Download Sectoral Productivity Growth in Saudi Arabia

Sectoral Productivity Growth in Saudi Arabia
Dr. Hamad M. Alhoshan
Department of Economics
College of Business, King Saud University
Saudi Arabia
1
Introduction:
The importance of productivity growth to economy-wide growth
and to the growth of different sectors within an economy probably cannot
be overstated. Economists generally recognize three main reasons why
economic growth takes place and standards of living improve over time.
These include expansion of the work force, accumulation of capital
(including human capital – educated and trained employees), and
innovation. When innovation and invention take root in an economy, they
allow that economy or at least some sectors of that economy to make
more with the same sets of inputs.1 Productivity is the term used in the
economic jargon to refer to the ability of an economy or a sector or
industry of that economy to produce goods and services with given
quantities of inputs.
Productivity and the growth in productivity are of crucial
importance to an economy or sector for the vital reason that they are
major determinants of the level and growth of per-capita income. This
last point is highlighted by the fact that a number of studies have found
that the so-called newly industrialized economies (NIEs) have in fact
experienced greater technological progress ( in terms of know-how) and
higher growth in their total factor productivity than other countries in
comparable situations, a factor which has gives a big boost to their
overall economic growth and might have put them ahead of other less
fortunate LDCs.2 This means by and large the relatively high per-capita
1
Barrow, Roberta j. and Xavier sala-j-martin, Economic Growth, Mcgrow Hill, Inc., New York 1995,
p 44.
2
Although the evidence on this issue is rather mixed, several studies have confirmed that the share of
TFP in the overall GDP percapita growth rate was relatively high. See for example A.Young, "The
Tyranny of Numbers: Confronting The Statistical Realities of East Asian Growth Experience'',
Quarterly Journal of Economics, 110, 1995.
2
income growth in those countries can be attributed to growth in
productivity.
However, for single commodity economies like that of Saudi
Arabia, the concept of productivity growth assumes added importance for
yet another very important reason. Owing to the fact that the country
depends on a primary depletable resource, and faced with the prospects
of fluctuating world oil prices and unstable world market conditions, the
issue of the diversification of the economic base is of paramount
importance to the country. A more healthy future-oriented economy
would require diversification away from oil sectors into other non-oil
sectors such as manufacturing, agriculture and non-oil extractive
industries. It is no wonder then that the Saudi government has pushed in
the direction of diversifying the economic base of the country and raising
the contribution of the non-oil sectors of the economy. In fact in
corroboration to the close attention paid by the Saudi government to it,
diversification has been among the major stated objectives of all the nine
five-years plans drafted by the government.3
It appears that the diversification objective is very closely related
to the productivity growth issue. In the light of significant capital flight
from the country and the rising labor costs, it may be that the scope for
enhancing productivity growth through expanded use of labor and capital
inputs is rather limited. Additional factor that militate against these latter
options is the difficulties and costs of launching a nation-wide training
effort to increase the supply of skilled workers. On the other hand, opting
for promotion of productivity growth 4 would allow the country to get
3
Ministry of planning kingdom of Saudi Arabia, seventh development plan, 2000-2004, p 114.
4
In a very illuminating study, Griliches concludes the process of innovation and technological change
and hence productivity growth) is amenable to economic analysis. The policy implications of the
conclusion can be of great importance generally speaking for government policy aimed at promoting
3
more from the existing labor and capital resources while attempting at the
same time to expand these resources at a moderate rates.
For example, by identifying higher productivity growth sectors, the
government might be able to redirect resources and attention from low
productivity growth sectors to high productivity growth sectors and thus
give a needed impetus to overall growth within the economy and at the
same time allow more diversification within the economy as the non-oil
productive sectors acquire a greater role in the production activities and
increase their share therein.
Research Problem and Objectives:
The extent of diversification within the Saudi economy can be seen
most clearly by examining the sectoral contributions by various oil and
non-oil sectors into GDP.
Table (1) below probably gives a clear
indication of the relative contribution of the various sectors over selected
years from mid 1980s until 2009. Two observations of interest for the
purposes of the present study seem to stand out quite clearly from
examining the figures in the table.
The first is the dominance of the oil sector throughout the whole
period as a major contributor to the Saudi GDP. The contribution of the
oil sector appears to be remarkably stable, ranging from about 25 to 30
percent approximately. While the Saudi authorities would not want to do
anything to jeopardize the status of the oil sector as an effective
contributor to the economic wellbeing of the country, it would certainly
want to see the non-oil sector play a greater role in the productive arena
of the country thus reducing dependence on oil.
technological and productivity advancement, see Zin Griliches, Hybrid Corn: An Exploration in the
Economics of Technological Change, Econometrical ( Oct. 1957).
4
Table 1
Percentage Sectoral Distribution of GDP
(1999 Constant Prices)- Selected Years.
2009
2004
2000
1999
1995
1990
1989
1986
4.7
5.3
5.7
5.7
5.8
6.0
6.3
4.8
Mining & quarrying
0.4
0.4
0.4
0.4
0.4
0.3
0.4
0.4
Manufacturing
12.7
11.2
10.4
10.4
8.8
8.3
8.6
8.7
Electricity, gas & water
1.7
1.6
1.4
1.4
0.4
0.5
0.6
0.5
Construction
7.1
6.7
6.6
6.5
6.6
6.5
7.1
8.0
Trade etc
8.7
7.9
7.6
7.6
6.3
6.5
6.9
7.5
&
6.8
5.3
4.6
4.6
4.4
4.4
4.6
4.9
Finance, insurance, real estate
13.3
10.4
10.0
10.2
10.6
13.1
14.4
14.9
3.9
3.6
3.6
3.5
3.4
3.6
3.9
3.9
Government services
17.7
18.3
19.0
19.3
18.9
19.0
19.8
20.2
Oil sector
28.2
28.3
29.3
28.7
32.9
30.1
25.7
25.4
Agricultural,
forestry
&
fishing
Transport,
storage
communication
&business services
Community, social & personal
services
* The figures do not add up to 100 because important duties are not included. Source: achievements of
the development plans, facts and figures, (1970-2009), ministry of planning, Saudi Arabia..
The cause of diversification away from oil would be better served
if certain of the non-oil sectors loom larger in the economy than others.
For example a relatively large role for the manufacturing sector and to a
less extent for the agricultural sector would provide a solid base for a
well-diversified and stable economy. But as another observation; the role
of the manufacturing sector in the production activity within the Saudi
economy appears to be moderate if not limited, averaging around 9-13
percent throughout the years considered. Since the contribution of the
manufacturing sector is considered by economists as a strong indicator
for the long term health of the economy,5 the situation in the Saudi
5
See for example Malcolm Gillis et. Al, Economics of Development, W. W. Norton and Company
New York, 1983, p 544.
5
economy leaves a lot to be desired. What is noteworthy here is that the
relative contribution of the industry does not show any tendency to
increase significantly overtime as one would expect.
Another observation pertains to the relative contribution of the
services sector. This sector comprises a number of the subsectors listed in
table (1) including transport, storage and communications, finance,
insurance, real estate and business services, community social and
personal services as well as government services. The total percentage
contribution of these has been in the order of 45 percent during the period
considered. This figure would be deemed too large by development
economists as the typical figure for a more balanced economy would be
in the order of 20 percent.6 What is needed of course is not so much
a shrinkage of this sector as it is a rapid expansion of the other relevant
sectors, via manufacturing and agriculture.
The productive roles of different economic sectors in the Saudi
economy as described above are not unique to Saudi Arabia but reflect
certain structural malformations which are characteristic to single
commodity or single crop economies.
On order to restructure the
economy in a pro-diversification fashion the Saudi government has
special emphasis to economic growth in the manufacturing sector and
some of the components of the services sector whose performance was
expected to have a bearing on the manufacturing sector such as the
transport and finance sectors. The agricultural sector also appears to be
well within the domain of the government's special attention. This
emphasis on the oft-mentioned sectors was reflected in relatively high
6
Although the share of the services sector is much higher in the developed industrialized countries this
probably reflects the role of the services sector which acts as a supportive sector for the highly
developed industrial sector. In view of the relatively modest role if the industrial sector in the Saudi
economy, we suggest the much smaller figure of 20% as an appropriate share for the services sector.
6
projected growth rates for these sectors in the several five-year
development plans drafted and implemented by the government so far.
Table (2) shows the projected and actual for average annual growth
rates of the various economic sectors during the periods of the different
five-years development plans. Except for electricity, gas and water, the
manufacturing sector was consistently assigned the highest growth rate
almost throughout all the five-year development plans considered ranging
from 18.8 percent in the third development plan to 7.9 percent in the
eighth development plan. The agricultural sector also appeared to be the
object of considerable attention with a projected average growth ranging
from 5.4 percent in the third plan to 3.2 percent in the eighth plan. The
government had put much stock in the manufacturing sector to play a
pivotal role in restructuring and diversifying the economy. At the same
time it had also put some emphasis on the agricultural sector, for
developmental and probably strategic purposes.
However, the actual growth performance of the favored sectors as
well as all the other sectors did not live up so well to the expectations
placed upon them. Thus the actual growth rates of the manufacturing
sector were close to the targeted figures in the sixth and seventh five-year
(not shown) development plans. But during the other periods, they
appeared to be rather wide off target. The agricultural sector surpassed
the projected figures for growth in the third and fourth plans but fell
considerable short in the fifth, the sixth and the eighth five-year
development plans. The rest of the economic sectors did not fare any
better as can be seen from the relevant figures in table (2). This relative
lackluster in the overall growth was not conducive to much noticeable
change in the share contributions of the different sectors to GDP.
7
Table (2)
Projected VS actual Growth Rates of GDP by Sector (Third Through
Eighth Five-year Plans) Average Growth Rate.
Third plan
Fourth plan
Fifth plan
Sixth plan
eighth Plan
Sector
Projected
actual
projected
actual
projected
actual
5.4
8.7
6.0
13.8
7.0
3.1
Agricultu
ral,
forestry
& fishing
9.8
5.7
3.0
-1.4
4.0
4.9
Mining &
quarryin
g
18.8
14.1
10.9
3.9
6.8
4.3
Manufact
uring
29.5
24.0
5
5.7
6.9
4.5
Electricit
y, gas &
water
-2.5
-1.4
-2.8
-6.7
3.8
00
Construct
ion
8.4
8.8
2.5
-1.5
3.0
1.3
Trade etc
12.9
7.1
5
-1.9
3.2
1.6
Transpor
t, storage
&
communi
cation
7.3
13.1
9.0
-11.3
5.7
1.8
Finance,
insurance
,
real
estate
&busines
s services
3.0
7.9
3.5
0.5
1.7
0.7
Communi
ty, social
&
personal
services
7.2
5.8
0.0
1.5
0.8
2.8
Governm
ent
services
1.4
-14.6
5.6
1.0
2.2
9.0
Oil sector
Source: Third through ninth five-year plans ministry of planning, Saudi Arabia.
projected
actual
projected
actual
3.1
1.2
3.2
1.4
9
2.1
7.9
3.0
4.9
3.8
6.2
5.9
5.5
2.7
4.2
5.7
4.0
1.3
6.7
4.7
6.2
2.9
1.1
1.3
5.2
7.5
5.6
9.1
4.1
0.7
5.9
5.2
3.4
1.6
3.5
4.9
2.7
1.5
3.8
2.7
3.8
0.6
2.7
2.6
What caused sectoral growth to fall short of expectations and thus
hamper the diversification objective for the economy? Since productivity
growth is very important determinant of overall economic growth, it
seemed reasonable to investigate what role productivity growth has
8
played in the economic growth on the different sectors of the Saudi
economy. Therefore, the task of the present research is to look into
productivity growth performance of the different sectors of the economy.
In particular, the following questions will be investigated in this paper:
(1) what does productivity growth performance look like in the different
sectors of the economy?
(2) what are the relative contribution labor and capital to sectoral
growth?
(3) what policy suggestions could be made to enhance productivity
growth and increase its role in the overall economic growth and
diversification of the economic base of the Saudi economy?
Methodology:
One method for estimating total factor productivity (TFP) is
through an estimation of an aggregate production function. The form of
the production function is usually taken to be of the Cobb- Douglas or
CES type. In this paper we will assume a CES production function type,
in which the Cobb-Douglas form is a special case. The CES production
function takes the following form:
Yi  A0 et [ k -i   (1   ) L-i ] v /  ............. (1)
Where Yi represents the output, Ki is the capital input, and Li is the labor
input.
 and (1   ) are distribution parameters that measure the
relative shares of the two inputs in the total output. Under the newclassical assumption, they are equal to the elasticities of the output with
respect to the inputs.
v
is a returns to scale parameter, and not necessary
equal to one.   1 , is a constant, which determines the value of the
elasticity of substitution between capital and labor,

. That is  
1
.
1 
In the limiting case where   0 , The CES production function reduces to
9
the Cobb-Douglas production function. In this case, the value of the
elasticity of substitution is equal to one. At  A0 et is an index of total
factor productivity (TFP) that shifts the production function, and which is
presumed to grow at exponential rate.7 Furthermore, TFP as a measure of
technological change reflects the amount of output growth that is not
accounted by the measured inputs growth. It is also reflects other factors
that affect output growth such as economic of scale and reallocations.
Log linearizing the CES function using Taylor’s series expansion
around   0 (the Cobb-Douglas case) yield the following linear
function:8
ln Yi  ln A0  t  v ln K i  v(1   ) ln Li 
 (1   )
(ln K i  ln Li ) 2 ...... (2)
2
Where A0 represent the level of the initial technology (TFP), and  is the
growth rate of the TFP. If   0 , the last term will drop out and the
function reduces to the Cobb-Douglas type.
Equation (2) can be written in a more compact form as:
ln Yi  1  t   2 ln Ki   3 ln Li   4 (ln Ki  ln Li ) 2 ..............
(3)
After estimating Equation (3), we can recover the parameters of the CES
production function as follows:
A0 = e 
1
=
2
2  3
v  2  3

7
8
 2 4 (  2   3 )
2 3
The technical change is assumed to be Hicks neutral.
See Greene 1997 and Intriligator, etl, (1996).
10
Further, the standard errors of estimates are derived using the
approximate formula suggested by Klein (1953), P.258.
Number of hypotheses will be tested such as constant return to
scale hypothesis, the case of   0 and hence, the reduction of the CES
function to the Cobb-Douglas type, and the value of the elasticity of
substitution.
Literature Review:
The literature on the sectoral productivity is vast and growing.
Different studies using different methods appeared in recent years.
Although, much of these studies were applied to developed countries due
mainly to the availability of disaggregated data, there are some studies on
developing countries.
Among these studies is the paper of Wu (1995) in which he
estimated three production functions for three sectors in China, state
industry, rural industry and agricultural, using panel data from 19851991, in order to examine total factor productivity growth, technological
progress, and technical efficiency change.
His results show that
technological progress is the main source of total factor productivity
growth in all three sectors of the economy and it dominates the change in
technical efficiency in these sectors. Furthermore, his findings indicated
that technical efficiency has improved in the rural sector but not in the
state and agricultural sectors. In addition, there are variations in
productivity and efficiency at the regional level.
Another study is that of Hatziprokopiou, M., Karagiannis, G., and
Velentzas, K. (1996) of the Albanian agriculture sector during the period
from 1950 to 1990, in which they used a trans-log production function to
examine the production structure, technical change and total factor
productivity in that sector.
They found that although there was a
11
significant technical change in the agriculture sector, it was decreasing
annually due to structural problems faced by this sector during this
period.
In comparative study, Hayami, and Ogasawara (1999) used data
that span a long period of Japan’s history (1888-1990) to examine
whether the pattern of economic growth as measured by the growth of
real GDP depends more on total factor productivity or more on capital
accumulation. Comparing to the United State, their results show that
even though Japan’s experience was similar to that of the United State
with respect to significance contribution of the total factor productivity to
economic growth, this contribution is much smaller for Japan because
Japan’s growth is still based on technology browning.
Jorgenson, and Stiroh (2000), used productivity accounting method
to look to the TFP among US industries. The authors found that TFP
among US industries are considerably different. That is TFP for some
industries has been positive, while it has been negative for others.
Therefore, these differences in TFP at industry level affected the estimate
of the wide economy TFP figure.
Gu, and Ho (2000), used similar method of the previous study to
compare growth of industrial productivity between Canada and the USA.
Among their findings is that inputs growth rather than TFP is the main
source of growth in all industries in the two countries.
Gu, Lee, and Tang (2000) also used productivity accounting
method to look to the TFP among Canadian industries during 19961 to
1995. They found a similar result concerning the dominance of inputs
growth as a source of output growth over most of their period of study.
The only exception was the period from 1988 to 1995, in which TFP
accounted for more than half of growth in output.
12
Chow, and Lin, (2002), estimated TFP for Taiwan for the period
1951-1999, and for the Mainland China for the period 1952-1998. Their
conclusion was that "The capital accumulation has been the most
important factor for increasing output in both economies…" 9
That is capital input accounted for 40% in Taiwan, and for 70% in
Mainland China, while TFP accounted for 40%, and 16% in both
economies, respectively.
Mahadevan, (2002) estimated TFP for Malaysia’s manufacturing
sector over the period 1982-96 using two different approachesparametric and non-parametric.
He found that while the parametric
approach shows that the growth of the output has been mainly inputsdriven and the TFP has been negative, the non-parametric method, in the
other hand, shows that the TFP has been positive.
Both methods,
however, revealed that the TFP in the manufacturing sector is low and
declining.
Othman, and Jusoh (2001) estimated an agricultural production
function to study the main source of growth in Malaysia’s agricultural
sector during the period 1960-1996. Their main finding was that in
general, increases in land input followed by capital input were the most
important factors of output growth during this period. On the other hand,
the role of TFP in output growth gradually rises in importance in recent
years.
In their study of Sri Lanka’s Manufacturing Productivity, Bandara,
and Karunaratne (2010) found that growth of TFP was the main source of
output growth during the first period of trade liberalization, 1979-88. In
contrast, during the second period of trade liberalization, 1988-97, input
growth was the main factor behind the output growth.
9
Chow, and An-loh Lin (2002), P.529.
13
The two studies of Chen, Yu, Chang, Hsu (2008) for China's
agricultural sector, and Verma (2012) for India's services sector. both
studies show that the growth of TFP was the major factor of the output
growth in the respective sector.
The Data:
The data used for estimation in this paper is a panel data relates to
the nine main sectors of the Saudi economy. These sectors are:
(1) Petroleum and minerals, (2) Manufacturing, (3) Electricity, gas and
water, (4) Construction, (5) Wholesale, retail, trade, restaurant and hotel,
(6) Transportation, storage and communication, (7) Banking, insurance,
researchers, consultants, and recruit, (8) Real estate, (9)Education, health,
social services, personal and community services. The agricultural sector
was excluded due to the lack of data. The data on labor and capital inputs
were taken from the Central Department of Statistics (CDS) surveys of
the main economic activities published on its web home page. 10 The
CDS conducts surveys at the sectoral levels that cover different aspects of
the sectors activities. It has published these surveys from 1995 to 2001,
then from 2005 to 2008. The second period surveys, however, are more
disaggregated than the first period, therefore, we had to aggregate it
according to the first period level of aggregation. The data covering the
period from 2002 to 2004 were missing, either because CDS did not
conduct the surveys in these years or just it did not publish it.
Data on the GDP, in value added, for each sector were also
obtained from CDS publication and also published on its web site.
Furthermore, we divided the current GDP figures by their real GDP
counterpart to derive the implicit deflator for each sector.
10
My appreciation goes to Mr. Moeid Alotiby for his help in compiling the data.
14
The measure of capital stock used in this study is the gross capital
stock approach, K t  i 1 I t i . Where I  investment.
L
The net capital approach is probably a more appropriate concept, but is
not followed here because, first, the low number of observation we have
in this study; and second, the problems associated with estimating net
capital stock: the initial capital stock, and depreciation rate.
Labor input, on the other hand, is measured by the total number of
workers, Saudi and Foreign, working in each sector. It is not adjusted for
the level of education or the work hours.
Estimation Results:
63 and 36 of panel observations, for both periods, respectively, will
be used to estimate the last equation in the methodology section.
Repeated here for convenience:
ln Yi  1  t   2 ln Ki  3 ln Li   4 (ln Ki  ln Li )2 .......... (3)
The fixed effect estimator will be used and tested for the panel.
This estimator is implemented by using dummy variables for each
individual sector. The model was tested for the fixed effects by the F-test
(see Greene 1990 for this type of test). The value of the F-test with 8 and
42 degree of freedom was 31.25, hence the common effect model was
rejected in favor of the fixed effect at 1% significance level.
Furthermore, due to the variation in scales, the variances are
expected to differ between sectors. Therefore, the model is estimated
using general least square estimation method (GLS).
we will also
estimate the model using data in real term as well as using nominal terms
data.
Table (3) presents the estimation results with fixed effects for the
period 1995-2001, in real term.
15
Table (3)
Independent Variable
ln A0

(1   )
v


R
2
=.99
Coefficients
Stander Error
t-test
7.95
0.71
11.13***
0.43
0.22
1.96*
0.57
0.22
2.59**
0.21
0.06
3.5***
-0.43
0.30
1.4
0.03
0.003
8.3***
SER =0.03
DW =0.81
Ftest  1450.66
SER is stander error of regression, DW is the Durbin Watson test. *** is significant at 1% level, **is
significant at 5% level, and *is significant at 10% level.
The results in the table show the R 2 coefficient is very high,
however, there is a positive auto-correlation as indicated by the DW
statistic. The results show that all coefficients, except  , are significant.
The non- significance of the  coefficient is interesting, since it indicates
that the CES function does not fit the data and the production function is
probably of a Cobb-Douglas type.
As far as the individual coefficient is concerned, the results
indicate that the share of capital input and labor input (elasticties) in the
output are both significant at 10% level and 1% level respectively. The
capital share is 43 percent, and is less than the labor share, of 57 percent.
The v term is significant with value about 0.21, and it significantly
less than one; this result suggests that there is a decrease return to scale in
these sectors as a whole.
Further, the  coefficient is significant at 1% level, and indicates
that all sectors experience a significant and positive growth rate of TFP.
Furthermore, such a result indicates that technical progress contributes
positively to the growth of the output of these sectors, although, the
contributions of the TFP to the output growth in these sectors is rather
small; not exceeding 3% annually. In contrast, labor inputs and capital
16
input both contribute about 57 and43% on average to the growth rate of
the output in these sectors, respectively. This suggests that these sectors
rely heavily on labor, and on physical capital accumulation more then on
technical progress as a mean of attaining higher output growth. 11
We also estimated the CES function for the second period, 20052008; however, the result did not differ significantly.
Furthermore,
estimating both periods using deferent specifications such as using
investment instead of capital stock or estimating the CDS function one
time with foreign labor input and another with Saudi labor, did not have a
significant effects on the results.
Finally, we interpolated the missing data, 2002-2004, and used the
full sample from 1995 to 2008 in current prices to estimate the CES
function in a fixed effect setting, using SUR estimation method and
correcting for auto-correlation. Table (4) bellow shows the estimation.
Table (4)
Independent Variable
Coefficients
Stander Error
t-test
ln A0
3.07
0.98
3.14***

0.73
0.07
10.42**
(1   )
0.27
0.07
3.86**
v
0.41
0.05
8.2**

-0.18
0.07
2.57**

0.06
0.005
11.71**
SER =1.03
DW =1.78
Ftest  27282.05
R 2 =.99
SER is stander error of regression, DW is the Durbin Watson test. *** is significant at 1% level, **is
significant at 5% level, and *is significant at 10% level.
The results from the current prices estimation show that all
coefficients are significant at the 1% level,. The results also indicate that
11
Chow, and An-loh Lin (2002) draw a similar conclusion for Taiwan and Mainland China. See also
Hayamia, and Ogassawara (1999) for the case of Japan.
17
the share of capital input in the output ( the elasticity) is significant and
equals to 0.73, while the labor share is 0.27.
The  term is negative and significant with value about -0.18;
which suggests that the CES function is on average a plausible
specification of production function in these sectors. Further, from this
estimated value of  we can recover the estimate of the elasticity of
substitution between labor and capital,  , to be around 1.22, which is
well above unity, in contrast to the specification of the Cobb-Douglas
production function.
The return to scale parameter,
v,
is again highly significant with
value equals to 0.41, and is significantly different from one. Further, the
magnitude of the return to scale parameter in the case of current prices
estimate is double that of the fixed prices estimate reported in table (3).
However, the conclusion is the same that is there seem to be a decreasing
return to scale in these sectors.
Finally, the estimated value of the growth of productivity,  , is
significant at 1% level and is found to be around 6 percent annually.
However, even though the estimated value of  is about double the value
found using the sample from 1995 to 2001 in fixed prices, the conclusion
remain the same that is these sectors rely heavily on the physical capital
accumulation more then on technical progress as a mean of attaining
higher output growth.
We also re-estimated the model allowing for different growth rate
of productivity among sectors. The results are reported in table (5).
The interesting difference between the current results and the
results in table (4) relates to the parameter  . It is now insignificant, even
though its value remains the same. This probably related to the effects of
18
the large coefficients estimated under this specification, and hence the
loose of degree of freedom.
With exception of construction and real estate sectors, six sectors
experience significant and positive growth rate of TFP, this indicates that
technology contributes positively to the growth of the output of these
sectors. The contribution of the TFP to the output growth in these sectors
varies between 2% and 14%. Furthermore, the growth rate of TFP in four
of these six sectors is rather small; not exceeding 5% annually. While the
highest growth rates of TFP are 14% and 9%., and are attained by
petroleum sector and manufacturing sector, respectively. On the other
hand, the only sector that experiences a significant negative growth of
TFP is the transportation sector.
Table (5)
Independent Variable
Coefficients
Stander Error
t-test
ln A0
6.23
0.42
14.82***

0.76
0.12
6.33**
(1   )
0.24
0.12
2.06*
v
0.25
0.05
27.78**

-0.18
0.16
1.125
0.14
0.05
2.75**
0.09
0.03
3.2**
0.05
0.01
3.97**
0.11
0.23
0.49
0.05
0.005
9.33**
-0.03
0.007
-3.37**
0.06
0.005
11.70**
-0.009
0.009
-1.04
0.02
0.003
7.11**
SER =1.09
DW =2.16
1
2
3
4
5
6
7
8
9
R 2 =.99
Ftest 
28880.63
SER is stander error of regression, DW is the Durbin Watson test. *** is significant at 1% level, **is
significant at 5% level, and *is significant at 10% level.
19
Conclusion and Final Remarks:
This study attempts to analyses the productivity growth
performance of the different sectors of the Saudi economy. For this task,
it estimated a CES production function using different samples over the
period 1995-2008.
The main finding is that TFP is significant and
positive in most of the sectors considered. It varies between sectors, but
it is in general in the range of 3% to 6%.
However, the conclusion to be drawn from the various estimation
results is that it seems that the contribution of the growth of TFP in the
output growth in these sectors' growth is very low, when it is compared to
these sector overall growth rates reported in table (2). This suggests that
the sectoral output growth is dominated by the growth of capital and labor
inputs more than the growth of TFP.
On the policy ground, the government may be able to increase
overall economy total factor productivity by giving subsidies and
redirecting resources and attention from low productivity growth sectors
to high productivity growth sectors. Furthermore, giving incentives and
encouraging some key sectors to increase their expenditures on research
and development are an important policy options that would contribute to
fostering growth in technological progress ( in terms of know-how).
Finally, more research is needed on this ground such as growth
accounting and more sophisticated estimation methods. Furthermore,
more analysis and decomposition of TFP is needed, especially the issue
of technical efficiency, as a source of growth in TFP, is an important
question that deserve more investigation.
20
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