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THE CONTRIBUTION OF ICT and RELATED SECTORS TO TECHNOLOGICAL CHANGE and LABOR PRODUCTIVITY: AN INPUT-OUTPUT ANALYSIS FOR TURKEY 1. Introduction Economic growth can be achieved either through increased or improved use of labor and capital or through a rise in total factor productivity (TFP). The Information and Communication Technologies (ICT) as a factor of production is considered as a special type of capital with its own characteristics. In recent years, there has been a significant rise in interest on the role of ICT in the productivity growth and/or economic growth process. The emergency of the phenomenon of the “The New Economy” or “KnowledgeBased Economy” has also intensified further the research interest on the role of ICT. The ICT are generally defined as manufactured products and services intended to fulfil information processing, communication and use of electronic means to control physical processes1. The ICT are aimed to play a key role in increasing the speed of diffusion and use of new knowledge within industries, markets and nations. The ICT are also intended to help firms to acquire the information needed to change the production technology and optimize the use of factors of production. In general, ICT are regarded as contributing to economic growth in three ways (Schreyer, 2000). First, as a producer sector, ICT production has increased rapidly in many countries, especially in the last decade, and constitute between 2.5 and 4.5 of GDP in OECD countries. Therefore, ICT sectors themselves can be regarded an important contributor to output growth, particularly for countries where these sectors have a considerable share in overall output, such as the USA. Second, ICT sectors provide capital and intermediate inputs to ICT-using sectors. By this way, ICT sectors contribute capital accumulation and efficient use of inputs in overall economy, and hence embodied technical change and economic growth. Third, as a special capital input, ICT could 1 See OECD (2001b, p. 4) for a similar definition. 1 generate various positive externalities or spillovers and organizational changes, and hence enhance disembodied technical change or Total Factor Productivity (TFP) growth. The striking increase in the growth of US labor productivity that occurred in the second half of the 1990s was accompanied by an investment boom in ICT equipment. Empirical studies usually find a strong positive relation between ICT-use and productivity performance2. A large part of the increase in output can be accounted for by rapid growth in the stock of ICT capital. Most of the new studies were carried out at macro level and found a positive contribution of ICT to both output growth and productivity growth (see. Oliner and Sichel (2000), Schreyer (2000), OECD (2001a), Oulton (2001)). Measuring empirically the role of ICT in production performance is, however, a difficult and complicated task mainly because of the lack of detailed information about ICT investment flows and capital services among sectors and within the national economic accounts. Due to the limitation imposed by data, empirical studies on this subject were carried out mostly on developed countries. That’s why, on this issue, there has been very limited empirical studies on Turkey in which economic growth performance has a particular importance in her middle term perspective and needs to be improved to overcome economic struggle. The purpose of this paper is to analyse if there is a positive relation between ICTinput use and labor productivity in manufacturing industry in Turkey. Due to time series data limitation, the magnitude of ICT sector will be measured via aggregated 1996 inputoutput table. By employing some input-output techniques, inter-sectoral linkages of ICTrelated sectors will be measured and the relation between ICT input use and labor productivity will be discussed. The remainder of the paper is organised as follows. In section 2, the theoretical background of total factor productivity (TFP) and the connection between TFP and ICT capital stock and ICT service input is set. Then, the relevance of input-output tables in such studies is discussed. Due to limitations imposed by data, we explain in what way we are going to use input-output table in this paper. The linkage analysis is briefly explained in section 3. In section 4, data and the aggregation of 205 sector input-output table into 40 sector level, where the ICT sectors are seen separately is explained. The findings about ICT sectors, the sectoral linkage measures and the relation between ICT input use 2 In fact, earlier studies did not find a significant positive correlation between ICT and productivity. See, for example Bailey and Gordon (1988), Loveman (1988) and Berndt and Morrison (1995). 2 and labor productivity in manufacturing sector are discussed in section 5. The findings, limits, prospects and the further extentions of this study are summarised in section 6. 2. Theorethical Backgroud of TFP There are a variety of measures that can be used to measure the productivity performance of an economy. Partial (labour and capital) productivity and TFP, technical change and efficiency are commonly used performance measures. First, we briefly explain the growth accounting method for TFP. The traditional approach to estimate TFP, namely the Solow (1956, 1957) method is the commonly used methodology. The Solow method can be explained briefly by using the following aggregate Cobb-Douglas production function: Qt = At Kt1 Lt2 (1) where Q is output, A is TFP, K and L are factors of production, capital and labour, respectively. The variable t stands for time and the parameters 1 and 2 are output elasticities of capital and labour inputs respectively. The TFP, capital and labour inputs enter into the production function in multiplicative forms which mean that these three factors of production are independent of each other. Under the assumption of constant returns to scale in production, equation (1) above can be written as: Qt = At Kt1-2 Lt2 (2 By using equation (2), TFP at time t can be calculated as follows: TFPt = At = Qt / (Kt 1-2 Lt2 ) (3) Alternatively, the level of TFP can also be calculated by taking the logarithm of both sides of equation (3)above: 3 log(TFPt) = log(At) = log Qt – (1-2) logKt - 2 logLt (4 Given the values of variables K and L, the calculation of TFP requires estimation of coefficient 2, namely the output elasticity of labour. In order to calculate 2, Solow uses the assumption of presence of perfectly competitive markets. The assumption of perfectly competitive markets here stands for Euler’s theorem which states that factors of production are paid their marginal products. Using this assumption, it is possible to calculate contributions of factors of production to output growth simply by multiplying the rate of increase in factors of production by their respective shares in income generated. Hence, the coefficient 2 can be regarded as equal to the share of wage payments in total income or valued added. Accordingly, under the assumption of constant returns to scale in production, the output elasticity of capital (1) can simply be calculated by subtracting 2 from 1. In essence, by using this methodology, the level of TFP is calculated by subtracting the contributions of factors of production from the level of output, and the rate of TFP growth can be calculated by subtracting the contributions of factors of production from the rate of output growth or by calculating the rate of change in the level of TFP. Consequently, this methodology treats the rate of TFP growth as an unexplained residual in the production process. The methodology outlined above is usually called ‘growth accounting’. There are a number of empirical studies aimed at the estimation of TFP growth in the growth accounting framework3 (see Saygili et all. (2001) for detailed paper list)4. The empirical findings indicate a significant contribution of TFP growth to output growth. The growth accounting approach is extended to measure the contribution of ICT to TFP growth and output growth performance. The contribution of ICT as a capital stock and as a special service input to output growth are empirically measured in a number of studies (see, Colecchia and Schreyer (2001), Klein et.all (2001)). 3 As an alternative to the growth accounting approach, we could also mention the direct-econometric estimation of TFP growth by the estimation of the aggregate production function specified in equation (2). In this case, the rate of TFP growth is estimated by including a time trend variable into the production function. The time trend variable here stands for a shift in the production function over time. 4 See also Maddison (1987) and Denison (1993) for a survey. 4 The impact of ICT capital stock on economic growth is analysed in the framework of an extention to growth accounting methodology and translated into following framework: Qt = At Kct1 Knt Lt (5) where Q is output, A is TFP, Kc is ICT capital stock, Kn is all other capital and L is labour.1, etc. denotes each factor’s share in total cost. The contribution of ICT as a capital stock to overall output can be measured by the rate of change of ICT capital stock, weighted by its share in total income. It is argued that ICTs benefits are not limited to ICT capital stock contribution on output growth. The ICT capital stock creates externalities, or spillovers and improves overall productivity and aggreagte output growth. So, there is a link between TFP and use of ICT as a special capital input. The possible determinants of TFP are investment in knowledge, the number of international patents granted, foreign direct investments and the extent of high-technology goods exports and ICT investments and expenditures. Thus, the contribution of ICT to output growth could also occur via TFP growth. As a special service input, ICT could generate various positive externalities or spillovers and organizational changes, and hence could contribute to TFP growth. Following equation (5), TFPt = At = Qt / ((1+θ)Kct1 Knt Lt ) (6) where θ represents spillovers, TFP can be formulated as, log(TFPt) = log(At) + 1 θ log (Kct)= log Qt – 1 logKct – 2 logKnt - 3 logLt where the standard TFP calculation, as a residual, captures both the externality generated by ICT capital and the overall rate of technical change in case of externalities. A recent study5 that used KLEMI type production function in which the impacts of ICT capital stock and ICT service input are decomposed is of Klein et.all (2001). They defined the following production function as KLEMI production function: 5 For more studies see, Wolff and Nadiri (1993), Wolff (1997), 5 X = K c1 L c2 M c3 e [ c4 ( ICT) I – c5 / I.k 1] e [ c6 K / ( ICT . L) ] e t c 7 e c 8 (7) where X represents real output, K is the total real stock of capital divided into two components, ICT capital (ICT) and other capital (KO) stock, L is labor, I is the information technology service input, M is all other intermediate inputs, k1 is the ratio of the ICT capital stock to labor and t is the time trend to proxy disembodied technological change. In sum, the two contributions of ICT on output growth can be estimated by using extended production function. One is directly via ICT capital stock contribution and the other is via ICT service input and/or externalities through TFP growth. In production function estimation process, input-output tables are essential and natural instruments to look at the workings of the economy from inside. In sectoral level analysis, the intermediate flows of ICT inputs and sectoral final demand shares of ICT can easily be seen in input-output tables. The sectoral and/or total value added, the information technology service input (mainly telecommunications), energy inputs and all other input data series can be derived from annual input-output tables, if available in international classification. (see Klein, et all (2001) for an application to US economy). However, for the time being, production function estimation method is impossible for us to employ in this study due to serious data constraints. With the very limited data option, we feel it would be more convenient to use linkage analysis, the backward and forward linkage indicators of ICT-sector on overall economy. The estimating of ICT capital and the information technology service input and their contribution on TFP and output growth in production function form would be difficult but challenging topic of another study. 3. Sectoral Linkage Analysis The analysis of linkages is used to examine the interdependency in sectoral production structures. The backward and forward linkages have extensively been used for the analysis of interdependent relationship between economic sectors. Many different methods have been proposed for the measurement of linkage coefficients. This analytical tool has been improved and expanded in several ways. The intersectoral linkage methods may be summarized in two categories. One refers to “Traditional Methods” based on 6 input-output and Leontief inverse coefficients. The other is “Hypothesis Extraction Methods” which mainly measures what happens to overall production when a sector i is extracted hypothetically from the economy. 3.1 Traditional Methods 3.1.1 Chenery-Watanabe Method One of the most common and old method used in input-output studies is based on both Leontief demand driven model (x=Ax + y) and supply driven model (x’=x’B + v). Chenery and Watanabe (1958) suggested using the column sums of the input coefficient matrix A as measures of backward linkages (BL) of sector i. Similarly, the forward linkage (FL) of sector i is defined as the row sums of the input coefficient matrix A. The Chenery and Watanabe method measures only the first round of effects generated by the inter-relationships between sectors. Although this method has been used recently, it has gradually been set aside, mainly because of its neglect of indirect effects (Andreosso B.– O’Callaghan and Yue, Guoqiang, (2000)). 3.1.2 Rasmussen Method In linkage analysis, Rasmussen (1958) proposed to use the column (or row) sums of the Leontief inverse, (I – A)-1 , to measure intersectoral linkages. The column sums of the Leontief inverse matrix is defined as the “backward linkage”. Sector i’s backward linkage measures the effects of an increase in final demand of sector i on overall output; in other words, it measures the extent to which a unit change in the demand for the product of sector i causes production increases in all sectors. Similarly, the row sums of the Leontief inverse matrix is defined as the “forward linkage”. It measures the magnitude of output increase in sector i if the final demand in each sector were to increase by one unit. In other words, it measures the extent to which sector i is affected by an expansion of one unit in all sectors. Rasmussen linkage indicators were used in many studies to identify key sectors in an economy. However, several writers have criticised the use of Chenery-Watanabe’s and Rasmussen linkage coefficients employed in the identification of key sectors. Jones (1976) argued that Chenery-Watanabe’s method “has three deficiencies: double counting 7 of causal linkages, neglect of indirect impact, and failure to distinguish domestic effects from operating on foreign economies”. It seems that Rasmussen’s measures have overcome the neglect of indirect linkage. Unfortunately, Jones, argued it “measures direct plus indirect effects on supplier industries, but not on user industries: i.e. backward but not forward linkages”. Thus, Rasmussen’s measures of forward linkage (the row sum of the Leontief inverse) do not provide a measuer of forward linkages symmetrical to that provided by the column sum for backward linkages. Jones suggests using the row sum of the output inverse matrix instead of the row sum of the Leontief inverse matrix to measure total forward linkages. Formally, the supply driven model is expressed as x’=x’B + v where B and v denote the output coefficient matrix (i.e. intermediate sales as share of total sales including final demand) and the primary input row vector, respectively. The solution to that equation is x’ = v Z, where Z = (I – B)-1 So, the forward linkages based on the output coefficient matrix is the row sum of the output inverse matrix Z. The forward linkage of sector i measures the extent to which a unit change in the primary input of sector i causes production increases in all sectors. (Andreosso B.– O’Callaghan and Yue, Guoqiang, (2000)). 3.2 Hypothesis Extraction Methods 3.2.1 Original Extraction The basic idea of the hypothesis extraction method is to extract a sector i hypothetically from an economic system, and then to examine the influence of this hypothetical extraction on other sectors of the economy. Starting with the Leontief’s model: x = (I – A)-1 y, it may be assumed that one sector is extracted from the economy. Extraction of kth sector, for example, simply means that the kth row and column of the input coefficient matrix A are deleted (not replaced by zero). Thus, the new model can be written as x (k) = (I – A(k))-1 y(k), where A is nxn matrix and A(k) is (n-1)x(n-1) matrix by deleting kth sector from A, y(k) and x(k) are (n-1) dimension vectors corresponding to final demand and output vectors, respectively. Expectedly, the results of output vector x(k) should be less than x, since sector k is eliminated from the economic 8 system.. Then, the sum of the differential between the vector x and the vector x(k) measure the linkage effect of the extracted sector k on total output. However, there are two shortcomings in the above original extraction method. First, Cella (1984) argued that it can not distinguish the total linkages into backward and forward linkages. Second, Dietzenbacher (1997) argued that the hypothesis of simply extracting an entire sector from the economic system is unrealistic and excessive. 3.2.2 Cella’s Measure To overcome the drawback of the original extraction method, Cella (1984) presented an improvement on the original extraction method. Instead of starting with backward or forward linkage, he defined a total linkage effect of each sector, and then identified its two components as backward linkage and forward linkage. All sectors of an economy can be divided into two groups: group one consists of the sectors that are to be extracted from the economy, called “Sector 1”; group two encompasses all the other sectors of the economy, called “Sector 2”. Then, the basic Leontief’s model x = Ax + y may be written in 2x2 form. Cella (1984) argues that if there does not hypothetically exist any relations between the two groups of sectors, i.e., if sector 1 does not sell or but any intermediate products to or from sector 2, then a new input coefficient matrix A is defined where A12 and A21 are equal to zero. The solution to the new matrix gives the output of sector 1 and sector 2 after extracting. Thus, the total linkage effect (TL) can be defined as: TL = e’ (x- x’) where x’ is the output column vector of all sectors after extracting sector 1, e is the input coefficient matrix column summation vector. The decomposition of total linkage into backward and forward linkages is obtained with matrix manipulations. (see Andreosso B.– O’Callaghan and Yue, Guoqiang, (2000) for detailed mathematical presentation). Cella’s method excludes purely internal transactions between sectors, and it is based on a consistent input-output model of the economy with a fixed set of technical coefficients. The backward and forward linkages are not symmetrical and are not comparable to the corresponding linkages given by Chenery-Watanabe and Rasmussen because this method is based on the decomposition of the total output of all sectors. 9 3.2.3 Pure Linkage Sonis et al. (1995) made a modification on the basis of Cella’s measure of interindustry linkages and presented a concept of pure linkage. The basic idea is to eliminate the feedback and internal effect completely from Cella’s measure. According to the basic decomposition equation, the output of sector 1 is: x1 = H y1 + H A12 G22 y2 where H = (I – A11 – A12 G22 A21)-1 , G22 = (I – A22)-1. Then, the pure backward linkage defined by Sonis et al.(1995) is: BLp = e’2 [G22 A21 H] y1 + e’2 [G22 A21 H A12 G22] y2 The meaning of the pure backward linkage is the impact of sector 1’s input coefficient on the production of sector 2. Similary, the pure forward linkage is: FLp = e’1 [A12 G22][( G22 A21 H ) y1 + G22 (I + A21 H A12 G22 ) y2 ] However, Sonis et al. (1995) argued that in above equation the economic meaning is not clear, because the matrix G22 is an internal output multiplier matrix from final demand to output for sector 2. An alternative way to measure the pure forward linkages is to use an output coefficient matrix. Based on the supply-driven model equation x’=x’B+ v, the pure forward linkage based on output coefficient matrix can be derived as: FLp =[v1 H* B12 Z22 e1+ v2 Z22 B21 H* B12 Z22 e2 ] where H* = (I – B11 – B12 Z22 B21)-1 , Z22 = (I – B22)-1 . 3.2.4 The Dietzenbacher & van der Linden Method Dietzenbacher and van der Linden (1997)’s revised extraction method is similar to the Cella’s decomposition technique. Starting from the 2x2 form of the basic Leontief’s model x = Ax + y, in the case of backward linkages, it is assumed that all the elements of column j of the input coefficient matrix are equal to zero. In other words, sector j buys no intermediate inputs from any production sector. x(j) denotes the total output vector after extracting sector j from the economic system. Thus, the total absolute backward linkage of sector j is defined as: d(j) = e’ [ x – x(j) ] In other words, the total backward linkage of sector j equals to the difference between the original output vector and the output vector obtained when sector j is 10 extracted from the economic system, multiplied by the sum of the input coefficient matrix. Since the primary concern of linkage analysis is the structure of production, the size effect of sectors is eliminated in the linkage measurements. To this end, the result d(j) is normalised by dividing the absolute figures by the value of the sector j’s production: BLD j = d(j) / xj * 100. The corresponding forward linkages can be obtained similary by using the extraction technique, starting with the supply-driven model x’=x’B + v. Here, it is assumed that sector j sells no output to any of the production sectors. Row i in the output coefficient matrix B is set equal to zero. The solution to the new matrix gives the corresponding output x’’ (i). The difference between x’ and x’’ (i) multiplied by e is defined as the absolute forward linkage: d*(i) = [ x’ – x’’(i)] e. Normalizing with the sector i’s production value, the forward linkage of sector j is : FLD i = d*(i) / xi * 100. 3.2.5 Summary of the Methods Andreosso B.– O’Callaghan and Yue, Guoqiang (2000) has summarised all the methods discussed above. The measures of backward linkages explore the effects of a change in final demand on the total output of sectors, while the measures of forward linkages explore the impacts of a change in primary inputs on the total output of sectors. Rasmussen’s backward (forward) linkage indicators measure the effects of one unit change in final demand (primary inputs) of each sector on total output of all sectors. The backward (forward) linkage indicators of Dietzenbacher and van der Linden method measures the extent of the impact derived from the hypothetical extraction of a sector on total output, when final demand (primary inputs) increases by one monetary unit in all sectors. This actually includes the effect of all other sectors on total output through the feedback in connection with the inputs (or sales) of the extracted sector. Differing from the Dietzenbacher and van der Linden method, the pure linkage method measures only the effects of the sector on the output of other sectors (it excludes the efffect on its own output). The common characteristic of the methods is that their backward and forward linkages are all symmetric. The backward linkages are based on the same demand-driven model and the forward linkages on the supply-driven model. 11 4. Data The main sources of data is the 1996 Input-Output Use and Supply Tables in 205 sectors detail prepared by State Institute of Statistics (SIS) according to International Standard Industry Classification (ISIC) Revision 3. The 205x205 input-output table is aggregated into 40x40 level. The sectors 1 through 3 are agriculture, animal husbandry, forestry and fishing. The sectors 4 through 7 covers mining sectors. The sectors 8 through 27 are manufacturing sectors. The sectors 28 through 37 are services sectors. The sectors 38, 39 and 40 are ICT sectors (see. Appendix A for aggregation key of 40 sectors from ISIC Rev.3 industry codes). 4.1 The Definition of ICT-Sectors In this study, OECD (2001a, p.6) definition of ICT-producing sector is adapted. According to the (ISIC) Revision 3 classification, the ICT- sectors includes the following industries: Sector 38: ICT-Manufacturing 3000- Manufacture of office, accounting and computing machinery 3130- Manufacture of insulated wire and cable 3210- Manufacture of electronic valves and tubes and other electronic components 3220- Manufacture of television and radio transmitters and apparatus for line telephony and line telegraphy 3230- Manufacture of television and radio receivers, sound or video recording or reproducing apparatus, and associated goods 3313- Manufacture of industrial process control equipment Sector 39: ICT-Services 5150- Wholesale of machinery, equipment and supplies 7123- Renting of office machinery, equipment (including computers) 6420- Telecommunications 7200- Computer and related activities 12 In aggregated 1996 Input-Output table, to see the total magnitude of ICT sector in Turkish economy the ICT-manufacturing (38) and ICT-services (39) sectors are summed up in sector (40). There has been some data problems while aggregating input-output table into 40 sectors, especially in ICT-service sector. In 205 sector input-output table, one of the important components of ICT service sector, telecommunications (ISIC Rev.3, 6420), is accounted together with “National post activities (6411)” and “Courier activities other than national post activities (6412)”. To decompose the telecommunication sector from others, domestic telecommunication net revenues belong to 1996 are obtained from Turkish Telecommunication Company. By using these data, the 85 per cent of total value of 6411 & 6412 & 6420 sectors is taken as telecommunication data and accounted in sector 39 in 40 sector input-output table. Similarly, in 205 sector input-output table, the wholesale trade sector (5100) is accounted as one item where the “Wholesale of machinery, equipment and supplies sector” (5150) is covered in. The output value of sector 5150 belong to 1996 is obtained from SIS Services Department. By using this data, the 5 percent of total value of 5100 sector is decomposed and accounted in sector 39 in 40 sector input-output table. To calculate the sectoral labor productivity values, sectoral value added and employment statistics of manufacturing sectors for 1995-1997 period in ISIC Rev.3 classification are obtained from SIS Manufacturing Department. 5. Results 5.1 General Finding about ICT Sector in Input-Output Table The aggregated input-output table into 40 sectors where the ICT-manufacturing and ICT-services sectors are presented separetely (see App. B) gives an idea about the magnitute of ICT sector in Turkish economy for 1996. The ratio of total ICT sector production over total production is about 2.06 per cent, and the ratio of total ICT sector value added over total value added is about 2.24 per cent. It means that ICT sector in Turkish economy only accounts for a small share of the economy. In OECD (2001a), it is shown that, in general, across the countries ICT sector’s share in value added ranges from 4.1 per cent (in Austalia) to 10.7 per cent (in Korea) of total business value added. Korea and Ireland have the largest ICT manufacturing sector, in contrast Australia, New 13 Zealand and Turkey only have a very small sector producing manufactured ICT goods. In fact, in our study, the share of ICT-manufacturing sector’s production in total manufacturing production is about 2.62 per cent according to 1996 input-output table. However, a small sector can make a large contribution to growth and productivity performance if it experiences much more rapid volume growth than the remainder of the economy. It is beyond this study to measure the ICT sector’s volume growth for a certain time period due to data collection problems. Table 1 shows the ratio of sectoral ICT input use over sectoral total input uses. The ICT sector (40) has the highest ICT input use with 43.4 per cent among all sectors. Financial Intermediation (34) sector follows with 14.5 per cent. Medical Equipments (24), Business (35), Trade (31), Electrical Machinery (23), Motor Vehicles (25), Metal Equipments (21), Publishing (15) sectors are among the sectors in which ICT input use is relatively high. In OECD (2001) paper, certain services, such as telecommunications, financial services, insurance and business services are found among the key users of ICT, which is confirmed with our findings. On the other hand, some of the traditional manufacturing sectors and/or the sectors which have relatively high shares in manufacturing production, such as Textile (10), Clothing (11), Food (8), Plastic (18), Basic Metal (20), Chemical Products (17), Machinery (22), have very low ICT input-use ratio. 14 TABLE 1: THE RANK ORDER OF THE RATIO OF SECTORAL ICT INPUT USE OVER TOTAL INPUT USE (From Highest to Lowest) Sectors ICT input use over Share in total inputs (%) Total production (%) 38 -ICT Total 34 Financial intermediation 24 Medical Equip. 35 Business 31 Trade 23 Electrical machinery 25 Motor vehicles 21 Metal equipments 15 Publishing 30 Construction 7 Quarry. of stone, sand and clay 5 Petroleum and natural gas 22 Machinery 26 Other transportation vehicles 29 Water 27 Furniture 32 Hotels, motels, restaurants 2 Forestry 17 Chemical Product 20 Basic Metal 13 Wood Prod. 8 Food 12 Leather Prod. 19 Non-metallic Other Mineral 9 Tobacco 11 Clothing 33 Transportation 14 Paper Prod. 18 Plastic 3 Fishing 10 Textile 37 Ownership of dwelling 28 Electric + Gas 4 Coal and lignite 6 Metal ores 1 Agricultural&Animal husbandry 16 Refined Petrol Products 36 Public Services 43.44 14.52 8.93 8.92 8.10 2.73 2.34 2.22 2.10 1.82 1.79 1.77 1.57 1.50 1.45 1.25 1.12 0.90 0.82 0.78 0.77 0.77 0.76 0.74 0.70 0.69 0.69 0.61 0.61 0.54 0.42 0.33 0.28 0.23 0.18 0.17 0.16 - Source: Aggregated 1996 Input-Output Table. 15 2.06 3.63 0.04 4.33 12.16 0.56 1.91 1.60 0.71 7.06 0.28 0.16 2.13 0.23 0.35 1.10 3.00 0.34 2.57 2.57 0.82 6.27 0.48 1.60 0.66 2.06 10.91 0.56 1.11 0.37 3.47 2.42 1.58 0.31 0.08 13.24 2.94 4.30 Table 2 indicates the distribution of ICT expenditures over final demand items. The gross fixed capital expenditure has the highest share of 9.38 per cent among all the final demand items. Another words, the share of ICT gross fixed capital formation in total fixed capital formation accounts for 9.38 per cent. It is a positive sign that there is a considerable amounts of ICT-investment in economy. The relatively high value of ICT fixed capital expenditure may indicate that ICT capital expenditure may contribute capital accumulation and efficient use of inputs in overall economy, and hence embodied technical change and economic growth. The share of ICT intermediate consumption in total intermediate consumption is about 2.88 per cent, the ICT export share in total exports is about 2.66 percent and the ICT import share in total imports is about 5.96 percent. TABLE 2: TOTAL ICT EXPENDITURES and PRODUCTION (% of total) 1996 2.88 2.37 1.56 0.81 9.38 6.13 3.25 -1.78 2.66 5.96 1 Total intermediate consumption 2 Consumption -Private -Government 3 Gross fixed capital formation -Private -Government 4 Changes in stocks 5 Export 6 Imports Total demand1 Final demand2 2.50 1.26 The share of ICT production The share of ICT value added The share of ICT input use The share of ICT manufacturing 2.06 2.24 1.81 2.62 1The sum of intermediate consumption, consumption, gross fixed capital formation, changes in stocks and exports. 2The sum of consumption, gross fixed capital formation, changes in stocks and exports minus imports, import and production taxes plus subsidies. 16 5.2 The Results of the Linkage Analysis To see the impact of ICT sector on overall economy, the sectoral linkage analysis explained in Section 3 is carried out in 1996 aggregated input-output table. The linkage measures are presented in Table 3 and Table 4. Table 3 indicates the results of CheneryWatanabe and Rasmussen’s methods, not only for the ICT sector as in the remaining methods presented in Table 4, but for all the sectors in the economy. The reason is that Chenery-Watanabe’s coefficients are those of the sums of the columns and the sums of the rows of input coefficient matrix. Similarly, the backward linkages of Rasmussen’s method is the sum of the column of Leontief inverse matrix. These values are the summary indicators of the aggregated 40x40 input-output table which may give an idea to readers about overall inter-sectoral relations. In Table 3, the backward linkage of Rasmussen method for ICT sector is 1.64066 point. It means that if the final demand of ICT sector increases one unit, the total output in the economy will increase 1.64066 point. In general, the manufacturing sectors, such as Leather Products (12), Clothing (11), Textile (10), Wood Products (13), Basic Metals (20), Motor Vehicles (25) have higher backward linkage indicators compared to ICT sector. It may indicate that ICT sector has relatively low intersectoral backward linkages. The forward linkage of Rasmussen method for ICT sector is 2.22026, relatively higher than backward indicator. It means that if the primary inputs of ICT sector increases one unit, the total output in the economy will increase 2.22026 point. In general, Medical Equipment (24), Basic Metals (20), Paper Products (14), Metal ores (6), Refined Petrol Product (16), Chemical Products (17), Electric&Gas (28), Financial Intermediation (34) sectors have higher forward linkage indicators compared to ICT sector. It means that ICT sector products are relatively less used as an input in intermediate consumption compared to above mentioned sectors. 17 TABLE 3: INTERSECTORAL LINKAGES in 1996 I-O TABLE (Traditional Methods) Sectors 1 Agricult.l&Animal husbandry 2 Forestry 3 Fishing 4 Coal and lignite 5 Petroleum and natural gas* 6 Metal ores 7 Quar. Of stone, sand and clay 8 Food 9 Tobacco 10 Textile 11 Clothing 12 Leather Products 13 Wood Products 14 Paper Products 15 Publishing 16 Refined Petrol Products 17 Chemical Products 18 Plastic 19 Non-metallic Other Mineral 20 Basic Metals 21 Metal Equipments 22 Machinery 23 Electrical machinery 24 Medical Equipments 25 Motor vehicles 26 Other transportation vehicles 27 Furniture 28 Electric & Gas 29 Water 30 Construction 31 Trade 32 Hotels, motels, restaurants 33 Transportation 34 Financial intermediation 35 Business 36 Public Services 37 Ownership of dwelling 38 -ICT Total Chenery-Watanabe Backward Forward linkages1 linkages2 0.38960 0.13730 0.23507 0.20746 0.13304 0.35735 0.21653 0.64422 0.58993 0.67244 0.60834 0.66984 0.66101 0.59446 0.50540 0.41983 0.57151 0.63501 0.48467 0.66059 0.59826 0.52985 0.55686 0.42575 0.59772 0.36263 0.54776 0.33714 0.15729 0.55748 0.26957 0.48443 0.39188 0.30573 0.37006 0.00000 0.17133 0.37453 1.06525 0.22936 0.01533 0.12870 0.50603 0.03040 0.09851 0.52833 0.06698 0.78770 0.04776 0.34856 0.52242 0.60532 0.12011 0.78252 1.14623 0.25181 0.32441 1.30295 0.39193 0.52647 0.26663 0.05573 0.19499 0.14171 0.08206 0.80861 0.08821 0.05502 1.49768 0.07621 1.37703 0.84408 0.66275 0.00000 0.00000 0.45408 Rasmussen Backward Forward linkages3 linkages4 1.66242 1.22798 1.40661 1.33423 1.23116 1.59020 1.35944 2.13017 2.00701 2.30005 2.30983 2.44697 2.25230 2.10003 1.92028 1.54307 2.03450 2.19658 1.79235 2.23686 2.15946 2.02986 2.07614 1.77040 2.16097 1.62028 2.11893 1.47776 1.25169 2.01735 1.43223 1.87466 1.65107 1.50119 1.65674 1.00000 1.31159 1.64066 Source: Aggregated 1996 Input-Output Table. 1 The column sums of the input coefficient matrix A. The row sums of the input coefficient matrix A. 3 The column sums of the Leontief inverse matrix. 4 The row sums of the output coefficient matrix. * The very high value occurred in Rasmussen’s forward linkage measure may be ignorable due to very high import value,caused to high and negative final demand value. 2 18 1.67620 2.70864 1.12760 3.12100 24.45944 4.29287 2.58196 1.43686 1.08355 1.69813 1.06656 1.68632 2.28861 3.81818 2.01651 2.70684 3.01301 1.92905 1.87717 3.45254 1.85279 1.98368 1.99600 4.55196 1.50290 1.94411 1.29611 2.79155 2.26481 1.01870 1.55311 1.35742 1.56442 2.52618 1.72534 1.00000 1.00000 2.22026 The results of the hypothesis extraction method applied to ICT sector are presented in Table 4 . By using the 1996 aggregated input-output table, we try to answer what happens if the total ICT sector (40) is extracted from the economic system hypothetically. TABLE 4: THE FORWARD and BACKWARD LINKAGES of ICT SECTOR (Hypothesis Extraction Methods) 38 -ICT Total Traditional Methods Hypothesis Extraction Methods Chenery-Watanabe BL FL TL BL 0.37453 0.45408 0.82861 Rasmussen BL FL 38 -ICT Total TL 1.64066 2.22026 3.86092 Original Extraction FL TL - BL - 0.06221 Cella's Measure FL TL 0.01337 0.59028 0.60365 Pure Linkage (Sonis et all.(1995) BL FL TL 38 -ICT Total 0.19863 0.15245 0.35108 The Dietzenbacher & van der Linden BL FL TL 38 -ICT Total 0.23233 0.37556 0.60789 The magnitude of ICT backward linkages in Pure Linkage and the Dietzenbacher and van der Linden Methods is small, 0.19863 and 0.23233, respectively. It means that if ICT sector is extracted from the economic system hypothetically, the output effect will be small and limited. The low values may suggest that ICT sector’s direct backward inter-linkages with other sectors are weak. The relatively high value of Rasmussen backward linkage measure (1.64066) shows that the indirect connections of ICT sector in economy is in a sense much more complex and important than the direct ones. Although the magnitude of forward linkages in Pure Linkage and the Dietzenbacher and van der Linden Methods is small, 0.15245 and 0.37556, respectively, 19 they are relatively higher than backward linkages. The direct forward linkage indicators represent the ratio of intermediate demand to total output of ICT sector. The higher values of forward linkages relative to backward linkages may suggest that ICT sector is relatively intermediate demand oriented rather than final demand oriented in sectoral output. Another words, ICT products are relatively more used in intermediate consumption as an input. The parallel result may be seen in Table 2 where the share of total demand of ICT sector (including intermediate consumption) accounts for 2.5 per cent, while the share of final demand of ICT sector in total final demand accounts for 1.26 per cent. 5.3 The Sectoral ICT Input Use and Labor Productivity in Manufacturing Sector The correlation tool can be used to determine whether two ranges of data move together, that is, whether large values of one set are associated with large values of the other (positive correlation). The labour productivity measure can be calculated by dividing output or value added by total employment. In our study, the sectoral value added values are divided by the sectoral average number of employees for manufacturing sectors in which the ICT input use is relatively higher than agriculture and the part of the services sector. The manufacturing labor productivity growth is calculated for 1995-1997 period, in which the sectoral ICT input use ratio is assumed to be constant and equal to 1996 value as an average for this period. Table 5 shows the correlations between manufacturing sector ICT input use and labor productivity growth. It is found that the correlation between the ICT input use and labor productivity in manufacturing sector is positive (0.2511). Although its value is positive, it is small and near zero. It means that if the ICT input use in total manufacturing sectors increases, the labor productivity increases, too. But its effect seems to be limited. Increase in labor productivity may have a potential to lead higher economic growth rate overall economy. 20 TABLE 5: ICT INPUT USE and LABOR PRODUCTIVITY GROWTH in MANUFACTURING INDUSTRY (For 1995-1997 period) Sectors 8 Food 9 Tobacco 10 Textile 11 Clothing 12 Leather Products 13 Wood Products 14 Paper Products 15 Publishing 16 Ref. Petrol Products 17 Chemical Products 18 Plastic 19 Non-metallic Ot Min. 20 Basic Metals 21 Metal equipments 22 Machinery 23 Electrical machinery 24 Medical Equipments 25 Motor vehicles 26 Other trans. Vehicles 27 Furniture 38 ICT-Manufacturing ICT input use over total inputs 1996 Labor productivity (VAi / Li) 1995 1996 1997 0.00767 1567.7 2685.4 4303.6 0.00702 1922.1 3252.8 3702.1 0.00419 1039.1 1537.6 2990.0 0.00686 740.7 1198.9 2317.7 0.00762 692.5 1187.8 2593.6 0.00770 1035.5 1414.1 2859.5 0.00615 1996.2 2656.0 4371.6 0.02104 1147.7 3355.3 9809.5 0.00157 13510.5 57117.0 158256.0 0.00819 3705.2 16007.1 10870.7 0.00607 2421.2 3156.9 5674.4 0.00735 1635.8 2928.5 5727.7 0.00778 1810.6 3021.2 7403.7 0.02216 1251.1 1967.9 4029.3 0.01565 1621.4 2510.9 4664.9 0.02730 1529.7 2220.2 5489.0 0.08932 731.5 1163.4 3104.9 0.02342 2200.0 3757.1 8799.5 0.01502 868.2 1334.6 4788.3 0.01249 820.9 1618.2 3211.6 0.46546 2539.2 4137.0 6863.8 % Change in labor productivity 1996 1997 1995-97 1.71 1.69 1.48 1.62 1.72 1.37 1.33 2.92 4.23 4.32 1.30 1.79 1.67 1.57 1.55 1.45 1.59 1.71 1.54 1.97 1.63 1.60 1.14 1.94 1.93 2.18 2.02 1.65 2.92 2.77 0.68 1.80 1.96 2.45 2.05 1.86 2.47 2.67 2.34 3.59 1.98 1.66 1.66 1.39 1.70 1.77 1.94 1.66 1.48 2.92 2.27 1.71 1.53 1.87 2.02 1.79 1.70 1.89 2.06 2.00 2.35 1.98 1.64 Correlation coefficient = 0.251063094 Covariance = 0.155562911 As a summary, the results of linkage analysis and the labor productivity analysis suggest that the ICT sector inter-linkages are low, the correlation between ICT input use and labor productivity is positive but weak. It may indicate that although ICT sector share in total production is low and inter-sectoral linkages are weak, it has a positive effect on labor productivity which needs to be straigthen. The reasons of limited impact of ICT sector on labor productivity may be linked to the weakness in productivity enhancing variables or factors. 21 6. Conclusion and Further Perspective The share of ICT sector’s value added in Turkey is calculated from 1996 inputoutput table is about 2.24 per cent, which accounts for a small share in overall economic activity. This result is similar to that reported in OECD (2001a). A relatively high value of ICT gross fixed capital expenditure is found, 9.38 per cent. This may be a positive indicator of ICT capital expenditure contribution on ICT capital stock accumulation, on efficient use of inputs in overall economy, and hence on embodied technical change and economic growth. The backward and forward linkages of ICT sector are relatively low and weak compared to other sector’s indicators. This may indicate that the inter-sectoral relations of ICT sector are insufficient and weak. This paper has also shown that there is a positive but weak correlation between sectoral ICT input use and sectoral labor productivity growth. This result is something similar to the findings about Turkey in Saygılı et all.’s (2001) study in which they argued that there has been very low –in fact negative level of TFP growth in 1992-2000 period and the low level of TFP might indicate insufficient and/or ineffective ICTinput use. The result of this paper is based on one-year input-output analysis and is needed to be confirmed with comparative input-output analysis and/or with econometric analysis. Unfortunately, Turkey has only one input-output table constructed according to ISIC Rev.3 classification in which ICT manufacturing and ICT service sectors may be seen and decomposed easily. However, to see the structural change of ICT input use on economic growth performance and to compare 1990 and 1996 tables on the same base, we put some effort to measure and decompose ICT sectors from 1990 input-output table which was prepared according to ISIC Rev.2 classification. We regret to say that there has been many data problems in decomposition and accumulation of ICT sector in 1990 table that we feel it may not give sound results even if we measure it because of assumptions and generalizations. Looking on the positive side, 1998 input-output table is forthcoming. Moreover, the SIS is planning to construct and report input-output tables annually starting soon. We feel that this study may constitute a base and may be improved to analyse the relation between the ICT input use and labor productivity and output growth for following next periods in comparative structure. 22 By collecting and using time series data, further analysis could also focus on the estimation of ICT capital stock for overall economy and also for specific industries, such as industries which may have high growth potentials, and/or traditional industries. As in explained section 2, the impact of ICT capital stock and ICT input use on TFP and output growth performance can be analysed in production function form as an alternative method to input-output technique. 23 References Andreosso B.–O’Callaghan and Yue, Guoqiang, (2000), “Intersectoral Linkages and Key Sectors in China 1987-1997- An Application of Input-output Linkage Analysis”, 13th International Conference on Input-Output Techniques, Macerata, Italy, August 21-25, 2000. Bailey, M. and R. 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(1997), “Spillovers, linkages and technical change”, Economic Systems Research 9 (1), pp. 9-23. 26 APPENDIX A: SECTORAL AGGREGATION KEY Sector ISIC Revision 3 Code: 01 Agricultural and Animal husbandry 0111, 0112, 0113, 0121, 0122, 0140 02 Forestry 0200 03 Fishing 0500 04 Coal and lignite 1010, 1020 05 Petroleum and natural gas 1110 06 Metal ores 1310, 1320 07 Quarring of stone, sand and clay 1410, 1421, 1422, 1429 08 Food 1511, 1512, 1513, 1514, 1520, 1531, 1532, 1533, 1541, 1542, 1543, 1544, 1549, 1551, 1552, 1553, 1554 09 Tobacco 1600 10 Textile 1711, 1712, 1721, 1722, 1723, 1729, 1730 11 Clothing 1810, 1820 12 Leather Products 1911, 1912, 1920 13 Wood Products 2010, 2021, 2022, 2023, 2029 14 Paper Products 2101, 2102, 2109 15 Publishing 2211, 2212, 2219, 2221, 2222, 2230 16 Refined Petrol Products 2310, 2320 17 Chemical Products 2411, 2412, 2413, 2421, 2422, 2423, 2424, 2429, 2430 18 Plastic 2511, 2519, 2520 19 Non-metallic Other Mineral 2610, 2691, 2692, 2693, 2694, 2695, 2696, 2699 20 Basic Metals 2710, 2720, 2731, 2732 21 Metal Equipments 2811, 2812, 2813, 2891, 2892, 2893, 2899 22 Machinery 2911, 2912, 2913, 2914, 2915, 2919, 2921, 2922, 2924, 2925, 2926, 2927, 2929, 2930 27 23 Electrical Machinery 3110, 3120, 3140, 3150, 3190 24 Medical Equipment 3311, 3320, 3330 25 Motor Vehicles 3410, 3420, 3430 26 Other Transportation Vehicles 3511, 3512, 3520, 3530, 3591, 3592, 3599 27 Furniture 3610, 3691, 3692, 3693, 3694, 3699 28 Electric and Gas 4010, 4020 29 Water 4100 30 Construction 4510, 4520, 4530, 4540 31 Trade 5000, 5200, 5020, 5110, 95 per cent of 5100 32 Hotels, Motels, Restaurants 5510, 5520 33 Transportation 6010, 6021, 6022, 6023, 6030, 6110, 6210, 6301, 6302, 6303, 6304, 6309, 15 percent of 6420 34 Financial Intermediation 6511, 6519, 6591, 659, 6599, 6601, 6603, 6720, 6712, 6719 35 Business 7010, 7020, 7111, 7121, 7122, 7310, 7320, 7411, 7412, 7413, 7414, 7491, 7492, 7499, 7421, 7422, 7430, 7493, 7494, 7495, 8010, 8021, 8022, 8090, 8511, 8512, 8519, 8520, 8532, 9000, 9111, 9112, 9211, 9212 9213, 9214, 9219, 9220, 9233, 9241, 9249, 9301, 9302, 9303, 9309 36 Public Services 7511, 7512, 7513, 7514, 7521, 7522, 7523, 7530 37 Ownership of dwelling - 38 ICT – Manufacturing 3000, 3130, 3210, 3220, 3230, 3312, 3313 39 ICT – Services 5 percent of 5100 as 5150, 85 per cent of 6420, 7123, 7210-90 40 ICT - Total Sectors 38 plus 39 28 29