﬃcients,

conﬁrming the idea that individuals learn on the job. Controlling for a (daily

or weekly) time trend at the individual level does not aﬀect the estimated

coeﬃcient on hours worked much, compared to the results in Tables 3 and 4.

This suggests that diﬀerences in learning speed between individuals are not

an important confounder.18

To check for the role of selective tenure, we estimate the model for diﬀer-

ent sub-samples of our data: (1) including only shifts which take place during

the ﬁrst year of tenure of an individual, (2) including only shifts which take

place after the ﬁrst year of tenure of an individual, and (3) including only the

ﬁrst half year of observation, and only shifts of individuals who are present

during all of this ﬁrst half year, to proxy a balanced panel in terms of tenure.19

The results are presented in Table 8. The coeﬃcient on hours worked is much

larger for individuals with short tenure than for individuals with long tenure.

This suggests that fatigue plays a much larger role for agents early in their

career than for experienced agents.20 This ﬁnding is consistent with evidence

that the need for recovery increases as workers' skills are below what is re-

18Instead of assuming a linear individual time trend, we also estimated the same model

with ﬁxed eﬀects interacting the agent with months and year dummies to allow for non-

linear individual-speciﬁc time eﬀects. The results, which are available from the authors,

result in a very similar point estimate as the ones presented in Table 7.

19The panel is not exactly balanced, because the individuals of this sub-sample are not

observed for exactly the same number of days. But this sub-sample is useful to check

whether the potential overrepresentation of individuals with long tenure in the data leads

to bias in the estimations.

20The results for a sub-sample of individuals who are in their ﬁrst half year of tenure

are very similar to those for individuals in their ﬁrst year.

quired for their job (Gommans et al., 2016). If workers learn on the job, they

should be less subject to fatigue as they become more experienced. But the

ﬁnding of smaller fatigue eﬀects for agents with longer tenure is also consis-

tent with the idea that tenure is selective, in the sense that agents who are

less subject to fatigue are more likely to remain in the call centre. In any

case, the eﬀect of hours estimated in Table 3 actually covers heterogeneous

eﬀects which depend on the tenure of an agent. Further, the coeﬃcient on

hours obtained in Column (3) of Table 8 is very similar to the one obtained

in Table 3. As our results are similar whether we use a balanced panel or

not, attrition does not seem to be an important source of bias.

In estimations at the week level, we also observe a much stronger fatigue

eﬀect for agents with less than a year tenure than for agents with more than

a year of experience. The fatigue eﬀect even seems to be entirely driven

by individuals in their ﬁrst year of tenure, and to disappear entirely for ex-

perienced agents at the week level. A possible explanation could be that

experienced agents are better able to recover from one day to another. Esti-

mation of a balanced panel yields results similar to those obtained in Table

4, suggesting that attrition does not bias the results.21

6.2 Scheduling as a source of exogeneity in hours worked

In Section 3, we argued that scheduling by the planning department of the call

centre ensures that the number of hours worked by an agent on a given day or

in a given week is exogenous. We also mentioned potential objections to this

argument: individuals can express schedule preferences for a speciﬁc day or

week, and they may be asked to stay shorter or longer in case of unforeseen

changes in the number of incoming calls. We address these objections here.

In Table 9, we estimate our preferred models at the day and week level,

including additional controls for the number of hours scheduled (Columns

(1) and (3)) or the deviation from the number of hours scheduled, deﬁned as

21Results available from the authors. For individuals in their ﬁrst year of tenure and

for the balanced sample, the results described here are only found after the week ﬁxed

eﬀects and the time trend are dropped out of the model. Estimation of the model with

the full set of controls seems to be hampered as the number of observations is reduced.

the diﬀerence between actual hours worked and scheduled hours for a week

(Columns (2) and (4)).22

The positive coeﬃcient on hours scheduled in Columns (1) and (3) of

Table 9 suggests that individuals are indeed more (less) productive in weeks

for which their expressed preferences ensured that they were scheduled for

longer (shorter) hours. However, controlling for the length of schedules does

not lead to important changes in the estimated magnitudes of the coeﬃcients

on working time. They are only slightly more negative, hinting at a slight

underestimation of the fatigue eﬀect due to selective scheduling.

The results in Columns (2) and (4) show that hours worked on top of

scheduled hours are negatively related to productivity. It suggests that fa-

tigue becomes even stronger if the eﬀort to be made by agents is unexpected.

This is intuitive, and is evidence against self-selection of agents in the sense

that they would volunteer to work more hours on days on which they are

more productive. The coeﬃcient on working time becomes slightly less neg-

ative in this speciﬁcation, which is logical because the total fatigue eﬀect (as

estimated in Tables 3 and 4) is now decomposed into "simple" fatigue due to

working more hours and additional fatigue during unexpected extra hours.

All in all, the evidence presented in Table 9 suggests that self-selection by

agents is not an important source of bias in our estimations. Rather, it seems

plausible that scheduling exogenously determines the hours actually worked

by agents.

7 Conclusions

In this paper, we have estimated the impact of working time on productivity,

both at the daily and at the weekly level. We have used panel data on indi-

vidual workers of a call centre in the Netherlands, which contain information

about the number of hours worked, the number of calls answered, the average

22The number of observations drops because information about scheduled hours is not

available for every agent. If we limit the estimation sample to agents with strictly positive

scheduled hours, the results remain very similar, only the coeﬃcient on scheduled hours

becomes smaller. If we take up the log scheduled hours and log deviation in the models

instead of scheduled hours and deviation, the results remain virtually unchanged.

handling time of calls and indicators of call quality for every day worked by

an agent from mid-2008 until the ﬁrst week of 2010. The panel character of

the data enables us to control for time-invariant unobserved heterogeneity.

Scheduling by the ﬁrm generates variation in the daily and weekly hours of

individual workers which is hardly related to individual time-varying char-

acteristics, since expected consumer demand is leading in making schedules.

This yields estimates of the eﬀect of working time on productivity that are

not biased by individual shocks.

Our results show that an increase in eﬀective working time by 1 percent

leads to an increase in output, i.e. the number of calls answered, by about

0.9 percent. This corresponds to moderately decreasing returns to hours,

probably due to fatigue among agents. Given that agents in our sample

work on average 4.6 eﬀective hours per day, this shows that the call centre

environment is demanding and that fatigue sets in early. The fatigue eﬀects

would be larger if the call agents worked full-time. This study complements

the small number of studies which examine the eﬀect of working time on

productivity using an arguably exogenous source of variation in working time

(Crocker and Horst, 1981; Brachet et al., 2012; Pencavel, 2015; Dolton et al.,

2016), by providing evidence for medium-skilled jobs in the service sector.

As we are able to isolate eﬀective working time and concentrate on this

measure, our estimates form an upper bound for the fatigue eﬀect. Our

ﬁnding of decreasing returns to hours seems to hold best for individuals with

shorter tenure, while we ﬁnd much less evidence of fatigue eﬀects among more

experienced agents. Various indicators of call quality show that call quality

does not suﬀer as the number of hours worked increase. On the contrary, call

quality seems to slightly improve.

Our ﬁndings are most relevant for part-time medium-skilled jobs in the

service sector. They suggest that increasing the eﬀective working time in

these occupations would cause individual workers, in particular the relatively

inexperienced ones, to produce smaller quantities of output per hour, due to

fatigue. Such an increase in working time could, however, be beneﬁcial for

the quality provided. These ﬁndings are informative for ﬁrm management

and for working time regulation policies. The total economic eﬀects of such

policies, however, are dependent not only on the eﬀect of working hours on

labour productivity at the individual level, but also on many more factors

such as work organisation and availability of qualiﬁed workers.

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Figures

Figure 1: Density of daily productivity

0.002 .004 .006

Density

0200 400 600 800 1000

Average handling time (in seconds)

Note: The ﬁgure shows how the density plot of daily productivity, as measured by the average handling

time (in seconds).

Figure 2: Productivity by working hours

(a) Descriptive results

0 .005 .01 .015

Productivity (1/AHT)

0 2 4 6 8

Daily working hours

(b) Estimation results

-.001 0 .001 .002 .003

Effect on productivity (1/AHT)

0 2 4 6 8

Daily working hours

Note: Panel (a) shows average productivity over working time for 15-minute intervals. The capped spikes

present the corresponding 5th and 95th percentile of the productivity distribution. Panel (b) shows the

coeﬃcients on dummies for 15-minute working time intervals in a regression as in model (7) from Table

3. The capped spikes present the corresponding 95-percent conﬁdence intervals. The reference category

is 21 quarters of an hour, i.e. 5 hours and 15 minutes.

Tables

Table 1: Descriptive statistics

Variable Mean Std. Dev. Min. Max. N

Day level

Average handling time (AHT) 321.613 111.012 3 910.364 33,123

Productivity (1/AHT) 0.004 0.003 0.001 0.333 33,123

Calls Answered 55.795 22.992 1 129 33,123

Daily working hours 4.621 1.516 0.001 7.747 33,123

Starting time 11.445 3.079 1 24 33,120

Share of repeat calls 0.164 0.084 0 1 33,123

Front line completion rate 0.956 0.072 0 1 32,831

Working hours 4.621 1.516 0.001 7.747 33,123

Hours available for calls 4.812 1.414 0.001 7.903 33,123

Hours present at the workplace 6.05 1.476 0.01 9.52 33,123

Week level

Average handling time (AHT) 319.314 100.133 5 910.364 8,641

Productivity (1/AHT) 0.003 0.002 0.001 0.2 8,641

Calls answered 213.876 114.138 1 645 8,641

Weekly working hours 17.712 8.458 0.001 43.046 8,641

Days per week 4.020 1.282 1 7 8,641

Tenure in weeks 149.58 191.623 1 691 8,587

Share of repeat calls over week 0.167 0.066 0 1 8,641

Front line completion rate over week 0.950 0.083 0 1 8,641

Working hours 17.712 8.458 0.001 43.046 8,641

Hours available for calls 18.447 8.334 0.001 42.929 8,641

Hours present at the workplace 23.191 9.775 0.02 50.56 8,641

Hours scheduled for the week 19.805 9.573 0 51 6,513

Agent level

N of agents 332

Male 0.351 0.478 0 1 305

Age 29.542 10.578 17 65 284

Table 2: Variation in daily and weekly working hours

Day level:

Working hours

h(rounded at 1)

Observations with

hhours

Individuals with h

hours at least once

Conditional on working hhours

at least once, share of days in

which individual works hhours

0 473 135 2.74

1 1211 192 6.13

2 1663 215 6.49

3 3228 276 11.1

4 5325 315 17.4

5 14372 317 45.84

6 3461 275 12.56

7 3211 202 17.24

8 179 70 3.51

Total 33,123

Week level:

Working hours h

(rounded at 4)

Observations with

hhours

Individuals with h

hours at least once

Conditional on working hhours

at least once, share of weeks in

which individual works hhours

0 187 92 7.25

4 725 227 13.6

8 997 224 17.2

12 1124 245 15.46

16 1262 247 17.68

20 1229 249 20.37

24 1621 232 27.02

28 968 189 19.95

32 350 114 13.1

36 171 66 11.78

40 6 6 8.11

44 1 1 9.09

Total 8,641

Table 3: Relationship between productivity and working time at the day level

(1) (2) (3) (4) (5) (6) (7) (8)

Log working hours -0.175*** -0.152*** -0.151*** -0.151*** -0.137*** -0.128*** -0.113*** -0.139***

(0.007) (0.013) (0.013) (0.013) (0.013) (0.012) (0.013) (0.011)

Trend 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Day of the week dummies (reference: Monday)

Tuesday 0.004 0.007* -0.023 -0.452***

(0.004) (0.003) (0.024) (0.045)

Wednesday -0.000 0.001 0.029 -0.427***

(0.004) (0.004) (0.025) (0.052)

Thursday -0.001 0.001 0.066*** -0.609***

(0.004) (0.003) (0.025) (0.063)

Friday 0.004 0.006* 0.011 -0.424***

(0.004) (0.004) (0.026) (0.050)

Saturday 0.109*** 0.110*** -0.471*** -0.890***

(0.008) (0.007) (0.069) (0.074)

Sunday 0.809*** 0.417*** 0.806*** 0.608***

(0.058) (0.063) (0.039) (0.040)

Age -0.001***

(0.000)

Tenure 0.008***

(0.000)

Male -0.003

(0.004)

Individual ﬁxed eﬀects No Yes Yes Yes Yes Yes Yes No

Team ﬁxed eﬀects No No No Yes Yes Yes Yes Yes

Hour-of-the-day dummies No No No No No Yes Yes Yes

Day ﬁxed eﬀects No No No No No No Yes Yes

R-squared 0.085 0.094 0.152 0.160 0.198 0.285 0.385 0.403

N 33,123 33,123 33,123 33,123 33,123 33,123 33,123 31,525

Individuals 332 332 332 332 332 332 332

Note: * p< 0. 10; ** p< 0. 05; *** p< 0. 01; ordinary least squares regression; dependent variable =

ln(1 /AHT ); robust standard errors in parentheses.

Table 4: Relationship between productivity and working time at the week

level

(1) (2)

Log weekly working hours -0.078**

(0.031)

Log days per week -0.066**

(0.032)

Log average hours per day -0.079**

(0.031)

Trend (weekly) 0.004*** 0.004***

(0.001) (0.001)

R-squared 0.345 0.345

N 8,641 8,641

Individuals 332 332

Note: * p< 0. 10; ** p< 0. 05; *** p< 0. 01; ordinary least squares regression; dependent variable =

ln(1 /AHT ); robust standard errors in parentheses. All regressions include agent, week, and team ﬁxed

eﬀects. The regressions also include hour-of-the-week ﬁxed eﬀects, which are deﬁned as 168 dummies for

each hour of the week during which an individual works.

Table 5: Alternative working time deﬁnitions and the relationship between

productivity and working time

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

Day level Week level

Log daily hours available for calls -0.078***

(0.015)

Log daily hours present at the workplace -0.016

(0.015)

Trend (daily) 0.001*** 0.001***

(0.000) (0.000)

Log weekly hours available for calls -0.050

(0.035)

Log weekly hours present at the workplace -0.020

(0.029)

Trend (weekly) 0.004*** 0.004***

(0.001) (0.001)

R-squared 0.368 0.357 0.335 0.329

N 33,123 33,123 8,641 8,641

Individuals 332 332 332 332

Note: * p< 0. 10; ** p< 0. 05; *** p< 0. 01; ordinary least squares regression; dependent variable =

ln(1 /AHT ); robust standard errors in parentheses. All regressions include agent and team ﬁxed eﬀects.

Regressions at the day level include day ﬁxed eﬀects, hour-of-the-day ﬁxed eﬀects and day-of-the-week

ﬁxed eﬀects. Regressions at the week level include week ﬁxed eﬀects and hour-of-the-week ﬁxed eﬀects.

Day-of-the-week (hour-of-the-week) ﬁxed eﬀects are deﬁned as dummies for each hour of the day (week)

an individual works.

Table 6: Call quality and working time

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

Day level Week level

Dependent Repeat Completion Repeat Completion

Variable calls rate calls rate

Daily working hours -0.005*** 0.018***

(0.001) (0.002)

Trend (daily) 0.000*** 0.000

(0.000) (0.000)

Weekly working hours -0.001*** 0.012***

(0.000) (0.001)

Trend (weekly) 0.002*** -0.000

(0.000) (0.000)

R-squared 0.200 0.079 0.237 0.223

N 33,123 32,831 8,641 8,641

Individuals 332 332 332 332

Note: * p< 0. 10; ** p< 0. 05; *** p< 0. 01; ordinary least squares regression; robust standard errors in

parentheses. All regressions include agent and team ﬁxed eﬀects. Regressions at the day level include day

ﬁxed eﬀects, hour-of-the-day ﬁxed eﬀects and day-of-the-week ﬁxed eﬀects. Regressions at the week level

include week ﬁxed eﬀects and hour-of-the-week ﬁxed eﬀects. Day-of-the-week (hour-of-the-week) ﬁxed

eﬀects are deﬁned as dummies for each hour of the day (week) an individual works. Repeat calls are

deﬁned as the share of customers to whom an agent spoke who call back within seven days and is deﬁned

on a scale from 0 (best) to 1 (worst). Front line completion is deﬁned as diﬀerence between an agent's

number of inbound calls and outbound calls, divided by the number of inbound calls. It is deﬁned on a

scale from 0 (worst) to 1 (best).

Table 7: Individual-speciﬁc time trends and the relationship between pro-

ductivity and working time

(1) (2)

Day level Week level

Log daily working hours -0.104***

(0.013)

Trend (daily) -0.000***

(0.000)

Log weekly working hours -0.071**

(0.034)

Trend (weekly) -0.001*

(0.001)

Agent-speciﬁc day trend Yes No

Agent-speciﬁc week trend No Yes

R-squared 0.479 0.496

N 33,123 8,641

Individuals 332 332

Note: * p< 0. 10; ** p< 0. 05; *** p< 0. 01; ordinary least squares regression; dependent variable =

ln(1 /AHT ); robust standard errors in parentheses. All regressions team ﬁxed eﬀects. Regressions at

the day level include day ﬁxed eﬀects, hour-of-the-day ﬁxed eﬀects and day-of-the-week ﬁxed eﬀects.

Regressions at the week level include week ﬁxed eﬀects and hour-of-the-week ﬁxed eﬀects. Day-of-the-

week (hour-of-the-week) ﬁxed eﬀects are deﬁned as dummies for each hour of the day (week) an individual

works.

Table 8: Tenure, attrition, and the relationship between productivity and

working time at the day level

(1) (2) (3)

Log daily working hours -0.141*** -0.078*** -0.100***

(0.022) (0.014) (0.023)

Trend (daily) 0.002*** 0.000*** -0.000

(0.000) (0.000) (0.000)

R-squared 0.421 0.430 0.499

N 15,664 17,326 9,366

Individuals 249 131 104

Note: * p< 0. 10; ** p< 0. 05; *** p< 0. 01; ordinary least squares regression; sample is restricted to

individuals with a year tenure or less (Column (1)), with more than a year tenure (Column (2)), and only

the ﬁrst half year of observation and only shifts of individuals who are present during all of this ﬁrst half

year (Column (3)); robust standard errors in parentheses. All regressions include agent, day ﬁxed eﬀects

and team ﬁxed eﬀects. Regressions also include hour-of-the-day ﬁxed eﬀects and day-of-the-week ﬁxed

eﬀects, which are deﬁned as dummies for each hour of the day and day of the week an individual works.

Table 9: Relationship between productivity and working time, controlling

for weekly schedule

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

Day level Week level

Log daily working hours -0.132*** -0.113***

(0.015) (0.014)

Log weekly working hours -0.091** -0.077*

(0.041) (0.040)

Hours scheduled for the week 0.004*** 0.005***

(0.000) (0.001)

Deviation from schedule -0.006*** -0.006***

(0.001) (0.001)

Trend (daily) 0.001*** 0.000***

(0.000) (0.000)

Trend (weekly) 0.003*** 0.003***

(0.001) (0.001)

R-squared 0.350 0.350 0.285 0.290

N 25,129 25,129 6,513 6,513

Individuals 319 319 319 319

Note: * p< 0. 10; ** p< 0. 05; *** p< 0. 01; ordinary least squares regression; dependent variable =

ln(1 /AHT ); robust standard errors in parentheses. All regressions include agent and team ﬁxed eﬀects.

Regressions at the day level include day ﬁxed eﬀects, hour-of-the-day ﬁxed eﬀects and day-of-the-week

ﬁxed eﬀects. Regressions at the week level include week ﬁxed eﬀects and hour-of-the-week ﬁxed eﬀects.

Day-of-the-week (hour-of-the-week) ﬁxed eﬀects are deﬁned as dummies for each hour of the day (week)

an individual works.

... The management states employee productivity was also a parameter that needed to be measured as it is known to impact productivity and company finances. According to Collewet and Sauermann (2017), employee productivity can be measured using working hours [30]. In electrical disruption, unproductive working hours are measured when employees cannot use computers and communication equipment to complete their administrative tasks. ...

... To calculate employee productivity, the variables used were the ideal employee working time and the time of disruption [30,35]. This impact calculation was used to measure the decline in employee productivity after the disruption of the electricity supply. ...

A container terminal is a facility used to collect containers from the hinterland and ports for distribution to the end destination. Based on these essential functions, container terminals often reflect the economic conditions in a country. Many studies have recently examined the use of business impact analysis (BIA) when disruption happens at a container terminal. In sustainability and business impact research, the economy is one of the prioritized aspects of impact analysis. The impact related to the economic aspect is generally caused by disruptions resulting from natural disasters, pandemics, and human activities (terrorists, labor issues, and lockouts). Until this article was conducted, there was still little literature explaining the business impact analysis procedures with case studies of disruptions in container terminals related to operational constraints. This paper aims to provide a perspective in deepening and adapting the BIA procedure initially general into an analysis that can be applied to the container terminal business. This paper takes a case study of BIA in a container terminal caused by electrical disruption. Based on the research, six categories of disruption became a concern when the port's electricity supply stopped, i.e., decreased productivity, economic loss, reduced employee productivity, decreased numbers of customers, a decline in company reputation, and wasted energy.

... With improving information and communication technologies being a relevant driver, workplace flexibility in terms of time (e.g., flexible working hours), place (e.g., transportable teleworking), and task (e.g., job sharing/rotation) continues to spread and is accompanied by increasing demand on the employee side, with the provision of flexible scheduling policies representing a 'key decision for employers' to attract employees (Mas & Pallais, 2017, p. 3723). Moreover, research on the relationship between working hours and productivity shows that the reduction of working hours can increase output due to lower levels of work fatigue, work stress, and errors (e.g., Brachet et al., 2012;Pencavel, 2014;Collewet & Sauermann, 2017). We will call this the 'fatigue effect'. ...

... While recent studies (e.g., Brachet et al., 2012;Pencavel, 2014;Collewet & Sauermann, 2017) highlight the deteriorating effect of fatigue on performance, our results can be taken as complementary advice to carefully consider the other side of the issue. For instance, one might ask why the decision-makers in the medical sector (considered in Brachet et al. (2012)) do not simply increase the number of paramedics to reduce individual effort costs or quality of service. ...

Workplace flexibility in the context of collaborative tasks offers many benefits but also carries several risks. While more collaborators can reduce fatigue and enhance performance (we call this the 'fatigue effect'), coordination costs also increase (we call this the 'coordination costs effect'). We investigate this trade‐off in a complex team task with high effort costs in a natural setting. We use an instrumental variables approach combined with an extensive sensitivity analysis to identify the effect of in‐game substitutions on performance in professional basketball. Results indicate that an increased number of collaborators (players) has a negative effect on team performance. We also find that the most successful teams are able to optimally trade off both effects.

... From the literature on work conditions, the following hypothesis was derived. Collewet and Sauermann (2017) observe that due to countries having different labour laws concerning working hours, an individual can expect to work very different hours from country to country, and even from job to job. Agboza, Owusu, Hoedoafia and Atakorah (2017), Collewet and Sauermann (2017) as well as Okazaki, et al. (2018) all agree that working hours are related to employee productivity and satisfaction. ...

... Collewet and Sauermann (2017) observe that due to countries having different labour laws concerning working hours, an individual can expect to work very different hours from country to country, and even from job to job. Agboza, Owusu, Hoedoafia and Atakorah (2017), Collewet and Sauermann (2017) as well as Okazaki, et al. (2018) all agree that working hours are related to employee productivity and satisfaction. The working hours of TEFL teachers differ depending on the school, teaching schedule, and type of institution. ...

Gabriella Berman
Adele Potgieter

Background Expatriation has grown rapidly, and the TEFL (teaching English as a foreign language) industry relies on it heavily. As is the case with any expatriation adjustment, TEFL teachers' overall experience of living and working in China can be influenced by external factors. For the purpose of the study described in this article, both social influences and the work environment are highlighted as influences on the overall experience of teaching TEFL in China. Research aims The research aim of the study was to investigate the efect of social influences and work environment on the overall experience of TEFL teachers who teach in China. Methodology The study made use of a positivistic research paradigm and a descriptive research design. The sampling procedure utilised in the study was non-probability sampling, and the technique used was convenience sampling. The target population comprised TEFL teachers who were teaching in China, with the final sample consisting of 379 respondents. To collect data, the method used was an online survey, and the specific measuring instrument was a web-based, self-administered questionnaire, distributed via a link on Facebook, WeChat, LinkedIn, and Reddit groups that are specifically for TEFL teachers in China. Once the data were collected, both descriptive and inferential statistics were utilised to analyse and interpret the data. Findings From the empirical data collected in the study, it was found that, in general, both social influences and the work environment of TEFL teachers in China positively affected their overall experience, while the potential influence of a language barrier proved to have no negative effect on their overall experience.

... Although productivity and profit are very important goals for managers, employee job satisfaction should not be neglected either. Previous studies show that more satisfied workers are more productive (Bellet et al., 2020), and that longer hours do not increase productivity (Collewet & Sauermann, 2017). Our study, on the other hand, has found that both too-long and too-short hours are detrimental to employees' job satisfaction. ...

Overwork is a common phenomenon worldwide. Although previous studies have found that long working hours can cause physical and mental health problems in employees, the nature of the relationship between working hours and job satisfaction remains little understood. We have theorised that there is a curvilinear association between working hours and job satisfaction, and tested this hypothesis. A total of 771 adult Chinese employees submitted self-reported measures of working hours, job satisfaction, and job autonomy. The results show that working hours have an inverted U-shaped association with job satisfaction. Work scheduling autonomy and decision-making autonomy moderate this relationship. Here we present our data and discuss their theoretical and practical implications. Supplementary information: The online version contains supplementary material available at 10.1007/s12144-021-02463-3.

... In Uganda since most (91%) of the farmers were using or adopting hand spraying methods, any benefits, limitations and potential problems associated these must be caused by factors related to labour (physical work and time taken) and equipment. Recent literature (Collewet and Sauermann, 2017) shows that as the number of hours worked increases, the average work time increases, with workers becoming less productive due to fatigue. Unexceptionally, this study found that each worker using a bucket pump appeared fatigued after spraying only 14 head of cattle and thereafter signs of ineffectiveness were evident. ...

... As discussed in H2, fatigue could also be explained by work duration. Recent findings suggest that work performance deteriorates toward the end of long shifts among paramedics (Brachet et al. 2012) and part-time call center agents (Collewet and Sauermann 2017). Thus, we expect that the longer the workers have recently worked, the less likely they would continue working and, if they do work, the work duration would be shorter relative to those with a shorter past work duration. ...

Wichinpong Park Sinchaisri

This dissertation combines tools from operations management, econometrics, machine learning, and behavioral sciences to (i) study how on-demand workers learn and make decisions in complex environments, (ii) develop tools to help improve their decision-making, and (iii) inform the design of better policies to manage human-centered operations. Recent technologies create and accelerate new work arrangements that provide workers with flexibility in their schedule and choice of service. At the same time, the decisions a worker faces have become more complex. Platforms offer competing dynamic incentives, and the independent nature of gig work means that workers do not experience the benefits of learning from colleagues. The following three chapters investigate how behavioral operations management can be utilized to better manage the gig economy. (i) Behavioral and Economic Drivers of Decisions.We empirically investigate how on-demand workers decide on when to work and \emph{for how long} depending on varying financial incentives and personal goals. Using the comprehensive data from a ride-hailing industry partner, we develop an econometric framework that addresses empirical challenges such as sample selection bias and endogeneity. Our results demonstrate that, while workers exhibit positive income elasticity as predicted by standard income theory, their decisions are significantly influenced by their cumulative earnings (more likely to stop working when reaching their income goal) and recent work duration (tend to stay working after long hours of work or exhibit "inertia"), more akin to the behavioral theory of labor supply. Inertia captures both the formation of work habits and the tendency to stay with the focal platform, suggesting that, amidst intensifying competition among platforms, platform loyalty could be induced through optimal incentive design. Thus, we propose a heuristic to optimize incentive allocation and demonstrate through counterfactual simulations the monetary and capacity benefits of accounting for our behavioral insights. (ii) Dynamic Decisions and Multihoming Behavior. We leverage proprietary data from our ride-hailing industry partner and the publicly available trip record data to develop and estimate a structural behavioral model of gig workers' sequential dynamic decisions of when and where to work in the presence of alternative work opportunities. Our major contributions are in the modeling and estimation of dynamic decisions with temporal and spatial components and dynamic outside options, and the development of an efficient simulation-assisted machine learning-based estimation framework. Our results characterize gig workers' forward-looking behavior and heterogeneous cost of working. We find that workers are strategic in their choice of initial service location to ensure high utilization and are prone to multihoming behavior when facing longer idle times. Then, we study how the firm can influence multihoming behavior among workers. Our counterfactual analyses demonstrate the effectiveness of strategies commonly used in practice and offer insights that can help retain workers during high demand or nudge them to quit during low demand. (iii) Improving Human Decision-Making with Machine Learning. We propose a novel machine-learning algorithm to automatically extract best practices from the trace data and infer simple tips that can help workers learn to make better decisions. We use an approach based on imitation learning and interpretable reinforcement learning and consider simple if-then-else rules that modify workers' strategy in a way that most improve their performance, capture useful insights that are challenging for workers to learn by themselves, and are simple enough for workers to understand. To validate our approach and test the performance of our algorithm, we design a virtual kitchen-management game and conduct large-scale pre-registered behavioral studies on Amazon Mechanical Turk. Our experiments show that rules inferred from our algorithm are effective and significantly outperform rules from other sources at improving performance and speeding up learning among workers. In particular, we help workers identify optimal early actions that help them improve in the long term and discover additional optimal strategies beyond what is stated by our algorithm.

... A break can result in a boost to your productivity (rate of output) [42]. Studies show output of working hours to not increase linearly after a threshold and absence of a rest day to decrease output, as long hours result in errors and accidents, as well as fatigue, stress, and sickness [43,44]. It can be challenging to cut down on work hours when you feel that there is so much to get done. ...

... Work life balance is also a topic great importance for the companies as worker's well-being is found to be related to profitability of a company (Jangwoo and Miller, 1999). Also, working for long hours is associated with lower productivity (Collewet and Sauermann, 2017) and increases the probability of making mistakes (Rogers et al., 2004) and increase turn-over intentions (Allen et al., 2000). ...

nihan demir
Kerim Eser Afşar

Smith's perspective is very similar to the theoretical framework developed by psychologists, neuroscientists and behavioural economists. He also envisaged a wide range of estimates of the phenomena of harm avoidance, will and justice, which are the focus of modern behavioural economics. Adam Smith; by addressing all individuals' efforts to generate more income and to dominate other individuals from a different perspective, it refers to the social space as the address where individuals can find their own interests. The purpose of this study is to examine the economic policy origins of behavioural economics and to make a new reading of the parts in relation to individual preference of the Theory of Moral Sentiments from the perspective of behavioural economics. For this purpose, the concept of empathy and the moral philosophy of Smith in the first part of the paper, justice in the second part, factors that cause deviations from rationality in the third part and sentiments and reason are discussed in the last part.

... Basically, each company has the hope that someday it will experience rapid development within the scope of its activities and want to create high productivity in the field of work. To achieve this, a number of factors of production such as labor, capital and expertise are needed, where the four factors cannot stand alone, but must support each other to achieve goals effectively and efficiently [1]. And among these four factors labor or human factors in this case are employees, is the most important thing because humans are the users and drivers and determinants of all activities [2]. ...

Background The critical conditions of intensive care patients require providing them with a higher acuity of care. Thus, it is essential to focus on critical care nurses and improve their work environment in a way that maximizes productivity, collaboration, satisfaction, and leads to improved patient care. Purpose This study aims to explore the role the workplace layout design play in determining nurses' satisfaction in three intensive care units (ICUs) at a university hospital. Method A prospective, cross-sectional, single-center, survey-based design was employed in this study. Data were obtained, via a standardized questionnaire, from 36 morning shift nurses. The nurses' self-reported satisfaction scores in three different ICUs with differing overall layouts, nursing station locations, and workplace design were statistically compared. Results The study found that ICU 1 (private rooms, single corridor, central nursing station, close to supported services) had higher nursing satisfaction levels than ICUs 2 and 3 (open wards with separate service zones), F (2,34) = 5.054, p = .012. However, overall satisfaction was higher with the ICU 2 primary workspace design, possibly due to the perceived acoustic privacy in this configuration, F (2,34) = 4.492, p = .019. The ability of the ICU layout design to enhance teamwork and minimize traffic in patients care areas was found to be an important predictor of nurses' satisfaction. The primary workspace design capacity to minimize congestion and the presence of large numbers of providers in a confined workplace might account for variation in nurses' satisfaction. Conclusion Physical environment variables in the ICU design may contribute to staff workplace satisfaction scores and may help in guiding informed choices regarding the future ICU design.

During the 2000s, over a dozen U.S. states passed laws that prohibit health care employers from mandating overtime for nurses. Using a nationwide panel dataset from 2004 to 2012, we find that these mandatory overtime laws reduced the service quality of nursing homes, as measured by an increase in deficiency citations. This outcome can be explained by two undesirable changes in the staffing hours of registered nurses: decreased hours of permanent nurses and increased hours of contract nurses per resident day. We observe that the increase in deficiency citations concentrates in the domains of administration and quality of care rather than quality of life, and the severity levels of the increased citations tend to be minor rather than major. We also find that the laws' negative effect on quality is more severe in nursing homes with higher percentage of Medicare-covered residents. These observations are consistent with the predictions of a stochastic staffing model that incorporates demand uncertainty and operational flexibility. Further, we rule out an alternative hypothesis that the quality decline is induced by an increase in nurse wages.

Using detailed matched employer-employee panel data for Belgium over the period 1999-2006, the authors investigate the relationship between working time, wages and productivity. More precisely, they are among the first to examine how changes in the proportions of short and long part-time workers affect the productivity of firms (measured by the value-added per hour worked) and to test for the presence of productivity-wage gaps. Their findings, controlled for a large range of individual and firm characteristics and robust to various econometric issues (including time-invariant unobserved firm heterogeneity, endogeneity and state dependence), suggest that long part-time workers are significantly more productive than short part-time and full-time workers. Their results further indicate that average hourly wages within firms do not significantly depend on the incidence of part-time jobs. Overall, this leads to the conclusion that short part-time workers are paid at their marginal productivity while long part-time workers appear to be "underpaid".

This study investigates (1) the prevalence and dynamics of being underskilled among a sample of older workers from the Maastricht Cohort Study (n=1,111) and (2) the impact of being underskilled on need for recovery (NFR), the risk of losing employment and retirement intentions prospectively over two years follow-up. Being underskilled was associated with higher levels of NFR over time and with the risk to lose employment, whereas no associations with early retirement intentions were observed. To achieve sustainable employment, the course of underskilling should be monitored throughout the work career, enabling timely interventions to avoid the negative consequences of underskilling.

John Pencavel

Observations on munitions workers are organized to examine the relationship between their output each week, their working hours and days each week, and their working hours and days in adjacent weeks. The hypothesis is that workers need to recover from work, and a long working week results in greater fatigue and stress, and yet provides insufficient time for recuperation before the next week's work opens. Workers require time off the job to restore their physical, mental and emotional capacities, and if a long working week provides inadequate time to repair, then their subsequent work performance suffers.

This paper investigates how newly hired workers learn on the job and the extent to which this learning is affected by their co-workers' tenure. We estimate tenure-performance profiles using weekly panel data on individual workers' performance. The results show a performance increase of 64% in the first year of the employment. We show that, during the first three months, workers placed in teams with more experienced peers have significantly steeper tenure-performance profiles than those employed in teams with less experienced peers. Our results suggest that placing new workers in more experienced teams reduces the time new hires need to become equally productive as an experienced worker by 36%, compared to being placed in less experienced teams.

Jungmin Lee
Yong-Kwan Lee

Long working hours are considered as one of the major risk factors for workplace accidents and workers' health. In this paper, we estimate the causal effect of working hours on industrial injury rates. We exploit a quasi-natural experiment in South Korea, where standard hours were reduced at different times by industry and establishment size from 2004 to 2011. We find that a one-hour reduction in weekly working hours significantly decreases the injury rate by about 8%.

John Pencavel

Observations on munition workers, most of them women, are organised to examine the relationship between their output and their working hours. The relationship is nonlinear: below an hours threshold, output is proportional to hours; above a threshold, output rises at a decreasing rate as hours increase. Implications of this finding for the estimation of labor supply functions are considered. The findings also link up with current research on the effects of long working hours on accidents and injuries.This article is protected by copyright. All rights reserved.

What Is the Average Number of Hours Worked Per Year

Source: https://www.researchgate.net/publication/315670177_Working_Hours_and_Productivity

What Is the Average Number of Hours Worked Per Year

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