Economics Research

Abstract  Export diversification remains a pivotal theme in international trade literature. Statistical evidence indicates that the export baskets of developing economies are predominantly composed of traditional commodities. Consequently, these nations strive to diversify their export portfolios by incorporating a broader range of manufactured goods. This imperative is particularly acute for oil-exporting developing countries, where diversification is essential to mitigate the economic risks associated with high export concentration in the hydrocarbon sector. Producing a diversified range of goods is often technology-intensive—a capability that firms in many developing nations frequently lack. Access to a wider variety of imported intermediate and capital inputs—characterized by competitive pricing and superior quality—can play a crucial role in bolstering export competitiveness. These imports facilitate productivity enhancements, reduce production costs, and improve product quality. Given the significance of this issue, elucidating the role of technology spillovers embodied in imported intermediate and capital goods and examining their impact on export diversification are vital for formulating effective industrial development strategies. This study aims to determine whether the importation of intermediate and capital goods exerts a positive and significant impact on Iran's export diversification. To this end, the research employs a panel data model using data from Iranian manufacturing industries at the two-digit ISIC level for the period 2006–2021 (1385–1400 SH). Furthermore, the study conducts a granular analysis of how these imported inputs affect export diversification across specific industrial sub-sectors. Method and Material This study investigates the determinants of industrial export diversification in Iran from 2006 to 2021, with a specific focus on imported intermediate and capital goods. The analysis utilizes a panel data model based on Iranian industries classified at the two-digit level of the International Standard Industrial Classification (ISIC), Revision 4. To quantify the impact of imported inputs on industrial export diversification ( ), the following econometric specification is estimated:   In Equation (1): denotes export diversification; is the real effective exchange rate (defined as ); and  represent imports of intermediate and capital goods, respectively; denotes research and development expenditures;  indicates the degree of economic openness; and represents the Gross Domestic Product per capita. The export diversification index for two-digit industries is calculated using the Theil index, as shown in Equation (2):   Here,  represents the exports of the industry with the -th four-digit code in period . Consequently,  denotes the total exports of the two-digit industry. The real exchange rate ( ) is calculated using Equation (3). The data comprises the official exchange rate, the Producer Price Index (PPI) disaggregated by Iranian industries, and the value-added deflator of U.S. industries (as a proxy for foreign prices), with 2015 as the base year.   Data for imported intermediate goods is derived from the value of foreign raw materials, packaging supplies, and non-durable tools. Data for imported capital goods is obtained from the purchase or acquisition of foreign capital assets. The Research and Development (R&D) variable is sourced from statistics on research and laboratory expenditures, disaggregated by industry. These data were extracted from the Statistical Center of Iran's database on industrial establishments with 10 or more employees across 23 distinct industries. GDP per capita was retrieved from the World Bank. All nominal variables were deflated using the industry-specific Producer Price Index (PPI) with a base year of 2015. The degree of economic openness is calculated as per Equation (4):   Consistent with theoretical foundations, it is expected that all variables, except the real exchange rate, will have a positive impact on Iran's industrial export diversification. Results The empirical estimation results, reported in Table 3, indicate that imported intermediate and capital goods have a positive and statistically significant impact on Iran's industrial export diversification. Specifically, a 1% increase in the imports of capital and intermediate goods is associated with increases in export diversification of 0.01% and 0.036%, respectively. Furthermore, GDP per capita and economic openness exert a positive and significant influence on diversification. Conversely, the real exchange rate and R&D expenditures exhibit a negative impact on industrial export diversification. For a more comprehensive analysis, the impact of imported inputs was examined at a disaggregated level across two-digit industry codes. The results in Table 4 reveal significant heterogeneity across sub-sectors. Notably, the elasticity of export diversification with respect to capital goods imports is highest in the Manufacture of food products industry (coefficient = 0.02). Similarly, the impact of intermediate goods imports peaks in the Manufacture of beverages industry (coefficient = 0.15). However, in the Manufacture of tobacco products and the Manufacture of pharmaceuticals, medicinal chemical, and botanical products, the effect of these imported inputs on export diversification is statistically insignificant. Conclusion This study examined the influence of imported intermediate and capital goods, along with other factors, on industrial export diversification in Iran from 2006 to 2021. The analysis utilized a panel data model employing data from Iranian industries at the two-digit ISIC level. The empirical findings demonstrate that imports of both intermediate and capital goods exert a positive and statistically significant impact on the export diversification of Iran's manufacturing industries. Furthermore, disaggregated analysis indicates that the magnitude of this effect varies across different industrial sub-sectors. Consequently, trade strategies should prioritize facilitating the importation of intermediate and capital goods.

Moreover, support policies regarding these inputs should be designed in a targeted, sector-specific manner to maximize industrial development.

Abstract  
Given the importance of economic globalization and the role of intermediate goods in global value chains, this study aims to assess countries’ share of international trade. In this regard, the relationship between domestic value-added (DVA) in gross exports and vertical specialization (VS) among 45 countries was analyzed, and traditional and modern methods of measuring countries’ share in international trade were compared. Data from input-output tables of 43 countries for 2014 and data for Iran and Singapore for 2016 and 2015 were examined, respectively. The findings showed that the inverse relationship between the share of DVA/TGE and VS/TGE holds, as in previous studies, and modern methods provide a more accurate picture of countries’ trade status due to considering intermediate goods and value-added production. In general, in resource-based countries, the share of DVA is higher and the share of VS is lower than the average. Meanwhile, Iran, with DVA and VS shares of 0.94 and 0.06, respectively, ranks at the top and bottom of 45 countries, indicating a weak link with the global value chain and reliance on upstream activities.
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Introduction
Economic globalization has led to the emergence of new theories in international trade, emphasizing the critical role of intermediary goods in production processes and value chains. Countries participate in value chains differently based on their economic structures and trade patterns, impacting trade balances and related economic variables. Resource-based economies, such as Iran, with asymmetric trade patterns, tend to have lower integration in global value chains compared to non-resource-based economies with symmetric trade patterns. Quantifying value chains through domestic value-added (DVA) and vertical specialization (VS) helps to assess Iran’s position and its integration into international trade. The increasing importance of intermediary goods in exports has shifted focus from traditional trade theories, which prioritized final goods, to modern theories like “trade in stages” or “trade in tasks”, emphasizing the connection between production factors, intermediary goods, and final products. Modern methods address the shortcomings of traditional models and provide more accurate calculations of value chains.
This study explores (1) the relationship between DVA, VS, and international trade participation in resource-based and non-resource-based economies, and (2) the suitability of modern methods over traditional ones in assessing countries’ share in international trade. The paper is organized into six sections: theoretical framework, literature review, data foundations, research methodology, results, and discussion.
Methods and Materials
This study uses input-output tables from three key sources: the World Input-Output Database (WIOD) for 43 countries based on the 2014 benchmark year, the Central Bank of Iran (CBI) input-output table for 2016, and the Department of Statistics Singapore (DOS) table for 2015. According to the International Monetary Fund (IMF) classification in 2020, economies with more than 20% of their exports derived from resource-based sectors are categorized as resource-based economies. Based on this criterion, countries such as Norway, Australia, Iran, Russia, Brazil, Canada, and Indonesia are considered resource-based economies, while countries with less than 5% of their exports derived from resource-based sectors, such as Malta, Japan, Taiwan, Luxembourg, South Korea, Switzerland, and Singapore, are classified as non-resource-based economies. Countries with resource-based export shares between 5% and 20% are categorized as intermediate economies.
To address the research objectives and questions, this study adopts the modern hypothetical extraction method to calculate domestic value-added (DVA) in gross exports and vertical specialization (VS), equivalent to foreign value-added (FVA). The modern approach decomposes gross exports to measure domestic value creation, intermediate imports’ share in exports, and the specialization of countries in production stages. Using this method, the study highlights the inverse relationship between DVA/TGE and VS/TGE and compares these measures to traditional methods, which rely on gross export ratios. While traditional methods assume that exports directly generate value-added and neglect the role of intermediate goods, the modern approach provides a more accurate assessment of countries’ participation in global value chains.
Results and Discussion
In line with the research questions, the findings are presented in two main areas: (1) examining the shares of DVA and VS, and (2) analyzing the results of traditional and modern methods for measuring countries’ shares in international trade.

Examining the Shares of DVA and VS

Similar to previous studies, the findings confirm an inverse relationship between DVA and VS shares at the macroeconomic level. As shown in Figure 1, resource-based economies such as Canada, Russia, Iran, Australia, Norway, Brazil, and Indonesia generally have higher DVA shares and lower VS shares compared to non-resource-based economies. For instance, except for countries like Singapore, Hungary, Malta, and Luxembourg (small non-resource-based economies), VS shares are lower than DVA shares in all other countries. According to the 2019 WTO report, economies with skilled labor, such as Singapore, tend to integrate into global value chains (GVCs) in higher value-added segments like design and specialized services.
Table 1 illustrates the average shares of DVA/TGE and VS/TGE, both overall and within two distinct groups of countries. In Table 2, we present the average shares of DVA/TGE, VS/TGE, TGE/WTGE, and TGE/GDP for a sample of 45 countries, highlighting the differences in trade dynamics among them.
Figure 1. Comparison of the ratio of domestic value added and vertical specialization to total exports of each country with other countries
 
Source: Research findings
Table 1. Average shares of DVA/TGE and VS/TGE overall and between the two groups of countries




Country Groups


Average DVA/TGE


Average VS/TGE




All Countries


0.71


0.29




Resource-Based Countries


0.86


0.14




Non-Resource-Based Countries


0.66


0.34




Source: Research findings
Table 2. Average shares of DVA/TGE, VS/TGE, TGE/WTGE, and TGE/GDP for 45 countries




Average TGE/GDP


Average TGE/WTGE


Average VS/TGE


Average DVA/TGE




0.19


0.03


0.29


0.71




Source: Research findings
 

Analyzing Traditional vs. Modern Methods

Traditional methods, based on gross export ratios (TGE/GDP and TGE/WTGE), fail to account for imported intermediate goods and multiple border crossings of goods, leading to an incomplete picture of trade competitiveness. Modern methods, focusing on DVA/TGE and VS/TGE, offer a clearer understanding of the actual value created domestically and the role of intermediate imports. As shown in Table 2, the global averages for TGE/GDP and TGE/WTGE are 0.19 and 0.03, respectively, while for DVA/TGE and VS/TGE are 0.71 and 0.29. Table 3 highlights that resource-based economies like Iran, Russia, and Brazil rank low in VS/TGE, reflecting limited integration into GVCs. On the other hand, non-resource-based economies with higher VS shares, such as Singapore, demonstrate greater integration into global trade through specialization and intermediate imports. Table 3 displays the results obtained from both traditional and modern methods for measuring the contributions of individual countries to international trade.
The findings indicate that high DVA shares may reflect economic independence but also limited engagement with GVCs, reducing opportunities for technological transfer and productivity gains. Resource-based economies, reliant on upstream industries, need to diversify and increase their participation in GVCs to enhance competitiveness and benefit from international trade.
 
 
 Table 3. Results of traditional and modern methods for measuring countries’ shares in international trade (in order)




Rank


VS/
TGE


Country Name (Lowest)


TGE/ WTGE


Country Name (Highest)


TGE/
GDP


Country Name (Highest)




1


0.06


Iran (Lowest)


0.139


China (Highest)


0.79


Luxembourg (Highest)




2


0.08


Russia


0.111


United States


0.701


Singapore




3


0.13


United States


0.097


Germany


0.655


Ireland




4


0.13


Brazil


0.047


Japan


0.638


Malta




5


0.14


Australia


0.044


France


0.561


Hungary




6


0.17


Indonesia


0.043


United Kingdom


0.545


Slovakia




7


0.17


China


0.04


South Korea


0.533


Czech Republic




8


0.17


Norway


0.034


Italy


0.514


Lithuania




9


0.19


United Kingdom


0.033


Netherlands


0.505


Netherlands




10


0.21


India


0.032


Canada


0.504


Belgium




11


0.23


Japan


0.029


Singapore


0.499


Estonia




12


0.24


Canada


0.028


Russia


0.494


Slovenia




13


0.25


Switzerland


0.022


Spain


0.484


Taiwan




14


0.26


Italy


0.022


Belgium


0.419


Bulgaria




15


0.27


Romania


0.021


Taiwan


0.41


Switzerland




16


0.27


Croatia


0.021


Mexico


0.405


Denmark




17


0.28


Germany


0.021


India


0.396


Austria




18


0.28


France


0.02


Switzerland


0.387


Latvia




19


0.29


Turkey


0.016


Australia


0.385


South Korea




20


0.29


Sweden


0.016


Brazil


0.38


Poland




21


0.29


Cyprus


0.015


Ireland


0.378


Germany




 
 
 
Table 3.




Rank


VS/
TGE


Country Name (Lowest)


TGE/ WTGE


Country Name (Highest)


TGE/
GDP


Country Name (Highest)




22


0.03


Greece


0.014


Turkey


0.358


Sweden




23


0.31


Spain


0.014


Sweden


0.357


Croatia




24


0.31


Latvia


0.14


Poland


0.341


Cyprus




25


0.31


Poland


0.12


Austria


0.339


Norway




26


0.32


Portugal


0.12


Indonesia


0.325


Romania




27


0.34


Mexico


0.11


Norway


0.317


Finland




28


0.35


Finland


0.1


Denmark


0.287


Portugal




29


0.35


South Korea


0.09


Czech Republic


0.277


Canada




30


0.36


Austria


0.07


Luxembourg


0.275


Turkey




31


0.36


Netherlands


0.07


Hungary


0.263


Russia




32


0.36


Lithuania


0.06


Finland


0.251


Spain




33


0.37


Slovenia


0.05


Iran


0.251


Italy




34


0.37


Denmark


0.05


Slovakia


0.248


Mexico




35


0.38


Bulgaria


0.04


Portugal


0.241


France




36


0.42


Taiwan


0.04


Romania


0.231


United Kingdom




37


0.43


Estonia


0.03


Greece


0.22


Greece




38


0.46


Belgium


0.02


Slovenia


0.206


China




39


0.46


Czech Republic


0.02


Bulgaria


0.204


Indonesia




40


0.48


Ireland


0.02


Lithuania


0.195


Iran




41


0.48


Slovakia


0.01


Estonia


0.186


Australia




42


0.51


Singapore


0.01


Cyprus


0.16


Japan




43


0.52


Hungary


0.01


Croatia


0.153


India




44


0.65


Malta


0.01


Latvia


0.109


Brazil




45


0.66


Luxembourg


0.01


Malta


0.102


United States




Source: Research findings
Conclusion
This study highlights the importance of adopting modern methods to measure countries’ shares in international trade, emphasizing the need for resource-based economies to diversify their production structures and integrate deeper into global value chains. Future research could explore the impact of factors such as technological advancement, workforce productivity, and international trade agreements on enhancing participation in GVCs. Here are the key findings:
- There is a clear inverse relationship between domestic value-added (DVA) and vertical specialization (VS) shares across all countries. Greater participation in global trade is associated with higher reliance on imported intermediates and lower utilization of domestic raw resources for value-added creation.
- Resource-based economies (e.g., Iran, Russia, Australia) have higher DVA shares (above the global average of 0.65) and lower VS shares, indicating less integration into global value chains (GVCs). These economies rely heavily on upstream industries and domestic resources, focusing less on intermediate imports and downstream production stages.
- Non-resource-based economies (e.g., Singapore, Malta) exhibit higher VS shares, emphasizing vertical specialization and deeper integration into GVCs.
- Traditional methods (e.g., TGE/GDP and TGE/WTGE) fail to account for imported intermediates and overestimate a country’s trade performance, especially in economies reliant on imports for export production.
- Modern methods (DVA/TGE and VS/TGE) offer a more accurate assessment of countries’ contributions to global trade by focusing on the actual value-added rather than gross trade volumes.

Abstract

Abstract In international economics literature, there is a great emphasis on importance of exports on economic development and growth. Because of growth in international trade, all countries try to activate this engine of economy by implementing proper policies. The experience of emerging economies has shown that countries with high intensity of industrial exports have experienced faster economic growth than others. Thus, focusing on industrial export and studying factors which affect export intensity are very important. So, in this study we investigate the impact of firm- and industry-level characteristics on export intensity. For this purpose, the data for Iranian manufacturing firms during 2007 to 2013 have been used. The results of empirical model estimated by using Dynamic Panel-Data approach and GMM Estimator show that export intensity of firms is positively affected by research and development intensity, capital intensity, labor productivity of firms and export intensity of industry while firm size, industry concentration and industry-level labor productivity has negative and significant effect on export intensity.

Abstract Some researchers consider trade liberalization as a factor for increasing the quality of the environment. However, others argue that trade liberalization leads to some countries specialize in the production of pollution-intensive, energy-intensive or capital-intensive goods and therefore the quality of the environment is reduced. Since theory offers grounds for both positive and negative relationship between trade and the environment, the issue must be settled through empirical analysis. In this context, the main question here is how international trade affects quality of environment in oil exporting countries? For this purpose, in the form of composition, scale and technical effects, we construct a panel data analysis to identify the relationship between international trade and carbon emission of selected oil exporting countries during 1990–2011. Our results suggest that there is positive relationship between trade openness and carbon emission and thus, pollution heaven hypothesis for these countries is not rejected. Also, the relationship between per capita output and carbon emission is N-shape and scale effect is negative. Furthermore, increasing comparative advantage and foreign direct investment leads to a reduction in oil exporting countries’ carbon emissions; which respectively indicate scale and technical effects of the international trade to be negative.

Abstract Empirical evidence about recent financial crisis revealed the prominent role of financial sector in transmission of different shocks to the real sector of economy. Due to the importance of banking sector behavior during business cycles and for the purpose of explaining the credit channel of monetary transmission mechanism in the economic literature, analyzing the role of banking sector during business cycles in Iran can help us to better understand how the shocks can have impacts on the economy. In this paper, we evaluated macroeconomic fluctuations and monetary transmission channels in Iranian economy using a standard New Keynesian dynamic stochastic general equilibrium model that includes banking sector. The results of the study show that inclusion of banking sector in a DSGE model can improve evaluation of macroeconomic fluctuations. Also, the results of simulation show that variables related to banking sector have pro-cyclical movements in Iranian economy and these results provided enough room to explain the role of banks as financial accelerator and monetary transmission channels in Iranian economy.

Abstract The Purpose this paper, is to investigate the effect of oil revenues on real exchange rate in Iran’s Economy by using a macroeconomic model, which has been estimated by 3sls regression technique and annual data during 1965 to 2003 .Provided that the government has taken no policies, the simulation results depict that, a rise in oil revenues will increase GDP, domestic price levels, money supply and real import, respectively, but will decrease Non-oil export. By regarding simulation results, we could say that nominal exchange rate will primarily decrease by oil revenues increase both in short run and long run. Afterwards, it will take an increasing trend and eventually, will go back to the initial equilibrium level. We will also observe real exchange rate decrease (increase) via the nominal exchange rate decrease (increase) in short run. However, in the long run, the real exchange rate will decrease by oil revenues boom and before the oil revenues increase will reach a lower level relative to its initial one.