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Dr. Antonio Maria Costa,Editor in ChiefJournal of Policy Modeling7 Drève de Lansrode,1640 Rhode St. Genèse, Belgium
July 17, 2017
Dear Dr. Costa:
I am pleased to submit a research article entitled “Economic Impact of Trade Disruption in Nepal: A Recursive Dynamic Computable General Equilibrium Approach” for consideration for publication in the Journal of Policy Modeling.
This paper is an application of recursive dynamic computable general equilibrium model, which is applied to the actual event which happened to Nepal. By incorporating a simple dynamic mechanism in the savings rate adjustment along with the quantitative trade restriction, this paper illustrates how the short-term event impacts the economy over the medium-term horizon. The paper also simulates and compares the various countermeasures to help withstand the negative impact. I believe this approach matches the interest of the readers of your journal.
This manuscript has not been published and is not under consideration for publication elsewhere. We have not conflicts of interest to disclose.
Thank you for your consideration, and looking forward to hearing back from you.
Yours Sincerely,
Ryosuke NakataChief Representative, USA officeJapan International Cooperation Agency1776 I street, N.W., Suite 895Washington, D.C. 20006 [email protected]
Economic Impact of Trade Disruption in Nepal
A Recursive Dynamic Computable General Equilibrium Approach
Abstract: This study aims to quantify the macroeconomic impact of the trade disruption
imposed on Nepal in late 2015 and simulate the effect of countermeasure policies using the
Dynamic Computable General Equilibrium model. The simulation reveals that even after
the end of trade disruption, the economy will remain below the normal level of activities for
a prolonged period. Direct transfers policy may help narrow the income gap in the short-
term but will not be effective in closing the gap in the medium-term, while investment
policies have a certain impact on GDP in the medium-term but are not sufficient in the
short-term.
Keywords: dynamic CGE, Nepal, trade disruption, quantitative restrictions
1. Introduction
Nepal is a landlocked country that shares the border with India and China. India is
Nepal's largest trading partner, accounting for more than 60% of Nepal’s annual trade
through its border. In response to the new constitution Nepal adopted in September 2015,
which India claimed would harm the Indian-inclined residents in Nepal, India virtually
blocked its border with Nepal. As a result, the total imports of Nepal over the period of
September-December 2015 dropped to 40% of the level during the same period a year
earlier, which caused a significant macroeconomic impact, including inflation and
2
production activities.1 Exports also declined over the same period to half the level over the
previous year.
Commodity details of import decline are not available from the Nepalese
government. Since India accounts for the majority of Nepal's trade, we can estimate the
commodity details of trade disruption by using the Indian data.2 The export value from
India (i.e., imports by Nepal) in October significantly declined to only US$57 million
compared to US$300 million during the first half of the year—only 15% of the first half of
the year. Imports of mineral products including gasoline, which is indispensable for
economic activities, significantly declined to only US$14 million from the customary level
of US$100 million, which suggests that economic activities were significantly hampered by
this disruption. Machinery and electrical equipment declined to 10% of the first half of the
year and transport equipment to only 6%. If this level of disruption had continued, it would
have suppressed the recovery from the devastating earthquake which occurred in April
2015 as well as investment activities for long-term economic growth.
The border blockade was lifted soon, and the import volume recovered to the pre-
crisis level as early as January 2016. But combined with the impact of the large earthquake
in April, the economy experienced a significant downward pressure even after the end of
trade disruption. Real GDP growth rate dropped from 6.0% in 2014 to 2.7% in 2015, and
declined further to 0.6% in 2016, according to the IMF World Economic Outlook (Figure
1). The growth rate is expected to trend higher due to the low base effect, but even then, the 1 Since Nepal does not have a quarterly GDP or monthly industrial production index, its quantitative impact
cannot be observed with hard macroeconomic data.
2 The following is based on the UN Comtrade database.3
economy will continue to stay below the potential level going forward (Figure 2). To
analyze the potential medium-term macroeconomic impact of the trade disruption, this
paper simulates its effect using the Dynamic Computable General Equilibrium (CGE)
model.
In Section 2, we will briefly review past CGE literature on quantitative trade
restriction. Section 3 discusses the basic structure of the recursive dynamic CGE model of
Nepal, followed by the discussion on the simulation results in Section 4. Section 5
concludes.
2. Literature Review
Although the issue being studied is the impact of the imposition of trade
restrictions, this can be analyzed as the opposite effect of trade liberalization measures.
Therefore, in this section, the past literature on trade liberalization is reviewed.
Trade reform was one of the most studied subjects using CGE model.3 The
3 One important approach of using CGE model in trade policy analysis is the multilateral trade model of 4
majority of such literatures analyze the impact of tariff reduction, but a number of studies
analyze the impact of non-tariff barriers. Most analyses of quantitative restriction, however,
adopt the “tariff-equivalent” idea, such as Chemingui and Dessus (2008), and Fugazza and
Maur (2008). One exception to the analytical approach includes the study of Hosoe,
Hashimoto and Gamoe (2010), who suggest a simple model incorporating quantitative
restriction directly into the CGE model, which I adopt in this study.
CGE was originally developed as a static model, but many attempts were made to
develop it into a dynamic framework. One approach is a “recursive dynamic model” in
which a static model is solved for each period while some transition variables (typically
capital stock) are renewed every period and forwarded to solve the model in the next
period. The other approach is to assume the inter-temporal optimization of consumers’
utility, allowing the forward-looking behavior of consumers. Examples of recursive
dynamic CGE in the field of trade analysis include Annabi, Cissé, Cockburn, and Decaluwé
(2005)/ who analyze the short-run and long-run impact of trade liberalization on poverty in
Senegal; and Yimir (2012) and Bisrat (2009), both of whom analyze trade liberalization in
Ethiopia. Furthermore, many models analyze the effect of tariff reduction rather than
quantitative restriction. Decaluwé, Patry and Robichaud (2008) is one example of analyzing
the unilateral abolition of import ban in the case of Benin, but they use a static CGE model.
The studies applying CGE to analyze the trade issues specifically in Nepal include
Acharya (2010), Acharya and Cohen (2008), and Acharya, Holscher and Perugini (2012),
Global Trade Analysis Project (GTAP). Since our analysis focuses on the impact in a single country, studies
using GTAP are not discussed here.5
but these are all static CGE models analyzing the effect of trade tariff reduction. One
exception is Battarai (2011), who uses a forward-looking dynamic model (not a recursive
model as used in this paper), studying the effect of financial liberalization, not trade
liberalization.
This paper uses the recursive approach to analyze the medium-term impact of trade
disruption in Nepal, but it incorporates an additional simple dynamic factor not used in
previous studies, whereby consumers reactively adjust their saving behavior, which affects
investment activities over the medium-term.
3. Simulation of medium-term impact of trade disruption: Model description
Nepal depends significantly on the trade flow from India for sustaining its
economic activities. Annual GDP growth declined significantly in 2015 and 2016. although
the impact of a devastating earthquake also explains a large part of this stagnation. But
since the economic data on a quarterly or monthly basis is not available in Nepal except for
inflation, we cannot directly observe its immediate impact on economic activities.
Inflationary pressure should dissipate soon after the end of trade disruption. But
even if trade is disrupted for only a brief period, its impact on the economy may extend to
the medium term. Suppressed capital investment (Nepal depends heavily on imported
machinery) may constrain production capacity in the medium term. Limited availability of
savings due to the economic downturn may restrict investment expenditure. In addition, a
significant decline in consumption level may have to be compensated for by higher
consumption expenditures in the following periods, which may further limit the availability
6
of savings. The accumulation of capital stock to the normal level, therefore, may take time.
In addition, the higher price level reduces real income and wages, which constrains the
demand side of the economy. To bring the economy back to the normal level, a certain level
of policy support through fiscal policy may be required to compensate for economic losses
during the trade disruption.
Such impact on the economy across sectors and time should be analyzed not only
at the macro level, but also at the level of resource allocation among economic sectors and
income distribution among different households. A dynamic CGE model is most
appropriate for this purpose.
(1) Overview of the dynamic CGE model and the SAM used in the model
CGE model is constructed based on the Social Account Matrix (SAM), which
records economic transactions between economic sectors, households, production factors
and the rest of the world. Assuming that transactions represented on SAM are the
equilibrium state between the demand and supply of economic agents, the parameters of
behavioral equations are calibrated, applying the general equilibrium conditions derived
from standard economic theory to construct the whole economic model. A CGE model can
simulate the impact of certain changes in economic policies or exogenous variables.
Originally, the CGE model was developed as a static model, but the objective here
is to see the medium-term impact of the short-term trade restriction, which requires the
extension of the model to the dynamic setting. In this study, I adopt a recursive dynamic
model, that is, the economy achieves static equilibrium at each period, and then investment
7
expenditure in this period will result in capital stock in the next period, which, along with
other variables (growing typically in line with population growth rate), is used to calculate
the new static equilibrium at the next period.4
The CGE model is static by nature, and how long it takes to achieve static
equilibrium at each period is not explicitly considered in the model. In this model, we
assume one period as one quarter expediently, not annual, as is usually considered in the
CGE literature. Since trade restriction ended in one to two quarters, it is difficult to see its
economic impact by using annual data. Of course, there is no reason to believe that the
economy will complete its adjustment and reach equilibrium in one quarter. This is just a
conventional assumption of the model. The original SAM, which records annual transaction
data, is simply divided by four to represent quarterly transactions.5
SAM required to construct this model is from the one used in Acharya, Holscher
and Perugini (2012).6 The economy is divided into four sectors: agriculture (AGR), industry
4 We opted not to adopt the intertemporal optimization model since the savings rates for poor households in
Nepal are so low, although positive. A certain level of adjustment in savings decisions may be possible —as
we assume in this study—but it may not be realistic to assume that they are able to optimize the consumption
path over the infinite horizon.
5 One concern of this approach is the existence of a significant seasonality in the economic variables, which is
very probable for a country dependent on the agriculture sector. Unfortunately, however, Nepal does not
produce even quarterly national account data, which precludes such treatment of seasonal adjustment.
6 SAM of Acharya et al. is for the year 2006, which may not be ideal for simulating events in 2015. But the
most recent SAM for Nepal, to the author's knowledge, is Raihan and Khondker (2011), whose sectoral
disaggregation is more detailed but is constructed for the year 2007. 8
(IND), commercial services (CS) and other services (OS).7 All sectors have import
activities, but OS does not export. Households are divided into four categories: urban
households (U-HH), rural large landholders (LR-HH), rural small landholders (SR-HH) and
rural landless households (LLR-HH). Households have three types of production factors:
high-skill labor (WHSL), low-skill labor (WLSL), and land and capital (PROFIT), and
receive factor income as well as transfers from the government and the rest of the world.
As for the static CGE model, the standard model structure is adopted per Lofgren,
Harris, and Robinson (2001). Firms conduct domestic production activities (QA) by
combining production factors (QF) through the Cobb-Douglas technology. Sectoral
commodities (QX) are transformed from QA by fixed coefficients. Demand for
intermediate goods (QINT), which include both domestically produced goods and imported
goods, is derived from QA through the Leontief production technology. Then, the domestic
producer will allocate QX into domestic sales (QD) and exports (QE) by maximizing profit
from the sale of goods aggregated by the constant elasticity of transformation (CET)
technology. Consumers, on the other hand, maximize their (static) CES utility from the
Armington composite goods (QQ) consisting of domestically supplied goods (QD) and
imported goods (QM). Government consumption as well as investment expenditures are
allocated among QQ with fixed shares (Figure 3).
Households receive income from their factors of production as well as the transfers
from other institutions (i.e., the government and the rest of the world), which becomes their 7 CS consists of such sectors as construction, electricity and gas, water, hotels and restaurants, wholesale and
retail sales, communication and transportation, finance and real estates. OS consists of health, education,
government and other social services.9
budget constraints for consumption decision. The government receives tax revenues on
income (both on households and firms), import customs duty and sales tax. All the tax rates
are fixed. The government also receives transfers from the rest of the world. On the other
hand, government expenditures consist of non-transfer expenditures and transfers to
households and firms.
Export and import prices (in the foreign currency term at the foreign market) are
assumed constant and not affected by domestic economic activities in Nepal (small country
assumption). Consumer price index is assumed constant and used as a numeraire.
Figure 11. Concept of commodity flow of the model
Householdconsumption: QH
Investment:QINV
GovernmentConsumption: GC
Armingtoncomposite goods:
CES function
CET functionImport goods:
QMDomestic supply:
QDExport goods:
QE
Domesticproduction: QX
fixed coefficients
Imported goods Domestic goodsLeontief function Sectoral activities:
QACobb-Douglas
function
Productionfactors: QF
High skill labor:WHSL
Low-skill labor:WLSL
Capital and land:PROFIT
Intermediate goods: QINT
(2) Model closure and specification of dynamic model
In our model, additional specifications are considered with respect to trade
restrictions and savings rate adjustment. Since the trade restriction, the model closures and 10
the dynamic specification are closely related, these model’s assumptions are discussed
together.
We assume that the basic specification of the trade restriction is the same as the
quota imposed by the importer country. In the latter case, however, rent from higher prices
becomes additional profit to importing firms, while, in the case of exporters’ restriction, the
exporting country may raise prices to capture the rent for profit. Whether all the rent will be
captured by the exporters, however, may be uncertain. Exporters generally sell to domestic
retailers (not directly to consumers) at the border at an inflated price, and retailers may
further inflate the price to match the domestic excess demand. Here, we assume that a half
of rent is obtained by Indian exporters and the other half by Nepalese firms. This rent is
added to the firm’s profit, which in turn is subject to tax. Therefore, this assumption may, to
a certain degree, soften the negative impact relative to the case that all rent is captured by
Indian exporters.
The model specification of the quota is as follows (see Hosoe, Gasawa and
Hashimoto (2010)). When the quota of commodity c is above the level of import demand (
QM cquota>QM c), the quota is not binding, and the equilibrium level of import is allowed
(baseline scenario). If the quota is smaller than the import demand, then the actual import
quantity is bound by the quota (QM cquota=QMc). In this case, the domestic price of import
goods becomes higher than the equilibrium price without import restriction, and the
difference between the domestic price of imports and the world import price becomes the
11
rent (χc).8
χc ∙ (QMcquota−QM c )=0
QM cquota−QM c ≥0
χc ≥0
PM c=(1+ χc ) ∙e ∙PM c¿
where QMc :equilibriumimport quantity of commodity c ;
QM cquota:quota of commodity c ;
PM c∧PM c¿ : domestic∧ foreign price of import goods;
e : exchangerate ;∧¿
χc :rent generated by the quota .
With respect to export restriction, a similar specification is applied. But the small
country assumption does not allow the export price from Nepal to deviate from the world
price. The rent, therefore, is not generated in this case. Instead, the term χ (in negative term)
is considered an additional cost that reduces the price domestic producers face lower than
the world price. Then, the domestic producers will reduce the allocation to the export goods
and reallocate to the domestic sales.
Since one of the interests of this study is the impact of trade disruption on
8 The first equation means that when the quota is above the baseline imports (i.e., the number in the
parenthesis is non-zero), then the rent χ must be zero. On the other hand, χ takes the non-zero value only
when the number in parenthesis is zero (i.e., the quota is binding).12
production capacity through capital accumulation, we assume that the economy’s savings
(sum of the households, the government and the rest of the world) will determine the level
of investment expenditures (savings-driven investment closure). In the baseline scenario,
households will allocate a fixed share (MPS; assumed constant over the periods) of income
to domestic savings. In the shock scenario, however, households are assumed to
compensate for the lower consumption level in one period by higher consumption spending
in the next period (and vice versa). If their consumption level (QH t1; superscript 1 refers to
the shock scenario value) is different from the baseline consumption level (QH t0;
superscript 0 refers to the baseline scenario value) relative to the baseline household
income (YH t0) in one period, then the savings rate in the next period is adjusted as follows9:
MPS t1=MPS+(QH t−1
1 −QH t −10 )/YH t−1
0 ∙ μ
where MPS :margial prpensity ¿ save ;
QH :consumption expenditure (0 for baseline∧1 for alternative scenario ) ;
¿YH : household income
μ is the factor of degree of responsiveness to the consumption gap. In this model, we
arbitrarily set μ=0.3. Please note that even though the savings rate is adjusted in response
to the consumption level in the previous period, the model still adopts the savings-driven
investment closure since the investment expenditure in each period is determined by the
available savings. The adjustment process of the savings rate is related to the dynamic 9 Household type indices are omitted for notational simplicity.
13
aspect of the model, while the savings-investment closure rule is related to the static aspect
of the model.
Another specification with respect to the dynamic model is on capital
accumulation. The aggregate investment expenditure (flow) at each period is allocated to
four economic sectors (AGR, IND, CS and OS) depending on the relative domestic price,
and all available savings are employed for investment through the adjustment in the
average capital return. These sectoral capital expenditures are added to the capital stock of
each sector after adjusting depreciation and will become the capital stock in the next period.
Capital stock, however, is not perfectly flexible between production sectors. Even when the
(relative) domestic price changes and one sector’s profit level becomes more favorable to
others, the already invested stock of capital cannot move to another sector. Therefore,
sectoral differentials in capital return are allowed. The sectoral distortion (WFDIST a ,t) to
the average capital return (WF t) is assumed constant at the base period, leaving the sectoral
difference in the return to capital. This adjustment process is expressed as follows (see
Morley, Pineiro, and Robinson (2011) for more details):
1. Update of the sectoral share of investment
INVSHRa ,t=capshra , t ∙(βa ∙(WF t ∙ WFDIST a ,t
WFKAV a , t−1)+1)
where INVSHRa , t : the share o f se ctor a ∈total investment expenditure ;
capshr a ,t : the capital stock share¿ the sector a ;
WF t : the average returnon capital ;
14
WFDIST a ,t : the w age distortion factor∈s ec tor a;
WFKAV a ,t : the a veragecapital rental rate;∧¿
βa : thec apital mobil ity f ac ter .10
2. Update of the quantity of new real capital formation
DKAPSa , t=INVSHRa ,t ∙(∑cPQc ,t ∙ QINV c , t
PK t)
where DKAPSa , t : the gross¿capital formation for sector a ;
∑c
PQc ,t ∙QINV c, t : the aggregate gross¿investment expenditure ;∧¿
PK t : the p rice of capital goods .
3. Update of aggregate capital quantity
QFS t+1=QFSt ∙ (1−dep )+DKAPSa ,t
where QFSt : thetotal supply of capital ;∧¿
dep : depreciationrate
Labor is assumed to be sticky to the production sectors since our interest is the
relatively short to medium term. That means relative wage differentials between sectors are
fixed, which causes unemployment. There is a clear differential between high-skilled labor
(WHSL) and low-skilled labor (WLSL), and there is no movement between these two
10 In this model, we use βα=1.15
categories.
For the transition to one static model to the next, in addition to the capital stock
and the savings rate, real government expenditures, transfers among institutions (including
the rest of the world) and total labor supply are assumed to grow in line with the population
growth rate (1.2% annually, that is, almost 0.3% quarterly).11 These exogenously growing
values and the renewed capital stock determine the production and consumption level at the
next period. In many dynamic models, productivity coefficients of production functions are
assumed to grow with time as well. In our case, we do not simulate the GDP growth rate,
but rather analyze the percentage difference from the baseline results. We therefore do not
assume the growth of productivity coefficient, which is common to two cases.
The rest of the model closures and the system constraints are as follows. The
balance of payments is closed by the fixed exchange rate (as is actually adopted by Nepal),
which means that any disequilibrium in the current account is matched by flexible foreign
savings. As for the government budget, both revenue and expenditures are determined
separately, leaving the fiscal balance open. The resultant fiscal balance constitutes the
economy’s overall savings along with other agents’ savings, which in turn will determine
investment expenditure.
(3) Simulation scenario and result of trade disruption
Based on this model design, the impact of trade disruption spanning two periods is 11 In the baseline, the government transfers are assumed to grow at the population growth rate without any
change in allocation among the households. In the policy simulation, however, several different policy
assumptions are applied (see (4) Policy simulation).16
simulated for the next 10 periods (i.e., the trade shock at t=1 and 2 and its impact for the
subsequent periods from t=3 to 12). First, we solve the baseline CGE model without
binding trade restrictions. At each period, the equilibrium set of variables is calculated, and
the available savings decide the investment expenditure in this period, which is forwarded
to the next period and solves the equilibrium set.
On the shock scenario in which the trade restriction is applied, the following
assumptions are adopted. At t=1 and t=2, quantitative restrictions of certain percentages
against the baseline quantity are applied. Considering the actual decline in trade value with
India, we assume the following magnitude of quantitative restrictions: 68% (73%) of the
baseline import (export) demand at t=1 and 51% (59%) at t=2 for the agriculture sector;
65% (84%) at t=1 and 45% (73%) at t=2 for industrial sector; and no restriction for service
sectors.12 From t=3, no restriction is applied. We will discuss the impact on major variables
below.
First, it is not surprising that import prices jump due to the quantitative restriction
of imports (Figure 4). Import prices of both industrial and agricultural goods jump more
than 1.5 times at t=1 and 2.5 times at t=2.13 Suppliers (Indian exporters and Nepalese
12 As discussed, we cannot observe the monthly trade data of Nepal. The trade data from India to Nepal,
however, can be obtained at a fairly reliable level, but not necessarily for other countries. Therefore, the
restricted trade values are calculated using the declined trade data with India while assuming that trade with
other countries was not affected and that the share of import from India at under normal circumstances is 60%
for imports and 65% for exports.
13 The accurate data are not available, but the local newspaper reported that gasoline prices in Katmandu 17
importers) can capture this rent. After the end of trade disruption at t=3, the prices quickly
return to baseline level. On the other hand, impact on the domestic supply price is not as
high because of the availability of domestically produced goods (Figure 5). Relative price
of industrial commodity rises by about 5% at t=1 and 15% at t=2, while agricultural
commodity price declines by 5% at t=2. Note that this does not necessarily mean the
decline of absolute price but of the relative price, especially due to a large increase of
industrial price at t=2.14 Commercial service sector price declines too in a relative term by
almost 5% at t=1 and 2, while the other service sector is only marginally affected.
Next, we will review the impact on GDP. Again, we will review the changes
against the baseline results, not the growth rate. Real GDP declines by almost 5% against
the baseline at t=1 and 2 (Figure 6). This decline comes to 2.6% on an annual basis (from
increased to three times during the import disruption (Kathmandu Post, 2015.12.11). World Food Program
reported that the price of rice near earthquake-hit areas increased twice, while the price of cooking gas went
up five to seven times (“Nepal: Extreme Hardship Expected To Worsen As Food Prices Soar,” WFP News
2015.12.11).
14 In this model, the overall consumer price index is fixed as a numeraire.18
t=1 to 4) against the baseline GDP. This slightly understates the decline of growth rate from
6% in 2014 to 2.7%. Even after the lift of import disruption at t=3, the real and nominal
GDP continue to stay below the baseline levels by around 1%. Furthermore, the gap
gradually widens over the medium term. This result may suggest that unemployment of
labor and under-accumulation of capital during the trade disruptions continue unadjusted
(to be seen later).
As for the demand side of the economy, the trade disruption significantly
constrains both consumption and investment expenditures. Rural small landholders and
rural landless households suffer the most at t=1, while the rural large landholders suffers the
most at t=2, presumably reflecting the different composition in the consumption basket
(Figure 7). Urban households suffer the least, but even so, their consumption declines by
5% at t=1 and 2. Consumptions rise higher than the baseline at t=3 for all household types,
since they adjust down the savings rate in the face of significantly lower consumption level
at t=2 (Figure 8).15 But thereafter, consumption levels stay lower than the baseline for 15 Note that the savings rate at t=1 remains unchanged. Figure 16 shows the savings rates are adjusted down
at t=2 and 3 but adjusted up at t=4. Even though the savings rate declines at t=2, which should support the 19
almost the entire simulation period.
Investment expenditure (flow) declines by 8½% against the baseline at t=1 and
19% at t=2 (Figure 9). A significant gap (almost 10%) remains at t=3 due to the lower
savings rate. A positive gap is observed at t=4—savings rate is higher because of higher
consumption at t=3—but almost 2% of negative gaps are persistently observed thereafter,
which should constrain capital accumulation.
On the supply side of the economy, domestic production in the industrial sector
suffers the most (Figure 10). Domestic supplies are significantly affected in a similar
manner by both the lower domestic production and the lower import (Figure 11). The
industrial sector suffers more than the agriculture sector, although the impact of trade
restriction (both exports and imports) is smaller at t=1. This may be due to the more
interconnected nature of production with other sectors through demand for intermediate
goods.16 The commercial and other service sectors, which are not subject to the trade
consumption level, the negative impact of trade disruption exceeds this effect at t=2 and brings down the
consumption level.
16 The share of intermediate inputs to the total output is 40.9% for the industrial sector and 18.5%, 21.1% and 20
disruption, are not free from the impact, partly because of the intermediate inputs
requirement and also the depressed consumption and investment expenditures. In all the
sectors, the gaps do not just appear, but increase even after the lift of trade disruption.
Such sluggishness of the overall economy causes lower employment of production
factors for both labor and capital. The industrial sector shows the largest gap in
employment as expected, almost 15% at t=1 and 25% at t=2 for both low-skilled and high-
skilled labor (Figures 12-13). Like the domestic supply, service sectors (especially
commercial service sector) in which no import disruption occurred observe large
employment gaps too, reflecting the lower production activities due to the unavailability of
intermediate input as well as lower demand.
As for capital and land, the adjustment against the import disruption does not occur
at t=1 but does occur at t=2 when investment expenditure (flow) at t=1 is reflected to
capital stock. The decline in capital stock at t=2 is relatively small, but then continues to
grow larger and widen over the simulation period (Figure 14).17 This widening gap in
28.1% for the agriculture, commercial service and other service sectors.
17 In the case of capital, all factors are employed. But due to the lower savings, investment expenditures are 21
capital accumulation should affect the economy’s production capacity in the medium-term.
Lastly, because of the lower factor demand, household income levels decline by
around 6% at t=1 and about 8-10% at t=2 (Figure 15). Overall, all households suffer lower
income levels at almost the same magnitude.
(4) Policy simulations
Now we will simulate the impact of various policy options on the macro-economy
restricted, which causes the lower supply of capital. Therefore, these gaps do not represent the existence of
unemployment of capital, but rather the short supply of capital.22
as well as household income and consumption expenditures. When the economy is stagnant
and the population suffers lower income and consumption levels, the policy options
typically available to the government are the income supports through transfers to
households or economic stimulus packages such as public investment programs. In our
model, however, investment expenditures are constrained by available national savings. If
the government expands transfers to households by running higher fiscal deficits, then it
reduces the available national savings, which thereby reduces investment expenditures (the
crowding out effect). The government may opt for foreign financing to circumvent the
savings constraint, but the economy adopts the fixed exchange rate regime, which requires
foreign savings to be determined endogenously.
These model characteristics allow us the following policy options. The first is to
reallocate transfers among households. Income transfers should be allocated more to those
households having higher consumption propensity for the maximum impact. One way is to
reallocate transfers from large landholders to other households proportionately with the
current share of transfer receipts (Option 1).18 This option keeps total government
expenditures unaffected (only the reallocation within the total envelope of transfers), which
is supposed not to affect government savings (and therefore total investment
18 Currently, rural small landholders as well as landless households receive the majority of government
transfer (Rs. 551.0 million and Rs. 516.5 million, respectively). Urban households are not necessarily the
wealthy households, but they receive only a similar total transfer as the large landholders (Rs. 123.5 million
and Rs. 108.8 million). Saving propensities as proxy of poverty are 3.0% for landless households; 8.1% for
small landholders; 8.3% for urban households; and 38.7% for large landholders.23
expenditures).19
Another way is to reduce non-transfer government expenditures to finance
additional transfers to households (Option 2). To keep additional transfers roughly at the
same magnitude as Option 1, here we assume that the non-transfer government outlay is
reduced by 1.1%, which is allocated to landless households, rural small landholders and
urban households as additional transfers, while the rural large landholders receive only the
same amount as the baseline case.
The third option is to reduce the non-transfer government outlay without the
matching increases in household transfers (Option 3). This will increase available total
savings, and thereby investment expenditures. The resultant expansion of investment
demand in the same period as well as the production level in future periods may help raise
the income level of households.
The simulation results of these three options are discussed below. Note, however,
that the policy impact itself is small since we limit the intervention size to the current
transfer to large landholders.20 The simulation results are presented both against the
baseline and the shock scenario. These policy measures are implemented throughout the
simulation period (from t=1 to 12).
19 Of course, different incomed among the household types may have different impacts on the macro-
economy, which may affect total savings as well as investment expenditures.
20 The annual transfer receipt by rural large landholders is Rs. 435 million, which is less than 0.8% of total
government outlay or 0.16% of total household consumption expenditures. Therefore, it is unrealistic to
expect a significant effect on the macro-economy. Rather, we will focus on the direction of change and the
comparison of options against the shock scenario.24
The real GDP gap continues to deteriorate further below the shock scenario
(binding trade restrictions, but no policy measures) under Option 1 (Figure 16). The
reallocation of transfers to higher consumption propensity households does not generate
positive results either immediately or in the medium term. The gap deteriorates
significantly at t=1 under both Options 2 and 3 due to the decrease in the (non-transfer)
government expenditures. This indicates that the additional transfers under Option 2 and
the investment expenditure under Option 3 are not sufficient to compensate for the
negative effect of reduced government expenditures. The difference between Option 2 and
3 is that the GDP gap gradually narrows under Option 3 and turns positive after t=10. As
far as the impact on real GDP is concerned, Option 3 looks like the most effective option,
as was expected. On the other hand, the transfer programs (Options 1 and 2) affect the
overall GDP worse than the no policy case. The expectation that immediate income support
will pay for itself is not supported.
This is clearly seen for the investment gap. Investment expenditure is raised
25
compared with the shock scenario under Option 3 and continues to rise throughout the
simulation period (Figure 17). Under Options 1 and 2, however, the investment
expenditure remains below the shock scenario. Option 2, which finances additional
transfers to poorer households from the reduced government expenditure, generates the
better result than the program financed by the reduction in the transfer to rich households.
Higher overall consumption level constrains the available savings, and thereby the
investment, under Option 1.
When it comes to consumption as well as income level, however, Option 3 is not
necessarily the most effective policy (Figures 18-19). This option will not affect
consumption and income levels much relative to the shock scenario for most of the
simulation period, although the gap against the shock scenario tends to narrow gradually
and consistently. For urban households, this option returns the best score at the end of the
simulation period, while the effect on large landholders shows the best even from the early
stage of the simulation period.
Under Option 1, large landholders are deprived of their transfer receipt, which
naturally lowers their consumption and income levels relative to the shock scenario. But
other households will benefit from the reallocated transfers, especially landless households.
Urban household consumption are least affected since their share of additional transfer
allocation is smaller.
Option 2 gives similar results except for rural large landholders. The large
consumption gap for these households under Option 1 is avoided, but it still returns a lower
consumption path than the shock scenario. The demand effect from higher household
26
consumption does not compensate for the negative demand effect of reduced government
expenditures. The choice between Option 1 and 2 is ultimately the overall priority among
the society as to whether this reallocation from rich households to other households is
socially acceptable.
In terms of production gap, the production of other service sector declines under
Options 2 and 3 (Figure 20). This is natural since the government expenditures that are
curtailed under these options are all spent to this sector. Otherwise, Option 3 produces the
best among the policy options. Options 1 and 2 do not produce desirable effects and further
deteriorate in the medium term.
Such different effects on the production sector cause different effects on the factor
demand (Figure 21). Both high-skilled labor and low-skilled labor suffer at t=2 under
Options 2 and 3 even compared with no policy scenario, but their negative effects will be
narrowed going forward under Option 3. Impact on capital, on the other hand, is positive
from the beginning of the simulation period.
27
The comparison of these three policy options suggests the following. Income
transfer brings the income and consumption levels higher than no policy scenario in the
short-term, except for the large landholders whose transfers are reduced when Option 2 is
adopted, but its effect will decline gradually over the medium term. The choice between
two options calls for consideration of social priorities. With regard to the production side,
Option 3, i.e., the investment policy, returns the best result, but does not have an immediate
impact on income and consumption levels. In the medium term, however, the investment
policy that raises the production base is more effective for income and consumption levels
too. This choice of time preference calls for another consensus about social priorities.
4. Conclusion
This paper simulated the macro-economic impact of trade disruption on Nepal in
late 2015 by using the recursive dynamic CGE model. The result shows that the largest
impact on the economy occurs during the time of trade disruption, but its impact on the
28
economy does not disappear even after the trade disruption ends, and it also affects such
sectors on which the trade restriction is not imposed directly. Nepal is a country in which
the domestic industrial base is not matured enough, so it depends on imported goods for its
economic activities. Consequently, even if the trade disruption is only for a limited time, its
impact persists for a prolonged period.
The effects of different transfer programs to households as well as the investment
program are simulated too, but each policy has advantages and disadvantages. One policy
option which was not studied in this paper is the use of foreign financing because the
assumption of fixed exchange rate regime determines the foreign saving endogenously. One
way to circumvent such restriction may be to use foreign savings to finance import
expenditures which are used for government expenditures. Then, the higher foreign savings
can support the public expenditures without affecting foreign saving items. Such
mechanism is not incorporated into the model of this study, which may be explored in a
separate study.
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