Economics Research

Abstract This study examines the interaction between the financial sector and the real economy of Iran over the period 1999–2016 using a Financial Social Accounting Matrix (FSAM) and a three-layer analytical framework. The objective is to assess the allocation of financial resources and identify shifts in the functional role of the banking system across service subsectors. The findings indicate a significant reorientation of financial intermediation toward distributive, personal, and social services in 2016 compared to 1999, accompanied by a relative decline in support for productive activities. In 2016, distributive services emerged as a primary destination of bank credit, with multipliers of 3.01 (commodity account) and 3.35 (activity account), while the shares of industry and agriculture decreased. Structural multiplier analysis further reveals that the financial sector’s impact on real sector growth has been transmitted predominantly through less-productive service activities. This allocation pattern has reinforced a service-oriented structure and widened the gap from development objectives, underscoring the need to reconsider credit policies and redefine the role of the financial system in supporting productive growth and sustainable 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   The Multi-Regional Input-Output Table (MRIO) provides comprehensive information on the economic statistics of regions, with help of which economic structure of regions and economic relations among them are determined. Since regional tables and statistics data of inter-regional trade, which are necessary for the construction of multi-regional output tables, are not created by official institutions in Iran, using non-survey methods in regional Input-output literature is the only solution. The main aim of this paper is to provide a nine-zone Input-output table based on the CHARM method in Iran, and to estimate interregional trade. Regional accounts and the national statistical Input-output table in 2011 of Iran’s Statistical Center, have been used as statistical bases. The results of this study indicate that the total value of Interregional trade in Iran is 1000679 billion rials. The highest volume of interregional trade belongs to Khuzestan region is 337658, and the region of southern Alborz with a volume of 242225 and the lowest volume is related to the Azarbaijan region with a volume of 38,283 billion Rials. The largest volume of interregional trade of Iran is in the crude oil and natural gas sector, and then services. The highest volume of interregional trade in the Shomal, Azerbaijani, South-Eastern and Zagros regions are in the agricultural sector, Khuzestan region in the crude oil and natural gas sector, Fars region in the construction of petroleum products and chemicals, the south Alborze and Khorasan regions are in the service sector And the Central region in the manufacturing of metals and electronic and metal products.

Abstract In the mixed economic and natural resource domain, transactions on commodities, services and natural resources (water and land) are in either visible or invisible (hidden) layers. The prevailing monetary systems of macro and sectoral accounting take into account only market transactions and therefore neglect invisible transactions like intersectoral water consumption. Such a deficiency provides the ground for policymaking and water resource management to focus their attention on the aggregate data, e.g. agriculture consumes directly more than 90 percent of the total water resources. In this paper, on the basis of the extended Input – Output model, we use two approaches; namely: quantity and mixed quantity–value approaches. By using the aggregated quantity-value based Input – Output table and corresponding sectoral consumption of water (billion liters) for the year 1990, we found that sectoral water consumption multipliers and the intersectoral water consumption provide more suitable criteria for policy implications as well as for managing the consumption of water resources of the country. In addition, the agro-based industries have the largest water consumption multipliers. With respect to the hidden layer of intersectoral water consumption we found that  agriculture sector supplies 92.5 percent of the total water resources while demanding 58 percent of these resources.