The spatial distribution of telematics: Modeling and empirical evidence

TECHNOLOGICAL

FORECASTING

AND SOCIAL CHANGE

32, 281-293

(1987)

The Spatial Distribution of Telematics: Modeling and Empirical Evidence JOACHIM

GENOSKO

ABSTRACT

In this paper we deal with the spatial distribution of telematics (a conflation of telecommunication and informatics) and the regional characteristics influencing it. We are interested in this topic because basically telematics could essentially alter the regional structure. Therefore, in the first section we construe a simple cost-minimizing model in order to derive factors impacting on the telematics endowment of a region. In the next section the hypotheses derived from this model are completed by sociological determinants of the regional telematics equipment. In the second part of the paper we test our hypotheses with the aid of an econometric study and of a questionnaire study. The sample is confined to two telematics tools, namely telefax and teletex, and geographically to the German state of Bavaria. Our main conclusion is that potentially decentralizing techniques do not work decentralizingly. We substantiate this conclusion with the factors which, in our opinion, mainly determine the regional endowment of telematics and those factors which will not alter in favor of peripheral regions.

1. Some Introductory Remarks Research on the determinants of the spatial adoption, diffusion, and distribution of innovations is not a new topic of inquiry in regional science. Thus, for example, research by Swedish authors [lo] and analysis of the Swedish experience [16] contributed importantly to this developing topic. The present paper is motivated by recent technological change, especially in microelectronics, and its regional effects. In the field of regional science we deal typically with “spatial diffusion,” where this term means the dispersion of a phenomena within a definite area over time altering the locational pattern of the phenomena [3, p. 21. Hence, spatial diffusion is a dynamic concept. In this paper, however, we shall not describe the temporal dispersion of a phenomena using, say, a logistic function [13], but instead identify factors behind the locational pattern of an innovation. In other words, we are interested in the spatial distribution of new technologies, not in their spatial diffusion. Moreover, the emphasis will be upon the characteristics of regions which affect this pattern. Sectoral biases will only be considered marginally.

JOACHIM GENOSKO is Associate Professor at the Department of Economics and Econometrics, University of Regensburg, West Germany, and Visiting Scholar at the Department of Economics, University of South Carolina, Columbia. The main fields of research are regional economics, technological change, and economic policy. Address reprint requests to Dr. Joachim Genosko, Department of Economics and Econometrics, Universitlt Regensburg, UniversitLtsstrasse 31, 8400 Regensburg, West Germany. 0 1987 by Elsevier Science Publishing

Co., Inc.

0040-1625/87/$03.50

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J. GENOSKO

In this context of regional effects, telematics (a conflation of telecommunication and informatics) play a special role. First, telematics could remove the “tyranny of space.” This means that new methods of information and communication can loosen the locational ties of enterprises, because certain inputs and outputs are no longer geographically fixed [14, pp. 57-581. In principle, telematics could produce a tendency toward decentralization, that is, it could contribute to a stronger participation of the geographically peripheral regions in employment development. Concrete forms of such a reallocation of jobs are decentralized “tele-working centers” or “tele-homework.“’ Second, telematics could reduce the “contact disadvantages” of the peripheral regions. This aspect is especially important if it is accepted that a lack of information in some regions provides a fundamental obstacle to the equal spatial diffusion of innovations across regions. Telematics could mitigate this deficiency and hence promote the diffusion of other innovations. Third, telematics could reduce certain “problems of acceptance.” Because telematics requires sui generis the use of data processing, the access to dataprocessing-based innovtions seems to be facilitated.

II. Theoretical Considerations: The Case of Telematics THE MODEL

The intention of the model is to derive the response of a cost-minimizing firm in region r to different incentives influencing the introduction of telematics. The starting point of our considerations is a production function of the following form: Y, = A, X, “Qr pTr ‘-Y c

where Y is the output (with Y =

%P,Y > 0,

(1)

Y,), A is a positive (regional)

parameter,

X is the

i=l

combined factor of capital and labor, Q is the quality of the manpower, T is the input of telematics, and the subscript r denotes the region (r = 1 . . . j). 6 is a dichotomous variable with 6=

1 if the firm takes part in the telecommunication 0 otherwise

system

[see 22, pp. 21-231. The firm faces a cost function

of the form:

C, (Y,) = qlx,

+ v,Q, + 6mJ’,.

(2)

Later on we assume 6 = 1, i.e., the considered firm is a participant in the telecommunication system (the firm uses telematics). We can now form the Langrangian L: L, (Y,) = (q,X,

+ vrQr + m,TJ

According to the assumption differentiate Equation (3): aL, aT, -

mr

+ X,
of cost minimization

-

‘Telematics also extends decentralization “home banking.”

“Qr pTr 9.

and our further considerations

X,A,A,X, “Qr PyTr Y--l = 0.

to the household

sector,

as evidenced

(3) we

(4)

by “teleshopping”

and

283

THE SPATIAL DISTRIBUTION OF TELEMATICS

From Equation

(4) we get

(5) [see 11, p. 4054071. In the following

we split the “price” m for telematics

into two components:

where i is the average interest rate in the region r and 2 is the number of participants in the telecommunication system in region r. a and b represent, in some way, the “fix cost” and the “variable cost” part of m,, respectively. First, we assume 2

< 0, because it may be cheaper to install telematics,

the more

r qualified the firm’s employees probable that $!

are. Second,

it is evident that $

r

> 0. Third,

it seems

< 0. The latter results from the fact that in many cases the charges for

local calls are lower than those for long-distance calls. Therefore, a firm located in an area with many participants in the telecommunication system has a cost advantage. Proceeding from our previous explanations we do some exercises in regard to T,. First, we observe how T, alters if m, the price of T,, increases.

aTr am,

-=-

-bYYr < m,2

o

(7)

’

As expected the input of telematics declines. But this conclusion can also be converted. If there is a region with fine consulting services it could be possible that m, lowers, inter alia. Fine consulting services would be able to point enterprises to cheaper telematics installations and to investment allowances granted by the government. Both hints can produce the above-mentioned price effect. In this case Equation (7) infers an increasing employment of telematics. Basically, a strong competition between firms offering telematics installations would presumably work in the same direction. In reality, however, such a development does not take place because of particular regulations on this market in West Germany. What happens if the quality of the labor force is improved?

=r -=

CL P?IA,)X“Qr p-‘Try) (dQr,

aQr

[a,(Qr,

iJ + 6, (ZJ -A, ~4, X, “Qr

9 + br WI2

BTry

2 ‘. (8)

> 0, because, as assumed above, aa, < 0. Therefore, r aQr we can conclude that an improved labor-force quality promotes and facilitates the introduction of telematics . The question now is why a region has more qualified manpower than another region. Among other things, one reason is a greater extent of computerization in a region. A greater extent of computerization results in greater employee familiar@ with microelectronics-based installations like telematics. Therefore, on the one hand a region disposes From Equation

(8) follows 5

284

J. GENOSKO

of a greater extent of suitable manpower, which c.p. lowers m,, on the other hand the training cost is meaner and the supply of qualified manpower is more extensive-both facts lower c.p. v,, the price of labor-force quality. Summing up, we conclude that a greater degree of regional computerization diminishes C, via the price of telematics and the price of labor-force quality. Next we examine the response to an increased (regional) interest rate.

am, da, since -, > 0. Finally, we are interested aa, di, in the firm’s reaction to a greater number of participants in the telecommunication system per region. The derivation

in Equation

(9) is negative,

(10) Consequently,

as illustrated above, 2

< 0 and 9

=, . > 0, a~ is positive. In other words,

if the number of the local participan; in the teleEommunic:tion system rises, the firm will invest in additional telematics. This results from the positive externalities of the telecommunication system, which consist of the call and access externalities [see 22, pp. 15-161. SOME FURTHER

ELUCIDATIONS

In this section we shall examine, more exactly, the previously mentioned determinants influencing the regional distribution of telematics and add some new factors to them. The price of a telecommunication installation is unlikely to have a spatial dimension insofar as the transportation costs are concerned, since these costs are inconsequential. A spatial dimension, however, exists with respect to the operating costs of such an installation, since these costs vary with the number of participants in the telecommunication system per region. The same seems to be true for the liquidity situation of a firm, especially if one attaches to the firm’s liquidity situation access to the capital market. In our model the differences in the liquidity situation are expressed by differences in the interest rates. In general, it is hypothesized that small- and medium-sized firms, which form the economic core of the peripheral regions, have less-favored access to the capital market. This hypothesis is reinforced by the conservative business practices of the local banking institutions, which are relevant to finance innovations because of their scale and risk. But the regional aspect may be seen as a microcosm of nation-wide banking practices. The hierarchical structure of the national banks hinders face-to-face contact between potential creditor and debtor. Such personal contact, however, is necessary with risk financing [6, p. 41. If credit is refused, this will also imply, in many cases, the denial of public funds as well. The dispersion of the qualifications necessary to adopt new communication and information technologies may differ spatially. An empirical study of a number of German regions indicates a qualitative polarization between central regions and peripheral regions

THE SPATIAL

DISTRIBUTION

OF TELEMATICS

285

[ 151. These regional differences are caused by the selectivity of migration and by the increasing locational flexibility of firms. On the other hand, adverse location makes it more difficult for the firms in peripheral regions to entice qualified employees out of the agglomerations. And, if they are successful in this endeavor, such firms have to pay higher earnings of fringe benefits. Accordingly, firms in the peripheral regions have higher labor-procurement costs [ 12, p. 1521. American studies of the defense industry show that manpower is younger and more qualified in regions where R&D activities are carried out (20, p. 184].* In this context, the regional distribution of service jobs and the subgroup of advisers’ jobs are of interest. On the one hand, the service activities themselves are supposed to be in the vanguard of those adopting new communication and information technologies. On the other hand, some services can contribute to overcoming the adoption barriers confronted by other firms [5, p. 67; 4, p. 651. Service and advisory activities, however, are concentrated in urban centers, and as such are therefore unevenly distributed across the regions [l, pp. 185-1921. From the outset the research literature has recognized “information” to be an essential ingredient in the spatial diffusion of techniques and ideas, inter alia [3, pp. 19, 261. It is assumed that the geographically peripheral regions have information deficits which adversely impact on innovations [ 17, p. 2501. Such deficiencies enter on both the supply and demand sides. On the supply side, it is argued that research and advisory institutions are unevenly distributed by regions. Coupled with deficiencies in traffic and communication infrastructures, this factor compounds the informational difficulties confronting plants in the periphery [ 12, p. 1531. Whether this argument is valid or not depends on demand-side behavior, for example, the information channels employed in the procurement of innovations.3 In the empirical part of the paper we shall further address this point. The issue of “centrality” is closely linked to “information” and also plays an important role in the discussion of spatial diffusion [3, pp. 17-181. According to Brown, the term “locational centrality” means the proximity of one central place to another, which has a basis in the transportation or communication network. Obviously, Brown is referring to the flow of information between locations.4 Also, “attitudes” toward and, hence, the problems of acceptance of innovations are regarded as regionally variable. The issue is less one of sociocultural differences between regions than of the willingness of management to accept new technologies. The regional component is as follows. Between the regions there exist differences in the size-structure of the firms. The geographically peripheral regions are dominated by small- and mediumsized independent plants. In such firms, owner and manager are often the same person. Empirical research identifies such firms as having a defensive attitude toward technological change [2, p. 571.’

‘Regional differences are observed in R&D activities; they seem to be responsible for the regional variation in the diffusion of new technologies [8, p. 231. It is hypothesized that higher R&D activity is advantageous because the employees gain greater familiarity with new technologies. ‘Here the close link between “information” and “skill” is manifest. Qualified employees not only facilitate the external procurement of information but are indeed the condirio sine qua non that enterprises can undertake R&D and gather information from their own research activities. 4Brown [3, p. 191 predicts that cosmopolitan individuals--those individuals who obtain their own information from outside the regioeare early adopters. Brown also carries “cosmopolity” over to locations. ‘In principle, the analysis of Rothwell and Zegveld [19, pp. 45, 491 confirms this result, albeit only with respect to small- and medium-sized firms in traditional manufacturing. This reticence of small- and mediumsized firms can of course be rational because of risk considerations, inter ah.

286

J. GENOSKO

The remaining general determinants, including competitive pressure, organizational structure, and the firm’s growth rate, cannot a priori be identified as region specific. Next, we briefly outline the “hypotheses tableau,” developed by the Swiss MANTO group for analyzing the spatial diffusion and distribution of the new techniques of telecommunication in commercial applications [5, p. 691. The first MANTO argument is that large-scale plants have a greater demand for “steering and controlling”; and hence, will be early adopters of telematics. Additionally, it is supposed that techniques of telecommunication are used in what might be termed routine-information environments. Also, the local labor market is relevant for reasons of skill shortages of experts. The greater the required investment and the more a firm’lacks investment funds, the slower diffusion will be. Moreover, the presence of advisers influences the spatial dispersion of the new techniques as does the hierarchy of spatial-economic relations. Neighborhood effects also play a role, namely the rate of adoptions in neighboring areas. Localization advantages operate positively because of the associated spillovers. III. Empirical Evidence The variables introduced above provide the basis of our empirical study; although not all of the hypotheses and indicators will be used. This is partly because their quantification is elusive and partly because some of the indicators refer more to sectoral considerations than to regional considerations. The empirical study has two levels: econometric and qualitative. DATA

The research sample comprises all 27 labor office districts of the German state of Bavaria. Bavaria has been selected because it represents one of Germany’s principal technology centers whereas it also contains geographically peripheral regions that are designated as “assistance regions” within the framework of German regional policy. It is therefore especially suited for our topic. The choice of labor office districts as research regions was dictated by data availability (see Table 1). The qualitative (questionnaire) component of the analysis is restricted to Eastern Bavaria, an area bounded by the Czechoslovakian border. The sample is drawn from manufacturing and construction. Because data are unavailable on regional telematics endowments, we focus on the (current) pattern of the spatial distribution of two single telematics services, namely telefax and teletex. Data limitations notwithstanding, there are two reasons for selecting these services: l

l

Telefax is a telematics service which has been available through the Federal Post Service since 1979 via the telephone system. Therefore, the required network for telefax is in place in each region and allows us to abstract from regional differences in the network supply. Teletex is essentially an upgrading of an existing Federal Post Service, that has been in place for several decades (Teletex came into being in 1980). A comparison may thus be made with respect to the spatial pattern of the two services.

“Telefax” means distance-copying, that is, the transfer of picture copies such as texts and graphics over long distances. Typical applications include the transfer of notes, statistics, diagrams, and designs [5, p. 261. “Teletex” refers to the transmission of textual material and requires the user to have a word processor. Unlike the old-fashioned “telex,” teletex can transmit text more quickly. For instance, teletex needs 10 seconds to transmit a DIN A4-page, as compared with around 5 minutes for telex [5, pp. 23-241.

THE SPATIAL

DISTRIBUTION

287

OF TELEMATICS

TABLE 1 The Data Set

Area I

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

TELEFAX 1.1525 I .2498 1.3307 0.8784 1.5291 1.1230 1.5393 0.8738 I .6967 I .0753 1.0984 1.2435 1.0733 1.1862 4.9310 2.9201 0.4971 0.8796 1.1053 1.2833 0.9432 0.8748 0.7673 0.9065 1.1104 0.6907 I .5373

TELEFAT

ED

PD

1.9821 2.2866 2.6267 1.6502 3.2260 2.6740 0.9427 2.2123 2.6037 2.8247 1.9824 2.1924 1.6018 2.6548 8.2910 5.2171 1.2710 1.4403 2.1398 2.4555 1.6454 I .5084 1.9954 1.6732 1.9451 I .0035 2.7720

24 73 92 43 40 55 27 28 32 62 39 40 38 50 273 223 33 31 34 35 25 32 30 24 26 22 48

174.7 440.3 659.0 513.3 419.0 332.8 220.8 95.5 124.5 442.0 242.5 465.6 342.0 263.3 920.2 996.3 307.3 117.7 473.0 429.3 277.3 360.8 101.0 253.0 97.7 145.0 458.3

Sources: Federal Office of Statistics,

CENT 9.8 9.0 20. I 9.8 8.5 6.7 8.2 9.7 23.3 6.7 13.4 8.9 10.6 12.7 56.1 36.5 7.2 9.7 13.9 15.5 8.5 7.5 10.6 7.5 13.4 9.8 13.6

SW

SS

AS

SBC

wss

RES

0.195 0.234 0.332 0.204 0.248 0.207 0.201 0.241 0.295 0.238 0.233 0.271 0.186 0.298 0.496 0.405 0.189 0.229 0.247 0.395 0.184 0.250 0.294 0.176 0.267 0.239 0.273

0.2754 0.2269 0.3163 0.2913 0.3081 0.2100 0.2986 0.2474 0.3076 0.2223 0.2583 0.3519 0.2521 0.2826 0.3756 0.3334 0.2810 0.2325 0.3566 0.3771 0.2537 0.2885 0.3823 0.2752 0.4012 0.2498 0.3895

0.0779 0.0888 0.1121 0.1038 0.0995 0.0954 0.0806 0.0838 0.1321 0.0828 0.0778 0.0954 0.0829 0.0783 0.2022 0.1241 0.0981 0.0854 0.1274 0.0952 0.0767 0.0767 0.0939 0.0940 0.1123 0.0731 0.1437

0.0638 0.1684 0.2729 0.1197 0.1113 0.1093 0.0664 0.1137 0.1199 0.2221 0.1793 0.1786 0.1303 0.0984 0.3090 0.3264 0.1276 0.0945 0.2051 0.0455 0.1158 0.1003 0.0726 0.2241 0.0697 0.0706 0.1306

29108 34510 37986 32258 31090 29313 31007 34003 35705 29057 40220 34012 36325 35024 46459 39897 30961 34996 33373 35616 30998 35389 35349 29416 34153 30992 34561

4.27 -4.14 0.38 1.26 -0.91 -3.21 1.57 - 1.54 4.29 -2.23 0.57 - 1.49 1.59 -0.40 0.43 1.62 -0.93 1.93 I .53 1.41 3.44 - 1.11 -3.59 1.11 - 1.14 2.54 -3.41

Bavarian Office of Statistics,

and German Institute of Regional Research.

VARIABLES

Our first dependent variable is the telefax ratio (TELEFAX), measured by the number of telefax users per area code (as provided by the German Federal Institute for Regional Research) per 10,000 inhabitants. The second dependent variable is the telefaxiteletex ratio (TELEFAT). It is construed in a quite similar way to TELEFAX: We add telefax and teletex users per area code again per 10,000 inhabitants. The figures are subsequently aggregate for labor office districts. The CENT variable is used in different specifications as both a dependent and independent variable. It is a measure of the “centrality” (CENT) of a labor office district. Centrality is measured by a simple potential model. In regional economics potential models are usually employed to measure how agglomeration potentials scatter across the space, i.e., how agglomeration areas radiate to non-agglomeration areas. The chosen potential model has the form (P/d)‘.*, where P is the population of the main urban centres in Bavaria, and d is the distance between the most popuious town within the respective labor office district and the main urban centers of Bavaria. Of the several potential values for each labor office district, the highest is selected as the “centrality” measure. The exponent 0.8 is found by calibration. In other words, one tries to adjust the potential model by the aid of the exponent to the real dispersion of agglomeration potentials [ 15, p. 511. The first purely RHS variable is employee density (ED), namely the number of registered social security employees per square kilometer. A similar construed variable

288

J. GENOSKO

is population density (PD). The SW indicator is simply the share of the salaried workers in total insured employment within mining and manufacturing. SS measures the share of household-related service employment (such as the retail trade), while AS is the percentage of business service employment (e.g., economic and legal advisory services, banking and transportation). SS and AS do not contain the self-employed. This is a serious omission because the latter are of great importance in the services sector and are underrepresented in the peripheral regions vis-d-vis central regions. SBC is the ratio of the total number of plants with 500 or more employees in a region to the total number of plants in the same region. WSS is the yearly value of regional gross wages and salaries per employee. Our estimation procedure is conducted in several steps. First, we regress TELEFAX and TELEFAT on CENT. Second, we regress CENT on a number of independent variables that may reasonably be elected to influence it. In a final step, we reformulate our first specification of TELEFAX and TELEFAT. All estimations are done by OLS. Now let us explain our procedure in detail. At first we want to simply test whether or not TELEFAX and TELETEX, respectively, permit decentrahzing. For this purpose we introduce the variable CENT in a simple regression model of the form: TELEFAX

= o1 + o2 CENT + u

(114

TELEFAT

= B, + B2 CENT + u.

(11b)

Because CENT is a conglomerate model:

CENT-=

YI +

variable,

y&D + MD

we estimate the following

+ yd;w

+ ysss + %AS + y,SBC

multiple regression

+ ysWSs

+ u.

(12)

Subsequently, we reformulate Equations (1 la) and (1 lb), dividing CENT in two components. The first component consists of variables from Equation (12) significantly determining CENT, the second uses the residuals of CENT (RES) obtained from Equation (12). TELEFAX

= TELEFAX

(RES, Xi)

(134

TELEFAT

= TELEFAT

(RES, Xi),

(1%)

where Xi is the vector of variables significantly determining CENT. If TELEFAX and TELEFAT, respectively, allow decentralizing, the sign of CENT has to be negative or at least insignificant; in the decentralizing case the two telematics services are principally media for overcoming distances between central and peripheral locations. If the media work is centralizing, the sign of CENT has to be positive according to the derivations in our model; CENT measures namely the possibility of contacts, inter dia. In our model this fact is covered by the variable 2. We expect positive signs for the variables ED, PD, SW, SS, AS, SBC, and WSS. Nomially centrai locations are characterized by a high employee and population density, a large share of service employment and salaried workers, big companies, and high wages and salaries. ED and PD are proxies for the frequency of contacts and the exchange of information. The variables, 55, AS, and SBC are employed to proxy the telematicspromoting economic (industry) environment. SW and WSS should measure the quality of manpower. The salaries and wages paid in a region are especially influenced by the quality

THE SPATIAL

DISTRIBUTION

OF TELEMATICS

289

and qualifications of the work force, inter a&z. Highly qualified employees are necessary to handle new technologies and to process information. Moreover, they may be more open and willing to accept “new (technological) developments.” RES should also have a positive sign, because we suppose that RES contains other (latent) variables which have a positive impact on TELEFAX and TELEFAT, respectively. FINDINGS The results of the regression estimations (1 la) and (11 b) are represented in Table 2. CENT has a highly significant positive sign. The results suggest that the distribution of telefax and teletex across Bavarian labor office districts is essentially influenced by the distance between site and center. But there is an important difference. In comparison TELEFAT is less centralized than TELEFAX (measured by the R”s). Table 3 presents the results of the CENT estimations. In estimation (12a) PD and SS show an unexpected negative sign, where, however, the coefficient of SS is insignificant. SBC has the right sign, but the coefficient is insignificant too. The other variables have significant coefficients with the expected sign. Because we suppose that heavy multicollinearities bias the results, especially the PD coefficient, we do two additional estimations. In fact simple regression of CENT on PD shows a significantly positive sign of the coefficient (This result is not contained in Table 3). In Equation (12b) we eliminate PD, SW, SS, and SBC-these four variables are strongly correlated with the remaining variables-and regress CENT on ED, AS, and WSS [because of the multicollinearities ED, AS, and WSS provide the same information as Equation (12a)]. Now all coefficients are significant on the 0.005 level; the adjusted R-square is negligibly smaller. Hence we can conclude CENT is primarily determined by ED, AS, and WSS. This means that central places are characterized by good possibilities for contacts, by a good offer of services for enterprises, and by a high quality of manpower (additionally WSS is to some extent also a factor that reflects the size of the establishments). Using ED, AS, WSS, and RES we again estimate TELEFAX and TELEFAT. In Section II we have shown that a greater number of local participants in the telecommunication system increases the use of telematics. The same is true for an improved quality of manpower. Moreover a better access to the capital market promotes the introduction of telematics. As already mentioned above in our econometric estimation, ED stands for the potential number of local participants in the telecommunication system in a region, WSS mirrors partly the quality of manpower, and in some way AS is a proxy for the capital market-access, because AS contains also the employees of banking houses; therefore AS can be seen as a variable measuring indirectly and weakly the regional endowment with banking houses. The results of our estimations are represented in Table 4. In Equation (13a) all coefficients have the expected sign and are significant. TELEFAX is therefore positively influenced by a high employee density, by a large share of business service employment, and to a lesser extent by high wages and salaries. But latent variables

OLS-Estimation Variable CENT z F-statistic

a=

0.005.

TABLE 2 TELEFAX,

E!q. lla Coefficient (ItI) 0.7721 E-01” (14.8774) 0.8945 221.335

TELEFAT Eq. llb Coefficient (ItI) 0.1233” (10.4740) 0.8070 109.704

J. GENOSKO

290 TABLE 3 OLS-Estimation CENT

Variable ED PD SW ss AS SBC wss ?ii F-statistic

Eq. 12a Coefficient (ItI)

Ekj. 12b Coefficient (IfI)

0.1118” (3.9323) - 0.1131 E-01’ (-2.1417) 27.8356’ (1.6214) - 2.7916 (-0.1666) 114.562” (3.1522) 1.8314 (0.1368) 0.4575” (1.9549) 0.9339 53.5; 19

0.8661 E-01” (5.2022)

120.729” (3.8957)

0.7633 E-03” (3.4897) 0.9181 98.1349

o = 0.005 * = 0.05. ( = 0.10.

also impact on the regional distribution of TELEFAX. Unlike TELEFAX, the regional distribution of TELEFAT is only significantly determined by a high employee density and a large share of business service employment. RES and WSS, however, have insignificant coefficients. The result of the regression analysis were supplemented by a questionnaire study of a randomly selected 5% subsample of 207 industrial enterprises in Eastern Bavaria which has been carried through by order of the Bavarian government. This area is characterized by a level of innovative activity that falls significantly below that of West Germany as a whole. The enterprises were asked whether or not they use data processing in any form. Around 65% of the enterprises report the use of data processing techniques (cf. 92% for Bavaria as a whole). Among the data-processing-using enterprises only 53% employed

OLS-Estimation

Variable RES ED

TELEFAT

Eq. 13a Coefficient (IfI)

E.q. 13b Coefficient (ItI)

0.4046 E-Olb (2.4081) 0.9155 E-02”

0.2725 (0.7895) 0.1808 E-01” (6.5597) 14.9548” (2.9151) - 0.5497 E-05 (-0.1518) 0.8798 48.5679

wss

(6.8244) 10.2570” (4.1076) 0.2027 E-04

Rz F-statistic

(1.1498) 0.9198 75.5819

AS

o = 0.005. * = 0.05. c = 0.10.

TABLE 4 TELEFAX,

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data processing for technical applications (e.g., CAD, CAM, and NC), but around 87% for business administration applications. Because “innovation” refers to new products and processes, these findings are unsatisfying for the innovative performance of the firms. Another result is that the higher the percentage of a firm’s R&D employment, the more data processing is used. The firms were asked a set of questions designed to ascertain their knowledge of telematic installations. Around 65% of the enterprises did not know of telefax (cf. 29% for Germany as a whole). Ignorance of the telematic instruments was as follows: teletex 56.0 (30.5), btx 68.2 (34.7), data-telephone 91.6 (62.7), tele-conference 87.4 (52.4), and tele-phone 89.4 (77.4). These figures betray considerable ignorance of the various forms of telematics in a sample area that clearly lags behind the rest of Germany. The actual use of telematics is as follows: telefax 12%, teletax 12%, btx 2%, data-telephone 3%, tele-conference l%, and tele-telephone 1%. Clearly, the results indicate a close connection between use of data processing and the knowledge and use of telematics. Accordingly, the use of telematicsAevolving here on the use of telefax and teletex+an be used as a proxy for the innovative ability of a region (especially with regard to the capacity to use microelectronics). What therefore are the subjectively perceived obstacles behind the lack of innovation in Eastern Bavaria? Principal reasons cited by the enterprises are market and cost risks as well as the difficulties on the funding side. Second in importance is a perceived lack of necessary skills. The final factor is lack of information on innovative developments. More precisely, information is obtained via deliverers, customers, and fellow enterprises.

IV. Summary and Conclusions Our econometric results shows CENT as a highly significant variable influencing the regional distribution of telefax and teletex-a result which corresponds with the derivations in the theoretical model. The result that TELEFAT is less centralized than TELEFAX is not surprising, because TELEFAT contains also the users of teletex. Teletex, however, is as mentioned above, only an upgrading of a telecommunication service which has already been available for many years. In other words, the results show that we can expect to some extent a further decentralization of TELEFAX, but the basic tendency will continue; that is to say that telefax and teletex will be concentrated in the urban centers. Despite the fact that our econometric results are based on aggregate data, some tentative conclusions may be advanced on the basis of our findings (especially when taken in conjunction with the qualitative analysis). First of all the close connection between TELEFAX, TELEFAT, and CENT might erode the hope that telematics can promote decentralized economic structures and reduce the contact advantages of the agglomerations. Rather, the results suggest that synergetic effects exist between the use of the telecommunication technologies and the contact advantages of the agglomerations. Moreover, the new telecommunication media are especially valuable if they can be complemented by the face-to-face contacts between decisionmakers [14, pp. 59-611. Furthermore, our conclusion is supported by the results of Table 3 and by the following statement which show that important factors influencing the introduction of telematics are not sufficiently available in peripheral regions. On average the enterprises in central regions are bigger than in peripheral regions. Bigger enterprises, however, dispose of better qualified manpower, more openness and Trillingness to introduce telematics, operations which are well suited for the introduction of telematics, and better access to capital markets. Since one cannot expect that this situation alters in favor of peripheral regions we reckon that potentially decentralizing techniques do not work decentralizingly .

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Second, if we consider in detail the factors influencing TELEFAX and TELEFAT, the possibilities of contacts seem to be essential. This is empirically supported by the significantly positive sign of ED. But also the result for AS suggests this conclusion. Moreover, the business service industries belong to the early adopters of telematics and support the introduction of telematics in other industries (see, for example, the important role of the banking houses). In the particular case of TELEFAX there is an additional correlation with the quality of the employees and hence with the openmindedness of the employees, which is correlated with their quality; we can also find an impact of latent variables (e.g., cost risk, funding) on TELEFAX. The introduction of latent v riables via RES, however, only marginally improves the “value of explanation” of our results (R2 without RES 0.9031, with RES 0.9198). The significance of RES and WSS in the TELEFAT equation reflects, in our opinion, the fact that teletex is only an upgrading of an existing communications system. Therefore neither (certain) determinants, which are covered by RES, nor “innovative” attitudes and knowledge, which are covered by WSS, are important for the purchase of teletex. In principle these findings receive support from the qualitative analysis. Third, the qualitative analysis also shows a close link between the use of telematics and the employment of data-processing techniques. Thus, the spatial pattern of telematics mirrors the spatial dispersion of microelectronics. This transition from telematics to microelectronics has fundamental policy implications. If it is accepted that the government should promote the spatial diffusion on innovations, then the immediate question that arises is how this should be achieved. In particular, telefax is an interesting case in point because the infrastructure of telefax service is universal. Clearly, mere installation of an overall network is not a panacea. Implementation of a successful innovation-oriented regional policy implies the establishment of an appropriate framework. This means that regional educational, labor market, economic, and technological policies have to be fused. Only a coordination of the various policies makes it possible for an innovation-oriented regional policy to provide favorable “supply conditions.” Policies geared toward promoting a technological renewal of regions via spatial diffusion of innovations can only be effective in the medium and long runs [7, p. 201. This conclusion is important because a demand-side innovationoriented regional policy does not seem to be feasible because of the openness of regions and, in particular, the high import rates of peripheral regions. Acknowledgments I thank John T. Addison, Ake Andersson, Wolfram Mieth, Nancy Held, and Walter Oberhofer for their valuable comments and assistance. References 1. Bade, F. J., Die funktionale Struktur der Wirtschaft und ihre raumliche Arbeitsteilung, Wissenschaftszentrum Berlin JJMIJP 84-27, 1984. 2. Boulianne, L. M., Technological Change: Firm and Region. A Case Study, in Technology: A Key Factor for Regional Development. D. Maillat, ed., Saint-Saphorin, 1982, pp. 36-67. 3. Brown, L. A., Diffusion Processes andlocarion. A Conceptual Framework andBibliography, Philadelphia, 1968. 4. Cross, M. Technical Change, the Supply of New Skills, and Product Diffusion, in Technical Change and Regional Development. A. Gillespie, ed. London, 1983, pp. S-67. 5. Fritsch, M., and Ewers, H. J., Telematik und Raumentwicklung, Bonn, 1985. 6. Genosko, J., The Interregional Technology Transfer: Some Sceptical Remarks, Regensburg (unpublished paper!, 1986.

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7. Genosko, J., Die innovationsorientierte Regionalpolitik: eine wirksame Handhmgsaltemative?, “Raumforschung und Raumordnung” 44, 1986. 8. Gibbs, D. C., and Edwards, A., Some Preliminary Evidence for the Interregional Diffusion of Selected Process Innovations, in Technological Change and Regional Developmenr. A. Gillespie, ed. London, 1983, pp. 23-35. 9. Gmber, W. H., and Marquis, D. G., Research on the Human Factor in the Transfer of Technology, in Facrors in rhe Transfer of Technology. W. H. Gruber and D. G. Marquis, eds., Cambridge (Ma.) and London, 1969, pp. 255-280. 10. Hagerstrand, T., Innovution Diflusion as a Spark1 Process. Chicago and London, 1967. 11. Johansson, B., and Karlsson, C., Industrial Applications of Information Technology: Speed of Introduction and Labour Force Competence, in Technological Change, Employmenr and Sparial Dynamics. P. Nijkamp, ed., Berlin, 1986, pp. 401428. 12. Kleine, J., Location, Firm Size, and Innovativeness, in Technology: A Key Facror for Regional Development. D. Maillat, ed., Saint-Saphorin, 1982, pp. 147-173. 13. Mansfield, E., Industrial Research and Technological Innovation. London, 1968. 14. Marti, P., and Mauch, S., Wirtschaftlich-raumliche Auswirkungen neuer Kommunikationsmittel, Arbcitsbericht Nr. 46 des Nationalen Forschungsprogramms “Regionalprobleme in der Schweiz,” 1984. 15. Mieth, W., and Genosko, J., Qualitative Polarisienmg der Regionen als Folge der dumlichen Selektion der Wanderung und der Arbeitsplltze, in Qualirdr van Arbeirsmdrkren und regionale Enrwicklung. Akademie ftir Raumforschung und Landes planung, Bd. 143, Hannover, 1982, pp. 14-61. 16. Nabseth, L., The Diffusion of Innovation in Swedish Industries, in Science and Technology in Economic Growth. B. R. Williams, ed., London, 1973. 17. Oakey, R. P., Thwaites, A. T., and Nash, P. A., The Regional Distribution of Innovative Manufacturing Establishments in Britain, Regional Srudies 14, 235-253, 1980. 18. Romeo, A. A., Interindustry and Interfirm Differences in the Rate of Diffusion of an Innovation, The Review of Economics and Srurisrics 57, 3 1 l-3 19, 1975. 19. Rothwell, R., and Zegveld, W., Innovarion and rhe Small and Medium Sized Firm. Their Role in Employment and Economic Change. London, 1982. 20. Shapero, A., Effects of Government R and D Contracting on Mobility and Regional Resources, in Facrors in the Transfer of Technology. W. H. Gruber and D. G. Marquis, eds., Cambridge (Ma.) and London, 1969, pp. 179-201. 21. Stoneman, P., The Economic Analysis of Technological Change. London, 1983. 22. Taylor, L. D., Telecommunications Demand. A Survey and Critique. Cambridge (Ma.), 1980. Received 2 March I987