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Bank Credit and Economic Growth in
Nepal: An Empirical Analysis#
Neelam Timsina
Abstract
This study examines the impact of commercial bank credit to the private sector on the economic
growth in Nepal from supply side perspectives. The study has applied Johansen co-integration
approach and Error Correction Model using the time series data for the period of 1975-2014. The
empirical results show that bank credit to the private sector has positive effects on the economic
growth in Nepal only in the long run. Nevertheless, in the short run, it has been observed a
feedback effect from economic growth to private sector credit. More specifically, the growth in
real private sector credit by 1 percentage point contributes to an increase in real gross domestic
product by 0.40 percentage point in the long run. The empirical results imply that, policy makers
should focus on long run policies to promote economic growth – development of modern banking
sector, efficient financial market and infrastructure so as to increase the private sector credit
which is instrumental to promote growth in the long run.
Key Words: Economic Growth, Bank Credit, Co-integration
JEL Classification: E23, G21, C32
#

The earlier version of this paper is available at www.nrb.org.np under NRB Working Paper
series, NRB-WP-22, 2014.
Director, Nepal Rastra Bank, Research Department, Central Office, Baluwatar, Kathmandu,
Nepal. Email: [email protected]
Acknowledgement: I would like to express my sincere thanks to Mr. Guna Raj Bhatta, Assistant
Director of Monetary Division, Research Department for his valuable inputs in setting
methodological frameworks.
2 NRB ECONOMIC REVIEW
I. INTRODUCTION
Economic growth is one of the major objectives of macroeconomic policy. It is the
crucial means of uplifting living standards as well as achieving economic development.
Economists define economic growth from various perspectives. Some economists view
that it is an increase in the national income or the level of production of goods and
services by a country over a certain period of time. Generally economic growth is defined
as an increase in gross domestic product (GDP). Therefore, GDP is considered as proxy
of economic growth in the study.
Credit is the aggregate amount of funds provided by commercial banks to individuals,
business organizations/industries and government for consumption and investment
purposes. Individuals obtain credit for both consumption and investment purposes,
business organizations/industries borrow loans to invest in plant and machinery where as
government borrows loans to spend for recurrent as well as capital expenditure purposes.
More specifically, credit is understood as the provision of resources such as granting a
loan by the creditor/lender to the debtor/borrower where the debtor does not reimburse
the lender immediately, thereby generating a debt, and instead arranges either to repay or
return those resources at a later date (Mishra at all, 2009). Credit is considered as a key
to economic growth especially in developing countries as it lubricates the economy.
Therefore, the role of bank credit in economic growth has been accepted by many
researchers as various economic agents are able to invest money in various investment
opportunities.
Economic growth has been one of the major macroeconomic objectives of the
government of Nepal. Nepal Rastra Bank (NRB) considers that monetary policy should
also support growth. NRB always directs commercial banks to flow their credit to
productive sector. Credit channel of monetary policy is considered very important and
effective in Nepal. In this channel, money supply is expected to affect real variables
through the means of bank balance sheet and availability of credit. A large body of
evidence suggests that financial sector development plays a huge role in economic
development. Okwo (2012) examined the effect of bank credit to private sector on
economic growth in Nigeria and found that bank credit to private sectors has a statistical
strong positive relationship with GDP as expected. Bank credit to private sector promotes
economic growth through capital accumulation and technological progress by increasing
the savings rate, mobilizing and pooling savings, producing information about
investment, facilitating and encouraging the inflows of foreign capital, as well as
optimizing the allocation of capital (World Bank, 2013). One of the major indicators for
measuring financial development of a country is private sector credit to GDP ratio. The
role of credit provided by banks to private sector is considered more efficient to support
economic growth rather than the credit provided to government. Therefore, private sector
credit is taken as the proxy of bank credit here in the study.
Bank Credit and Economic Growth in Nepal: An Empirical Analysis 3
Bank credit has significant role in economic growth. Especially in developing countries
like Nepal, it caters resource need for economic growth. Hence, NRB and the government
have adopted many policies and programs to increase economic growth through the use
of bank credit. NRB has been playing a leading role to determine the proportion of bank
loans and advances to productive sectors (agriculture, energy, tourism, industry). The
main objective of this provision is to stimulate economic growth in the country. However,
the relationship between private sector credit and economic growth has not yet been
assessed properly in the Nepalese context. In this regard, this study attempts to fulfill the
gap. Therefore the main objective of this study is to examine the effects of commercial
bank credit to private sector on economic growth from supply side perspectives as well as
to suggest ways of improving bank credit to private sector so as to achieve better
economic growth in Nepal.
The rest of the paper is structured as follows. The second section describes the theoretical
framework. The third section reviews the related literatures. The fourth section presents
the status and trend of bank credit to private sector. The fifth section presents the data and
methodology and the sixth section shows results of the study. The last section concludes
the study.
II. THEORETICAL FRAMEWORK
Bank credit contributes to economic growth in several ways. For example, credit is an
important link in money transmission; it finances production, consumption, and capital
formation, which in turn affect economic activity. The transmission mechanism of
monetary policy can be strengthened, and the monetary policy objectives attained to a
large extent, if the financial system is well-operated and regulated. Credit extended to the
private sector in an environment of banking discipline will be instrumental in tapping the
productive potentialities and development prospects of the economy. It thereby ushers to
inculcate economic growth, generating employment opportunities, and strengthening the
competitiveness of the economy (Basyal, 2009). It is a means of generating self
employment opportunities, strengthening informal activities. Ademu (2006) explained
that credit can be used to prevent economic activity from total collapse in the event of
natural disaster such as flood, draught, disease or fire. By using credit, farmers increase
agricultural production by investing money in seed, fertilizers, tractor, and pump set etc.
Industrial production can be increased by using credit. Moreover service sectors need
credit to flourish. In fact all components of GDP need credit to grow. In performing the
financial intermediation role, it has been argued that by virtue of this function that banks
generate economic growth by providing needed resources for real investment (Kinnon,
1973).Sustainable economic growth depends on the ability to raise the rates of
accumulation of physical and human capital to use the resulting productive assets more
efficiently and to ensure the access of the whole population to these assets (Fitzgerald,
2006). This is possible only by having access to bank credit. Banks perform the act of
financial intermediation that collect money from the surplus sector in the form of deposits
and lend it to various sectors of the economy leading to economic growth. Extension of
credit is one of the major functions of banking institutions.
4 NRB ECONOMIC REVIEW
Neo-classical growth theory states that labor and capital are the major factors of
production. I.e. Y = f (K,L) where Y denotes aggregate output, K denotes aggregate
capital stock, and L is the labor force. If technology and human capital are added, then
equation becomes : Yi,t = AKα(Lh)1−α. ( Mankiw, Romer, and Weil, 1992). Bank credit
facilitates to acquire more capital in this production function. When a new technology is
available, the labor and capital need to be adjusted to maintain growth equilibrium. To
acquire new technology and thus to increase total factor productivity, the role of credit
provided by banks would be of immense help. Private sector credit fosters growth
through increasing investment and an efficiency/productivity. The capital accumulation
channel is particularly important for underdeveloped and emerging countries, while the
productivity channel is mostly relevant for advanced countries.
In standard neoclassical theories investment-savings is the engine of growth. In these
theories, there are no capital market frictions and thus financial intermediation is not
explicitly modeled. However these models assume that savings translate directly to
investment and thus one could argue that finance affects growth primarily through capital
deepening (investment) (Papaioannou,2007). A different class of theoretical models
argues that financial development may foster growth by raising human capital
accumulation. In Galor and Zeira (1993) model income inequality and credit market
frictions impede growth, since not all individuals can invest in education. They argue thus
that financial intermediation can spur growth (and eventually decrease inequality) by
fostering human capital accumulation.
III. LITERATURE REVIEW
A large body of literature is available on the extensive empirical work with regard to the
nexus between finance and economic growth. Largely, this task has been performed by
King and Levine (1993) and Levine(1997). They showed that financial development has
predictive power for future growth and interpret this finding as evidence for a casual
relationship that run from financial development to economic growth. Although the
literature regarding the role of financial development on economic growth has grown
rapidly in recent time, studies that examine bank credit or access to private sector credit
and how it affects the economic performance of industries or economic sectors have been
overshadowed by the increasing number of empirical studies that largely focus on
financial development and growth. Nevertheless, private sector credit is one of the
important indicators of financial development. Therefore the literatures on financegrowth nexus are helpful for the study on bank credit –growth nexus. King and Levine
(1993) provided the evidence that financial sector proxied by the ratio of bank credit
granted to the private sector to GDP, affects economic growth both through the
improvement of investment productivity ( better allocation of capital) and through higher
investment level. Financial system could impact positively on real economic performance
by affecting the composition of savings (Bencivenga and Smith, 1991), providing
information (Greenwood and Jovanovic, 1990), and affecting the scope for credit
rationing (Boyd and Smith, 1997).
Bank Credit and Economic Growth in Nepal: An Empirical Analysis 5
Schumpeter (1971) identified banks' role in facilitating technological innovation through
their intermediary role. He argued that the role of bank of channelizing resources from
surplus sector to deficient sector plays crucial role in promoting growth. Several others
such as (Kinnon 1973), Shaw (1973), Adekanye (1986), Fry (1988), King and
Levine(1993), and Adeniyi (2006)have focused on the significance of private sector
credit to economic growth. Similarly studies by Gurley and Shaw (1967), Goldsmith
(1969), Jayaratne and Strahan (1996), Kashyap and Stein(2000), Beck et al.(2000), Beck
et al (2003), Driscoll (2004) etc, found that financial development can foster economic
growth by raising saving, improving allocative efficiency of loanable funds, and
promoting capital accumulation. In their opinion, well developed financial markets are
necessary for the overall economic advancement of less developed and the emerging
economies. King and Levine (1993a) said that the banking sector's development in
Europe was not only correlated with economic growth but was also a cause of long-term
growth. Adekanye (1986) argued, by providing credit; banks are rendering a great social
service which leads to increase in production, capital investment and improving living
standard. Akpansung (2011) by using two stage least square, found that private sector
credit impacts positively on economic growth in Nigeria. However, lending interest rate
impedes economic growth. Moreover, that paper recommends the need for more financial
market development that favours more credit to private sector with minimal interest rate
to stimulate economic growth.
A low rate of expansion of the credit volume is not only a symptom of weak economic
growth, but can also be one of its causes (Bundesbank, 2005). Bayoumi and Melander
(2008) found that a 2.5 percent reduction in overall credit caused a reduction in the level
of GDP by around 1.5 percent. Dey and Flaherty (2005) used a two stage regression
model to examine the impact of bank credit and stock market liquidity on GDP growth.
They found that banking development is significant determinant of GDP growth.
However Koivu (2002) found that growth in credit has not always been sustainable and in
some cases it may have led to a decline in growth rates. Murty at al (2012) by using
multivariate Johansen co integration approach, examined the long run impact of the bank
credit on economic growth of Ethiopia and found that bank credit to the private sector
affected economic growth through its role in efficient allocation of resources and
domestic capital accumulation. Thus the policy makers should focus attention on long run
policies to promote economic growth – the creation of modern banking sector so as to
enhance domestic investment, which is instrumental to increasing output per capita and
hence promoting economic growth in the long run. Ugoani (2013) examined the power of
bank credit on economic growth in Nigerian perspective and found that bank credit has
significant relationship with economic growth and socio-infrastructural development. He
argues that on the one hand, bank credit is the oil on the wheel of economic growth. On
the other, there is strong empirical evidence that the development of sound financial
markets and institutions has significant relationships with long term economic growth.
Financial sector plays a key role in channeling savings into productive investment
especially in the formal sectors of the economy. The banking sector is well recognized as
6 NRB ECONOMIC REVIEW
a key conduit of financial intermediation in the economy. Access to credit enhances the
productive capacity of businesses (Were Nzomi and Rutto, 2012). Private sector credit is
considered as proxy of bank credit in many international studies. Beck and Levine (2001)
measure bank development as bank credit to private sector divided by GDP. Also the
endogenous growth theory sheds light on the role of finance on economic growth. Solow
(1956, 1957) in his two factor neoclassical growth model incorporated the role of credit.
The supply of credit, both in terms of volume and in terms of credit standards applied on
loans to enterprises, have significant effects on real economic activity. In other words, a
change in loan growth has a positive and statistically significant effect on GDP
(Cappiello, Kadareja, and Sarensen, 2010). In the same way that financial services
increase income of poor by expanding the supply of financial services which can be
accessed by the poor. It will generate income growth for the poor, thus having a direct
impact on poverty reduction (Jalilian & Kirkpatrick, 2001). The role of private sector
credit as transmission channel of monetary policy cannot be ignored. Monetary policy
may affect real economic activity, and ultimately inflation, via its impact on the banking
sector credit through a number of transmission channels (Brunner and Meltzer, 1963 and
Bernake, 1983).
IV. THE STATUS AND TREND OF BANK CREDIT
TO PRIVATE SECTOR IN NEPAL
A significant portion of credit in Nepal is provided through the banking system, though
there are some institutions such as savings and credit cooperative societies, finance
companies, development banks and micro finance institutions. However, availability of
time series data for the latter institutions is very limited. Therefore, in this study, private
sector credit provided by commercial bank only is taken into consideration.
The ratio of bank credit to private sector and nominal GDP has not increased steadily
over the study period in Nepal. Before 1980s, such ratio was very low. The financial
sector of Nepal witnessed revolutionary changes in 1980s. A broad based program of
reforms was launched since 1980s. The banking sector in Nepal had been transformed
from a highly dominated inefficient state-owned sector to a dynamic private sector. NRB
and the Government of Nepal took a number of steps to further enhance the pace of this
transformation process of the development of financial sector in the country. A
substantial increase in private sector credit to GDP ratio took place only after
implementation of financial liberalization in early 2000s.The last five years from 2009 to
2014 recorded ratios of 44.0 percent, 41.7 percent, 40.3 percent, 41.2 percent , 45 percent
and 47 percent respectively.
Bank Credit and Economic Growth in Nepal: An Empirical Analysis 7
50.0
45.0
40.0
35.0
30.0
25.0
20.0
15.0
10.0
5.0
0.0
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
2007
2009
2011
2013
percent
Chart 1
Private Sector Credit to GDP Ratio (Nominal Term)
year
Source: Data from Quarterly Economic Bulletin July,2013 (NRB) and figure from Author's calculation.
It has recorded significant increases only in 2009, 2013 and 2014. The stringent measures
adopted by NRB to limit the real estate and margin lending loan of the banking sector,
short term nature of loans, low growth of remittances and resulting liquidity crunch, low
growth of government expenditure etc. were accountable for the low private sector credit
to GDP growth in year 2010 and 2011.
In Nepal, economic growth rate is low compared to other developing countries (Annex 5).
Real GDP growth rate is only 3.6 percent in 2013 and 5.2 percent in 2014. Average real
GDP growth rate is 4.08 percent over the last twenty years. One of its main reasons is low
private sector credit growth. Private sector credit (provided by commercial banks) is only
47 percent in 2014 but it was 21 percent of nominal GDP on average over the sample
period. Therefore, to boost the economic growth of the country, private sector credit
should be increased to productive sector.
8 NRB ECONOMIC REVIEW
Chart 2
Private Sector Credit and GDP Growth (Nominal Term)
50.0
40.0
percent
30.0
20.0
Pvct
10.0
GDP
-10.0
-20.0
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
0.0
year
Chart 2 shows that there is positive relationship between nominal private sector credit and
nominal GDP. As private sector credit increased, GDP also increased, but at a lower rate.
Except some years their growth rate also seems to move in the same direction. Chart 3
also shows that real private sector credit has positive relationship with real GDP during
the period 1975-2014.
Chart 3
Private Sector Credit and GDP Growth (Real Term)
40.0
30.0
percent
20.0
GDP
10.0
PVCT
-10.0
-20.0
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
0.0
year
Up to now, the Nepalese banking system does not seem to have the investment bank for
long term loans, venture capital for viable projects, which in turn leads to inadequate
economic growth. Moreover, Nepal is experiencing still a significant credit transaction in
informal sector despite government's efforts to channel credit to the productive sector
Bank Credit and Economic Growth in Nepal: An Empirical Analysis 9
through commercial banks, development banks, finance companies and micro credit
development banks.
Sector wise Distribution of Bank Credit to Private Sector
Sector wise distribution of private sector credit has great meaning to economic growth.
Generally it is assumed that credit to productive sector caters economic growth where as
credit to consumption sector can not contribute in this regard. In Nepal, of the total
private sector credit provided by commercial banks in July 2014, wholesale and retail
trade constituted 22 percent followed by production (20 percent), others (16 percent),
construction (10 percent), finance, insurance & fixed assets (8 percent), service industries
(8 percent), transportation, communication & public services (4 percent), agriculture
sector (4 percent) and metal, machinery, tools & fitting (1 percent).
Though agriculture constitutes
32.40 percent of GDP in Nepal,
only 4 percent of total private
sector credit was provided in
this sector as of 2014. Though
Nepal Rastra Bank has made
policy provision that banks
should provide at least 20
percent of their total loan to
productive sector and at least 12
percent of their loan to
agriculture, energy and tourism
sector, this percent was merely
6 percent only. Commercial
banks seem to provide only 5
percent of their total loan to
consumption sector (very small
figure in total) which helps to
establish
the
relationship
between bank credit and
economic growth.
Chart 4
Sectorwise Distribution of Bank Credit (July, 2014)
Agriculture
Mines
4%
0%
Local
Government
0%
Consumable
Loan
5%
Others
16%
Productions
20%
Service
Industries
8%
Finance,
Insurance &
Fixed Assets
8%
Construction
10%
Wholesaler &
Retailers
22%
Transportation
Communications
& Public Services
4%
Metal,
Machinary,
Tools and
Fitting
1%
Transportation
Equipment
Production &
Fitting
2%
V. DATA AND METHODOLOGY
Secondary data that captured the whole population of all commercial banks in Nepal for
the period 1975 –2014 are used in the study. Secondary data are gathered from Quarterly
Economic Bulletin and Quarterly Financial Indicators (NRB).
In Nepal, the bank credit is allocated to both the public and private sector of the economy.
However, private sector credit is considered to be more effective to stimulate economic
10 NRB ECONOMIC REVIEW
growth. Several studies such as Beck et al (2005), Levine(2002), Odedokun (1998), King
and Levine(1993), Boyreau-Debray (2003), Liang (2007) and Crowley (2008) have
suggested that bank credit to the private sector is more significant for economic activities
than bank credit to the public sector. Therefore, in this study bank credit to private sector
is taken as appropriate variable. Since the paper attempts to assess relationship between
private sector credit and economic growth, variables such as bank credit to the private
sector (lnrpvct), economic growth (lnrgdp) are taken as the main variables. Government
expenditure (lnrgexp) and interest rate (ir) have been included in the study as control
variables. Mathematically, GDP = f( pvct, gexp, ir). Murty et al (2012) suggested this
type of variables to examine the effects of private sector credit on growth. Also, Okyo et
al (2012) emphasized the interest rate and inflation as control variables in their study. The
study has applied co-integration approach error correction model and granger causality
for the empirical examination of the relationship between the private sector credit and
economic growth.
Empirical Model
Firstly, we form the following regression equation to estimate the effects of private sector
credit on real gross domestic product. Government expenditure and interest rate are taken
as control variables.
Where,
lnrgdpt = α0 + α1lnrpvctt+ α2lnrgexp t + α3ir t+ μ
rgdp = real gross domestic product
rpvct = private sector credit provided by banks in real terms
ir = interest rate
rgexp = real government expenditures
Unit root test and co-integration tests should be performed first before performing the
ordinary least square method. If the variables are found I(I) and co-integrated to each
other, then co-integration and error correction test should be run. If the variables are
found I(I) but no co-integration found between the variables of interest, then it is better to
run OLS. Therefore in this case we first perform the unit root test and co-integration test.
Unit Root Tests
The pre-requisite of co-integration test is the stationarity test of each individual time
series over the sample period. Co-integration analysis has increasingly become the
appropriate methodological approach for analyzing time series data containing stochastic
trends. Hence before turning to the analysis of the long run relationships between the
variables, we should check for the unit root properties of the data, as non stationary
behavior is a prerequisite for including them in the co-integration analysis.
Bank Credit and Economic Growth in Nepal: An Empirical Analysis 11
Table 1
ADF Test Results (Unit Root Tests)
Intercept
Variables
lnrgdp
lnrpvct
lnrgexp
ir
Intercept and Trend
Level
t-stat
p-value
First Difference
t-stat
p-value
Level
t-stat
p-value
First Difference
t-stat
p-value
-0.2191
0.9275
-6.2461
0.0000
--1.5065
0.8103
-6.1634
0.0000
-0.5403
0.8722
-4.6902
0.0005
-3.0366
0.1361
-4.738
0.0026
-1.6497
0.4483
-6.1444
0.0000
3.6743
0.0363
-5.9839
0.0001
-1.3167
0.6119
-4.6169
0.0006
-2.7912
0.2091
-4.5509
0.0043
Source: Author's computation
ADF statistics in the above table shows that all the variables included found to be I(1)
with one variable lnrgexp having deterministic trend. Hence, although it can be modeled
at first difference with OLS and extracting trend and cycles for trend-stationary variables,
this is possible only if variables are not co-integrated. The Johansen co-integration test
has been carried out as follows to identify whether there exists a co-integrated
relationships.
Table 2
Johansen's Cointegration Test (LNRGDP LNRPVCT LNGEXP IR )
Trace Statistics
Hypothesized
No.
of CE(s)
None*
Trace
Statistic
Maximum Eigenvalue
P-value
58.3249
0.05
Critical
Value
47.85613
0.05
Critical
Value
27.58434
P-value
0.0039
MaxEigen
Statistic
26.1242
At most 1*
32.2006
29.79707
0.0259
17.1239
21.13162
0.1662
At most 2
15.0767
15.49471
0.0577
14.1104
14.26460
0.0528
At most 3
0.9663
3.841466
0.3256
0.9663
3.841466
0.3256
0.0759
* denotes the rejection of null hypothesis at 5 percent level of significance. Trace test
indicates 3 cointegrating equations at o.05 level whereas maximum Eigen Value test
indicates 1 cointegrating equation at o.05 level.
The Johansson cointegration tests for cointegration shows conflicting results with trace
test and maximum eigenvalues test. The trace test indicates a 2 cointegration relation,
however, eigenvalue shows none. Hence it is desired to test the cointegration relation
only with the variables of interest, credit flow and its impact to GDP. The test results for
cointegration between economic growth ( lnrgdp) and private sector credit (lnrpvct) are as
follows:
12 NRB ECONOMIC REVIEW
Table 3
Johansen's Cointegration Test (LNRGDP LNRPVCT)
Trace.
Maximum Eigenvalue
Hypothesized
No. of CE(s)
Trace
Statistic
0.05
Critical
Value
P-value
MaxEigen
Statistic
0.05
Critical
Value
P-value
None*
15.6358
15.4947
0.0476
15.5774
14.2646
0.0308
At most 1
0.05843
3.8414
0.8090
0.0584
3.8414
0.8090
Both the results consistently show a single co-integration relationship, at a highest level
of confidence which further confirms the earlier test of co-integration with four variables.
Hence, it can be decided that there exists a long term relationship between private sector
credit and country real GDP.
Causality Test
A number of studies have been carried out to examine the direction of causality between
bank credit and economic growth. Mishra at al (2009) examines the direction of causality
that runs between credit market development and economic growth in India through the
application of Granger Causality Test and find that credit market development spurs
economic growth. Mukhokadhya and Pradhan (2010) assess the causal relationship
between financial development and economic growth of seven Asian developing
countries and concluded that no general consensus can be drawn about finance growth
relationship in developing countries. Odedokun (1989) find the case of unidirectional
causality from the real sector to the financial sector and concludes that money is causally
prior to income.
Here in the study, Granger Causality Test has been conducted to find out the direction of
causality between the bank credit and economic growth. The results show evidence of
unidirectional casual relationship from GDP to private sector credit (annex 3, annex 4).
With different lag structure at 2 and 5 lags, the estimated F-stat suggests that private
sector lending does not Granger causes the real GDP but the other way is true. Hence, the
preliminary relationship is something different than expected. Nepalese economic growth
is led by feedback effect from the growth, rather than multiplier effect that of investment.
Based on these results, a bivariate error correction model is being estimated in the
following sections. Using the representation theorem of Engle and Granger (1987) to
establish a link between the co-integration and Error Correction Model (ECM), we can
show the long-run relation as:
 t  ln rgdpt    1 ln rpvctt
….. (1)
Bank Credit and Economic Growth in Nepal: An Empirical Analysis 13
By transforming the equation 1, we can develop an error correction model as:
l
l
 ln rgdpt   nrgdp   ln rgdp t 1   a1h  ln rgdpt h   b1h  ln rpvctt h  uln rgdp.t …… (2)
h1
h1
l
l
h1
h1
 ln rpvctt  ln rpvct   ln rpvct t 1   a2 h  ln rpvctt h   b2 h  ln rgdpt h  uln pvct .t ..... (3)
Where, u ln rgdpt and u rpvct.t are stationary white noise processes for some number of lags l.
The coefficients in the co-integration equation give the estimated long-run relationship
among the variables and coefficients on the error correction model (ECM) describe how
deviations from that long-run relationship affect the changes on them in next period. The
parameters  ln rgdp and  ln rpvct of the equation (2) and (3) measure the speed of
adjustment of private sector credit and economic growth respectively towards the longrun equilibrium
VI. RESULTS
Estimates of the equation (1) for co-integration
 t  ln rgdp t 1  8.153  0.410 ln rpvctt 1
(0.0087)*
….. (4)
The error correction estimates of equation (2) and (3) have been presented as follows:
 ln rgdpt  0.056  0.0553ˆt 1  0.0149 ln rpvctt 1  0.004 ln rpvctt  2  0.0878 ln rgdpt 1  0.215 ln rgdpt  2
.
(0.012)
(0.07)
(0.046)
(0.005)
(0.188)
…..
(5)
(0.199)
Adj. R2 = 0.054, F-Stat = 0.35
 ln rpvctt  0.181 0.916ˆt 1  0.534 ln rpvctt 1  0.0786 ln rpvctt  2  1.39 ln rgdpt 1  1.59 ln rgdpt  2
(0.0329) * (0.215) *
(0.125) *
(0.134)
(0.505) *
(0.53) *
… (6)
Adj. R2 = 0.59, F-Stat = 9.05
Note: * Significant at 5 percent or lower level.
Values in parenthesis indicates the standard errors of the respective estimates
The estimates of the model show interesting results. By rearranging the estimates of cointegration equation (4), it can be inferred that one percentage point increase in the real
private sector credit may cause the increase in real GDP by 0.41 percentage points over
the long run equilibrium relationships. Nevertheless, the short run equilibrium effects are
more induced by the feedback effects of GDP growth to the private sector lending, not
from the private sector lending to GDP growth, which is against our hypothesis. All the
coefficients of error correction estimates with the dependent variable as ∆lnrgdpt are
found to be insignificant including  ln rgdp , very low adjusted R2 value (0.054) and
insignificant F-Stat (0.35). In the contrary, with the dependent variable ∆lnrpvctt, the
14 NRB ECONOMIC REVIEW
error correction estimate is significant showing that the estimate of  ln rpvct is 0.916;
significant at 5 percent or lower level. It includes relatively high R2 value (0.593) and FStat (9.05). It indicates that any deviation in real GDP in any given time will affect the
real private sector lending by 0.916 in the next period and the effect of such deviation in
private sector credit to the real GDP is almost zero. Hence, the finding is that, although
there is a long-run relationship can be observed from private sector lending to overall
growth of the economy, there is no immediate multiplier effect from investment to
growth and such a long-run relationship became only possible through feedback effects.
Diagnostics tests shows estimations are valid. Residuals Plots move around zero (annex
6). LM Test for Autocorrelation shows no serial correlation in error terms (annex 7).Since
p-value is higher while we include up to three lags, we do not reject null, in favor of this,
there is no serial correlation in residuals (annex 7).
Spikes of the correlogram graphs are also found to be within the bands (annex 9) and
also, all inverse roots of AR Polynomial lie inside the circle (annex 10 ).
VII. CONCLUSION
Credit is an important link in monetary transmission as it finances production,
consumption, and capital formation, which in turn affect economic growth. Especially in
developing countries like Nepal, it caters resource need for economic growth. NRB and
the government have adopted many policies and programs to increase economic growth
through the use of bank credit. However, the relationship between private sector credit
and economic growth has not yet been assessed properly in the Nepalese context.
Applying Johansen co-integration approach and estimating Error Correction
Model, the study found that the banks credit to private sector has positive impact on
economic growth only in the long run. Nonetheless the short run equilibrium effects are
more induced by the feedback effects of GDP growth to the private sector lending, not
from the private sector lending to GDP growth, which is against the proposed hypothesis.
The empirical results imply that, policy makers should focus attention on long run
policies to promote economic growth such as development of modern banking
sector, efficient financial market, infrastructures so as to increase private sector
credit which is instrumental to promote growth in the long run.
*****
Bank Credit and Economic Growth in Nepal: An Empirical Analysis 15
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Bank Credit and Economic Growth in Nepal: An Empirical Analysis 19
APPENDICES
1.
Co-integration Test (Four Variables)
Date: 10/20/14 Time: 14:39
Sample (adjusted): 1977 2014
Included observations: 38 after adjustments
Trend assumption: Linear deterministic trend
Series: IR LNRGDP LNRGEXP LNRPVCT
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
0.05
Critical Value
Prob.**
None *
At most 1 *
At most 2
At most 3
0.497158
0.362774
0.310181
0.025108
58.32488
32.20065
15.07670
0.966282
47.85613
29.79707
15.49471
3.841466
0.0039
0.0259
0.0577
0.3256
Trace test indicates 2 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical Value
Prob.**
None
At most 1
At most 2
At most 3
0.497158
0.362774
0.310181
0.025108
26.12422
17.12396
14.11042
0.966282
27.58434
21.13162
14.26460
3.841466
0.0759
0.1662
0.0528
0.3256
Max-eigenvalue test indicates no cointegration at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
20 NRB ECONOMIC REVIEW
2.
Co-integration Test (Two Variables)
Date: 10/20/14 Time: 14:42
Sample (adjusted): 1977 2014
Included observations: 38 after adjustments
Trend assumption: Linear deterministic trend
Series: LNRGDP LNRPVCT
Lags interval (in first differences): 1 to 1
Unrestricted Cointegration Rank Test (Trace)
Hypothesized
No. of CE(s)
Eigenvalue
Trace
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
0.336304
0.001536
15.63582
0.058428
15.49471
3.841466
0.0476
0.8090
Trace test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue)
Hypothesized
No. of CE(s)
Eigenvalue
Max-Eigen
Statistic
0.05
Critical Value
Prob.**
None *
At most 1
0.336304
0.001536
15.57739
0.058428
14.26460
3.841466
0.0308
0.8090
Max-eigenvalue test indicates 1 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Bank Credit and Economic Growth in Nepal: An Empirical Analysis 21
3.
Pairwise Granger Causality Tests (With two lags)
Sample: 1975 2014
Lags: 2
Null Hypothesis:
Obs
LNRGDP does not Granger Cause LNRPVCT 37
LNRPVCT does not Granger Cause LNRGDP
4.
F-Statistic Prob.
7.57364
0.00338
0.0020
0.9966
Pairwise Granger Causality Tests (With five lags)
Sample: 1975 2014
Lags: 5
Null Hypothesis:
Obs
LNRGDP does not Granger Cause LNRPVCT 34
LNRPVCT does not Granger Cause LNRGDP
5.
F-Statistic Prob.
3.87323
1.60409
0.0108
0.1988
Economic Growth in Developing Countries
Countries
2009
2010
2011
2012
2013
2014
China
9.2
10.4
9.3
7.8
7.7
-
India
8.5
10.5
6.3
4.7
5.0
-
Indonesia
4.6
6.2
6.5
6.3
5.8
-
Malaysia
-1.5
7.4
5.1
5.6
4.7
-
Nepal
4.5
4.8
3.4
4.9
3.6
5.2
Source: World Bank
22 NRB ECONOMIC REVIEW
6.
Residuals Plots move around zero.
LNRPVCT Residuals
.12
.08
.04
.00
-.04
-.08
-.12
1980
1985
1990
1995
2000
2005
2010
2005
2010
LNRGDP Residuals
.06
.04
.02
.00
-.02
-.04
-.06
1980
7.
1985
1990
1995
2000
VEC Residual Serial Correlation LM Tests
Null Hypothesis: no serial correlation at lag order h
Sample: 1975 2014
Included observations: 36
Lags
LM-Stat
Prob
1
2
3
9.450576
5.579553
5.590300
0.0508
0.2328
0.2319
Probs from chi-square with 4 df.
Bank Credit and Economic Growth in Nepal: An Empirical Analysis 23
8.
Vector Error Correction Estimates
Date: 10/20/14 Time: 14:37
Sample (adjusted): 1978 2014
Included observations: 37 after adjustments
Standard errors in ( ) & t-statistics in [ ]
Cointegrating Eq:
CointEq1
LNRGDP(-1)
1.000000
LNRPVCT(-1)
-0.409841
(0.00877)
[-46.7222]
C
-8.153330
Error Correction:
D(LNRGDP)
D(LNRPVCT)
CointEq1
0.055931
(0.07941)
[ 0.70431]
0.916661
(0.21503)
[ 4.26285]
D(LNRGDP(-1))
-0.087868
(0.18502)
[-0.47491]
-1.392763
(0.50100)
[-2.77997]
D(LNRGDP(-2))
-0.215248
(0.19596)
[-1.09841]
-1.595705
(0.53063)
[-3.00717]
D(LNRPVCT(-1))
-0.014957
(0.04599)
[-0.32525]
0.534695
(0.12452)
[ 4.29405]
D(LNRPVCT(-2))
0.003950
(0.04950)
[ 0.07980]
-0.078660
(0.13404)
[-0.58684]
C
0.056417
(0.01218)
[ 4.63310]
0.180577
(0.03297)
[ 5.47649]
0.054060
-0.098511
0.013521
0.020884
0.354324
93.91664
-4.752251
-4.491021
0.042574
0.019926
0.593335
0.527744
0.099137
0.056551
9.045982
57.05943
-2.759969
-2.498739
0.104796
0.082290
R-squared
Adj. R-squared
Sum sq. resids
S.E. equation
F-statistic
Log likelihood
Akaike AIC
Schwarz SC
Mean dependent
S.D. dependent
Determinant resid covariance (dof adj.)
Determinant resid covariance
Log likelihood
Akaike information criterion
Schwarz criterion
1.36E-06
9.52E-07
151.4944
-7.432128
-6.822591
24 NRB ECONOMIC REVIEW
9.
Autocorrelations with 2 Std.Err. Bounds
Cor(LNRPVCT,LNRPVCT(-i))
Cor(LNRPVCT,LNRGDP(-i))
.4
.4
.2
.2
.0
.0
-.2
-.2
-.4
-.4
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
Cor(LNRGDP,LNRPVCT(-i))
4
5
6
7
8
9
10
11
12
10
11
12
Cor(LNRGDP,LNRGDP(-i))
.4
.4
.2
.2
.0
.0
-.2
-.2
-.4
-.4
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
10.
Inverse Roots of AR Characteristic Polynomial
1.5
1.0
0.5
0.0
-0.5
-1.0
-1.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
6
7
8
9
Determinants of Stock Market
Performance in Nepal#
Prakash Kumar Shrestha, Ph.D.*
Biggyan Raj Subedi**
Abstract
This paper empirically examines the determinants of the stock market performance in Nepal using
monthly data for the period of mid-August 2000 to mid-July 2014. The impact of major changes in
politics and Nepal Rastra Bank’s policy on lending against share collateral has also been
assessed. Empirical results obtained from OLS estimations of behavioural equations reveal that
the performance of stock market is found to respond positively to inflation and broad money
growth, and negatively to interest rate. This suggests that, in Nepal, share investors seem to take
equity as a hedge against inflation and consider stock as an alternative financial instrument.
Further, availability of liquidity and the low interest rates stimulate the performance of the
Nepalese stock market. More importantly, stock market has been found to respond significantly to
changes in political environment and the policy of Nepal Rastra Bank. These findings help to
design policies to stabilize or stimulate the share market in Nepal.
Key Words: Stock Market, Macro Variables, Nepal
JEL Classification: G10, E44
#
The earlier version of this paper is available at www.nrb.org.np under NRB Working Paper
series, NRB-WP-24, 2014.
*
Director, Nepal Rastra Bank, Research Department, Central Office, Baluwatar, Kathmandu,
Nepal. Email:[email protected]
**
Deputy Director, Nepal Rastra Bank, Research Department, Central Office, Baluwatar,
Kathmandu, Nepal. Email:[email protected]
We would like to thank Mr. Shalikram Pokharel and Mr. Nanda Dhakal, Assistant Directors,
Research Department, Nepal Rastra Bank for their help in preparing this paper. In addition, we
are grateful to the Editorial Board and an anonymous external reviewer for providing valuable
comments to revise this paper.
26 NRB ECONOMIC REVIEW
I. BACKGROUND
The history of stock market is not long in Nepal. Securities Exchange Centre (SEC) was
established in 1976 with an objective of facilitating and promoting the growth of capital
market (Gurung, 2004). However, it opened its floor for secondary trading of shares only
in 1981, which was only for government bonds (NRB, 1996). With enactment of
Securities Exchange Act 1984, SEC opened its floor for corporate share trading also, but
it was very limited. The organized and full fledged stock market began with the
conversion of Securities Exchange Centre into Nepal Stock Exchange (NEPSE) Limited
in 1993. The NEPSE opened its trading floor in the beginning of 1994. Till now, it is the
only stock exchange in Nepal. Hence, the stock market in Nepal is still in evolving stage
but of special interest as it has grown significantly since its establishment. It was
established in order to mobilize capital alternative to traditional banking sector for
promoting economic growth and development in the country.
Within a short period of time since its inception, the NEPSE index witnessed significant
ups and downs. Recently, after the results of the second CA election in November 2014,
the NEPSE index took an upward trend until August 2014. On July 14, 2014 the
benchmark index reached 1036.1, the highest in the last six years. Earlier on August 31,
2008, the NEPSE index had reached its all-time high of 1175 points before plunging to a
record low of 292 on June 15, 2011.
Normally, the stock market index is taken as a barometer of an economy. Growth in stock
index is normally considered as a good sign since it implies the investors are confident
about the future prospect of the economy. It helps promote investment in the economy.
However, a rapid increase in the stock market index is always a matter of concern. If the
increase in the index is not justified by the fundamentals, such a rise cannot be sustained
and eventually the index will plummet endangering the economic and financial stability.
Hence, it is essential that the policymakers keep eyes on the stock market development
and be ready to take appropriate measures, if needs arise, to prevent the build up of
bubbles and collapse in the market. For this, it is necessary to understand the relationship
between the stock market index and the factors that influence it. Several factors may
affect the stock market. Any factors that have an effect on cash flows of firms or discount
rate will have impact on the stock market. However, which factors affect to what degree
will vary from country to country, depending on the size, type and other characteristics of
the economy and the market. In this context, this paper aims to analyze the relationship
between the performance of NEPSE index and major macroeconomic variables in Nepal
using monthly data that span from mid-August 2000 to mid-July 2014. In addition to
main variables, this paper also assesses the impact of changes in politics and Nepal Rastra
Bank's policy on lending against share collateral. It is expected that the findings of
this study would provide some meaningful insights to understand the determinants
behind the performance of Nepalese stock market, useful for both policymakers and
investors.
Determinants of Stock Market Performance in Nepal 27
There are a lot of research studies on the determinants of stock market in other countries
such as Asprem (1989), Yosuf and Majid (2007), Rahman et al. (2009), Singh (2010),
Hsing (2011, 2014), Eita (2012), Quadir (2012), Naik and Padhi (2012), Jauhari and
Yadav (2014), and Khan (2014). A very few studies have been done on the Nepalese
stock market such as Dangol (2008, 2010), Pradhan and KC (2010), Bhatta (2010) and
Regmi (2012). These studies mainly focused on micro perspective rather than macro and
policy perspectives. This study differs from them since we have examined the impact of
macroeconomic variables as well as politics and NRB’s policy changes on the stock
market performance.
The paper is structured as follows. Section 2 presents the glimpse of the Nepalese stock
market, which is followed by the review of literature in section3. Section 4 describes the
data and methodology used and section 5 presents the empirical results and discussion.
Finally, Section 6 concludes the study.
II. GLIMPSE OF THE NEPALESE STOCK MARKET
The Nepalese stock market is still in infant stage. However, there has been some progress.
In the last two decades, the number of listed companies at NEPSE has increased from 79
in 1995 to 237 in 2014. During the same period, market capitalization has increased from
5.9 percent to 54.8 percent of GDP (Table 1). The growth in the listed companies mostly
includes banks and financial institutions that were opened with the adoption of financial
liberalization. Existing regulations require bank and financial institutions to publicly float
at least 30 percent of shares and get listed in the stock exchange within a specific period
of time. However, there is no such a mandatory requirement for companies in the real
sector. As such, very few real sector companies have been listed in the stock market. As
of mid-July 2014, there were 182 (76 %) financial institutions out of 237 listed companies
at NEPSE (Table 2). Similarly, banks and financial institutions contributed to 64.3
percent of the total market capitalization followed by insurance (13.3 percent) and
hydropower (8.7 percent). Market capitalization of manufacturing and processing firms
remained just at 1.9 percent.
Table 1: Glimpse of the Nepalese Stock Market
Year
No. of listed
companies
Market Capitalization
(Rs in million)
Market Capitalization/GDP
(percent)
1995
79
12963
5.9
2000
110
43123
11.4
2005
125
61366
10.4
2010
176
376871
31.6
2014
237
1057166
54.8
Source: Quarterly Economic Bulletin and Current Macroeconomic Situation of Nepal
(2013/14), NRB
28 NRB ECONOMIC REVIEW
Table 2: Structure of the Nepalese Stock Market
(Mid-July 2014)
Type of Institution
Number
Market Capitalization (%)
182
Financial Institutions
64.3
22
Insurance Companies
13.3
18
Manufacturing & Processing
1.9
4
Hotel
2.4
4
Trading
0.1
5
Hydro Power
8.7
2
Others
9.3
237
Total
100.0
Source: Current Macroeconomic Situation of Nepal (2013/14), NRB
As regards the movement of the NEPSE index, it hovered around 200 points between
1994 and 1999. This was also the period when Nepalese stock market was evolving in
terms of number of listed companies and the market capitalization. From 2000 onwards,
the NEPSE index observed a greater fluctuation. In Figure 1, we can see the NEPSE
peaking up three times in the past such as in November 2000, December 2007 and August
2008 before taking a sharp plunge. Now again in 2014, after the election of second
Constituent Assembly, the NEPSE index reached as high as 1036.1 points in mid-July
2014. What factors can explain the movement of the NEPSE is a matter of study in this
paper.
Figure 1: NEPSE Index (mid-month)
1200
1000
800
600
400
200
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2006
2007
2008
2009
2010
2011
2012
2013
0
Source: www.nepalstock.com.np
Determinants of Stock Market Performance in Nepal 29
III. REVIEW OF LITERATURE
The Arbitrage Pricing Theory, introduced by Ross (1976), establishes the theoretical
framework to link stock returns with several variables which can influence the source of
income volatility (Rahman, et al. 2009). Mukherjee and Naka (1995) showed that
economic variables influence stock market returns through their effects on future
dividends and discount rates. Macroeconomic variables selected to examine the
determinants of stock market tend to differ slightly across studies, however
(Rahman, et.al. 2009). Most common variables are the rate of inflation, money growth,
interest rates, industrial production and exchange rates for explaining the stock
market movement. Selection of these macroeconomic variables has theoretical
justifications as follows.
Higher interest rates or discount rates would reduce the present value of cash flows,
which would reduce the attractiveness of investment, hence, shrinks the value of
stock returns (Rahman, et al. 2009). Another impact could be through portfolio
substitution, a rise in the rate of interest increases the opportunity cost of holding
cash, which later on leads to a substitution effect between stocks and other interest
bearing securities like bonds (Rahman, 2009, p.98). In the literature, the common
interest rate proxies are the treasury bills rates as being employed by Mukherjee and
Naka (1995), Ratanapakorn and Sharma (2007), Yusof and Majid (2007), and Eita
(2012)1. In case of money supply, Mukherjee and Naka (1995) argue that if an increase
in money supply leads to economic growth, stock prices would benefit from
expansionary monetary policy. In another way, with increase in money supply, the
availability of liquidity at a lower interest rate increases, which can flow into the stock
market. In contrast, Fama (1981) argues that an increase in money supply leads to
inflation (or expected inflation) in the economy, which in turn increases the discount rate
and lowers the stock market returns.
Moreover, inflation is also an important variable that investors consider before making
any investment decisions. Theoretically, Asprem (1989) put forward that inflation should
be positively related to stock return if stocks provide a hedge against inflation. This is
based on Fisher (1930) who posits that stock markets are independent of inflation
expectations since equities are a claim against real assets of the company. Fama (1981)
however, disagrees with the generalized Fisher hypothesis on the basis that an increase in
inflation causes uncertainty and reduces future economic activity, which reduces the
stock price.
Another variable of interest used in the literature is the exchange rate. The exchange
rate influences the firm’s cash flow and the amount of dividend to be paid, especially in
open economy (Eita, 2012). A depreciation of the local currency makes exporting
goods less expensive and may lead to an increase in foreign demand and sales for the
1
Lending rate used by Hsing (2014).
30 NRB ECONOMIC REVIEW
exporting firms (Pan et al., 2007). As a result, the value of exporting (importing)
firms would increase (decrease). Rehman et al. (2009) argue that the importance of
international trade in the economy determines the impact of exchange rate on stock price.
However, we do not consider exchange rate in our case because of several reasons. First,
the Nepalese stock exchange is overwhelmingly dominated by banks and financial
institution; there are no any trading companies. Second, Nepal has not opened the capital
account so that there is no foreign portfolio investment in stock market. Third, Nepal has
been following the pegged exchange rate with India currency so that exchange rate may
not be the important variable for stock market.
Other than monetary variables mentioned above, the level of real economic activity
is the crucial factor in determining the stock market returns (Rehman et al. 2009).
There is a general consensus that an increase in economic activity causes stock market
returns to increase (Eita, 2012, p874). The most popular measure of real economic
activity is the gross domestic product (GDP). Unfortunately, data on GDP is normally on
annual basis and only in some countries, it can be available on a quarterly frequency.
Some use industrial production index as another measure for real economic indicator
(Rashid, 2008; Rehman et al., 2009). In addition, researchers have used other additional
variables as well such as debt/GDP ratio and yields of alternative financial assets by
Hsing (2014), foreign reserves by Rahman et al. (2009), and variables like capital
formation and gold price by Jauhari and Yadav (2014), gross capital formation relative to
GDP, credit to the private Sector to GDP and net remittance relative to GDP by El-Nadar
and Alraimony (2013) and federal fund rate by Yusof and Majid (2007) as factors
affecting the performance of stock market.
Empirical results regarding macroeconomic determinants are mixed types. Estonian and
Hungarian stock market index have a positive relationship with debt/GDP ratio, real GDP
and the German stock market index and a negative relationship with the exchange rate,
the domestic interest rate, the expected inflation rate, and the euro area government bond
yield (Hsing 2011; 2014). In case of Namibia, an increase in economic activity and the
money supply increases stock market prices, while increases in inflation and interest rates
decrease stock prices (Eita, 2012). The results suggest that equities are not a hedge
against inflation in Namibia, and contractionary monetary policy generally depresses
stock prices. In Jordon, money supply, gross capital formation, inflation, and credit to the
private sector have significant positive relationship, and income and net remittance have
negative relationship with stock market (El-Nadar and Alraimony, 2013). Moreover, there
is a co-integrating relationship of Malaysian stock market index with changes in money
supply, interest rate, exchange rate, reserves and industrial production index (Rahman et
al., 2009). In case of India, the macroeconomic variables like GDP, savings, capital
formation, gold price, industrial output, money supply, exchange rate, WPI, and interest
rate have concurrence with the variability of the Sensex index (Jauhari and Yadav, 2014).
On the other hand, Naik and Padhi (2012) also examined the Indian stock market index
(BSE Sensex) and observed the positive relationship between stock price and money
supply and industrial production but negative relationship with inflation. The exchange
rate and the short-term interest rate were found to be insignificant in determining stock
prices in India. However, Rashid (2008) showed the long run relationship between stock
Determinants of Stock Market Performance in Nepal 31
prices and macroeconomic variables like exchange rate, industrial index, interest rate,
inflation in Pakistan. Specially, in Pakistan, exchange rate, inflation and GDP growth rate
were positively related with stock prices (KSE-100 index) while the interest rate was
negatively related as found by Khan (2014). Yusof and Majid (2007) found a significant
direct impact of US federal fund rate on the Malaysian stock market, reflecting the impact
of capital flows on the stock market.
Most studies use either monthly or quarterly data for examining the determinants of stock
performance. Ratanapakorn and Sharma (2007), Eita (2012), and Kemboi and Tarus
(2012) use quarterly data, while Yusof and Majid (2007), Rahman et al. (2009), Singh
(2010), El-Nadar and Alraimony (2013) use monthly data. With regards to methodology,
Rahman et al. (2009), Eita (2012) employ VAR framework. Kwon and Shin (2001),
Rashid (2008), and El-Nadar and Alraimony (2013) use cointergration and variance
decomposition, while Hsing (2011, 2014) uses GARCH method and Rashid (2008),
Singh (2010), and Jauhari and Yadav (2014) apply Granger causality test. On the other
hand, Yusof and Majid (2007) apply the ARDL approach. Hence, there is no unique way
to investigate the determinants of stock market performance.
3.1
Politics and Stock Market
The stock market index, in general, is considered as the reflection of the expectation of
future profitability of the companies. This market, therefore, tends to be influenced not
only by macroeconomic fundamentals, but also by the unexpected political events as well
as policy changes. Several studies have found the relationship between the political event
and the stock market performance. For example, Beaulieu et al. (2006) investigated the
short run impact of the political uncertainty associated with the 1995 Quebec referendum
on the stock returns. The study found that the uncertainty surrounding the referendum
outcome had short run impact on stock returns of Quebec firm, implying that the stock
market was directly influenced by the political risk and uncertainty. Similarly, Jensen and
Schmith (2005) estimated the impact of the four main Brazilian presidential candidates on
the mean and variance of the Brazilian stock market using a number of time-series
regressions. They argue that political events, such as the election of a politician that is
expected to enact “market-friendly” policies, lead to increases in stock market returns
while political events that are expected to have a negative impact on the economy and
specific firms lead to decreases in stock market returns.
3.2
News and Stock Market
Stock markets are heavily affected by news and rumours, like a “beauty context” as
described by Keynes (1936). News can affect sentiments as well as expectation of the
investors and performance of the companies. Most importantly, people interpret news
differently based on their own cognitive power. There are some empirical examinations
on the impacts of news on the performance of stock. For example, Boudoukh et.al.(2013)
investigated the relation between news and the stock prices of 795 S&P500 companies,
32 NRB ECONOMIC REVIEW
covering the period of January 1, 2000 to December 31, 2009. Using advanced textual
analysis method, they find that, when information can be identified and that the tone (i.e.,
positive versus negative) of this information can be determined, there is a closer link
between stock prices and information.
Similarly, Alanyali et. al. (2013) investigated daily print issues of the Financial Times
from 2nd January 2007 to 31st December 2012 to quantify the relationship between
decisions taken in financial markets and developments in financial news. They find a
positive correlation between numbers of times the name of a company mentioned daily in
the Financial Times and the daily transaction volume of a company's stock both on the
day before and on the same day of the news released. Their results provide quantitative
support for the suggestion that movements in financial markets and movements in
financial news are closely interlinked.
3.3
Past Empirical Evidence from Nepal
There are a few other studies on the explaining stock market performance, mainly from
micro perspectives. For example, Joshi (2012) examined the impact of dividends on stock
price in the context of Nepal and found the impact of dividends is more pronounced than
that of retained earnings on stock prices in Nepal. Dangol (2008) studied the reaction of
Nepalese stock market to announcements of unanticipated political events using the event
analysis methodology. His analysis covered the period from 2001 to 2006. He found that
good-news (bad news) political announcements generate positive (negative) abnormal
returns in the post-event period. This finding suggests that there is a strong linkage
between political uncertainty and common stock returns in Nepal.
In another study of Dangol (2010) examined the random walk behaviour on daily market
returns of the Nepal Stock Exchange for the period between July 2000 and January 2010
and found that the Nepalese stock market does not show any characteristics of random
walk and thus, is not weak form efficient. Findings of Bhatt (2010) are also similar. This
means news affects the movement of the stock market index. Further, Pradhan and KC
(2010) assessed equity share price behaviour in Nepal and tested the hypothesis that share
price changes are independent using weekly data of 26 listed companies from mid-July
2005 to mid-July 2008. They found that random walk hypothesis holds for less frequently
traded stocks but do not hold for highly traded stocks at NEPSE.
IV. DATA AND METHODOLOGY
4.1
Data and Sample
Based on the availability of data and their relevancy as guided by the literature and
considering the feature of Nepalese stock market, the following data are taken to examine
the determinants of stock market index in Nepal as shown in Table 3.
Determinants of Stock Market Performance in Nepal 33
Table 3: Variables and their Description
Variable
SI
GDP
CPI
M2
TB91
D1
D2
Description
Unit
NEPSE Index
Annual real GDP
Rs in million
CPI index, monthly average (base year = 2005/06)
Broad Money Supply
Rs in million
91 day Treasury bill rate
Percent
Political Event Dummy (takes value 1 if negative scenario, 0 otherwise)
Policy Change Dummy (takes value 1 if margin lending is tightened, 0 if it is
relaxed)
The level of real economic activities is one of the crucial determinants of the stock market
performance as a scale variable. The traditional measure for such activities is the gross
domestic product (GDP). However, GDP data are unavailable on a monthly basis (not
even on a quarterly basis). Hence, GDP variable has been dropped in further empirical
estimation. All other data are collected on a monthly basis. Given the data availability and
relevancy, the sample period of August 2000 to July 2014 has been chosen. Though the
formal trading in Nepalese stock market started in 1994, the stock market was in evolving
stage and highly immature until 2000. This fact is also reflected in Figure 1, which shows
that NEPSE remained relatively flat until 2000.
4.2
Methodology
Based on the literature and the availability of data, the study has used the following
general behaviour model.
SIt = f (CPIt,M2t, TB91t, SIt-1, D1, D2)
…… (1)
where the meanings of symbols are same as described in Table 3. The two dummies d1
and d2 are introduced to capture the impact of political changes and the NRB's policy
changes. All other variables are standard in the literature.
The first lag of stock market index is also included in our model as the literature suggests
that stock prices tend to be highly persistent. A large section of investors are “chartist”
who just follows the trend of movement of stock market index. Moreover, information on
fundamental comes late so a majority of stock investors apply their own gut feeling.
Though stock returns are theoretically assumed to follow random walk as argued by the
efficient market hypothesis, many studies have found that the stock returns are autocorrelated. Boudoukh et al. (1994) points out that time series patterns occur in stock
returns because investors either overreact or partially adjust to information arriving to the
market.
Prior to deciding on the appropriate method, a preliminary examination of the nature of
the data is necessary. We follow the standard procedure of unit root testing by
employing the Augmented Dickey Fuller (ADF) test. Since the ADF test is often
34 NRB ECONOMIC REVIEW
criticized for low power, we complement this test with the Phillips Perron (PP)
test. Based on the unit root test results in Table 4, all variables, except TB91 are I(1).
Table 4: ADF and Philip Perron test
Variables
ADF Test
H0:
Variable
non-stationary
is
Philips Perron Test
H0:
Variable
is
non-stationary
Order of
Integration
log(SI)
-1.552
-1.304
I(1)
dlog(SI)
-10.297***
-10.297***
log(CPI)
-2.419
-2.612
I(1)
dlog(CPI)
-0.582**
-10.435***
log(M2)
-1.516
-1.516
I(1)
dlog(M2)
-12.408***
-13.768***
TB91
-2.821*
-2.918**
I(0)
*** implies significant at 1% level, ** implies significant at 5% level and * implies
significant at 10% level.
Source: Authors’ calculation
Since not all selected variables are in same order, we cannot follow VAR or cointegration approach. More importantly, application of VAR method may not be
appropriate when the real sector variable is missing. Rather, the following models are
estimated by OLS using first difference of I(1) variable2. It seems that following models
are able to capture the performance of stock market. Considering the possibility of multicollinearity among explanatory variables, we do the estimation on step by step basis, and
finally all explanatory variables are included in equation (5).
dlog(SI)t =  + 1 dlog(CPI)t + 1D1 + 2D2 + dlog(SI)(t–1) + t
…… (2)
dlog(SI)t =  + 2 dlog(M2)t + 1D1 + 2D2 + dlog(SI)(t–1) + t
…… (3)
dlog(SI)t =  + 3 TB91t + 1D1 + 2D2 + dlog(SI)(t–1) + t
…… (4)
dlog(SI)t =  + 1 dlog(CPI)t + 2 dlog(M2)t + 3 TB91t + 1D1 + 2D2 + dlog(SI)(t–1) + t
…… (5)
V. EMPIRICAL RESULTS
5.1
Correlation Analysis
Based on annual data from 2000/01 to 2013/14, the correlation between the real GDP and
NEPSE index is found to be 0.57 (P-value=0.03), both positive and significant. Lack of
GDP (or industrial production index) at a monthly frequency prevent us to use it in
econometric estimation. The correlation of other macro variables (except interest rate,
TB91) such as Consumer Price Index (CPI), Broad Money (M2) with NEPSE index (SI)
2
However, this study can be extended using ARDL approach with the expansion of stock
market in Nepal.
Determinants of Stock Market Performance in Nepal 35
are found to be statistically significant at 5 percent level of significance on monthly data
covering mid-August 2000 to mid-July 2014 (Table 5). This suggests that there must be
some relationship between stock market index and these macro variables.
Table 5: Correlation between Log (SI) and macroeconomic variables
LOG(CPI)
Correlation
0.552
P-Value
0.000
Source: Authors’ calculation
5.2
LOG(M2)
0.563
0.000
TB91
0.033
0.671
Empirical Estimation and Discussion
Table 6 presents the empirical results of the above model, estimated by using Eviews
software. Each of the macro variables CPI, M2 and TB91 rate are found to be statistically
significant, separately as well as taking all together. The dummy variables for political
changes as well as for NRB's policy on lending against share collateral are also found to
be significant. The signs of the coefficient are also as expected.
Table 6: Regression Results
Dependent Variable: dlog(SI)
Number of observations: 166
Eq 5
0.038***
(0.005)
dlog(CPI)
0.713**
(0.039)
dlog(M2)
0.800***
0.754***
(0.005)
(0.007)
tb91
-0.005*
-0.005*
(0.042)
(0.064)
d1
-0.042***
-0.041***
-0.037***
-0.035***
(0.001)
(0.001)
(0.005)
(0.005)
d2
-0.039**
-0.047***
-0.026
-0.039**
(0.019)
(0.005)
(0.130)
(0.022)
dlog(SI(-1))
0.149*
0.175**
0.115
0.130*
(0.049)
(0.020)
(0.140)
(0.089)
Adj. R-squared
0.119
0.138
0.117
0.169
D-W stat
2.000
2.048
2.034
2.036
Note: *** implies significant at 1% level, ** implies significant at 5% level and * implies
significant at 10% level. Figures in parenthesis are the respective P-values.
Source: Authors' calculation
const
Eq 2
0.035***
(0.002)
0.755**
(0.034)
Eq 3
0.029**
(0.013)
Eq 4
0.054***
(0.000)
36 NRB ECONOMIC REVIEW
The performance of the stock market is found to be positively related to inflation and
growth of M2, and inversely related to TB91. This implies that higher inflation induces
investors to invest in equity as a hedge against inflation, thereby pushing up stock prices.
Likewise, growth in money supply leads to greater demand for stocks as result of
portfolio substitution with ample liquidity. Given the limited supply of stocks, this exerts
upward pressure on stock prices. Negative relationship between interest rate and stock
market index implies that low interest rate make stocks more attractive because of low
cost of credit as well as low opportunity cost foregone by holding bank deposits. Hence,
in case of low interest rates, depositors may use their deposits to buy stock on the one
hand and on the other hand, people can borrow at the low interest rates from banks and
financial institution to make investment in share market. Our findings are similar to Khan
(2014).
The negative signs for the coefficients of both dummies indicate that political uncertainty
and tightening of loans against share collateral by the NRB have negative impact on the
NEPSE index. The positive coefficient for lagged stock market index term indicates the
past month's stock price has a significant impact on the current month stock index. It
shows the persistence behaviour, in other words, chartist behaviour in stock market. In all
four equations, R2 is not so high which indicates that news, rumours and speculations
must have played the important role in fluctuating stock market index. Moreover, stock
market changes daily while other macroeconomic data are not available on a daily basis.
For the results of equation (5) to be robust, it is necessary that it should not suffer from
the problem of multicollinearity. Though CPI, M2 and TB91 have significant correlation
in level form, no such correlation was found in log difference form (Table 7)3. This gives
the indication that the possibility of multicollinearity in equation (5) is very low.
Table 7: Cross -correlation between Explanatory Variables
CPI
M2
TB91
DLOG(CPI) DLOG(M2)
CPI
1
DLOG(CPI)
1
--------M2
0.994
1
DLOG(M2)
0.036
1
(0.000)
----(0.643)
----TB91
-0.257
-0.271
1
TB91
-0.034
0.018
(0.000)
(0.000)
----(0.663)
(0.814)
Note: Figures in parenthesis are the P-value for the null hypothesis of no correlation.
Source: Authors’ calculation
3
TB91
1
-----
Most researchers appear to consider the value of 0.9 as the threshold beyond which problem of
multicollinearity can occur (Asteriou and Hall, 2007).
Determinants of Stock Market Performance in Nepal 37
VI. CONCLUSIONS
This paper examines the determinants of stock market performance in Nepal, which has
been passing through up and down in recent years. Since stock market tends to be highly
sensitive and volatile, we examine the determinants of stock market index on monthly
data. We have found the Nepalese stock market has been behaving as we expected
theoretically. It has strong positive relationship with inflation and growth of money
supply, and negative response to interest rate. It shows that people have been gradually
taking stock market as a hedge against inflation and invest in this market when there is
ample liquidity available at a low interest rate. More importantly, the stock market
performance has been found to be influenced by political changes similar to finding of
Dangol (2008) and the NRB’s policy. The positive outlook for political stability has
positive impact on stock market index. Similarly change in NRB’s policy on lending
against share collateral has significant impact on the movement of stock market index.
A number of policy implications can be drawn from this study. First, Nepalese stock
market has been quite responsive to macroeconomic development, especially monetary
sector development. Second, a loose monetary policy could trigger an asset price bubble
in share market, which is mainly dominated by banks and financial institutions. Third,
share investors seem to watch the political development closely. Hence, a positive
political development with stability can promote share market further which can play a
vital role for financial intermediation and resource mobilization through capital market.
Fourth, NRB’s policy on lending against share collateral has been effective in influencing
the share market. This indicates the significant role of NRB’s policy in the share market.
As our results reveal that share market is also influenced by rumours, news and
speculations, transparency should be increased in this market by making information
related to listed companies easily accessible. Transparency and communication should, in
fact, be enhanced by the concerned authorities in order to clear gossips and rumours in
the market.
*****
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Rahman, A. A., N. Z. M. Sidek and H. T. Fauziah. 2009. "Macroeconomic Determinants
of Malaysian Stock Market", African Journal of Business Management, 3(3) : 95106.
Rashid, A. 2008. “Macroeconomic Variables and Stock Market Performance: Testing for
Dynamic Linkage with a Known Structural Break.” Saving and Investment, 32(1) :
77-102.
Ratanapakorn, O. and S. C. Sharma. 2007. "Dynamics analysis between the US Stock
Return and the Macroeconomics Variables." Applied Financial Economics 17(4) :
369-377.
Ross, S. A. 1976. "The Arbitrage Theory of Capital Asset Pricing."
Economics Theory, 13 : 341-360.
Journal of
Singh, D. 2010. "Causal Relationship Between Macro-Economic Variables and Stock
Market: A Case Study for India.", Pakistan Journal of Social Sciences, 30(2) : 263274.
Yusof, R. M. and M. S. A. Majid. 2007. “Macroeconomic Variables and Stock Return in
Malaysia: An Application of ARDL Bound Testing Approach." Saving and
Investment, 31(4) : 449-469.
40 NRB ECONOMIC REVIEW
Appendix 1
List of Major Political Events and Likely Impact on Share Market
S.
N.
1
June 2001
2
Feb. 2005
3
4
5
Oct. 2005
Jan. 2006
Apr. 2006
6
Nov. 2006
7
Apr. 2007
8
Jan. 2008
9
Apr. 2008
10
Aug. 2008
11
May 2009
12
13
Jun. 2010
May, 2011
14
Aug. 2011
15
May 2012
16
Nov. 2013
Date
Event
The Royal massacre.
King Gyanendra dismissed Prime Minister Sher Bahadur Deuba and took up executive
power.
Cease fire by the Maoists.
Cease fire withdrawn by the Maoists
Restoration of Parliament and start of peace process
Peace agreement between the government and Maoists; Maoists agreed to lay down
arms.
Maoists joined interim government, a move that took them into the political
mainstream.
A series of bomb blasts killed and injured dozens in the southern Terai plains, where
activists were demanding regional autonomy.
Former Maoist rebels became the largest party in elections of the new Constituent
Assembly (CA), but failed to get an outright majority.
Maoist leader Puspa Kamal Dahal (Prachanda) formed coalition government, with
Nepali Congress in opposition.
Prime Minister Prachanda resigned in a row with President Yadav. Maoists left the
government after other parties opposed integration of former rebel fighters into
national army.
PM Madhav Kumar Nepal quit under Maoist pressure.
Constituent Assembly failed to meet deadline for drawing up new constitution.
PM Jhalnath Khanal resigned after government failed to reach compromise with
opposition on new constitution.
Prime Minister Baburam Bhattarai dissolved CA, called elections for November 2012,
after politicians missed a final deadline to agree on a new constitution.
Election for CA second time. Nepali Congress party, Nepal Communist Party (UML)
became the first and second largest party with two-third majority together. These two
parties have some common political agenda.
Possible
Impact
Bad
Bad
Good
Bad
Good
Good
Good
Bad
Bad
Good
Bad
Bad
Bad
Bad
Bad
Good
Sources: Dangol (2008) and BBC News, South Asia: http://www.bbc.com/news/world-south-asia-12499391
Appendix 2
List of Major Policy Changes by NRB on Loans against Share Collateral and
Likely Impact on Share Market
S.
N.
Date
1
Oct7, 2007
Event
Margin lending limit reduced to 50 % of last 90 days average price of shares;
restriction on restructuring of margin loan; regulation requiring maximum period of
margin loan not to exceed 1 year.
2
Jan 22, 2008
Margin lending limit not to exceed 50 % of the last 180 days average price of shares
or 50 % of market price, whichever is minimum.
3.
Jan 15, 2009
Regulation requiring to make a margin call if the collateral is seen not sufficient to
secure the loan.
4.
Oct 30, 2009
Restructuring of the margin loan was allowed provided that the 50 % of principal
and interest has been repaid.
5.
Feb 22, 2010
No need to make margin call if the price fall of the share is not more than 10%;
About 75 % of margin loan amount was allowed to restructure
6.
Aug 10, 2010
Margin lending limit increased to 60% of the last 180 days average price of shares or
50 % of market price, whichever is minimum.
7.
Jul 14, 2011
BFIs were allowed to make self decision on the limit of margin lending based on the
last 180 days average price of shares or 50 % of market price, whichever is
minimum; Revaluating the shares and extending loan limit was restricted.
8.
Jun 10, 2012
Loan could be extended with the guarantee from the broker instead of pledging
original share certificates.
Source: Various NRB Circulars
Possible
Impact
Bad
Bad
Bad
Good
Good
Good
Good
Good
Structural Change and Per Capita Income in
Nepal: Empirical Evidences#
Guna Raj Bhatta
Abstract
This paper empirically examines Nepalese economic structure by applying OLS technique on the
annual series of sectoral growth, population and capital related variables ranging from 1975 –
2012. The estimates obtained with due consideration of stationarity of the series including HP
filter revealed that industrial sector is significant to increase per capita income compared to the
agriculture and service sectors in Nepal. Moreover, health as indicated by life expectancy and
population at working age are found to be substantial to increase the income but, education and
capital formation are found insignificant. It is inferred that employment matters for raising per
capita income, requiring employment-led growth rather mere growth of economic sub-sectors.
Hence, it is needed to have balanced contribution of economic sub-sectors and their employment
share to national economy along with healthy workforce to raise the per capita income.
Key Words: Structural Change, Employment, Per Capita Income, Nepalese Economy
JEL Classification: O10, O49, L16, N10
#

The earlier version of this paper is available at www.nrb.org.np under NRB Working Paper
series, NRB-WP-23, 2014.
Assistant Director, Nepal Rastra Bank, Research Department, Central Office, Baluwatar,
Kathmandu, Nepal. Email: [email protected], [email protected]
Acknowledgement: I would like to express my sincere thanks to the Editorial Board of NRB
Economic Review for the comments and suggestions.
42 NRB ECONOMIC REVIEW
I.
INTRODUCTION
Economic growth of the country is always a major concern worldwide since rise in GDP
is one of the major human welfare indicators. Direct correlation is found between
increased real output and income, with improvements in development factors in the
history (Welker, 2012). Higher GDP growth not only provides better opportunities to
improve access over basic requirements for the livelihood, but also provides more saving
and revenue to the government. Nevertheless, economic transformation from rural
agricultural to modern industrial or service sectors is the fundamental requirement to
achieve high and sustainable growth. This can be said as the rapid and sustainable
economic development in most of the developed as well as emerging economies has been
achieved with the permanent shifts in their economic structure over the long-run. They
have experienced a gradual transformation of the economy from rural subsistent
agriculture to the modern industrial and then ultimately to the service dominant.
Although there are ample resources such as sufficient arable land, natural resources and
labour force, Nepal is still among the poorest countries in the world as the latest human
development index ranked the country 157th out of 187 and the rank for per capita income
is 207th out of 229 countries (based on purchasing power parity). Nevertheless, the rank is
35th in labor force availability and 46th in percentage of arable land (CIA Fact Book,
2013). Likewise, Nepal is ranked fifth in employees per hectare, requiring 3.6 people to
cultivate one hectare of land.
Economic growth is predominantly determined by the performance of agricultural sector
in Nepal. This sector contributes more than one third to the country's gross domestic
product (GDP) and employs about two-thirds of the total labour force inferring a low
productivity. Moreover, the country experiences a monsoon-based growth as it witnesses
an improved agricultural GDP at the time of favorable rainfall (Acharya & Bhatta, 2013).
With these scenarios, Nepal witnessed a 4 percent growth of the economy on an average
in recent ten years, in which agriculture and industry sectors had grown by 3.3 percent
and 2.7 percent respectively whereas services sector had witnessed a growth of 5.3
percent. The share of agriculture was gradually declining over the study period whilst the
share of services steadily increasing, being more than 50 percent in 2013 and 2014.
However, the industrial share to GDP was found to be increasing until late 1990s and
started declining.
The aforesaid facts and figures clearly depicts Nepalese economy's gradual structural
shift from agro to services sector lead economy. However, problem can be witnessed in
the employment pattern. The agriculture sector contributes only one-third to the economy
but more than 64 percent of the total employment is on this sector. Similarly, the
contribution of service sector to the economy has been growing rapidly but the total
employment share of it is around 15 percent. In this milieu, this paper attempts to
examine the Nepalese economic structure more closely by comparing and contrasting
with the prominent literatures and prescribing some perceived policies for high and
sustainable growth of the economy.
Structural Change and Per Capita Income in Nepal: Empirical Evidences 43
The rest of the paper flows as follows. The next section reviews the prominent literature
of structural change. Section three portrays the structural change of the Nepalese
economy. Data and methodology are discussed in section four. Section five explains the
results and findings and finally section six concludes the paper with some policy
prescriptions for high and sustainable growth.
II.
STRUCTURAL CHANGE MODELS AND LITERATURE
The economic structural change is often considered as a permanent shift in the
fundamental structure of an economy, basically an agrarian economy shifts to either
industry or service based. In many countries, it primarily involves a decline in share of
agriculture to the GDP and a rise in share of services (Maddison, 1991; Buera and
Kaboski, 2012). It is believed that without the structural change, modern economic
development is impossible (Kuznets, 1971) which is mostly associated with promising
growth and continuous transformation (Pasinetti, 1981) in the globalized and dynamic
economic system. Although employment shares in manufacturing were previously
thought to be increasing monotonically as countries develop (Uy et. Al., 2013), the rise of
new world economic powers has been primarily determined by the rapid structural
change of their economies, that is, the shift from mining and agriculture to manufacturing
and then to skill and technology-intensive sectors (Olga and Lelio, 2010).
Lewis (1954) emphasizes the need to transform the structure of an economy from low
labour productive agriculture sector to the high labour productive modern industrial
sector. In the least developed countries (LDCs), a large population depends upon
traditional rural subsistence sector with surplus labour and hence, such surplus labour can
be transferred to a highly productive modern sector in the process of development.
Observing the happenings in the United States, Fuchs (1980) emphasized the importance
of services sector in the economy, particularly, the changing patterns of employment,
which grew across western economies as time passed. Likewise, Fuchs (1980) argues that
to augment the contribution of services sector, it is required to increase participation of
females in labor force as working-wives are likely to spend more out of their earnings to
the services compared to males.
Besides the development of primary and secondary sectors, Fisher (1939) advocated
about the emergence of large services sector for the economic progress, also known as
tertiary sector development. Later on, Clark (1940) established the Fisher's theme as a
tertiary sector development model. Fisher-Clark approach of structural transformation
explains that large amount of labour force working in the services sector will lead the
country to the development and high-growth. The model proposes two significant factors
in the emergence of service sector, i.e., high income elasticity of demand and low
productivity of labor in services. Fisher-Clerk analogy is further supported by Cost
Disease Hypothesis of Baumol (1967). This hypothesis argues that there will be shift to
service from manufacturing due to low productivity, less progressiveness, higher costs
and higher relative prices of service compared to manufacturing.
44 NRB ECONOMIC REVIEW
In the stage of economic development, innovation led by dissemination and imitation
seems to be most dominant factor for structural change of the economy (Schumpeter,
1939) and structural changes especially in specific industry are significant determinants
of aggregate income and growth (Pender, 2002). Todays' advanced economies had
followed two most prominent growth strategies, short-run strategy for stimulating growth,
and a medium to long-run strategy to sustain that growth (Ocampo, 2003; Haggard and
Kaufman, 1983).
The emergence of international trade has also shifted the pattern of employment as we
observe the decline in U.S. manufacturing employment as an effect of its trade with
China (Autor, et.al., 2011). In addition, the gain received today by China and India from
the external sector has been realized by the transformation of their economies. If they had
not have emphasized on innovation and change towards industry and services, traditional
garments and agricultural products would not have been sufficient to get advantage of
international trade and investment to their economies (Rodrik, 2007). Nevertheless, the
pattern of structural transformation varies with region, for instance, the path followed by
developed economies and SAARC countries is different being heterogeneity in the
transformation processes (Sawhney, 2010).
III. CHANGES IN ECONOMIC STRUCTURE: GLOBAL AND
NEPALESE SCENARIO
3.1 Global Change in Economic Structure
As discussed earlier in section II, the structure of the advanced economies has a very low
contribution of agriculture sector and predominance of service sector. Depending upon
the individual economy, the contribution of industrial sector to GDP is found less than 50
percent from the beginning of study period, at the middle of agriculture and service.
Likewise, the pattern of employment from agriculture, industry and services are similar in
the contribution to GDP. A significant dominance of service sector in job opportunities
has been observed as compared to the agricultural sector in most of the advanced
economies (Figure 1 and Figure 2).
In emerging economies, share of each sector to the GDP has been oriented to catch the
path of advanced economies, though some countries are still far behind. It can be
identified as a declining share of agriculture and increasing share of services to GDP over
time. But sectoral contribution to employment has yet to be balanced with the
contribution to GDP in these economies. Thus, the structure of developed and emerging
economies shows a similar trend in contribution to GDP and employment, however, the
perfect balance on employment and sectoral share can only be observed in the advanced
economies (Figure 1, Figure 2 Figure 3 and Figure 4). This issue is also discussed on the
trend of Nepal's abut neighbors, India and China in the following section 3.2.
Structural Change and Per Capita Income in Nepal: Empirical Evidences 45
Figure 1: Economic Structure and
Employment of United States
100
Figure 2: Economic Structure and
Employment of Japan
80
USA: Value Added, % of GDP
Japan: Value Added, % of GDP
60
50
40
20
0
0
1970 1975 1980 1985 1990 1995 2000 2005 2010
Agriculture
100
Industry
1970 1975 1980 1985 1990 1995 2000 2005 2010
Services
Agriculture
100
USA: Sectoral Employment Share, %
50
50
0
0
Industry
Services
Japan: Sectoral Employment Share, %
1980 1985 1990 1995 2000 2005 2010
1980 1985 1990 1995 2000 2005 2010
Agriculture
Industry
Agriculture
Services
Industry
Services
Data Source: World Development Indicators, World Bank.
Figure 3: Economic Structure and Employment
of Philippines
Figure 3: Sectorwise Contribution of GDP to the Economy
of PCI
Figure 4:Economic Structure and
Employment of Thailand
Figure 3: Sectorwise Contribution of GDP to the Economy
of PCI
Data Source: World Development Indicators, World Bank.
46 NRB ECONOMIC REVIEW
3.2 The Indian and Chinese Economic Structure
Economic structures of two giant Nepalese neighbors namely China and India are
substantially different than the structure of advanced economies. Although Chinese
economy gives a different picture, Indian economy still possess some fundamental
structural problem. In India, service sector has 57 percent share to GDP whereas
agriculture sector accounts for 17 percent in 2012.The problem for India is observed in
employment pattern as in Nepal. In 2010, for instance, the contribution of agriculture and
service to GDP are 18 percent and 54 percent respectively but contribution to the
employment of those sectors for the same year accounted for 51 percent and 27 percent
respectively, indicating a low productivity in agriculture and little contribution of service
sector towards the employment generation. Nonetheless in China, the contribution of
industrial sector to GDP has been 47 percent in 2010 being continually the largest subsector of the economy but for providing employment, the sector is at the lowest level with
29 percent. Similarly, service sector in China has 43 percent shares in the GDP with 35
percent contribution to employment generation in 2010. Compared to Nepal and India,
Chinese economic structure has been better in productivity and in employment
generation. But the problem is in industrial employment in China; though the contribution
to GDP is the highest, it is the lowest in employing population (Figure 5 and Figure 6).
Figure 5: Economic Structure and Employment of
China
Figure 6: Economic Structure and Employment of
India
Data Source: World Development Indicators, World Bank.
3.3
Structure of Nepalese Economy
Even though Nepal has sufficient natural resources and labour force among others, the
historical average GDP growth rate is just about 3.7 percent since 1960 onwards
Structural Change and Per Capita Income in Nepal: Empirical Evidences 47
(Figure 7). The industrial growth remained more unstable throughout the study period
relative to the growth of agriculture and service (Table 1and Figure 8).
Table 1: Standard Deviations of Nepal's Growth
Sector
Agriculture
Industry
Services
Aggregate
Full Sample
3.3
7.2
3.0
2.5
1991 Onwards
2.2
4.0
2.3
1.6
Source: Author's Calculation
Figure 7: Real GDP Growth Rate in Nepal
Figure 8: Sectoral GDP Growth Rate in Nepal
Data Source: World Development Indicators, World Bank
Data Source: World Development Indicators, World Bank.
According to Nepal Labor Force Survey (NLFS) 1998/99, of the total labour force, 76
percent were engaged in agriculture, 10 percent in industry and 14 percent in service.
After about one decade, as NLFS-2008 presented, 74 percent were in agriculture, 11
percent in industry and 15 percent in services – not much different from 1998/99.
Nevertheless, the share of these three sectors to the GDP had changed over that period. In
1998/99, the contribution of agriculture sector was 38 percent, industry 23 percent and
services 39 percent. The share of service sector in GDP jumped up to 48 percent in
2008/09, while the share of industry declined to 16 percent. The share agriculture
declined marginally to 36 percent in 2008/09. In 2012/13, agriculture sector contributed
34 percent; industry 15 percent and service sector 51 percent. In this way, the
contribution of service sector has been increasing while that of agriculture and industrial
sector has been declining (Figure 9). In short, a gradual change is observed in economic
structure since the share of services sector to GDP exceeded the sum total of agriculture
and industry sectors so far.
However, the major bottleneck in Nepalese economic transformation is employment
pattern. It is believed that increased employment opportunities are the prerequisites for
continued and sustained economic growth. In Nepal, nonetheless, we can observe a
massive underemployment with very low productivity in agriculture. The opposite is the
case of services as the contribution to economy is more than half but it provides
employment for only 15 percent of work force. From the economic sense, however, the
industrial sector is still playing vital role with closer similarities in contribution to both
GDP and employment opportunities (Figure 10).
48 NRB ECONOMIC REVIEW
Figure 10: Share of Sectoral Employment in Nepal
Figure 9: Sectoral Contribution in the GDP in Nepal
100
80
14.1
9.8
15.3
10.8
76.1
73.9
60
40
20
0
1998/99
Agriculture
Data Source: World Development Indicators, World Bank
IV.
2008
Industry
Services
Data Source: Nepal Labor Force Survey, 1998/99 and 2008.
DATA AND METHODOLOGY
The paper follows the methodology of Pender (2002) to identify the determinants of
structural change variables with slight modification. As Pender (2002) uses the concept
with dynamic panel data analysis of OECD countries, the same technique has been
adopted here only for Nepalese data to model ordinary least squares (OLS).
Since per capita income of an economy is total production of the country within a year
divided by the total population, factors that may cause to change income can be
hypothesized as:
PCI = f(agri_growth, ind_growth, ser_growth, edu, health, pop, popw, capital, others)…(1)
Here, PCI refers to per capita income, agri_growth, ind_growth and ser_growth is the
growth of three major economic sectors namely, agriculture, industry and service. The
level of education (edu), health condition, total population and working population (pop,
popw), capital injection and other variables are presumed to be the major determinants of
per capita income. More precisely, based on this income hypothesis, the income model
can be estimated as:
𝑃𝐶𝐼𝑡 = 𝛼 + 𝛽1 𝐴𝐺𝑅𝐼_𝐶𝐺𝑡 + 𝛽2 𝐼𝑁𝐷_𝐶𝐺𝑡 + 𝛽3 𝑆𝐸𝑅_𝐶𝐺𝑡 + 𝛽4 𝐸𝐷𝑈𝑡
+ 𝛽5 𝐺𝐹𝐶𝐹𝑡 + 𝛽6 𝐿𝐸𝑡 + 𝛽7 𝑃𝑂𝑃𝑡 + 𝛽8 𝑃𝑂𝑃𝑊𝑡 + 𝜀𝑡
… (2)
Where, per capita income (PCI) is the nominal annual US dollar per capita income in
purchasing power parity. Growth rate of share of agriculture, industry and services are
termed as AGRI_CG, IND_CG and SER_CG in the model, which is the percentage
growth of sectoral contribution into the total Nepalese GDP, calculated as follows.
Agricultural GVA
100} Total GVA
t
AGRI_CG = [{
Agricultural GVA
100} ] ×100-100
Total GVA
t-1
{
… (3)
Structural Change and Per Capita Income in Nepal: Empirical Evidences 49
Industrial GVA
100} Total GVA
t
{
Industrial GVA
100} ] ×100-100
Total GVA
t-1
… (4)
Services GVA
100} Total GVA
t
Services GVA
100} ] ×100-100
Total GVA
t-1
… (5)
IND_CG = [{
and
SER_CG =[{
{
Life expectancy (LE) is the expected years of life at birth, total population (POP) is the
total number of population in million residing in the country and population at working
age (POPW) is the population in million, with 15 to 64 years. The above data are
obtained from World Bank Database.
Gross fixed capital formation (GFCF) is the annual fixed capital formation in million
rupees, obtained from national accounts statistics published by central bureau of statistics
(CBS). Years of schooling (EDU) is the average year of schooling of working age
population, calculated by multiplying currently available information of enrollment of the
students ranging from primary school to advanced university degree that is obtained from
Economic Survey (Various Editions).
The augmented Dicky-Fuller (ADF) test for unit root has been presented below (Table 2).
Table 2: Augmented Dickey Fuller (ADF) Test for Unit Root
Variable
AGRI_CG
IND_CG
SER_CG
LOG(GFCF)
EDU
LE
LOG (LE)
Log (PCI)
Log (PCI) @ Trend (AIC)
POP
Log(POPW)
t-Stat
-5.93
-4.59
-7.57
-0.78
-1.51
-2.90
-3.24
0.61
-3.303
0.72
-0.16
Level
First Diff
P Value
t-Stat
P Value
0.000
0.001
0.000
0.813
-6.786
0.000
0.518
-8.183
0.000
0.058
0.028
0.988
-6.475
0.000
0.086
0.991
2.822
0.066
0.934
3.134
0.034
Source: Author’s calculation
The Augmented Dickey Fuller (ADF) test for unit root shows the variables AGRI_CG,
IND_CG, SER_CG and LE are stationary at level whilst rests are found to be nonstationary. After first difference, log (GFCF), EDU, POP and log(POPW) become
stationary. Nonetheless, per capita income (PCI) variable shows a trend stationary nature.
When time trend is included in the test equation, PCI is found to be stationary at 10
50 NRB ECONOMIC REVIEW
percent significance level and log(PCI) at 5 percent significance in even in level data
(Table 2).
Hence, to address the trend stationarity of PCI, the Hodric-Prescott (HP) filter is applied
to extract trend and cycle from PCI. The HP filter generates new cycle and trend from the
trend-stationary series that minimizes the variance of the old series around the new one,
subject to a penalty constant . Once trend and cycle is extracted, we can use cycle in the
regression equation. Hence, in case of PCI, the filter chooses PCI_Cyclet to minimize:
∑𝑇𝑡=1(𝑃𝐶𝐼𝑡 − 𝑃𝐶𝐼_𝐶𝑦𝑐𝑙𝑒𝑡 )2 + 𝜆 ∑𝑇−1
𝑡=2 ((𝑃𝐶𝐼_𝐶𝑦𝑐𝑙𝑒𝑡+1 − 𝑃𝐶𝐼_𝐶𝑦𝑐𝑙𝑒𝑡 ) − (𝑃𝐶𝐼_𝐶𝑦𝑐𝑙𝑒𝑡 −
2
𝑃𝐶𝐼_𝐶𝑦𝑐𝑙𝑒𝑡−1 ))
… (6)
By applying this method, the new series of per capita income, pci_cyclet, which is trendstationary free and contains all the information of PCI too.
Based on Pender (2002)’s modeling framework and considering the nature of data
and properties, the best fit model can be presented as follows.
𝑃𝐶_𝐶𝑦𝑐𝑙𝑒𝑡 = 𝛼 + 𝛽1 𝐴𝐺𝑅_𝐶𝐺𝑡 + 𝛽2 𝐼𝑁𝐷_𝐶𝐺𝑡 + 𝛽3 𝑆𝐸𝑅_𝐶𝐺𝑡 + 𝛽4 ∆𝐸𝐷𝑈𝑡 ++𝛽5 ∆ (log(GFCF))𝑡 +
𝛽6 log(𝐿𝐸)𝑡 + 𝛽7 ∆(𝑃𝑂𝑃)𝑡 + 𝛽8 ∆(log(𝑃𝑂𝑃𝑊))𝑡 + 𝛽9 𝑑𝑢𝑚01 + 𝜀𝑡
… (7)
Equation (7) illustrates the prime factors in influencing per capita income of the
citizens in an economy. In addition to the equation (2), one additional dummy
variable is introduced. Dummy variable (dum01) is the variable with value one if
the year of analysis is 2001 and zero otherwise, which is used to capture
compilation break from 2001 as Nepal switched in accounting GDP with new
system of National Accounts (SNA), 1993 with the broad categorization of the
sectors especially that of services.
The impact of sectoral growth variables is assumed to be positive for per capita
income. Level of education, as explained by EDUt is also expected to increase the
income since education is a human capital. Gross fixed capital formation is
assumed to impact positively to income as capital is most significant factor for
productivity increment and high growth. Effect of Life expectancy (LE) and
population at working age (POPW) are also hypothesized to have positive impact
on per capita income. Nevertheless, total population (POP) is presumed to reduce
the income, as the population rises, income is to be distributed among citizens.
There may be the possibility of multi collinearity among the regressors. To
identify whether there exists serious collinearity problem, variance inflation factor
(VIF) has been estimated. VIF helps quantifying the inflation of the variance due
Structural Change and Per Capita Income in Nepal: Empirical Evidences 51
to the collinearity with other regressors in the estimated equation. The VIF factor
for β̂i have been calculated as follows:
𝑉𝐼𝐹 =
1
1−𝑅𝑖2
…… (8)
V. MODEL ESTIMATION AND RESULT ANALYSIS
Equation (7) is estimated by applying ordinary least squares (OLS) method of estimation
in EViews 8. The estimated coefficients of equation (7) have been presented in Table 3.
Table 3: Empirical Results
S.N. Variable Name
Coefficient
t-Stat
1.
Constant
- 166.24
-2.196**
2.
AGRI_CGt
0.695
0.597
3.
IND_CGt
0.936
2.251**
4.
SER_CGt
- 0.092
-0.139
-0.452
5.
-5.017
EDUt
0.314
6.
8.32
log(GFCF)t
7.
Log(LE)t
32.62
1.742*
-2.081**
8.
-93.12
POPt
3.431**
9.
3032.91
log(POPW)t
10.
Dum01
50.71
3.088**
*=significant at 10 percent level ** = significant at 5 percent or less level
Adj. R2= 0.41, DW = 1.6, F-Stat = 3.60**
Source: Author’s calculation.
In contradiction to the hypothesis, coefficient of AGRI_CGt, represented by the growth
rate of the agricultural share to the GDP, is found with positive sign and SER_CGt with a
negative, both the coefficient are insignificant though. The IND_CGt, which represents
the growth rate of industrial share to GDP, has expected sign and is significant at 5
percent level indicating that increased share to industrial GDP has a vital role in
increasing per capita income.
The reason behind the insignificance of agriculture sector could be justifiable.
Agricultural productivity matters for other sectors development too, as very low
agricultural productivity can severely damage modernization of economy (Kim &
Whang, 2012). Moreover, Nepal's agriculture is largely at sustenance level, being high
level of underemployment and only 40 percent farmer produce sufficient foods for one
year's consumption (CBS, 2013)4. As discussed before in section three and proved
statistically, the industrial sector is still playing vital role in raising per capita income only
4
Nepal Living Standards Survey-III reveals 32 percent underemployment in all sectors and
share of wage employment in agriculture is just 2.8 percent compared to 12.6 percent in nonagriculture. Moreover, share of self-employment in agriculture is still 61 percent, only 10
percent down from 1996 level.
52 NRB ECONOMIC REVIEW
due to the closer similarities in contribution to both GDP and employment opportunities.
As the sector contributes about 15 percent to the economy by employing 11 percent of
total employment, it is the closest combination in the share to employment and to the
GDP so far.
Nonetheless, there is also strong evidence on the insignificance of the service sector. The
case of service sector is like the opposite the case of agriculture in terms of employment.
Service sector's contribution to economy is more than half but it provides employment
only for 15 percent of the total employed. With this, from income perspective, service
sector's growth is still playing no role till date. Unless it absorbs the workforce at a speed
of its growth and then its sectoral contribution to the national economy, it would not raise
the living standards of societies.
As hypothesized earlier, both life expectancy and population at working age have
significant positive impact to per capita income; the coefficient of log(LE)t is significant
at 10 percent level and log(POPW)t at 5 percent or lower level. These statistical results
can be inferred as the improved health condition and young working groups foster the
overall per capita income. As presumed before, increase in country's population reduces
per capita income, POPt significant at 5 percent or lower level. The dummy variable,
dum01 is significant at 5 percent or lower level. Hence, it has captured the compilation
break of services sector in 2001.
Nevertheless, education and gross fixed capital formation have been found insignificant
to raise income. Although the sign of log(GFCF)t is positive as expected, the sign of
EDUt is even negative. This contradictory finding, that is, the growth of industrial share
to GDP is significant but capital formation is not increasing the income can be argued as
follows. These two phenomena have been regressed with different scenarios, as the
former indicates the sectoral growth in the share to the total production of the economy,
and the later, with one of the factors of production that usually input for all three sectors
in aggregate (agriculture, industry and services). Although capital injection may increase
the productivity, the productive use of capital matters which may be suffering in Nepal
(Bhatta, 2014) 5. Most importantly, capital injection should directly hit the income of the
people, especially in employment creation; this might have missing in Nepalese context.
On the other hand, the insignificance of the education variable as measured by years of
schooling of working age population could be due to the couple of reasons. The increased
number of outgoing migrants in the recent years (and impact of remittance on the
education is still to witness), lack of labor movement from agriculture, being high share
of underemployment, lower level of vocational trainings etc. may be impeding the role of
education to the national economy. Besides, it is also witnessed a large chunk of educated
unemployment in Nepal, educated youths being unable to get job due to the 'lack of
access to relevant education and training, and lack of information' among others (United
5
This issue has been highlighted in an article at The Himalayan Times, March 11, 2014.
Structural Change and Per Capita Income in Nepal: Empirical Evidences 53
Nations, n.d., and Sharma, 2013). The unemployed youths tend to get higher education,
which is easily accessed without any qualifying exam restrictions in Nepal. Besides these
all, there may the minor adjustment possibility of education data used in analysis since
the education data is computed self. Nevertheless, change in sign with large variation in
the coefficient couldn't be expected even after the revision of the series.6
So that, the insignificance of education and capital variable indicates that both the current
level of education and capital injection have not contributed significantly to increase per
capita income. Thus, it is essential to enhance the level of education and capital formation
drastically in the days to come if Nepal intends to increase income of the people through
education and investment as in advanced and emerging economies. This can be inferred
on the basis of literature supports in the importance of capital, both human and physical,
in OECD and other emerging economies.
The goodness of fit, diagnostic and stability tests satisfy the minimum criteria required
for the statistical inference. The Lagrange multiplier (LM) test for autocorrelation shows
no serial correlation in residual as p-value of the test is 0.64. The residual plot of the
model shows a random move around mean (Annex I - Figure 12). The stability test of the
model is also significant since the recursive estimates represented by CUSUM and
CUSUM squares test for stability lie within 5 percent range (Annex I - Figure 13).The
adjusted R2, Durbin-Watson statistics and F-Stat for overall model significance show the
satisfactory results.
The VIF estimates for identifying the multi-collinearity among the regressors has been
presented in Table 4.
Table 4: Variance Inflation Factor (VIF) Estimates
Variable
AGRI_CG
IND_CG
SER_CG
D(POP)
D(LOG(POPW))
D(EDU)
LOG(LE)
DUM01
D(LOG(GFCF))
6
Centered VIF
7.82
3.22
6.39
4.48
3.82
1.07
1.81
2.43
1.51
Source: Author’s calculation
The negative sign of coefficient of EDUt is something weird in our estimation. Perhaps, a
significant variation may not result even if the education data is revised. The estimated data are
near to the official published series for the specific years. See annex II-A for the details.
54 NRB ECONOMIC REVIEW
Generally, a very low value of VIF is the indication of no multi-collinearity problem, in
which some researchers say only below 5 is the tolerable, for instance, Rogerson (2001).
However, many researchers such as Neter et al., 1989: 409; Hair et al., 1995; Marquardt,
1970; Mason et al., 1989 have set the centered VIF below 10 as a tolerable limit for
collinearity. In our VIF estimates, all the values of the centered VIFs are below 10. The
VIFs of AGRI_CGt and SER_CGt have been found relatively higher but within the
tolerable limit.
The empirical findings, hence, suggest the requirement of an employment-generating
economic growth. Even though we may achieve a higher sector-specific growth, the
concern would be whether there is new employment generation. The message is that the
balance of contribution to the GDP and to the total employment is a must for increasing
income of people.
VI. CONCLUSION
Although the contribution of industrial sector does not change much in Nepal, historical
data shows a gradual shift in the share of economy from agriculture to services. But the
employment pattern has not changed in line with the change in sectoral composition of
GDP. Unbalanced contribution of agriculture, industry and service sectors is found in the
share of GDP and total employment.
In Nepal, empirical estimates show that industry is the most significant sector to increase
income compared to agriculture and service sectors. Improvement in health is also found
significant to increase per capita income. Besides, working age population contributes to
enhance per capita income of total population. Nevertheless, as against the theory and
international empirics, capitals both human and physical have been found not
contributing to raise per capita income, being investment and education variables
insignificant in the empirical analysis. This could be because increased educated
unemployment and lack of productive investment.
The unbalanced contribution of employment, that is, high subsistence on agriculture and
very low employment by the service sector could be blamed as the insignificance of these
sectors in increasing the income. Hence, it is the major structural problem in Nepaldeviation in economic and the employment structure especially higher level of
underemployment and eroded productivity in agriculture and employment unfriendly
service sector. Industrial sector relatively observed better in increasing per capita income
as the sector is much closer in employment generation and the share of the economic
growth.
Thus, employment generation is the utmost importance in an economy to raise the income
of the people, so is for Nepal. In addition, improved health and larger share of working
age people are also needed. The focus should be on increasing the productivity of the
agriculture sector and move agriculture-based labors to other sectors of economy.
Nonetheless, massive employment can only be generated with increased productive
investment in the aforesaid sectors.
Structural Change and Per Capita Income in Nepal: Empirical Evidences 55
The paper can be further improved by analyzing the panel data of similar economies that
helps in identifying random and fixed effect estimations much comprehensively.
*****
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58 NRB ECONOMIC REVIEW
Appendix I
Figure 11: Hodrick Prescott Decomposition
Figure 12: Residual Plot of the Model
40
20
0
20
-20
10
-40
0
-10
-20
-30
1980
1985
1990
Residual
1995
2000
Actual
2005
2010
Fitted
Figure 13: Stability Tests of the Model
10.0
1.6
7.5
1.2
5.0
2.5
0.8
0.0
0.4
-2.5
-5.0
0.0
-7.5
-10.0
-0.4
2002
2003
2004
2005
2006
CUSUM
2007
2008
2009
5% Significance
2010
2011
2002
2003
2004
2005
2006
CUSUM of Squares
2007
2008
2009
5% Significance
2010
2011
Structural Change and Per Capita Income in Nepal: Empirical Evidences 59
Appendix II
A. Data on Average Years of Schooling for Population and Working age, 15-64 years
Year
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Total Population,
Million
Population at Working Age,
Million
Average Years of
Schooling
12.87
13.16
13.45
13.75
14.06
14.38
14.72
15.06
15.42
15.78
16.14
16.51
16.89
17.27
17.68
18.11
18.57
19.05
19.55
20.07
20.59
21.12
21.65
22.18
22.69
23.18
23.66
24.10
24.53
24.92
25.29
25.63
25.95
26.25
26.54
26.85
27.16
27.47
7.16
7.31
7.46
7.62
7.78
7.95
8.12
8.30
8.48
8.67
8.85
9.03
9.21
9.39
9.60
9.83
10.10
10.39
10.71
11.03
11.36
11.69
12.02
12.34
12.65
12.95
13.22
13.47
13.72
13.96
14.20
14.45
14.69
14.96
15.24
15.56
15.92
16.31
0.86
0.93
1.05
1.15
1.34
1.42
1.47
1.54
1.60
1.55
1.70
1.67
1.73
1.83
2.07
2.31
2.40
2.47
2.41
2.40
2.37
2.47
1.89
2.50
2.58
2.50
2.63
2.66
2.74
2.87
3.01
2.98
3.01
3.13
3.28
3.22
3.21
3.09
UNDP
Data7
0.6
2.0
2.4
2.7
2.8
2.9
3.0
3.1
3.2
3.2
3.2
Data Source: Total Population and Population at Working Age Data is downloaded from World Bank
Database and Average Years of Schooling Data is self-calculated by using school
enrollment data available fr om Economic Survey,. Various Issues.
7
Average number of years of education received by people ages 25 and older, converted from
education attainment levels using official durations of each level. The data is put here for the
reference, obtained from United Nations Human Development Indicators Accessed: 2/25/2013,
at http://hdr.undp.org.
60 NRB ECONOMIC REVIEW
B. Other data series used in analysis
Year
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
GFCF
Million, NPR
2223.0
2443.0
2580.0
3294.0
3263.0
3681.0
4299.0
5465.0
6576.0
6907.0
9386.0
9431.0
11825.0
13414.0
16392.0
17002.0
22780.0
29277.0
37278.0
42032.0
48370.0
56081.0
60794.0
65375.0
65269.0
73324.0
84750.5
89889.3
98072.8
109181.3
117538.9
135532.0
153336.9
178445.5
211039.0
264888.0
292730.0
307384.0
IND_G
NA
8.301994
26.50899
6.529822
0.370499
-0.518811
3.736784
3.999741
-0.467740
-1.562553
19.92926
4.953289
-0.174051
2.159217
2.274153
-1.812131
6.940153
17.47837
1.290158
5.101934
4.783464
0.710507
-0.228906
-1.624526
-3.036574
1.480300
-19.60559
1.672383
0.315668
-1.603388
-0.900514
-2.815206
-0.604771
1.403537
-5.551835
-4.502185
-1.939240
-1.552970
SER_G
NA
8.890165
13.87903
0.561690
-5.285280
11.00017
1.614016
-2.237120
2.925061
-1.819503
25.63819
-1.540583
2.383194
-1.540314
0.471022
-2.897028
10.24382
-1.934237
6.833271
-5.102471
0.767073
0.258643
0.351942
5.315892
-1.869622
0.404737
20.28716
-2.793550
2.293923
1.482795
2.190292
4.798017
2.461346
1.198472
-0.686357
-3.374023
-2.733647
4.311903
AGRI_G
NA
-3.432291
-7.764521
-1.363254
2.027867
-3.956940
-1.408213
0.170157
-1.154514
1.143192
-15.21721
-0.458424
-1.458986
0.342023
-1.027216
2.497816
-8.557058
-4.976672
-5.877285
1.987817
-3.039191
-0.606958
-0.175306
-3.684482
3.473210
-1.143537
-7.782908
2.516736
-2.723048
-0.975243
-2.217146
-4.695526
-3.121590
-2.477033
3.987479
7.327898
4.408893
-4.645298
PCI, PPP USD
281.6
287.6
289.8
295.9
296.3
282.9
327.6
352.5
347.3
386.2
412.9
431.6
441.7
481.3
509.1
539.7
578.9
599.8
620.3
667.4
688.7
719.7
751.7
766.6
793.7
842.8
885.3
884.0
922.1
976.6
1028.9
1083.1
1138.5
1220.6
1272.8
1336.8
1402.1
1484.3
PCICYCLE
(HP Filtered)
19.05878
15.29110
7.541765
3.168187
-7.985212
-34.18853
-4.086187
4.321822
-19.03958
-0.192662
4.774010
0.195859
-14.24678
-0.560529
0.312850
3.265048
14.31836
6.774899
-1.438216
16.79517
9.083011
10.83688
12.95151
-2.929753
-7.870098
7.363763
13.84589
-26.02243
-29.71056
-20.39875
-16.86215
-14.64082
-14.19630
10.54934
3.586678
7.142556
11.21697
31.97413
Note: The data of 1975 represents the Nepal's fiscal year 1974/75, 1976 as fiscal year 1975/76 and so on in the whole data
sets.
Data Source: World Development Indicators, the World Bank and Central Bureau of Statistics, Kathmandu, Nepal.
Determinants of Inflation in Nepal:
An Empirical Assessment
Shoora B. Paudyal Ph.D.
Abstract
This paper examines short term and long term effects of the macroeconomic variables on the
inflation in Nepal during 1975-2011. The variables considered are budget deficits, Indian prices,
broad money supply, exchange rate and real GDP. The regression results from Wickens-Breusch
Single Equation Error Correction model suggest that all variables considered are significant in
long run implying that these variables are the determinants of inflation in Nepal. However, only
budget deficit, money supply and Indian prices cause inflation in the short run. The results are
consistent with monetarists’ hypothesis of money matters and inflationary gap theory of Keynesian
as well as supply constraints approach to inflation.
Key Words: Inflation, Budget Deficits, Single Equation ECM, Imported Prices,
Consumer Price Index, M2, GDP
JEL Classification: E31; C22

Dr. Paudyal is a faculty member of economics, Tribhuvan University, Kathmandu.
Email: [email protected]
The author acknowledges the valuable comments from Editorial Board and external reviewers on
the original version of this paper.
62 NRB ECONOMIC REVIEW
I. INTRODUCTION
A continuous rise in price level is termed as inflation (Parkin, et al, 1986: 367). Inflation
is an ongoing process whereby prices are rising persistently year after year. Shapiro
(1996:468) defines inflation as a rising price level. If such a rise in price level persist for
long it is known as inflation. Consumer price index, gross domestic product deflator and
other several indices measure the changes in price level. The use of these measures is
purposely applied wherever appropriate. However, the rate of percentage change in
consumer price index as a measure of inflation is widely used. We also here adopt this
definition of inflation for our purpose.
Inflation is everywhere and is interestingly touchy issue in macroeconomics. All daily
newspapers cover the news about inflation. There is no dearth of literature on inflation. It
is the mostly discussed issue all over the world among policy makers and academia. It is
because of the fact that its effects are widespread and severe and the impacts are far
reaching. Inflation has been the major concern for the government since it has serious
implication for the living of common peoples. Moreover, it affects several
macroeconomic variables such as saving, investment, real interest, real wage, real income
and level of employment. Inflation depreciates domestic currency and the imports
become more expensive which further push up the domestic prices. In short, inflation is a
burning issue in the macroeconomics and main objective and function of central bank is
to control inflation.
Table 1: Consumer price index
Year
CPI Year
CPI Year
CPI Year
1974/75
9.8 1984/85
20.8 1994/95
59.7 2004/05
1975/76
9.6 1985/86
24.7 1995/96
65.2 2005/06
1976/77
10.5 1986/87
27.4 1996/97
67.7 2006/07
1977/78
11.3 1987/88
29.9 1997/98
75.4 2007/08
1978/79
11.7 1988/89
32.5 1998/99
81.0 2008/09
1979/80
13.4 1989/90
35.2 1999/00
83.0 2009/10
1980/81
14.9 1990/91
40.7 2000/01
85.2 2010/11
1981/82
16.6 1991/92
47.6 2001/02
87.8
1982/83
18.7 1992/93
51.2 2002/03
92.8
1983/84
19.2 1993/94
55.5 2003/04
93.6
Source: IMF, International Financial Statistics, various issues base year 2005=100
CPI
100.0
107.6
114.1
126.6
141.3
155.4
170.2
Table 1 shows that price index (base year 2005) has increased persistently over the
years. It has increased by little over seventeen times (from 9.8 to 170.2) during 19752011. This means the purchasing power of the rupee has eroded by the same speed.
Aforementioned, the impact of rising prices on the real sector is stylised fact. It constrains
the rise of per capita real GDP and thereby reduces the standard of livings of the common
people in the country. The stationary price level has thus been one of success parameters
of the elected government. However, it has been a Herculean task to achieve in
Determinants of Inflation in Nepal: An Empirical Assessment 63
developing countries. In case of Nepal, however, there appear some positive signals in
slowing down the speed of price rise in the later years. For instance, CPI took ten years to
double from 9.8 in 1975 to 20.8 in 1985; it doubled even faster within six years between
1985 and 1991 and within eight year period between 1991 to 1999. This has, however,
turned upside down since the doubling period lengthened to twelve years between 1999
and 2011. This clearly indicates that prices have accelerated at slower motion especially
after 1991’s political change. One of the reasons for this might be relatively improved
supply situation of the commodities during this period. Partly because Nepal’s improved
bilateral relation with India in the changing context and partly because of the sharply
improved trade openness index due to trade liberalisation policy adopted by the
government during early 1990s (Bowdler, et al; 2004)8. Some empirical studies
substantiate that trade openness index has important bearing on the combating hyper
inflation. This paper attempts to examine the relation between inflation and other related
variables that influence the inflation in the country and suggest policy implications.
There may be a bunch of factors that may influence the inflation. In Nepal, price level,
budget deficits, money supply, real GDP are continuously rising for many years.
However, this does not prove that one causes other. We examine in this paper effects of a
number of variables including budget deficits on prices dividing the paper into six
sections: Section I is introduction, section II presents a literature review, and section III
analytical framework, while section IV displays regression results and interpretation. The
last section V concludes the paper.
II. REVIEWS OF LITERATURE
This section is divided into two parts: a) review of theoretical foundation and b) reviews
of empirical studies. The theoretical review shape the relationship between inflation and
other macroeconomic variables under classical/monetary hypothesis, Keynesian, New
Keynesian and New Classical theories, while the empirical review examines both the
short run and long run relationship between the variables under consideration.
The classical economists’ famous quantity theory of money can be summarised in
MV=PT, in which velocity of money in circulation (V) and quantity of goods (T) remain
constant in the short run. So an increase in stock of money (M) brings a proportionate rise
in price level (P). This equation became the foundation of monetarists economic thought.
The monetarist economists opine that too much money chasing too few goods is the cause
of inflation. As per the modern quantity theory of money, demand for money is given by
1/v* PQ. If the economy is at full employment, real income does not change and v being
equals, there is a direct and proportionate relationship between changes in quantity of
money and price level. The central bank prints more paper notes that directly causes
inflation. But under the less than full employment economy, real income does not remain
constant. So, v being constant if real income increases by 4% together with a 10%
increase in money supply implies a 6% increase in inflation- a less than proportionate rise
in price level.
8
They claim that greater trade openness decreases the probability of inflation.
64 NRB ECONOMIC REVIEW
Historically, it is seen that if deficits are financed by money created by central bank that
increases inflation directly (Friedman,1984). Friedman opines strongly that inflation is
always and everywhere a monetary phenomenon. Monetarists including Friedman believe
that money supply has direct effects on the inflation i.e., too much money to chase a few
goods causes inflation. This implies that inflation is the function of money supply and
real output. Friedman (1968) believed that central bank can control the inflation in the
long run by controlling money supply. It is now well accepted that the primary goal of
central bank is achieve a stable and low rate of inflation. However, the governments in
many countries seem fail to support such contractionary monetary policy because of their
temptation toward achieving higher economic growth and employment.
Figure 1 and Table A5 in appendices show the growth of money supply and that of prices
in the country. Both move in the same direction but broad supply has alway been higher
than inflation. High positive correlation between money supply and budget deficits
(Appendices, Table A1) exhibits a very close association between these two variables.
Keynesian views of stimulating economy through aggregate demand often lead to a
situation where government spending exceeds the mobilisation of taxes. It becomes thus a
customary for the government from the developing economies to present deficit budget.
However, it is widely believed that higher budget deficits have serious impacts on the
macroeconomic variables. Among these most striking effect of budget deficits is on
inflation. For this reason, budget deficit is largely considered as one of the major
determinants of the inflation. Deficit compels the central bank to print new money to
finance the budget deficit which led to an increase in money stock9.
Deficits can be financed by other sources such as borrowing from the market which also
indirectly affects inflation. Moreover, many believe that such effects of the budget
deficits pass through interest rates to the major macroeconomic variables such as
investment, employment, price level, consumption, exports and imports in an economy.
9
See Malcolm Gillis, Dwight H Perkins, Michael Roemer and Donald R Snodgrass. 1983.
Economics of Development, Second edition, p326. A country’s money supply may be defined
as the sum of all liquid assets in the financial system.
Determinants of Inflation in Nepal: An Empirical Assessment 65
Because, the additional resources that are borrowed from the financial market by the
government to finance the budget deficits which may crowd out the private investment.
This increases the demand for credit, other things being equal; this will lead to a higher
interest rate. Its immediate effects will be seen in the reduced private sector investment
and consumption that implies lower aggregate demand, which virtually results in a lower
income and employment. On the other hand, reduced private investment will result in
lower aggregate supply. So, central bank generally attempts to monetise deficits to
control the interest rate from rising. For this, it will purchase back the government
securities pumping the new money in the market. This generally pushes up the price level
through increase in money stock.
The monetary authority adopts thus the expansionary monetary policy that nullifies the
rise in interest rate by increasing money supply but it causes inflation. So, deficits have
indirect effects on the inflation through neutralised interest rate. This is consistence with a
study that finds budget deficits in Nepal are interest rate neutral (Paudyal, 2013).
However, the growth of budget deficits has been lower in later years but not the growth of
money supply. This indicates that deficit is not only the cause of an increase in money
supply.
The rise in budget deficits in the annual budget of the government of Nepal has been
substantial over the years. The amount of budget deficits has increased to Rs 496 billion
in 2010/11, which accounts for about 4% of GDP but this is lower than 5.5% in 2000/01
and about 10% in 1988/89 (NRB, 2010; MoF, 2012). This clearly indicates that budget
deficits, though still at upper ladder, has substantially declined in recent years.
Figure 2 reveals that the relationship between inflation and deficits. The deficits as a
percent of GDP almost coincided with inflation only for few years (1989/90, 1992/93 and
others), but for the rest of years inflation departs either downwards or upwards of deficits.
For instance, in 1979/80 deficit was little over 3% of GDP, but inflation rate was about
15%. This means inflation exceeds deficits by 12%. In 1982/83, deficits rose to 9% of
GDP but inflation rate slipped to around 12% from the level of 1979/80 (see Table A2 in
Appendices). The gap between two rates became only 3%. But interestingly they moved
to opposite direction, that is, the increment in deficits appeared with reduction in
inflation. Both declined in the next year (1983/84), but deficits decreased marginally by
66 NRB ECONOMIC REVIEW
one percent point (from 9% to 8%) while prices fell dramatically by over nine percentage
point (from 12% to 2.8%). See Table A2 in Appendices. This indicates that deficits have
not neck to neck relationship with inflation. However, two rates follow a similar positive
trend for most of the years during the study period. To note, inflation rate exceeds deficits
for the most of the years. It may be indicative that deficits are not only factors influencing
inflation. However, the positive association between budget deficit and prices is high
enough (Table A1). Some empirical studies such as Vuyyuri (2004) for developing
economies suggest a strong influence of budget deficits on inflation rate. However, others
such as Blanchard (1989) claim that budget deficits and inflation rate rarely show a
positive association.
Figure 6 in Appendices also indicates that the growth rates of money supplies and
domestic borrowing through securities to finance budget deficits are associated to each
other. However, the government borrowing is highly fluctuating against the relatively
stable money supply. The reason for the fluctuation might be explained in association to
the disbursed amount of external loan and absorptive capacity of bureaucracy in the
country. The growth curves of deficits and domestic borrowings reveal that they move
concurrently.
The qualitative discussion clearly reveals that some variables seem to have very close link
between them and others contain some distant relationship. But almost all of the variables
mostly move in the same direction and exceptionally they move in opposite direction.
Figures 7, 8, 9 and 10 in appendices show that the positive association between growth
rates of broad money and budget deficits, that of borrowing and budget deficits, that of
debt and deficits, and lastly relationship between three variables namely deficits, narrow
money supply and inflation rate respectively.
Once Keynes says, “in the long run we all are dead.” So, Keynesian economics focuses
on the analysis of the short run behavior of the economic variables. For Keynesians, only
aggregate demand matters. To them, in the short run, price level is determined by the
aggregate demand and fixed aggregate supply. Any rise in aggregate demand in the short
run thus causes inflation. For this reason, any pressure of the excess demand over fixed
quantity of aggregate supply of goods and services causes inflationary gap. This leads to
demand pull inflation (Shapiro, 1982).
Keynesian Philips curve comfortably explains tradeoff between inflation rate and
unemployment rate, that is lower the unemployment rate higher the inflation. Keynesian
aggregate demand (AD) policy to increase employment leads to a wider inflationary gap
in the short run as aforementioned output cannot be increased immediately. Up to this
point inflation was thus the resultant of demand shocks.
The oil shock of 1974 created a new problem of stagflation i.e. higher inflation appears
with lower employment/real output (Shapiro, 1982). In other words, the world faced a
new type of economic problem of hyper inflation and recession simultaneously that could
not be explained by the old Keynesian Philips curve. Many countries faced double digit
inflation rate in 1974 and 1979/80 due to supply shocks originated outside the country
Determinants of Inflation in Nepal: An Empirical Assessment 67
(Shapiro, 1982). This suggested that supply shocks may have a dominant role in
determination of inflation. The supply shocks generated outside the country cannot be
controlled by the domestic policy. In addition, it is seen that past experiences on inflation
may influence the future expected rate of inflation. This pushed the Keynesian
economists to formulate new theory which is popularly known as New Keynesian
economics that claims that inflation rate is determined by demand pull inflation, cost push
and built-in-inflation (Gordon, 1988)10.
The supply shocks from within and outside the domestic economy are accepted as one of
the major causes of inflation. The economy wide shortages of goods and services cause
the cost push inflation, that is, supply bottleneck causes this inflation. The cost push
inflation may have many sources. One of these is the rising cost of production which
causes inflation. The reasons behind such rise in cost may be either wage push, profit
push or supply shock due to crop failure or lockouts or drought or foreign blockade, or
war or supply shocks originated from outside country such as OPEC oil price rise in 1974
and 1979/80 (Shapiro, 1982). Supply shocks typically become rule, where domestic
production shares small proportion of the total supply of the commodities in the market
and agriculture largely depends on rain water in a small landlocked country like Nepal.
Supply constraints, to some extent, might demonstrate higher domestic prices as
compared to the international imported prices.
The built-in-inflation is another component of current inflation is the carryover from the
past events and persists in the present time. The past events in the built in inflation may
be persistence of either demand pull inflation or large cost push inflation or both in the
past. Besides, inflationary expectations theory and conflict theory of inflation also
contribute to the built in inflation. The past experiences of high inflation rate lead to the
higher current inflation. The subjective judgments of workers and employers that current
inflation will persist in future primarily push them to make an agreement to increase the
prices of goods and money wages that causes built-in-inflation. The conflict theory of
prices caused by wage-price spiral states that demand for higher money wages to protect
real wages leads to built-in-inflation. This is also based on the subjective judgment of the
workers. An increase in money wages pushes the prices upward and the purchasing
power of the money again fall down. The built-in-inflation in New Keynesian seems to
have largely influenced by the New Classical theory of rational expectation. The good
thing is that this notion brings two major schools of thought-Keynesian and Monetaristthat differ for the century closer to each other.
10
Gordon built triangle model of New Keynesian Theory of Inflation.
68 NRB ECONOMIC REVIEW
Figure 3 shows that growth in prices and real GDP move in the same direction. The
growth of prices is higher for almost years and that of real output seems to be lower for
almost all years. However, the high positive correlation coefficient between prices and
real output (Table A1) demonstrates strong association between two variables.
The Keynesian view that a modern capitalist system is not self-regulating and thus
requires government intervention has given the rebirth of classical theory of economics
(Cornwall, 1990:47). Among the advocates of New Classical theory of rational
expectation, Lucas (1976) is in forefront. Lucas theory is very close to rational
expectation in financial (stock) markets. This theory states that the predictions of
economic agents about the future value of the variables are not systematically wrong in
that all errors are random. This theory of inflation argues that rational expectation is the
key cause of inflation. The actual price will only deviate from the expectation due to
information shock caused by unforeseeable information at the time of expectation. So,
actual price is the sum of expected price and error term. The control of inflation largely
depends on the credibility, integrity and autonomy of the central bank. The economic
actors look rationally into the future and try to maximize their well-being. They carefully
watch the activities of the central bank and make their own perception about the decision
of monetary authority and future expectations about the rate of inflation. Accordingly
they frame their own strategies for maximization of benefits. They hardly believe the
central bank which remained soft in the enforcement of its own decision in the past. In
such backdrop, central bank policy to curb the inflation fails rather than succeed because
market actors will hardly believe in the enforcement of the decision and so they expect
higher inflation in future.
In short, Keynesian aggregate demand policy is considered as appropriate to reduce
unemployment but inappropriate for regulating inflation (Cornwall, 1990:5). Rational
expectation theory has some virtues and getting popularity explaining inflation. Modern
macroeconomics theory posits that the output supplied depends on the unexpected
movement in price level as well as on the actual and unexpected technological shocks
(Blanchard, et. al, 1989: 519). So, inflation is not only the effect of demand and supply
shocks but also is the cause of supply.
Determinants of Inflation in Nepal: An Empirical Assessment 69
Some empirical studies such as Pahlavani (2009) find that even the international inflation
and expected inflation have influential bearings on domestic inflation. Others such as
Khan (2007) constructed econometric model to study inflation incorporating fiscal and
monetary policies of the government. In reality, evident from empirical studies that
several factors including money supply play roles in macro-economy (Kennedy, 2012:
189). McMillan (1986) finds that budget deficits cause inflation (Vuyyuri, et. al, 2004).
In a study for Pakistan’s inflation, Khan (2007) finds that the most important
determinants of inflation in 2005-06 were adaptive expectations, private sector credit and
rising import prices whereas, the fiscal policy’s contribution to inflation was minimal.
Bayo on the study for Nigeria reveals that fiscal deficits, money supply, interest and
exchange rates are cause of inflation in Nigeria during the period under review. Pahlavani
(2009) states that inflation in Iran is largely determined by money supply, exchange rate,
GDP, expected inflation rate and imported inflation along with dummy variable. Kumar
(2013) finds that money supply and imports index is the most important variables in
explaining inflation in India while Laryea (2001) states that inflation in Tanzania is
largely influenced by monetary factors both in the short run or the long run.
A study for NRB notes that Indian prices have a significant bearing on variation of domestic
prices in the country (NRB, 1994:100). Besides, they find that an increase in money stock
causes price rise and the gradual depreciation of the exchange rate of domestic currency has
been partly responsible for the price rise in Nepal. Thapa (2010) attempts to study the
determinants of inflation in Nepal. A study by Neupane (1992) finds that one year lagged
money supply, cost of holding real balances, budget deficits, low output growth rates and
import prices are the important determinants of price inflation in Nepal. NRB (2001) reveals
that there is no structural shift in money price relationship in Nepal. This study finds that
broad money has stronger relationship with inflation compared to narrow money. Mathema
(1998) finds that a rise in wages in industrial sector causes national inflation while Koirala
(2008) discloses a significant relationship between inflation and inflation expectations in
Nepal. Koirala (2013) again finds non-constant time varying parameters of both the
constant and autoregressive of order one AR(1) coefficient of inflation over the long run.
He opines that the changes in the expectations of rational economic agents on
macroeconomic policies due to the lack of policy commitment, credibility and dynamic
consistency might have contributed for this.
Empirical studies in several other countries have shown that a set of explanatory variables
such as real gross domestic product (RGDP), budget deficits (BD) or government
expenditure, exchange rate (EXC), imported price (MP), broad money (M2) and expected
inflation (Pe) explain the variation in price level (P). We examine these variables to explain
the inflation in Nepal. In our case, CPI series is a measure of price level and among the
independent variables the budget deficits after grants (BD), real GDP, the liquidity or money
supply (M2) and the imported price is consumer price index of India (CPII) since Nepal’s
imports from India accounts about 66 per cent for 2013, which includes goods of daily
consumption such as vegetables, clothes, medicines, transport equipments and petroleum
products (MoF, 2014).
70 NRB ECONOMIC REVIEW
Two curves in Figure 4a shows that the movement of the price series in Nepal and India.
The coincided curves to each other imply that they are very close mates and move
together. They not only move to the same direction but also almost coincides each other.
This implies that these two variables are perfectly co-related.
Figure 4b presents the inflation rates in Indian and Nepali economies. This clearly shows
that inflation, with an exception for a few years, is always higher in Nepal compared to
India. Table A1 in appendices shows a strong positive association between imported price
and inflation. Besides, it also reflects the higher dominance of imported Indian goods in
the domestic market.
Determinants of Inflation in Nepal: An Empirical Assessment 71
Two curves in Figure 5 demonstrate that how closely last year price and current year
price move together. The inflationary expectation in future is based on past inflation in
built-in-inflation of new Keynesian economics that affects the current inflation. This may
be examined with the help of the one year lagged cpi variable in the Nepali context. The
lagged cpi will be interpreted as adoptive expectation.
III. ANALYTICAL FRAMEWORK
We discuss about the methodology in this section. This study covers time period 19752011. The data series on CPI for India and Nepal are retrieved from the international
financial statistics of IMF. The rest data series are extracted from the quarterly economic
bulletin of Nepal Rastra Bank and from various issues of Economic Survey published by
Ministry of Finance of Nepal government. We use Wickens-Breusch Single Equation
Error Correction model for the analysis of the data discussed somewhere in this section.
The regression of a non-stationary time series on a set of non-stationary time series can
likely produce spurious results. But the difference of the non-stationary time series is
individually stationary. However, the regression results from difference variables may
results in loss of information about long run relationship between the variables. So it
necessitates the regression of variables at level. Sometimes, the linear combination of the
two or more non-stationary time series gives stationary results (Pindyck,et al, 1991,
Enders, 2014, Gujarati, 2007). So, it is customary these days to test whether time series of
economic variables are individually stationary followed by co-integration test of these
variables at level. Dickey-Fuller or Augmented Dickey-Fuller test are generally apply to
see whether such time series are individually stationary. The time series of economic
variables are shown non-stationary or follow the random walk at level I(0) and stationary
at first difference, I (1). The results from the augmented DF test at level and first
difference are given by Table 2.
72 NRB ECONOMIC REVIEW
Table 2: Augmented Dickey-Fuller tests at the level and at the first difference
Level
CPI
rGDP
BD
EXC
-1.37555
-3.012338
-2.53712
0.217641
First
Difference
-5.175140**
-7.498887**
-5.869865**
-4.87361**
CPII
-1.405367
-6.564345**
M2
-1.98365
-3.872057*
**, * significance at 1%, 5% level
1%
5%
10%
-4.243644
-4.243644
-4.243644
4.243644
-3.54033
-3.54033
-3.54033
-3.544284
-4.243644
-4.243644
-3.54033
-3.54033
-3.20244
-3.20244
-3.20244
3.204699
-3.20244
-3.20244
Order of
Integration
I(1)
I(1)
I(1)
I(1)
I(1)
I(1)
Augmented Dickey-Fuller tests (1979, 1981), shows that variables such as logarithm of
CPI, RGDP, BD, EXC, CPII and M2 series are non-stationary at level but stationary at
first difference as expected. So, we can regress difference of logarithm of prices (CPI) on
the difference of logarithm series of budget deficits (BD), real gross domestic product
(RGDP), exchange rate (EXC), Indian prices (CPII), and broad money supply (M2).
Besides, Johansen co-integration test results presented in Table 2 suggest that these
variables are co-integrated with dependent variable. It implies that there exist long run
relation between inflation and other variables. This means that these variables can be
regressed at level form also although individual series are stationary only at first
difference. The coefficients from the regression of the variables at level measure the
effects of independent variables on the dependent variable in the long run. Table 3 reveals
that there are multiple cointegrating equations but we apply only one for the analysis of
the data.
Table 3: Johansen co-integration test at level
[series CPI , GDP, BD, EXC, M2 and CPII]
Trace test
Critical
Max-Eigenvalue
Critical value
statistics
value 0.5%
statistics
0.5%
None
129.4204*
95.75366
45.56497*
40.07757
At most 1
83.85546*
69.81889
33.09859
33.87687
At most 2
50.75687*
47.85613
23.18014
27.58434
At most 3
27.57673
29.79707
16.28424
21.13162
At most 4
11.29249
15.49471
8.732777
14.26460
At most 5
2.559717
3.841466
2.559717
3.841466
Note:*Trace test indicates 3 cointegrating equations while max-eigenvalue test indicates 1
cointegrating equation at the 0.05 level
Variables at level
Economic theories are based on the long run equilibrium relation between a set of
variables. However, their relation can be disturbed by short term shocks and thus,
disequilibrium occurs in short run. But the economists believe that if the variables are
integrated, it implies that they have long run relation. So sooner or later, the
disequilibrium in the short run returns to the long run equilibrium path. Such relation is
stable and produces optimal results.
Determinants of Inflation in Nepal: An Empirical Assessment 73
In this paper we examine the long run and short term effects of the related variables on
inflation with the help of the W-B single error correction mechanism. So, this model is
useful to examine the short run disturbances as well as long run equilibrium, which was
first used by Sargon (1984; Cited in Gujarati, 2007) and later popularized by Engle and
Granger (Cited in Gujarati, 2007) for the correction of disequilibrium between economic
variables in the short run. The Engle and Granger method is two steps EC method.
However, we estimate here error correction mechanism in a different way. The WickensBreusch approach (1988) is a single equation error correction model. This model
evaluates the state of equilibrium between dependent and independent variables as well as
estimates the speed at which a dependent variable returns to long run equilibrium path.
So, this embodies a number of desirable properties which are as follows: a) it estimates
the short and long term effects in one step; b) it provides easy interpretation of short and
long term effects; c) applications to both integrated and stationary time series data; d) can
be estimated with OLS; and e) model is built as per the theoretical relation between
variables; f) useful for small sample data analysis; g) it assumes only one co-integration
relationship among the variables in the equation and only one error correction model is
formulated. So, this model is consistent with our economic theories. We can estimate
long term effects of Xs on Y, short run effects of Xs on Y and measure the speed of Y
variable moving toward the long run equilibrium level with the help of single equation
error correction model.
As its name suggests, this is a multi-variables single equation model with first order
difference and lag variables of both dependent and independent variables. However, we first
begin our discussion with the specific error correction model which takes the following
form:
Y     0 X t  1 (Yt 1   2 X t 1 )   t
---- (i)
Where ß1 is error correction component, which measures the rate of return per time unit; or
speed of rerun to the equilibrium path; -1< ß1<0, i.e. the value of ß1 lies between -1 and 0; ß0
is short term effects while ß2 estimates long term effects of X on Y.
Where Y is a dependent variable and X is explanatory or independent variable, t-l is one year
lag length, where ß1 is the error correction component estimated from the equation, which is
expected significant and to appear with negative sign. This model is based on the
behavioural assumption that two or more time series exhibit an equilibrium relationship
in the long run but short run disturbances push the dependent variable away from the
equilibrium path. One of the important virtues of this model is that it estimates both short
run effects and long run effects of right hand variables in the model. We extend model (i)
by including aforementioned variables. Our single equation error correction model which
makes the model in its full extent is as follows:
∆Pt = α + ß01 ∆RGDPt + ß02∆BDt + ß03 ∆EXCt + ß04 ∆MP+ ß05∆M2t +Pe- ß1(Pt-1 – ß21RGDPt-1- ß22
BDt-1- + ß23 EXCt-1 + ß240MPIt-1 + ß25 M2t-1+ ß25Pet-1+ εt
---- (ii)
Where ∆ denotes first difference of log of variable and t-1 is one year lag of the variable.
In the next section we present the regression results from this model.
74 NRB ECONOMIC REVIEW
IV. REGRESSION RESULTS AND INTERPRETATION
We already discussed about the movement and relationship between the time series of
macroeconomic variables. We now are analysing the time series annual data of budget
deficits and other macroeconomic variables to see their effects on price level of Nepal.
Besides we tried to include the dummy variables for external supply shocks such as 1989
trade embargo imposed by India, 1979/80’s oil price rise and policy change variable in
1991 onward. However, all these dummies appeared with insignificant t-statistics and for
the reason we dropped them from the model. Aforementioned we use log form of all
variables for the regression purpose.
Table 4: Single equation ECM Model
Dependent
D(CPI)
Coefficient
Std. Error
t-Statistic
Prob.
Intercept
D(rGDP)
D(BD)
D(EXC)
D(dCPII)
D(M2)
CPIt-1
rGDPt-1
BDt-1
EXCt-1
dCPIIt-1
M2
-5.648542**
0.159783
0.062471*
0.036576
0.055639**
0.227992*
-0.760707**
0.433375*
0.072413**
0.169250**
0.109898**
0.142841+
1.644366
0.164301
0.027578
0.083414
0.011400
0.109064
0.180061
0.160304
0.025298
0.052021
0.018445
0.082091
-3.435089
0.972499
2.265231
0.438494
4.880555
2.090450
-4.224709
2.703459
2.862440
3.253465
5.958097
1.740025
0.0024
0.3414
0.0337
0.6653
0.0001
0.0483
0.0003
0.0130
0.0091
0.0036
0.0000
0.0958
Long run
multiplier
Adj R2=0.71;
F=8.3**;
SE=0.02;
DW=1.93;
AIC=-4.62;
SIC=-4.08;
N(0)=0.68;
SC(1) = 2.33;
HC(1)= 19.2;
N=34
0.56*
0.10**
0.22**
0.14**
0.19+
**, * & + significant at 1%, 5% and 10% respectively, ns=not significant
Table 4 displays the regression results from the single equation ECM model which pass
all diagnostic tests shown in the far right column of the table. As priori expectation, the
error correction component in this model is highly significant at 1% and appears with
negative sign. This shows that the system distracted temporarily out of long run
equilibrium path but it will return soon. The speed of return to the equilibrium path is
0.76% next year and the remaining will be corrected following years.
All variables considered including real income are influencing inflation in long run while
only variables such as budget deficits, change in imported prices and broad money are
significant in both short run and long run. The exchange rate, budget deficits and change
in imported prices are significant at 1% level whereas real income at 5% level and broad
money supply at 10% in the long run. Similarly, in short run change in imported price is
significant at 1% while budget deficits and broad money at 5% level. This clear shows
that the change in import prices is the most important variable both in short run and long
run. Obviously, in the short run, the change in import prices, budget deficits and broad
money supply have critical role in determining the rise in prices in Nepal while in the
long run almost all variables have influenced inflation. Among these budget deficits,
Determinants of Inflation in Nepal: An Empirical Assessment 75
exchange rate and imported prices have played dominant role in determining the level of
price in the country.
The estimated long run income elasticity of inflation is inelastic or less than one. In other
words, long run multiplier is only 0.56, which implies that a 1% rise in real income leads
to 0.56% increase in price level followed by exchange rate multiplier (0.22), broad money
supply (0.19) and change in imported prices (0.14). However, broad money supply is
significant at only 10% in long run as compared to at 5% in short run implies that the
effect of this variable on price level slips low in long run.
Short run elasticity of the variables is lower than long run elasticity except in case of
broad money. The elasticity for budget deficits and change in imported prices is estimated
at 0.06 each while that of the broad money supply at 0.23. This implies that prices in
Nepal are more sensitive to the change in broad money supply than the changes in
imported prices which are the most influential variables in the short run. So, this can have
important policy implication. The regression result that broad money supply is significant
and real GDP is insignificant in the short run seems to obey the quantity theory of money
that says only money supply matters to the inflation as real output and velocity of money
remaining the equal. As regression results show that both money supply and real GDP are
important variables in the long run, this further satiates the modern monetarists’ approach
to inflation theory under less than full equilibrium where real output changes and affects
the price level. Furthermore, this result is consistent with the previous study which
suggests interest neutrality in Nepal (Paudyal, 2013).
Nepal Rastra Bank seems to have monetised deficits to contain the interest rate. It might
have taken expansionary monetary policy to offset the effect of deficits on interest rate.
This led to increase in money supply causing inflation. On the other hand, budget deficit
from the perspective of the Keynesian aggregate demand approach is the resultant of
increase in aggregate demand. Such increase in aggregate demand due to excess
government expenditure leading to budget deficits shows an excellent influence on
inflation (inflationary gap theory) in both periods since this variable is significant at 5%
and 1% in short run and long run. Moreover, highly significant effects of imported prices
(cpii) variable at 1% both in short run and long run is consistent with the skyrocketing
increase in imports which may be also the reflection of domestic supply constraints in the
country. This is also comparable with the previous findings of NRB that finds Indian
prices are the most important source of Nepali inflation (1994). However, the highly
significant imported price variable implies that inflation is largely the function of
external price discloses the difficulty of Nepal Rastra Bank which by law has to make
attempts to keep the inflation rate low and stable. Still, policy variables such as money
supply and budget deficits have important implications. Moreover, the empirical findings
reveal that the growth of real GDP has an excellent influence on the inflation in the long
run, through growth of real sector and increased supply.
In the sum, this study finds that the variations in inflation can be explained largely by
imported price, exchange rate, budget deficits, real GDP and broad money in long run.
However, in the short run, only the variables like budget deficit, broad money supply and
imported prices can be considered as the major determinants of inflation in this country.
76 NRB ECONOMIC REVIEW
V. CONCLUSIONS
Controlling inflation is not easy task in a country like ours which shares open borders
with neighbours and heavily dependent on the imported goods for the daily consumption
and materials for other development purposes. In this backdrop, the dominant cause of
domestic inflation becomes supply shock generated outside the country. In this context,
the control of inflation typically becomes much more complicated and challenging for
monetary authorities since the monetary and fiscal policies framed to tame the inflation
seem to have lesser implications. However, it does not imply that there is no room for
such policy implications at all. This empirical study suggests that prices in Nepal became
highly dependent on Indian prices especially after 1991’s political change. It is because of
a weaker supply of domestic production supplemented by the increased imported goods
from India. The movement of Nepali prices are very close to Indian prices after 1991/92.
Because of liberalisation and privatisation policy, the existing limited domestic profit
making import substituting enterprises like Basbari shoe factories were closed down
partly because they were sold to the private sector. Besides, some of the domestic
products could not compete with Indian goods in domestic market in the changing context
of reduced import duties under preferential trade agreement with India. This led further
rise in the imports from Indian goods and thereby the influence of Indian prices in Nepali
prices. So, this has obviously increased the dominance of Indian prices in the domestic
prices in the later years.
In short run following Keynesians, demand management is a major concern of the
government to control the inflation. Well synchronized fiscal and monetary policies
targeting the reduction in budget deficits through cutting down recurring expenditures and
augmenting revenues of the government together with restrained money supply can be the
effective measures to control inflation. Still, there remain some doubts about the
effectiveness of the monetary and fiscal policies in the short run since this empirical study
suggests the high domination of Indian prices. So far long run is concerned, an increase in
domestic supply is the major step to control the inflation. The long run strategy thus to
combat inflation is to increase the production of goods and services through the use of
productive resources from money and capital markets. The government efforts to create
conducive environment for foreign direct investment and ODA in the areas of energy and
other infrastructure development along with investment in the productive sector thus can
be the effective measures to improve the supply situation/real out and thereby control the
inflation. Even so, as suggested by this empirical study, given the open border with India
and liberalised trade regime, there exists higher prospect of Indian domination on the
domestic prices in this country. Noting that there is a higher prospect of lower prices in
India as revealed by its expected higher growth and prosperity, however, suggests that
better connectivity with the Indian and Chinese economies will pave the road to deeper
integration of Nepali economy with these Asian power houses help Nepal to control
inflation. This along with other monetary and fiscal policy measures may be appropriate
strategy to combat the inflation in Nepal.
*****
Determinants of Inflation in Nepal: An Empirical Assessment 77
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APPENDICES
Table A1: Correlation Among Variables
LOG(CPI)
LOG(rGDP)
LOG(BD)
LOG(EXC)
LOG(CPII)
LOG(M2)
LOG(CPI)
1
0.992
0.972
0.975
0.999
0.997
LOG(GDP)
0.998
1
0.962
0.971
0.999
0.999
LOG(BD)
0.972
0.943
1
0.945
0.965
0.959
LOG(EXC)
0.975
0.967
0.945
1
0.971
0.965
LOG(CPII)
0.999
0.995
0.965
0.971
1
0.999
LOG(M2)
0.997
0.996
0.959
0.965
0.999
1
Source: Author’s Calculation
Table A2: Budget Deficits and Inflation
Year
BD/GDP
Inflation rate
Year
BD/GDP
Inflation rate
1975/76
2.5
-2.2
1993/94
5.8
8.4
1976/77
3.6
9.8
1994/95
4.8
7.6
1977/78
3.2
7.4
1995/96
5.6
9.2
1978/79
2.7
3.6
1996/97
5.1
3.9
1979/80
3.4
14.7
1997/98
5.9
11.3
1980/81
2.9
11.1
1998/99
5.3
7.5
1981/82
5.4
11.7
1999/00
4.7
2.5
1982/83
9.0
12.4
2000/01
5.5
2.7
1983/84
8.0
2.8
2001/02
5.0
3.0
1984/85
7.6
8.1
2002/03
3.3
5.7
1985/86
7.1
19.0
2003/04
2.9
0.9
1986/87
6.7
10.8
2004/05
3.1
6.8
1987/88
6.1
9.0
2005/06
3.8
7.6
1988/89
9.6
8.8
2006/07
4.1
6.0
1989/90
8.1
8.2
2007/08
4.1
11.0
1990/91
8.9
15.6
2008/09
5.0
11.6
1991/92
7.5
17.1
2009/10
3.5
9.9
1992/93
7.0
7.5
2010/11
3.6
9.6
Source: MoF/GoN, Economic Survey
Determinants of Inflation in Nepal: An Empirical Assessment 81
82 NRB ECONOMIC REVIEW
Relevance of Keynesianism in Nepal:
An Empirical Analysis
Hom Nath Gaire
Abstract
In this paper, the relevance of Keynesian postulates has been examined in the Nepalese context for
the period 1975-2012 using annual time series data. The empirical results from the Johansen cointegration tests clearly show that there is long run equilibrium relationship between government
expenditure and real GDP, private consumption and gross fixed capital formation. Likewise,
Granger Causality test confirms that there is bilateral causal relationship between government
expenditure and gross fixed capital formation in Nepal. However, no causal relationship is
observed between government expenditure and real GDP and private consumption. Thus, it is
confirmed by this study that the Keynesian postulates are relevant for capital formation rather
than for increasing real GDP growth and private consumption in Nepal.
Key Words: Keynesianism, Effective Demand, Casual Relationship, Government
Expenditure, Gross Fixed Capital Formation and Real GDP
JEL Classification: E12

Lecturer of Economics, Greenfield National College, Kathmandu.
Email: [email protected]
Remarks: The author is grateful to the Editorial Board and anonymous referees for their valuable
comments that helped greatly in the improvement of this paper.
84 NRB ECONOMIC REVIEW
I. INTRODUCTION
Keynesianism is a macroeconomic school of thought based on the ideas of 20th century
British Economist John Maynard Keynes. The concepts forming the basis of
Keynesianism were first published in “The General Theory of Employment, Interest and
Money” in 1936. This book is a repudiation of the foundations of laissez-faire and
advocacy of active government because unemployment is primarily a matter of the
volume of effective demand. Keynes argues that some individually-rational
microeconomic actions, if taken collectively by a large proportion of individuals and
firms, can lead to ineffective aggregate macroeconomic outcomes, where in the economy
operates below its potential output level (Keynes, 1936). It is further argued that such low
level economic situation can be corrected by the Government through active monetary
and fiscal policies.
One of the tenets of Keynesian theory is that government spending on consumption and
investment, tax cuts and lower interest rates can stimulate demand and induce investment
which would have otherwise remained idle to produce wealth (Keynes, 1936). Similarly,
redistribution of wealth from wealthy to poor, who are perceived to have higher marginal
propensity to spend would generate higher economic growth. Therefore, for four decades
from mid-1945 to mid-1970 Keynesianism dominated the thinking of professional
economists and public policy makers not only in the United States, and Europe but also in
a number of developing countries. However, the Keynesian principles have also been
subjected to considerable criticisms during the same period. The critics argue that
macroeconomic policies based on Keynesianism are counter-productive to stabilize the
economy and these will lead to inflation, income inequality, and incite consumers to
spend even more in anticipation of future tax increase (Michael, 2006). At the same time,
Keynesians advocate an active stabilization policy for reducing the magnitude of the
business cycle, which they rank as the most serious economic problem by raising
aggregate demand thereby stimulating economic activities, reducing unemployment and
avoiding deflation.
Governments in Nepal have used expansionary fiscal policy since long back to stimulate
demand as a countercyclical measure as well as for political reasons. It is believed that
large budgets can play influential role in generating higher growth and increasing
employment. However, the reality does not confirm this as government expenditure and
growth do not seem to move together. Hence, testing causality between government
expenditure and economic growth or examining the relevance of Keynesianism would be
a worthwhile exercise.
The main objective of this paper is to gauge the relevance and implication of Keynesian
notions in the Nepalese context. For this, the study aims to test the causality between the
government expenditure and real GDP, private consumption and gross fixed capital
formation for the period between 1975 and 2012. Conclusions drawn from the study
would provide useful insights to fiscal policy makers of Nepal.
Relevance of Keynesianism in Nepal: An Empirical Analysis 85
The rest of the paper is organized as follows. The second section provides a precise
review of evolution of Keynesianism which covers origin of Keynesian thoughts,
dominance of Keynesian policy and monetarist revolution followed by the counter
revolution of Keynesianism. Section three covers the review of empirical studies on
relevance as well as effectiveness of Keynesian thoughts available so far both in global
and Nepalese context. Section four briefly describes the data and methodology used in
this study. Section five presents the results and discussion of empirical analysis. The last
section concludes the discussion.
II. EVOLUTION OF KEYNESIANISM
Origin
John Maynard Keynes (1883–1946) had acquired an international reputation shortly after
World War-I by “The Economic Consequences of Peace”. In his 1924 book, “A Tract on
Monetary Reform”, Keynes declared that gold was a “barbarous relic” and that
governments should control money supply to maintain a stable domestic price level as
well as a stable foreign exchange rate (Anderson, 1925). In 1930 Keynes published “A
Treatise on Money”, a two-volume work which established him as the reputed leading
monetary theorist for the next five years. Keynes' “The General Theory of Employment,
Interest, and Money” published in February 1936 is widely regarded as the cornerstone of
Keynesian thought. By the end of World War-II, The General Theory became the
foundation of the new “Macroeconomics”, which in turn was popularized as
Keynesianism (Hutt, 1963).
Keynesian Dominance: 1941–1979
From the end of the Great Depression, Keynesian ideas quickly established in America
and Europe also was a leading inspiration for the English speaking common wealth
countries of Asia and Africa from 1941 to the mid-1960s. In late 1965, Time Magazine in
a cover story entitled "We are all Keynesians now" scaled Keynes's central theme by
stating that Keynes was one of the three most important economists ever, and that his
General Theory was more influential than the ‘magna opera’ of his rivals i.e. Adam
Smith’s ‘The Wealth of Nations’ and Karl Marx's ‘Das Capital’.11 Hence, from early
1940s to the mid-1970s, which is also known as the Golden Age of capitalism,
Keynesianism provides the main inspiration for economic policy makers and for
prominent economists including the academia.
Monetarist Revolution: 1979-1999
The stagflation of 1970s including the oil crisis of 1973 followed by the recession
questioned the logic behind Keynesianism and lead to the development of new classical
macroeconomics. Thus, Austrian School of thoughts and Monetarism charged
11
"We are all Keynesians now". Time Magazine, 1965-12-3, Retrieved 2008-11-13.
86 NRB ECONOMIC REVIEW
Keynesianism and demand management as tools for 'fools' because wealth, in a better
society and cleaner world along with a higher level of development, cannot be directed by
the government. Meanwhile, the “Washington Consensus” which propagates that markets
work best if they are unregulated came to be used as a notable anti-Keynesian view. That
created the space to proliferate the Monetarism and new classical economics, which in
turn displaced the Keynesianism for 1979-1999 (Hoover, 2003).
Keynesian Counter Revolution: 1999–2007
The Asian Financial Crisis of 1997 in the developing world and market failure as well as
Dotcom crash of the 2000 in advanced economies caused a turn back from free market
policies to Keynesianism. In the meantime, Britain and Japan had shown keenness to
Keynesianism saying "the real challenge was to interpret Keynes's insights for the
modern world" (Carabelli, 2010). By 2007 there had been high promotion of
Keynesianism in the English speaking countries including China, India and south East
Asia. In the academic world, the advent of the global financial crisis in 2007 had caused
the resurgence of Keynesian thought (Anthers, 2010).
Keynesianism After 2008
During the global financial crisis (2007–2009), the Keynesianism was receiving most
attention as fiscal stimulus was widely launched across the world. It was mid-2010 that
the earlier global consensus for ongoing Keynesian stimulus had broken, especially in
Europe, as there was an increasing demand for immediate fiscal tightening. By mid-2012,
with the on-going Euro crisis and persistent unemployment problem in the US, there has
been renewed consideration of stimulus policies by European and American policy
makers, although there is no return to the pro stimulus consensus that existed in 2009
(Farrell and Quiggin, 2012).
III. LITERATURE REVIEW
Numerous studies have been conducted to investigate the relationship between
government spending and economic growth with mixed results. Landau (1983) found that
the share of government consumption to GDP reduced economic growth which was
consistent with the pro-market view that the growth in government constrains overall
economic growth. Ram's (1986) study made a rigorous attempt to incorporate a
theoretical basis for tracing the impact of government expenditure to growth through the
use of production functions specified for both public and private sectors. The author
found government capital expenditure to have significant positive externalities on growth
particularly in the developing countries. Lin (1994) used a sample of 62 countries (196085) and found that non productive spending had no effect in growth in the advanced
countries but a positive impact in LDCs. Josaphat et al. (2000) investigate the impact of
government spending on economic growth in Tanzania (1965-1996) using time series
data for 32 years. The results revealed that expenditure on human capital investment was
Relevance of Keynesianism in Nepal: An Empirical Analysis 87
insignificant in their regression and confirm the view that public investment in Tanzania
was not productive. Junko and Vitali (2008) in an investigation of the impact of
government expenditure on economic growth in Azerbaijan suggested that the initial
growth performance largely depends on the efficiency of scale-up expenditure.
Komain and Brahmasrene (2007) gauged the relationships between government
expenditure and economic growth for Thailand during the period 1970-2005. The results
suggest that there was a long-run relationship between government expenditure and
economic growth, thus supporting the Keynesian hypothesis. Jamshaid et al. (2010) found
a wide range of evidences on the impacts of government expenditure on economic
development and concludes that government expenditure contributes to economic growth,
both through supply and demand channels in the USA, Japan, Germany, France, United
Kingdom, Italy and Canada. The study suggested government expenditure contributes in
raising the quality of life by creating amenities, providing consumption goods and
contributing to macroeconomic stability.
Amid inconclusive evidences, Keynesian policies have been able to exert some positive
impact in the global economy, especially during crises since 1930s great depression to the
latest financial crisis of 2007-2009. Skidelsky (2011) made a comparison between the
performance of the world economy during the Golden Age period (1951–1973) where
Keynesian policies were dominant and the Washington Consensus period (1981–2008)
where free market policies were adopted. The study reveals that the 'golden age' period
was substantially more stable with higher growth, employment and low inequality.
However, during the 'Washington Consensus' period the world economy was quite
unstable with increasing inequality.
In Nepalese context, Shrestha (2009) investigated the role of composition of public
expenditure, particularly the expenditure on physical infrastructure, on economic growth
in Nepal based on the endogenous growth model using time series data. The results
suggest that the impact of public expenditure on economic growth was positive.
However, Chaudhary (2010) found no causality between real GDP and government
expenditure in Nepal. The findings suggest that the increase in the size of government
expenditure has no influence on economic growth of Nepal.
Recently, Sharma (2012) tested the impact of government expenditure on economic
growth of Nepal. The results reveal that although there is a weak influence on economic
growth, growth depends on the size, spending capacity, and effective use of capital
expenditure in the development process. Similarly, Kharel (2012) develops a
macroeconomic forecasting model focusing on fiscal policy and economic growth in
Nepal using annual data from 1992/93 to 2009/10. The evidence suggests that fiscal
policy, particularly government' capital expenditure affects economic growth positively
and also crowds-in private investment.
However, there exists a trade-off between fiscal stability and high level of economic
growth as the policy goal of achieving both objectives seems to be unattainable. Within
88 NRB ECONOMIC REVIEW
the above theoretical and empirical evidences this study analyzes causality between
government expenditure and economic growth in Nepal.
IV. DATA AND METHODOLOGY
Many empirical studies of macro impact on government spending were based on the
Vector Autoregressive (VAR) model of major macroeconomic variables. A number of the
studies were focused to estimate the effect of government spending and fiscal deficit on
growth variables. Blanchard and Perotti (1999) used data pertinent to the United States
during the postwar period for VAR specification of taxes, government spending and GDP
in real per capita terms. Similarly, Heppke-Falk, Tenhofen and Wolff (2006) used
Structural Vector Autoregressive (SVAR) approach to investigate short-run effects of
fiscal policy shocks on the German economy.
As this study is primarily based on the time series secondary data of Government
Expenditure (GE) and Economic Growth, Johansen Co-integration method based on VAR
approach has been used. In order to test the causality between natural log values of GE
vis-à-vis real GDP, Private Consumption (PC) and Gross Fixed Capital Formation
(GFCF), time series annual data for the period 1975 to 2012 have been used.
Model Specification
In order to find out the causality between GE and Economic Growth Variables, natural
log value of GE is taken as independent variables while natural log values of real GDP,
PC and GFCF are taken as dependent variables. For this purpose the following models
have been developed.
GDPt = 0 + 1 GEt + t1
…… (1)
PCt = 0 + 1 GEt + t2
…… (2)
GFCFt = 0 + 1 GEt + t3
…… (3)
Where, i, i,i are parameters to be estimated and ti are white noise error terms
Unit Root Tests
Many economic and financial time series data exhibit trending behavior or non-stationary
in the mean. A series is said to be stationary if the mean and auto covariance of the series
do not depend on the time. A series whose mean and auto covariance depend on time is
said to be non-stationary. An important econometric task is determining the most
appropriate form of trend in the data. If the data are trending then some trend removal
measures are required to transform the data into stationary form prior to analysis. Two
common trend removal or de-trending procedures are first differencing and time trend
Relevance of Keynesianism in Nepal: An Empirical Analysis 89
regression. First differencing is appropriate for time series and time trend regression is
appropriate for trend stationary time series.
As the present study is based on the time series data, it is important to check whether a
series is stationary or not before analysis. For this purpose, first differencing procedure
i.e. Augmented Dickey-Fuller (ADF) test has been performed in this study. Since the
ADF test of unit root does not follow the conventional Student's t-distribution,
Mackinnon (1991, 1996) t-values have been used.
Co-integration Test
Economically speaking, two variables will be co-integration if they have a long term or
equilibrium relationship. Although there are a number of methods for testing the cointegration, the following Vector Auto Regression (VAR) method of order p developed
by Johansen has been utilized.
yt = t + A1yt-1 + … + Apyt-p + Bxt + t
…… (4)
Where, yt is an n×1vector of variables that are integrated of order one - commonly
denoted I (1) - t is an n×1 vector of innovations.
In this test, the null hypothesis of r co-integrating vectors is tested against the alternative
of r +1 co-integrating vectors. Thus, the null hypothesis r=0 is tested against the
alternative r=1 against r=2, and so forth. Johansen proposes two different likelihood ratio
tests of the significance of these canonical correlations and thereby the reduced rank of
the Π matrix: the trace test and maximum Eigen value test as follows:
𝑗𝑡𝑟𝑎𝑐𝑒 (r/p) = -T∑𝑛𝑖=𝑟+1) 𝐼𝑛(1 − 𝑖 )
…… (5)
𝑗𝑚𝑎𝑥 (r/r + 1) = -T 𝐼𝑛(1 − 𝑖+1 )
…… (6)
Here T is the sample size and  is the ith largest canonical correlation.
As the co-integration tests are very sensitive to the choice of lag length, following Akaike
Information Criteria (AIC) and Schwarz Information Criteria (SIC) after existence of cointegration between the variables in the equations, the Granger Causality test has been
performed.
Granger Causality Test
The common practice in testing the direction of causation between two variables is the
Granger Causality test. According to Granger (1969), series X causes Y if the past values
of X can more accurately predict Y than simply the past values of Y. In simple words, if
past value X improves the prediction of Y with statistical significance, then we can
90 NRB ECONOMIC REVIEW
conclude that X “Granger Causes” Y. The Granger Causality test for the above equations
(1), (2) and (3) has been performed on the basis of the following fundamental model.
𝑌𝑡 = 0 + 1 𝑌𝑡−1 + 2 𝑌𝑡−2 + ⋯ 2 𝑌𝑡−𝑛 + 1 𝑋𝑡−1 + ⋯ + 𝑚 𝑋𝑡−𝑚 + 𝑈𝑡
…… (7)
Where, 𝑈𝑡 white noise error is term series.
V. RESULTS AND DISCUSSIONS
Findings
In order to gauge the relevance of Keynesianism in Nepal, in this study, first of all ADF
tests have been performed to examine the unit root in all the set of 4 series comprising log
values of GE, GDP, PC and GFCF for the period of 1975-2012. The results of ADF tests
presented in the table-1 support that the log value series under consideration are not
stationary at both level and first difference. This is confirmed as the calculated values of
t-statistics, in absolute sense, are smaller than the tabulated values at both 1% and 5%
level of significance accepting the null hypotheses that the series are non-stationary. This
indicates that there is trending behavior in mean of all the series under consideration.
Table 1: Unit Root Tests
Variables
NLGDP
NLGE
NLPC
NLGFCF
At Level
0.535
-1.411
-2.192
-2.532
t-statistics
First Difference
0.300
-0.787
-2.179
-2.779
MacKinnon p-value
At Level
First Difference
0.9859
0.9774
0.5770
0.8229
0.2092
0.2141
0.1079
0.0614
Critical values for level at 1% and 5% are respectively -3.668 and -2.966
Similarly critical values for first difference at 1% and 5% respectively are -3.675 and 2.969
Figure-1: Log Value of Variables under Consideration
Relevance of Keynesianism in Nepal: An Empirical Analysis 91
Johansen Co-integration Tests
After confirming the non-stationary nature of series under consideration, it is required to
test whether the variables are co-integrated or not i.e. whether they exhibit the tendency
of co-movement over the long run and converge towards equilibrium.
Table 2 depicts the results of the Johansen Co-integration tests. Both the trace test and
maximum Eigen value test reject the null hypotheses of all models that there is no cointegration between the variables under consideration at 99 percent confidence level.
Table 2: Results of Johansen Co-integration Tests
GDP and GE (Sample-1976 – 2012), Trend- Linear, Lags-1
Null
Hypothesis
(H0)
r=0
r≤1
Eigen Value
Trace
Statistics
Critical Value
5%/1%
Max-Eigen
Statistics
0.59018
0.37549
47.6995
16.4778
15.41/20.04
3.76/6.65
31.2217
16.4778
Critical
Value
5%/1%
14.07/18.63
3.76/6.65
PC and GE (Sample-1976 – 2012), Trend- Linear, Lags-1
Null
Hypothesis
(H0)
r=0
r≤1
Eigen Value
Trace
Statistics
Critical Value
5%/1%
Max-Eigen
Statistics
0.56200
0.31429
42.0988
13.2053
15.41/20.04
3.76/6.65
28.8935
13.2053
Critical
Value
5%/1%
14.07/18.63
3.76/6.65
GFCF and GE (Sample-1976 – 2012), Trend- Linear, Lags-1
Null
Hypothesis
(H0)
r=0
r≤1
Eigen Value
Trace
Statistics
Critical Value
5%/1%
Max-Eigen
Statistics
0.61558
0.37636
49.9874
16.5266
15.41/20.04
3.76/6.65
33.4608
16.5266
Critical
Value
5%/1%
14.07/18.63
3.76/6.65
The above result of Johansen co-integration tests confirms that there is co-integration of
the Government Expenditure vis-à-vis real GDP, Private Consumption and Gross Fixed
Capital Formation of Nepal. The existence of co-integration implies that there is long-run
relationship between the Government Expenditure variables and Economic Growth
Variables in Nepal partially supporting the Keynesian notion.
Granger Causality Tests
The results of Granger Causality Test are reported in the following Table 3. The Wald
F-statistics and the corresponding critical values indicate there is no any causality
between the Government Expenditure vis-à-vis real GDP and Private Consumption, since
the null hypotheses of equations (1) and (2) that GE does not Granger Cause real GDP
and PC accepted with high probability values. However, there is a bilateral causality
92 NRB ECONOMIC REVIEW
between the Government Expenditure and Gross Fixed Capital Formation. This is
confirmed since the null hypothesis of equation (3) that GE does not Granger Causes
GFCF is rejected at 5 % level of significance to very low probability values.
Table 3: Pairs wise Granger causality (Wald) tests (Sample-1976 – 2012), Lags-1
Null Hypothesis (H0)
F-Statistics
Probability
Decision
GE does not granger cause GDP
.40944
0.6675
(H0) Accepted
GE does not granger cause PC
.39971
0.6738
(H0) Accepted
GE does not granger cause GFCF
4.9003 (3.32)*
0.0139
(H0) Rejected
GFCF does not granger cause GE
5.3341 (3.32)*
0.0100
(H0) Rejected
* indicates the rejection of H0 at 5% level of significance respectively. Figures in parenthesis are
the tabulated values of F-distribution for corresponding degree of freedoms.
VI. CONCLUSION
This paper examined co-integration and causality between the Government Expenditure
(GE) vis-à-vis real Gross Domestic Product (GDP), Private Consumption (PC) and Gross
Fixed Capital Formation (GFCF) with an aim of testing the relevancy of Keynesianism in
the context of Nepal using time series data of 1975 to 2012. Using the methods of the unit
root tests and co-integration tests, the study confirmed that there is long-run equilibrium
relationship between the Government Expenditure variables and Economic Growth
variables in Nepal. However, Granger Causality test revealed that there is no causality
between the Government Expenditure and real GDP as well as private consumption for
the review period. However, there is bilateral causality between Government Expenditure
and Gross Fixed Capital Formation (GFCF) in Nepal.
The evidence from this study reveals that Keynesian notion, which claims positive impact
of Government Expenditure on real GDP and private consumption, is not valid for Nepal.
This may be because that the GDP of Nepal is mainly dependent on agriculture
production which is subject to the favorable weather conditions and the private
consumption is highly depends on remittance received from foreign employment.
Similarly, because of high propensity to consume and supply side constraints in the
economy a given increment in government expenditure is leaked out of the country in the
form of imports. But the Keynesian notion that the Government can play pivotal role in
capital formation through its expenditure, which in turn stimulate the private investment
and growth of the economy is proved. Thus the Government can contribute in creating
favorable environment for private sector and business community through infrastructure
development and capital formation by raising capital expenditure.
The results of this study, in line of some literatures, confirm that the notion of
Keynesianism to promote economic activities and growth through government
intervention is partially relevant in Nepal. This means the Keynesian notion which is
based on industrialized economies could not fully perform in the agriculture dominated
least developed economies like Nepal. However, there is a role of Government in such
economies where there are market imperfections and the private sector is not capable
enough for huge investment in infrastructure development and capital formation.
Relevance of Keynesianism in Nepal: An Empirical Analysis 93
The findings of this study suggest that the Government should not be involved in general
kind of business activities such as production and distribution of goods and services
rather should focus on effective governance and mobilization of resources in order to
increase the capital expenditure for capital formation and infrastructure development.
*****
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Relevance of Keynesianism in Nepal: An Empirical Analysis 95
Appendix 1
Data Used in the Study (Figure in Rs 10 Million)
Year
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Government
Expenditure
151.4
191.3
233.0
267.5
302.5
347.1
409.2
536.1
697.9
743.7
839.5
979.7
1151.3
1410.5
1800.5
1966.9
2355.0
2641.8
3089.8
3359.7
3906.0
4654.2
5072.4
5611.8
5957.9
6627.3
7983.5
8007.2
8400.6
8944.3
10256.0
11088.9
13360.5
16135.0
21966.2
25968.9
29536.3
34514.6
Real GDP
13106.2
13609.4
13838.9
14288.6
14524.0
14573.4
15874.7
16644.1
16820.4
18299.2
19552.9
20483.8
20915.2
22390.3
23597.9
24749.1
26395.5
27687.5
28644.9
30911.5
31840.7
33668.1
35358.6
36559.2
38234.8
40574.6
44151.8
44204.9
45948.8
48100.8
49773.9
51448.6
53203.8
56451.7
58941.9
61625.7
64255.3
67232.6
Source: Economic Survey Various Issues (CBS)
Private
Consumption
1365.2
1406.0
1368.9
1572.9
1774.1
1919.5
2241.1
2527.2
2745.8
3186.0
3597.7
4478.2
5074.6
6240.7
7017.3
8631.4
9777.1
12137.2
13340.2
15406.5
16644.3
19146.9
21636.4
23139.2
26494.4
28794.7
34898.9
36094.7
37142.1
37405.7
39221.9
41321.7
42541.9
43076.3
45546.8
48298.4
48524.9
51025.7
Gross Fixed Capital
Formation
222.3
244.3
258.0
329.4
326.3
368.1
429.9
546.5
657.6
690.7
938.6
943.1
1182.5
1341.4
1639.2
1700.2
2278.0
2927.7
3727.8
4203.2
4837.0
5608.1
6079.4
6537.5
6526.9
7332.4
8475.1
8486.3
8806.9
9094.9
9142.7
10157.0
10694.0
10892.2
10945.9
12764.7
12672.3
12197.9
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