How Spending on Defense versus Human Capital impacts Economic Productivity in South Asia? by Vaqar Ahmed and Muhammad Zeshan

1. International Journal of Economics and Empirical Research
http://www.tesdo.org/Publication.aspx
How Spending on Defense versus Human Capital impacts Economic Productivityin South Asia?
Muhammad Zeshana , Vaqar Ahmed a
a
Sustainable Development Policy Institute, Pakistan
Highlights
 The paper investigates the production function for South Asia.
 The panel unit root tests, fixed and random effect models have been used for empirical analysis.
 Health care and research expenditures stimulate economic growth.
Abstract
Purpose: South Asiahosts the highest number of poor people yet it continues to attach priority to its defence
spendings. The present study investigates the relationship between defence spendings, health care expenditures,
research, and economic growth of 5 South Asian countries including Bangladesh, India, Nepal, Pakistan, and
Sri Lanka. Methodology: The empirical results are based on the panel unit root tests, fixed and random effect
models while the Hausman test has been used for the diagnostic analysis for the period 1994-2010. Findings:
The short-run results indicate that a 1% increase in health care expenditures and research input boost
economic growth by 1.43% and 1.17% respectively. The long-run results indicate that defence spending is not
contributing to economic growth whereas health care expenditures and research contribute 3.62% and 2.28%
in long-run productivity. Recommendations: Hence, the present study suggests the South Asian government to
focus more on health care and research activities which have higher social returns.
Keywords: South Asia, Defence Spending, Health Care, Research, Economic Growth
JEL Classification: N1, O3, P3, R5

Corresponding Author: muh.zeshan@gmail.com, zeshan@sdpi.org
Citation: Zeshan, A. and Ahmed, V. (2014). How Spending on Defense Versus Human Capital impacts Economic
Productivity in South Asia? International Journal of Economics and Empirical Research. 2(2), 74-83.
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2. International Journal of Economics and Empirical Research. 2014, 2(2), 74-83.
I. Introduction
South Asia is home to the highest number of poor living below the poverty line, yet it continues to attach priority to
budget expenditures which are counterproductive. The defence spending for example can influence a country
through different transmission channels and each channel has a varied impact on the economic milieu (Abu-Bader
and Abu-Qarn, 2003). Atthe end, the dominant effect will prevail having multiple macroeconomic implications.
However, a fraction of empirical literature also reports positive aspects of defence spending suggesting that it
improves aggregate consumption (Yildirimet al. 2005) and at times good governance resulting in higher assets and
profits (Hirnissa, 2009). The former has a direct influence on economic productivity while the latter affects through
gross fixed capital formation. Higher aggregate demand also allows firms to boost manufacturing activities to clear
market and production of additional manufacturing units requires more workers in turn generating employment. In
the long run if higher defence expenditure enables improved security for business then there is a likelihood of
increased domestic and foreign investment (Hassan et al. 2003). There are also examples where defence institutions
are providing education and technical skills in regions where civilian institutions do not exist (Benoit, 1978). In
contrast, a part of empirical research argues that defence spending in the long run might influence economic
productivity negatively through exhaustion of national savings (Deger, 1986). The higher defence spending leaves
fewer savings available for future investment. If such expenditures are prolonged then there exists a tendency where
government borrowing crowds out private sector. Less liquidity in the market increases interest rate and general cost
of doing business. These studies also indicate that the multiplier effect of a fall in investment is dominant as
compared to higher defence spending; hence, macroeconomic losses are very obvious. This trend is very obvious in
South Asia,a region that already is capital constrained(Siddiqui, 2007).Contrary to the case of defence spending
there is a near consensus in the literature suggesting the long lasting impact of increase in human capital on
economic productivity. Neoclassical growth theories have emphasized how high-end labour and capital are
substitutes for generating productivity (Solow, 1994). Endogenizing effects of knowledge and technology put
nations on higher productivity trajectories (Romer, 1986). Innovation in production techniques cause permanent rise
in output productivity. The spillover effects also introduce many positive externalities which pave the way for
sustaining longr un productivity. Empirical literature finds that investment in Rand might provide social return in the
range of 20% to 30% at industry level whereas this return is much higher in case of aggregated economy (Jones and
Williams, 1998).
II. State of Defence Spending and Human Capital in South Asia
South Asia’s likening for a large defence arsenal is not a new phenomenon. Several of these countries have been in a
state of war for the past several decades. For example India and Pakistan have fought at least four full blown wars
since 1947. Sri Lanka was troubled with civil war for two decades and similar was the case of Nepal confronted with
Moist regime. Bangladesh is a result of civil war in united Pakistan of late 1960s.Under the above mentioned milieu
and the persistent distrust among the leaders of these countries it is not surprising to observe the stubbornly high
defence spending in the region (see
Figure-1).
Figure-1 Pattern of Defence Spending in South Asia
$Billion (2005) PPP
India ,,2009, 91.5
India 2010, 90.2
India , 2008, 79.6
India , 2007, 70.2
India ,India , 2006, 68.8
2005, 68.8
India
India , 2004, 65.1
India ,India ,India , 2003, 58.7
2001, 2002, 57.3
57.2
Bangladesh
India ,India , 2000, 54.9
1999, 53.0
India , 1998, 45.2
Pakistan
India , 1997, 41.3
India 1994, 1996,
1995, 35.9
India ,,India , 34.5 37.1
Sri Lanka
Nepal
Pakistan, Pakistan, Pakistan, Pakistan, Pakistan, Pakistan, 2007, 13.
Pakistan, 13. 13. 13. 13. 12. 12. 10. 11. 11. 12.Pakistan,13.
Pakistan, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 13.
2010,
Pakistan, Pakistan, Pakistan, Pakistan, Pakistan, 13. 2008,
2009,
9 Bangladesh Bangladesh Bangladesh Bangladesh Bangladesh
6 Bangladesh Bangladesh Bangladesh Bangladesh Bangladesh Bangladesh
6LankaLankaLankaLankaLankaLankaLankaLankaLankaLankaLankaLanka6 Lanka
1 Sri9 Sri8 Sri7 Sri0 Sri9 Sri5 Sri0 Sri7 Sri63
0
Sri
Sri
Lanka
Bangladesh
Bangladesh Bangladesh Sri
Sri Lanka Sri Sri
Bangladesh
Nepal,2008, 0.4
2010,
2009,
2008,
Nepal,,1994,,1995,,1997,,1998,,1999,,2000,,2001,,2001,,2002,,2004,,2005,,2006,,2007, 2.62.2
Nepal, 0.2 , 2.6 Nepal, 0.2 Nepal, 2.9 Nepal, 0.3 , 1.8 Nepal, 0.5 0.52009, 3.2
2010,
2010,
, 1995,2.6 Nepal, 0.2 Nepal, 0.2 , 0.2 , 0.2 , 2.0 Nepal, 0.4 Nepal, 2007, 2.23.1
,,2009, 0.5
,,1994, 1995, 1996, 1996, 1997, 1998, 1999, 2000, 2002, 2003, 2003, 2004, 2005, 2006, 0.52.4
1994,1.6 Nepal, 0.2 , 2.3 , 2.4 , 2.2 Nepal, 2.4 Nepal, 0.3 , 2.0 , 1.8 , 2.12008,2.7
1.3 , 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2.12007, 2.6
1.4 1.4 1.5 1.6 1.7 1.8 1.8 1.7 1.7 1.8 1.9
Source: World Development Indicators
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3. How Spending on Defense Versus Human Capital impacts Economic Productivity in South
Asia?
Today India plans to exercise its ambitions of South Asian big brother and in doing so it plans to balance China’s
dominance in this part of the world. While both China and India stand at very different development trajectories,
however this level of economic development has not prevented India from multiplying its defence spending several
times over and it currently stands 9th in the list of top countries with highest defence spending (Table-1).
S. No.
1
2
3
4
5
6
7
8
9
Table-1: The Top 10 Defence Spenders, 2010
Country
Spending ($ bn.)
World share (%)
USA
698
43
China
119
7.3
UK
59.6
3.7
France
59.3
3.6
Russia
58.7
3.6
Japan
54.5
3.3
Germany
45.2
2.8
India
41.3
2.5
Italy
37.0
2.3
Source: SIPRI (2010)1
At this point one is forced to indicate the commitment by South Asian countries towards Millennium Development
Goals (MDGs) at the start of the previous decade. As most of it has been mere rhetoric and not backed by tangible
action, therefore this region today houses most of the malnourished and uneducated population. This is the region
where high levels of infant mortality continue to exist alongside epidemics such as polio which is nowhere else in
the world, (Table-2).
Panel of
Countries
Mortality rate,
infant
(per 1,000 live
births)
Bangladesh
India
Nepal
Pakistan
Sri Lanka
73.3
68.0
72.7
84.9
21.1
Bangladesh
India
Nepal
Pakistan
Sri Lanka
48.2
54.4
50.7
74.2
16.4
Table-2: Mortality Rates and Poverty Gap
Mortality rate,
Poverty gap at $1.25
under-5
a day(PPP, %)
(per 1,000 live
births)
Averages for 1990s
102.2
19.2
93.8
13.5
99.4
25.5
108.3
8.9
25.0
2.9
Averages for 2000s
63.0
12.6
72.
9.0
63.7
11.9
92.9
4.9
19.2
1.8
Poverty gap at
$2 a day(PPP, %)
40.1
34.1
46.2
27.8
13.6
32.3
26.9
27.8
20.4
9.6
Source: World Development Indicators
Despite of these challenges the budgetary priorities attached with health sector is the lowest as compared to the
global standards (Table-3). It is not surprising to note that average health expenditures as percentage of GDP in two
of the largest countries of South Asia i.e. India and Pakistan have declined since 1990s.Several multilateral
institutions have been financing project related to health, education, research and development in South
1
Stockholm International Peace Research Institute 2010, ‘SIPRI Yearbook 2010’, viewed 15 May, 2012,
<http://www.sipri.org/research/armaments/milex>.
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4. International Journal of Economics and Empirical Research. 2014, 2(2), 74-83.
Asia. For example World Bank portfolio shows several projects for these countries in recent past. In 2008, 14
projects were approved for Bangladesh worth $ 1.7 billion, 7 projects were approved for India worth $ 10.3 billion,
and several small projects approved for Pakistan worth $ 25 million.
Table-3: Average Health Expenditures (as % of GDP)
Countries
1990s
2000s
Overall
Bangladesh
3.2
3.3
3.25
India
4.4
4.3
4.35
Pakistan
3.4
2.7
3.05
Sri Lanka
3.6
3.7
3.65
Nepal
5.2
5.5
5.35
United Kingdom
6.8
8.4
7.6
United States
13.3
15.7
14.6
Source: World Development Indicators
Panel of
Countries
Bangladesh
India
Nepal
Pakistan
Sri Lanka
Bangladesh
India
Nepal
Pakistan
Sri Lanka
Bangladesh
India
Nepal
Pakistan
Sri Lanka
Bangladesh
India
Nepal
Pakistan
Sri Lanka
Table-4: Scientific Journal Articles and Patent Applications
Scientific Journal
Patent Applications
Patent Applications
Articles
(Nonresidents)
(Residents)
Averages for 1990s
151
175
52
9869
5575
1911
35
3
4
284
945
33
82
150
70
Averages for 2000s
207
269
49
15198
18762
4767
60
5
5
580
1239
95
121
228
154
Per Million Person for 1990s
1.23
1.43
0.43
9.86
5.57
1.91
1.54
0.13
0.15
2.12
7.03
0.24
4.43
8.13
3.78
Per Million Person for 2000s
1.47
1.91
0.35
13.23
16.34
4.15
2.17
0.18
0.18
3.62
7.73
0.59
6.09
11.45
7.73
Source: World Development Indicators
Several multilateral institutions have been financing project related to health, education, research and development
in South Asia. For example World Bank portfolio shows several projects for these countries in recent past. In 2008,
14 projects were approved for Bangladesh worth $ 1.7 billion, 7 projects were approved for India worth $ 10.3
billion, and several small projects approved for Pakistan worth $ 25 million. The World Bank has also been focusing
on education enhancement projects. It provided $ 7.19 million to India for a project that aims to increase number of
school going kids at the primary level. Furthermore, in Pakistan, it provided funding to National University of
Science and Technology (NUST) for RD in engineering discipline. India certainly tops the regional list in terms of
attainment in RD and its application to commercial returns (see Table-4). While quantitatively India is followed by
Pakistan and Bangladesh however in per capita terms Sri Lanka stands second after India.
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5. How Spending on Defense Versus Human Capital impacts Economic Productivity in South
Asia?
It should be pointed out that the collapse of public sector education system in these countries forced deregulation of
this sector and today it is the private sector that is bridging the gaps and keeping enrollment rates on the rise
III. Literature Review
The debate whether defence spending causes economic productivity formally started in 1970s and Benoit, (1978)
investigated causal relationship between the two variables. Using a panel of 44 developing countries the author
concluded that higher defence spending contributes positively towards economic productivity. After this pioneering
study, extensive research work started which aimed to uncover all possible transmission channels and their effects.
Today we know that the empirical literature is unable to have a consensus and reports mixed results. For a panel of
developing countries Deger, (1986) reports a negative correlation between defence spending and economic
productivity. It asserts that such spending causes misallocation of productive resources which ultimately lower the
economic productivity. Similar results are reported by Robert and Alexander (2012).Hassan et al. (2003) used panel
data and covered the period 1980-1999. The author finds a positive correlation between defence spending and
foreign direct investment (FDI). The explanation indicates that higher defence spending provides elements of
security over time which in turn encourages FDI. Extending the Barro and Sala-i-Martin theoretical framework,
Aizenman and Glick, (2006) proposed that higher defence spending owing to external threats, might augment
productivity in the long run. Dakurahet al. (2000) analyzed a panel of 62 developing countries for this purpose but
gets mixed results. More specifically13 countries reported unidirectional causality running from defence spending to
economic productivity, 10 countries reported causality running from economic productivity to defence spending, 7
countries reported bidirectional causality whereas the remaining 18 countries reported no causal relationship
between the defence spending and economic productivity. Kollias et al. (2004a) also finds mixed results for a panel
of 15 European Union countries. However, most of them reported unidirectional causality running from economic
productivity to defence spending. In a subsequent study, Kollias et al. (2004b) investigated the causal linkages for
Cyprus. It finds bidirectional causality between defence spending and economic productivity indicating that each
variable reinforces other. Yildrim and Ocal, (2006) investigated the impact of defence spending on two South Asian
countries, Pakistan and India, and found unidirectional causality running from defence spending to economic
productivity. This explains that defence spending in both countries is not dependent on economic productivity; such
spending might be discretionary and counterproductive for productivity.
Human capital is impacted primarily by investment in social sectors such as health and education. Intuitively higher
income might cause higher health care expenditures and vice versa. Furthermore a wealthy society may be more
health conscious because they face higher opportunity cost of being ill. Health may also be regarded as a capital
good because it accounts for labour productivity, and a stream of income is connected with good health. Similarly
investment in education is long lasting; the higher the education acquisition, the greater is the return. Expenditures
on health and related social sectors contribute more in development (Abu-Bader and Abu-Qam; 2003). Bloom et al.
(2004) utilized 2SLS framework and find a positive relationship between health care expenditures and economic
productivity. The growth rate increases by 4% if there is one year increase in life expectancy. With the help of
various social indicators, Weil (2005) found health an essential element of income variations. The same evidence is
provided by Arora (2001) who worked with a panel of 10 industrial countries. It claimed that good health can
contribute 30% to 40 % higher growth in the long run. Another important element for upgrading the productivity
performance is acquiring of higher education whose output is essentially exhibited as RD in the available datasets2.
It brings innovations in market structure, productivity of labour and capital increases which ultimately causes
economic productivity. Using different indicators of the RD for empirical analysis, Samini and Alerasoul (2009)
worked with a panel of 30 developing countries and asserted that it did not contribute to productivity. Hence, a more
thorough analysis is required to find its transmission mechanism. For a panel of G5 countries, Griffith (2000)
uncovered that United Kingdom (UK) had the lowest RD intensity. On the other hand; all other countries were
having rising trend overtime. Furthermore stagnant RD expenditures were providing lower level of productivity in
UK.
To our knowledge, a simultaneous analysis of economic productivity, social sector indicators and military spending
is missing for South Asia. Present study aims to fill this gap with the help of South Asia specific panel data.
Secondly it is common to see literature trying to draw a relationship between economic productivity and higher
educational expenditures or spending on R & D.
2
Cameron (1996) reviews extensive literature that analyzes the impact of research and development on economic productivity.
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6. International Journal of Economics and Empirical Research. 2014, 2(2), 74-83.
This stream of literature as suggested above has inconclusive results owing to the large time period required for
research efforts to impact economic productivity. Therefore in this paper we have taken output indicator for RD
which includes journal papers and patents. Following is a brief description of estimation strategy that is adopted.
IV. Estimation Strategy and Data
Short time span of variable data makes conventional analysis less powerful. Extending this limited dataset over
many cross sections might provide a rich panel dataset. Contempory literature provides a number of methods for
analyzing a panel of countries. These methods combine information of time series and cross sectional dimensions,
and provide robust results. Hence, panel econometric techniques are more popular among econometricians for
empirical analysis in such situation. Based on its cross sectional units and time span (N and T respectively), it also
preserves asymptotic behavior in model (Levin et al. 2002).
IV.I Panel Unit Root Tests
Unit root is an important empirical issue and therefore the present study intends to run different panel unit root tests
for an in-depth investigation. Levin et al. (2002) proposes following equation for panel unit root test:
yi ,t   i   i t  t   i yi ,t 1   i ,t ; i  1,2,3,....... N ; t  1,2,3........T
(1)
 indicate the difference operator whereas y denote a given variable. It incorporates fixed effects in
two ways; fixed effect is introduced through  and  while unit specific time trend is introduced through  where t
indicates time trend. Since the above mentioned model contains lagged dependent variable ( yt 1 ), which assumes
slope homogeneity for all units, unit specific fixed effects become very important. Finally  is the error term in this
system. This test is estimated under the null of  i (for all i) whereas alternative becomes  i    0 (for all
In this system,
i).Furthermore it assumes cross-sectional independence among all individual processes. It derives a correction factor
which crafts distribution of pooled OLS estimation in a standard normal distribution. One basic shortcoming in this
test is the assumption of collective stationarity of all the series under analysis. It is not necessary that all the series
must be integrated or stationary; a fraction of series might be integrated while other may be stationary. Im et al.
(1997) provide a test that handles this shortcoming and provides robust results. Basically, this test is an extension of
Levin et al. (2002) and considers heterogeneity in  i under different hypothesis. For the same equation (1), null and
alternative hypotheses are as follows:
H 0 :  i  0 i
H A : i  0
i  1,2,.... N1; i  N1  1, N1  2,.... N
,
In this set up, null ( H 0 ) assumes non-stationarity of all the variables (  ) whereas alternative ( H A ) specifies that a
fraction of series is stationary.
IV.II Data and Estimation Technique
The present study investigates the contribution of defence spending, health care expenditures and R&D in economic
productivity of 5 South Asian countries including Bangladesh, India, Nepal, Pakistan, and Sri Lanka3. Assuming a
log-linear relationship between the variables, it utilizes health care expenditures and scientific research input as a
proxy of human capital4. For estimation purpose, it acquires data from the World Bank data base covering the period
1994-2010 with 2000 as base year5. All the variables are adjusted to purchasing power parity, and are in natural
logarithmic form. The basic model is as follows:
Y it   i   t M
it
 H it  R it  U it
(2)
3
Present study excludes other remaining South Asian countries from the analysis owing to data limitations.
This includes research papers published in internationally renowned journals, and patents.
5
The World Bank database 2012, ‘World Development Indicators (WDI) and Global Development Finance (GDF)’, viewed
15May, 2012, <http://databank.worldbank.org/ddp/home.do?CNO=2andStep=12andid=4>.
4
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7. How Spending on Defense Versus Human Capital impacts Economic Productivity in South
Asia?
Where Y , M , H and R indicate real GDP, defence spending, health care expenditures and research input. All the
variables are in level form and stands for country specific effects and U indicates error term6. Estimation procedure
is completed in two steps. The first step estimates fixed effect (FE) model (Allison, 1996) and random effect (RE)
model (Laird and Ware, 1982) while the second step utilizes Hausman test (Fielding, 2004). This test selects the
appropriate model between FE and RE model. The FE model assumes that cross country differences might cause
biasness in estimates and there is a need to control these differences. Nonetheless, this method ignores time invariant
characteristics within models because these characteristics are unique for each cross sectional unit and are supposed
not to be correlated with other cross sectional units. On the contrary, if these individual traits are correlated with
error term then one should avoid FE model. In this situation, use of FE model would generate biased estimates while
RE model provides robust results. This provides the rational for the use of the Hausman test to choose between FE
and RE model. This test works under the null of the true FE model against the alternative of true RE model.
V. Empirical Results
Levin et al. (2002) unit root test indicates that all the variables are non-stationary except defence spending whereas
Im et al. (1997) test specifies all the variables are non-stationary (Table-5: Results of Panel Unit Root Tests
). These mixed results create ambiguity and make it difficult to comprehend the examination. At this stage, it is
important to remind that standard time series practices require stationary data. In the presence of non-stationary data,
standard errors become biased and t-test loses its significance. As panel data is a mixture of time series and cross
sectional units, same rule also applies here. Although differencing might overcome the problem of non-stationarity
but it also causes loss of information in data.
Table-5: Results of Panel Unit Root Tests
Levin Test
Variable Name
Unadjusted t-stat
Adjusted t-stat
GDP
-5.6
-0.5
Defence Spending
-7.8**
-1.6**
Health Spending
-6.0
5.7
Research Input
-3.8
-0.7
Note: ** indicates 5% significance level.
Im Test
w-t-bar stat
0.7
-1.1
-0.3
0.5
This biasness can be minimized with the help of a Jackknife redistribution procedure, a method developed by E fron,
(1982). This procedure takes repeated sub-samples from the original sample in such a way that it acquires some
specified number of observations for each sub-sample while leaving one single observation. In this case, a sample
having ‘k’ observations would have sub-samples having‘k-1’ observations. These sub-samples are formed by
omitting one observation from sample and are used for approximating Jackknife estimate and standard error. To
overcome the biasness resulting from level form estimation of non-stationary variables, the present study also takes
the help of Jackknife re-sampling technique. Results of FE model indicate that defence spending does not contribute
to economic productivity whereas health care expenditures and research input are contributing significantly. A 1%
increase in health care expenditures and research input improves economic productivity by 1.43% and 1.17%
respectively. It indicates that social sector improvements specifically geared towards human capital can bring greater
productivity in South Asia. Furthermore point estimates indicate that health care sector requires more attention of
policy makers than research input because higher marginal social returns are associated with health sector. Though
the point estimates of defence spending are insignificant but it seems counterproductive in South Asia. Our results
are consistent with Aizenman and Glick (2006). This short run analysis points out that South Asia needs to develop
its social sector and realign budget priorities towards human capital rather than piling up arms and ammunition. This
analysis also exhibits that defence spending crowds out private investment in South Asia which lowers economic
productivity. Furthermore, the impact of the health care expenditures and research inputs goes up to one year
indicating that both are contributing to long-run economic productivity.
6
This study uses data of number of research articles, and patents as a proxy of RD. Griliches (1990) asserts that research articles
and patents are good indicators of RD.
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8. International Journal of Economics and Empirical Research. 2014, 2(2), 74-83.
Table-6: Results of Fixed Effect Model and Random Effect Model
Dependent Variable: GDP
Fixed Effect Model
Random Effect Model
Defence Spending
-0.02
(0.13)
1.43**
(0.70)
1.17**
(0.46)
0.12
(0.17)
0.21
(0.23)
0.17
(0.33)
0.08
(0.17)
-0.06
(0.11)
0.02
(0.56)
0.06
(0.67)
1.9***
(0.21)
0.03
(0.12)
0.54
(0.97)
0.19
(0.29)
1.5***
(0.11)
0.13
(0.10)
-0.07
(0.23)
-0.23
(0.19)
9.90***
(1.3)
0.51
(0.76)
R Square – Within
0.90
0.50
R Square – Between
0.96
0.52
R Square – Overall
0.96
0.52
Health Spending
Research Input
Defence Spending Lag1
Defence Spending Lag2
Health Spending Lag1
Health Spending Lag2
Research Input Lag1
Research Input Lag2
Constant Term
Hausman test
7.14
Note: *** and ** indicate 1% and 5% significance level respectively.
Standard errors are reported in parenthesis.
Nonetheless, insignificant point estimates for lagged values indicate that the contributions of these sectors are short
lived. This can be attributed to the lack of consistency in budgetary spending towards social sector particularly high
end education, research and development. Another issue is that lack of accountability and prudent monitoring and
evaluation in health sector prevents the total absorption of allocated funds. Summing up all the parameters of
exogenous variables provides us its long run impact on economic productivity. In the present case defence spending
is not contributing towards economic productivity whereas health expenditures and research input contributed
3.63% and 2.8% in long run productivity respectively. The above mentioned point estimates are based on FE model
because data of countries is not selected randomly. For a definite analysis, RE model is also estimated and takes the
help of Hausman test for model selection. This test operates under the null that FE model is correct against the
alternative of true RE model. Last row indicates the point estimate of the Hausman test indicating inability of
rejecting the null. Hence it can be concluded that FE model is the proper model for empirical analysis.
VI. Conclusion and Policy Implications
Millions of people in South Asia lack access to basic social services while India alone spent more than $ 41 billion
in 2010 to satiate her desire for expanding arsenals of arms and ammunition7. Defence spending in this region
7
Stockholm International Peace Research Institute 2011, ‘SIPRI Yearbook 2011’, viewed 15 May, 2012,
<http://www.sipri.org/research/armaments/milex>.
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9. How Spending on Defense Versus Human Capital impacts Economic Productivity in South
Asia?
crowds out a substantial share of development expenditures and private investment. This is a region having more
than 25% of the world population which provides it ample capacity to develop given proper resource utilization
towards human development. To our knowledge, no study in South Asia simultaneously assesses the impact of
defence spending and human capital developments on economic productivity. The present study aims to fill this gap
for a panel of South Asian countries which includes Bangladesh, India, Nepal, Pakistan, and Sri Lanka. It covers the
period 1994-2010 and uses World Bank database. It assumes a log-linear relationship between health care
expenditures, research input, defence spending and economic productivity. We applied panel unit root tests rather
than employing time series unit root tests. Literature specifies the use of Levin et al. (2002) test and Im et al. (1997)
test for this purpose but these tests are unable to provide consistent results. Although differencing might convert
non-stationary variables in stationary variable but it also causes the loss of information in data. The Jackknife fixed
effect model (used in this paper) not only preserves this information but also produces unbiased standard errors.
Short run results demonstrate that defence spending is not contributing to economic development whereas health
care expenditures and research input are contributing significantly to productivity. A 1%increase in health care
expenditures and research input boost productivity by 1.43% and 1.17% respectively in short-run. On the other
hand, long run results also indicate that defence spending is not contributing to economic productivity whereas
health care expenditures and research input contribute 3.62% and 2.28% in long-run productivity. Hence, the present
study suggests the South Asian governments to revisit their current stance on defence spendings. These countries
should increase their national budgets of health care and research activities which have higher marginal and social
productivities. The recent meeting, which took place after more than a decade, between the Indian and Pakistani
senior army officers is a good sing towards a friendly environment. This milieu would bring more investment in this
region, translating into employment generation. This environment can help to reduce the poverty in South Asia.
Such meeting should be scheduled on regular basis among the South Asian countries. Further, these countries should
design their regional poverty reduction targets. The cultural exchange programs among the national universities and
research institutes at regional level can build a consensus on regional policy making process.
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