The macroeconomic determinants of trade openness in Latin American countries: A panel data analysis

1. Introduction

The expansion of trade in countries around the world has proven to be a crucial mechanism for addressing some of the world’s most pressing crises, offering solutions to challenges like food security, which has been exacerbated by inflationary pressures. Moreover, the deepening of this trade liberalization fosters the development of key elements shaping economic complexity, such as the diversification of products that reveal comparative advantages, while also creating an environment that encourages innovation, thereby driving economic growth (Bardi & Hfaiedh, 2021; Fusco et al., 2020; Gallego & Zofío, 2018; Tabash et al., 2022). Additionally, sustainable trade practices are essential for promoting long-term economic resilience, environmental protection, and social well-being, aligning with broader sustainable development goals.

The growing international trade flow, the development of technological capabilities, research and development, and economic complexity have had a direct and significant impact on sustainable economic growth. From the theoretical perspective of economic policy for sustainable growth, trade policy plays a fundamental role in the state, as market economies require the existence of an efficient and effective state that can ensure compliance with free trade rules (Cetin et al., 2018; Chávez, 2021; Lima & Alvarez, 2018).

It is important to acknowledge the existence of an ongoing debate surrounding the so-called resource curse. In this regard, evidence shows that, after the 1980s and 1990s, Latin American economies developed strategies to diversify exports and foster the reorientation of external sectors, producing goods with medium to high technological complexity. Meanwhile, other countries such as Peru, Ecuador, Bolivia, Chile, Colombia, Paraguay, and Venezuela based their export industries on primary sector goods as the backbone of their commercial basket (Alvarado & Guzmán, 2019; Gnangnon, 2022; Tabash et al., 2022).

In the South American region, according to data from the Economic Commission for Latin America (2021), the average growth in trade openness—used to assess the magnitude of foreign trade relative to a country’s domestic output—in Argentina reached 28% over the period 1990–2022. This measure has been volatile, with the highest levels of openness observed between 2002 and 2008, followed by a decline, ultimately reaching 31.7% in 2022.

In Bolivia, the average trade openness indicator was 60.1% over the 1990–2022 period, showing an upward trend from 1990 to 2008 and reaching 67.8% in 2022. In Brazil, the average trade openness growth reached 24.3% between 1990 and 2022, following an upward trend to reach 38.8% by 2022.

In Chile, trade openness averaged 62.4% from 1990 to 2022, reaching 75% in 2022; while in Colombia, the average for the same period was 35.3%, with an increase to 48.4% in 2022. Ecuador’s trade openness averaged 50.9%, reaching 57.7% in 2022. For Paraguay, the trade openness level was 82.8% during 1990–2022, while Peru reached 42.4% over the same period, and Uruguay reached 52.7%.

Arguments in favor of trade liberalization emphasize greater accumulation of physical capital, although this faces diminishing returns. Instead, the growth driver focuses on knowledge accumulation, which creates significant, dynamic impacts, leading companies that need access to large markets to innovate and become more competitive. This is due to the fluidity in their capacity to learn and assimilate new technologies (Corrales Sillo, 2021; Mesquita & Stein, 2019).

While trade liberalization has empirically demonstrated its positive externalities in economies, clear empirical factors fully supporting this phenomenon are still lacking, particularly in Latin American economies. In this region, the impact of trade liberalization is estimated to reflect an acceleration in annual per capita GDP growth of between 0.6 and 0.7 percentage points, on average. This effect is largely due to the roles of political, institutional, and structural factors, particularly concerning technology and innovation. However, the unique characteristics of South American countries may have limited the benefits of trade liberalization, given the low levels of human capital and their distance from the technological frontier. These conditions can diminish the positive impact of trade liberalization, as economic theory suggests (Grossman & Helpman, 2015; Goldberg & Pavcnik, 2016).

Trade liberalization has undoubtedly allowed Latin America to move beyond decades of economic stagnation. The shift in trade policy has been a key factor in the region’s development and well-being (Herreros and Rosales, 2013; Lopez & Munoz, 2020). As Boloña (1993) held, “precisely because we are a small country in economic terms, we must diversify our markets, expand the scale of our economy, benefit from international markets to grow and accumulate capital, to overcome poverty and achieve the desired levels of well-being and prosperity” (p.13).

In this regard, Falcón (2021) points out that one of the main challenges for trade policy is to guide economies toward greater diversification of productive structures. This shift would allow countries in the region to advance toward higher economic complexity based on knowledge accumulation, moving beyond physical or labor capital alone. Such a transformation includes technological development and an evolution of the export basket toward higher value-added manufacturing, reducing dependency on commodities and generating sustainable comparative advantages. There is, therefore, a clear relationship between trade liberalization, economic complexity, innovation, research and development, and economic growth (Lee & Lee, 2020; Shahzad et al., 2022).

Understanding the productive transformation unique to each country enables the connection of these key variables for public policy decision-making. In this way, an external sector transformation can be promoted that fosters sustainable growth with a developmental perspective, particularly in emerging economies (Yang et al., 2023; Cheong, 2023).

In this context, the present article aims to analyze the macroeconomic determinants of trade liberalization in Latin American countries. To this end, it poses the question: What are the macroeconomic determinants of trade liberalization in Latin American countries? A panel data analysis will be employed, accounting for variables such as economic complexity, innovation, research and development, and economic growth during the period 2000–2020.

The primary contribution of this study lies in the specificity of its model, which is crucial for policymakers. By focusing exclusively on South American countries within a defined period (2000–2020), the study adds to the empirical evidence on the importance of better integration into the global economy through enhanced competitiveness. In this framework, trade liberalization is viewed as a relevant factor for income distribution and poverty reduction, promoting investment and driving international trade dynamics within the region.

The growth of international trade flows, driven by technological development and investment in R&D, has been a key factor in sustainable economic growth, especially within economies with trade liberalization policies. According to Castañeda Martínez and López González (2022) trade liberalization should promote technological diffusion and economic growth, but in some cases, such as Mexico, the expected effects do not materialize due to structural limitations. However, Grande González (2023) highlights that innovation and digitalization have significantly improved the competitiveness of developed economies, provided that appropriate regulatory frameworks are in place. In this context, economic policies aimed at trade liberalization must be accompanied by investment strategies in technological infrastructure and the strengthening of the R&D ecosystem to ensure that the benefits of trade are not concentrated in just a few sectors and economic actors (Orquera Carranco, 2023).

Moreover, the impact of trade agreements on attracting foreign investment reinforces the relationship between trade openness and economic development. Castañeda Martínez and López González (2022) how treaties such as NAFTA have increased foreign direct investment in Mexico, strengthening its industry and economic growth. However, Orquera Carranco (2023) emphasizes that companies must play an active role in sustainable development, aligning their strategies with the Sustainable Development Goals (SDGs) to ensure equitable economic growth. In this regard, Castañeda Martínez and López González (2022) highlight the role of international organizations, such as the OECD, in promoting free trade and fiscal regulation, though challenges remain regarding the fairness of global governance. Thus, trade liberalization will only be effective in terms of sustainability if complemented by policies that strengthen technology, incentivize research, and promote inclusive development strategies.

However, a knowledge gap remains regarding the specific macroeconomic determinants influencing trade liberalization in Latin America. Despite evidence suggesting that factors like economic complexity and innovation play significant roles, it is not entirely clear how these factors interact with other structural and political aspects unique to the region. This study seeks to address that gap by providing a detailed analysis that can serve as a foundation for more effective public policy formulation across the region.

2. Literature review

Recent empirical evidence underscores the relevance of the external sector, driven by trade liberalization policies, as a key factor in the growth of emerging economies in Latin America, especially amid current health, food security, and economic uncertainty crises. This context has highlighted the need to diversify and enhance the complexity of exports, prompting the restructuring of the productive matrix. This transformation demands not only the accumulation of physical capital and expansion of capacities but also the development of an innovation-friendly business environment and increased investment in research and development (Carrasco, 2021; Chávez, 2021; Bashir et al., 2020; Chacha et al., 2024; Guo et al., 2023; Mania & Rieber, 2019; Nieminen, 2020).

In the international context, Wang et al. (2023) conduct a study evaluating economic complexity, trade diversification, renewable energy consumption, and environmental taxes as drivers of growth from 1995 to 2018. The findings indicate that these factors significantly promoted growth in BRICTS countries, with relevant policy implications for achieving marginal contributions to economic development. Similarly, Li and Rigby (2023) find a positive and significant relationship between economic growth and complex technological diversification in China, specifically in 1991-1995 and 2011-2015. Here, knowledge complexity and smart specialization demonstrate a knowledge development pathway toward achieving regional competitive advantage.

A panel VAR analysis across 11 European countries (2001-2016) reveals a unidirectional relationship between economic growth and financial development, and between trade and economic growth, but a negative association between innovation and economic growth, as well as between trade and innovation (Mtar & Belazreg, 2023). This suggests that each country’s role depends on innovation and productivity, and recommends that institutional quality is essential for improving trade regulation and fostering innovation in local companies to mitigate the effects of trade liberalization.

Various studies demonstrate relationships between economic complexity, innovation, research and development, and economic growth. In 28 African countries (1995-2019), analysis shows how economic complexity moderates the impact of natural resources on economic growth in both the short and long term, proposing policies to effectively link these factors (Mesagan & Vo, 2023). In Russia, the creation of high-tech products and markets is viewed as essential for long-term economic development, where technological diversification in high-tech sectors drives competitiveness and market expansion (Goryushkin & Khalimova, 2023).

Additionally, research shows that export diversification and economic complexity positively correlate with per capita growth, which influences GDP and economic vulnerability, especially in countries facing challenges such as low growth, high unemployment, rising debt, and regional vulnerability (Ben Saad et al., 2023). Rahman et al. (2023) in Bangladesh highlight that technological innovation, foreign investment, trade, and human capital have both short- and long-term associations with economic growth. Bardi and Hfaiedh (2021) also demonstrate that trade liberalization positively impacts economic growth, supported by classical theories (Barro & Sala, 2009; Lucas, 1988; Romer, 1986; Banday et al., 2021; Zaman et al., 2021).

Canh and Thanh (2022) for their part, evidence that the diversification of exports and economic complexity show a positive dynamic, presenting bidirectional causality given the role played by the quality of export products which is measured from the productive capacities of a country to add value to its export products achieving more competitiveness through the structural transformation of economies and the diversification of the productive matrix of countries (Armas et al., 2020; Shahzad et al., 2022; Lapatinas et al., 2019; Koch, 2021).

While Mesquita and Stein (2019) they consider that although the great trade liberalization in Latin America has led to a positive impact on economic growth and well-being being the channel that helped recover much of the economies of Latin America from their participation in trade in the gross domestic product in the region that reached 28% they still face challenges in terms of technological aspect and innovation to continue the solid growth of international trade; this because there are comparative advantages that allow productivity leaps (Llanos & Brown, 2021; Vela et al., 2021).

For Chávez (2021) considers in the panel data model two related equations innovation as the dependent variable and trade openness as the independent variable and as a second equation was obtained as the dependent variable to the predictions of innovation and trade openness as the explanatory variable.

Meanwhile, Fusco et al. (2020) considers in their analysis of dynamic panel data where they analyze food security and trade openness developing three regression equations with indicators of trade openness, tariffs, and globalization. While Cetin et al. (2018) considers economic growth, energy consumption, trade openness, and financial development as explanatory variables and carbon emissions as the dependent variable for the case of Turkey.

3. Theoretical basis

4. Methods

4.4 Data analysis

The databases used for this research stem from international institutions such as the World Bank, Harvard University, and the Economic Commission for Latin America for the period 2000-2020.

In the estimation, we have used the panel data model based on economic literature that demonstrates a positive impact of trade openness on economic growth (Romer, 1986; Barro & Sala, 2009; Lucas, 1988; Katz, 1999).

Highlighting the importance of export diversification and complexity, leading to restructuring the production matrix, which in turn requires the accumulation of phys-ical capital and expansion of capacities, thereby necessitating the development of a climate of entrepreneurial innovation, as well as allocating greater resources to research and development (Carrasco, 2021; Chávez, 2021).

Equation 1 establishes the relationship of the dependent variable trade openness with exports, imports, and gross domestic product per country in South America i =1, 2, …………. 11 in the year t = 2000-2020.

(1)

$$\mathrm{AC}\mathrm{i},\mathrm{t}=\mathrm{Bo}+\text{B1}(\text{Export}\mathrm{i},\mathrm{t})+\text{B2}(\text{Import}\mathrm{i},\mathrm{t})+\text{B3}(\mathrm{Pbi}\mathrm{i},\mathrm{t})+\mathrm{ei},\mathrm{t}$$

On the other hand, the incorporation of the variable’s economic complexity (eco-nomic complexity index), innovation (proxy: exports of high-tech products), research and development (percentage expenditure on research and development), the logistic performance index, and total interregional trade of goods are evidenced in the following equation:

(2)

$$\mathrm{AC}\mathrm{i},\mathrm{t}=\mathrm{Bo}+\text{B1}(\text{Export}\mathrm{i},\mathrm{t})+\text{B2}(\text{Import}\mathrm{i},\mathrm{t})+\text{B3}(\mathrm{Pbi}\mathrm{i},\mathrm{t})+\text{B4}(\text{ICEi},\mathrm{t})+\text{B5}(\mathrm{Export}\_\text{teci},\mathrm{t})+\text{B6}(\mathrm{i}\_\mathrm{d}\mathrm{i},\mathrm{t})+\text{B7}(\mathrm{i}\_log\mathrm{i},\mathrm{t})+\text{B8}(\mathrm{c}\_\text{inter}\mathrm{i},\mathrm{t})+\mathrm{ei},\mathrm{t}$$

Next, the Generalized Least Squares (GLS) model is estimated because the equations present problems of heteroskedasticity and autocorrelation detected with the Breusch and Pagan (1979) (as cited in Onali et al., 2017) and Wooldridge (2010) Lagrange Multiplier tests. Furthermore, to choose between a fixed or random effects model, the Hausman test (1978) (as cited in Onali et al., 2017) is utilized.

The variables used in the proposed panel data model are detailed in the following Table 1.

Table 1. Variables used in the model.

Variable	Description	Source
Trade openness (ac i,t)	Economic openness index measuring the degree of a country's economic openness; calculated as the sum of exports plus imports divided by gross domestic product.	World Bank 2000-2020
Exports (Export i,t)	Merchandise exports (US$ at current prices)	World Bank 2000-2020
Imports (Import i,t)	Merchandise imports (US$ at current prices)	World Bank 2000-2020
Gross Domestic Product (Pbi i,t)	Value of all goods and other market services exported to the rest of the world by department, US$ at current prices	World Bank 2000-2020
Export of High-Tech Products (Export_tec i,t)	Value of exports of high-tech products, which are highly research and development intensive products	World Bank 2000-2020
Research and Development Expenditure (i_d i,t)	Expenditure on research and development (% of GDP)	World Bank 2000-2020
Logistic Performance Index (i_log i,t)	Logistic Performance Index: Total (From 1=low to 5=high)	World Bank 2000-2020
Economic Complexity Index (ice i,t)	Economic Complexity Index, a set of export products from a region showing a country's productive capacity based on existing capabilities and know-how	Harvard University, 2000-2020
Total Interregional Trade of Goods (c_inter i,t)	Value of trade in goods between countries, FOB exports in millions of dollars	Economic Commission for Latin America, 2000-2020

The data were analyzed using an econometric panel data model that captures the bidimensionality of the data. This model allows for the quantification of the impact of economic complexity, innovation, research and development, and economic growth on the trade openness of South American countries during the period 2000-2020. The data were obtained from the World Bank, Harvard University, and the Economic Commission for Latin America and the Caribbean and processed in Eviews 12 and is used for the estimation of the panel data model, considering the control variables that are subjected to the proposed equations to analyze the interrelationship between the variables ( Figure 1). This allows for both descriptive statistical treatment and the estimation of the proposed panel data model and the Generalized Least Squares (GLS) model. An academic license for the use of Eviews 12 software is available, registered under the name Lindon Vela Meléndez. The license details are as follows: Serial number: Q1208886 - D49010AF - 9D854485. The software can be downloaded from the following link: http://www.eviews.com/download/student12.

Figure 1. Correlation of model variables.

Note: Own elaboration with data from World Bank (2024), Harvard University (2024), Economic Commission for Latin America (2024), for the period 2000-2020, using Eviews 12.

Furthermore, the Generalized Least Squares (GLS) model was employed due to issues of heteroscedasticity and autocorrelation identified in the structural equations. These issues were detected using the Breusch and Pagan (1979) (as cited in Onali et al., 2017) Lagrange Multiplier test and the Wooldridge (2010) test. Additionally, the Hausman (1978) (as cited in Onali et al., 2017) test was used to decide between a fixed or random effects model, which helps in choosing the most suitable model based on the consistency of the estimators.

5. Results

The research utilizes data sourced from the World Bank, Harvard University, and the Economic Commission for Latin America and the Caribbean. Descriptive statistics for each variable analyzed are presented in Table 2, including the mean, standard deviation, minimum, maximum, skewness, and kurtosis. Additionally, the correlation matrix between the variables under analysis is provided. It shows that exports, imports, gross domestic product (GDP), the economic complexity index, total interregional trade of goods, and research and development had a negative relationship with trade openness, whereas the export of high-technology products and the logistics performance index had a positive correlation with trade openness during the period 2000-2020. The negative relationship indicates that South American countries, except for Chile and Brazil, have shown less trade openness in their economies over the last decade with respect to the international market. According to the Economic Commission for Latin America (2021) economies in South America have demonstrated reduced trade openness due to the slowdown in international trade despite their high dependence on imports and extensive trade relations with major global economic powers in a scenario of economic uncertainty and a political shift to the left, which helps to understand which variables are influencing reduced trade openness ( Figure 2).

Figure 2. Trade openness index by country.

Note: Own elaboration with data from World Bank (2024), Harvard University (2024), Economic Commission for Latin America (2024), for the period 2000-2020, using Eviews 12.

The standard deviation indicates that there is greater variability among the variables of export of high-technology products, exports, imports, gross domestic product, and total interregional trade of goods, with a total of N data points equivalent to 231 observed data per variable representing the total number of 2079 observations across the 9 variables studied during the period 2000-2020 for the 11 South American countries. This variability and the detailed analysis of the correlations provide insights into the complexities of trade dynamics and economic interactions in the region, underscoring the nuanced impact of various economic factors on trade openness.

The estimated regression results, considering the Wooldridge (2010) test, indicate the presence of autocorrelation across the panels. Additionally, the Breusch and Pagan (1979) (as cited in Onali et al., 2017) Lagrange Multiplier test shows that the estimations had issues with heteroscedasticity; these issues have been corrected using Generalized Least Squares (GLS) to address both heteroscedasticity and autocorrelation.

Table 3 displays the results of the panel data estimation, revealing that public health spending, rural population, unemployment, and gross domestic product are statistically significant at the 5% level considering equation 2.

To determine the best model (fixed or random effects), the Hausman Test is used, which compares the beta coefficients obtained through the fixed and random effects estimators, identifying whether the differences between them are significant. In both cases, the weight matrix is homoscedastic. Thus, considering that the Prob > Chi² is less than 5%, the fixed effects model is employed, which shows a higher global significance of 94.5%.

The interpretation of the fixed effects details that for every 1% increase in the exports of high-technology products, trade openness increased by 0.05%. Similarly, a 1% increase in exports raises trade openness by 0.30%; a 1% increase in imports leads to a 0.09% increase in trade openness; a 1% increase in total interregional trade of goods increases trade openness by 0.005%; and a 1% increase in spending on research and development results in a 0.14% increase in trade openness. Regarding gross domestic product, a 1% increase led to a 0.42% decrease in trade openness. For the economic complexity index, a 1% increase resulted in a 0.08% decrease in trade openness, and for the logistics performance index, a 1% increase led to a 0.13% decrease in trade openness during the analysis period 2000-2020.

These findings provide valuable insights into the factors that influence trade openness in South American countries, highlighting the complex interplay between various economic and logistical variables. The results underscore the importance of considering both economic structures and policy environments when assessing the openness of trade regimes.

6. Discussion

The impact of economic complexity, innovation, research and development, and economic growth on the trade openness of South American countries in the period 2000-2020 shows a differential behavior. These countries have exhibited lower trade openness in their economies over the last decade compared to the international market. Additionally, there is a reduced economic complexity due to lower diversification of productive matrices in export baskets, resulting in economies still heavily reliant on commodities or primary export products.

The greatest decline in trade openness is observed in Venezuela, with a negative impact of 104%. This is attributed to the country’s current economic crisis characterized by inflation and high prices of its oil commodity, coupled with a closed economy model. Among the remaining market economy countries, negative impacts are noted in Peru (15%), Ecuador (16%), Argentina (16%), Uruguay (10%), Bolivia had the greatest decline in Trade Openness at 18%, Ecuador and Argentina had 16%, Colombia had 13%, and Paraguay had 10%; these declines were due to diminishing returns and the accumulation of physical capital. Conversely, Chile, French Guiana, and Brazil have experienced positive impacts of 12%, 31%, and 25%, respectively. These economies are characterized by better performance in the economic complexity index, higher percentage of budget allocated to research and development expenses, and trade policies focused on industrializing value-added products such as high-tech products ( Figure 3).

Figure 3. Impact on trade openness by country.

Note: Own elaboration with data from World Bank (2024), Harvard University (2024), Economic Commission for Latin America (2024), for the period 2000-2020, using Eviews 12.

These results align with the findings of the Economic Commission for Latin America and the Caribbean, which has shown that economies in South America have exhibited lower trade openness due to the slowdown in international trade. Countries such as Peru, Ecuador, Bolivia, Chile, Colombia, Paraguay, and Venezuela have relied on their primary sector as the backbone of their export industry (Economic Commission for Latin America, 2021).

Venezuela muestra datos atípicos en el análisis de apertura comercial debido a su profunda crisis económica y su modelo de economía cerrada. A diferencia de otros países sudamericanos, Venezuela experimentó una caída del 104% en su apertura comercial, la más pronunciada en la región. Este comportamiento se explica por su dependencia extrema del petróleo como principal commodity, combinada con una inflación descontrolada y políticas económicas que han limitado la diversificación productiva y la integración con mercados internacionales. Mientras otros países, como Chile y Brasil, han logrado impactos positivos gracias a una mayor complejidad económica y políticas orientadas a la industrialización y la innovación, Venezuela ha visto un retroceso significativo en su capacidad para competir en el mercado global. Esta situación resalta la importancia de políticas comerciales y económicas que fomenten la diversificación y la inversión en investigación y desarrollo para evitar la vulnerabilidad asociada a la dependencia de recursos primarios.

Furthermore, these results are consistent with the findings of Mesquita Moreira and Stein (2019), who found that while significant trade liberalization in Latin America has led to a positive impact on economic growth, challenges remain in terms of technological aspects and innovation to sustain solid international trade growth. There are still comparative advantages that allow for productivity leaps, yet South American countries have not yet taken off in terms of innovation and thus achieving higher productivity.

Corrales Sillo (2021) and Mesquita Moreira and Stein (2019) also suggest that the aforementioned results indicate that South American economies still lack knowledge accumulation, which translates into a lack of innovation. To address this, policies focusing on innovation and development are needed to add more value to export products and access larger markets, thereby enhancing competitiveness. These economies are characterized by diminishing returns due to the accumulation of physical capital.

7. Conclusions

In the analysis period of 2000-2020, South American countries exhibited differential behavior as they showed lower trade openness in their economies compared to the international market. Additionally, there was lower economic complexity due to less diversification in export baskets, resulting in economies still based on commodities or primary export products.

Based on panel data analysis using fixed effects, a total of 8 countries—Peru, Ecuador, Argentina, Uruguay, Bolivia, Colombia, Paraguay, and Venezuela—experienced a negative impact on trade openness. Only the economies of Chile, French Guiana, and Brazil showed a positive impact on trade openness, characterized by better performance in economic complexity indices. These countries allocate a higher percentage of their budget to research and development and pursue trade policies aimed at industrializing products with added value.

Interpretation of fixed effects reveals that the variables with the greatest impact on trade openness during 2000-2020 were gross domestic product, logistics performance index, economic complexity index, exports, imports, and research and development expenditure.

While significant trade liberalization in Latin America has led to a positive impact on economic growth, challenges remain in terms of technological aspects and innovation to sustain solid international trade growth. South American countries still struggle to innovate and achieve higher productivity, facing a slowdown in international trade despite high dependence on imports and extensive trade relations with major global economic powers amidst economic uncertainty and a shift towards left-wing political systems.

For South American countries, it is recommended to strengthen policies that promote investment in research and development to drive innovation, as well as enhance education in science, technology, engineering, and mathematics to foster creative industries that add value to primary export products.

Developing strategic investment planning for infrastructure such as roads, ports, airports, and information technologies is essential to improve connectivity and facilitate the transportation of goods and services, thereby boosting energy efficiency and reducing logistical production costs.

Identifying the positive effect of trade liberalization on economic growth in Latin American countries, albeit unsustainable, calls for the development of clusters as a key strategy to foster competitiveness and specialization in specific sectors of the economy. This could be accompanied by fiscal and financial incentive programs to encourage investment in research and development, as well as the establishment of industrial parks, shared laboratories, and innovation centers to optimize connectivity and logistics.

The results of the panel data analysis for the period 2000–2020 reveal that trade openness in South American countries is influenced by factors such as economic complexity, innovation, research and development (R&D) spending, and economic growth. However, the findings show that most countries in the region have experienced a decline in trade openness, with the exception of Chile, Brazil, and French Guiana, which have maintained a positive impact thanks to policies focused on industrialization and export diversification. In this context, it is crucial to propose specific policy recommendations tailored to each country's realities to strengthen their integration into the global market and promote sustainable economic growth.

a. Promotion of Research and Development (R&D):

b. Improvement of Logistics Infrastructure:

c. Export Diversification:

d. Strengthening Education in Science and Technology:

e. Creation of Clusters and Industrial Parks:

In conclusion, the implementation of these specific recommendations, tailored to each country's realities, would not only enhance trade openness but also promote sustainable and diversified economic growth in the region. Collaboration between governments, the private sector, and international organizations will be key to achieving these goals.