Sage Journals: Discover world-class research

Abstract

The current study compares trade performance between India and China during COVID period (January–December 2020). The merchandise trade and commercial service exports and imports from both countries were obtained from the official World Trade Organization website to compare the output (deviation of actual from projected during COVID-19) of India and China’s foreign trade with the rest of the world. The present study employs models of autoregressive integrated moving average using a three-step model creation process. As per the results obtained, we can conclude that India is behind China in achieving the amount of merchandise exports and imports in relation to the output of its previous years, while China’s variance between expected and real is relatively lower. When comparing service exports and imports, it can be found that the gap between service exports is not much, but in the case of service imports, China’s deviation from India is much greater.

Keywords

International trade autoregressive integrated moving average (ARIMA)Box-Ljung trade forecast merchandise trade service trade

Introduction

In the current pandemic, the global economy is expected to contract by 3% as quarantines and lockdowns cripple output, even worse than during the financial crisis of 2008–2009, as mentioned in the World Economic Outlook report recently published by the International Monetary Fund (IMF). The World Trade Organization (WTO) stressed ‘As the COVID 19 pandemic disrupts normal economic activity and life across the world, world trade is projected to plunge by between 13% and 32% in 2020’ (WTO, 2020). By comparing a number of different lockdown speeds, studies show that the economic impacts of COVID-19 are responsive to the pace at which lockdown restrictions are enforced globally (Guan et al., 2020). In this situation, the first major economy to shut down to combat the virus was China, where the pandemic started in December 2019, and also the first to begin the battle to restore normal economic activity after the ruling Communist Party claimed victory over the epidemic in March. China’s foreign trade was affected during the most grievous time of combat against COVID-19 in the first quarter of 2020. Later, in April and May, while the domestic economy was rebounding rapidly, foreign trade was still under pressure as the pandemic spread to many of China’s trading partners. The exports from China were also contracted. It is also an outcome that underlines the permanent position of the nation in development, even in the midst of simmering tensions with the USA, which have sparked talk of changing supply chains.

A reshaping of the global supply chain was on the cards, with companies searching for alternative supply choices, with the current instability in China and with some countries planning to pull their businesses out of China. India has already earned the reputation of being a responsible and trustworthy nation with efficient and participatory governance in these times of crisis. India’s response to the pandemic was swift and prudent. Knowing the severity of an uncontrolled outbreak, the government took a timely decision on a full shutdown to carry out social distancing effectively. The impact of COVID-19 was everywhere; even in the education institutions, and the future strategies, challenges and long-term plans to combat such crisis began to be discussed (Rana et al., 2020a). The efforts to revive the domestic economy are in the right direction; we can also simultaneously begin to look at the effects of the crisis on international trade and investment and the lessons that India can learn to devise its future trade and investment strategy. During this turbulence period, international trade and investment are directly affected. According to WTO, ‘In 2020, world trade is expected to decline as COVID 19 pandemic disrupts normal economic activity and worldwide life’. With this kind of prediction, in the near future, the prospects for global trade and investment look rather bleak. Economies diversified as regards export baskets will suffer less compared to those reliant on fewer products. Similarly, countries that depend heavily on the travel, hospitality and entertainment sectors will also be lagging behind other recovering countries due to significant disruptions in these sectors. The heavy reliance of the USA on China and India for pharmaceutical drugs had disrupted the US drug supply during this period and resulted in shortages, as a large number of Chinese drug factories shut down during the coronavirus outbreak. Although India is the world’s largest supplier of generic medicines, it imports a significant portion of active pharmaceutical ingredients from China, thereby raising its dependency on China.

This motivates India to put an effort to improve its competitiveness in areas with export potential that can become essential components of the global value chain. Certain studies discuss the competitiveness of Indian industries (textile industries and products) and suggest that export competitiveness needs to be addressed for sustaining in global marketing environment (Dhiman et al., 2020b). Several studies suggest that India can also utilize its trade competitiveness with its neighbouring trading block where it can make the benefit from similar cultural environment. In the multicultural global climate, Rana et al. (2020) have suggested four powerful factors for useful impact estimation. These variables are sources of beneficial effects, operating efficiency, strategic option for foreign marketing and beneficial outcomes (Sharma & Shrivastava, 2021a). In the light of this backdrop, the current study contrasts the success of Indian and Chinese foreign trade with the rest of the world.

Considering all of the above which shows an opportunity for India to improve in terms of global trade, as China is no longer a trusted venue on the world market, we would like to make a comparison of merchandise and service trade in the COVID-19 era to understand India’s increased share in this time as compared to China. The analysis is categorized into six parts. The second section presents the theoretical framework on the subject. The third section provides the details of the methodology and describes the data used for analysis. The fourth section discusses the validity of research along with the empirical findings and explanations thereof. The fifth section discusses the threat to validity of the current work. Finally, the sixth section identifies the outcome of the analysis undertaken, its implications and future research directions.

Theoretical Framework

After its independence in 1949, China’s economy has remained largely closed to the rest of the world, but it has begun to open up its economy since the late 1970s and early 1980s. Since then, China has begun to achieve double-digit growth rates, partly fuelled by the rapidly increasing commercial market. China has been continuously changing its trading structures with its fast-growing international trade (Caporale et al., 2015). China’s economy is globally linked via trade, investment and tourism, and any slowdown or ongoing travel and transportation constraints are likely to place pressure on global supply chains and potentially generate global economic fallout. Several studies have been conducted to assess the effect of COVID-19 on China’s economy because of the large contribution of China’s economy to the global economy (Liu et al., 2020; Maffioli, 2020; Vasiev et al., 2020). In 1991–1992, India embarked on an ambitious programme of economic reform to turn its centrally planned, inward-looking economy into a market-driven economic system focused on export-led development. (Haralambides & Behrens, 2000) also explored India’s competitiveness in global trade. Indian manufacturing industries are already going through a phase of transformation, and studies also indicate the accelerated actions of the Government of India towards making more policy decisions to make Indian firms more globalized (Rana et al., 2018). This is the right time for Indian industries to make their mark in the global market. While both economies start their economic recovery at different periods of time, both economies are competitive and have important positions in the global market.

Both China and India have huge populations and economies that are increasing rapidly. At current exchange rates, they now account for almost 5% and 2% of the global gross domestic product (GDP), respectively. If we extrapolate their recent growth rates over half a century, we would find that China and India, the giants, are among the world’s largest economies (Winters & Yusuf, 2007). Dahlman (2011) also concentrated on India and China’s relationship with the global system and found that both countries have their own strategies and are equally competitive in the global arena. Hölscher et al. (2010) also studied the competitiveness between India and China in relation to international trade. The study also examines how regional economic integration influences trade in COVID-19 products, and it identifies a range of factors that are crucial for positive trade between the two countries (Uttama, 2021).

Khan and Ahmad (2017) analysed the export pattern and competitiveness of India–China in global and bilateral market. The emerging Chinese and Indian multinationals (Pradhan, 2017) dealt with the root and development of outward foreign direct investment (OFDI) and investigated the locational determinants of such investment. Quer et al. (2017) argue on the influence of host country’s institutional factors on location. In their study, Li and Zhang (2018) empirically analyse the contrast of the international competition between China and the manufacturing industries of India by drawing up specific indicators.

Zhao et al. (2018) compared domestic value added differences between China and the USA, Japan, Korea, and India based on generalized logarithmic mean divisia index by considering value-added coefficient effect, effect of input–output structure, effect of domestic scale and effect of international scale. Ahmad et al. (2018) studied India and China’s short- and long-term trade patterns using the revealed comparative advantage (RCA) and bilateral RCA; this study specifically attempts to identify export patterns and specialization areas of the economies under investigation. Dwesar and Kesharwani (2019) examined the intensity of intra-industry trade (IIT) between India and China using the A-Index over a period of two decades (1999–2018). Using 99 commodity data of 10 years, the study also investigates trade trends of commodities moved between countries and how they have changed over time.

Several studies have investigated the impact of COVID-19 on international trade of counties Gruszczynski (2020), Kerr (2020), Barichello (2020), Vidya and Prabheesh (2020), Baldwin and Tomiura (2020), and Sharma and Shrivastava (2021) have studied the negative impact of COVID-19 on international demand of tradable goods and services and future of trade in G7 plus China. Mandal and Mandal (2020) compared the COVID-19 data from India with China and rest of the world. Alon (2020) studied the impact of COVID-19 on international business and globalization. Gereffi (2020) investigated the requirement for change in the global medical supply value chains and the impact of current pandemic on the same. Recently, Nag and Puniani (2021) analysed the trend of export and import between India and China during post-COVID era. Their study focusses on the global value chain participation of India and China.

From the conclusive summary of related literature, A large gap can be seen in the final synthesis of connected literature to provide a scope for the study on the presented issue, which is the comparison of India and China’s international commerce with the rest of the globe. The gap can be used to create dynamic autoregressive models of merchandise and service imports and exports of both the countries. Therefore, the present study intends to make some conclusive contribution to the trade performance comparison of both the countries in the current COVID-19 context.

Descriptive Data and Analytical Framework

To compare the performance (deviation of actual from estimated during COVID-19) of international trade of India and China with the rest of the world, data pertaining to the merchandise trade and commercial service exports and imports of both the countries have been obtained. The data have been taken on a monthly basis starting from January 2013 to December 2020. The data pertaining to commercial service exports and imports of China could be obtained from January 2015; hence, the study extrapolates China’s commercial service imports and exports on the basis of available data. The data have been sourced from the official website of WTO. The data from the period ranging from 2013 to 2019 have been used for developing the predictive model using autoregressive integrated moving average (ARIMA) model, and the data pertaining to the remaining 12 months (January 2020 to December 2020) have been used to compare the performance of the countries by calculation of deviation between estimated and actual trade.

The Variables

The following data have been collected from the official website of WTO:

Chi_Mxp: China’s total merchandise exports (million US dollar)

In_Mxp: India’s total merchandise exports (million US dollar)

Chi_Mmp: China’s total merchandise imports (million US dollar)

In_Mmp: India’s total merchandise imports (million US dollar)

Chi_Sxp: China’s commercial services exports (million US dollar)

In_Sxp: India’s commercial services exports (million US dollar)

Chi_Smp: China’s commercial services imports (million US dollar)

In_Smp: India’s commercial services imports (million US dollar)

Econometric Model Building

When the time series forecast is to be done on the basis of its own previous values, lags, and lagged forecast errors, the Box-Jenkins method or ARIMA are popular models. The econometric modelling through ARIMA includes the following steps:

Step 1: Identification of a Suitable Model

Model identification (to find the order p, d, q) is the first step in constructing the ARIMA model.

p denotes the number of autoregressive terms;

d denotes the number of differencing done to make series stationary; and

q denotes the number of lagged forecast errors in the prediction equation.

According to George P. Box and Gwilym Jenkins, these models work on both stationary and non-stationary (made stationary after differencing) time series. Hence, the first stage in ARIMA model building involves checking the stationarity of times series.

There are a number of tests available to check the stationarity of time series data, but the current study uses augmented Dickey–Fuller (ADF) and Philips–Perron (PP) test to check the stationarity of series. Both ADF and PP are sometimes unable to reject the null hypothesis about non-stationarity; hence to confirm stationarity of the results, the study also employs the Kwiatkowski–Philips–Schmidt–Shin (KPSS) unit root test. This practice of combined use of unit root (ADF and PP) and stationarity (KPSS) tests is known as confirmatory data analysis (Brooks, 2002). Here, it is to be noted that the first two tests have null hypothesis about non-stationarity, but KPSS test has null hypothesis about stationarity of the series. Trend plots of trade series (export and import data of merchandise and service trade) also have been created to check the stationarity in the series. The number of series’ differencing necessary to make the series stationary is used to determine the value of d.

Step 2: Estimation Through Model

The second stage in identification involves finding suitable values of p in AR and q in MA terms. To find the lags of AR and MA, the plots of correlogram (Autocorrelation function [ACF]) and partial correlogram (Partial autocorrelation function [PACF]) of stationary series have been created. The plots show the values of p and q that were used to create prediction models for trade data (export and import series).

Step 3: Diagnostic Checking of Models

For diagnostic checking of models, forecast errors (residuals) have been plotted; to investigate the correlation between successive forecast errors (residuals), correlogram (ACF) and partial correlogram (PACF) have been created; and to check whether the residuals are white noise or not, Box–Jung test has been conducted.

Empirical Results

Tables 1 and 2 show the results of ADF unit root test, PP test and KPSS unit root test. Table 1 presents the unit root test results at level, while Table 2 shows the results at first differencing. Both the figures and tables confirm the non-stationarity of our seven series at level and stationarity at first differencing. Only the time series of China’s merchandise exports is stationary at level (As per test values). Hence, order (d = 0) can be accepted for China’s merchandise exports. While order (d = 1) can be taken for remaining series. On observing the results of PP test, it can be noticed that the null hypothesis about non-stationarity of series is rejected even at level in case of India’s merchandise trade, China’s merchandise imports, China’s commercial service exports, China’s commercial service imports and India’s commercial service imports, but the other two tests confirm non-stationarity at level; hence the series have been differenced once to make them stationary.

Table 1.

Stationarity Test: Series at Level

Variable	ADF		PP Test		KPSS
Variable	Test Statistic	p-Value	Z-Alpha	p-Value	Test Statistic	p-Value
Chi_Mxp	–3.37	0.02	–43.30	0.02	0.61	0.17
In_Mxp	–1.63	0.59	–34.63	0.02	0.37	0.07
Chi_Mmp	–1.752	0.68	–38.41	0.01	0.61	0.014
In_Mmp	–3.11	0.44	–15.42	0.10	0.42	0.04
Chi_Sxp	–2.32	0.31	–43.72	0.02	1.29	0.01
In_Sxp	–2.21	0.64	–15.13	0.17	1.46	0.01
Chi_Smp	–3.57	0.07	–38.65	0.02	1.18	0.01
In_Smp	–3.11	0.62	–23.47	0.01	1.79	0.01

Source: The author.

Table 2.

Stationarity Test at First Difference

Variable	ADF		PP test		KPSS
Variable	Test Statistic	p-Value	Z-alpha	p-Value	Test Statistic	p-Value
In_Mxp	–6.39	0.01	–114.76	0.01	0.062	0.1
Chi_Mmp	–4.87	0.01	–95.89	0.01	0.084	0.1
In_Mmp	–6.05	0.01	–112.87	0.01	0.152	0.1
Chi_Sxp	–6.47	0.01	–61.87	0.01	0.179	0.1
In_Sxp	–5.88	0.01	–96.43	0.01	0.243	0.1
Chi_Smp	–5.71	0.01	–51.43	0.01	0.052	0.1
In_Smp	–4.98	0.01	–99.66	0.01	0.149	0.1

Source: The author.

Figures 1–16 present the correlogram (ACF) and partial correlogram (PACF) plots of differenced times series for lags 1 to 20. As per the results of ACF and PACF plots, the p and q orders for ARIMA models have been selected.

Figure 1.

Source: R studio output.

Figure 2.

Source: SPSS output.

Figure 3.

Source: R studio output.

Figure 4.

Source: SPSS output.

Figure 5.

Source: R studio output.

Figure 6.

Source: R studio output.

Figure 7.

Source: R studio output.

Figure 8.

Source: R studio output.

Figure 9.

Source: R studio output.

Figure 10.

Source: R studio output.

Figure 11.

Source: R studio output.

Figure 12.

Source: R studio output.

Figure 13.

Source: R studio output.

Figure 14.

Source: SPSS output.

Figure 15.

Source: R studio output.

Figure 16.

Source: R studio output.

Table 3 shows different ARIMA models selected for different time series. Hence, after identifying different models, the estimation has been conducted to find the coefficient of ARIMA models.

Table 3.

ARIMA Models for Different Time Series

Variable	ARIMA Model
China’s total merchandise exports (million US dollar)	ARIMA(0,1,2)
India’s total merchandise exports (million US dollar)	ARIMA(2,1,2)
China’s total merchandise imports (million US dollar)	ARIMA(3,1,2)
India’s total merchandise imports (million US dollar)	ARIMA(1,0,1)
China’s commercial services exports (million US dollar)	(0,1,1) with drift
India’s commercial services exports (million US dollar)	(0,1,1) with drift
China’s commercial services imports (million US dollar)	(1,1,1)
India’s commercial services imports (million US dollar)	(1,1,1)

Source: The author.

Table 4 presents the coefficients of ARIMA models applied after identification of models. All the coefficients are significant at 1% level of confidence except China’s merchandise imports (significant at 5% level).

Table 4.

Coefficients of AR and MA Terms (ARIMA models)

Variable		Estimate	Std. Error	z-Value	Pr(>\|z\|)
Chi_Mxp	ma1	–0.575	0.088	–5.591	2.26e–08***
	ma2	–0.344	0.091	–3.339	0.0008***
In_Mxp	ar1	–0.872	0.087	–11.112	< 2.1e–19***
	ar2	–0.920	0.094	–9.217	< 2.2e–14***
	ma1	0.659	0.128	4.579	3.411e–08***
	ma2	0.900	0.154	4.672	2.811e–08***
Chi_Mmp	ar1	–1.314	0.176	–5.754	7.49e–012***
	ar2	–0.782	0.185	–3.026	0.0027***
	ar3	–0.638	0.127	–3.947	1.59e–05***
	ma1	0.812	0.239	4.123	0.0020***
	ma2	–0.164	0.324	–0.589	0.4942
In_Mmp	ar1	0.929	0.0441	21.12	5.04e–099***
	ma1	–0.407	0.109	–3.740	0.0002***
Chi_Sxp	ma1	–0.689	0.094	–8.687	<2.2e–23***
In_Sxp	ma1	–0.413	0.121	–3.336	0.0005726***
Chi_Smp	ar1	0.241	0.139	1.811	0.0645*
	ma1	–1.000	0.066	–17.30	8.06e–034***
In_Smp	ar1	0.405	0.164	2.477	0.0133**
	ma1	–0.387	0.121	–4.150	0.0006191***

Source: The author.

Table 5 shows the results of Ljung–Box test statistic for examining the null hypothesis of independence in a given time series, which is accepted in case of all time series. Hence, we can conclude that ARIMA models of time series are best fit.

Table 5.

Box-Ljung Test (diagnostic checking)

Variable	X-squared	p-Value
Chi_Mxp	0.074	0.729
In_Mxp	0.028	0.746
Chi_Mmp	0.413	0.543
In_Mmp	1.526	0.347
Chi_Sxp	0.089	0.871
In_Sxp	2.892	0.065
Chi_Smp	8.237	0.098
In_Smp	4.127	0.069

Source: The author.

Table 6 presents the actual and predicted merchandise exports of India and China. Figures 17 and 18 show the estimated and actual merchandise exports of China and India, respectively. The figures clearly show that China has been able to perform better than India in case of merchandise exports. The table also presents the percentage deviation of actual merchandise exports from the projected merchandise exports separately for each nation. The negative deviation shows that the actual performance of exports is better than estimated, and positive values show that the counties could not achieve the estimated level of trade performance. We can observe from the results that India’s merchandise exports are weaker than China’s. After June 2020, China’s merchandise exports seem to be more stable, although we can observe that India’s exports are still in an adverse condition. China’s export performance is improving, and it can be observed from the table that China has outperformed India in the month of November and December 2020. The negative impact on China can be observed only for 4 months (March, April, May and June), while India is affected more. The actual values for China’s merchandise exports were not available for the month of January 2020. Hence, the comparison for the month of January has been omitted. The maximum impact on India is observed in the month of April; the reason attributed to this can be complete lockdown in India in the month of April. India has achieved satisfactory export performance in the month of December.

Table 6.

Estimated Versus Actual (merchandise exports)

Obs.	Chi_Mxp (actual)	Chi_Mxp (predicted)	D	In_Mxp	In_Mxp	D
Obs.	Chi_Mxp (actual)	Chi_Mxp (predicted)	D	(actual)	(predicted)	D
2020: January		221382		25866	26235	1.41
2020: February	292449	213613	–36.91	27727	26415	–4.97
2020: March	185133	214019	13.5	21434	26595	19.41
2020: April	200114	214424	6.67	10169	26359	61.42
2020: May	207147	214829	3.58	19230	26478	27.37
2020: June	213574	215235	0.77	22014	26459	16.80
2020: July	237631	215640	–10.2	23761	26417	10.05
2020: August	235259	216045	–8.89	22849	26449	13.61
2020: September	239756	216450	–10.77	27559	26427	–4.28
2020: October	237183	216856	–9.37	24905	26421	5.74
2020: November	268074	217261	–23.39	23519	26424	11.01
2020: December	281928	217666	–29.52	27145	26413	–2.77

Source: The author.

Note: D Is the percentage deviation of actual from predicted.

Figure 17.

Source: Author’s formation based on data.

Figure 18.

Source: Author’s formation based on data.

Table 7 reflects the actual and expected prices of merchandise imports from the rest of the world in both countries. Figures 19 and 20 show the estimated and actual merchandise imports of China and India, respectively. On comparing the figures, we notice that China’s imports are stable, while India’s imports are falling from 2019 onwards. In Table 7, we have measured the percentage deviation of actual imports from the estimate. The negative deviation indicates better actual performance of imports, and positive values show that the counties could not achieve the estimated level of imports. We note more deviation in India than in China in the case of imports as well. Again, after June 2020, China’s imports are stable, but India is still at the height of fluctuations. Again, we can note that China has been able to perform well, as predicted in the month of December 2020. If we compare the deviation of exports and imports of China with that of India, we can observe that imports are more impacted than exports for both the countries. Due to unavailability of the values of merchandise imports of China for the month of January 2020, we have omitted the calculation of deviation for that month.

Table 7.

Estimated Versus Actual (merchandise imports)

Obs.	Chi_Mmp	Chi_Mmp	D	In_Mmp (actual)	In_Mmp (estimated)	D
Obs.	(actual)	(estimated)	D	In_Mmp (actual)	In_Mmp (estimated)	D
2020: January		188012		41170	39017	–5.52
2020: February	299544	177905	–68.37	37883	38959	2.76
2020: March	165074	191093	13.62	31388	38905	19.32
2020: April	154912	185648	16.56	17100	38854	55.99
2020: May	144115	188353	23.49	22855	38807	41.11
2020: June	167153	183676	8.99	21326	38764	44.99
2020: July	175302	191592	8.5	28512	38724	26.37
2020: August	176334	185192	4.78	29562	38686	23.58
2020: September	202759	190592	–6.38	30316	38651	21.56
2020: October	178737	186063	3.94	33552	38618	13.12
2020: November	192673	192068	–0.31	33393	38588	13.46
2020: December	203754	186555	–9.28	42586	38560	–10.44

Source: The author.

Note: D Is the percentage deviation of actual from predicted.

Figure 19.

Source: Author’s formation based on data.

Figure 20.

Source: Author’s formation based on data.

In Table 8, we can observe the comparison between the deviations of actual from estimated service exports of India and China. Figures 21 and 22 show the estimated and actual service exports of China and India, respectively. Again, we have omitted calculation for China’s service exports for the month of January 2020 due to non-availability of data. The negative deviation shows that the actual performance of exports is better than estimated, and positive values show that the counties could not achieve the estimated level of trade performance. As per the results, we have compared the average deviation of China with that of India. The average deviation in case of China is 12.61%, while average deviation in case of India is 11.84%. We can observe that China’s service exports are more impacted from COVID-19 in comparison to India’s service exports (though the difference is not much). We can also conclude from the figures as well as the tables that both India’s and China are observed to have a similar impact due to COVID-19 in case of service exports, since the positive values indicate that actual service exports have been less than the estimated service exports.

Table 8.

Estimated Versus Actual (service exports)

Obs.	Chi_Sxp	Chi_Sxp	D	In_Sxp	In_Sxp	D
Obs.	(actual)	(estimated)	D	(actual)	(estimated)	D
2020: January	19255	24928.9	22.76	18985	19190.7	1.072
2020: February	20274	25044	19.05	17725	19262.8	7.983
2020: March	24131	25159	4.09	18163	19334.9	6.061
2020: April	22825	25274	9.69	16450	19407	15.237
2020: May	21663	25389.1	14.68	16766	19479.1	13.928
2020: June	21659	25504.1	15.08	16995	19551.2	13.074
2020: July	24611	25619.1	3.93	17031	19623.3	13.210
2020: August	21895	25734.2	14.92	16441	19695.3	16.523
2020: September	23847	25849	7.74	17285	19767	12.556
2020: October	22098	25964	14.89	16583	19840	16.416
2020: November	22969	26079	11.93	17080	19911	14.218
2020: December		26194			19983

Source: The author.

Note: D Is the percentage deviation of actual from predicted.

Figure 21.

Source: Author’s formation based on data.

Figure 22.

Source: Author’s formation based on data.

In Table 9, we have made a comparison between the deviations of actual from estimated service imports of India and China. Figures 23 and 24 present the estimated and actual service imports of China and India, respectively. Here also, we have omitted calculation for China’s service imports for the month of January 2020 due to non-availability of data. The negative deviation indicates that the actual performance of imports is higher than expected, and the positive values mean that the counties were unable to reach the estimated level of trade performance. As per the results from Table 9, we have calculated the average deviation of China and India for making comparison. The average deviation of China is 30.85% and that of India is 17.26%. Thus, we can observe that China’s service imports are impacted more than India’s service imports. The higher positive values in case of China indicate that China is lagging behind in case of service imports (the difference between estimated and actual is high).

Table 9.

Estimated Versus Actual (service imports)

Obs.	Chi_Smp (actual)	Chi_Smp (estimated)	D	In_Smp (actual)	Chi_Smp (estimated)	D
2020: January	38859	44877	13.41	12001	12282	2.29
2020: February	27995	44549	37.16	11067	12209	9.35
2020: March	34596	44589	22.41	11112	12217	9.04
2020: April	28339	44714	36.62	9301	12257	24.12
2020: May	28223	44859	37.09	9938	12311	19.28
2020: June	28920	45007	35.74	9960	12370	19.48
2020: July	31339	45157	30.60	10047	12431	19.18
2020: August	31535	45307	30.40	9597	12493	23.18
2020: September	34887	45457	23.25	10135	12555	19.28
2020: October	27657	45608	39.36	9525	12618	24.51
2020: November	30501	45758	33.34	10120	12681	20.20
2020: December		45908			12743

Source: The author.

Note: D Is the percentage deviation of actual from predicted.

Figure 23.

Source: Author’s formation based on data.

Figure 24.

Source: Author’s formation based on data.

Threats to Validity

It is difficult to calculate the effect in the same pandemic situation because of massive uncertainty, distinct period and length of COVID-19. The current study is limited to the comparison of merchandise and service trade of India and China (with the rest of the world). The analysis is restricted to a particular time frame, and a comparison was made only for the pandemic era. The contrast for the month of January could not be made due to the non-availability of data for that month (China’s goods and service imports and export data).

Conclusion and Future Research Direction

The current study successfully created the ARIMA models of times series. The findings of the current study suggest that India’s merchandise exports fluctuate more than China’s. China’s merchandise exports appear to be more stable after June 2020, but we can observe that there is still an unfavourable situation for India’s exports. We note more fluctuations in India than in China in the case of imports as well. Again, after June 2020, China’s imports are stable, but India is still at the height of uncertainty. In contrast to India’s service exports, China’s service exports are more affected by COVID-19. However, in the case of service exports, the effect of COVID-19 on both India and China is observed to be minimal. Given that the positive values suggest that actual exports of services were lower than expected and there are almost equal differences between actual and estimated exports of services, India’s service imports, as compared, are less affected than China’s service imports. The higher positive values for China suggest that, in the case of service imports, China is lagging behind. When we speak about the overall effect of COVID-19 on the foreign trade (merchandise and service) of both countries, we realize that India is more affected than China. In the month of April, the maximum impact of COVID-19 on India’s foreign trade can be observed (due to the complete lockdown in the country). India continues to face fluctuations in foreign trade, whereas China is now in the stage of revival. This could be due to the fact that the disease is transmitted in different countries at different times. The other reason for the same may be China is better positioned than other emerging economies to win the war against the pandemic (Liu et al., 2020). There is ample scope for researching foreign trade in various (underdeveloped, emerging and developed) world economies. A comparison spectrum can offer a more complex area for comparison during the pandemic phase and can be contrasted with its after-effects too. Studies for comparing India’s and China’s success can be extended in line with availability of data.

Managerial Implication

Evaluating the economic effect of the coronavirus pandemic (COVID-19) on the country’s foreign trade is important for policymakers but difficult because of the immense uncertainty surrounding COVID-19 and the lack of timely data. The consequences of the lockdown, supply chain disturbances and other factors are included in the current study’s trade data to demonstrate their impact on foreign trade. The outcomes of the study may be useful to policymakers. The current study provides insights for foreign trade policymakers to take advantage of the current situation in which, due to changing supply equations in the current pandemic, India can create an enormous market base for itself. The current study stimulates India to devise its future trade and investment strategy to place itself in the international trade arena.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

References

Ahmad

, Kunroo

M. H.

, & Sofi

I. A.

(2018). An RCA analysis of India–China trade integration: Present, potential and prospects. Foreign Trade Review, 53(1), 49–58.

Alon

(2020). COVID-19 and international business: A viewpoint. FIIB Business Review. https://doi.org/10.1177/2319714520923579

Baldwin

, & Tomiura

(2020). Thinking ahead about the trade impact of COVID-19. In Baldwin

, & Tomiura

, (Eds.), Economics in the Time of COVID-19 (p. 59). Hitotsubashi University.

Barichello

(2020). The COVID-19 pandemic: Anticipating its effects on Canada’s agricultural trade. Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie, 68(2), 219–224.

Brooks

(2002). Introductory econometrics for finance. Cambridge University Press.

Caporale

G. M.

, Sova

, & Sova

(2015). Trade flows and trade specialisation: The case of China. China Economic Review, 34, 261–273.

Dahlman

(2011). The world under pressure: How China and India are influencing the global economy and environment. Stanford University Press.

Dhiman

, Kumar

, & Rana

(2020). Why export competitiveness differs within Indian textile industry? Determinants and empirical evidence. Review of International Business and Strategy. https://doi.org/10.1108/RIBS-03-2020-0021

Dwesar

, & Kesharwani

(2019). Examining intra-industry trade between India & China: Is India on the right track? Theoretical Economics Letters, 9(6), 1834–1851.

10.

Gereffi

(2020). What does the COVID-19 pandemic teach us about global value chains? The case of medical supplies. Journal of International Business Policy, 3(3), 287–301.

11.

Guan

, Wang

, Hallegatte

, Davis

S. J.

, Huo

, Li

, Bai

, Lei

, Xue

, Coffman

, Cheng

, Chen

, Liang

, Xu

, Lu

, Wang

, Hubacek

, & Gong

(2020). Global supply-chain effects of COVID-19 control measures. Nature Human Behaviour, 4, 577–587.

12.

Gruszczynski

(2020). The COVID-19 pandemic and international trade: Temporary turbulence or paradigm shift? European Journal of Risk Regulation, 11(2), 337–342.

13.

Haralambides

, & Behrens

(2000). Port restructuring in a global economy: An Indian perspective. International Journal of Transport Economics/Rivista internazionale di economia dei trasporti, 27(1), 19–39.

14.

Hölscher

, Marelli

, & Signorelli

(2010). China and India in the global economy. Economic Systems, 34(3), 212–217.

15.

Kerr

W. A.

(2020). The COVID-19 pandemic and agriculture: Short-and long-run implications for international trade relations. Canadian Journal of Agricultural Economics/Revue canadienne d’agroeconomie, 68(2), 225–229.

16.

Khan

M. A.

, & Ahmad

(2017). An analysis of export pattern and competitiveness of India–China in global and bilateral market. FIIB Business Review, 6(2), 9–18.

17.

, & Zhang

(2018). Empirical research on comparison of international competitiveness between China and India manufacturing industries. 2nd International Conference on Education Science and Economic, Management (ICESEM 2018) (pp. 661–665). Atlantis Press.

18.

Liu

, Sun

, & Zhang

(2020). Is the Chinese economy well positioned to fight the COVID-19 pandemic? The financial cycle perspective. Emerging Markets Finance and Trade, 56(10), 2259–2276.

19.

Maffioli

E. M.

(2020). How is the world responding to the novel coronavirus disease (COVID-19) compared with the 2014 West African Ebola epidemic? The importance of China as a player in the global economy. The American Journal of Tropical Medicine and Hygiene, 102(5), 924–925.

20.

Mandal

, & Mandal

(2020). COVID-19 pandemic scenario in India compared to China and rest of the world: A data driven and model analysis. medRxiv.

21.

Nag

, & Puniani

(2021). Post-covid world economy and India–China bilateral trade. Trade and Development Review, 13(2).

22.

Pradhan

J. P.

(2017). Emerging multinationals: A comparison of Chinese and Indian outward foreign direct investment (pp. 113–148). Institutions and Economies.

23.

Quer

, Claver

, & Rienda

(2017). Cultural distance, political risk and location decisions of emerging-market multinationals: A comparison between Chinese and Indian firms. Journal of the Asia Pacific Economy, 22(4), 587–603.

24.

Rana

, Saikia

P. P.

, & Barai

M. K.

(2018). Globalization and Indian manufacturing enterprises. FIIB Business Review, 7(3), 167–175.

25.

Rana

, Anand

, Prashar

, & Haque

M. M.

(2020a). A perspective on the positioning of Indian business schools post COVID-19 pandemic. International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-04-2020-0415

26.

Rana

, Prashar

, Barai

M. K.

, & Hamid

A. B.

(2020b). Determinants of international marketing strategy for emerging market multinationals. International Journal of Emerging Markets. https://doi.org/10.1108/IJOEM-09-2019-0742

27.

Sharma

, & Shrivastava

A. K.

(2021a). Economic activities and oil price shocks in Indian outlook: Direction of causality and testing cointegration. Global Business Review. https://doi.org/10.1177/0972150921990491

28.

Sharma

, & Shrivastava

A. K.

(2021b). Marketing strategy for financial services in Indian financial service institutions. FIIB Business Review. https://doi.org/10.1177/2319714521994510

29.

Uttama

N. P.

(2021). Is trade in COVID-19 products in ASEAN economies a building or stumbling block? Journal of Economic Integration, 36(1), 46–64.

30.

Vasiev

, Bi

, Denisov

, & Bocharnikov

(2020). How COVID-19 pandemics influences Chinese economic sustainability. National Research University Higher School of Economics, 14(2), 7–22.

31.

Vidya

, & Prabheesh

(2020). Implications of COVID-19 pandemic on the global trade networks. Emerging Markets Finance and Trade, 56(10), 2408–2421.

32.

Winters

, & Yusuf

(2007). Dancing with giants: China, India, and the global economy. The World Bank.

33.

WTO. (2020). Trade set to plunge as COVID-19 pandemic upends global economy (Press release). World Trade Organization. https;//www.wto.org/english/news_e/pres20_e/pr855_e.htm

34.

Zhao

, Liu

, Qiao

, Wang

, Zhang

, & Li

(2018). Tracing value added in gross exports of China: Comparison with the USA, Japan, Korea, and India based on generalized LMDI. China Economic Review, 49, 24–44.

Merchandise and Service Trade Deviations During COVID-19: A Performance Comparison Between India and China

Abstract

Keywords

Introduction

Theoretical Framework

Descriptive Data and Analytical Framework

The Variables

Econometric Model Building

Step 1: Identification of a Suitable Model

Step 2: Estimation Through Model

Step 3: Diagnostic Checking of Models

Empirical Results

Threats to Validity

Conclusion and Future Research Direction

Managerial Implication

Footnotes

Declaration of Conflicting Interests

Funding

References