Economic and Sectoral Convergence in Latin America and the Caribbean: An Analysis of Beta, Sigma, and Gamma Convergence

Abstract

The purpose of this study is to examine the economic and sectoral convergence of 32 countries in Latin America and the Caribbean (LAC) region from 1980 to 2022. The economic convergence hypothesis suggests that two economies with similar structural characteristics but different per capita income levels can tend to equalize in terms of income level in the long run. Confirming economic convergence has led to the development of various methodologies, among which dynamic and static disparity measures stand out. To achieve the objective of this research, both types of measures were calculated, determining beta and sigma convergence for dynamic disparity and gamma convergence for static disparity. This was accomplished by adopting the methodological approaches proposed by Sala-I-Martin and Marchante, Ortega, and Sánchez. The results show a gradual but steady evolution towards economic and sectoral convergence in LAC region during the 1980–2022 period. However, inequalities and divergences persist, requiring less developed countries to strengthen their institutions, implement sound macroeconomic policies, and diversify their economies. These measures are essential to driving economic growth and fostering more balanced and sustainable development across the region.

1. Introduction

During the 1980–2022 period, the Gross Domestic Product (GDP) of LAC saw a significant increase of 160.6%, rising from USD 2.09 trillion in 1980 to USD 5.4 trillion in 2022. This growth can be attributed to the development strategies implemented by the countries in the region, along with the dynamism shown in employment, public spending, foreign trade, and foreign direct investment. Consequently, the region’s per capita GDP increased by 15% over the same period. Dividing the study period, it is observed that the per capita income growth rate was −10.6% from 1980 to 1990, while it reached 23.7% from 1995 to 2022. The countries with the highest per capita income levels during the 1980–2022 period were the Bahamas, Barbados, Saint Kitts and Nevis, Antigua and Barbuda, and Uruguay, whereas Haiti, Nicaragua, Honduras, Bolivia, and El Salvador recorded the lowest levels. Analyzing the economic sectors during the 1980–2022 period reveals a 35.4% increase in the per capita value added of the region’s agricultural sector, with Suriname, Uruguay, Barbados, the Bahamas, and Argentina standing out. In the industry sector, the increase was 3.3%, with the Bahamas, Trinidad and Tobago, Argentina, Saint Kitts and Nevis, and Uruguay having the highest per capita value added. The service sector saw a 5.2% growth in per capita value added, with the Bahamas, Antigua and Barbuda, Saint Kitts and Nevis, Barbados, and Saint Lucia achieving the highest levels (WB, 2024).

In this context, this paper examines the economic and sectoral convergence of 32 countries in Latin America and the Caribbean region (Antigua and Barbuda, Argentina, the Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, and Uruguay) during the 1980–2022 period. The economic convergence hypothesis posits that two economies with similar structural characteristics, but different per capita income levels may eventually equalize in terms of income over the long run. To confirm economic convergence, researchers have developed various methodologies, including dynamic and static disparity measures (Solow, 1956; Baumol, 1986; Barro, 1991; Barro & Sala-I-Martin, 1991, 2004; Sala-I-Martin, 1996; Marchante et al., 2006). To achieve the objective of this research, both types of measures were calculated, determining beta (β) and sigma (σ) convergence to analyze dynamic disparity, as well as gamma (γ) convergence to assess static disparity. This was done by adopting the methodological approaches proposed by Sala-I-Martin (2000) and Marchante et al. (2008).

This research contributes to the understanding of economic dynamics in LAC through the combined application of three economic convergence metrics, the analysis of 32 regional economies over a period of four decades, the study of recent empirical evidence, and a detailed examination of economic sectors. Furthermore, this work enriches the debate on how structural and strategic differences influence the economic performance of the region and raises new questions for exploring interregional comparisons and club convergence analysis.

This paper is organized into five sections. The first analyses the dynamics of economic growth in the region and its countries. The second establishes the theoretical framework of economic convergence. The third presents the methodological development of the measurement of β, σ, and γ convergences. The fourth describes and discusses the results obtained. Finally, the main conclusions of the work are presented.

2. Economic Growth in Latin America and the Caribbean: Analysis of Sectoral Dynamics

This section examines the evolution of the main macroeconomic indicators in LAC to assess the performance of the 32 economies in the region. The analysis aims to offer preliminary insights into the economic growth trends from 1980 to 2022.

2.1. Contextualization of Growth in the Economies of the Region

During the study period, the GDP of the region grew by 160.6%, increasing from USD 2.09 trillion in 1980 to USD 5.4 trillion in 2022. Gross Fixed Capital Formation (GFCF) also saw significant growth, rising by 124.3% from 1980 to 2022, reflecting the evolution of infrastructure in the region’s economies. This infrastructure development has facilitated trade, with exports of goods (X) increasing by 5642% and imports of goods (M) growing by 4997%. Public spending (PS) expanded 313.8%, positively impacting social welfare. This growth in PS has fostered a significant increase in investment, particularly foreign direct investment (FDI), which rose by 955.3% during the 1980–2022 period, from USD 18 billion in 1980 to USD 196 billion USD in 2022. As a result of the dynamism in macroeconomic indicators such as GDP, GFCF, X, M, PS, and FDI from 1980 to 2022, the GDP per capita in LAC grew by 15%. The countries with the highest levels of per capita income include the Bahamas, Barbados, Saint Kitts and Nevis, Antigua and Barbuda, and Uruguay, while Haiti, Nicaragua, Honduras, Bolivia, and El Salvador recorded the lowest levels. These economic disparities highlight the heterogeneity within the region (WB, 2024).

Analyzing the periods separately, it is observed that between 1980 and 1990, the per capita income growth rate was −10.6%, while from 1995 to 2022, it increased to 23.7%. During the 1980–1990 period, the countries with the highest growth rates were Saint Kitts and Nevis, Saint Lucia, Antigua and Barbuda, Dominica, and Grenada. In contrast, from 1995 to 2022, the highest growth rates were found in Guyana, Panama, the Dominican Republic, Cuba, and Chile. Regarding the countries with the lowest GDP per capita levels, they were Nicaragua, Guyana, Peru, Trinidad and Tobago, and Haiti during the 1980–1990 period. From 1995 to 2022, the lowest levels were recorded in Jamaica, Belize, Haiti, Saint Lucia, and Suriname. The changes in per capita income reflect the development strategies implemented by these countries during the study periods, highlighting economic convergence at specific times (WB, 2024).

2.2. Diagnosis of Value Added per Worker of Economic Sectors in the Region

2.2.1. Agricultural Sector

Between 1980 and 2022, the value added per worker (VApw) in the agricultural sector of the region increased by 35.4%, rising from USD 7099 in 1980 to USD 9611 by 2022. Countries such as Suriname, Uruguay, Barbados, the Bahamas, and Argentina stood out for their high levels of VApw, while Haiti, Bolivia, Peru, Honduras, and Nicaragua recorded the lowest levels. For the 1980–1990 period, the growth rate of VApw in the agricultural sector was −9.3%, but it increased significantly by 80.7% between 1995 and 2022. During 1980–1990, Saint Vincent and the Grenadines, Saint Lucia, Cuba, Chile, and Guyana had the highest growth rates, while between 1995 and 2022, Guyana, the Dominican Republic, Brazil, Trinidad and Tobago, and Argentina stood out. The countries with the lowest growth rates of VApw in the agricultural sector for the 1980–1990 period were El Salvador, Barbados, Uruguay, Nicaragua, and Peru, while the Bahamas, Cuba, Ecuador, Suriname, and Guatemala have the lowest growth rates in the sector between 1995 and 2022 (WB, 2024).

2.2.2. Industry Sector

From 1980 to 2022, the VApw in the industry sector of the region increased by 3.3%, rising from USD 26,860 in 1980 to USD 27,735 in 2022. The Bahamas, Trinidad and Tobago, Argentina, Saint Kitts and Nevis, and Uruguay stood out as the countries with the highest VApw levels, while Nicaragua, Honduras, Bolivia, El Salvador, and Haiti recorded the lowest levels. A detailed analysis reveals that between 1980 and 1990, the growth rate of VApw in the sector was −15.7%, rising to 27.1% from 1995 to 2022. During the 1980–1990 period, the countries with the highest growth rates were Saint Kitts and Nevis, Grenada, Antigua and Barbuda, Saint Lucia, and Paraguay, however, between 1995 and 2022, they were Guyana, Cuba, Dominican Republic, Panama, and Trinidad and Tobago. Regarding the countries with the lowest growth rates, Guyana, Trinidad and Tobago, Suriname, Nicaragua, and Peru stood out during the 1980–1990 period, while Belize, the Bahamas, Jamaica, Haiti, and Mexico had the lowest rates from 1995 to 2022 (WB, 2024).

2.2.3. Service Sector

During the 1980–2022 time frame, the VApw of the service sector in the region grew by 5.2%, increasing from USD 21,446 in 1980 to USD 22,564 in 2022. The countries with the highest levels of VApw were the Bahamas, Antigua and Barbuda, Saint Kitts and Nevis, Barbados, and Saint Lucia, while Nicaragua, Haiti, Honduras, Bolivia, and El Salvador recorded the lowest levels. The analysis by periods shows that the growth rate of VApw in the sector was −11.2% between 1980 and 1990, reaching 8.6% between 1995 and 2022. Between 1980 and 1990, the countries with the highest growth rates were Saint Kitts and Nevis, Saint Lucia, Antigua and Barbuda, Dominica, and Grenada, between 1995 and 2022, Guyana, Panama, Costa Rica, Uruguay, and Chile stood out. As for the countries with the lowest growth rates, Guyana, Peru, Bolivia, Nicaragua, and Haiti were notable between 1980–1990; while in 1995–2022, Saint Lucia, Jamaica, Suriname, Haiti, and Belize stood out (WB, 2024).

3. Economic Convergence: A Theoretical Retrospective

For over a century and a half, economic growth has been a recurring topic in the studies of various economic schools (Sarmiento, 2009). These schools have explored the determinants of economic growth through economic growth theory, identifying factors such as labor, investment, technology, and exports as fundamental drivers of a country’s economic growth. This analysis is conducted using models and tools that examine production performance.

Economic growth models are generally classified into two main categories (Sarmiento, 2009; Martín, 2010): (a) Demand-side models: These include the works of Keynes (1936), Harrod (1939), Domar (1946), Kaldor (1957), Thirlwall (1972), and Pasinetti (1978); (b) Supply-side models: These are represented by Solow (1956), Swan (1956), Romer (1987), and Lucas (1988). Among them, the most influential is the Solow–Swan model (1956). Based on a neoclassical production function and the presence of diminishing marginal returns to capital, it establishes savings, technology, depreciation, and population growth as exogenous variables that determine the equilibrium level of per capita income in the long term. Studies such as those by Bracamontes and Camberos (2010), Martín (2010), and Rondón (2016) have found that disparities in income levels across countries are due to long-term labor productivity and the capital–labor ratio being out of long-term equilibrium.

The concept of economic convergence originates from the Solow model, which posits that two countries with similar structural characteristics, but different per capita income levels can eventually achieve a similar per capita income level. This equalization occurs due to the mobility of capital, which moves from economies where it is abundant and has low marginal productivity to those where it is scarce and has high marginal productivity. This process helps equalize the capital–labor ratio, profitability, and wages between the two economies (Solow, 1956).

Although the notion of convergence or divergence in economic growth originated with the Solow model, it was Abramowitz’s work that formally evaluated this hypothesis. Abramovitz (1986) suggests that reducing economic disparities between countries can be achieved through rapid capital accumulation or more efficient resource allocation. He further emphasizes that economic convergence is influenced by the natural resource endowment and capital stocks of economies.

Baumol (1986) rigorously analyses the convergence hypothesis, establishing a relationship between the growth rate of countries’ per capita income and their initial per capita income level, suggesting that a negative correlation indicates convergence. Similarly, Barro and Sala-I-Martin (1991), building on Barro’s (1991) work, conduct a cross-sectional regression of the initial and current per capita income values of multiple countries, concluding that there is an inverse relationship between these variables and, therefore, absolute beta (β) convergence among these economies (Barro & Sala-I-Martin, 2004; Cuervo, 2004; Gómez & Santana, 2016; Rondón, 2016).

Given that economies show variations in their economic and institutional indicators, the analysis of conditional or relative beta (β) convergence is proposed. Barro and Sala-I-Martin (1992) were the first to introduce this concept, suggesting that different steady states can be established by accounting for these disparities (Jaramillo, 2013; Rondón, 2016). This form of convergence indicates that countries tend to reduce their disparity, although this does not disappear completely, as each economy converges towards its own steady state in terms of income and per capita production (Odar, 2002; Rodríguez-Benavides et al., 2016a).

Alongside absolute and conditional β convergence, Sala-I-Martin (1996) introduces the notion of sigma (σ) convergence in response to criticisms by Quah (1994, 1996). This convergence aims to demonstrate that the dispersion of per capita income across economies tends to decrease over time and is measured using the standard deviation or coefficient of variation of the logarithm of per capita income. Furthermore, it is argued that σ convergence is an important condition for β convergence, although not necessarily the opposite. This highlights the complementarity of these two measures rather than considering them substitutes (Cuervo, 2004; Martín, 2010; Pérez, 2015; Rondón, 2016).

Arellano (2006) and Pérez (2015) indicate that, along with the concepts of σ and β convergence, the notion of gamma (γ) convergence emerges. This type of convergence examines fluctuations in the rankings of per capita income among economies, determining whether there is convergence based on changes in their ranking positions (Marchante et al., 2006). According to Barro and Sala-I-Martin (2004), Banerjee and Duflo (2005), Beyaert and Camacho (2008), and Cermeño and Llamosas (2007), variations in institutional and macroeconomic variables can lead to periods in which both convergence and divergence coexist among countries, implying the presence of two different steady states (Rondón, 2016).

According to Moncayo (2004), the presence of convergence or divergence among economies has multiple implications. Internationally, it raises questions about the processes of internationalization and integration among countries, as well as the role of international institutions promoting these processes. Domestically, within countries or subnational regions, it suggests the need to adjust public policies to promote interregional equity and balanced regional development.

Moncayo (2004) notes that a substantial amount of research on economic convergence has been conducted at the international level by authors such as Barro and Sala-I-Martin (1991), Williamson (1996), Dowrick and Delong (2003), Rasler and Thompson (2009), Basel et al. (2021), and Martinho (2023a, 2023b), among others. In the Latin American context, significant studies include those by Cáceres and Sandoval (1999), Dobson and Ramlogan (2002), Cuervo (2004), Barrientos (2007), Álvarez et al. (2009), Dabús et al. (2014), King and Ramlogan-Dobson (2016), Martín (2010), Rodríguez-Benavides et al. (2014b), Rodríguez-Benavides et al. (2014a, 2016b), Osorio (2019), Badia-Miró et al. (2020), Delbianco and Dabús (2020), Zhao and Serieux (2020), and Hurtado et al. (2021).

These studies generally indicate the presence of economic convergence among countries in the region since 1980. This trend has been influenced by the history, geography, and socioeconomic characteristics of each economy, as well as by the international context.

4. Methodological Development: Beta, Sigma, and Gamma Convergence

Several methodologies have been proposed in the literature to analyze how countries tend toward economic convergence over time. These are based on measures of static disparity (gamma convergence, alpha convergence, weighted coefficient of variation, and Theil index) and dynamic disparity (absolute and conditional beta convergence, and sigma convergence) (Rondón, 2016). In this context, this research will focus on analyzing economic beta (β) and sigma (σ) convergence for dynamic disparity, as well as economic gamma (γ) convergence for static disparity.

4.1. Beta Convergence

Beta (β) convergence focuses on determining whether a relative income gap at a given time tends to decrease over time; it analyses whether economies starting from less favorable positions experience higher growth rates than more advanced economies, thereby creating a catching-up phenomenon (Bracamontes & Camberos, 2010; Caballero & Caballero, 2016). This type of convergence evaluates the speed at which the GDP per capita of a less developed economy increases relative to that of a more prosperous economy (Arellano, 2006; Pérez, 2015). According to Sala-I-Martin (2000), its mathematical formulation is:

$$log{Y}_{it}-log{Y}_{it-1}=\alpha +\beta Log{Y}_{it-1}+e_{it}$$

(1)

where $Log{Y}_{it}$ represents the logarithm of GDP per capita of each economy; $Log{Y}_{it}-Log{Y}_{it-1}={\gamma }_{it}$ indicates the growth rate of the logarithm of GDP per capita of economy i between year t and year t − 1; $e_{it}$ corresponds to the error term, which captures various types of temporary disturbances affecting the production function; and $\beta$ is the convergence parameter to be estimated.

From Equation (1), it follows that to demonstrate regions with lower initial per capita incomes experience higher growth rates, the parameter $\beta$ must be negative and statistically significant (Gómez & Santana, 2016; Pérez, 2015). Furthermore, it is important to note that the estimates of the $\beta$-convergence model are conducted using a direct, linear, and cross-sectional approach over time, employing ordinary least squares (OLS) or nonlinear least squares methods for its determination (Jaramillo, 2013; Rodríguez-Benavides et al., 2016a).

From a graphical perspective, $\beta$ convergence is evidenced by an inverse relationship between the growth rate of the logarithm of per capita income for economy i in year t and the logarithm of per capita income for economy i in year t−1 (Bracamontes and Camberos 2010). This implies that the association must have a negative slope. An absolute $\beta$ convergence process is observed when the analyzed economies align with the trend line (Caballero & Caballero, 2016). Absolute $\beta$ convergence presupposes that the per capita incomes of countries tend to converge regardless of their initial conditions or characteristics (Jaramillo, 2013; Rodríguez-Benavides et al., 2016a).

4.2. Sigma Convergence

Sigma ($\sigma$) convergence refers to the reduction of per capita income disparity, measured by variance (Bracamontes & Camberos, 2010). A trend towards $\sigma$ convergence is observed when income disparity among countries decreases over time, implying that they are approaching a common steady state (Caballero & Caballero, 2016; Gómez & Santana, 2016; Rodríguez-Benavides et al., 2016a).

In the literature, two measures of dispersion have been used to calculate $\sigma$ convergence (Morales & Pérez, 2007): (a) the standard deviation of logarithms, and (b) the coefficient of variation (CV). The former is defined as follows (Pérez, 2015; Sala-I-Martin, 2000; Villca, 2013):

$${\sigma }_t=\sqrt{{\displaystyle \sum }_{i=1}^n{\displaystyle \frac{{\left(log{Y}_{it}-{\mu }_t\right)}^2}{n}}}$$

(2)

where $log{Y}_{it}$ represents the logarithm of the per capita GDP of each economy i in year t; n indicates the number of economies considered; and ${\mu }_t$ is the sample mean of the $log{Y}_{it}$. $\sigma$ convergence is confirmed when the indicator nears 0, while divergence is indicated when it approaches 1.

The second measure of dispersion is the coefficient of variation (CV), which is mathematically expressed as follows (Morales & Pérez, 2007; Pérez, 2015):

$$CV={\displaystyle \frac{\sqrt{{{\displaystyle \sum }}_{i=1}^n\frac{{\left(Y_{it}-\overline{Y_t}\right)}^2}{n}}}{\overline{Y_t}}}$$

(3)

where $Y_{it}$ indicates the per capita GDP of each economy i in year t; n represents the number of economies considered; and $\overline{Y_t}$ is the sample mean of per capita GDP in year t. $\sigma$ convergence is present if the CV tends to decrease over time, while an increase in the CV indicates divergence.

$\sigma$ convergence does not aim to analyse the speed at which convergence occurs, but rather to determine if, over time, the per capita income of a group of economies is converging between them. Consequently, the fact that a low-income country grows at a faster rate than a high-income country does not necessarily imply a reduction in disparity between them. This is because various economic, political, social, technological, and demographic factors, among others, may contribute to increasing disparities (Arellano, 2006; Morales & Pérez, 2007; Pérez, 2015; Sala-I-Martin, 2000).

4.3. Gamma Convergence

Gamma ($\gamma$) convergence indicates whether there is mobility in the ranking of countries according to their per capita GDP over time (Boyle & McCarthy, 1997). $\gamma$ convergence is present when changes are observed in the positions of the analysed economies during a specific period (Arellano, 2006; Pérez, 2015).

$\gamma$ convergence is evaluated using Kendall’s binary concordance index, which measures the mobility in the ranking of the economies between periods t and t−1. The mathematical formulation of this type of convergence is expressed as follows (Marchante et al., 2008):

$$\gamma ={\displaystyle \frac{var\left[R{\left(Y\right)}_{it}+R{\left(Y\right)}_{it-1}\right]}{var\left[2\ast R{\left(Y\right)}_{it-1}\right]}}$$

(4)

where $R{\left(Y\right)}_{it}$ represents the position in the per capita GDP ranking of economy i in year t, while $R{\left(Y\right)}_{it-1}$ indicates the position in the per capita GDP ranking of economy i in year t−1. This index ranges from 0 to 1, indicating convergence when it is close to 0 and divergence when it is closer to 1. If the index is exactly 0, it signifies absolute convergence (Arellano, 2006; Marchante et al., 2008).

5. Economic Convergence in Latin America and the Caribbean: Analysis of Results

This section analyses economic and sectoral convergence of 32 LAC countries during 1980–2022 and the sub-periods 1980–1990 and 1995–2022, applying Equations (1), (3), and (4) for $\beta$, $\sigma$, and $\gamma$ convergence, respectively. The analysis uses WB (2024) data on per capita GDP and VApw, measured in 2015 international dollars, to determine these convergences.

5.1. The Economic Convergence of Latin America and the Caribbean

The results of $\beta$, $\sigma$, and $\gamma$ convergences are presented below, illustrating the economic convergence in LAC during the 1980–2022 period.

5.1.1. β Convergence

During the period from 1980 to 2022, an inverse relationship was observed between the growth rate of the logarithm of GDP per capita and the logarithm of per capita GDP of the countries in the region, relative to the reference year 1980. The convergence parameter $\beta$ in this period had a value of −0.0974, indicating a speed of convergence at 9.7%. Thus, during this period, the less developed economies of LAC tended to approach the more developed ones. A dynamic of convergence $\beta$ is evident in the region during the 1980–2022 period (see Figure 1).

In the 1980–1990 sub-period, a positive association was observed between the growth rate of the logarithm of GDP per capita and the logarithm of GDP per capita of the economies in the region, relative to the reference year 1980. The convergence parameter $\beta$ was 0.0523, indicating a divergence speed of 5.2% among LAC countries (see Figure 2).

Through the 1995–2022 sub-period, a negative relationship was observed between the growth rate of the logarithm of GDP per capita and the logarithm of per capita GDP of LAC economies, relative to the reference year 1995. Within this period, the $\beta$ convergence parameter stood at −0.1183, indicating a speed of convergence at 11.8% among the countries of LAC (see Figure 3).

5.1.2. σ Convergence

σ convergence is then assessed to determine whether, during the periods of analysis, the per capita GDP of the LAC countries tends to converge between them. In the overall period between 1980 and 2022, a pattern of σ convergence is observed among the region’s economies (see Figure 4 and

Table A5. σ convergence of GDP per capita in LAC countries, 1980–2022.

σ Convergence										Period Rate of Change
1980	1985	1990	1995	2000	2005	2010	2015	2020	2022	1980–1990	1990–2022	1980–2022
0.745	0.789	0.775	0.692	0.691	0.681	0.634	0.601	0.561	0.617	4.0%	−10.9%	−17.2%

Source: Authors’ design based on Table A1 of the Appendix A, using the methodology of Sala-I-Martin (2000).

).

The sub-period analysis reveals that countries tended to converge between them, in terms of per capita income between 1995 and 2022. This is evidenced by a 10.9% decrease in the CV during these years, indicating σ convergence. In contrast, during the 1980–1990 sub-period, the CV increased by 3.9%, denoting σ divergence (see Table A5).

5.1.3. γ Convergence

The calculation of $\gamma$ convergence shows that throughout the entire 1980–2022 period, there was considerable mobility in the ranking positions of the per capita GDP of LAC countries. This provides evidence of $\gamma$ convergence during the period under study. In the sub-period analysis, the 1980–1990 period stands out, showing greater mobility in the ranking positions of per capita income. Notably, this variation is mainly concentrated in the middle positions of the ranking, as countries with the highest and lowest levels of per capita income maintained their positions throughout the analysis (see

Table A1. Gross Domestic Product per capita (International Dollars, base 2015).

Country	1980	1985	1990	1995	2000	2005	2010	2015	2020	2022	1980–1990	1995–2022	1980–2022
Antigua and Barbuda	22,498	27,636	38,221	37,942	43,596	44,872	40,317	38,409	38,093	42,079	69.9%	10.9%	87.0%
Argentina	33,511	29,659	26,819	32,529	32,463	32,513	38,209	39,263	35,403	35,315	−20.0%	8.6%	5.4%
Bahamas	70,637	74,856	75,149	67,036	71,236	73,356	70,471	65,828	53,479	65,057	6.4%	−3.0%	−7.9%
Barbados	41,624	39,458	42,845	39,298	40,038	40,475	41,876	43,291	40,654	43,699	2.9%	11.2%	5.0%
Belize	13,207	11,557	15,964	18,637	18,980	20,054	17,118	16,155	13,608	15,970	20.9%	−14.3%	20.9%
Bolivia	5529	4471	4452	4897	5229	5406	5835	7378	7152	7227	−19.5%	47.6%	30.7%
Brazil	18,740	17,474	16,985	17,587	18,209	18,438	21,473	21,625	22,708	21,986	−9.4%	25.0%	17.3%
Chile	14,251	12,573	15,717	21,207	26,199	30,128	31,861	35,110	36,789	37,898	10.3%	78.7%	165.9%
Colombia	9445	9046	9962	10,997	11,870	11,816	13,056	14,840	15,781	17,387	5.5%	58.1%	84.1%
Costa Rica	19,101	16,601	18,246	20,543	21,626	22,652	27,190	29,176	34,475	35,688	−4.5%	73.7%	86.8%
Cuba	12,613	16,915	15,293	10,280	12,060	14,261	18,008	20,317	20,103	19,696	21.3%	91.6%	56.2%
Dominica	10,066	11,726	15,379	16,529	18,331	18,401	20,841	19,108	16,995	17,813	52.8%	7.8%	77.0%
Dominican Republic	9538	8991	9022	10,466	12,664	14,116	16,693	18,397	20,430	22,319	−5.4%	113.3%	134.0%
Ecuador	12,700	12,200	12,025	12,322	11,719	12,414	14,087	15,061	14,330	13,262	−5.3%	7.6%	4.4%
El Salvador	9207	7611	7538	8639	8945	9502	9672	10,391	10,670	11,075	−18.1%	28.2%	20.3%
Grenada	11,276	12,169	16,220	15,615	20,180	22,302	19,499	21,319	21,885	22,646	43.8%	45.0%	100.8%
Guatemala	10,550	8772	8784	9413	9908	10,085	10,889	11,379	11,952	12,302	−16.7%	30.7%	16.6%
Guyana	11,536	8698	7715	10,691	12,558	12,811	15,756	18,517	33,533	61,872	−33.1%	478.7%	436.3%
Haiti	6455	5622	4971	3941	4150	4102	3933	4006	3824	3530	−23.0%	−10.4%	−45.3%
Honduras	5505	5155	5293	5161	5271	5913	5940	6025	6277	6511	−3.9%	26.1%	18.3%
Jamaica	11,462	10,354	12,231	13,782	13,034	13,465	13,313	12,026	10,865	11,390	6.7%	−17.4%	−0.6%
Mexico	28,970	27,576	25,989	24,667	27,369	26,329	25,887	26,617	25,781	25,423	−10.3%	3.1%	−12.2%
Nicaragua	6840	6154	4516	4223	4544	4537	4618	5142	4894	5257	−34.0%	24.5%	−23.1%
Panama	9949	10,070	8466	9691	11,310	11,963	16,129	21,128	19,059	23,432	−14.9%	141.8%	135.5%
Paraguay	17,684	16,935	19,518	21,264	19,136	18,238	21,518	23,241	27,506	25,327	10.4%	19.1%	43.2%
Peru	11,816	10,094	7902	8939	8641	8846	10,679	13,218	13,823	14,337	−33.1%	60.4%	21.3%
Saint Kitts and Nevis	18,115	21,584	31,715	34,517	39,129	41,246	43,677	47,976	48,975	49,911	75.1%	44.6%	175.5%
Saint Lucia	16,438	19,165	27,937	27,459	27,789	26,757	27,068	26,195	21,741	25,231	70.0%	−8.1%	53.5%
Saint Vincent and The Grenadines	9593	10,999	13,330	14,979	16,366	19,040	19,714	21,063	23,792	24,039	38.9%	60.5%	150.6%
Suriname	29,644	26,529	24,708	23,341	23,840	25,227	27,570	26,482	22,753	21,727	−16.7%	−6.9%	−26.7%
Trinidad and Tobago	28,385	23,629	19,712	22,005	26,804	33,726	39,629	44,471	38,597	38,901	−30.6%	76.8%	37.0%
Uruguay	26,464	21,241	24,816	29,147	33,490	33,623	38,547	44,011	46,202	47,875	−6.2%	64.2%	80.9%
Latin America and the Caribbean	18,992	17,631	16,974	17,659	18,894	18,909	20,843	21,732	21,796	21,848	−10.6%	23.7%	15.0%

Source: Authors’ design based on data published by WB (2024).

and Figure 5).

These economic convergence results indicate that during the overall period from 1980 to 2022, less developed countries tended to converge with more developed countries in the region. In the comparative analysis by sub-periods, it is noteworthy that the convergence speed was higher between 1995 and 2022, whereas there was a tendency toward divergence from 1980 to 1990. The sigma convergence results show that during the 1995–2022 sub-period, countries exhibited a greater tendency to converge between them, while during the 1980–1990 period, their coefficient of variation tended to stabilize. The gamma convergence results indicate that the greatest mobility among economies was observed during the 1980–1990 sub-period, while periods of both mobility and stagnation coexisted from 1995 to 2022.

5.2. Sectoral Economic Convergence in Latin America and the Caribbean

5.2.1. Agricultural Sector

Between 1980 and 2022, there was a trend toward economic convergence in LAC’s agricultural sector, with less developed economies moving closer to more developed ones. This was reflected in an inverse relationship between the growth rate of VApw and the logarithm of VApw, with a β convergence parameter indicating a convergence speed of 38.5% over the entire period. However, the sub-periods exhibited different convergence speeds: 12.8% from 1980 to 1990 and 20.9% from 1995 to 2022. A decrease in the dispersion of the CV also indicated a tendency toward σ convergence during the study period. Similarly, γ convergence showed significant mobility in the VApw ranking of the countries in the region, particularly during the 1995–2022 sub-period (see

Table A6. Sectoral β convergence of LAC countries, 1980–2022.

Economic Sector	1980–1990	1995–2022	1980–2022
Agricultural Sector	−0.12806	−0.20958	−0.38560
Industry Sector	−0.20360	−0.27771	−0.37858
Service Sector	0.17021	−0.12596	−0.04096

Source: Authors’ design based on Table A2, Table A3 and Table A4 of the Appendix A, using the methodology of Sala-I-Martin (2000).

and Figure A1 and Figure A2 of the Appendix A).

5.2.2. Industry Sector

From 1980 to 2022, there was a clear trend towards economic convergence in the LAC industry sector, as the least developed countries closed the gap with the most developed economies in the region at a speed of 37.8%. The convergence speed was more pronounced in the 1995–2022 sub-period, reaching 27.7%, compared to 20.3% in the 1980–1990 period. The sigma convergence results indicate that during the 1995–2022 sub-period, countries showed a greater tendency to converge, except in the final year of the analysis, where divergence was observed. Finally, the gamma convergence results showed the greatest mobility among the economies in the 1995–2022 sub-period (see Table A6 and Figure A1 and Figure A2 of the Appendix A).

5.2.3. Service Sector

The results of economic convergence in the service sector indicate a marked trend toward convergence in LAC from 1980 to 2022, with less developed economies catching up with more developed ones. This dynamic was evidenced by an inverse relationship between the growth rate of the logarithm of VApw and the logarithm of VApw, resulting in a sectoral convergence speed of 4.0%. However, the sub-period analysis revealed different trends: from 1980 to 1990, there was a divergence at a speed of 17.0%, while from 1995 to 2022, there was a convergence at a speed of 12.5%. Regarding the CV, overall convergence was evident over time, with the highest levels of sigma convergence occurring in the 1995–2022 sub-period. Gamma convergence in the service sector demonstrated significant mobility in the VApw rankings of countries in the region throughout the review period, particularly during 1995–2022 (see Table A6 and Figure A1 and Figure A2 of the Appendix A).

5.3. Discussion of Results

The results presented here indicate that between 1980 and 2022, the 32 economies of the LAC region exhibited a trend towards convergence in per capita income, achieving a 9.7% convergence speed. By sub-periods, the lowest income disparity (σ) and the highest economic mobility (γ) were achieved between 1995 and 2022. In contrast, the region experienced a phase of β divergence from 1980 to 1990. By economic sector, over the entire study period, the region’s economies gradually converged towards a common steady state in the agricultural, industry, and service sectors, each with different convergence speeds, reductions in VApw disparities, and mobility in sectoral rankings. It is important to mention that a σ divergence was observed among the region’s countries in 2022.

These findings are consistent with those of Cáceres and Sandoval (1999), Dobson and Ramlogan (2002), Barrientos (2007), Álvarez et al. (2009), Dabús et al. (2014), King and Ramlogan-Dobson (2016), Rodríguez-Benavides et al. (2014a, 2016b), Osorio (2019), and Badia-Miró et al. (2020). Economic convergence is expected given the shared language, culture, religion, and history among LAC countries. However, the literature shows that the region has experienced periods of greater and lesser convergence throughout its history (Badia-Miró et al., 2020; Barrientos, 2007).

Since 1960, LAC countries have experienced periods of steady growth alternating with economic declines. Although the region recorded an average growth rate of around 2% annually in the 1960s and 1970s, most countries experienced negative growth rates in the 1980s (Dobson & Ramlogan, 2002). According to Cáceres and Sandoval (1999), LAC showed a trend towards reducing per capita income disparity until 1979, indicating a significant process of economic convergence. However, from that year onward, the economies of the region began to show a less pronounced trend toward convergence and entered a new phase of growing inequality (Badia-Miró et al., 2020).

The 1974 oil crisis led LAC countries to rely more on external debt. Thus, in the early 1980s, the region faced its most severe recession since the 1930s. The adjustments and consequences of this crisis varied, with some countries opting for orthodox policies and others for heterodox approaches. However, institutional weaknesses, lack of price adjustments, high indebtedness, and internal conflicts led to slow or zero growth in many countries during the 1980s. Even though some more developed countries such as Brazil, Mexico, Chile, Colombia, and Uruguay managed to recover growth in the second half of the 1980s, most countries did not. This marked a shift in the region’s dynamics of economic convergence (Badia-Miró et al., 2020; Barrientos, 2007).

To stabilize their economies, many countries adopted neoliberal measures, which included liberalization and reduced state involvement. However, the results were mixed having devaluations failing to boost exports and rising unemployment rates. While some countries experienced modest growth improvements, social inequalities persisted, and the absence of specific policies to address these issues led to a reassessment of the link between growth and equality (Barrientos, 2007). Despite the structural reforms implemented in several countries of the region, the economic performance of the nations during the 1990s did not suggest a return to pre-crisis levels of convergence (Dabús et al., 2014; Dobson & Ramlogan, 2002).

According to Álvarez et al. (2009), the first years of the 21st century showed an accelerated pace of economic convergence in the region compared to the 1980s and 1990s. This acceleration was driven by an increasing integration of LAC economies into the international market. Despite this trend, however, the evolution of per capita GDP and other economic indicators during this period indicates persistent economic inequality among the countries in the region (King & Ramlogan-Dobson, 2016). As a result, the convergence of per capita income in the region occurred at a much slower speed compared to the periods before 1980 (Badia-Miró et al., 2020; Barrientos, 2007; Osorio, 2019).

Despite the continuous reduction of disparities between the richest and poorest areas throughout the 20th and 21st centuries, LAC remains one of the most economically unequal regions in the world today. Industrialization, fluctuations in the exploitation of natural resources, and the advance of trade in a context of national and international economic integration have combined over the years to produce a pattern of limited economic convergence.

The evolution of economic activity in LAC reveals periods when economic differences between countries have narrowed and other periods when they have widened (Badia-Miró et al., 2020). These fluctuations are known as convergence and divergence processes, respectively. The transition points between these phases are called structural changes, such as the shift in economic model in the late 1980s and early 1990s (Rodríguez-Benavides et al., 2016b).

For the time frame of this research (1980–2022), there was a steady decrease in disparities between the most prosperous and the most disadvantaged economies in the region. Despite this progress, LAC still stands out as one of the most economically unequal areas in the world. The combination of factors such as industrialization, varying rates of natural resource extraction, and trade growth within the context of national and international economic integration has generated a pattern of limited economic convergence in the region.

6. Conclusions

The analysis of economic convergence is based on supply-side models that investigate the reasons for economic growth. In this regard, Solow (1956) argues that two nations with structural similarities but disparities in their per capita income levels can eventually reach an identical per capita income level, that is, converge towards a long-term steady state.

Following the formulation of the economic convergence hypothesis, mathematical models have been developed to quantify economic disparities and develop strategies to mitigate them. Notable studies on convergence in LAC include those by Cáceres and Sandoval (1999), Dobson and Ramlogan (2002), Cuervo (2004), Barrientos (2007), Álvarez et al. (2009), Dabús et al. (2014), King and Ramlogan-Dobson (2016), Martín (2010), Rodríguez-Benavides et al. (2014a), Rodríguez-Benavides et al. (2014a, 2016b, 2016c), Osorio (2019), Badia-Miró et al. (2020), Delbianco and Dabús (2020), Zhao and Serieux (2020), and Hurtado et al. (2021), among other authors. These studies show the presence of economic convergence among the countries in the region, which is affected by their socioeconomic characteristics and varies according to the period under analysis.

The purpose of this study is to examine the economic and sectoral convergence of 32 countries in LAC (Antigua and Barbuda, Argentina, the Bahamas, Barbados, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Cuba, Dominica, Dominican Republic, Ecuador, El Salvador, Grenada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Suriname, Trinidad and Tobago, and Uruguay) from 1980 to 2022. To achieve this goal, β, σ, and γ convergence were calculated using the methodological approaches proposed by Sala-I-Martin (2000) and Marchante et al. (2008).

Based on the results, it is observed that during the 1980–2022 period, the economies of LAC converged in per capita income at a speed of 9.7%. The 1995–2022 sub-period is notable for its lower income dispersion and greater economic mobility, while 1980–1990 was a period of economic divergence. Additionally, the countries in the region tended to gradually converge by sector in terms of VApw towards a steady state, with variations in speed, dispersion, and mobility, while maintaining patterns like the convergence of per capita GDP. Unlike the performance in per capita income, the sectoral analysis shows that the countries in the region tended towards divergence in 2022, reflecting the heterogeneity of development strategies employed by the economies in response to the COVID-19 crisis.

These results generally align with a series of research studies, notably those by Cáceres and Sandoval (1999), Dobson and Ramlogan (2002), Barrientos (2007), Álvarez et al. (2009), Dabús et al. (2014), King and Ramlogan-Dobson (2016), Rodríguez-Benavides et al. (2014a, 2016b), Osorio (2019), and Badia-Miró et al. (2020). These studies support the idea of slow economic convergence in the LAC region since 1980, as well as the presence of fluctuations and periods of divergence over time, highlighting the inherent complexity of economic processes in LAC.

Based on the economic convergence analysis conducted in this research, several important elements could be proposed to contribute to economic convergence in the LAC region. Less developed countries should not only avoid depending exclusively on the production and export of natural resources but also strengthen their institutions, implement sound macroeconomic policies, and adopt measures to improve their competitiveness in international markets while focusing on knowledge-intensive and technology-intensive goods (Badia-Miró et al., 2020; King & Ramlogan-Dobson, 2016; Rodríguez-Benavides et al., 2016b).

Future lines of research could include analyzing club convergence among LAC countries, identifying the conditional factors driving economic convergence within the region, and examining economic convergence between the main countries of LAC and Southeast Asia. Specifically, this last line of research would seek to explain the differences in convergence rates between the two regions, with a particular focus on the more rapid progress achieved by Southeast Asian economies. In this context, the analysis of industrial sector convergence is especially critical due to its substantial impact on the economic development of both regions.