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 U.S.A.1-202-293-2334nakata.ryosuke@jica.go.jp

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:

QQ

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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