COVID-19 ECONOMIC AND SOCIAL IMPACT ASSESSMENT IN CAMBODIA

Policy Brief 01

COVID-19
ECONOMIC AND SOCIAL IMPACT
ASSESSMENT IN CAMBODIA

CGE & GTAP Simulation Exercises

October 2020

Acknowledgment

The analyses were conducted by Dr. Bazlul Khondker and Dr. Oum Sothea, UNDP Cambodia’s Senior Consultants, with
a close consultation with the Ministry of Economy and Finance and technical support from Dr. Richard Colin Marshall,
Former Country Economist, UNDP Cambodia.



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4 Summary: Impacts of COVID 19 in Cambodia (Version 2) August 12, 2020

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1. Introduction and Background

The COVID-19 (novel coronavirus) has exerted tremendous distributional impacts of the gathering crisis. These include:
socio-economic impacts on every nation on the earth. The clustering around the poverty line and the potential for large
COVID-19 outbreak has had dramatic global impacts on increases in the poverty headcount; high levels of household
trade, production, and other economic activities. Multi- indebtedness; and weak channels of social protection.
lateral agencies such as the World Bank (WB), International
Monetary Fund (IMF), the Asian Development Bank (ADB) and UNDP used General Equilibrium (GE) approach and GTAP
UN system have estimated the cost of COVID 19. All these approach to assess economic and social impacts of COVID
agencies came up with high costs of COVID 19. However, all 19 for Cambodia using already identified channels through
of them estimated cost by representative regional blocks which the Cambodian economy would be affected by the
such EU; OCED; South Asia; East Asia; Asean and Africa COVID-19 outbreak . The general equilibrium model is a
etc. Following these, UNDP undertook studies to assess the simulation environment suited for various types of policy
socio-economic impacts of COVID 19 on Cambodia on an simulation. It is anchored to Social Accounting matrix (SAM)
exclusive country level as opposed to the assessment as a 2020 and used the macro-economic data (March 2020)
nation belonging to the Asean region. to design the Business as Usual (BAU) scenario. It also
captures the secondary indirect impacts across all agents of
Crucially, the crisis operates through both demand and supply the economy (e.g. households, government, and rest of the
channels. Cambodia is exposed as it relies on a narrow world). The outcomes generated in the CGE model are fed
economic base (comprising garments, tourism, agriculture into the employment and
and construction) and is a highly open economy (exports
and imports are around 62% and 63% of GDP, respectively, poverty modules to assess impacts on employment, and
and FDI is 11% of GDP). On the supply side, Cambodia is poverty levels. In this particular simulation task, the starting
highly reliant on China for raw material inputs for its garment points are the external (exogenous) demand shocks.
industry and as a source of tourists. Equally, on the demand
side, China is an important export destination for agricultural GATP model being a global trade model has the advantage of
products; and, capital inflows from China are key to various tracking trade flows between Cambodia and representative
sectors, particularly in the construction sector. Furthermore, countries as well as by major economic blocks as classified
Cambodia’s higher value-added exports are concentrated on in the GTAP model compared to the single country CGE
countries in Europe, the new epicentre of the virus outbreak. model (as discussed above) where imports and exports
trade with Cambodia are treated as single external flows.
It is also important to recognize that any COVID-19 effects The latest version of the GATP model is calibrated to 2014
would tend to exacerbate the pre-existing loss of trade global data sets. The main demerit of the GATP model is
preferences from the partial suspension of EBA (Everything that due its use of single household (only one household
But Arms), which will take place from August 2020. The as opposed multi-household groups in the single country
UNDP’s macroeconomic model-based estimates suggested CGE model) it fails to capture the import impacts on income
a range of impacts, with the central projection being a distribution. However, impacts outcomes from Cambodia
loss of around 0.6% in GDP, and the headline year-on- CGE model and GTAP model not only supplement each
year figure declining to 6.5% (PPT to UNCT, March 2020). other but also enhance the robustness of the impact
Moreover, Cambodia needs to recognize at the outset, that assessment. Even tough, we have simulated the impacts
a set of pre-existing socioeconomic vulnerabilities in the of high case scenario, the simulation results of expected
Cambodian context, which would exacerbate the welfare and case scenario is only reported here.

1. For details please refer to Kongchheng Poch and Richard Colin Marshall (2020), “Potential Impacts of the COVID-19 Outbreak on the Cambodian Economy”.

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2. Identified Channels and Assumptions

The identified channels along with extent of shocks and the time frames are summarised below:
Table 1: Assumptions and Description of Scenarios (Cases)

No Assumptions MEF Expected Case: Interpretation of MEF case: High case: shock
projected sector decline interpreted % impact parameter
[key driver variables/ basis] [% full year]
[explanation of shock parameter]

Duration of the global

1 outbreak impacts 2020 full year, given by MEF 2020 full year 8 months (2/3)
(base month of the year

is February)

2 Garment (including 13.1% Output decline [20.1% 20% reduction in demand 60% Reduction in
textile and footwear) reduction in export demand] [used MEF base driver rounded] export demand
[40% full year]

3 Construction 10.6% Output decline [24% 11% decline in investment 30% Reduction as
reduction in project approval] demand [used MEF output indicated before
decline as accept base load [20% full year]
of projects]

13.3% Output decline

4 Hotel and restaurant [42% reduction in international 40% reduction in demand [use 60% Reduction in all
tourist arrival and 30% MEF base driver for both H&R tourists, i.e. in demand

in domestic tourists] and T&C; but deducted 2%, as [40% reduction full

mainly international i.e. year - as indicated

5 Transportation 1.5% Output decline at 80:20 ratio] before]

and communication [as above]

6 Agriculture Based on MEF subsector 0.9 % System derived
output data

Government MEF: Based on programmes given by MEF: Social Protection +1.9% of GDP,

response: (1) per MEF Tax Reliefs +0.7% of GDP, savings on capital schemes -3.5% of GDP [net -1.2% of GDP]

7 data; and (2) based

on UN proposals UN SP: RGC Programme (above), plus remaining level 1 and costed level 2

(Level 1 and 2) proposals [net + 3.1% of GDP]

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3. BAU (PRE-COVID 19) SCENARIO

Using the simulation model, we generated the BAU scenario the sectoral GDP estimated under the BAU scenario exactly
using the sectoral GDP information for 2020 projected in matches the sectoral GDP reported in the macro-economic
the MEF Macro-economic framework (as of March 2020). framework. BAU scenario provides the benchmark data
Noting this is before adjustments for EBA impacts and the against which the GDP, employment, household consumption
provisional Covid impacts (the former is re-included via the and poverty outcomes under the demand shock as well as
scenarios). As mentioned above, an important feature is that stimulus scenarios are compared.

Figure 1. Simulated GDP, Employment and Poverty under BAU scenario

Source: Cambodia SAM 2000, employment matrix and poverty modules

BAU scenario key observations: The size of Cambodian 2017. Following the availability of CSES 2017 and the preference
economy has been estimated at $ 27.6 billion at current prices of the policy makers to used CSES for employment estimation,
and $ 13.9 billion in constant prices. GDP growth without any previous employment estimates based on LFS 2012 have been
demand shock is projected to achieve 6.5 per cent in FY 2020. replaced with employment statistics based on CSES 2017. Head
Total number employed persons are 9.2 million implying an count poverty rate drops further to 9.56 per cent of the total
extremely low unemployment rate of 0.7 per cent. These are population (i.e. 16.7 million).
revised and updated employment statistics based on CSES

2. This is done as the Covid scenarios will necessarily alter the EBA impact as quantum and value of exports will decline prior to re- imposition of some tariffs.

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4. DEMAND SHOCK SCENARIOS

The most widely used and accepted indicator to measure
economic well-being is GDP. It is the sum of values of all
goods and services produced in an economy in a time (e.g.
usually a quarter or a year).
Figure 2. Simulated impacts on GDP growth rates
– CGE & GTAP model (% change over 2019)

Source: Cambodia Static CGE Model 2020 and GATP model Source: Cambodia Static CGE Model 2020, Employment and
Poverty Module 2020
GDP Key findings: Ceteris paribus, demand shocks of the
scales discussed above, likely to contract the economy severely. GDP Key findings: Employment and poverty key findings: Like
GDP growth rate reduction is large and negative at – 4.11 per many other developing nations, Cambodia does not have a robust
cent under the expected case scenario. The impact on GDP system to track employment and thus employment rate. Large
growth rates are even higher under the GTAP simulation. More number workers are employed in low productive informal sectors
specifically, the COVID-19 would result in a -4.6% contraction in (i.e. mainly in services and agriculture) where their average
GDP growth from 2019 for the expected case scenario. working hour per week is low (less than 40 hours threshold)
leading to high rate under-employment. Thus, the estimated
Figure 3. Simulated impacts on employment & poverty employment intensities across activities are high – suggesting
that decline in domestic output may spur unemployment rate.
Unemployment rates under the post covid 19 period may increase
to 4.8 per cent of total labour force in the expected case scenario.
The unemployment rates are even higher under the GTAP model.

Poverty impacts are high in the post-COVID 19 scenarios. GDP
or national income loss led to the loss of household income and
consumption. As a result, head count poverty rate may increase
to 17.6 per cent under the expected case scenario implying an
8.0 percentage jump in poverty compared to the pre-COVID 19
period. In the expected case scenario, as many as 1.34 million
persons may slip back to poverty.

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5. STIMULUS

In response to the Covid 19, the government has proposed MEF Stimulus key findings: Stimulus package as designed
series of stimulus packages involving expansion of social by MEF may not improve the already deteriorated social and
protection and tax relief. According to government budget economic conditions since they do not inject additional funds into
information, the funds for the social protection and tax relief the economy. In the MEF, the resources are withdrawn from the
is likely to generate through savings from non-execution system by 1.2 per cent of GDP. Even though, GTAP model found
of some capital projects – i.e. reallocation of resources some improvements in GDP growth rate but they remain in the
from one budget line to other lines. The description of this negative zone (i.e. -2.5%). But when the single country CGE model
stimulus is provided below. is employed, GDP loss worsened under MEF stimulus. As a result,
unemployment rates deteriorated under the stimuli compared to
Stimulus (MEF): Based on programmes the demand shock scenarios (i.e. expected and high). However,
given by MEF: Social Protection +1.9% despite worsening of income and employment situation, heads
of GDP, Tax Reliefs +0.7% of GDP, savings count poverty improved under these stimulus – due mainly to
social protection transfers to three vulnerable household groups.
on capital schemes -3.5% of GDP
[net -1.2% of GDP]. 5.2. SOCIAL PROTECTION-FOCUSED

5.1. Government Expenditure Stimulus STIMULUS PACKAGE

GDP, employment, and poverty outcomes under the proposed Social protection – especially social assistant has emerged
government stimuli are presented here. as the most important stimulus to save lives and livelihood
during COVID 19. Rich countries have already allocated around
Table 2. Impact on government stimuli (MEF) 6% of their GDP in SP to mitigate the negative impacts of
COVID 19. Higher allocation with universal coverage (even for
Indicators Expected Expected + MEF a temporary basis) has come from unlikely sources such as
IMF and WB. Martin Ravallion (2020) suggested to allocate at
Cambodia CGE Model least 2% of GDP to SP programme. SP system also witnessed
unprecedent expansion – vertical and horizontal during the
GDP growth rate -4.11 -4.41 last 4 to 5-month period (see adjacent chart). Following these
(% over 2019) 5.02 developments at the global level, UN system proposed 3.5 %
-14.53 SP stimulus for Cambodia must be considered to save live,
Unemployment rate 4.77 16.55 livelihood as well to propel the economy.
(% of LF)

Households consumption -15.86
(% over BAU)

Poverty rate 17.58
(% of population)

GTAP Model

GDP growth rate -4.60 -2.50
(% over 2019)

Source: Cambodia Static CGE Model 2020 and GTAP Source: Gentilini et al. (2020)

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Chart 2. Distribution of SP simulus by progrmmes Figure 4. Simulated GDP impact under SP stimulus
scenario

Panel A: Cambodia CGE model

Expected case scenario

Panel B: GTAP model

Expected case scenario

Accordingly, a separate exemplification of a SP-focussed Source: Cambodia Static CGE Model 2020 and GATP model
stimulus package was undertaken. Here we assume that cash
transfers are allocated to the all 7 representative household GDP growth key findings: Impacts of SP stimulus – found
groups according to their population share and types of SP to improve GDP or national income compared to COVID shock
interventions. We acknowledge this is a simplified approach scenarios. Impacts on GDP growth rates under SP stimulus
and taken without reference to the ability to deliver transfers scenarios (i.e. -3.33 %) are slightly improved than the GDP
in this manner and scale. Modifications of this assumption growth rates found under the demand shock scenarios (i.e. -4.11
can be made if needed %). Growth impacts are larger under the GTAP model simulation:
1.9% under the expected case scenarios with SP.
The results and key findings are necessarily provisional at this
stage. Nevertheless, important differences on the other two
results do emerge. We see a small decline in the abatement of
GDP loses (is around 0.3% of GDP). And the wider socioeconomic
impacts (on employment and poverty) and the distributional
impacts are more positive and, in many cases, significant.

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Figure 5. Simulated employment and poverty effects of SP Stimulus

Panel A: Employment effects Panel A: Impact on poverty (% of total population)

Source: CGE Model 2020 and Employment and Poverty Modules

Employment and Poverty key findings: Due to Overall consumption decline is less by -4.64 percentage points
improvement in GDP or national income under the SP between expected SP and expected demand shock scenarios. The
stimulus compared to the MEF stimulus, the employment impacts are higher for rural household groups compared to the
effects are better under the SP stimulus than the MEF urban household groups as SP system is overwhelming biased
stimulus as well compared to the demand shock scenarios. towards to the rural poor or population. Poverty rate improved
In the case of expected case scenario, the difference in to 14.2 per cent with 0.56 million fewer poor under expected SP
unemployment rate is -0.4 percentage points. Number of stimulus compared to the expected demand shock scenario.
jobless persons is likely to be less by 28.5 thousand in SP The percentage points difference is -3.35 between the SP and
stimulus than the demand shock under the expected case demand shock scenarios under the expected case. Superiority
scenario. Employment effects have been found similarly of SP stimulus on salutary effects on poverty is clearly captured
high in the case of GTAP simulation. here by the falling poverty rates under the SP stimulus compared
to the demand shocks as well as MEF stimulus.

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