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The Peace Dividend: Military Spending Cuts and Economic Growth

Author(s):
International Monetary Fund. Research Dept.
Published Date:
January 1996
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There 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 ratcheted up throughout a region as each country tries to outspend its neighbors to ensure its own security, resulting in higher levels of military expenditure but no increase—or even a decrease—in the security of the region. 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 international cooperation that succeeded in reducing tensions, and thus in lowering military spending, would be to the long-run economic benefit of all countries. 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 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 is usually argued by recourse to casual empiricism. For example, the post-World War II experiences of the Federal Republic of Germany and Japan lend support to the notion that there are substantial economic benefits from sustaining low rates of military expenditure over long periods of time. The strict postwar limits on defense spending in 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 proportions 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 to improving the educational level and discipline of the labor force and may 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 on the military can also have productive uses: many developing countries still benefit from transport and communications networks that were originally constructed for military purposes. These counterexamples suggest that the question of 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 empirical 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 occur with a long lag. Thus, even in cases where military spending cuts are large, the ultimate beneficial effects may be hard to disentangle empirically from other factors that also influence economic growth. Given these considerations, it is not surprising that the existing empirical literature yields ambiguous results, not only on 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 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 GDP absorbed by military spending had remained unchanged?

This 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-sectional 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. 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.

The paper is organized as follows. Section I summarizes trends in military expenditures since the early 1970s and reviews the empirical literature on the relationship between military spending and economic growth. Section II outlines our extension of a standard neoclassical growth model and the estimation technique used. Section III first presents standard cross-sectional 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 IV describes two simulation experiments that help to indicate the rough order of magnitude of the peace dividend from military spending cuts. We first simulate the long-run growth effects of the developments in military spending that have already taken place in various geographic regions during the latter half of the 1980s. We then simulate the potential effects of further declines in military spending that might occur in various regions in the future if a lasting global peace could be secured. Section V concludes.

I. Data and Empirical Research on Military Spending

Throughout this paper we define the military spending ratio, m, as the ratio of total military expenditure (M) to GDP; the data used here are those published by the Stockholm International Peace Research Institute (SIPRI).1 We define the peace dividend narrowly as the percentage difference between the level of real capacity output per capita that would result from a given sustained reduction in the military spending ratio, and the baseline path of capacity output that would have prevailed in the absence of such a reduction.

Trends in Military Spending, 1972–1990

The descriptive literature on trends in military expenditures (see Hewitt (1993) and the primary data sources cited there) indicates the large extent to which the world’s productive resources have been devoted to the military throughout the period since World War II.2 Hewitt’s data show important differences in military spending across country groupings as well as over time. On average during the period 1972–90, for example, over 5 percent of world resources, as measured by the combined GDP of the countries considered by Hewitt, was devoted to military spending. Table 1 summarizes the main patterns of military expenditure for 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 the regional total GDP level measured in U.S. dollars using official exchange rates.

Table 1.Ratios of Military Spending to GDP for Various Country Groups and Time Periods a
Period weighted averages, in percentb
Full periodPeriod IPeriod II
(1972–90)(1972–85)(1986–90)
Full sample5.105.194.84
Industrial countries3.903.973.70
Developing countries5.205.544.26
Regional groupings
Asia5.706.353.88
Eastern Europe12.4011.7514.22
Middle East10.0010.369.06
North Africa7.208.124.60
Sub-Saharan Africa3.203.123.42
Western Hemisphere2.102.161.94
Source: Hewitt (1992).

See Appendix I for lists of countries included in each grouping for Hewitt’s sample.

The weights are each country’s share in the group GDP measured in U.S. dollars using official exchange rates.

Source: Hewitt (1992).

See Appendix I for lists of countries included in each grouping for Hewitt’s sample.

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 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 1986–90, 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 regional average 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 in 1985 to 4.3 percent five years later. Table 1 shows that this feature is the most striking in North Africa and Asia. For the group of Middle East countries, where military spending absorbed over 10 percent of GDP during 1972–85, 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 is 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. While these averages were higher for 1986–90 than for Period I, a year-by-year analysis shows that military spending ratios were on a declining trend in both regions after 1987.3

The Relationship Between Military Spending and Growth

The relationship between military spending and 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 stimulative Keynesian impact on the growth rate by inducing a rise in capacity utilization—that is, it raises the growth of current output relative to that of capacity output.4 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 at least two channels by which a sustained increase in military expenditure might depress a country’s secular growth performance. The first 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 may 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. Alternatively, the rise in military spending may be accomodated by a fall in public sector investment. These channels are likely to be particularly important in net-debtor developing countries. Since such countries are faced with external financing constraints, a rise 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.5

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 deadweight 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. A higher deadweight loss to domestic production results from either an increase in contemporaneous taxes or heavier borrowing to finance higher military spending. In developing countries this financing often involves increased government borrowing from the domestic banking system, which raises inflation and distorts resource allocation.6 Furthermore, research and development activities may concentrate on military progress at the expense of technological advances in economically productive areas. 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 preferences for certain firms and industries, and compulsory military service. Finally, rent-seeking activities grow around the military because of its noncompetitive 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-sectional 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 to empirical testing—Benoit (1973 and 1978) and Frederiksen and Looney (1982)—find apparent empirical support for the thesis that military expenditures are not detrimental to growth. Benoit (1978), using data for 44 developing countries over 1950–65, finds a positive association between military 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 adverse 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.7 Both the model we specify in the next section and the technique we suggest for estimating it are intended to address the limitations of past empirical research on this relationship.

II. 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 allow for 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.8 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 restrictiveness of the country’s international trade regime (i.e., a weighted average of its tariff rates on intermediate and capital goods); the Barro-Lee (1993) proxy for the incidence of wars;9 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.10

Equation (1) specifies the per capita output growth rate, Zi,t—Zi,t-l, where Z is the natural logarithm of output per capita:

a natural logarithm is indicated by In; the indices i and t represent the country and time period, respectively; n is the average population growth rate; g is the rate of technical progress, δ is the rate of depreciation of the stock of physical capital, and g + δ is assumed to be equal to 0.05;11sk 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; ξt, represents time-specific factors; μi, represents country-specific factors; and ε 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 that specifies the ratio of real investment to real GDP as a function of the rate of investment in human capital, sh; the restrictiveness of the trade system, f; the proxy for the incidence of wars, w; and the military spending ratio, m. The investment equation is

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 variables that enter into equations (1) and (2), our estimation sample is a subset of the countries covered by Hewitt.12

The basic estimation sample covers 79 countries over the period from 1971 to 1985. The countries 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 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 features that are quite close to those highlighted by Hewitt. In particular, the declines in the weighted average military spending ratios in each region are about the same size in the two samples.13 This broad correspondence gives us a degree of confidence that although the sample we use to estimate the model has a less comprehensive country coverage than Hewitt’s, it nevertheless retains broadly similar characteristics.

Since available statistics 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 consistent estimates of the effect of military expenditures on investment and economic growth.14 Specifically, we employ the econometric technique proposed in our earlier paper (Knight, Loayza, and Villanueva (1993)) to deal with time-series cross-sectional data.

Table 2.79-Country Sample: Ratios of Military Spending to GDP for Various Country Groups and Time Periods a
79-country sample Period averages, in percentComparison of changes (Period II minus Period I)
Full period

(1972–90)
Period I

(1972–85)
Period II

(1986–90)
79-country sampleHewitt’s sample
Full sample
Weighted average3.803.893.59–0.30–0.35
Simple average3.353.443.08–0.36
(Standard deviation)(0.21)(0.12)(0.21)
Industrial Countries
Weighted average3.903.993.71–0.28–0.27
Simple average3.013.072.85–0.22
(Standard deviation)(0.13)(0.07)(0.12)
Developing Countries
Weighted average3.103.142.80–0.34–1.28
Simple average3.483.583.17–0.41
(Standard deviation)(0.26)(0.16)(0.24)
Regional Groupings
Asian Developing
Weighted average3.903.943.75–0.19–2.47
Simple average3.663.643.710.07
(Standard deviation)(0.28)(0.28)(0.33)
Middle East
Weighted average6.806.936.49–0.44–1.30
Simple average10.7011.249.21–2.03
(Standard deviation)(1.35)(1.07)(0.82)
North Africa
Weighted average6.507.593.38–4.21–3.52
Simple average6.066.724.19–2.53
(Standard deviation)(2.27)(2.29)(0.48)
Sub-Saharan Africa
Weighted average2.702.832.52–0.310.30
Simple average2.702.752.58–0.17
(Standard deviation)(0.24)(0.26)(0.12)
Western Hemisphere
Weighted average2.202.281.80–0.48–0.22
Simple average2.432.492.26–0.23
(Standard deviation)(0.40)(0.44)(0.25)
Source: Hewitt (1992).

See Appendix I for lists of countries included in each grouping for both Hewitt’s and our sample.

Source: Hewitt (1992).

See Appendix I for lists of countries included in each grouping for both Hewitt’s and our sample.

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 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 five-year interval, rather than a single year. This procedure provides a simple means of smoothing out short-run 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.15 Similarly, the data for the investment ratio sk are averaged over the same five-year intervals. The variables for which panel data are available are indexed by both time, t, and country, i; these variables are z, h, sk, m, and sh. 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 five-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 1971–75, 1976–80, 1981–85. For the cross-sectional variables—sh,f, w—observations for each country correspond to averages over the 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 country-specific and time-specific effects. Country-specific effects are especially important in the present analysis. There are a host of factors that are peculiar to each country—government policies, resource endowments, social institutions, geographic location, 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. To account for country-specific effects, we use the methodology proposed by Chamberlain (1982 and 1984), commonly known as the Π-matrix technique. We control for the time-specific effects by removing the time means from each variable. Given that the growth regression contains a lagged dependent variable, the fixed-effects “within” estimator that is commonly used to control for specific effects would yield inconsistent estimates.

A detailed exposition on the application of the Π-matrix technique to growth regressions estimated using panel data is presented in our earlier paper. Basically the application has two steps. First, we replace the country-specific factor μi,t by its linear predictor E*(μi,t) 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.

III. Estimation Results

We now present the estimation results for our two-equation growth model. To illustrate the difficulties (discussed in Section I) 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-sectional estimates of equations (1) and (2) using the data on output growth and the investment ratio for each country in our sample averaged over the whole period 1971–85 and average levels for each country of the values of the right-hand-side variables over the same period. This approach will show us what the parameter estimates for our model would look like if they were obtained without taking full advantage of the time-series dimension of the data set. Next, we re-estimate the model on our sample of panel data observations using our proposed econometric approach, and compare the parameter estimates obtained using the two alternative estimation methods.

Standard Cross-Sectional Estimation

Table 3 reports standard cross-sectional estimates of the growth equation (equation (1)). To account for geopolitical and developmental differences across regions we consider two regional dummy variables in our cross-country regressions, one for Africa (AFR) and the other for the developing countries in the Western Hemisphere (WH). We find that only the investment ratio, the proxy variable for international trade restrictions, and the dummy variable for the developing countries in the Western Hemisphere enter significantly with the expected signs. Indeed, in line with the results of several of the empirical studies cited in Section I above, in the cross-sectional regression for the growth rate the estimated coefficient of military spending is positive but statistically insignificant. Note also that when the military spending ratio is included in the regression, the estimated coefficients of the other variables change very little. The same results are obtained when the proxy for the incidence of wars is included. We believe that these rather inconclusive cross-sectional results help to illustrate why the past empirical work cited in Section I has been affected by the application of estimation procedures that do not take advantage of all the information available in the data set.

Table 3.Standard Cross-Sectional Regressions for the Growth Rale a
VariableCoefficient (t-ratio)Coefficient (t-ratio)Coefficient (t-ratio)
Zt–1–0.0057

(–1.17)
–0.0055

(–1.17)
–0.0057

(–1.19)
In (n + 0.05)0.0103

(1.51)
0.0091

(1.37)
0.0088

(1.28)
In sk0.0112

(2.73)
0.0110

(2.68)
0.0107

(2.57)
In sh0.0013

(0.86)
0.0012

(0.87)
0.0012

(0.89)
In f–0.0189

(–2.12)
–0.0181

(–2.12)
–0.0184

(–2.11)
In m0.0023

(0.69)
0.0026

(0.72)
w–0.0043

(–0.50)
constant0.0537

(1.79)
0.0471

(1.70)
0.0489

(1.74)
AFRb–0.0079

(–1.09)
–0.0072

(–2.98)
–0.0070

(–0.95)
WH–0.0139

(–2.78)
–0.0128

(–2.48)
–0.0125

(–2.30)
Adjusted R20.2440.2410.231

Number of countries = 79.

“Africa” is defined as countries in the North and sub-Saharan African regions.

Number of countries = 79.

“Africa” is defined as countries in the North and sub-Saharan African regions.

Table 4 reports the estimation results for a standard cross-sectional 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 investment in physical capital.

Table 4.Standard Cross-Sectional Regressions for the Ratio of Investment to GDP a
VariableCoefficient (t-ratio)Coefficient (t-ratio)
In (n+ 0.05)–0.1400

(–0.79)
–0.1478

(–0.89)
In sh0.0702

(3.47)
0.0664

(3.43)
In f–0.0895

(–0.36)
–0.0985

(–0.41)
In m0.0401

(0.54)
0.0809

(1.01)
w–0.5190

(–1.51)
constant2.1726

(4.16)
2.1620

(4.31)
AFRb–0.4054

(–2.10)
0.3723

(–1.89)
WH–0.1736

(–1.37)
–0.1362

(–1.05)
Adjusted R20.4630.483

Number of countries = 79.

“Africa” is defined as countries in the North and sub-Saharan African regions.

Number of countries = 79.

“Africa” is defined as countries in the North and sub-Saharan African regions.

In particular, the cross-sectional regression cannot identify a significant relationship, whether positive or negative, running from the level of military spending to the rate of capital investment. When the proxy for the incidence of wars is added to the regression equation the parameter estimates change only slightly; as expected, the proxy exerts a negative effect on investment that is statistically significant at the 10 percent level.

Panel Data Estimation

We now contrast these standard results with our panel data estimates of the investment and growth equations. Table 5 reports estimates of the growth equation (equation (1)) using our panel data and estimation technique. First, consider the 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 per capita 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.

Table 5.Panel Regressions for the Growth Rate a
VariableCoefficient (t-ratio)Coefficient (t-ratio)Coefficient (t-ratio)
Zt–1–0.0989

(–3.83)
–0.6656

(–2.96)
–0.0262

(–1.27)
In

(n + 0.05)
0.00003

(0.003)
0.0064

(0.48)
–0.0004

(–0.03)
In sk0.0227

(3.65)
0.0225

(3.94)
0.0165

(2.92)
In sh0.0603

(3.73)
0.0404

(2.99)
0.0158

(1.32)
In f–0.0286

(–4.35)
–0.0204

(–3.39)
–0.0091

(–1.63)
In m–0.0081

(–2.67)
–0.0060

(–2.06)
w–0.0132

(–1.51)
Wald test for uncorrelated effects

(p-value)
28.19

(0.0000)
51.61

(0.0000)
55.59

(0.0000)

Number of countries = 79.

Number of countries = 79.

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 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.

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 restrictions.16 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 fostering the adoption of various types of trade restrictions.17 Owing 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.Panel Regressions for the Investment to GDP Ratioa
VariableCoefficient (t-ratio)Coefficient (t-ratio)Coefficient (t-ratio)
In (n +0.05)0.5081

(3.37)
0.6506

(4.65)
0.6950

(4.74)
In sh0.2707

(2.99)
0.2511

(2.99)
0.2294

(2.99)
In f–0.1225

(–2.03)
–0.0947

(–1.69)
–0.0766

(–1.52)
In m–0.0742

(–1.98)
–0.0754

(–2.14)
w–1.3232

(–6.78)
Wald test for uncorrelated effects

(p-value)
5.46

(0.1410)
38.57

(0.0000)
65.85

(0.0000)

Number of countries = 79.

Number of countries = 79.

Table 6 reports the panel data estimates for the investment equation. In contrast to the standard cross-sectional 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 at 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 higher military spending does indeed lead to crowding out of investment in productive fixed capital.

IV. The Peace Dividend: Simulation Experiments

As mentioned in Section I, 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 military 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 that region in future years. Thus it is interesting to use the panel data 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.

Simulated Long-Run Effects of the Changes in Military Spending 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 geographic regions to achieve reductions in their military spending ratios.

While our empirical 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 the six regional groups of developing countries analyzed by Hewitt and described in Section I: Asia, Eastern Europe, the Middle East, North Africa, sub-Saharan Africa, and the Western Hemisphere. The exogenous development 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.

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 1986–90,18 and that after reaching its new level in 1990 the military ratio remains constant. 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 6.19

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 the rise is about 0.15 percentage points.

The middle panel in Table 7 shows the difference between the simulated growth rate of capacity output per capita and its baseline path that will eventually result from the changes in military spending levels that actually occurred in each region in the late 1980s. The bottom panel shows the percentage difference in the level of capacity output per capita in each region relative to its baseline path.

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 the baseline rates. As a result, the percentage deviation in the levels of per capita GDP (shown in the 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 steady states are reached in each region.

Table 7.The Peace Dividend: Simulated Lung-Run Effects of Changes in Military Spending Ratios in the Late 1980s on Capacity Outputa
Country groups
Developing countries
Industrial countriesAsiaEastern EuropeMiddle EastNorth AfricaSub-Saharan AfricaWestern Hemisphere
(in percent of GDP)
1.Average military spending ratio. 1972–85b3.976.3511.7510.348.133.122.16
2.Average military spending ratio, 1986–90b3.703.8814.229.064.603.421.94
3.Change in military spending ratio (line 2 minus line 1)–0.27–2.472.47–1.28–3.530.30–0.22
4.Associated change in ratio of investment to GDP0.140.66–0.480.250.70–0.110.15
Time horizonSimulated minus baseline growth rates of per capita GDP
(years)(in percent per annum)
10.0100.071–0.0280.0190.082–0.0130.015
50.0490.339–0.1310.0910.391–0.0630.073
100.0430.297–0.1150.0790.343–0.0550.064
250.0290.199–0.0770.0530.230–0.0370.043
500.0150.103–0.0400.0270.119–0.0190.022
........
........
0.0000.0000.0000.0000.0000.0000.000
Time horizonSimulated minus baseline levels of per capita GDP
(years)(in percent of baseline GDP)
10.00.10.00.00.1–0.00.0
50.11.0–0.40.31.2–0.20.2
100.42.6–1.00.73.0–0.50.6
250.96.2–2.41.77.2–1.21.3
501.49.8–3.82.611.3–1.82.1
........
........
2.013.6–5.33.615.7–2.32.9

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 II.

Derived from data in Hewitt (1992).

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 II.

Derived from data in Hewitt (1992).

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

Note: Solid line assumes military spending falls from 1972–85 to 1986–90 average over five years. Dotted line assumes regions decrease military spending form 1972–85 average to that of the Western Hemisphere in the period 1986–90 over 15 years.

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 0.3–0.4 percent per year higher than the baseline growth rate during years 5–10; the effects are of course smaller for other country groups, particularly the industrial countries and the developing countries in the Western Hemisphere.

Owing to the dynamic properties of our estimated growth model (relatively slow conditional convergence), these modest deviations in growth rates persist for a long time, and as a result their ultimate effects on the levels of capacity output per worker are noteworthy. For example, our simulations indicate that over 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 substantial effects on the levels of capacity output.20 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.

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 could 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 discernible effects on the growth paths of capacity output in various regions? How large might these effects ultimately be? These are, of course, highly speculative questions. Even if there were 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 (about 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 simple approximation of the minimum level that could be attained in other regions if a lasting world peace were achieved.

Our second set of simulations assumes that the military spending ratio in each region declines steadily over a ten-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 1972–85. Thus these simulations include 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 summarizedin 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 (a rise equivalent to 4.9 percent of GDP) and the Middle East (a rise of 3.3 percent of GDP), the regions that—since they currently have high levels of military spending—stand to gain the most from reducing military spending to the minimum level associated with a generalized peace.

Table 8.The Peace Dividend: Simulated Effects of Decreasing Regional Military Spending Ratios from Their 1972–85 Average Levels to the 1986–90 Level for Western Hemisphere Developing Countries a
Country groups
Developing countries
Industrial countriesAsiaEastern EuropeMiddle EastNorth AfricaSub-Saharan AfricaWestern Hemisphere
(in percent of GDP)
1.Average military spending ratio, l972–85b3.976.3511.7510.348.133.122.16
2.“Minimum” military spending ratiob1.941.941.941.941.941.941.94
3.Change in military spending ratio (line 2 minus line 1)–2.03–4.41–9.81–8.40–6.19–1.18–0.22
4.Associated change in ratio of investment to GDP1.501.634.903.301.810.580.15
Time horizonSimulated minus baseline levels of per capita GDP
(years)(in percent per annum)
10.0100.071–0.0280.0190.082–0.0130.015
50.0490.339–0.1310.0910.391–0.0630.073
100.2650.5350.5700.6090.6390.1400.064
250.3480.5420.9080.8150.6570.2430.043
500.1790.2790.4670.4200.3380.1250.022
........
........
0.0000.0000.0000.0000.0000.0000.000
Time horizonSimulated minus baseline levels of per capita GDP
(years)(in percent of baseline GDP)
10.00.1–0.00.00.1–0.00.0
50.11.0–0.40.31.2–0.20.2
101.13.31.12.33.90.10.6
256.912.616.116.015.24.11.3
5013.222.432.430.727.08.52.1
........
........
19.832.749.746.239.613.12.9

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 II.

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

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 II.

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

The dotted lines in the upper panels in Figure 1 show the hypothetical downward paths of military spending for each region over the next ten years. The dotted lines in the lower panels represent the percentage deviation of each region’s per capita capacity output from the baseline path over a 50-year period starting from 1986. As the simulations indicate, the further gradual declines in military spending in all regions that might be associated with a lasting improvement in international security would exert very marked stimulative effects on the growth paths of per capita output in all regions (except Western Hemisphere, where there is assumed to be no further fall in military spending after 1990).

The simulated transitory effects on growth rates are much larger than in the first simulation. For example, in Eastern Europe where—on our assumptions—the total decline in the military spending ratio would be the largest (on the order of 9.8 percent of GDP), the rate of growth of per capita GDP would rise to a maximum during years 10 to 25, when it would be 0.9 percent per annum higher than the baseline. In the Middle East, Asia, and North Africa, growth rates would reach a maximum that would be more than 0.6 percent a year higher.

As a result of these differences in growth rates, the ultimate long-run effects on the levels of capacity output would differ widely across regions, but they would eventually be very large indeed. When all lagged effects had worked their way through after more than 50 years, capacity output levels in Eastern Europe and the Middle East would be 50 percent and 46 percent higher, respectively, than they would have been if the permanent reductions in military spending ratios had not occurred. In the developing countries of Asia and North Africa the long-run gain would be 30 to 40 percent, and in sub-Saharan Africa, over 10 percent. For industrial countries, capacity output per capita would eventually be higher by 20 percent. This second set of simulations, therefore, suggests that military spending cuts of a size that could 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.

V. Summary and Conclusion

There are a number of good reasons to expect 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. This 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-sectional 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 output growth equation and an investment 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 standard cross-sectional estimates, which give no clear empirical 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 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, through 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.

Deeper cuts in military spending that would be made possible by a generalized peace would result in an even larger peace dividend. 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 over 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 long-run peace dividend effects for other regions, though less spectacular than in these cases, are still very large.

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 assume. Universal peace, after all, would be a 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.

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 more open trading systems, increased global economic integration, 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. Although the effects of military spending cuts may cumulate gradually over many years, the peace dividend is likely to be very substantial in the long term. Thus, for policymakers who can exercise sufficient patience, reductions in military spending should be attractive structural policy elements of economic reform programs designed to enhance economic growth performance.

APPENDIX I Data Sources and Definitions, and Sample of Countries

Data Sources and Definitions

The basic data used in this study are annual observations. The following variables were obtained from Summers and Heston’s (1991) Penn World Tables:

z : natural logarithm of real GDP per capita;

sk : ratio of real investment to real GDP (five-year average);

n : population growth rate (five-year average).

The following variable was obtained from Mankiw, Romer, and Weil (1992):

sh : percent of working-age population enrolled in secondary schools (average for the period 1960–85).

Data on tariffs were obtained from Lee (1993):

f : import-share-weighted average of tariffs on intermediate and capital goods (from various years in the early 1980s).

Data on military expenditures were provided by Daniel Hewitt, who collected the data from the 1992 Yearbook of the Stockholm International Peace Research Institute (SIPRI):

m : ratio of SIPRI military expenditures to GDP (data are annual for 1971–1990).

Data on the incidence of wars were obtained from Barro and Lee (1993):

w : ratio of years spent in international wars to the total number of years in the period 1960–1985.

List of Countries Included in Hewitt’s 124-Country Sample, and Used for Estimation in Our 79-Country Panel Data Set

(Countries from Hewitt’s sample that are included in our panel data estimation are marked with an asterisk).

Industrial Countries
Australia*Italy*
Austria*Luxembourg
Belgium*Netherlands*
Canada*Norway*
Denmark*Portugal*
Finland*Singapore*
France*Spain*
Germany, Federal Republic of*Sweden*
Greece*Switzerland*
Ireland*United Kingdom*
Japan*United States*
Developing Countries
Algeria*Bulgaria
AngolaBurkina Faso*
Argentina*Burundi*
BahrainCameroon*
Bangladesh*Central African Republic*
Benin*Chad
Bolivia*China
BotswanaChile*
Brazil*Colombia
Congo*Mozambique
Costa Rica*Myanmar
Cöte d’IvoireNepal*
CubaNicaragua*
CyprusNiger
CzechoslovakiaNigeria*
Dominican RepublicOman
Ecuador*Pakistan*
Egypt*Panama
El Salvador*Paraguay*
Ethiopia*Peru*
FijiPhilippines*
GabonPoland
German Democratic RepublicRomania
Ghana*Rwanda*
Guatemala*Saudi Arabia
Guinea-BissauSenegal*
GuyanaSierra Leone*
Haiti*Somalia*
HondurasSouth Africa
HungarySri Lanka*
India*Sudan*
Indonesia*Swaziland
IranSyrian Arab Republic*
IraqTaiwan Province of China
IsraelTanzania*
Jamaica*Thailand*
Jordan*Togo
Kenya*Trinidad and Tobago*
Korea*Tunisia*
KuwaitTurkey*
LebanonUganda*
LiberiaUnited Arab Emirates
LibyaUSSR
Madagascar*Uruguay*
Malawi*Venezuela*
Malaysia*Yemen Arab Republic
MaliYemen People’s Democratic Republic
MauritaniaYugoslavia
Mauritius*Zaïre*
Mexico*Zambia*
Morocco*Zimbabwe*
APPENDIX II Simulation Exercise

There are three elements that determine the gains in GDP over time for a given reduction in the ratio of military spending to GDP (M/GDP = m). First, the effect of m on the ratio of investment to GDP, and the latter’s effect on per capita GDP growth; second, the direct effect of m on per capita GDP growth; and third, the effect of the current per capita GDP on its growth rate (the convergence effect.) From equations (1) and (2), the percentage gain in per capita GDP (Δzt) for a given percentage change in the military spending ratio (Δln m) is given by

The estimates for θm, θk, and γ are taken from Table 5, column 4; and the estimate for ηm, from Table 6, column 4.

The change in the level of the investment ratio produced by a given percentage change in the military spending ratio can be approximated as follows:

Δln m for each group of countries is computed as follows:

Δln m = ln [Average M/GDP( 1986–1990)] – ln [Average M/GDP( 1972–1985)]

REFERENCES

Malcolm Knight, Deputy Director of the IMF’s Middle Eastern Department, holds a Ph.D. from the London School of Economics and Political Science. Norman Loayza is an Economist in the Macroeconomics and Growth Division of the World Bank’s Policy Research Department. He received his doctorate from Harvard University. Delano Villanueva, who holds a doctorate from the University of Wisconsin, was Assistant to the Director of the IMF’s Middle Eastern Department when this article was written. He is currently on a year’s sabbatical leave at the University of Hawaii. 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.

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

Our paper makes use of the data on military expenditures presented in Hewitt (1992 and 1993), which are based mainly on statistics published by SIPRI. Hewitt’s data cover 124 industrial and developing countries.

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.

Hewitt (1992) hypothesizes that military expenditures can have a net positive or negative impact on economic growth depending on the alternative use of the funds. He argues that military expenditures on general-use public infrastructure and promotion of research, as well as demobilization of trained personnel, contribute to economic growth; however, military spending is an inefficient way to enhance growth compared with 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 on national security grounds, since the economic benefits are limited.

Hewitt (1992) notes that increases in military spending 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.

See Chan (1985) for a selected bibliography.

For details of this derivation, see Knight, Loayza, and Villanueva (1993). The growth effects that we discuss in the current paper apply to the transition to the steady state.

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).

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.

The assumption that g + δ = 0.05 follows Mankiw, Romer, and Weil (1992). We find that although changes in this number affect the estimated θn, they do not significantly affect the other estimated coefficients.

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 other 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 resources. The list of countries and the data sources for the variables used to estimate our model are presented in Appendix I.

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 the weighted average military spending ratio for these countries, while Hewitt’s more comprehensive data show a small rise.

In this paper the term investment, used alone, refers to investment in physical capital. When we refer to human capital investment, we say so explicitly.

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.

As expected, the 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 with increases in military expenditures.

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 difficulties when these purchases are made. This may be one of the reasons why developing countries also tend to operate more restrictive trade regimes.

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

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 would have 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 actual output relative to capacity output.

Even for the industrial countries and Western Hemisphere developing countries, where initial military spending levels were low and where the cuts during the latter half of the 1980s were modest, per capita output levels would eventually be 2.0 percent and 2.9 percent, respectively, above the baseline paths.

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