Introduction

The observation of economic growth trends is particularly intriguing within regional unions, as one of their main objectives is to foster economic integration among member states, thereby promoting growth and convergence of prosperity within the union: “The convergence of prosperity (growth) of countries belonging to a regional union means that the poorest countries tend to approach the growth trends of the richest one. An opposite development would be called a divergence of prosperity” (Gugler & Vanoli, 2017, p. 610).

A regional integration process can be viewed as a mechanism to accelerate the pace at which economies narrow the gap of their economic growth. As mentioned by Gammadigbe “It has been argued in the literature that regional integration promotes shared economic growth and income convergence among member countries through direct and indirect channels of increased intra-regional trade, economies of scale, dissemination of knowledge and technology, and structural transformation.” (Gammadigbe, 2021, p.23). The Association of Southeast Asian Nations (ASEAN)—regrouping 10 Southeast Asian nationsFootnote 1—is particularly interesting in this regard, given the divergence in economic stages among its member states and the organization’s goal of promoting economic convergence (Gugler & Vanoli, 2017, pp. 610). Indeed, the ASEAN member countries exhibit significant disparities in terms of GDP and economic growth, as demonstrated by Fig. 1. A major objective of ASEAN is to strengthen the economic integration of its member countries. This aims not only to consolidate the economic growth of the member countries but also to address inequalities, particularly in terms of economic growth, due to the disparities in economic performance among the member countries. Given this major objective of ASEAN, it seemed important to us to examine whether the ASEAN countries were sufficiently resilient during the COVID-19 period and whether the goal of strengthening the economic convergence of the weaker countries was not undermined by the pandemic. In other words, did the weaker countries suffer more from the pandemic than the stronger ASEAN countries?

Fig. 1
figure 1

GDP growth (annual %) of ASEAN economies, 2000–2022. Elaboration based on World Bank, WDI database, data retrieved in July 2023

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The aim of this paperFootnote 2 is to analyze the economic development trends regarding economic convergence within ASEAN from 2000 to 2022. This study focuses on the period from 2000 to 2022 for several reasons. Firstly, it aims to explore whether a convergence process among ASEAN countries has occurred since the inclusion of the last member, Cambodia, in 1999. Secondly, the research seeks to analyze the potential impact of the 2020 pandemic on this convergence process. The period concludes in 2022 due to data availability.

This paper comprises three parts. The first part presents the economic convergence measurement tools as well as the main studies dedicated to this phenomenon within the ASEAN. The second part introduces the methodology used and presents the results of our analysis. Specifically, the analysis focuses on the difference in the convergence before and after the Corona Virus Disease 2019 (COVID-19) pandemic. Finally, the conclusion will synthesize the key findings and highlights of this study.

Literature Review on Economic Convergence of ASEAN Countries

Over the past two centuries, significant works addressing economic growth and its determinants have emerged. While the scope of this paper does not allow for an exhaustive presentation of these theories, Table 1 presents the main theoretical contributions concerning the drivers of economic growth. The quantitative studies of these drivers have contributed to the development of theories on economic convergence. These theories identify the main drivers of economic growth in countries as well as the different growth rates recorded by various countries. Indicators such as productivity, human capital, and innovation, to name a few, are important for understanding what lies behind the economic growth data of countries. Moreover, they highlight the importance of economic and social policies that influence a country’s economic performance, such as education policies, innovation policies, competition policies, and trade policies.

Table 1 Main determinants and factors driving economic growth classified by doctrines and author(s)
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GDP per capita (GDPC) and GDP per worker (GDPW) have both found consensus in the scientific literature to assess economic convergence rate (Aghion & Howitt, 2009, pp. 1–2). GDPC and GDPW are, respectively, obtained by dividing the aggregate level GDP by the number of inhabitants and the number of inhabitants actively working.

The Neo-classical growth theory model or Solow’s model (1956) is among the first contributions dedicated to economic convergence. Consequently, these theories, particularly those of Solow, form the basis of our study, which, let us remember, is dedicated to the question of whether the economic growth trends of ASEAN countries have been convergent or divergent. Solow hypothesizes absolute β-convergence, meaning that poor economies tend to grow faster per capita than rich ones due to diminishing returns to capital (Barro & Sala-i-Martin, 2004, pp. 45–46). In other words, the lower the initial level of GDPC, the higher or faster its growth rate, giving shape to the “catch-up” effect. All countries will reach the same steady-stage level. “If countries differ significantly from each other, conditional β-convergence may be observed” (Gugler and Vanoli, 2017, p. 612), meaning that countries will converge to different steady-state levels. Thus, convergence depends on pre-determined conditions, i.e., each economy converges to a stationary point that is different from others (Aghion & Howitt, 2009, p. 21). “This approach is very important in the case of ASEAN countries, as this Association is characterized by a significant disparity in the economic performance of its member countries” (Gugler and Vanoli, 2017).

According to Verico (2022, p. 1), convergence needs to meet two conditions. The β-coefficient represents the speed of convergence, and it hypothesizes that the phenomenon of convergence occurs, i.e., that a negative correlation between the initial level of prosperity and the growth rate of prosperity is assumed (Barro & Sala-i-Martin, 2004, pp. 462–466). β-Convergence is a necessary condition for convergence; however, this is not sufficient.

The second necessary hypothesis for convergence is the reduction in income dispersion, given by “σ-convergence”. This measure is needed in order to observe the disparity between GDPs per capita (GDPC) levels over time. In other words, σ-convergence occurs only if the dispersion economies decrease over time. Different measures can be used calculated σ-convergence: the most used is the Coefficient of Variation (CV). Other measures are the Standard Deviation of logs, Gini, the Theil index or the Standard Deviation of the logarithm of the GDPC (Barro & Sala-i-Martin, 2004, p. 462).

Our literature review on the economic growth of ASEAN countries in general, and on convergent or divergent developments, demonstrates significant divergences in methodology, choice of countries considered, and periods analyzed. Moreover, it is difficult to find studies that specifically examine the impact of COVID-19 on the economic growth trends of member countries and address the question of whether the pandemic had a convergent or divergent effect. Indeed, Our literature review on economic trends within the ASEAN reveals that recent studies and their conclusions may differ in their coverage of ASEAN Member States (AMS), in the periods they consider, and, in the methods, they apply to test β- and the σ-convergence. Insights may focus on either the original ASEAN-5 members—Indonesia, Malaysia, the Philippines, Singapore, and Thailand—or the expanded ASEAN-10, which includes the additional member countries of today (ASEAN-10).

Regarding contributions on ASEAN-5, the study by Michelis and Neaime (2004) rejects the σ-convergence hypothesis because they show an increase in GDPC disparities over the period 1960–1990. Michelis and Neaime (2004), test β-convergence using Pooled Ordinary Least Squares (POLS) method over the period 1960–1990. Besides rejecting the necessary σ-convergence hypothesis, Michelis and Neaime (2004, p. 486) provided weak evidence of conditional β-convergence. In contrast, Ismail (2008) observes both conditional and absolute β-convergence. Jetin (2019, pp. 60–61) rejects the σ-convergence hypothesis because the Theil index of GDP over the period 1970–2014 was higher in 2014 (0.14) than in 1960 (0.06). The Asian Financial Crisis (AFC) 1997–1998 provoked a steep rise of divergence because Malaysia, Singapore and Thailand bounced back swiftly while it took several years for Indonesia to recover. The Theil index is higher than for the founders ASEAN-5, which reflects the disparities among AMS. Magazzino et al. (2021, p. 1002) investigated how economic integration has affected the country income levels from 1960 to 2017. The analysis confirms convergence within ASEAN-5, supporting the theory that “promoting trade liberalization and economic integration would draw important development benefits for the area” (Magazzino et al., 2021, pp. 1011–1012).

Other studies have considered all members, i.e., ASEAN-10. Park (2000) investigated the hypothesis of convergence among Southeast Asian region over the 1960–1997, however, without any success as no related evidence was found. For σ-convergence, the results are not homogeneous. Chowdhury (2005), using time regressions by Ordinary Least Squares (OLS), does not show any σ-convergence of GDPC for the period 1960–2001. Chowdhury et al. (2011), analysing the decade 1998–2008 and using the Standard Deviation (SD) of GDCP, confirm the hypothesis of σ-convergence, i.e., a lower dispersion of the GDPC and thus synonymous with convergence. The findings regarding β-convergence exhibit notable diversity. While studies such as Chowdhury (2005) and Alavi and Ramadan (2008) utilized a time-series approach and failed to discern any convergence pattern, Chowdhury et al. (2011) examined the 1990–2008 period, revealing a divergent trajectory pre-Asian Financial Crisis (AFC) of 1997–1998, followed by a convergence trend post-crisis. This trend post-crises is confirmed by Blizkovsky (2012, p. 41) whose study reports a β-coefficient of 1.90 (thus annual convergence rate of 1.90%.) Gugler and Vanoli (2017, p. 622), analyzing the period from 2000 to 2014, employed diverse methodologies such as OLS, POLS, and POLS with time period effects, all of which confirm the convergence trend. In summary, absolute convergence is evident, with coefficients ranging between 0.4% and 0.6%. The reduction in disparities is confirmed by the diminishing GDPC dispersion in ASEAN countries, confirming the σ-convergence hypothesis with approximately a − 14% reduction over the 2000–2014 period (equivalent to an average annual reduction of 1%). These results showed a weak and slow convergence among AMS primarily caused by productivity differences. Over the same period, Jena (2018, pp. 3–9) examines GDPC convergence. The hypothesis of σ-convergence is confirmed as the Theil Index decreased from 1.46 in 2000 to 1.39 in 2014, reducing inequalities within the ASEAN. The GDP’s inequality has been decreasing within ASEAN over 2000–2014, finding that the industrial and services sector have contributed to income convergence. Lastly, Verico (2022, pp. 3–6) considered the period between 2000 and 2019 and some of AMS plus four non-members. A convergence’s coefficient of − 1% is founded, showing that the convergence of prosperity within ASEAN is still a long-term issue. To sum-up, we can conclude that the literature varies in many aspects, and all this hinder the homogeneity and uniformity of results concerning convergence within ASEAN.

Most of the studies considered lack consideration of economic and social policies to be developed within the member countries and also actions to be undertaken by the Association itself. Admittedly, a detailed analysis of the strengths and weaknesses of the numerous studies goes beyond the scope of this paper, which nevertheless demonstrates significant gaps, justifying the writing of our contribution.

Methodology and Results

As indicated above, this paper focusses on the period 2000–2022 for the following reasons. First, the goal is to scrutinize whether a convergence trend among ASEAN countries can be observed since the inclusion of the last member (i.e., Cambodia) in 1999. Second, the research aims to analyse the impact of the 2020 pandemic that can hamper the convergence process. The period ends in 2022 due to the availability of data. The study test β-convergence and σ-convergence of GDPC and of GDPW of the 10 AMS.

Testing β-Convergence of GDPC and of GDPW

The dependent variables of the model (y) are GDPC as a proxy for welfare and GDPW as a proxy for productivity. The independent variables are GDPC and GDPW at their initial period. Following Gugler and Vanoli, “based on Monfort (2008, p. 4) and more recently König (2014, p. 76), the absolute β-convergence of y can be modelled by the following Eq. (1) which stands for the relationship between the log of the Compound Annual Growth Rate (CAGR) of y over N years and the initial level of y” (Gugler & Vanoli, 2017, pp. 613–614):

$$\mathit{ln}\left(\Delta {y}_{i,{t}_{0}+N}\right)=\alpha +\beta \mathit{ln}\left({y}_{i,{t}_{0}}\right)+{\varepsilon }_{i,N}$$
(1)

where \(\alpha\) is a constant, \(\Delta {y}_{i,{t}_{0}+N}\) is the CAGR of y over the period and \({y}_{i,{t}_{0}}\) the initial level of y. In situations of absolute β-convergence, the estimated coefficient β should be negative (Monfort, 2008, p. 4). According to the neoclassical Solow’s growth model (1956) and of Barro and Sala-i-Martin (1991), to test conditional convergence further independent variables potentially affecting growth rate have to be included: “the saving rate (s), the population growth rate (n), the rate of technical progress (g) and the depreciation rate (δ)” (Gugler & Vanoli, 2017, p.614). “The following Eq. (2) which stands for the relationship between the log of the CAGR of y over N years and the initial level of y, including other factors potentially affecting the growth rate” (Gugler & Vanoli, 2017, p. 614):

$$\mathit{ln}\left(\Delta {y}_{i,{t}_{0}+N}\right)=\alpha +{\beta }_{1}\mathit{ln}\left({y}_{i,{t}_{0}}\right)+{\beta }_{2}{\text{ln}\left({s}_{i,N}\right)+{\beta }_{3}\text{ln}\left({n}_{i,N}+g+\delta \right)+\varepsilon }_{i,N}$$
(2)

where \(\alpha\) is a constant, \(\Delta {y}_{i,{t}_{0}+N}\) is the CAGR of y over the period and \({y}_{i,{t}_{0}}\) the initial level of y. “In situations of conditional β-convergence, the estimated β1 should be negative, while, according to the Solow model, the estimated β2 should be positive and the estimated β3 should be negative (König, 2014, p. 79)” (Gugler & Vanoli, 2017, p. 614). Table 2 summarizes the variables, their proxies and their sources which are taken from.

Table 2 Description of variables
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β-Convergence of GDPC

Figure 2 indicates “an absolute convergence of GDPC reflected by a negative relationship between the initial level of GDPC and the growth rate over the period 2000–2019” (Gugler & Vanoli, 2017, p.614). For instance, Myanmar has a lower initial level of GDPC in 2000 but, at the same time, it grew at a higher CAGR of almost 10% from 2000 to 2019. On the other side, Brunei has the lower (even negative) CAGR GDPC 2000–2019 with the higher initial level of GDPC in 2000. This observation is confirmed by the regression of column (1) in Table 3.

Fig. 2
figure 2

Initial level and growth of GDP per capita, 2000–2019. Elaboration based on World Bank, WDI database, data retrieved in April 2023

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Table 3 Absolute β-convergence of GDP per capita for ASEAN members
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Figure 3 does demonstrate the divergence of GDPC “due to a positive relationship between the initial level of GDPC and the growth rate” (Gugler & Vanoli, 2017, p.616) over the period 2020–2022: the higher the prosperity of a country in 2020, the higher its rate of growth. For instance, Lao and Myanmar have a lower initial level of GDPC in 2020 and, at the same time, they grew at the lowest pace from 2020 to 2022; on the other side, Malaysia and Singapore grow faster even with a higher level of GDPC in 2020.

Fig. 3
figure 3

Initial level and growth of GDP per capita, 2020–2022. Elaboration based on World Bank, WDI database, data retrieved in April 2023

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The neoclassical Solow’s model is tested using various methods (see Wooldridge, 2002; see Gugler & Vanoli, 2017, p.614). Table 3 shows “the results of an OLS regression with robust Standard Errors (SE), accounting for heteroskedasticity and considering one observation per country” (Gugler & Vanoli, 2017, p.614). Columns (1), (2) and (3) provide the results, respectively, over the period pre-pandemic (2000–2019), with the pandemic (2020–2022) and over the whole period (2000–2022). Columns (4) and (5) are based on the entire period and they provide the results of a Pooled OLS (POLS) regression as each country counts three observations since the time period 2014–2022 is divided into three different shifts counting the same number of years: 2014–2016 (base period), 2017–2019 (post AEC integration) and 2020–2022 (COVID-19). The observed period in column (5) starts suddenly by 2014 for the reason that each time period has to be it must last the same way. As per the data analysis, the pandemic period lasted 3 years, going backwards the analysis stopped in 2014 because the author wanted to focus on the most recent economic trends. Column (5) counts for time period effects to identify whether the period has a strong impact on the dependent variable of the regression. Before the pandemic, column (1) reports a negative coefficient of 0.283. Column (2) has a positive coefficient of ln (GDPC): 0.270 (statistically different from 0 at the 1%-level), demonstrating there is a divergence of prosperity among ASEAN countries after the 2020 pandemic. In column (5), the time period effect is not statistically significant. Over the whole period 2000–2020, an absolute β-convergence is reflected by a negative coefficient of 0.219 in column (3) per annum means a 5.037% growth rate difference over the 23 years considered. Apart from column (3), the adjusted R-squared (R2) is above 0.800, meaning that the dependent variable (y) is explained by more than 80% by the independent variables.

Table 4 presents the outcomes of conditional β-convergence analysis for GDPC among ASEAN members. Similarly to Table 3, the observed period in column (5) starts suddenly by 2014 for the reason that each time period has to be it must last the same way. As per the data analysis, the pandemic period lasted three years, going backwards the analysis stopped at 2014 because the author wanted to focus on the most recent economic trends. In column (1), the β-convergence appears positive (0.190), thus indicating more of an absolute convergence of the less advanced states towards the more industrialized ones when considering the pre-pandemic period yet lacks statistical significance. However, as shown in column (2) which went from 2020 to 2022, the convergence coefficient takes a negative sign, indicating conditional convergence, where each member progresses towards its own steady-state level, i.e., each economy converges to a stationary point that is different from others. However, this coefficient is not statistically significant. Overall, all five columns are not statistically significant. Savings rates are positively related to the CAGR just in column (3); the coefficient (g + n + δ) is always positively related to the CAGR (unexpected). It can be that a higher technical progress rate can increase the GDPC over the period considered. The convergence of GDPC does not condition on other variables as they are not significantly.

Table 4 Conditional β-convergence of GDP per capita for ASEAN members
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β-Convergence of GDPW

Figure 4 shows the absolute convergence of GDPW indicating a negative relationship between the initial level of GDPW and the growth rate over the period 2000–2019. This observation is proved by the regression of column (1) in the following Table 4.

Fig. 4
figure 4

Initial level of and growth of GDP per worker, 2000–2019. Elaboration based on World Bank, WDI database, data retrieved in April 2023

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Figure 5 shows a slight divergence of GDPW reflected by a slight positive relationship between the initial level of GDPW and the growth rate over the period 2020–2022. For instance, Lao and Myanmar have a lower initial level of GDPW in 2020 and, at the same time, they grow at the lowest pace from 2020 to 2022; on the other side, Malaysia and Singapore grow at faster even with a higher level of GDPW in 2020.

Fig. 5
figure 5

Initial level of and growth of GDP per worker, 2020–2022. Myanmar: dot blue on the x-axis line due to missing data in 2021 and 2022. Elaboration based on World Bank, WDI database, data retrieved in April 2023

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Table 5 reflects the results regarding the absolute β-convergence of GDP per worker (GDPW) with robust Standard Errors (SE), accounting for heteroskedasticity and considering one observation per country. Columns (1), (2) and (3) provide the results, respectively, over the period pre-pandemic (2000–2019), with the pandemic (2020–2022) and over the whole period (2000–2022). Columns (4) and (5) are based on the entire period and they provide the results of a Pooled OLS regression as each country counts three observations since the time period 2014–2022 is divided in three different shifts counting the same number of years: 2014–2016 (base period), 2017–2019 (post AEC integration) and 2020–2022 (COVID-19). The observed period in column (5) starts suddenly by 2014 for the reason that each time period has to be it must last the same way. As per the data analysis, the pandemic period lasted three years, going backwards the analysis stopped at 2014 because the author wanted to focus on the most recent economic trends. Column (5) counts for time period effects to analyse whether the period has a significant impact on the dependent variable of the regression. Before the pandemic, column (1) reports a negative coefficient of 0.302 per annum, indicating an absolute convergence rate of 6.04% over the 20 year period considered. Column (2) shows an opposite trend as the coefficient of 0.050 (statistically significant at 1%-level) is positively signed, and thus rejecting the hypothesis of absolute convergence of GDPW when considering the pandemic period 2020–2022. In column (3), over the whole period 2000–2020, an absolute β-convergence is reflected by a negative coefficient of 0.308, which is enhanced to 0.334 when using the POLS regression in column (4). Apart from column (3), the adjusted R-squared (R2) is above 0.800, meaning that the dependent variable (y) is explained by more than 80% by the independent variables. Similarly to GDPC, the results of GDPW proof an absolute convergence before the 2020 pandemic and then a divergence of GDPW over the period 2020–2022.

Table 5 Absolute β-convergence of GDPW for ASEAN members
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Table 6 presents the outcomes of conditional β-convergence analysis for GDPW among ASEAN members. Similarly to Table 5, the observed period in column (5) starts in 2014 for the reason that each time period has to be it must last the same way. As per the data analysis, the pandemic period lasted three years, going backwards the analysis stopped at 2014 because the author wanted to focus on the most recent economic trends. When considering the pre-pandemic period in column (1), the initial GDPW is positively related to the CAGR over the period considered, i.e., 0.134, thus indicating more of an absolute convergence of the less advanced states towards the more industrialized ones yet lacks statistical significance. Column (2) has a positive and statistically significant coefficient of + 1.344 which does not support the hypothesis of conditional convergence. Regarding the period 2000–2022 of column (3), whatever the method used, the initial GDPW is positively related to CAGR over the period (unexpected and not statistically significant coefficient). The savings rate coefficient is positive whatever the method used (expected but not always statistically significant at 5%-level), meaning that the speed of convergence is positively related to the savings.

Table 6 Conditional β-convergence of GDPW for ASEAN members
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Testing σ-Convergence of GDPC and GDPW

Figure 6 illustrates a general decline in GDPC inequalities across ASEAN Member States (AMS) from 2000 to 2022. For GDPC, the Coefficient of Variation (CV) decreased by 17.95%, the Standard Deviation of logs by 28%, and the standard deviation of logs by 28.09%, with average annual reductions of 1.296% and 1.162%, respectively. Differences in inequality reductions emerged when considering 2000–2019 and 2020–2022 separately. Looking at the period 2000–2019, both the reduction over the period and the average reduction per year are higher than considering the period 2000–2022; this for both measures. It means that before the pandemic, the inequalities within AMS were reducing faster than usual. With COVID-19, both measures of inequality show an increase as confirmed graphically: CV increased by 3.76%, while the standard deviation of logs by 2.91%, meaning that inequality has increased during the pandemic. That is for GDPC, the hypothesis of σ-convergence is confirmed before the pandemic, but it is rejected with the pandemic. Conversely, for GDPW the hypothesis of σ-convergence is rejected over the whole period 2000–2022 as inequality reduces as well as before and with the pandemic as can be shown in the Fig. 7.

Fig. 6
figure 6

Coefficient of Variation for ASEAN countries, GDP per capita, 2000–2022. Elaboration based on World Bank, WDI database, data retrieved in August 2023

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Fig. 7
figure 7

Coefficient of Variation for ASEAN countries, GDP per worker, 2000–2022. Elaboration based on World Bank, WDI database, data retrieved in August 2023

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The final Table 7 summarizes all the results according to the observed variable (GDPC and GDPW), type of convergence (Absolute β-convergence, conditional β-convergence and σ-convergence) and the analyzed time period (2000–2019, 2020–2022 and 2000–2022). In the pre-pandemic period 2000–2019, there is a general phenomenon of absolute convergence, demonstrating a reduction in the difference in absolute values between less advanced and more modern countries, which also leads to a reduction in inequality as shown by the confirmation of the σ-convergence hypothesis. This is true for both variables GDPC and GDPW. The catch-up effect of absolute β-convergence is not evident when looking at the period 2020–2022. In this timeframe, the absolute β-convergence and σ-convergence hypotheses are both rejected, which slows down the economic growth of poorer economies towards the more advanced ASEAN countries. Overall, the entire 2000–2022 time period confirms the unconditional β-convergence and σ-convergence assumptions of the pre-pandemic period. These results are not surprising. They confirm Michael Porter’s theories (Porter, 1990), which argue that a country’s economic performance depends on its microeconomic competitiveness, which in turn can be strengthened or weakened by macroeconomic competitiveness. As demonstrated in particular by the annual reports of the World Economic Forum (WEF, 2019), there is a clear correlation between macroeconomic and institutional performance (including economic and political stability, corruption, intellectual property rights, and institutional reliability) and microeconomic performance (including innovation and productivity). Indeed, the relatively low resilience of the poorest countries in ASEAN is partly explained by political shortcomings that do not foster effective domestic entrepreneurship and the establishment of Foreign Direct Investment (FDI) that could strengthen essential economic and social structures crucial for the development of these countries. This observation underscores the importance of economic growth theories mentioned earlier in this contribution, highlighting key political factors crucial for enhancing national economies, namely education and innovation policies within a stable context. The various economic performance rankings containing numerous indicators, such as those from the WEF (2019) and the World Bank (2021), clearly demonstrate the strengths and weaknesses of ASEAN countries. It would be interesting to study the correlation between these indicators and the economic growth outcomes and economic convergence of ASEAN countries. While some results regarding economic convergence among ASEAN countries were mixed during the COVID period, one might wonder what the outcomes would have been in the absence of ASEAN, which significantly bolstered economic integration procedures among its member states, notably through the establishment of the Asean Economic Community (AEC) in 2015 followed by the AEC Blueprint 2025 (ASEAN, 2023).

Table 7 Summarizing results according to the observed variable, type of convergence and the analyzed time period
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Conclusion–Limitations

One of the objectives of creating free trade zones is to promote the economic prosperity of their members through stronger integration. The challenge is particularly significant when the group of countries is highly divergent in terms of economic development levels. The case of ASEAN illustrates this point. It was therefore interesting to examine the evolution of economic growth over a period of more than twenty years, and especially to examine the evolution of the prosperity of member countries during the Covid period. The interest was focused on both highly competitive members such as Singapore and on the poorest members such as Myanmar.

This study focused on the period from 2000 to 2022 for several reasons. Firstly, it aimed to explore whether a convergence process among ASEAN countries has occurred since the inclusion of the last member, Cambodia, in 1999. The period ends in 2022 due to the availability of data.

We based our study on Solow’s (1956) neoclassical model. Our study based on the GDPC shows an absolute convergence over the period 2000 and 2019. Indeed, the poorest ASEAN countries tend to grow faster than the richest members, albeit at a rather low rate of convergence: − 0.283% per year. During the pandemic (2020–2022) the poorest ASEAN countries tend to grow slower than the richest ones (+ 0.269% per annum), shaping a divergence of prosperity. Regarding the second hypothesis σ-convergence, the analysis finds an average annual rate of 1.00–1.30%, showing a reduction in inequality from 2000 to 2019. However, the divergence of prosperity increased during the pandemic (2000–2020). situations among member countries.

Since 2000 (the start date of our study), it has been observed that the poorest countries have managed to improve their prosperity compared to the average of ASEAN member countries. However, due to their low macroeconomic and microeconomic competitiveness, the poorest countries in ASEAN have been particularly affected by the pandemic and have consequently experienced divergence. Indeed, although the observations for the short period 2020–2022 were predictable, caution is warranted in interpreting the data as the resilience of countries may depend on unmeasurable factors, such as those stemming notably from domestic policy measures that could have affected member countries differently. It would be interesting to continue this study over a longer post-pandemic period to examine the resilience capacity of member countries, particularly those with the weakest economies. Indeed, this study has certain limitations. It was not possible to examine the impact of COVID-19 after 2020 due to the lack of data at the time of writing. Furthermore, it would be valuable to conduct a detailed examination of the impact of numerous indicators of macroeconomic and microeconomic competitiveness on the economic performance of ASEAN countries. Additionally, it would be insightful to thoroughly explore the role of key economic and social policies, as emphasized by theories of economic growth (innovation policy, education policy, trade policy, etc.).

Last but not least, one may wonder about the reason for studying the evolution of economic growth, particularly economic convergence within a regional union association. Such studies are crucial for several reasons. Firstly, the findings are important for developing common economic and social policies within the Association. Indeed, garnering the support of all member countries can be challenging when considering binding policies. They also enable each member country to persuade national shareholders and stakeholders of the benefits of economic and social integration, which involves liberalizing flows of goods and services, capital, and people.

Key Questions Reflecting Applicability in Real Life

  1. 1.

    Should economic integration agreements be promoted to strengthen the growth and resilience of member countries?

  2. 2.

    Should poorer countries be encouraged to join or create free trade associations to boost their economic growth?

  3. 3.

    What priorities should the leadership of free trade institutions set to promote growth and the catch-up of the poorer member countries?

  4. 4.

    What national microeconomic policies can enhance the resilience of countries in the face of economic and social crises.