Economic diversification in Saudi Arabia: A disaggregated analysis

1. Economic diversification in Saudi Arabia: A disaggregated analysis Mohammad Asif, Wasim Ahmad and Raj Bahadur Sharma College of Business Administration, Salman bin Abdulaziz University, Al-Kharj, Kingdom of Saudi Arabia

2. Introduction and motivation of the study • It is a well known fact that Saudi economy is an oil driven economy; • But despite oil led economic development, the economy has been passing through the challenging phases of economic transformation. Where it is not only facing the developmental challenges from its neighbouring countries but also domestically owing to high growth rate of population and highly volatile oil revenue; • The highly capital intensive oil sector has provided limited scope of employment generation and due to social and cultural constraints, the economy has also not been able to catch-up the faster pace of development as seen in other member GCC countries; • In case of oil dependent economy, economic diversification implies the development of non-oil sectors with high employment generation potentials; • Technically, the economic diversification also implies the non-synchronization between oil and non-oil sectors;

3. Introduction and motivation of the study • In the literature especially in case of Gulf Co-operation Council (GCC) countries, not many studies have evaluated the process of economic diversification and from policy perspective, there is special need to analyse the process of economic diversification so that it may guide the policy makers to outline necessary measures; • Especially, in case of Saudi economy, it is critical issue because the economy is currently passing through the challenging phases of economic interdependence as the development of non-oil sectors is on high priority; • The another motivation of this study comes from the recent change in oil outlook of global economy; • Until, now US economy was largest importer of oil but after the crisis, it has started utilizing its own oil reserves and there is speculation that it may lead the global oil market. If it happens, Saudi economy may have to see the decline in its economic importance; • The discovery of oil reserves in some part of old Russian federation countries have also expected to have impact on the demand side of the Saudi economy;

4. Considering these facts, this study attempts to study the process of economic diversification at disaggregated (sectoral) level in case of Saudi economy by analyzing the synchronicity between oil sector and non-oil sectors for the period 1970-2011; • More specially, the empirical part of this study is divided into two sections: • First, we analyse the non-synchronization properties of oil and non-oil sectors; • Second, we empirically evaluate the critical determinants of economic diversification such as public spending and oil revenue.

5. An overview about existing literature • The existing studies in the literature which have examined the issue of economic diversification in an international setting are Meijer, 1990; Wilson and Zurbruegg, 2001; Al-Faris, 2002; Chaudhary and Al-Sahlawi, 2000; Gylfason, 2004; Hargis and Mei, 2006; Payne, 2008; Basher, 2010; Havro and Santiso, 2011; Espinoza, 2012; Hvidt, 2013; Schiliro, 2013; • Of these, the works of Chaudhary and Al-Sahlawi (2000) and Basher (2010) in case of Saudi and GCC countries, stand out as detailed econometric studies of the process of economic diversification; • Both these studies use the annual data and analyses the important sectors of the economy; • Both studies confirm the presence of visible patterns of economic diversification in Saudi economy and GCC countries; • Analytically, the study of Basher (2010) appears to be more rigorous because it applies not only the parametric methods but also the non-parametric techniques to examine the non-synchronized properties of non-oil sector with oil sector in case of GCC countries.

6. Percentage share of oil and non-oil sectors in total revenue of Saudi Arabia Percentage share of oil exports in total exports of Saudi Arabia Source: SAMA (2012), Figures are authors’ own calculations.

7. Empirical methodology • The empirical methodology is divided into three sections: • At first stage, we calculate sectoral output gaps by applying two non-parametric high pass filters viz., Hodrick and Prescott (HP, 1997) and Christiano and Fitzgerald (CF, 2003); • These two filters possess many characteristics such as low of observations and are also able decipher any asymmetry present in the data. • In the second stage, we calculate the sectoral synchronization by applying a non-parametric technique developed by Mink et al (2007) • In the third and last stage, we apply logit model to examine the impacts of public spending and volatile oil prices on the binary outcomes of sectoral synchronization.

8. The model of Mink et al. (2007) is explained as follows: Suppose is reference output gap for a country. Then, the synchronicity between individual sectors i and the reference cycle in period t, as proposed by Mink et al. (2007) is given by: Where denotes for the output gap of sector ifor the output gap of sector i in period t. As above mentioned, the present study uses HP and CF filters to compute the output gap of each sector. • Synchronization is defined when and in equation (1) have the same sign. When the value of is it is considered that both the sectors under investigation satisfy the synchronization property and when its value is -1 then it is characterized as decoupling (or diversification). • Finally, equation (1) is further extended to the multivariate case by examining the synchronicity between the reference cycle and sample sectors in period t. • where N is the number of non-oil sectors within a country. In order to obtain the values of non-aggregate oil sector. At last, the sums of individual non-oil sector i, equation (2) is used to examine the synchronicity of economic cycles between the reference sector and aggregate non-oil sector.

9. Data • The present study uses the annual data of Saudi Arabia’s oil and non-oil sectors for the period 1970-2011; • The source of this data is Saudi Arabian Monetary Agency (SAMA). We retrieve the spot price of crude oil (West Texas Instrument (WTI)) traded on NYMEX from Federal Reserve Bank of St. Louis’s FRED economic database; • The sample sectors considered in this study are Agriculture, Community (Community, Social & Personal Services), Construction, Finance (Finance, Insurance, Real Estate and Business Services), Manufacturing, Retail (Wholesale & Retail Trade, Restaurants and Hotels), Transport (Transport, Storage & Communication); • Oil-GDP, non-oil GDP (sum of all sample sectors GDP, utilities (electricity, gas and water) and import duty and less imputed bank service charge.

10. Empirical results Descriptive statistics of sample series, 1970-2011 • The average growth rate of GDP during sample period is about 5%; • Analysing the non-oil sectors individually, the figures indicate that Retail sector (9.41%) exhibits the highest average growth followed by Transport (7.62%), Manufacturing (7%), Construction (6.64%), Finance (6.13%), and Community (5.75%). The lowest average growth is exhibited by agriculture sector (5.43%).

11. Sectoral synchronization and decoupling analysis • The synchronicity between non-oil sectors and oil sector exhibits fluctuation during sample period; • Since, the computed values of synchronicity varies between -1 and 1, we have superimposed each sector’s cyclical trend extracted using HP and CF filters; • As it is discernible that in case of all sample sectors, there is clear evidence of ups and downs during first oil crisis (1973-74), second oil crisis (1979-81) and the Gulf war (1990-91) with the exceptions of Agriculture and Community; • More importantly, these figures also capture the recent oil price shock observed in sample non-oil sectors during global financial crisis (2008) with the exception of Agriculture and Communication; • These results are also substantiated by the synchronization results computed using CF filter; • Overall, these findings indicate that in case of Saudi Arabia, the long-run synchronicity has increased modestly before 1990 and declined thereafter; • This is in agreement with Basher (2010).

12. Time average of synchronicity measure between oil and non-oil sectors (percent)

13. The non-oil sector synchronicity of sample sectors becomes easier to explain when it is transformed into [0, 1] scale; • For example, the value for synchronicity in non-oil sector in Saudi Arabia is calculated in this way: [100% multiplied by (average value of the synchronicity of all non-oil sectors+1)/2]; • At aggregate level, based on the results of HP and CF filters, the value of synchronicity in non-oil sector for Saudi Arabian economy indicates that on average 49% (from HP filter) and 51% (from CF filter) of times the non-oil sector had the output gap with the same sign as the output gap of oil sector; • Analysing sector-wise, the results of HP filter indicates that the value of synchronicity appears to be higher in case of manufacturing sector, followed by agriculture and construction; • The lowest synchronicity appears to be in case of Finance followed by community, transport and retail. These results are further substantiated by the results of CF filter with negligible differences.

14. Sectoral synchronization, public expenditure and oil price movements • we apply logit model by converting the binary values of sectoral synchronicity between oil and non-oil sectors. The model is specified as follows: 1 implies when there is synchronicity between oil and non-oil sectors and 0 indicates sectoral non-synchronicity which also means decoupling between oil and sample non-oil sectors; After converting values of in to binary outcomes, we specify the logit model for each individual sector as follows: where i= 1,…..,N and N=7 (number of non-oil sectors). denotes the explanatory variables. In this study, the logit model is specified as Where is public/government spending and shows the change in oil price (in our case we have considered WTI price).

15. Proxy of government expenditure • The first non-oil fiscal indicator is the ratio of non-oil primary balance (NOPB) to non-oil GDP. • This particular indicator is often cited as one of the most trusted indicators to gauge the direction of public spending in case of oil rich economies. The indicator NOPB is calculated by subtracting the non-oil revenue from total public spending. • This implies that an increase in NOPB would indicate increasing trend in public spending either by higher allocation of funds or a reduction in the non-oil revenue collection. A reduction in NOPB will imply fiscal consolidation. • The second promising indicator of public spending is ‘fiscal impulse’. • The calculation of fiscal impulse involves two important steps. • The first step is to measure the cyclically adjusted non-oil balance (CANOB), which does not take into account the impacts of automatic stabilizers and other non-discretionary factors on the non-oil balance. • The CANOB reveals the portion of the government’s budget balance that is directly affected by specific purpose fiscal policies. The CANOB is calculated as follows:

16. Proxy of government expenditure • The CANOB is calculated as follows: Where is the non-oil revenue to non-oil GDP ratio in period t. denotes the primary expenditure to non-oil GDP ratio of Saudi Arabia in period t. is real non-oil output divided by potential trend in period t. The second step of calculating fiscal impulse is as follows: where a negative (positive) value of is indicates a contractionary (expansionary) fiscal policy.

17. Structural fiscal impulse in Saudi Arabia Note: A positive (negative) value of Fiscal impulse indicates an expansionary (contractionary) fiscal policy. • Over the sample period, the average value of fiscal impulse indicates that there is fiscal contraction in case of Saudi Arabia; • For instance, in this case, the average value of fiscal contraction appears to be around 0.76 percent of non-oil GDP in case of Saudi Arabia; • The figure is surprising because a close appraisal of government’s plans indicates that there is strong emphasis on increasing the public expenditure.

18. Estimation results: Logit model • The estimated results show that when public spending is measured by the NOPBt, the synchronization between oil sectors and non-oil sectors is visible as it is partly explained by the expansionary fiscal policy; • The results further indicate that in cases of agriculture, community and finance, the negative coefficients indicate fiscal consolidation; • Among sample sectors, construction, manufacturing, retail and transport, the results confirm the strong case of fiscal expansion; • However, among these four sectors, the results of only two sectors are only statistically significant viz., manufacturing and transport, indicating that these sectors are on expansion in their expansionary stage supported by short as well as long-term fiscal stance; • The estimated results based on HP filter, indicate that among all sectors, the coefficient of NOPBt is only significant in case of manufacturing, indicating fiscal expansion. The sign as well as statistical significance of NOPBt in case of other sectors is in agreement with the results of CF filters;

19. Estimation results: Logit model • The results of interaction terms of NOPB and change in oil price do not significantly explain the sectoral synchronicity using both the filters with the exception of retail and transport. • The positive and significant interaction terms indicates fiscal expansion owing to increase in oil revenue. Surprisingly, the estimated coefficients of changes in oil price do not significantly explain the sectoral synchronization. •  Considering the case when public spending is measured by fiscal impulse, the results of both filters indicate that the coefficients of fiscal impulse is negative and statistically insignificant for all the sample sectors, implying that it does not significantly explain the sectoral synchronization. • The negative coefficient indicates that a tighter government’s expenditure policy is associated with sectoral synchronization. • The change in oil price, either directly or via the interaction term does not significantly explain the sectoral synchronization for all the sample non-oil sectors. • This is in contrast with the findings of Basher (2010) who reports significant impact for Saudi Arabia.

20. Conclusion and policy implications • The results of the study can be summarized as follows: • From diversification perspective, among all non-oil sectors of Saudi economy, finance, community, transport and retail appear to be relatively less synchronized with oil sector compared to other sample sectors like agriculture, manufacturing and construction; • Non-synchronization between oil and non-oil sectors implies that there is less dependence of these non-oil sectors on oil sector; • The overall outcome of empirical results is that there is visible trend of economic diversification in some of the sectors of Saudi economy, though the degree of overall economic diversification is not very encouraging. • Some important sectors of Saudi economy with large employment potentials like manufacturing and construction are still appeared to be dependent on oil sector; • One of the major features of this study is that the empirical results clearly reveal the impact of major global events including the recent global financial crisis (2008) on the sectoral synchronization between oil and individual non-oil sectors; • Considering the major determinants, the non-oil primary balance along with changes in oil price significantly explain sectoral synchronicity in some of the important non-oil sectors of Saudi economy;