ISSN 0798 1015

Vol. 39 (Number 34) Year 2018 • Page 11

A study on cross border trade policies & developments with special reference to emerging economies

Un estudio sobre políticas y desarrollos comerciales transfronterizos con especial referencia a las economías emergentes

Rahul BANERJEE 1; Padmabati GAHAN 2; Shyamali SATPATHY 3; Sisir R. DASH 4

Received: 17/03/2018 • Approved: 25/04/2018

Content

1. Introduction

2. Methodology

3. Results

4. Conclusions

Bibliographic references

1. Introduction

It is a commonly accepted assumption that liberalization of trade in the cross border front stimulates and accelerates economic growth. But, an effective foreign trade policy has to be accompanied with policy interventions, transformations on restrictions, changes in monetary policy and a series of structural reforms (Salvatore, 1995). The General Agreement on Tariff and Trade (GATT) has enabled better access to markets of developed economies by the emerging and frontier economies in terms not only in terms of international trade but also in terms of cross border investments. The present shape of the international trading system however has been governed by principles as well as pragmatism (World Trade Report, 2003). And this new international trading system is also meant for pursuing emerging and frontier economies to implement their national trade policy with an orientation towards economic development (Gibbs, 2007). Apart from visible improvements in the positions of foreign exchange, trade policy also has impacts on resource mobilization though it is complex to identify this linkage empirically. Theoretically at least it is accepted that foreign trade policy has an impact on government revenues, income distribution and also foreign investments (Laird, 2001). Hence, it has become a well accepted view of the researchers that one single indicator provide a measure for impacts of cross border trade policies and developments on economies (Bineau and Montalbano, 2011).

1.1. Relevance of the study

India adopted the policy of liberalization, privatization and globalization as a national agenda in the year 1991 and the economic philosophy of the country in its post globalization era strongly advocates enhancement of economic integration (Nayar, 2006). Since the nature and extent of a country’s economic integration with the world can be accessed on the basis of (1) trade in goods and services, (2) capital flows and (3) migration of people; in specific a study on cross border trade policies and developments on different economies of the world is legitimate within the world of international business management.

The present study has been undertaken with the broad objectives to study the cross border trade policies and developments in the world level in general and in India in specific, this articles has been written with the following specific objectives:

1. To study the trends in international trade in the global economy in general and in Indian economy in specific.

2. To study the trends in international trade in the global economy through cross country comparative analyses.

3. To compare the international trade and development scenario of Indian economy in pre and post globalization period.

4. To detect (if any) the impact of globalization on international trade and development scenario of Indian economy.

2.1. Research hypotheses

The following hypotheses need to be tested to bring out meaningful conclusions of the study:

H01: There is no significant difference in international trade and development scenario of Indian economy and the selected economies.

H11: There is significant difference in international trade and development scenario of Indian economy and the selected economies.

H02: There is no significant difference in international trade and development scenario of Indian economy in pre and post globalization period.

H12: There is significant difference in international trade and development scenario of Indian economy in pre and post globalization period.

H03: There is no significant impact of globalization on international trade and development scenario of Indian economy.

H13: There is significant impact of globalization on international trade and development scenario of Indian economy.

2.2. Sample economies

From the review of extant literature it has been discovered that foreign trade policy has also a significant impact on foreign investments. Hence, for the selection of economies to be included in the sample, the following two criterions have been considered:

1. International trade in merchandise

2. International trade in services

The first indicator under consideration for country selection is ‘international trade of merchandise’. So, the data on international trade i.e. exports and imports of merchandise have been collected from the official website of United Nations Conference on Trade and Development (UNCTAD). It demonstrate that there are only a few comparable developing economies along with India which are coming in the top positions in exports. The second indicator under consideration for country selection is ‘international trade in services’. So, the data on international trade i.e. exports and imports of services have been collected from the official website of United Nations Conference on Trade and Development (UNCTAD).  In order to select the countries to be included in the sample, first of all their performance on the basis of international trade i.e. exports and imports has been considered. But, after a thorough analysis of the top exporting and importing economies globally, it has been found that they cannot be compared with Indian economy since they are economically much more developed than India and India after remaining as a developing economy for decades now has shifted its position to the category of emerging economies. So, it became necessary to select a few economies which are similar to Indian economy. Now keeping in mind (1) geographical areas, (2) population and (3) other stylized features of the economies (especially the GDP and time period of globalizing their economies) countries like Argentina, Brazil, China, India, Mexico and South Africa have been taken. All of these economies are similar in terms of their geographical areas, density of population and other stylized features of their respective economies. Hence, it would be judicious to compare India with these economies in terms of cross border trade and development.

All of these countries have started liberalizing their economies either in 1980s or in 1990s and their population growth rate is also found to be competing with each other. 

Table 2.1
Features of the Sample Economies

2.3. Data and period of study

The annual data on various parameters selected for the study have been taken from the World Bank Indicators Database available online in www.worldbank.org for the period 1991 to 2014. The study spans the period 1991 through 2014 and there is a valid reason for taking this period under consideration. As one of the objectives of the study is to measure the impact of globalization on international trade scenarios of Indian economy and to analyze the implications of globalization on it, the period ideal for analysis should start in 1991 only. It is because India started globalization only after the launch of NEP in the year 1991 and also the economies in comparison to India have adopted the policy of globalization in a true sense only in late 1980s or early 1990s. Table 2.2 shows the detail about period of the study and number of observations in different selected economies.

Table 2.2
Period of Study and Number of Observations

The period of study and the number of observations for India is different from other selected economies and the reason behind it is that the present study has been undertaken with special reference to India. Since the study focuses on cross border trade policies and developments of India in specific in comparison to the selected economies, the impact of economic reforms on international trade scenario of the country is of utmost interest. Hence, the period of study and number of observations for India is different than the other selected economies.

2.4. Variables

Table 2.3
Description About Selected Trade Indicators

2.5. Techniques of data analysis

The present study uses various appropriate statistical techniques which are listed below:

1. t-Test: Paired Two Sample for Means

2 Regression Analysis using Dummy Variables

One of the tasks of the present study is to detect whether the selected variables are significantly different in the selected economies than in India or not and secondly whether they have significantly changed in post reforms period of India not. In this connection, t-Test: Paired Two Sample for Means seemed to be most suitable. It enables one to make comparison on a one to one basis and since it is a small sample test, it can also be used for large sample sizes. For detecting whether the selected variables are significantly different in the selected economies than in India, the sample sizes are ‘24’ (twenty four only) in each of the cases while for detecting whether they have significantly changed in post reforms period or not the sample sizes are ‘24’ (twenty four only) each in pre reforms and post reforms period. For the second purpose, “The Before-After Approach” has been followed in which the years before reforms and the years after reforms have been selected. And in order to detect any significant change between these groups of years, difference between the means method is the most ideal one. One of the most robust statistical techniques in this context i.e. paired t-test for difference of means has been selected in this study to determine the significance of difference between years with reforms and years without reforms. It has been found that since the inherent job in this study is to test a predefined hypothesis, an appropriate method of hypothesis testing would be ideal to implement. For this purpose, t-Test: Paired Two Sample for Means has been chosen. One can use a paired test when there is a natural pairing of observations in the samples, such as when a sample group is tested twice — before and after an experiment. This analysis tool and its formula perform a paired two-sample Student's t-Test to determine whether observations that are taken before a treatment and observations taken after a treatment are likely to have come from distributions with equal population means. This t-test form does not assume that the variances of both populations are equal. In this case, the treatment is implementation of economic reforms and it is required to study the mean value of the chosen indicators before reforms and after reforms. The t-Test: Paired Two Sample for Means works as follows: For example, India implemented NEP in 1991 after which radical changes in Indian economy got witnessed and if we will take it    as an event then we can consider the years before 1991 as pre reforms period and years after 1991 as post reforms period. The t-Test: Paired Two Sample for Means requires equal number of observations in both samples and that is why if we take 1991 to 2014 as post reforms period i.e. 24 observations; then we are required to go back 24 years back from 1991 and take 1967 to 1990 as pre reforms period. The mean values of selected variables in pre reforms period and post reforms period are then has to be compared by calculating the t-value and then comparing it with the critical value of t at the given degrees of freedom and chosen significance level (0.05 in this case).

The implementation of t-Test: Paired Two Sample for Means tells about the presence or absence of significant differences between sample mean values. Hence, the student’s t test has given answer of the question whether the average positions of selected variables of India have significantly changed in the post reforms period or not. But, in addition to this there is another thing which may interest researchers, academicians and policy makers that is what is the magnitude of impact of economic reforms on these variables. Here, we have chosen linear regression analysis to know the magnitude of impact of economic reforms. The present study used dichotomous variable as independent variable in the regression equation. These dichotomous variables are often referred to as “dummy” variables when scored as either 0 or 1. We have taken ‘0’ values for independent variable in pre reforms period and ‘1’ values for independent variable in post reforms period. The value ‘0’ refers to zero presence of reforms i.e. in pre reforms period and the value ‘1’ refers to cent percent presence of reforms i.e. in post reforms period. Regression analysis enables us to mathematically measure the average relationship between two or more variables by taking original units of the data. If the regression analysis is confined to only two variables at a time, then it is called ‘simple regression’ and when it is more than two variables are considered at a time, it is called ‘multiple regression’. In the present study, there is multiple numbers of variables involved under trade indicators but the main objective of the regression analysis in the present context is to measure the magnitude of impact of economic reforms on trade indicators. Hence, the study has adopted simple regression by taking the trade indicators as dependent variables and economic reforms as independent variable represented by dummy variables ‘0’ and ‘1’. The variable whose influence is to be measured is called ‘independent variable’ while the variable on which the influence is measured is called ‘dependent variable’ in regression analysis. Data on the above mentioned variables have been taken from the official website of World Bank. It is popularly known as World Development Indicators Database. One can avail time series data on various real sector indicators, trade indicators, financial sector indicators and human development indicators of more than two hundred countries from this data base.

Since there are a few selected trade indicators for analysis in the present study, the key findings have also been narrated below as per these indicators.

3.1. Exports

For the first trade indicator ‘exports’, exports (as % of GDP) has been considered that stand for exports of goods and services which represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments. From the results of t-Test: Paired Two Sample for Means of India with the selected economies, it is evident that the growth in exports have been significantly higher in China, Mexico and South Africa than in India over the period of study while it is significantly lower in Brazil than in India over the period of study. Exports growth in Argentina is not significantly different than in India over the period of study.

3.2. Imports

For the second trade indicator ‘imports’, imports (as % of GDP) has been considered that stand for Imports of goods and services which represent the value of all goods and other market services received from the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude compensation of employees and investment income (formerly called factor services) and transfer payments.  From the results of t-Test: Paired Two Sample for Means of India with the selected economies basis exports, it is evident that the growth in imports have been significantly higher in China, Mexico and South Africa than in India over the period of study while it is significantly lower in Argentina and Brazil than in India over the period of study.

3.3. Exports/imports

For the third trade indicator ‘Exports/Imports’, export to import ratio has been taken that represent Total Exports of goods and services divided by Total Imports of goods and services. From the results of t-Test: Paired Two Sample for Means of India with the selected economies basis exports/imports, it is evident that the export to import ratio have been significantly higher in Argentina, Brazil, China, Mexico and South Africa than in India over the period of study.

3.4. Export as a capacity to import

For the fourth trade indicator ‘export as a capacity to import’, export as a capacity to import (constant LCU) has been considered that stand for Exports as a capacity to import which equals the current price value of exports of goods and services deflated by the import price index. Data are in constant local currency. From the results of t-Test: Paired Two Sample for Means of India with the selected economies basis exports as capacity to import, it is evident that the growth in exports as a capacity to import have been significantly lower in Argentina, Brazil, China, Mexico and South Africa than in India over the period of study. It is noteworthy here that the data for China basis this indicator is not available after 2009 and that is why while comparing it with India, the data for India was also taken till 2009.

3.5. Total reserves

For the fifth trade indicator ‘total reserves’, total reserves (Including Gold, Current US $) has been considered that stand for total reserves comprise holdings of monetary gold, special drawing rights, reserves of IMF members held by the IMF, and holdings of foreign exchange under the control of monetary authorities. The gold component of these reserves is valued at year-end (December 31) London prices. Data are in current U.S. dollars. From the results of t-Test: Paired Two Sample for Means of India with the selected economies basis total reserves, it is evident that the growth in total reserves have been significantly higher in China than in India over the period of study while it is significantly lower in Argentina, Mexico and South Africa than in India over the period of study. The growth in total reserves in Brazil is not significantly different than India over the period of study.

3.6. Net oda received per capita

For the sixth trade indicator ‘net ODA received per capita’, Net ODA received per capita (current US $) has been considered that stand for net official development assistance (ODA) per capita consists of disbursements of loans made on concessional terms (net of repayments of principal) and grants by official agencies of the members of the Development Assistance Committee (DAC), by multilateral institutions, and by non-DAC countries to promote economic development and welfare in countries and territories in the DAC list of ODA recipients; and is calculated by dividing net ODA received by the midyear population estimate. It includes loans with a grant element of at least 25 percent (calculated at a rate of discount of 10 percent).       From the results of t-Test: Paired Two Sample for Means of India with the selected economies basis net ODA per capita, it is evident that the growth in net ODA per capita have been significantly higher in Argentina, Mexico and South Africa than in India over the period of study while it is significantly lower in China than in India over the period of study. The growth in net ODA per capita in Brazil is not significantly different than India over the period of study.

3.7. Merchandise trade

For the seventh trade indicator ‘merchandise trade’, merchandise trade (% of GDP) has been considered that stand for merchandise trade as a share of GDP is the sum of merchandise exports and imports divided by the value of GDP, all in current U.S. dollars. From the results of t-Test: Paired Two Sample for Means of India with the selected economies basis merchandise trade, it is evident that the growth in merchandise trade have been significantly higher in China, Mexico and South Africa than in India over the period of study while it is significantly lower in Brazil than in India over the period of study. The growth in net ODA per capita in Argentina is not significantly different than India over the period of study.

3.8. Comparison of pre and post globalization trade scenario of india

Till now the understanding of the trends in international trade in the global economy in general and in Indian economy in specific is over. Additionally, through the testing of hypothesis exercises undertaken above the overview of the trends in international trade in the global economy through cross country comparative analyses is also over. But, there is another objective of the present study and it is to compare the international trade and development scenario of Indian economy in pre and post globalization period. And this objective has been fulfilled in the following manner by using t-Test: Paired Two Sample for Means for the selected trade indicators of India in pre and post globalization period.

Table 3.1
Trade Scenario in India – Pre Globalization

Table 3.1 and Table 3.2 show the positions of the selected trade indicators in post reforms period of India. In these two tables also after taking the time series data on selected trade indicators of India during the post reforms period, the arithmetic mean of each of the indicators has been calculated. In addition to it, the t stat has been calculated for performing t-Test: Paired Two Sample for Means and the respective p values are shown in the tables (See Table 3.1 and Table 3.2). Since the computation of t-Test: Paired Two Sample for Means require equal number of observations in each of the samples taken into consideration, it has been strictly followed at the time of analysis here in this study. For India, time series data from 1967 to 1990 has been taken as pre reforms period and data from 1991 to 2014 has been taken as post reforms period (i.e. 24 numbers of observation in each case).

Table 3.2
Trade Scenario in India – Post Globalization

 The hypothesis testing results based on t-Test: Paired Two Sample for Means given in the Table 3.2 reveals that in case of India all the selected trade indicators except Exports/Imports ratio and net ODA per capita have changed significantly. The exports have increased significantly as well as the imports have increased significantly. Probably it is because of this, the exports to imports ratio has not changed significantly. However, a very good sign of the trade scenario is that the import capacities as well as the total reserves have increased significantly in the post globalization period.  Apart from the above analysis, a regression analysis has also been conducted to detect (if any) the impact of globalization on international trade and development scenario of Indian economy.

Table 3.3
Results of Regression Analysis Using Dummy Variables

In the above table the R Square and Adjusted R Square values of regression analyses using dummy variables for each of the trade indicators has been given. By taking the F values and the corresponding significance levels, we can say that for Indian economy the trade indicators like Exports (as % of GDP), imports (as % of GDP), Export as a Capacity to Import (in Current LCU), Total Reserves (Including Gold, Current US $) and Merchandise Trade (as % of GDP) has been influenced by reforms significantly. In the rest of the trade indicators even if the magnitude of impact is significant, but still we cannot consider it due to insignificant values of R Square and Adjusted R Square.

The results of the above analysis has given an in-depth idea about the scenario of international trade and development in the selected economies in general and in India in specific. As per the findings, first of all the trade liberalizations going on all over the globe did not impact all of the emerging economies uniformly. Secondly, globalization has impacted significantly the trade scenario of Indian economy.

4.1. Limitations of the study and scope for further research

There are two limitations of the present study. First, it considers only a few trade indicators for understanding the trade scenario globally. Secondly, it has taken into account only a few emerging economies for narrating the trade scenario of the world with special reference to emerging economies. These two limitations are expected to be taken care of in future researches.

4.2. Acknowledgements

This article is mainly based on the unpublished doctoral thesis of the first author. The second author is the thesis supervisor in his doctoral programme. The third and fourth authors have greatly contributed by improving the standard of this article through their analytical insights.

Books and Journals

Dominick, S.  (1995). Industrial Trade Policies, Indusrialization and Economic Development, 95(4), MEDIT, 4-11.

Gujarati, D. N. (1995). Basic Econometrics. Singapore: McGraw-Hill, Inc. 122-131.

Gupta, S.C. (2011). Fundamentals of Statistics. Mumbai: Himalaya Publishing House, 16.26-16.33.

Handbook of Statistics 2017, United Nations Conference on Trade and Development (UNCTAD)

Key Statistics and Trends in International Trade 2016, United Nations Conference on Trade and Development (UNCTAD)

Key Statistics and Trends in Trade Policy 2016, United Nations Conference on Trade and Development (UNCTAD)

Kothari, C.R. (2005). Research Methodology. New Delhi: New Age International, 184-225.

Levin, R.I., & Rubin, D.S. (1991). Statistics for Management. New Delhi: Prentice Hall of India, 567 – 588.

Majhi, P., & Khatua, P. (2011). Research Methodology for Management: Concepts, Technologies and SPSS, New Delhi: Himalaya Publishing House, 100-103.

Malhotra, N.K. (2005). Marketing Research - An Applied Orientation, New York: Pearson Education Inc., 343-347.

National Development Strategies Policy Notes 2007, United Nations, 8-9.

World Trade Report 2003, World Trade Organization, 78-117.

Yannick, B., & Pierluigi, M. (2011), Selected Developmental Aspects of Trade and Trade Policies, Project No. 2011/265311 funded by European Commission, 1-2.

Zdenek, D. (2001). Can Trade Policy Help Resources for Economic Development?, 16 (1), CREDIT Research Paper, 1-55.

Websites

www.cia.gov.in

www.imf.org

www.unctad.org  

www.worldbank.org

www.wto.org

1. Doctoral Research Scholar, Department of Business Administration, Sambalpur University – 768019, India. Contact e-mail: rahul.skyshop@gmail.com

2. Chief Mentor, Professor, Department of Business Administration, Sambalpur University – 768019, India. Contact e-mail: pgahan7@gmail.com

3. Associate Professor, IILM, Graduate School of Management, Greater Noida – 201306, India. Contact e-mail: shyamalisatpathy@gmail.com

4. Assistant Professor, Trident Academy of Creative Technology (TACT), Bhubaneswar – 751024, India. Contact e-mail: dash_sisir@rediffmail.com