INTERNATIONAL REAL ESTATE REVIEW

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INTERNATIONAL REAL ESTATE REVIEW A Publication Associated with the Asian Real Estate Society and the Global Chinese Real Estate Congress Editorial Office: Executive Editors Rose Neng Lai Hongyu Liu Ko Wang

Editors Chin-Oh Chang Kyung-Hwan Kim Miki Seko Tsuriel Somerville Hongwei Wang Jing Yang Tyler T. Yang

Associate Editors Brent Ambrose Steven Bourassa Dennis R. Capozza Su Han Chan Kwong Wing Chau Jie Chen Shu-Mei Chen Yen-Jong Chen Ping Cheng Jichang Dong Robert H. Edelstein Fang Fang Chang-Chun Feng Jeffrey Fisher Yuming Fu Stuart Gabriel Bo Gao John L. Glascock William Goetzmann Joseph E. Gyourko Lu Han William Hardin Martin Hoesli Eddie Hui Shenghua Jia Jyh-Bang Jou Yuichiro Kawaguchi Jinu Kim Michael LaCour-Little Charles Ka Yui Leung Youguo Liang Kim Hiang Liow Chu-Chia Lin Colin Lizieri Chiuling Lu Bryan D. MacGregor Jiangping Mei Graeme Newell Kanak Patel Liang Peng Weidong Qu Timothy J. Riddiough Stephen E. Roulac James D. Shilling Tien Foo Sing Sheridan Titman Piyush Tiwari Kerry D. Vandell Neng Wang K. C. John Wei Joseph Williams Jianping Ye Siqi Zheng Yinggang Zhou

International Real Estate Review, Department of Finance and Business Economics, Faculty of Business Administration, University of Macau, Taipa, Macau, China

November 22, 2013

Professor Albdulkarim K. Alhowaish Urban and Regional Planning Department College of Architecture and Planning University of Dammam P.O. Box 2397, Dammam 31451 Saudi Arabia E-mail: [email protected]

Dear Professor Alhowaish We are pleased to inform you that your paper entitled “Causality between the Construction Sector and Economic Growth: The Case of Saudi Arabia” has been accepted for publication in the International Real Estate Review, assuming that you will be willing to make some minor editorial revisions if our copy editor deems them necessary. We ask that you comply fully with the “submission guidelines”, which we are enclosing for your convenience. Please pay particular attention to the sections entitled Headings, Exhibits and Footnotes. Please make sure that you sign the enclosed copyright release form and return to me together with the final version of the manuscript in Microsoft Word or WordPerfect formats either via e-mail or facsimile. We wish to express our sincere gratitude to you for allowing us an opportunity to review your manuscript. As with all other high quality journals, we rely heavily on the submission of outstanding articles like yours to maintain and improve the reputation and readership of the Journal. Again, we thank you very much for considering the International Real Estate Review as the outlet for your fine research. We look forward to receiving more papers from you in the near future.

Sincerely,

Rose Neng Lai Executive Editor

Telephone: +(853) 8397-4728 Fax Number: +(853) 2883-8320 E-mail: [email protected] Website: http://www.umac.mo/fba/irer/

The International Real Estate Review is associated with the Asian Real Estate Society and Global Chinese Real Estate Congress. It serves as a vehicle for communication between real estate academics, professionals, and policy makers.

Volume 8, Number 1 – Spring 2015

Financial and editorial support is provided by The University of Macau and the Johns Hopkins University

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Book: Silberberg, E. (1990). The Structure of Economics: A Mathematical Analysis (2 McGraw Hill Pub. Co.: New York.

nd

edition).

Joumal article: Granger C.W.J. (1969). Investigating Causal Relationship by Econometric Methods and CrossSpectral Methods, Economtrica, 34, 4, 424-43 8.

Article in book: Dhrymes, P.J. (1971). Price and Quality Changes in Consumer Capital Goods: An Empirical Study in: Price Index and Quality Change: Studies in New Methods of Measurement, Grilielses, Z. (ed.), Harvard University Press, Cambridge, Mass., 88-149.

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Construction Sector and Economic Growth in Saudi Arabia

131

INTERNATIONAL REAL ESTATE REVIEW 2015 Vol. 18 No. 1: pp. 131 – 147

Causality between the Construction Sector and Economic Growth: The Case of Saudi Arabia Albdulkarim K. Alhowaish Associated Professor, Urban and Regional Planning Department, College of Architecture and Planning, University of Dammam. P.O. Box 2397, Dammam 31451, Saudi Arabia. Email: [email protected] Tel. +966 56677 6060 Fax. +966 3 857 8580

This study empirically investigates the relationship between construction flow and economic growth for Saudi Arabia during the 1970–2011 period. Integration and cointegration techniques are applied to investigate the relationship between the construction sector and economic growth. Given the fact that Saudi Arabia is an oil-rich country, oil revenue has also been included in the model as a conditioning variable. The findings reveal that there is strong causality that runs from economic growth and oil revenues to the construction industry with feedback effects that only run from construction to economic growth (i.e. bi-directional relationship). However, the construction industry does not Granger-cause oil revenues in the longrun (i.e. uni-directional relationship). The findings also reveal that there exists no causal relationship between economic growth and oil revenues in the long-run. Economic growth and oil revenue are “independent” effects on construction growths in the long-run. However, oil revenues have significant effects on economic growth just in the short-run. Therefore, oil revenues play a critical role in economic growth in the short-run and therefore, the growth of the construction industry in the long-run. The accuracy of the estimated results is validated by performing several diagnostic tests. Keywords Construction Sector, Economic Growth, Oil Revenues, Granger Causality, Saudi Arabia

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1.

Alhowaish

Introduction

Worldwide, the construction sector is considered a prime source of employment generation, which offers job opportunities to millions of unskilled, semi-skilled, and skilled workers. The sector is also considered to play a key role in generating income in both the formal and the informal sectors of a national economy. Development organizations, researchers, and policy makers maintain that the construction sector is an essential component of national economic growth and development because of its strong linkages to other economic sectors (Hirschman 1958; World Bank 1984; Bon and Pietroforte 1990; ILO 2001; Ewing and Wang 2005; Khan 2008; Jackman, 2010). According to Hillebrandt (2000), the most significant factor that affects construction demand is the general economic situation and expectations about how it will change. In a buoyant economy with a high and growing gross domestic product (GDP), a satisfactory balance of payments, and a reasonable level of employment—and with expectations that this situation will continue—generally, standards of living rise, consumer expenditures increase, and the government is able to spend to improve services to the community. In a depressed situation, the entire position is reversed and less demand for construction will be created. Given a buoyant economy, the construction sector in Saudi Arabia is the largest and fastest growing market in the Gulf region. As of 2010, ongoing construction projects in the Gulf were valued at $1.9 trillion, and one-quarter of the developments were located in Saudi Arabia (US-SABC 2011). The Saudi construction sector has great potential for growth. Demand for housing project construction is sharply rising, given the 3.5 percent annual growth in the Saudi population. Demand is also increasing from the massive industrial expansion through the National Industrial Cluster Development Program and from the completion of the six Economic Cities. Demand for commercial and institutional construction projects is also growing. All of these factors play a critical role in reviving growth in the construction sector in the short- and long-run. The Saudi government has committed itself to spending more than $400 billion on different construction projects during the next five years to boost the national economy and provide more job opportunities for its citizens (9th NDP 2007). In light of these growing development factors, a critical investigation into the effect of the construction sector on economic growth in Saudi Arabia is needed, and hence, whether a construction promotion strategy is a relevant growth factor for the Saudi economy needs to be examined. Specifically, this study attempts to answer the following main question: Is there a relationship between the construction sector and economic growth in Saudi Arabia? And if a relationship exists, what is the direction of causality between these two variables?

Construction Sector and Economic Growth in Saudi Arabia

133

Given the fact that Saudi Arabia is an oil-rich country, oil revenues could be the cause and the consequence of both growth in the national economy and construction industry. To validate this assumption, oil revenue will be included in the analysis as a conditioning variable along with the construction and economic growth variables. To the best of the author’s knowledge, no study of this nature has been conducted on Saudi Arabia or any area in the Gulf region. Furthermore, studies on the construction sector and its effect on economic growth have been largely restricted to developed countries. Hence, by focusing on Saudi Arabia as a developing country, this study adds to the rather sparse body of knowledge on the effect of the construction sector on developing countries in general and on the Gulf-states in particular. The remainder of this paper is organized as follows. Section 2 presents a review of the literature. Section 3 presents the data, methodology, and results. Section 4 concludes the study.

2.

Literature Review

A number of researchers have addressed the contributions from the construction sector to a country’s aggregate economy, and valuable literature is available on the linkage between the construction sector and other sectors of an economy. Several researchers conclude that the construction sector has strong forward and backward linkages with other sectors of a national economy. Hillebrandt (1985) defines construction as a complex sector of the national economy that involves a broad range of stakeholders and linkages with other economic activities. Park (1989) argues that the construction industry generates one of the highest multiplier effects through its extensive backward and forward linkages with other sectors of the economy. The World Bank (1984) states that the importance of the construction industry stems from its strong linkages with other sectors of the economy. Many studies on construction economics (Turin 1969; Wells 1986; Field and Ofori 1988; Bon and Pietroforte 1990; Bon 1992; Green 1997; Hillebrandt 2000; Lean 2001; Rameezdeen 2007; Myers 2008; Dlamini 2011) emphasize the important role of the construction sector in national economic growth. They all argue that construction makes a noticeable contribution to the economic output of a country. Construction generates employment and income for the people; therefore, the effects of changes in the construction industry on the economy occur at all levels and in virtually all aspects of life. These studies also emphasize that construction has a strong linkage with many economic activities, and that whatever happens to the industry directly and indirectly influences other industries and, ultimately, the wealth of a country. Hence, the construction industry is regarded as an essential and highly visible contributor to the growth process (Khan 2008, p. 282). However, many of these studies are largely restricted to developed nations. Little consensus

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seems to exist on the relationship between the construction sector and economic growth in developing nations. Authors such as Wang and Zhou (2000), Hassan (2002), Tan (2002), Kim (2004), and Dlamini (2011) all argue that the construction sector and its related activities are not drivers of economic growth, but followers of fluctuations in the national economy. In contrast, researchers such as Bon et al. (1999), Hongyu et al. (2002), Abdullah (2004), Khan (2008), and Jackman (2010) find that bi-directional causality exists between the construction sector and economic growth. Hence, no clear-cut evidence exists on the issue as it relates to developing countries, possibly suggesting that the ability of the construction sector to boost economic growth depends on the economy at hand. These results indicate that, to promote construction strategies to boost the national economy, the government and policy makers in developing countries should carefully make their decisions based on empirical analysis. In other words, the extent to which a construction promotion strategy is relevant to national economic growth needs to be carefully investigated; otherwise, construction and related activities are believed to have extremely high capitaloutput ratios compared with other types of investments and are deemed to be “resource absorbers,” which negatively affects the national economy and its socio-economic development plans in the long run.

3.

Data Methodology and Results

The main objective of this study is to investigate the causal relationship between the construction sector and economic growth in Saudi Arabia. The dataset employed consists of annual data on construction output and GDP from 1970 to 2011. Data on construction flows, real GDP and oil revenues in local currency were directly obtained from the Saudi Arabian Monetary Agency (SAMA) database. All variables are expressed in natural logarithms, thus allowing the estimated coefficients to be considered as the elasticity of the relevant variables. The maximum likelihood estimation technique of Johansen and Juselius (1990) is employed to determine the existence of a cointegration equation. This cointegration technique determines only the existence of a relationship between variables and not the direction of causality. Hence, Granger causality and the vector error correction model (VECM) are employed to determine the direction of causality in both the short run and the long run. A basic estimation model is mathematically presented as follows: (1) 𝐿𝑛(𝐺𝐷𝑃𝑡 ) = 𝛽0 + 𝛽1 𝐿𝑛(𝐶𝑜𝑛𝑠𝑡𝑡 ) + 𝜀𝑡 𝐿𝑛(𝐶𝑜𝑛𝑠𝑡𝑡 ) = 𝛼0 + 𝛼1 𝐿𝑛(𝐺𝐷𝑃𝑡 ) + 𝑣𝑡 (2) where coefficient (𝛽1 ; 𝛼1 ) is expected to positively determine construction (𝐿𝑛𝐶𝑜𝑛𝑠𝑡) and economic growth (𝐿𝑛𝐺𝐷𝑃) in both the long run and the short run.

Construction Sector and Economic Growth in Saudi Arabia

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The Johansen and Juselius (1990) maximum likelihood test of cointegration requires the same order of integration I(1) of all of the variables. The augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) unit root tests are employed to determine the unit root. The ADF test checks for serial correlation by adding lagged values of explanatory variables and is represented as: 𝑛

∆𝑦𝑡 = 𝛽1 + 𝛽2𝑡 + 𝛿𝑦𝑡−1 + 𝛼𝑖 ∑ ∆ 𝑦𝑡−𝑖 + 𝜀𝑡

(3)

𝑖=1

where 𝜀𝑡 represents the white noise error term, ∆𝑦 = 𝑦𝑡 − 𝑦𝑡−1 , and t represents the time trend. The PP unit root test uses a non-parametric method to take care of serial correlation in the error term without adding a lagged difference term. The PP test estimates the modified t-value associated with the estimated coefficient so that serial correlation does not affect the asymptotic t distribution. (4) ∆𝑌𝑡 = 𝛽0 + 𝛽1 𝑌𝑡−1 + 𝜀𝑡 Table 1 presents the results of the ADF and PP unit root tests, which imply that both variables are stationary at their first difference level - I(1). The unit root result is encouraging for further econometric estimations when using the Johansen and Juselius (1990) technique for long-run relationships. If two nonstationary time series regressions result in stationary residuals, then both variables are said to be cointegrated or have a long-run association. Table 1 Variables LnGDP LnConst

Results of Unit Root Test Level -2.777 (0.070) -0.486 (0.881)

ADF 1st difference -4.901 (0.000) -4.186 (0.002)

Level -3.021 (0.041) -3.865 (0.004)

PP 1st difference -4.944 (0.000) -4.959 (0.000)

Decision I(1) I(1)

Notes: Figures in the parenthesis are the probability value. Source: Author estimation by using EViews8.

The lag length for a vector autoregressive (VAR) system is selected based on the minimum Akaike Information Criterion (AIC), Schwarz information criterion (SC) and Hannan-Quinn information criterion (HQ) – see Appendix (I). The estimation of cointegration by using this method involves an estimation for following an unrestricted VAR model: 𝑛

𝑌𝑡 = 𝐴0 + ∑ 𝐴𝑖 𝑌𝑡−1 + 𝜀𝑡

(5)

𝑖=1

where 𝑌𝑡 is a n × 1 vector of non-stationary I(1) variables and, in this study, Y= GDP and construction; hence, 𝐴0 is a 3 × 1 constant vector. 𝑛 is the

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number of lags, 𝐴𝑖 is a 3 × 3 matrix of the estimated parameters, and 𝜀𝑡 is a 3 × 1 independent error term. To determine the existence of the cointegration of 𝑌𝑡 , the unrestricted VAR is converted into a vector error correction model (VECM): 𝑛−1

∆𝑌𝑡 = 𝐴0 + ∑ 𝜑𝑖 ∆𝑌𝑡−1 + 𝛽𝑌𝑡−1 + 𝜀𝑡 𝑖=1

where

𝑛−1

𝑛

𝜑𝑖 = − ∑ 𝐴𝑖 𝑎𝑛𝑑 𝛽 = ∑ 𝐴𝑖 − 𝐼. 𝑖=1

(6)

(7)

𝑖=1

where I is the identity matrix (n × n) and Δ is the difference operator. Johansen and Juselius (1990) derive two cointegration tests: a trace test and the maximum eigenvalue test. The null hypothesis of no cointegration between construction and economic growth is tested against the alternative hypothesis of the existence of cointegration. Table 2 presents the result of the cointegration between construction and economic growth. The result shows that the trace statistic is higher than the 5% critical value; hence, it rejects the null hypothesis of no cointegration in favor of one cointegrating vector. Similarly, the maximum eigenvalue test statistic is also higher than the 5% critical value, which indicates rejection of the null hypothesis of no cointegration. Thus, this result suggests that there exists a long-run stable relationship between the GDP and the construction sector. Table 2

Results of Johansen-Juselius Cointegration

Hypothesis Null

Alternative

r=0 r≤1

r=1 r=2

Trace Test 5% Statistic Critical P-Value Value 28.6678 15.4947 0.0003 0.0001 3.8414 0.9930

Maximum Eigenvalue 5% PStatistic Critical Value Value 28.6677 14.2646 0.0002 0.0001 3.8414 0.9930

Note: See Appendix II – for more information Source: Author estimation by using EViews8.

As previously mentioned, the Johansen and Juselius cointegration technique only determines the existence of cointegration between variables, but not the direction of causality. The Granger causality test is employed to determine the direction of causation. If a variable Y is found to Granger-cause U, this means that past values of Y are useful in forecasting values of U without considering past values of U. Similarly, for a variable Y that is found to Granger-cause U, this means that past values of U are useful in forecasting values of Y without considering past values of Y. The Granger causality test consists of estimating the following equations:

Construction Sector and Economic Growth in Saudi Arabia 𝑛

137

𝑛

∆𝐿𝑛𝐺𝐷𝑃𝑡 = 𝛽0 + ∑ 𝛽1𝑖 𝐿𝑛𝐺𝐷𝑃𝑡−1 + ∑ 𝛽2𝑖 𝐿𝑛𝐶𝑜𝑛𝑠𝑡𝑡−𝑖 + 𝑈𝑡 𝑡=1 𝑛

𝑡=1

𝑛

∆𝐿𝑛𝐶𝑜𝑛𝑠𝑡𝑡 = 𝛼0 + ∑ 𝛼1𝑖 𝐿𝑛𝐶𝑜𝑛𝑠𝑡𝑡−1 + ∑ 𝛼2𝑖 𝐿𝑛𝐺𝐷𝑃𝑡−𝑖 + 𝑉𝑡 𝑡=1

(8) (9)

𝑡=1

where 𝑈𝑡 and 𝑉𝑡 are uncorrelated and white noise error term series. Causality may be determined by estimating Equations (1) and (2) and testing the null hypothesis that (𝛽2𝑖 ; 𝛼2𝑖 = 0) against the alternative hypothesis that (𝛽2𝑖 ; 𝛼2𝑖 ≠ 0) for Equation (8) or (9) respectively. If the coefficients of 𝛽2𝑖 are statistically significant, but those of 𝛼2𝑖 are not statistically significant, then economic growth (GDP) is said to have been caused by construction growth (uni-directional causality relationship). The reverse causality holds, if the coefficients of 𝛼2𝑖 are statistically significant while those of 𝛽2𝑖 are not. However, if both 𝛽2𝑖 and 𝛼2𝑖 are statistically significant, then causality runs both ways (bi-directional causality relationship). The results of the Granger causality test are presented in Table 3. Table 3

Granger Causality Test

Causality Economic Growth ↛ Construction Construction ↛ Economic Growth Economic Growth ↛ Construction Construction ↛ Economic Growth Economic Growth ↛ Construction Construction ↛ Economic Growth

Lag Length 1 1 2 ** 2 ** 3 3

F-Statistic 6.195 3.183 6.923 2.520 3.398 2.448

P-Value 0.017 0.082 0.002 0.094 0.029 0.081

Notes: 1. The notation Economic Growth ↛ Construction represents the null hypothesis: Economic growth (GDP) does not Granger-cause Construction sector. A similar interpretation follows for the reverse test. 2. Denotes optimal leg length based on (AIC), (SC) and (HQ) test. Source: Author estimation by using EViews8.

The results indicate that economic growth does Granger cause construction sector in the first-differences of the data. The F statistics is significant at the 1% level, which means that in the case of Saudi Arabian economic growth, GDP does greatly affect the construction industry. The results also indicate that there is a causal effect that runs from the construction sector to economic growth, and the null hypothesis is rejected (i.e. the construction sector does not Granger cause economic growth) at a 10% significance level. Therefore, the Granger causality test indicates that there is a bi-directional relationship between the construction sector and economic growth in Saudi Arabia. Engle and Granger (1987) suggest that if cointegration exists between two variables, then proper statistical inference is obtained only by analyzing causality based on an error correction model (ECM). The VECM is employed

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to determine the short-run and long-run causalities between construction and economic growth. The VECM estimation is performed by using the following VAR framework: 𝑛

𝑛

∆𝐿𝑛𝐺𝐷𝑃𝑡 = 𝛽0 + ∑ 𝛽1𝑖 𝐿𝑛𝐺𝐷𝑃𝑡−1 + ∑ 𝛽2𝑖 𝐿𝑛𝐶𝑜𝑛𝑠𝑡𝑡−𝑖 + 𝜆1𝐸𝑐𝑡𝑡−1 + 𝑈𝑡 𝑡=1 𝑛

𝑡=1 𝑛

(10)

∆𝐿𝑛𝐶𝑜𝑛𝑠𝑡𝑡 = 𝛼0 + ∑ 𝛼1𝑖 𝐿𝑛𝐶𝑜𝑛𝑠𝑡𝑡−1 + ∑ 𝛼2𝑖 𝐿𝑛𝐺𝐷𝑃𝑡−𝑖 + 𝜆2𝐸𝑐𝑡𝑡−1 + 𝑉𝑡 𝑡=1

𝑡=1

(11)

where 𝛽1𝑖 , 𝛽2𝑖 , 𝛼1𝑖 &𝛼2𝑖 are the short-run coefficients,1𝐸𝑐𝑡𝑡−1 &2𝐸𝑐𝑡𝑡−1 are the error correction terms, and 𝑈𝑡 & 𝑉𝑡 are the residuals. The error correction terms 𝐸𝑐𝑡𝑡−1 are derived from a long-run cointegration relationship and measure the magnitude of past disequilibrium. The coefficient 𝜆 of the error term represents deviations in the dependent variable from the long-run equilibrium. Table 4 presents the results of the VECM test. The error correction term for both ∆𝐿𝑛𝐺𝐷𝑃𝑡 and ∆𝐿𝑛𝐶𝑜𝑛𝑠𝑡𝑡 is correctly signed and significant at 5% and 1% respectively. These results support the result obtained from the Granger causality test, which can be concluded that there is a long-run (two ways) causality relationship between economic growth and the construction industry in Saudi Arabia for the period of 1970-2011. Table 4 Dependent variable ∆𝐿𝑛𝐺𝐷𝑃𝑡 ∆𝐿𝑛𝐶𝑜𝑛𝑠𝑡𝑡

Results of VECM Test Source of Causation (Independent variable) Long-run Short-run ∆𝐿𝑛𝐺𝐷𝑃𝑡 ∆𝐿𝑛𝐶𝑜𝑛𝑠𝑡𝑡 T-statistic 𝐸𝑐𝑡𝑡−1 χ2-Statistic -2.158 2.750 __ -0.213 (0.038) (0.252) -5.551 __ 3.002 -1.194 (0.000) (0.222)

Note: Figures in the parenthesis are the probability value (see Appendix III). Source: Author estimation by using EViews8.

The Wald test is also employed to check the short-run causality between construction and economic growth. The estimated results show that construction does not Granger-cause economic growth and economic growth also does not Granger cause construction in the short-run in Saudi Arabia. Both variables have a positive but insignificant effect on each other in the short run. The above results are consistent with several previous studies, such as: Bon and Pietroforte (1990); Tan (2002); Fadhlin (2004); Khan (2008) and Jackman (2010). The ECM also passes a range of diagnostic tests (Table 5). The accuracy of the ECM estimated results is validated by performing several diagnostic tests, such as the tests of normality, serial correlation (LM), and

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heteroskedasticity. These tests imply that the model is well-behaved (i.e. the errors appear to be normal; free of autocorrelation and nonheteroskedasticity). Table 5

Diagnostic Tests

Test VAR Residual Normality Test (JarqueBera)

VAR Residual Serial Correlation LM Test (Lags 1 to 12)

Heteroskedasticity Test (ARCH)

Test Statistic 2

P-Value

χ = 4.530

0.338

LM = 6.392 LM = 7.941 LM = 2.408 LM = 6.406 LM = 2.557 LM = 0.139 LM = 2.395 LM = 0.988 LM = 0.230 LM = 5.591 LM = 2.225 LM = 1.615 χ2 = 2.374

0.171 0.095 0.661 0.170 0.634 0.997 0.663 0.911 0.993 0.231 0.694 0.806 0.305

Source: Author estimation by using EViews8.

As previously mentioned, given the fact that Saudi Arabia is an oil-rich country, oil revenues could be the cause and the consequence of both growth in the construction sector and the national economy. Movements in construction activities and economic growth may be driven by underlying factors such as oil revenues. To validate this assumption, oil revenues (𝐿𝑛𝑂𝑖𝑙𝑟) have been included in the VECM model as a conditioning variable along with the construction ( 𝐿𝑛𝐶𝑜𝑛𝑠𝑡 ) and economic growth ( 𝐿𝑛𝐺𝐷𝑃 ) variables (see Appendix IV). The results reveal the following points: 

There is strong causality that runs from economic growth and oil revenues to the construction industry with feedback effects that run from construction to economic growth only (i.e. bi-directional relationship). The construction industry does not Granger-cause oil revenues in the long-run (i.e. uni-directional relationship).



There exists no causal relationship between economic growth and oil revenues in the long-run. These two variables are “independent” effects on construction growths in the long-run. However, oil revenues have significant effects on economic growth just in the short-run.

The above results validate the assumption that oil revenues do, indeed, play a critical role in economic growth in the short-run, and therefore, the growth of the construction industry in the long-run.

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Conclusion and Policy Implications

The purpose of this study is to test for Granger causality between the construction sector and economic growth for Saudi Arabia over the period of 1970-2011. The empirical results suggest that historically, bidirectional causality had existed between construction and economic growth. The estimated results of the Wald test show that neither construction nor economic growth has a short-run causal effect on each other. These results suggest two important factors: 1.

2.

3.

Historically, causality had existed from the construction sector to the aggregate economy, thereby justifying the Saudi government’s intention to intervene in construction-driven activities. The Granger causality test also lends support to the growth-driven construction hypothesis; in other words, the construction sector is largely influenced by the general national economic situation. During expansionary periods, the Saudi government appeared to spend more on construction projects, whereas recessionary periods are associated with a reduction in construction activities. Saudi Arabia as a rich-oil-resource country needs to stabilize a new institutional mechanism in order to cushion shocks from oil revenue instability and related boom-bust economic cycles.

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Dickey, D.A. and Fuller, W.A. (1979).Distributions of the Estimators for Autoregressive Time Series with a Unit Root, Journal of the American Statistical Association, 74, 427-31. Dlamini, S. (2011).Relationship of construction sector to economic growth. School of Construction Management and Engineering, University of Reading, UK. Engle, R., and Granger, C.W.J. (1987). Cointegration and error-correction: Representation, estimation, and testing, Econometrica, 55, 251-276. Ewing, B.T., and Wang, Y. (2005). Single Housing Starts and Macroeconomic Activity: An Application of Generalised Impulse Response Analysis, Applied Economics Letters, 12, 187-90. Field, B. and Ofori, G. (1988).Construction and economic development – a case study. Third World Planning Review, 10, 41–50. Granger, C.W.J. (1969).Investigating Causal Relations by Econometric Methods and Cross-spectral Methods, Econometrica, 34,541-51. Green, R.K. (1997). Follow the leader: how changes in residential and nonresidential investment predict changes in GDP. Real Estate Economics, 25, 253–70. Hassan, S.A. (2002). Construction Industry. (Pakistan) published by Economic Review 2002. Hillebrandt, P. M. (2000). Economic Theory and the Construction Industry. Third Edition. London: Macmillan Press LTD. Hillebrandt, P. (1985). Analysis of the British Construction Industry, Macmillan, London. Hirschman, A.O. (1958). The Strategy of Economic Development. Yale University Press: New Haven. CT. Hongyu L., Park, Y.W. and Siqi, Z. (2002). The Interaction between Housing Investment and Economic Growth in China, International Real Estate Review, 5, 40-60. ILO Geneva (2001). The construction industry in the twenty first century: Its image, employment prospects and skill requirements, International Labor Office Geneva

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Jackman, M. (2010). Investigating the Relationship between Residential Construction and Economic Growth in a Small Developing Country: The Case of Barbados, International Real Estate Review, 13, 109-116 Johansen, S., & Juselius, K. (1990).Maximum Likelihood Estimation and Inference on Cointegration with applications to the Demand for Money. Oxford Bulletin of Economics and Statistics, 52, 169-210. Khan, R. A. (2008). Role of Construction Sector in Economic Growth: Empirical Evidence from Pakistan Economy. First International Conference on Construction in Developing Countries (ICCIDC–I), Karachi, Pakistan: 4-5 August. Kim, K.H. (2004). Housing and the Korean Economy, Journal of Housing Economics, 13, 321-41. Lean, S.C. (2001). Empirical tests to discern linkages between construction and other economic sectors in Singapore, Construction Management and Economics, 13, 253-262 Mackinnon, J.G., Haug A.A., and Michelis L. (1999). Numerical distribution functions of likelihood ratio tests for cointegration, Journal of Applied Econometrics, 14, 563-577. Ministry of Economic and Planning, 9th National Development Plan – Construction Sector, Ch. 13: 207-218, www.mep.gov.sa Myers, D. (2008). Construction Economics. Second Edition ed. London and New York: Taylor & Francis. Park, S.H. (1989) Linkages between industry and services and their implications for urban employment generation in developing countries. Journal of Development Economics, 30, 359–79. Rameezdeen, R. (2007). Image of the construction industry. Department of Building Economics, University of Moratuwa, Sri Lanka. Saudi Arabian Monetary Agency (SAMA), http://www.sama.gov.sa/ Tan, W. (2002). Construction and economic development in selected LDCs: past, present and future. Construction Management & Economics, 20, 593632. Turin, D.A. (1969). Construction Industry, based on the Proceedings of the International Symposium on Indusm'al Development held in Athens in NovDec 1967, New York, Monograph no. 2.

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U.S.-Saudi Arabian Business Council (U.S-SABC), (2011).The Construction Sector in the Kingdom of Saudi Arabia, Bulletin Report. http://www.ussabc.org/ Wang, K. and Zhou, Y. (2000).Overbuilding: A Game-theoretic Approach. Real Estate Economics, 28, 493-522. Wells, J. (1986). The Construction Industry in Developing Countries: Alternate Strategies for Development, Croom Helm Ltd, London. World Bank (1984). The Construction Industry: Issues and Strategies in Developing Countries, The World Bank, Washington, D.C.

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Appendices Appendix I

VAR Lag Order Selection Criteria

Endogenous variables: LOGCST LOGGDP Exogenous variables: C Date: 09/29/13 Time: 01:10 Sample: 1970 2012 Included observations: 40 Lag LogL LR FPE AIC SC HQ 0 -52.97174 NA 0.053550 2.748587 2.833031 2.779119 1 47.64148 186.1345 0.000428 -2.082074 -1.828742 -1.990477 2 81.15322 58.64554* 9.80e-05* -3.557661* -3.135441* -3.405000* 3 82.48227 2.192925 0.000112 -3.424113 -2.833005 -3.210387 * indicates lag order selected by the criterion LR: sequential modified LR test statistic (each test at 5% level) FPE: Final prediction error AIC: Akaike information criterion SC: Schwarz information criterion HQ: Hannan-Quinn information criterion

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Appendix II

145

Johanson-Juselis Cointegration Test

Sample (adjusted): 1973 2012 Included observations: 40 after adjustments Trend assumption: Linear deterministic trend Series: LOGCST LOGGDP Lags interval (in first differences): 1 to 2 Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05 No. of CE Eigenvalue Statistic Critical Value None * 0.511636 28.66787 15.49471 At most 1 2.66E-06 0.000107 3.841466 Trace test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05 No. of CE Eigenvalue Statistic Critical Value None * 0.511636 28.66776 14.26460 At most 1 2.66E-06 0.000107 3.841466 Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I) LOGCST LOGGDP -3.406348 1.872387 3.461189 -3.990911 Unrestricted Adjustment Coefficients (alpha) D(LOGCST) 0.057032 -3.59E-05 D(LOGGDP) 0.062653 -0.000258 1 Cointegrating Equation(s): Log likelihood 82.48221 Normalized cointegrating coefficients (standard error in parentheses) LOGCST LOGGDP 1.000000 -0.549676 (0.07205) Adjustment coefficients (standard error in parentheses) D(LOGCST) -0.194269 (0.03500) D(LOGGDP) -0.213417 (0.09889)

Prob.** 0.0003 0.9930

Prob.** 0.0002 0.9930

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Appendix III

Vector Error Correction Estimates

Date: 09/27/13 Time: 18:01 Sample (adjusted): 1973 2012 Included observations: 40 after adjustments Standard errors in ( ) & t-statistics in [ ] CointegratingEq LOGCST(-1)

CointEq1 1.000000

LOGGDP(-1)

-0.549676 (0.07205) [-7.62860]

C Error Correction CointEq1

-3.147306 D(LOGCST) -0.194269 (0.03500) [-5.55126]

D(LOGGDP) -0.213417 (0.09889) [-2.15817]

D(LOGCST(-1))

0.707129 (0.20744) [ 3.40886]

0.744089 (0.58617) [ 1.26942]

D(LOGCST(-2))

-0.122387 (0.13120) [-0.93280]

-0.203101 (0.37075) [-0.54782]

D(LOGGDP(-1))

0.071940 (0.07968) [ 0.90287]

-0.357191 (0.22515) [-1.58645]

D(LOGGDP(-2))

-0.070590 (0.08108) [-0.87061]

-0.300427 (0.22911) [-1.31126]

C

0.039604 (0.01319) [ 3.00163] 0.921934 0.910454 0.143544 0.064976 80.30579 55.84225 -2.492113 -2.238781 0.106862 0.217135

0.110281 (0.03728) [ 2.95797] 0.374720 0.282767 1.146166 0.183605 4.075122 14.29161 -0.414580 -0.161248 0.101736 0.216797

R-squared Adj. R-squared Sum sq. resids S.E. equation F-statistic Log likelihood Akaike AIC Schwarz SC Mean dependent S.D. dependent

Determinant resid covariance (d of adj.) Determinant resid covariance Log likelihood Akaike information criterion Schwarz criterion

7.68E-05 5.55E-05 82.48221 -3.424111 -2.833003

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Appendix IV Causality Test &VEC Estimates – Including Oil Revenues Causality Test Causality Economic Growth ↛ Construction Construction ↛ Economic Growth Oil Revenues ↛ Construction Construction ↛ Oil Revenues Oil Revenues ↛Economic Growth Economic Growth ↛ Oil Revenues

Lag Length

2

F-Statistic 6.923 2.520 3.539 0.540 2.427 0.815

P-Value 0.002 0.094 0.039 0.587 0.102 0.450

VEC Estimates Dependent variable ∆𝐿𝑛𝐺𝐷𝑃𝑡 ∆𝐿𝑛𝐶𝑜𝑛𝑠𝑡𝑡 ∆𝐿𝑛𝑂𝑖𝑙𝑟𝑡

Source of Causation (Independent variable) Long-run Short-run ∆𝐿𝑛𝐺𝐷𝑃𝑡 ∆𝐿𝑛𝐶𝑜𝑛𝑠𝑡𝑡 T-statistic 𝐸𝑐𝑡𝑡−1 χ2-Statistic -1.824 __ 5.207 -0.196 (0.077) (0.074) -4.904 1.524 __ -0.204 (0.000) (0.466) -2.072 0.215 0.004 -0.561 (0.046) (0.897) (0.995)

∆𝐿𝑛𝑂𝑖𝑙𝑟𝑡 5.192 (0.074) 0.176 (0.915) __