Defense vs. Human Capital Spending

International Journal of Economics and Empirical Research http://www.tesdo.org/Publication.aspx

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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: [email protected], [email protected] 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.

International Journal of Economics and Empirical Research. 2014, 2(2), 74-83.

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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

Source: World Development Indicators

India , 1994, 34.5India , 1995, 35.9India , 1996, 37.1India , 1997, 41.3India , 1998, 45.2India , 1999, 53.0India , 2000, 54.9India , 2001, 57.2India , 2002, 57.3India , 2003, 58.7

India , 2004, 65.1India , 2005, 68.8India , 2006, 68.8India , 2007, 70.2India , 2008, 79.6

India , 2009, 91.5India , 2010, 90.2

Bangladesh , 1994, 1.3

















Pakistan, 1994, 13.6
















Pakistan, 2010, 13.6Sri Lanka

, 1994, 1.6Sri Lanka
















, 2010, 2.6Nepal, 1994, 0.2Nepal, 1995, 0.2Nepal, 1996, 0.2Nepal, 1997, 0.2Nepal, 1998, 0.2Nepal, 1999, 0.2Nepal, 2000, 0.2Nepal, 2001, 0.3Nepal, 2002, 0.3Nepal, 2003, 0.4Nepal, 2004, 0.5Nepal, 2005, 0.5Nepal, 2006, 0.5Nepal, 2007, 0.4Nepal, 2008, 0.5Nepal, 2009, 0.5Nepal, 2010, 0.5

$Bill

ion

(200

5) P

PP India

Bangladesh

Pakistan

Sri Lanka

Nepal

How Spending on Defense Versus Human Capital impacts Economic Productivity in South Asia?

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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).

Table-1: The Top 10 Defence Spenders, 2010

S. No. Country Spending ($ bn.) World share (%) 1 USA 698 43 2 China 119 7.3 3 UK 59.6 3.7 4 France 59.3 3.6 5 Russia 58.7 3.6 6 Japan 54.5 3.3 7 Germany 45.2 2.8 8 India 41.3 2.5 9 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).

Table-2: Mortality Rates and Poverty Gap

Panel of Mortality rate,

infant Mortality rate,

under-5 Poverty gap at $1.25

a day(PPP, %) Poverty gap at

$2 a day(PPP, %)

Countries (per 1,000 live

births) (per 1,000 live

births)

Averages for 1990s

Bangladesh 73.3 102.2 19.2 40.1 India 68.0 93.8 13.5 34.1 Nepal 72.7 99.4 25.5 46.2 Pakistan 84.9 108.3 8.9 27.8 Sri Lanka 21.1 25.0 2.9 13.6

Averages for 2000s

Bangladesh 48.2 63.0 12.6 32.3 India 54.4 72. 9.0 26.9 Nepal 50.7 63.7 11.9 27.8 Pakistan 74.2 92.9 4.9 20.4 Sri Lanka 16.4 19.2 1.8 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

1Stockholm International Peace Research Institute 2010, ‘SIPRI Yearbook 2010’, viewed 15 May, 2012, <http://www.sipri.org/research/armaments/milex>.


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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

Table-4: Scientific Journal Articles and Patent Applications Panel of Countries

Scientific Journal Articles

Patent Applications (Nonresidents)

Patent Applications (Residents)

Averages for 1990s

Bangladesh 151 175 52 India 9869 5575 1911 Nepal 35 3 4 Pakistan 284 945 33 Sri Lanka 82 150 70

Averages for 2000s

Bangladesh 207 269 49 India 15198 18762 4767 Nepal 60 5 5 Pakistan 580 1239 95 Sri Lanka 121 228 154

Per Million Person for 1990s

Bangladesh 1.23 1.43 0.43 India 9.86 5.57 1.91 Nepal 1.54 0.13 0.15 Pakistan 2.12 7.03 0.24 Sri Lanka 4.43 8.13 3.78 Per Million Person for 2000s Bangladesh 1.47 1.91 0.35 India 13.23 16.34 4.15 Nepal 2.17 0.18 0.18 Pakistan 3.62 7.73 0.59 Sri Lanka 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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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.

2Cameron (1996) reviews extensive literature that analyzes the impact of research and development on economic productivity.


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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:

TtNiyty titiitiiti ........3,2,1;,.......3,2,1;,1,,

(1) In this system, 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 ( 1ty ), 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 0 i (for all 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:

iiH 0:0 NNNiNiH iA ,....21,11;1,....2,1,0:

In this set up, null ( 0H ) assumes non-stationarity of all the variables ( ) whereas alternative ( AH ) 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:

itititittiit URHMY (2)

3Present study excludes other remaining South Asian countries from the analysis owing to data limitations. 4This 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>.


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Where RandHMY ,, 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 Im Test Variable Name Unadjusted t-stat Adjusted t-stat w-t-bar stat GDP -5.6 -0.5 0.7 Defence Spending -7.8** -1.6** -1.1 Health Spending -6.0 5.7 -0.3 Research Input -3.8 -0.7 0.5

Note: ** indicates 5% significance level. 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.

6This 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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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) 0.12

(0.17) Health Spending 1.43**

(0.70) 0.21

(0.23) Research Input 1.17**

(0.46) 0.17

(0.33) Defence Spending Lag1 0.08

(0.17) 0.02

(0.56) Defence Spending Lag2 -0.06

(0.11) 0.06

(0.67) Health Spending Lag1 1.9***

(0.21) 0.54

(0.97) Health Spending Lag2 0.03

(0.12) 0.19

(0.29)

Research Input Lag1 1.5*** (0.11)

-0.07 (0.23)

Research Input Lag2 0.13 (0.10)

-0.23 (0.19)

Constant Term 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 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

7Stockholm International Peace Research Institute 2011, ‘SIPRI Yearbook 2011’, viewed 15 May, 2012, <http://www.sipri.org/research/armaments/milex>.


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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. Reference Abu-Bader, S.and Abu-Qarn, A. S. (2003). Government Expenditure, Military Spending and Economic Growth:

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Education

Defense vs. Human Capital Spending