KGEMM Simulations

Abstract

As mentioned earlier, MEMs are evaluated and validated using in-sample and out-of-sample simulations, and policy analysis, among other validation methods. In this section, we run KGEMM for in-sample forecasting and out-of-sample policy analysis to evaluate its predictive ability. Hasanov and Joutz (2013) provide an overview of the literature that covers in-sample and out-of-sample forecasts and other methods for evaluating the predictive ability of MEMs. This includes Calzolari and Corsi (1977), Beenstock et al. (1986), Klein et al. (1999), Fair (1984, 1994, 2004), Bardsen and Nymoen (2008).

As mentioned earlier, MEMs are evaluated and validated using in-sample and out-of-sample simulations, and policy analysis, among other validation methods. In this section, we run KGEMM for in-sample forecasting and out-of-sample policy analysis to evaluate its predictive ability.¹ Hasanov and Joutz (2013) provide an overview of the literature that covers in-sample and out-of-sample forecasts and other methods for evaluating the predictive ability of MEMs. This includes Calzolari and Corsi (1977), Beenstock et al. (1986), Klein et al. (1999), Fair (1984, 1994, 2004), Bardsen and Nymoen (2008).

MEMs can be run using either the static simulation method, in which the model takes historical lagged values, or the dynamic simulation method, in which the model takes predicted lagged values. The predicted lagged values are the combination of historical lagged values and any errors in the model’s predictions. The smaller the prediction errors of the model, the better the model can perform/approximate in simulations. Therefore, it would be advisable to run the model with dynamic simulation to see how significant or insignificant its prediction errors are in simulating the in-sample or out-of-sample values of the endogenous variables. Static, dynamic, deterministic, and stochastic MEM simulation methods have been comprehensively discussed in Klein et al. (1999) and Fair (1984, 1994, 2004), among others. This section employs the dynamic simulation method.

8.1 In-Sample Simulation

As mentioned previously, an in-sample simulation exercise is an evaluation/validation method to check how well a model can approximate historical data. We run KGEMM for the period 1999–2019 to check its in-sample predictive ability of approximating the Saudi Arabian historical data.²

The results of the in-sample simulations are plotted in Appendix D. Figures D.1, D.2, D.3, D.4, D.5, and D.6 in the appendix illustrate selected key endogenous variables from each block. Interested readers can refer to the details of the in-sample forecasting for each variable in Appendix D. The figures show that the model closely approximates the historical time path of the endogenous variables under consideration. It performs especially well in capturing the historical turning points and sudden changes in data.

It can be concluded that KGEMM’s in-sample predictive ability for the historical values of the endogenous variables is quite high. The literature suggests that if a model successfully approximates historical time paths of the variables, then there is a high probability that the model will also perform well in out-of-sample simulations or policy analyses.

8.2 Out-of-Sample Simulations

This sub-section simulates KGEMM to evaluate the economic, energy, and environmental effects of domestic and global changes. It should first be stated that these simulations aim to illustrate the model’s ability in addressing domestic and global changes through the linkages across its blocks. Two things have to be noted in this regard: (i) for economic, energy and CO₂ emissions variables, input values are just the authors’ calculations and output values are the results of the KGEMM simulations both based on many assumptions (which might not be adequate representations of the real life). Therefore, either input or output values in the simulations do not represent any official and or policy views at all. (ii) The simulations conducted here do not aim to evaluate any policy options. SV2030 provides a number of targets and initiatives to consider, related to fiscal stance, energy transitions (e.g., domestic consumption of fossil fuels and renewable deployments), competitiveness, investments, non-oil export expansion, and diversification among others. Hasanov et al. (2020) simulated KGEMM to assess the effects of domestic energy price reform (and also fiscal reform at some extent). Also, Hasanov et al. (2022c) simulated KGEMM to assess the non-oil exports effects of the expansions in the non-oil tradable and non-tradable sectors. Moreover, Hasanov and Razek (2022) assessed the positive impacts of the Public Investment Fund’s new investment strategy for 2021–2025 on the Saudi competitiveness by using KGEMM. Furthermore, Elshurafa et al. (2022) used KGEMM to quantify the macroeconomic and sectoral effects of diesel displacement from the agriculture sector. Lastly, Hasanov et al. (2022a) couple KGEMM with power generation models to evaluate economic, energy, and environmental effects of renewable deployments at the distributed generation and utility farm scales.

Given the above-mentioned KGEMM simulations done already, we do not want to repeat the same or very similar exercises here. Rather to provide readers with new insights here, we simulate KGEMM to assess the effects of the following scenarios: changes in (i) global oil prices, (ii) foreign direct investments to Saudi Arabia, and (iii) renewable penetration in the electricity generation. The scenario analyses cover the period 2022–2030. We simulate two scenarios in each out-of-sample exercise—business as usual (BaU) scenario and one of the three scenarios (denoted by S1, S2, and S3) mentioned above. The simulations end in 2030 to be in line with the time span of the SV2030 although the model can be solved till 2035. In the BaU, it is assumed that the Saudi Arabian economy moves forward as it did in 2021. Precisely, the BaU scenario includes the fiscal reform items (in particular, the implementations of expat levy in 2017, and 5% and 15% VAT rates in 2018 and 2020, respectively)³ and domestic energy price increases in 2016 and 2018, and does not include the goal of having 50% of renewable and 50% of natural gas in power generation by 2030 because we simulate this here as a scenario (S3) in 8.2.3. Thus, the differences between the BaU scenario and given scenario will be stemmed only from the inputs in a given scenario. As an output of each exercise, we consider not only economic indicators but also energy and environmental indicators. The idea is to assess the effects of given changes from the sustainable development perspectives by considering economic-energy-environmental dimensions. To this end, we consider non-oil value added (GVANOIL), non-oil exports (XGNOIL), non-oil government budget revenues (GREVNOIL), households’ disposable income (DI), total energy consumption (DEN_TOT_KSA), and total CO₂ emissions from energy consumption in the kingdom (CO₂_EN_TOT_KSA). The rationale behind this selection would be as follows: the first three indicators show developments in the non-oil sector, its exports and revenue collection, which are the main economic streamlines in the SV2030 and its realization programs.  A wellbeing of the nation and hence increasing disposable income of the households is the end goal of any economic policies. Lastly, energy consumption and associated emissions are considered from the  United Nations sustainable development goals standpoint. The description of each variable is given in Appendix B.⁴ We discuss policy background, inputs, and outputs of each scenario in the next sub-sections. We keep our discussions very brief, since we have three scenarios to examine and since out-of-sample simulations are not the entire objective of this book.

8.2.1 The Effects of Global Oil Price Changes

As in any oil-exporting country, oil prices and related revenues play an important role in economic activities including non-oil sector in Saudi Arabia. Oil constitutes large shares in total exports, budget revenues, and total economy in the Saudi economy as mentioned earlier in this book. There is a consensus in the literature that oil-related revenues are important for the development of the non-oil sector mainly through fiscal spending. Given this, we simulate KGEMM to examine effects of the international price of Arabian light crude oil (WPO_AL) on economic, energy, and environmental relationships of the Kingdom. As an input, the international price of Arab light crude oil in Scenario 1 (S1) is increased by 25% in each year of the 2022–2030 period compared to the values in the BaU scenario, as shown in Fig. 8.1.⁵

Fig. 8.1

Arabian light crude oil international price, US$ per barrel

Figure 8.2 documents the effect of this increase on the selected indicators.

Fig. 8.2

Projections of the selected indicators in S1 and BaU scenarios, 2021–2030

The graphs in the figure demonstrate increases in the selected indicators in S1 compared to BaU when the Arabian light crude oil international price increases. This is pretty much expected given the nature of the Saudi economy as discussed above. High oil price increases oil income, which is channeled into the rest economy through increased oil sector’s demand and increased government demand for other sectors’ goods and services as Eqs. (7.1)–(7.26) describe. This increases total demand and sectoral economic activities (see Eqs. (7.27)–(7.39) and (7.61)–(7.76)). This is the first round and demand-side effect, and it translates into the supply-side effect over time mainly through investment-capital accumulation as the Eqs. (7.43)–(7.51) and (7.114)–(7.138) explains. XGNOIL increases because domestic production measured by GVANOIL increases (see Eq. (7.193). An expansion in economic activities leads to more employment and wages and resultantly, the disposable income of the household’s raises (see Eqs. (7.264)–(7.274) and (7.285)–(7.291), and (7.306) and (7.109)). Expanded economic activities demand more energy (see Eqs. (7.61)–(7.76) and (7.307)–(7.321)) in S1 compared to BaU. Numerically, calculated implied elasticities for the average of 2022–2030 show that a 1% increase in the international price of Arabian light crude oil leads to a 0.4%, 0.4%, 0.2%, and 0.14% increase in GVANOIL, XGNOIL, GREVNOIL, and DI, respectively, while DEN_TOT_KSA and CO₂_EN_TOT_KSA only increase by 0.2% each.⁶

8.2.2 The Effects of Foreign Direct Investments Inflow to Saudi Arabia

The National Investment Strategy (NIS) has been announced in October, 2021. The strategy highlights a significant increases in foreign direct investments (FDI) inflow and domestic investments in the coming years. Precisely, the cumulative total (FDI inflow+Domestic) investment of 12.4 SAR trillion in KSA during 2021–2030 (Jadwa Investment 2021). 388 SAR billion of FDIs inflow and 1.65 SAR trillion of domestic investment and hence the total of 2 SAR trillion in 2030 have been indicated.⁷ Table 8.1 records the shares of the sources in the total investment by 2030.

Table 8.1 Breakdown of the total investment by stakeholder by 2030

The strategy targets to raise FDI inflow from SAR 17 billion in 2019 to SAR 388 billion in 2030—this is about 23 times increase in 10 years. Likewise, it is targeted to increase domestic investments and overall investments by 2.6 times and 3.1 times, respectively, during 2019–2030.

Obviously, the NIS and associated domestic and foreign investment targets have large policy implications regarding their impacts on the development of Saudi economy. They also have implications for energy and environmental dimensions of the Kingdom. It was also discussed that accomplishing the above-mentioned targets necessitates well-designed development measures determined by the NIS and this is based on four main milestones, namely, enhancing investment opportunities, targeting different investor types, diversifying financing options, and improving competitiveness.⁸ We do not discuss more policy implications of the NIS here to save space, but vividly this deserves a scenario analysis to quantify its impact on the Kingdom. To do so, we focused on the FDI inflow aspect of the NIS. This is because FDI inflow is exogenous to the Saudi economy, and it is considered so in the KGEMM framework. Thus, it is more realistic to simulate the impact of FDI inflow, as a global factor, on Saudi Arabia. As the input for scenario 2 (S2), we obtained nominal values of FDI inflow for 2022–2030 in SAR billion from the online media source of https://www.argaam.com/en/article/articledetail/id/1503922 and then converted them to US$ billions using the exchange rate.⁹ Figure 8.3 illustrates the FDI inflow values in the S2 scenario (FDI$IN_Z S2) and those in the BaU scenario (FDI$IN_Z BaU) over the simulation period.

Fig. 8.3

Projected values of FDIs inflow in the S2 and BaU scenarios, US$ billions

The figure shows that values for the FDIs inflow are significantly large in S2 scenario compared to the values in the BaU scenario. The difference between the FDIs inflow values in two scenarios increases from about 3 times in 2022 to 10 times in 2030; these are large numbers and should have sizeable growth effects. Figure 8.4 reports the results for the selected indicators.

Fig. 8.4

Projections of the selected indicators in S2 and BaU scenarios, 2021–2030

The graphs in the figure convey heterogeneous information. In other words, for GVANOIL, XGNOIL, DEN_TOT_KSA, and CO₂_EN_TOT_KSA, the deviations of the S2 scenario values from the BaU scenario values are considerable, whereas that for the GREVNOIL and DI are not. Numerically, in 2030, the percentage change deviations of the S2 values from the BaU values for GVANOIL, XGNOIL, DEN_TOT_KSA, and CO₂_EN_TOT_KSA are 23%, 25%, 14%, and 14%, respectively. While the deviations for GREVNOIL and DI are 4% and 1%, respectively. At the first glance, small deviations in the case of latter variables might be seen puzzling as the increases in FDIs inflow in the S2 scenario are quite large compared to that of the BaU scenario. However, a closer look reveals out a few points that are worth considering. First, historically, the size of FDIs inflow in total domestic investments and thus its role in the economy was quite limited and even diminishing over time. In terms of numbers, the size rose from 19% in 2005 to a peak of 33% in 2008 and has been steadily declining since then, falling to just below 3% in 2019. A simple cointegration analysis shows that the magnitude of the impact of the FDIs inflow on the non-oil activities was very small compared to that of the domestic investments.¹⁰ Second, if we calculate the implied elasticities of the most largely impacted variables, i.e., XGNOIL and GVANOIL, with respect to FDIs inflow, they would be as small as 0.0216 and 0.0197, respectively, for 2022–2030, pointing to minor growth effects of FDIs inflow for the non-oil activities. Third, another reason why GREVNOIL and FDI are less affected by the increase in FDIs inflows may be due to outflow remittances. Eq. (7.109) illustrates that net national disposable income declines when outflow remittances (REMOF) increase. In this regard, in 2030, percentage deviation of REMOF value in S2 from the value in BaU is 25%, indicating a quite large amount of leakage. Fourth, the growth in the other component of the net national disposable income, namely, labor compensation (LABCOMP), which positively affects it, is very small. Numerically, the percentage deviation of LABCOMP value in S2 from the value in BaU is as small as 0.2% in 2022 and it grows to 1.5% only in 2030. This is not surprising because of the two reasons: the expansion in the economic activity in this scenario is investment-driven and the labor elasticity of output is smaller than that of capital elasticity for a number of the non-oil activity sectors, namely, manufacturing less petrochemical; transportation and communication; petrochemical; agriculture; utility; retail, wholesale, hotels, and catering (see identities for the estimated potential output equations by economic activity sector in Sect. 7.1). Both reasons indicate a limited role of employment in non-oil economic activities in this scenario. In addition, the increase in non-oil sector employment (ETNOIL) in the S2 scenario compared to the BaU scenario is 0.6% in 2022 and 4.8% in 2030. As a result, limited role of employment coupled with its limited increase makes LABCOMP to increase with a minor magnitude in the S2 scenario. Moreover, the third component of DI, namely, government transfers to households (GCGPE) does not increase in the S2 scenario compared to the BaU scenario and even gets slightly small due to the decrease in government oil revenues (GREVOIL) as explained below. Fifth, low growth in employment and wages and thus in net national disposable income and household consumption makes GREVNOIL to grow less too as the VAT collections is one of its main components. Sixth, another reason for a small increase in GREVNOIL would be none to negative growth rates of government expenditure (GEXP). Numerically, its growth in S2 compared to BaU is on average −0.4%. The main reason for it is that an expansion in economic activity demands more energy to be consumed domestically (see changes in DEN_TOT_KSA), and this results in less amount of oil being available for export (XOILC) since the oil production (OILMBD) in KGEMM is treated as exogenous due to the global oil market conditions such as OPEC+ agreements (see Eq. (7.199)). Consequently, government oil revenues (GREVOIL) in S2 decrease by an average of 2.5% over 2022–2030 compared to BaU. Since the share of GREVOIL in total government revenues (GREV) is considerably large (the average of 1969–2019 was almost 80%), the latter declines slightly in S2 compared to BaU although GREVNOIL increases in S2 as figure above shows. As a result, GEXP declines and it is one of the key drivers of economic growth including non-oil economic activity, which is the base for GREVNOIL collections.

In the conclusion of the scenario 2 exercise, some insights can be derived from the simulation. The FDIs inflow has a positive impact on the economy, but historical structure and business environment in the economy should be changed to make the magnitude of this impact larger. The simulation results here support the four main milestones (enhancing investment opportunities, targeting different investor types, diversifying financing options, and improving competitiveness) that are already considered in the NIS framework as the main policy measures to make FDIs inflow more impactful in the economy. Second, the authorities may wish to think about measures to further increase the labor contribution to output in some non-oil economic activity sectors. Investments in research and development, education, trainings, and other human capital enhancing activities might be fostered in this regard. Third, the authorities also may wish to think about measures to reduce leakages, such as outflow remittances, which would further increase aggregate demand and domestic economic activities. Increasing the expat levy rate does not seem a best measure in this regard because it might encourage foreign workers to consider another Gulf countries for work. One measure, which seems reasonable, is to further facilitate business and investment opportunities, as well as ownership and property rights for foreigners. In this regard, it is acknowledged that the government successfully implemented reforms, measures, and initiatives in line with Saudi Vision 2030 recently.¹¹ This would encourage foreign workers to invest, establish, or expand their business and own properties in Saudi Arabia. The other measure might be further development of service sectors including entertainment, such as cinema, sport games, so that foreigners spend their money domestically, which would boost economic activities through spillover effects. Fourth, none to very small growth in GEXP in the S2 scenario brings up two policy insights: (i) the share of renewable in total domestic energy consumption (DEN_TOT_KSA) should be increased. The point here is that increased economic activity will demand more energy, resulting in less oil to export. Increasing the share of renewable to meet domestic energy demand will displace more oil from domestic use. The government has already announced its strategy of completely displacing liquid fuels in the electricity generation and making it based on renewable and natural gas only with the shares of 50% and 50% by 2030. This would allow to save more oil that can be either exported as a crude or refined domestically for export purpose, which would bring more foreign exchange reserves, which can be used for covering the government debt or put in human capital, research and development, innovation, and other long-run drivers of productivity. Renewable deployments also will reduce CO₂ emissions, which would help to achieve environmental targets. (ii) weaken the role of government spending in the development of the non-oil sector activities and putting more emphasis on the private sector. Note that this is one of the key targets of SV2030—to increase the private sector’s contribution to GDP from 40% to 65% by 2030.

8.2.3 The Effects of Raising the Share of Renewables in Power Generation Mix to 50% by 2030

Saudi Arabian government has very comprehensive strategies aiming at increasing the share of renewables in energy consumption. Energy and Sustainability strategy is one of the important streamlines of SV2030, a roadmap for the development of the Kingdom. The National Renewable Energy Program (NREP) is an important initiative under this strategy with the purpose of increasing the share of renewable energy production, achieve a balance in the mix of local energy sources, and fulfill Saudi Arabia’s obligations toward reducing CO₂ emissions.¹² One of the key targets of NREP is achieving optimum energy mix to produce electricity.¹³ It targets removing liquid fuel from the mix to make it comprised of renewables and gas, each with a share of 50% by 2030.¹⁴ Obviously, the targeted figures make it important to assess their potential economic, energy, and environmental implications. Such a green energy transition brings two main benefits at least: reducing environmental pollution and gaining extra revenues from exporting displaced fossil fuels.

Given that the share of natural gas in the power mix was almost 50% in 2019 (see source in footnote 19), the target of the optimum energy mix is to increase the share of renewable to 50% by 2030. Put differently, the use of crude oil, diesel, and HFO in the electricity generation mix will be replaced by natural gas over time, so that the shares of these liquid fuels in the mix will be zero by 2030. This opens a great avenue to export this replaced liquid fuels to make more foreign exchange reserves or use them domestically (for example, crude can be used to produce refined products). These are the two options to deal with the displaced liquid fuels from the power mix, but the first option might be seen more attractive.

Obviously, increasing the share of renewables to 50% by 2030 requires a large amount of investments among other efforts. Although we have not came across any announced investment figures for this purpose, some media resources mention investing 380 billion SAR by 2030.¹⁵

In this scenario (S3), we simulate KGEMM to assess economic, energy, and environmental effects of the above given renewable initiative. First, we calculated the needed amount of renewable energy for the power mix. We considered solar given the fact that it is the main renewable energy source in Saudi Arabia so far, as discussed earlier in this book. Solar energy was 0.063 MTOE and 0.075 MTOE in 2019 and 2020, respectively, the Kingdom according to IEA (2021). We forecasted it to be 0.09 MTOE in 2021 assuming the same growth rate of 2020. We also forecasted total electricity production to be 37.74 MTOE in 2030 using different factors including efficiency of fossil fuels and contribution of renewable, i.e., solar energy. Given that it is targeted the half of the total generation will be solar (and the other half will be generated from natural gas) and considering solar energy forecasted for 2021, we extrapolated solar energy between 2022 and 2030 in S3 scenario. Next, we calculated crude oil equivalent of the projected solar energy using the power generation efficiency factor of crude oil in Saudi Arabia from the electricity-related agencies for S3 scenario.¹⁶ Finally, we extrapolated the announced investment figure for renewable energy and deflated the resulted values by investment deflator for 2022–2030. In scenario S3, we assume that domestic oil use (OILUSE) will be decreased by the crude oil values to be displaced and utility investment will be increased by the calculated real investment values. These two are the inputs that differentiates S3 from BaU. Note that we included these two inputs in the S3 as add factors (named OILUSE_ADD and IFU_ADD) rather treating OILUSE and IFU exogenous. This is because both variables are the endogenous variables and hence should change in line with economic activity performance in S3 scenario, but treating them exogenous would ignore this reality. Figure 8.5 illustrates calculated crude oil to be displaced and subtracted from domestic crude oil use, and renewable investment to be added into the utility sector investment.

Fig. 8.5

Projected OILUSE_ADD in million barrel per day and IFU_ADD in SAR 2010 billion in S3 scenario, 2021–2030

The figure demonstrates that both variables are zero in 2021. This is to show that our scenario simulation starts in 2022 and this does not mean that either OILUSE or IFU are zero in 2021. The displaced crude oil in S3 (OILUSE_ADD S3) is on average 15% of OILUSE in BaU in 2022–2030. In the same token, additional utility sector investment due to the renewable projects in S3 is on average 112% of IFU in BaU in the same period. These mean that there will be increases in the crude oil export revenues (associated with the displaced crude oil) and increases in utility sector investments in S3 compared BaU. We expect that they will create positive changes in the economy and environment in the S3 scenario. The results of the simulations for the selected variables are illustrated in Fig. 8.6.

Fig. 8.6

Projections of the selected indicators in S3 and BaU scenarios, 2021–2030

We would like to point out some observations from the graphs briefly. First, all the indicators demonstrate growth, whereas CO₂ emissions declines in the S3 scenario compared to the BaU scenario. Second, macroeconomic indicators increase with different paces. Numerically, on average in 2022–2030, GVANOIL S3 and XGNOIL S3 increase by 3.3% and 3.6%, respectively, while GREVNOIL S3 and DI S3 rise by 1.0% and 0.6%, respectively. GVANOIL S3 increases due to the aggregate demand increases in the short-run and due to the supply-side expansions (mainly capital stock but non-oil employment also increased albeit slight) in the long run both caused by injecting displaced crude oil revenues and renewable investments into the economy. GREVNOIL S3 rises because GVANOIL S3, i.e., non-oil economic activities increase. Increases in DI S3 are mainly caused by increases in LABCOMP S3, which grows by 1.0% on average in 2022–2030 compared to LABCOMP BaU. Third, DEN_TOT_KSA S3 increases on average by 2.0% compared to BaU. The increase is caused by the increasing aggregate demand and economic activities. Last, but not least, CO₂_EN_TOT_KSA declines in a growing pace in the S3 scenario compared to the BaU scenario. Numerically, the drop is 1.8% in 2022 and reaches to 18.2% in 2030. Apparently, the declining  CO₂ effect of the renewable deployments, which removes crude oil use, outpaces the increasing effect of additional fossil fuel energy consumption due to the expanded economic activities in the S3 scenario.