QUANTIFYING ECONOMY-WIDE IMPACTS OF REDUCING SUBSIDY TO THE ELECTRICITY SECTOR IN KUWAIT

Paper to be presented at International Conference on Economic Modeling, 2014

Bali – Indonesia, July 16–18, 2014

Ayele GelanEconomic Public Policy Research Programme (EPP)

Techno-Economics Division (TED)Kuwait Institute for Scientific Research (KISR)

P.O Box 24885Safat 13109

KuwaitTel.: +9652489495 (office)

+96560677415 (mobile)

Abstract

This paper examined economy-wide impacts of reducing electricity subsidy in Kuwait. A social accounting matrix (SAM) was constructed with 2010 as a base year and a computable general equilibrium (CGE) model was developed. The CGE model was used to simulate effects of a 30% reduction the subsidy. This policy shock was applied to the model in two scenarios. In the first scenario, the subsidy reduction was applied and results were compared with the baseline scenario given in the SAM. This yielded contractionary effects across a range of endogenous variables. Electricity tariff increased by three fold, rising from 2.0 to 6.1 fils/kWh. GDP fell by 1.2% and aggregate household welfare declined by 1.6%. Sectoral value-added declined with substantial variations across the sectors, notably the crude oil extraction as well as the petroleum production subsector of manufacturing being among those that experience the largest declines. The electricity sector out itself experienced a 21% decline in output. In scenario 2, the 30% subsidy reduction was accompanied with cash transfers to households by the amount of subsidy reduction to compensate for welfare loss. The results indicated that such transfers would reduce the adverse effects of the policy reform. The effects on GDP and household welfare were reversed, each rising by 0.3% and 0.2% respectively. Finally, sensitivity of the model results were examined by varying the elasticity of substitution of electricity use with factor uses in the process of production. It was found out that greater possibility of substitution causes the subsidy reform to have much larger positive effects compared to scenario 2 (or smaller contractionary effects compared with scenario 1). The simulation experiments suggest that subsidy reform would need to be accompanied by demand side stimulus or supply side initiatives to encourage electricity conservation.

Key worlds: Electricity subsidy reform, welfare, cross-price elasticity, Kuwait, SAM, CGE.JEL: E61, E64, H20, L94, Q43, Q48.

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1. Introduction

Kuwait’s electricity is the most highly subsidized sector in the country - the rate of subsidy is

such that it amounts to generating electricity and almost freely distributing to users. The

extremely generous subsidy is intended to serve as a means of allocating welfare transfers to

resident households and businesses. The government owns a vertically integrated monopoly

and manages the entire supply chain all along electricity generation to retails (Wood and

Alsayegh 2014, Burney, 1998).

In recent years, however, it has become increasingly clear that the welfare oriented electricity

production and distribution have had adverse economy-wide effects, specifically conflicting

with other policy priorities such as environmental protections and misguiding resource

allocation (BuShehri and Wohlggenant 2012). There is a growing awareness among policy-

makers and researchers that the existing policy is not sustainable (Alotabi 2011, Darwish, et

al 2008; Darwish and Darwish 2008). The necessity for economic reforms in wider areas of

public utilities management is rooted in recent shifts in economic development strategies as

well as initiatives related to regional integration among GCC member countries (EIU 2010).

Despite the consensus on the necessity to reform electricity tariff, there are very few

analytical studies providing insights into options for optimal mix of policy instruments.

Burney and Al-Matrouk (1996) developed an econometric model and examined factor

substitution possibilities in the Kuwait electricity generation. Their findings implied that

“using factor prices, particularly fuel prices, as instruments will not only help with energy

conservation, but will also induce more demand for capital.” (p. 78). Since there can be many

options to apply fuel price as instrument of reform, it is important to go further and specify

alternatives ways to put the price policy into practice. For instance, the most straightforward

way to cause changes in fuel prices is to withdraw or reduce their subsidies.

BuShehri and Wohlggenant (2012) examined possible conflicts in objectives of reforming the

Kuwaiti electricity sector. The specifically drew attention to the inevitable welfare loss when

fuel prices are used as instruments of policy reform. They suggested that “the government

may, in the short run adopt a cash payment plan (i.e., credit balance) not exceeding the loss in

user welfare for the target group. This plan would allow consumers to enjoy the same level of

satisfaction as before the price increase, offsetting any political backlash…” (p. 425).

However, it should be noted that the rationale for compensating households for welfare loss

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goes wider than equity considerations and gaining popular support for the reform. Given the

extent of the current subsidy, its withdrawal or reduction is bound to cause substantial

increases in electricity tariff. This will inevitably cause adverse demand side shock and hence

reductions in economic activities. Therefore, compensation schemes can also be seen as a

means of mitigating short to medium run adverse effects on economic activities.

This objective of this paper is to quantify economy-wide effects of reducing subsidy to the

Kuwait electricity sector and shed some light on the likely impacts of the envisaged reform.

A computable general equilibrium (CGE) model is applied to conduct simulation

experiments1. The simulation results indicate that reforming the electricity sector is likely to

have adverse economy-wide consequences unless it is accompanied by demand or supply

side measures to offset adverse consequences of the price increases. The former may include

compensating households for welfare loss through cash transfers to households while the

latter may include policy interventions to introduce and promote energy saving technologies.

The remaining part of this paper is classified into four sections. The next section highlights

study context. This is followed by description of approaches and methods. Results of

simulation results are discussed in the fourth section. The final section provides concluding

remarks.

2. Study context

2.1. Electricity tariff

Electricity users in Kuwait pay a nominal tariff of 2 fils (about 0.7 cents) per KWh2.  In fact,

all GCC member countries are known for having extremely low electricity tariff rates

compared to the rest of the world.  Qatar has relatively low tariffs among GCC member

countries other than Kuwait.  Its residential tariff rate is about 2.2 cents which is about three

times the corresponding figure for Kuwait.   This means Kuwait’s electricity tariff is the

lowest even by the standard of GCC member countries. The rest of GCC have differential

tariff rates for residential and business premises but their tariff levels are much higher

compared to corresponding figures for Qatar and Kuwait.  For instance, tariffs in Saudi 1 An early and abridged version of this paper was published in: Gelan, A. 2014, Simulating impacts of reducing subsidy to Kuwait’s electricity sector. Oxford Energy Forum, 96: 32-35

2 This section heavily relied on data in a project report: TED-KISR 2014 (forthcoming). Household Conservation Behavior: A Case Study of Electricity and Water Demand in Kuwait, project progress report.

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Arabia are close to 4.1 cents/KWh.  This means that perhaps Kuwait’s tariff rate is the lowest

in the world.  

Kuwait’s 2 fils (0.7 cents)/KWh was introduced in 1966 and has been retained at that level

ever since. On the other hand, the cost of generating electricity has sharping risen over the

years.  For instance, cost of production per KWh escalated from 20 fils (7 cents) in 2000 to

about 40 fils (14 cents) in 2010, nearly doubling during that decade.  This suggests that rate

of Kuwait’s electricity subsidy was 95% in 2010.  

2.2. Patterns of consumption

The extremely generous subsidy has given rise to a pattern of unsustainable behaviour in

electricity use. Krane (2013) described the situation as a ‘dichotomy between energy value

and price’ to say that excessively low energy pricing induced ‘wanton consumption’ whereby

‘low pricing encourages consumption at rates above those warranted by the opportunity cost

of these fuels on global energy markets. Low prices also distort energy allocation preferences

while undercutting upstream investment and efficiency incentives’ Krane (2013).

These are reflected in a number of key aggregate indicators. In terms of economic efficiency

in use of electricity, measured in terms of GDP generated per unit of KWh used, Kuwait does

not only stand among the lowest in the world but also the situation has gotten worse over the

years. In 1990 GDP/KWh was USD 1.4 but this fell to USD1.2 in 2005. This contrasts with

experiences of other countries in the world; for instance, USA which was doing already much

better in 1990 (about USD 2.2/KWh) but also electricity use efficiency improved and reached

about USD2.7 in 2005.

In terms of electricity consumption per capita in 2005 Kuwait was the second highest in the

world (after Norway). This figure doubled between 1985 and 2005, rising from about 8

thousands KWh to 17 thousand KWh. It should be noted that Kuwait’s electricity is

generated entirely by using fossil fuels but other countries like Norway mostly use

renewables mainly hydroelectric power.

2.3. Prospects for reform

The pressure to reduce subsidy comes from various sources that can be classified into

domestic and regional policy environments. The first one is related to the latest medium term

development plan which expressed the government’s commitment to implement far reaching

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liberalization of Kuwait’s economy (General Secretariat of Higher Council for Planning and

Development 2010). These are planned to be implemented through two firmly interrelated

strategies: (a) diversifying the structure of the economy by reducing the dominance of the oil

sector and encouraging growth of the non-oil sectors; and (b) promoting private sector

development and reducing the dominance of the public sector. Liberalization of public

utilities including electricity and water are prime targets in achieving these goals.

The regional policy environment is related to the GCC electricity grid interconnection.

Tabors (2009) provide interesting summary and economic analysis of the GCC grid

interconnection. The primary goal of this initiative is to provide power supply stability and

reliability by integrating high voltage transmission systems of all GCC member countries.

The economic rationale for this lies in the need to improve competitiveness in generation and

distribution capacity which is badly needed in the medium to the long-run in each country.

The system encourages countries to engage in trading electricity among each other based on

their comparative marginal costs. Cross border electrical energy trading has already been

taking place starting from summer 2010 although information on quantity traded is not yet

made available.

The relevance of GCC electricity grid connection to reform and regulation by each member

country lies in the pressure the interconnection is expected to cause in each country to

improve its efficiency so that its marginal cost of production and distribution would be

competitive relative its neighbours. In this regard, Kuwait is already at a relatively

disadvantageous position since its marginal cost is relatively high compared to other GCC

member countries. For instance, Qatar’s marginal cost of electricity production at peak is

less than half of that of Kuwait, $88/MWh and $188/MWh respectively. These differences

are largely explained by types of turbine or types of fuel used to fire electricity generating

plants – mostly natural gas in Qatar and heavy oil in Kuwait.

3. Approaches and methods

The structure of the Kuwaiti economy in the baseline year (2010) is displayed in a social

accounting matrix (SAM) developed for that year. Simulation experiments were conducted

by formulating a CGE model and utilizing the baseline SAM. These are discussed in detail

below.

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3.1. The Social Accounting Matrix

A SAM was constructed for Kuwait with 2010 as a base year. The choice of the base year is

influenced by the existence of Kuwait Input-output Table produced for that year (CSB 2011).

The micro-SAM, the detailed SAM with its full dimension, has 85 accounts. The condensed

SAM (Table 1) has 13 accounts. The latter is obtained mainly by aggregating 37 activities

into a aggregate activity (labelled as ACT row 1 and column 1) and 37 commodities accounts

into a aggregate commodity account (labelled as COM in headings of row 2 and column 2).

Complete list of the activities and commodities is presented in Appendix A-1.

Table 1 – Condensed SAM for Kuwait 2010 (million KD)

ACT

1

COM

2

LAB-K

3

LAB-N

4

CAP

5

PTAX

6

SUBS

7

ITAX

8

HHD

9

GVT

10

S-I

11

ROW

12

Total

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ACT 1 - 57,876 - - - - - - - - - - 57,876

COM 2 21,646 1,822 - - - - - - 9,916 5,203 6,878 22,054 67,520

LAB-K 3 4,469 - - - - - - - - - - - 4,469

LAB-N 4 4,795 - - - - - - - - - - - 4,795

CAP 5 26,879 - - - - - - - - - - 3,044 29,923

PTAX 6 87 - - - - - - - - - - - 87

SUBS 7 - -2,447 - - - - - - - - - - -2,447

ITAX 8 - 228 - - - - - - - - - - 228

HHD 9 - - 4,469 4,771 1,929 - - - - 5,786 - - 16,954

GVT 10 - - - - 27,675 87 -2,447 228 - - - - 25,543

S-I 11 - - - - - - - - 5,167 12,684 - - 17,851

ROW 12 - 10,040 - 24 320 - - - 1,870 1,871 10,973 - 25,098

Total 13 57,876 67,520 4,469 4,795 29,923 87 -2,447 228 16,954 25,543 17,851 25,098ACT : activities or industries COM : commodities or productsLAB-K : Kuwaiti labourLAB-N : Non-Kuwaiti labourCAP : capitalPTAX : production tax

ITAX : import taxesHHD : householdsGVT : governmentS-I : saving-investment balanceROW : rest of the worldTotal : control totals of each account

SUBS : product subsidies

Details of the remaining 11 accounts in the condensed SAM appear as they are in the micro-

SAM. Descriptions for the abbreviated account headings in Table 1 are presented below the

table.

In each SAM account, entries in columns denote out-goings or payments and entries in the

corresponding row represent in-coming or receipts. A particular cell in the matrix represents

a payment by the account in the column heading and a receipt by the account in the row

heading.

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Column 1 represents cost of production which consists of intermediate inputs worth 21,646

million (in row 2) and 36,143 million value-added (sums of intersections with rows 3, 4, and

5). The CGE model is implemented with three factors of production: Kuwaiti labour, Non-

Kuwaiti labour, and producer surplus. This represents GDP at factor cost during 2010, whose

details are presented in Appendix-2. Intersection with Row 4 represents production tax

(PTAX) which is given as 87 million. Row 1 has only one entry, domestic commodity output

supply, which is estimated 57,876 million, intersection with column 2. Although it is a single

entry in the condensed version of the SAM, the activity-commodity linkage, the make matrix

has a dimension of 37 rows (number of activities) by 37 columns (number of commodities) in

the full SAM

Total commodity supplied to the domestic economy is presented in column 2. This consists

of commodity output from domestic activities at producer prices (in row 1) and aggregate

imports at world prices (in row 12). The remaining entries in this column are net commodity

tax (intersection with rows 7 and 8) and trade margins (intersection with row 2). Aggregate

commodity supply at market price (inclusive of tax and trade margins) is 67,520 million (in

row 13). Row 2 allocates total commodity supply to different destinations: intermediate

demand, transaction demand, final demand, investment demand, and exports.

Column 3, 4 and 5 distribute national income, generated in the process of production and

factor income earned from abroad to domestic institutions (rows 9 and 10). This model has

only one aggregate household group whose receipts (in row 9) consists of labour income,

capital income, and transfers from the government. The main item in household expenditure

(column 9) is final consumption expenditure (intersection with row 2); other outgoings from

this account are transfer to the ROW (in row 12) and private household savings (in row 11).

The bulk of government revenue comes from the oil sector, which in this SAM framework

comes as a transfer from the capital account (through column 5). The other two sources with

negligible contributions are production tax (in column 6) and import taxes (in column 8).

The other figure in the government revenues row denotes net commodity taxes, which is a

negative number (-2,447), representing product subsidy. When expanded into 37 separate

cells, it denotes subsidy by commodity group. It is the particular cell against the electricity

sector (IO class 40, see Appendix A-3) which is the focus of this study – economy-wide

impacts of reducing electricity subsidy. While final expenditure on goods and services and

transfers to households and transfer to the rest of the world represent major items of

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government expenditure, the balance between the sum of these items and total government

revenue gives government surplus (in row 11).

In the Kuwaiti SAM, substantial surplus in the external sector is transferred to the ROW

through the capital account (in row 12). It should be noted that domestic capital formation

6,878 million (in row 2) constitutes only 40% of the total savings made available by domestic

institutions (household savings plus government savings).

In addition to SAM data, the implementation of the CGE model requires supplementary

satellite accounts such as employment are separately estimated in line with flow variables in

the SAM. In appendix – 2, columns 1 and 2, come from such satellite data while the other

columns provide value-added related micro SAM version the concisely presented macro

SAM discussed in this section. The satellite accounts are compiled by making use of data

obtained from CSB (2011) and reconciling the latter with other sources (PACI 2014, CSB

2011, CSB 2010).

3.2. Description of the CGE Model

The model used for this study was previously adapted from IFPRI standard CGE model

(Lofgren et al 2002) to the Kuwait Economy and used for policy analysis (TED-KISR 2004).

In this study we have undertaken further reformulation and adaptations of the previous model

to conduct the simulation experiments. The focus in this version of the model is on

application of key elasticity values obtained through previous econometric studies using

Kuwaiti data. Furthermore, the model is calibrated with the recently constructed Kuwaiti

SAM which is discussed in the preceding section.

Figure 1 displays the structure of the model. Interactions between its components are

classified into three blocks (see Panels 1, 2 and 3). In Panel 1, starting from activity level

(QAA), the process of production is modelled as a nested multi-level structure. The first level

of the nesting structure determines sectoral output (QAA) as aggregation of intermediate

inputs (QINTAA) and value-added and electricity (QVEA) using a Leontief functional form.

This means substitution between material inputs is not allowed at this level (subscripts A and

C denote activities and commodities respectively). At the second nest of the production

function, the value-added and electricity bundle QVEa is split into demand for aggregate

factor input (QVAA) and electricity (QINTEA). At this stage, the constant elasticity of

substitution (CES) is employed to allow for substitution in input uses. In other words, this

specification allows electricity saving in the process of production. Burney and Al-Matrouk

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(1996, p.77) have used Kuwaiti data and estimated cross-price elasticity between energy and

labour 0.1691 with labour and 0.3597 with capital. In this study electricity-Gross value-

added substitution elasticity of (ve) of 0.3 in such a way that it would fall within the range of

the previous estimates. During the simulation experiment, a sensitivity analysis is conducted

to examine the role of variations in this parameter value on endogenous variables in the

model.

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Figure 1 – Structure of the Kuwaiti CGE Model

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CD+CD

PANEL 3

PANEL 2

PANEL 1

Investment demand

(QINVC | PQC)

Government final demand(QGC | PQC)

Household final demand(QHC | PQC)

LEONTIEF

LEONTIEF

CET

Imports(QMC | PMC)

Composite commodity(QQC | PQC)

Exports(QEC | PEC)

Domestic demand(QDC | PDC)

CD

LEONTIEF

Aggregate commodity output(QXC | PXC)

CES

>>> Commodity output(QXACA,C | PXACA,C)

Commodity output(QXAC1,1 | PXAC1,1)

Activity output(QAA | PAA)

>>>Other int. inputs(QINT36,A | PQ36,A)

Other int. inputs(QINT1,A | PQ1,A)

Other composite int inputs(QINTAA | PINTAA)

VA & Elec composite(QVEA | PVEA )

CES (0.3)

Electricity(QINTEE,A | PQE)

Value-added(QVAA | PVAA)

Capital(QFCAP,A | PFCAP,A)

Labour (QFLAB,A|PFLAB,A)

CD

Non-Kuwaiti labour (QFLABN,A| PFLABN,A)

Kuwaiti labour (QFLABK,A| PFLABK,A)

The value-added and other intermediate composites are split into their component parts.

Using the Leontief functional form, the composite quantity of intermediate demand by each

producing sector is disaggregated into demand for different commodities (QINTC,A). The

value-added composite is disaggregated into aggregate labour and capital using a Cobb-

Douglass functional form. Finally, the aggregate labour is disaggregated into demand for

Kuwaiti labour and non-Kuwaiti labour, again using Cobb-Douglass specification.

Panel 2 displays specifications and functional forms used in transforming activity output to

commodity outputs. This corresponds with the make matrix part of the SAM that provides

linkages between commodity supply and activity outputs. Totals of domestic commodity

outputs (QXC) are obtained by aggregating over their activity origins of QXACA,C

(commodity C produced by Activity A). On the other hand, the sum of commodities C

produced by an activity A should equal the level of activity output (QAA). This model

follows the functional form applied by Lofgren et al (2002) - CES for the former and Leontief

for the latter.

Panel 3 displays flows of commodity supply. The upper part shows a constant elasticity of

substitution (CET) function that allocates domestic commodity output (QXC) to exports to the

rest of the world (QEC) and domestic supply (QDC). The lower part of the diagram shows

determination of domestic demand for a composite commodity (QQC) which is obtained

through a two-way aggregation. The first one is the Armington assumption implies that

commodities from different geographical origins are treated as imperfect substitutes

(Armington, 1969). This is implemented by applying a CES aggregation of commodities by

source of supply – domestic output (QDC) and imports from the rest of the world (QMC). The

second one is summation of commodity destinations by domestic institutions – intermediate

demand by producing sectors (QINTC,A), household final consumption (QHC,H), government

final consumption (QGC), and capital formation or investment demand (QINVC).

4. Simulation results

4.1. Scenarios

The model was run three rounds to conduct simulation experiments. The first run replicates

the base year situation or the baseline scenario. This represents the status quo whereby the

subsidy system remains as in the baseline SAM. The second and third runs impose policy

shock under different scenarios. These yield results which are different from the base run.

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The differences between the base scenario and the policy shocks yield the effects of the

policy change.

A 30% reduction in electricity subsidy was applied and economy-wide impact of this change

was evaluated. This rate of subsidy reduction was arbitrarily chosen, it can be set at lower or

higher rate of changes. However, it is not feasible to implement much larger shocks such as

abolishing electricity subsidy altogether in a CGE modelling context, particularly when the

rate of subsidy at the base is as high as 91% (see appendix 3). In such cases, policy shocks

can be evaluated by applying relatively small changes to get a sense magnitude regarding

impacts.

For this policy experiment, the 30% subsidy reduction is simulated in two scenarios.

Scenario 1 was run without compensating households for any welfare loss resulting from

inevitable rises in electricity tariffs due to the partial withdrawal of subsidy. Scenario 2 is

inspired by the recommendation BuShehri and Wohlgenant (2012) that the Kuwait

government would need to compensate target household groups for their welfare loss.

4.2. Intra-sectoral effects

Sectoral effects are discussed in the subsequent section but it is important to examine impacts

on the electricity sector itself. Table 1 displays changes in selected indicators. Clearly, the

intra-sectoral effects of the policy shocks are about equal in both scenarios. The reason is that

scenario 2 is an increase in transfer payments which is not likely to cause the amount of

electricity consumed by households, given the functional forms applied to specify the

household consumption function.

The most immediate effect is about three fold increase in electricity tariff in both scenarios.

This means a 30% reduction in electricity subsidy in Kuwait is likely cause tariff to rise from

2.0 to 6.1 fills / KWh. The amount of electricity generated and gross value added in the

sector is expected to fall by 21% and 24% respectively.

Table 2 - Effects on the electricity sector

Scenario 1 Scenario 2Tariff 307.58 306.02Output -20.90 -20.44Value added -24.16 -23.74Employment:

Total -3.73 -3.66Kuwaiti 0.00 0.00Non-Kuwaiti -24.06 -23.58

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Demand for electricity:Final demand -5.00 -4.96Intermediate demand -35.10 -34.31

Employment in the sector declines from the baseline level by 3.7%. It should be noted that

Kuwaitis constitute the bulk of the work force in the electricity and water production and

distribution sectors, accounting 84% of total employees (see appendix 2). Given that the

number of Kuwaiti employees is not allowed to vary (by assumption). It follows that the

brunt of changes in employment falls on the non-Kuwaiti labour force that decline by about

24% in both scenarios.

The last two rows of Table 2 indicate the extent to which electricity users will reduce their

consumption as a result of subsidy reduction and hence increase in tariff rates. In the

framework of this model, household final consumption will fall by about 5% from the base

line level while total intermediate demand for electricity declines by 35%. It should be noted

that the difference in the rate of energy saving between the final and intermediate demand is

explained by the functional forms employed in specifying the two categories of demand for

electricity. Household demand specification followed the LES system, where demand for

each community group cannot fall below a certain minimum. Expenditure elasticity are

borrowed from Burney and Al-Mutairi (1993). As far as are aware, this is the only

econometric study on Kuwait economy on the relationship between income and expenditure.

On the other hand, intermediate demand by the production sector is specified using the CES

functional form which allows substitution between electricity and factor inputs.

4.3. Macroeconomic effects

Now we turn to effects on key macroeconomic indicators. Figure 1 displays percentages

changes for the two scenarios from the baseline levels for selected macroeconomic variables.

Figure 1 - Effects on key macroeconomic variables

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GDP

BOP

Govt Saving

Welfare

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

-1.2

-2.1

-2.1

-1.6

0.3

0.9

-0.5

0.2

Scenario 2Scenario 1

Change from the base line (%)

Scenario 1 results indicate electricity subsidy withdrawals without compensating households

for welfare loss is likely to lead to adverse macroeconomic outcomes. GDP, measured in

gross value-added, terms decline by 1.2%. The government budget surplus and balance of

payments (GSV and BOP) decline by equal proportions, 2.1%. Aggregate welfare indicator

declines by 1.6%.

Scenario 2 results show that relocating subsidy from producer to consumer, at least in the

context of Kuwait, is likely to lead to noticeable positive macroeconomic effects. GDP

increases by 0.3% when a subsidy reduction is accompanied by household transfer payment

of equivalent magnitude with the quantity of subsidy withdrawn. The BOP surplus increases

from the baseline level by 0.9% but government savings declines by 0.5%, which is a smaller

proportion compared to corresponding results for scenario 1.

Aggregate household welfare marginally rises, by 0.2%, from the baseline level. The fact

that scenario 2 has a consistently positive effects on most macroeconomic variables indicates

that shifting subsidy from producer to consumers leads to positive economy-wide effects. On

the one hand, the reduction of producer subsidy improves resource allocation across sectors,

resources moving out of less productive to more productive sectors. On the other hand, the

relocation of subsidies to households causes consumer demand, which in turn stimulate more

production activities.

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Figure 2 presents effects of the policy shock on aggregate expenditure components. GDP,

expenditure measure, declines by 1.2% in scenario 1 and rises by 0.3% in scenario 2. As we

expect, these are equal to the corresponding changes in GDP net of taxes, production

approach, reported in Figure 1. It is interesting to note that the effects on household

consumption in the two scenarios are about equal but in opposite directions. Without

compensation, household consumption declines by about 0.9% but with compensation

household expenditure it increases by about the same proportion.

Figure 2 - Effects on domestic expenditure

Gross domestic expenditure

Household demand

Government demand

Investment demand

Exports

Import

-2.50 -2.00 -1.50 -1.00 -0.50 0.00 0.50 1.00 1.50

-1.17

-0.91

0.07

-1.13

-2.33

-1.28

0.25

0.93

0.14

-1.70

-0.89

-0.73

Scenario 2Scenario 1

Change from the base line (%)

The model is implemented with fixed government expenditure and flexible government

budget surpluses. As a result, the effect on government expenditure is minimal. On the other

hand, the saving-investment balance was obtained by allowing investment to vary but fixing

private savings. Consequently, investment declined from the baseline level in both scenarios.

Both exports and imports decline in each scenario but they declined in scenario 2 by

relatively smaller proportion than in scenario 2.

4.4. Labour market effects

The Kuwaiti labour market is highly segmented by nationality (Kuwaiti and non-Kuwaiti) as

well as in pay structure. One of the indicators for labour market segmentation is the huge

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wage differentials between Kuwaiti and non-Kuwaiti employees. Additionally, there are

differences in degree of flexibilities in employment contracts as well as sectoral mobility. The

Kuwait government guarantees full employment of nationals (Kuwaitis), which means

employers are not free to lay off Kuwaiti employees regardless of the economic conditions.

Also, Kuwaiti employees are highly concentrated in certain sectors with limited possibility of

their relocation or mobility across sectors regardless of differentials in sectoral employment

opportunities. On the contrary, employment contracts for non-Kuwaitis is highly competitive

in that they can be employed with high degree of flexibilities in employment contracts both in

terms of in mobility between sectors and payment conditions.

Table 2 – Labour market effects

Scenario 1 Scenario 2Employment level Total -0.89 -0.12

Kuwaiti 0.00 0.00Non-Kuwaiti -1.09 -0.15

Labour income Total -1.05 -0.30Kuwaiti -0.86 -0.23Non-Kuwaiti -1.21 -0.37

The model specifications have taken into account the underlying labour market conditions

outlined above. Accordingly, the labour market closures were specified in such a way that

wage and employment of Kuwaitis were fixed, which means they are not allowed to vary

during the model run. However, non-Kuwaiti labour is characterized by full flexibility in

wage levels and mobility across sectors.

Table 2 presents aggregate labour market effects that taken into account details of the labour

market closures outlined above. In scenario 1, total employment income decline by 1.05%,

with the Kuwaiti and non-Kuwaiti labour income declining by 0.86% and 1.21%

respectively. In terms of levels of employment, overall employment level falls by 0.89%.

This is mostly explained by declines in non-Kuwaiti labour (by 1.09%) but Kuwaiti labour

remains at the baseline level as expected under fixed employment closure for this category of

labour. Scenario 2 shows much less adverse labour market effects. Kuwaiti labour income

declines by only 0.23 and non-Kuwaiti labour income by 0.37%. Total employment declines

by 0.12%.

16

4.5. Sectoral value-added effects

As noted earlier, the reduction of subsidy causes electricity tariff to rise by around 307% in

both scenarios. The marginal differences between the two scenarios in their price effects can

be explained by the role in demand side stimulus on commodity prices in scenario 2. Now

we turn our attention to explaining how the three-fold increase in electricity tariff triggers

further increases in prices of other commodities and economic activities measured by sectoral

gross value added.

Figure 3 – Effects on sectoral gross value-added

Base metalsReal estate activitiesPetroleum products

Crude oil and natural gasThe furniture and related products

Repair of goodsAgriculture and livestock

Hotels and restaurantsLeather products

Machinery and equipmentFin. Insur. and related services

Food products and beveragesPrecision instruments

Business services ActivitiesWater production and distribution

Computer and related activitiesTextiles

Other non-metallic mineral productsWholesale Trade

Clothing and fur productsRoad transport services

Transport and storage servicesRetail Trade

Printing and publishingWood and wood products exc furn.

Fabricated metal products Construction

Other servicesPaper and paper productsEquipment rental services

Waste and scrapsPlastics and rubber products

Postal and Telecom. servicesEducation and health services

Public administration and defenseChemicals products

-5.00 -4.00 -3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00

Scenario 2Scenario 1

Figure 3 displays changes in sectoral value added in both scenarios, the data is sorted in

increasing order of scenario 1 effects. Clearly, the highest declines in value-added happen in

manufacturing sectors that are use electricity more intensively. It should be noted that the oil

sector, both primary crude oil extraction and petroleum products sub-sector of manufacturing,

are among the most adversely affected production sectors. Chemical products subsector is an

exception, its output increased in both scenarios. Service sectors encounter contractions but

only by a relatively small percentage points compared to the industrial sectors.

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4.6. Sensitivity to elasticity values

It is useful to examine responsiveness of the simulation results to variations in elasticity

values. Energy demand elasticity play in play a critical role in quantifying effects of energy

sector reforms, particularly reduction of energy subsidies (He et al 2011; 2010, Burney 1996).

Table 3 presents variations in simulation results in response to changes in the elasticity value

for substitutions between electricity and aggregate factors of production. The simulation

results discussed so far were obtained with the electricity -value-added aggregate elasticity of

substitution (ve) being held at 0.30. The corresponding results for the selected variables are

displayed in the middle (shaded) column in Table 3. Now we conduct sensitivity of results

by varying the values of ve and then examining how simulation results change from those

reported in the preceding section.

Table 3 - Sensitivity of simulation results to variations in demand elasticity (ve)

(ve =0.25 (ve =0.30 (ve =0.35Tariff 307.51 306.02 304.63Output -17.98 -20.44 -22.72Value added -20.95 -23.74 -26.32Employment (Total) -3.20 -3.66 -4.08Intermediate demand for electricity -29.58 -34.31 -38.70Output, savings and welfare: Gross Domestic Product (GDP) 0.22 0.25 0.28 Balance of payments 0.74 0.92 1.08 Government saving -0.53 -0.51 -0.49 Household welfare 0.19 0.22 0.25Expenditure: Gross domestic expenditure (GDE) 0.22 0.25 0.28 Household demand 0.91 0.93 0.96 Government demand 0.06 0.14 0.22 Investment demand -1.48 -1.70 -1.91 Exports -0.82 -0.89 -0.97 Import -0.65 -0.73 -0.81

It proves useful to start with changes in intermediate demand for electricity. It is

straightforward that higher price elasticity means greater change in quantity demanded.

Accordingly, change in intermediate demand for electricity is directly related to the sizes of

the elasticity values. When the value of ve reduced to 0.25, then electricity use fell by less

percentage point, about 30% compared to the 34% decline with ve = 0.30. On the contrary,

when the value of ve increased to 0.35 then we get a larger fall in demand for electricity,

39%. The rises in tariff rates go in the opposite directions, i.e., greater increase in electricity

18

tariff with smaller elasticity values and vice versa. It should be noted that “substitution

elasticity” in the context of this simulation analysis can only mean savings in electricity use,

change in quantity of electricity demand. The latter imply presence of excess use of

electricity over and above the essential requirement in technical relationships in the process

of production.

The variations with parameter values in the price and quantity demanded for electricity

causes changes in the rest of the economy. Within the electricity production sector,

employment value-added and total output rise and fall with possible extents of electricity

savings. Similarly, much greater possibility of electricity savings will cause subsidy

reductions to have greater expansionary effects on most macroeconomic variables.

Electricity subsidy withdrawals can have expansionary effects or positive stimulus on the

economy only when electricity users are in a position to reduce electricity use. This may

indicate the importance of accompanying measures to enable consumers to conserve

electricity.

5. Conclusion

The simulation experiments indicate that subsidy reduction does not necessarily cause any

substantial contractions in economic activities or declines in household welfare. The

differences between the two policy scenarios indicated that the adverse demand side effect of

the subsidy reform dominates unless the reform is accompanied with other measures.

Specifically, when households are compensated for their welfare loss, then this changes the

effects of the policy reform to become positive and hence aggregate GDP and household

welfare effects became positive as well. However, the results of the simulation experiments

reported in this study should be interpreted with caution. The model used for this analysis is

highly aggregate, and hence it does not account for distributional effects, particularly

differential impacts on households in different income brackets.

Policy reforms, such as reduction of subsidy to Kuwait’s electricity, can realize positive and

desired results by implementing accompanying measures in addition to the actual change to

the policy instruments. The accompanying measures may include organizational or

technological changes, both of which mean innovations which are not quantifiable in a

modelling framework. This means depending on whether or not these innovations

accompany the policy change, the results reported in this study can overestimate or

19

underestimate the effects of reducing subsidy. Similarly, the applications of packages of

reform measures can influence the speed with which the economy will realize potential

benefits from reducing or abolishing subsidies to public utilities.

For instance, the 21% contraction in the electricity sector was likely to overstate the adverse

effects since the reform package will not be confined to just reducing or removing the

subsidy but also partial or full privatization of the public utility which in turn would lead to

substantial efficiency gains through organizational changes and introduction of latest

technology in the process of generating electricity. If this is the case, then the economy-wide

positive stimulus of the reform can be much greater or adverse effect much smaller than the

reported simulation results.

References

Armington, P. S. 1969. The geographic pattern of trade and the effects of price changes. IMF Staff Papers 16: 176 – 199.

Al-Hasan, A.Y.; A.A. Ghoneim; and A.H. Abdullah. 2004. Optimizing electrical load pattern in Kuwait using grid connected photovoltaic systems. Energy Conversion and Management 45 (4):483-494.

Alotaibi, Sorour. 2011. Energy consumption in Kuwait: Prospects and future approaches. Energy Policy 39(2): 637-643.

Ayyash, S.; M. Salman; and N. Al-Hafi. 1985. Modelling the impact of temperature on summer electricity consumption in Kuwait. Energy 10(8): 941-949.

Burney, Nadeem A. 1998. Economies of scale and utilization in electricity generation in Kuwait. Applied Economics 30: 815-819.

Burney, Nadeem A. and Faisal T. Al-Matrouk. 1996. Energy conservation in electricity generation: A case study of the electricity and water industry in Kuwait. Energy Economics. 18(1–2): 69-79.

Burney, Nadeem A., and Naief Al-Mutairi. 1993. Household consumption patterns in Kuwait. The Middle Eastern Business and Economic Review 5(2): 1-12.

BuShehri, Mahmoud A.M. and Michael K. Wohlgenant. 2012. Measuring the welfare effects of reducing a subsidy on a commodity using micro-models: An application to Kuwait's residential demand for electricity. Energy Economics 34(2): 419-425.

CSB. 2010. National Accounts Statistics 2011. Central Statistical Bureau, Ministry of Planning Kuwait. http://www.csb.gov.kw/Socan_Statistic_EN.aspx?ID=26. (last accessed on 07/05/2014)

20

CSB. 2011. Annual Survey of Establishments. Central Statistical Bureau, Ministry of Planning Kuwait. http://www.csb.gov.kw/Socan_Statistic_EN.aspx?ID=26. (last accessed on 07/05/2014)

CSB. 2014. National Accounts Statistics Input & Output Tables 2005-2010. Central Statistical Bureau, Ministry of Planning Kuwait. http://www.csb.gov.kw/Socan_Statistic_EN.aspx?ID=26 . (last accessed on 07/05/2014).

Darwish, M.A ; F.M. Al-Awadhi; and A.M. Darwish. 2008. Energy and water in Kuwait Part I. A sustainability view point. Desalination 225(1–3): 341-355.

Darwish, M.A. 2001. On electric power and desalted water production in Kuwait. Desalination 138 (1–3): 183-190.

Darwish, M.A. and A.M. Darwish. 2008. Energy and water in Kuwait: A sustainability viewpoint Part II. Desalination 230(1–3): 140-152.

EIU. 2010. The GCC In 2020: Resources for the Future – debating how the GCC states will manage key natural resources (oil and gas, minerals, water and food) over the next decade. A report from the Economist Intelligence Unit Sponsored by the Qatar Financial Centre Authority, London. www.eiu.com/gulfin2020

Gelan, A. 2014. Simulating impacts of reducing subsidy to Kuwait’s electricity sector. Oxford Energy Forum 96: 32-35

General Secretariat of Higher Council for Planning and Development. 2010. General Frame of Development Plan For the State of Kuwait 2010/2011 – 2013/2014 . State of Kuwait, February 2010

He, Y. X.; L. F.Yang; H. Y. He; T. Luo; and Y. J. Wang. 2011. Electricity demand price elasticity in China based on computable general equilibrium model analysis. Energy 36(2): 1115-1123.

He, Y.X.; S.L. Zhang, L.Y. Yang; Y.J. Wang; and J. Wang. 2010. Economic analysis of coal price–electricity price adjustment in China based on the CGE model. Energy Policy. 38(11): 6629–6637.

Koesler, Simon and Michael Schymura. 2012. Substitution Elasticities in a CES Production Framework An Empirical Analysis on the Basis of Non-Linear Least Squares Estimations. ZEW Discussion Paper No. 12-007. Centre for European Economic Research, ftp://ftp.zew.de/pub/zew-docs/dp/dp12007.pdf

Krane, Jim. 2013. Stability versus Sustainability: Energy Policy in the Gulf Monarchies. EPRG Working Paper 1302, Cambridge Working Paper in Economics 1304.

Lofgren, H.; R. L. Harris; and S. Robinson. 2002. A Standard Computable General Equilibrium (CGE) Model in GAMS. International Food Policy Research Institute. Microcomputers in Policy Research, Volume 5, Washington, D.C.

PACI. 2014. Population: Statistical reports. The Public Authorities for Civil Information (PACI).

21

http://stat.paci.gov.kw/englishreports/#DataTabPlace:view1ArcGISRegionMap. (last accessed on 12/06/2014)

Tabors, Richard D. 2009. Interconnection in the GCC Grid: The economics of change. Presented at System Sciences, HICSS 2009: 42nd Hawaii International Conference, Big Island, Hawaii, 5-8 Jan. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4755521.

TED-KISR. 2014(forthcoming). Household conservation behavior: a case study of electricity and water demand in Kuwait. Project progress report, Kuwait.

TED-KISR. 2004. A detailed specification of the standard static computable general equilibrium model for Kuwait developing quantitative planning tools. Technical report No. 3. Submitted to Ministry of Planning State of Kuwait

Wood, Michael and Osamah A. Alsayegh. 2014. Impact of oil prices, economic diversification policies and energy conservation programs on the electricity and water demands in Kuwait. Energy Policy 66: 144-156

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Appendix – A: Baseline Data

Appendix – A-1: Compensation of employees by sector

Input-output categoriesCodes Descriptions

01 Agriculture and Livestock11 Extraction of crude oil and natural gas15 Food products and beverages17 Textiles18 Clothing and the making and dyeing of fur19 Leather preparation and tanning plus manufacture handbags and leisure bags plus saddles20 Wood and wood products and cork excluding furniture21 Paper and paper products22 Printing publishing and copying of recorded media science23 Coke and refined petroleum products and nuclear fuel24 Chemicals and chemical products25 Plastics and rubber products26 Other non-metallic mineral products27 Base metals28 Fabricated metal products excluding machinery and equipment29 Machinery and equipment that were excluded in previous category31 Precision instruments36 The furniture industry and furniture upholstery and paint37 Waste and scrap that is of non-ferrous metals40 Electricity and gas41 Water production and distribution45 Construction51 Wholesale Trade52 Retail Trade526 Repair of goods55 Hotels and restaurants60 Road transport services

61-63 Transport and storage services64 Postal and Telecommunications services

65-67 Financial insurance and related services70 Real estate activities71 Machinery and equipment rental and personal goods72 Computer and related activities74 Business services Activities75 Public administration and defense

80-85 Education and health services90+ Other services

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Appendix – A-2: Sectoral distribution of value-added (2010, Thousands KD)

No. personsemployed

Compensationof Employees

Othervalue-added

Grossvalue-added

%share

Codes Kuwaiti Non-Kuwaiti

Kuwaiti Non-Kuwaiti

Total

01 705 30,891 347 3,486 3,833 121,448 125,281 0.311 8,115 12,193 255,665 164,667 420,332 18,032,054 18,452,386 51.115 487 21,942 7,771 66,542 74,313 120,448 194,761 0.517 18 1,972 127 3,921 4,048 4,203 8,251 0.018 112 14,877 522 25,559 26,081 22,671 48,752 0.119 2 227 27 592 619 596 1,215 0.020 6 1,194 47 2,353 2,400 2,682 5,082 0.021 26 2,268 323 7,308 7,631 13,180 20,811 0.122 288 5,427 4,590 31,093 35,683 15,347 51,030 0.123 4,087 1,163 185,315 37,432 222,747 1,613,573 1,836,320 5.124 1,401 4,090 48,428 29,227 77,655 247,644 325,299 0.925 77 4,235 971 12,255 13,226 24,732 37,958 0.126 145 10,206 2,032 35,313 37,345 84,290 121,635 0.327 11 1,172 90 5,574 5,664 6,484 12,148 0.028 177 11,775 1,790 31,871 33,661 32,363 66,024 0.229 32 4,490 286 10,870 11,156 4,231 15,387 0.031 370 14,519 7,654 58,472 66,126 47,610 113,736 0.336 48 6,506 278 14,665 14,943 17,084 32,027 0.137 11 763 80 1,918 1,998 1,841 3,839 0.040 6,568 1,206 38,300 1,614 39,914 224,704 264,619 0.741 4,863 893 28,360 1,195 29,555 414,567 444,123 1.245 2,611 138,318 16,977 422,803 439,780 287,705 727,485 2.051 1,393 35,912 7,085 113,142 120,227 282,943 403,170 1.152 4,309 120,752 24,563 347,865 372,428 515,390 887,818 2.5526 425 17,563 3,364 36,092 39,456 40,353 79,809 0.255 1,346 62,198 17,087 174,894 191,981 43,378 235,359 0.760 1,659 21,921 12,736 54,907 67,643 59,881 127,524 0.4

61-63 6,356 55,541 140,170 382,586 522,756 144,445 667,201 1.864 955 17,243 23,676 261,465 285,141 750,646 1,035,787 2.9

65-67 22,701 39,522 687,288 659,391 1,346,679 1,527,006 2,873,685 8.070 1,431 9,778 14,592 65,862 80,454 1,714,585 1,795,039 5.071 156 4,218 1,628 15,089 16,717 62,532 79,249 0.272 189 6,401 3,487 56,914 60,401 15,062 75,463 0.274 2,001 65,510 15,730 149,548 165,278 53,607 218,885 0.675 187,002 67,863 1,399,870 116,593 1,516,463 140,408 1,656,871 4.6

80-85 68,670 53,047 1,476,981 681,720 2,158,700 104,877 2,263,578 6.390+ 8,139 619,007 40,682 710,111 750,793 84,439 835,231 2.3

Totals 336,891 1,486,802 4,468,917 4,794,910 9,263,828 26,879,012 36,142,839 100.0

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Appendix – A-3: Sectoral distribution of subsidy payments (2010, Thousands KD)

Codes Value ofcommodity output Amount of subsidy Subsidy rate (%)

01 182,995 -38,537 -21.111 18,930,170 - -15 583,682 -64,514 -11.117 23,593 - -18 79,437 - -19 2,447 - -20 20,120 - -21 76,108 - -22 73,066 -543 -0.723 11,673,717 -710,777 -6.124 1,109,152 - -25 121,378 - -26 341,584 -4,244 -1.227 87,591 - -28 214,163 - -29 38,690 - -31 260,594 - -36 95,572 - -37 12,627 - -40 663,905 -606,541 -91.441 1,056,271 -1,007,717 -95.445 2,324,698 - -51 617,465 - -52 1,204,622 - -526 112,757 - -55 567,137 - -60 217,624 - -

61-63 2,227,697 - -64 2,046,854 - -

65-67 3,483,180 - -70 2,230,729 - -71 197,745 - -72 153,802 - -74 462,335 - -75 2,629,140 - -

80-85 2,679,173 - -90+ 1,074,265 -14,226 -1.3

Totals 57,876,085 -2,447,100 -4.2

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