Download The Contribution of ICT and Related Sectors to Technological Change

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 Kt1 Lt2
(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 Lt2
(2
By using equation (2), TFP at time t can be calculated as follows:
TFPt = At = Qt / (Kt 1-2 Lt2 )
(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 Kct1 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)Kct1 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
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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
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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