1. Trends in military spending

Military spending in developing countries has increased substantially in real terms over the past three decades. Spending surged in the 1960s and then again in the 1970s. ^2/ Military spending in developing countries during 1960-86 grew on average almost three times as fast as in the industrial countries and quintupled in real terms during the period (Table 1). Developing countries now account for a much larger proportion of worldwide military spending. Their share of military spending in 1986 was almost three times as great as in 1960 (see Table 2).

Table 1.

Percentage Change in Real Military Expenditures ^1/

Source: Sivard (1987), p. 42.

^1/Percentage changes are based on data expressed in 1984 prices and in general converted to dollars at three-year average exchange rates (1982-84).

Table 1.

Percentage Change in Real Military Expenditures ^1/

Years	World	Industrial Countries	Developing Countries
1960-65	22.0	19.9	50.0
1965-70	24.2	20.5	63.9
1970-75	9.2	2.2	64.4
1975-80	13.0	12.4	15.5
1980-85	24.5	22.0	36.6
1960-86	139.1	107.5	562.5

Source: Sivard (1987), p. 42.

^1/Percentage changes are based on data expressed in 1984 prices and in general converted to dollars at three-year average exchange rates (1982-84).

View Table

Table 1.

Percentage Change in Real Military Expenditures ^1/

Years	World	Industrial Countries	Developing Countries
1960-65	22.0	19.9	50.0
1965-70	24.2	20.5	63.9
1970-75	9.2	2.2	64.4
1975-80	13.0	12.4	15.5
1980-85	24.5	22.0	36.6
1960-86	139.1	107.5	562.5

Source: Sivard (1987), p. 42.

^1/Percentage changes are based on data expressed in 1984 prices and in general converted to dollars at three-year average exchange rates (1982-84).

Table 2.

Military Expenditures in Developing Countries as a Percentage of World Military Expenditure ^1/

Source: Sivard (1987), p. 42.

^1/Percentages are based on data in 1984 prices and in general converted to dollars at three-year average exchange rates (1982-84).

Table 2.

Military Expenditures in Developing Countries as a Percentage of World Military Expenditure ^1/

Year	Percentage of World Total
1960	7.0
1965	8.6
1970	11.3
1975	17.0
1980	17.4
1985	19.1
1986	19.3

Source: Sivard (1987), p. 42.

^1/Percentages are based on data in 1984 prices and in general converted to dollars at three-year average exchange rates (1982-84).

View Table

Table 2.

Military Expenditures in Developing Countries as a Percentage of World Military Expenditure ^1/

Year	Percentage of World Total
1960	7.0
1965	8.6
1970	11.3
1975	17.0
1980	17.4
1985	19.1
1986	19.3

Source: Sivard (1987), p. 42.

^1/Percentages are based on data in 1984 prices and in general converted to dollars at three-year average exchange rates (1982-84).

It has been suggested that this rise in developing country military expenditures can be partly explained by the increase in external conflicts in developing regions, the amount of protection necessary for the domestic ruling elites, the influence of the military in the political realm, and the increase in the cost and maintenance of weapon systems and the armed forces. ^3/ Also contributing to the explanation is the near tripling of the number of independent states in the developing world since the 1950s. Many of these countries, and especially those in Africa, lacked military establishments at the time of independence. As each new country allocated resources to create an armed force, military spending for developing countries surged. The decline in military grants to developing countries that began in the late 1960s could also help explain this rise in recorded military spending. To the extent that military aid went previously unrecorded in the budgets of recipient countries, the cessation of aid forced these countries not only to finance their own military operations but also to more clearly reflect these expenditures in their budgets.

The growth in military spending by developing countries has been distributed unevenly both across regions and in countries within regions. Table 3 displays regional differences in the growth of military spending. ^4/ The jump in Middle East expenditures in nominal terms during 1970-75 is striking; spending increased by 152 percent, outpacing the average rate of growth in developing country military spending by 112 percentage points. However, in 1975-85, the rate of growth of military spending of Middle Eastern countries dropped off sharply to less than 10 percent, while most other developing country regions outpaced the average developing country rate of growth in military spending. Whereas developing country military spending increased by 12 percent over this period, spending in Africa increased by 39 percent, in East Asia by 17 percent, and in Latin America by 26 percent.

Table 3.

Percentage Change in Real Military Spending by Region, 1965-85 ^1/

Source: United States Arms Control and Disarmament Agency (1988).

^1/Percentage changes are based on data in U.S. dollars. Note that for 1965-70 data are in 1973 prices and converted from local currencies to U.S. dollars by 1973 exchange rates. Data for 1970-75 are in constant 1978 U.S. dollars and are converted from local currencies by 1978 exchange rates. The data for 1975-85 are in constant 1984 prices and converted from local currencies to U.S. dollars based on 1984 exchange rates.

^2/For 1965-70 data are for OPEC countries rather than the Middle East region.

Table 3.

Percentage Change in Real Military Spending by Region, 1965-85 ^1/

		1965-70	1970-75	1975-80	1980-85
World		20.6	11.6	10.1	17.4
Developed economies		17.1	5.6	9.6	19.0
	Europe	14.1	18.7	8.1	7.4
	North America	22.4	-15.4	9.7	42.2
Developing economies		48.9	40.5	11.9	11.3
	Africa	79.2	25.5	38.8	-5.6
	East Asia	49.3	13.8	16.5	15.3
	Latin America	34.3	40.8	25.6	15.7
	Middle East	95.6 2/	152.1	8.6	8.4
	Oceania	10.2	-3.1	16.7	28.6
	South Asia	-1.5	32.4	27.8	49.3

Source: United States Arms Control and Disarmament Agency (1988).

^1/Percentage changes are based on data in U.S. dollars. Note that for 1965-70 data are in 1973 prices and converted from local currencies to U.S. dollars by 1973 exchange rates. Data for 1970-75 are in constant 1978 U.S. dollars and are converted from local currencies by 1978 exchange rates. The data for 1975-85 are in constant 1984 prices and converted from local currencies to U.S. dollars based on 1984 exchange rates.

^2/For 1965-70 data are for OPEC countries rather than the Middle East region.

View Table

Table 3.

Percentage Change in Real Military Spending by Region, 1965-85 ^1/

		1965-70	1970-75	1975-80	1980-85
World		20.6	11.6	10.1	17.4
Developed economies		17.1	5.6	9.6	19.0
	Europe	14.1	18.7	8.1	7.4
	North America	22.4	-15.4	9.7	42.2
Developing economies		48.9	40.5	11.9	11.3
	Africa	79.2	25.5	38.8	-5.6
	East Asia	49.3	13.8	16.5	15.3
	Latin America	34.3	40.8	25.6	15.7
	Middle East	95.6 2/	152.1	8.6	8.4
	Oceania	10.2	-3.1	16.7	28.6
	South Asia	-1.5	32.4	27.8	49.3

Source: United States Arms Control and Disarmament Agency (1988).

^1/Percentage changes are based on data in U.S. dollars. Note that for 1965-70 data are in 1973 prices and converted from local currencies to U.S. dollars by 1973 exchange rates. Data for 1970-75 are in constant 1978 U.S. dollars and are converted from local currencies by 1978 exchange rates. The data for 1975-85 are in constant 1984 prices and converted from local currencies to U.S. dollars based on 1984 exchange rates.

^2/For 1965-70 data are for OPEC countries rather than the Middle East region.

Even within regions, military spending has not been evenly distributed among countries. Africa is especially characterized by uneven spending patterns across countries. For the continent as a whole, military expenditures increased in real terms by less than 50 percent on average between 1965 and 1980, but rose by some 300 percent in a subset of 1A African countries. ^5/ For several of these countries (Algeria, Libya, Gabon, and Tunisia), earnings from oil exports provided additional resources that were allocated to the military; for others (Morocco, Zimbabwe, South Africa, Ethiopia, and Tunisia), internal or external threats or conflicts resulted in greater military spending. ^6/

2. Data sources and problems

The preceding discussion of trends in military spending is based on several sources of military data often criticized for their unreliability and inconsistency. Because we will use these data sources in Section III to analyze how military spending is affected by Fund-supported adjustment programs, it is appropriate to review some of these criticisms. ^7/

Researchers have expressed skepticism about military data published by governments because they suspect that national governments have reason to hide the true figures. For example, foreign governments may interpret the news of another country’s increase in military spending as an act of aggression while the country’s citizens may protest that government resources should be allocated to social policy programs. ^8/ Researchers have tended to rely on data published by the following organizations: United States Arms Control and Disarmament Agency (ACDA), the Stockholm International Peace Research Institute (SIPRI), the International Institute for Strategic Studies (IISS), and the Fund (in its Government Finance Statistics (GFS)). Even though these organizations attempt to be reliable, the data they provide can be only as reliable as those released to them by national governments.

In the analysis of Section III we utilize three of the four sources of data: ACDA, GFS, and SIPRI. By using various data sets, we were able to verify the sensitivity of qualitative results and judgments as regards the effect on military expenditure of data set variations. Note, however, that each source has slightly different definitions of military expenditure, and produces the data in slightly different formats, which one should keep in mind when reviewing the empirical results. Appendix I provides detailed descriptions on the various definitions of military expenditure.

The ACDA and SIPRI collect data from the individual country’s ministry of defense and are therefore unable to impose any cross-country consistency on how military expenditure is defined. GFS data are based on a standardized reporting format to which all countries are supposed to adhere. Other explanations for the differences among these data sources are related to timing procedures (whether the data are recorded on a fiscal or calendar year basis), deflation methods, and currency conversion methods. ^9/

Again, all data sources have one problem in common: reported military expenditures may not represent the true expenditures. Nicole Ball has identified five reasons for the discrepancy. ^10/

a. Double bookkeeping. Governments may keep two sets of budget accounts, one for internal purposes, the other for external purposes.

b. Extrabudgetary accounts. Military spending is funded by sources that are omitted from the national government budgets, as are the outlays they finance.

c. Highly aggregated budget categories, including for military spending. Military-related outlays can be easily hidden in other budget categories and may not be traceable.

d. Military assistance. Particularly when military budgets are highly aggregated, military aid to and from foreign governments is not necessarily reported in expenditure and financing accounts.

e. Foreign exchange manipulation. A portion of foreign exchange that is earned by a country through exporting is not reported in the budget. These funds can then be used to purchase goods, such as military items, that would go unreported.

Conclusive evidence on the frequency and importance of any of these methods of underreporting military expenditure is difficult to obtain; it would be even more difficult to attempt to correct the published data on the basis of such evidence. However, one should bear these issues in mind when analyzing the data; they clearly limit the reliability of any empirical results.

1. Introduction

Fund-supported stabilization programs in the form of stand-by arrangements or extended Fund facilities primarily focus on achieving medium-term balance of payments viability. To the extent that nonsustainable external current account deficits tend to reflect fiscal imbalances, they generally involve reducing the government’s budget deficit, often through expenditure restraint, given limits on the feasibility and desirability of increased taxes.

Even though stabilizing fiscal policy, including expenditure restraint, is central to many Fund-supported adjustment programs, it is not central to all. Particularly when a country has experienced disequilibria for long periods of time, as well as a greatly overvalued exchange rate and the quasi-disappearance of foreign financing, a forced and disorderly adjustment of government expenditures may have occurred long before the Fund’s involvement. In such cases, Fund-supported adjustment programs are likely to emphasize adjusting relative prices, restructuring production, and rehabilitating the administrative and economic infrastructure, which, in addition to promoting domestic activity, will spur new foreign investment from bilateral, multilateral, and private sources. Under such catalytic programs, both government expenditure and the deficit could temporarily rise, largely reflecting the greater availability of foreign financing, but also possibly additional domestic resources.

If, however, governments must restrain spending to achieve balance of payments objectives, there is unlikely to be any obvious economically, socially, and politically optimal way of implementing such a policy. Limiting capital, rather than current, expenditure is often expedient but could prove detrimental to long-term growth. On the other hand, some current expenditures may be more “productive” than many capital projects, particularly if (in the case of maintenance expenditures) curtailment today could contribute to the future deterioration of the economic infrastructure.

The above discussion underscores how difficult it is to evaluate the impact of Fund-supported programs on government military spending. Indeed, evaluating the implications of Fund-supported programs for public expenditure in general is a complicated issue likely to involve a broad range of considerations and particular circumstances. Yet, we seek to explore the question one step further; to the extent that Fund-supported adjustment policies affect government spending, how have they influenced military spending? In the following section, a formal conceptual framework is developed to guide to the subsequent empirical analysis.

2. A conceptual framework

We will assume that, in the short run, the government can be viewed as having a preference ordering between “security,” y₁, “other public services,” y₂, and “burden on private sector,” occasioned by inflationary finance or crowding-out, for which the size of domestic (bank and nonbank) financing, D_d, is a reasonable proxy. ^11/ This preference ordering results in a utility function

with the following partial derivatives: U₁ > 0, U₂ > 0, U₃ < 0; an increase in D_d is viewed as bad, and hence lessens welfare.

A complex interaction of political, economic, and strategic considerations is likely to contribute to the amount a country chooses to spend on the military. All interact with each other at the national, regional, and global levels. They may include such factors as the perceived or actual domestic or external security risk, ^12/ the nature of the political regime (military or quasi-military rule vs. civilian government, repressive vs. liberal), ^13/ the existence of a local arms industry, the existence and degree of regional conflicts or insecurity, and adherence to a global power bloc. The level of development might be a factor, to the extent that increased per capita wealth may require greater protection from external and domestic threats.

The degree of urbanization may also be a factor. The protection requirements of a country appear to be directly related to the level of infrastructural development associated with large cities. Urbanization also contributes to a country’s potential for external and domestic conflict, and hence to protection needs, both because it introduces greater complexity in individual’s interactions and because it renders existing inequalities between socioeconomic groups more transparent and visible.

For the purpose of the model we assume that “security”, y₁, will depend for any given level of defense expenditure undertaken, x₁, on a vector of such variables, S, which are defined as negatively related to y₁, ceteris paribus. For example, other things being equal, more defense expenditures, x₁, yield higher security, y₁ but for a given level of defense expenditures, the security obtained depends negatively on the extent of a security threat, the level of income per head, the degree of urbanization, etc. ^14/

Assuming for simplicity a one-to-one correspondence between “other public services,” y₂, as it appears in the utility function (1) and actual expenditure, x₂, on such goods, the relationships (2.a)-(2.c) are postulated

Expression (2.a) can be viewed as a “production function”; the signs of partial derivatives (indicated in parenthesis) imply that, other things being equal, more defense expenditures, x₁, yield higher security, y₁, but that for a given level of defense expenditures, the security obtained depends negatively on the variables in S. Expression (2.c) is the government budget constraint where revenue, R, and foreign financing, D_f, are for the moment assumed to be exogenous.

In the absence of a Fund-supported program, the variables x₁, x₂ and D_d can be viewed as endogenously determined, in that they are objects of policy choice. After y₁ and y₂ in (1) are replaced by their expression from (2.a)-(2.b), utility maximization subject to (2.c) is postulated to yield a set of expenditure functions such as ^15/

and a domestic financing choice function

implying, given (2.c), an aggregate expenditure function

Again, the signs of partial derivatives are indicated in parentheses; note, in particular, that (a) higher values of S, imply both an increase in military and total expenditure, but a decline in other government spending; and (b) higher revenue and/or foreign financing should lessen the demand for domestic financing. However, aggregate government expenditure demand should be positively related to R + D_f;

i.e., $\text{1+}\frac{\partial {\text{D}}_{\text{d}}^{*}}{\partial {\text{R+d}}_{\text{f}}}>0$.

The government’s expenditure choice decision in the presence of a Fund-supported program is often affected by the fact that domestic financing D_d (especially, but not exclusively, in the form of domestic bank resources) is no longer a choice variable, but rather exogenously constrained at some binding level D_d < ${\text{D}}_{\text{d}}^{*}$ (). ^16/ In this case, utility maximization along the lines detailed earlier, yields

Note that the system (4.a)-(4.d) can always be approximated around the unconstrained case (3.a)-(3.d) to find, in particular

The above re-writing of constrained demand functions in the presence of a Fund-supported program clearly illustrates the link between the two “regimes”: total expenditure will be reduced by the full difference between ${\text{D}}_{\text{d}}^{*}\left(\right)$ and D_d, but the impact of defense expenditure will depend on the size of the partial derivative ∂x₁/∂D_d, which is a measure of how defense expenditures would share in the adjustment imposed by D_d < D^*().

The above discussion implicitly assumes that developments in revenue and foreign financing can be viewed as independent of whether a Fund-supported program is or is not in effect. Clearly, they are often not independent. Even as a share of GDP, government revenue could tend to increase systematically under a Fund-supported adjustment program either because of specific revenue mobilization efforts or because of the positive effect that macroeconomic policies pursued in the context of the program could have on government revenue. Perhaps, more important, and as indicated earlier, foreign financing availability could be strongly correlated with the implementation of a Fund-supported adjustment program, particularly catalytic programs that stress economic rehabilitation. This would relieve the constraint on expenditures imposed by D_d < ${\text{D}}_{\text{d}}^{*}$ () in (5.a)-(5.b) in the sense that R + D_f in (5) would actually turn out to be larger than in (3), and allow total and military expenditure, to expand; at the limit, the effect of these additional nondomestic financing resources could be dominant.

The conceptual framework described here suggests that both the structure of government expenditure functions and their stability are likely to be critically affected not only by whether or not a Fund-supported program is in effect, but also by the nature of this program, particularly its implications for government revenue and external resources. Thus, simple econometric specifications are not likely to perform well, while with more complex ones, the limited number of time series/cross-section observations becomes a problem. Accordingly, we propose an alternative methodological approach in Section III.3, which nevertheless follows the spirit of the above conceptual framework.

One might also add that other characteristics of military expenditures may make them less susceptible to sudden changes, particularly within the relatively short duration of a Fund-supported program. Military expenditures involve acquisition of new and relatively expensive weapons systems and technologies as well as the training of new personnel. Both activities may require a long period of implementation. Military supply pipelines, especially when they are in the form of purchases from one of the major national suppliers of arms, are often quite long. Training schedules may be very complex and prolonged, for technical and political reasons. This particularly applies to the more advanced technologies or forms of equipment that are either highly complicated to operate or that are politically sensitive. Once committed to such expenditures, countries may find it difficult to reverse decisions, even in the context of a Fund-supported program. Finally, developing countries are often not in a position to dictate either the terms or timing of military purchases. As a result, there is often a mistiming of events (e.g., the emergence of a current account deficit and the arrival of a large military package).

In any analysis of resilience, these factors particular to the military sector may need to be considered in understanding the observed results, recognizing however that other non-military outlays are also subject to factors that limit a government’s flexibility for adjustment.

3. The empirical methodology

Using the complete sample of countries for which data are available, we first investigate how a Fund-supported adjustment program may have affected the share of overall military expenditure in GDP (MIL/GDP) and in overall expenditure (MIL/EX). In terms of the earlier conceptual framework, it amounts to analyzing the behavior of x₁ and x₁/x as provided from (5.a) and (5.b) versus (3.a) and (3.d), where the variables x₁, x₂, D_d, R, and D_f must now be interpreted as a share of GDP. As previously indicated, we use three different data sources, ACDA, GFS, and SIPRI, to check for consistency among the different data sources as well as to address the problem of limited country coverage of some sources. We also examine the percentage change in armed forces during Fund-supported programs. ^17/ The results confirm the need to distinguish between cases of fiscal tightening versus fiscal accommodation, as anticipated earlier; fiscal tightening will be defined as coinciding with a decrease of total expenditure as a share of GDP, and fiscal accommodation with an increase.

Next, we reconsider in more detail the behavior of MIL/GDP, MIL/EX, and the percentage change in armed forces in each of the two subsamples of Fund-supported programs. The term “resilience” is introduced to describe a situation in which despite budgetary expenditure restraint, the share of military expenditure in total expenditure, MIL/EX, actually increased during the Fund-supported program. Again, in terms of the conceptual framework introduced earlier, this will occur when the adjustment coefficient ∂x₁/∂D_d in (5.a) is sufficiently small; this implies that x₁ is close to the unconstrained level x₁(), and accordingly, that x₁/x will tend to rise.

Finally, the discussion will specifically focus on those countries that experienced fiscal tightening during Fund-supported programs to see if resilience can be related to some of the variables, or proxies to them, identified in the formal framework as likely to be key determinants of military spending in the first place. Formally, we investigate whether the size of the adjustment coefficient ∂x₁/∂D_d in (5.a) can be systematically related to variables that explain defense expenditures themselves. Thus, we seek to formulate a profile of characteristics that could assist in clarifying the resilience of military expenditure in specific countries. In particular, we try to establish whether, relative to the sample average levels, resilient countries tend: (a) to have higher pre-program levels of MIL/GDP and MIL/EX; (b) to have higher per capita GDP; (c) to be more highly urbanized; and (d) to have a larger percentage of its population in the military. The explanatory variable (a) could be viewed as synthetic for all others, while the variable (d) could be viewed as a proxy to the index of security risk indicated earlier, for which no comprehensive satisfactory time series data could be found (and which, in any case, may not be amenable to objective quantification).

b. Overall behavior

To provide an overall picture of the effect of a Fund-supported program on military spending, Table 4, Part I compares data on trends in military spending, before and during the program period, as a share of GDP, as a share of total expenditure, and as the percentage change in the size of armed forces. The original sample of countries included all those that had either a stand-by arrangement or an extended Fund facility during 1978-34, regardless of the fiscal policy stance they adopted in the context of the program. In the top panel (A.), preprogram is defined as equal to the calendar year before the Fund-supported program was implemented. In the bottom panel (B.), preprogram is defined as the average of the three years prior to implementation.

Table 4.

Military Indicators Before Versus During Fund-Supported Program Periods

I. All Countries

Source: See Appendix I.

^1/Increase also includes those countries in which there was no change.

Table 4.

Military Indicators Before Versus During Fund-Supported Program Periods

I. All Countries

A. Pre-Program Year = One Year
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	56	52	48
	Fund (GFS)	43	54	46
	SIPRI	51	39	61
Military spending/total expenditure
	ACDA	56	57	43
	Fund (GFS)	42	50	50
	SIPRI	44	48	52
Percentage change in armed forces		56	34	66
B. Pre-Program Year = Three Years
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	57	53	47
	Fund (GFS)	42	57	43
	SIPRI	53	40	60
Military spending/total expenditure
	ACDA	57	65	35
	Fund (GFS)	43	63	37
	SIPRI	44	55	45
Percentage change in armed forces		58	36	64

Source: See Appendix I.

^1/Increase also includes those countries in which there was no change.

View Table

Table 4.

Military Indicators Before Versus During Fund-Supported Program Periods

I. All Countries

A. Pre-Program Year = One Year
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	56	52	48
	Fund (GFS)	43	54	46
	SIPRI	51	39	61
Military spending/total expenditure
	ACDA	56	57	43
	Fund (GFS)	42	50	50
	SIPRI	44	48	52
Percentage change in armed forces		56	34	66
B. Pre-Program Year = Three Years
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	57	53	47
	Fund (GFS)	42	57	43
	SIPRI	53	40	60
Military spending/total expenditure
	ACDA	57	65	35
	Fund (GFS)	43	63	37
	SIPRI	44	55	45
Percentage change in armed forces		58	36	64

Source: See Appendix I.

^1/Increase also includes those countries in which there was no change.

Table 4.

Military Indicators Before Versus During Fund-Supported Program Periods

II. Fiscal Tightening

Source: See Appendix I.

^1/Increase also includes those countries in which there was no change.

Table 4.

Military Indicators Before Versus During Fund-Supported Program Periods

II. Fiscal Tightening

A. Pre-Program Year = One Year
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	23	66	34
	Fund (GFS)	17	76	24
	SIPRI	19	53	47
Military spending/total expenditure
	ACDA	23	43	57
	Fund (GFS)	18	28	72
	SIPRI	18	33	67
Percentage change in armed forces		23	26	74
B. Pre-Program Year = Three Years
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	22	73	27
	Fund (GFS)	14	79	21
	SIPRI	19	47	53
Military spending/total expenditure
	ACDA	22	58	42
	Fund (GFS)	16	44	56
	SIPRI	16	25	75
Percentage change in armed forces		22	23	77

Source: See Appendix I.

^1/Increase also includes those countries in which there was no change.

View Table

Table 4.

Military Indicators Before Versus During Fund-Supported Program Periods

II. Fiscal Tightening

A. Pre-Program Year = One Year
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	23	66	34
	Fund (GFS)	17	76	24
	SIPRI	19	53	47
Military spending/total expenditure
	ACDA	23	43	57
	Fund (GFS)	18	28	72
	SIPRI	18	33	67
Percentage change in armed forces		23	26	74
B. Pre-Program Year = Three Years
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	22	73	27
	Fund (GFS)	14	79	21
	SIPRI	19	47	53
Military spending/total expenditure
	ACDA	22	58	42
	Fund (GFS)	16	44	56
	SIPRI	16	25	75
Percentage change in armed forces		22	23	77

Source: See Appendix I.

^1/Increase also includes those countries in which there was no change.

Table 4.

Military Indicators Before Versus During Fund-Supported Program Periods

III. Fiscal Accommodation

Source: See Appendix I.

^1/Increase also includes those countries in which there was no change.

Table 4.

Military Indicators Before Versus During Fund-Supported Program Periods

III. Fiscal Accommodation

A. Pre-Program Year = One Year
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	33	43	57
	Fund (GFS)	25	40	60
	SIPRI	32	31	69
Military spending/total expenditure
	ACDA	33	67	33
	Fund (GFS)	24	67	33
	SIPRI	26	58	42
Percentage change in armed forces		33	39	61
B. Pre-Program Year = Three Years
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	35	40	60
	Fund (GFS)	27	44	56
	SIPRI	34	35	65
Military spending/tota1 expenditure
	ACDA	35	69	31
	Fund (GFS)	26	73	27
	SIPRI	28	71	29
Percentage change in armed forces		36	44	56

Source: See Appendix I.

^1/Increase also includes those countries in which there was no change.

View Table

Table 4.

Military Indicators Before Versus During Fund-Supported Program Periods

III. Fiscal Accommodation

A. Pre-Program Year = One Year
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	33	43	57
	Fund (GFS)	25	40	60
	SIPRI	32	31	69
Military spending/total expenditure
	ACDA	33	67	33
	Fund (GFS)	24	67	33
	SIPRI	26	58	42
Percentage change in armed forces		33	39	61
B. Pre-Program Year = Three Years
		Total Number of Countries	During Fund-Supported Program Percent of Countries Exhibiting
			Decrease	Increase 1/
Military spending/GDP
	ACDA	35	40	60
	Fund (GFS)	27	44	56
	SIPRI	34	35	65
Military spending/tota1 expenditure
	ACDA	35	69	31
	Fund (GFS)	26	73	27
	SIPRI	28	71	29
Percentage change in armed forces		36	44	56

Source: See Appendix I.

^1/Increase also includes those countries in which there was no change.

In the top panel, the ACDA and GFS data reveal that slightly more countries experienced a decrease in MIL/GDP during their program period than experienced an increase in MIL/CDP. The SIPRI data, however, indicate that more countries experienced an increased or stable than a decreased MIL/GDP. These results are consistent with those displayed in the lower panel of the table under the alternative definition of preprogram period.

The indicator MIL/EX reveals less consistent results with regard to data sources and pre-program period definition. When the pre-program period is defined as one year, the following results are obtained: the ACDA data suggest that more countries experienced a decrease in MIL/EX than experienced an increased or steady ratio combined. In contrast, the GFS data indicate that as many countries experienced a decline in MIL/EX as experienced increased or steady ratios. Finally, the SIPRI data indicate that more countries experienced an increased or steady ratio than a decrease. When the pre-program period is defined as a three-year average, the results from all three data sources indicate that more countries exhibited a decrease in MIL/EX over the program period than exhibited an increased or a stable ratio.

For the indicator percentage change in armed forces, the results uniformly indicate that more countries exhibited either a constant or increasing rate of change than a decreasing one.

d. Focus on fiscal tightening

So far, we have discussed the diverse patterns of military spending among Fund-supported program countries without focusing on specific explanatory variables, except to emphasize the importance of the fiscal policy stance adopted under the program. The following set of exercises attempts to establish links between specific characteristics of countries and their observed military spending, concentrating on those countries that have experienced fiscal tightening during Fund-supported program years.

As indicated in Section III.2, a number of variables can explain why one country is likely to spend relatively more than another on defense, as measured, for instance, by MIL/GDP and MIL/EX. These same variables should play a role in determining why countries react differently to adjusting their military expenditures under Fund-supported programs, and, in particular, why some countries display greater resilience. Not all these variables can easily be quantified and, even for those that can, data are not always readily available, as in the case of the security risk index referred to earlier. Accordingly, a first exercise consists in establishing whether countries that display above average shares of military expenditures in total expenditures prior to a Fund-supported program, compared with the sample mean for all countries, tend to exhibit resilience or, on the contrary, greater flexibility when expenditure constraints become more effective in the context of Fund-supported programs. With reference to the formal framework of Section 2, this exercise may be viewed as testing whether the size of the adjustment coefficient ∂x₁/∂D_d is negatively or positively related to the set of variables, S, that generally tends to explain higher military spending. ^23/

Turning to the empirical evidence, we calculate the average preprogram value of MIL/EX over the entire sample of countries for each of three data sources using both pre-program definitions. We then compute the pre-program year MIL/EX for each country in which fiscal restraint occurred during the Fund-supported program, and rank the countries according to whether they are above or below the sample average. In subsection e. we shall look at how many countries in each group exhibited resilience during a Fund-supported program in the sense that MIL/EX increased.

The results of this exercise, reported in Tables 5 and 6, are somewhat ambiguous, varying over pre-program period definition as well as data sources. Where the pre-program level of MIL/EX is greater than the sample average for all countries, the results indicate that during Fund-supported programs almost an equal number of countries exhibited increases in this ratio—that is, resilience—as exhibited decreases. (When pre-program is defined as a three-year average, slightly more countries experienced an increase in MIL/EX during the Fund-supported program.)

Table 5.

Relationship Between Pre-Program Level of Military/Expenditure Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = One-year average)

Source: See Appendix I.

Table 5.

Relationship Between Pre-Program Level of Military/Expenditure Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = One-year average)

Data Source	Pre-Program Level of Military/Expenditure Above Sample Average			Pre-Program Level of Military/Expenditure Below Sample Average
		Behavior of military/ expenditure during Fund-supported program			Behavior of military/ expenditure during Fund-supported program
	No. of observations	Increase (resilience)	Decrease	No. of observations	Increase (resilience)	Decrease
ACDA	10	5	5	14	8	6
Fund (GFS)	8	4	4	10	9	1
SIPRI	7	3	4	11	9	2

Source: See Appendix I.

View Table

Table 5.

Relationship Between Pre-Program Level of Military/Expenditure Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = One-year average)

Data Source	Pre-Program Level of Military/Expenditure Above Sample Average			Pre-Program Level of Military/Expenditure Below Sample Average
		Behavior of military/ expenditure during Fund-supported program			Behavior of military/ expenditure during Fund-supported program
	No. of observations	Increase (resilience)	Decrease	No. of observations	Increase (resilience)	Decrease
ACDA	10	5	5	14	8	6
Fund (GFS)	8	4	4	10	9	1
SIPRI	7	3	4	11	9	2

Source: See Appendix I.

Table 6.

Relationship Between Pre-Program Level of Military/Expenditure Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = Three-year average)

Source: See Appendix I.

Table 6.

Relationship Between Pre-Program Level of Military/Expenditure Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = Three-year average)

Data Source	Pre-Program Level of Military/Expenditure Above Sample Average			Pre-Program Level of Military/Expenditure Below Sample Average
		Behavior of military/ expenditure during Fund-supported program			Behavior of military/ expenditure during Fund-supported program
	No. of observations	Increase (resilience)	Decrease	No. of observations	Increase (resilience)	Decrease
ACDA	9	5	4	13	4	9
Fund (GFS)	5	3	2	10	5	5
SIPRI	6	4	2	10	8	2

Source: See Appendix I.

View Table

Table 6.

Relationship Between Pre-Program Level of Military/Expenditure Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = Three-year average)

Data Source	Pre-Program Level of Military/Expenditure Above Sample Average			Pre-Program Level of Military/Expenditure Below Sample Average
		Behavior of military/ expenditure during Fund-supported program			Behavior of military/ expenditure during Fund-supported program
	No. of observations	Increase (resilience)	Decrease	No. of observations	Increase (resilience)	Decrease
ACDA	9	5	4	13	4	9
Fund (GFS)	5	3	2	10	5	5
SIPRI	6	4	2	10	8	2

Source: See Appendix I.

For the countries in which the relative pre-program level of MIL/EX is below the sample average, the results of this exercise are somewhat more informative. When the pre-program period is equal to one year, all data sources indicate that a majority of countries experience an increase in MIL/EX—that is, resilience. However, when the pre-program period is equal to an average of three years, only the SIPRI data confirm this result. The ACDA data suggest that more countries exhibit a decrease, while the GFS data are split evenly between increases and decreases in MIL/EX.

The result that emerges, albeit weak, is that if a country has a below average pre-program-period level of MIL/EX, its military spending is likely to exhibit resilience. We also explored how the behavior of MIL/EX during Fund-supported programs relates to the pre-program level of MIL/GDP relative to the sample average. The results are quite similar to those presented above and are displayed in Tables 7 and 8.

Table 7.

Relationship Between Pre-Program Level of Military/GDP Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = One-year average)

Source: See Appendix I.

Table 7.

Relationship Between Pre-Program Level of Military/GDP Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = One-year average)

Data Source	Pre-Program Level of Military/GDP Above Sample Average			Pre-Program Level of Military/GDP Below Sample Average
		Behavior of military/ expenditure during Fund-supported program			Behavior of military/ expenditure during Fund-supported program
	No. of observations	Increase (resilience)	Decrease	No. of observations	Increase (resilience)	Decrease
ACDA	13	6	7	11	7	4
Fund (GFS)	8	4	4	9	8	1
STPRI	7	3	4	10	8	2

Source: See Appendix I.

View Table

Table 7.

Relationship Between Pre-Program Level of Military/GDP Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = One-year average)

Data Source	Pre-Program Level of Military/GDP Above Sample Average			Pre-Program Level of Military/GDP Below Sample Average
		Behavior of military/ expenditure during Fund-supported program			Behavior of military/ expenditure during Fund-supported program
	No. of observations	Increase (resilience)	Decrease	No. of observations	Increase (resilience)	Decrease
ACDA	13	6	7	11	7	4
Fund (GFS)	8	4	4	9	8	1
STPRI	7	3	4	10	8	2

Source: See Appendix I.

Table 8.

Relationship Between Pre-Program Level of Military/GDP Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = Three year average)

Source: See Appendix I.

Table 8.

Relationship Between Pre-Program Level of Military/GDP Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = Three year average)

Data Source	Pre-Program Level of Military/GDP Above Sample Average			Pre-Program Level of Military/GDP Below Sample Average
		Behavior of military/ expenditure during Fund-supported program			Behavior of military/ expenditure during Fund-supported program
	No. of observations	Increase (resilience)	Decrease	No. of observations	Increase (resilience)	Decrease
ACDA	10	5	5	12	4	8
Fund (GFS)	4	2	2	11	6	5
SIPRI	3	1	2	13	10	3

Source: See Appendix I.

View Table

Table 8.

Relationship Between Pre-Program Level of Military/GDP Relative to Sample Average and Behavior of Military/Expenditure During Fund-Supported Programs (With Fiscal Restraint)

(Pre-program = Three year average)

Data Source	Pre-Program Level of Military/GDP Above Sample Average			Pre-Program Level of Military/GDP Below Sample Average
		Behavior of military/ expenditure during Fund-supported program			Behavior of military/ expenditure during Fund-supported program
	No. of observations	Increase (resilience)	Decrease	No. of observations	Increase (resilience)	Decrease
ACDA	10	5	5	12	4	8
Fund (GFS)	4	2	2	11	6	5
SIPRI	3	1	2	13	10	3

Source: See Appendix I.

e. A closer look at resilience

Based on the results of the previous section, we were able to conclude tentatively that countries with below average pre-program MIL/EX or MIL/GDP relative to other countries are likely to exhibit resilience during a period of fiscal tightening. One may go a step further and determine whether the country specific characteristics that would explain higher military expenditure could also, individually, explain the lesser resilience predicted theoretically, or the opposite.

Tables 9 and 10 list those countries that have exhibited resilience in military spending during Fund-supported programs, defining the pre-program period as equal to one-year and a three-year average, respectively. ^24/ Again, in line with our discussion on the determinants of military spending and given the availability of data, the following limited sample of pre-program period variables are considered for each country exhibiting resilience in military spending and are compared with the sample average for all countries: armed forces as a percentage of total population, urban population as a percentage of total, and per capita GNP. For the reasons already indicated, military expenditure is expected to be positively related to each one of these variables.

In Tables 9 and 10, columns 1 and 2 report values of MIL/EX before and during program periods for each country. They quantify the extent to which MIL/EX has actually increased and therefore how resilient military spending in each of these countries actually was. MIL/EX and MIL/GDP relative to (i.e., as a ratio of) sample averages in columns 3 and 4 reveal what was previously suggested about the relationship between resilience and each of these variables. In Table 9.a (where the program period equals one year), the pooling of the three data sources yields 38 observations. Of the 38 observations, 25 indicate values of MIL/EX below their respective sample average while 13 are above. In Table 10.a (where the pre-program period equals a three-year average), the pooling of data sources yields 29 observations. Of the 29, 18 indicate a value of MIL/EX below average and 11 above. With regard to MIL/GDP, in Table 9.a, pooling the three data sources yields 36 observations. Of the 36 observations, 23 are below their respective sample average while 13 are above. In Table 10.a, pooling the three data sources yields 29 observations. In this case, 19 observations are below their sample average while only 10 are above. These results seem to confirm that countries exhibiting resilience are dominated by below average MIL/EX and MIL/GDP before a Fund-supported program.

Table 9.a.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = One year)

Source: For military/expenditure and military/GDP, see Appendix I.

^1/ACDA, United States Arms Control and Disarmament Agency; SIPRI, Stockholm International Peace Research Institute; and GFS, the Fund’s Government Finance Statistics.

Table 9.a.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = One year)

				Pre-Program Relative Levels
		Military/Expenditure		Military/expenditure relative to sample average (3)	Military/GDP relative to sample average (4)
	Source	Before (1)	During (2)
Barbados	ACDA 1/	2.5	2.9	0.18	0.23
	GFS 1/	2.5	2.8	0.22	0.30
Chile	ACDA 1/	12.5	12.7	0.90	1.21
	GFS	12.5	12.7	1.07	1.41
China	ACDA	37.3	40.3	2.68	2.96
Costa Rica	ACDA	2.7	2.8	0.19	0.20
	GFS	2.7	2.8	0.23	0.25
	SIPRI	2.8	3.1	0.19	0.21
Ecuador	GFS	10.7	11.9	0.91	0.60
	SIPRI	10.7	12.9	0.74	0.51
Gabon	ACDA	1.9	6.7	0.14	0.31
Ghana	GFS	5.1	5.1	0.43	0.38
Mauritius	ACDA	0.5	1.0	0.04	0.06
	GFS	0.5	1.0	0.05	0.07
	SIPRI	0.7	1.1	0.05	0.06
Pakistan	ACDA	23.6	26.6	1.69	1.41
	GFS	23.6	26.6	2.01	1.92
	SIPRI	26.6	29.2	1.85	1.40
Romania	ACDA	10.9	17.6	0.78	1.21
	GFS	3.8	4.9	0.32	0.61
	SIPRI	3.8	4.9	0.26	0.51
Sierra Leone	GFS	2.7	4.2	0.23	0.30
	SIPRI	3.2	4.6	0.22	0.27
Somalia	ACDA	17.0	21.5	1.22	1.89
Sri Lanka	ACDA	3.2	4.0	0.23	0.37
	GFS	1.8	2.0	0.15	0.26
	SIPRI	3.3	4.1	0.23	0.39
Tanzania	ACDA	9.2	12.2	0.66	0.73
	GFS	9.2	11.9	0.78	1.01
	SIPRI	13.8	21.0	0.96	1.16
Togo	SIPRI	4.0	6.6	0.28	0.71
Uganda	ACDA	19.1	21.4	1.37	0.28
	GFS	19.1	21.4	1.62	…
	SIPRI	25.4	30.6	1.76	…
Yugoslavia	ACDA	45.3	48.9	3.25	1.21
	GFS	45.3	48.9	3.86	1.71
	SIPRI	46.3	48.4	3.21	1.43
Zambia	SIPRI	6.8	9.4	2.37	0.83

Source: For military/expenditure and military/GDP, see Appendix I.

^1/ACDA, United States Arms Control and Disarmament Agency; SIPRI, Stockholm International Peace Research Institute; and GFS, the Fund’s Government Finance Statistics.

View Table

Table 9.a.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = One year)

				Pre-Program Relative Levels
		Military/Expenditure		Military/expenditure relative to sample average (3)	Military/GDP relative to sample average (4)
	Source	Before (1)	During (2)
Barbados	ACDA 1/	2.5	2.9	0.18	0.23
	GFS 1/	2.5	2.8	0.22	0.30
Chile	ACDA 1/	12.5	12.7	0.90	1.21
	GFS	12.5	12.7	1.07	1.41
China	ACDA	37.3	40.3	2.68	2.96
Costa Rica	ACDA	2.7	2.8	0.19	0.20
	GFS	2.7	2.8	0.23	0.25
	SIPRI	2.8	3.1	0.19	0.21
Ecuador	GFS	10.7	11.9	0.91	0.60
	SIPRI	10.7	12.9	0.74	0.51
Gabon	ACDA	1.9	6.7	0.14	0.31
Ghana	GFS	5.1	5.1	0.43	0.38
Mauritius	ACDA	0.5	1.0	0.04	0.06
	GFS	0.5	1.0	0.05	0.07
	SIPRI	0.7	1.1	0.05	0.06
Pakistan	ACDA	23.6	26.6	1.69	1.41
	GFS	23.6	26.6	2.01	1.92
	SIPRI	26.6	29.2	1.85	1.40
Romania	ACDA	10.9	17.6	0.78	1.21
	GFS	3.8	4.9	0.32	0.61
	SIPRI	3.8	4.9	0.26	0.51
Sierra Leone	GFS	2.7	4.2	0.23	0.30
	SIPRI	3.2	4.6	0.22	0.27
Somalia	ACDA	17.0	21.5	1.22	1.89
Sri Lanka	ACDA	3.2	4.0	0.23	0.37
	GFS	1.8	2.0	0.15	0.26
	SIPRI	3.3	4.1	0.23	0.39
Tanzania	ACDA	9.2	12.2	0.66	0.73
	GFS	9.2	11.9	0.78	1.01
	SIPRI	13.8	21.0	0.96	1.16
Togo	SIPRI	4.0	6.6	0.28	0.71
Uganda	ACDA	19.1	21.4	1.37	0.28
	GFS	19.1	21.4	1.62	…
	SIPRI	25.4	30.6	1.76	…
Yugoslavia	ACDA	45.3	48.9	3.25	1.21
	GFS	45.3	48.9	3.86	1.71
	SIPRI	46.3	48.4	3.21	1.43
Zambia	SIPRI	6.8	9.4	2.37	0.83

Source: For military/expenditure and military/GDP, see Appendix I.

^1/ACDA, United States Arms Control and Disarmament Agency; SIPRI, Stockholm International Peace Research Institute; and GFS, the Fund’s Government Finance Statistics.

Table 9.b.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = Three years)

Sources: For armed/population see Appendix I. Urban population as a percentage of total population is from the World Bank’s World Tables, Vol. II. GNP per capita data are from World Bank Atlas, 1980; both of these variables are for 1978 rather than the pre-program period as defined elsewhere.

Table 9.b.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = Three years)

	Pre-Program Relative Levels
	Armed/population relative to sample average (1)	Urban population as a percentage of total relative to sample average (2)	Per capita income relative to sample average (3)
Barbados	0.73	1.14	2.58
Chile	1.90	2.34	1.82
China	…	0.57	0.29
Costa Rica	0.52	1.25	2.0
Ecuador	0.79	1.28	1.18
Gabon	0.75	0.99	4.18
Ghana	0.33	1.01	0.47
Mauritius	—	1.47	1.14
Pakistan	1.07	0.81	0.30
Romania	1.82	1.36	2.05
Sierra Leone	0.17	0.63	0.29
Somalia	2.63	0.84	…
Sri Lanka	0.17	0.75	0.25
Tanzania	0.54	0.31	0.30
Togo	0.39	0.54	0.40
Uganda	0.09	0.27	0.35
Yugoslavia	2.19	1.20	2.60
Zambia	0.71	1.15	0.63

Sources: For armed/population see Appendix I. Urban population as a percentage of total population is from the World Bank’s World Tables, Vol. II. GNP per capita data are from World Bank Atlas, 1980; both of these variables are for 1978 rather than the pre-program period as defined elsewhere.

View Table

Table 9.b.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = Three years)

	Pre-Program Relative Levels
	Armed/population relative to sample average (1)	Urban population as a percentage of total relative to sample average (2)	Per capita income relative to sample average (3)
Barbados	0.73	1.14	2.58
Chile	1.90	2.34	1.82
China	…	0.57	0.29
Costa Rica	0.52	1.25	2.0
Ecuador	0.79	1.28	1.18
Gabon	0.75	0.99	4.18
Ghana	0.33	1.01	0.47
Mauritius	—	1.47	1.14
Pakistan	1.07	0.81	0.30
Romania	1.82	1.36	2.05
Sierra Leone	0.17	0.63	0.29
Somalia	2.63	0.84	…
Sri Lanka	0.17	0.75	0.25
Tanzania	0.54	0.31	0.30
Togo	0.39	0.54	0.40
Uganda	0.09	0.27	0.35
Yugoslavia	2.19	1.20	2.60
Zambia	0.71	1.15	0.63

Sources: For armed/population see Appendix I. Urban population as a percentage of total population is from the World Bank’s World Tables, Vol. II. GNP per capita data are from World Bank Atlas, 1980; both of these variables are for 1978 rather than the pre-program period as defined elsewhere.

Table 10.a.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = Three years)

Source: For military/expenditure and military/GDP see Appendix I.

^1/ACDA, United States Arms Control and Disarmament Agency; SIPRI, Stockholm International Peace Research Institute; and GFS, the Fund’s Government Finance Statistics.

Table 10.a.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = Three years)

				Pre-Program Relative Levels
		Military/Expenditure		Military/expenditure relative to sample average (3)	Military/GDP relative to sample average (4)
	Source	Before (1)	During (2)
China	ACDA 1/	37.7	40.3	2.67	3.26
Dominican Republic	SIPRI 1/	10.5	11.2	0.72	0.48
Ecuador	GFS 1/	11.2	11.9	0.95	0.63
	SIPRI	11.2	11.9	0.82	0.55
Gabon	ACDA	1.7	6.7	0.12	0.24
Haiti	ACDA	6.5	8.7	0.46	0.36
	SIPRI	7.1	7.6	0.49	0.38
Côte d’Ivoire	GFS	3.5	3.5	0.30	0.44
	SIPRI	3.5	3.5	0.24	0.34
Mauritius	ACDA	0.5	1.0	0.04	0.05
	GFS	0.5	1.0	0.04	0.07
	SIPRI	0.6	1.1	0.04	0.06
Pakistan	ACDA	23.2	26.6	1.65	1.41
	GFS	23.2	26.6	1.98	1.98
	SIPRI	26.1	29.2	1.78	1.43
Romania	ACDA	10.4	17.6	0.74	1.25
	GFS	3.9	4.9	0.33	0.71
	SIPRI	3.9	4.9	0.27	0.58
Sierra Leone	GFS	3.3	4.2	0.28	0.33
	SIPRI	3.6	4.6	0.25	0.27
Somalia	ACDA	19.2	21.5	1.36	1.50
Togo	SIPRI	4.5	6.6	0.31	0.74
Uganda	ACDA	20.0	21.4	1.42	1.66
	GFS	20.0	21.4	1.70	0.88
	SIPRI	22.7	30.6	1.56	0.60
Yugoslavia	ACDA	44.2	48.9	3.13	1.22
	GFS	45.1	48.4	3.77	1.81
	SIPRI	45.1	48.4	3.08	1.50
Zambia	GFS	6.8	9.4	…	…
	SIPRI	6.8	9.4	0.47	0.85

Source: For military/expenditure and military/GDP see Appendix I.

^1/ACDA, United States Arms Control and Disarmament Agency; SIPRI, Stockholm International Peace Research Institute; and GFS, the Fund’s Government Finance Statistics.

View Table

Table 10.a.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = Three years)

				Pre-Program Relative Levels
		Military/Expenditure		Military/expenditure relative to sample average (3)	Military/GDP relative to sample average (4)
	Source	Before (1)	During (2)
China	ACDA 1/	37.7	40.3	2.67	3.26
Dominican Republic	SIPRI 1/	10.5	11.2	0.72	0.48
Ecuador	GFS 1/	11.2	11.9	0.95	0.63
	SIPRI	11.2	11.9	0.82	0.55
Gabon	ACDA	1.7	6.7	0.12	0.24
Haiti	ACDA	6.5	8.7	0.46	0.36
	SIPRI	7.1	7.6	0.49	0.38
Côte d’Ivoire	GFS	3.5	3.5	0.30	0.44
	SIPRI	3.5	3.5	0.24	0.34
Mauritius	ACDA	0.5	1.0	0.04	0.05
	GFS	0.5	1.0	0.04	0.07
	SIPRI	0.6	1.1	0.04	0.06
Pakistan	ACDA	23.2	26.6	1.65	1.41
	GFS	23.2	26.6	1.98	1.98
	SIPRI	26.1	29.2	1.78	1.43
Romania	ACDA	10.4	17.6	0.74	1.25
	GFS	3.9	4.9	0.33	0.71
	SIPRI	3.9	4.9	0.27	0.58
Sierra Leone	GFS	3.3	4.2	0.28	0.33
	SIPRI	3.6	4.6	0.25	0.27
Somalia	ACDA	19.2	21.5	1.36	1.50
Togo	SIPRI	4.5	6.6	0.31	0.74
Uganda	ACDA	20.0	21.4	1.42	1.66
	GFS	20.0	21.4	1.70	0.88
	SIPRI	22.7	30.6	1.56	0.60
Yugoslavia	ACDA	44.2	48.9	3.13	1.22
	GFS	45.1	48.4	3.77	1.81
	SIPRI	45.1	48.4	3.08	1.50
Zambia	GFS	6.8	9.4	…	…
	SIPRI	6.8	9.4	0.47	0.85

Source: For military/expenditure and military/GDP see Appendix I.

^1/ACDA, United States Arms Control and Disarmament Agency; SIPRI, Stockholm International Peace Research Institute; and GFS, the Fund’s Government Finance Statistics.

Table 10.b.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = Three years)

Sources: For armed/population see Appendix I. Urban population as a percentage of total population is from the World Bank’s World Tables, Vol. II. GNP per capita data are from World Bank Atlas, 1980; both of these variables are for 1978 rather than the pre-program period as defined elsewhere.

Table 10.b.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = Three years)

	Pre-Program Relative Levels
	Armed/population relative to sample average (1)	Urban population as a percentage of total relative to sample average (2)	Per capita income relative to sample average (3)
China	…	0.57	0.29
Dominican Republic	0.85	1.12	1.44
Ecuador	0.81	1.18	1.28
Gabon	0.64	0.99	4.18
Haiti	0.27	0.78	0.30
Côte d’Ivoire	0.16	1.08	1.18
Mauritius	0.07	1.47	1.14
Pakistan	1.24	0.81	0.30
Romania	1.91	1.36	2.05
Sierra Leone	0.14	0.63	0.29
Somalia	2.80	0.84	…
Togo	0.36	0.54	0.40
Uganda	0.18	0.27	0.35
Yugoslavia	2.32	1.20	2.60
Zambia	0.67	1.15	0.63

Sources: For armed/population see Appendix I. Urban population as a percentage of total population is from the World Bank’s World Tables, Vol. II. GNP per capita data are from World Bank Atlas, 1980; both of these variables are for 1978 rather than the pre-program period as defined elsewhere.

View Table

Table 10.b.

Resilient Countries: Pre-Program Levels Relative to Sample Average

(Pre-program period = Three years)

	Pre-Program Relative Levels
	Armed/population relative to sample average (1)	Urban population as a percentage of total relative to sample average (2)	Per capita income relative to sample average (3)
China	…	0.57	0.29
Dominican Republic	0.85	1.12	1.44
Ecuador	0.81	1.18	1.28
Gabon	0.64	0.99	4.18
Haiti	0.27	0.78	0.30
Côte d’Ivoire	0.16	1.08	1.18
Mauritius	0.07	1.47	1.14
Pakistan	1.24	0.81	0.30
Romania	1.91	1.36	2.05
Sierra Leone	0.14	0.63	0.29
Somalia	2.80	0.84	…
Togo	0.36	0.54	0.40
Uganda	0.18	0.27	0.35
Yugoslavia	2.32	1.20	2.60
Zambia	0.67	1.15	0.63

Sources: For armed/population see Appendix I. Urban population as a percentage of total population is from the World Bank’s World Tables, Vol. II. GNP per capita data are from World Bank Atlas, 1980; both of these variables are for 1978 rather than the pre-program period as defined elsewhere.

As displayed in Table 9.b, for the variable armed forces as a percentage of population, of the 17 available observations of resilient countries, 12 are below the sample average. This result is confirmed in Table 10.b; of the 14 observations, 10 are below the average. The data support the claim that countries with resilient military expenditure are characterized as having a below average level of armed forces as a percentage of the total population.

The variables—urban population as a percentage of total population and GNP per capita—yield particularly mixed results. For both variables, approximately half of the available observations lie above the sample average and half below, irrespective of program period definition.