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: sothea.oum@buseco.monash.edu.au

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

4

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

10

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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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