Document Type : Original Article from Result of Thesis
Highlights
Introduction
International trade performance evaluation is one of the important and favorite issues for policymakers and macroeconomic planners, which can be reflected in indicators related to international trade and economic development. The expansion of international trade in addition to direct effects on economic development, can also be affected by improving its performance and lead to welfare changes. Therefore, it is important to measure relative trade performance and examine its components with the Human Development Index (as an important indicator of economic development) in order to determine the position of countries and International relationships. In order to act as a driving force for development, trade must lead to continuous improvement by expanding people's choices (concept that some aspects of economic development, especially human dimension, have attempted to achieve(. Data extracted from trade performance assessment are essential for determining the trade countries position, making economic policies, development planning and understanding the competitiveness. Therefore, it is important to measure international trade performance and understand the relationship between its components and the human development index. This study focuses on explaining the performance of international trade and examines the measurement of trade technical efficiency, ranking and efficiency fluctuations. In addition, it measures the growth of total trade factor productivity in the time horizon of 2014 to 2020 in 32 selected countries. In the following and based on the defined classifications, the relationship between Human Development and trade performance indicators is examined.
Methodology
In order to measure trade performance, the method of the current study is output oriented Data Envelopment Analysis with constant return to scale in the period from 2014 to 2020. This method is a mathematical programming technique used to evaluate the relative efficiency of a set of units with multiple inputs and outputs. It is a non parametric method (meaning it doesn't assume a specific functional form for the production process). The aforementioned method, constructs an efficient frontier based on the observed data and then compares each unit to this frontier to determine its efficiency score. The model used in this study to measure trade efficiency is the Window Data Envelopment Analysis. This model is a technique used to assess the dynamic efficiency of units over time by treating each unit in different periods as a separate unit. It applies a moving average principle, creating overlapping windows of data to analyze efficiency trends. This approach helps overcome limitations of traditional Data Envelopment Analysis by allowing for the analysis of panel data and providing more granular insights into efficiency changes. Also the Malmquist Productivity Index has been used in order to measure changes in trade productivity. In fact, this model is a tool used to measure changes in productivity over time, specifically for units with multiple inputs and outputs. It decomposes productivity change into two components: efficiency change (catching-up effect) and technical change (frontier-shift effect). In this regard and after selecting the variables (input variable: Country Risk Index, Real Effective Exchange Rate, Export Product Concentration Index, Product Diversification Index, Tariff Rate Index and output variable: Export Market Penetration Index, Export-Import Ratio Index, High tech Manufacturing Export Index, Industrial Export Quality Index), trade efficiency has been measured with using a Window Model and the Malmquist index has been used to measure the growth of total trade factor productivity in 32 selected countries. It also examines the relationship between the growth index of total trade factor productivity and its related sub components. In the following, in a matrix space and based on the defining and classifying the indicators, the relationship and correlation of the Human Development Index (as a comprehensive indicator of economic development) with trade technical efficiency and growth of total trade factor productivity are evaluated.
Results and Discussion
According to the window trade efficiency measurement with output oriented and constant returns to scale, Germany with a relative trade efficiency 100 percent is at the highest score, followed by Italy and United States. By evaluating the top 10 countries in the ranking of trade efficiency, the European continent (with the highest frequency), the East Asian continent and the American continent are in the top ranks. In the opposite direction, mainly Asian countries and especially Middle Eastern countries are at the bottom of the table. Iran, Egypt and Pakistan have the most unfavorable trade efficiency situation with averages of 46.7, 41.2 and 34.4 percent, respectively. Also, the average of the 4 window trade efficiency periods is 84.37, 85.42, 85.41 and 85.34 respectively. The countries with the highest standard deviation of trade efficiency, are Saudi Arabia, Ukraine and Russia, which indicates high instability in their scores.
Also, the average of the 4 periods of window trading efficiency is 84.37, 85.42, 85.41, and 85.34. The highest standard deviation is for Saudi Arabia, Ukraine and Russia, which indicates instability in trade efficiency scores. Annual average efficiency, based on the country window, during 2014 to 2020 is 85.2, 83.3, 83.8, 84.5, 87.7, 85.2, 86 percent and also horizontal and vertical average of the window during the period is 85 percent. In accordance with the results of the Malmquist output oriented model with constant returns to scale, the average change in total trade factor productivity in the selected countries and during the period has increased relatively, which is mainly due to positive technological changes. Australia, Turkey and Saudi Arabia are at the top with the highest average changes in total trade factor productivity, 45.1, 27.5 and 18.2 percent and Malaysia, Ukraine and Iran are at the bottom of the table with -6.1, -11.5 and -20.2 percent. Also, the correlation coefficient of the total trade factor productivity index with its components, is positive and significant. Then, in a matrix space and based on the defining and classifying the indicators, the correlation coefficient between Human Development Index and trade efficiency shows a positive and significant relationship. Also, this coefficient, shows a weak (negligible) and non significant correlation between Human Development Index and total trade factor productivity growth. Certainly, the above classifications, contribute to a more appropriate view of trade performance and economic development.
Conclusions
The importance of the issue, lies in the fact that improving trade performance is an effective factor in competitiveness and economic development. it is important to measure relative trade performance and examine its components with the Human Development Index, in order to determine the position of countries and International relationships. The ranking of trade efficiency scores indicates the relative level and position of countries in terms of trade. Also, the growth of total trade factor productivity shows the rate of changes in factors. The correlation coefficient of the total trade factor productivity growth with its components, has positive and significant relationship. The performance of the efficiency component indicate that the use of trade capacity and the combination of resources over the period, improved. On the other hand, increases in the average changes in technology, indicate that, the use of new technologies has improved and the driving forces behind the growth of total trade factor productivity is supported by technological changes. Also, the correlation coefficient between Human Development Index and trade efficiency shows a positive and significant relationship. It describes that most countries with high Human Development rankings, have high trade efficiency and vice versa. This result is consistent with the thinking of supporters of the positive effect of trade on economic growth and development. Also, the correlation coefficient between Human Development Index and total trade factor productivity growth is weak and insignificant. Meanwhile, Iran's trade performance indicators are in a critical condition compared to other countries and require urgent reforms and revisions in its policies. In order to improve relative trade performance and economic development, countries must adopt more effective technology, combinations of inputs and reconsideration in some policies. At the end, the present study can provide a useful insight into the prediction of trade performance and hence it will be a light for prioritizing trade policies, economic growth and development.
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