metadata: authors: 0: author: Knight, Malcolm
1: author: Loayza, Norman
2: author: Villanueva, Delan
docna: 0: docna: The peace dividend : military spending cuts
            and economic growth
docty: Policy Research Working Paper
subtopic: Conflict and Fragile States,Economic Theory & Research,Industrial Economics,International Trade and Trade Rules,Economic Growth,Armed Conflict
teratopic: Poverty Reduction
abstracts: cdata!: Conventional wisdom suggests that
            reducing military spending may improve a country's
            economic growth, but empirical studies have produced
            ambiguous results on this point. Extending a standard growth
            model, the authors exploit both cross-section and
            time-series dimensions of available data to get consistent
            estimates of the growth-retarding effects of military
            spending. Military spending is growth-retarding because of
            its adverse impact on capital formation and resource
            allocation. Model simulation results suggest a substantial
            long-term peace dividend - in the form of higher capacity
            output per capita - that may result from: 1) markedly lower
            military spending in most regions in the late 1980s; and 2)
            future cuts in military spending if global peace is secured.
repnme: repnme: The peace dividend : military spending cuts
            and economic growth
display_title: The peace dividend : military
            spending cuts and economic growth
listing_relative_url: /research/1996/02/696766/peace-dividend-military-spending-cuts-economic-growth
topicv3: Economic Growth m1326241 471,Trade m1326924 698,Political Violence and War m1327310 2462,Population, Aging and Health m1327736 640,Military m1505651 3036
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Poverty Reduction
origu: PRDMG
no_of_pages: 60
model_extractions: POLICY RESEARCH WORKING PAPER 1577

# Summary findings

Conventional wisdom suggests that reducing military spending may improve a country's economic growth, but empirical studies have produced ambiguous results on this point.

Extending a standard growth model, Knight, Loayza, and Villanueva exploit both cross-section and time-series dimensions of available data to get consistent estimates of the growth-retarding effects of military spending. Military spending is growth-retarding because of its adverse impact on capital formation and resource allocation.

Model simulation results suggest a substantial long- term peace dividend in the form of higher capacity - output per capita that may result from (1) markedly lower military spending in most regions in the late 1980s and (2) future cuts in military spending if global peace is secured.

- This paper a joint product of the Macroeconomics and Growth Division, Policy Research Department, and the - International Monetary Fund is part of a larger effort to understand the link between policies and growth. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rebecca Martin, room N11-059, telephone 202-473-1320, fax 202-522-3518, Internet address rmartin@worldbank.org. February 1996. (49 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be used and cited accordingly. The findings, interpretations, and conclusions are the authors' own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries._ Produced by the Policy Research Dissemination Center
# The Peace Dividend: Military Spending Cuts and Economic Growth

by Malcolm Knight(IMF), Norman Loayza (PRDMG), and Delano Villanueva (IMF)1/ _JEL Classification Numbers: 041, 047_ I/ This paper has also been issued as IMF Working Paper 95/53. The authors are indebted to Paul Cashin, Daniel Hewitt, Mohsin Khan, Anne McGuirk, Peter Montiel, and Michael Sarel for comments. Daniel Hewitt also provided data and advice on interpreting available statistics on military expenditures. Peter Kunzel assisted with the empirical research.
I
# Contents

- I. Introduction

- II. Data and Empirical Research on Military Spending

1. Trends in military spending, 1972-1990

2. The relationship between military spending and growth

- III. Model and Empirical Methodology

- IV. Estimation Results

1. Standard cross-section estimation

2. Panel data estimation

- V. The Peace Dividend: Simulation Experiments 1. Simulated long-run effects of the changes in military spending that took place in the late 1980s

2. Simulated effects of a generalized peace in all regions

- VI. Summary and Conclusion

Appendix I. Data Sources and Definitions, and Sample of Countries Appendix II. Simulation Exercise Tables

1. Ratios of Military Spending to GDP for Various Country Groups and Time Periods

2. 79-Country Sample: Ratios of Military Spending to GDP for Various Country Groups and Time Periods 3. Standard Cross-Section Regressions for the Ratio of Investment to GDP 4. Standard Cross-Section Regressions for the Growth Rate 5. Panel Regressions for the Investment to GDP Ratio 6. Panel Regressions for the Growth Rate 7. The Peace Dividend: Simulated Long-Run Effects of Changes in Military Spending Ratios in the Late 1980s on Capacity Output 8. The Peace Dividend: Simulated Long-Run Capacity Output Effects of Decreasing Regional Military Spending Ratios From Their 1972-85 Average Levels to the 1986-90 Level for Developing Countries in the Western Hemisphere

- Figure 1. Various Country Groups: Simulated Long-Run Effects of Actual Past Changes in Military Spending (solid line) and Possible Future Reductions (dotted line)

References
I
# I. Introduction

It is a widely held view that political tensions and associated high levels of military spending are likely to detract from a country's long-run economic growth performance. In an insecure region, so the argument goes, each country must devote a disproportionate share of its endowment of scarce economic resources to "unproductive" military spending. In the absence of international cooperation to reduce political tensions, military spending may be pushed higher and higher throughout a region as each country tries to outspend its neighbors to ensure its own security, raising regional military expenditure levels and yielding no increase--or even a decrease--in the security of all. While political tensions themselves can weaken various aspects of economic performance, there are two direct and interrelated avenues by which higher military spending may adversely affect long-run output growth. First, increases in military spending may reduce the total stock of resources that is available for alternative domestic uses such as investment in productive capital, education, and market-oriented technological innovation. Second, high spending on the military may aggravate distortions that reduce the efficiency of resource allocation, thereby lowering total factor productivity.

If these effects turn out to be empirically significant, then a converse proposition is also likely to be valid: the sustained military spending cuts that would become feasible as a result of improved international security should yield a "Peace Dividend" in the form of higher long-run levels of capacity output. It would then follow that forms of
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international cooperation that succeeded in reducing tensions, and thus in lowering military spending, would be to the long-run economic benefit of all. Interest in the potential size of this Peace Dividend has risen considerably in recent years with the improvements in international security that have become evident for both industrial and developing countries with the end of the Cold War and with the more recent initiatives aimed at achieving a comprehensive peace in the Middle East.

The view that low levels of military spending are associated with strong growth performance, and vice versa, is usually argued by recourse to casual empiricism. For example, the post-World War II experiences of the Federal Republic of Germany and Japan appear to lend support to the notion that there are substantial economic benefits from sustaining low levels of military spending over long periods of time. The strict post-war limits on military expenditures that were imposed on these countries--combined with the Allies' effective guarantee of their security--were factors that allowed the Federal Republic of Germany and Japan to devote relatively large fractions of their total factor endowments to productive capital formation, thereby contributing to their impressive economic growth performance during the succeeding five decades. Such general but striking observations have left most economists with a strong presumption that, on average, a country that has a relatively low ratio of military expenditure to GDP is likely to display relatively strong long-run growth performance.

Yet not all military spending is unambiguously counterproductive, or even unproductive, in an economic sense. It is often argued, for example, that expenditure on military training in developing countries may contribute
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to improving the educational level and discipline of the labor force<sup>and may</sup> act as a stabilizing influence in the society. Likewise, it has been argued (see, for example, Thompson (1974)) that military expenditure can be economically productive to the extent that it enhances the state of national security and improves the enforcement of property rights, thereby encouraging private investment and growth. Capital expenditure<sup>on the</sup> military can also have productive uses: many developing countries still benefit from extensive transport networks that were originally<sup>constructed</sup> for military purposes. These counter-examples suggest that the question<sup>of</sup> whether, and to what extent, military spending is economically unproductive cannot be resolved by recourse to anecdotal evidence and historical generalizations, but instead requires rigorous theoretical and<sup>empirical</sup> analysis.

The analysis must also be able to confront formidable estimation problems: even if cuts in military spending do improve growth performance substantially, these effects are likely to appear with a long lag.<sup>Thus in</sup> empirical studies the beneficial effects from large military spending<sup>cuts</sup> may be hard to disentangle in the estimation work from other factors<sup>that</sup> influence economic growth. Given these considerations, it is not<sup>surprising</sup> that the existing empirical literature yields ambiguous results, not<sup>only on</sup> the magnitude of the impact of military expenditure on long-term economic growth, but even on whether the effect is positive or negative. Nevertheless, if national governments are to be convinced that it is to their economic advantage to stockpile fewer guns in order to make room for more investment in productive capital, they need to be presented with robust
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quantified estimates of the costs that military spending imposes on the economic welfare of their citizens and to have convincing evidence of the improvements in living standards that can result over the long run from military spending cuts.

Such a quantification is attempted in this paper. We address several questions. Is there a Peace Dividend from military spending cuts? If so, how large might it eventually be? More specifically, how much is productive investment likely to increase in response to military spending cuts, and how strongly will the associated improvements in the efficiency of resource allocation increase long-run capacity output, relative to the level it would have attained if the fraction of GNP absorbed by military spending had remained unchanged? Our paper extends a standard neoclassical growth model to take account of important linkages between military spending, productive investment, and the long-run growth of per capita capacity output. It implements an econometric technique for obtaining empirical estimates of the model from a panel of time-series and cross-section data for a large sample of developed and developing countries.

We then use the estimated model in simulation experiments designed to gauge the size of the Peace Dividend--that is, the impact of cuts in military spending on economic growth performance--in a number of major geographic regions of the world. To summarize, our estimation and simulation analyses suggest that these Peace Dividend effects would take some time to emerge, but would eventually be large, especially for countries in regions--such as Eastern Europe, North Africa and the Middle East--where levels of military spending have traditionally been high.
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The next sections are organized as follows. Section II summarizes trends in military expenditures since the early 1970s and reviews the empirical literature on the relationship between military spending and economic growth. Section III outlines our extension of a standard neoclassical growth model and the estimation technique used. Section IV first presents standard cross-section estimates of the effect of military spending on investment and economic growth and then contrasts them with the results of our panel-data estimation. Section V describes two simulation experiments that help to indicate the rough order of magnitude of the longer-run Peace Dividend from military spending cuts. We first simulate the long-run effects that will eventually become visible as a result of the developments in military spending that have already taken place in various geographic regions during the latter half of the decade of the 1980s. We then simulate the potential effects of further declines in military spending that might be expected to occur in the future in various regions if a lasting global peace could be secured. Section VI concludes.

# II. Data and EmRirical Research on Military SRending

Throughout this paper we define the _military spending ratio,_ m, as the ratio of total military spending to GDP; the data used here are those published by the Stockholm International Peace Research Institute
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(SIPRI). _]/_ We define the "Peace Dividend" narrowly<sup>as the percentage</sup> difference between the level of real capacity output<sup>per capita that would</sup> result from a given sustained reduction in the military<sup>spending ratio, and</sup> the baseline path of capacity output that would have prevailed<sup>in the</sup> absence of such a reduction.

# 1. Trends in military spending. 1972-1990

The descriptive literature on trends in military expenditures<sup>(see</sup> Hewitt (1993) and the primary data sources cited there)<sup>indicates the large</sup> extent to which the world's productive resources have<sup>been devoted to the</sup> military throughout the period since the Second World War. _1/_ Hewitt's data show important differences in military spending across country groupings as well as over time. On average during the period 1972-90,<sup>for</sup> example, over 5 percent of world resources as measured<sup>by the combined GDP</sup> of the 124 countries considered by Hewitt, were devoted to military spending. Table 1 summarizes the main patterns of military<sup>expenditure for</sup> industrial countries and for developing countries in various geographic regions. The entries in this table are weighted averages of national military spending ratios, where the weights are each country's share of<sup>the</sup> regional total GDP level measured in U.S. dollars using official exchange rates.

_1/_ Appendix IA provides definitions and sources for all<sup>data used in this</sup> study. A detailed discussion and analysis of the<sup>SIPRI data on military</sup> spending, as well as that provided by other sources,<sup>is given in Hewitt</sup> (1992, 1993). Based on his detailed analysis, Hewitt<sup>concludes that SIPRI</sup> data are to be preferred to other sources for empirical<sup>work of the sort we</sup> undertake here.

_.2_ Our paper makes use of the data on military expenditures presented in Hewitt (1992, 1993), which are based mainly on statistics published<sup>by</sup> SIPRI. Hewitt's data cover 124 industrial and developing<sup>countries.</sup>
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Table 1. Ratios of Military Spending to GDP for Various Country Groups and Time Periods 1/

||Period Wei|ehted Averages. in|Percent 2/|
|---|---|---|---|
||<br>Full Period<br>(1972-90)|<br>Period I<br>(1972-85)|<br>Period II<br>(1986-90)|
|Full Sample|5.10|5.19|4.84|
|Industrial Countries|3.90|3.97|3.70|
|Developing Countries|5.20|5.54|4.26|
|Regional Groupings:||||
|Asia|5.70|6.35|3.88|
|Eastern Europe|12.40|11.75|14.22|
|Middle East|10.00|10.36|9.06|
|North Africa|7.20|8.12|4.60|
|Sub-Saharan Africa|3.20|3.12|3.42|
|Western Hemisphere|2.10|2.16|1.94|

Source: Hewitt (1992).

I/ See Appendix IB for lists of countries included in each grouping for Hewitt's sample. Z/ The weights are each country's share in the group GDP measured in U.S. dollars using official exchange rates.

Table 1 presents these averages for nine country groupings: the full sample of 124 countries; a group of 22 industrial countries; and 102 developing countries subdivided into six regional groups--Asia, Eastern Europe, Middle East, North Africa, sub-Saharan Africa and Western Hemisphere. This table also regroups these regional data into two time
-8-

periods. Period I extends over 1972-85; it covers roughly the years when the Cold War was still at its height and when initiatives toward improved security in the Middle East had not yet borne fruit. Period II covers 198690, which can be characterized as a period of diminishing tensions associated with the gradual end of the Cold War and somewhat improved security conditions in Asia, the Middle East, and North Africa.

The data in Table 1 yield several broad observations. First, in both periods military spending ratios varied widely between the industrial and developing country groups, and among developing countries in different geographic regions. For example, among the developing country groups during Period I military spending ratios ranged from a high of over 11 percent of GDP for countries in Eastern Europe to only just over 2 percent for the Western Hemisphere. These differences correspond broadly to what one would expect given the different levels of security across regions. For example, in both periods countries in Eastern Europe and the Middle East had the highest military spending ratios, reflecting the failure to achieve comprehensive improvements in security in those regions. Next in ranking were Asia and North Africa, followed by sub-Saharan Africa. Finally, the very low ratio for Western Hemisphere developing countries in both periods reflects the low incidence of major armed conflicts in this region.

A second striking feature of the data is that world military spending has been declining in recent years. When Hewitt compares the ratios for the mid-1980s with those for 1990, he finds that total military expenditures of all countries in his sample fell sharply, from 5.6 percent of combined GDP
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in 1985 to 4.3 percent five years later. This feature is the most striking in North Africa and Asia. For the group of Middle East countries, where military spending initially absorbed over 10 percent of GDP, the weighted average ratio also fell markedly in the latter half of the 1980s. The industrial countries had a modest decline in military spending during the late 1980s associated with the end of the Cold War. Developing countries in the Western Hemisphere region, which already had very low levels relative to other developing regions, experienced only very small further reductions. An unfortunate contrast with these trends was evident in Eastern Europe and sub-Saharan Africa, where ongoing internal and regional tensions caused weighted average military spending ratios to _rise_ by 2.5 and 0.3 percentage points, respectively, in 1986-90. 1/

# 2. The relationshiR between military spending and growth

The relationship between military and spending output growth is complicated by the fact that it has both short-run and long-run components, which may act in opposite directions. In the short run, as with increases in other types of government expenditure, a rise in military spending on final goods and services may increase aggregate domestic demand, thereby exerting a short-run stimulative Keynesian impact on the growth rate by _1/_ Despite the adverse trends in these two regions, it is noteworthy that the weighted average military spending ratio for the whole group of developing countries fell by considerably more than it did in the industrial countries. Whereas the weighted average ratio of developing countries had been nearly 1.6 percentage points higher than that of the industrial countries in Period I, in Period II it fell to a level only 0.6 percentage points higher.
10

-

-

inducing a rise in capacity utilization--that is, it raises the growth of current output _relative to_ that of capacity output. 1/ However, these short-run stimulative effects do not necessarily lead to higher levels of capital formation and capacity output. Indeed, over the longer term increases in military spending are likely to exert a negative effect on capacity output.

There are two channels by which a sustained increase in military expenditure might be expected to depress a country's secular growth performance. The first channel results from the likelihood that, other things equal, a rise in military spending exerts a negative impact on the rate of investment in (public and private) productive fixed capital. This occurs because of well-known crowding-out effects: an increase in military spending must be financed either by raising current taxes or by borrowing (future taxes). In either case, it will lower the expected after-tax return on productive fixed capital, while simultaneously reducing the flow of (domestic plus foreign) savings that is available to finance productive fixed capital formation in the domestic economy. This channel is likely to be particularly important in the case of net-debtor developing countries. Since such countries are faced with external financing constraints, a rise 1/ Hewitt (1992) hypothesizes that military expenditures can have a net posi,tive or negative impact on economic growth depending on the alternative use of the funds. He argues that specific military expenditures on generaluse public infrastructure and promotion of research, as well as demobilization of trained personnel contribute to economic growth; however, military spending is an inefficient means to enhance growth compared to private investment expenditure or government expenditure on social infrastructure and education. In the context of developing countries, Hewitt contends that the justification for military expenditures must be from national security grounds, since the economic benefits are limited.
- II -

in military spending--to the extent that it is not associated with larger net capital inflows to finance a higher external current account deficit-can be expected to crowd out capital investment and/or private consumption. 1/ A second channel by which military expenditures may affect the growth path of capacity output is through their direct impact on the efficiency of resource allocation. Since military spending is not governed by market processes, it tends to create distortions in relative prices that result in a dead-weight loss to total productive capacity. In addition, it **may exert** negative externalities on capacity output. There are several ways in which these inefficiencies directly affect the growth rate. First, a higher deadweight loss to domestic production results from either an increase in contemporaneous taxes or heavier borrowing to finance higher military spending; borrowing from the banking system often leads to higher inflation, which distorts resource allocation. _2!_ Second, research and development activities may concentrate on military progress at the expense of technological advances in economically-productive areas. Third, policies implemented to support a military program are often detrimental to efficient resource allocation and market growth: examples are trade restrictions, nationalization of military equipment producers, military procurement 1/ Hewitt (1992) notes that higher military expenditures may be,financed through higher external borrowing, lower private consumption, lower private investment, and lower expenditures on other government programs, including productive ones such as education and health services, public infrastructure, and the police and judicial systems. In general, the likely consequences would be lower current consumption and investment levels and lower future growth, the exact mix being dependent on the particular financing channel.

_2/_ See Tommasi (1995) and de Gregorio (1991).
12 -

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preferences for certain firms and industries, and compulsory military service. Finally, rent-seeking activities grow around the military because of its non-competitive allocation of resources. In this way, _over and above_ their depressing effect on the level of investment, military expenditures may exert a direct adverse impact on the economy's productive efficiency.

These considerations suggest that the net effect of a rise in military expenditure on a country's growth rate and its steady state level of capacity output is likely to be negative. Therefore, one would expect to find evidence of this negative impact in longer-run economic data both across countries and over time. However, it is obviously difficult to disentangle empirically the potential positive short-run effect of the demand stimulus associated with an increase in military spending from the depressing effect of high military spending on the longer-run growth path of capacity output, particularly if the estimation work fails to exploit both the time-series and cross-section dimensions of the data. The striking ambiguity of past econometric results in the face of strong anecdotal evidence on the long-run economic benefits of lowering military expenditure suggests that weaknesses in the econometric techniques used to test these hypotheses may be a problem.

Thus it is not surprising that a number of past attempts to subject the relation between military spending and growth relationship to empirical testing--Benoit (1973, 1978) and Frederiksen and Looney (1982)--seem to have uncovered empirical support for the thesis that military expenditures were not detrimental to growth. Benoit (1978), using data for 44 developing countries over 1950-65, finds a positive association between military
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spending and growth of civilian per capita output. In contrast, Rothschild (1977) on the basis of rank correlations on growth, exports, and military spending for 14 OECD countries during 1956-69, concludes that higher military spending is associated with lower exports and lower economic growth. Deger and Smith (1983) find that the direct impact of military expenditures on growth is positive, while the effect on savings is negative; in their view the net impact of military expenditures on growth is negative because the negative indirect effect on savings outweighs the positive direct impact. Biswas and Ram (1986) conclude that military expenditures neither help nor hinder economic growth. Aschauer (1989) finds that government expenditure on infrastructure in the United States has a positive effect on growth, while military capital expenditures have virtually no impact. Using data for 71 countries over the period 1969-89, Landau (1993) concludes that military expenditure is not associated with lower rates of economic growth, capital formation, or government social and infrastructure spending. Some other studies have obtained a negative, but weak empirical relationship between military spending and economic growth. 1/ In the next section we specify a model and suggest a technique of estimation that are both intended to address the limitations of past empirical research on this relationship.

1/ See Chan (1985) for a selected bibliography.
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# III. Model and Empirical Methodology

We now extend the standard neoclassical growth model of Solow (1956) and Swan (1956) to incorporate the linkages between military spending, productive investment, and the growth of capacity output. We do this by expanding the empirical analysis of this model that we undertook in Knight, Loayza, and Villanueva (1993) to incorporate the possible effects of military spending on the growth path of per capita capacity output. First, we expand the basic neoclassical growth equation: in addition to the investment ratio and other factors considered in our earlier paper, it now includes the military spending ratio as a determinant of capacity output. Second, we specify an explicit investment function in which the ratio of investment to GDP is determined by several standard factors, and also by the fraction of GDP that is devoted to military spending.

Our equation for the rate of economic growth is based on the Mankiw, Romer, and Weil (1992) version of the Solow-Swan model. It is derived by linearizing the transition path of output per capita around its steady-state level. j/ The resulting equation specifies output growth as a function of initial output and variables that condition for the economy's steady state. The conditioning variables that we include are the ratio of investment to GDP; the rate of population growth; a proxy for the degree of openness of the economy to international trade (i.e., an index of the degree of restrictiveness of its system of tariff and non-tariff barriers to I/ For details of this derivation, see Knight, Loayza, and Villanueva (1993). The growth effects that we discuss in this paper apply to the transition to the steady state.
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international trade); the Barro-Lee (1993) proxy for the incidence of wars; 1/ the ratio of military spending to GDP; and a dummy variable that catches any otherwise unspecified country-specific effects. In accordance with the Solow-Swan model we assume that the conditioning variables are exogenous with respect to output growth; in particular, the ratio of military expenditures to GDP is assumed to be unaffected by the rate of output growth. 2/ Equation (1) specifies the per capita output growth rate, Zi,t-Zi,t1j, where z represents the natural logarithm of output per capita: where ln indicates a natural logarithm; the indices i and t represent the country and time period, respectively; n is the average population growth rate; g is the technological growth rate, 6 is the rate of depreciation of 1/ The Barro-Lee proxy variable for the incidence of wars is defined for each country as the number of war-years as a fraction of total years in the period 1960-85. See Barro and Lee (1993).

2/ There are three differences between the growth equation specified in this paper and the one used in our 1993 paper. First, in this paper we do not include the ratio of government fixed investment to GDP as an explanatory variable, since we found it to be statistically insignificant in our previous study. Second, and more important, we now include as a regressor the ratio of military expenditures to GDP. Finally, to isolate the effect of military expenditures on the allocation of productive resources, we control for the incidence of wars on economic growth by including the above-mentioned Barro-Lee proxy.
- 16 -

the stock of physical capital, and g+6 is assumed to be equal to 0.05 1/; _sk_ is the ratio of physical capital investment to GDP; _m_ is the ratio of military expenditures to GDP; _sh_ is a proxy for the ratio of human capital investment to GDP; f is a proxy for the degree of restrictiveness of the economy's international trade system; w is the proxy for the incidence of wars; (t represents time-specific factors; pi represents country-specific factors; and E is a white-noise error term.

In order to allow for the indirect effect of military spending on growth via its impact on productive investment, we extend the model of our earlier paper to include a second equation which specifies the ratio of investment in fixed capital as a function of the rate of investment in human capital, _sh;_ the restrictiveness of the trade system, f; the incidence of wars proxy, w; and the military spending ratio, m. The investment equation is: _l( ski, t) =_ 77n ln(ni,t _+g +6 ) +77mln(mi, t) +77h_ ln(shi) +r7f ln(f i) +i7wln(wi) _+ Hi_ + £i,t (2) As already noted, we use Hewitt's annual data on the ratios of military expenditure for all the countries in our sample. However, owing to limitations on the availability of data for some countries on the other 1/ The assumption that g+6=0.05 follows Mankiw, Romer, and Weil (1992). We found that although changes in this number affect the estimated _en,_ they do not significantly affect the other estimated coefficients.
- 17 -

variables that enter into equations (1) and (2), our estimation sample is a subset of the countries covered by Hewitt. _.1/_ It covers 79 countries; the period is 1971-1985. The countries and regions included, together with the simple and weighted means and standard deviations for their ratios of military spending to GDP over 1971-85 and 1986-90, are presented in Table 2. A close comparison of the weighted averages for each of the country groups in our sample with those for the full group of 124 countries discussed by Hewitt shows that, for the regions we include, our sample has characteristics that are quite similar to those highlighted in the Hewitt sample. In particular, the magnitudes of the declines in the weighted average military spending ratios in each region are quite similar in the two samples. Z/ These broad similarities between the sample we use for estimation and Hewitt's data give us a degree of confidence that although our estimating sample has a less comprehensive country coverage it nevertheless retains broadly similar characteristics.

_1/_ The countries excluded from our sample are those in Eastern Europe (including the countries of the former Soviet Union and the former Democratic Republic of Germany), and countries for which complete data were not available for other variables in the model. The latter include several developing countries in Asia, sub-Saharan Africa and the Western Hemisphere. Consistent with other empirical studies of long-term economic growth, we also exclude from the estimation sample a few countries--mostly in the Middle East and North Africa--whose main source of GDP comes from the extraction of petroleum reserves. The list of countries and data sources for the variables used to estimate our model are presented in Appendix IA and B.

2/ The exception is sub-Saharan Africa, where the country coverage of our sample is much less comprehensive than Hewitt's owing to the unavailability of data on the other variables in equations (1) and (2). As a result of these differences in coverage, our data show a small decline in military spending ratios in these countries, while Hewitt's more comprehensive data show a small rise.
> - 18 -

Table 2. 79-Country Sample: Ratios of Military Spending to GDP for Various Country Groups and Time Periods 1/

||Period av<br>|erages, in perce<br>|nt<br>|Comparison of chang|es (Period 11 minus Period I)|
|---|---|---|---|---|---|
||Full Period<br>(1972-90)|Period I<br>(1972-85)|Period 11<br>(1986-90)|Our 79-Country<br>Sample|Hewitt's Sample|
|Full Sample||||||
|Weighted Average<br>|3.80<br>|3.89<br>|3.59<br>|-0.30<br>|-0.35|
|Simple Average<br>|3.35<br>|3.44<br>|3.08<br>|-0.36|..|
|(Standard deviation)|(0.21)|(0.12)|(0.21)|.||
|Industrial Countes||||||
|Weighted Avcrage<br>|3.90|3.99|3.71|-0.28|-0.27|
|Simple Average<br>|3.01<br>|3.07<br>|2.85<br>|-0.22|...|
|(Standard deviation)|(0.13)|(0.07)|(0.12)|...|..|
|Developing<br>Countries||||||
|Weighted Average<br>|3.10<br>|3.14<br>|2.80|-0.34|-1.28|
|Simple Average<br>|3.48<br>|3.58<br>|3.17<br>|-0.41|...|
|(Standard deviation)|(0.26)|(0.16)|(0.24)|...||
|Regional Grouaping||||||
|Asian Developing||||||
|WeightedAverage<br>|3.90|3.94|3.75|-0.19|-2.47|
|Simple Averge<br>|3.66<br>|3.64<br>|3.71|0.07|...|
|(Standard deviation)|(0.28)|(0.28)|(0.33)|...|...|
|Middle East||||||
|Weighted Average<br>|6.80<br>|6.93<br>|6.49<br>|-0.44|-1.30|
|Simple Average<br>|10.70<br>|11.24<br>|9.21|-2.03||
|(Standarddeviation)|(1.35)|(1.07)|(0.82)|...|...|
|North Africa||||||
|Weighted Average<br>|6.50<br>|7.59<br>|3.38|-4.21|-3.52|
|Simple Average<br>|6.06<br>|6.72<br>|4.19<br>|-2.53|...|
|(Standard deviation)|(2.27)|(2.29)|(0.48)|...||
|Sub-Saharan Africa||||||
|Weighted Average<br>|2.70|2.83|2.52|-0.31|0.30|
|Simple Average<br>|2.70|2.75|2.58|-0.17|...|
|(Standard deviation)|(0.24)|(0.26)|(0.12)|...||
|Western Hemisphere||||||
|Weighted Average<br>|2.20<br>|2.28<br>|1.80<br>|-0.48<br>|-0.22|
|Simple Average<br>(Standard deviation)|2.43<br>(0.40)|2.49<br>(0.44)|2.26<br>(0.25)|-0.23|...|

Source: Hewitt (1992).

I/ Sec Appendix lB. for lists of countries included in each grouping for both Hewitt's and our sample.
- 19 -

Since the available data on military activity indicate that the fraction of GDP devoted to the military varies widely _both_ across countries and over time, our empirical methodology is designed to exploit these two dimensions of the data to overcome some of the shortcomings of past empirical work and obtain robust estimates of the effect of military expenditures on investment and economic growth. 1/ Specifically, we employ an econometric technique that was proposed in our earlier paper (Knight, Loayza, and Villanueva (1993)) to deal with time-series crosssection data.

To construct the panel data set we work with non-overlapping intervals of five years each. We have cross-sectional data covering output growth and several other variables in three separate five-year time periods: 1971-75, 1976-80, and 1981-85. Since data for all variables are available for 79 - countries, this gives us a relatively large panel-data sample of 79 x 3 237 observations on the dependent variables in equations (1) and (2). We measure the growth of per capita output over a 5-year interval, rather than a single year. This procedure provides a simple way of averaging out shortrun cyclical variations in the rate of capacity utilization, thereby helping to ensure that this variable approximates output growth at the average rate of capacity utilization in each five-year time period for each country in the sample. 2/ Similarly, the data for the investment ratio _sk_ are _i/_ In this paper, the term investment, by itself, refers to physical capital investment. When we refer to human capital investment, we say so explicitly.

_.Z/_ This follows the technique used by Phillips (1958) to ensure that his estimated relation between the rate of change of nominal wages and the level of the unemployment rate was approximately a phase line.
- 20 -

averaged over the same 5-year intervals. Thus some variables are indexed by both time, t, and country, i; these are the variables _z, h, sk, m_ and _sh,_ for which panel data are available. The remaining variables f and w, for which we have only cross-sectional data, are indexed only by country. The observations for the level of per capita output that are used to obtain the growth rate for each 5-year interval correspond exactly to the years 1970, 1975, 1980, and 1985. For the rest of the panel variables--n, _sh, m--_ observations correspond to the averages over the five year intervals 197175, 1976-80, 1981-85. For the cross-sectional variables--sh, f, w--observations for each country correspond to averages over tne whole 15-year period (1971-85) under consideration or, in a few cases, that portion of it for which data are available.

The fact that panel data are available for most of the variables of interest allows us to account for both time-specific and country-specific effects. Country-specific effects are especially important in the present analysis. There are a host of factors that are peculiar to each country (e.g., government policies, resource endowments, social institutions, and cultural traits) and these may well be correlated with the regressors considered in the model. Failure to account for them would lead to inconsistent estimates of the parameters. We control for the time-specific effects by removing the time means from each variable. To account for the country-specific effects, we use the methodology proposed by Chamberlain (1982, 1984), commonly known as the H-matrix technique. Given that the growth regression contains a lagged dependent variable, the fixed-effects
- 21 -

"within" estimator that is commonly used to control for specific effects would yield inconsistent estimates.

A detailed exposition on the application of the fl-matrix technique to growth regressions estimated using panel data is presented in our earlier paper (Knight, Loayza, and Villanueva (1993)). Basically, the application has two steps. First, we replace the country-specific factor pi t by its linear predictor E*( it) plus an error term in the regression equation for each time period. The linear predictor is a linear function of the regressors for all time periods. This yields a system of reduced-form regression equations, with one equation for each time period. Second, we estimate the reduced-form parameters in the system and, from them, obtain the structural parameter estimates through a minimum-distance estimation procedure. Chamberlain (1982) shows that this procedure results in consistent and asymptotically efficient estimates.

# IV. Estimation Results

This section presents the estimation results for our two-equation growth model. To illustrate the difficulties (discussed in Section II) that may have arisen in past empirical work on the impact of military spending on growth, we first employ a standard estimation procedure that is widely in use in empirical research in growth economics. Specifically, we obtain standard "cross-section" estimates of equations (1) and (2) using the data on output growth for our 79-country sample averaged over the _whole period_ 1971-85 and average levels for each country of the values of the right-hand variables over the same period. This approach will show us what the
- 22 -

estimated parameters of our model would look like if it was estimated without taking full advantage of the time-series<sup>dimension of the data set.</sup> Next, we re-estimate the model on our sample of panel<sup>data observations</sup> using our proposed econometric approach, and<sup>compare the parameter estimates</sup> obtained using the two alternative estimation<sup>methods.</sup>

# 1. Standard cross-section estimation

Table 3 reports standard cross-section estimates<sup>of the growth equation</sup> (equation 1). To account for geopolitical and<sup>developmental differences</sup> across regions we consider two regional dummy<sup>variables in our cross-country</sup> regressions, one for Africa and the other for<sup>the developing countries in</sup> the Western Hemisphere. We find that only<sup>the investment ratio, the proxy</sup> variable for international trade restrictions,<sup>and the dummy variable for</sup> the developing countries in the Western Hemisphere<sup>enter significantly with</sup> the expected signs. Indeed, in line with the<sup>results of several of the</sup> empirical studies cited in Section II above,<sup>in the cross-sectional</sup> regression the estimated coefficient of military<sup>spending has a</sup><sup>_positive_</sup> but statistically insignificant estimated effect<sup>on growth. Note also that when</sup> the military spending ratio is included in<sup>the cross-section regression for</sup> equation 1, the coefficient estimates for the other<sup>variables change very</sup> little. The same results are obtained when the<sup>Barro-Lee proxy for the</sup> incidence of wars is included.

We believe that these rather inconclusive cross-sectional<sup>results help</sup> to illustrate why past empirical work cited<sup>in Section II has been affected</sup> by the application of estimation procedures<sup>that do not take advantage of</sup> all the information available in the dataset.
- 23 -

Table 3. Standard Cross-Section Regressions for the Growth Rate

|No. of Countries|79|79|79|
|---|---|---|---|
|VARIABLE|COEFFICIENT<br>(T-RATIO)|COEFFICIENT<br>(T-RATIO)|COEFFICIENT<br>(T-RATIO)|
|**Zt_**I|-0.0057|-0.0055|-0.0057|
||(-1.17)|(-1.17)|(-1. 19)|
|ln(n+0.05)|0.0103|0.0091|0.0088|
||_(1.51)_|(1.37)|(1.28)|
|ln(sk)|0.0112|0.0110|0.0107|
||(2.73)|(2.68)|(2.57)|
|ln(sh)|0.0013|0.0012|0.0012|
||(0.86)|(0.87)|(0.89)|
|n(f)|-0.0189|-0.0181|-0.0184|
||(-2.12)|(-2.12)|(-2.11)|
|In(m)||0.0023|0.0026|
|||(0.69)|(0.72)|
|w|||-0.0043|
||||(-0.50)|
|constant|0.0537|0.0471|0.0489|
||(1.79)|(1.70)|(1.74)|
|AFRICA 1/|-0.0079|-0.0072|-0.0070|
||(-1.09)|(-2.98)|(-0.95)|
|WESTERN|-0.0139|-0.0128|-0.0125|
|HEMISPHERE|(-2.78)|(-2.48)|(-2.30)|
|Adjusted R<sup>2</sup>|0.244|0.241|0.231|

1/ 'Africa' is defined as countries in the North and sub-Saharan African regions.

Table 4 reports the estimation results for a standard cross-section regression of the investment equation (equation 2). Only the proxy for investment in human capital and the dummy variable for Africa exert positive and statistically significant effects on physical capital investment. In particular, the cross-section regressions cannot identify a significant
- 24 -

Table 4. Standard Cross-Section Regressions for the Ratio of Investment to GDP

|No. of Countries|79|79|
|---|---|---|
|VARIABLE|COEFFICIENT<br>(T-RATIO)|COEFFICIENT<br>(T-RATIO)|
|ln(n + 0.05)|-0.1400|-0.1478|
||(-0.79)|(-0.89)|
|_ln(sh)_|0.0702|0.0664|
||(3.47)|(3.43)|
|In|-0.0895|-0.0985|
||(-0.36)|(-0.41)|
|ln(m)|0.0401|0.0809|
||(0.54)|(1.01)|
|w||-0.5190|
|||(-1.51)|
|constant|2.1726|2.1620|
||(4.16)|(4.31)|
|AFRICA 1/|-0.4054|0.3723|
||(-2.10)|(-1.89)|
|WESTERN HEMISPHERE|-0.1736|-0.1362|
||(-1.37)|(-1.05)|
|Adjusted R<sup>2</sup>|0.463|0.483|

1/ "Africa" is defined as countries in the North and sub-Saharan African regions.

relationship, whether positive or negative, running from the level of military spending to the rate of investment in productive fixed capital. When Barro and Lee's proxy for the incidence of wars is added to the regression equation, the parameter estimates change only slightly; as expected, it exerts a negative effect on investment, and the effect is statistically significant at the 10 percent level.
- 25 -

# 2. Panel data estimation

We now contrast these standard results with our panel data estimates of the investment and growth equations. Table 5 reports our panel-data estimates of the growth equation (equation 1). First, consider the estimation results when military expenditure is not included in the regression. The lagged value of per capita output is significant and negatively related to the growth rate. This is the standard result in the empirical growth literature, known as "conditional convergence." Our results imply that the growth rate of population is not a significant determinant of output growth. This is somewhat surprising in the light of previous studies which find a negative relationship on the basis of data from 1960 to 1985. Investments in physical capital and human capital both exert a positive effect on growth, and trade restrictions have a negative influence.

When we include the military spending ratio as an explanatory variable in our panel data regression, we find that it has a negative and significant effect on growth. It implies that, in addition to crowding out physical investment (as analyzed below and reported in Table 6), a rise in military spending also exerts an independent direct negative impact on economic growth. This is true even though we are additionally controlling for human capital investment, population growth, and trade restrictions. The panel data estimation results of the growth equation that are reported in Table 5 are therefore consistent with the view that a rise in military spending adversely affects the growth performance of the economy.
- 26 -

Table 5. Panel Regressions for the Growth Rate

|No. of countries|79|79|79|
|---|---|---|---|
|VARIABLE|COEFFICIENT<br>(T-RATIO)|COEFFICIENT<br>(T-RATIO)|COEFFICIENT<br>(T-RATIO)|
|ZI_l|-0.0989|-0.6656|-0.0262|
||(-3.83)|(-2.96)|(-1.27)|
|ln(n+0.05)|0.00003|0.0064|-0.0004|
||(.003)|(0.48)|(-0.03)|
|ln(sk)|0.0227|0.0225|0.0165|
||(3.65)|(3.94)|(2.92)|
|_ln(sh)_|0.0603|0.0404|0.0158|
||(3.73)|(2.99)|(1.32)|
|In(J)|-0.0286|-0.0204|-0.0091|
||(-4.35)|(-3.39)|(-1.63)|
|ln(m)||-0.0081|-0.0060|
|||(-2.67)|(-2.06)|
|w|||-0.0132<br>_(-1.51)_|
|Wald test for|28.19|51.61|55.59|
|uncorrelated effects<br>(p-value)|(0.0000)|(0.0000)|(0.0000)|

A further important result of our empirical work is that inclusion of the military spending ratio reduces the absolute size of the estimated coefficients of physical investment, human investment, and trade restrictions in the growth equation. This follows from the fact that military expenditures are generally negatively correlated with both types of investment, and positively correlated with the intensity of trade
- 27 -

Table 6. Panel Regressions for the Investment to GDP Ratio

|No. of Countries|79|79|79|
|---|---|---|---|
|VARIABLE|COEFFICIENT<br>(T-RATIO)|COEFFICIENT<br>(T-RATIO)|COEFFICIENT<br>(T-RATIO)|
|In(n+0.05)|0.5081|0.6506|0.6950|
||(3.37)|(4.65)|(4.74)|
|_tn(sh)_|0.2707|0.2511|0.2294|
||(2.99)|(2.99)|(2.99)|
|In(l)|-0.1225|-0.0947|-0.0766|
||(-2.03)|(-1.69)|(-1.52)|
|_ln(m)_||-0.0742|-0.0754|
|||(-1.98)|(-2.14)|
|w|||-1.3232<br>(-6.78)|
|Wald test for|5.46|38.57|65.85|
|uncorrelated effects<br>(p-value)|(0.1410)|(0.0000)|(0.0000)|

> restrictions. 1/ Given that both human capital investment and the openness of the trade system have a significant positive impact on output growth, their negative correlation with military expenditures indicates the possibility of other channels through which military spending adversely affects growth; namely, through crowding out human capital investment and

1/ As expected, the Barro-Lee proxy for the incidence of wars exerts a direct negative and significant effect on economic growth. The inclusion of this variable in the panel estimates also alters the magnitude of the estimated coefficients of the other regressors. In fact, all such coefficients decrease in absolute size, reflecting the fact that levels of both physical and human capital are negatively correlated with the incidence of military conflict, whereas the incidence of military conflict is positively correlated with intensification of trade restrictions and thus with increases in military expenditures.
- 28 -

fostering the adoption of various types of trade restrictions. j/ Due to the lack of panel data on the proxies for human capital investment and trade restrictions, we cannot run separate regressions explaining the variables _sh_ and f, and thus we are unable to quantify such effects. However, as explained below, we do estimate the effect of military spending on physical capital investment.

Table 6 reports the panel data estimates for the investment equation. In contrast to the standard cross-section results, in our panel regressions of this equation all the variables now enter with the expected sign and all but one are significant at the 5 percent level; the effect of f is significant about the 20 percent level. The inclusion of the proxy for the incidence of wars does not importantly modify the parameter estimates but, as expected, it affects investment in a negative and significant way. Population growth and human capital investment have positive effects on physical capital investment, while a more restrictive trade system has a negative impact. Most interesting for our purposes--and consistent with our priors--the panel data estimates reveal that a rise in the ratio of military spending has a _statistically significant negative impact_ on investment.

Thus our results for equation 2 are consistent with the hypothesis that a rise in military spending does indeed lead to crowding out of investment in productive fixed capital.

1/ The positive correlation between military spending and trade restrictions is particularly strong in developing nations. Given that most of these countries import military armaments from industrial countries, they are more exposed to balance of payments problems. Partly for this reason, developing countries also tend to operate more restrictive trade regimes.
- 29 -

# V. The Peace Dividend: Simulation Experiments

As mentioned in Section II, the ending of the Cold War in Europe and the other improvements in international security that occurred in the latter half of the 1980s were associated with significant reductions in militarv spending in a number of major geographic regions. In addition, the ongoing peace process in the Middle East raises the prospect that substantial further cuts in military spending could take place in this region in future years. Thus it is interesting to use our empirical estimates of the quantitative impact of military expenditures on investment and growth obtained in the preceding section to assess the timing and rough order of magnitude of the Peace Dividend effects that might occur in each region.

1. Simulated long-run effects of the changes in military spending that took place in the late 1980s

As a first step, we run simulations to see what our model has to say about the likely long-run effects of the major changes in military spending ratios that took place in a number of regions during the late 1980s. As the data in Table 1 above make clear, the improvements in international security that became evident during the 1980s permitted governments in all but two
- 30 -

geographic regions to achieve reductions in their military spending ratios. 1/ While our estimation results indicate that the effects of these changes may eventually be substantial, the estimated lags also suggest that they will take time to appear. The simulation experiments provide a useful gauge of the timing and size of these effects. We undertake these simulations for the industrial countries and for each of the groups of developing countries in the six regions analyzed by Hewitt and described in Section II: Asia, Eastern Europe, the Middle East, North Africa, subSaharan Africa, and the Western Hemisphere. The exogenous shock that generates each simulation is the change in the military spending ratio that took place in each region during the second half of the 1980s. Specifically, we take the difference between Hewitt's weighted average military spending ratio for Period I (1972-85) and the corresponding ratio for Period II (1986-90) for each country group in Table 1. The levels and the resulting changes are reprinted in the first three lines of Table 7.

1/ For example, when the average ratio of military spending to GDP for the period 1972-85 is compared to the average for 1986-90, it is evident that between these two subperiods military expenditure declined as a percentage of GDP in all regions except Eastern Europe and sub-Saharan Africa. Although the ratio fell only modestly in the industrial countries (from 4 percent to 3.7 percent) it declined sharply in a number of regions of the developing world. The largest reduction occurred in North Africa, from 8.1 percent in 1972-85 to 4.6 percent in 1986-90; followed by Asia (6.3 percent to 3.9 percent); the Middle East (10.4 percent to 9.1 percent); and the Western Hemisphere (2.2 percent to 1.9 percent). By contrast, there was a relatively large _rise_ in the military spending ratio in Eastern Europe (11.8 percent to 14.2 percent) and a modest rise (3.1 percent to 3.4 percent) in sub-Saharan Africa.
- 31 -

In these simulation experiments we assume for simplicity that the change in the average military spending ratio in each region is spread over the whole period 1986-90, 1/ and that after reaching its new level in 1990 the military ratio remains constant thereafter. The stylized paths of the changes in military spending ratios that occurred in each region during the late 1980s are illustrated by the solid lines in the top panels of Figure 1. The numerical parameters of the model are our panel estimates from Tables 5 and 6. Z/ Table 7 and Figure 1 (solid lines) summarize the simulation results for each of the seven regions. Line 4 indicates the change in the investment ratio that results from the shift in the military spending ratio in each region. For example, in the Asian developing and North African countries, which had the biggest cuts in their military spending ratios over 1986-90, the resulting increase in investment is nearly 0.7 percent of GDP; in the Middle East investment rises by 0.25 percent of GDP; in the industrial countries and the developing countries of the Western Hemisphere<sup>the rise is</sup> about 0.15 percentage points.

1/ Specifically, we assume that the natural logarithm of the military spending ratio declines linearly over this period.

_2/_ To implement the simulation we first substitute equation (2) into equation (1) to obtain the reduced-form relationship between the military spending ratio and the growth path. From this reduced-form equation we obtain the deviation of the simulated growth path for each region owing to the change in the military spending ratio from the path that<sup>would have</sup> prevailed if this exogenous change had not occurred (for a detailed explanation see Appendix II). Note that since ours is a long-run model the simulations trace the dynamic effects of this change on regional levels of _capacity_ output. We are not interested in the short-run Keynesian multiplier effects of military spending cuts, since these affect<sup>actual</sup> output _relative to_ capacity output.
- 32 -

Table _7._ The Peace Dividend: Simulated Long-Run Effects of Changes in Military Spending Ratios in the Late 1980s on Capacity Output I/ Country Groups

||Industrial|||Developin|g Countries|||
|---|---|---|---|---|---|---|---|
||Countries|Asia|Eastem<br>Europe|Middle<br>East|North<br>Africa|Sub-Saharan<br>Africa|Westem<br>Hemisphere|
||||(I|n percent of G|DP)|||
|IAverage Military||||||||
|Spending Ratio,|3.97|6.35|11.75|10.34|8.13|3.12|2.16|
|1972-1985 2/||||||||
|2.Average Military||||||||
|Spending Ratio,<br>1986-19902/|3.70|3.88|14.22|9.06|4.60|3.42|1.94|
|3. Change in Military<br>Spending Ratio<br>|-0.27|-2.47|2.47|-1.28|-3.53|0.30|-0.22|
|_(2minus 1)_||||||||
|4. Associated Change in||||||||
|Ratio ofInvestment|0.14|0.66|-0.48|0.25|0.70|-0.11|0.15|
|toGDP3/||||||||
|Time||||||||
|Horizon||Simula|ted Minus Base|line Growth R|ates of Per Ca|pita GDP||
|___(Years)||||||||
||||(In<br>|percent**per**an<br>|num)<br>|||
|I|0.010|0.(071|-0.028|0.019|0.082|-0.013|0.015|
|5|0.049|0.339|-0.131|0.091|0.391|-0.063|0.073|
|10|0.043|0.297|-0.115|0.079|0.343|-0.055|0.064|
|25|0.029|0.199|-0.077|0.053|0.230|-0.037|0.043|
|50|0.015|0.103|-0.040|0.027|0.119|-0.019|0.022|
|co|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|Time||||||||
|Horizon||Sim|ulated Minus B|aseline Levels|of Per Capita|GDP||
|(__<br>Years)||||||||
||||(In per<br>|cent of baselin<br>|e GDP)<br>|||
|I|0.0|0.1|-0.0|0.0|0.1|-0.0|0.0|
|5|0.1|1.0|-0.4|0.3|1.2|-0.2|0.2|
|10|0.4|2.6|-1.0|0.7|3.0|-0.5|0.6|
|25|0.9|6.2|-2.4|1.7|7.2|-1.2|1.3|
|50<br>___<br>____|1.4<br>2.0|9.8<br>13.6|-3.8<br>-5.3|2.6<br>3.6|11.3<br>15.7|-1.8<br>-2.3|2.1<br>2.9|

I/ The simulation exercise is based on the parameter estimates given in Table 5, column 4, and Table 6, column 4. Details on how the simulations were performed are given in Appendix 11.

2! Derived from data in Hewitt (1992).

3/ The change in investment ratio is produced by the total change in military spending ratio with respect to the baseline military spending ratio (average during 1972-85).
- 33 -

The middle panel in Table 7 shows the difference between the simulated _growth rates_ of capacity output per capita and their baseline paths that will eventually result from the changes in military spending levels that actually occurred in the late 1980s. The lower panel shows the percentage difference in the _levels_ of capacity output per capita in each region relative to their baseline paths.

In our model, a one-shot increase in the military spending ratio causes a permanent rise in the level of GDP as a result of a transitory rise in the growth rate: since our first set of simulations assumes that military spending ratios are held constant at their new levels from 1990 onward, the growth rates of per capita output in each region gradually decelerate again until they return to their baseline levels. As a result, the percentage deviation in the levels of per capita GDP (shown in the lower bottom panel of Table 7 and the solid lines in the lower panel of Figure 1) continue to rise at decreasing rates until the new levels are reached in each region.

Obviously, the geographic regions that experienced the largest reductions in military spending ratios in the late 1980s are the ones that will eventually benefit from the largest gains in capacity output per worker. As indicated in the middle panel of Table 7, these past cuts are projected to result in modest but persistent gains in annual growth rates. For example, in the cases of Asia and North Africa, where military spending cuts were largest, the gains in annual growth rates reach a maximum that is about 0.3 percent per year higher than the baseline growth rate; the effects are of course smaller for other country groups, particularly the industrial countries and the developing countries in the Western Hemisphere.
- 34 -

Owing to the dynamic properties of our estimated growth model (relatively slow conditional convergence), these modest deviations in growth rates persist for quite a long time, and as a result their ultimate effects on the _levels_ of capacity output per worker are substantial. For example, our simulations indicate that in the long run the changes in military spending ratios of the late 1980s would--if sustained--result in a gain in the capacity level of per capita output in North Africa of nearly 16 percent relative to the baseline level that would have prevailed in the absence of such cuts; for Asia, output would eventually be nearly 14 percent higher; and for the Middle East it would be 3.6 percent higher.

These results suggest that the long-run Peace Dividend from the military spending cuts that have already taken place in several regions during the late 1980s will eventually cumulate to large effects on the levels of capacity output. 1/ By contrast, because of the rise in military spending ratios that occurred in Eastern Europe and sub-Saharan Africa during the latter half of the 1980s, per capita GDP in these regions would be _lower_ by some 5.3 percent and 2.3 percent, respectively, in the long run.

1/ Even for the industrial countries and Western Hemisphere countries, where the military spending cuts during the latter half of the 1980s were modest (from their relatively low initial levels), per capita output levels would eventually be 2.0 percent and 2.9 percent, respectively, above the baseline paths.
- 35 -

# 2. Simulated effects of a generalized peace in all regions

Our second set of simulation experiments looks at the long-run gains in capacity output that might result from _future_ large military spending cuts that might be associated with the achievement of a generalized peace in all geographic regions of the world. Specifically, we pose the following questions. If global peace were achieved, by how much might military spending ratios decline? What might be the size of the stimulus to productive investment? How soon might the resulting Peace Dividend exert positive effects on the growth paths of capacity output in various geographic regions? How large might these effects ultimately be? These are, of course, highly speculative questions. Even if there was a sustained improvement in global security, it is not clear how large the resulting cuts in military spending ratios would actually be, since most countries would probably still wish to maintain at least some minimal level of military preparedness.

We have already emphasized that--reflecting the differing levels of political tensions and risks of military conflict in different parts of the world--there has been a wide regional variation in ratios of military spending to GDP throughout the period for which comparable data are available. In particular, as seen in Table 1, developing countries in the Western Hemisphere have maintained the lowest average military spending ratios of any region (around 2 percent) over a long period of time. Since the Western Hemisphere developing countries have avoided major armed conflicts throughout this period, it is plausible to assume that the average military spending ratio already observed for this region can be taken as a
- 36 -

simple approximation of the minimum level that could be attained in other regions if a lasting world peace were achieved.

Thus our second set of simulations assumes that the military spending ratio in _each_ region declines steadily over a 10-year period from its regional average level over 1986-90 to the average level observed for the Western Hemisphere developing countries over the same period--that is, just under 2 percent of GDP. We then simulate the effects of these reductions in military spending on the growth paths of per capita capacity output for each of the regions, and compare them to the baseline paths that would have been traced out if military spending ratios had remained at the average levels observed over 1972-1985. Thus this second set of simulations includes _both_ the effects of the changes in military spending that occurred in 1986-90 (relative to the average levels for 1972-85) _and_ the additional effects that would eventually result from a generalized international peace. The results are summarized in Table 8 and Figure 1 (dotted lines).

In our model, these large further reductions in military spending ratios would act as a strong stimulus to productive investment in all regions. The fourth line of Table 8 shows that the resulting increases in investment ratios would be especially striking for Eastern Europe (4.9 percent of GDP) and the Middle East (3.3 percent of GDP), the regions that--since they currently have high levels of military spending--stand to gain the most from reducing them to the minimum level associated with a generalized peace.

The dotted lines in the upper panels in Figure 1 show the hypothetical downward paths of military spending for each region over the next 10 years.
# Figure 1. Various Country Groups: Simulated Long-run Effects of Actual Past Changes in Military Spending (solid line) and Possible Future Reductions (dotted line)

<!-- Start of picture text --> Path of Military Spending Ratio<br>(In percent of GDP)<br>Industrial Countries Asia Eastern Europe<br>16 16 16 16 16 16<br>14- 14 14 14 14/ 114<br>12 12 12 -12 12/ 12<br>10 10 10 10 10 10<br>8 8 8 8 8 8<br>6- 6 6 6 6 6<br>2 / ..at ..... 0 2.2 ............... . 2 2,,,.2 . 2 .-12<br>2/ 2/<br>0 0 5 10 15 20 25 30 35 40 45 50 , 1 I 10 o'' 0 5 , 10 15 20 25 30 35 40 45 50 -. I0 0 0 5 00 10 15 20 25 30 35 40 45 50 ,' ''0<br>Gain In GDP per Capita<br>(in percent of baseline level)<br>industrial Countries Asia Eastern Europe<br>40 40 40 40 40 - 40<br>30 - 0 3 0-30 30.* 3<br>20 -20 20 -I ~20 20 2.20<br>10 ~~~~~~~~~~~~2/ ......<br>10 - ~~ ~~~~10 10 10 10 . 10<br>0 0 0 0 0 0<br>0 510 1520 25 30 35 404550 0 510 1520 2530 35 4045 50 0 510 15 2025 3035 40 4550<br>I/ If military spending falls from 1972-85 to 1986-90 average over five years.<br>2/ Assuming regions decrease military spending from 1972-85 average to that of the Western Hemisphere in the period 1986-90 over 15 years.<br><!-- End of picture text -->
# Figure 1 (cont'd). Various Country Groups: Simulated Long-run Effects of Actual Past Changes in Military Spending (solid line) and Possible Future Reductions (dotted line)

<!-- Start of picture text --> Path of Military Spending Ratio<br>(In percent of GDP)<br>Middle East North Africa Sub-Saharan Africa Western Hemisphere<br>16 16 16 16 16 16 16 _ 16<br>14 14 14 14 14 14 14 14<br>12 -12 12 12 12 12 12 12<br>10 0 10 10 10 10 10 10<br>:~~~~~~~~~~~<br>3 *.~~~~~~~~ 8 8\.- S 8 888<br>6 -6 \ 11 - ' 6 6<br>4 ~ 4~~~~~/ 4 4 412<br>2 2 " , , ~~~~~~2 2 2/ 2 -',.,..,,,,2 2 2~~~~~2 1/2/~~~~~~~2 2 " ' /-22.,,,,,,,,<br>0 5 10 15 20 25 30 35 40 45 50 0 0l' 0 5 10 15 20 25 30 35 40 45 50 10 0''' 0 5 10 15 20 25 30 35 40 4550 I I o '. 05 10 15 20 25 30 35 40 45 .. 50 '<br>Gain in GDP per Capita<br>(In percent of baseline level)<br>Middle East North Africa Sub-Saharan Africa Western Hemisphere<br>40 40 40 40 40 40 40 40<br>30 30 ~~~~~~~~~~~3 30 030-3 3030 3 30- 30 30<br>,,,." ,,,,."" 20 20<br>20 2/ 20 20 20 20 20<br>10 2/ 10<br>1 10 10 ................. 10 - 10<br>1/ ~~~~~~~~~~~~~~~~0 0 /2/<br>0 0 ., / 0<br>-10 -.10 I 1<br>0 5 10 15 20 25 30 35 40 45 50 0 S 1 O15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 404 50 0 5 10 15 20 25 30 3540 45 50<br><!-- End of picture text -->

**1/** If military spending falls from 1972-85 to 1986-90 average over five years.

2/ Assuming regions decrease military spending from 1972-85 average to that of the Western Hemisphere in the period 1986-90 over 15 years.
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Table 8. The Peace Dividend: Simulated Effects of Decreasing Regional Militay Spending Ratios from their 197245 Aveage Levels to the 1986-90 Level for Western Hemisphere Developing Countries I/ Country Groups

||Industrial|||Develop|ing Countries|||
|---|---|---|---|---|---|---|---|
||Countries|Asia|Eastern<br>Europe|Middle<br>East|North<br>Africa|Sub-Saharan<br>Africa|<br>Western<br>Hemisphere|
||||(|In percent of G|DP)|||
|1. Averge Military||||||||
|SpaxdingRatio,<br>1972-1985 2/|3.97|6.35|11.75|10.34|8.13|3.12|2.16|
|2. "Minimum"Military||||||||
|SpandigRatio2/|1.94|1.94|1.94|1.94|1.94|1.94|1.94|
|3.Change in Militry<br>Spending Ratio|-2.03|-4.41|-9.81|-8.40|-6.19|-1.18|-0.22|
|_(2 minu_1)||||||||
|4.Asaociated Chanp in||||||||
|Ratioof Investnent|1.50|1.63|4.90|3.30|1.81|0.58|0.15|
|tDGDP 3/||||||||
|Time<br>Horizon||Simul|ated Minus Bas|eline Growth|Rates of Per C|apita GDP||
|(YOMrs)||||||||
||||(In|pecment pera|numn)|||
|I|0.010|0.071|-0.028|0.019|0.082|-0.013|0.015|
|5|0.049|0.339|-0.131|0.091|0.391|-0.063|0.073|
|10|0.265|0.535|0.570|0.609|0.639|0.140|0.064|
|25|0.348|0.542|0.908|0.815|0.657|0.243|0.043|
|S0|0.179|0.279|0.467|0.420|0.338|0.125|0.022|
|co|0.000|0000|0000|0000|0000|0000|0000|
|<br>Timo<br>Horizon<br>r||.<br>Si|.<br>mulated Minus|.<br>Baseline Leve|.<br>ls of Per Capi|.<br>ta GDP|.|
|(yeas)||||||||
||||(In pe|rcent of baseli|ne GDP)|||
|I|0.0|0.1|-0.0|0.0|0.1|-0.0|0.0|
|5|0.1|1.0|-0.4|0.3|1.2|-0.2|0.2|
|10|1.1|3.3|1.1|2.3|3.9|0.1|0.6|
|25|6.9|12.6|16.1|16.0|15.2|4.1|1.3|
|S0|13.2|224|32.4|30.7|27.0|8.5|2.1|
|ao|19.8|32.7|49.7|46.2|39.6|13.1|2.9|

1/ The simulation exmis is based on the parumeter estimates given in Table 5, column 4, and Table 6, column 4. Details on how the simulations wer pefoired ae given in Appendix II.

2/ Derived from data in Hewitt (1992). Line 2 is the 1986-90 average military spending ratio of developing countries in the Western Hanisphee.

3/ The change in investment ratio is produced by the total change in military spending ratio with respect to the baseline military **spending** ratio (averge during 1972-85).
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The dotted lines in the lower panels represent<sup>the percentage deviation of</sup> each region's per capita capacity output<sup>from the baseline path over a fifty</sup> year period starting from 1986. As the simulations<sup>indicate, the further</sup> gradual declines in military spending in<sup>all regions that would be</sup> associated with a lasting improvement<sup>in international security would exert</sup> very marked stimulative effects on the<sup>growth paths of per capita output in</sup> all regions (except Western Hemisphere,<sup>where there is assumed to be no</sup> _further_ fall in military spending after 1990).

The simulated transitory effects on growth<sup>rates are much larger than</sup> in the first simulation. For example, in Eastern<sup>Europe where--on our</sup> assumptions--the total decline in the military spending ratio would<sup>be the</sup> largest (on the order of 4.9 percent of GDP), the<sup>rate of growth of per</sup> capita GDP would rise to a maximum where it was 0.9 percent<sup>per annum higher</sup> than in the baseline simulation. In the Middle East, Asia<sup>and North Africa,</sup> growth rates would reach a maximum that was more that<sup>0.6 percent a year</sup> higher from years 10 to 25 of the simulation.

As a result of these differences in growth rates, the<sup>ultimate effects</sup> on the levels of capacity output would vary widely<sup>across regions, but would</sup> generally be large. When all lagged effects had worked<sup>their way through,</sup> the output levels for Eastern Europe and<sup>the Middle East would be</sup> 50 percent and 46 percent higher, respectively,<sup>than they would have been if</sup> the reductions in military spending had not<sup>occurred. In the developing</sup> countries of Asia and North Africa the long-run gain<sup>would be 30 to</sup> 40 percent, and in sub-Saharan Africa<sup>over 10 percent. For industrial</sup> countries, capacity output per capita would<sup>eventually be higher by</sup>
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20 percent. This second set of simulations, therefore, suggests that military spending cuts of a size that might plausibly be expected to occur in each region if a comprehensive global peace were achieved would exert large positive Peace Dividend effects on capacity output in most geographic regions.

# VI. Summary and Conclusion

There are a number of good reasons for expecting that military spending cuts associated with improved international security would be likely to enhance long-run economic growth performance. Thus it is surprising that the empirical literature, taken as a whole, yields an ambiguous answer to the question whether military spending cuts have a positive impact on growth. The present paper was motivated by our suspicion that the ambiguous results of past studies may reflect weaknesses in estimation methodology, particularly the failure to exploit both the cross-section and time-series dimensions of available data using appropriate econometric techniques.

To unravel the contradictory empirical findings, we estimate an extension of a standard growth model that includes an investment equation and a growth equation, both of which are functions of the military spending ratio as well as other determinants. We estimate the model on panel data for a large sample of industrial and developing countries.

In contrast to standar'd cross-section estimates which give no clear significant results, the panel estimates of both the investment and the growth equations are robust in the sense that all variables enter significantly with the expected sign. The empirical results provide a clear
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answer to the question whether military expenditure is economically unproductive. Our answer is in the affirmative. When the military spending ratio is added to a growth equation that already includes the determinants suggested by standard theory the direct effect of higher military spending on per capita output growth is unambiguously negative and large. The indirect impact of military spending on economic growth, via its negative impact on productive investment, is also found to be statistically **significant.** Thus our empirical estimates clearly indicate that high levels of military expenditure detract from economic growth both because they reduce productive fixed capital formation and because they act more generally to distort resource allocation.

Using simulations with the estimated model we quantify the likely size of **the Peace** Dividend that would result over the long run from sustained cuts in military spending ratios. We find that the improved security **conditions** and associated military spending cuts in most regions in the late 1980s will lead--provided they are sustained--to substantial gains in capacity output over the long run. On the other hand, the unsettled security conditions and the associated increases in military spending in Eastern Europe and sub-Saharan Africa in the late 1980s have further weakened the already low rates of growth of per capita output in these regions. These simulated effects are large enough in themselves to justify our belief that there will be a substantial Peace Dividend from the cuts in military spending that have already taken place in most regions.

Deeper cuts in military spending that would be made possible by a generalized peace would result in an even larger Peace Dividend.
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Specifically, we find that a generalized improvement in security that allowed military spending ratios in all regions to fall to the levels actually observed in the Western Hemisphere in recent years would result in very large long-run gains in capacity output in most regions. For example, in Eastern Europe and the Middle East--where military spending ratios have been high in the past--the salutary effects of military spending cuts on investment and growth could increase capacity output in the very long run by nearly 50 percent relative to the levels that would have prevailed if military spending ratios had remained fixed at the high average levels that were prevalent in these regions over 1972-85. The Peace Dividend effects for other regions, though less spectacular than in these cases, are still very large in the long run.

It is also relevant to note that these simulation results may actually tend to _understate_ the positive output-growth effects of enhanced international security. First, a sustained global peace might eventually reduce the world military spending ratio by more than our simulations assumed. Universal peace, after all, would be the classic example of a public good. Furthermore, although our simulations explicitly assume that all determinants of investment and growth other than military spending would remain unchanged even if a generalized peace were achieved, it is likely that there would be positive synergies in the evolution of productive technology. Since improved security would allow a greater proportion of research and development expenditures to be devoted to nonmilitary goals, it would stimulate market-oriented technological innovation, thus enhancing the growth of total factor productivity.
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Over the long run, improvements in international security would almost certainly result in improvements in the other economic variables that are significant determinants of economic growth. As political tensions subsided, more and more countries would be able to concentrate on improving economic performance by dismantling barriers to free international exchange of goods, services, and financial assets. In this way, a generalized peace would foster economic interdependence, more open trading systems, and associated specialization gains. For analogous reasons, a better international security situation would also allow national education programs to concentrate on productive skills, and participation in the educational systems could rise markedly in a number of populous countries where political insecurity has long limited educational opportunities.

Given these considerations, the key policy implication of this study is straightforward: The Peace Dividend from military spending cuts is likely to be very substantial over the longer term. Thus reductions in military spending should be viewed as attractive structural policy elements of macroeconomic packages designed to enhance the growth path of capacity output.