Productivity Effects of Business Process Outsourcing (BPO)
A Firm-level Investigation Based on Panel Data∗

Jörg Ohnemus†
Centre for European Economic Research (ZEW), Mannheim
April 2009

Work In Progress, Please Do Not Cite!

Abstract

This paper analyses the impact of business process outsourcing (BPO) on firm productivity based on a comprehensive German firm-level panel data set covering manufacturing and service industries. The growing importance of service inputs into
the production process is undisputed. Firms increasingly decide to go to the market and buy all or at least parts of selected services they need from external service
providers. This is especially true for services which rely to great extend on new
information and communication technologies. Doing so, outsourcing firms can concentrate on their core competencies. Additionally, they benefit from the expertise of
the external service provider. Finally, external vendors are able to provide services
at lower price because of scale effects. By estimating a production function using asystem-generalised method of moments (system-GMM) estimation procedure, I showthat BPO has a positive and highly significant impact on firm-level productivity inboth manufacturing and service industries.

Keywords: Business Process Outsourcing (BPO), Productivity, Panel Data, System-GMM
JEL-Classification: C23, D24, L24, L60, L80∗

This paper was written as part of the research project “Business Process Outsourcing: Determinants andConsequences for Competitiveness” commissioned by the Förderkreis Wissenschaft und Praxis at the Centre
for European Economic Research.

†

Centre for European Economic Research (ZEW), Research Group Information and Communication Technologies, P.O. Box 10 34 43, D–68034 Mannheim, Germany, Tel.: +49–(0)621–1235–354, Fax: +49–(0)621–1235–
4354, ohnemus@zew.de.

1

Introduction

The overall importance of services as an input into the production process of firms is undisputed.
Firms can choose between two different forms of acquiring those inputs: they can produce
services themselves or they can contract out the services to an external service provider.1 Figure
1 reflects the growing importance of external service inputs for the production process of theGerman economy. Between 1995 and 2005 (the latest point of time for which input-output data
is available) the share of intermediate inputs from the “other business activities sector” (NACE
74) in the total production value in Germany increased from 5.45 percent to 6.38 percent.
Although this increase in percentage terms seems rather moderate, the absolute numbers arequite substantial. For the period from 1997 to 2005 the increase in the share of intermediate
input of 1.3 percentage points reflects an additional amount of business services requestedby the market of around e91.5 billion. The import share only represents an additional e3.9
billion. The rest of the increase therefore comes from service providers located in Germany.
Since concentrating solely on the “other business activities sector” is quite narrow when focusing
on business process outsourcing activities, a wider definition which includes additional sectors
providing business services is displayed in Figure 2. Here I show results for the “corporate service
sector” which comprises the sectors “computer and related activities” (NACE 72), “researchand development” (NACE 73) and “other business activities” (NACE 74). In this case, theincreasing importance of services as inputs to the production process is even more pronounced.
Between 1997 and 2005 (again the latest point in time with input-output data availability) the
increase of services from the above defined sectors as inputs into other sectors increased by 1.5
percentage points to a total amount of almost e307 billions (including imports of around e24
billions). Data made available by the EU-Klems project allows to take a look at the share ofintermediate service inputs in total value added in Germany of the past years until the year
1978. Since then the share of service input rose from 24.8 percent to 34.0 percent in 2005 forthe service sector, an increase of more than 9 percentage points. Regarding the manufacturingindustries, the increase is smaller but still amounts to almost 6 percentage points in the sametime span. A study conducted by the Centre of European Economic Research (ZEW) in 2007
1

Sometimes firms, especially larger ones and those with several subsidiaries, found their own service division
which then provides services to all the other parts of the group. Sometimes those service divisions also begin
to offer their service to external companies.

1

analyses the involvement of German firms in business process outsourcing. They find that
54 percent of German manufacturing firms and 59 percent of firms belonging to the service
industries are involved in BPO (ZEW, 2007). This holds especially in the fields of accounting
and human resource managements where the services of BPO providers are highly demanded byfirms in both industries (see Figure 4).

The aforementioned figures illustrate the growing importance of external service provision for
the German economy. The question now is to find out if this usage also has an effect on thebusiness performance of the outsourcing firm, e.g. in terms of productivity gains. Why shouldfirms resort to external providers and give away decision power and (maybe) flexibility if it does
not help them to improve their economic performance in some way? Therefore, the purpose ofthis paper is to analyse the impact of business process outsourcing on firm-level productivity byusing a comprehensive panel data set from the German manufacturing and service industries
for the years 2000, 2002, 2004 and 2007. I apply different estimation methods to a production
function framework, among others system-GMM estimation. This controls for unobserved firm
effects, measurement errors in the variables and simultaneity of inputs and output, which may
induce substantial biases in pooled OLS regressions. The results clearly show a positive andhighly significant impact of business process outsourcing on the productivity of outsourcing firms.

The remainder of the paper is structured as follows: Section 2 gives a definition of businessprocess outsourcing and develops the main hypothesis. Furthermore, an empirical literature
review focusing on business process outsourcing and productivity research is presented. Section
3 introduces the estimation procedure. In Section 4 the data set and the applied transformation
steps are presented. Section 5 discusses the estimation results. Section 6 concludes.

2

2

Background Information

2.1

Business Process Outsourcing

Business Process Outsourcing (BPO) is a broad term referring to outsourcing in all fields of
economic activity. It can be defined as ‘an organisation entering into a contract with anotherorganisation to operate and manage one or more of its business processes or sub-processes’(Sharma, 2004). Focusing on the more technical side of BPO an alternative definition wouldbe ‘BPO is the leveraging of technology or specialist process vendors to provide and manage
an organisation’s critical and/or non-critical enterprise processes and applications’ (Mukherjee,2007).

A BPO service provider differentiates itself from a typical third party Application

Service Provider (ASP) by either putting in new technology or applying existing technology ina new way to improve a (business) process. The application of BPO usually also means thata certain amount of risk is transferred to the vendor which runs the process on behalf of theoutsourcer. Business segments which are typically outsourced include information technology(IT), human resources, facilities and real estate management, and accounting. In recent years,
because of the rapid development of information technology, business process outsourcing has
increasingly become the delegation of one or more IT-intensive business processes to an external
provider that in turn owns, administers and manages the selected process based on definedand measurable performance criteria. With BPO, the outsourcing provider not only takes overadministrative responsibility for a technical function but also assumes strategic responsibility forthe execution of a complete, business-critical function. This additional step can introduce new
efficiencies and cost savings while it also enables the outsourcing vendor to deliver important
strategic benefits to the customer. BPO is positively related to the search for more efficientorganisational designs: cost reduction, productivity growth and innovative capabilities. Hence,a source of strategic advantage.

An important fact of business process outsourcing is its ability to free corporate executives fromsome of their day-to-day process management responsibilities. Executives usually spend mostof their time managing everyday business and only some time on formulating strategies for a
successful advancement of the company. This can look quite different when business processes
are outsourced. Once a process is successfully outsourced, the ratio can be easily reversed. As
3

a result, executives have more time. This saved time helps them tremendously to explore newrevenue areas, accelerate other projects and focus on their customers. This, beyond any doubt,
leads to productivity improvements.

Companies that outsource their business processes are often able to generate new efficiencies andimprove their productivity. They are in a better position to reallocate their resources to otherimportant projects. This also helps their employees to increase their efficiency and productivity.
In most cases highly qualified experts are brought in to design and manage these processes.
Those experts bring with them increased productivity and years of experience that mostcompanies previously did not have access to or could not afford on their own. The availabilityof highly qualified skill pools and the faster adoption of well-defined business processes leads to
productivity improvements without compromising on quality.

2.2

Outsourcing of Services

Business Process Outsourcing is very closely related to service outsourcing since it can be seen as
the outsourcing activity of a sub-group of services. The empirical literature dealing with service
outsourcing and offshoring (the outsourcing to a service provider abroad) is much diversified.
Some papers deal with the outsourcing drivers, e.g. with the factors that affect the outsourcing
decision of the firm (Abraham and Taylor, 1996; Girma and Görg, 2004; Antonietti and Cainelli,
2007; Merino and Rodríguez Rodríguez, 2007).2

Another topic the empirical literature on

service outsourcing is dealing with, is the effect of outsourcing on firm success, e.g. in terms of
productivity increases of the outsourcing and/or offshoring firms. Important contributions weremade by Siegel and Griliches (1992); Fixler and Siegel (1999); Görg and Hanley (2005); Jiang
and Qureshi (2006); Sako (2006); Hijzen (2007) and Görg et al. (2008). All those papers analyse
the relationship between productivity and service outsourcing in general. Heshmati (2003) and
Olsen (2006) provide surveys of the more general literature on outsourcing and its relationshipto efficiency and productivity. The extent to which services are outsourced is treated by the
paper of Amiti and Wei (2005). They find that the international outsourcing of material inputs
2

Theoretical aspects about the determinants of outsourcing and offshoring can be found in Grossman andHelpman (2003, 2005) and Antràs et al. (2006)4

is still more important than the international outsourcing of services. However, taking a look on
the dynamics of both outsourcing aspects, the growth rate of service outsourcing is much higher
compared to the growth rate of material outsourcing.

2.3

Outsourcing and ICT

Business process outsourcing is very closely related to information technology outsourcing. BPO
even comprises IT outsourcing to some extend. The increasing importance of information andcommunication technologies (ICT), especially the usage of computers and intra and internet
network connections, has revolutionised the provision of services. Even more important, abroad variety of new services were created because of the possibilities offered by new andfast developing information and communication technologies. Therefore, the vast majority ofbusiness processes today rely in some way on information and communication technologies. In
addition, the operation of a firm’s ICT infrastructure itself can be interpreted as business process.

Various authors have analysed ICT/IT outsourcing and offshoring, including Loh and Venkatraman (1992) as well as Barthêlemy and Geyer (2001; 2004; 2005), who took a close look on thedeterminants of IT outsourcing. Additionally, further research was devoted to the outsourcing
firms’ performance, basically trying to identify (labour) productivity effects of IT outsourcing.
Maliranta et al. (2008) thereby find out that IT outsourcing enhances an organisation’s IT use
and thus boosts its labour productivity. In contrast, Bertschek and Müller (2006) cannot findany significant differences in key variables between outsourcing and non-IT outsourcing firms.
They even find that firms without IT outsourcing produce more efficiently than those involvedin IT outsourcing. Ohnemus (2007) in turn finds the opposite. He shows that IT outsourcing
firms are more efficient in their production processes. Furthermore, he finds that employees
working at a computerised workplace are more productive in IT outsourcing firms. Besides theempirical literature dealing with IT outsourcing, there is also a variety of theoretical papers

5analysing various aspects of IT outsourcing. For a comprehensive overview see Dibbern et al.
(2004).3

The analysis of Abramovsky and Griffith (2006) rather attempts to identify the impact of
information and communication technologies on the make or buy decision of business services.
They rely on British firm-level data for the years 2001 and 2002. Information and communication technologies reduce the transaction costs and the costs of adjustment of outsourcing
relationships. Therefore, long distance service outsourcing (offshoring) has become increasingly
popular during the last few years.
decreased significantly.

Additionally, the costs of these outsourcing activities

Abramovsky and Griffith (2006) find out that ICT-intensive firms

tend to buy a greater amount of services on the market (compared to in-house production).
Furthermore, these firms are more involved in international service outsourcing (offshoring).

3

Empirical Framework

In order to estimate the impact of business process outsourcing on a firm’s productivity, I referto a production function framework using a Cobb-Douglas specification:

Yit=

F (Ait , Lit , Kit , BP Oi )=Ait Lαit Kitβ eγBP Oi ,(1)

where Yit refers to output of firm i at time t, Lit represents labour input and Kit represents
capital input. BP Oi is a dummy variable indicating if firm i has been involved in businessprocess outsourcing since the year 2000 or before. After taking logs on both sides, one canrewrite Equation (1) in the following way:

yit = α lit + β kit + γ BP Oi + ηi + λj(i),t + it ,3(2)

In their literature overview Dibbern et al. (2004) analyse 84 papers published between 1992 and 2000. Theyfind that most of the studies focus on Transaction Cost Theory, Agency Theory or Strategic Management
Theory as a reference framework to explain IT outsourcing.

6

where small letters denote the corresponding logarithmic value and multifactor productivityln(Ait ) = ηi + λj(i),t + it is decomposed into a firm-specific and time-invariant fixed part
ηi , a time-variant industry-specific part λj(i),t ,4 and a time-variant firm- specific residual it .
Firm-effect ηi captures fixed or quasi-fixed factors affecting productivity, e.g. management
ability, organisational capital, branding or location. The residual it comprises measurement
errors, mit , and firm-specific productivity shocks, µit , such that it = mit + µit . In this analysis, both mit and µit are assumed to be serially uncorrelated and only their sum it is considered.

The industry time–variant part λj(i),t captures variations in productivity that are specificto a particular industry and that are left unexplained by the input variables. In this sense,
λj(i),t helps to ensure that outputs of firms are more readily comparable across industries.
In particular, demand fluctuations induced by industry–specific business cycles may lead tovariations in factor utilisation that are similar across firms of one industry. The resulting industry–specific changes of productivity are then captured by λj(i),t . While the industry–specificcomponent λj(i),t will be controlled for by including time–variant industry dummies, distorting
effects from unobserved ηi and it will be addressed by econometric techniques. I account for
the fact that both ηi and it may be correlated with the inputs if, for example, firms witha good management (i.e. a high ηi ) are both more productive and more inclined to makeuse of capital input, or if a demand shock (high it ) raises both productivity as well as investment.

The endogeneity of the explanatory variables can be removed by an instrumental variable
regression. In this respect, it is convenient to use GMM estimations with internal instruments,
i.e. other moments of the same variable (Hempell, 2006). More precisely, the first differencesof the explanatory variables are instrumented here by the levels of the lagged variables. The
prediction power of the internal instruments could be small, however, given the only minorchanges in some of the variables (e.g. number of employees) from one year to another. That
could evoke biases in the GMM estimator in first differences (Blundell and Bond, 1998).
Therefore I prefer the so-called system-GMM estimator of Arellano and Bover (1995). Here,the differences are instrumented again with lagged levels as internal instruments. The levels

4

With j(i) denoting the industry j that firm i is operating in.

7

of the covariates are simultaneously instrumented by adequate lagged differences. The main
advantage of this approach is that besides the temporary differences, differences among firmsin levels are also taken account of in the estimation. That improves the information used for
identifying the effect and usually enhances the precision of the estimator. A necessary condition
for the system-GMM estimator is that the correlations between the unobserved fixed effects
and the covariates remain constant over time (Arellano and Bover, 1995). The productivity
estimations are carried using the command xtabond2 in STATA 10.1 (Roodman, 2006). I
applied the available two-step estimation variant which is asymptotically more efficient than the
one-step alternative. Unfortunately, the reported two-step standard errors tend to be severely
downwardly biased (Arellano and Bond, 1991; Blundell and Bond, 1998).

To resolve thisproblem, Windmeijer’s adjustment process for variances is applied (Windmeijer, 2005). Thismethod helps to make the two-step system-GMM estimation more efficient than the one-stepestimation.

4

Data

The firm-level data used for the empirical analysis are taken from a survey conducted by theCentre for European Economic Research (ZEW) between 2000 and 2007. It is a representative
survey of information and communication technologies in the German manufacturing andselected service sectors.5 In each wave a total of approximately 4,400 firms were surveyed.
The data is stratified according to industries (seven manufacturing industries and seven service
sectors), size (three distinct classes) and region (East or West Germany). Merging all four
existing waves of the collected data results in an (unbalanced) panel structure in important
key variables. Besides a huge amount of variables dealing with information and communication
technologies, the ZEW ICT-survey contains annual data on sales, number of employees (and theirskill structure) and expenditures on gross investment. In the last wave, which was conducted
in 2007, information about ICT and business process outsourcing was collected. The survey

5

The first wave of the so called ZEW ICT-survey was conducted in the year 2000, the second wave followed two
years later, the third wave in 2004 and the hitherto last survey wave took place in 2007.

8

did not only focus on the present state of the outsourcing activities of each firm but also asked
for the starting year of various BPO activities (for a further discussion of this point, see page 11).

In order to conduct meaningful production function estimations, same of the available variables
have to be transformed using external data sources. In the following I will illustrate how thisexternal data is used to transform the available survey data which enables me to run production
function regressions. For output Yit I use a measure of firms’ value added. Alternatively, theoriginally available data on total sales from the survey could be used with including firm–levelintermediate goods as an additional input. However, the available data lacks reliable information
on intermediate inputs. Using sales for output instead of value added without the inclusion of
the amount of intermediate inputs might lead to an omitted variable bias in the regressions sinceindustries that operate rather at the end of the value chain (such as wholesale and trade) resort
more to intermediate goods in terms of quantity than other industries do. In order to control
for these differences and to deflate the corresponding outputs, I calculate the shares of real value
added in nominal gross output at the NACE two–digit industry level.6 The firm–level data onsales are then multiplied by these industry–specific shares.7 For labour input, the number of
employees in full-time equivalences is used.

Some firms did not report investment figures for one or more of the survey periods. Since thetotal number of firms (and observations) in the sample is already at the lower limit, I tried
to keep as many observations in the sample as possible. Therefore I imputed investments forfirms with missing values by multiplying the total number of employees with industry and year
specific average investment intensities (investment per employee) obtained from the full survey
sample (full cross section) in each specific survey year. In order to construct a capital stockfrom investment data, I use official producer price deflators for investment goods to deflate theinvestments of firm i. Given the deflated investments for capital, I apply the perpetual inventory6

For these calculations I used Table 81000-0103 and Table 81000-0101 from the German Statistical Office.

7

If Zit and Yit are sales and value added of firm i in period t, and if Zj(i),t and Yj(i),t are sales and value added
aggregated over all firms of the same industry j(i) that firm i is operating in. Then the unknown value addedof firm i is approximated by Yit ' Zit · Yj(i),t /Zj(i),t .

9method with constant, geometric depreciation to construct the capital stock. Accordingly, thecapital stock Kit of firm i in period t results from investment Ii,t−1 in the following way:

Kit = (1 − δj(i) )Ki,t−1 + Ii,t−1(3)with δj(i) denoting the industry-specific depreciation rates of capital stocks for firm i.8 Since no
information is available on the level of capital stocks, I construct initial capital stocks employing
the method proposed by Hall and Mairesse (1995). Under the assumption that investment
expenditures on capital goods have grown at a similar, constant average rate g in the past in
all firms, Equation (3) can be rewritten for period t = 1 (2000) by backward substitution in thefollowing way:9

K1 = I1 + (1 − δ)I−1 + (1 − δ)2 I−2 + . . .
∞X=
I−s (1 − δ)s
s=0
∞ 
X

= I0

s=0

=

I1
g+δ


1−δ s
1+g
(4)

In order to derive the initial capital stocks, assumptions about the pre-period growth rate gof investments have to be made. Hereby, I assume an annual growth rate of approximately
5 percent (g = 0.05). Since there are time lags between the installation and the productive
contribution of capital goods, I employ the capital stock at the beginning of each period (orat the end of the corresponding previous period) as a measure of capital input. In order to8

I calculated the depreciation rates δj(i) by industries as the shares of capital consumption in net fixed assets
evaluated at replacement prices (time series 81000-0107 and 81000-0117 of the German Statistical Office). The
unweighted mean over all industries amounts to 4.8 percent with a maximum in NACE 72 (data processing)of 26.0 percent and a minimum in NACE 70 (real estate) with 2.3 percent.

9

In fact, the initial value of investment
for firm i Ii,1 is replaced by the average of the observed values ofP
investment such that Ii,1 ' T1 Tt=1 Iit .

10

apply suited econometric techniques (system-GMM estimation), I consider only firms for which
consistent information is available for at least three consecutive periods.10

The resulting unbalanced sample consists of 700 firms with a total of 2371 observations.
The statistics of the total sample, the outsourcing firms and the non-outsourcing firms are
summarised in Table 1. The figures report the means and medians of the unweighted firms’means over time. The majority of firms in the reference sample are small and medium–sized
firms with a median of 49 employees. The median value of the number of employees is higher
for firms involved in outsourcing (61 employees) than for non-outsourcing firms (38 employees).
The bottom rows of Table 1 report the means and medians of the firms’ (longitudinal) averagesof output and capital intensity (value added and capital per employee). At the median firm,a workplace is equipped with capital worth e60,950. The median value added per employee is
e62,900. Again value added per employee and capital per employee is higher for the outsourcing
sub-sample compared with the non outsourcing sample. 11

The business process outsourcing variable , the main variable of interest in this analysis, is adummy variable indicating if firm i partly or completely outsources business processes to an
external service provider. The variable is time-invariant, taking the value one if the firm began
to outsource business processes in the year 2000 or before, with 2000 being the first year forwhich data from the survey is available. The questionnaire of the ICT survey asked firms about
their outsourcing engagement in certain business activities, the starting year of this engagement
and the extend of their outsourcing (fully or partly). A firm belongs to the outsourcing group if
at least one of the following business processes is provided by an external service provider: (i) IT
services, (ii) Marketing, (iii) Procurement, (iv) Customer Services and (v) Sales and Distribution.

10

Since information about BPO is essential for the analysis conducted in this paper and BPO information was
only collected in the 2007 survey, all firms included in the final sample were observed in the year 2007. The
panel observations then reach back at least to the year 2002 and sometimes even further to the first wave of
the ZEW ICT-survey in 2000.

11

The corresponding mean values are substantially higher than the median since some firms display very highvalues for both inputs and outputs per employee.

11Taking a look at Table 2 reveals the decomposition of the sample by industry affiliation. One
can easily see that the greatest share of firms in the sample belongs to the metal and machine
construction industry. The smallest share of firms on the other hand belongs to the wholesaletrade industry.12 As the last two columns of Table 2 indicate, business process outsourcing is
extremely popular in the chemical industry. More than 61 percent of the firms belonging tothis industry are involved in BPO. The industry with the least involvement in business process
outsourcing is the electronic processing and telecommunication industry. Here, only 26 percentof the firms outsource business processes to an external service provider. Overall, 43 percent of
all firms are outsourcing companies.

Table 3 shows descriptive statistics of labour productivity (value added per employee), separated
into firms with BPO and firms without BPO. The mean of labour productivity is substantiallyhigher for firms that are involved in outsourcing compared to those firms without any involvement in BPO. Outsourcing firms reach a mean value for labour productivity of about e114,063
and non-outsourcing firms show a value of e74,208. According to a t-test the difference isalso highly significant at any conventional level. When calculating the logarithm of labour
productivity (the logarithm of productivity is later on used in the estimation procedures) andcomparing the means in the two sub-samples a t-test again shows that the mean of the logarithmof labour productivity is significantly higher in the sample of firms with outsourcing activities.

5

Results

Tables 4 and 5 show the results for pooled OLS, random effects and system-GMM estimation.
The first two columns of Table 4 contain the results of the pooled OLS regression for a pureproductivity estimation with labour and capital inputs and productivity estimation.

The

additional dummy variable indicates if firm i is involved in business process outsourcing. In
both estimations the labour and capital input coefficients are highly significant, reaching values

12

For a detailed description and composition of the sectors included in the survey, see Table 6. In the ZEW
ICT-survey banks and insurances are also surveyed, but since there is no output data available for those firms(only the balance sheet total and insurance premiums, respectively), these industries are excluded from theanalysis in this paper.

12

of about 0.96 for labour input and 0.08 (0.07) for capital input. The coefficient for BPO is also
positive and highly significant, reaching a value of 0.25. The pooled OLS estimates are possibly
biased because observations of the same firm in different years are considered as independentand unobserved heterogeneity cannot be taken into account.

To account for this possible bias, I estimated a random effects model.13 The estimation results
are shown in the third column of Table 4. In contrast to the pooled OLS regression of column
two, the capital coefficient is significantly smaller and not significant at the usual significancelevel any more. Additionally, the labour coefficient increased to 0.99. Moreover, the BPO
coefficient slightly increased to a value of 0.27 and is still highly significant.

The endogeneity problem of labour and capital is further addressed in the system-GMM
regressions. Here, the lagged endogenous variables are used as instruments. Labour and capitalare regarded as endogenous variables, the dummies for industry, time, and the location of
the firm (East or West Germany) are assumed to be exogenous. Besides, the BPO dummy
variable, which indicates if firm i has done business process outsourcing since at least theyear 2000 is assumed to be exogenous.

System-GMM estimation results are presented in

5. I estimated a basic production function without the ‘BPO-input’ variable as a starting
point. The results for the labour and capital inputs are again highly significant, as well as thecoefficient for the dummy variable indicating if a firm is located in East or West Germany.
In contrast to the OLS and random effects regressions, the coefficient for labour is smaller
reaching a value of 0.81. However, the capital input coefficient increased to around 0.26, whichis considerably higher than 0.08 in the pooled OLS regression. Adding BPO as an input factor
to the production function estimation and focusing on all firms in the sample, I can find asignificant positive impact of business process outsourcing on productivity at the firm level. The
coefficient reaches a value of 0.21 and is highly significant. In this setup, the labour and capital input coefficients do not differ much compared to the basic setup of the productivity equation.

13

A fixed effect model would also be an appropriate alternative in this case. However, since the variable of maininterest is a time-invariant dummy, this variable would not be identified in a fixed effect model.

13In a further step, I want to see if there are differences concerning the productivity effects ofbusiness process outsourcing in the different sectors of the manufacturing or service industries.
The results of these two estimations are reported in columns three and four of 5. In bothsettings the labour input coefficient is highly significant but the difference in absolute terms isquite substantial. While the coefficient for labour is around 0.72 in the manufacturing industry,
it is almost 0.96 in the service sector. This result is not surprising, since manufacturing is
generally less labour intensive than the service sector. Consequently, the capital coefficient ishigher for manufacturing than for services, albeit in neither estimation the coefficient achieves
significance. The most interesting finding is that in both estimations the impact of businessprocess outsourcing is positive and highly significant (at the five percent level in manufacturing
and at the one percent level in services). In absolute terms, the impact seems to be more
pronounced in the service sector (0.33 compared to 0.27).

In all system-GMM estimations, there is significant first order correlation (of the first differenced
residuals) and no second order correlation at the usual levels. This result indicates the validity of
the applied instruments. This is further undermined by the Hansen J -Test of overidentification.
In all settings the null hypothesis could not be rejected.

6Concluding Remarks

The aim of this paper is to analyse the impact of business process outsourcing on firm-levelproductivity.

An augmented production function approach is used which takes account offirms’ BPO activities. For the empirical analysis system-GMM estimation is applied for apanel data set of German firms. The results show that business process outsourcing has aconsiderably positive and significant effect on firm-level productivity. This result holds for firmsof the manufacturing as well as for firms from the services sector. The coefficient indicating if
a firm is involved in BPO is again highly positive and significant in separate estimations for
manufacturing firms and service firms.

14

The outsourcing of business processes to external providers seems to be a good choice for firms.
The allows managers to focus on the core business of the firm. Moreover, the qualified andexperienced work of the service provider and the cost savings finally result in an improved
business performance. This research, however, is still at the beginning. Further research efforts
are necessary and should focus for example on a decomposition of labour input by qualification
and age. Moreover, interactions between BPO and specific input factors could be taken into
account.

15

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17Appendix
A – Data
Output (Y): Firms’ value added is used as output measure. Because of data restrictions
only data of sales is available. I constructed firm-specific and deflated value added figures bymultiplying sales with the shares of real value added in nominal gross output terms at the
NACE two–digit industry level.

Employment (L): For labour input, the number of employees in full-time equivalences is used.
Besides the total number of employees, the firms were asked about their share of part-time
employees. I accounted for that share, assuming that part-time employees work on average half
the time of full-time employees.

Capital Stock (K): The capital stock variable is constructed out of deflated investment
information available for each firm and year. I apply the perpetual inventory method to achieve
capital stock data for each firm (and year).

Business Process Outsourcing (BPO): The business process outsourcing variable is adummy which takes on the value one if firm i started to partly or completely outsource (al least)
one of the following business processes before or in the year 2000: (i) IT services, (ii) Marketing,(iii) Procurement, (iv) Customer Services and (v) Sales and Distribution.

East or West Germany: Dummy variable indicating if the location of firm i is in East orWest Germany.

18

B – Figures and Tables
Figure 1: Share of intermediate inputs from “other business activities” sector in the total production value inGermany (1995-2005)8

percentage share

7

6,33

5,97

6

6,38

6,31

6,19

6,12

5,99

5,71
5,45

5,37
5,08

5

4
1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

share of intermediate input plus imports from "other business activities" sector at total production value
share of intermediate inputs from "other business activities" sector at total production value

Source: Based on input-output tables provided by the Germany Statistical Office and authors’ calculations.

Figure 2: Share of intermediate inputs from “corporate service sector” ∗ in the total production value inGermany (1995-2005)
8

7,37
7,26

7,03

6,97

percentage share

7

7,30

7,27

6,85
6,54

6,07

6,06

6

5
1995

5,79

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

share of intermediate input plus imports from the corporate service sector* at total production value
share of intermediate inputs from the corporate service sector* at total production value

Note: ∗ The “corporate service sector” comprises the sectors “computer and related activities” (NACE 72), “research anddevelopment” (NACE 73) and “other business activities” (NACE 74).
Source: Based on input-output tables provided by the Germany Statistical Office and authors’ calculations.

19

Figure 3: Share of intermediate service inputs at total value added in Germany (1978-2005)40

35

percentage share

30

25

20

15

10

5

0
1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

Service Industry

1998

2000

2002

2004

Manufacturing

Source: EU-Klems 2008 and authors’ calculations.

Figure 4: Share of firms outsourcing business processes in Germany 2007
54

Overall

59

36

Accounting

44
23

Human Resources

28
8

Marketing

124Procurement

7
4

Customer Service

4
Manufacturing
Service Industries

4

Sales and Distribution

4
0

10

20

30

40

50

60

70

percentage share

Source: ZEW ICT-survey, first quarter 2007.

20

Table 1: Descriptive Statistics
IndustryAll

Outsourcing

Non-Outsourcing

Firms

Firms

Firms

Mean

MedianMeanMedianMeanMediansales

54071.27

7008.6182827.79

10866.67

32807.15

4833.33

value added

23379.33

3270.9134541.41

4932.43

15125.47

2496.82

employees

223.7248.75

305.41

60.83

163.31

38.33

153961.96

2933.57

265760.78

4334.9771291.86

2003.06

log value added

8.08

8.03

8.53

8.44

7.75

7.65

log employees

3.92

3.83

4.18

4.08

3.73

3.53

log capital

8.13

7.97

8.55

8.36

7.82

7.60

value added p. employee

91.15

62.90

114.06

67.20

74.21

60.89

capital p. employee

2261.4960.95

3215.8668.89

1555.77

57.61

capital

Number of firms

701

298

403

Note: The figures are calculated at the means and medians of the unweighted firms’ means over time. Monetary values
are in 1,000 Euros in prices of 2000. Source: ZEW ICT-survey.

Table 2: Share of Observations by Industry
Industry

All
Firms

thereof ...
Outsourcing

Non-Outsourcing

consumer goods

7.9

45.5

54.6

chemical industry

6.3

61.4

38.6

other raw materials

7.6

39.6

60.4

metal and machine construction

13.4

41.5

58.5

electrical engineering

8.7

32.8

67.2

precision instruments

9.3

33.9

66.2

automobile

5.3

56.8

43.2

wholesale trade

4.0

57.1

42.9

retail trade

7.4

55.8

44.2

transport and postal serv.

7.0

53.1

46.9

electronic process. and telecom.

10.6

25.7

74.3

technical services

7.4

34.6

65.4

other business-related serv.

5.1

41.7

58.3

Total

100.0

42.6

57.4

Source: ZEW ICT-survey.

21

Table 3: Comparison of Mean Labour Productivity
Quantile
N

10%

50%

90%

Mean

Std.Dev.

298

40

67

227

114.063130.931

403

27

61

146

74.20854.417

Outsourcing Firms
Labour Productivity
Non-Outsourcing Firms
Labour Productivity

t-test on the equality of the means of Labour Productivity
H0 : mean(w/ BPO) - mean(w/o BPO) = diff = 0 → t = 5.5028
diff 6= 0 → [ p > |t| ] = 0.0000

H1 :t-test on the equality of the means of log Labour Productivity
H0 : mean(w/ BPO) - mean(w/o BPO) = diff = 0 → t = 6.5412
diff 6= 0 → [ p > |t| ] = 0.0000

H1 :Note: Labour productivity is value added per employee in 1,000 Euro in prices of 2000. It is calculated at the means andmedians of the unweighted firms’ means over time. Source: ZEW ICT-survey.

Table 4: Estimation Results – OLS and Random Effects

LabourCapital
BPO

(1)

(2)

(3)

OLS

OLS

Random Effects

0.9641***

0.9577***0.9946***(0.0233)(0.0224)(0.0642)0.0778***0.0723***0.0471(0.0180)(0.0170)(0.0539)––0.2571***(0.0466)

East Germany
Constant

R2
Number of observations
Number of firms

0.2690***(0.0491)-0.2346***-0.2131***-0.1928***(0.0624)(0.0612)(0.0597)

3.4538***3.4404***4.6081***(0.2179)(0.2300)(0.3410)0.84180.84660.8469

2371

2371

1671
700

Note: *,** and *** indicate significance at the 10%, 5% and 1% level respectively. Robust standard errors are reported.
Source: ZEW ICT-survey and own calculations.

22

Table 5: Estimation Results – SYS-GMM

LabourCapitalBPO
East Germany
Constant

(1)

(2)

(3)

(4)

Basic

All

Manufact.

Services

0.8060***0.8423***0.7238***0.9580***(0.1543)(0.1426)(0.1932)(0.1462)0.2569**0.2240*0.10100.0599(0.1254)(0.1340)(0.2062)(0.0841)––0.2053**0.2721**

0.3281***(0.0858)(0.1075)(0.1039)-0.1786**-0.1634**-0.2506**-0.1808(0.0880)(0.0810)(0.0982)(0.1117)2.9602***3.0126***4.4526***3.9048***(1.0392)(1.0277)(1.4946)(0.7575)

AR(1) (p-values)

0.00000.00000.00000.0010AR(2) (p-values)

0.56800.54010.27480.5633

Hansen J -Test (p-values)

0.56000.56470.3432

0.6139R2

0.82290.83340.86630.7995

Number of instruments

63

64

40

36

Number of observations

2371

2371

1383

988

Number of firms

700

700

409

291

Note: *,** and *** indicate significance at the 10%, 5% and 1% level respectively. Robust standard errors are reported.
Source: ZEW ICT-survey and own calculations.

23

Table 6: Industry Classification
Industry

Explanation

consumer goods
manufacture of food products, beverages and tobacco
manufacture of textiles and textile products
manufacturing of leather and leather products
manufacture of wood and wood products
manufacturing of pulp, paper and paper products; publishing and printing
manufacturing n.e.c.

NACE
15-16
17-18
19
20
21-22
36-37

chemical industry
manufacture of coke, refined petroleum products and nuclear fuelmanufacture of chemicals, chemical products and man-made fibres

23
24

other raw materials
manufacture of rubber and plastic products
manufacture of non-metallic mineral products
manufacture of basic metal

25
26
27

metal and machine construction
manufacture of fabricated metal products (except machinery and equipment)
manufacture of machinery and equipment n.e.c.

28
29

electrical engineering
manufacture of office machinery and computersmanufacture of electrical machinery and apparatus n.e.c.
manufacture of radio, television and communication equipment and apparatus

30
31
32

precision instruments
manufacture of medical, precision and optical instruments, watches and clocks

33

automobile
manufacturing of transport equipment

34-35

wholesale tradewholesale trade and commission trade (except of motor vehicles and motorcycles)51

retail tradesale, maintenance and repair of motor vehicles and motorcycles; retail sale of automotive fuelretail trade (except of motor vehicles and motorcycles), repair of personal and household goods

50
52transportation and postal services
land transport, transport via pipelinewater transport
air transport
supporting and auxiliary transport activities; activities of travel agencies
post and courier activities

60
61
62
63
64.1

electronic processing and telecommunication
computer and related activities
telecommunications

72
64.2

technical services
research and development
architectural and engineering activities and related technical consultancy
technical testing and analysis

73
74.2
74.3

other business-related services
real estate activities
renting of machinery without operator and of personal and household goods
legal, accounting, book keeping and auditing activities; tax consultancy; market research andpublic opinion pools; business and management consultancy; holdings
advertisinglabour recruitment and provision of personnel
investigation and security services
industrial cleaning
miscellaneous business activities n.e.c.
sewage and refuse disposal, sanitation and similar activities

70
71
74.1
74.4
74.5
74.6
74.7
74.8
90

24