metadata: authors: 0: author: Landau, Daniel
docna: 0: docna: The economic impact of military expenditures
docty: Policy Research Working Paper
subtopic: Conflict and Fragile States,Economic Theory & Research,Industrial Economics,Health Care Services Industry,Educational Sciences,Economic Growth,Armed Conflict
teratopic: Poverty Reduction
abstracts: cdata!: The author addresses three questions
            about military spending in developing countries: What are
            the levels of (and trends in) military spending as a
            percentage of gross national product? What impact does
            peacetime military spending have on growth, government
            spending on social welfare and infrastructure, and other key
            economic variables? What major factors influence the level
            of military spending? The author finds that military
            spending as a share of GNP generally fell in the 1980s, even
            in the Middle East and North Africa. The mean level of
            military expenditure as a share of GNP (MES) was 3.9
            percent, well below the peak of 5.3 percent in 1976. In
            1989, MES averaged only 2.7 percent in Latin America and 2.0
            percent in sub-Saharan Africa - the two regions with the
            most severe economic problems. He finds no evidence of a
            negative relationship between military spending as a share
            of GNP and the peacetime growth rate of developing countries
            - except where military spending is high. He finds that
            higher shares of MES are not associated with lower shares of
            government spending on education, health, and
            infrastructure. As MES increases, government spending as a
            share of GNP increases, which allows the level of spending
            on health, education, and infrastructure to be maintained.
            The author finds some evidence that increased military
            spending in the developing countries has a weak negative
            impact on investment and the balance of trade. He finds no
            evidence of a statistically significant relationship between
            military spending and inflation. The most important
            determinant of peacetime military spending is the spending
            level of neighboring countries - in other words, the
            potential external threat. Regional conciliation and
            disarmament may be an important step toward reduced military spending.
repnme: repnme: The economic impact of military expenditures
display_title: The economic impact of military expenditures
listing_relative_url: /research/1993/05/699017/economic-impact-military-expenditures
topicv3: Economic Growth m1326241 471,Education m1327048 470,Political Violence and War m1327310 2462,Health m1504661 3480,Military m1505651 3036
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: Policy Research Department
no_of_pages: 55
model_extractions: # **Policy Research Pubilc Economics**

WPS 1138 This paper-aproduct ofthe Public Economics Division, Policy Research Departmnent-is part of a larger effort in the department to study public expenditure issues in developing countries. This study was funded by the Bank's Research Support Budget under research project "The Economic Impact of Military Expenditures" (RPO 676-85). Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Carlina Jones, room NIO-063, extension 37699 (May 1993, _45_ pages).

Landau addresses three questions about military spending in developing countries:

* What are levels of (and trends in) military spending as a percentage of gross national product?

* What impact does peacetime military spending have on growth, government spending on social welfare and infrastructure, and other key economic variables?

of GNP and the peacetime growth rate of - developing countries except where military spending is high.

He finds that higher shares of MES are not associated with lower shares of government spending on education, health, and infrastructure. As MES increases, government spending as a share of GNP increases, which allows the level of spending on health, education, and infrastructure to be maintained.

* What major factors influence the level of military spending?

Landau finds that military spending as a share of GNP generally fell in the 1980s, even in the Middle East and North Africa. The mean level of military expenditure as a share of GNP (MES) was 3.9 percent, well below the peak of _5.3_ percent in 1976. In 1989, MES averaged only 2.7 percent in Latin America and 2.0 percent in Sub-Saharan Africa - the two regions with the most severe economic problems.

He finds some evidence that increased military spending in the developing countries has a weak negative impact on investment and the balance of trade. He finds no evidence of a statistically significant relationship between military spending and inflation.

The most important determinant of peacetime military spending is the spending level of neighboring countriesin other words, the potential external threaL Regional conciliation and disarmament may be an important step toward reduced military spending.

He finds no evidence of a negative relationship between military spending as a share T'hePolicy Research Working PaperoSeriesdisseminates thefodings of work under way in theBank. Anobjectiveof the<sup>series</sup> is to get these findings out quickly, even if presentations are less than fully polished. Th - findings, interpretations, and conclusions in these papers do not necessarily represent of ficial Bank policy.

Produced by the Policy Research Dissemination Center
# THE ECONOMC IMACT OF TARY EXES

by Daniel Landau
Table _of QW=_

|_Abstnct_<br>_....................................._<br>_.................._|_..._<br>_........._<br>_i_|
|---|---|
|Summary ...............<br>.......................|fi|
|I. lNTRODUCIION<br>........................................<br>...<br>....|1|
|U.LEVELS OFMILTARYEXPENDITURE<br>...............................|2|
|m.MILITARY SPENDING AND ECONOMIC GROWTH ...........<br>............|4|
|A.<br>Hypothesis ......................................|4|
|<br>B.<br>Model Specification .......................................|6|
|<br>C.<br>Data<br>................................<br>.|_8_|
|D.<br>Results<br>.<br>.....................................<br>.|9|
|Tl e Relationship Between Milex and Ecowomic Growd<br>.<br>.|9|
|<br>Ot.&er Regressors..........<br>..............<br>.9||
|<br>Sensitivity Analysis..|_9_|
|<br>Changing the Specification<br>..|10|
|<br>Robustness ........................<br>,,|10|
|Robustness vis-a-vis Outliers .........|11|
|E.<br>Military Expenditres and Growth: Channels of Impat_..............._|_.._14|
|IV. OTHER ECONOMIC IMPACTS OF MILITARY EXPENDITURES .....<br>..........|16|
|A.<br>Impact on Government Social and Ifrastrucure_SpeWing._....<br>..........|16|
|<br>B.<br>hImact on Other Economic Variables. .......<br>.............<br>.|is|
|V. DETERMINANTS<br>OF MLITARY EXPENDITURE ......<br>...................|19|
|VI. CONCLUSIONS ................................................|20|
|REFERENCES..................................|30|
|Variable List||
|Table I<br>Patterms of Military Experditure||
|<br>Table 2<br>Cross Section Regressions||
|<br>Table 3<br>Regressions of the Difference in Growth Rata Betwee Succouive Mulid-yer||
|Periods on the Differences in the Regressors||
|<br>Table 4<br>Summary of Regressions by Geographic Regiom||
|Table S<br>Dropping Various Regions||
|Table 6<br>Channels of Imact||
|<br>p<br>Table 7<br>Testing the Efficiency Hypcthesis||
|<br>Table 8<br>Impact of Milex On The Level and Composiion of Cal<br>GovNment||
|Expediture 197089||
|Table 9<br>Impact of MES on Key Economic Variables||
|<br>Table 10<br>The Dem<br>ir of Military Exp<br>_end - MES||
. i -

# Abstract

Levels of military spending in developing countries have been falling and are relatively low in areas with economic problems. Military spending is mostly motivated by external threats. In general, at typical current levels (about 4 percent of GDP), military expenditure is not associated with lower rates of economic growth, government social and infrastructure spending, or capital formation, or with higher inflation.

_l7ds research project was fiaded by a grant from the World Bank's Research Support Budget, managed by Sanjay Pradhan. The author would like to dtnk Shanta Devarajan, Sawjay Pradhan and Vlnaya Swaroopfor many helpfid comments and suggestions._
- ii -

Summary This paper presents the results of research on three aspect of military spending in the developing countries. What are the levels and trends in military spending as a percentage of national product? What is the impact of peacetime military spending on growth, governmeni social and infrastructur **e** xpenditure, and other key economic variables? What are the major factors influencing the level of military spending? In terms of the levels of military spending, Landau finds that military spending shares in GNP were generally falling during the 1980s, including the highest spending areas of the Middle East and North Africa. The mean level of military expenditure as a share in GNP (MES) in 1989 wss 3.9%, much below the peak of _5.3_ percent in 1976. In 1989, in the areas with the most severe economic problems, Latin America and Sub-Saharan Africa, MES averaged only 2.7% and 2.0%, respectively.

Landau studies the impact of military spending on economic growth with regressions of the growth rate of real GNP on MES and other important determinants of growth. The regressions use a sample of 71 countries with a population of 2 million or more. The data cover the time period 1969-89. Landau's hypothesis is that the impact of military expenditure (milex) on growth is a combination of three effects: (1) increased security - positive impact on growth; (2) milex is related to external threat and hence pressure for more efficient government policies in response to external threat (or "policy efficiency effects" - positive impact; and (3) diversion of resource' rom productive investment - negative impact. Further, he hypothesizes that the combination of these effects will produce a non-linear (quadratic) relationship between milex and growth: at low levels of milex, there will be a positive impact on growth due to increased security and efficiency, while at higher levels of milex, the negative resource-use impact will lead to lower growth. For the full sample of 71 countries, he finds a non-linea **r** elationship between MES and the growth rate. Initially increases in MES are associated with faster growth and beyond a cerain level they are associated with slower growth. However, this result is being driven by the 24 countries in the sample from Asia, the Middle East, North Africa, and Southern Europe, which account
> - iii -

for onethird of total observations. When the regressions are run without these countries, there is no significant relationship, positive or negative, between MES and the growth rate. Landau concludes that there is no evidence of a negative relationship between the share of military spending in GNP and the growth rate of the developing countries (in peacetime) until the military expenditure share is quite high. However, it is uncertain whether the non-linear milex-growth relationship can be generalized beyond the Eurasia/North Africa region.

Landau also attempts to determine empirically the channels through which military expenditure influences economic growth. He finds evidence for the hypothesized 'policy efficiency effect,' i.e., military expenditure is associated with more efficient policies in respoise to an external threat. However, he flnds no other statistically significant channels )f impact. Specificaily, the impact of military spending on growth cannot be explained by its effects on the levels of investment in human or physical capital or by its impact on the balance of payments.

Regarding other economic effects of military spending, he fnds that higher levels of MES are not associated with lower levels of government spending on education, health, and infrastructure as shares in GNP. As MES increases, the share of total govc-nment spending in GNP increases which allows the spending on health, education and infrastructure as shares in GNP to be maintained aithough their shares in total central governnent expenditure are reduced. 'here is some evidence that increased military spending in the developing countries has a very weak negative impact on investment and the balance of trade. Landau finds no evidence of a stadstically significant relationship between military spending and the inflation rate.

The major deerminants of military spending as a percemtage of GNP are the average level of military spending of neighboring countries (the potendal threat), per capita product, and the existence of actual iternational wars. The most important determint of peacetime military spending is neighbors' military spending levels. This result suggests that, in general, military spending in the developing
- iv -

countries is a response to potential foreign threats. This in turn suggests that a country can lower military expenditures if its neighbors do so as well w that regional conciliation and disarmament could be critical positive steps for reductions in military expenditures.

As with all studies of the determinants of growth, this study is constrained to work from an incomplete theoretical basis, using proxies for some explanatory factors and using imperfect data. In addition, these particular findings apply to peacetime military spending in the developing countries and are based on the years studied. War is an economic as well as human disaster. With the changes in the world in the 1990s, the relationship between developing countries' military spending and their economies could also change.
- I -

# I. INTROD),XrION

In recent years, there has been growing concwn about the possibly harmful effecls of unfettered military expenditures in developing countries. It Is alleged that these expenditures worsen balance of payments deficits, undermine 7rowth and *crowd out" critical economic and social sector expenditures, with adverse implications for the poor. Furthermore, some did donors fear that development assistance Is directdy or indirecy financing military spending. While these concerns have been rdised in the past, the Gulf War and the collapse of the Soviet Union have brought the debate into sharp focus. Indeed, several observers have suggested making development assisance explicitly conditional on reductions in military expenditures.' While defense conditionality is ruled out for the World Bank,<sup>2</sup> some bilateral donors are beginning to impose conditions on military spending. In this context, it is worth asking what the economic effects of military expenditures have been.

This paper studies several aspects of this question. First, it briefly reviews trends in military expeditures over time and across regions to provide background to the magnitude and location of the problem. Second, and most important for policy, it analyzes the effects of military expenditure on the economic performance of developing countries. How does military expendtture affect economic growth? What are the channels, and what evidence do we have that these are the med1uvdsms? What is the impact of military spending on government social and infrastructure spending and on other key economic variables? The analysis thus provides insights into the desirability of reducing military expenditures. Finaly, the paper analyzes the deerminants of military penditure, which need to be taken into account in Initives **to** reduce militarv spending.

The rest of the paper is organized as foilows. Section II of the paper discusses trends in military aependiture. Section M analyzes the relationship between military spending and growth. Section IV emine other economic impacts of military spending, and Section V idendfies the determinants of miitay
-2-

# spending. Section VI summarizes our findings.

# 11. LEVELS OF MIL1TARY EXPENDiTURE

In order to analyze the economic effects of military expenditures, it is<sup>useful to examine trends</sup> in such spending. Do some regions si 'ud more on the military than others?<sup>Are military expondita.ts</sup> increasing over time? As discussed in detail below, the available data suggest<sup>that military expenditure</sup> has generally been falling in recent years, that it is particularly high in specific<sup>regions, and that the poor-</sup> growth regions have relatively low levels of military spending.

A key problem in analyzing military expenditures Is obtaining reliable<sup>and accurate data on</sup> aggregate military spending. Data from the Stockholm International Peace Renearch<sup>Institute (SIPRI) are</sup> generally more comprehensive than those of the IMP's Government<sup>Financial Statistics (GFS) or the U.S.</sup> Arms Control and Disarmament Agency (ACDA). This paper uses military expenditure<sup>data from SIPRI,</sup> supplemented by data from ACDA for some earlier years.

Table 1 gives the mean levels of the military expenditure share (percentage)<sup>in GNP -</sup> abbreviated MES hereafter - for seven regions over the period<sup>1969-89. In addition, it provides the</sup> mean for all 71 countries in the sample ("PF!! Sample") and the mean<sup>excluding the Middle East and</sup> North Africa ("Full Sample 2").3 As can be seen from Table 1<sup>and Figure 1, the full sample was at its</sup> lowest level in 1939 at 3.9 percent of GDP, much below the peak of 5.3<sup>percent in 1976. In the sample</sup> excluding the high-spending Middle East and North Africa, military<sup>spending has varied very little on</sup> average over the years. In the Middle East and North Africa,<sup>spending in 1989 was far below their peak</sup> levels.' More important, from the standpoint of the relationship between<sup>military spending and growth,Sub-Saharan Africa and Latin America-have</sup> the two regions with the most serious growth problems<sup>-</sup> on average been the lowest spenders on the military. Overall,<sup>therefore, the figures show that the bigest</sup>
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spenders are spending less and that the average is down from peal' levels for the LDCs as a whole and in most regions. Consequently, this inial examination suggests neither a heavy "military burden" on the economy - outside the Middle East and North Africa - nor a growing military burden. There are of course regions (South Asia) and countdes (Honduras) which are excepdons to these general trends in military expenditure.'

# m. MILifARY SPENDING AND ECONOMIC GROWTH

There has been considerable research on the relationship between militay expenditure ("mllexc for short) and economic growth. Most of the work is empirical; attempts to resolve the issues theoretically have not been successful. The empirical studies work with a wise rray of specifications, country and time period samples, and esdmating methods. They also reach differing conclusions with regard to the impact of miiitary spending on growth.

&AlaA presents brief survey of the existing literature. As further explained therein, the existing literature on military expenditure and economic growth is beset by several problems: (I) there are significant omitted vt.iables in the analysis; (ii) regressors are not lagged; (iii) the studies often cover too short a time period; (iv) the regressors for military exnditure do not allow for non-linear relationships; (v) the regressors include factors influenced by the level of military expenditure; and (vi) military expenditure is treatd as a cost which could not possibly be directly beneficial by providing security. Given the problems with the existing literature, we developed our own approach, making use of the results of the author's previous work on the determna ts of economic growth (Landau 1986, 1990).

A. HIgeis Our hypothesis is that there are three types of effects of military expenditure on economic growth: (i) resource use; (ii) security; and (iii) policy efficiency. One, the resources used for military
expenditares are in gene not availablo for invement; a a result, incresed<sup>military spending will tend</sup> to diminish the growth rate. Two, for any given level of foreign threat,<sup>the higher the millIary</sup> expenditure of a country, the more saure the country i8. The increased security will<sup>tend to increase</sup> private investmeL 4 and accelerate growth. Three, governments of countries under strong foreign<sup>threat</sup> need a tax base that can provide the tax revenue necessary to build strong armed forces.<sup>As a result,</sup> developing countries facing strong foreign threats will tend to modify their<sup>policies in directions which</sup> facilitate faster economic growth.7 The combination of the three effects of military spending is hypothesized to produce<sup>a quadrtic</sup> relationship betwoen the level of military spending and the growth rate.<sup>The</sup><sup>_initidal_impact of low levels</sup> of milex will be posidve due to the security and policy efficiency<sup>effects. Beyond a certain level, the</sup> impact of military spending on growth will be negative as the resource use effect<sup>comes to dominate.</sup> Our measure of military epditure is the miitary expenditure share<sup>(MES) in the national product. The</sup> hypothesis predicts that the coeffiriet for MES will be positdve and<sup>that the coefficient for MES squared</sup> QMES2) will be negative.

The level of MES and the impact of milex on growth will both<sup>depend on the degree of threat</sup> a country faces. We measure th threat by the share of military expenditure<sup>of neighboring countries</sup> (NMES). NMES is the unweigted average of the military expenditure<sup>shares for all neighboring</sup> countries of over two million population.' There are two possible effects<sup>of the threat represented by</sup> NMES: (i) decreased investmt ad growth due to the threat to human<sup>and physical assets; and (ii)</sup> increased policy efficiency in response to the threat. The second effect is<sup>the same as that hypothesized</sup> for expenditure MES. Our priors were that the first effect would dominate<sup>and NMES would have a</sup> negative coefficient In the growth regessions.
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# B. Model Specification

We need to embed our hypothesis about the effects of milex on growth<sup>in a general model of the</sup> growth process. We can view the growth rate, Y, as a function of six basic<sup>economic derminants: labor</sup> (L); natural resources (N); physical capital (Kp); human capital (Kh);<sup>technology (M); and efficiency (E).</sup> (1) y = f(L, N, Kp, Kh, T, E) The correct functional form for this equation is not known. It would probably<sup>depend on the rates of</sup> growth of the six factors and also their levels. These six factors are, in<sup>any case, only the proximate</sup> economic determinants of growth. The real questions concern what determines<sup>the levels and rates of</sup> change of these six factors for each country in each time period.

We could model each of the six proximate determinants of growth<sup>as a function of various</sup> fundamental factors. For example, the growth rate of physical capital<sup>would depend on the factors which</sup> influence the expected return to investment: internal protection<sup>of property rights, political</sup> stability/instability, threats of civil and international war, the level and rate of growth of human<sup>capital,</sup> technology and its rate of change, international economi **c** onditions. After<sup>we had written down the six</sup> functions for the six proximate determinants of growth, we would combine<sup>them with the function in</sup> equation (1) **to** come up with a reduced form eWtion for growth<sup>in terms of its fundamental</sup> determinants. However, knowledge of the determinants of growth is not sufficient<sup>even to specify the</sup> six functions for the proximate determinants of growth, let alone the reduced<sup>form. In addition, many</sup> of the variables have no quantitative measures - for example, protection<sup>of property rights, government-</sup> caused market distortions, historical and cultural factors.

In order to test the central hypothesis of this study, the key regressors<sup>constituted MES, MES2</sup> and NMES, as discussed above. In addition, those variables were selected<sup>as regressors that met the</sup> following criteria: (i) quantitative data were available; (ii) previous research<sup>has shown them to</sup> significantly influence the growth rate (either in the reduced form equation or<sup>as proxies for the proximate</sup>
-7 -

determinants of growth); and (iii) they are exogenous with regard to the military expenditure regressors -

- MES and MES2. Based on these criteria, the following regressors were chosen: the growth rate of the developed countries (GRW); the change in the terms of trade (CTOT); per capita product (PCP); the debt burden as a share of GDP (DEBTS); the average life expectancy at birth (LIFE); political condition variables - instability (PI), civil war (CW) and international war (W); and the share of fuel exports in national product (OILS).' The basic OLS regression equation is given below. (CW and IW were not statistically significant in the growth rate regressions.)

(2) y = bo + b,MES + b2 MES2 + b3NMES + b4 GRW + b5CTOT + b6PCP + b,DEBTS + bELIFE + bPI + b10OILS + error Since we are interested in studying the impact of military expenditures on long-term growth, the dependent variable in the regressions must be the average growth rate over multi-year periods rather than single-year growth rates. The change in real product over one year, or even a few years, is a mix of the real long-run growth of the economy and cyclical changes in the level of production. Since we do not have an adequate measure of the cyclical effects for the majority of developing countries, the only way to get a dependent variable which is mainly real growth and **to** minimize the cyclical element is to use time periods as long as the data allow. Consequendy, the dependent variable is the averagc annual growth rate over six- and seven-year periods of real GNP (in domestic currency at 1987 prices). This variable was used in two forms: the growth rate of total GNP (abbreviated "GRT"), and the growth rate of GNP minus military expenditure (abbreviated 'GRNM').

In order to examine the long-term impact of the regressors rather than their current impact, the regressors were lagged.'° If the regressors are not lagged, no matter how long a period is included in each observation, we are only looking at the average impact of the current level of 'X' on current GNP; we are not looling at the long-run effect of 'X'. But it is the long-run effects we are interested in: if military expenditure increases today, will future GNP be higher or lower? Lagging the regressors avoids
-8-

- the problem of causality current milex could cause changes in the current growth rate or changes in the current growth rate could cause changes in the current level of milex. In this light, the most - important regressors MES, MES2, NMES, etc. - are the average of the three years immediately preceding the six- or seven-year period of the dependent variable.'1

Since the dependent variable is the average growth over six to seven years and since the regressors are typically threeyear lagged averages, the regressions using data for 196949 basically test the cross-section relationship between military expenditures and growth. 7"tare are 71 countries and at most two observations per country. Using multi-year growth rates as the dependent variable, it is difficul **t** o test the time series relationship because the share of milex in GNP changes slowly. To test the effects of changes in MES on the growth rate, we also regressed differences in the growth rates between successive non-overlapping multi-year periods on differences in the regressors.12 The sample was also broken down by geographic regions: Sub-Saharan Africa (SSA), Latin America, and all other - regions (Asia, Middle East, North Africa and Southern Europe ASMENASE). Finer breakdowns would result in too few degrees of freedom in the regressions.

C. D=a The data source for the economic, social, and human capital variables was the World Bank's World Tables. This data series provides annual data for the years 196949 and defines the time period for the study. As discussed earlier, the military expenditure data were from the SIPRI Yarbo, supplemented by USACDA data when necessary. The political condition variables were created by the author, and consist of dummy variables assessed annually for political instability, civil war, and international war. Further details about data sources and variable definitions are provided in the endnote.'4 The countries in the sample are non-Communist developing countries with a population of over 2 million for which there are data in the World Table. It i importan **t** o note that some of the
# -9 -

countries involved in international wars - e.g., Iran and Iraq - have nkot provided data to the _Nol_ Table, and thus are not in the sample.

## D. Results

The, RelationshjD BDetween Milex andg EconomicGrow. The 'cross-section" regressions for the full sample of 71 countries show the predicted non-linear relationship between military spending and the growth rate, with a statistically significant positive coefficient for MES and a statisdcally significant negative coefficient for MES2. These results which constitute the basic regressions for the study are presented in Table 2. The results are similar for both the seven- and six-year growth periods and the two forms of the growth rate - the growth rate of total GNP (GRl), and the growth rate of GNP excluding military expenditures. For the differences regressions that test the 'dme-series' relationship between a change in MMIS and the change in the growth rate, the coefficients show the predicted non-linear relationship in three of the four cases (see Table 3). The mnmerical values of the coefficients for MES and MES2 predict hat increaes- in milex are associated with faster growth up to 4 - 9 percent of GNP (depending on which regression one uses) and with slower growth thereafter. In view of the lack of robustness of any precise coefficient, no particular importance is attached to the implied point of m --imal growth.

Other RWe&ressor. The results for neighborig countries' military expenditures (NMES) are quite unexpected. The coefficients for NNMES are positive and statistically significant for all the full sample regressions, the ASMENASE region, and in most of the regressions where regions and outliers are dropped. As discussed further below, this appears to be due to more efficient policies induced by countries facing external threats. In the regressions for the SSA and Latin Ameica regions, NMES is not statistically significant. The results for the other regressors are discussed in the endnote.U

> Sensitivity ADalvss. The results for the full sample support the hypothesis of a non-linear relationship between milex and growth. However, the question arises whether these results are sensitive
- 10-

to changes in the specification (e.g., inclusion/exclusion of other regressors) and the sample of countries in the data set.

Changing the Specification. Further analysis showed that the results for the full sample of 71 countries are not sensitive to the inclusion or exclusion of other explanatory variables besides MES, MES2, and NMES. Speciffcally, cross section seven-year growth rate regressions with GRT as the dependent variable were repeated removing successively NMES, PI, DEBT, OILS and CTOT, PCP and GRW. The predicted quadratic relationship remains as the other regressors are removed. The predicted relationship also generally holds for the differences regressions when the only regressors are the ch&ges in MES and MES2.<sup>16</sup> The results are also not sensitive to the addition of other explanatory variables, such as investment, central government expenditure, education, and components of the balance of payments.

kobustness. While the full sample results are not sensitive to the inclusion/exclusion of other regressors in the regression equation, the results are sensitive to changes in the countries in the sample. In particular, the results by the three geographic regions outlined earlier (atin America, Sub-Saharan Africa and Asia/Middle East/North Africa/Southern Europe - ASMENASE) are mixed. Table 4 presents summary statistics for regression equations for each of the three geographic regions. For the ASMENASE region (Panel A, Table 4), we find the statistically significant quadratic relationship in all cases (regressions 2 and 4). However, Latin America (Panel B, Table 4) shows no statistically significant relationship between MES and the growth rate. Sub-Saharan Africa does not have the quadratic relationship and shows a weak tendency towards a simple linear positive relationship between MES and the growth rate for the six-year growth periods (regressions 1 and 3).

This suggests that the foil sample results are being driven by the ASMENASE region. In Table _5,_ the regions are successively dropped from the sample. The results show that a significant, non-linear quadratic relationship holds when Sub-Saharan Africa or Latin America are dropped from the fill sample
- 11 -

(regressions 4 and 5). However, when the entire ASMENASE region is dropped, the coefficients for MES and MES2 become totally Insignificant (regression 6). When either the Asia or the rest of the ASMENASE region is dropped, the non-linear relationship exists but one of the coefficients is no longer significant at the 10 percent level.

Based on the above, it would appear that the non-linear relationship between milex and growth holds because of the ASMENASE countries. Without them, there is no statistically significant relationship. Ihe ASMENASE region is generally one of higher military spending and more rapid growth, while Latin America and SSA are, in general, regions of slower growth and lower military spending.

This conclusion can be interpreted in two ways: the non-linear relationship only holds for the ASMENASE region, or the results from these countries are the important ones. The second interpretation would be based on the following arguments. The 24 countries for ASMENASE constitute more than onethird of all the observations, and contain the majority of the population in the total sample. The view that Eurasia/North Africa is the real test of the hypothesis is strengthened if one considers some particular features of Latin America and Sub-Saharan Africa. Latin American countries do not, by and large, face significant threats from one another, and protection has effectively been provided by the United States. As a result, it can be conjectured that levels of military spending are generally too low to exhibit a quadratic relationship. In Sub-Saharan Africa, in countries not in an actual war, we observe relatively low levels of MES due to the combination of low per capita product and small size (see Hewitt, 1991b). Thus SSA is at the left end of the inverted U relationship, and exhibits some tendency towards a simple positive relationshir ',etween milex and growth rajer than a quadratic one.<sup>17</sup> By this reasoning, SSA and Latin America do not reject the hypothesis; they simply do not test it adequately.

Robustness vis-a-vis Oudiers. We further tested the sensitivity of the results by looking at the impact of outliers. The extreme - highest and lowest - observations by growth rates (GRT), per capita
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product (PCP), neighbor's military expenditure (NMES), and own military expenditure (MES) were dropped and regresslome run without these observations. When the two top and bottom outliers by ORT or MES are dropped, the same non-linear relationship as the full sample holds, with only small re'uctions in significance levels for some of the regressions. Dropping the 10 highest and lowest outliers by per capita product and NMES also does not change the non-linear relationship. However, if we drop the 10 outliers by growth rates and MES, the hypothesized relationship does not always hold. In sum, dropping individual outlier observations leads to a conclusion similar to dropping regions. A few extreme outlier observations are not producing the fill sample results, but a subset of observations are.

Additional, but less rigorous, information is provided by Fiare 2 on the next page. It plots the mean MES against the annual growth rate of per capita GNP from 1970-89 for seven regions: South Europe, North Africa, Middle East (minus Saudi Arabia), South Asia, East Asia, Sub-Saharan Africa and Latin America. The plot shows a wn-linear relationship with the growth rate by regions increasing with MES up to roughly 4 percent and then decreasing.

In =m, for the full sample of 71 countries, we find a significant, non-linear relationship between milex and the growth rate. The coefficients of MES and MES2 indicate that the change from a positive to a negative relationship between growth and MES comes when MES is between 4% and 9% of GNP, depending on which regression one uses. However, this result **is** being driven by the 24 countries in the sample from Asia, the Middle East, North Africa and Southern Europe, and the specific coefficients are not robust. When the regressions are run without these countries, there is no significant relationship - - positive or negative between MES and the growth rate. The only general conclusion from the growth regressions is that there is no evidence of a negative relationship between milex and growth in peacetime until the military expenditure share is quite high.
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# E. Miltary Expenditures and Gowth: Channels of Inact

In order to study the channels through which military expenditures affect growth, we add investment, education, and other key variables to the growth regressions. The test of whether a variable - - e.g., education - is an important channel of impact involves three elements: (1) causality can be assumed to run from milex to the variable, not the reverse; (2) there is a significant (partial) correlation between the variable and the growth rate; and (3) there is a significant (partial) correlation between milex and the variable. The causality issue must be determined independently of the growth regressions. The relation between the proposed channel of impact and the growth rate is tested by the significance of its coefficient when it is added to the growth regressions. The correlation between the proposed channel of impact and milex is tested by the change in the absolute value and significance of the milex coefficient when the "channel of impact" variable is added to the growth regressions. If there is a major change in the absolute value of the milex coefficient (and especially if the significance of the milex coefficient also changes), and the other two conditions are met, then the variable is indeed a major channel of impact of milex on growth.

In Table 6, six economic variables are added to the growth regressions, first, one at a time and then four variables together. The variables (all shares in GNP except for ED) are: the investment share in GNP - IS; the share of central government expenditure - CGES; the weighted sum of enrollment rates at the secondary and primary levels (see Landau 1986) - ED; the balance of trade - BT; official transferq received (net) - TRANS; and the "overall balance" - OB - which is the sum of the balance of trade, official transfers, and net long-term capital movements. In none of the regressions - 2 to 9 - do we see a material change in the coefficients for MES and MES2 from those in regression 1. These results suggest that none of these variables is an important channel of impact of milex on growth.
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Our basic hypothesis is that milex should impact growth through resource use, increased security, and increased efficiency of govermnent policies. The results in Table 6 do not support the resource use or increased security explanation, though it is possible the two are canceling each other out. The results of testing the efficiency hypothesis are in Table 7.

We do not have a direct measure of efficiency. However, we can use the ratio of output increase to investment in physical capital as a proxy measure. We measure the ratio of output increase to - investment by IOCR 'incremental output capital ratio'. The numerator in IOCR is the mean annual increase in real GNP over the 7 years of the growth rate. The denominator is mean annual real investment over the 3 years of the lagged regressors. The higher the ratio of output increase to investment in physical capital, ceteris paribus, the greater the efficiency.

In Panel A of Table 7, we use the full set of regressors, and in Panel B, only MES, MES2, and NMES. Regression 1 in Panel A is the base regression for comparison; in regression 2, IOCR is added **to** the base regression. The coefficient for IOCR is highly significant and the coefficients for MES and MES2 decrease markedly in absolute value, but their significance does not change. The same test is done in regressions 1 and 2 of Panel B. Here, when IOCR is added in regression 2, not only do the coefficients of MES and MES2 change, they also become statistically insignificant.

These results suggest that an important channel of impact of milex on growth is increased efficiency. This is consistent with our hypothesis that governments which feel threatened tend to increase the efficiency of their economic policies to build the tax base to finance a larger armed forces.

Table 7 also tests whether the positive impact of NMES on the growth rate is due to greater efficiency - in response to the threat. This is tested in regressions 1 and 2 of Panel A and regressions 4 and 5 of Panel B. In both cases, when IOCR is added to the regression, the absolute value and the significance of the NMES coefficient decrease markedly."'
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To summarize our results on the channels of the growth impact of milex, we find empirical evidence that increased milex is associated with greater efficiency in the developing countries. We do not find significant impact of milex through the levels of investment, education or the balance of payments.

# IV. OTHER ECONOMIC IMPACTS OF MILITARY EXPENDrTURES

Military expenditure is though **t** o hurt economic variables beyond growth. One frequent concern is that increased milex crowds out government social and infrastructure spending. Other economic variables believed to be negatively affected by military spending are the inflation rate, investment, and the balance of payments. The paper also analyzes the impact of military expenditures on these variables.

# A. Ipac on Government Social and Infrastucture Spendin.

Resources used for the military cannot be used for education, health or infrastructure. Accordingly, it would appear that milex must have an opporunity cost of less spending on these three categories which both contribute to growth and poverty alleviation.19 Data from the IMF's Government Financial Statistics were used to test this hypothesis.' The results are shown in Table 8, and are of two types: (i) the government expenditure shares in GDP regressed on the share of milex in GDP; and (ii) government expenditure category shares in total (central) government expenditure regressed on the share of milex in GDP. The share of exenditure categories in GDP more clearly indicates the impact of milex on the overall level of funding for other public services.

The most important result (regression 1, Table 8) is the positive relationship between the share of central government expenditure in GDP and the share of milex in GDP. The coefficients are statistically significant and numerically large. The coefficients imply that an increase in milex is
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- accommodated by an increase in total government expenditure a 1 percent increase in the milex GDP share would be associated with a 2 percent increase in the government expenditure share in GDP.

Table 8 also shows that the coefficients for the Impact of milex on the GDP share of education, health, combined education and health, and infrastructure are all positive, except for the three-year health share. However, only the regression of the education share shows a statistically significant and numerically important coefficient, and that only for the three-year period. Thus, for the full sample, increased military expenditures as a share of r-DP does not cause major changes in the shares of education, health or infrastructure expenditure in GDP. Separate regressions were also run for Latin America and Sub-Saharan Africa only. While the results for total government expenditure and for health expenditure were similar to those for the full sample, the coefficients for education and infrastructure were positive and statistically significant.<sup>21</sup> For the sub-sample of Latin America and Sub-Saharan Africa, therefore, increased military spending is associated with increased government expenditure (as a percent of GDP) on both education and infrastructure.

When the dependent variables are the shares of the various expenditure categories (e.g., education, health) in total government expenditures, all coefficients but one are negative, statistically significant and numerically important. When milex is increased, its share in total government expenditure increases and the shares of other types of expenditures are reduced.

In I=, increased milex does not reduce expenditure on education, health, and infrastructure as shares of GDP in developing countries in general. For Latin America and Sub-Saharan Africa alone, it actally increases government spending on education and infrastructure. When milex increases, the shares of these three categories in government expenditure fall, but total government expenditure expands sufficiently to keep their shares in GDIP from falling. Again, as with economic growth, there is no evidence of a general negative imnpact from military spending in the developing countries.
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## B. Inaft onhrEsZarW Viables.

The impact of milex on other key economic variables was also tested. Inflation, investment, education, and other key variables were regressed on MES and the other regressors used in the growth equations. No claim is made that this is the correct spucification of the determinants of these variables. Rather, we are merely looking at the impact of MES on them, holding constan **t** he other factors included in the growth regressions. These same regressions were also run using MES and MES2; in the regressions which Included MES2, none of the coefficients for either MES or MES2 as statistically signiflcant. Therefore, these regressions are not discussed here.

# Table 9 shows the regression res"lts, and the findings are summarized below:

- Inflation. Military expenJiture - as measured by MES - does not have a statistically significant impact on the inflation rate (INF).

- Education. The weighted sum of enrollment rates at the primary and secondary level (ED) was used as an 'output' measure of education (see Landau 1986) as contrasted to the 'input' measure of government expenditure (see above). The coefficient for MES in the equation for ED is insignificant.

- Investment. The coefficient for MES in the equation for the share of investment in GNP (IS) is negative, but statistically insignificant. This suggests any impact of increased military expenditure on the overall level of investment is weak.

- 3 Balance of Trade. In the equation for the balance of trade as a share of GNP (BT), the coefficient for MES is negative but not quite significant. An increase in the milex share in GNP may worsen the balance of trade.

- Official Transfers. The equation for net official transfers in GNP (TRANS), shows that increased military expenditure is associated with increased receipt of transfers.

- Overall Balance. The final variable was a proxy for the 'over all balance' on current and capital accounts. It is the sum of the balance of trade, official transfers, and net long term capital flows as a share of GNP. The coefficient for MES is negative but statistically insignificant.

To summarize, with inflation, investment, education, and the balance of payments, as with growth and government social spending, there is no evidence of a strong negative impact from military spending in the developing countries.
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# V. DEiTEMINANTS OF MlLITARY EXPENDTIURE

The desirability of reducing military spending depends not only on its economic effect but also on what motivates miliary spending. Undetanig the motivation also helps shed light on the feasibiy, scope and limits of reducing militay spending. We cannot study the motivations for military expendi directly. However, we can study what empirically measurable factors appear to influence the level of spending. The most comprehensive work on the ddminas of military spending is by Hewitt (1991a, 1991b). In this paper, we introduce some additonal variables (e.g., NMES, political instablity) which pwvide new and significant results.

Table 10 preset the raults of our regressions of the share of milary spending in GNP (MES) on key _. be variables in the regressions are three- and five-year averages. Our prors were _that_ neios mitary spending (NMES), political instability (PO), civil war (CW), and int tiona war would mcrease miliry spending, as would transfers (FRANS).22 As _sem_ from Table 10, the most importa determinat of MES is NMES. The coeffici is posive, hihly _si_ an nd nmeically iponu Specifically, a 1 percent cee in the averge of NMES is associated with ougy a 0.5 pecent icrease in MES. The rc_eatip betwee NMES and MES is presumably threat response, with a high level of military eq endi by one's ndghbors bemg eilher a direct threat or a repne to a threat by a large, more distant country. In either case, the increase in the given coouy's MES in response to a higher NMES coud be quite ratonal. Among oher variables, rasfe unexectedy have no signfica impact.' Inatial wam, of couas, increa the ratio of milex to GNP. The IW regess is a dummy variable. For the five-yea averages, the coeffiients predict that the exstec of a war inrees MES by round 3 percen of GNP. lhe military expendi share is aLso an incrasing functo of per capita product.25 One definitely supisin result iatMES is sigficanldy negatvely reled to political intabit (PI). Thi relt _could_ be explai by the obseraton tat large milary exenditu are Ar sophistcated
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weapons, which are used for international war rather than internal instability or for controlling dissatisfied civilians.26 Overall, the results suggest that a major determinant of levels of military spending is external threats as measured by NMES. These results suggest that for individual countries, military spending is rationally motivated by foreign threats. Such a conclusion is what an economist should expect, given that economics is built on the assumption that people are rational with regard to their expenditures. However, much of the literature assumes military expenditure in the developing countries is not rationally motivated. The threat response nature of military expenditure also implies that while a country can lower its military expenditures ifits neighbors do so as well, regional conciliation and disarmament might be important preconditions for reductions in military spending.

# VI. CONCLUSIONS

The purpose of our research was to analyze the economic impact of military expenditures. This entailed reviewing trends in military spending across regions and over time, analyzing the impact of military expenditures on economic growth as well as the channels through which this impact takes place, identifying the impact of military expenditures on other key economic variables, and analyzing the determinants of military spending. The key conclusions are summarized below.

* Levels of Military Spending. The share of military spending in GNP has been generally falling, including in the high spending areas of the Middle East and North Africa. The regions with the most serious econcmic problems - Sub-Saharan Africa and Latin America - spend relatively low shares of GNP on the military.

* The Impact of Military Expenditures on Economic Growth. For the full sample of 71 countries, our hypothesis of a quadratic milex-growth relationship was supported. As
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military spending increases, the growth rate at first increases and then decreases. However, this result was being driven by 24 countries in the Eurasia/North Africa region accounting for a third of our observations; these countries have relatively high milex and high growth. When these regions are dropped from the sample, we find no relationship - - positive or negative - between milex and growth. From these results, we conclude that there is no evidence of a negative relationship between the level of military spending and the growth rate, but it is uncertain if in general there is a non-linear relationship with growth initially increasing and then decreasing as milex increases.

- **0** Channels of Impact of Milex on Economic Growth. We found empirical evidence that increased milex is associated with greater efficiency in the developing countries. The particular caannel appears to be external threat (as measured by neighbor's military expenditures) which induces increased military expenditures and also greater efficiency to produce a stronger economy which can support the required spending. We do not find significant impact of milex through the levels of investment, education, or the balance of payments.

- Other Economic Iimpacts of Milex. Total government spending as a share of GNP increases with MES. As a result, with an increase in milex, the GNP shares of education, health and infrastructure spending do not decrease even though their shares in total government spending decrease. Consequently, as with economic growth, there is no evidence of a negative impact of milex on government social and infrastructure spending. There is no strong or consistent impact of increased milex on other key economic variables; however, there is some evidence of a weak negative impact of milex
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on investment and the balance of payments while there is no statistically significant evidence of impact on education or the inflation rate.

* Determinants of Military Spending. The most statistically significant and numerically important determinant of military spending is neighbor's military spending. Neighboring country's military spending represents either a direct threat to any country or a response to a larger, more distant threat. In general, this implies: 1) that military spending by individual countries is rationally motivated; 2) that regional conciliation and disarmament may be more important in determining the feasibility of unilateral reductions in military spending. Other significant determinants of military spending were per capita income and nternational wars.

The concern about military spending in the developing countries starts from the hypotheses that: 1) there is high and rising burden of military expenditure on the developing countries; 2) such spending is not rationally motivated; 3) the developing country military spending has a negative impact on growth, government social spending, and other key economic variables. We find that all three of these assumptions are inconsistent with the data. Developing-country military spending is: moderate and falling, apparently motivated by external threats, and at typical current levels (about 4%), is not associated with lower rates of economic growth, government social and infrastructure spending, or other economic variables.

As with all studies of the determinants of growth, this study is constrained to work from an incomplete theoretical basis, using proxies for some explanatory factors and using imperfect data. In addition, these particular findings apply to peacetime military spending in the developing countries and are based on the years studied. War is an economic as well as human disaster. With the changes in the
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world in the 1990s, the relationship between developing countries' military spending and their economies could also change.
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# Eam

1. In a paper presented at the World Bank Anua Conference on Development Economics, Robert McNamara endorsed a proposal made by the Independent Group on Financial Flows to Developing Countries (chaired by Helmut Schmidt) that 'when decisions concrning allocations of foreign aid are made, special consideration be given to countries spending less than 2 percent of their GNP in the security sector." Dennis Healey, the former British defense secretary, was quoted in the Financial [iM (July 12, 1991) as advocating 07 nations to link aid to developing countries to cuts in defense expenditures.

2. World Bankl, Military Expenditures." Report No. SecM91-1563 (December 1991). Washington, D. C.

3. The regional (and full sample) means are unweighted averages of the country MESs. There do not appear to be any unambiguously superior weighing mechanisms; consequendy, only unweighted averages have been presented.

4. However, it is important to note that Iraq and Iran are not in the sample.

S. 1 1988, Saudi Arabia was spending 19.8 -ercent of GNP on the military compared with 8.4 percat in 1969. Honduras experienced a very rapid increase in MES from 2.8 percent in 1982 to 8.4 percent in 1989. Zimbabwe was spending 7.9 percent of GNP on the military in 1989 compared to 3.2 percent by Southern Rhodesia in 1969. However, these are the exceptions. The more typical trend is of a reduction in military expenditures over time. Egypt, which had reached 36.5 percent of GNP in 1974 was down to _4.5_ percent in 1989. Israel was down from 34 percent of GNP in 1973 and 25 percent in 1981 to 9.2 percent in 1989.

6. Some researchers of the relationship between military spending and economic growth attribute significant creation of human capital to the armed services. There are problems with this hypothesis. First, looking at the numbers in the armed forces in most developing countries, it is implausible that the human capital created during military service would significantly change the growth of the civilian economy. It must also be remembered that significant numbers of the more highly trained soldiers are career soldiers whose training does not aid the civilian economy.

7. If threatened governments modify their policies in ways which promote growth, what pattern of policy change would we expect to see in seriously threatened countries? The necessary policy approach to encourage rapid growth is quite well spelled out in the World Dvelopmnt K (1991) in terms of aiding the efficiency of markets, rather than replacing them, and flexible pragmatic policies.

Historically, we find re 4ence that at key points in their modernization, and under conditions of serious external threats, Japad and the countries of Western and Central Europe made these changes in their policies. The political scientist Pempel (1982) describes the policies which Japan used to became a developed country before World War Two: "When Japan was forced to open to Western commerce in 1854, it had to overcome a 250 year history of centralized feudalism.... The Meiji reformers realized that a strong state apparatus, parallel to that of Prussia, was essential if Japan was to develop the "rich country
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and strong army" designed to preserve national autonomy from the threat of Western Imperialism. **.** (emphasis added, p. 12).

Where the prewar Japanese state was most different from totalitarian states was in its toleration, even actual encouragement, of the private sphere. State institutions, while powerful, were not comprehensive in their activities.... Unlike Sweden, however, Japan did not develop a widespread public system of social services. Although the government supported a public system of ... education, private systems developed parallel and in competition with the state.(p. 14).

Private initiative rather than government direction; selective government intervention or direction rather than constant presence; and a small and efficient government rather than a lumbering bureaucratic monstrosity tended to characterize Japanese politics (emphasis added, p. 21)." Imperial Japan consciously followed the pattern of helping rather than fighting the market in order to build a strong economy as the basis for a strong army which it believed was needed for defense.

Eric Jones (1987) describes how Europe got the market economy first in his book Ih. Erean Miracl,. He wrote: Rulers, whose schemes for glory drove them to prepare for war, began to do so by actively improving the economic base. In addition there were clients for modernization among the 'middle' and merchant classes. What they prayed for was more public order and fewer obstructions to business, ranging from the abolition of legal and customary restrictions on factor mobility to the removal of nuisances like narrow town gates and constricting walls. They desired the enforcement at law of freely negotiated contracts, the improvement of communications, and all measures to unify the market. The wishes of the ruler and of rising group in society were thus in many ways confluent. Internal barriers to trade began to be removed, both institutional and physical ones (p. 135).

Thus like Japan in the first half of the 20th century, or Taiwan, South Korea, and Thailand since World War Two, the rulers of modernizing Europe were forced to facilitate the efficient working of a market economy to build the tax base for their armies.

The explanation for any positive association between military spending and economic growth is not specific policies. Rather it is the pattern of policies - spending, regulatory, etc. - which aided the growth of the market economy rather than hindering it (as is typical in so many LDCs). When the governments of LDCs have felt the need to spend large sums on the armed forces they typically - but not in every case - also have felt the need to facilitate the smoother functioning of the market economy (in order to build a tax base which could finance the military spending). Military spending does not in itself contribute to faster growth; rather, the policy changes made along with the increased military spending accelerate economy growth. Such positive policy changes can offset only so much resource use by the armed forces, therefore, beyond a certain percentage of GNP, the impact of increased military spending turns negative.
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8. The best way to measure the threat from naighbors' military expenditure seems to be NMES, the average of their MES's. There is also some evidence that countries respond to threats in terms of shares of GNP spent rather than dollar values (McKinlay, 1989, Thkir Wozld Militak Eendi ) One could find rationales for this response pattern in terms of smaller countries indicating a willingness to fight and thus inflict costs on a larger country, but they are unable to match expenditures in dollar terms. Furthermore, if the smaller country is not technologically superior, and thus unable to substitute quality of arms for quantity of armed men, diminishing returns may set in fairly soon if the smaller country tried to substitute capital for labor. Whatever theoretical qualms one might have about NMES, it works very well both in the growth regressions and in the regressions of the determinants of MES, Table 10. In contrast to the success of NMES, if we measure the foreign threat as the ratio of foreign military expenditure to home country GNP. the variable is insigmificant. The sample is 71 non-Communist LDCs with over two million p,, .lation for which the 1990/91 World Tablea (World Bank) has data. China was also tried as an addition to the sample set of 71 countries, but this changed nothing. Some of the neighbors of countries in the sample are over two million population, but are not in the sample either because they are not LDCs, they were communist, or the World Tabes do not have data for them - Iran and Iraq for example. The source for MES - SIPR[ Yearbook of World Armaments and Disarmament - had data on most of these countries so they could be included in the calculation of average neighbor's MES - NMES. A secondary source - U.S. Arms Control & Disarmament Agency, World Milit ExIenditures and Arms Transfers allowed filling in a few more.

9. Ihe exogenous and predetermined variables included in the regressions were chosen because they could be expected to influence the growth rate significantly and they are available. The expectation that they would significantly influence the growth rate comes from general theoretical considerations and the literature on growth, especially the author's previous work on the empirical determinants of growth (Landau 1986, 1990). Ihe growth rate of the developed - OECD - countries (GRW) could influence the growth rate of the LDCs in terms of the export opportunities and perhaps also the amount of foreign investment. The change in the terms of trade (CTOT) would influence the returns to an LDC from any given level of exports. The share of oil exports in GNP (OILS) was included because many workers in the field believe major oil exporters can increase MES without a negative impact on growth (Looney).

Per capita product (PCP) is included because of the results in the author's previous work. Allowing for the level of investment in human capital, there is a strong negative relationship between the level of per capita product and the growth rate. There is of course an enormous literature suggesting that debt burdens will slow economic growth both due to the direct impact of the debt service payments and the indirect threat of inflationary or other disruptive government policies if the burden becomes economically or politically unsustainable. Thus the literature predicts a strong negative relationship between the ratio of debt to GNP (DEBTS) and the growth rate. Life expectancy at birth (LIFE) is a proxy for the general level of investment in human capital.

GRW and CTOT are not hypothesized to be basic determinants of long run growth, rather they serve to remove some of the transitory effects on realized levels of production of external conditions. For this reason, they are mean values over the same years as the dependant variable, not lagged.

As indicated above, some of the limited number of regressor we have are proxies for the arguments in equation 1. OILS - the share oil exrorts in GNP - is a proxy for natural resources. We tried the population growth rate, which is a proxy for the growth of the labor force, but it was insignificant. LIFE - life expectancy - is a proxy for the general level of investment in human capital. It is better proxy than education measures. The two most important forms of investment in human
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capital - health and education - are highly correlated, so there is a need to choose<sup>one or the other</sup> (see Table 7, Panel B). Current levels of investment in education - like enrollment rates - could<sup>be</sup> influenced by the current level of MES, where as LIFE would not be influenced<sup>by current MES.</sup> The other regressors used would be in the reduced form equation influencing<sup>a number of the</sup> proximate determinants of growth. PCP - per capita product - may influence the<sup>rate of technological</sup> change. Lower per capita product countries face a larger 'shelf' of borrowable<sup>technologies than</sup> higher per capita product countries, provided they make the necessary investments in<sup>human capital.</sup> DEBTS - the ratio of debt to national product - will influence investment levels,<sup>and probably also</sup> efficiency - investors would tend to choose more inflation safe assets as debt<sup>levels rise. We would</sup> expect the political variables - MES, NMES, PI, CW, & IW - to have<sup>an impact on a number of the</sup> proximate determinants of growth. The military expenditure regressors have already been discussed. Political instability, civil wars, and international wars would influence investment, efficiency, technological change, etc. The political instability dummy - PI - is the only<sup>one of the 3 political</sup> conditions variables which turned out to be statistically significant in the growth regressions.

Other possible regressors, besides those mentioned, were seen as potentially<sup>endogenous and</sup> thus including them in the basic estimate of the MES impact would produce biased estimates.<sup>For</sup> example, if an increase in military expenditure results in a decrease in the level of investment,<sup>then</sup> including investment in the basic regressions would bias our coefficient of MES upward. The reader should keep in mind that our purpose here is not to 'explain' economic growth, but rather to estimate the impact of military expenditure<sup>on economic</sup> growth. We are intentionally omitting from the basic regressions factors which would be influenced either directly by the level of MES, or indirectly by the policy changes induced by the<sup>threat which</sup> produced a higher level of MES.

10. A secondary benefit of lagged regressors is that it avoids contemporary correlation<sup>between the</sup> explanatory variables and the residual which would necessitate a simultaneous equation system.<sup>The</sup> appropriate variables for a simultaneous equation system are far from clear theoretically.<sup>In addition,</sup> the data to estimate a good simultaneous system ior the relationship between military spending and economic growth in the LDCs is simply not available.

11. The regressions were also run for shorter growth periods - _5_ & 4 years - and different<sup>starting</sup> and ending years within the 1969-89 period. These results generally are consistent<sup>with those</sup> presented in this report, however, as expected, the hypothesis fits longer growth periods<sup>better than</sup> shorter ones. These other results are available from the author.

12. Thus for 7 year growth rates, the difference in growth rates is the difference<sup>between the growti</sup> rate from year **'t'** to year 't+6' and the growth rate from year 't-7' to year 't-l'.<sup>For the lagged</sup> regressors, their differences would be the difference between their mean for years 't-3'<sup>to 't-1' and</sup> their mean for the years 't-10' to 't-8'.

13. One reviewer of an earlier version of the paper raised the question, why didn't we use a fixed effects - country dummy model? Since most of the variation in growth and milex is cross sectional, using country intercepts almost guarantees a finding of minor - or no - effects for milex irrespective of the true relationship. Country dummies will capture the effects of cross
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section differences in milex along with most other cross section differences.

14. The assessment to define the political conditions variables was done by the author using the capsule political summaries for the post World War Two period in The Europe Year Book 1990 (two _-_ volumes). These summaries run 5 10 normal sized pages per country. Occasionally The Poliical Handbook of the World (1989) was used as a supplemental source. The following definitions were used. A time period for country 'Xi was considered one of political instability if there were coupes, attempted coupes, violent riots - dozens or more killed, significant gueriliA wa-fare, or major terrorist incidents 5 years or less apart. The situation was defined as one of civil war if regular internal armies fought, there was major guerilla warfare, or the suppression of dissent killed thousands of people - e.g. the peak of the "Dirty War" in Argentina. The situation was defined as international war if the country fought engagemernts - with significant casualties - with foreign country military forces. For the civil war and international war dummies, the situation was defined to exist only for the years hostilities were active. The years of civil war and international war were relatively few, but political instability, as here defined, existed for some (or all) years for the majority of the countries in the sample.

The regressors formed from the political dummy variables were also the average of 3 annual lagged values. The author had to produce his own political conditions measures because there are no suitable published indexes. Internal World Bank data on Central Government Expenditure - CGE - for the years 1972-88 - was also used. This CGE series was extended back to 1969 using the USACDA data. Where the above sources had missing values, that usually means an observation on that variable is missing. There are two exceptions to that rule. First, for the lagged regressors, they are the mean of available years, so that if one year of the three is missing, the value is the mean of two years. Second, the human capital series - enrollments, etc. - are generally not collected for every year and they would not normally jump around between collection dates. These series were interpolated, but not extrapolated. No other series were either interpolated or extrapolated.

15. The coefficients for PCP - per capita product - have the predicted negative sign and they are significant for the cross section regressions both on the full sample and the regions (full regional regression results are available from the author). In the differences regressions, DPCP has the predicted sign and is usually statistically significant, but the significance levels are lower than the cross section regressions (compare Tables 2 and 3). The negative and significant coefficients are consistent with the findings in Landau (1986) that, holding constant human capital, their is a 'catch up' effect among the LDCs. That is lower per capita countries would tend to grow faster if they invest sufficiently in human capital. The most plausible explanation for the 'catch up effect' is that the available 'shelf' of borrowable technologies is larger for lower per capita income countries.

The human capital regressor is the life expectancy - LIFE. LIFE has positive and highly signiflcant coefficients in virtually all the regressions. The ratio of official debt to GNP - DEBTS - has the expected negative coefficient and is highly significant in most regressions. The results for the change in terms of trade - CTOT, the share of oils exports in GNP - OILS, and the growth rate of the OECD countries - GRW - are mixed. Sometimes the coefficients have the expected signs and they are significant, and sometimes the coefficients are insignificant or the 'wrong' sign.

16. For the differences regressions with only DMES and DMES2 as explanatory variables, the predicted quadratic holds with DGRT as the dependent variable and there is a simple positive linear relationship with DGRNM as the dependent variable. These regressions are not in the Tables with this report, they are available from the author.
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17. If we look at shorter growth periods of five and four years for SSA, the simple positive linear relationship is statistically significant.

18. In regressions 3 and 6 of Table 7, IS, the share of investment in GNP is ade-' to the regressions which already include IOCR. In these regressions, IS is statistically significant, whereas, in regression 2 of Table 6, without IOCR in the regression, is not statistically significant. This result underscores the importance of efficiency of investment.

19. Developing country armed forces do spend funds on education and health care for the troops as well as on some infrastructure. However, these exenditures are not the bulk of military spending.

20. While the data includes almost all the 71 countries already used to test the milex-growth reladonship, over half of the potendal observations during the 1970-90 period are missing. Still, there are up to 191 three-year observations and 119 five-year observations, and regressions were run using three- and five-year averages of the expenditure data.

# 21. Detailed regression results are available from the author.

22. The major regressors tested (besides population - POP) are per capita product (PCP), neighbor's average military expenditure share (NMES), the dummies for political instability (PI, CW and IW), and the share of official transfers received in national product (TRANS). The civil war regressor was dropped because it proved insignificant. PCP tests whether richer countries are willing to spend a higher share of GNP on the military.

23. Without India (and China), population is not a statistically significant derminant of MES for the multi-year periods. Regressions were also run with anmual observations, though these are not reported in the table. For the anmnal observations, population and population squared influence MES; these are scale effects, but the coefficients are reversed with and without India. The annual regressions were run including and excluding India. India changes the effects of population on MES, but does not change other coefficients materially. Since India is obviously a population outdier, the muld-year period regressions reported here exclude India.

24. However, with annual observations, TRANS has positive and statistically significant coefficients.

25. In these regressions, PCP is measured in thousands of 1987 U.S. dollars. In some of the other tables and regressions, PCP is in 1987 U.S. dollars.

26. Janowitz (1977) noted that the developing countries experienced much greater increases in paramilitary forces (militia, political police, etc.) from 1966 to 1974/75 than in active duty regular forces. He also discusses the preference among military governments to use para-military forces rather than the regular army for internal control.