Homicide Rate and Net Inflow of Foreign Capital as a Proportion of GDP in Latin America and the Caribbean

ABSTRACT

The aim of this study is to analyze the effects of crime, represented here by homicide rates, on the net inflow of foreign capital as a proportion of Gross Domestic Product (GDP) in Latin America and the Caribbean. To do so, the study used the dynamic panel method which allows one to control for the endogenous relationship between variables, using data from the World Bank Data for the 1990-2020 period. The main result showed that homicide rates tend to reduce the net inflow of foreign capital as a proportion of GDP in Latin America and the Caribbean countries. In view of these results, which highlight the devastating effects of crime, it is expected that this study can contribute to the scientific production on the subject, and also serve as a support for the possible drafting of public policies to reduce crime in Latin America and the Caribbean countries, which would lead to a greater inflow of foreign capital and increase economic growth rates.

KEYWORDS:
Inflow of foreign capital; Homicide rates; Economic growth; Dynamic panel

RESUMO

O presente estudo tem como objetivo analisar os efeitos da criminalidade, representada pelas taxas de homicídios, sobre o influxo líquido de capital externo como proporção do Produto Interno Bruto (PIB) na América Latina. Para tal, utilizou-se o método de painel dinâmico, que permite o controle da relação endógena entre as variáveis, a partir de dados do World Bank Data para o período 1990-2020. Como principal resultado, foi possível verificar que as taxas de homicídios tendem a reduzir o influxo líquido de capital externo como proporção do PIB nos países latino-americanos. Diante dos resultados obtidos que evidenciam os efeitos nefastos da criminalidade, espera-se que este trabalho possa contribuir para a produção científica que tange o tema, além de servir de subsídio para a possível formulação de políticas públicas que reduzam a criminalidade nos países latino-americanos, o que propiciaria maior influxo de capital externo e elevação das taxas de crescimento econômico.

PALAVRAS-CHAVE:
Influxo de capital externo; Taxas de homicídios; Crescimento econômico; Painel dinâmico

1 INTRODUCTION

In an integrated economy, foreign investment is crucial for economic growth, and enables nations facing internal financing challenges to develop. Foreign Direct Investment (FDI) can thus benefit both investors and host countries, depending on their objectives and specific macroeconomic characteristics (Carminati & Fernandes, 2013).

This type of investment contributes to the dissemination of innovative technologies and ideas, and drives economic progress. It also confers other benefits, such as reduced unemployment, increased revenue, greater competitiveness on the part of companies, higher wages, opening or consolidation of new consumer markets, and reduced risk because of the diversification of operations (Carvalho, 2022; Holland & Barbi, 2010; Kubicová, 2013; Lascurain Fernández, 2012).

To attract more FDI, a country must adopt strategic measures and consider factors, such as qualification of the workforce, size and growth rate of Gross Domestic Product (GDP), economic resilience when crises hit, receptivity to foreign capital, and the performance of stock exchanges, which are all essential determinants (Nonnenberg & Mendonça, 2005).

On the other hand, despite its prime importance in a globalized world, excessive dependence on the inflow of such resources can damage local economies, as the entry of transnational companies can reduce the market share of national companies in the competitive market. In addition, the competitive disadvantages must be highlighted, taking into account the facilitation of intra-firm transfers by transnational companies between one subsidiary and another (Chiarini, 2016; Galvão & Pereira, 2017).

In the 1990s, market deregulation enabled Latin America and the Caribbean countries to access the international financial market, driven by the adoption of trade liberalization policies and the quest for external financing (Colombo & Sartório, 2022). Trade liberalization, along with local government receptiveness and low interest rates, resulted in an increase in the inflow of foreign capital to the region. However, these investments were not durable and began to slow down due to low industrial growth and economic and political uncertainties (UNCTAD, 2015).

According to Cepal (2011), after the 2010 international financial crisis, the Latin America and the Caribbean region experienced impressive growth in FDI driven by increased domestic demand and the pursuit of new markets by transnational companies. The Latin American countries which benefited most were Brazil (48.4 billion dollars), Mexico (17.7 billion), Chile (15.1 billion) and Peru (7.3 billion), with the United States and China as main investors. However, in 2020, due to the Covid-19 pandemic, foreign investment in the region reduced by around 34.7 % compared to the previous year. It should be noted that this drop in FDI was a global phenomenon (CEPAL, 2022).

In 2021, after the vaccination campaign and the stabilization in numbers of Covid-19 cases, FDI recovered in this region with a 40.7 % increase when compared to the previous year, representing around 9 % of global foreign investment. However, forecasts for the coming years indicate that all countries will still suffer from the aftermath of the pandemic, which will impact foreign investment (CEPAL, 2022).

Given the vital importance of FDI for developing countries, especially those of Latin America and the Caribbean, and considering the deregulation of markets, it is essential to analyze the factors which influence the inflow of foreign capital. Such factors include: structure of firms, characteristics of the host countries and their regional attributes, such as the size of the economy (GDP), average growth rate in previous years and the coefficient of openness of the economy (Nonnenberg & Mendonça, 2005). Amal and Seabra (2007) also emphasize the importance of influential institutional factors, such as the degree of freedom and political risk, when attracting foreign investment, while Freitas and Prates (1998) declare that, in addition to the degree of financial openness, the macroeconomic management of capital flows is essential.

In addition to these aspects, socioeconomic factors, such level of schooling, can also influence foreign capital inflows (Nonnenberg & Mendonça, 2005). Another highly relevant socioeconomic component is the level of crime, as an unstable and violent environment may not attract investments (Ashby & Ramos, 2013; Carneiro & Oliveira, 2020).

During the period of increased FDI, there was considerable growth in crime rates in Latin America and the Caribbean, making it one of the world’s most violent regions. According to a Report from the UN Offices on Drugs and Crime (UNODC, 2019), homicides in Central America reached a rate of 62.1 per 100,000 inhabitants, and was correlated with the increase in inequality resulting from the considerable influx of commodities in recent years. A comprehensive analysis of all countries, according to the InSight Crime Magazine (Appleby et al., 2023), shows that the 50 most violent cities in 2021 are predominantly located in Latin America and the Caribbean (Appleby et al., 2023).

Drug trafficking and the use and smuggling of weapons are indicated as factors associated with the high rates of violent deaths by homicide in the region, as indicated by the Instituto Humanitas Unisinos (IHU) in March 2023 (BEHS, 2023). The Institute for Economics and Peace (IEP) highlights the consequences of this increase in crime in Latin America and the Caribbean, when analyzing the global economic cost of violence. The economic impact was estimated at US$16.5 trillion in 2021, equivalent to 10.9 % of global GDP, or US$2,117 per person (IEP, 2022).

The aforementioned economic impact resulting from the increase in crime can be categorized into direct costs, which directly affect those involved, and indirect costs, such as the loss of productivity and inflow of capital. These aspects highlight the direct impact of crime on countries’ economic indicators, including the inflow of foreign capital.

The IEP (2022) suggests that there is an inverse relationship between crime incidence and FDI inflow. Countries which did not record an increase in crime rates presented an annual increase of 5.2 % in their FDI rates, while those in which crime decreased until 2019 had an increase of 2.6 %, thereby highlighting the possible inverse relationship between crime and foreign investment.

Despite the importance of the topic, the literature on the relationship between crime incidence and FDI inflow is limited, with generally consistent results. In the scenario analyzed by Loría (2020), Mexico faced economic challenges due to high crime rates in the previous decade, which resulted in a reduction in FDI. Torres et al. (2018) intensify this analysis, highlighting that homicides and robberies are the crimes that most damage the inflow of foreign investment to the country. Daniele and Marani (2008) corroborate this trend by noting the strong relationship between the increase in crime and the decrease in FDI in Italy, especially in areas with high crime rates. Santos (2017) also identified a negative relationship between certain types of crime and the inflow of foreign capital.

Considering that Latin America and the Caribbean is one of the world’s most violent regions, where the quality of life of individuals and the economic environment are negatively impacted, this study sets out to analyze the effects of crime, represented by homicide rates, on the net inflow of foreign capital as a proportion of GDP in the region, over the 1990 to 2020 period. The study is pertinent in that it contributes to the literature by considering Latin America and the Caribbean countries and using recent data. It comprises four more sections, covering Empirical Evidence, Methodology, Results, and Final Comments.

2 EMPIRICAL EVIDENCE

There is an intrinsic relationship between the rise in criminality and the inflow of foreign capital. On the one hand, the inflow of FDI can reduce crime rates because of improved living conditions (Amundsen, 2023), while on the other, there could be an increase in crime, because of the acceleration of urbanization (Levchak, 2024).

Nevertheless, the focus of this study is to investigate how an increase in crime rates impacts the inflow of foreign capital. According to Carneiro and Oliveira (2020), violent crimes have various implications for society, including a reduced level of trust in the respective region. Latin America and the Caribbean is a developing region which has been receiving a relatively high flow of foreign investment since 1980. Considering that the level of trust and stability are vital factors for the inflow of foreign investment in countries, it is crucial to understand how ,and to what extent, crime can interfere with that inflow.

Islam and Hussain (2018) undertook a study to analyze the impact of crime, especially homicides and corruption, on the economic performance of more than one hundred countries, by estimating an Ordinary Least Squares (OLS) model for 110 countries, for the year 2014. The results show that a reduction in crime rates, along with an increased feeling of security among citizens, the rule of law, and intensified efforts to curb corruption are all factors which positively impact GDP. When there is a 1 % decrease in crime rates, there is a 0.8 % positive effect on GDP, which generates incentives for foreign investment.

Ramos and Ashby (2017) also showed how the feeling of security can affect foreign investment into Mexico. They initially estimated the model with static panel data using the fixed effects model, and then used dynamic panel data, controlling for various determinants of FDI at state level and the potential for geographic reproduction of this violence in the country for the 2001 to 2015 period. The results indicated that investors’ prior knowledge of the existence of high crime rates makes them wary of investing. Thus, a 1 % increase in crime rates leads to a reduction of the order of 41 to 46 million dollars in foreign investment.

Agyapong et al. (2016) used the Solow growth model in the form of a Cobb-Douglas production function to analyze the relationship between crime and foreign investment in Ghana, over the 2000 to 2014 period, by means of an Autoregressive Distributed Lag model. The results suggest that organized crime generates insecurity, inefficiency, and uncertainty among domestic and foreign investors, which directly affects the country’s growth and development. In this respect, crimes of various types affect trade and foreign investment, as also analyzed by Blomberg and Mody (2005) for the 1980 to 2000 period.

In turn, Afriyanto (2017) highlighted that, in addition to the negative image that crime gives to countries, the inverse relationship between foreign investment and crime is also a reflection of the high cost of additional security equipment, according to the estimation of an econometric model with panel data for the 2005 to 2015 period, involving 31 provinces in Indonesia. In particular, he concluded that an increase in crime levels has a negative effect of approximately 0.95 % on FDI. The fact that investors have to increase their spending on security as crime increases is a disincentive to investment in the country.

Carreira (2011) sought to analyze the effects of one type of crime on foreign investment which generates controversy in the literature, namely corruption. While some authors suggest that corruption facilitates the inflow of capital (as seen in Huntington, 1975), Carreira (2011), on analyzing 49 countries, showed that the level of corruption generates uncertainty and reduces the inflow of foreign investment. He used the estimation of a Tobit econometric model, in which he found that a one-unit increase in the level of corruption implies a 21 % reduction in the inflow of FDI.

It is also worth noting that according to UNCTAD (2015), Mexico was the thirteenth largest recipient of FDI in 2014 and 2015. On the other hand, it was consistently considered one of the world’s most violent countries, with thousands of deaths resulting from drug trafficking violence. In the same vein, Garriga and Phillips (2015) and Ashby and Ramos (2013) analyzed the impact of crime on foreign investment in various sectors of the country for the 2000 to 2012 and 2004 to 2010 periods, respectively.

Their results show that crime has marked effects which reduce foreign investment. According to the results found by Ashby and Ramos (2013), one additional homicide per 100,000 people is associated with a 2 % to 4 % reduction in investment in the financial, administrative and real estate sectors, in addition to a 4 % to 5 % decrease in investment in the trade and communication and transportation sectors. These results reflect the expected inverse relationship between crime and the inflow of foreign capital.

By estimating a time series model for Mexico for the 1997 to 2017 period, Loría (2020) found that high rates of kidnappings and intentional homicides discourage foreign investment in Mexico by 1.19 % and 0.28 %, respectively.

More recently, Garriga and Phillips (2022), using static panel data (Fixed Effects) and dynamic panel data for the 2000 to 2018 period, innovated by considering the effects of organized crime at regional level. They found that a greater presence of criminal groups in Mexican states reduces new inflows of foreign investment by approximately 31 %, or in other words, a state with no criminal activities would receive approximately US$1,235 billion in the form of FDI.

As regards Brazil, Marchezini et al. (2022) corroborate the existence of this relationship when analyzing the state of São Paulo for the 2002 to 2018 period. Using an econometric model composed of a system of equations for panel data, they found that crime reduces the pace of economic growth.

3 METHODOLOGY

3.1 Data

The data for constructing the variables used in this study were extracted from the World Bank Data, a platform which provides different economic data and statistics at international level for all countries, including Latin America and the Caribbean. The period analyzed is from 1990 to 2020, which resulted in 930 observations. This period was delimited in order to undertake a study that brings the most recent results possible to the literature on the subject.

3.2 Econometric Model

To analyze the effects of crime on the inflow of foreign capital to Latin America and the Caribbean countries, the dynamic panel data methodology is adopted. Models with panel data present notable advantages, as they allow for an analysis of the impact and evolution of different variables over several periods, even when the information for some specific variables is incomplete. In addition, this method provides a significant amount of information by increasing the number of observations, which results in more degrees of freedom, greater efficiency of estimates and reduced collinearity. This, in turn, allows for a more in-depth analysis of the data (Marques, 2000).

The use of dynamic panel data provides additional advantages, as the inertial relationship is taken into account when using the dependent variable as a lagged explanatory variable. Another advantage is the possible control of the endogenous relationship between certain variables by considering the variables themselves delayed by two lags as tools. Such control is fundamental, as endogeneity implies biases, inconsistencies, and inefficiency in econometric estimation (Arellano & Bond, 1991).

Therefore, an unbalanced panel was constructed with data from 30 Latin American and Caribbean countries for the 1990 to 2020 period, a total of 930 observations. According to Arellano and Bond (1991), the estimation of the model with dynamic panel data is performed using the Generalized Method of Moments (GMM), described in its general form as follows:

$$y_{i,t}=\alpha y_{i,t-1}+\beta X_{i,t}+{\epsilon }_{i,t}$$ (1)

where y is the dependent variable, X is the vector of explanatory variables and ε corresponds to the regression error term. The subscript 𝑖 indicates the country, while 𝑡 represents the period. It is also assumed that the error term includes the specific effects on crime rates for each country ( 𝑢 𝑖 ), and the unobserved random shocks over time, ( 𝑣 𝑖,𝑡 ).

$${\epsilon }_{i,t}=u_{i,t}+v_{i,t}$$ (2)

Thus, the regression equation can be rewritten as:

$$y_{i,t}=\alpha y_{i,t-1}+\beta X_{i,t}+\mathrm{}{u}_{i,t}+v_{i,t}$$ (3)

Assuming that 𝑢 𝑖 ∼ IID (0, 𝛼 𝑢 2 ) and 𝑣 𝑖 ∼ IID(0, 𝛼 𝑣 2 ), where the 𝑢 𝑖 represent the time-invariant individual fixed effects of each country and 𝑣 𝑖,𝑡 represent the time-varying random shocks specific to each country, which are heteroscedastic and correlated in time between countries, but not between themselves, thus:

$$\mathrm{E}\mathrm{}\left(u_i\right)\mathrm{}=\mathrm{}\mathrm{E}\mathrm{}\left(v_{i,t}\right)\mathrm{}=\mathrm{}\mathrm{E}\mathrm{}\left(u_{i,t}{v}_{i,t}\right)\mathrm{}=\mathrm{}0.$$ (4)

$${\mathrm{E}\mathrm{}(v}_{i,t}{v}_{j,s})\mathrm{}=\mathrm{}0,\mathrm{}\forall i,j,t,\mathrm{}\mathrm{s}\mathrm{}\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{m}\mathrm{i}\mathrm{n}\mathrm{g}i\ne j.\mathrm{}$$ (5)

The regression using the GMM method can eliminate the fixed effects on the crime rate of each country when the transformation moment is applied to the model. The first regression is thus performed with the aim of removing from the model the components which do not vary over time. Hence, the specific estimation equation calculated for this study can be defined as:

$$Inflow\_invi,t=a+\beta 1Inflow\_invlagi,t-1+\beta 2Homicidesi,t+\beta 3Exp\_health\_(i,t)+\beta 4Pop\_growthi,t+\beta 5Exp\_educ\_govi,t+\beta 6Imfi,t+\beta 7Gfcf\_gdpi,t+\beta 8Pcgdp\_6i,t+\beta 9Covidi,t+ui,t;i=1,2,...30;t=1990,...2020$$ (6)

where (Inflow_inv) corresponds to the net inflows of FDI as a proportion of GDP and is the dependent variable, while the β’s are the estimated parameters for each independent variable, which were included based on the literature on the subject and are presented in Chart 1, below, where they were summarized and described with their respective expected signs.

It is important to highlight that Inflow_invlag has a positive expected sign, as the literature points to an inertial effect on the trajectory of foreign investments. Therefore, past foreign investment has a positive effect on current investment (Axarloglou, 2007).

For the Homicides variable, a negative relationship with net foreign investment inflows is expected. Loría (2020) argues that high homicide rates have the effect of reducing foreign investment, while Blomberg and Mody (2005) highlight the negative relationship between violence and FDI inflow to the host country, especially in developing countries. In this context, in regions where crime is prevalent, an environment characterized by insecurity and fear tends to inhibit the country's economic activity, especially that involving external agents.

Thus, high homicide and kidnapping rates can significantly reduce foreign investment (Bernal & Castillo, 2012). This variable was considered endogenous in the estimation, as the inflow of foreign capital can also decrease (Amundsen, 2023) or increase (Levchak, 2024) homicide rates, thereby signaling reverse causality.

Chart 1
Variables used in estimating the econometric model

Variable	Description	Expected sign
Inflow_inv	Net inflows of foreign investment as a proportion of GDP	Dependent variable
Inflow_invlag	Net inflows of foreign investment as a proportion of GDP, lagged by 1 period	Positive
Homicides	Intentional homicide rate per 100,000 inhabitants	Negative
Exp_health	Percentage of expenditure on health in relation to GDP	Positive
Pop_growth	Population growth rates (em %)	Ambiguous
Exp_educ_gov	Percentage of expenditure on education in relation to total expenditure	Positive
Imf	Use of IMF credit (in US$)	Ambiguous
Gfcf_gdp	Gross fixed capital formation (as % of GDP)	Ambiguous
Pcgdp	Per capita Gross Domestic Product (GDP)	Positive
Covid	Dummy which assumes a value of 1 for the year 2020, and 0 if the period is from 1990 to 2019	Negative

• Source: Drawn up by the authors.

A positive relationship is expected between the variable indicating expenditure on health in relation to the country’s GDP and the dependent variable. According to Alsan, Bloom and Canning (2006), an improvement in health conditions, which could come about through increased health expenditure, tends to increase the inflow of FDI.

An ambiguous relationship is expected between Pop_growth and the dependent variable. On the one hand, a country which presents greater potential in terms of labor could be attractive for foreign investment (Nonnenberg & Mendonça, 2005), while on the other, Negri and Acioly (2004) found that some investors demand better qualified labor rather than a certain quantity of workers which means that there could be a negative relationship.

Furthermore, a positive relationship is expected between the dependent variable and Exp_educ_gov, given that an increase in public investment in education tends to improve the country’s productivity, and directly affects the pace of economic growth. In this respect, according to Silva Filho (2015), expansion in the stock of human capital is an indispensable requirement for joining FDI projects.

In turn, the Gfcf_gdp variable, that is, expansion of an economy’s future productive capacity, is considered endogenous in the estimation, since it has a reverse causality relationship with the net inflow of foreign capital. According to Mordecki and Ramírez (2018), gross fixed capital formation is positively related to GDP, as an expansion in production capacity will imply an increase in national wealth, which is measured by GDP. As defined by Islam and Hussain (2018), macroeconomic factors, such as the expansion of GDP are essential for driving FDI.

On the other hand, Krkoska (2001) states that the relationship between FDI and Gfcf_gdp is not so simple and not necessarily positive. In certain cases, such as the occurrence of foreign privatizations, the relationship could be negative, as seen in the transfer of productive capacity to other countries. Thus, an ambiguous result is expected for this variable.

The variable related to the use of IMF credit (Imf) also has an endogenous relationship with the inflow of investment. According to Biage et al. (2008), the inflow of capital tends to intensify as the expectation of returns increases and the risk associated with investments diminishes. By using IMF credit as a proxy for risk, a positive relationship is expected between the variables, as greater credit concession is reflected in lower risk. On the other hand, an increase in FDI could be a sign of the country’s stability for investors, which, in turn, will have a positive impact on the concession of IMF credit. This result is corroborated by Coelho (2012), where volatility in international business also reflects the volatility of credit concession.

The variable related to per capita GDP (Pcgdp) also has an endogenous relationship with the dependent variable. On the one hand, an increase in per capita GDP tends to increase the inflow of foreign capital, given the greater purchasing power of the population (Alshamsi et al., 2015). However, analyzing from another perspective, Sghaier (2022) points out that the inflow of foreign capital tends to increase the pace of economic growth of countries. Furthermore, the variable to control the period related to the beginning of the Covid-19 pandemic (Covid) tends to present a negative estimated sign for its coefficient (Koçak & Bariş-Tüzemen, 2022).

Finally, to verify the robustness of the models estimated with dynamic panel data, Arellano and Bond (1991) suggest the Sargan and Serial Correlation tests. The former verifies the validity of the tools, where rejecting the null hypothesis indicates that the tools are valid. The latter is a Lagrange Multiplier test, responsible for checking if there is a first- and second-order serial correlation of residuals in the first difference in the variation of random shocks specific to each country, which vary over time. The null hypothesis of this test is that the errors are autocorrelated in the first difference, and it is expected that there is a first-order but not a second-order correlation.

4 RESULTS

This section, divided into two subsections, presents the results of the research. The former presents the descriptive analysis, while the latter presents the results found through econometric estimation, both covering the 1990 to 2020 period.

4.1 Descriptive Analysis

This descriptive analysis describes key aspects related to the variables used in the model being studied, such as mean, standard deviation, and minimum and maximum values, with a view to facilitating a better understanding of the sample and subsequently helping to understand the econometric results. Table 1, below, describes the results of the descriptive statistics.

Table 1
Descriptive statistics

Variable	Mean	Standard Deviation	Minimum	Maximum
Inflow_inv	472.99	276.69	1	952
Homicides	283.98	238.44	1	726
Exp_health	194.87	200.17	1	601
Pop_growth	1.20	0.80	-2.90	3.52
Exp_educ_gov	140.71	169.03	1	520
Imf	214.53	212.96	1	644
Gfcf_gdp	247.02	228.02	1	688
Pcgdp	473.80	277.60	1	951
Covid	0.32	0.18	0	1

• Source: Drawn up by the authors.

In general, all variables present wide disparity, a signal of the heterogeneity between Latin America and the Caribbean countries. It is important to highlight the great variability of the controls which represent government spending on education and health, whose standard deviations exceed the means.

As a complement, Figures 1 and 2 show, respectively, the homicide rates and net inflow of foreign capital in 1990 and 2020, the first and final years considered in the sample. Using these figures, the net inflow of FDI in Latin America and the Caribbean countries can be related with the variation in homicide rates, used as a proxy for crime in the years under analysis.

It was seen that, in general, there was a reduction in homicide rates in a few countries in the region between 1990 and 2020, notably Colombia and Ecuador. Similarly, net foreign capital inflows to these countries generally increased between 1990 and 2020. In addition, countries showing an increase in their homicide rates during the period analyzed generally had reductions in foreign capital inflows, such as Jamaica and Saint Lucia.

Figure 1
Homicide rates for Latin America and the Caribbean countries in 1990 and 2020

It is also worth highlighting the case of Antigua and Barbuda, the country which received the largest net inflow of investments as a percentage of GDP in 1990, and which fell to less than half in 2020. Its homicide rates increased by 82.6 % over the period. That result indicates of an inverse relationship between homicide rates and net inflow of foreign capital as a proportion of GDP, which will be confirmed in the next subsection, the presentation of econometric results.

Figure 2
Net inflow of foreign capital as a proportion of GDP for Latin America and the Caribbean countries in 1990 and 2020

4.2 Econometric Results

Table 2 below presents the results of the estimation of the econometric model with dynamic panel data, taking robust standard errors into account. Considering a significance level of at least 10 %, the Exp_health, Pop_growth, Exp_educ_gov, Gfcf_gdp and Pcgdp variables were not statistically significant. Furthermore, to analyze the robustness of the estimation, the Arellano-Bond serial correlation and the Sargan instrumental validation tests were performed, which indicated that there was no serial correlation of the errors and that the tools used are valid.

Firstly, it can be said that the variable of greatest interest in the study, the homicide rate, proved significant at 5 % to explain the net inflow of foreign capital as a percentage of GDP. In this regard, a 1 % increase in homicide rates reduces the net inflow of foreign capital as a proportion of GDP in Latin America and the Caribbean countries by 4.87 % in the 1990 to 2020 period. This result is in line with studies by Blomberg and Mody (2005) and Bernal and Castillo (2012). They set out to analyze the impact of crime in its various forms (homicide rates, kidnappings, or organized crime) on the inflow of foreign investment in certain countries with similar results.

Table 2
Econometric Results

Variable	Coefficient	p-value
Inflow_invlag	0.4957*** (0.0427)	0.000
Homicides	-0.0487** (0.0248)	0.049
Exp_health	-0.0142NS (0.0610)	0.815
Pop_growth	-28.3973NS (22.5705)	0.208
Exp_educ_gov	0.0882NS (0.0659)	0.181
Imf	0.0837** (0.0393)	0.033
Gfcf_gdp	0.0310NS (0.0455)	0.496
Pcgdp	-0.0041NS (0.0227)	0.855
Covid	-155.6353*** (39.0439)	0.000
Constant	262.2433*** (52.4361)	0.000

• Note: *** significant at 1 %; ** significant at 5 %; * significant at 10 %; ^NS not significant. Standard errors in parenthesis.
• Source: Drawn up by the authors.

It is important to highlight that the endogenous relationship between crime and foreign capital inflow was controlled in estimating the model. It can thus be seen that crime, given its effects of insecurity and instability, disincentivizes potential investors, and also has a negative impact on the economic and social indicators of countries. Detotto and Otranto (2010) associate criminal activities with levies on the economy, as they reduce the attractiveness of national or foreign investments, inhibit the competitiveness of companies, and generate uncertainty and economic inefficiency. In addition, an increase in crime can substantially increase the cost of setting up a company (Dadzie et al., 2014).

In the case of Latin America and the Caribbean specifically, the literature has stressed that high crime rates are responsible for increasing fear, uncertainty, and insecurity among investors (Ashby & Ramos, 2013; Garriga & Phillips, 2015). Furthermore, Dadzie et al. (2014) point out that investors’ expenditure on private security could increase with the increase in crime and thereby contribute to disincentivizing investment.

It should be noted that crime also has indirect consequences, such as high expenditure in an attempt to minimize its occurrence and deal with its consequences (Cerqueira, 2014). Such considerable spending compromises the distribution of a country’s financial resources to its public security agenda, reduces the amount that could be allocated to infrastructure, and increases dependence on FDI to make up for this lack of resources (Fialho, 2020).

On the subject of the other control variables, the inflow of foreign investment lagged by one period was also significant at 1 % and presented the expected result, thereby showing the inertial effect of the trajectory of foreign investments, as suggested by Axarloglou (2007). Thus, a 1 % increase in the net inflow of foreign capital as a proportion of GDP in the previous period increases the indicator by approximately 49.57 % in the subsequent period.

In turn, the variable related to the use of IMF credit (Imf) was statistically significant and presented a positive estimated sign for its coefficient. Thus, a 1 % increase in the use of credit from the IMF increased the inflow of foreign capital as a proportion of GDP by approximately 8.37 %. According to Biage et al. (2008), the inflow of capital increases as the expectation of return rises and the risk associated with investments reduces, which justifies the result found.

The Covid variable was included in order to control the initial effect on the inflow of capital of the Covid-19 pandemic, which affected all countries worldwide beginning in 2020. The result indicates that it exerted a strong negative influence on the inflow of foreign capital, a fact corroborated by Koçak and Barış-Tüzemen (2022), who analyzed foreign investments in emerging countries and found evidence of the pandemic having a considerable impact on investment flows.

5 FINAL COMMENTS

The goal of attracting foreign investment in an economy is motivated by several factors. It contributes to the balance of payments, adoption of new technologies, workforce qualification, productivity gains and, most importantly, the possibility of external financing in capital formation.

Because of the many positive effects which result from the inflow of foreign investment, the primary objective of this study was to investigate the effects of crime, represented by homicide rates, on the net inflow of foreign capital as a proportion of GDP in Latin America and the Caribbean countries during the 1990 to 2020 period, using the methodology of estimating a model with dynamic panel data from information extracted from the World Bank Data.

It can be concluded from the results that homicide rates negatively impact net foreign capital inflows as a proportion of GDP. Regions with very high crime rates tend to generate an environment of instability and insecurity, which reduces social well-being and inhibits economic activities, particularly foreign investment. On the basis of these results, the study emphasizes the importance of promoting measures which limit homicide rates, given their impact on economic indicators and on social well-being.

Furthermore, apropos of other controls included in the econometric estimation, the Inflow_invlag and Imf variables showed a positive relationship with the dependent variable. Thus, the greater the inflow of foreign capital in the previous period and use of IMF credit, the higher the foreign capital inflows as a proportion of GDP tend to be in Latin America and the Caribbean countries. On the other hand, the Covid dummy presented a negative estimated sign for its coefficient, which indicates that the initial period of the Covid-19 pandemic led to a sharp decline in the inflow of foreign capital as a proportion of GDP in Latin America and the Caribbean countries.

Therefore, given these results showing the harmful effects of crime on the net inflow of foreign capital as a proportion of Latin American and the Caribbean GDP, it would be essential to formulate public policies to mitigate the high homicide rates which plague these countries. Such policies would include greater quality spending on public security, particularly in technology to make policing more efficacious. With a more stable and safer environment, the region could become more attractive to foreign investment, which would provide various benefits.

That said, it should be noted that this study presents certain limitations becauseit only uses the homicide rate as a proxy for crime, a broad concept which is difficult to measure given the number of instances of underreporting. However, the results obtained were robust and can be used as a starting point for different analyses on the topic and for possible further studies.

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• DATA AVAILABILITY
  All the data supporting the results of this study were published in the article itself.

Edited by

• EDITOR-IN-CHIEF
  Márcia D’Angelo

• ASSOCIATE EDITOR
  Lucas Godeiro

Data availability

All the data supporting the results of this study were published in the article itself.

Publication Dates

• Publication in this collection
  20 July 2026
• Date of issue
  2026

History

• Received
  15 Feb 2024
• Reviewed
  18 Aug 2024
• Accepted
  29 Aug 2024