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Military Expenditure and Sustainable Economic Growth in Latin America

March 2025
Business and Society Review 22(84):170-231

DOI:10.3232/UBR.2025.V22.N1.04

Authors:

María Gabriela Cueva-Jiménez

Camilo José Cela University

Ronny Correa-Quezada

Universidad Técnica Particular de Loja

Ana Belén Tulcanaza-Prieto

Universidad de Las Américas

Lucía Cueva-Rodríguez

Universidad Técnica Particular de Loja

Military expenditure or military budget comprises the amount of financial resources of total spending on all three armed forces (army, navy, and air force) and peacekeeping provided by the central government in a country. The (Stockholm International Peace Research Institute (SIPRI), 2023) established that the military expenditure includes current and capital spending for the armed and peacekeeping forces, military space activities, defense ministries, and other government agencies that are focused on defense projects. Moreover, it should comprise expenditures on current military and civil personnel, retirement pensions of military personnel, social services for personnel and their families, operations and maintenance, procurement, military research and development, military construction, and military aid. (Smith, 2000) defined the defense of society as one of the primary functions of government and there is the reason for reasonable taxation; therefore, the military budget is recognized as one area where there is no private solution.

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Military Expenditure

and Sustainable

Economic Growth in

Latin America

Gasto Militar y Crecimiento Económico

Sostenible en América Latina

1. INTRODUCTION

Military expenditure or military budget comprises the amount of

financial resources of total spending on all three armed forces

(army, navy, and air force) and peacekeeping provided by the

central government in a country. The (Stockholm International

Peace Research Institute (SIPRI), 2023) established that the

military expenditure includes current and capital spending for the

armed and peacekeeping forces, military space activities, defense

ministries, and other government agencies that are focused on

defense projects. Moreover, it should comprise expenditures on

current military and civil personnel, retirement pensions of military

personnel, social services for personnel and their families, operations

and maintenance, procurement, military research and development,

military construction, and military aid. (Smith, 2000) defined the

defense of society as one of the primary functions of government

and there is the reason for reasonable taxation; therefore, the military

budget is recognized as one area where there is no private solution.

The primary challenge of the analysis of military expenditure in a

country is how much security is required by a nation and how much

security can be afforded by that country. These questions are also

related to military capability, which does not necessarily only depend

on the military budget associated with the Gross Domestic Product

(GDP) but is also related to the economic, political, geographic,

cultural, and strategic factors in a country. Therefore, the importance

of military spending is aligned with the constant efforts to make a well-

built and efficient defense system, which also involves internal and

Received: 2024.10.16 Accepted: 2025.02.14 DOI: 10.3232/UBR.2025.V22.N1.04

JEL CODES:

H56, O11, C32, F52, N46

Business and Society Review

UCJC

(Formerly known as Universia Business Review)

María Gabriela Cueva-

Jiménez

Estudios de Doctorado en

Universidad Camilo José

Cela, Madrid, Spain.

 gabrielacuevaj@gmail.

com

Ronny Correa-Quezada

Departamento de Economía

de la Universidad Técnica

Particular de Loja, Ecuador.



Ana Belén Tulcanaza-

Prieto

Grupo de Investigación Lugar

Medio Sociedad (LMS),

Universidad de las Amèricas,

Quito, Pichincha, Ecuador.



Lucía Cueva-Rodríguez

Magister in Economic

Development en la Facultad

Latinoamericana de Ciencias

Sociales, Quito Ecuador,

Investigadora y Consultora en

Economía.



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EXECUTIVE SUMMARY

This research seeks to thoroughly examine the interconnectedness and directional

influence between military spending and sustainable economic growth while considering

the role of gross fixed capital formation (investment) as a controlling factor. Drawing on a

comprehensive dataset from 13 distinct Latin American economies, spanning the extensive

time frame of 1990 to 2019, the study investigates the patterns of cointegration and causal

relationships within the variables. The results of the cointegration analysis do not provide

substantial support for the existence of a strong and enduring relationship between all the

examined variables over the long term. Notably, the research identifies a unidirectional

causality running from (i) economic growth to military expenditure, (ii) economic growth to

investment, and (iii) investment to military expenditure. This suggests a complex interplay

between economic dynamics, investment patterns, and military spending behaviors in the

context of Latin American economies, highlighting the need for a nuanced understanding

of the underlying causal mechanisms at play. Moreover, it is necessary to consider

national and international policies to promote sustainable development in countries, given

it produces benefits for investment, safety, and security. As nations strive for sustained

development, the research underscores the need for policymakers to grapple with the

intricate implications of military expenditure on economic and investment landscapes. This

holistic perspective advocates for a balanced and inclusive approach, cognizant of the

multifaceted challenges and opportunities that characterize the evolving socio-economic

and geopolitical landscape of the region.

RESUMEN DEL ARTÍCULO

Esta investigación busca examinar a fondo la interconexión y la influencia direccional entre

el gasto militar y el crecimiento económico sostenible, considerando el papel de la formación

bruta de capital fijo (inversión) como factor de control. Basándose en un conjunto de datos

exhaustivo de 13 economías latinoamericanas distintas, abarcando el extenso período de

tiempo de 1990 a 2019, el estudio investiga los patrones de cointegración y las relaciones

causales entre las variables. Los resultados del análisis de cointegración no brindan un

respaldo sustancial para la existencia de una relación fuerte y duradera entre todas las

variables examinadas a largo plazo. Específicamente, la investigación identifica una

causalidad unidireccional que va desde (i) el crecimiento económico hacia el gasto militar,

(ii) el crecimiento económico hacia la inversión y (iii) la inversión hacia el gasto militar. Esto

sugiere una interacción compleja entre la dinámica económica, los patrones de inversión

y los comportamientos de gasto militar en el contexto de las economías latinoamericanas,

destacando la necesidad de comprender de manera matizada los mecanismos causales

subyacentes en juego. Además, es necesario considerar las políticas nacionales e

internacionales para promover el desarrollo sostenible en los países, dado que produce

beneficios para la inversión, la seguridad y la estabilidad. A medida que las naciones buscan

un desarrollo sostenido, la investigación subraya la necesidad de que los formuladores de

políticas aborden las implicaciones intrincadas del gasto militar en los paisajes económicos

e de inversión. Esta perspectiva holística aboga por un enfoque equilibrado e inclusivo,

consciente de los desafíos y oportunidades multifacéticos que caracterizan el paisaje

socioeconómico y geopolítico en evolución de la región.

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external security. Generally, the measure of military burden and the

importance of the military sector in a country is defined by military

expenditure as a share of GDP or as a share of government spending

(Kumar, 2017). However, these metrics suffer problems of reliability,

validity, and comparability.

Each country dedicates financial resources to defense purposes.

As mentioned by (P. Dunne & Tian, 2013), these resources are

needed to handle any internal or external security threads. However,

even when there are opportunities, such as the generation of jobs,

there is also an associated cost, known as opportunity cost, as

these resources could be also used in more productive sectors.

Several studies analyzed the impact of military spending

on economic growth, but there is not a clear consensus

about their effects. Literature in defense economics warns

that resources allocated to the defense sector can have

adverse effects on economic growth, as it can reduce the

availability of resources for other more productive sectors of

the economy, as well as displace or substitute research and

development in the civilian sector. However, it has also been

argued that it can produce positive economic effects, as it

can create security and thereby promote trade, development,

and investment, increase aggregate demand through the

multiplier effect of spending, and generate spillover effects

from military research and development (Desli et al., 2017; J.

P. Dunne et al., 2005; Sempere, 2018). As shown by (Safdari

et al., 2011), developing economies in Asia (Iran and Saudi

Arabia) did not show a clear relationship between military expenditure

and economic growth. However, this relationship is evidenced in

industrial countries such as South Korea and Malaysia. Nevertheless,

this situation is similar to larger economies. For instance, (Das et al.,

2015) demonstrated in their study that Italy and Australia showed

a bidirectional causality between military spending and economic

growth, while other countries such as the USA, Canada, France,

Germany, China, and India did not identify any causality.

In this context, the objective of the present research is to analyze the

relationship between military spending and economic growth in 13

Latin American countries (namely Argentina, Bolivia, Brazil, Chile,

Colombia, Dominican Republic, Ecuador, El Salvador, Guatemala,

Mexico, Paraguay, Peru, and Uruguay) from 1990 to 2019, using

econometric techniques of cointegration and causality for panel data.

e primary

challenge of the

analysis of military

expenditure in a

country is how much

security is required

by a nation and how

much security can

be aorded by that

country.

MARÍA GABRIELA CUEVAJIMÉNEZ, RONNY CORREAQUEZADA,

ANA BELÉN TULCANAZAPRIETO & LUCÍA CUEVARODRÍGUEZ

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KEYWORDS

Military expenditure,

economic growth,

Latin America,

cointegration,

causality.

PALABRAS CLAVE

Gasto militar,

crecimiento

económico, América

Latina, cointegración,

causalidad

The results are in line with previous investigations and the case of

cointegration, most of the different tests implemented did not reject

the null hypothesis of the absence of a long-term relationship. In

the case of causality, three unidirectional relationships were found

economic growth to military spending and investment and from

investment to military spending.

The rest of this investigation is organized as follows. The theoretical

framework is presented in Section 2, where the theoretical and

empirical aspects related to the link between military spending

and economic growth are developed. The third section details the

methodology, the variables of interest, sources of information, and

different econometric procedures to verify cointegration and causality

in panel data. The results are shown in Section four. Finally, in

Section fifth, the study concludes by describing the most relevant

findings and offering recommendations and research directions for

future researchers.

2. LITERATURE REVIEW

In the extensive study of the determinants of long-term economic

growth, fundamental variables have been identified, for instance,

investment (Manuelito & Jiménez, 2013; Serebrisky et al., 2015).

However, the economic importance of defense spending is not

similarly emphasized (J. P. Dunne et al., 2005). Empirically, (Barro,

1989) presented evidence that defense spending is not significant for

economic growth. Similarly, (Sala-i-Martin et al., 2004) investigated

the determinants of economic growth with a significant number of

variables, including defense spending. The results indicated that

out of 67 variables, 18 were significant, but none were related to the

defense sector, suggesting its irrelevance.

On the other hand, the literature on defense economics has generated

a variety of theoretical and especially empirical discussions about the

effect of defense spending on economic growth, but without a solid

consensus. Theoretically, it has been argued that there are several

channels through which military spending can impact economic

growth. In this regard, (Sempere, 2018) mentioned that defense

spending can have a favorable economic effect through security, as it

encourages trade and investment, through infrastructure and human

capital that can be accessible to other sectors, and through the

multiplier effect of spending that boosts aggregate demand. Similarly,

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(Desli et al., 2017) stated that military spending can create spillover

effects due to military research and development, and stimulate

aggregate demand (through Keynesian militarism), contributing to

economic growth.

According to (J. P. Dunne et al., 2005), the defense sector affects

economic growth through demand, supply, and security. On the

demand side, the essential elements are the level and composition

of spending (the multiplier effect of spending). On the supply side,

the availability of production factors is highlighted, and through the

security effect, which promotes investment and innovation and,

consequently, production. The transmission channel of the economic

effects of defense spending can be indirect, through investment or

employment (Heo & Ye, 2016). In this regard, (Barro, 1989, 1991)

pointed out that military spending can influence private sector

productivity or property rights and, consequently, private investment.

(Heo & Ye, 2016) mentioned that defense spending can generate

employment through the hiring of active-duty soldiers, civilian

personnel, external firms, and subcontractors that provide goods and

services to the military.

On the other hand, the expenditure allocated to the defense sector

reflects high opportunity costs, as it can displace more productive

expenditures and public needs. Additionally, military research

and development can disturb, substitute, or diminish its civilian

counterpart (Cowan & Foray, 1995; Desli et al., 2017; Sempere,

2018). According to (Cowan & Foray, 1995), in the life cycle of

technology, military research and development are only beneficial for

civilian research and development during the experimentation stage,

where new general knowledge about emerging technology arises. In

the rationalization phase, where the use of technology is specialized

in each sector, military research and development has little value for

the civilian sector. In this sense, military research and development

would no longer be as important for the civilian sector.

The first studies that analyzed the nexus between military spending

and economic growth belong to (Benoit, 1973, 1978). The author

found a positive association, mainly coming from defense spending.

Subsequent research has raised questions regarding the validity

of Benoit's hypothesis, as a substantial body of empirical evidence

indicates that military expenditure yields not only positive but also

negative and neutral effects. Consequently, there exists evidence

suggesting that in certain instances, the causal relationship originates

MARÍA GABRIELA CUEVAJIMÉNEZ, RONNY CORREAQUEZADA,

ANA BELÉN TULCANAZAPRIETO & LUCÍA CUEVARODRÍGUEZ

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from economic growth, particularly prevalent in developing nations,

rather than the conventional assumption of military spending driving

economic growth. Moreover, this research highlights the existence of

bidirectional causality in some cases, emphasizing the intricate and

multifaceted nature of the relationship between military spending

and economic dynamics. However, some subsequent studies that

evaluated the same link confirmed Benoit's hypothesis, while others

reached opposite conclusions, and there is even evidence suggesting

a null economic effect. In terms of causality, there is evidence that

the direction of causality originates from economic growth (mainly

in developing countries) and not the other way around, as well as

bidirectional causality. Overall, the various types of findings contribute

to the lack of consensus on the economic effect of military spending.

(Aizenman & Glick, 2006; Alptekin & Levine, 2012; Dakurah et al.,

2001; Deger & Sen, 1983; Desli et al., 2017; Dritsakis, 2004; P. Dunne

& Tian, 2013; P. Dunne & Vougas, 1999; Wijeweera & Webb, 2009;

Yakovlev, 2007; Yesilyurt & Yesilyurt, 2019).

Regarding the null effect, (J. Dunne & Smith, 2020), through

econometric techniques for panel data, argued that from 1960 to

2014, there is no evidence of a strong relationship between military

spending, economic growth, and investment. A group of European

Union countries, (J. Dunne & Nikolaidou, 2012) concluded that

defense spending did not stimulate economic growth; on the contrary,

it can have a negative or null effect. In the case of the U.S. economy,

(Heo, 2000, 2010) mentioned that the resources allocated to the

defense sector do not have significant economic effects.

Despite a plethora of empirical evidence on the military spending-

economic growth nexus, there is no consensus, which can be

explained for various technical reasons: i) sample composition,

model specification, and estimation methods (Aziz & Asadullah,

2017; J. Dunne & Smith, 2020); ii) problems of endogeneity of the

variable linked to the defense sector that have not been adequately

addressed (D’Agostino et al., 2017), iii) sample heterogeneity and

non-linearity (P. Dunne & Tian, 2013; Huang et al., 2017); and iv)

weaknesses of theoretical models, such as the Feder-Ram model,

which is prone to misinterpretations, and its econometric estimation

contains simultaneous bias problems. Alternatively, augmented Barro

and Solow models offer better results compared to the Feder-Ram

model (J. P. Dunne et al., 2005). Therefore, it is necessary to analyze

empirical evidence and the relationship findings between military

spending and economic performance.

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2.1. Empirical Evidence

The empirical evidence regarding the link between military spending

and economic growth is extensive. (Yildirim & Öcal, 2016) studied the

economic effect of military spending from a broad perspective (128

countries) through spatial econometrics methods and the augmented

Solow growth model for the period of 2000-2010. The findings

indicate a positive effect of military spending on economic growth with

significant spatial dependence. (Kollias et al., 2017) analyzed the link

between military spending, economic growth, and investment using

the panel vector autoregressive methodology (PVAR) for 65 countries

from 1971 to 2014. The authors also disaggregated the information

into panel subgroups classified by income level. In the high-income

group, they found that the economic effect of military spending is

positive, which can be explained by effective demand and spillover

effects generated in the defense industry sector. In medium- and

low-income countries, the opposite occurs; economic growth has a

positive effect on military spending, suggesting that as the economy

grows, more resources are available for the defense sector.

Negative effects have also been found. For instance, (Huang et al.,

2017) studied the causality between military spending and economic

growth in 77 countries from 1996 to 2014, taking into account the

country's level of development through the Human Development

Index (HDI). They found that causality is bidirectional, negative,

non-linear, and varies over time and between countries. As the HDI

becomes higher, the negative causality of the defense sector on

economic growth and vice versa decreases. Similarly, (Chang et al.,

2011), for different groups of countries from the causality approach

from 1990 to 2006, found that there is no causal link between the

variables of interest in the high-income and medium-income country

groups. However, in low-income countries, military spending

negatively affects economic growth.

(Heo & Ye, 2016) studied the direct and indirect economic effects of

military spending on economic growth in 161 countries from 1990 to

2012. Using seemingly unrelated regressions (SUR), they found that

the effect of the defense sector is negative and statistically significant;

however, the magnitude of the coefficient is extremely small,

suggesting an almost insignificant impact. (P. Dunne & Tian, 2013),

using an exogenous growth model and methods for dynamic panel

data in 106 countries from 1988 to 2010, identified that the effect of

military spending on economic growth is negative and significant in

MARÍA GABRIELA CUEVAJIMÉNEZ, RONNY CORREAQUEZADA,

ANA BELÉN TULCANAZAPRIETO & LUCÍA CUEVARODRÍGUEZ

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185

the short and long term. The findings are consistent across different

sample specifications and independent variables.

(Aziz & Asadullah, 2017), for a panel of countries from 1990 to 2013

and through econometric techniques such as ordinary least squares

(OLS), fixed effects, random effects, and the generalized method

of moments (GMM), concluded that the economic effect of military

spending is negative and statistically significant. Similarly, (D’Agostino

et al., 2017), using the pooled mean group (PMG) estimator and

the dynamic fixed effects method for country panel data from 1970

to 2014, showed a long-term negative effect of military spending on

economic growth. The results are robust to different study period

specifications and countries.

(D’Agostino et al., 2019), employing the instrumental variables

method to control for the endogeneity of defense spending, in a panel

of 109 low-income countries from 1998 to 2012, found evidence in

favor of a significant negative economic effect of military spending,

which has been underestimated by the OLS method. The results are

robust to heterogeneity and different time specifications. (D’Agostino

et al., 2020), on their part, implemented the autoregressive distributed

lag (ARDL) approach for panel data from 1984 to 2014. They found

that both military spending and corruption have a significant long-

term negative economic effect.

(Desli et al., 2017) constructed a panel of 138 countries from 1988

to 2013. Additionally, the authors disaggregated the information into

subgroups according to the income level and applied econometric

methods of cointegration and causality. The results confirmed a

long-term relationship in the full sample formed by developed and

developing countries, but in less developed countries, the tests tend

to mostly reject the cointegration hypothesis. Regarding causality, in

the long term, there is a bidirectional relationship between the general

sample and developing countries, while in developed countries, it is

unidirectional from economic growth to military spending. In the short

term, there is no causality for the less developed country group, and

in the rest of the groups, causality comes from economic growth to

military spending.

Specifically for the case of Latin America, (Kung & Min, 2013), using

Granger causality, following the Bootstrap procedure, for 16 Latin

American economies from 1988 to 2010, found that military spending

causes economic growth in the case of Nicaragua and Belize.

Conversely, growth causes military spending for Ecuador and Bolivia;

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while for the remaining countries (Argentina, Brazil, Chile, Colombia,

Dominican Republic, El Salvador, Guatemala, Mexico, Paraguay,

Peru, Uruguay, and Venezuela), there is no evidence of causality.

(Kollias et al., 2017), The findings rely on the specification of various

samples with varying country numbers: i) 46 countries in total with

available information for the period of interest; ii) 25 OECD countries;

and, iii) 17 OECD countries. Additionally, they disaggregate the entire

period into two sub-periods, the first encompassing the Cold War era

(1960-1985) and the second corresponding to the post-Cold War

period (1986-2014); in addition to the variables of interest (military

spending and economic growth), added investment and employed

cointegration methods, linear causality, and non-linear causality for

a time series analysis in 13 Latin American economies from 1961 to

2014. The results indicated that there is at least one cointegrating

vector for each country; however, in terms of causality, not all

countries showed a causality relationship in at least one direction;

therefore, the authors concluded that, in general terms, the causality

link is weak.

Understanding the relationship and causality between economic

growth and military expenditure is especially important in developing

economies, principally in those where the foreign exchange rate

plays an important role (Paul Dunne, 1996) and where public financial

resources are limited. Moreover, the share of military spending in the

GDP of some Latin American countries is not insignificant, especially

considering that they are developing economies with important public

needs. During the last three decades (period of 1990-2019), the

country with the highest military spending, on average, was Colombia,

with a figure equal to 3.14% of the GDP, followed by Chile, Ecuador,

and Uruguay, where the share of military spending exceeded 2% of

their GDP. On the other hand, the country that has allocated the least

resources to the defense sector is Mexico, distributing an average

of 0.45% as a share of GDP. If we consider the last year of the study

period (2019), the behavior of expenditure did not differ from the

average observed over the three decades, as Colombia continues

with the allocation of the highest resources for the defense sector

(3.14% of GDP), followed by Ecuador (2.18%), and Uruguay (2.06%).

About the other countries that are part of this study, in the last year,

Chile, Bolivia, Brazil, Peru, and El Salvador allocated defense of

figures less than 2%; while Paraguay, Argentina, the Dominican

Republic, Mexico, and Guatemala, military spending is less than 1%

as a percentage of GDP (World Bank, 2023) (Figure 1).

MARÍA GABRIELA CUEVAJIMÉNEZ, RONNY CORREAQUEZADA,

ANA BELÉN TULCANAZAPRIETO & LUCÍA CUEVARODRÍGUEZ

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By way of analysis, Peru, Colombia, and Ecuador share borders and,

consequently, common security concerns such as drug trafficking,

smuggling, and guerrilla activity. These threats have led to an

increase in military spending in these countries, aiming to strengthen

their defense and national security capabilities.

In the case of Peru, its military expenditure has historically been

directed towards combating drug trafficking and controlling its

extensive border with Colombia, where the activities of guerrilla

groups and drug traffickers have posed a threat to internal security.

Additionally, Peru has faced challenges related to organized crime

and terrorism, especially in rural and border areas.

Colombia, on the other hand, has faced decades of internal conflicts

with illegal armed groups such as the FARC and the ELN, as well as

challenges related to drug trafficking. Military spending in Colombia

has focused on combating these internal threats, as well as protecting

its borders and ensuring stability in the region.

In the case of Ecuador, although it has not faced internal conflicts as

intense as Peru and Colombia, it has experienced border tensions

with Peru in the past and shares similar concerns regarding regional

security. Ecuador's military spending has focused on strengthening

Figure 1. Military spending as a percentage of GDP

Note: Argentina (ARG), Bolivia (BOL), Brazil (BRA), Chile (CHL), Colombia (COL), Dominican Republic (DOM), Ecuador

(ECU), El Salvador (SLV), Guatemala (GTM), Mexico (MEX), Paraguay (PRY), Peru (PER), and Uruguay (URY). Source:

(World Bank, 2023).

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its defense capabilities and border surveillance, as well as addressing

threats such as drug trafficking and smuggling.

Therefore, the hypothesis of the study is:

Hypothesis 1. There is a relationship between economic growth

to military spending (unidirectional causality) in the short term.

During a specific short-term period, an increase in economic

growth directly leads to a corresponding rise in military spending,

indicating a unidirectional causal relationship between these two

variables.

3. METHODOLOGY

The study of the relationship between military spending and

economic growth for some Latin American countries is conducted

based on cointegration and causality methods for panel data; these

methods allow for verifying whether there is a long-term equilibrium

relationship between the variables of interest and identifying the

direction of causality. The choice of cointegration and causality

methods for panel data in the study of the relationship between military

spending and economic growth in certain Latin American countries is

justified by their suitability for examining dynamic and interdependent

relationships over time across multiple entities. These methods are

particularly appropriate as they enable the analysis of both long-term

equilibrium relationships and short-term causal dynamics, allowing

for a comprehensive understanding of the complex interactions

between the variables under investigation. Additionally, panel data

analysis facilitates the consideration of individual country-specific

effects alongside overall trends, providing a more nuanced and robust

assessment of the relationship within the context of the diverse Latin

American economies. A balanced data panel was constructed for 13

Latin American economies (namely Argentina, Bolivia, Brazil, Chile,

Colombia, Dominican Republic, Ecuador, El Salvador, Guatemala,

Mexico, Paraguay, Peru, and Uruguay) from 1990 to 2019. The

variables of interest are GDP per capita and military spending.

Additionally, gross fixed capital formation (investment) is added as a

control variable. GDP per capita and investment are obtained from the

World Bank's World Development Indicators (World Bank, 2023), and

military spending is collected from the (Stockholm International Peace

Research Institute (SIPRI), 2023). All variables are measured at

constant prices and transformed into logarithms. Table 1 summarizes

MARÍA GABRIELA CUEVAJIMÉNEZ, RONNY CORREAQUEZADA,

ANA BELÉN TULCANAZAPRIETO & LUCÍA CUEVARODRÍGUEZ

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the variables of interest, abbreviations, and sources of information

(Most of the methodological procedures were conducted in Stata 16,

except the (Pesaran, 2007) unit root test and the Johansen-Fisher

cointegration test, which were performed in EViews 12, owing to the

availability of the procedure).

Table 1. Variables

VARIABLES ABBREVIATION SOURCE

GDP per capita lnpib_pc (World Bank, 2023)

Military expenditure lngm (Stockholm International Peace Research

Institute (SIPRI), 2023)

Gross xed capital formation

(investment)

lnfbkf (World Bank, 2023)

The cointegration analysis involves determining the order of

integration of the series through non-stationarity tests. The choice

of unit root tests is delimited by the assumption of cross-sectional

dependence because in the presence of cross-sectional correlation

of errors, unit root tests can exhibit significant size distortions

(Baltagi et al., 2007). Therefore, the Lagrange multiplier (LM) spatial

dependence tests of (Breusch & Pagan, 1980) are used, which

are appropriate when the time dimension (T) is greater than the

number of cross-sectional units (n) (T>n), and the cross-sectional

dependence (CD) test of (Pesaran, 2004). In the presence of spatial

correlation, three unit root tests are used: i) the cross-sectionally

augmented panel unit root tests (CIPS test) of (Pesaran, 2007) that

allows for spatial dependence; ii) the (Breitung, 2001) test, following

the approach of (Breitung & Das, 2005) that provides robustness

for cross-sectional correlation; and iii) the panel unit root test (IPS

test) proposed by (Im et al., 2003) but subtracting cross-sectional

averages to reduce the effect of spatial dependence, as suggested

by (Levin et al., 2002). The null hypothesis indicates the existence of

a unit root in the panels and is common for all tests.

To verify whether there is a long-run relationship between the series,

non-cointegration tests by (Kao, 1999), (Pedroni, 1999, 2004),

(Westerlund, 2005), and the Johansen-Fisher type by (Maddala &

Wu, 1999) are implemented. (Kao, 1999) suggested various unit root

tests based on Dickey-Fuller and augmented Dickey-Fuller. (Pedroni,

1999, 2004)’s tests allow for considerable heterogeneity and are

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organized into two groups, the first including four tests related to the

within-dimension where the autoregressive parameter is common

to all panels, and the second comprising three tests related to the

between-dimension where the autoregressive term is specific to

each panel. (Westerlund, 2005) proposed two non-parametric tests

based on residuals, and (Maddala & Wu, 1999) developed a test

that combines the cross-sectional p-values to construct a panel test

statistic.

The aforementioned tests are based on residuals (except the

Johansen-Fisher type test); therefore, the analysis is complemented

with the tests of (Westerlund, 2007) based on an error correction

model, which has good properties in small samples and high power

compared to tests based on residuals. The author proposed four

non-cointegration tests, two corresponding to group-mean statistics

(G_τ and G_α) and with the alternative hypothesis that there is

cointegration in at least one cross-sectional unit, and the other two

refer to panel statistics (P_τ and P_α) with the alternative hypothesis

indicating cointegration for the entire panel. The tests relax the

assumption of spatial independence using the bootstrap approach. In

the tests based on residuals, the cross-sectional mean is subtracted

to reduce the effect of spatial correlation (Levin et al., 2002).

In addition to the cointegration approach, the causality test suggested

by (Dumitrescu & Hurlin, 2012) is implemented, which will reveal the

direction of causality in the variables of interest. According to the

authors, the test is based on the cross-sectional average of individual

Wald statistics of Granger non-causality. It has good properties

in small sample sizes, and in the presence of cross-sectional

dependence, the bootstrap approach can be used. The method

requires the series to be stationary and the choice of the lag length,

so the optimal lag length is based on the Akaike information criterion

(AIC).

4. RESULTS

Table 2 shows the descriptive statistics of the variables. The data

panel is balanced with 390 observations, containing 13 cross-

sectional units (countries) and 30 annual frequency periods (1990-

2019). In the three variables of interest, there is greater dispersion

among countries than within a country over time (between dispersion

is higher than within), although the difference is smaller in the case of

economic growth.

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ANA BELÉN TULCANAZAPRIETO & LUCÍA CUEVARODRÍGUEZ

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Table 2 serves as a crucial gateway into the heart of our empirical

exploration, presenting the descriptive statistics that encapsulate the

essence of the variables under scrutiny. This statistical tableau unveils

a balanced data panel, meticulously crafted with 390 observations.

This comprehensive dataset spans a considerable temporal

landscape, encompassing 13 cross-sectional units, representing

distinct countries, and spanning 30 annual frequency periods from

1990 to 2019. Within this dataset, a trove of information awaits,

shedding light on the distribution and variability of the key variables of

interest—military spending, economic growth, and investment. These

metrics stand as pillars, supporting the edifice of our investigation

into the intricate dance between defense expenditures, economic

prosperity, and investment patterns across the diverse tapestry of

Latin American nations.

One notable characteristic that leaps from the statistical canvas is the

discernible dispersion among countries relative to within a country

over time. This divergence is particularly pronounced in the context

of the between-country dispersion, where variations among the 13

distinct nations surpass those occurring within a specific country

across the temporal spectrum. It's a revealing revelation, painting

a vivid picture of the heterogeneity that exists among the nations

comprising our study.

Now, let's delve into the specifics of the three variables—military

spending, economic growth, and investment. These components,

fundamental to our exploration, exhibit intriguing patterns that merit

meticulous examination.

Military Spending: Unveiling Cross-Country Disparities

Our first protagonist, military spending, unfolds as a variable of

substantial interest and intrigue. As we peruse Table 2, the dispersion

among countries stands out prominently. Each nation, it seems,

charts its unique trajectory in allocating resources to the defense

sector. This cross-country variability paints a diverse panorama

of strategic priorities, geopolitical considerations, and perhaps,

economic capacities influencing the defense expenditure decisions

of these Latin American nations.

However, a compelling nuance arises when we juxtapose this cross-

country variability with the within-country variation over time. The

between-country dispersion, while substantial, underscores that

the variations in military spending within a specific nation across

the years are not negligible. This duality sets the stage for a deeper

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inquiry—what factors contribute to the divergences in military

spending policies among nations, and what internal dynamics drive

fluctuations within a country over time?

Economic Growth: Between Stability and Flux

Turning our gaze to economic growth, the second protagonist in our

narrative, we encounter a nuanced tale. The descriptive statistics

reveal a comparable pattern of greater dispersion among countries

than within a country over time. However, a notable subtlety arises—

the magnitude of this difference is somewhat less pronounced in the

case of economic growth.

This finding prompts us to ponder the underlying factors that contribute

to the relatively more stable trajectory of economic growth within a

nation over the years. Is it indicative of resilient economic policies,

structural stability, or perhaps, a convergence of developmental

strategies among these Latin American nations? Conversely, the

variability in economic growth among countries invites us to explore

the diverse economic landscapes that shape the growth trajectories

of these nations.

Investment: A Crucible of Economic Dynamism

Our third key player, investment, takes center stage, embodying the

pulse of economic dynamism. The descriptive statistics spotlight a

conspicuous trend—once again, the dispersion among countries

eclipses the variations within a country over time. This accentuates

the distinctive investment patterns that characterize each nation,

reflecting diverse economic structures, policy frameworks, and

perhaps, responses to global economic dynamics.

Synthesizing Insights: A Call for Deeper Exploration

In the symphony of descriptive statistics presented in Table 2, a

harmonious yet complex melody emerges. Cross-country disparities

resonate across military spending, economic growth, and investment,

shaping a mosaic that reflects the diverse economic, political, and

strategic realities of the Latin American region. The interplay between

between-country and within-country variations beckons further

investigation, inviting researchers to unravel the intricate threads that

weave the fabric of these disparities.

As we embark on the journey into the econometric intricacies that lie

ahead, Table 2 serves not just as a numerical compendium but as

a compass guiding us through the uncharted territories of military-

economic dynamics in Latin America. The disparities unveiled within

these statistics beckon us to explore the driving forces, the nuanced

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ANA BELÉN TULCANAZAPRIETO & LUCÍA CUEVARODRÍGUEZ

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policies, and the intricate interdependencies that underlie the

economic and defense landscapes of these nations. This statistical

prelude, rich in insights, sets the stage for a deeper dive into

causality, cointegration, and the underlying mechanisms that govern

the symbiotic relationship between military spending and economic

growth in the complex tapestry of Latin America.

Table 2. Descriptive statistics

VARIABLE GROUP MEAN STD. DEV. MINIMUM MAXIMUM

lnpib_pc

overall 8.612 0.537 7.431 9.692

between 0.516 7.722 9.344

within 0.204 8.022 9.170

lngm

overall 20.991 1.422 18.001 24.002

between 1.439 19.230 23.607

within 0.327 19.560 21.822

lnfbkf

overall 23.625 1.431 21.133 26.689

between 1.420 21.933 26.214

within 0.428 22.363 24.565

Note: Number of observations for “overall group”=390, number of observations for “between group”=13 (coun-

tries), and number of observations for “within group”=30 (years of study).

The initial exploration preceding the selection of unit root tests

delves into the realm of spatial dependence. The outcomes of the

tests conducted, as elucidated by (Breusch & Pagan, 1980; Pesaran,

2004) are meticulously detailed in Table 3. Strikingly, the results from

both tests align, unequivocally pointing towards the existence of

spatial dependence across all three variables under scrutiny.

The spatial dependence, as unveiled by these tests, introduces

a layer of complexity to our understanding of the relationships

between the variables. This phenomenon implies that the values of

the variables are not independent or randomly distributed across the

geographical units, in this case, the various countries in our study.

Instead, there is a discernible spatial pattern or interdependence that

transcends mere temporal correlations.

Breusch and Pagan's (1980) test, a stalwart in spatial econometrics,

and (Pesaran, 2004)’s test, a contemporary approach known for its

robustness, converge in their verdict—each variable exhibits spatial

dependence. This concordance fortifies the credibility of our findings

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and underscores the robustness of the spatial dependence observed

in the dataset.

The presence of spatial dependence in all three variables—military

spending, economic growth, and investment—holds profound

implications for our understanding of the dynamics within the Latin

American economies under study. It prompts a critical examination

of the interconnectedness and spatial patterns that may underpin the

observed variations in these key economic and defense indicators.

The spatial dimension introduces a geographical context to our

analysis, suggesting that neighboring countries may influence

each other's military spending, economic growth, and investment

decisions. This spatial interdependence could stem from shared

regional characteristics, geopolitical considerations, or even

economic spillover effects that transcend national borders. As such,

our investigation transcends a mere temporal examination and

ventures into the spatial intricacies that shape the economic and

defense landscapes of the Latin American region.

The acknowledgment of spatial dependence also opens avenues

for further exploration. Researchers may delve into the specific

mechanisms through which spatial interdependence operates in

the context of military spending, economic growth, and investment.

Are there clusters of countries exhibiting similar patterns, and if so,

what factors contribute to this spatial homogeneity? Conversely, what

divergent forces lead to spatial heterogeneity among nations?

Moreover, this spatial lens invites considerations of policy implications.

If neighboring countries do indeed influence each other in terms of

military spending, economic growth, and investment, policymakers

may need to adopt a regional perspective. Collaborative efforts

among neighboring nations could be essential for fostering stability,

economic development, and security in the broader regional context.

Table 3. Spatial dependence tests

TEST LNPIB_PC LNGM LNFBKF

Breusch y

Pagan

Test statistic 2038.857 817.875 1661.720

Probability 0.000 0.000 0.000

Pesaran Test statistic 45.095 17.968 40.414

Probability 0.000 0.000 0.000

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ANA BELÉN TULCANAZAPRIETO & LUCÍA CUEVARODRÍGUEZ

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Due to spatial dependence, two tests are implemented: i)the CIPS

test by (Pesaran, 2007), which relaxes the assumption of spatial

independence, and ii) the Breitung (2000) and IPS tests by (Im et

al., 2003) with control for cross-sectional dependence to mitigate

their impact. The findings are presented in Table 4. The variable of

GDP per capita, according to the CIPS test, is stationary only when

specified with an intercept, while without a deterministic term, it is

non-stationary. Breitung and IPS suggest non-stationarity. Regarding

military expenditure, Breitung rejects the non-stationarity hypothesis

when including trend or excluding trend and intercept, as does the IPS

test with a constant. As for investment, when the constant is specified,

there is stationarity according to the CIPS, however, when the trend

is included or the deterministic term is excluded, it is non-stationarity.

The remaining tests indicate non-stationarity. Upon obtaining the first

difference of the variables, all tests converge towards the stationarity

of the series, suggesting that the variables are integrated of order one

(I(1)).

Table 4. Unit Root Tests

VARIABLE DETERMINISTIC TERM CIPS BREITUNG Y DAS IPS

lnpib_pc

(level)

Constant -2.265 * 4.269 0.822

Trend -1.578 0.928 1.233

Excludes deterministic term -0.854 6.040 -

lngm

(level)

Constant -2.090 0.848 -2.489 ***

Trend -3.115 *** -1.015 -0.842

Excludes deterministic term -2.112 *** 2.439 -

lnfbkf

(level)

Constant -2.602 *** 2.124 -1.021

Trend -2.477 -0.324 -0.697

Excludes deterministic term -1.302 3.698 -

dlnpib_pc

(difference)

Constant -3.202 *** -4.960 *** -10.145 ***

Trend -3.278 *** -5.081 *** -8.932 ***

Excludes deterministic term -3.080 *** -5.333 *** -

dlngm

(difference)

Constant -4.554 *** -3.710 *** -19.350 ***

Trend -4.440 *** -5.207 *** -18.638 ***

Excludes deterministic term -3.710 *** -13.977 *** -

dlnfbkf

(difference)

Constant -3.963 *** -5.447 *** -12.388 ***

Trend -4.240 *** -7.042 *** -10.033 ***

Excludes deterministic term -3.556 *** -8.036 *** -

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

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Following the identification of the order of integration of the series,

various cointegration tests are implemented to verify if there is a long-

term equilibrium relationship. The results are shown in Table 5. Out of

the five Kao tests, three indicate cointegration at the 10% significance

level. (Pedroni, 1999, 2004) suggests in four out of seven tests

that there is a long-term relationship at the 1% and 5% statistical

significance levels, respectively. The non-parametric (Westerlund,

2005) tests do not reject the null hypothesis of no cointegration, while

the Johansen-Fisher type test suggests the existence of at least one

cointegrating vector at the 1% significance level. The (Westerlund,

2007) tests, which consider spatial dependence using the bootstrap

procedure, do not reject the null hypothesis of no cointegration.

Table 5. Cointegration Tests

COINTEGRATION TESTS STATISTIC PROBABILITY

Test Kao

Modied Dickey-Fuller t 1.156 0.124

Dickey-Fuller t 1.265 0.103

Augmented Dickey-Fuller t 1.468 0.071 *

Unadjusted modied Dickey-Fuller t 1.457 0.073 *

Unadjusted Dickey-Fuller t 1.586 0.056 *

Test Pedroni

Modied Phillips-Perron t 0.963 0.168

Phillips-Perron t -0.596 0.276

Augmented Dickey-Fuller t 0.448 0.327

Modied variance ratio -2.849 0.002 ***

Modied Phillips-Perron t 2.132 0.017 **

Phillips-Perron t 1.908 0.028 **

Augmented Dickey-Fuller t 2.143 0.016 **

Test Westerlund (2005)

Variance ratio

(Alternative hypothesis: Some panels are cointegrated) 0.327 0.372

Variance ratio

(Alternative hypothesis: All panels are cointegrated) 0.970 0.166

Test Johansen-Fisher

Null hypothesis: No cointegrating equation

Trace test 74.210 0.000 ***

Maximum Eigenvalue Test 62.500 0.000 ***

MARÍA GABRIELA CUEVAJIMÉNEZ, RONNY CORREAQUEZADA,

ANA BELÉN TULCANAZAPRIETO & LUCÍA CUEVARODRÍGUEZ

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Some cointegration tests do not reject the null hypothesis. Therefore,

the evidence for a long-term equilibrium relationship is quite weak.

Regarding the causality approach, Table 6 shows the results of the

(Dumitrescu & Hurlin, 2012) test. The statistics values are presented

with their probability and the probability values are calculated

using the bootstrap procedure to control for spatial dependence.

Additionally, the optimal lag length (AIC) is specified.

Null hypothesis: At least one

Trace test 34.850 0.115

Maximum Eigenvalue Test 30.520 0.247

Westerlund (2007) (Bootstrap)

Gτ-1.516 0.881

Gα-5.453 0.713

Pτ-2.734 0.993

Pα-1.507 0.994

Note: ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels, respectively.

Table 6. Causality

CAUSALITY STATISTIC

Z-BAR PROBABILITY PROBABILITY

(BOOTSTRAP) AIC

dlngm does not cause the dlnpib_pc 1.114 0.265 0.281 1

dlnpib_pc does not cause the dlngm 4.706 0.000 0.005 1

dlngm does not cause the dlnfbkf 1.792 0.073 0.138 2

dlnfbkf does not cause the dlngm 5.511 0.000 0.003 1

dlnfbkf does not cause the dlnpib_pc 0.834 0.404 0.484 1

dlnpib_pc does not cause the dlnfbkf 2.410 0.016 0.093 1

Note: Alternative hypothesis: There is causality in at least one cross-sectional unit.

The insights gleaned from Table 6 pave the way for a nuanced

understanding of the causal relationships embedded within the

economic and defense dynamics of Latin American nations. The

unidirectional causality unveiled in the analysis serves as a key

revelation, shedding light on the intricate interplay between economic

growth, military expenditure, and investment.

According to the findings, a unidirectional causal link exists between

economic growth to both military expenditure and investment. This

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implies that as the economic prosperity of Latin American countries

advances, there is a subsequent positive impact on both military

spending and investment. This unidirectional causality underscores

the pivotal role that economic growth plays in shaping resource

allocation decisions within these nations.

Equally noteworthy is the identified unidirectional causality from

investment to military spending. This suggests that increased

investment levels contribute to a subsequent rise in military

expenditure. The implications of this finding are manifold, hinting

at the symbiotic relationship between economic investment and

the defense sector. It beckons further exploration into the factors

that drive this causality—whether it is driven by security concerns,

geopolitical considerations, or a combination of various economic

and strategic factors.

Contrastingly, the hypothesis of non-causality from military spending

to economic growth cannot be rejected based on the results. This

implies that at least in the examined period and countries, military

spending does not appear to be a driving force behind economic

growth. This challenges conventional wisdom in certain economic

theories that posit military spending as a potential catalyst for

economic development. The nuanced findings here suggest that, in

the Latin American context, economic growth shapes the trajectory of

military spending and investment, rather than the reverse.

The crux of these results suggests that the defense sector's dynamics

are intricately tied to the performance of the broader economy. In

essence, the resources available for military spending can expand

if Latin American economic growth improves. This aligns with the

broader narrative in the economic literature that emphasizes the

importance of a robust and growing economy in supporting other

sectors, including defense.

These findings echo the work of (Kung & Min, 2013) in the case of

Ecuador and Bolivia, as well as the research conducted by (Kollias

et al., 2017) for middle- and low-income countries. The argument that

as the economy expands, more resources can be allocated to the

defense sector resonates across various contexts, substantiating the

idea that economic prosperity acts as a catalyst for bolstering military

capabilities.

In contemplating the policy implications of these results, a critical

consideration emerges for Latin American policymakers. The

prioritization of strategies that foster economic growth may not only

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ANA BELÉN TULCANAZAPRIETO & LUCÍA CUEVARODRÍGUEZ

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enhance overall national prosperity but also fortify the resources

available for defense and strategic investment. It underscores the

need for a comprehensive and integrated approach to policymaking

that recognizes the symbiotic relationship between economic

development and the capacity to address security and defense

concerns. Similar findings are presented in the study by (Desli et al.,

2017), which stated that in developing countries from 1988 to 2013,

the direction of causality in the short term is from economic growth to

military spending and not the other way around. Similarly, (Kollias &

Paleologou, 2019) for middle- and low-income countries concluded

that there is a positive effect from economic growth to military

spending, while the reverse only occurs in high-income countries.

5. CONCLUSIONS

In the quest to unravel the intricate relationship between military

spending and economic growth in Latin American economies,

this comprehensive research employed advanced econometric

techniques, specifically cointegration and causality analysis, using

panel data spanning the period from 1990 to 2019 across 13 distinct

nations in the region. The multifaceted analysis yielded nuanced

insights that challenge conventional wisdom and carry substantial

implications for both economic and defense policy in the region.

The cointegration analysis, a fundamental aspect of this study, aimed

to discern the existence of a long-term relationship between military

spending and economic growth. The various tests conducted did not

unequivocally reject the null hypothesis, indicating an absence of a

robust and enduring relationship between these variables over the

extended time frame. While the results did not conclusively establish

a causal link, they refrained from affirming a sustained connection.

This challenges prevailing assumptions in the literature and warrants

a reevaluation of the perceived long-term impact of military spending

on economic growth in Latin America.

In delving into the causal relationships, the study identified three

unidirectional causal links. First, a causal relationship was observed

between economic growth to military spending, suggesting that a

thriving economy contributes to increased allocation of resources

to the defense sector. Second, a unidirectional causality emerged

from economic growth to investment, indicating that economic

expansion precedes and fosters higher levels of investment. Third,

a unidirectional link from investment to military spending was

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established, implying that increased investment may lead to higher

military expenditures.

These findings challenge the conventional narrative that military

spending is a catalyst for economic growth. Instead, they suggest that

economic growth plays a pivotal role in determining the resources

allocated to the defense sector. This nuanced understanding

reshapes the discourse surrounding military spending policies,

cautioning against strategies that prioritize defense expenditure as a

means to stimulate aggregate demand.

The implications of these findings for public policy are profound.

The traditional notion of increasing defense resources to stimulate

economic growth is challenged, as the study suggests that such a

strategy may be ill-advised. Policymakers need to carefully consider

the economic dynamics at play and avoid overlooking the importance

of fostering a robust and growing economy to sustain the defense

sector.

The research also emphasizes that the efficacy of the defense sector

extends beyond financial resources. Military capability is shaped

by a myriad of factors, including economic, non-economic, political,

geographical, geopolitical, and geostrategic considerations. The

study underscores the importance of adopting a holistic approach to

understanding military capability, moving beyond simplistic metrics

such as the share of GDP allocated to defense.

In light of contemporary geopolitical tensions, the study notes a

global trend of increased military spending across nations. This

observation aligns with the notion that rapidly growing countries

are likely to allocate higher proportions of their budgets to military

protection. The implication is that as nations develop, they are likely to

prioritize security measures, linking the development of a country to

increased financial resources for both military spending and broader

investments.

This research challenges preconceived notions about the

relationship between military spending and economic growth in Latin

America. The identified causal links highlight the intricate dynamics

at play, emphasizing the need for nuanced development strategies

that prioritize economic growth as a fundamental driver for both

investment and military expenditures. Policymakers are urged to

adopt a comprehensive understanding of the multifaceted factors

shaping military capability, taking into account economic, geopolitical,

and strategic considerations. As Latin American nations navigate the

complex interplay between defense and development, this research

serves as a guide for formulating informed and effective policies that

foster sustainable growth and security in the region.

MARÍA GABRIELA CUEVAJIMÉNEZ, RONNY CORREAQUEZADA,

ANA BELÉN TULCANAZAPRIETO & LUCÍA CUEVARODRÍGUEZ

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1. INTRODUCCIÓN

El gasto militar o presupuesto militar comprende la cantidad de

recursos financieros de gasto total en las tres fuerzas armadas

(ejército, armada y fuerza aérea) y mantenimiento de la paz

proporcionado por el gobierno central en un país. El Instituto

Internacional de Investigación para la Paz de Estocolmo (SIPRI, por

sus siglas en inglés) estableció que el gasto militar incluye gastos

corrientes y de capital para las fuerzas armadas y de mantenimiento

de la paz, actividades espaciales militares, ministerios de defensa

y otras agencias gubernamentales enfocadas en proyectos de

defensa. Además, debería incluir gastos en personal militar y civil,

pensiones de jubilación del personal militar, servicios sociales

para el personal y sus familias, operaciones y mantenimiento,

adquisiciones, investigación y desarrollo militar, construcción militar

y ayuda militar. Smith (2000) definió la defensa de la sociedad como

una de las funciones principales del gobierno y como la razón para

una tributación razonable; por lo tanto, el presupuesto militar es

reconocido como un área donde no hay una solución privada.

El principal desafío del análisis del gasto militar en un país es

cuánta seguridad requiere una nación y cuánta seguridad puede

costear ese país. Estas preguntas también están relacionadas

con la capacidad militar, que no depende necesariamente solo del

presupuesto militar asociado al Producto Interno Bruto (PIB), sino

que también está relacionada con factores económicos, políticos,

geográficos, culturales y estratégicos en un país. Por lo tanto, la

importancia del gasto militar se alinea con los esfuerzos constantes

Recepción: 16.10.2024 Aceptación: 14.02.2025 DOI: 10.3232/UBR.2025.V22.N1.04

CÓDIGOS JEL:

H56, O11, C32, F52, N46

Business and Society Review

UCJC

(Formerly known as Universia Business Review)

Gasto Militar y

Crecimiento Económico

Sostenible en América

Latina

Military Expenditure and Sustainable Economic

Growth in Latin America

María Gabriela Cueva-

Jiménez

Estudios de Doctorado en

Universidad Camilo José

Cela, Madrid, Spain.

 gabrielacuevaj@gmail.

com

Ronny Correa-Quezada

Departamento de Economía

de la Universidad Técnica

Particular de Loja, Ecuador.



Ana Belén Tulcanaza-

Prieto

Grupo de Investigación Lugar

Medio Sociedad (LMS),

Universidad de las Amèricas,

Quito, Pichincha, Ecuador.



Lucía Cueva-Rodríguez

Magister in Economic

Development en la Facultad

Latinoamericana de Ciencias

Sociales, Quito Ecuador,

Investigadora y Consultora en

Economía.



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