The Impact of Financial Sector Development on Economic Growth in the Non-Oil Sector in Saudi Arabia

Transcription

1. WP /16/5 SAMA Working Paper: Comparative Analysis: The Impact of Financial Sector Development on Economic Growth in the Non-Oil Sector in Saudi Arabia November 2016 By Mohammed Alghfais Financial Sector Development Department Saudi Arabian Monetary Agency The views expressed are those of the author(s) and do not necessarily reflect the position of the Saudi Arabian Monetary Agency (SAMA) and its policies. This Working Paper should not be reported as representing the views of SAMA
2. Comparative Analysis : The Impact of Financial Sector Development on Economic Growth in the Non-Oil Sector in Saudi Arabia* Mohammed Alghfais Financial Sector Development Department November 2016 ABSTRACT The aim of this paper is to examine the relationship between financial sector development and economic growth in the non-oil sector in Saudi Arabia. Six measures of financial sector development are used in this study, The data used in this study are from Saudi Arabia and consisted of time series data for the period of 1985 to 2015. The data are analyzed using the Autoregressive Distributed Lag (ARDL) model. The main conclusion is that there is a positive and significant impact of financial sector development on the total economic growth of the non-oil sector1 and the economic growth of the non-oil public and private sector. Keywords: Financial development, Economic growth, Non-oil, Saudi Arabia, Autoregressive Distributed Lag (ARDL). JEL classification codes: O11, O16, O47 1 Growth of total non-oil sector consist of non-oil private sector and non-oil public sector * Contact Details: Mohammed Alghfais, Email: malghfais@sama.gov.sa 2
3. 1 . Introduction Over the last few decades, the standard of living in oil-exporter countries has reached a remarkable level, which reflects these countries’ total significant economic growth. Standard factors of production of goods and services can affect economic growth, including capital, labor, knowledge or effectiveness of labor, and land and other natural resources. By the same token, financial development plays a major role in affecting economic growth in both developing and developed countries. Saudi Arabia, an oil-based economy, has recently devoted more attention to financial sector development. The financial sector in Saudi Arabia has grown rapidly in the last several years, particularly in the banking, stock market, and insurance sectors. The relationship between economic growth and financial development has been widely discussed in the literature. Economic researchers have used several different indicators to measure financial development. In this paper, a wide range of these measurements within financial sector development and their impact on the Saudi Arabian economy in the non-oil sector is discussed. One of the most influential studies relevant to the present research is found in Schumpeter (1911). Schumpeter (1911) studied the relationship between financial development and economic growth and highlighted that finance plays a major role in enhancing economic growth. In addition, Shandre and Jiunn (2004) analyzed the impact of financial development on economic growth. Their study covers the period from 1960 to 1999 in Australia, and it examined how financial development affects economic growth using three indicators to assess financial sector development. They found no evidence suggesting that economic growth has an effect on financial sector development; 3
4. however , greater attention could have been paid to several levels of measurement, particularly since the study examined six models with two explanatory variables for each model. Models with more than two explanatory variables for each model should have been examined to obtain a more accurate, credible, and reliable results. Lewis (1955), who expanded on the work of Schumpeter (1911), analyzed the relationship between economic growth and financial sector development using different measures, while Samargandi, Fidrmuc, and Ghosh (2013) devoted on this relationship in oil-exporter countries. Both of these papers included similar measures of financial development. The aim of this paper is to examine the relationship between financial development and economic growth in Saudi Arabia using an Autoregressive Distributed Lag (ARDL) model. This study builds on the work of the papers mentioned previously. The model used for this research has similar characteristics to those previous studies. The analysis is carried out using annual time series data from Saudi Arabia from 1985 to 2015. Specifically, this study sought to determine whether these indicators, in conjunction or independently, affect Saudi Arabian economic growth of non-oil sector and, if so, in what way and to what extent. In addition, the aim is to compare the magnitude of such effects on Saudi Arabian economic growth of non-oil as a whole, non-oil of the private sector, and non-oil of the public sector based on how they have been affected by financial development. This paper will add to the current literature by providing updated data along with a wide array of explanatory variables that have yet to be analyzed collectively. In December of 2015, The Custodian of the two Holy Mosques, King Salman bin Abdulaziz, announced economic reforms to diversify sources of 4
5. income and to reduce the high dependence on oil in Saudi Arabia . Thus, conducting a comparative analysis of economic growth in the non-oil sector that has been impacted by financial sector development is crucial to enhance the kingdom’s economic growth, making this study extremely relevant and significant. It is vital for policymakers to identify what type of economic growth (total non-oil sector, non-oil private sector, and non-oil public sector) has been the most directly impacted by financial sector development to determine what policy changes can be made to enhance future economic growth in Saudi Arabia. The paper is structured as follows. Section 2 discusses the most important previous papers on financial development and economic growth. Section 3 describes the model under the assumption that the data perfectly match the ideal theoretical and empirical characteristics for the model. Section 4 discusses how closely the data obtained matches the theoretical “ideal” for the variables and the identified time period, sample, and type of data. Section 5 discusses and interprets the results. Section 6 outlines the conclusion and policy implications. 2. Literature Review The subject of financial sector development and how it contributes to economic growth is an ongoing debate. Over the past several decades, a plethora of studies have estimated how financial sector development affects economic growth, including banking sector indicators and stock market indicators. Nonetheless, there has not been a study that combines the majority of these indicators in one cohesive model. This paper will build from previous research that has examined financial sector development to determine what type of 5
6. economic growth (total non-oil sector, non-oil private sector, and non-oil public sector) has been impacted by financial sector development. Several theoretical models have been proposed to examine the relationship between financial sector development and economic growth. Schumpeter (1911) asserted that financial intermediation is a significant action to enhance the economy. In fact, financial intermediations affect the allocation of savings, therefore enhancing productivity along with the rate of economic growth. Several empirical studies have been conducted to test the relationship between financial development and economic growth. Samargandi, Fidrmuc, and Ghosh (2013) examined the impact of financial development on economic growth in Saudi Arabia using a sample of 252 observations and five variables during the period from 1968 to 2010. Their research used five variables and three models in total. The autoregressive distributed lag (ARDL) is used to analyze the factors or indicators it examined. Their study also contained three levels of measurements, including broad money, liquid liabilities, and credit to the private sector. A principal component analysis is used as a single composite indicator of financial development. Furthermore, based on their study, it is found that financial sector development has a positive significant impact on the economic growth of the total non-oil sector; however, the effect of financial sector development on the economic growth of the oil sector as well as on the economy as a whole is insignificant. They used an interesting modeling technique in examining the relationship between financial sector development and economic growth; however, the scope of the study is undoubtedly too narrow. The study devoted solely on the predictive power of three explanatory variables (broad money, liquid liabilities, and credit to the private sector) in determining economic growth. The inclusion of additional potentially 6
7. significant indicators should improve the model ’s predictive ability at all levels of economic growth. Similarly, Ibrahim (2013) examined the relationship between economic growth and financial development using three indicators of financial development, including the real general stock market, credits to the private sector, and the real industrial production index. Ibrahim’s (2013) study used annual data from 1989 to 2008 and implemented fully-modified ordinary least squares (FMOLS) to analyze the indicators of financial sector development that affect economic growth in Saudi Arabia.Ibrahim (2013) found that the domestic bank credit to the private sector ratio has a significant and positive impact on economic growth in the long-term but an insignificant and negative impact on economic growth in the short-term. In the long-term, the stock market and economic growth are positively related but not significant. In the short-term, the stock market has a negative but insignificant impact on economic growth; however, the real industrial production index 2 has a positive and significant impact on economic growth in both the short and long-terms. In addition, Inanga and Emenuga (1997) and Adjasi & Biekpe (2006) claimed that the performance of the stock market is a significant indicator of financial development affecting economic growth because it measures changes in economic activity and how financial sector development behaves. For example, if the stock market is active, it positively impacts economic growth; however, Ibrahim (2013) should have looked beyond the typical financial development indicators linked to economic growth to consider the influence of the entire spectrum of financial development indicators on economic growth. 2 This index is used as a measure of financial depth 7
8. Furthermore , numerous studies have examined the relationship between financial development and economic growth in Asia. For instance, Jalil and Ma (2008) compared two countries in terms of the effect of financial development on economic growth and found conflicting results. They studied both China and Pakistan using an ARDL model and used the deposits liability ratio and the credit to the private sector ratio to assess financial development. Their findings are contradicted in their study. They found that financial development has a positive and significant effect on economic growth in the case of Pakistan, whereas in the case of China, the results are positive but not significant for the deposits liability and significant for the credit to the private sector. Although Jalil and Ma (2008) have implemented an interesting modeling technique for both countries, they should have looked beyond these two levels of measurement to assess the financial development because it is possible that there are other indicators that affect economic growth that are not considered, which are therefore captured in the error term in their model. It would be interesting to include other variables in the model and to use different measures for financial development to test whether or not the relationship found by these studies still holds. The results in the case of Pakistan are similar to Samargandi, Fidrmuc, and Ghosh’s (2013) study in which they used similar levels of measurement of financial development. Similarly, numerous papers have devoted on the effect of financial sector development on economic growth using different measurements of financial sector development in different regions in Europe. For example, Caporale, Rault, Christophe, Sova, and Sova (2009) studied the relationship between financial sector development and economic growth in ten new European Union members’ economies. They estimated a dynamic Generalized Method of 8
9. Moments (GMM) method using panel data during the period from 1994-2007. Their main findings are that financial development has a positive effect on economic growth but not vice versa. Although they utilized an extensive list of explanatory variables throughout their analysis, they failed to consider other important financial development indicators, such as the insurance sector. Caporale (2009), Rault (2009), and Sova’s (2009) studies has similar results to Shandre and Jiunn (2004): financial development has a positive effect on economic growth but not vice versa. Patrick (1966), Greenwood and Jovanovic (1990), Greenwood and Bruce (1997), and Demetriades and Hussein (1996) asserted the existence of a two-way relationship between financial sector development and economic growth. Finally, other studies found that financial development has a negative effect on economic growth. For instance, De Gregorio and Guidotti (1995) examined the impact of financial development on economic growth using credit to the private sector as an indicator of financial development. They used panel data of 12 Latin American countries, which included 95 observations from 1950 to 1985. They found that credit to the private sector has a negative impact on economic growth. They justified their result by the existence of poor regulations in Latin American countries, which are the reasons for the negative impact on economic growth. Similarly, Al-Malkawi, Marashdeh, and Abdullah (2012) examined the impact of financial development on economic growth in United Arab Emirates (UAE). They indicated that financial development has a negative effect on economic growth in the UAE and they justified their results by the fact that the financial system in UAE is still in the transition phase. This study contributes to the current body of knowledge on the relationship between economic growth and financial sector development 9
10. through the examination of the effects of economic growth of total non-oil , nonoil of the private sector, and non-oil of the public sector. The results determine whether or not financial development has an effect on the economic growth levels of the non-oil sector. This paper’s dissemination of the above findings will undoubtedly aid in providing a stronger theoretical framework. As a whole, the previous research strongly bolsters the hypothesis that economic growth is associated with financial sector development. The focused of the present study is Saudi Arabia. 3. Theoretical and Empirical Model This paper examines the relationship between economic growth and financial sector development. The main hypothesis is that financial development has a positive effect on the economic growth of total non-oil, nonoil of the private sector, and non-oil public sector. The more a country is financially developed, the more the country will experience economic growth. Thus, expanding financial development is important for increasing economic growth. It is important to know whether there is a relationship between the economic growth of the non-oil sector and financial development. Determining the size and the direction of the effect, if any, could help in making policy decisions. Samargandi, Fidrmuc, and Ghosh (2013) constructed three models with different economic growth measures as the dependent variables to establish the relationships between the economic growth of the oil sector, the non-oil sector, the economy as a whole, and financial development. Likewise, three models with similar explanatory variables are used in this study but with different 10
11. dependent variables : gross domestic product of the total non-oil sector per capita (GDPN), GDPN per capita of the non-oil private sector (GDPNP), and GDPN per capita of the non-oil public sector (GDPNG). The model is as follows:
12. 5.2 Cointergration Test, Long-term Impact, and Short-term Impact for total Non-oil GDP For the total non-oil GDP, two models6 are estimated to determine which model best predicted the total non-oil GDP. Bound tests are conducted on models 5 and 6, and they are 34.29 and 51.73, respectively, which both are higher than the upper bound critical value of Narayan (2005) table at 1 percent significance level. Thus, they indicate that there is enough evidence that there is a long-term relationship among the variables for both models; however, Model 5 is the appropriate model because model 6 indicates there is an error with the functional form. ARDL (1, 4, 4) selected on the basis of AIC for model The regression outcomes demonstrate that the trade openness and financial development coefficients have a positive relationship, as expected under the alternate hypothesis, and that it is significant at the 1 percent significance level using the appropriate one-tailed or two-tailed hypothesis tests. Ceteris paribus, a 10 percent increase in trade openness, a total non-oil GDP increase on average by 7.3 percent on the long-term, while a 10 percent increase in financial development, the total non-oil GDP increase on average by 1.2 percent in the long-term. The magnitude of the effect of financial sector development is very small; this is plausible because financial development grows slower in an oilbased economy than in a non-oil based economy 7 . This conclusion bolsters previous research that financial development grows slower in an oil-based 6 Trade openness and financial development are regressed against the total non-oil GDP on the model 5, for model 6, oil prices and financial development are regressed against the total non-oil GDP. 7 The higher the country depends on oil, the slower financial sector development grow. 21
13. economy than in a non-oil based economy . In particular, this is consistent with findings by Nili and Rastad (2007) and Samargandi (2013). 5.3 Cointergration Test, Long-term Impact and Short-term Impact for the Non-oil GDP of the Private Sector Bound tests are conducted on models8 5 and 6, and they are 34.29 and 51.73, respectively, which both are higher than the upper bound critical value of Narayan (2005) table at 1 percent significance level. Thus, that indicate there is evidence that there are long-term relationships among the variables for both models. Model 5 indicated that there is an heteroscedasticity issue at 10 percent significance level. However Fosu and Magnus (2006) argue that it is reasonable to spot heteroscedasticity because they are co-integrated at different order(I(0) and I(1)). Model 5 is the most appropriate model because model 6 indicated that there is an error with the functional form. ARDL (1, 0, 2) selected on the basis of AIC for model 5. The regression results show that the trade openness and financial development coefficients have a positive relationship and it is significant at the 1 percent significance level using the one-tailed hypothesis tests. Ceteris paribus, a 10 percent increase in trade openness, a non-oil GDP of the private sector increase on average by 8.7 percent in the long-term, while a 10 percent increase in financial development, a non-oil GDP of the private sector increase on average by 1.7 percent on the long-term. The magnitude of the effect of financial sector development is very small but larger than the total non-oil GDP; 8 Trade openness and financial development are regressed against the non-oil GDP of the private sector for the model 5, while for model 6, oil prices and financial development are regressed against the non-oil GDP of the private sector. 22
14. this might indicate that financial sector development performs better under the non-oil GDP of the private sector . 5.4 Cointergration Test, Long-term Impact and Short-term Impact for the Non-oil GDP of the Public Sector Two models9 are estimated to determine which model best predicted the non-oil GDP of the public sector, as shown Table 8. Bound tests are conducted on models 5 and 6, and they are 5.2 and 10, respectively, which are higher than the upper bound critical value of Narayan (2005) table at 1 percent significance level. Thus, they indicated that there is a long-term relationship among the variables for both models. Model 5 is the most appropriate model and ARDL (3, 4, 4) selected on the basis of AIC for model 5. The regression results indicated that financial development has an expected positive sign under the alternate hypothesis, and is significant at the 1 percent significance level using the appropriate one-tailed or two-tailed hypothesis tests. Ceteris paribus, a 10 percent increase in trade openness, a non-oil GDP of the private sector increase on average by 4.3 percent in the long-term, while a 10 percent increase in financial development, a non-oil GDP of the private sector increase on average by 0.97 percent on the long-term. Financial development has the least impact on the non-oil public sector among all other types of the economic growth of non-oil sector in this study. The reason might be due to the fact that the economic growth of non-oil public sector is less efficient than the economic growth of non-oil private sector. 9 Trade openness and financial development are regressed against the non-oil GDP of the public sector for model 5, while for model 6, oil prices and financial development are regressed against the non-oil GDP of public sector. 23
15. 5 .5 Error Correction Model (ECM), Short-term Impact for the Total Non-oil GDP, Non-oil GDP Private Sector and Non-oil GDP Public Sector Short-term relationships exist among the variables. The coefficients of ECMt-1 have a negative sign, as expected under the alternate hypothesis, and are significant at the 1 percent significance level for the total non-oil GDP, non-oil GDP private sector and non-oil GDP public sector, as shown in Table 5, 7 and 9 respectively. This confirms the long-term relationship among the total non-oil GDP, non-oil GDP private sector and non-oil GDP public sector. The coefficients of ECMt-1 are -0.13, -0.114 and -0.097, which indicate that the speed of the adjustment process is 13 percent, 11.4 percent and 9.7 percent of the disequilibria in the total non-oil GDP, non-oil GDP private sector and nonoil GDP public sector growth of the previous year’s shock adjust back to the long-term equilibrium in the present year respectively. In particularly, the system corrects its previous period disequilibrium at a speed of 13 percent, 11.4 percent and 9.7 percent annually to reach at the steady state. 5.6 Diagnostic test No evidence is found of a serial correlation, error functional form, or heteroscedasticity at 5 percent significance level. A cumulative sum (CUSUM) test and cumulative sum square (CUSUMSQ) test are conducted to ensure the stability of the models, as shown in Figures 7, 8, 9, 10, 11 and 12. All tests remained within the critical boundaries of 5 percent and indicated that the model is stable. 24
16. 6 . Conclusion & Policy Implications Existing research has found that there are three major contributors to the economic growth of the non-oil sector. Previous research has not combined these contributors in one cohesive model to determine how financial sector development impacts the total non-oil GDP and non-oil GDP of the private sector. This study has included a number of financial development indicators in an effort to better predict the economic growth of the non-oil sector in Saudi Arabia. An appropriate regression analysis with time series data is used to identify significant predictors of the economic growth of the non-oil sector. The study used PCA to construct an index for financial development using six measurements of financial development, which is a constructed index that has not been used in the previous studies and is an incredibly strong predictor of the economic growth of the total non-oil sector and the non-oil private sector. One conclusion is that financial development does not have a statistically significant impact on non-oil GDP of the public sector. Another conclusion is that financial development and trade openness are significant predictors of the total non-oil GDP and non-oil GDP of the private sector. The effect of financial development on non-oil GDP of the private sector is larger than the effect on the total non-oil GDP, that might indicate that the nonoil public sector contributes less than the non-oil GDP of the private sector. Thus, privatizing some of the public sectors is crucial because this should lead to greater productivity and more transparency and should reduce the overall cost. In addition, financial development can be greatly improved by easing credit constraints on the Small and Medium-sized Enterprises (SMEs) and can improve the allocation of capital, thereby accelerating economic growth. 25
17. This study supports the Saudi vision 2030 , which has been proposed by deputy crown price and defense minister Mohammad Bin Salman. The findings of this study are vital because they can inform policy decisions on financial development in Saudi Arabia, which can develop specific models to boost the growth of the non-oil sector. Future research can devote on examining financial development by dividing it into two sectors. In fact, it can be examined by constructing two indexes: one for the banking sector and one for the stock market sector. In addition, future research should use panel data on oil export countries because a panel approach estimation would provide more precise estimates of variable coefficients than is possible with the currently implemented ARDL approach. 26
18. Appendix Table 1 : Summary Statistics Variables Mean 31983.97 GDPN 11742.1 GDPNG 20241.88 GDPNP 0.357631 GOV 41.38065 OIL 0.208357 INV 1.554982 INF 0.297033 CTP 0.405744 MGDP2 0.500721 MGDP3 0.562963 SMC 0.695997 TR 9.602899 VT 0.604486 T Table 2: PCA of FD Median 28830.79 11196.52 17273.64 0.333423 24.32 0.196933 0.907563 0.274262 0.399061 0.503671 0.09985 0.304005 0.780819 0.5698 Maximum 45107.81 14095.24 31514.63 0.576195 110.22 0.317152 9.868752 0.540016 0.645035 0.724247 3.727868 4.291885 48.54107 0.824545 Minimum 26026.5 10692.09 15203.48 0.266806 12.2 0.137665 -3.203331 0.152361 0.321311 0.399236 0.002019 0.011343 0.01046 0.451704 Cumulative Value 4.625350 5.847709 5.922789 5.967977 5.989214 6.000000 Std. Dev. 6160.996 1009.483 5441.349 0.103042 32.83118 0.058532 2.854822 0.095919 0.071269 0.067735 0.926619 0.89175 14.02885 0.103622 Cumulative Proportion 0.7709 0.9746 0.9871 0.9947 0.9982 1.0000 Component Eigenvalue Difference Proportion PCA 1 4.625350 3.402992 0.7709 PCA 2 1.222358 1.147278 0.2037 PCA 3 0.075080 0.029892 0.0125 PCA 4 0.045188 0.023951 0.0075 PCA 5 0.021237 0.010451 0.0035 PCA 6 0.010786 --0.0018 Table 3: Unit Root Test Variables ADF Test ADF Test Level I(0) First difference I(1) Intercept Intercept and trend Intercept Intercept and trend GDPN 0.234 -5.546*** -3.789*** -3.661** GDPNG -1.135 -3.086 -2.582 -3.535* GDPNP 0.466 -2.167 -4.287*** -4.208** OIL -0.970 -2.989 -5.843*** -5.497*** INF -2.629* -2.8336 -6.895*** -6.864*** INV -1.299 -3.594** -5.083*** -4.988*** T -1.729 -1.747 -4.747*** -4.821*** GOV -2.276 -2.313 -7.218*** -7.251*** FD -1.199 -3.489* -5.234*** -5.101*** *** indicates 1% significance level, **indicates 5% significance level,*indicates 10% significance level 27
19. Table 4 : ARDL Estimate Long Term Dependent Variable: GDPN Variables Model 1 Model 2 OIL 0.177* 0.188** (0.102) (0.086) INF 0.009 0.0052 (0.01) (0.006) INV -0.087 (0.219) T 0.423 0.38 (0.392) (0.317) GOV -0.135 -0.107 (0.157) (0.125) FD 0.0413 0.034** (0.026) (0.014) Const 9.71*** (0.444) Diagnostic Test Statistics Serial correlation 2.52** χ2(1) Functional form 0.002 2 χ (1) Normality χ2(1) 0.247 Heteroscedasticity 1.055 χ2(1) Bounds χ2(1) 17.02*** Model 3 0.261*** (0.053) Model 4 Model 5^ 0.236*** (0.033) Model 6 0.25*** (0.028) -0.056 (0.181) 0.0216 (0.062) 0.025** (0.01) 0.058 (0.114) 0.036*** 0.119*** (0.005) (0.027) 0.036*** (0.006) 9.8 *** (0.44) 9.45*** (0.27) 9.59*** (0.152) 10.8*** (0.150) 9.5*** (0.09) 2.20** 1.3 1.3 0.793 0.31 0.035 4.35** 4.38* 2.40 6.84** 0.952 0.715 1.47 1.303 1.46 1.40 1.403 0.903 0.784 0.854 25.3*** 32.4*** 40.7*** 34.29*** 51.73*** 0.730*** (0.289) *** indicates 1% significance level, **indicates 5% significance level,*indicates 10% significance level ^ ARDL (1, 4, 4) selected on basis of Akaike information criterion (AIC). Standard error in parentheses Table 5: ARDL Model ECM Results Dependent Variable: ΔGDPN ΔT 0.053** (0.029) ΔFD -0.001 (0.002) ECM(-1) -0.126*** (0.01) Diagnostic Test Statistics R-squared 0.98 Adjusted R-squared 0.97 Durbin-Watson stat 2.18 *** indicates 1% significance level, **indicates 5% significance level,*indicates 10% significance level Standard error in parentheses 28
20. Table 6 : ARDL Estimate Long Term Dependent Variable: GDPNP Variables Model 1 Model 2 OIL -0.149 0.30*** (0.350) (0.062) INF -0.051 -0.00051 (0.043) (0.005) INV 0.084 (0.106) T 0.047 0.0407 (0.232) (0.228) GOV 0.047 0.0311 (0.067) (0.065) FD 0.116 0.036*** (0.070) (0.012) Const 11.36*** (0.316) Diagnostic Test Statistics Serial correlation 2.30 χ2(1) Functional form 1.87 2 χ (1) Normality χ2(1) 0.714 Heteroscedasticity 2.36* χ2(1) Bounds χ2(1) 7.72*** Model 3 0.30*** (0.061) Model 4 Model 5^ 0.298*** (0.061) Model 6 0.265*** (0.036) 0.033 (0.196) 0.0313 (0.064) 0.036*** (0.012) 0.0509 (0.188) 0.035*** 0.170*** (0.012) (0.0242) 0.082*** (0.017) 8.91 *** (0.323) 8.91*** (0.301) 8.90*** (0.30) 10.47*** (0.12) 9.024*** (0.130) 2.80* 0.45 0.532 1.81 0.35 0.725 2.3 2.35 0.979 3.77* 0.157 1.88 0.51 2.01* 1.68 2.88 1.057 2.4* 0.11 1.227 9.92*** 31.07*** 33.75*** 43.58*** 0.866*** (0.258) 46.10*** *** indicates 1% significance level, **indicates 5% significance level,*indicates 10% significance level ^ ARDL (1, 0, 2) selected on basis of Akaike information criterion (AIC). Standard error in parentheses Table 7: ARDL Model ECM Results Dependent Variable: ΔGDPNP ΔT 0.079** (0.036) ΔFD 0.00164 (0.0041) ECM(-1) -0.114*** (0.0076) Diagnostic Test Statistics R-squared 0.97 Adjusted R-squared 0.96 Durbin-Watson stat 2.6 *** indicates 1% significance level, **indicates 5% significance level,*indicates 10% significance level Standard error in parentheses 29
21. Table 8 : ARDL Estimate Long Term Dependent Variable: GDPNG Variables Model 1 Model 2 OIL 0.192 0.287 (0.171) (0.239) INF -0.009 0.051 (0.153) (0.041) INV 0.717 (0.44) T -1.052 -1.841 (0.714) (1.129) GOV 0.248 0.967 (0.28) (0.992) FD -0.062 001 (0.05) (0.05) Const 16.62*** (1.24) Diagnostic Test Statistics Serial correlation 0.101 χ2(1) Functional form 48.2*** 2 χ (1) Normality χ2(1) 1.525 Heteroscedasticity 0.942 χ2(1) Bounds χ2(1) 2.29 Model 3 0.472 (0.285) Model 4 0.438* (0.241) Model 5^ Model 6 0.125** (0.043) -1.563 (0.869) 0.148 (0.356) -0.085 (0.065) -1.45* (0.804) 0.429 (0.476) -0.082 (0.054) 0.097* (0.064) 15.51 *** (1.26) 14.25*** (1.35) 14.23*** 9.678*** (1.204) (0.255) 8.975*** (0.162) 0.37 0.556 2.18** 1.51 3.330** 51.6*** 53.01*** 49.5*** 0.905 0.717 0.687 3.07** 0.726 2.73** 0.856 2.38* 1.95 0.575 2.938 0.439 2.42 2.73 2.30 5.208*** 9.96*** 0.012 (0.022) *** indicates 1% significance level, **indicates 5% significance level,*indicates 10% significance level ^ ARDL (3, 4, 4) selected on basis of Akaike information criterion (AIC). Standard error in parentheses Table 9: ARDL Model ECM Results Dependent Variable: ΔGDPNP ΔT 0.0515 (0.036) ΔFD 0.009 (0.052) ECM(-1) -0.097*** (0.0076) Diagnostic Test Statistics R-squared 0.98 Adjusted R-squared 0.96 Durbin-Watson stat 2.53 *** indicates 1% significance level, **indicates 5% significance level,*indicates 10% significance level Standard error in parentheses 30
22. The Ratio of CTP to GDP The Ratio of M 2 to GDP .6 .7 .5 .6 .4 .5 .3 .4 .2 .1 1985 1990 1995 2000 2005 2010 2015 .3 1985 1990 The Ratio of M 3 to GDP .05 .7 .04 .6 .03 .5 .02 .4 .01 1990 1995 2000 2005 2000 2005 2010 2015 2010 2015 2010 2015 The Ratio of TR to GDP .8 .3 1985 1995 2010 2015 .00 1985 1990 The Ratio of VT to GDP 1995 2000 2005 The Ratio of SM C to GDP .05 4 .04 3 .03 2 .02 1 .01 .00 1985 1990 1995 2000 2005 2010 2015 Source: Author's Calculation 31 0 1985 1990 1995 2000 2005
23. Figure 7 : Cumulative Sum of GDPN 12 8 4 0 -4 -8 -12 01 02 03 04 05 06 07 08 CUSUM 09 10 11 12 13 14 13 14 15 5% Significance Source: Author's Calculation Figure 8: Cumulative Sum Squared of GDPN 1.6 1.2 0.8 0.4 0.0 -0.4 01 02 03 04 05 06 07 08 CUSUM of Squares 09 10 11 12 5% Significance Source: Author's Calculation 32 15
24. Figure 9 : Cumulative Sum of GDPNP 15 10 5 0 -5 -10 -15 94 96 98 00 02 04 CUSUM 06 08 10 12 14 10 12 14 5% Significance Source: Author's Calculation Figure 10: Cumulative Sum Squared of GDPNP 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 94 96 98 00 02 04 CUSUM of Squares 06 08 5% Significance Source: Author's Calculation 33
25. Figure 11 : Cumulative Sum of GDPNG 12 8 4 0 -4 -8 -12 03 04 05 06 07 08 09 CUSUM 10 11 12 13 14 15 5% Significance Source: Author's Calculation Figure 12: Cumulative Sum Squared of GDPNP 1.6 1.2 0.8 0.4 0.0 -0.4 03 04 05 06 07 08 09 CUSUM of Squares 10 11 12 13 5% Significance Source: Author's Calculation 34 14 15
26. Billions Riyals Figure A . Economic Growth of the Non-Oil Sector 200 180 160 140 120 100 80 60 40 20 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 0 Non-oil Private sector Non-oil Public sector Total Non-Oil sector Source: SAMA Bank Credit by Economic Activity Agriculture and Fishing Manufacturing and Processing Mining Quarrying Electricity, Water, Health Services and Gas Building and Construction Commerce Transport and Communications Finance Services Miscellaneous Source: SAMA 35 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987 1986 1985 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%
27. References Adjasi , C.K.D. and Biekpe, N.B. (2006). Stock Market Development and Economic Growth: The Case of Selected African Countries. African Development Review, 18(1), 141-161. Al-Malkawi, H., A., Marashdeh, H., and Abdullah, N. (2012). “Financial Development and Economic Growth in the UAE: Empirical Assessment Using ARDL Approach to Co integration.” International Journal of Economics and Finance Vol. 4, No. 5; Published by Canadian Center of Science and Education. Caporale, G. M., Rault, C., Sova, R. & Sova, A. (2009). Trade Specialisation and Economic Convergence: Evidence from Two Eastern European Countries (April). DIW Berlin Discussion Paper No. 875. De Gregorio, J and Guidotti, E. (1995). “Financial Development and Economic Growth.” 99 World Development, March, 23(3), pp. 43348. Demetriades, P., Hussein, K. (1996). “Does financial development cause economic growth? Time series evidence from 16 countries.” Journal of Development Economics, 51, 387–411. Diamond. D. (1984). Financial intermediation and deleaated monitorinn. Review of Economic Studies, 51, 393-414. Fosu, O, and Magnus, F (2006). Bound Testing Approach to Cointegration: An Examination of Foreign Direct Investment Trade and Growth Relationships. American Journal of Applied Sciences 3, pp 20792085. 36
28. Greenwood , J and Bruce, S. (1997), “Financial Markets in Development, and the Development of Financial Markets.” Journal of Economic Dynamics and Control, 21, 145-181. Greenwood, J., Jovanovic, B., (1990). “Financial development, growth, and the distribution of income.” Journal of Political Economy, 98, 10761107. Hakkio, C.S. and Rush, M (1991). “Cointegration: How short is the long run?” Journal of International Money and Finance, 10, 571-581. Ibrahim, M. A. (2013). “Financial development and economic growth in Saudi Arabian economy.” Applied Econometrics and International Development, 13(1), 133–144. Inanga, L. and Emenuga. C. (1997) “Institutional, Traditional and Asset Pricing Characteristics of the Nigerian Stock Exchange.” African Economic Research Consortium Research paper 60 March 1997. Inder, B. (1993). “Estimating Long Run Relationships in Economics.” Journal of Econometrics, 57, 53-68. Jalil, A and Ma, Y. (2008) “Financial development and economic growth: Time series evidence from Pakistan and China.” Journal of Economic Cooperation among Islamic Countries, 29(2), 29–68. Lewis, A. (1955). The Theory of Economic Growth. London, Allen and Unwin. Mahran, Hatim Ameer, and Khalid A. Al Meshall. "Bounds testing approach to cointegration: An examination of the impact of foreign direct investment and trade on growth in Saudi Arabia, 1970-2010." Journal of Economics and International Finance 6, no. 11 (2014): 258-271. 37
29. Narayan , P. (2005), “The Saving and Investment Nexus for China: Evidence from Cointegration Tests.” Applied Economics, Vol. 37, No. 17, pp. 1979–1990. Nili, M. and Rastad, M. (2007). “Addressing the Growth Failure of the Oil Economies: The Role of Financial Development.” The Quarterly Journal of Economics and Finance, 46, 726–40. Patrick, H., (1966). Financial Development and Economic Growth in Underdeveloped Countries. Economic Development Cultural Change, 14, 174-189. Pesaran, M.H. and B. Pesaran (1997). Microsoft 4.0: Interactive Econometric Analysis. Oxford University Press. Samargandi, N., Fidrmuc, J., & Ghosh, S. (2014). Financial development and economic growth in an oil-rich economy: The case of Saudi Arabia. Economic Modelling, 43, 267-278. Schumpeter J.A. (1911). The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle. 1934 translation, Cambridge, Mass: Harvard University Press. Shandre, M. T. and Ang Beng Jiunn, J. (2004). “Financial Development and Economic Growth in Australia: An Empirical Analysis.” Empirical Economics, 29, 247-260. Williamson, D. (1986). “Costly Monitoring, Financial Intermediation, and Equilibrium Credit Rationing.” J. Monet. Econ, 18(2), 159-79 38