Knowledge-Intensive Business Services in Transition ...

Knowledge-Intensive Business Services in Transition Economies ´ Zˇ V L A D I M ´I R B A L A

Knowledge-intensive business services accounted for a rapid growth in transition economies after 1989. The growth in value added outpaced growth in employment, which indicated increasing labour productivity in this sector. Studies based on input–output tables found that development of business services was closely related to development of communication services in advanced EU member countries. The input–output analysis did not confirm this relation for Slovakia and the Czech Republic and found a medium to strong level of correlation for Hungary. Development of a market economy was likely to be a major factor behind development of business services. This assumption was tested on empirical data. The use of communication and business services could be a proxy for introduction of new technologies in production functions. The functions indicated that these industries made a significant contribution to economic growth both in advanced and transition economies. Output elasticity coefficients were quite similar in the Czech Republic, Hungary and Slovakia and the EU member countries.

INTRODUCTION

Knowledge-intensive business services (KIBS) are usually: . .

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performed by private companies and organisations, relying heavily on professional knowledge i.e. knowledge or expertise related to a specific (technical) discipline or (technical) functional domain, and supplying intermediate products and services that are knowledge based [Bilderbeek et al., 1998].

Vladimı´r Bala´zˇ is at the Institute for Forecasting, Slovak Academy of Science, Sˇancova´ 56, 813 64 Bratislava, Slovak Republic. The Service Industries Journal, Vol.24, No.4, July 2004, pp.83–100 ISSN 0264-2069 print=1743-9507 online DOI: 10.1080=0264206042000275208 # 2004 Taylor & Francis Ltd.

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THE S ERVI CE INDUSTRIES JOURNAL

Accounting, management consultancy, technical engineering services, R&D activities, design, computer and information technology-related services, and legal and financial services are typical examples of KIBS. Most works concerned with KIBS are based on Schumpeter’s assumption that new combinations of existing means of production drive economic development in an economic system [Schumpeter, 1934]. Innovations per se are not restricted to technological innovation, but to all kinds of knowledge generation and diffusion [Marklund, 2000]. The service sector is the prime user of information and communication technologies. Firms engaged in generation and diffusion of KIBS, in turn, contribute to product and process innovation much more than firms involved in manufacturing or agriculture [Cowan and van de Paal, 2000]. Innovation often goes together with increasing shares of highly qualified staff (IT specialists in particular). Conventional views of innovation as an industry-related matter have gradually been replaced by recognising the role of KIBS in innovation processes.1 This shift was generated via growing shares of KIBS in total employment and generation of wealth. Traditional manufacturing and service industries will be able to compete only via introduction of significant inputs from KIBS industries. KIBS frequently have an important role in creation and diffusion of knowledge [Larsen, 2001]. Firms involved in consultancy and advisory activities, for example, facilitate exchange of tacit knowledge [Bessant and Rush, 1995] and enable sharing learning experiences and create learning opportunities [Miles et al., 1995]. Small and medium-sized firms, which do not have in-house R&D departments, can particularly benefit from diffusion of knowledge provided by KIBS-based firms. KIBS-based firms make a significant contribution to enhancement of a system’s innovative capability [Lundvall, 1992]. In this way, KIBS may account for a significant contribution to development of other sectors and growth in total output. Knowledge-intensive business services became the most rapidly growing sector in advanced OECD-member countries in the 1980s and 1990s. While high- and medium-high technology manufacturing accounted for about 9 per cent of total OECD value added by the end of the 1990s, knowledgebased ‘market’ services accounted for 18 per cent [OECD, 2001]. Demand for these services was limited in centrally planned economies, but increased significantly after 1989 (Figure 1). The share of KIBS in total value added doubled in Poland and increased by 30 – 50 per cent in the Czech and Slovak Republics and Hungary in the last decade. Data subtracted from OECD National Accounts [OECD, 2002] confirmed that growth in value added in the KIBS industries was much faster than growth in employment in the same sector and there ware great increases in the labour productivity.

KIBS IN TRANSITION ECONOMIES

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FIGURE 1 SHARES OF BUSINESS SERVICES IN TOTAL VALUE ADDED (VA) AND TOTAL EMPLOYMENT IN SELECTED TRANSITION ECONOMIES (IN PERCENT, NACE 65 – 74)

Source: OECD [2002]. Data on employment were missing for the Czech Republic.

In this way, KIBS significantly contributed to increasing efficiency in national economies. Particular KIBS can be found among all industrial and service sectors. For practical reasons, most KIBS are ranked to sectors 64– 74 of the NACE classification. Information- and communication-related KIBS account for a special position among other KIBS. Communication services enhance transfer of KIBS to virtually all users in a national economy. In this way, they enable the diffusion of KIBS to other sectors of the economy and generation and adoption of technological and organisational innovations. A strong correlation between both the levels and rates of growth of business services and communication services should be visible in an economy based on knowledge generation and adoption. These assumptions have been tested by several authors on a sample of developed economies. .

The largest sample, based on value added as dependent variable, was analysed by Antonelli [2000] and included the UK, Germany, France and Italy. A production function model of the relation between sectoral value added on the output side and labour costs, capital stock and the flow of inputs of either communication and or the business services on the input side, was developed.

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Tomlinson’s [2000] two-way analysis was based on gross output and labour costs and included Japan and the UK. First, gross output was assumed as a function of sector purchases of intermediate material goods, sector purchases of communication and business services as well as the total wages paid in the sector. Second, the gross output per wage unit was conceived as a function of the purchase of intermediate material goods per wage unit and the purchase of communication and business services per wage unit. Drejer’s [2001] analysis for Denmark used the Tomlinson-type model. Katsoulacos and Tsounis’s [2000] study was concerned with residual growth output and value added and concentrated on Greek industries. The residuals from a traditional Cobb-Douglas function of output growth were assumed to be a function of the increase in inputs from business services.

This article is based on input – output tables from three transition countries (the Czech and Slovak Republics and Hungary) and follows concepts developed in Antonelli’s pioneering study [1998], which used macro data to demonstrate a correlation between use of communication and business services and growth in output. Input – output tables allowed the testing of several assumptions on correlation between development of communication and business services and economic growth. The first assumption was that development of business services was significantly aided via diffusion of communication services. The regression equation BS C ¼aþb (1) AV AV measured the relation between the intensity of communication services purchased by each industry in terms of value added (C/AV) and the intensity of business services purchased by the same industries (BS/AV). Any significant association between development of business services and communication services should be reflected also in the correlation of growth rates in intensities of these industries. The regression equation BSt Ct ln ¼ a þ b ln (2) BSt1 Ct1 assumed that the rates of growth in the intensities of communication services were correlated with growth in the intensity of business services. Ln Ct, Ct21, BSt and BSt21 were natural logarithms of the growth rates of communication and business services’ consumption in particular time periods. Writing the equation in logarithm form provided for direct measures of elasticity of growth in business services consumption against the growth in communication services consumption.


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The third assumption sought a causal relation between use of communication and business services and output increases. The use of communication and business services could be employed as a proxy for introduction of new technologies in the production function: ln Y ¼ d þ a ln L þ b ln K þ g ln C

(3)

ln Y ¼ d þ a ln L þ b ln K þ g ln BS

(4)

and

where Y ¼ the natural logarithm of value added in particular sectors, L ¼ the natural logarithm of labour costs, K ¼ the natural logarithm of capital stock (estimated from investments), C, BS ¼ inputs of communication and business services in particular industries, and d, a, b, g ¼ parameters to be estimated. The logarithm form of the production function provided for coefficients to read directly as output elasticity parameters. Transition economies accounted for lower expenditure on R&D, but higher intensities of capital and labour use than developed economies in the EU. They also accounted for lower levels of penetration by communication services. This implied the following hypotheses: .

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the role of communication services in development of business services was lower in transition economies than in developed countries, development of business services was strongly related to market reforms after 1989, and both the communication and business services had important roles in stimulating output growth during the transition period.

DATA

Slovak data were provided by the Slovak Statistical Office in Commodity Industry Input – Output Tables. Czech data were taken from the World Wide Web home page of the Czech Statistical Office, while Hungarian data were provided by courtesy of the Hungarian Central Statistical Office. The input –output tables presented data on supply and consumption of particular commodities and services in particular industries and data on labour costs,

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gross fixed capital formation and value added. Data for capital stock were approximated via investments. A yearly 20 per cent depreciation rate was used, so Kt ¼ It þ 0.8It21 þ 0.6It22 þ 0.4It23 þ 0.2It24. Input–output tables have been produced since 1993 in the Slovak Republic and 1995 in the Czech Republic. The 1993 and 1994 Slovak editions and the 1995 Czech tables, however, were rather experimental. Both republics came into being in 1993 and the System of National Accounts replaced the Material Product System in 1992. With experience by both national statistical offices increasing, input – output tables were becoming more reliable. Results computed from these time series should be observed with care. The Hungarian data were available for one year only (1999). Lack of comparative statistics at a sufficiently disaggregated level was a major obstacle for comparative analysis of KIBS in particular countries [Preissl, 1997]. Each OECD member, for example, used its own classification. Many sub-sectors were not directly comparable among various countries and could not be aggregated. There were differences between national GDP statistics, EURASTAT and OECD publications. The OECD National Accounts provided the most comprehensive data, but even the most detailed subgroups embraced very heterogeneous production sectors. The detailed NACE Rev.1 schemes should allow for more exact analyses. These schemes, however, were fully implemented by only a few OECD members. For these reasons, most comparative studies were based on highly aggregated data and results computed from the OECD statistics were not directly comparable between and among different countries. This analysis relies on sectors in input – output tables sorted according to the NACE Rev.1 scheme (Table 1).

TABLE 1 NACE REV.1 CATEGORIES

NACE Rev. 1 64 65 66 67 70 71 72 73 74

Description Post and telecommunications Financial intermediation services, except insurance and pension funding services Insurance and pension funding services, except compulsory social security services Services auxiliary to financial intermediation Real estate services Renting services of machinery and equipment without operator and Computer activities and software supply Research and development Other business services (Legal activities, accountancy, advertising)

National statistical offices accounted for following coverage of particular KIBS sectors in transition economies: Czech Republic: 64, 65, 66, 67, 70, 71, 72, 73, 74; Hungary: 64, 65, 66, 67, 70, 71, 72, 73, 74; Slovakia: 64, 65, 66 þ 67, 70 þ 71, 72, 73, 74.

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As mentioned above, differences in statistical coverage did not allow direct comparisons with other OECD members, but enabled comparisons of relative importance of particular KIBS industries.

RESULTS AND DISCUSSION

Levels of usage of communication and business services were strongly correlated in the EU member countries. This observation, however, did not hold for Slovakia and the Czech Republic (Table 2). Explanation power (R2) and significance levels (t-values) were much lower for all periods observed than in the EU countries. Hungary provided a partial exception, with medium to strong correlation between developments in these sectors. Shares of IT expenditure in GDP were higher in the Czech Republic (3.9 per cent), Hungary (3.3 per cent) and Slovakia (2.9 per cent) than the EU average (2.7 per cent) in 2000. The production of IT services, however, was not a necessary precondition of its use. These shares reflected mainly high levels of post-privatisation foreign direct investment (FDI) and modernisation-related investments in this sector. If, for example, ratios of Internet hosts and users were taken as proxies for IT diffusion, all these countries were significantly lagging behind EU levels [Radosevic and Mickiewicz, 2002]. This gap hampered diffusion of information in the economy and society and at least partly explained the low correlation between use of communication and business services. Hungary was the only country which tried to implement policies oriented towards attraction of knowledge intensive and high-value-added activities [European Commission, 2000]. The so-called TABLE 2 REGRESSION EQUATION RESULTS

Italy UK Germany France Slovakia Czech Republic Hungary

Years

a

b

N

R2

F

1988 1990 1990 1990 1994 1996 1998 1995 1999 1998

0.047 0.247 0.168 0.297 0.018 0.073 0.012 0.106 0.175 0.109

3.382 (10.211) 2.885 (8.211) 3.312 (9.901) 2.966 (12.311) 0.157 (1.446) 1.531 (4.312) 0.096 (1.902) 3.205 (3.331) 0.685 (1.628) 2.126 (5.146)

32 32 23 25 47 47 47 58 58 57

0.781 0.881 0.741 0.901 0.045 0.297 0.076 0.165 0.045 0.325

104.258 124.212 104.867 96.578 2.092 18.590 3.619 11.092 2.651 26.482

Equation: BS/AV ¼ a þ b(C/AV) Source: Antonelli [1998] for Italy, UK, Germany, France and Greece and author’s own computations for Slovakia, Czech Republic and Hungary. Notes: Goods sectors only for the UK. T-values in parenthesis.

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Sze´chenyi Plan (a government programme promoting industrial and technology parks and attracting foreign investments) comprised schemes aimed at consulting companies providing structured knowledge-intensive services for SMEs [Havas, 2001]. These policies were successful in attracting several foreign investors involved in the manufacture of electronics. If development of business services is associated with development of communication services, the growth rates of intensities of communication services should be associated with growth rates of intensities of business services. Results of a comparative regression analysis are presented in Table 3. The b coefficient is a direct measure of the elasticity of the growth in business services consumption against growth in communication services consumption. The elasticity coefficient was statistically significant in all countries compared, but much higher for Italy, the UK, Germany and France than for Slovakia and the Czech Republic. The correlation coefficient was also much lower in Slovakia than in other countries included in the comparison. There were, however, visible increases both in total explanation power and significance levels in Slovakia (R2 ¼ 0.129, t ¼ 2.555 in 1996/1994 and R2 ¼ 0.190, t ¼ 3.217 in 1998/1996). As for the Czech Republic, both the correlation coefficient (R2 ¼ 0.348) and significance level (t ¼ 29.325) for the period 1999/1995 fell between the Slovak and EU member levels. The experimental nature of the 1994 Slovak and 1995 Czech data, however, must be considered and computed results observed with care. Unfortunately, the same regression could not be computed for Hungary, as no time series were available. Antonelli’s study found significant contributions from inputs of both communication and business services to sectoral value added (Tables 4 and 5). For all EU member countries total explained variance (in terms of R2) TABLE 3 REGRESSION EQUATION RESULTS

Italy UK Germany France Slovakia Czech Rep.

Years

a

b

N

R2

F

1988/85 1990/84 1990/86 1990/86 1996/94 1998/96 1999/95

24.905 23.705 29.905 26.535 1.181 0.234 0.074

0.607 (8.819) 0.789 (9.819) 0.753 (7.908) 0.831 (8.115) 0.273 (2.555) 0.484 (3.217) 0.687 (5.415)

32 32 23 25 47 47 58

0.733 0.739 0.813 0.782 0.129 0.190 0.348

77.771 57.987 71.951 77.549 6.528 10.351 29.325

Equation: ln(BSt/BSt21) ¼ aþb ln(Ct/Ct21) Notes: Goods sectors only for the UK. T-values in parenthesis. Source: Antonelli [1998] for Italy, UK, Germany, France and Greece and author’s own computations for Slovakia and the Czech Republic.

TABLE 4 REGRESSION EQUATION RESULTS

Italy UK Germany France Slovakia Czech Rep. Hungary

Years

d

a

b

g

N

R2

F

1988 1990 1990 1990 1994 1996 1998 1995 1999 1998

2.258 24.281 0.945 2.185 1.522 1.433 1.155 1.150 0.488 1.986

0.693 (7.479) 0.349 (3.956) 0.779 (3.615) 0.161 (1.879) 0.347 (2.926) 0.354 (3.838) 0.371 (4.066) 0.500 (6.761) 0.491 (3.347) 0.540 (6.453)

0.321 (3.389) 0.354 (5.483) 0.296 (0.741) 0.662 (9.389) 0.381 (3.842) 0.437 (5.839) 0.435 (5.636) 0.410 (5.897) 0.349 (2.682) 0.092 (3.774)

0.085 (3.211) 0.208 (2.34) 0.155 (1.498) 0.088 (2.211) 0.208 (5.034) 0.164 (4.507) 0.113 (2.887) 0.076 (1.738) 0.272 (3.723) 0.341 (5.800)

32 32 23 25 47 47 47 58 58 57

0.749 0.953 0.859 0.913 0.805 0.878 0.898 0.944 0.879 0.935

47.162 182.635 24.162 73.162 57.727 100.634 123.548 295.374 130.803 249.738

Equation: ln Y ¼ d þ a ln L þ b ln K þ g ln C Note: Goods sectors only for the UK. T –values in parenthesis. Source: Antonelli [1998] for Italy, UK, Germany, France and Greece and author’s own computations for Slovakia, Czech Republic and Hungary.

TABLE 5 REGRESSION EQUATION RESULTS


Years

d

a

b

1988 1990 1990 1990 1994 1996 1998 1995 1999 1998

3.945 24.281 3.945 3.945 1.358 1.229 1.089 1.151 0.182 1.489

0.433 (7.279) 0.473 (5.479) 0.473(6.429) 0.273 (3.422) 0.417 (3.780) 0.351 (4.562) 0.262 (2.886) 0.512 (7.166) 0.505 (3.888) 0.546 (8.189)

0.516 (3.489) 0.332 (6.415) 0.216 (7.989) 0.616 (4.89) 0.288 (3.023) 0.350 (5.335) 0.412 (5.827) 0.363 (4.691) 0.254 (2.050) 0.063 (3.098)

0.271 0.424 0.287 0.263 0.251 0.287 0.234 0.057 0.340 0.359

g

N

R2

F

(3.211) (4.567) (2.211) (3.811) (5.323) (6.379) (4.320) (1.789) (5.013) (7.492)

32 32 23 25 47 47 47 58 58 57

0.749 0.929 0.899 0.889 0.813 0.908 0.916 0.944 0.896 0.949

47.162 154.342 231.162 76.890 60.919 138.122 151.763 296.382 155.527 319.873

Equation: ln Y ¼ d þ a ln L þ b ln K þ g ln BS Note: Goods sectors only for the UK. T–values in parenthesis. Source: Antonelli [1998] for Italy, UK, Germany, France and Greece and author’s own computations for Slovakia, Czech Rep. and Hungary.


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was higher than 0.7 and in most cases approached 0.9. Except for communication services in Germany, all coefficients were significant at the 0.01 level. This indicates that the usage of communication and business services is a good proxy for levels of general efficiency of the technology production function. These assumptions held for three transition economies as well. The explanation power was higher than 0.8 and coefficients significant at the 0.01 level in all countries and sectors involved in the analysis. There were visible differences between parameters estimated for 1994, 1996 and 1998 for Slovakia, both for communication and business services. The explanation powers and significance levels increased in both communication and business services. The rather experimental character of the 1994 database and the rapid pace of market reforms in this period did not allow for a straight conclusion that this increase was generated via ongoing increasing efficiency of diffusion by KIBS to other sectors of the national economy. However, the increases in significance levels for business services in particular were so strong that they were not likely to originate solely in statistical discrepancies. As for the Czech Republic, explanation powers and significance levels for communication and business services in 1999 were very close to those of Slovakia in 1998. The output elasticity in both communication and business services was significantly higher in the Czech Republic than in Slovakia. This may indicate higher use of KIBS in the Czech economy. This assumption, however, could not be directly verified due to differences in data aggregation. Hungary was the strongest performer in the analysis in terms of explanation power, significance levels and output elasticity. With differences in data aggregation kept in mind, the contribution to output provided by communication and business services seemed significantly higher in Hungary than in the Czech and Slovak Republics. Output elasticity (g coefficient) is a direct measure of the elasticity of the growth in communication or business services consumption against growth in output. These coefficients were similar in the communication sector and somewhat lower for business services in Slovakia than in the EU member countries. This finding was consistent with data on cost shares of communication and business services in total value added (Table 6). Slovakia accounted for higher cost shares of communication services and lower shares of business services than the EU members. It should be noted that the explanation power of the model (R2 ¼ 0.805, 0.878, 0.898) and significance levels (F ¼ 57.727, 100.634, 123.548) in the model increased both in communication services in the period 1994–1998 (Table 4). The same development was observed for the business services (R2 ¼ 0.813, 0.908, 0.916; F ¼ 60.919, 138.122, 151.763; Table 5). As for the cost shares and output elasticity in communication services, the situation was slightly different in the Czech Republic and Hungary. These shares were much higher than in the EU member countries in the 1980s.

TABLE 6 OUTPUT ELASTICITY AND COST SHARES FOR COMMUNICATION AND BUSINESS SERVICES

Communication services


1985 1988 1984 1990 1986 1990 1986 1990 1994 1996 1998 1995 1999 1998

Business services

Output elasticity

Cost shares

Ratio OE/CS

Output elasticity

Cost shares

Ratio OE/CS

x 0.085 x 0.208 x 0.155 x 0.088 0.208 0.164 0.113 0.076 0.272 0.341

0.0148 0.0178 0.0120 0.0102 0.0123 0.0139 0.0089 0.0095 0.022 0.026 0.031 0.024 0.033 0.036

x 4.78 x 20.39 x 11.15 x 9.26 9.39 6.36 3.68 3.18 8.20 9.39

0.148 0.164 0.166 0.193 0.149 0.155 0.141 0.163 0.251 0.287 0.234 0.057 0.340 0.359

x 0.271 x 0.424 x 0.287 x 0.263 0.160 0.158 0.159 0.172 0.176 0.398

x 1.65 x 2.20 x 1.85 x 1.61 1.57 1.82 1.47 0.33 1.93 0.90

Note: Goods sectors only for the UK. T-values in parenthesis. Source: Antonelli [1998] for Italy, UK, Germany, France and Greece and author’s own computations for Slovakia, Czech Republic and Hungary.


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As for business services, Hungary accounted for by far the highest output elasticity and very high cost shares. Cost shares by communication services increased in all countries observed, except the UK. The UK data, however, were not comparable, because they included telecommunication services only, while data from other countries combined post and telecommunication services. Increases in cost shares by communication services were even more important, if decreasing unit costs of telecommunication services were taken into account. Costs share of business in total value added also increased in all EU member states studied. The small decrease for Slovakia in the period 1994–1996 may not have been significant, given the rather experimental character of the 1994 data. Table 6 presents data on output elasticity and cost shares for communication and business services. Comparison of output elasticity and cost shares (OE/CS ratio, the disequilibrium coefficient) indicates remarkable discrepancies between these parameters for each country observed. High values of disequilibrium coefficients suggested that usage of both communication and business services were far from equilibrium in the countries and time periods observed. Absorption of communication and business services generated a marginal product, which was much higher than the share of these industries in total value added. The coefficients indicated that firms which absorbed knowledge diffused and/or generated by KIBS agents were able to increase their market shares and profitability. The higher the coefficient, the higher the disequilibrium and the higher the transient rents collected by early KIBS adopters. Disequilibrium was significantly higher for communication than business services for all countries. Output elasticity, for example, was 4.78 times higher than cost shares in communication services in Italy, 11.15 times higher in Germany and 9.26 times higher in France. The UK accounted for the highest value (20.39 times), but the UK data were not comparable due to different statistical coverage. The disequilibrium coefficients for Slovakia were broadly comparable with other countries and seemed to decrease over time. This decrease was consistent with growth in competition and inputs of labour and capital in these sectors over the transition period. As for business services, the disequilibrium coefficients were much lower and fairly similar for particular countries observed. Slovakia accounted for the lowest values. This indicates somewhat lower levels of marginal products in these industries and lower levels of transient quasi-rents in Slovakia than in the EU member countries. Czech data accounted for similar values to those of the EU members. Hungary provided for an interesting exemption, with oversupply of business services (OE/CS ratio ¼ 0.90). Models based on the input–output tables pointed to the fact that development of business services was not related to development of communication services in the Czech and Slovak Republics in the 1990s. Correlation coefficients for

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Hungary were somewhat higher than those in the above-mentioned countries, but significantly lower than in the EU member countries in the 1980s. Which factors were likely to start a boom in business services in transition economies, if not communication services? Data on employment and value added in business services in transition economies (Figure 2) indicated that these services increased considerably over the transition period. Development of a market economy in general and privatisation, price liberalisation, financial sector deregulation and institutional reforms in particular, were likely to be major factors behind development of business services. Business services were of little importance in a centrally planned economy, but accounted for rapid growth after the introduction of market reforms.2 Rapid development of business services after 1989 in some transition countries suggests that the latter might be the result of strong inward-outward FDI linkages, primarily in financial institutions. Stare [2002] found some empirical evidence for this assumption. There seemed to be a logical relation between the advance of market reforms and expansion of business services. This assumption could be tested on empirical data. Expansion of business services can be approximated via increasing shares of business services (NACE 65–74) in the GDP (dependent variable bsgdp). OECD statistics on National Accounts [OECD, 2002] contained data on value added and employment for four transition economies: the Czech and Slovak Republics, Hungary and Poland. Transition reports by EBRD [1998–2002] included rich data on market reforms in transition economies: .

.

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shares of private enterprises in total output ( prsecgdp), EBRD index of small privatisation (spindex) and EBRD index of large privatisation (lpindex) as proxies for privatisation process, share of administered prices in total output as proxy for price liberalisation (adpgdp), share of domestic credits in GDP (domcrgdp) and share of broad money in GDP (M2gdp), interest margin (bank lending rate minus deposit rate, netrate) as proxies for financial sector development, EBRD index of enterprise reform (erindex) and EBRD index of bank reform (brindex) as proxies for business environment development, share of employment in business services in total employment (bsemp) as proxy for structural changes in labour inputs.

The relation between expansion of business services and development of market reforms can be formalised as: bsgdp ¼ f ð prsecgdp; spindex; lpindex; adpgdp; domcrgdp; M2gdp; netrate; erindex; brindex; bsempÞ

(5)

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Cross-country OLS estimates generated a model with high explanation power (R2 ¼ 0.903, F ¼ 9.286). The model, however, accounted for high levels of multicollinearity. Some independent variables were closely correlated, e.g. share of domestic credits with share of broad money in GDP or share of private enterprises in total output with EBRD index of large privatisation. After collinear variables were excluded, a simplified model brought more convincing results (t-values in parenthesis): bsgdp ¼ 1:596 0:373adpgdp þ 3:030lpindex þ 2:410M2gdp, (2,246)

(2:825)

(4:036)

R2 ¼ 0:766, F ¼ 18:559: All variables were significant at the 0.01 level and had expected signs. The regression equation indicated that development of business services was positively related to introduction of large privatisation programs and development of the domestic financial system sector and negatively with state administration of prices in four transition economies in the period 1991 –2000. These variables explained over three-quarters of the total variance in the share of business services in total value added. CONCLUSIONS

Shifts in sectoral structure in 1980s and 1990s were closely related to the rise in importance of KIBS. While manufacturing industries shrunk, KIBS accounted for major increases in terms of both total wage bill and value added in the late 1990s. Analyses performed by Antonelli, Drejer, Tomlinson, and Katsoulacos and Tsounis found strong correlations between growth in intensities of communication services purchased by other industries and the intensity of business services purchased by the same industries for EU member countries in terms of both levels and growth rates. This correlation was statistically significant in Slovakia and the Czech Republic as well, but accounted for much lower levels of total variance explained. Hungary provided a partial exemption, with a medium to strong correlation. Low values of correlation coefficients suggested that development of business services was not primarily driven by development of communication services. It was change in the system environment, rather than technological changes, which prompted spread of these industries in Hungary, the Czech Republic and Slovakia. A model using OECD and EBRD data provided some evidence for this assumption. There were some market reforms in the EU member countries (e.g. privatisation of telecommunication industries in the 1990s). These reforms, however, were aimed at improvement rather

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than change of economic system. With the economic system basically unchanged, the effect of technological innovations (e.g. introduction of new ICT standards and technologies) on business services was much more visible. Production functions for both communication and business services indicated that these industries made a significant contribution to economic growth in all countries observed. Output elasticity coefficients were quite similar in Slovakia and the EU member countries. Decreasing disequilibrium coefficients indicated that usage of communication services moved to equilibrium levels during the 1990s, due to high levels of labour and capital inputs (including FDI) and increasing competition in the sector. Hungary and the Czech Republic accounted for higher output elasticity of communication services than other countries. Business services seemed to be a particularly strong production factor in Hungary. KIBS have had an important role in creation and diffusion of knowledge and enhancing output capacity. Transition economies will be able to compete with advanced OECD members only via introduction of significant inputs from KIBS industries. Growing shares of KIBS in total generation of wealth and high levels of output elasticity in transition economies indicate that the process of convergence has already started. While highly interesting, cross-country comparisons in use of KIBS and their contribution to economic growth havea number of limitations: . . .

there is no consistent database on KIBS using the NACE classification, levels of disaggregation varied significantly across particular countries, and analysis of time series was difficult due to problems with deflation. Most countries did not publish data in constant prices and there was limited information on price indices and structural distribution of price changes in particular industries.

These limitations frequently restrict the explanation power of the input– output analysis to a couple of countries. Thorough implementation of advanced statistical standards in national accounts in the OECD member area and refining and re-computation of historical data are likely to make a significant contribution to increasing the explanation power of cross-country comparisons. ACKNOWLEDGEMENTS This research was kindly sponsored by the APVT Agency grant No. 51–023602 NOTES 1. Hauknes [1998] exemplifies several technological and non-technological service innovations in trade (formats and formulae in retailing, automated inventory), transport and logistics


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(containerisation, third party logistics), financial services (derivatives, share funds, database management, internet banking), consultancy services (Intangible asset valuation, rapid design and prototyping, environmental impact analysis) and telecom services (cellular telephony systems, broad band ISDN). 2. Hungary, for example, has been introducing some early market reforms since the 1970s and accounted for the highest shares of KIBS in total value added in the 1990s.

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