A RECURSIVE DYNAMIC CGE ASSESSMENT OF THE CAMBODIAN MILLENNIUM POVERTY REDUCTION TARGET
Sothea Oum
Centre of Policy Studies Monash University
Ph: +61 3 9905 5561
Fax: +61 3 9905 2426 E-mail: [email protected]
Preface Title of Thesis: “A Recursive Dynamic Computable General Equilibrium Model for
Poverty Analyses in Cambodia”
Supervisor: Professor Philip Adams
Summary of the thesis
The contribution of the thesis is the first estimated input – output table and the building of
a major computable general equilibrium (CGE) model for Cambodia. The CGE model
features an income distribution and poverty module, which is fully incorporated into the
recursive dynamic CGE model. The model can be used for many pressing policy issues in
Cambodia. Among the applications illustrated in the thesis are: a forecast simulation of the
economy for 2005 – 2025, an assessment of expected oil windfalls from 2011 onward, and
an improvement of the agricultural productivity. The effects on income distribution and
poverty are discussed for each simulation.
Chapter 1: Introduction and Summary
Chapter 2: Theoretical Structure of the Cambodian CGE model
Chapter 3: Incorporating Income Distribution and Poverty Framework into the Cambodian
CGE Model
Chapter 4: Estimation of Input – Output Table and Other Required Data
Chapter 5: The Application of the Cambodian CGE Model for Policy Analyses
Chapter 6: Conclusion and Plan for Future Research
The following paper is based on the ongoing research project.
Abstract: The main objective of this paper is to apply a recursive dynamic computable general
equilibrium model to assess the likelihood of Cambodia meeting her poverty reduction
target of her millennium development goals (CMDGs) in 2015. Results from the model’s
forecast simulation imply that Cambodia could potentially reduce its poverty headcount
from 35 percent in 2004 to 21 percent in 2015, which is 4 percent below its CMDG’s
target. However, this optimistic forecast is entirely based on the assumption that the
pattern of income distribution throughout the forecast period is the same as in the base
period.
A RECURSIVE DYNAMIC CGE ASSESSMENT OF THE CAMBODIAN MILLENNIUM POVERTY REDUCTION TARGET
1. INTRODUCTION
After years in conflict, Cambodia has re-emerged and become one of the best performing
economies in the last ten years. The country’s annual economic growth has been 8 – 9
percent over a decade, due largely to economic reforms to attract foreign direct investment,
foreign aids, and to significant increases in garment and tourism exports. In 2004,
Cambodia became the first least-developed country admitted to the World Trade
Organization (WTO). The country has enjoyed double-digit growth rates since then.
According to the International Monetary Fund (IMF), Cambodian economy is expected to
grow at about 9 percent in 2007, IMF (2007).
Despite high economic growth, the country’s achievement in poverty reduction is
moderate. Cambodia aims to reduce poverty by half in 2015, according to her millennium
development goals (CMDGs). The original CMDG’s poverty reduction target was to
reduce poverty from an old estimate at 39 percent in 1993/94 to 19.5 percent in 2015.
However, using backward extrapolation from the socioeconomic survey 2004, the World
Bank (WB) has revised the estimation of the poverty headcount in 1993/94 up to 47
percent, WB (2006). The rate in 2004 is estimated to be at 35 percent, and the CMDG’s
poverty reduction target in 2015 is revised to be at 24 percent. In its assessment, the WB
maintains that assuming a 10 percent per annum growth rate in industry and services, and a
2.5 percent growth rate for agriculture would reduce the poverty rate to 29 percent in 2015.
But the rate would fall to 21 percent, were the annual growth in agriculture 4 percent. In
both cases, the growth rate of the economy was assumed to be 7 percent per annum from
2007 – 2015.
The purpose of this paper is to employ a recursive dynamic computable general
equilibrium model (CGE) to re-assess the above assertion based on economic forecast for
the period in question. The rest of paper is organized as follows. In Section 2, we briefly
overview the CGE model of Cambodia. We then, in Section 3, describe how to link CGE
results with household survey data to conduct poverty analyses. Section 4 discusses
poverty implications from the forecast. The conclusion of the paper is in section 5.
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2. THEORETICAL STRUCTURE OF THE CAMBODIAN CGE MODEL
The staring point of the core model is ORANI, the Australian static CGE model. The main
theoretical features of the model can be found in Horridge (2000) and the detailed
description of the model is provided by Dixon et al. (1982). The model consists of
equations describing, for some time period:
• producers' demands for produced inputs and primary factors;
• producers' supplies of commodities;
• demands for inputs to capital formation;
• household demands;
• export demands;
• government demands;
• the relationship of basic values to production costs and to purchasers' prices;
• market-clearing conditions for commodities and primary factors; and
• numerous macroeconomic variables and price indices.
Demand and supply equations for private-sector agents are derived from the solutions to
the optimisation problems (cost minimisation, utility maximisation, etc.) which are
assumed to underlie the behaviour of the agents in conventional neoclassical
microeconomics. All markets are cleared and the agents are assumed to be price takers,
with producers operating in competitive markets which prevent the earning of pure profits.
Following Johansen (1960), the model is solved by representing it as a series of linear
equations relating percentage changes in model variables using GEMPACK developed by
Harrison and Pearson (1996).
The model was calibrated with our estimated input – output dataset for Cambodia, Sothea
(2007). The data represents the economy in 2004. All relevant elasticises are taken from
the Global Trade Analysis Project (GTAP) due to the lack of data specific to Cambodia for
econometrically estimated parameters.
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2.1 Production Structure
We assume that producers minimise their input costs given level of output with nested
Leontief/constant returns to scale (CES) production. Production is assumed to be separable
in order to reduce the dimension of parameter space in the optimisation problem. At the
top level, commodity composites, a primary-factor composite and 'other costs' are com-
bined using a Leontief production function. Consequently, they are all demanded in direct
proportion to output. Each commodity composite is a CES production function of a
domestic good and the imported equivalent, following the Armington (1969) imperfect
substitution. The primary-factor composite is a CES aggregate of land, capital and
composite labour, of which land and capital stock are assumed to be industry-specific.
Composite labour is a CES aggregate of occupational labour types.
2.2 Final Demands
The demand for investment goods are derived from two-part cost-minimization. First, the
total cost of each imported and domestic commodity is minimized subject to the CES
function. At the aggregated level, the total cost of commodity composites is minimized
subject to the Leontief production function. No primary factors are used directly as input to
capital formation.
The household demand is modelled similar to that of the investment demand. The only
difference is that commodity composites are derived by a Klein – Rubin utility
maximization subject to its aggregate budget constraints, leading to the linear expenditure
system (LES). The imported and domestic commodities substitute for each other according
to a CES aggregation. We simply allow the aggregate demand of each household to
response proportionately to their disposal income from wages, capitals, land rentals, and
transfers.
Government spending is assumed to be exogenously determined. Finally, export demands
are modelled as a reverse function of their price in foreign currency and a constant own
price elasticity of demand.
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2.3 Regional Extension
We follow the top-down regional extension as described in Dixon et al. (1982) by
assuming that each industry uses the same technology in each region. The main data
required is a matrix showing how industry output is distributed between regions
(provinces) and how other final demands are split using data on provincial output, value-
add, and employment. No regional trade in each commodity is needed.
The regional industries are divided into national and local industries. The former produce
freely tradable commodities and their outputs are assumed to move proportionate to
national output, whereas commodities produced by the latter (mainly services) are scarcely
traded across regional borders and their outputs move in line with the local demand for the
corresponding commodities.
Household consumption in each region is tied to regional labour income. The alternative
treatment is to relate regional consumption to total factor payments in the region.
However, the employment of non-labour factors in the region does not necessary generate
income payments in that region. In any case, the movement in labour income and total
income are likely to be similar.
These assumptions produce local multiplier effects: regional benefits from both expansion
in national industries and from increased demands for local commodities. This extension
allows us to translate national simulations results into the regional ones such as regional
output, employment as well as the regional consumption that can also be used in the
poverty analyses.
2.4 Simple Dynamic Features
In order to capture inter-temporal changes in main variables in question, additional
recursive dynamics are needed to accommodate stock-flow relations in physical capital
accumulation and real wage-employment adjustment. There are 3 main mechanisms added
into the core model: (i) a stock-flow relation between investment and capital stock, which
assumes a 1 year gestation lag; (ii) a positive relation between investment and the rate of
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profit; (iii) a relation between wage growth and employment. The formal mathematical
forms of these features are found in Horridge (2002).
Annual rates of growth of capital stocks are linked to investment; investment in turn is
guided by rates of return. Starting point of each computation represents the economy as it
was both at the end of the previous period and at the beginning of the current period.
Similarly, the 'updated' data base produced by each computation represents the economy as
it will be both at the end of the current period and at the beginning of the next. Changes in
variables compare their values at the end of the current period with those at the beginning
of the current period.
We allow for real wages to adjust to employment levels as follows: If end-of-period
employment exceeds some trend level, then real wages will rise. Since employment is
modelled as negatively related to real wages, this mechanism causes employment to adjust
towards the trend level, which may be thought of as the level of employment
corresponding to the natural rate of unemployment (NAIRU) hypothesis.
3. LINKAGE BETWEEN CGE MODEL AND POVERTY ANALYSES
The applications of CGE models in poverty analyses are becoming more common. Filho
and Horridge (2004), and Savard (2003 & 2005) provide very helpful literature reviews
and good discussions on the topic. According to them, the application of CGE in income
distribution and poverty analyses can be classified into three main categories depending on
how households are integrated into the CGE models.
The first approach is a model with a single representative household (RH) through which
poverty analysis can be performed by using the variation of income or expenditure of the
RH generated by the model with household survey data to conduct ex ante poverty
comparison. Even though this approach is easy to implement, its main drawback is it
provides no information on the intra-group income distribution.
The second approach is the integrated multiple-household model (MH), in which there are
a number of representative households. The main advantages of this approach are that it
provides richer information on intra-group income distribution changes and prevents pre-
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judgement from aggregating households into categories. However, the data reconciliation
and the size of the model can become a constraint.
The third approach is the application of the micro-simulation (MS) techniques. This
approach provides richer information on household behaviour (consumption and labour
supply) for large record units of household survey data. However, the main drawback of
this approach is the lack of consistency and the feedback between the CGE model and the
micro-simulation model.
In this paper, we apply the method in-between the first and second approach. What we
mean by that is we integrate 15 household categories into the model according to their
geographical location. We also make use of results from previous surveys to impose intra-
group income distribution. We use expenditure to conduct poverty analyses. Using the
results from our CGE model, we then perform poverty analyses in two ways.
In one way, if the mean consumption of a household category increases, the expenditure of
each household within the group is simply raised by the same proportion. In another, rather
than updating expenditure of every household by the same proportion, we calculate
expenditure elasticities of the sub-group households within each household category based
on data from previous surveys. We use these elasticites to calculate the changes in
expenditure of each sub-group household in response to changes in the household
category’s mean consumption.
We then compare the poverty levels obtaining in the post-simulation case with those in the
pre-simulation case using the FGT index of Foster, Greer, and Thorbecke (1984). Given a
vector of household incomes (expenditures) y = (y1, y2, . . . ,yn) in increasing order, a
predetermined poverty line z > 0. Where gi= z -y, is the income shortfall of the ith
household, q = q(y; z) is the number of poor households (having income no greater than z),
and n = n(y) is the total number of households, the index is given by the following
formula:
q
αi=1
1P (y; z) =n
igz
α⎛ ⎞⎜ ⎟⎝ ⎠
∑
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where: when 0α = , P0 is commonly known as the poverty headcount index, the percentage
of the population with per capita consumption below the poverty line, when 1α = , P1 is the
poverty gap index which is the average shortfall of income from the poverty line, and
when 2α = , P2 is the poverty severity index which gives greater weight to those that fall
far below the poverty line than those that are closer to it. When the y vector is broken
down into subgroup expenditure vectors y(1),…,y(m). The index can also be written as: m
( )α
j=1P (y; z) = ( ; )j j
nP y z
n α∑
Therefore, the total index is the weighted sum of the subgroup levels.
4. POVERTY IMPLICATIONS FROM THE ECONOMIC FORECAST
In this section, we discuss steps in conducting the forecast over the period 2005 – 2015
based on macroeconomic data published by the WB and IMF, and Cambodia’s National
Institute of Statistics (NIS). We follow the methodology pioneered by Dixion and Rimmer
(2002) for the MONASH model.
4.1 Forecast Closure and the Stylized Model
In the forecast simulation, the solution is on annual basis: the base solution for the year-t
computation is the solution for year t – 1. This means that, for instance, start-of-year
capital stock for year t is completely determined by end-of-year capital stock in the base
solution. However, end-of-year capital stock of each year is endogenous and determined
through changes in real rate of return (ROR) and end-of-the year investment. We illustrate
the closure by Figure 1. Gross Domestic Product (GDP) and agricultural land in each
industry are exogenous and can be shocked by the forecast values. The primary-factor
efficiency is endogenized to capture economic-wide changes in productivity. Employment
and real wage are allowed to adjust with the employment trend (NAIRU).
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Figure 1: Causation in the Forecast Closure
Start-of-year Capital Stock
GDP = + + + Trade
balance
Employment
ROR
Aggregate investment
Government spending
Employment Trend
Primary-factor efficiency
End-of-Year Capital Stock
End-of-Year Investment
Household consumption
Real Wage
Endogenous
Legend
Exogenous
On the expenditure side, real consumption is endogenous. Real aggregate investment and
real government spending are exogenous, allowing them to take shocks from the macro
forecasts. Foreign currency prices of imports are naturally fixed as Cambodia is a small
and open country. The consumer price index (CPI) is used as numeraire is shocked by the
forecast value. National population is also allowed to change with official forecast. Other
variables in this closure are such as taxes, tariffs, and quantity shift variables are assumed
to be fixed.
To track the causal relation between these variables, we adopt a commonly used strategy to
illustrate the mechanisms of the core model through the sketch model version of
MONASH by Dixon and Rimmer (2002). It is presented in Table 1.
Table 1: Equations of the Stylized Model
(1) Y=C + I + G + X M−
(2) Y = A F (K, L)
(3) C + G = APC*Y
(4) C / G = Γ
(5) M = H (Y, TOT)
(6) TOT = J (X, V)
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(7) I = N (ROR,RROR)
(8) K / L = Q (ROR, A, TOT)
(9) W = U (K/L, A, TOT)
(10) L/ L = Z(W )
Equation (1) is the GDP (Y) identity in constant-price terms. Equation (2) is the
economy’s production function relating real GDP to inputs of labour (L), and capital (K)
and to input-saving technology term (A). In writing (2) and elsewhere in the stylised model
we ignore the existence of agricultural land and the presence of distortions due to indirect
taxes and subsidies. Equation (3) links total consumption (C+G) to GDP via a given
propensity to consume (APC). Equation (4) defines Γ, the ratio of private (C) to public (G)
consumption spending. Equation (5) summarises the determination of import volumes (M).
In the absence of changes in preferences, import volumes are positively related GDP and
the ratio of domestic to imported prices (represented here by TOT, i.e., the price of exports
relative to the price of imports). Commodity exports are inversely related to their foreign
currency prices via constant elasticity demand functions. This is summarised by equation
(6), which relates the terms of trade to the volume of exports (X) (movements along
foreign demand schedules) and a shift variable V (movements in foreign demand
schedules). Equation (7) links aggregate investment to the rate of return (ROR) and the
shift variable (reflecting investors’ confidence, RROR). With constant return to scale
assumption, the marginal product functions are homogeneous of degree 0 and so can be
expressed as functions of K/L and A. This accounts for equations (8) relating the profit
maximising capital/labour ratio to the rate of return on capital, technological change (A),
and the terms of trade (TOT). Similarly, the movement in the real consumer wage (W) can
be related to changes in the capital / labour ratio, technology, and the terms of trade in
equation (9).
Following Giesecke (2004), we deriving (8) & (9) by solving the firm’s profit
maximization problem: Π = P.Y – WL.L – WK.K, subject to Y = A f(L,K); where Π is
profit, WL is the wage rate, WK is the rental price of capital, and P is the price of output Y.
From this problem we have the f.o.c. ΠK = P.A.fK – WK = 0, or fK = WK/(A.P), or
equivalently fK = (WK/PI)(PI/A.P). Noting that fK is a monotonically decreasing function
of K/L, that (WK/PI) is the rate of return (ROR in equation 8) and that PI/P is a negative
9
function of the terms of trade (since PI – the investment price index – includes import
prices but excludes export prices, while P – the price of domestic output – includes export
prices but excludes import prices) provides (8). This implies that QROR < 0, QTOT > 0, and
NA > 0. By the same token, ΠL = P.A.fL – WL = 0, or fL = WL/(A.P) , or equivalently fL =
(WL/PC )(1/A)(PC/P). Noting that fL is a monotonically increasing function of K/L, that
(WL/PC) is the real consumer wage (W in equation 9) and that PC/P is a negative function
of the terms of trade (since PC – the consumer price index – includes import prices but
excludes export prices) provides (9). This implies that UK/L > 0, UTOT > 0, and UA > 0. The
real wage must also adjust according the NAIRU rule via equation (10) where L is the
employment trend.
The stylized model can now be used to describe the main features of the base forecast
closure. Under this closure, Y, P, I, G, APC, J and V are determined exogenously. Most
equations can be readily associated with the determination of a specific endogenous
variable. Hence, we might think of (8) as largely determining K and ROR which in turn
determines I in each industry. C and G are determined by equations (3) and (4). Equation
(5) determines M leaving (1) to determine X. Equation (6) determines TOT. The real
consumer wage is determined by (10) and L in (9). Linkages like these will be used to
explain the results of the forecast simulation.
4.2 The Economic Forecast and Poverty Implications
4.2.1 The Economic Forecast for 2005 -2015
We use forecasts published by the NIS, the WB and IMF as inputs into the model. These
forecasts are for main macroeconomic variables and for structural variables. However, for
the current exercise, we use only the information presented in table 2.
We select these variables for the following reasons. First of all, the forecast of GDP
growth is used to compare with the extent of poverty implication done by the WB (2006)
over the same period. Land clearances and land concessions for agricultural purposes are
prevalent. We use the trend of annual growth in the past decade of arable land reported by
NIS (2006) and assume that it is carried over projected period. The latest projection in
population growth by NIS is also used. Even though labour supply is normally linked
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closely with population growth, we adopt a higher rate of growth in employment trend
(NAIRU) to reflect underemployment and the trend rate of growth in labour participation
in Cambodia. The forecasted changes in consumer price are also used in the forecast.
Table 2. Main Macroeconomic Forecast 2005 – 2015
(percentage change)
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Gross Domestic Product 13.5 10.8 9.1 7.9 7.5 7.5 7.5 7.5 7.5 7.5 7.5
Consumer Price Index 5.8 4.7 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0
Aggregate Investment 30.7 20.8 11.7 10 9.3 9 9 9 9 9 9
Government Spending 8.1 8.0 8.5 8.8 8.9 9.1 9.2 9.3 9.3 9.3 8.1
Employment Trend 5 5 5 5 5 5 5 5 5 5 5
Population Growth 2.1 2.1 2.1 2.2 2.2 2.3 2.3 2.3 2.3 2.3 2.3
Cultivated Land Area 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5
Source: NIS, WB, and IMF
Taking these extraneous forecasts into the model, we are ready to investigate their impact
on other endogenous variables at both macro and some industrial results using causation
diagram and the stylized model. The details of forecasts are presented in table 3.
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Table 3. Macroeconomic Results (percentage change)
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
1 Real GDP 13.5 10.8 9.1 7.9 7.5 7.5 7.5 7.5 7.5 7.5 7.5 2 Aggregate household consumption 14.9 11.8 7.2 6.0 5.6 5.7 5.8 5.9 6.0 6.1 6.1 3 Aggregate real investment 30.7 20.8 11.7 10.0 9.3 9.0 9.0 9.0 9.0 9.0 9.0 4 Aggregate real government demands 8.1 8.0 8.5 8.8 8.9 9.1 9.2 9.3 9.3 9.3 9.3 5 Export volume 9.3 7.8 10.5 9.5 9.1 9.1 8.9 8.8 8.6 8.5 8.3 6 Import volume 15.0 12.2 8.9 7.8 7.4 7.4 7.5 7.5 7.5 7.4 7.4 7 All primary-factor efficiency 3.4 0.1 - 0.9 - 1.0 - 0.7 - 0.2 0.2 0.4 0.6 0.8 0.9 8 Aggregate land stock 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 9 Aggregate employment 9.9 8.2 6.1 5.0 4.6 4.4 4.4 4.5 4.5 4.6 4.6 10 Aggregate capital stock 10.0 14.1 15.9 15.3 14.5 13.8 13.2 12.7 12.4 12.1 11.8 11 Capital/labour ratio 0.1 5.9 9.8 10.3 10.0 9.4 8.7 8.2 7.8 7.5 7.2 12 Trend employment 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 13 Average real wage 2.4 4.0 4.6 4.6 4.3 4.0 3.7 3.5 3.2 3.0 2.8 14 Real devaluation -2.3 -1.6 3.3 3.0 2.7 2.5 2.2 2.0 1.9 1.8 1.7 15 Nominal exchange rate 4.6 4.0 4.5 4.2 3.9 3.8 3.6 3.5 3.5 3.4 3.4 16 GDP deflator 7.1 5.7 1.2 1.1 1.2 1.2 1.4 1.5 1.5 1.6 1.7 17 Consumer price index 5.8 4.7 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 3.0 18 Rate of return index -4.5 -10 -9.9 -10.2 -9.7 -9 -8.5 -8.2 -7.8 -7.7 -7.5 19 Terms of trade -1.3 -1.1 -1.6 -1.4 -1.3 -1.3 -1.3 -1.2 -1.2 -1.2 -1.2
Assuming for the moment that wage is sticky and that A are unchanged, it is clear from the
stylized model that: with K exogenous (row 10), L (row 9) is effectively determined (via
equation 2). However, when the actual employment grows higher than the employment
trend the real wage must adjust upward, and vice versa, to reinforce the NAIRU rule via
equation (10). The employment moves back to the trend in 2008 and down afterward
before picking up toward the trend along with slowing real wage (row 13). With Y, L and
K given, so too is A (row 7) as a residual. With K, L, A, and W given, equation (9)
determines TOT (row 19) which is consistent with the movements of the balance of trade
described below. They together determine ROR, RROR, X, and M via equation (5) – (8).
Via equation (8), the increase in capital-labour ratio (row 12) must cause the marginal
product of capital to fall (that is, ROR must fall). This accounts for the reduction in the rate
of return index (row 20). This in turn causes the marginal product of labour to rise,
justifying the increase in the real wage. With aggregate investment I (row 3) given,
equation (7) implies that investors are more confident in the country’s economy, thus
willing to supply more capital at a lower required rate of return (RORR), i.e. rightward
shifting in the capital supply curve. With Y, G, and APC exogenous, the household real
consumption C (row 2) is determined via equation (3).
From equation (1), even though with lower increase in G, the higher increases in real
absorption C and I relative to GDP are sufficient to cause the balance of trade moving
towards deficit in the first two years. This is achieved through an appreciation of the real
exchange rate (row 14). The appreciation in the real exchange rate causes import volumes
to rise (row 6) in conjunction with growing domestic demand via equation (5). From
equation (6), the increase in export volumes (row 5) reaffirms the deterioration in the terms
of trade. The real devaluation causes the improvement in the balance of trade from 2007
onward.
The sectoral results, presented in table 4, largely follow from the macroeconomic results.
With higher GDP growth driven by both domestic demand and international trade, so too
is the activity of all sectors to satisfying these demands.
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Table 4. Industrial External Trade Structure and Output Results
Share in total
country’s export
Export share of
total output
Import share in local market
Share in total
imports
% Accumulated changes in output
2010 2015 1 Paddy 0.038 0.2832 0.0070 0.0006 16.4 39.2 2 OtherCrops 0.001 0.0130 0.1039 0.0114 37.8 68.6 3 Livestock 0.003 0.0261 0.0004 0.0000 51.1 92.9 4 Forestry 0.006 0.1716 0.0000 0.0000 99.4 242.7 5 Fishery 0.023 0.1637 0.0001 0.0000 45.4 83.7 6 Mining 0.000 0.0000 0.0000 0.0000 121.3 253.8 7 FoodProds 0.000 0.0016 0.0715 0.0189 49.7 90.5 8 BevTbacco 0.014 0.6412 0.7545 0.0260 50.6 85.5 9 Textiles 0.008 0.2176 0.9184 0.3009 88.4 199.1
10 WearingApp 0.659 0.9679 0.3730 0.0273 87.5 199.4 11 LeatherFtw 0.014 0.4224 0.4316 0.0127 64.2 115.8 12 WoodPrd 0.006 0.1966 0.0160 0.0004 110.5 276.3 13 PaperPrt 0.001 0.0714 0.2676 0.0048 94.1 200.9 14 OilGasPrd 0.000 0.0000 0.9757 0.1984 107.0 261.3 15 ChemRubPlas 0.038 0.7302 0.7447 0.0401 48.8 77.4 16 NonMetlMin 0.000 0.0000 0.6017 0.0228 120.8 321.2 17 FabBasMtlPrd 0.000 0.0000 0.8785 0.0299 105.9 271.8 18 MotorVehicle 0.000 0.0000 0.9604 0.1685 103.5 256.8 19 TrasportEqui 0.000 0.0000 0.6916 0.0165 119.8 260.9 20 ElectronicEq 0.000 0.0000 0.6527 0.0199 115.4 314.9 21 Machinaries 0.000 0.0000 0.8613 0.0250 110.5 297.2 22 OthManuf 0.035 0.6955 0.5349 0.0171 75.3 160.7 23 Electricity 0.000 0.0000 0.0000 0.0000 73.6 148.8 24 Water 0.000 0.0000 0.0000 0.0000 76.5 155.5 25 Construction 0.000 0.0000 0.0246 0.0039 128.4 254.2 26 WholeSales 0.000 0.0000 0.0004 0.0000 73.6 152.3 27 RetailTrades 0.000 0.0000 0.0004 0.0000 75.5 158.4 28 Repairs 0.000 0.0000 0.0354 0.0005 76.8 159.4 29 HotelRestau 0.074 0.6936 0.1918 0.0067 72.3 142.6 30 OthTransport 0.025 0.1622 0.0500 0.0058 75.1 151.8 31 WtrTransport 0.002 0.1099 0.1627 0.0027 72.9 150.0 32 AirTransport 0.014 0.2412 0.2594 0.0137 71.2 143.0 33 PostComm 0.013 0.6280 0.5809 0.0091 79.1 153.8 34 FinanceServ 0.000 0.0000 0.0327 0.0008 82.5 174.4 35 RealEstBus 0.003 0.0308 0.1215 0.0123 72.7 148.8 36 PubAdmin 0.001 0.0339 0.0342 0.0007 56.6 130.8 37 Education 0.001 0.0339 0.0342 0.0009 57.7 132.5 38 Health 0.001 0.0339 0.0342 0.0005 56.8 131.1 39 OtherServ 0.020 0.1677 0.0135 0.0012 63.8 123.6 40 Dwellings 0.000 0.0000 0.0000 0.0000 112.4 272.2
14
The table shows the expansions in all 40 sectors of the economy. However, some sectors
gain more than the others due to the underlying input - output linkages of and between
sectors, and their sale pattern. For instance, among the top winners are industries 16 – 26.
They are mostly importing industries and main suppliers of capital goods. Construction
sells largely to dwelling and both gain significantly from the strong growth of the
economy. Most of textile imports are used by the wearing & apparel industry. They also
both stand to gain together. The second most advantageous industries are in the service
sectors which are a mixture of labour intensive, domestic and traded-oriented industries.
The last group are agricultural sectors except forestry which enjoy a moderate gain due to
their labour intensity. Moreover, these agricultural sectors causes slow growths in the other
sectors that use outputs from them as their main inputs. Those are foods and beverages,
tobacco, and rubber industries.
4.2.2 Household Consumption and Poverty Implications
As briefly discussed above, the household expenditure is modelled as a linear expenditure
system (LES) derived from a Klein – Rubin utility. The results of the real expenditure of
the 15 household categories are presented in table 5.
Table 5. Accumulated Changes in Real Household Consumption by Categories (percent)
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 20151 Banteay Mean Chey 11.9 22.1 28.0 32.6 37.0 41.6 46.6 51.9 57.5 63.5 69.72 Bat Dambang 12.0 22.3 28.2 32.9 37.3 42.0 47.1 52.5 58.2 64.2 70.63 Kampong Cham 11.8 21.9 27.7 32.2 36.5 41.1 45.9 51.2 56.7 62.5 68.74 Kampong Chhnang/Pursat 11.3 20.9 26.4 30.7 34.7 39.0 43.6 48.5 53.8 59.3 65.15 Kampong Speu 10.5 19.5 24.7 28.7 32.5 36.6 40.9 45.5 50.5 55.7 61.16 Kampong Thum 10.8 20.1 25.4 29.5 33.5 37.6 42.1 46.8 51.9 57.2 62.87 Kampot 11.7 21.7 27.1 31.3 35.2 39.4 43.9 48.8 53.9 59.4 65.18 Kandal 12.3 22.9 28.9 33.6 38.1 42.9 48.0 53.4 59.2 65.3 71.79 Phnom Penh 16.6 31.0 40.2 47.7 54.9 62.5 70.6 79.2 88.3 98.0 108.1
10 Prey Veaeng 11.4 21.0 26.4 30.7 34.7 38.9 43.4 48.3 53.4 58.9 64.611 Siem Reab 11.6 21.5 27.4 32.1 36.6 41.3 46.4 51.7 57.5 63.5 69.812 Sihanouk/Kep/Koh Kong 13.2 24.4 30.9 36.1 41.0 46.1 51.6 57.5 63.8 70.4 77.413 Svay Rieng 11.4 21.0 26.3 30.3 34.1 38.1 42.4 47.1 52.0 57.3 62.814 Takaev 12.0 22.2 28.1 32.8 37.2 41.8 46.8 52.2 57.8 63.8 70.115 Other* 11.1 20.5 25.8 30.0 33.9 38.0 42.4 47.1 52.2 57.5 63.1
*Kratie, Mondul Kiri, Preah Vihear, Ratanak Kiri, Strung Treng, Oddar Meanchey, and Pailin
15
It can be seen that the very optimistic forecast causes strong growth in households’
consumption for all categories. The households in the capital Phnom Penh enjoy the
largest gains. If these average gains are applied to the base period consumption of every
household in each category, it would drastically drive down poverty in any measure. We
believe that it is not a plausible scenario. In stead, we derive the household’s expenditure
by categories and deciles using the elasticity of decile-household consumption in response
to changes in the average consumption of the household category they belong to. It is
shown in table 6.
Table 6. Accumulated Changes in Real Household Consumption by Categories and Deciles (percent)
2010 Provinces/Cities D1 D2 D3 D4 D5 D6 D7 D8 D9 D10
1 Banteay Mean Chey 5.0 12.2 15.3 19.8 24.7 30.2 36.2 40.3 52.5 66.52 Bat Dambang 5.0 12.3 15.5 20.0 24.9 30.4 36.6 40.7 53.0 67.23 Kampong Cham 4.9 12.0 15.1 19.6 24.4 29.8 35.8 39.8 51.7 65.54 Kampong Chhnang/Pursat 4.7 11.5 14.5 18.7 23.2 28.3 34.0 37.8 49.1 62.05 Kampong Speu 4.4 10.8 13.6 17.6 21.9 26.6 31.9 35.4 45.9 57.96 Kampong Thum 4.6 11.1 14.0 18.0 22.4 27.4 32.8 36.4 47.2 59.67 Kampot 4.7 11.6 14.6 18.8 23.5 28.6 34.3 38.2 49.6 62.78 Kandal 5.1 12.5 15.8 20.4 25.4 31.0 37.3 41.5 54.1 68.69 Phnom Penh 7.0 17.5 22.1 28.8 36.2 44.6 54.1 60.6 80.3 103.7
10 Prey Veaeng 4.7 11.4 14.4 18.6 23.2 28.2 33.9 37.6 48.9 61.811 Siem Reab 4.9 12.1 15.2 19.7 24.5 30.0 36.0 40.0 52.1 66.012 Sihanouk/Kep/Koh Kong 5.4 13.3 16.8 21.8 27.2 33.3 40.1 44.7 58.3 74.213 Svay Rieng 4.6 11.2 14.1 18.3 22.7 27.7 33.2 36.9 47.9 60.414 Takaev 5.0 12.2 15.4 19.9 24.8 30.3 36.4 40.5 52.7 66.815 Other* 4.6 11.2 14.1 18.2 22.6 27.6 33.1 36.8 47.7 60.2
2015 Provinces/Cities D1 D2 D3 D4 D5 D6 D7 D8 D9 D10
1 Banteay Mean Chey 7.5 18.7 23.7 30.9 38.9 48.1 58.6 65.7 87.5 113.92 Bat Dambang 7.6 18.9 23.9 31.2 39.4 48.7 59.3 66.5 88.7 115.53 Kampong Cham 7.4 18.4 23.4 30.5 38.4 47.4 57.7 64.7 86.2 112.04 Kampong Chhnang/Pursat 7.1 17.6 22.3 29.1 36.6 45.1 54.8 61.3 81.5 105.55 Kampong Speu 6.7 16.7 21.1 27.4 34.5 42.4 51.5 57.6 76.2 98.46 Kampong Thum 6.9 17.1 21.6 28.1 35.4 43.6 52.9 59.2 78.5 101.57 Kampot 7.1 17.6 22.3 29.0 36.5 45.0 54.7 61.2 81.3 105.48 Kandal 7.7 19.1 24.3 31.7 40.0 49.4 60.2 67.5 90.2 117.59 Phnom Penh 10.6 27.0 34.5 45.6 58.2 72.9 90.1 102.0 140.0 187.9
10 Prey Veaeng 7.0 17.5 22.1 28.8 36.3 44.7 54.3 60.8 80.8 104.611 Siem Reab 7.5 18.7 23.7 31.0 39.0 48.2 58.7 65.8 87.8 114.212 Sihanouk/Kep/Koh Kong 8.2 20.4 25.9 33.9 42.9 53.1 64.8 72.9 97.7 128.013 Svay Rieng 6.9 17.0 21.6 28.1 35.3 43.5 52.7 59.0 78.3 101.214 Takaev 7.5 18.7 23.8 31.0 39.1 48.3 58.9 66.0 88.0 114.615 Other* 6.9 17.1 21.7 28.2 35.5 43.7 53.0 59.4 78.7 101.8
*Kratie, Mondul Kiri, Preah Vihear, Ratanak Kiri, Strung Treng, Oddar Meanchey, and Pailin
16
Applying these changes to each household’s consumption in the deciles of every
household category in the base year, we are able to recalculate the poverty indices. The
base year household consumption and poverty indices are estimated by James, (2005). We
use the same dataset to estimate poverty indices for 2010 and 2015 as presented in table 7.
Since the household consumption is in real terms, we do not need to update the poverty
line from the base period.
Table 7. The FGT Poverty Indices (percent)
Base 2004
2010
2015
P0 P1 P2 P0 P1 P2 P0 P1 P2
Cambodia 34.7 9.0 3.3 25.3 6.6 2.5 21.1 5.8 2.31 Banteay Mean Chey 37.2 9.8 3.6 27.3 7.2 2.7 23.6 6.3 2.32 Bat Dambang 33.7 7.9 2.6 22.8 5.3 1.8 17.2 4.5 1.63 Kampong Cham 37.0 9.3 3.3 26.3 6.7 2.5 21.4 5.9 2.24 Kampong Chhnang/Pursat 39.6 10.3 3.8 29.9 7.7 2.8 25.0 6.7 2.55 Kampong Speu 57.2 17.0 6.7 45.9 13.6 5.4 41.0 12.3 4.96 Kampong Thum 52.4 15.5 6.2 42.5 12.5 5.0 38.6 11.2 4.57 Kampot 30.0 6.6 2.3 19.2 4.5 1.7 15.7 3.9 1.58 Kandal 22.2 4.8 1.7 13.9 3.2 1.2 10.0 2.7 1.19 Phnom Penh 4.6 1.2 0.5 2.7 0.7 0.3 1.9 0.6 0.3
10 Prey Veaeng 37.3 8.1 2.7 25.2 5.5 1.9 19.4 4.7 1.611 Siem Reab 51.8 17.3 7.5 43.2 14.2 6.2 38.7 13.0 5.712 Sihanouk/Kep/Koh Kong 23.2 4.6 1.4 13.0 2.7 0.8 11.3 2.1 0.613 Svay Rieng 35.9 8.3 2.8 25.1 5.8 2.0 20.5 5.0 1.714 Takaev 27.7 6.3 2.1 18.9 4.2 1.4 15.1 3.5 1.215 Other* 46.1 13.2 5.0 36.4 10.1 3.8 32.2 9.0 3.4
*Kratie, Mondul Kiri, Preah Vihear, Ratanak Kiri, Strung Treng, Oddar Meanchey, and Pailin
In the base year, poverty is prevalent in Kampong Speu, Kampong Thum, Siem Reab, and
other small provinces. Capital Phnom Penh and its adjunct Kandal province have the
lowest rate of poverty in all measures. The average poverty headcount of the country in the
base year is 35 percent. With significant increase in households’ consumption all over the
country, the poverty headcount falls below the CMDG’s target of 24 percent in 2015. The
poverty headcount in 2015 is 21 percent, coincidently the same rate as the best scenario of
the WB’s assumption of a 4 percent per annum growth in agricultural output.
Not surprisingly, Phnom Penh and Kandal manage to reduce more than half of their
poverty headcount compared with the base period. They are the centre of the country’s
industry and main economic activities. The initial poverty-stricken provinces maintain the
17
same status at the end of the forecast. These results reflect the fact that we impose the
same pattern of income distribution as in the base year. We ignore of the possibility of the
dynamics movement of households in and out of poverty. A poverty analysis in a real
dynamic sense is still a challenging research issue.
Even though our poverty results are the same as the WB’s second scenario, we depart from
its assumptions as follows. Firstly, we adopted a higher GDP growth rate throughout the
forecast period, while the World Bank assumes a straight annual 7 percent growth in GDP
from 2007 – 2015. Secondly, rather than plainly assuming a sectoral growth composition,
we allow the model to project the sectoral results based on the forecast on main
macroeconomic data.
5. CONCLUDING REMARKS In this paper, we apply a recursive dynamic CGE model to forecast the Cambodian
economy for the period 2005 – 2015. The results from the model’s forecast are used to
assess the likelihood of the country meeting her poverty reduction target in 2015. The
model’s forecast simulation implies that Cambodia could potentially reduce its poverty
headcount from 35 percent in 2004 to 21 percent in 2015, which is 4 percent below her
CMDG’s target. Our poverty headcount result in 2015 is coincidently the same as the best
scenario of the WB’s assessment, without imposing strict assumptions on the sectoral
growth pattern.
However, this optimistic forecast is entirely based on the assumption that the pattern of
income distribution throughout the forecast period is the same as in the base period. As
discussed above, if we plainly use the consumption results from the 15 household
categories to update consumption of every house belong to them, we would end up having
a very low rate of poverty headcount. This intra-group distribution problem, perhaps, can
be solved by integrating as many as more households into the model or by using the micro-
simulation approach. These will be agenda items for the forthcoming research.
18
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