Development of Latin American trading blocs
Source: Telesur
Latin America, disintegrated in integration
In which position do we find ourselves? Despite numerous integration efforts, Latin America remains the region with the lowest level of intra-regional trade in the world – just 14% of total trade according to ECLAC and 15% according to the Inter-American Development Bank, well below the 63% in East Asia or the 65% in Europe according to the World Trade Organization statistical report of 2023. Second, amid rising geopolitical tensions and shifting global value chains, regional blocs have gained renewed importance in fostering economic development. As production becomes more geographically concentrated, neighboring countries must strengthen agreements to facilitate trade and maximize mutual gains. This approach is crucial not only for global competitiveness but also for regional resilience. For Latin America, deeper intra-regional cooperation promises significant economic benefits by leveraging shared strengths and reducing external dependencies. Then, the question arises: if there are trading blocs and integration movements, then why do intra-regional exports have a low level in Latin America?
New approach, old problem
To answer these questions, we address the issue by using an innovative methodological approach based on regression analysis. Regression is a statistical technique that attempts to determine the strength of a relationship between one subject (dependent variable) and one or many factors (independent variables). The idea is to measure how well the integration process is working.
Example: To determine the grade of influence of Colombia’s labor force and quantity of tariffs on the GDP growth of Peru.
Here there is an example:
We want to know how the quantity of study hours affects the final result of an exam.
We have 20 students with different quantities of studying hours and their grades. Please click on the following link to check the explaining data.
Independent variable (X): Study hours, we are free to decide the quantity.
Dependent variable (Y): Exam grade depends on the study hours.
Regression analyzes data by observing how many times one present factor influences the subject. In the present example, the analysis shows a correlation between the number of hours and the final grade; it concludes that the students who spent more time studying have a bigger probability of scoring better in the exam.
Calculation in Excel of this example.
From the regression analysis, we get all types of results, but today just two parts are important:
R-squared: It is the magnitude of the effect of an independent variable on the dependent one. The closer to 1, the stronger the influence.
The p-value will determine how significantly each variable affects the dependent variable; it is possible that the variable is not related in any meaningful way. The p-value must be less than 0.05 to be considered relevant.
As we prove, there is a real influence shown by the P value, not strong though, shown by R square.
Coming back to our case study, we gather information from 1995 to 2023 (29 observations), a sample large enough to check trends and data sourced from the World Development Indicators | DataBank.
The following indicators were chosen based on how the GDP in one country may be influenced by economic changes in other economies.
-Export volume -Employers (% of total employment)
-Tariff rate applied -Purchasing power parity
-Total labour force
The idea is to quantify how susceptible economies are to variations in the economies of regional partners. This relational approach will allow us to quantify the real degree of economic interdependence created by these integration processes.
It is worth mentioning that different analyses were carried out between GDP and the indicators until some result was obtained that confirmed the relationship.
These particular countries were chosen in order to have different countries’ positions: Brazil as a regional economic power, Peru as an average regional economy and Bolivia as one of the less developed economies in South America.
Furthermore, the countries in red are those in which no possible relationship with the independent variables was found.
For each trading bloc, we will choose one country to make the comparison.
Country chosen: Its GDP will be taken as the dependent variable.
Other countries in the trading bloc: indicators will be independent variables.
Pacific Alliance – Peru (country chosen)
Andean Community - Bolivia (country chosen)
MERCOSUR – Brazil (country chosen)
To check tables and complete regression charts, we recommend using the provided links.
According to the grade of influence and correlation existence, we observe some results:
Smaller economies show a clear relationship like the one in the Pacific Alliance between Peru, Chile and Colombia. However, when compared to a much larger economy, such as Mexico, the analysis shows a weak relationship between the growth of both economies.
Bolivia is the smallest economy assessed; its economy is closely linked to the growth of its neighbors.
Brazil has a big impact on smaller countries' economies; they are clearly linked when analyzing the P-value. However, for a large economy like Argentina, Brazil has a weak relationship.
Integration without direction
To conclude, Latin America's economic interdependencies are shaped by asymmetrical relationships where size and external trade ties determine integration. Large economies like Brazil influence smaller neighbors (e.g., Bolivia, Paraguay) but show limited interconnection with peers like Argentina.
Mid-sized economies such as Peru and Chile trade regionally but struggle to deepen ties with much larger partners like Mexico, whose economy is structurally linked to North America. Meanwhile, small economies remain heavily dependent on neighboring markets, exposing them to regional volatility. These dynamics reveal a fundamental pattern: Latin American trade remains shallow, with countries prioritizing global markets over regional partners due to stronger demand and higher-value opportunities.
The region's trade blocs face existential challenges due to persistent structural barriers. Intra-regional commerce languishes at just 15-20% of total trade—far below other emerging markets—as countries export similar raw materials rather than developing complementary supply chains. Chronic issues like poor infrastructure, inconsistent policies, and lack of competitive industries reinforce dependence on extra-regional partners (U.S., China, EU). Without reforms to boost competitiveness and reduce trade costs, Latin America's integration projects will remain secondary to global trade relationships, limiting their potential to drive meaningful economic convergence. The region must choose between deepening functional cooperation or accepting fragmented dependent economies in an increasingly competitive world.