DISCUSSION PAPER SERIES IN ECONOMICS AND MANAGEMENT

Is There Really No Place Like Home For Productivity?

Ulrich Kaiser

Discussion Paper No. 04-03

GERMAN ECONOMIC ASSOCIATION OF BUSINESS ADMINISTRATION – GEABA

Is there really no placelike home for productivity?

Large–sample evidence from a small countrywith high work–at–home adoption rates§

Ulrich Kaiser∗

This version: March 15, 2004

Abstract: I study the effects of working at home (of ‘teleworking’ or ‘telecom-muting’) on firm performance using cross–sectional data for a sample of 647manufacturing and 276 retail and wholesale trade firms based in Denmark. Amain result from OLS and quantile regression analyzes is that I find positive,significant and quantitatively sizeable effects of teleworking on labor productiv-ity (measured by profits, value added and sales per worker) for manufacturingindustries and trade.

JEL classification: J24, C21

Keywords: work at home, telecommuting, teleworking, quantile regression

§Helpful comments from participants of a colloquium on personnel economics at theUniversity of Bonn are gratefully acknowledged. I in particular appreciated commentsreceived from Uschi Backes–Gellner and Thomas Zwick. I am indebted to AndersHertz Larsen and Frederik Silbye for valuable insights into the data set and to Statis-tics Denmark for making the data accessible to me. I also gratefully acknowledgethe help of Robert Brautigam and Andreas Michelfeit for sharing their expertise ontelecommuting–related software with me.

∗University of Southern Denmark at Odense, Dept. of Economics, Cam-pusvej 55, 5230 Odense M, Denmark; email: uka@sam.sdu.dk, internet:www.sam.sdu.dk/staff/uka, Centre for Economic and Business Research, Copenhagen,and Centre for European Economic Research, Mannheim.

1 Introduction

There’s no place like home for productivity.

B. McAllister, Washington Post March 1, 1993, p. A15

The importance of working at home, often also termed ‘telecommuting’ or ‘tele-

work’, is rapidly growing. According to US Department of Labor (2002) statistics

some 13 to 19 million workers (between about ten and 14 percent of the total

workforce) telecommute in the US, some at a full–time, most at a one or two days

a week basis. Comparable figures also exist for the European Union (EU) where

the share of telecommuters is around 13 percent (Empirica 2003). Denmark is a

particularly important adopter of telecommuting, 21.5 percent of the workforce

regularly telecommute. The only EU countries with a higher share of telecom-

muters are the Netherlands (26.4 percent) and Finland (21.8 percent).

A large body of case studies shows that enormous productivity gains and cost

savings can be associated with the introduction of such an organizational inno-

vation. Some particular prominent examples for very successful telecommuting

programs are American Express, AT&T, British Telecom, Compaq, IBM, J.C.

Penney, Merrill Lynch & Co., Mobil, Texas Instruments and Xerox.1 Interest-

ingly, however, some of the largest productivity increases are reported by firms

that also supply the equipment to establish telecommuting workplaces. IBM for

example reports savings in annual real estate costs of 700 million US dollars and

productivity increases of 20 percent, British Telecom, which started a ‘change the

1The references I use here are as follows: Bainbridge (1998) — reference to British Telecom’s

“Change the way you work” program, Croner Consulting (2003) — BT cost savings, Lorek

(2003) — list of global players that use telecommuting, cost saving figures for IBM, Rylander

(2000) — Texas Comptroller analysis, Canadian Telework Association (2003) — AT&T office

cost savings, Sirhal (2003) — reference to US Patent and Trademark Office.

1

way to work campaign’ in 1998, claims to have saved 42 million British Pounds

each year in rent and AT&T reports to have reduced office space costs by 550

million US Dollars annually per office.

The “pure” (more physical output per worker) productivity increases that are

attributed to teleworking are also impressive: American Express telecommuters

are said to handle 26 percent more calls and to produce 43 percent more business

than their office–based counterparts, Compaq Computer Corporation documents

productivity increases between 15 percent and 45 percent and case studies at IBM

Canada have shown that productivity can increase by as much as 50 percent.

Additional empirical evidence comes from government agencies. The Texas Comp-

troller (2000) for example lists evidence for a number of US state agencies that

report large cost decreases due to the introduction of telecommuting. The report

does not contain data on productivity but mentions that workers’ morale went up

after telecommuting was introduced. More evidence comes from the US Patent

and Trademark office that relinquished three floors of work space due telecom-

muting which saves 1.5 million dollars office cost.

These figures seem to be impressive, suggestive and to make a strong argument

in favor of teleworking. Apart from the fact that it is a non–trivial exercise to es-

timate cost savings and, even more so, productivity gains due to telecommuting,

there are at least two additional grains of salt here: it is suspicious that firms that

produce Information and Communication Technologies (ICT) are among those

who report the most impressive payoff numbers since they may have incentives

to overestimate the gains from telecommuting in order to increase their customer

base. The second one is that unsuccessful work–at–home programs go unnoticed.

These two issues cast serious doubts on the validity of the numbers published

in the business press and at the same time make a strong case for a large–scale

econometric study on the effects of telecommuting on firm performance.

While there is abundant, although presumably upward biased, case study evi-

dence on the effects of telecommuting on firm performance, there is little empirical

2

evidence from the telecommuter’s perspective. In a study for the US sponsored

by AT&T, ITAC (2000) conducted 1,877 phone interviews with teleworkers in

2000. A main finding of the ITAC study is that the survey respondents self–

reported an average productivity increase by 15 percent due to telecommuting.

Their supervisors, as the study shows, do not find that their workers’ productiv-

ity changed in any direction, whatsoever.

For most of the global players that introduced telecommuting two decisive factors

led to their introduction of flexible human resources management practices: (i)

productivity increases and (ii) cost savings. In this study I quantify the effects

of working at home on (i) total sales, (ii) value added and (iii) total profits per

worker or, in other words, on labor productivity. I think it is a particular virtue

of my data that it contains information on firm profits since profits combine cost

and “pure” productivity effects.

Cross–sectional data from 1998 for a sample 647 manufacturing and 276 retail

and wholesale trade sector firms that are based in Denmark form the basis of the

analysis.2 The data stems from a business survey that was carried out by Den-

mark’s national statistical agency, Statistics Denmark, that collected information

on firms’ use of information technologies. This data is enriched by combining it

with information on sales, value added, profits and the number of workers that

is taken from the Danish firm registry which is assembled by Statistics Denmark

as well.

Apart from the fact that Denmark is one of very few countries where firm prof-

itability data is generally available (and not restricted to stock–listed firms),

looking at Denmark is also interesting since firms, citizens and government au-

thorities particularly rapidly adopted not only of telecommuting but also ICT as

2The data originally contains information on a few firms from the following sectors as well:

hotel, transport, financial services, ‘other’ business–related services and consultancy. After

correcting for item–nonresponse, only one firm (from consultancy) was left for the econometric

analysis from these sectors.

3

shown by Jensen et al. (2003). Danish adoption rates of ICT adoption are even

above those of the US.

Ordinary Least Squares (OLS) and quantile regression methods (also termed

‘least–absolute value’ or ‘minimum L1–norm’ models) are used in the empirical

analysis. Quantile regression models allow to analyze the effects of telecommut-

ing on firms with different overall productivity. This appears attractive since it

is to be expected for example that firms at the top end of the productivity dis-

tribution do not gain much from changing their human resource practices while

firms at the lower end of the productivity distribution might benefit significantly.

The main finding of this paper is that there are statistically significant and eco-

nomically quite sizeable positive effects of telecommuting on all my measures of

labor productivity and for both manufacturing industries and the trade sector.

Telecommuting goes along with both cost savings and “pure” productivity in-

creases since I find significant effects on profits as well as sales and value added

per worker. In general, only firms of median and higher productivity realize pro-

ductivity gains due to telecommunting. Firms of low productivity do, by contrast,

not benefit from telecommuting.

2 Why introduce telecommuting?

InnoVisions Canada, a Canadian teleworker consultancy, published a long and

presumably complete list of the advantages and disadvantages from an employer’s

and an employee’s perspective at http://www.ivc.ca/proemployer.html. Below I

just briefly summarize the key arguments.

Advantages

Savings in office space is probably the most apparent and most direct advantage

of introducing telecommuting. Improvements in workers’ morale and an increased

4

job satisfaction that lead to an improved employee retention are also high on the

agenda. This is also true for productivity effects that might occur. Most advo-

cates of telecommuting believe that the productivity effects are positive through

less distraction by fellow employees, phone calls or office talk. This argument

might obviously also go the other way around: distraction from children, neigh-

bors or sunny weather could equally well lead to productivity decreases.

There are some other advantages of telecommuting such as decreased pollution,

less traffic congestion etc. that do not matter, however, in this firm–level study.

Disadvantages

A potentially increased distraction would not constitute a problem if monitoring

was effective. Monitoring a worker at her home is hard to maintain, however,

so this problem goes to the ‘disadvantages’ side. Telecommuting can also dis-

rupt teamwork and a firm’s organizational culture. Lastly, there are startup and

operating cost associated with the introduction of teleworking such as the devel-

opment of guidelines, technical support, training etc.

Summary

In summary, there are both strong advantages and strong disadvantages asso-

ciated with teleworking. Case study evidence suggests large positive effects of

telecommuting on firm performance but these studies are usually hardly general-

izable as pointed out in the Introduction.

Extent of telecommuting in Denmark

Whatever their motives are, Danish firms and Danish employees have embraced

telecommuting as shown in the introductory section of this paper. Empirica

(2003) estimates the share of telecommuters in total workforce to be 21.5 percent

in 1998. This figure does not compare with the numbers that I display in Table

1 and Table 2 since there are aggregation problems, my data refers to 1998 and

the Empirica (2003) figures stem from employee surveys while my data is gen-

5

erated from employer surveys. Table 1 and Table 2 display the share of a firms

workers that regularly work at home. The mean share of telecommuters across

firms is 12.5 percent in trade and 6.3 percent in manufacturing. The difference

in manufacturing and trade are unsurprising since blue collar work in manufac-

turing can hardly be performed at home. The median share of telecommuters is

zero percent in both sectors meaning that half of the firms do not employ any

telecommuters. For firms at the top end of the telecommuter–per worker distri-

bution, as displayed in Table 1, the adoption rates are quite large: ten percent of

the trade sector firms have a telecommuter share of more than 50 percent with

the corresponding figure for manufacturing being 20 percent.

Table 1 and further inspection of the raw data (not shown in this paper) suggest

that the survey respondents did not exactly know how large the share of the

telecommuting workforce actually is since the responses cluster around ‘round’

values such as 5, 10, 20 and 80 percent etc. Due to this issue and to make sure to

properly distinguish between telecommuting non–adopters and adopters, I clas-

sify the firms according to their shares of employees that telecommute. For the

econometric analysis I create dummy variables for each of the telecommuter share

categories and use them as explanatory variables in the estimation.

I choose the telecommuting intensity categories such that shares of each category

are comparable and densely populated, both in relative and in absolute terms.

At the same time, I aim at defining the telecommuting intensity categories not

too broad. I end up having four telecommuting intensity categories for manufac-

turing and services. For manufacturing these are: 0 percent of telecommuters,

between 0 and 5 percent, between 5 and 20 percent and more than 20 percent.

For trade my classification is 0 percent, between 0 and 10 percent, between 10

and 40 percent as well as more than 40 percent. There may of course be po-

tentially large differences between firms with a telecommuter share of 40 percent

and a firms with a telecommuter share of 95 percent. These differences get lost in

the estimation since they are grouped in the same category. A finer grouping of

6

the telecommuting intensities is, however, infeasible since the statistical inference

would then be based on very few firms only.

Table 2 shows the distribution of firms according to their telecommuter intensity.

Insert Table 1 about here!

Insert Table 2 about here!

3 Empirical framework

3.1 Model

Following Bertschek and Kaiser (forthcoming) I assume that firm i produces

according to a Cobb–Douglas production technology. Output yi is a function of

capital, Ki, labor, Li, energy Ei, materials, Mi and a set of variables capturing

observable firm heterogeneity, often termed ‘observable differences in production

efficiency’, which are summarized in variable Ai:

yi = Ai KαKi LαK

i EαEi MαM

i . (1)

The α’s denote the elasticities of output with respect to capital, labor, energy

and materials respectively. Taking logs and adding an i.i.d. normally distributed

error term, denoted by ui, leads to

ln(yi) = ln(Ai) + αK ln(Ki) + αK ln(Li) + αEln(Ei) + αM ln(Mi) + ui. (2)

Log–labor productivity, i.e. output per worker, is then given by:

ln( yi

Li

)= ln(Ai) + αK ln(Ki) + (1− αL)ln(Li) + αEln(Ei) + αM ln(Mi) + ui. (3)

7

3.2 Specification

Output measurement

I use three measures of output: (i) account profits, (ii) value added and (iii) total

sales. I consider it as a great virtue of my data that I can use all three perfor-

mance measures. Accounting profits measure the total effect of telecommuting

since they combine both cost information and worker productivity. By contrast,

value added and total sales measure the effects of telecommuting in terms of pro-

duction per worker. If, for example there is a positive effect of telecommuting on

sales but an equally large change on costs, then the effect of telecommuting on

profits is zero. My data hence allow to differentiate between direct productivity

and cost effects of telecommuting.

Input measurement

My labor productivity specifications contain — apart from the ‘core’ produc-

tion inputs capital, labor, energy and materials (with energy and materials being

omitted in the specifications for the service sector since both input factors are

unimportant for the production of services) — other factors that I expect to in-

fluence productivity as well (the factors summarized by scalar Ai). These include

ICT usage variables, ownership structure and sector dummy variables.

ICT usage variables: a good representation of a firms’ usage of ICT is of height-

ened importance for this paper. If ICT usage does not enter the productivity

estimations in itself, the dummy variables for telecommuting will very likely pick

up productivity effects from ICT usage since telecommuting is associated with

firms’ ICT strategies. In other words: the coefficient estimates on the telecom-

muting dummies would then represent a mixture of ICT usage and the “pure”

telecommuting effect, thus leading to an upward bias in the coefficient estimates

for the telecommuter variables.

My specifications include four variables that represent a firms use of the internet:

8

(i) the share of workers that uses PCs or workstations, (ii) the share of workers

that is connected to a firms intranet, (iii) the share of workers that is permanently

connected to the internet and (iv) a variable that counts how many different in-

ternet applications are used by a firm. The internet application includes access

to the internet, email possibility, to an intranet and to an extranet (access to

internal websites for a selected group of external individuals). Hence, the “ICT

count” variable ranges between zero (none of the four internet applications is

used) to four (they are all used).

Ownership structure: a firm’s ownership structure might also influence productiv-

ity since a dependent firm could receive spillovers — monetarily, knowledgewise

etc. — that have effects on labor productivity. I include three dummy variables

for (i) firms that have other owners, (ii) firms that have a Danish parent and (iii)

firms that have a parent from another EU country. Having a parent from outside

the EU is the base category.3

Sector dummy variables are included to account for productivity differences across

sectors. The specification for manufacturing includes eight sector dummies (food,

textile, pulp and paper, chemicals, metals, machinery, electricity, and ‘other’

manufacturing with utilities as the base category) and the specification for ser-

vices contains one sector dummy (for retail trade with wholesale trade as base

category).

Estimation methods

Equation (3) is estimated both by Ordinary Least Squares (OLS) and by quantile

regression methods since I am not only interested in the effects mean effects of

working at home on productivity (which is estimated by OLS) but also in the ef-

fects of telecommuting on firms of different productivity. For example a firm that

is at the lower tail of the productivity distribution might encounter very different

impacts of telecommuting than a firm that is at the upper tail. This could be so

3The quantile regression specification contains a dummy variable for being a subsidiary only

since the inclusion of the variables for parent firm origin caused convergence problems.

9

since highly productive firms have more productive workers and highly produc-

tive workers are those who are most likely to receive an opportunity to work at

home.

The baseline idea behind quantile regression is to regress the explanatory variables

to the quantiles of the dependent variable (usually the 25 percent, the median

and the 75 percent quantile) instead of regressing it to the mean as in OLS. In

other words: a quantile regression estimates the quantile of the dependent vari-

able, conditional on the values of the explanatory variables while OLS estimation

estimates the mean of the dependent variable, conditional on the values of the

explanatory variables.

Quantile regression methods are also robust against outliers. In general, there are

— if there are no outliers in the dependent variable — little differences between

OLS regression results and the median regression results, both qualitatively and

quantitatively.4

A practical problem associated with quantile regression estimation is that the

corresponding variance–covariance matrix is inconsistent. I cure this problem

by using bootstrapped variance–covariance matrices (see Greene 2003, Ch. E.4,

for a reference). Bootstrapped variance–covariance matrices are also robust to

heteroscedasticity by construction.

4 Data

I use two different but complementary data sets that were both collected by

Statistics Denmark. The main data source is a business survey in Danish in-

dustries that was carried out in 1999, asking retrospective questions related to

4Note that the median is a consistent estimator for the mean so that both results should be

similar in the absence of outliers.

10

1998. This survey asked 2,954 firms in Denmark with more than 20 employees.

In some sectors the threshold was set below 20 employees, an issue that is not

further described by Statistics Denmark so that I cannot provide further details

either. A total of 1,832 firms replied to the survey, leading to a response rate of

61 percent.

The questionnaire is mainly concerned with the use of information technologies

and also contains information about the share of telecommuters. A data set

documentation, which unfortunately is available in Danish only, is provided by

Statistics Denmark (1999).

I use the share of employees who regularly work from home. The wording of the

original question is: “How large is the fraction of employees that regularly work

at home?”.

The second data set I use is also collected by Statistics Denmark. This data

contains accounting information on profits, sales, value added, the total number

of employees (in fulltime equivalents), energy costs, materials costs and capital

stock. Both data sets are merged by Statistics Denmark on the basis of unique

firm identifier that are attached to both data sets. Statistics Denmark granted

remote access to its file servers via the internet to me.5 Descriptive statistics of

the data are presented in Appendix A.

5 Caveats

Before turning the estimation results, some words of caution are in order since

there are some apparent caveats inherent to this study. All caveats are due to

5Statistics Denmark generally allows academic researchers to use its data. Data users need

to, however, be physically be present in Denmark even if they access the data via the internet.

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binding data restrictions: (i) Measurement of working at home: I only know what

fraction of workers regularly works at home. I do neither observe how many hours

are worked at home nor what workers work from home. There might be large

productivity differences associated with both the degree of telecommuting and

the types of workers who telecommute.

(ii) Generalizability: My analysis is concerned with the Danish services and man-

ufacturing industries. Denmark differs markedly from other countries in many

dimensions, most importantly in size and with respect to the social security sys-

tem for example. By contrast, labor legislation is much more US–type than

German type. Workers can easily be hired and fired without much restrictions. I

therefore believe that the results are generalizable to other countries with flexible

labor markets as well.

(iii) Assumption of Cobb–Douglas production: This assumption is along the lines

of much of the productivity estimation literature. Using a Cobb–Douglas spec-

ification implies an elasticity of substitution of unity between the input factors

by construction and does not take account of the possibility that organizational

change might vary the elasticity of substitution between input factors, for example

between labor and ICT. A popular alternative to the Cobb–Douglas production

function is the Translog approach which is more flexible in terms of elasticities

of substitution. In the estimation of such a Translog production function we

would encounter the well-known problem of high collinearity between the input

factors which, coupled with our relatively low number of observations, made our

Translog estimates implausible e.g. with negative mean production elasticities so

that we believe that our restricted Cobb–Douglas specification is more reliable

than the Translog specification. Brynjolfsson and Hitt (1995) for example, apply

both specifications, Cobb–Douglas and Translog, to a data set of 1,185 U.S. firms.

The estimated elasticities resulting from the Translog specification turn out to be

comparable to those of the Cobb-Douglas specification (p. 192 of their paper).

(iv) Selection of highly productive workers into telecommuting programs: This is

12

potentially the most severe flaw of this study. What is meant here is that is seems

likely firms pick the workers with highest productivity to work at home. This

would lead to a significantly positive effect of telecommuting that is simply due to

the fact that the best workers work at home. The US Bureau of Labor Statistics

(2002) reports that among all telecommuters almost two–thirds are indeed man-

agers and professionals. In a country with a strong taste for equality and where

workers are well organized through the profession’s unions, such winner–picking

seems unlikely, an impression that is shared with interviews I have conducted.

If at all, then such a selection of the most productive workers might take place

for groups of workers such as IT professionals, sales representatives etc. I do

not, however, possess any information about those workers that telecommute. In

manufacturing industries it seems to be clear that only managers and possibly

clerical staff — and not assembly line workers — telecommute. Managers, pos-

sibly clerical staff and, presumably most importantly, sales and purchase people

are among the groups that are likely to telecommute in the trade sector as well.

My estimation results do also not provide strong support for this selection is-

sue since I find highly significantly positive and quantitatively sizeable effects of

telecommuting on labor productivity. Selection problems imply a positive corre-

lation between the probability to be selected into a telecommuting program and

labor productivity. This induces a downward bias in the coefficient estimates for

telecommuting, something that is not consistent with my empirical findings.

(v) Outdated data: The data I use date back to 1998 so that it is naturally ques-

tionable if my results have any validity for current policy and management analy-

sis. I believe they clearly have since the only marked changes in telecommuting–

related technology is that both the costs for internet use have declined while the

speed of the internet has improved since 1998. A key issue for telecommuting is

the ability to communicate with colleagues, customers and suppliers. Technolo-

gies enabling this communication such as network clients (for example Novell

Netware), groupware (for example GroupWise or Access) and email existed in

13

1998 already (and have not changed very much since then).

6 Results

6.1 Empirical findings

OLS estimation of the labor productivity equation, Equation (3), are shown in Ta-

ble 3, quantile regression estimation results are displayed in Table 4. For brevity,

both tables contain parameter estimates and standard errors corresponding to

the telecommuting dummy variables only. All other OLS regression output is

moved to Appendix B, all other quantile regression output is moved to Appendix

C1 and C2.

Insert Table 3 about here!

Insert Table 4 about here!

A general — and important — result from both OLS and quantile regression

results is that I do find positive and (generally) statistically significant effects

of telecommuting on all measures of productivity for retail and wholesale trade.

The estimation results for manufacturing industries differ a bit between OLS

and quantile regression. In the OLS regressions there are significant effects of

telecommuting on sales per worker only while the median regressions find sig-

nificant effects on profits and value added per worker as well. These differences

might be due to outliers that affect OLS estimation results while quantile regres-

14

sion evidence is robust to that problem.6

The magnitude of the coefficient estimates also suggests that telecommuting in-

deed has quite sizeable impacts on labor productivity. For example, a share of

telecommuters of between 10 and 40 percent is associated with an increase in

median profits per worker by 48.8 percent (standard error 21.3 percent) in trade

(compare Table 3). This is at the same time the largest effect I find in my es-

timates. My estimation results by and large do not contradict the case study

evidence I reviewed in the introductory section of this paper.

For both manufacturing and trade, the quantitatively largest effect of telecom-

muting is found for profits per worker as productivity measure. Since there are

also significantly positive effects on value added and sales per worker, this sug-

gests that telecommuting is both associated with “pure” productivity increases

(more physical output per worker) and with cost savings. With regard to the fact

that the explanatory power of the profit–per–worker equations are comparatively

low (as measured by the R2s), this results should be regarded with some caution.

Promoting too much telecommuting might, however, backfire at some point. For

highly productive trade sector firms with a telecommuter share above 40 percent,

the effect of telecommuting on profits per worker is significantly negative.

Interestingly, the estimation results indicate that primarily firms of medium and

high productivity gain from telecommuting while there are no significant effects of

telecommuting on the least productive firms. Hence, introducing a work–at–home

program might not be a means to spur productivity for firms of low productivity.

This might of course be due to the fact that firms of low productivity have a

smaller chance of allowing highly productive workers to telecommute than firms

of high productivity. This is the problem of selecting highly productive workers

into telecommuting programs that I discussed in the “Caveats” section.

Marked differences exist between manufacturing and trade firms with respect to

6The five percent most productive firms were discarded in the estimations in order to avoid

that the OLS estimation results are too heavily influenced by few highly productive firms.

15

productivity–maximizing telecommuter shares. The estimation results show that

for manufacturing industries the optimal telecommuter share is above 40 percent

while for trade it is between 10 and 40 percent. Manufacturing and trade follow

inherently different production processes so that these differences are not surpris-

ing.

The adjusted R2 for the OLS regressions and the pseudo R2 of the quantile regres-

sions differ considerably between the different measures for labor productivity. As

it is to be expected, accounting profits per worker are least well explained while

the goodness of fit for value added and total sales per worker is high for estima-

tions on cross–sectional data.

6.2 Strategic management implications

The strategic management implications are trivial, but presumably a truism:

telecommuting indeed seems to be a way to increase productivity so that imple-

menting a telecommuting program might be a means to boost labor productivity.

Clearly, however, the magnitude of these effects differs from firm to firm due to

differences in the workforce, in production technology and the way telecommut-

ing is implemented. This study generates evidence on average effects across firm

and can of course not provide any specific recommendations that have value for

individual management decisions.

My estimation results indicate economically sizeable and significant effects of

telecommuting on labor productivity, in particular if measured by profits per

worker. I also find significant effects of telecommuting on value added and sales

per worker. Taken together, this suggests that there are both “pure” productivity

increases generated by telecommuting and that there are also cost savings.

Telecommuting does, however, not benefit all firms alike. Profits per workers

16

of the 25 percent most productive firms are for example unaffected by telecom-

muting both in manufacturing and in trade so that telecommuting is at least for

highly productive firms not a strategy to further improve profitability.

There may, however, also be productivity losses that can be associated with

telecommuting, as found for trade sector firms of high productivity.

6.3 Economic policy implications

My main economic policy recommendation directly falls from the observation

that already highly productive firms do not greatly benefit from telecommuting:

if economic policy decides to promote telecommuting (which is debatable, see

below), it then should focus on firms of medium or lower labor productivity —

e.g. on the less successful market players.

Economic policy tends to be at risk to reward winners — in the present case

firms with a high productivity — which makes any economic policy evaluation

quite cumbersome (Heckman et al. 1999). It is quite likely that economic policy

will tend to promote telecommuting at highly productive firms since these firms

will be eager to receive a piece of any governmental promotion program (which

is maybe why they became highly productive in the first place).

It is, however, if governments should promote telecommuting at all. From an

economic point of view, government intervention is only justified if market fail-

ures exist and need to be cured. It is not apparent what type of market failure

telecommuting policy would cure so that government intervention is not justified

on economic grounds, at least unless the domestic economy is to be put at a com-

parative advantage. One argument in favor of telecommuting policy is, however,

environmental protection. If (and only if) telecommuting helps to protect the

environment, it is justified on economic grounds.

17

As a matter of fact, governments do promote telecommuting. The Bush ad-

ministration has singled out telecommuting as the fastest growing application

of information society and plans to promote is (Hodson 1995). The European

Commission has also sponsored various telecommuting project, among which the

evaluation project ‘Teleurba’ is probably the best known one.

7 Conclusions

This paper empirically quantifies the effects of a specific form of organizational

innovation — working at home (of “telecommuting” or “telework”) — on labor

productivity. Three measures of labor productivity are considered: accounting

profits per worker, value added per worker and total sales per worker.

A sample of 647 firms from manufacturing industries and 276 firms from retail

trade and wholesale trade that are based in Denmark is used in the study. The

data stem from accountancy and business survey data that is collected by Den-

mark’s federal statistical agency.

Simple Ordinary Least Squares regressions (OLS) and quantile regression meth-

ods are applied in the empirics. Quantile regression methods allow to quantify

the effects of explanatory variables on the quantiles — in the present paper the

25 percent, the median and the 75 percent quantile — of the distribution of the

dependent variable. By contrast, OLS estimates the mean of the dependent vari-

able.

The key finding of this paper is that telecommuting is associated with signifi-

cantly positive and quantitatively sizeable gains in labor productivity. For both

manufacturing industries and trade these productivity gains are mainly due to

cost savings as indicated by significantly positive and quantitatively large effects

of telecommuting on profits per worker and by quantitatively smaller (but still

18

significant) effects on value added and sales per worker.

Other findings are that the largest productivity gains are related to telecommuter

shares above 20 percent in manufacturing industries while in retail and wholesale

trade the optimal ratio of telecommuting to office based workers is between 10

and 40 percent.

The estimation results also suggest that telecimmuting is not a means to spur

productivity for less productive firms: significantly positive effects are only found

for firm of median and high productivity.

Further research will focus on the selection of workers that are allowed to telecom-

mute since it might be the case that only the most efficient workers receive access

to telecommuting programs. Although this paper does not find strong support

for this claim, such selection effects are likely to be in place.

19

References

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Bertschek, I. and U. Kaiser (forthcoming). Productivity effects of organiza-tional change: microeconometric evidence, ZEW Discussion Paper 01–32;forthcoming in Management Science.

Brynjolfsson, E. and L. Hitt (1995). Information technology as a factor of pro-duction: the role of differences among firms, Economics of Innovation andNew Technology 3(4), 183–200.

Canadian Telework Association (2003), Cost–benefits;http://www.ivc.ca/costbenefits.htm

Croner Consulting (2003). Flexible working can save £1000 says BT;http://www.humanresources-centre.net/cgi-bin/croner/jsp/Editorial.do?cache=true&contentID=97136

Empirica (2003). Verbreitung der Telearbeit in 2002: Internationaler Vergleichund Entwicklungstendenzen, Empirica, Bonn.

Heckman, J.J., R.J. LaLonde and J.A. Smith (1999). The economics and econo-metrics of active labor market policy, in: Ashenfelter, O. and D. Card(Eds.): Handbook of Labor Economics, Vol. 3a, North Holland, Amster-dam, 1865-2097.

Hodson, N. (1995), The economics of teleworking; speech at the Telecommute’95 meeting in Santa Clara, California;http://www.teleworker.com/papers/economic.html.

ITAC (2000), Telework America 2000 Research;http://www.workingfromanywhere.org/pdf/ITACTeleworkAmerica2000KeyFindings.pdf

Jensen, S.E.H., N. Malchow–Moller, J.R. Skaksen and A. Sorensen (2003). Den-mark and the Information Society: Challenges for Research and EducationPolicy”, DJOEF Publishing, Copenhagen

Lorek, L.A. (2002), Telecommuting saves businesses, agencies millions, San An-tonio Express–News Oct. 28, 2002;http://ww.fortwayne.com/mld/newssentinel/4386078.htm

20

Rylander, C.K. (2000), Encourage Telework among state agencies, Texas Comp-troller of Public Accounts; http://www.e-texas.org/recommend/ch04/hrm05.html.

Sirhal, M. (2003), Agency sees gains from telework initiative, National Journal’sTechnology Daily April 15, 2003; http://www.govexec.com/dailyfed/0403/041503td2.htm

Statistics Denmark (1999), Danske virksomheders brug af informationsteknologi1998, Statistics Denmark Serviceerhverv 1998:8.

US Department of Labor (2003), Evaluation of the department of labor’s tele-work program; http://www.oig.dol.gov/public/reports/oace/fy2002/2E505980005.pdf.

21

Table 1: Share of employees that regularly work at home

10% 25% 50% 75% 90% Mean Std. dev. # of obs.ManufacturingShare of workers that

regularly telecommute 0.0 0.0 0.0 5.0 20.0 6.3 17.0 682TradeShare of workers that

regularly telecommute 0.0 0.0 0.0 10.0 50.0 12.5 26.8 291

Reading example: on average 12.5 percent of a firms’ workers telecomute in the trade sector. The median share

of telecommuters is 0 percent, the 90% percentile is 50 percent which means that 90 percent of the trade sector

firms have telecommuter shares of less than 50 percent.

Note: the figures for manufacturing refer to 682 firms, the figures for trade refer to 291 firms.

Table 2: Distribution of firms by extent of telecommuting

ManufacturingTelecommuter share: 0% 0–5% 5–20% 20–100%Corresponding firm share 65.4 15.0 12.0 7.6Number of firms 446 102 82 52

TradeTelecommuter share: 0% 0-10% 10–40% >40%Corresponding firm share 64.6 14.1 10.7 10.7Number of firms 188 41 31 31

Reading example: 14.1 percent of the firms from the trade sector have a telecommuter share of between zero

and ten percent.

Note: the figures for manufacturing refer to 682 firms, the figures for trade refer to 291 firms.

22

Table 3: OLS regression results: primary interest parameters

ManufacturingProfits Val. add. Sales

Coeff. std. err. Coeff. std. err. Coeff. std. err.> 0 and ≤ 5 0.1212 0.1209 -0.0105 0.0389 0.0490 0.0381> 5 and ≤ 20 0.0277 0.1557 0.0687 0.0461 0.0863** 0.0399> 20 0.1083 0.1651 0.0640 0.0605 0.0738 0.0509Trade

>0 and ≤ 10 -0.0735 0.1804 0.1331* 0.0804 0.0878 0.0841>10 and ≤ 40 0.4879** 0.2007 0.1955 0.1274 0.1501 0.1108>40 -0.0964 0.2015 0.2072** 0.0992 0.1765* 0.1017

Table 3 displays OLS regression results for the coefficients related to the dummy variables for the telecommuting

dummy variables. The dependent variables are the natural logarithms of profits per worker (‘Profits’), value

added per worker (‘Val. add.’) and sales per worker (‘Sales’). All other regression output is moved to Appendix

B. The asteriks’ ∗∗ and ∗ indicate that the corresponding coefficient is significantly different from zero at the one,

five and ten percent marginal significance level respectively. Standard errors are robust to heteroscedasticity.

A total of 647 manufacturing firms and a total of 276 service sector firms are involved in the estimation. The

coefficient estimates are to be interpreted as percentage changes in labor productivity.

Reading example: a telecommuter share of between 10 and 40 percent is associated with an increase in per–

worker profits by 48.8 percent (standard error 20.1 percent) for a trade firm of mean productivity.

23

Table 4: Quantile regression results: primary interest parameters

ManufacturingProfits Val. add. Sales

Coeff. std. err. Coeff. std. err. Coeff. std. err.25% quantile> 0 and ≤ 5 0.1722 0.1850 -0.0420 0.0461 -0.0056 0.0317> 5 and ≤ 20 -0.0681 0.2099 0.0658 0.0490 0.0143 0.0329> 20 0.1785 0.3377 0.0477 0.0806 0.0242 0.0446Pseudo R2 0.0561 0.2318 0.359450% quantile> 0 and ≤ 5 -0.0901 0.1389 -0.0024 0.0508 -0.0184 0.0320> 5 and ≤ 20 0.1218 0.1979 0.0282 0.0495 0.0252 0.0466> 20 0.2620* 0.1503 0.0984* 0.0613 0.0436 0.0480Pseudo R2 0.0516 0.2047 0.312275% quantile> 0 and ≤ 5 0.1793 0.1463 -0.0154 0.0553 0.0603 0.0761> 5 and ≤ 20 0.1416 0.1449 0.0570 0.0713 0.1114* 0.0588> 20 0.0438 0.1506 0.0588 0.0849 0.0683 0.0621Pseudo R2 0.0748 0.1849 0.2301Services

25% quantile>0 and ≤ 10 -0.0306 0.2913 0.1700* 0.1050 0.1891 0.1331>10 and ≤ 40 0.7866 0.3806 0.0906 0.2121 0.1196 0.2455>40 0.0261 0.3166 0.2129 0.1444 0.1266 0.1616Pseudo R2 0.1008 0.1008 0.064350% quantile>0 and ≤ 10 -0.0225 0.2332 -0.0114 0.0923 0.0622 0.1065>10 and ≤ 40 0.4809** 0.2153 0.2249 0.1935 0.2646* 0.1453>40 -0.0800 0.2662 0.1843 0.1327 0.1577 0.1756Pseudo R2 0.0873 0.0873 0.066375% quantile>0 and ≤ 10 -0.0565 0.2332 0.0867 0.1363 0.0372 0.1351>10 and ≤ 40 0.2556 0.1809 0.3275** 0.1459 0.1331 0.1368>40 -0.4180* 0.2376 0.0936 0.1281 0.2270 0.1651Pseudo R2 0.0915 0.0915 0.0819

Table 4 displays quantile regression results for the coefficients related to the dummy variables for the telecom-

muting dummy variables. The dependent variables are the natural logarithms of profits per worker (‘Profits’),

value added per worker (‘Val. add.’) and sales per worker (‘Sales’). All other regression output for manufactur-

ing is moved to Appendix C1, for services it is moved to Appendix C2. The asteriks’ ∗∗ and ∗ indicate that the

corresponding coefficient is significantly different from zero at the one, five and ten percent marginal significance

level respectively. Standard errors are bootstrapped (10,000 replications are used). A total of 647 manufacturing

firms and a total of 276 service sector firms are involved in the estimation. The coefficient estimates are to be

interpreted as percentage changes in labor productivity. The ‘Pseudo R2’ is the R2 of the entire regression.

Reading example: a telecommuter share of more than 20 percent is associated with an increase in per–worker

profits by 17.9 percent (standard error 33.8 percent) for a manufacturing firm situated at the 25 percent quantile

of the productivity distribution.

24

Appendix A: descriptive statistics

Manufacturing TradeMean Std. dev. Mean Std. dev.

ln(profits per worker) 3.3116 1.2065 3.7355 1.0412ln(value added per worker) 6.7862 0.3939 7.5615 0.5127ln(sales per worker) 12.9307 0.3939 12.9633 0.5777Explanatory variablesShare of workers connected to intranet 45.0850 34.3710 58.9588 35.6222Share of workers w/ internet access 23.0147 32.6427 24.3058 34.2383ICT count variable 1.9751 1.1202 1.9107 1.1592Dummy for subsidiary firms 0.4589 0.4987 0.4467 0.4980Dummy for Danish parent 0.2933 0.4556Dummy for EU parent 0.1100 0.3131ln(capital stock) 9.7202 1.8805 8.7258 1.7381ln(number of employees) 4.6801 1.0768 4.0381 1.0792ln(materials) 10.3946 2.4894ln(energy) 6.6222 1.6448Food 0.0836 0.2770Textile 0.0352 0.1844Paper 0.1100 0.3131Chemicals 0.1041 0.3056Metals 0.1217 0.3272Machinery 0.1657 0.3721Electricity 0.1012 0.3018‘Other’ manufacturing 0.1144 0.3185Wholesale trade 0.8351 0.3718

25

Appendix B: additional output from OLS regressions

Profits Val. add. SalesCoeff. std. err. Coeff. std. err. Coeff. std. err.

ManufacturingShare of workers connected to intranet 0.0021 0.0016 0.0020*** 0.0005 0.0019*** 0.0004Share of workers w/ internet access -0.0002 0.0014 0.0002 0.0005 0.0005 0.0004ICT count variable -0.0380 0.0513 0.0106 0.0150 -0.0087 0.0141Dummy for subsidiary firms 0.3136 0.1957 0.0645 0.0514 0.1439*** 0.0592Dummy for Danish parent -0.3391* 0.2089 0.0433 0.0552 -0.0442 0.0600Dummy for EU parent -0.4006* 0.2328 0.0178 0.0581 -0.0044 0.0692ln(capital stock) 0.0476 0.0526 0.0295* 0.0183 0.0260** 0.0133ln(number of employees) -0.1785** 0.0832 -0.1457*** 0.0348 -0.1405*** 0.0314ln(materials) 0.0149 0.0234 0.0417** 0.0211 0.0551*** 0.0213ln(energy) 0.1959** 0.0631 0.0797*** 0.0188 0.0924*** 0.0187ServicesShare of workers connected to intranet -0.0009 0.0018 0.0037*** 0.0010 0.0018** 0.0009Share of workers w/ internet access 0.0044** 0.0022 -0.0008 0.0013 -0.0005 0.0011ICT count variable -0.1379** 0.0699 0.0087 0.0355 -0.0222 0.0316Dummy for subsidiary firms 0.1991 0.1316 0.1999*** 0.0662 0.1175** 0.0596ln(capital stock) 0.1213** 0.0529 0.0775** 0.0337 0.0313 0.0275ln(number of employees) -0.1013 0.0857 -0.0245 0.0538 -0.0104 0.0433

Appendix A displays OLS regression results for the coefficients that are not of key interest. The dependent

variables are the natural logarithms of profits per worker (‘Profits’), value added per worker (‘Val. add.’) and

sales per worker (‘Sales’). The asteriks’ ∗∗∗, ∗∗ and ∗ indicate that the corresponding coefficient is significantly

different from zero at the one, five and ten percent marginal significance level respectively. Standard errors

are robust to heteroscedasticity. A total of 647 manufacturing firms and a total of 276 service sector firms

are involved in the estimation. The coefficient estimates are to be interpreted as percentage changes in labor

productivity.

26

Appendix C1: additional output from quantile regressions, manufacturing

Profits Val. add. SalesCoeff. std. err. Coeff. std. err. Coeff. std. err.

25% quantileShare of workers connected to intranet 0.0018 0.0021 0.0007 0.0005 0.0010** 0.0004Share of workers w/ internet access 0.0005 0.0025 0.0001 0.0005 0.0000 0.0004ICT count variable -0.0932 0.0844 0.0194 0.0185 0.0081 0.0135Dummy for subsidiary firms 0.2573 0.3270 0.0278 0.0617 0.0345 0.0518Dummy for Danish parent -0.0849 0.3404 0.0607 0.0627 0.0179 0.0508Dummy for EU parent -0.1961 0.4372 0.0213 0.0661 0.0018 0.0569ln(capital stock) 0.1014 0.1077 0.0231 0.0239 0.0364** 0.0162ln(number of employees) -0.1525 0.1497 -0.2904*** 0.0502 -0.3940*** 0.0440ln(materials) 0.0100 0.0762 0.2041*** 0.0477 0.3261*** 0.0521ln(energy) 0.1569 0.1063 0.0766** 0.0259 0.0442** 0.018650% quantileShare of workers connected to intranet -0.0001 0.0017 0.0017*** 0.0005 0.0015** 0.0005Share of workers w/ internet access -0.0025 0.0019 -0.0002 0.0006 0.0001 0.0005ICT count variable -0.0091 0.0555 0.0163 0.0201 0.0022 0.0128Dummy for subsidiary firms 0.2996 0.2406 0.0187 0.0681 0.0678 0.0525Dummy for Danish parent -0.2521 0.2421 0.0543 0.0634 -0.0362 0.0559Dummy for EU parent -0.2376 0.2768 0.0394 0.0757 -0.0687 0.0758ln(capital stock) 0.0240 0.0720 0.0184 0.0195 0.0126 0.0198ln(number of employees) -0.1450 0.1088 -0.2391*** 0.0501 -0.3191*** 0.0749ln(materials) 0.0601 0.0665 0.1533** 0.0570 0.2820*** 0.0789ln(energy) 0.1485** 0.0684 0.0657** 0.0240 0.0622*** 0.024475% quantileShare of workers connected to intranet 0.0018 0.0014 0.0029*** 0.0008 0.0018** 0.0008Share of workers w/ internet access -0.0012 0.0014 -0.0005 0.0007 0.0007 0.0006ICT count variable -0.0352 0.0512 0.0125 0.0209 -0.0038 0.0205Dummy for subsidiary firms 0.2124 0.2337 0.0136 0.0870 0.0205 0.0993Dummy for Danish parent -0.1535 0.2425 0.1016 0.0906 0.0378 0.0917Dummy for EU parent -0.2155 0.2703 0.0253 0.0842 0.1167 0.1170ln(capital stock) 0.0083 0.0522 0.0235 0.0313 0.0110 0.0191ln(number of employees) -0.2532*** 0.0893 -0.1462** 0.0633 -0.2565*** 0.1030ln(materials) 0.0470 0.0562 0.0654 0.0612 0.1993* 0.1151ln(energy) 0.1805*** 0.0648 0.0569** 0.0261 0.0892*** 0.0336

Appendix C1 displays quantile regression results for the coefficients that are not of key interest for manufactur-

ing industries. The dependent variables are the natural logarithms of profits per worker (‘Profits’), value added

per worker (‘Val. add.’) and sales per worker (‘Sales’). The asteriks’ ∗∗∗, ∗∗ and ∗ indicate that the correspond-

ing coefficients is significantly different from zero at the one, five and ten percent marginal significance level

respectively. A total of 647 firms are involved in the estimation. Standard errors are bootstrapped (10,000 repli-

cations are used). The coefficient estimates are to be interpreted as percentage changes in log–labor productivity.

27

Appendix C2: additional output from quantile regressions, services

Profits Val. add. SalesCoeff. std. err. Coeff. std. err. Coeff. std. err.

25% quantileShare of workers connected to intranet -0.0011 0.0033 0.0040*** 0.0014 0.0012 0.0015Share of workers w/ internet access 0.0029 0.0036 -0.0021 0.0019 0.0007 0.0018ICT count variable -0.1695* 0.1004 -0.0290 0.0456 -0.0297 0.0447Dummy for subsidiary firms 0.0201 0.2606 0.1129 0.0806 0.2330** 0.0974ln(capital stock) 0.1936** 0.0934 0.0503 0.0432 0.0463 0.0413ln(number of employees) -0.0436 0.1551 0.0662 0.0754 -0.0286 0.0689R2 0.0576 0.1031 0.068250% quantileShare of workers connected to intranet -0.0013 0.0023 0.0028** 0.0014 0.0006 0.0011Share of workers w/ internet access 0.0030 0.0037 -0.0004 0.0016 0.0003 0.0012ICT count variable -0.1155 0.1164 0.0455 0.0484 -0.0232 0.0417Dummy for subsidiary firms 0.2571 0.1618 0.2217*** 0.0858 0.0582 0.0859ln(capital stock) 0.1117 0.0934 0.0621 0.0396 0.0596 0.0396ln(number of employees) -0.0261 0.1286 -0.0295 0.0633 -0.0018 0.0617R2 0.0550 0.0867 0.073475% quantileShare of workers connected to intranet 0.0030 0.3200 0.0030*** 0.0011 0.0014 0.0012Share of workers w/ internet access 0.0051* 0.0860 0.0002 0.0017 -0.0011 0.0014ICT count variable -0.1379 0.2440 0.0132 0.0577 0.0136 0.0561Dummy for subsidiary firms 0.3364** 0.0540 0.2116** 0.0892 0.0859 0.0919ln(capital stock) 0.0846 0.1740 0.0457 0.0352 0.0611 0.0426ln(number of employees) -0.0914 0.4600 -0.0209 0.0627 -0.0762 0.0751R2 0.0413 0.0970 0.0939

Appendix C2 displays quantile regression results for the coefficients that are not of key interest for retail

and wholesale trade. The dependent variables are the natural logarithms of profits per worker (‘Profits’), value

added per worker (‘Val. add.’) and sales per worker (‘Sales’). The asteriks’ ∗ ∗ ∗, ∗∗ and ∗ indicate that the

corresponding coefficient is significantly different from zero at the one, five and ten percent marginal significance

level respectively. A total of 276 firms is involved in the estimation. Standard errors are bootstrapped (10,000

replications are used). The coefficient estimates are to be interpreted as percentage changes in labor productivity.

28