Volume 35: Paper 7

Trade Openness and Economic Growth in Asia: An Empirical Analysis of the COVID-19 Shock and Recovery (2019 – 2022)

Ngoc (Nancy) Nguyen
Department of Economics, The College of Wooster

 

The reduction of global trade operations caused by the onset and aftermath of COVID-19 created major financial problems for numerous nations worldwide. Southeast Asian economies are widely documented as being highly trade-dependent (World Bank, 2023), which increased their exposure to global supply chain disruptions during COVID-19. UN ESCAP (2020) documents that Southeast Asian countries experienced significant health system strain alongside sharp declines in economic activity due to lockdowns and global supply chain disruptions. Several empirical studies have analyzed the statistically significant trade-growth relationship. Tahir and Lodhi (2016) find a positive effect of trade openness across developing countries, while Hye and Lau (2015) identify a long-run relationship using time-series techniques, suggesting that the impact of openness may depend on country-specific dynamics. This research project examines whether trade openness increased or decreased the GDP growth in Asian countries throughout the pandemic shock and the initial recovery period (2019 – 2022). These years include the early outbreak of the pandemic, the peak, and the beginning of economic recovery, providing a unique window to analyze economic resilience during a global shock. Recent research emphasizes that international trade can generate both efficiency gains and exposure to external risk. For example, Adamopoulos and Leibovici (2024) show that while trade improves average food security outcomes, greater integration into global trade networks increases vulnerability to international supply disruptions. This trade-off between efficiency and risk highlights the importance of examining how trade openness interacts with economic performance during the COVID-19 shock. The analysis of this question will help Asian governments produce informed policy solutions to navigate upcoming international economic challenges.

The basic concept of trade openness suggests that it can enhance GDP growth by enabling nations to reach larger markets while attracting investment from abroad. However, during a global crisis, greater integration into international trade networks may increase exposure to external supply disruptions and volatility, as documented in the trade risk literature (Adamopoulos & Leibovici, 2024). This paper tests whether trade openness facilitated a faster economic recovery following the global shutdowns, while controlling for domestic factors such as gross fixed capital formation (percent of GDP) as a proxy for capital accumulation, health expenditure, inflation, and population. The hypothesis is that GDP growth recovered more robustly in countries with high trade openness, even after controlling for other key factors over the years 2019-2022. This study analyzes data from 30 Asian countries and incorporates five variables in its regression analysis, with GDP growth as the dependent variable and trade openness as the main independent variable, together with health expenditure, government spending, inflation, gross fixed capital formation (percent of GDP) as a proxy for capital accumulation, and population as controls. Using panel data for 30 Asian countries from 2019 to 2022, this paper finds that trade openness did not have a statistically significant effect on GDP growth during the pandemic shock and early recovery period. While prior literature documents a positive long-run relationship between openness and growth, these results suggest that the short-run dynamics of a global crisis may weaken or temporarily disrupt that relationship.

I. Theoretical Framework

The link between trade openness and economic growth is explained in both classical and modern growth theory. In long-run growth models, openness allows countries to access larger markets, new technologies, and foreign ideas. Romer (1990) and Grossman and Helpman (1991) argue that trade increases innovation and technology diffusion, which raises productivity. Barro and Sala-i-Martin (1995) suggest that poorer countries can grow faster by adopting technologies from more advanced economies. Empirical work reviewed by Edwards (1997) finds that more open economies tend to experience faster productivity growth, and broader surveys also conclude that openness is generally associated with higher long-run growth (e.g., Alam & Sumon, 2020; Nasreen & Anwar, 2014).

However, these theories mainly focus on long-run growth under normal economic conditions. The COVID-19 pandemic was a short-run global shock, which is better understood using aggregate demand and aggregate supply. In the short run, openness increases the role of exports in total output. In theory, when foreign income falls, export demand declines, which reduces aggregate demand in open economies. Similarly, many countries rely on imported inputs and global supply chains; if international transport or production is disrupted, firms face higher costs or shortages that compress output.

Because of these two effects, the short-run impact of trade openness during a global crisis is uncertain. Openness could help countries recover through export growth, but it could also make them more vulnerable to external demand shocks and supply chain disruptions. This paper tests which effect was stronger for Asian economies during the COVID-19 period. Based on these competing channels, the coefficient on trade openness is expected to be ambiguous in sign: positive if export-recovery effects dominate and negative if supply-chain disruption effects dominate. Government spending is expected to carry a positive sign if fiscal stimulus effectively supports aggregate demand, though its short-run impact may be muted during a synchronized global shock.

II. Literature

The majority of studies that relate trade openness to GDP growth use the World Development Indicators (WDI) from the World Bank as their primary data source. The identical data source across research papers enhances the ability to make results comparable between studies. The most frequently used dependent variable in studies is GDP growth, measured either as a rate or level, although trade openness is usually defined as total trade (exports plus imports) relative to GDP. Hye and Lau (2015) and Tahir and Lodhi (2016) empirically estimate the relationship between trade openness and economic growth using panel data for developing countries. Both studies find a positive long-run relationship between openness and growth, suggesting that outward-oriented trade policies are associated with higher economic performance in lower- and middle-income economies. The unified methodology enables researchers to establish significant connections between time-based macroeconomic data for a better understanding of long-term patterns.

Researchers use fixed effects (FE) panel regressions as their primary method across the literature, together with OLS or instrumental variables to resolve endogeneity issues. Hye and Lau (2015) enhanced their model by combining panel FE with instrumental variables, and Bashir and Shahbaz (2010) designed time-series regressions suitable for Pakistan’s data. Most empirical research uses shared control variables, which comprise inflation together with labor force size, capital formation, education, and government expenditure. Trade openness studies incorporate these control mechanisms to separate their effects from other structural and macroeconomic variations that exist between countries or time groups.

Most empirical studies focus on whether trade openness leads to economic growth rather than the reverse. Because higher income levels may also increase trade, several studies address potential endogeneity using panel methods or instrumental variables. Tahir and Lodhi (2016), for example, apply fixed effects and instrumental variable techniques to a panel of developing countries and find that a 1 percent increase in trade openness is associated with approximately a 0.35 percent increase in GDP growth. Similarly, Hye and Lau (2015) estimate a positive long-run effect of openness on China’s GDP, ranging from 0.25 percent to 0.30 percent. Other studies, including Shahbaz et al. (2008) and Sinha (2022), also report positive effects, although the magnitude varies across regions. Differences in estimated effects may reflect variations in country samples, time periods, econometric methods, and control variables included in the models.

The current research is based on how trade openness affects GDP growth in 30 Asian countries from 2019 to 2022, after direct adaptation of this literature research approach. The methodology of this study draws parallel elements from the articles reviewed by using WDI data and panel regression while implementing inflation, population size, and government spending controls. The control variable of health expenditure draws inspiration from Bashir and Shahbaz (2010) to measure how public investments affect growth. Moreover, two tests were performed to check for the presence of multicollinearity and heteroskedasticity by referring to the methodology used in Hye and Lau (2015). This research examines trade dynamics spanning the COVID-19 shock and the subsequent economic reopening in Asian countries while bridging the gap in understanding global shock responses. Taken together, the literature provides strong evidence of a positive long-run trade-growth relationship. These findings motivate the hypothesis that countries with greater trade openness would recover more robustly from the COVID-19 shock by accessing larger export markets and maintaining foreign investment flows. However, because the pandemic simultaneously disrupted both global supply and demand, it remains unclear whether these long-run mechanisms would operate effectively in the short run, which is the question that this paper tests.

III. Method

To investigate how trade openness influenced economic resilience and recovery throughout the 2019 – 2022 period, a panel regression approach was used with macroeconomic indicators from 30 Asian countries. The dataset includes annual measures of GDP growth as the dependent variable, trade as a percentage of GDP as the independent variable of interest, and five control variables: gross fixed capital formation (percent of GDP) as a proxy for capital accumulation, health expenditure, government expenditure, inflation, and population.

The specification follows the standard empirical growth regression framework used in trade–growth literature, where GDP growth is modeled as a function of trade openness and key macroeconomic controls. While some studies, such as Hye and Lau (2015), employ time-series approaches for individual countries, this paper adopts a panel fixed-effects framework to capture short-run variation across countries during the COVID-19 period.

After cleaning and reshaping the dataset in Stata, the empirical analysis ultimately relies on a fixed-effects (FE) panel regression framework. While a simple OLS specification was initially estimated for comparison, the main results use country fixed effects to control for time-invariant unobserved heterogeneity across countries. Year fixed effects are included to capture common global shocks, including the COVID-19 pandemic. Standard errors are clustered at the country level to account for serial correlation and heteroskedasticity within countries over time.

A dynamic specification including lagged GDP growth was also estimated as a robustness check to capture persistence and short-run adjustment dynamics. Including the lagged dependent variable allows the model to account for growth momentum or mean reversion during the pandemic and recovery period.

Finally, to determine whether a fixed or random effects model was more appropriate, a Hausman test was conducted using stored estimates from both specifications. The test supported the use of fixed effects, which became the basis for the main regression results. The Hausman test rejected the null hypothesis that random effects are consistent, confirming that country fixed effects provide the appropriate specification for this panel dataset.

All regressions were summarized in a formatted regression table using outreg2, and the table includes models with and without controls, with robust errors, and with the fixed effects specification. This step-by-step approach ensures a thorough and credible evaluation of the link between trade openness and GDP growth in the context of a global economic shock.

The regression model for this research:

(1) $$\displaylines{GDPGROWTH_{i,t}=β_1TRADEGDP_{i,t}+β_2CAPITAL_{i,t}+β_3 HEALTHEXP_{i,t} \\ +β_4GOVEXP_{i,t}+β_5INFLATION_{i,t}+β_6POPULATION_{i,t}+γ_i+γ_t+ϵ_{it}}$$

where:
GDPGROWTH_i,t is the dependent variable, and TRADEGDP_i,t is the independent variable of interest, and the rest are control variables. γ_i represents country fixed effects, γ_t represents year fixed effects, and ϵ_it is the error term.

IV. Data

All data for this project comes from the World Bank’s World Development Indicators. Thirty Asian nations within the timeframe of 2019 through 2022 serve as the basis for this research. The dataset includes key macroeconomic indicators, a proxy for capital accumulation (gross fixed capital formation as a percentage of GDP), and a demographic control variable, allowing for panel regression analysis across countries and time.

GDP growth represents the dependent variable through measurements of annual percentage changes in gross domestic product. Trade openness serves as the main independent variable in the study based on its measurement as trade relative to GDP. This indicator reflects the degree to which a country is involved in international trade and is commonly used in development and growth studies.

Other factors affecting GDP growth rates are explained through multiple control variables included in the model. Gross fixed capital formation (percent of GDP) is included as a proxy for capital accumulation, consistent with standard empirical growth regressions. Economic performance determines health expenditure levels as a proportion of the GDP in each national economy. Government consumption represents public sector spending as a share of GDP. Inflation is measured by the annual change in consumer prices and helps account for macroeconomic volatility. Population size is also included as a demographic control, which helps adjust for country scale and market size differences.

The original dataset was reshaped using the reshape command to create a long-format panel and then converted back to wide form after cleaning and labeling. The population dataset was merged using country code and year to ensure alignment across indicators. Observations with missing values in any of the variables used in the regression were dropped. The dataset is structured as a country-year panel, enabling the use of fixed-effects estimation to control for unobserved country-specific characteristics.

Summary statistics were generated using the outreg2 command, which created a formatted table including the number of observations, mean, standard deviation, minimum, and maximum for each variable. The final balanced panel includes up to 120 country-year observations, though the effective sample size varies across specifications due to missing data and the inclusion of lagged variables.

All variables are sourced from the World Development Indicators and measured in annual frequency. The fixed-effects estimation sample includes 26 countries due to missing observations in some control variables.

Table 1: Summary Statistics Table

VARIABLES	(1) Observations	(2) Mean	(3) Standard Deviation	(4) Minimum	(5) Maximum
GDP growth (annual percentage)	120	2.832	6.757	-32.910	37.510
Government expenditure (percentage of GDP)	111	13.660	4.742	4.809	25.430
Health expenditure (percentage of GDP)	92	5.500	2.471	2.060	12.340
Inflation, consumer prices (annual percentage)	109	6.377	8.935	-1.370	49.720
Trade (percentage of GDP)	112	85.960	61.790	26.270	333.000
Total population	120	145,100,000	346,100,000	442,680	1,425,000,000
Gross fixed capital formation (percentage of GDP)	111	27.420	7.960	7.769	55.110

V. Identification challenges

This analysis may be subject to omitted variable bias due to unobserved factors that influence both trade openness and GDP growth. While country fixed effects help control for time-invariant unobserved heterogeneity across countries, time-varying unobserved shocks may still bias the estimated relationship. One important missing factor could be COVID-19 policy responses, such as lockdowns, travel restrictions, or government support programs. These actions likely differed across countries and over time. For example, strict lockdown policies may simultaneously reduce trade activity and economic growth, creating endogeneity concerns if not properly accounted for. The analysis acknowledges that strict domestic lockdowns in 2020 may have temporarily dampened the transmission mechanism between trade openness and growth. The exclusion of trade-related policy variables in the analysis could produce a false interpretation of trade effects since their presence would alter the model results.

Another possible omitted variable is institutional quality, which evaluates governmental efficiency and political system stability. Single institutions that demonstrate robustness enable countries to engage in increased trade as well as achieve accelerated growth. The analysis could produce misleading results about the strength of trade because this information is excluded. Although a dynamic specification including lagged GDP growth was estimated to account for persistence, the short panel length (2019-2022) limits the ability to fully capture long-run adjustment processes.

Lastly, each country’s economic structure raises concerns about the model’s reliability. Countries that depend mainly on tourism, together with manufacturing industries, show a higher impact from global trade shifts during the pandemic. Failing to account for these variations might lead researchers to overestimate or misread the effects of trade. Because some important variables are not included, the results should be interpreted as relationships rather than clear cause-and-effect conclusions. In addition, the short time period and the unusual COVID-19 crisis make it difficult to draw strong causal claims.

VI. Results

Table 1 presents summary statistics for the 2019–2022 sample. GDP growth averaged 2.83 percent, with substantial cross-country variation. Trade openness averaged 85.96 percent of GDP, though dispersion was wide. Government expenditure, health expenditure, inflation, and capital formation also show meaningful variation across countries and over time, supporting the use of panel estimation methods.

Table 2: Baseline FE Model (Trade Only)

VARIABLES	(1) GDP Growth
Trade (percentage of GDP)	0.159 (0.152)
Year (2019-2022) = 2020	-7.017*** (1.173)
Year (2019-2022) = 2021	1.093 (1.071)
Year (2019-2022) = 2022	-1.106 (1.489)
Constant	-8.871 (12.984)
Observations	112
Number of countries	28
R-squared	0.405

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table 2 reports the baseline fixed effects model with country and year effects. Trade openness is not statistically significant, with a coefficient of 0.159, suggesting that a one percentage point increase in trade-to-GDP is associated with roughly 0.16 percentage points of additional growth, an effect indistinguishable from zero. The 2020-year indicator is large and negative (−7.017, p < 0.01), confirming the severe contraction associated with the COVID-19 shock. The model explains approximately 40 percent of the variation in growth rates, driven primarily by the pandemic year effects.

Table 3: Full FE Model (Trade + Capital + Controls)

VARIABLES	(1) GDP Growth
Trade (percentage of GDP)	-0.040 (0.153)
Gross fixed capital formation (percentage of GDP)	-0.609 (0.963)
Health expenditure (percentage of GDP)	-3.650* (2.073)
Government expenditure (percentage of GDP)	-1.959 (1.208)
Inflation, consumer prices (annual percentage)	-0.181 (0.318)
Total population (in millions)	-0.055 (0.197)
Year (2019-2022) = 2020	-4.900** (1.774)
Year (2019-2022) = 2021	4.809** (2.075)
Year (2019-2022) = 2022	8.891** (3.403)
Constant	79.936 (68.608)
Observations	80
Number of countries	26
R-squared	0.561

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table 3 adds capital formation and macroeconomic controls. Trade remains statistically insignificant. Health expenditure is negative and weakly significant, while other controls are not robustly related to growth. Year effects indicate a strong recovery in 2021 and 2022. Model fit improves with the inclusion of controls.

Table 4: FE COVID Interaction Model (Trade × 2020)

VARIABLES	(1) GDP Growth
Trade (percentage of GDP)	-0.037 (0.156)
COVID-19 Shock (2020)	-4.220* (2.264)
Trade × 2020 Interaction	-0.009 (0.018)
Gross fixed capital formation (percentage of GDP)	-0.605 (0.966)
Health expenditure (percentage of GDP)	-3.632* (2.055)
Government expenditure (percentage of GDP)	-1.922 (1.189)
Inflation, consumer prices (annual percentage)	-0.186 (0.315)
Total population (in millions)	-0.054 (0.196)
Year (2019-2022) = 2020	omitted
Year (2019-2022) = 2021	4.781** (2.040)
Year (2019-2022) = 2022	8.854** (3.377)
Constant	78.828 (68.025)
Observations	80
Number of countries	26
R-squared	0.563

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table 4 tests whether trade had a differential effect during the pandemic by interacting trade with a 2020 indicator. The interaction term is not statistically significant, suggesting that trade exposure did not amplify or mitigate the contraction in 2020. Furthermore, an additional interaction model (reported in Table 8 of the Appendix) confirms that trade exposure also did not have a statistically significant differential effect during the recovery years of 2021 and 2022.

Table 5: Dynamic FE Model (Lagged GDP Growth)

VARIABLES	(1) GDP Growth
Lagged GDP Growth (annual percentage)	-1.357*** (0.211)
Trade (percentage of GDP)	-0.288** (0.120)
Gross fixed capital formation (percentage of GDP)	-1.222** (0.513)
Health expenditure (percentage of GDP)	-3.268** (1.416)
Government expenditure (percentage of GDP)	1.031 (1.307)
Inflation, consumer prices (annual percentage)	0.513** (0.203)
Total population (in millions)	0.561** (0.264)
Year (2019-2022) = 2021	0.985 (1.861)
Year (2019-2022) = 2022	17.918*** (4.321)
Constant	-31.972 (49.976)
Observations	54
Number of countries	26
R-squared	0.921

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table 5 introduces lagged GDP growth as a robustness check. The lagged term is negative and highly significant (−1.357, p < 0.01), indicating strong mean reversion: countries that grew rapidly in one period tended to contract in the next, consistent with the sharp boom-bust cycle of the pandemic years. Trade openness becomes negative and significant in this specification (−0.288, p < 0.05). This shift likely reflects a compositional effect rather than a true structural relationship; more trade-open economies tended to record sharper pre-pandemic growth, and controlling for that prior growth exposes the subsequent correction as a negative association. This dynamic result should therefore be interpreted cautiously and does not overturn the main finding that trade openness had no significant contemporaneous effect on short-run GDP growth. Capital formation, inflation, and population also become significant, and the model’s R-squared rises to 0.921, driven largely by the lagged growth term capturing pandemic-era fluctuation.

Table 6 shows low VIF values, indicating no multicollinearity concerns. Table 7 reports evidence of heteroskedasticity, which is addressed using cluster-robust standard errors.

Overall, the evidence suggests that trade openness does not consistently explain short-run GDP growth during 2019–2022. The dominant factor is the COVID-19 shock and subsequent recovery. Trade effects appear only in the dynamic specification, indicating that its impact may operate through more complex growth adjustments rather than immediate contemporaneous effects.

VII. Analysis and Robustness Check

Several diagnostic tests and alternative model specifications were conducted to assess the reliability of the regression results. Full outputs are reported in the appendix.

First, multicollinearity was evaluated using the Variance Inflation Factor (VIF). All VIF values were low, with a mean of 1.21 and no variable exceeding the common threshold of 5. The VIF for trade_gdp was 1.20, indicating minimal correlation with other regressors. These results suggest that multicollinearity does not bias the coefficient estimates in the main specifications.

Second, the Breusch–Pagan test was performed to assess heteroskedasticity. The test produced a chi-squared statistic of 4.70 with a p-value of 0.030, rejecting the null hypothesis of constant variance. This indicates the presence of heteroskedasticity, violating a key OLS assumption. To address this issue, all models were re-estimated using cluster-robust standard errors at the country level. While coefficient magnitudes remained unchanged, standard errors increased, reducing the statistical significance of some variables. Government expenditure remained negatively associated with GDP growth and approached significance at the 10 percent level.

The findings differ from much of the established trade–growth literature. Prior studies, including Tahir and Lodhi (2016) and Hye and Lau (2015), document a positive long-run relationship between trade openness and economic growth. In contrast, this study finds no statistically significant short-run effect of trade openness during 2019–2022. This divergence likely reflects differences in time horizon and economic context. Earlier research examines structural growth under relatively stable macroeconomic conditions, whereas this paper focuses on a period characterized by a synchronized global shock. During the COVID-19 pandemic, supply chain disruptions, lockdown measures, and demand collapses may have temporarily weakened the conventional mechanisms through which trade openness promotes growth. Therefore, the absence of a significant short-run effect does not contradict the long-run literature but instead highlights the conditional nature of the trade–growth relationship during crisis periods.

VIII. Conclusion

This paper examined the relationship between trade openness and GDP growth in 30 Asian countries from 2019 to 2022, capturing both the COVID-19 contraction and subsequent recovery. The central hypothesis that higher trade openness would support a stronger economic recovery was not supported by the empirical results. Across baseline fixed effects models, trade openness did not exhibit a statistically significant impact on GDP growth during this period.

Government expenditure showed a negative but statistically insignificant association with growth across several specifications. This pattern may tentatively suggest that reactive fiscal spending during the crisis did not translate into immediate output gains, but given the lack of statistical significance, this finding should be interpreted with caution rather than treated as a firm conclusion. Other control variables, including health expenditure and inflation, did not display consistent statistical significance.

These findings suggest that while trade openness may support long-run growth, it does not function as a reliable short-run buffer during globally synchronized disruptions that constrain both supply and demand. Policymakers should therefore consider complementary domestic stabilization mechanisms when designing resilience strategies for future crises.

Future research could extend this analysis by incorporating more granular pandemic-related variables, such as lockdown stringency indices, stimulus timing, or measures of supply chain resilience. Additionally, examining post-2022 data may clarify whether the growth benefits of trade re-emerge once global economic conditions stabilize.

Appendix

Table 1: Summary Statistics Table

VARIABLES	(1) Observations	(2) Mean	(3) Standard Deviation	(4) Minimum	(5) Maximum
GDP growth (annual percentage)	120	2.832	6.757	-32.910	37.510
Government expenditure (percentage of GDP)	111	13.660	4.742	4.809	25.430
Health expenditure (percentage of GDP)	92	5.500	2.471	2.060	12.340
Inflation, consumer prices (annual percentage)	109	6.377	8.935	-1.370	49.720
Trade (percentage of GDP)	112	85.960	61.790	26.270	333.000
Total population	120	145,100,000	346,100,000	442,680	1,425,000,000
Gross fixed capital formation (percentage of GDP)	111	27.420	7.960	7.769	55.110

 

Table 2: Baseline FE Model (Trade Only)

VARIABLES	(1) GDP Growth
Trade (percentage of GDP)	0.159 (0.152)
Year (2019-2022) = 2020	-7.017*** (1.173)
Year (2019-2022) = 2021	1.093 (1.071)
Year (2019-2022) = 2022	-1.106 (1.489)
Constant	-8.871 (12.984)
Observations	112
Number of countries	28
R-squared	0.405

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table 3: Full FE Model (Trade + Capital + Controls)

VARIABLES	(1) GDP Growth
Trade (percentage of GDP)	-0.040 (0.153)
Gross fixed capital formation (percentage of GDP)	-0.609 (0.963)
Health expenditure (percentage of GDP)	-3.650* (2.073)
Government expenditure (percentage of GDP)	-1.959 (1.208)
Inflation, consumer prices (annual percentage)	-0.181 (0.318)
Total population (in millions)	-0.055 (0.197)
Year (2019-2022) = 2020	-4.900** (1.774)
Year (2019-2022) = 2021	4.809** (2.075)
Year (2019-2022) = 2022	8.891** (3.403)
Constant	79.936 (68.608)
Observations	80
Number of countries	26
R-squared	0.561

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table 4: FE COVID Interaction Model (Trade × 2020)

VARIABLES	(1) GDP Growth
Trade (percentage of GDP)	-0.037 (0.156)
COVID-19 Shock (2020)	-4.220* (2.264)
Trade × 2020 Interaction	-0.009 (0.018)
Gross fixed capital formation (percentage of GDP)	-0.605 (0.966)
Health expenditure (percentage of GDP)	-3.632* (2.055)
Government expenditure (percentage of GDP)	-1.922 (1.189)
Inflation, consumer prices (annual percentage)	-0.186 (0.315)
Total population (in millions)	-0.054 (0.196)
Year (2019-2022) = 2020	omitted
Year (2019-2022) = 2021	4.781** (2.040)
Year (2019-2022) = 2022	8.854** (3.377)
Constant	78.828 (68.025)
Observations	80
Number of countries	26
R-squared	0.563

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

 

Table 5: Dynamic FE Model (Lagged GDP Growth)

VARIABLES	(1) GDP Growth
Lagged GDP Growth (annual percentage)	-1.357*** (0.211)
Trade (percentage of GDP)	-0.288** (0.120)
Gross fixed capital formation (percentage of GDP)	-1.222** (0.513)
Health expenditure (percentage of GDP)	-3.268** (1.416)
Government expenditure (percentage of GDP)	1.031 (1.307)
Inflation, consumer prices (annual percentage)	0.513** (0.203)
Total population (in millions)	0.561** (0.264)
Year (2019-2022) = 2021	0.985 (1.861)
Year (2019-2022) = 2022	17.918*** (4.321)
Constant	-31.972 (49.976)
Observations	54
Number of countries	26
R-squared	0.921

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table 6: Multicollinearity (VIF Test)

VARIABLES	Variance Inflation Factor	1 / Variance Inflation Factor
Trade (percentage of GDP)	1.200	0.835
Gross fixed capital formation (percentage of GDP)	1.120	0.891
Health expenditure (percentage of GDP)	1.170	0.855
Government expenditure (percentage of GDP)	1.170	0.853
Inflation, consumer prices (annual percentage)	1.120	0.893
Total population	1.230	0.815
Year (2019-2022) = 2020	1.400	0.716
Year (2019-2022) = 2021	1.410	0.711
Year (2019-2022) = 2022	1.080	0.926
Mean Variance Inflation Factor	1.210

Table 7: Heteroskedasticity (Breusch–Pagan Test)

Null Hypothesis	Chi-squared Statistic	p-value
Constant variance	4.700	0.030

Table 8: FE Recovery Interaction Model (Trade × Year)

VARIABLES	(1) GDP Growth
Trade (percentage of GDP)	-0.130 (0.170)
Year (2019-2022) = 2020	-6.467** (2.686)
Year (2019-2022) = 2021	1.832 (1.762)
Year (2019-2022) = 2022	2.218 (14.430)
Trade × 2020 Interaction	0.011 (0.021)
Trade × 2021 Interaction	0.037 (0.023)
Trade × 2022 Interaction	0.094 (0.216)
Gross fixed capital formation (percentage of GDP)	-0.658 (0.992)
Health expenditure (percentage of GDP)	-4.274 (2.653)
Government expenditure (percentage of GDP)	-1.867 (1.198)
Inflation, consumer prices (annual percentage)	-0.211 (0.323)
Total population (in millions)	0.068 (0.165)
Constant	71.565 (64.822)
Observations	80
Number of countries	26
R-squared	0.574

Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Lit Review Tables

Article 1: Trade Openness, COVID-19 Shock, Foreign Direct Investment, Inflation, and Output Volatility in Six ASEAN Member States

(Behera & Rath, 2024)

Research Question	Dependent Variable & Data source	Independent Variable & Data source	Controls	Regression Technique(s)	Finding
What is the effect of trade openness and FDI on output volatility during COVID-19 in ASEAN countries?	Output volatility (GDP growth rate fluctuations) World Bank WDI, UNCTAD, IMF databases	Trade openness (percent of GDP) Foreign Direct Investment (percent of GDP) World Bank & UNCTAD	Inflation Exchange rates External debt	GMM (Generalized Method of Moments) Panel Estimation Dynamic panel model	A 1 percent increase in trade openness is associated with a 0.12 to 0.18 percentage point decrease in output volatility across most ASEAN countries, suggesting that more open economies experience greater macroeconomic stability.

Article 2: Causal Relationship between Trade Openness, Economic Growth, and Energy Consumption: A Panel Data Analysis of Asian Countries

(Nasreen & Anwar, 2014)

Research Question	Dependent Variable & Data source	Independent Variable & Data source	Controls	Regression Technique(s)	Finding
Does trade openness cause economic growth in Asian countries?	GDP per capita World Development Indicators (World Bank)	Trade openness (percent of GDP) WDI	Energy consumption Labor force Capital stock	Panel Cointegration Tests Panel Granger Causality	A 1 percent increase in trade openness leads to a 0.32 percent increase in real GDP, highlighting a strong positive long-run relationship between openness and economic performance in Asian countries.

Article 3: Trade Openness and Economic Growth in the Asian Region

(Tahir & Khan, 2014)

Research Question	Dependent Variable & Data source	Independent Variable & Data source	Controls	Regression Technique(s)	Finding
How does trade openness influence economic growth in the Asian region?	GDP growth rate Asian Development Bank (ADB) and World Bank	Trade openness (exports + imports)/GDP ADB/WDI	Investment Labor force Government expenditure	Fixed Effects and Random Effects Models	The results show that a 10 percent increase in trade openness results in a 0.9 percent increase in economic growth, indicating a robust and positive effect in the developing economies of Asia.

Article 4: Causal Relationship between Trade Openness and Economic Growth: A Panel Data Analysis of Asian Countries

(Alam & Sumon, 2020)

Research Question	Dependent Variable & Data source	Independent Variable & Data source	Controls	Regression Technique(s)	Finding
Is there a causal link between trade openness and economic growth in Asian countries?	GDP (PPP-adjusted) World Bank and IMF	Trade openness World Bank WDI	Exchange rate Labor force Inflation	Granger Causality Panel Regression with Fixed Effects	A 1 percent increase in trade openness is associated with a 0.28 percent increase in GDP growth in the long run, based on the fully modified OLS panel analysis of 14 Asian countries.

Article 5: Econometric Analysis of Trade Openness and Economic Growth for Developing Countries

(Tahir & Lodhi, 2016)

Research Question	Dependent Variable & Data source	Independent Variable & Data source	Controls	Regression Technique(s)	Finding
How does trade openness affect GDP in developing countries?	GDP growth World Bank WDI	Trade openness World Bank WDI	FDI Capital formation Labor participation	OLS and Fixed Effects Model	For every 1 percent increase in trade openness, real GDP increases by approximately 0.35 percent in developing countries, according to the fixed-effects panel regression results.

Article 6:  Trade Openness and Economic Growth: Empirical Evidence from India

(Hye & Lau, 2015)

Research Question	Dependent Variable & Data source	Independent Variable & Data source	Controls	Regression Technique(s)	Finding
What is the relationship between trade openness and economic growth in developing countries?	GDP growth rate World Development Indicators (World Bank)	Trade openness (measured as the sum of exports and imports divided by GDP) World Development Indicators (World Bank)	Domestic investment Labor force Education Democracy index Inflation	Panel Fixed Effects Estimation Instrumental Variables to address endogeneity	A 1 percent increase in trade openness leads to a 0.25 to 0.30 percent increase in India’s GDP, leads to a consistent positive impact of openness on economic growth.

 

REFERENCES

Adamopoulos, T., & Leibovici, F. (2024). Trade, food security, and climate change. Journal of International Economics, 146, 103858.

Alam, K. J., & Sumon, K. K. (2020). Causal relationship between trade openness and economic growth: A panel data analysis of Asian countries. International Journal of Economics and Financial Issues, 10(1), 118–126.

Barro, R. J., & Sala-i-Martin, X. (1995). Economic growth. McGraw-Hill.

Bashir, F., & Shahbaz, M. (2010). Trade openness and economic growth: Empirical evidence from Pakistan. The Pakistan Development Review, 49(4), 793–805.

Behera, C., & Rath, B. N. (2024). Trade openness, COVID-19 shock, foreign direct investment, inflation, and output volatility in six ASEAN member states (ERIA Discussion Paper Series No. 503). Economic Research Institute for ASEAN and East Asia.

Edwards, S. (1998). Openness, productivity and growth: What do we really know? The Economic Journal, 108(447), 383–398.

Grossman, G. M., & Helpman, E. (1991). Innovation and growth in the global economy. MIT Press.

Hye, Q. M. A., & Lau, W. Y. (2015). Trade openness and economic growth: Empirical evidence from India. Journal of Business Economics and Management, 16(1), 188–205.

Nasreen, S., & Anwar, S. (2014). Causal relationship between trade openness, economic growth and energy consumption: A panel data analysis of Asian countries. Energy Policy, 69, 82–91.

Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5, Part 2), S71–S102.

Shahbaz, M., Ahmad, K., & Chaudhary, A. R. (2008). Economic growth and trade openness in Pakistan: A multisectoral analysis. Pakistan Economic and Social Review, 46(1), 45–60.

Sinha, A. (2022). Trade openness and economic growth in developing countries: A review of the literature. International Journal of Social Science Research.

Tahir, M., & Khan, I. (2014). Trade openness and economic growth in the Asian region. Journal of Chinese Economic and Foreign Trade Studies, 7(3), 136–152.

Tahir, M., & Lodhi, S. A. (2016). Econometric analysis of trade openness and economic growth for developing countries. Colombo Business Journal, 7(1), 24–36.

United Nations Economic and Social Commission for Asia and the Pacific. (2020). COVID-19 and South-East Asia. United Nations.

World Bank. (2023). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators