Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 et [ 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 et 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 References: Bandara, Yapa M. W. Y., and Neil D. Karunaratne (2010), "An Empirical Analysis of Sri Lanka Manufacturing Producivity slow-down", Journal of Asian Economics, 21 PP., 391-403. Barrow, Roberta j. and Xavier sala-j-martin (1995), Economic Growth, McGraw Hill, Inc., New York, p. 44. Chena, Po-Chi, Ming-Miin Yu, Ching-Cheng Chang, and Shih-Hsun Hsu (2008), "Taotal factor Productivity Growth in China Agricultural Sector", China Economic Review, 19, PP., 580-593. Chow, and An-loh Lin (2002), " Accounting for Economic Growth in Taiwan and Mainland China: A comparative Analysis", journal of Comparative Economics, Vol., 30, PP.207-530. Greene, William (1990), Econometric analysis, Macmillan Publishing Company. United State. Griliches, Zin, and Hybrid Corn (1957), “ An Exploration in the Economics of Technological Change”, Econometrical, Oct. Gu, Wulong, and Musn S. Ho, " A Comparison of Industries Productivity Growth In Canada and The United State", AEA Papers and Proceedings, Vol. 90, No. 2, PP. 172-175. Gu, Wulong, Frank C. Lee, and Jianmin Tang (200), " Economic and Productivity Growth in Canadian Industries", AEA Papers and Proceedings, Vol. 90, No. 2, PP. 168-171. Hatziprokopiou, Michalis, Gannis Karagiannis, , Kosta Velentzas, (1996)," Production structure, Technical Change, and Productivity Growth in Albanian Agriculture" journal of Comparative Economics, Vol., 22, PP.295-310. 21 Hayami, Yujiro, and Junichi Ogasawara (1999), “ Changes in the Sources of Modern Economic Growth: Japan Compared with the United State”, journal of the Japan and International economies, Vol., 13, PP. 1-21. Intriligator, M., Ronald Bodkin, and Cheng Hsiao (1996), Econometric Models, Techniques, and Applications, Prentice-Hall, Inc., Second Edition. Jorgenson, D., and Kevin Stiroh (2000), " Industry-level Productivity Competitivenss between Canada and the United State" AER; May, Vol., 90, No.2, PP.161-167. Klein, L. R. A (1953) A Textbook of Econometrics, Row, Peterson, Evanston, Illinois. Mahadevan, Renuka (2002), “ Is There a Real TFP Growth Measure for Malaysia’s Manufacturing Industries”, ASEAN Economic Bulletin, Vol., 19, No., 2, PP.178-90. Malcolm Gillis et. Al (1983), Economics of Development, W. W. Norton and Company New York, p. 544. Othman, Jamal, and Mansor Jusoh (2001), " Factor Share, Productivity, and Sustainability of Growth in the Malaysian Agriculture Sector", Asean Economic Buletin, Vol. 18, No., 3, PP. 320-330. Verma, Rubina (2012), "Can Total factor Productivity explain value added growth in services", Journal of Development Economics, Doi:10.1016/j.jdeveco. 2011.12.003. Wu, Yanrui (1995), “ Productivity Growth, Technological Progress, and Technical Efficiency Change in China: A Three-Sector Analysis”, journal of Comparative Economics, Vol., 21, PP.207-229. Young, A. (1995), "The Tyranny of Numbers: Confronting The Statistical Realities of East Asian Growth Experience'', Quarterly Journal of Economics, 110. 22