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Diffusion and economic impacts of biotechnology - A case study for Germany

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While the economic impact of biotechnology is disputed, empirical data and research about diffusion and economic impacts of biotechnology in application sectors is rather limited. In this paper, actual and prospective diffusion of biotechnology in different application industries is analysed on a disaggregated level by combining production data with a written expert survey. On the basis of these results, an input-output model was used to calculate the direct and indirect employment effects of biotechnology in Germany for the years 2004 and 2020. The results suggest a further diffusion of biotechnology in all application sectors. The subsequent employment occurs in both application industries and upstream sectors.
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Int. J. Biotechnology, Vol. 12, Nos. 1/2, 2011 87
Copyright © 2011 Inderscience Enterprises Ltd.
Diffusion and economic impacts of biotechnology –
a case study for Germany
Sven Wydra*
Department Emerging Technologies,
Fraunhofer Institute for Systems and Innovation Research (ISI),
Breslauer Str. 48, 76139 Karlsruhe, Germany
E-mail: sven.wydra@isi.fraunhofer.de
*Corresponding author
Michael Nusser
Fachhochschule Hannover – University of Applied Sciences and Arts,
Ricklinger Stadtweg 120, 30459 Hannover, Germany
E-mail: michael.nusser@fh-hannover.de
Abstract: While the economic impact of biotechnology is disputed, empirical
data and research about diffusion and economic impacts of biotechnology in
application sectors is rather limited. In this paper, actual and prospective
diffusion of biotechnology in different application industries is analysed on a
disaggregated level by combining production data with a written expert survey.
On the basis of these results, an input-output model was used to calculate the
direct and indirect employment effects of biotechnology in Germany for the
years 2004 and 2020. The results suggest a further diffusion of biotechnology
in all application sectors. The subsequent employment occurs in both
application industries and upstream sectors.
Keywords: biotechnology; technology diffusion; employment; input-output
model; Germany.
Reference to this paper should be made as follows: Wydra, S. and Nusser, M.
(2011) ‘Diffusion and economic impacts of biotechnology – a case study for
Germany’, Int. J. Biotechnology, Vol. 12, Nos. 1/2, pp.87–103.
Biographical notes: Sven Wydra is a Researcher at the Department of
Emerging Technologies at the Fraunhofer Institute for System and Innovation
Research (Germany). He holds a PhD in Economics from the University of
Hohenheim. His research interests include the economic impacts of emerging
technologies on innovation and growth, and innovation policy.
Michael Nusser is a Professor of Economics at the Fachhochschule Hannover –
University of Applied Sciences and Arts (Germany). He holds a PhD in
Economics from the University of Bamberg. His research concentrates on
issues of economic impacts of emerging technologies and R&D/knowledge
intensive sectors, international competitiveness, systems of innovation and
innovation policies.
88 S. Wydra and M. Nusser
1 Introduction
It is often claimed that biotechnology will be the next technology to deeply transform the
economy and society. According to such opinions, the development and application of
biotechnology has the potential for far-reaching economic, social and environmental
impacts. Consequently, biotechnology would gain in strategic importance to knowledge-
based economies and their governments. Nations that fail to develop biotechnology
capabilities will lag behind others in realising economic impacts (Arundel, 2003). On the
other hand, the potentially revolutionary impact of biotechnology is called into question.
In particular, the failure as yet to achieve major breakthroughs in industry, the public’s
fears and opposition, as well as the currently limited applications to certain
manufacturing and resource sectors raises scepticism about the future (Hopkins et al.,
2007; Pisano, 2006; Arundel, 2003; Freeman, 2003).
To gain further insight into this debate empirical data accompanied by adequate
foresight procedures are needed. But, to date, measurement of biotechnology is still in its
early stages and there is a lack of comparable data on the impacts of biotechnology
(OECD, 2005; Rose and McNiven, 2007; Senker et al., 2007).1 Moreover, existing
conceptualisations and surveys provide little information about the diffusion of
biotechnological techniques in application sectors and their consequential economic
effects.
Therefore in this paper, actual and prospective diffusion of biotechnology in different
application industries is analysed and related employment impacts are assessed. Diffusion
of biotechnology is assessed using a bottom-up approach. Values for production groups
on a disaggregated level are combined with techno-economic data and expert judgement
to determine the extent of diffusion. The combination of these various types of
information is often acknowledged to give accurate results. To gain further insight,
potential developments in future diffusion scenarios are considered. This approach is
applied to four application sectors in Germany for the years 2004 and 2020.2
Biotechnology-related employment is analysed using an integration of the results of
biotechnology diffusion in an adjusted input-output model.3
This paper begins with a short overview of the theoretical arguments regarding
economic impacts of emerging technologies. Following this overview, it discusses
statistical and measurement issues relating to the impacts of biotechnology. The focus
lies on the usage of biotechnological methods in application industries. In Section 3, the
methodology applied in this study for measuring diffusion and economic impacts of
biotechnology is introduced. The results of current and potential diffusion paths as well
as related employment figures are given in Section 4. Finally, Section 5 summarises the
main findings and discusses the limitations of the research results.
2 Background
2.1 Theoretical overview
The understanding of how a new technology impacts on the economy is a pre-requisite
for developing an adequate methodology for impact measurement. But theories differ in
their arguments and emphasise different channels of impact (Smith, 2001). In the
Diffusion and economic impacts of biotechnology 89
following, interesting implications for the long-term impact of generic technologies like
biotechnology from concepts of general purpose technologies (GPTs) are discussed, as
these account more properly for the differentiated effects of different types of
technological change (Palmberg and Nikulainen, 2007).
According to Lipsey et al. (2005) a GPT “is a single generic technology, recognisable
as such over its whole lifetime, that initially has much scope for improvement and
eventually comes to be widely used, to have many uses, and to have many spillover
effects”. The evolution of the GPT is indicated by the increasing efficiency with which it
delivers its services and the increasing range of its applications. New products are
created, therefore, and process improvements increase the productivity of existing
products. When the GPT matures it spreads throughout the economy and generates an
impact on the long-term growth of production and productivity. But such technologies
take a long time to diffuse and even longer to have an economic impact. The respective
facilitating structure or techno-economic paradigm has to be adjusted to fit the changes in
the new technologies. These adjustments involve new forms of best-practice organisation,
new skill profiles in the labour force, new location patterns, new infrastructures, new
consumption patterns, new types of dominant firms, new regulations, etc. (Smith, 2001).
But, because of coordination problems and uncertainty among the actors, as well as the
inertia effects of fixed capital and the need for complementary innovations, this process is
often costly and slow.
Hence the economic impact of a GPT depends on a myriad number of factors.
Lipsey et al. (2005) emphasise the importance of the productivity of the GPT relative to
that of the technology it is replacing; whether the new GPT is a substitute or a
complement to existing technologies; the relation of the GPT to the existing facilitating
structure of the economy, and the inherent properties of the GPT itself. For this reason,
no clear-cut conclusions can be drawn on the economic impacts of such technologies.
This is especially true in respect to employment, which has long been debated in
compensation theory. On the one hand, the claim is that technological change mainly
increases the potential for rationalisation, which leads to displacement of labour. On the
other hand, various market compensation mechanisms can counterbalance the initial
labour-saving impact of process innovation (Vivarelli, 1995; Petit, 1995; Pianta, 2004).
One example of a GPT is ICT. Computers and related equipment are used in most of the
industries making up the economy, and have made possible new ways of organising
firms, of facilitating the emergence of new sectors (e.g., software) and of boosting
productivity after a certain time-lag (Guerrieri and Padoan, 2007). But the employment
impact of ICT is rather unclear, as impact differs widely between sectors and depends on
different factors such as institutional settings and demand dynamics (Antonucci et al.,
2003).
As noted above, the potential of biotechnology to become an important
technology like a GPT is hotly debated and controversial. It is widely accepted that
biotechnology appears to meet the requirement of having a wide range of applications
(within human and animal health, industrial processing and almost all resource-based
sectors, such as agriculture, forestry, aquaculture, and mining) while its range (e.g.,
shares of production in application sectors) and impact on productivity, as well as its
scope for creating new products, is disputed. Accordingly, we focus our empirical
approach on these aspects.
90 S. Wydra and M. Nusser
2.2 Measurement of the impact of biotechnology
The measurement of the economic impact of biotechnology is still in its early stages and
faces significant challenges. Questions arise with regard to constructing an adequate and
useful definition for biotechnology4, the conceptualising of various indicators and
especially the collection of the data and integration with other statistics (Rose and
McNiven, 2007).
Most of the economic effects from technologies with wide applications like ICT and
biotechnology are expected from their diffusion and adoption by user firms in application
industries (OECD, 2005). However, production or provision of technologies only
generates limited impact as the rather small production shares of ICT-producing sectors
illustrate. Unfortunately, the definition and separation of users and producers is not
straightforward for biotechnology. The main activities carried out by biotechnology firms
are research and development, the use of biotechnology techniques to produce goods or
services and the use of biotechnology products as an input for manufacturing (OECD,
2005; Lim and Choi, 2006). The latter is excluded from analysis by the OECD, although
it admits that the boundaries between key activities and some end uses are not always
clear. A similar concept is used in this study. To be counted as a biotechnological product
there must be an additional biotechnological step which builds on an intermediate step.
Moreover, the provision of biotechnology know-how and techniques on the one hand
(technology producers), and the use of biotechnology techniques to produce goods or
services on the other hand, are separated. While the former mainly includes universities
and R&D institutions, small and medium-sized dedicated biotechnology enterprises, and
biotechnology suppliers, the latter includes application industries like the pharmaceutical
industry, the chemical industry, the food-processing industry and so on.5 But it has to be
remembered that some firms can be engaged in biotechnology research and also use
biotechnology as a production technique.
Most surveys of biotechnology are conducted as special biotechnology-firm surveys
from private consulting or official institutes (e.g., E&Y, 2007). Although they are
important in providing some basic information about research and commercialisation in
biotechnology, they have, up until now, demonstrated several shortcomings. First they
tend only to cover dedicated biotechnology firms, which are predominantly active in
biotechnology. Some of them cover diversified ‘innovative active firms’, which conduct
R&D in biotechnology amongst other non-biotechnological activities. But, to date, there
are hardly any surveys on firms that use biotechnology but which do not perform R&D in
biotechnology (IPTS, 2007)6. Second, while these surveys usually provide information
about basic indicators like turnover, employment and R&D, there is for the most part no
information about important indicators like trade, investments, value added
or productivity. An important step in obtaining useful and comparable indicators
would be the integration of biotechnology-specific data in official production statistics.
This would help to connect it with other economic measures and to construct
biotechnological-specific indicators like productivity, value added, trade, and so on. This
could be realised on the basis of the firm or by changes in commodity classifications,
but both options present difficulties: in terms of the firm, it is almost impossible to
calculate separate indicators for biotechnology in diversified firms. Moreover,
biotechnology-related applications are spread across a number of very diverse existing
industrial sectors, e.g., food production, textile finishing, pulp and paper, agriculture,
power generation, chemicals and petrochemicals, and pharmaceuticals. So the
Diffusion and economic impacts of biotechnology 91
construction of a NACE-based definition of a sector that provides or uses
biotechnological methods is highly unfeasible, as it is difficult to set up clear-cut criteria
to determine which sector diversified firms belong to.
In order to identify biotechnology by commodity classifications, would necessitate
major changes to the classification system. Product classification does not usually
distinguish between alternative production processes. So for many products a distinction
between biotech and conventional (e.g., chemical) production processes as well as the
introduction of some new ones would be necessary. Moreover, biotechnology is a
dynamic field and certain biotechnological production methods will probably soon be
expanded to other, or completely new, products (Brink et al., 2004).
Up-to-date official statistics and existing firm surveys can therefore provide only
limited information about the diffusion of biotechnological techniques in application, and
the economic impact of this (OECD, 2005; Rose and McNiven, 2007). To fill this
information gap and to provide some foresight and scenarios for the future, various
techno-economic studies have been carried out using a mix of methods and sources, often
based mainly on expert judgement, case-studies, and various statistical data. For example,
Dewick et al. (2006) investigate the effects of biotechnology on economic structure by
examining expert judgements on potential change of input-output coefficients. Menrad
et al. (2003) analyse the employment impact of biotechnology in Germany with a
hypothetical diffusion rate of 100% in 25 years within relevant product groups.
An alternative for measuring the diffusion of biotechnology in application for major
application sectors is the biotechnology-related-sales (BRS) concept (see Section 3.2,
OECD, 1998; McKinsey & Company, 2003; Deke, 2005; Reiss et al., 2008). This raises a
lot of methodological questions, such as the correctness and comparability of the data
used, the usefulness of expert judgement and the underlying assumptions. Existing
studies have used quite different approaches for measurement (definition of
biotechnology, aggregation level, examined sectors, scenario assumptions, etc.), some of
them with very optimistic results, which has led to criticism (Herrera, 2004; Arundel,
2002). In the following, a proposition for an alternative measurement for the BRS
concept is applied to the case of Germany in the years 2004 and 2020. Methodological
challenges and potential means of improvement are discussed. Moreover, in order to
assess the impact of biotechnology diffusion the indicator is linked with an input-output
model to study employment effects.
3 Methodology
3.1 Overview
The methodological approach used in this study consists of two phases: on the one hand
the analysis of diffusion and on the other hand the analysis of the employment impact.
First the present and potential future diffusion of biotechnology in application sectors was
ascertained. As a wide diffusion of technology is the main condition for economy-wide
impacts, but this is still unclear in the case of biotechnology, the main emphasis of our
research focuses on this aspect. The four sectors with the highest assumed diffusion of
biotechnology were analysed: chemicals, pharmaceuticals, food-processing and
agriculture. On the basis of these results, the related employment effects of biotechnology
were assessed. In order to study the impact on the whole economy, the indirect
92 S. Wydra and M. Nusser
employment effects of those economic activities that make biotechnological production
possible also have to be considered. So, besides direct employment in application sectors
related to the use of biotechnology, employment impacts via backward linkages using an
input-output model were also calculated.7 The overall methodological approach is
summarised in Figure 1 and discussed in more detail in the following.
Figure 1 Methodological approach for the analysis of diffusion and economic impact of
biotechnology (see online version for colours)
selection of biotechnological-related
sub-segments
Expert ratings of diffusion
in sub-segments
literature/ expert
judgements of
Product groups 9th-
digit-code
Written survey
with 72 experts
Employment impact in
application sectors
Employment impact in
upstream sectors
Simulation with
adjusted Input-
output model
MethodStep
Diffusion of
Biotechnology
Employment
impact
Assessment of
labour coefficients
and production
volume
3.2 Diffusion of biotechnology
In order to analyse the diffusion of biotechnology across various sectors in a comparable
way it must be measured in monetary terms. Therefore, the BRS concept is used for
major application sectors. This concept evaluates the extent of adoption of biotechnology
in monetary terms within the different application sectors. It captures different categories
of market contributions from biotechnology products, the sales of new products directly
attributable to the use of modern biotechnology, and sales of products manufactured by
improved processes that make direct use of modern biotechnology. Where modern
biotechnology or derived products are used at certain stages of a production process, the
output is calculated as being entirely due to the impact of modern biotechnology,
although the modern biotechnology-based process may be just one amongst several
non-biotechnological steps in the production process. An important assumption behind
Diffusion and economic impacts of biotechnology 93
this interpretation is that biotechnological methods have been taken up by the user in
order to be, and to remain, competitive in the respective product market.
To assess the BRS for the applications sectors, production data were combined with
information taken from the literature and expert judgements. One major concern here is
the aggregation level. Information derived from the literature, or expert judgements on a
highly disaggregated level are far from being widely available. But a high aggregation
level may be a possible source of overestimation when using such an approach. Experts
are often unaware of aggregate values, e.g., the share of turnover of different product
groups in the chemical sector. So their estimations for the share of biotechnology in a
sector may be biased, even if they are able to give plausible estimations for the share of
biotechnology in the production of specific products. Therefore, in order to investigate
the impact of aggregation level on the results, the use of a combination of experts’
bottom-up (disaggregated production groups in an application sector) and top-down
(whole application sectors, e.g., the pharmaceutical industry) ratings is proposed in this
paper in order to check for consistency. In the ‘bottom-up’ approach the biotechnology
turnover share for product groups is analysed. For each product group the turnover share
of biotechnology was multiplied by the total sector share of sales of these products and
added up to provide a total share of biotechnology turnover in the respective sector.8 In
the ‘top-down’ approached the turnover share for the whole application sector is
assessed.
The main problem in conducting a bottom-up approach is getting a sufficient number
of experts for specific products, such as the 800-odd chemical products on the 9-digit
code of product classifications set out in the European Community Production survey
(PRODCOM). Because of that, products that are already produced by biotechnological
methods or where the technological feasibility of biotechnological production is assumed,
were selected on the basis of expert opinions and literature. These products were
aggregated to 10–15 product groups mainly using the 6-digit code of the PRODCOM
classification were aggregated (see Annex, Table 1). On this basis experts from
universities, research institutes and industry were asked about the share of biotechnology
turnover in 2004 and 2020 from a ‘bottom-up’ and ‘top-down’ perspective. In addition,
the effects of biotechnology innovation (e.g., new products) in each sector and
aggregated product groups were assessed by the experts. Most of these experts were from
firms using bio techniques for the production of goods and services. It is assumed that
they have the best knowledge when it comes to comparing competing technologies and
that they are probably less biased in terms of providing over-optimistic estimations, as
their own reliance on the prosperity of biotechnology is relatively low. In total, 72 experts
answered the questionnaire. A modification of this approach is carried out for the
agriculture sector, in which the measurement of biotechnology is very challenging. While
genetic-modifying techniques (GM) are often considered to have the highest potential in
increasing yields and competitiveness, future diffusion is especially uncertain. It relies
heavily on consumer acceptance and regulation. Because of this, expert ratings for GM
techniques and other biotechnological processes were asked for separately. As expected,
the latter showed an enormous divergence in opinion even for 2020, and was not
considered further, with only non-GM-related sales being included in the indicator.
In order to check the consistency of this approach the results were discussed in expert
interviews, and triad patent analysis for the share of biotechnology in the application field
in 2004 was carried out. The share of biotechnology patents amongst all patents in an
94 S. Wydra and M. Nusser
application field was found to be within the range of the biotechnology turnover share.
The patent analysis thus confirms the results for market diffusion of biotechnology, but it
must be kept in mind that comparability of these indicators is limited.
3.3 Employment impact
On the basis of these diffusion indicators the employment effects of biotechnology for
Germany are considered. For the respective upper and lower limits in 2004 and 2020
scenario values for employment are calculated. The numbers can be interpreted as a
plausible corridor of employment that is affected by the use of biotechnology methods in
the main application sectors.
In a first step, the biotechnology-related production volume was calculated by
multiplying the biotechnology turnover share by the sectoral production volume.
Problems could arise as the former is based on sales, while the latter is measured by
production values. As there were no major differences in value for the sectors in 2004,
they could be assumed to be roughly equal. For 2020, the sectoral production volume was
estimated based on past trends from 1995 and 2004, and the results were fairly consistent
with other prospective outlook studies (e.g., Prognos, 2002).
Directly-related employment effects in application sectors were calculated by
multiplying the biotechnology-related production volume and employment coefficients
(gainfully employed persons per unit of gross production value). There are no indications
for major differences in labour intensity between biotechnological and conventional
products (see Section 4.2), and the same coefficients used for respective sectors by the
Federal Statistical Office were assumed. In order to include employment effects in
upstream supplier sectors, an input-output model is used to calculate the employment
impacts of biotechnology in Germany for the years 2004 and 2020. Input-output
modelling reveals supply and demand linkages between different industries. For example,
a particular industry will use the output of other industries as intermediate inputs
in its own production processes. The basic IO model used in this study is based on the
input-output tables produced by the Federal Statistical Office for the year 2002. They
divide the German economy into 71 production and service sectors and six end-user
sectors (including, among others, private and state consumer demand, and export).
Since the production value of each sector is made up of the sum of supplies to
intermediate and final demand, it is true that:
X = AX + Y
where X is total output, Y is the final demand and A is the technology matrix. The
correlation between final demand and production can be formulated within this static
input-output model as follows:
()
XIA1*Y.=
Assuming a roughly linear correlation between the level of employment in a sector and
the level of production in a sector, the following employment effects result:
L l X=∗
where l stands for the employment coefficient li in sector i. L is the total employment in
the economy.
Diffusion and economic impacts of biotechnology 95
It is sometimes argued that IO models are not appropriate for long-term studies as the
IO production function does not allow for substitutions among inputs and technological
change.9 But as demonstrated in several studies (e.g., Faber et al., 2007) adjustments
can be made, and such adjustments were made here for biotechnology. To adjust and
project the input-output coefficients under biotechnological progress, it is best to have the
input-output data for each biotechnical process, which in fact requires the establishment
of unprecedented biotechnological input-output tables (Pan, 2004). However, as such
data do not exist, the most appropriate sectors within existing input-output tables were
chosen and adjustments were made accordingly, based on expert and literature-based
information. One example is the higher backward linkages of the chemical sector to
agriculture as a result of higher production of bioethanol and biotechnologically-
produced polymers on the basis of biomass. Sensitivity analysis for input-output
coefficients shows that variances are rather small, as it is mainly the degree of diffusion
of a technology that triggers this kind of impact. In order to analyse the employment
impact in 2020, developments in production shares and productivity were estimated on
the basis of past trends, available theoretical and empirical evidence and results from
other relevant studies (e.g., IPTS, 2002) in order to avoid biases in estimates of
employment.
4 Results
4.1 Diffusion of biotechnology
As Table 1 shows, biotechnology is already influencing major application sectors. As is
usual in new technology fields, the market penetration in 2004 and 2020 contains
uncertainties. The diffusion rates are therefore quoted with upper and lower limits.
Especially high variances appeared for 2004 in the food-processing and agricultural
sectors. One reason for this seemed to be different interpretations of the proposed
definition of modern biotechnology. For all applications the bottom-up results were
below the estimates of experts for the whole sector. This may be a hint that studies on a
high aggregation level might overestimate the impact of biotechnology. Hence the values
were based on bottom-up results that were chosen and rated slightly higher (on average
about 1%), as the top-down estimates may capture certain biotechnological applications
that were not considered in the bottom-up product groups.
Table 1 Biotechnology turnover share for 2004 and 2020 (in per cent, referring to the
production value of the whole industry)
Application industries
Biotechnology
turnover share
2004
Biotechnology
turnover share
2020
Scenario figures chemicals 4–6% 9–18%
Scenario figures pharmaceuticals 11–18% 18–40%
Scenario figures food-processing 9–23% 17–32%
Scenario figures agriculture 11–20% 26–42%
96 S. Wydra and M. Nusser
The numbers indicate a more or less similar diffusion rate in pharmaceuticals,
food-processing and agriculture, while the overall diffusion rate in the large
heterogeneous chemicals sector is the lowest. In all sectors diffusion is expected to rise
significantly by 2020. This conclusion also holds on the basis of product groups
where the growth rates also differ significantly. In general, those product groups
with the highest penetration in 2004 are also expected to have the highest diffusion rate in
2020.
As indicated by the wide range of upper and lower limits, there is quite a high level of
uncertainty about the diffusion of biotechnology. The expectations for 2020 in particular
are highly uncertain, as fast and slow diffusion can differ widely. The diffusion of
biotechnology is influenced by various factors whose development is rather uncertain.
The analysis of diffusion barriers shows some parallels with the development of former
GPTs, although analogies should not be stretched too far. In the following literature
diffusion barriers are highlighted:10
Biotechnological knowledge is still in its early stages. Despite all the progress
achieved over recent years, insights are still limited and there is uncertainty as to
how much is yet to be discovered, how long progress may take and what sort of
unfavourable side-effects may emerge (Pisano, 2006; Hopkins et al., 2007).
Moreover, just as former GPTs’ complementary technical and organisational
innovations in surrounding infrastructure required adequate financing structures, so
business models and integration within broader systems like the linked structures of
chemicals production need to be achieved (Kern and Enzing, 2003). But these can
take decades to be generated, which means that new technologies are often subject to
high and increasing development costs in subsequent decades and the timescale of
possible effects are unclear.
An important barrier in the past was the lack of appropriate skills (e.g., in
biochemistry) and a lack of willingness among chemists and chemical engineers
within firms to invest in this competing technology (Freeman, 2003; Nusser and
Wydra, 2007). Also, limits to growth in the future will probably be only partially
dictated by the availability of natural resources but also by access to verified
scientific knowledge and specialised skills (IPTS, 2002).
One major factor hampering progress in the past was public acceptance (Lipsey et
al., 2005; Freeman; 2003). Although in certain fields (e.g., health, industrial
biotechnology) an increasing acceptance seems to be emerging, this may still be a
significant barrier in other fields (e.g., GMO-modified food), (Nusser and Wydra,
2007). Demand from consumers may remain low and public policy in regard to
biotechnology may be influenced in various ways.
In addition, several other diffusion factors exist (regulation, costs of natural resources
etc.), (Nusser and Wydra, 2007). Hence the future development of biotechnology
depends on various factors that may interact with each other and are hard to predict.
4.2 Employment impact
The results of biotechnology-related employment impacts are listed in Table 2.
Diffusion and economic impacts of biotechnology 97
Table 2 Employment impacts in the 2004 and 2020 scenarios in full-time equivalents (FTE)1
Direct biotechnology-related
jobs gross employees
(in thousand FTEs)
Upstream supplier jobs
indirect/induced gross employees
(in thousand FTEs)
2004 2020 2004 2020
Pharmaceuticals 12,4–20,4 17,8–39,6 13,1–21,5 22,7–50,5
Chemicals 9,8–14,7 18,0–35,9 27,2–40,8 64,2–128,3
Food-processing 69,6–177,7 108,0–203,2 123,4–315,2 185,7–349,5
Agriculture 68,8–125,1 107,4–173,5 44,3–80,6 74,2–119,8
Total 160,6–337,9 251,2–452,2 208,0–458,1 346,8–648,1
Notes: 1No sectoral sums for direct and indirect employment are calculated as significant
double counting could occur. E.g., the pharmaceutical industry purchases
biotechnology-produced inputs from the chemical firms with biotechnology or
biotech-SMEs inputs of the biotech suppliers/outfitters. Owing to a lack of
differentiated statistical data it is not feasible to identify the magnitude of the
double counting and it was therefore considered best to avoid this calculation.
In 2004, a total of about 160,600 to 337,900 persons were employed in direct
biotechnology-related jobs. The majority of these employees were working in the
food-processing and agricultural industry in which the proportion of the total costs or the
total value added as a result of being influenced by modern biotechnology is often low. In
this sense it can be assumed that a high proportion of these jobs are currently only
slightly affected by modern biotechnology methods, technologies or products. In
upstream sectors the figure is around 208,000 to 458,100 employees. These impacts
mainly occur in upstream service sectors (e.g., biotechnological suppliers).
Because of future diffusion the number of employees in direct biotechnology-related
jobs increases in 2020 to around 251,200 to 452,200. The main impact emerges in the
food-processing industry with 108,000 to 203,200 employees and in agriculture with ca.
107,400 to 173,500 employees. But the greatest change compared to 2004 is estimated to
occur in the chemical industry (ca. 18,000 to 35,900 employees) and pharmaceutical
industry (approx. 17,800 to 39,600 staff). In upstream sectors around 346,800 to 648,100
employees for the application sectors result. In sum, the upstream employment effects are
greater than the direct biotechnology-related employment impacts of biotechnology, not
only in 2004 but also in 2020.
To gain further insight into the kind of economic impact and consequences possible
for employment, the experts in the written survey were asked in more detail about their
estimation of the kind of economic impact biotechnology has/will have. The results are
listed in Annex Table 2. The greatest importance is attributed to the creation of new
products or quality improvement as well as to innovation in processes. Only negligible
effects are estimated in terms of reduction of labour. Thus, in contrast with other
technologies (e.g., ICT), biotechnology does not seem to have a high direct labour-saving
effect. More surprisingly, relatively little importance is attributed to savings in energy
and materials, which are often suggested in studies. However, some differences between
the sectors are noted. As supposed, the importance of new products is believed to be
especially high in the pharmaceutical industry. In the food-processing industry the main
impact is the reduction in the use of inputs and less pollution to the environment, while in
agriculture all possible advantages are of lesser significance. In looking ahead to 2020 the
98 S. Wydra and M. Nusser
importance of biotechnology is estimated to increase, while the kinds of advantages are
estimated to remain the same.
5 Conclusions
5.1 Discussion
We conclude that biotechnology is on the way to becoming increasingly relevant to the
economy and is likely to gain in importance in several application sectors in the next 10
to 15 years. Comparison of the employment estimations for 2004 and 2020 reveals an
interesting point: the increasing share of biotechnology in application sectors and its
backward linkages to suppliers exceeds the tendency of these application sectors to
reduce employment via productivity effects. The economic importance of biotechnology
increases through its diffusion in mature manufacturing sectors. On the other hand, the
results also show limitations in the impact of biotechnology. Diffusion in these main
application sectors will affect only limited parts of overall employment – around 40
million persons in Germany – in the whole economy. Only if biotechnology increases in
other sectors might a major economy-wide impact emerge. But to date the diffusion in
other sectors can be assumed to be rather low (Reiss et al., 2008). Moreover, there are
uncertainties as to the level and timing of future diffusion. Several diffusion barriers
exist, which could hamper the development and diffusion of biotechnology. How deeply
biotechnology may transform the economy depends especially on its potential to enable
the creation of new products and to improve the efficiency in the production of existing
products (Section 2.1). In the written survey part of this study, experts expected that
biotechnology would enable the creation of many products but the potential for
increasing labour productivity is considered to be limited. This corresponds to other
findings in the literature (e.g., Hopkins et al., 2007). So what does this mean with respect
to questions about revolutionary and economy-wide impacts? One suggestion is that
biotechnology may transform the economy and society in a different way from that of
GPTs like ICT. Major boosts for economic growth may emerge in combination with
other technologies or efficiency gains, which raise productivity and purchasing power to
realise the fruits of biotechnology.
5.2 Measurement issues
Due to limited data availability the methodological approach shows certain limitations,
but it also opens up views worthy of further research. In this study, the BRS concept was
used for four application sectors and does not therefore capture all the application fields
within biotechnology. Moreover, there are two major criticisms against the BRS concept.
First, the sales of a product are totally valued to biotechnology although other
technologies or machines contribute to production and the application segments are to a
varying degree specialised in biotech-related applications. While in some applications the
assumption that the use of biotechnology fosters competitive advantage is reasonable
(e.g., new biopharmaceutical products) it is more crucial in traditional fields like food
processing (Arundel, 2002). Second, to study the impact of the value added by
biotechnology instead of sales would be a more appropriate indicator (Rose and
McNiven, 2007). Nevertheless, this concept is in our view an important starting point in
Diffusion and economic impacts of biotechnology 99
evaluating biotechnological commercialisation. To obtain a more detailed picture, the use
and impact (value added, productivity) of biotechnology in application sectors should be
focused on more in official government studies. Studies by the Canadian statistical office
provide positive examples of this (e.g., Lonmo and McNiven, 2005). But in other
countries this would require new surveys, changes to existing ones, changes in the
classification system and the combination of existing databases. Moreover, to get an
accurate picture, in particular about changes in the economy arising from the use of
biotechnology, this should in our view be complemented by non-monetary application-
specific diffusion indicators (e.g., GMO field trials, biopharmaceutical new molecular
entities).
The explanatory power of the results obtained for biotechnology-related employment
is limited, as substitution effects (e.g., fewer jobs in conventional chemistry) could not be
taken into account. In regard to analysis of the economy-wide impact of biotechnology
net-impact modelling would be desirable. Besides the calculated positive gross
employment effects arising from the growth in the utilisation of biotechnology
know-how, negative employment effects also emerge. Conventional processes, products
and services (e.g., chemical-based pharmaceutical products, fossil energy sources) could
be utilised less, avoiding expenditure and investment. In addition, negative budget effects
arise, such as extra costs caused by the provision and application of biotechnology
processes, products and services as opposed to conventional processes, products and
services (e.g., biotechnology research funding, subsidies and tax exemptions for
biofuels). Moreover, dynamic effects like knowledge spillovers or learning effects for
biotechnology and competing technologies should be considered. The calculation of such
negative employment effects is difficult at the present time, however, as a lot of
additional data and crucial assumptions would be necessary.
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Notes
1 An exception is the recently published BIO4EU study, which provides a comprehensive
assessment of the adoption and impact of biotechnology (IPTS, 2007; Reiss et al., 2008).
2 The empirical results were conducted within a project funded by the Hans-Böckler
Foundation, the German Association of Biotechnology Industries (DIB) and the German
Miners’, Chemical and Energy Workers’ Union, (IGBCE). The full study is available at
http://www.isi.fraunhofer.de/t/projekte/wettbewerb_beschaeft_pot_bt.pdf.
3 This paper concentrates on the economic impacts of biotechnology, although these are likely
to be substantially less than its impacts on environmental conditions and on quality-of-life
issues (Arundel, 2003).
4 In this study, the OECD definition is used (OECD, 2005). The single definition defines
biotechnology as “the application of science and technology to living organisms, as well as
parts, products and models thereof, to alter living or non-living materials for the production of
knowledge, goods and services.”
5 While in this paper only diffusion and employment effects in application branches is shown,
the impact on provision sectors was also calculated in the project.
6 The current definitions of a ‘biotechnology firm’, DBF, or ‘biotechnology active firm’, all
require the firm to perform R&D in biotechnology.
7 Some studies include employment effects in supply firms with the use of backward linkages in
input-output models. But these results also indicate that the macro-economic importance of the
biotechnology industry is rather low (Hermans and Kulvik, 2005).
8 For 2020, possible changes in the composition of sectoral output, e.g., higher share of fine and
speciality chemicals on the basis of past trends, were considered, but led to negligible changes
in the biotechnological share of production.
9 For a detailed discussion see Faber et al. (2007).
10 Unfortunately, these factors could not be explicitly considered in the calculation of scenarios
values, because the exact functioning and interactions of the various influencing factors is
mostly unclear and important data are missing. The factors are implicitly considered by the
optimistic/pessimistic valuations of the experts.
102 S. Wydra and M. Nusser
Annex Table 1 Aggregation of product groups for the bottom-up approach to estimate
biotechnology-related turnover
Sector (number of
product groups on
9-digit code)
Selected biotechnology-related sub-segments (mostly on 6-digit code of
PRODCOM product classification)
Chemicals (791) 1. enzymes; 2. organic acid and derivatives; 3. amino acids; 4. sugar
and derivatives; 5. vitamins; 6. monomers for polymers; detergents;
7. modified fats, fatty acids, fatty derivatives; 8. surface active
substances, tensides or surfactants; 9. body-care and cosmetics;
10. finishing agents for surface treatment in textile finishing and paper
production; 11. colouring and varnish; 12. adhesives based on
starch/protein; 13. odorous substances, flavours; 14. diagnostic and
analytic reagents; 15. lubricants; 16. fertiliser based on biomass;
17. pesticides; 18. alcohols
Pharmaceuticals (94) 1. plant-based drugs and medicines (e.g., alkaloids); 2. antibiotics;
3. hormones; 4. insulin; 5. anticoagulants, antiserum, blood fractions,
blood products; 6. human vaccines; 7. veterinary vaccines; 8. vitamins;
9. peptones and protein derivatives; 10. nucleic acids; 11. other
chemical agents not mentioned
Food processing (452) 1. bread and baked goods (e.g., barm, sourdough, enzymes); 2. pasta;
3. modified starch; 4. sugar and derivatives; 5. candies and chocolate;
6. alcoholic beverages; 7. fermented beverages, 8. yogurt, fermented
milk beverages, milk components; 9. cheese; 10. (bio)chemical
modified fats and oils, 11. meat processing; 12. fruit and vegetable
processing; 13. fruit and vegetable juices; 14. yeast
Agriculture* 1. Grain; crops; 2. potatoes; 3. forage crops; 4. sugar beets; 5. oil
seeds; 6. vegetables; 7. fruits; 8. must/wine; 9. flowers; ornamental
plants; 10. tree nursery products; 11. cattle; 12. swine; 13. poultry;
14. other animals (e.g., sheep; goats); 15. fish; 16. agricultural services
Notes: *For agriculture the product groups were based on the national statistics of the
Federal Ministry of Food, Agriculture and Consumer Protection
Annex Table 2 Economic impact of biotechnology
Present impact Future expectations
(for 2020)
Low Average High Shrink Remain Increase
Increased range of goods and
services
7% 25% 68% 3% 18% 79%
Improved quality in goods or
services
19% 31% 50% 2% 27% 71%
New methods and process
steps
10% 32% 58% 6% 16% 78%
Improved methods and
process steps
21% 31% 48% 9% 23% 68%
Reduced labour costs per
unit output
62% 21% 17% 10% 65% 25%
Reduced materials and
energy per unit output
26% 37% 37% 2% 30% 68%
Source: Written survey by Fraunhofer ISI (n = 72)
Diffusion and economic impacts of biotechnology 103
Annex Table 2 Economic impact of biotechnology (continued)
Present impact Future expectations
(for 2020)
Low Average High Shrink Remain Increase
Accelerated processes (faster
time-to-market)
31% 31% 38% 3% 30% 67%
Improved flexibility of
production (e.g., small
plants) or service provision
24% 47% 29% 0% 34% 66%
Reduced environmental
impacts or improved health
and safety
16% 38% 46% 1% 25% 74%
Source: Written survey by Fraunhofer ISI (n = 72)
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