Developments of biotechnology in German-speaking countries: An analysis based on economic and occupational data

NORTH- HOLLAND

Developments of Biotechnology in German-Speaking Countries: An Analysis Based on Economic and Occupational Data BERND MARTENS and THOMAS SARETZKI

ABSTRACT A database containing information about biotechnology in German-speaking countries is used to describe certain economic and occupational features of organizations that are engaged in this field. Regression methods enable us to give overviews based on averages, to describe relationships in a static and dynamic perspective, and to compute forecasts. The results indicate very heterogeneous developments in the industrial use of biotechnology. Particularly, small enterprises show a large growth of economic variables. Occupational and economic features are connected, but the forecasts suggest a stronger development of the economic variables over the next years. In general, the empirical findings do not confirm some enthusiastic hopes about a key role of biotechnological industry in the foreseeable future.

Introduction Prospects and economic achievements of new biotechnology as well as hurdles and strategies to o v e r c o m e t h e various hindrances in this field o f strong i n t e r n a t i o n a l c o m p e t i t i o n are widely discussed for e x a m p l e by Office o f T e c h n o l o g y A s s e s s m e n t ( O T A ) [1], T h o m a s [2], B a l m e r and Sharp [3], W a l s h [4], W h e a l e and M c N a l l y [5]. T o date, the p h a r m a c e u t i c a l industry is generally seen as the m o s t p r o m i s i n g a r e a o f r a p i d a n d c o m m e r cially successful applications o f new b i o t e c h n o l o g y . O n the o n e h a n d , this sector has s h o w n s o m e m a j o r successes, but on t h e o t h e r h a n d , biotech c o m p a n i e s h a v e still suffered a severe lack o f profits. It is estimated t h a t b i o t e c h c o m p a n i e s in the U n i t e d States lost $3.6 billion in 1993 [6] a n d [7]. C o n s i d e r i n g the impacts o n e c o n o m i c g r o w t h and e m p l o y m e n t , it seems w o r t h w h i l e to have s o m e current descriptions a n d empirically i n f o r m e d conjectures a b o u t future BERND MARTENS is assistant professor at the University of Tuebingen, Department of Sociology. THOMAS SARETZKI is a research associate at the University of Hamburg, Institute for General Botany. Both cooperate in a research project about trends in biotechnology funded by the Hans-Boeckler-Stiftung and the German Federal Ministery for Research and Technology. Address reprint requests to Dr. Bernd Martens, University of Tuebingen, Department for Sociology, Wilhelmstr. 36, D-72074 Tuebingen, Germany. Technological Forecasting and Social Change 48, 45-57 (1995) © 1995 Elsevier Science Inc. 655 Avenue of the Americas, New York, NY 10010

0040-1625/95/$9.50 SSDI 0040-1625(94)00036-V

46

B. MARTENSAND T. SARETZKI TABLE 1 Distribution of Organizational Types In the Data Set (417 Cases) and in the Whole Database BIKE (2,497 Records), in Percentages

Type Governmental agencies Collection of cultures Consultants Financing Industry Information centers Associations Nonacademicresearch Technologytransfer University research

BIKE 0.9 1.5 4.6 0.4 41.3 2.7 2.0 15.4 1.7 29.5

Data set 0.5 0.2 4.1 0.7 85.1 1.4 0.2 6.2 1.0 0.5

developments of biotechnology at hand. A lack of valid statistical data on this field can be observed however. Recently Reiss and Huesing [8] used traditional empirical social research methods to gather such information at the company level by survey techniques. In the study described in this paper we pursued a slightly different approach: we used an existing database containing a broad scope of information about organizations located in German-speaking countries that are engaged in the new field of biotechnology. This database was established to promote technology transfer, and is mainly used as a source of scientific and technological information. A subset of this database contains additional economic information as well. Our analysis of this subset enables us to give an empirically informed overview about economic trends and prospects in the case of the Federal Republic of Germany, Switzerland, and Austria. As the data cover different years, it is also possible to give some forecasts in the form of extrapolations based on statistical analysis of the information available. THE DATA Since 1984 the Gesellschaft fuer Biotechnologische Forschung (GBF, Institute for Biotechnological Research) located at Brunswick (Lower Saxony, FRG) has built up a database called BIKE (Biotechnology Information Knot Europe). The database contains all relevant information about biotechnological activities in German-speaking countries (the Federal Republic of Germany without the former German Democratic Republic, Switzerland, and Austria). The information is collected by voluntary notification of relevant institutions, by analysis of important exhibitions (for example BIOTECHNICA, ACHEMA, or MEDICA), by evaluation of journals, or by systematic surveys [9]. As far as German-speaking countries are concerned, the database BIKE is generally regarded as the most extensive and valid information source available for institutions (companies, universities, and research institutes) active in the field of new biotechnology [10], [11], and [8]. Table 1 gives an overview about the distribution of organizational types in the database BIKE. Nearly 45°70 of the records refer to research institutions (universities and other research sites). A similar percentage (41.3070) of the information entities relates to genuine industrial enterprises, whereas other organizational types occur only infrequently. BIKE mainly contains information about scientific or technological fields of interest, but the database is not restricted to scientific and technological data. It also comprises information about economic variables and data about the number of people employed in specific institutions, in research and development (R & D) departments, and in the

BIOTECHNOLOGY IN GERMAN-SPEAKING COUNTRIES

47

TABLE 2 Distribution of German.Speaking Countries in the Data Set and in the Database BIKE, in Percentages

Country Switzerland Austria FRG (without the former GDR)

BIKE 9.2 6.8 81.7

Data set 11.8 5.3 83.0

field o f new biotechnology. Following our focus o f research, we looked at all data sets o f BIKE with economic or occupational information. In December 1991, the whole database BIKE contained 2497 records. A subset of 417 records covered information relevant in our context o f investigation. Records with extreme values were checked and in a few cases obvious errors were rectified. This data set is the basis o f the following analyses. For each institution occurring in the limited database of these 417 records, at least one o f six different variables is available: • counts o f the overall number of people working in the specific institution (we denote this variable as staff); • the number o f people occupied in the field of R & D (the variable rdstaf/); • the number o f people working in the field o f new biotechnology (biostaff); • the a m o u n t o f the overall turnover (sales); • the a m o u n t of costs devoted to R & D (rdcosts); and • the a m o u n t of turnover in the field o f new biotechnology (biosales). The economic variables are measured in German marks (DM). The process of data collection was accomplished with the cooperation o f the institutions in question. They gave the information voluntarily and they differentiated between the different variables (for example the differentiation between sales in general and biosales). The variable biostaff also includes all personnel with no academic grades [12]. Additionally, the data set embraces information on the type of the institution, its national setting, founding year, and the year in which the biotechnological activities started (Tables 1-3).

In comparison with the whole database, the relative frequency o f companies is much stronger in the subset (85.1070 of the records refer to industrial companies), whereas research institutions are only o f minor importance (Table 1). With respect to different countries, the distributions are roughly equal (Table 2). Insofar it seems reasonable to regard the d a t a set in question as a representative one. According to the supplementary descriptive variables, it can be concluded that a m a j o r part o f the organizations we investigated is relatively young: The median o f the founding year is larger than the mean (Table 3). One half o f all institutions were founded in 1975 or later. Moreover, biotechnological activities were first performed by 5007o o f the organizations only after 1983. Therefore, it can be expected that information about

TABLE 3 Distribution of Additional Descriptive Variables in the Data Set (n = 417)

Variable m med s Founding year 1956 1975 40 Start of bioactivities 1976 1983 20 Abbreviations: m = mean, med = median, s = standard deviation, n = number of valid cases.

n 363 253

48

B. MARTENS AND T. SARETZKI

TABLE 4 Basis of the Regressions for the Whole Data Set and for the Subset of the German Biotechnological Industry

Variable

Valid cases

Mean

s

Median

411 223 113 35 194 99

2.5 2.6 2.4 1.5 2.5 2.6

1.4 1.3 1.4 0.9 1.3 1.3

2 2 2 1 2 2

288 176 78 25 136 73

2.5 2.6 2.5 1.6 2.6 2.6

1.4 1.3 1.5 1.0 1.4 1.1

2 2 2 1 2 2

Whole data set

staff sales rdstaff rdcosts biostaff biosales German industy

staff sales rdstaff rdcosts biostaff biosales

Q

companies and institutions busy in the field o f n e w biotechnology are recorded in the d a t a set. The oldest c o m p a n y was founded in 1802 and biotechnological activities are visible since 1836. The correlation between the two starting points in time is 0.52. Regarding the different economic and occupational variables, considerable variation in the number o f valid records was observed: The variable stqffpossesses 411 valid values, whereas in the cases o f r d s t a f f a n d b i o s t a f f t h e number of valid records is 113 and 194, respectively. These frequencies are equivalent to 27~/0 and 47% o f the d a t a set. If economic variables are taken into account, the number o f valid cases for sales reaches 53% (corresponding 223 records). In contrast to such a percentage, the sample sizes for the variables biosales and rdcosts are much smaller (24°?0 and 8°/0, Table 4). The variations o f valid cases and accordingly the small sample sizes are a drawback that must be regarded during the analyses. In particular the very small empirical basis o f the variable rdcosts restricts the reliability o f estimation and forecasts. I n f o r m a t i o n is available for different points in time. These d a t a are not necessarily related to successive years. Each record consists o f a variable number o f data. In order to give valid empirical estimations, regression methods are used for the valid cases o f the six economic and occupational variables. F o r each record i, each valid value y~t o f the variable y, and the number o f years in question xit a linear regression equation was computed [13]. y~, = a~ + b~xj,

(1)

A n average o f the variable y can be estimated for each record i [m,~vt)]. The same is possible for the mean year [m,(xt)l. Additionally, the regression provides a measure b/ that shows the degree o f change between different years according to the linear equation. The regression coefficient bj denotes the change o f the variable y in dependence o f the course o f time. The slope bj o f the equation depicts the a m o u n t of increase or decrease in the dependent variable during one year. If there is only one valid case (t = 1), the regression coefficient cannot be computed (b -- 0). The portion of these cases ranges between 20-300/o o f the sample size for the

49

B I O T E C H N O L O G Y IN G E R M A N - S P E A K I N G C O U N T R I E S

TABLE 5 Means (m), Medians (reed), and Standard l)eviatiom (s) of the Variables (y) and of the

Regres~ion( ~ ' ~ l e n t s Co) Variable

re(y)

re(b)

med0,)

med(b)

sO,)

s(b)

Units

1818.6 827.8 71.9 76.4 26.7 5.5

12.5 - 21.6 1.1 0.0 2.3 0.4

34.0 11.4 4.0 0.6 7.0 1.6

0.0 0.5 0.0 0.0 0.0 0.1

10683.0 3659.4 594.3 360.6 69.6 12.3

142.5 455.4 5.1 0.8 17.0 2.1

Persons Mill. DM Persons Mill. DM Persons Mill. DM

2152.1 887.3 88.0 97.2 24.7 6.4

10.8 - 14.9 0.5 - 0.0 2.9 0.4

35.0 13.3 3.2 0.5 7.1 1.6

0.3 0.4 0.0 0.0 0.1 0.1

11785.7 3823.3 713.2 425.6 66.9 14.1

152.0 470.4 1.7 0.8 20.3 2.5

Persons Mill. DM Persons Mill. DM Persons Mill. DM

Whole data set staff sales rdstaff rdcosts biostaff biosales

German industry staff sales rdstaff rdcosts biostaff biosales

The computations are based on all valid cases for the particular variable and the mean year 1987.

respective variable. Only the variable rdcosts shows 66070 of the records with one value. The estimations of all other variables are based on roughly three points in time (Table 4). The following analyses relate to the estimated values and the regression coefficients.

Results D E S C R I P T I O N S OF E C O N O M I C AND O C C U P A T I O N A L V A R IA B LES ON T H E BASIS OF MEANS

Descriptions can be given on the basis of aggregated values (for example, the total number of people employed in the field of the new biotechnology) or they are founded on average values. Throughout these analyses, the second possibility will be pursued, because the distributions of the six variables in question are influenced by some outliers. Weighted sums, for instance, estimated on the basis of the whole database BIKE, will presumably exaggerate the situation. In Table 5, parameters of the distributions are presented. The means can be seen as a description of a hypothetical "average" organization (company) engaged in the field of the new biotechnology in German-speaking countries or in German industry respectively. The distributions of the values can be assessed by comparing the mean and the median. This comparison reveals that the differences are especially due to the size of the organization: The average size is more than 1800 employees with an overall sales of 828 million DM. However, 50070 of the institutions have less than 35 employees, and the sales do not exceed 11.4 million DM. All numbers refer to the average year 1987. Regarding the other variables, the differences between the two statistical parameters are smaller, but, nevertheless, they exist also in the cases of rclsta2~, rdcosts, biostaff, and biosales. One interesting fact is the declining difference between the three types of variables (overall sales and staff, R & D, and the variables for the biotechnological sector): It can be deduced that a lot of biotechnological activities are performed by small organizations. The relatively small amount of R & D expenditures (the variable rdcosts) is astonishing, as industrial biotechnology is research intensive. In the context of the other variables, it can be expected that the median of this variable should be higher. Perhaps a

50

B. MARTENSAND T. SARETZKI

connection can be drawn to conclusions of the German Federal Ministery of Research and Technology, which states that the R & D budgets of private enterprises are too small in Germany [ 14]. The scope of our empirical findings are confined due to the small sample size of rdcosts (Table 4). The mean and the median of the variables y permit a description of an "average" organization. Additionally, the distributional parameters of the regression coefficients b provide some impressions of the changes during time. The decline of sales is most remarkable. On the average, a loss of 21.6 million DM per year appears. Nearly 1007o of the organizations reveal negative regression coefficients for this variable. This amount of negative dynamic rates resembles that of the other variables (the percentages of negative coefficients vary between 6.0% and 10.1070). Especially heavy losses of large enterprises cause an overall negative economic achievement in German-speaking countries as well as in German industry, at least during the 1980s. One additional feature of our data is a large similarity between the distributional parameters for all institutions in German-speaking countries and for the German industry. This can be seen as a hint of the general validity of the data set. RELATIONSHIPSBETWEEN THE VARIABLES This description of an "average" organization engaged in the field of biotechnology will be enhanced by the analysis of relationships between the variables. Because of outliers that can distort results to a large extent, we transformed the original data and compared the results: (a) We computed bivariate correlations between the six variables and their regression coefficients b. (b) The means were dichotomozed according to the median. The influence of extreme values is correspondingly restricted. (c) Eventually the original data were weighted by sales and staff in order to exclude the dependence on the size of organizations. All analyses are carried out for the whole data set and for a subsample containing records only for German industry. The missing values are excluded in a pairwise manner. This leads to different sample sizes for each bivariate correlation. The results possess the drawback that they do not refer to one sample of data.

Case A The correlations between the variables and their regression coefficients can be divided in two groups. The first one refers to the means of the variables. Their correlations with each other are essentially positive, and their relationships with the founding year of the organization and the starting year of biotechnological activities are mostly negative. This describes a general size effect: There is a strong mutual dependence of staff and sales, that is, large companies are normally older, have larger R & D expenditures, etc. Though, the values of the correlation coefficients of the variables sales and staffwith the starting point of biotechnological activities are much smaller than the ones with the founding year (r = - 0.10 compared with r = - 0.3 I). This can be seen as a hint that biotechnologically relevant activities are not performed by large companies exclusively. The conclusion is confirmed by the small correlations between biostaffand the size variables staffand sales (r = 0.08 and 0.05, respectively). The correlation for biosales amounts to 0.37, hence the mutual dependence of biotechnological and overall economic variables seems to be stronger than one of the occupational features. Also large positive relations between rdstaff, biosales, and biostaffoccur (the correlations vary between 0.8 and 0.9). But high R & D endeavors do not necessarily yield high sales of biotechnological products: the correlation coefficient is only 0.1. Nevertheless, the correlations between the means show a clear tendency toward positive interdependences, which can be interpreted as a size effect. This does not hold true in the case of the

BIOTECHNOLOGYIN GERMAN-SPEAKINGCOUNTRIES

51

variables biostqffand biosales. Visible biotechnological activities are exhibited by smaller organizations, too. The second group of correlations refers to the regression coefficients b as estimations of the dynamic alterations. There are some correlations with values larger than 0.4. One impressive example is the negative correlation between the changing rate of sales on the one hand and the variables staff, sales, rdstaff, and rdcosts on the other hand. These relationships are always smaller than - 0.77. It seems that the very large companies reveal some losses. This holds true not only for German industry but for some large Swiss enterprises, too. Similar figures for the two size variables corroborate this statement: the correlation between staff and the regression coefficient of sales is -0.80. According to the data, rather strong negative relationships exist between the R & D expenditures and the changing rates b of staffand sales (the correlations are nearly "perfect", r = - 0.9). This means that in the eighties strong R & D endeavors are sometimes connected with extensive losses of sales and staff. In general correlations with the regression coefficients b form an inhomogeneous picture of relationships. This finding indicates a heterogeneous economic dynamic in the field of new biotechnology. The relationships between the group of the six regression coefficients are essentially small. An exception is the changing rate of rdstaff: If the number of personnel in R & D departments increases this will often be connected with positive changes in the number of the general staff (r = 0.6), R & D expenditures (0.7), biostaff(0.6), and biosales (0.4). It should, however, be kept in mind that particularly large companies show some decreases of the variables in question. Outliers can distort results. As a remedy, correlations were computed once more with dichotomized variables in order to prove the reliability of results under data transformations.

Case B The means of the variables y are dichotomized according to the median (smaller or greater equal the values in question, Table 5). In the case of the regression coefficients a similar binary transformation is not reasonable because the distributions do not have a lower boundary. Hence the correlation matrix is merely partly dichotomized. Overall, the relationships between the dichotomized variables y are still all positive. Due to the data transformations the values of the correlations are smaller (lying mainly in the range of 0.3 and 0.5). Nevertheless, the coefficients show the size effect mentioned previously. Regarding the relations between the variables and the changing rates almost all significant coefficients vanish due to the dichotomization. The values of all correlations are less than 0.3, mostly less than 0.1. Further principal component analyses [15, pp. 39-50] of the partly dichotomized correlation matrix reveal a common size factor correlating with the variables y, but this factor is not connected with the changing rates b. The very heterogeneous picture of the dynamics is confirmed once more. Other describing factors correlate with the variables biostaffand biosales or rdstqff and rdcosts. These principal components do not correlate with some of the regression coefficients b. It seems that, according to the data set as a whole (without some extreme values owing to dichotomization), only minor relationships exist between economic and occupational variables on the one hand, and their changing rates (estimated by linear regressions) on the other hand. Features of the variables at a point in time do not coincide with features of changing rates. The dynamics are much more complicated. Thus, our empirical results do not verify some enthusiastic statements about a glorious future of biotechnology as a whole within the next years [16] and [17]. At least the data of the

52

B. M A R T E N S A N D T. SARETZKI TABLE 6 Correlation Matrix of the Relative Values 1

2

3

4

5

6

7

8

Variables 1 2 3 4

rdstaff/staff biostaff/staff rdcosts/sales biosales/sales

1.00 0.65 0.05 0.58

1.00 - 0.11 0.73

1.00 -0.18

1.00

0.37 0.29 -0.07 0.25

0.23 0.96 0.20 0.33

0.11 - 0.01 0.04 -0.08

0.26 0.40 -0 .15 0.41

Regression coefficients b 5 6 7 8

rdstaff/staff biostaff/staff rdcosts/sales biosales/sales

1.00 0.65 -0.28 0.16

1.00 0.21 -0.04

1.00 -0.01

1.00

The variables are divided by staffor sales, respectively. The matrix refers to the whole data set.

1980s do not deliver a foundation for such expectations. On the contrary, the data exhibit a broad scope of various differing dynamics.

Case C Eventually the original data of the occupational variables were divided by staffand the one of the economic variables by sales. By doing so, the resulting values are independent of the organizational size. The results can be summarized in five statements (Table 6): 1. High proportions of R & D personnel are connected with high percentages of biosWff and biosales (t = 0.6). 2. The same holds true for the portions of biosales and biostaff (the correlation coefficient is 0.7). 3. If the percentage of biostaffis high, so is the relative changing rate (0.9). 4. In the case of biosales the situation is comparable to the former one, but the correlation is much weaker. 5. Relative changes in R & D staff are correlated with changing rates in biostaff(0.7). In the perspective of the relative values, the development of a biotechnological potential is primarily connected with occupational activities in R & D. Astonishingly there is no clear relationship with R & D expenditures. According to the experiences with the economic situation in the 1980s, a weaker relation in the case of biotechnological sales and its changing rates can be seen as in the case of occupational developments in the biotechnological field. This seems to be a hint for a lack of biotechnologlcal products, which has influenced industrial biotechnology and will presumably continue to do so over the next years [18] and [19]. FORE CAST S A N D F O R E S I G H T

In the last years, the difference between forecasts and foresight has been discussed in social science context [20]. The former term is connected with the notion of one single future that can be predicted in a precise way. On the other hand, the word "foresight" is used to stress the notion that several different futures are possible. The question what will come about depends on decisions in several social systems. Exact predictions of social, political, or economic situations influencing the development of a technology are consequently neither conceivable nor desirable. If we use the term "forecast" in the following analyses, we do so because our method of giving some foresight follows the usual standards of quantitative predictions. These computations ought to be seen only as illus-

BIOTECHNOLOGY IN GERMAN-SPEAKING COUNTRIES

53

TABLE 7 Esflmaltons of Vwrlables for the Year 1990 and 2000 Variable Whole data set staff sales rdstaff rdcosts biostaff biosales German industry staff sales rdstaff rdcosts biostaff biosales

1990 m0,)

med0,)

1852.2 683.0 76.6 76.5 35.3 7.0

41.4 14.0 5.5 0.6 9.9 2.0

2173.5 771.3 90.3 97.2 35.8 8.0

44.8 15.8 4.1 0.5 10.0 1.9

2000 m0')

med0,)

10,640.4 2230.0 595.2 360.5 118.7 13.5

1977.0 773.6 88.3 76.8 58.4 13.0

66.5 21.2 7.0 0.7 14.8 3.0

10,759.7 2875.4 599.8 360.1 277.0 24.8

Persons Mill. DM Persons Mill. DM Persons Mill. DM

11,578.9 2429.2 713.0 425.5 133.1 15.4

2281.9 891.8 95.8 96.9 64.7 14.3

70.6 22.0 5.8 0.7 14.8 2.7

11,390.9 3180.8 712.6 425.2 327.0 28.2

Persons Mill. DM Persons Mill. DM Persons Mill. DM

sO')

s(y)

Units

m, reed, and s denote the mean, median, and standard deviation, respectively.

trations of one possible f u t u r e - a future based on extrapolations of observations in the recent past. The regression method permits forecasts on the basis of the coefficients b. As already mentioned, some variables show great fluctuations, for example, heavy declines in companies' sales. As the analyses of the correlations have revealed, the relationships are partly influenced by some extreme values. If regression coefficients are especially small, this can cause negative values of the variable y when used in forecast computations. In these cases we set the variables in question to zero. This was necessary in 2.2o/0 of all records. Hence, as already stressed, the forecasts based on the regressions do not deliver "true" values. They are conjectures guided by an empirical basis of data mainly gathered in the 1980s, and can be seen as a special foresight based on statistical methods. We computed values for the six variables at two different points in time (1990 and 2000, Table 7). In general, the results for the whole data set and the ones for the German industry are comparable. A difference can be observed insofar as the later ones reveal a slightly higher numeric level. In comparison with the average year 1987 (Tables 5 and 7), the predicted sales are relatively small due to some negative regression coefficients of large firms. The same does not hold true for the medians, which confirms the dependence of the mean on the organizational size. The empirical median of sales (Table 5) and the predicted ones can be hierarchically ranked with regard to its value and time. If the predicted means of the two years (1990 and 2000) are compared, the dynamics are relatively modest with regard to the variables staf£, sales, rdstaf£, and rdcosts. The percentages of change between the two points in time do not exceed 15°70 for the first four variables. The extent of changes for the means of biostaffand biosales is much higher: it varies between 65070 and 85070. The means show some tendency of an extraordinary increase in staff and sales in the field of biotechnology in German-speaking countries as well as in German industry (Table 7). Although the numbers will almost double, however, in comparison with the overall occupational and economic variables the portion of the biotechnological ones will remain rather small during the time frame of the forecasts

54

B. M A R T E N S A N D T. SARETZKI TABLE 8

Proportions of Occupational and Economic Variables Based on Estimated Mean Values for the Years 1990 and 2000, in Percentages Variables

1990

2000

Whole data set rdstaff/staff biostaff/staff rdcosts/sales biosales/sales

4.1 1.9 11.2 1.0

4.4 3.0 9.9 1.7

German industry rdstaff/staff biostaff/staff rdeosts/sales biosales/sales

4.1 1.6 12.6 1.0

4.2 2.8 10.8 1.6

(Table 8). Accordingly, a great industrial take off cannot be expected in the next years, as other studies based on different empirical information also suggest [21], [22], and [23]. The comparison of the predicted medians (Table 7) shows a second trend in biotechnology: The changes between the 2 years are significantly larger for this distributional parameter as for the means. Consequently, small companies engaged in biotechnology will probably reveal greater dynamics in general. One prominent issue of political discussion about the impacts of industrial biotechnology is the connection between economic and occupational variables in the future. Will biotechnology have an increasing or decreasing effect on employment? Remarks about this topic are far from certain [23]. Our results confirm these difficulties of evaluating the occupational impacts of biotechnology. In this study, we restrict our attention to the direct impacts on the number of persons employed within the biotechnological sector (the variable biostafJ). Figure 1 gives a graphical impression of the developments during the time frame of investigation for the proportion of the variables biostaff/staff. It reveals that the dynamics mostly vary within a certain range in the surrounding of the line that denotes no change (the line with the slope 1 and a = 0). In the case of the occupational variables, the regression lines for small and large institutions, measured according to the median of the variable staff,, are slightly but apparently different: the slope of the former one is bsm~al = 1.16, whereas the regression coefficient of the second group is b~rge = 0.98. The predictions for each group are comparable in the case of the economic variables (1.05 and 1.03, respectively). In general, the relationships between the occupational variables at the two points in time are weaker compared with the economic ones. According to a path model of relationships [13] in Figure 2, it seems that the development of the (relative) sales within the biotechnological field will be in some extent more steady than the dynamics of the occupational variables.

Conclusions A comprehensive database was used to give some impressions about connections between different occupational and economic variables in the field of new biotechnology and German-speaking countries. Regressions are computed in order to give static and dynamic overviews. The results can be summarized in four statements: 1. Especially small organizations are strong in new biotechnology, and they will probably show larger dynamics in the future.

BIOTECHNOLOGY IN GERMAN-SPEAKING COUNTRIES

55

biostaff/staff in the year 2 0 0 0

100 I !

,° I r

mijn •

0,1

0,01

0,001

i

0 0

i IIiilil

i

I L iJiiL;

I

i i JJiiLi

i

L I i~lJt

0,001 0,01 0,1 biostaff/staff in t h e " staff

< 34

~

i

i i liJLli

1

10

I

i i liiLt

100

year 1990

staff

>= 3 4

Fig. 1. Forecast of the variables biostaff/staff. The points are marked according to the number of people working in the organization.

2. In general, the picture of developments is a very heterogeneous one. In the 1980s, examples of heavy losses can be depicted and at the same time large increases of economic features occurred. Because of that, it is very difficult to draw valid conclusions with a broad scope. 3. Extraordinary increases in sales of biotechnological products and services, as well as people working in this field, will probably be occurring. This does not imply that biotechnology will become the key industry in the foreseeable future as some interested projections expect. When it comes to new jobs, its relevance will remain relatively small compared to other sectors of the economy. 4. Some results of the forecasts suggest that the development of sales in biotechnology will probably be more steady than the development of its occupational features.

56

B. MARTENS AND T. SARETZKI

At the year 1990

At the year 2000 O,Z7

0,38 biostaff/st3ff

060 biosales/sa/es

0

b/ost

ff/st

0003,06 0,g8

ff

049 " " " " b/osales/sa/e.s

0,82 Fig. 2. Path model of the relationships between the proportional variables biostaff/sta~'and biosales/ sales at the 2 years 1990 and 2000. Two-headed arrows indicate correlations, one headed arrows denote standardized regression coefficients or the error term (n = g0).

References 1. OTA, Biotechnolog~y in a Global Economy, U.S. Government Printing Office, Washington, D.C., 1991. 2. Thomas, S. M., Global Perspective 2010. The Case o f Biotechnology. A Report o f the FAST Programme, Commission of the European Communities, Brussels 1993. 3. Balmer, B., and Sharp, M., The Battle for Biotechnology. Scientific and Technological Paradigms and the Management of Biotechnology in Britain in the 1980s, Research Policy 22, 463-478 (1993). 4. Walsh, V., Demand, Public Markets and Innovation in Biotechnology, Science and Public Policy 20, 138156 (1993). 5. Wheale, P., and McNally, R., Bintechnology Policy in Europe: A Critical Evaluation, Science and Public Policy 20, 261-279 (1993). 6. Spalding, B. J., U.S. biotech firms embrace "virtual integration," Bio/Technology 11, 1216-1217 (1993). 7. Burrifi, G. St., and Lee, K. B. Jr., Biotech 94. Long-Term Value, Short-Term Hurdles. Ernst & Young's Eighth Annual Report on the Biotechnology Industry, Ernst & Young, San Francisco, 1993. 8. Reiss, T., and Huesing, B., Potentialanalyse fuer Auftragsforschung in der Biotechnologie, FrannhoferInstitut fuer Systemtechnik und Innovationsforschung, Karlsruhe, 1992. 9. Wagner-Doebler, I., and Klein, J., Zum Aufban und Inhalt des Adressteils: Das BIKE-Projekt der GBF, in BioTechnologie-Das Jahr- undAdressbuch 87. A. Mietzsch, ed., Polycom, Brunswick, 1987, pp. 19-24. 10. Henckel, D., Die raeumiiche Verteilung yon Unternehmen der Biotechnik und der Informatinnstechnik, lnformationen zur Raumentwicklung Heft 4, 237-244 (1989). 11. Volkert, B., Der Diffusionsprozess der neuen Biotechnologie steht noch am Anfang. Anmerkungen zu den Biotech-Standorten in der Bundesrepublik Deutschland, Raumforschung und Raumordnung 48, 109-116

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57

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