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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
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;
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;
 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.
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.
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
100
90
80
70
60
40
50
20
30
1969
1972
1975
1978
1981
1984
1987
1990-1991
1994
1997
2000
2003
2006
2009
2012
0
% share of non-oil sector
% share of oil sector
Source: SAMA (2012), Figures are authors’ own calculations.
10
2005 2006 2007 2008 2009 2010 2011
% share of oil sector
% share of non-oil sector
Empirical methodology
The empirical methodology is divided into three sections:
At first stage, we calculate sectoral output gaps by applying two nonparametric 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.
 The model of Mink et al. (2007) is explained as follows:
SupposeOr (t )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:
oi (t )or (t )
Oir (t) 
.....(1)
| oi (t )or (t ) |
Where oi (t ) denotes for the output gap of sector i for 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
oi (t ) and or (t ) in equation (1) have the
same sign. When the value ofoir (t ) 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.
1 N oi (t )or (t )
ir (t)  
.....(2)
N i 1 | oi (t )or (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.
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.
Empirical results
Descriptive statistics of sample series, 1970-2011
GDP
Oil GDP
Mean
4.961
3.634
Std. Deviation
7.205
16.596
Skewness
0.560
0.256
Kurtosis
0.932
0.659
Obs.
41
41
Non-oil GDP
6.774
8.847
2.585
10.230
41
Agriculture
5.432
10.005
2.874
13.751
41
Community
5.750
13.943
4.148
24.087
41
Construction
Finance
6.647
6.137
16.428
12.821
2.611
3.230
9.238
15.455
41
41
Manufacturing
Retail
Transport
7.030
9.419
7.625
4.823
9.983
8.562
1.205
0.808
0.127
3.440
0.331
0.965
41
41
41
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%).
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).
Time average of synchronicity measure
between oil and non-oil sectors (percent)
Avg. of sync.
based on HP
filter
Avg. of sync.
based on CF
filter
Agriculture
Manufacturing
Construction
Retail
Transport
Finance
Community
54.76
57.14
52.38
50.00
47.62
30.95
40.00
50.00
57.14
57.14
51.67
50.00
42.86
35.71
Non-oil GDP
48.98
51.36
Economic Sectors
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.
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
1
ir (t )   sectors and 0 indicates sectoral non-synchronicity which also
0 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).
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:
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.
Structural fiscal impulse in Saudi Arabia
1.00
0.80
0.60
0.40
0.00
-0.20
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
0.20
-0.40
-0.60
-0.80
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.
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;
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.
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;