What influences bank lending in Saudi Arabia?

Ken Miyajima (International Monetary Fund, Washington, District of Columbia, USA)

ISSN: 1319-1616

Publication date: 22 April 2020

Abstract

Purpose

Determinants of credit growth in Saudi Arabia are investigated.

Design/methodology/approach

A panel approach is applied to macroeconomic and bank-level data spanning 2000 ‐15.

Findings

Bank lending is supported by strong bank balance sheet conditions (high capital ratio, and growth of NPL provisioning and deposits), and higher growth of both oil prices and non-oil private sector GDP. Lower bank concentration also helps, likely through greater competition, so does stronger institution. Consistent with the literature, lending by Islamic banks may be more responsive to economic activity. Lending remained robust in 2015 despite oil prices having declined, helped by strong bank balance sheets and as banks reduced their holdings of “excess liquidity”. To support bank lending in the period ahead, bank balance sheets need to remain strong. Fiscal adjustment and a reduced reliance on banks to finance the budget deficit would support credit provision to the private sector.

Originality/value

The paper is first to analyze in detail determinants of bank lending in Saudi Arabia applying a panel approach to bank level data, and draws critical policy implications.

Citation

Miyajima, K. (2020), "What influences bank lending in Saudi Arabia?", Islamic Economic Studies, Vol. 27 No. 2, pp. 125-155. https://doi.org/10.1108/IES-07-2019-0018

Publisher

:

Emerald Publishing Limited

1. Introduction

As oil prices fell since mid-2014, inflows of oil receipts declined and fiscal spending was scaled back, weakening economic activity. Funding conditions in the banking system tightened, evidenced by the increase in the 3-month Interbank Offered Rate (SIBOR) to the highest level in many years (Figure 1, upper right panel). Lower oil prices also dampened confidence (Husain et al. (2015)). Deposit growth fell and remained mostly in negative territory on a year-on-year basis during 2016, but growth of credit to the private sector remained robust, particularly to the construction sector, partly reflecting efforts by businesses to manage their cash positions as government payments were delayed (Figure 1, lower left panel). Banks reduced their holdings of both excess reserves at the Saudi Arabian Monetary Authority (SAMA) and SAMA bills to help fund private sector credit and purchases of bonds, which the government restarted issuing in 2015 (Figure 1, lower right panel).

In response, SAMA undertook measures to help ease funding conditions. The central bank placed more than SAR 20 billion ($5.3 billion) of government entity deposits with the domestic commercial banks in September. It also announced the introduction of 7-, 28- and 90-day repos, which had only been overnight previously. As bank funding conditions eased, the 3-month SIBOR declined to the lowest level in six months in mid-December [1]. Deposit growth rebounded to marginally above 0 percent and bank holdings of excess liquidity rose in November. The issuance of$17.5 billion international bonds by the government in October, which was the largest by an emerging market and heavily oversubscribed, also helped ease funding conditions. Earlier, SAMA also relaxed the loan to deposit ratio, allowing the ratio to exceed the 85 percent limit.

As banks face funding pressure, one key question is prospects for bank lending in Saudi Arabia. Bank credit represents a key channel of transmission from oil prices to the real economy in Saudi Arabia and an important driver of economic growth more generally. Cross-country data for the past several years presented in Figure A1 in the Appendix suggest that the nation's credit deepening (bank credit to the nonfinancial private sector relative to Gross Domestic Product (GDP)) was broadly consistent with the stage of economic development (GDP per capita in US dollars during 2010–2015). However, low oil prices could adversely affect bank credit extension and economic activity. Indeed, weak bank balance sheet conditions (e.g. higher non‐performing loan (NPL) ratios and lower deposit growth) can spillback to further weaken macroeconomic conditions (Miyajima (2017)).

Against this backdrop, this paper analyzes determinants of bank credit in Saudi Arabia. It complements the literature, which primarily relies on cross-country panel data by single-country estimations that account for country-specific characteristics. The paper applies a panel econometric approach to bank-level balance sheet and macrolevel data for Saudi Arabia spanning 2000–2015. It finds that bank lending is supported by strong balance sheet conditions (high capital ratio and growth of provisioning and deposits), higher growth of both oil prices and nonoil private sector GDP. Lower bank concentration helps, likely through greater competition, so does stronger institution.

The rest of the paper is organized as follows. Section II reviews the literature and Section III describes the methodology and data. Section IV discusses results. Section V extends the baseline model to address several questions key to Saudi Arabia. Section VI concludes.

2. Literature

Bank credit is one important element of financial development, which enhances economic growth. A vast literature finds that greater financial development helps spur economic growth (see, e.g Levine (1997, 2005) and Demirguc-Kunt and Levine (2008), for an extensive survey of the literature). However, more recent studies provide nuanced messages. Sahay et al. (2015) argue that many benefits in terms of growth and stability can be reaped from further financial development in most emerging market economies but that the effect of financial development on economic growth is bell-shaped and weakens at higher levels of financial development.

Some studies, however, have suggested the effects of financial development on growth are weak in oil exporting countries. For example, Naceur and Ghazouani (2007) analyze the Middle East and North Africa (MENA) countries, including Bahrain, Kuwait, Oman and Saudi Arabia and find no significant relationship between the development of the banking sector or the stock market and economic growth [2]. Barajas et al. (2013) argue that the beneficial effect of financial deepening (including private credit) on economic growth is generally smaller in oil exporting countries and lower-income countries due to weaker regulatory and supervisory characteristics and more limited access to financial services. Hakura (2004) argues that, in the MENA, where oil revenues are significant (including the GCC countries), large governments have likely limited private sector growth and diversification. However, such cross-country, panel data analyses may fail to capture Saudi Arabia's country-specific characteristics and can be usefully complemented by single-country estimations. Indeed, Miyajima (2017) finds that higher bank lending strengthens real GDP growth in Saudi Arabia.

Given the importance of bank credit, a large volume of literature focuses on its determinants. One strand of literature studies the issue in the context of monetary policy transmission or the bank lending channel, which has attracted particular attention after the global financial crisis. These studies find that bank-specific characteristics, such as size, liquidity, capitalization and lenders' default probabilities, have a large impact on the provision of credit (for instance, Altunbasa et al. (2010); Gambacorta and Marques-Ibanez (2011); Guizani (2015); Gambacorta and Shin (2016)). For emerging markets, global factors increased in importance in affecting the bank lending channel as capital flows became larger and more volatile against the backdrop of very easy global monetary conditions (Kohlscheen and Miyajima (2015)).

Another strand of literature more directly focuses on determinants of bank credit in emerging markets, including the GCC countries. Focusing on a sample of emerging economies, Chen and Wu (2014) confirm the importance of strong balance sheet conditions and banking regulation in supporting robust credit growth. Moreover, state-owned banks played a counter cyclical role during the global financial crisis in 2008–2009, particularly in Latin America and emerging Europe. That is, credit by state-owned banks grew faster than credit by private banks. More generally, emerging economies displayed remarkable resilience during the global financial crisis in 2008–2009 compared with their experience in previous crises, such as those in the late 1990s, partly as bank balance sheet conditions were stronger (BIS, 2010). Saudi Arabia was not materially affected either owing to a range of buffers, including the conservative supervisory framework and a strong banking system (Al-Hamidy, 2010). Guo and Stepanyan (2011) examine a large number of emerging economies for a decade and identify several key determinants of bank credit: domestic economic activity, bank balance sheet conditions, domestic and external monetary conditions, and foreign funding. Amidu (2014) analyses determinants of bank lending in 24 countries in Sub-Saharan Africa using both bank and country-level data and finds linkages between bank balance sheet health and lending. Barajas et al. (2010) find that bank characteristics (capitalization and loan quality) help explain bank credit slowdown among MENA countries in the aftermath of the global financial crisis in 2008. Studies zooming in on the Gulf Cooperation Council (GCC) banks identify a range of determinants affecting bank credit (Ghosh (2013); Gani and AlMuharramil (2016)): bank capital, concentration, financial deepening (credit to GDP), economic growth, and institutional quality (enforcement, regulatory quality and rule of law).

Islamic banks tend to increase credit more rapidly than non-Islamic banks, according to results based on a larger sample of GCC banks and the classification of banks by type provided by Bankscope. Barajas et al. (2010) conjecture that Islamic banks' business models are geared more toward investments and lending in high growth areas such as real estate.

3. Methodology and data

3.1 Methodology

As commonly done in the literature, determinants of real growth of bank-level credit are modeled using the following multivariate panel data specification for bank i in year t [3].

(1)rcgi,t=jα1,jBankj,i,t1+kα2, kMacrok,t+kα3,l Macrol,t1+lα4,mtimem+θi+εi,t
where rcgi,t is real growth of bank credit to the private sector and Bankj,i,t1 is bank level variables (j=1,2, ) lagged by one period to reduce potential endogeneity issues. That is, banks may adjust balance sheet composition in response to lending activity. Macrok,t represents macro level variables (k=1, 2, ), which are contemporaneous on the premise that these variables are exogenous to balance sheet conditions of individual banks. Macrol,t1 also represents macro level variables (l=1, 2, ) and are lagged by one period to reduce potential endogeneity issues. Time dummies for 2008 and 2009, timem (m=1, 2) help capture the potential effects of defaults of two large family-owned domestic conglomerates on loans [4]. Finally, θi is bank fixed effects and εi,t is random errors.

3.2 Data

A range of bank- level and macrolevel explanatory variables are considered to explain bank-by-bank real credit growth guided by the literature [5]. All data are annual and span 2000–2015. Bank-level balance sheet data from Bankscope are available for longer history, back to 1987 but with limited data availability. There are 12 domestic Saudi banks, but the analysis focuses on 10 of them dictated by data availability (Table 1). The 10 banks together represent more than 90% of the size of the banking system. All variables are expressed in real terms except for ratios. Figure 2 visually summarizes the data, while Table 2 presents summary statistics. Table A1 in the Appendix reports detailed description of the data.

Four bank level balance sheet variables, standard in the literature, capture bank characteristics (which perhaps represent supply factors): the capital ratio, growth of NPL provisions, deposit growth and net income growth [6]. Higher capital allows bank to lend more while maintaining the same level of capital ratio. Higher NPL provisions would negatively affect capital and could reduce lending but would also improve the bank's credit risk, lower funding cost and allow the bank to lend more. Higher deposits increase resources to lend. Finally, higher profitability could encourage the bank to lend more. A lagged dependent variable, bank-level credit growth, is often included in the literature but not in this paper's analysis because its coefficient is generally statistically insignificant. Therefore, the rest of the paper considers a standard panel fixed-effects model [7]. However, to help account for remaining potential endogeneity issues, the models are also estimated using a system generalized method of moments (GMM) approach as robustness checks.

Five macrolevel variables control for key global and domestic developments (which could perhaps represent a combination of supply and demand factors.) Oil price growth captures an important external shock given the nation's large reliance on oil exports. Oil revenues affect the nonoil sector through government spending on domestic goods and services and payment of government wages. The US Fed funds rate aims at controlling for changes in global monetary conditions which, given the Saudi riyal's peg to the US dollar, are expected to impact domestic monetary conditions. Nonoil private sector GDP growth captures domestic economic activity not directly affected by oil price movements. Domestic money market interest rates (3-month SIBOR) are expected to capture domestic monetary conditions and bank profitability [8]. Bank holdings of excess liquidity (sum of bank holdings of excess reserves at the central bank and those of central bank bills) represent banks' liquidity conditions.

Figure 2 summarizes the behavior of the explanatory variables. As far as bank-level variables, real growth of bank credit rose in the early 2000s but declined to negative territory in the late 2000s, primarily due to defaults of two large domestic conglomerates, rather than spillovers of international financial shocks. Saudi banks have been very well capitalized, with the Tier 1 ratio remaining at around 15%. NPLs was very well provisioned, by about 1.6 times in Q4 2015 [9]. Banks increased the pace of NPL provisioning as NPLs rose in the late 2000s. It fell as NPLs have fallen and remained very low. Deposit growth has behaved broadly in line with credit growth, as banks rely primarily on customer deposits to fund their assets (the size of balance sheets has grown in line with deposits). Saudi banks are profitable, but net income growth started to moderate on low oil prices, tightening funding conditions and weaker economic activity.

Turning to macrolevel variables, oil prices registered the worst performance in at least 3 decades, which in turn dampened nonoil private sector GDP growth. It fell to the lowest level since the early 2000s. As the US Federal Reserve started to gradually normalize its policy rate from very low levels and as domestic liquidity conditions have tightened, the 3-month money market rate has surged. Bank holdings of excess liquidity (in percentage point change of bank assets) declined notably. The US and domestic interest rates are both detrended using liner time trend to help reduce issues of spurious coefficients.

Results from a correlation analysis suggest both bank-level and macrolevel variables affect bank credit growth. Table 3 reports correlation coefficients which are statistically significant at the 5% level. Credit growth increases when bank funding (deposit growth) and profitability (net income growth) improve. It also strengthens with economic activity (nonoil private sector GDP growth). Bank profitability improves as short-term interest rates increase. Deposit growth is positively correlated with economy activity, bank profitability and liquidity conditions (bank holdings of excess liquidity). Domestic money market rates rise with the US Fed funds rate and as liquidity conditions tighten (lower excess liquidity and oil prices).

4. Results

4.1 Bivariate regressions

To start the analysis, bivariate panel fixed-effects regressions of bank credit growth are estimated on each one of the independent variables (see Table 4). Bank-level data are lagged by one period to reduce issues of reverse causality. Macrolevel data are introduced contemporaneously, except for nonoil private sector GDP growth, which can be contemporaneously affected by bank lending and therefore is lagged by one period. All models control for time effects for 2008 and 2009.

Results confirm the importance of bank balance sheet and macroeconomic conditions. A rise in the capital ratio increases the bank's capacity to lend. A higher growth rate of NPL provisioning reduces resources for additional lending and leads to a decline in credit growth. Higher deposit growth increases resources to fund bank lending. Higher growth of oil prices and nonoil private sector GDP creates tailwinds for lending. Interest rates, net income growth and bank holdings of excess liquidity do not systematically affect lending growth [10].

4.2 Multivariate regressions

We subsequently proceed to the multivariate panel fixed effects models of bank credit growth by combining all variables [11].

Empirical evidence confirms that bank credit is affected by bank characteristics and macroeconomic conditions (Table 5). Starting from bank characteristics, the supply of bank credit improves as the capital ratio increases and deposit growth strengthens. An increase in Tier 1 capital by 1% point of risk-weighted assets leads to 0.5%–1.0% increase in credit growth. Around 0.2–0.4 of a rise in deposit growth in real terms is transmitted to credit growth [12]. Credit supply weakens as banks provision for NPLs. Every 1% growth in NPL provisions in real terms reduces credit growth by 0.1%–0.2%. Net income growth does not systematically affect credit growth as it may capture offsetting factors, both funding cost and profitability.

Turning to macrolevel variables, bank lending increases as oil price growth accelerates and activity in the nonoil private sector strengthens (Table 5). A 10% increase in oil price growth leads to 0.8%–1.0% increase in credit growth. A 1% rise in nonoil private sector economic growth leads to 0.8%–1.2% increase in credit growth. Lower bank holdings of excess liquidity support credit extension. In some specifications, a 1% point reduction in the variable accelerates credit growth by 0.4%–0.6%. Interest rates do not systematically affect credit growth either probably as the variables are capturing a combination of both profitability and cost of capital.

The results are broadly unchanged when the model is estimated using system GMM. In the ordinary least squares (OLS) regression discussed above, bank-level variables and nonoil private sector GDP growth are all lagged by one period in a bid to reduce endogeneity issues. To check whether coefficients are biased due to remaining endogeneity issues, we apply system GMM approach proposed by Arellano and Bover (1995) and Blundell and Bond (1998). Estimated results summarized in Table 6 shows that the coefficients are similar to those summarized in Table 5 in terms of sign, size and statistical significance. Therefore, in the rest of the paper, we proceed with OLS estimations [13].

4.3 Model predictions

Model predictions help understand why credit growth remained robust through 2015, despite oil prices having fallen. Figure 3 plots the actual average credit growth and the model prediction (using Model 16). It also shows contributions to the change in the predicted credit growth

The result plotted in Figure 3 suggests that the sharp decline in credit growth around the global financial crisis was due to a combination of bank specific factors (a lower capital ratio, weaker deposit growth) and macroeconomic factors (lower growth of oil prices and nonoil private sector GDP). In addition, time dummy variables are required to fully capture the magnitude of the fall in credit growth, consistent with the view that defaults of two large domestic conglomerates dented market confidence around the global financial crisis [14]. Credit growth held up well in 2015 despite a large fall in oil prices, supported by resilient bank balance sheet conditions and economic activity but also by a reduction in bank holdings of excess liquidity. Looking ahead, credit growth could slow further, reflecting lagged effects of slower deposit growth, and if the capital ratio declines, provisioning for NPLs accelerates and economic activity slows further.

Additional factors are considered to account for a range of characteristics. These are bank holdings of government bonds, lending by specialized credit institutions (SCIs), banking system concentration, state ownership, regulation and governance, and Islamic versus other banks.

First, bank lending can slow as domestic banks continue absorbing bonds after the Saudi government restarted issuing debt securities. The government used to issue domestic bonds actively during previous periods of low oil prices (see Figure 4). For instance, Bloomberg reports that during 1997–2007, the Saudi government issued 17–18 bonds per year on average. The maturity ranged from one year to 10 years [15]. As a result, the amount outstanding of Saudi government bonds peaked at close to SAR 700 billion in the early 2000s. Domestic banks held 10%–20% of the total amount outstanding during the 1990s and early 2000s. Bank holdings of government bonds as a share of total bank assets peaked at close to 30%. From the early 2000s, the share continued to decline until the Saudi government restarted domestic bond issuance in mid-2015 (Figure 5, first panel).

Second, lending by SCIs may affect bank lending [16]. SCIs lend to some of the same sectors as banks do, which may increase or reduce bank lending. SCI lending growth accelerated during the 2000s and has remained relatively high (Figure 5, second panel). The stock of lending by SCIs represents some one-fourth of that of by banks [17].

Third, greater banking system concentration may limit competition and reduce credit growth. An indicator of banking system concentration suggests that, among the GCC banking systems, Saudi Arabia's is the least concentrated (Figure A2). Moreover, concentration has been declining in recent years in Saudi Arabia (Figure 5, third panel).

Fourth, greater state ownership can affect bank lending behavior. One view is that banks with greater state ownership may lend more counter cyclically to fill the gaps left by other commercial banks as the latter reduce lending in response to a negative macroeconomic shock (Chen and Wu, 2014). Another view is that if state ownership comes with greater prudence or reduced risk taking, lending behavior may appear pro-cyclical. State ownership in 2015 is estimated based on two definitions (Table 7). Under the “wide” definition, which accounts for ownership by the Saudi government, the Public Investment Fund (PIF) and two domestic pension funds (General Organization for Social Insurance (GOSI) and Public Pension Agency (PPA)), state ownership is considered to be high when it is 50% or above [18]. Under the “narrow” definition, which accounts only for the ownership by the Saudi government and PIF, the threshold above which state ownership is considered to be high is lowered to 30%. Econometrically, a dummy variable representing high state ownership is interacted with several variables.

Fifth, the stronger institution could lead to stronger credit extension. Gani and Al-Muharrami (2016) argue and find that conventional institutional quality measured by the time taken to enforce a contract, regulatory quality, the rule of law and government effectiveness are inversely correlated with the lending by the banks. We use the six components of the World Governance Indicators in a bid to capture different aspects of institutional strength [19].

Sixth, lending by Islamic banks could be more affected by economic activity relative to lending by other banks. As mentioned earlier, Barajas et al. (2010) conjecture that Islamic banks’ business models are geared more towards investments and lending in high growth areas such as real estate. These areas may be “cyclical” sectors and more sensitive to economic activity.

Results from econometric models including those variables indicate that bank characteristics and most of macro variables remain key determinants of bank credit growth (Tables 8–11). A higher capital ratio, lower provisioning growth and higher deposit growth all lead to higher bank credit growth. Similarly, higher oil price growth, which can represent higher demand, supply or confidence, support bank credit growth. A reduction in bank holdings of excess liquidity also helps.

Results also show that most of the additional macroeconomic factors affect bank credit growth. First, bank credit growth declines as banks increase their holdings of government bonds (suggesting “crowding out”). As shown in Table 8, a 1% point of bank balance sheet increase in the holdings of government bonds reduces credit growth by 1%–1.5%. However, the coefficient loses statistical significance when combined with nonoil private sector GDP growth [20].

Second, lending by SCIs does not appear to systematically complement commercial bank lending (Table 8). The estimated negative coefficients in most models allude to the existence of competition rather than complementarity between these institutions and banks in the segments of the market they work in. In one specification, the estimated coefficient is not statistically significant. More research is needed to understand the role of SCI lending.

Third, higher banking system concentration leads to lower bank lending growth (Table 9) [21]. The recent decline in concentration in domestic banking system should have helped improve credit growth. When the index of concentration is interacted with bank-level and macrolevel variables, results suggest that the negative impact of excess liquidity holdings on lending declines with concentration, probably as banks have more leeway to mitigate the impact.

State ownership does not appear to systematically affect bank lending growth (Table 10). However, tentative evidence, which is statistically significant at the 10% level, suggests that lending by banks with greater state ownership appears procyclical with respect to oil price performance. As oil prices increase, lending by banks with high state ownerships tend to rise more than lending by other banks. One interpretation is that when oil prices decline and the fiscal balance becomes strained, banks with high state ownership tend to purchase government bonds and reduce lending more than other banks do.

Another tentative finding relates to the capital ratio (Table 10). For banks with high state ownership, credit growth is little affected by the capital ratio. The coefficient on the interaction term between the capital ratio and the high state ownership dummy broadly cancels the coefficient on the capital ratio. One interpretation is that banks with larger state ownership are less constrained by capital. For instance, in 2015, banks with high state ownership had a higher average capital ratio compared to other banks by 1%–1.5% points.

We find evidence that stronger institution facilitates bank lending (Table 11). Results suggest that improvements in the indicators of voice and accountability, political stability and government effectiveness all contribute to greater credit extension. Some of the indicators of institution, such as regulatory quality, may be already captured by bank balance sheet conditions [22].

Finally, tentative evidence suggests that lending by Islamic banks may be more responsive to economic activity compared to other banks (Table 12). However, those analyses whereby banks are separated into Islamic and non‐Islamic following the classification presented by data provider Bankscope yielded few statistically significant coefficients for Islamic banks due probably to the relatively small number of observations used in this paper. Results were weaker when all banks were included in a panel and the key variables were interacted with Islamic bank dummies.

6. Concluding discussion

This paper investigated determinants of bank credit growth in Saudi Arabia. The results, relying on bank level balance sheet data, suggest that, consistent with the literature, bank lending is influenced importantly by bank balance sheet conditions and macroeconomic developments. A rise in the capital ratio and deposit growthand a reduction in bank holdings of “excess liquidity” all lead to higher credit growth. But greater NPL provisioning reduces it. Bank lending growth rises with stronger oil prices and domestic economic activity. Interest rates, either domestic or foreign, do not systematically affect bank lending. In 2015, bank credit growth remained robust despite oil prices having declined as banks maintained strong balance sheet conditions and reduced holdings of excess liquidity.

The benchmark model was extended to assess the impacts of Saudi Arabia–specific characteristics on bank lending. A rise in bank holdings of government bonds diversifies bank asset portfolios but also crowds out bank lending. Lending by SCIs does not lead to higher bank lending. The recent decline in bank concentration should have helped strengthen bank lending. Tentative results suggested that banks with relatively large state ownership may have been lending procyclically with respect to oil price performance (the higher is oil price growth, the higher is lending growth) and that their lending is less sensitive to the capital ratio. This is likely because when oil prices are low, those banks may be buying government bonds than other banks do. Also, lending by banks with higher state ownership may be less constrained by the capital ratio, which is on average higher than that of other banks. Stronger institution appears to support credit extension. Finally, consistent with the literature, lending by Islamic banks may be more responsive to economic activity compared to lending by other banks.

These results suggest that to support bank credit provision in the period ahead, bank balance sheets need to remain strong. This is particularly the case as the commitment to increase the role of the private sector in the economy under the National Transformation Program and Vision 2030 would present many opportunities for lenders. Generally, banks in Saudi Arabia are profitable, liquid and well-capitalized. SAMA's regulation and supervision of the banking system continued to strengthen in recent years, including through the early adoption of Basel III capital and liquidity standards. Looking ahead, banks need to maintain sufficient capital even as low oil prices start putting bank balance sheets under pressure. Provisioning for NPLs reduces credit extension but is essential for safeguarding financial stability. Greater confidence in the banking system also helps attract customer deposits and support credit supply. A reduced reliance on the banking system to finance the budget deficit would also support credit provision to the private sector.

First, one could analyze the composition of lending and its implications for growth. The impact of lending on growth would differ by borrower (retail or corporate; wealthier or not) or use of funds (consumption or investment). Second, the impact of bank lending on financial inclusion and stability is another useful area of research. This involves the role of formal banking system in facilitating financial inclusion, while paying due attention to financial stability risk. The role of informal lending could be also assessed if data are available.

Figures

Figure 1

Oil prices and bank funding conditions

Figure 2

Saudi Arabia: Macroeconomic and bank-level variables

Figure 3

Real bank credit growth in Saudi Arabia

Figure 4

Saudi government bond issuance and stock outstanding

Figure A1

Financial Deepening and Economic Development

Figure A2

GCC: Indicator of Banking System Concentration

Table 1

Saudi Arabian domestic banks analyzed

NamePercent share of total banking system assets 2015
1. National Commercial Bank20.7
2. AI Rajhi Bank14.5
3. Samba10.8
5. Banque Saudi Fransi8.5
6. Saudi British Bank8.7
7. Arab National Bank7.9
8. Saudi Hollandi Bank5.0
9. Saudi Investment Bank4.3
10. Bank Aljazira2.9
Sum of above93.6

Source(s): Bankscope and author's calculations

Table 2

Data summary

VariableObsMeanStd. Dev.MinMax
Bank-level data
Credit growth24410.412.2−10.835.3
Capital ratio16316.23.211.823.6
NPL provision growth2253.717.3−26.643.3
Deposit growth2458.28.8−5.727.8
Net income growth2377.310.2−10.428.3
Macro-level data
Oil price growth297.542.2−61.8130.3
Nonoil private sector GDP growth295.24.2−0.419.6
US Fed funds rate, detrended290.01.4−2.32.3
Saudi 3M interest rate, detrended230.02.4−4.44.6
Excess liquidity, ppt chg.220.92.9−7.77.6

Note(s): Data for 2000–2015. See Appendix Table A1 for data description

Source(s): Author's calculation

Table 3

Correlation Coefficient (Significant at the 5 percent level)

1. Credit growth2. Capital ratio3. NPL provision growth4. Deposit growth5. Net income growth6. Oil price growth7. Nonoil private sector GDP growth8. US fed funds rate, detrended9. Saudi 3M interest rate,detrended10. Excess liquidity,change
1.Credit growth1.00
2.Capital ratio1.00
3.NPL provisions growth1.00
4.Deposit growth0.651.00
5.Net income growth0.390.431.00
6.Oil price growth0.281.00
7.Nonoil private sector GDP growth0.370.281.00
8.US Fed funds rate, detrended0.281.00
9.Saudi 3M interest rate, detrended0.54−0.280.511.00
10.Excess liquidity, change0.270.37−0.411.00

Note(s): “…” when estimated correlation coefficients are not statistically significant at the 5 percent level. See Appendix Table A1 for data description.

Source(s): IMF staff calculations

Table 4

Determinants of real bank credit real growth–bivariate regressions

Model number123456789Lagged
Bank characteristics
Capital ratio0.881**
0.016
Y
NPL provisions, real growth−0.162***
0.000
Y
Deposits, real growth0.271*
0.095
Y
Net income, real growth0.092
0.433
Y
Macro variables
Oil prices, real growth0.128***
0.002
N
Nonoil PS GDP.real, growth0.863
0 002
Y
Domestic mm rate, real, detrended−0.332 0.579N
Fed funds effective, real, detrended−0.829 0.239N
Excess liquidity, ppt change0.140 0.490N
_cons−2.560
0.614
12.242***
0.000
9.006***
0.000
10.993***
0.000
10.471***
0.000
4.656**
0.010
11.390***
0.000
11.328***
0.000
11.138***
0.000
N
Bank fixed effectsYYYYYYYYYN
N142145145142155155155155155
r2_a0.1710.2020.1670.1430.2190.2110.1250.1310.124

Note(s): Dependent variable is real bank credit growth. Using OLS. *, ** and *** indicate statistical significance at the 1%, 5% and 10% level. p-values underneath coefficients. See Table A1 for variable definition

Source(s): Author's calculation

Table 5

Determinants of bank credit real growth

Model number101112131415161718Lagged
Bank characteristics
Capital ratio0.969**
0.010
0.947***
0.007
0.834***
0.006
0.913**
0.035
0.850**
0.019
0.705**
0.043
0.664*
0.059
0.694**
0.039
0.544*
0.071
Y
NPL provisions, real growth−0.126***
0.005
−0.131***
0.005
−0.161***
0.002
−0.127***
0.004
−0.139***
0.002
−0.179***
0.001
−0.151***
0.002
−0.151***
0.002
−0.181***
0.001
Y
Deposits, real growth0.382***
0.000
0.395***
0.00
0.287**
0.039
0.314***
0.000
0.333***
0.000
0.233***
0.044
0.297***
0.000
0.311***
0.000
0.190*
0 053
Y
Net income, real growth−0.082
0.440
−0.052
0.671
−0.12
0.311
−0.054
0.700
−0.138
0.230
−0.101
0.442
Y
Macro variables
Excess liquidity, ppt change0.129
0.470
0.072
0.683
−0.011
0.943
−0.045
0.793
−0.218
0.171
−0.500***
0.001
−0.431*
0.058
−0.423*
0.053
−0.586***
0.000
N
Nonoil PS GDP. real, growth0.900***
0.001
1.071***
0.000
1.167***
0.000
0.755**
0.012
0.877***
0.004
0.892**
0.023
Y
Oil prices, real growth0.101**
0.030
0.076*
0.098
0.086
0.135
N
Domestic mm rate, real, det.−0.771
0.379
−2.124**
0.033
−0.955
0.494
N
Fed funds, real, det.−0.812
0.425
−1.983**
0.045
−0.997
0.293
N
−6.097
0.283
−6.054
0.242
−3.497
0.469
−10.876
0.135
−11.683*
0.063
−8.895
0.134
−5.865
0.339
−7.5
0.218
−4.616
0.380
N
Bank fixed effectsYYYYYYYYYN
2008, 09 dummiesYYYYYYYYYN
N138138141138138141138138141
r2_a0.3020.3030.2660.3850.4120.3810.4230.4230.392

Note(s): Dependent variable is real bank credit growth. Using OLS. *, ** and *** indicate statistical significance at the 1%, 5% and 10% level. p-values underneath coefficients. See Table A1 for variable definition

Source(s): Author's calculation

Table 6

Determinants of bank credit real growth system – GMM approach

Model number192021222324252627Lagged
Bank characteristics
Capital ratio1.035*
0.062
0.891*
0.069
0.835
0.286
0.668**
0.02
0.508*
0.059
0.715
0.372
0.964**
0.013
0.768**
0.028
0.748*
0.056
Y
NPL provisions, real growth−0.151***
0.000
−0.156***
0.000
−0.209***
0.000
−0.166***
0.000
−0.163***
0.000
−0.232***
0.000
−0.194***
0.000
−0.181***
0.001
−0.206***
0.000
Y
Deposits, real growth0.432***
0.000
0.466***
0.001
0.274**
0.035
0.303***
0.000
0.304***
0.000
0.328***
0.001
0.324***
0.000
0.351***
0.000
0.350***
0.000
Y
Net income, real growth−0.104
0.383
−0.028
0.868
−0.253**
0.036
−0.221
0.136
−0.052
0.64
−0.101
0.414
Y
Macro variables
Excess liquidity, ppt chg.−0.023
0.959
−0.921*
0.098
−0.877***
0.001
0.38
0.102
0.097
0.765
−0.900**
0.011
−0.534*
0.069
−0.671**
0.02
−0.885**
0.048
N
Nonoil PS GDP, real growth1.474***
0.000
1.442***
0.000
1.248***
0.000
0.848***
0.007
0.914***
0.001
1.007***
0.005
Y
Oil prices, real growth0.102**
0.044
0.098**
0.023
0.085
0.197
N
Domestic mm rate, real, det.−0.831
0.378
−2.750***
0.001
−1.616
0.298
N
Fed funds, real, det.−3.298**
0.044
−1.28
0.236
−1.081
0.281
N
−6.917
0.420
−4.604
0.542
−2.332
0.862
−10.215**
0.033
−7.447
0.103
−9.484
0.456
−11.923*
0.059
−8.825
0.149
−9.334
0.156
N
Bank fixed effectsYYYYYYYYYN
2008, 09 dummiesYYYYYYYYYN
N138138141138138141138138138
AR(1)0.0050.0060.0150.0060.0040.0060.0040.0030.004
AR(2)0.4530.9310.5070.5710.8950.5560.7810.6490.778
Hansen0.9851.0000.9991.0001.0001.0001.0001.0001.000

Note(s): Dependent variable is real bank credit growth. Using System GMM approach by Arellano and Bover (1995) and Blundell and Bond (1998). *, ** and *** indicate statistical significance at the 1%, 5% and 10% level. p-values underneath coefficients. See Table A1 for variable definition. Treating bank-level variables and nonoil private sector GDP growth as endogenous. To help reduce the number of instruments and avoid over-fitting the model, we follow Roodman (2009) and limit lag depth and “collapse” the matrix, or drop zeros from the instrument matrix. AR(1) and AR(2) signify p-values associated with the null hypothesis of lack of first and second order serial correlation. Hansen signifies p-value associated with the null hypothesis that the instruments are exogenous

Source(s): Author's calculation

Table 7

State ownership of domestic banks (2015) (percent of total)

Table 8

Determinants of bank credit growth-additional factors

Government bond holdingsSCI lendingLagged
Model number2829303132333435
Bank characteristics
Capital ratio1.145**
0.014
0.839**
0.037
0.689*
0.081
0.688*
0.058
0.708**
0.027
0.554**
0.047
0.5
0.158
0.503
0.148
Y
Provisions growth−0.128***
0.007
−0.151***
0.002
−0.148***
0.002
−0.151***
0.003
−0.124***
0.005
−0.148***
0.002
−0.135***
0.003
−0.136***
0.004
Y
Deposit growth0 379***
0.000
0.363***
0.000
0.312***
0.000
0.301***
0.000
0.353***
0.001
0.348***
0.001
0.278***
0.001
0.276***
0.001
Y
Net income growth−0.041
0.711
−0.08
0.469
−0.139
0.253
−0.132
0.279
−0.135
0.175
−0.143
0.154
−0.197*
0.091
−0.194
0.102
Y
Macro variables
Bank holdings of gov. bonds, ppt chg.−1.532**
0.018
−1.180*
0.092
−0.013
0.985
−0.171
0.812
N
SCI credit growth−0.402**
0.02
−0.25
0.129
−0.446**
0.017
−0.425**
0.031
N
Oil prices growth0.091**
0.025
0.079*
0.051
0.101**
0.031
0.087**
0.015
0.034
0.397
0.042
0.404
N
Nonoil PS GDP growth0.741**
0.015
0.706**
0.021
0.943***
0.006
0.936***
0.008
Y
Excess liquidity, ppt chg.−0.443*
0.063
−0.098
0.653
N
Dummy variables
BankYYYYYYYYN
2008YYYYYYYYN
2009YYYYYYYYN
Constant−8.695
0.221
−4.02
0.519
−6.481
0.334
−5.895
0.344
1.502
0.613
2.525
0.877
−0.969
0.859
−1.087N
N138138138138138138138138
r2_a0.3480.3860.4120.4180.3430.3730.4530.449

Note(s): Dependent variable is real bank credit growth. Using OLS. *, ** and *** indicate statistical significance at the 1%, 5% and 10% level. p-values underneath coefficients. See Table A1 for variable definition.

Source(s): Author's calculation

Table 9

Determinants of bank credit growth-additional factors

ConcertationLagged
Model number3637383940414243444546
Bank characteristics
Capital ratio0.910***
0.006
0.706***
0.006
0.752**
0.027
0.654**
0.025
0.950***
0.007
0.712***
0.006
0.886**
0.026
0.649*
0.057
0.751**
0.028
0.576*
0.055
0.559**
0.047
Y
Provisions growth−0.107**
0.018
−0.112***
0.007
−0.112**
0.011
−0.114**
0.011
−0.091*
0.067
−0.109***
0.007
−0.102**
0.035
−0.085***
0.007
−0.112**
0.02
−0.098***
0.006
−0.087**
0.028
Y
Deposit growth0.344***
0.004
0.342***
0.001
0.284***
0.001
0.275***
0.001
0.372***
0.005
0.337***
0.001
0.276***
0.002
0.217**
0.01
0.284***
0.001
0.221***
0.003
0.221***
0.004
Y
Net income growth−0.054
0.576
−0.124
0.158
−0.14
0.146
−0.135
0.184
−0.045
0.661
−0.126
0.163
−0.101
0.33
−0.165
0.139
−0.14
0.148
−0.179
0.108
−0.165
0.166
Y
Macro variables
Concentration index, ppt chg.−2.831**
0.04
−2.974**
0.03
−2.912**
0.035
−1.724
0.254
−5.737
0.223
−3.825
0.149
−4.489
0.294
−7.409***
0.006
−2.852
0.477
−5.074*
0.07
−8.114*
0.069
N
Oil prices growth0.137**
0.015
0.096
0.124
0.146**
0.032
0.150**
0.026
0.096*
0.097
0.0970.137
0.413
N
0.161
Nonoil PS GDP growth0.721**
0.016
0.685**
0.021
1.071*
0.072
0.712
0.228
0.428
0.115
1.015*
0.077
Y
Excess liquidity, ppt chg.−1.186**
0.034
−0.951*
0.064
−1.312**
0.017
−2.016*
0.081
N
Interaction with concentration index (CI)
Capital ratio * CI0.115
0.635
Y
Provisions growth * CI0.037
0.479
Y
Deposit growth * CI0.070
0.407
Y
Oil prices growth * CI0.025
0.619
0.028
0.825
N
Nonoil PS GDP growth * CI0.172
0.634
−0.006
0.985
0.384
0.359
Y
Excess liquidity, ppt chg. * CI2.264***
0.001
1.433**
0.041
1.038
0.400
N
Dummy variables
BankYYYYYYYYYYYN
2008YYYYYYYYYYYN
2009YYYYYYYYYYYN
Constant−5.533
0.254
−4.197
0.303
−9.117
0.108
−5.651
0.232
−6.82
0.222
−4.751
0.249
−12.663
0.144
−1.2
0.816
−9.045
0.242
−2.878
0.555
−6.619
0.334
N
N118118118118118118118118118118118
r2_a0.3620.4050.4560.4710.3490.3820.4290.350.4520.460.472

Note(s): Dependent variable is real bank credit growth Using OLS. *, ** and *** indicate statistical significance at the 1%, 5% and 10% level. p-values underneath coefficients. See Table A1 for variable definition

Source(s): Author's calculation

Table 10

Determinants of bank credit growth-additional factors

State ownershipLagged
Model number474849505152
Bank characteristics
Capital ratio0.990**
0.033
0.924**
0.027
1.145***
0.007
1.034***
0.007
1.091**
0.012
0.996**
0.011
Y
Provisions growth−0.114**
0.022
−0.142***
0.004
−0.109**
0.03
−0.139***
0.006
−0.113**
0.026
−0.143***
0.005
Y
Deposit growth0.471***
0.002
0.402***
0.002
0.419***
0.002
0.362***
0.003
0.400***
0.004
0.343***
0.006
Y
Net income growth−0.141
0.159
−0.152
0.143
−0.168*
0.078
−0.192**
0.05
−0.168*
0.075
−0.192*
0.051
Y
Macro variables
Concentration index, ppt chg.N
Oil prices growth0.085**
0.01
0.093***
0.003
0.054
0.106
0.064**
0.043
0.074*
0.051
0.083**
0.021
N
Nonoil PS GDP growth0.814***
0.002
0.694***
0.006
0.801***
0.002
0.697***
0.005
Y
Excess liquidity, ppt chg.−0.28
0.370
−0.328
0.296
N
Interaction with state ownership
0.259
−1.053*
0.086
−0.953
0.122
Y
0.115
−0.113
0.163
−0.111
0.176
Y
0.359
−0.165
0.327
−0.144
0.396
Y
0.057
0.061*
0.076
0.061*
0.08
N
Nonoil PS GDP growth * SO_broad−0.049
0.905
−0.013
0.973
Y
Excess liquidity, ppt chg. * SO_broad−0.223
0.578
N
Capital ratio * SO_narrow−0.865
0.226
−1.210*
0.06
−1.135*
0.073
Y
Provisions growth * SO_narrow−0.052
0.507
−0.038
0.671
−0.031
0.727
Y
Deposit growth * SO_narrow−0.096
0.615
−0.123
0.514
−0.05
0.795
Y
Oil prices growth * SO_narrow0.045
0.185
0.039
0.267
0.039
0.267
N
Nonoil PS GDP growth * SO_narrow0.281
0.512
0.289
0.489
Y
Excess liquidity, ppt chg. * SO_narrow−0.214
0.590
N
Dummy variables
BankYYYYYYN
2008YYYYYYN
2009YYYYYYN
Constant−6.495 0.394−4.254
0.552
−13.834*
0.051
−9.959
0.135
−12.601*
0.095
−9.094
0.197
N
N138138138138138138
r2_a0.3480.3320.4060.3910.4050.39

Note(s): Dependent variable is real bank credit growth. Using OLS. *, ** and *** indicate statistical significance at the 1%, 5% and 10% level. p-values underneath coefficients. See Table A1 for variable definition

Source(s): Author's calculation

Table 11

Determinants of bank credit growth–additional factors

InstitutionLagged
Model number53545556575859
Bank characteristics
Capital ratio0.664*
0.059
0.618*
0.066
0.560*
0.061
0.381
0.254
0.667*
0.061
0.691*
0.06
0.680*
0.068
Y
Provisions growth−0.151***
0.002
−0.116**
0.033
−0.139***
0.004
−0.101***
0.006
−0.149***
0.004
−0.152***
0.002
−0.153***
0.001
Y
Deposit growth0.297***
0.000
0.301***
0.001
0.264***
0.000
0.200**
0.011
0.298***
0.000
0.305***
0.000
0.301***
0.000
Y
Net income growth−0.138
0.230
−0.037
0.820
−0.105
0.319
−0.264**
0.022
−0.131
0.324
−0.122
0.351
−0.13
0.283
Y
Macro variables
Oil prices growth0.101**
0.030
0.068
0.133
0.086*
0.055
0.122**
0.017
0.105**
0.020
0.098*
0.055
0.109**
0.028
N
Nonoil PS GDP growth0.755**
0.012
1.021***
0.002
0.849***
0.005
0.919***
0.004
0.790**
0.022
0.789**
0.023
0.718**
0.018
Y
Excess liquidity, ppt chg.−0.431*
0.058
−0.071
0.722
−1.027***
0.003
−0.351*
0.088
−0.479**
0.028
−0.463**
0.011
−0.457**
0.023
N
Institution variables
Voice and accountability0.022***
0.004
Y
Political stability0.015**
0.021
Y
Government effectiveness0.022**
0.018
Y
Regulatory quality−0.001
0.742
N
Rule of law−0.003
0.719
Y
Control of corruption−0.001
0.768
N
Dummy variables
BankYYYYYYYN
2008YYYYYYYN
2009YYYYYYYN
Constant−5.865
0.339
−8.758
0.188
−1.933
0.72
0.335
0.957
−6.343
0.353
−7.051
0.336
−6.011
0.348
N
N138138138138138138138
r2_a0.4230.4790.4480.4670.4180.4190.418

Note(s): Dependent variable is real bank credit growth. Using OLS. *, ** and *** indicate statistical significance at the 1%, 5% and 10% level. p-values underneath coefficients. See Table A1 for variable definition

Source(s): Author's calculation

Table 12

Determinants of bank credit growth - additional factors

Islamic abnksOther banks
Model number606162636465Lagged
Bank Characteristics
Capital ratio0.5980.3530.50.651*0.674**0.650**Y
0.5080.6790.5660.0610.040.046
Provisions growth−0.158−0.174−0.151−0.155***−0.149***−0.152***Y
0.130.1680.2400.0010.001
Deposit growth0.1930.2650.3220.365***0.319***0.303***Y
0.5250.2760.1410.0010.0020.003
Net income growth−0.273−0.51−0.512−0.097−0.119−0.119Y
0.4000.1680.1500.3340.2190.214
Macro variables
Oil prices growth0.0530.011−0.0480.109***0.080***0.104***N
0.5880.8910.6260.0000.0050.002
Nonoil PS GDP growth2.071**2.051**0.730***0.741***Y
0.0150.0170.0010
Excess liquidity, ppt chg.0.908*−0.457*N
0.0770.084
Dummy variables
BankYYYYYYN
2008YYYYYYN
2009YYYYYYN
Constant5.639−3.411−6.398−1.789−6.66−5.956N
0.7310.8270.640.7720.2530.317
N212121138138138
r2_a0.0440.4530.4760.3340.3860.393

Note(s): Dependent variable is real bank credit growth. Using OLS. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% level. p-values underneath coefficients. See Table A1 for variable definition.

Source(s): Author’s calculation.

Table A1

Data description

Variable nameDescriptionUnitSourcesAggregation level
Bank scopeHaverSAMAWolrd bankBank levelMacro level
Credit growthYear on year growth of gross loans deflated by Saudi CPI indexPercentxx x
Capital ratio, Tier1Tier 1 capital to risk weighted assetsPercentx x
NPL provisions growthYear on year growth of NPL provisions deflated by Saudi CPI indexPercentxx x
Deposit growthYear on year growth of total customer deposits deflated by Saudi CPI indexPercentxx x
Net income growthYear on year growth of net interest income deflated by Saudi CPI indexPercentxx x
Nonoil private sector GDP growthYear on year growth of non oil private sector real GDPPercent x x
Oil price growthYear on year growth of Brent oil prices deflated by US CPI indexPercent x x
US Fed funds rateUS Fed funds interest rate effective minus US CPI inflation detrended by linear trendPercent x x
Domestic 3 months interest rateSaudi 3 months SIBOR minus Saudi CPI inflation detrended by linear trendPercent x x
Excess liquiditySum of bank holdings of (1) current and other deposits at SAMA and (2) SAMA bills. Year on year differences divided by bank assets in the base yearPercent x x
Bank holdings of government bonds, changeYear on year difference in bank holdings of government bonds scaled by bank total assetsPercentxx x
SCI lending growthYear on year growth of lending by Specialized Credit Institutions deflated by Saudi CPI indexPercentx x
Bank concentration index, changeAssets of five largest banks as a share of total commercial banking assets, year on year percentage point changePercent x x

Note(s): SAMA = Saudi Arabian Monetary Authority

Source(s): Author

Notes

1.

The 3-months SIBOR ended 2016 at 2.035%, marginally up from its low registered in mid-December.

2.

The authors argue that underdeveloped financial systems in the MENA region hamper economic growth and that more needs to be done to improve the institutional environment and functioning of the banking sector.

3.

We also considered specifications with time dummies for each year in which case all variables were statistically insignificant, likely reflecting the importance of common shock related to global oil price movements. In order to generate policy-relevant results, where bank-level and macro variables capture transmission of shocks, the paper limited time dummies to those in 2008 and 2009 to capture identifiable potential shocks (defaults of a large domestic conglomerate).

4.

Data on the international investment position and the BIS banking statistics suggest that the Saudi banking system' cross-border exposures are small.

5.

Bank credit, deposits, NPL provisions, net income, are deflated by domestic inflation. Oil prices (in dollar terms) are deflated by US inflation. Non-oil private sector GDP is available in real terms. Capital and excess liquidity are expressed in terms of ratios.

6.

Growth of NPL provisions is a more direct and likely a better measure of banks' capacity to extend credit than NPLs in Saudi Arabia. This is because in Saudi Arabia, banks have been provisioning for NPLs counter-cyclically, weakening the linkage between NPL ratios and credit growth. Indeed, the author did not find plausible results using NPL ratios instead of growth of NPL provisions.

7.

A panel fixed-effects approach suffers from a downward Nickell bias when a lagged dependent variable is included in the right-hand side of the regression equation. In such circumstances, a system Generalized Method of Moments (GMM) approach proposed by Arellano and Bover (1995) and Blundell and Bond (1998) is used commonly.

8.

Given the Saudi riyal's peg to the US dollar, we do not include the US Fed funds rate and the 3-months SIBOR together in regression models. Some of the deposit base in Saudi Arabia is interest free.

9.

More than 100% is considered as very prudent. When 160% of NPLs are provisioned, when all NPLs default, the bank can fully write of the loans and still maintain another 60% of NPLs worth of reserves.

10.

The 3-month SIBOR spread to US dollar 3-months LIBOR is not significant in bivariate nor multivariate specifications.

11.

Key messages in sections III and IV were generally unchanged when a dummy variable for 2006 was introduced to capture a large decline in domestic stock prices.

12.

The estimated coefficient appears low despite bank credit in Saudi Arabia being primarily funded by deposits. However, the value of the estimated coefficient (i) doubles when real credit growth is regressed on contemporaneous real deposit growth and (ii) increases to around unity when median values of bank-level data (as shown in Figure 3) are used to regress real credit growth on contemporaneous real deposit growth.

13.

Treating bank-level variables and nonoil private sector GDP growth as endogenous. To help reduce the number of instruments and avoid over-fitting the model, we follow Roodman (2009) and limit lag depth and “collapse” the matrix, or drop zeros from the instrument matrix. Nonetheless, the Hansen statistics of 1 could indicate over-fitting as warned by Roodman (2009).

14.

Predicted credit growth using regression results without 2008 and 2009 time dummies is not shown.

15.

Information from Bloomberg on the amounts issued is scant. In 2007, the Saudi government issued two 10 years bonds for SAR 200 million each and one 10-years bond for SAR 2 billion.

16.

SCIs are unlevered nondeposit taking entities that rely mainly on budgetary support by the MoF. They target lending to, for instance, housing, critical industrial projects, and SMEs, some of which do not have access to bank lending. The Saudi Industrial Development Fund (SIDF) finances industrial projects, the Public Investment Fund (PIF) large scale government and private industrial projects, the Real Estate Development Fund (REDF) individual/corporate residential and commercial real estate, and the Saudi Agricultural Development Fund (SADF) farmers and agricultural projects. The Saudi Credit and Saving Bank (SCSB) provides interest-free loans to small and emerging businesses and professions. Al-sadig (2013) finds that private domestic investment is positively associated with SCI lending.

17.

Banks offer bridge financing to construction projects financed by SCIs. One SCI provides SME credit guarantees in collaboration with banks (kafala). Another SCI offers top-up financing for mortgage borrowers to meet the recent 70% LTV limit.

18.

Bankscope's classification of state ownership include “General Investment Funds” and “Government of Saudi Arabia via various funds” which are interpreted as the PIF and remaining ownership by the government.

19.

Voice and accountability, political stability, government effectiveness, regulatory quality, rule of law and control of corruption. The point estimates are subject to uncertainty.

20.

This is consistent with the result from Alhumaidah et al. (2016). The chapter uses an asset-liability management framework to discuss the benefits and risks as well as the macroeconomic implications of different financing strategies for the fiscal deficit, and illustrates some of these aspects through a simulation analysis. It also reviews a number of policies that will help expand the investor base and reduce financing costs, while having broader positive implications for the economy.

21.

The author is grateful to an anonymous reviewer for the suggestion on the specification following Brambor et al. (2006).

22.

The indicators are expressed in terms of the distance from historical average in the number of standard deviation (so-called Z score), and introduced in the model in percent change.

Figure A1

Figure A1

Table A1

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Corresponding author

Ken Miyajima can be contacted at: kmiyajima@imf.org