Socio-economic environments and the budgetary allocation process in developing countries: The case of defense expenditures

Socio-Econ. Plann. Sci. Vol. 22, No. 2, pp. 71-81. 1988 Printed

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0038-Ol21/88 $3 00 + 0.00 1988 Pergamon Press plc

SOCIO-ECONOMIC ENVIRONMENTS AND THE BUDGETARY ALLOCATION PROCESS IN DEVELOPING COUNTRIES: THE CASE OF DEFENSE EXPENDITURES ROBERT

E. LOONEY

National Security Affairs, Naval Postgraduate School, Monterey, CA 93943-5100, U.S.A (Received

17 November

1986; in revised

form 10 January 1987)

Abstract-The purpose of this paper is to extend the discussion on the determinants of budgetary allocations in the Third World. More often than not, policy analysts have assumed that military budgets are determined by exogenous factors (such as the threat of aggression) and are thus not directly measurable or can only be crudely quantified by some form of proxy variable. Hence, the link between the determinants of defense spending on the one hand and the level of military expenditures on the other hand has not been amenable to traditional economic analysis. This paper suggests that any forecast of military expenditures which excludes economic factors is likeiy to be substantially inaccurate.

extent of their economic impact” [2]. Chan recently summarized the major research efforts and discussed some of the major problems in the area [3]. Furthermore, he noted the lack of almost any effort investigating the direction of causality, i.e. does defense cause growth or does economic growth allow nations to “indulge in” more military programs-or both. In his conclusions Chan suggested that:

INTRODUCTION

During the last two decades, there has been a keen interest and a growing literature on the many dimensions of military expenditures in developing countries (LDCs). Four of these avenues of research have been (a) to examine whether military spending helps, hinders, or has no effect on economic growth, (b) to compare the effects of civilian and military regimes on economic performance, (c) to see why some LDCs produce weapons and others do not, and finally (d) to examine the major determinations of defense programs in Third World countries. The purpose of this paper is to extend the discussion on the determinants of defense spending. More often than not, policy analysts have assumed that military budgets are determined by exogenous factors (such as the threat of aggression) and are thus not directly measurable or can only be crudely quantified by some form of proxy variable. Hence the link between the determinants of defense spending on the one hand and the level of military expenditures on the other hand has not been amenable to traditional economic analysis. This paper suggests that any forecast of military expenditures which excludes economic factors is likely to be substantially inaccurate. We hypothesize that economic factors play a significant role and often are the predominant influence determining levels of military expenditures in many Third World countries.

We have probably reached a point of diminishing returns in relying on aggregate crossnational studies to inform us about the economic impact of defense spending. Instead, it appears that future research will profit more from discriminating diachronic studies of individual countries [4]. Another major debate has focused on the effect different political regimes (civilian vs military) have had on budget allocations, economic performance, and policy outcomes. Remmer recently summarized this research by noting that The empirical studies of regime type, public policy, and policy outcomes conducted so far, whether focused on Latin America or including other areas as well, tend to support the conclusion that regime differences have little or no impact on public policy [5]. She suggested that a country’s relative performance was a function of endogenous economic factors and not regime type. Neuman’s efforts to investigate why only certain LDCs produce weapons led her to conclude that there exists “a hierarchically shaped arms production system based largely on factors of scale.” In addition she suggested that “the existence of a large military to provide an adequate market, combined with a generous national income and sizable population to support the necessary infrastructure,” will be important in determining whether a country becomes a producer [6]. Looney and Frederiksen built on Neu-

REVIEW OF THE LITERATURE

One of the earliest attempts to quantify the relationship between military spending and economic growth was completed by Emile Benoit [l]. While Benoit tentatively found that defense spending and eCOnOmiC performance were positively correlated, the link is still not clear today. As Nawaz noted, while military spending rose 63% in the 1970s “, . . no clear agreement has emerged about the nature and 71

72

ROBERTE. LOONEY

man’s work and suggested that while poplation and size do play an important role, an endogenous economic environment conductive for weapons production to start and continue must be present. Their results pointed to factors such as contact with the world economy, the extent of public debt, and the extent and growth of foreign trade as being additional factors which are important in the process of arms production [7]. Recent research on the determinants of military spending has spanned many disciplines. Kim pointed out “the military spending level of any nation is likely to be a product of a number of separate forces” [8] and suggested that the following factors play important roles: arms races, military alliances, status and status discrepancies in international systems, military aid, size of country, the form of the government, the extent of military involvement in the country, internal social division, internal political conflict, and the wealth of the country. Westing examined 1975 data for 159 nations and noted the positive correlation of military spending levels with certain internal factors such as population, size, the extent of productive land area, and Gross National Product (GNP). He suggested that military expenditures in many LDCs will increase as these countries become wealthier [9]. Two recent studies examined military expenditure levels in developed countries. Griffin et al. examined defense spending in the United States between 1949 and 1976 and concluded that “military outlays (as of GNP) do appear to be employed as a counter-cyclical fiscal instrument by the state” [lo]. Treddenick tested for the impact of economic factors by examining the recent pattern of Canadian military expenditures. Specifically, he wished to see whether: the level and composition of a nation’s military expenditures may be significantly influenced by domestic economic imperatives which are independent of any security considerations. Thus, military expenditures may be undertaken to promote economic objectives but rationalized in terms of providing for national security [I 11. He concluded that “recent large increases in Canadian defense expenditures have been influenced more by economic than by security considerations,” [12] and that changes in military budgets have been used as a policy instrument by the Canadian government. Using data for 83 countries between 1978 and 1980, Maize1 and Nissanke suggested three potential influences on military expenditures in any countrythe political framework, military activity and economic linkages [13]. However, the relative importance of each factor in explaining military spending levels will be determined by national, regional or global conflicts or interactions in the individual country. For example, at the national level the following economic factors are considered important determinants of military spending levels; the level of economic development (urbanization, inequalities in wealth and income, and opportunities for advancement), real income growth, the size of the state budget, and the influence of the military-industrial complex. While not listing any economic linkages at the regional level, the authors hypothesize that the

growth of foreign exchange, the influence of foreign capital and the influence of major aid donors are important determinants of military spending if the country is involved at the global level. After estimating regression equations for the entire sample and for three regions (Africa, Asia and Latin America), they concluded that differences in military spending among the sample countries is not merely a result of wars or local conflicts. As they noted, “domestic factors . and external factors including relations with the global power blocs and the availability of foreign exchange to purchase arms from abroad, are also major determinants for government decisions in regard to military expenditures” [14]. Somewhat surprisingly, they found that investments by transnational corporations did not appear as a major determinant of military spending levels. As Harris noted recently, the search for economic factors which affect defense spending levels in developing countries has received scant attention in the literature. [15] In a study broadly similar to the present one, he examined the importance of domestic economic conditions on the levels of defense spending in five ASEAN countries (Indonesia, Malaysia, Philand Thailand) since the early ippines, Singapore 1960s. The independent variables in his regression analysis included the country’s GNP level, government revenue, the inflation rate, and the balance of payments condition. He concluded his study by noting that:

. . . economic forces do exert at least a moderate influence on defense expenditures. Defense expenditure in the current year was positively related to defense expenditure in the previous year, and to the governments budgetary position in that year. The balance of payments position exerted a strong indirect influence through its effect on government revenue [ 161. The purpose of this paper is to extend the discussion on the ecomomic determinants of defense spending levels in LDCs. METHODOLOGY Presumably, the various motives for defense expenditure manifest themselves in economic, social and political data. Whether defense expenditures are largely reflected and presumably determined by economic factors or whether defense expenditures, everything else being equal, are largely a function of political and social considerations, is of course an empirical matter. The data base used for cross section analysis differs from those used in previous expenditure studies in two respects. First, the sample is much larger-the initial data base includes 96 countries. Second, the data base comprises both economic and sociopolitical variables. Economic variables were taken from the World Bank data base [17], the International Monetary Fund [18], and the Yale Data Base on Political and Social Indicators [19]. Military expenditure variables were taken from the U.S. Arms Control and Disarmament Agency [20]. In order to formulate relationships between military expenditures and certain dimensions of the

Defense expenditure in developing countries economy, it was first essential to gain some general idea of how these dimensions are interrelated. This was done through factor analysis [21]. In brief, the factor analysis patterned a number of variables into a smaller number of linear combinations-factors. For example, in the analysis of the economic data for our sample of developing countries, 33 variables were selected for the factor analysis. Seven factors were constructed out of these 33 variables. One can interpret this result as meaning 33 economic variables are simply manifestations of 7 basic factors which can only be measured indirectly by using mathematical constructions. When each factor is correlated with the 33 variables, some interesting interpretations of the likely association of the variables can be made. The extent of the correlation between each factor and each variable is indicated by the coefficients of these linear combinations, the factor loadings. The factors constitute different weightings of the original variables so as to explain as much as possible their variations. In all the factor analyses presented below, the program used specified that 99% of the variation in the selected variables be accounted for by the factors formed in the analysis [22]. An initial factor analysis omitted expenditure and measures of income (gross domestic product (GDP) and per capita GNP in an attempt to determine the main trends in the underlying economic data. Variables were selected to reflect aspects of the sample countries’ internal and external characteristics. The variables included those depicting: (1) External debt situation: Variables were chosen to reflect the historical evolution of the current debt situation, i.e. values for 1970 and 1982 were included. The variables selected for inclusion represented the actual levels of public indebtedness in both 1970 and 1982, the payment of interest and principal, debt service as a percentage of export earnings-the debt service ratio, and several measures of the debt/export situation in 1982-public debt/exports, public external borrowing commitments, and gross inflow of external loans. (2) Structural conditions: Here measures of the share of public and private consumption in GDP and the openness of the economy, as measured by the ratio of exports and resource balance to GNP were chosen. (3) Dynamic growth movements during the last decade: The rates of growth of exports, imports, and public and private consumption were included to depict the relative expansion of the sample countries in the recent past. (4) Balance of payments: The current account and terms of trade were included to complete the identification of external facets of the sample countries.

RESULTS-ECONOMIC

FACTORS

The results of the orthogonally rotated factor pattern (Table 1) indicated that 99% of the observed variance in the data set could be accounted for by seven factors. The factors were:

13

(1) Variables facilitating public consumption. These included gross inflow of public loans, external borrowing commitments and the resource balance. Clearly, the inflow of capital facilitated the expansion of public consumption for sample countries. In addition, it appears that the expansion of public consumption was at the expense of private consumption (as evidenced by the negative factor loadings for private consumption in 1960 and 1982). (2) Variables contributing to the absolute level of external public debt in 1982. The level of total public debt in 1982 is, as might be expected, a function of past inflows of public loans, past external debt and the current account deficit (as indicated by the negative loading). (3) Variables depicting the level of gross international reserves. The average maturity of the public debt is higher, the larger the amount of accumulated reserves; while the negative sign in the balance of payments indicates that the level of reserves may be in large part a result of external capital flows needed to supplement balance of payments deficits. (4) The relative share of public external debt to GDP. The factor has a relatively high loading for exports of GDP, as a percentage perhaps indicating credit-worthiness is a function of export orientation. (5) The growth in imports 197&1982. (6) Public external debt as a percentage of GDP in 1982. (7) Public external debt as a percentage of GDP in 1970. Forty countries were retained in the analysis-the remaining 56 having been eliminated from the factor analysis due to missing values. A logical case can be made that each of these factors could exert an influence on the overall level of military expenditure. In addition, several previous studies have indicated that a proxy for overall economic conditions is the GNP and this variable alone is sufficient to explain patterns of military spending. M. O’Leary and W. Coplin [23], for example, found a correlation coefficient of 0.88 between military expenditures and GNP for a sample of 19 Latin American countries for the period 1967-1971 [24]. In other words, if we know the level of the nation’s GNP, we can make an accurate guess to what the level of its military spending will be. If we guess that military spending is 3% of GNP, we will not be far wrong most of the time. Including total military expenditures and GDP as variables in the factor analysis (Table 2) indicated that both loaded on Factor 3-the level of international reserves-with a loading for military expenditures of 58. O’Leary and Coplin also noted that military expenditures as a share of GDP might be a more appropriate measure of the link between military spending and economic variables [25]. Replacing total military expenditures with military expenditures as a share of GDP slightly improved the factor loadings, with the highest loading being a 59 on Factor 4 (Table 3).

-4

91’ 86* 63* 62* -72* -82* -s3* 9 14 9 4 -5 -6 -5 I5 19 -4 -2 15 -8 -8 23 13 9 13 8 47 36 -8 50 -5 12 7 -15 21 94: 92* 90* 89* 86’ 85* 82* 80* 17s 73’ 39* -8O* 19 29 -18 -25 -9 -26 2 -11 -18 27 0

7

0 3 II 3

97f 96* 94* 92’

ConsumPtlo”

2 Factors contributmg to 1982 external debt

I

-13 -8 -9 IO -15 -16 21 11 20 13 10 10 14 5 19 23 21 25 -I 89’ 85* -48* -59* -29 -59s -6 -12 19 -7 -23

-5

-14 -13

3 Gross international reserves

*Note; All mihtary varmbles together with Gross Domestic Product and per capita income are omltted.

Gross inflow public loans/exports 1982 Pubhc debt/exports 1982 Resource balance as % of GDP 1982 Growth in pubhc consumption 197&1982 Public external borrowing commitments/exports 1982 Gross inflow public loans/GDP 1982 Public consumption as % of GDP 1982 Growth in pnvate consumption l97C-1982 Private consumption as % of GDP 1960 Private consumpiton as % of GDP 1982 Terms of trade 1982 Total public external debt 1982 Gross inflow pubhc loans 1970 Interest payments on external debt 1970 Repayment of principal on public loans 1982 Gross inflow pubhc loans 1982 Pubhc external borrowing commitments 1982 Interest payments on external debt 1982 Total public external debt 1970 Net inflow of public external loans 1970 Repayment of principal on external loans 1982 Growth in exports 197C-1982 Current account balance 1970 Gross international reserves 1982 Gross international reserves 1970 Average maturity of public external debt Current account balance 1982 Public external debt as % of GDP 1982 Exports as % of GDP 1982 Growth in exports 1960-1970 Public consumption as % of GDP 1960 Growth in imports 197kl982 External debt service as % of GDP 1982 Public external debt as % of GDP 1970

Variables

I

4

9 2

8 -2 55 I -16 -28 9 0 -1 -16 -11 1 -4 2 -14 -2 5 3 -6 -11 -5 5 10 26* IO 67’ 55* -I 5 20

-14

4 Share of I982 pubhc external debt m GDP

Factors

I. Orthogonally rotated pattern: (loadmgs) economic variables

Factors faclhtating public

Table

2

-11 IO 20 -18 -20 -15 29 34 30 -23 -17 31 5 -29 9 -7 -11 -22 -15 -22 I 20 71* -6 5

I

12 -11 -13 48

7 26

-4

5 Growth in exuorts

I

8 -13 -19 17 20 -7 2 12 28 18 38 -30 -19 37 -8 1 -7 I -43 0 12 0 -27 -18 -6 59* -I

-II -13 -5

-7

-9 -13

1982

SW”llX

6 External debt

5 I 2 23 -25 21 2 -44 -10 -5 -4 9 -10 -17 -11 -4 -8 IO 25 7 -16 IO 9 -6 23 21 17 21 -24 37 I 2 55’

-6

7 Pubhc external debt 1970

P

-_I

Variables

Average maturity external debt 1982 Current account balance 1982 Growth in exports 3960-1970 Public external debt as % of GDP 1982 Exports as % of GDP 1982 Public consumption as % of GDP 1960 Public consumption as % of GDP 1982 Net inflow public external loans 1970 Pubhc external debt 1970 Public external debt as % of GDP 1970 Growth in Imports 1970-1982 Growth in private consumption 197G-1982 Debt service as % of exports 1982 Growth m exports 197&1982

Gross inflow public loans 1982 Interest payment external public debt 1982 External public debt 1982 Repayment of prinnpal on public external loans 1970 Pubhc external borrowing commitments 1982 Interest payment on external public debt 1970 Repayment of principal on public external loans 1982 Gross in&w public loans 1970 Current account balance 1970 Gross inflow public loans/GDP 1982 Gross inflow public loans/ exports 1982 External public debt/exports 1982 Public borrowing (external) commitments/exports 1982 Resource balance as % of GDP 1982 Growth in public consumption 1970-1982 Private consumption as % of GDP 1970 Private consumption as % of GDP 1982 Temw of trade 1982 Gross mternational reserves 1982 Gross international reserves 1970 Gross Domestic Product 1982 Military expenditure 1981

^

9 8 -1; -3 34 41 -5 18 -1 34 39

-18 -17

0 12 3 17 -12 14 -25 2 40 16

66* 65* -85* 0 2 0

9 4 3

-8 3

0 46 3

-8 -11

-5

-1

97+ 92* 89. -51’ - 70’ -82’ -9

7 19 98’ 97* 97*

-5

3

- 50* -62* -1 -25 15 -10 4 9 IO -41 II -2 -24 21

-5 -19 -20 15 96’ 96’ 59’ 58*

-10

8 7 13 -3 -8 -7

0

2

0 6 2

4

0 5 81* 78* 77* 68; 67* -1 -9 12 -8 -3 3 19

-14 15

-12 -32 15 -8

-15

-3 -10

-10

14

83*

5

-4

-1

-2

89*

2 2 2

4 Pubhc consumptmn % of GDP

-5

-3 -6 -2

3 Mihtary expenditures

11

2

2 Growth in public consumptmn

92*

94*

99*

99s

1 Overall external debt

33 22 -5 0 -16 28 8 77* 68* 58’ -2 11 9 -5

2 9 13 18 -10

-1

0 0

3

0

0 -15 5 -22 -2 I5 -16 8 -13 8 77’ 66* -12 0

5 3 32 10 -9 17 11 -11 4 6

5

23 -7 -14 8

11 52 -10 2

22

0 28

-29

5

9

12 10 8

6 Growth in consumption

17

-11 -16

5 External debt 1970

Table 2. Obhque rotated factor pattern (standard regression coefficients): economic variables, total mditary expenditure, GDP 1

-23 21 -51 12 12 -8 0 6 -4 25 -20 10 62* -45*

-6 15 -9 -19 -21 19 5 -9 -16 -28

33 4 17 -7 -2 -5

0

4

0

6 16 9

Debt serwce 1982

8

5 9 w

E’

a E a

9 B

m

t8 s9* 89* 88* X8* s4* g2* -54f -6s -73* -8 0 -5 9 5 5 -8 -5 41 1 -11

-15* 0 2 0 0 9 3 17 -10 12 -18 5 39 -12 -15 12 0 6 -2 -12 0 36 43 -4 15 -2 2s

Net mflow public external loans 1970 External public debt 1970 External pubtic debt as % of GDP 1970 Growth in imports 1970-1982 Growth in private consumption 1970-1982 Debt service as % of exports 1982

7 8 -37 10 -2 21

-6 11 41 -7

7 15 12 -2 -7 -6 -6 2 -4 -34 -16 13 83* 83* 51* -43* -53* -1 12 -21 3 -8 6

3 3

1

-5

-3 13 7

-4 -1 0 5 5

1 1

1

-2

3 Gross domesttc product

1 IO

55* 37*

84* a2* 82* 81* If?+ 75* 61*

2 Growth in public consumption

Gross inRow public loans 1982 Repayment of principal pubhc loans 1982 Interest payments on external public debt 1982 External pubhc debt 1982 Public borrowing commitments (external) 1982 External public debt 1970 Gross inflow public loans 1970 Repayment of prmcipal external public loans 1982 Growth in exports 1970-1980 Current account balance 1970 Gross inflow public loans mcome 1982 Gross inflow public loans/exports 1982 External public debt/exports 1982 Public borrowing commitments/exports 1982 Resource balance/GDP 1982 Growth m public consumption 1970-1980 Private sector ~nsumpiton as % of GDP 1960 Private sector consumption as % of GDP 1982 Terms of trade 1982 Gross international reserves 1982 Gross international reserves 1970 Gross Domestic Product 1982 Average maturity of external debt 1982 Current account balance 1982 Export growth 1960-1970 Exports as % of GDP 1982 External public debt as % of GDP 1982 Public consumptron as % of GDP 1982 Public consumpiton as % of GDP 1960 Military expenditures/GNP 1981

Variables

1 External pubhc debt 1982

0

4 -3 12 -5 -3 -3

76* 65* 6S* 64* 64* 59*

I

2 22 --13 2 5 2 2 -24 0 -9 -25 II -3 4 -10 3

-3 -10

0 1

1 -5

Factors 4 Determinants of military expenditures

65* 56* 4s* -2 9 6

27 16 -7 -16 -4 8 23 35

I

10 -1 -9 2 ._ 1 2 2 0 -2 0 1 9 18 -5

8 0 13 43

-13

2

-9

7

-11

7 12f 61*

-13

- 14 8 I 5 4 3 31 0 -1 15 II -10 4 0 -15 5 -3 -23 -15 13 4

1

21

12 -27 10 8 21 -26 -7

6 Growth tn con.sumDtlon

Gross Domestrc Product

5 External public debt 1970

Table 3. Oblique rotated factor pattern (standard regression coefficients) economtc vanables, rmhtary expendnures-GNP.

60*

4 -6 24 -18 9

32 -35 13 -7 -2 -5 -6 12 -8 -34 -23 20 0 -~ 11 -16 -22 21 -39 19 20 3 -2 -15

7 2 17 10 4 1 3

7 External debt serwce 1982

-II -9 -20 -6

coefficlentsk

5 9

4 0

-9 -2 0

5

9 -5 -8 I3 26 I3 -7 0 I5

-7 -8

3

-I II 9 0 -2 loo* 94’ 71* 66* 49* -69* -8 -6 -4

-3

-12

2 DemographIc growth

reeression

1 Measure of level of develooment

(standard

Political nghts mdex (Gastil) maxImum shift 1973-1979 Civil rights mdex (Gastll) maximum shift 1973-1979 Potential separatism, % involved, 1975 Potenttal separatism, mtenslty, lY75

factor oattern

x9* 88* s7* 84* 82; 82* 67’ 60* -52* -91’ -93* I4 20 -2 -24 -18 31 0 IO I6 3 4 I4 -6 -18 -12 -14 -5 0 -28 -9 -22 24

rotated

Percentage of labor force in industry in 1960 Urban population as % of total POP 1982 Urban population as % of total POP 1960 Percentage of labor .force m mdustry 1982 Percentage of labor force m services 1982 Percentage of labor force m services 1960 Number enrolled m higher education as % age group 1960 Number enrolled in higher education as % age group 1982 Organized labor as % total labor force Percentage of labor force in agriculture 1960 Percentage of labor force in agruxlture 1982 Growth of population 196&1970 Growth of labor force 196@1970 Growth of labor force 197&1982 Growth of population 197&1982 Growth of urban population 1960-1970 Percentage of populatmn working age 1960 Percentage of male students primary school age 1982 Percentage of total students primary school age 1982 Percentage of female students primary school age 1982 Percentage urban population m largest city 1982 Percentage urban population in cities over 500,000 Percentage urban population in largest city 1960 Pohtlcal rights index (Gastll) mean 1973-1979 CIVII rights index (Gastil) mean 1979 Pohtical discrimination, % discriminated against 1975 Economic discrimination, % discriminated against 1975 Economic discrimination, intensity of discrimmatlon 1975 Pohtical dlscnmination, intensity of dlscrimmatlon 1975 Civil rights index (Gastd) 1979 Number of cities over 500,000 1961 Number of cities over 500,000 1982 Total mihtary expenditure (ACDA) 1981

Variables

Table 4. Obliaue

^

21 -9 -9 -7

If -6 I9 4 -3 -I

I

-If I7 -7 96* 95* 87* 0 8 -9 8 4 -26

-3 I3 -3 -10 28 -4 -2 26 27 5 -15 -10 -II

3 % Age group m primary school 3 4

0 -5 -I -18

-13 -23 I3 -4 3 -6

8

-I -9 26 30 -I 23 -10 8 -2 23 -18 -19 27 2 0 0 2 89* 19* 19* 3 24

-15

4 Urbanization

nolitical-social-demoeraohx

-6 -13 -2 9

5 I4 0 96* 68* 5 -14 -II 6 -21 -8 -8 19

-3

-9 10 0 6 -4 -9 -3 I4 -13 22 I -14 -12 6 14 I3 -I 6

exoendltures.

-17 -13 -26

9

-8 -15 -21 -I -II I5 30 -19 5 I3 -I II -4 -II I4 0 -15 -4 6 0 -8 6 5 -18 78* 71* 5x* 54; -27 -15 -12 - I9

9

6 Political economx dlscrlmmation

total mihtarv

5 Political civd rights mdex

variables.

5 3

-6

-3 0

0 27 9 -10 9 I5 -2 I2 -3 -II -3 0 7 -3 -I -20 21 -23 -8 -13 -22 -19 0 3 -18 96* 96* -35*

-14

-8

-17

500.000

OVU

I C&s

samole

x4* 8* -7 0

IO 9 0 -20 9 -8 -33 -13 29 -1 -II 25

-II 24 -24 -21 IO 19 3 I2 8 4 6 -8 I2 22 26 4 4 4

0

8

8 Maximum shift political civil rights index

total countrv

-II -3 84* 74*

-6 I3 6 -34 -21 23 0 -6 -2 -9 -2 0 -17 0 3 -12 -2 -21 -10 0 -5 -2 4

0

-1: -23 -13 I8 0 -8 9

^

9 Potential seuaratism

Variables

factor pattern

regression

60* -48’ -89’ -89* I3 18 -I -22 -22 32 II 4 16 1 I3 2 -4 8 -15 -12 -12 -6 -3 -2 -16 -12 -10 -26 -24 -10

88* 87* 87* 82* SO* 76% 701 62*

I Measure of level of development

(standard

Percentage aged 20-24 m higher education 1982 Organized labor as % labor force Percentage labor force in agriculture 1960 Percentage labor force in agriculture 1982 Growth in population 196&1970 Growth in labor force 1960-1970 Growth in labor force 197tS-1982 Growth in population 197CLl982 Growth in urban population 196Sl970 Percentage of population working age 1960 Percentage of total age group m pnmary school 1982 Percentage of male age group in primary school 1982 Percentage of female age group in primary school 1982 Percentage of urban population in largest city 1982 Percentage of urban population in largest city 1960 Percentage of urban population m cities over 500,000 Political rights index (Gastil) mean 1973-1979 Civil rights index (Gastil) mean 1973-1979 Political rights index 1979 score Political discrimination, % dwriminated against 1975 Economic discnmmation, % discnminated agamst 1975 Economic discrimination-intensity of discrimination 1975 Political discrimination-intensity of discrimination 1975 Number of cities over 500,000 1960 Number of cities over 500,000 1982 Political rights index (Gastil) maximum shift 1973-1979 Civil rights index (Gastil) maximum shift 1973-1979 Civil nghts index (Gastil) 1979 score Potential separatism % mvolved 1975 Potential separatism mtenslty of involvement 1975

Urban population as % of total population 1960 Percentage of labor force in industry 1960 Urban population as % of total population 1982 Percentage of labor force in industry 1982 Percentage of labor force in services 1960 Percentage of labor force in services 1982 Percentage aged 20-24 in higher education 1960 Military expenditures as % GNP (Yale) 1978

Table 5. Oblique rotated

3

”

-13 23 -8 94; 93’ 87* 0 -9 9 5 -7 I -23 I 16 -5 0 -5 24 -8 18 -2 0

0

2

1 loo* 92* 72* 68* 46* -7o* -4 -4 -3 0 -7 9 -7 -52 I2 -7 -7 14 25 -4 3 3 -12 I5 -5 -2 -1 -7

29 23

^ L

1: -10 -3 35 -6 -19

-3

3 % Age group in primary school

^

-2 24 -10 -10 -5 21 -18 -21 25 I 0 0 2 90f SO* 79* 2 0 24 4 8 -9 -22 -7 0 -3 -8 12 -I -20

27 -8 28 0

-;

-18

3

0

9 7

-7 -8 -3 -10 -21

-18 -10

0 9

7

ii;*

I9 -15 10 IO -12 -9 4 11 16 -I 0 4 -5 4 -I 14 96* RF

-8 -3 -2

-I -10

-3 -19 79 74* 55* 52* -20 -16 -2 -17 -25 -6 -18

-10

36 -15 4 4 0 10 -8 -12 14 0 -4 -15 5 1

-16 I2 -10 -28 -11 0 13 0 4

0

93’ 93* -6 -3 -19 5 -3

^ :

-I I4 -1 -16 0 0 -5 4 -3 -21 -24 21 -9 0 -15 -24 21

I3 -15

0 -8 25 -10

-9

-18

500,000

OVU

7 Cities

sample

3

4 -10 -32 -16 -4 -10 86: 78* 32* -21 -7

-7 -17

-9 11 20 28 6 8 4 -9 -10 8

;

-1 24 -23 -28 I3 25

-12

8 Maximum shift pohtlcal awl rights mdex

as % GDP, total country

6 Political economic discrimination

defense expenditures,

5 PolitIcal cwil rights mdex

variables,

4 Urbanization

pohtlcal-soaal-demographic

10 3

7 3 0 30

-5

-14

0

2 Demographic growth

coefficients):

^ -35 -15 I8 0 0 -2 -7 -3 -16 -3 -I 7 4 -7 3 -38 -15 -3 2 -15 -6 2 81* 79;

-4 I4

3 7 0

9 -13 -13 -3 16 -8 40

-20

9 Potential separatism

rotated

factor pattern

population in largest city 1960 % urban population in largest city 1981 % urban population in cities over 500,000 1980 Political rights index (Gastil) mean 1973-1979 Civil rights index (Gastil) mean 1973-1979 Political rights index (Gastil) 1979 score Political rights index maximum shift 1973-1979 Civil rights index maxunwn shift 1973-1979 Civil rights index 1979 score Potential separatism, intensity of involvement 1979 Intensity of economic discrimination 1975 % politically discriminated against 1975 % economically discriminated against 1975 Intensity of political discrimination Number of cities over 500,000 1980 Number of cities over 500.000 1960

% urban

1981

(standard

Urban population as % of total 1982 Percentage of labor force in industry 1960 Percentage of labor force in industry 1982 Urban population as % of total 1960 Percentage of labor force m service sector 1982 Percentage of labor force m service sector 1960 % of age group enrolled in higher education 1960 % of age group enrolled in higher education 1982 Potential separation % involved 1975 Organized labor % total labor force 1975 % labor force in agriculture 1960 % labor force in agriculture 1982 Growth in labor force 1970-1982 Population growth 196&1970 Population growth 197&1982 Growth in labor force 196&1970 Growth m urban population 196&1970 % population working age 1960 % male age group m primary school 1981 % total age group in primary school 1981 % female age group in primary school 1981 Defense expenditures/total government expenditures

Variables

Table 6. Obhque

regresslo”

0 15 8 -3 6 -24 1 -7 -21 -25 -3 -7 -12 -6 -18 -6

81* 79’ 76’ 75* 751 66’ 58’ 58* -35* -45s -75% -83* 9 6 -17 12 -26 29 3 13 17 14 8

0

6

-4 II -5 -5 -2 20 -14 -4 -14 IO -8 -6 1 -I -8

0

-4 I3 -5 -9 5 8 -4 X3* 74* 69* 59* 45* -69* -8 -5 -5 8

-3

-II

2 DemographIc growth

variables,

4 0 I2 8 -14 0 31 -2 20 -20 I6 -16 I6 -16 -4 2

IO 0 -2 0 I2 -7 24 -I -16 31 I -6 3 -16 -31 -18 4 -2 85* 84* 71* -39

3 % age group in prunary school

political-social-demographic

I Measure of level of development

coefficients):

0

19’ 73’ 62* 9 6 42 -12 -11 I9 -22 -9 3 -I 23 -2 -8

0 I2 -4 25 -16 23 -9 24 -8 4 -4 2 -9 21 8 0 6 5 5 28

-21

4 Urbanization

defense expenditures

2

-I 25 88* 82; 52 7 -30 -34 I3 -10 I7 2 -7 -5 -3

7

-12 22 -7 20 -6 9 -2 5 -19 5 -22 9 -4 3 1 -4 I6

-I

9 0 3

1973-179

SCOE

5 Political civd rights

-5 -II 13 -10 -6 -18 70* 55’ 411 371 -54 I -6 -39 7 0

-2 II 23 -12 -II -14 11 1 I7 I5 7 0 -16 19 4 I2 42 20 12 8 3 37

6 Political civil rights shift 1960-1971

5 -12 11 0 -I 4 0 -9 -17 45 80* 16* 41 -9 -13

-2

-9 -16 -18 -7 -10 -9 32 23 -6 -5 5 I6 -12 6 -5 18 6 II -5 4 IO I5

I Political discrimination activity

as % of total current budget, total country

sample

2

-19 -15 25 -1 2 -15 0 -I -17 -8 0 -13 -1 -6 84’ 82*

I

0 -12 -3 14 2 I -7 IO 12 0 -4 -9 6 -8 0 6 -4 -3

-17 -5

8 Cities over 500,000

80

ROBERT E. LCONEY RESULTS-POLITICAL, SOCIO-ECONOMIC VARIABLES

To test whether political and social variables were more highly correlated with military expenditures, a similar factor analysis was performed using a set of selected variables from the Yale Data Set [26]. The variables reflected: (1) Popular participation and government restraints-here various measures of political nghts index, civil rights index, political discrimination, economic discrimination, potential separatism, voter turnout, partly factionalization and organized labor were included, (2) Sectoral composition of the labor force and growth of the population, and (3) Measures of urbanization and school enrollment and literacy. Since the political literature is not clear as to the most appropriate measure of military expenditure to correlate with the social, political and demographic variables, three measures of military expenditures were factor analyzed: total military expenditure, military expenditure as a percentage of GNP and military expenditure as a proportion of the government budget. In general, the results were quite poor. The loadings for each measure of military expenditure were much lower than those obtained for the total sample of countries using the economic variables set. The factor analysis performed without military expenditures produced 9 factors: (1) Measures of the level of developmentvariables reflecting the share of the work force in industry, proportion of the population in higher education, the share of the population in industry, etc.; (2) Measures of demographic growthvariables reflecting the growth in the labor force, growth in overall population, and growth in urbanization. (3) The percentage of the population of primary school age attending school; (4) Urbanization-variables indicating percentage of the population in the largest city and in cities over 500,000; (5) Political-civil rights index-variables for which a positive value indicates a growth in freedom and a negative value indicates a growth in repression (index computed annually by Raymond Gastill); (6) Politicaleconomic discrimination-the scope and intensity of discrimination in these areas; (7) The number of cities over 500,000. (8) The maximum shift in political&vi1 rights index from 1973-1979-maximum shift representing the largest change from lowest to highest score, or vice versa; (9) Potential separatism-the relative sizes and intensities of separatist movements; As noted, total military expenditures loaded quite weakly on the political, social and demographic variables (Table 4), with the highest loading of 35 on

Factor 7, the number of cities over 500,000. Clearly, the number of cities over 500,000 should be associated with certain measures from the economic data set. AS noted above (Table 2), military expenditure loads more heavily at 58 on GDP. Adding total population to the economic data set should (along with the GDP) pick up all of the effects that Factor 7 in the political, social and demographic index implies for military expenditures. Defense expenditures as a percentage of GNP loaded most heavily on Factor 1, general measures of the level of development (Table 5). Again, this measure can be depicted as well with per capita income as with the various labor force-sectoral occupation measures found in this factor. Therefore, per capita income was added to the economic data set for regression analysis. It should be noted that military expendituresmilitary expenditure as a percentage of the total government budget-loaded most heavily at - 39 on the percentage of studies in their age group in primary schools (Table 6). This result is somewhat hard to explain. Are military expenditures a residual after schooling (and associated expenditures), the higher share of education, the lower share of money left for defense? A regression of the share of government expenditures allocated to education showed no statistically significant correlation. CONCLUSIONS

To summarize, the consistently low loading of military expenditures on political-social and demographic variables is evidence that these variables of and by themselves are of limited use in accounting the observed patterns of defense expenditure. If, in fact, these variables impact on military expenditure, the data presented here indicates that this impact manifests through economic variables. In general, the above results indicate that economic variables should not be overlooked when trying to explain observed patterns of military expenditures in the Third World. Further research is necessary to determine if this is a generalized relationship which covers all developing countries. What is clear though is that any credible forecast of future military expenditures should consider not only the usual threat analysis or arms race patterns but and perhaps more importantly also the financial capability of the country. REFERENCES 1, E. Benolt. Growth and change m developing countries. Econ. Dev. Cult. Change 26, 271-280 (1978).

2. S. Nawaz. Economic impact of defense expenditures. Fin. Dev. 20, 34 (1983). 3. S. Chan. The impact of defense spending on economic performance: a survey of evidence and problems. Orbm 29, 403434 (1985). 4. Ibid., p. 433. The topic remains one of current interest however. See R. Looney and P. C. Frederiksen. Defense expenditures, external public debt and growth in developing countries. J. Peace Res. 23, (1986). To be published. 5. K. L. Rimmer. Evaluating the policy impact of military regimes in Latin America. Lat. Am. Res. Rev. 13,4142 (1978).

Defense

expenditure

International stratification and Third 6. S. Neuman. World American arms producers. Znt. Org. 38, 16761697 (1984). Profiles of current I. R. Looney -and P. C. Frederiksen. Latin American arms producers. Znt. Org. 40, 745-752 (1986). linkages, and 8. K. Hill. Domestic politics, international military expenditures. Stud. Comp. Znt. Dev. p. 53. (1978) This article provides an excellent summary of the literature prior to 1978. and their reduc9. A. H. Westing. Military expenditures tion. Bull. Peace Propo&ls~pp. 2429 (1978). 10. L. J. Griffin. M. Wallace and J. Devine. The political economy of military spending: evidence form the United States. Camb. J. Z&on. pp. l-14 (1982). The arms race and military Key11. J. M. Treddenick. nesianism. Can. Publ. Policy p. 78 (1985). 12. Ibid., p. 77. The determinants of 13. A. Maizels and M. Nissanke. military expenditures in developing countries. Discussion Paper 85-18, Dept. of Political Economy, Univ. of London. 14. Ibid., pp. 22-23. of defense expenditure in 15. G. Harris. The determinants the ASEAN region. J. Peace Res. pp. 4149 (1986). 16. Ibid., p. 41. 17. The World Bank data consists of the entire statistical supplement to its World Development Report 1984.

in developing

18.

19.

20.

21.

23.

24. 25. 26.

countries

81

OUP, New York (1984); plus data for 1975 contained in its World Development Report 1978. OUP, New York (1978). The IMF data consists of government expenditures by type and is taken from the International Monetary Fund Government Financial Statistics Yearbook. IMF, Washington (1983). Charles Taylor and David Jodice. World Handbook of Political and Social Indicators, 3rd edn, Vol. Z, Cross National Attributes and Rates. Yale University Press, New Haven 1983. The entire data base was nut on-line for analysis. U.S. Arms Control and Disarmament Agency, World Military Expenditures and Arms Transfers, 1975-82. ACDA, Washington, DC (1984). All of the empirical findings in the present study were uroduced bv using Statistical Analysis System (SAS). see SAS User’s Glide: Statistics, 1982 Edn. SAS. Ins& tute, NC (1982) Cary, for a description of the factor, regression, and discriminant analysis procedures. Michael K. O’Leary and William Coplin (Eds), Explaining military expenditures in Latin America. In Quantitative Techniques in Foreign Policy Analysis and Forecasting, pp. 112~142. Praeger, New York (1955). Ibid., p. 115. Zbid., p. 116. Taylor and Jodice, op. cit.