Sadegh Mohit; Kowsar Yousefi; Salman Farajnia; Hossein Abbasinejad
Abstract
This study investigates labor market shocks in Iran using quarterly data from 2009 to 2022, employing a Bayesian sign-restricted Structural Vector Autoregression (SVAR) model to disentangle supply and demand shocks. The analysis evaluates the effects of monetary policy on these shocks across the ...
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This study investigates labor market shocks in Iran using quarterly data from 2009 to 2022, employing a Bayesian sign-restricted Structural Vector Autoregression (SVAR) model to disentangle supply and demand shocks. The analysis evaluates the effects of monetary policy on these shocks across the aggregate economy and three key sectors: industry, services, and agriculture. The real exchange rate is included as a control variable. The findings reveal heterogeneous responses to monetary policy across sectors. An expansion in the money supply positively affects labor demand in the industrial sector but negatively impacts it in agriculture. Moreover, interest rate reductions have a more pronounced employment-enhancing effect in industry compared to other sectors. In contrast, inflationary pressures dominate labor demand dynamics in agriculture and services. On the supply side, monetary shocks exert a negative effect on labor supply in agriculture—unlike in other sectors—likely due to persistently low wages, which reduce workers’ willingness to increase hours and encourage sectoral migration during inflationary episodes.
Introduction
The examination of Iran's labor market in recent years is of particular significance due to the young population, high labor supply, and lack of proportional demand growth, especially since the unemployment rate has consistently been above 10% from 1388 to 1398 (2009–2019), peaking at 14.7% in 1390 (2011). The decline in the share of employment in the agricultural sector and its shift to the industrial sector is another notable development in Iran's labor market over the past decade. Another current reality of Iran's labor market is the reduction in the economically active population, which has experienced significant fluctuations over the past ten years, dropping from 47.7% in 1384 (2005) to 41% in 1401 (2022). Analyzing this issue requires an examination of supply-side labor policies. Therefore, distinguishing the policy effects on the supply and demand sides of Iran's labor market appears essential for providing precise analyses.
Methods and Materials
In this study, supply and demand shocks are first decomposed, and then the effects of monetary policies on these supply and demand shocks in the labor market are examined using the results of this decomposition. The aim of this research is to separate supply and demand shocks, and given the macroeconomic nature of the question, it is necessary to employ macro econometric methods. Among the available methods, the use of the Structural Vector Autoregression (SVAR) model has been selected, and the model is identified using the sign restriction method, which is a modern approach for identifying coefficients and is defensible under methodological assumptions. This research utilizes the method proposed by Baumeister and Hamilton (2015) to identify supply and demand shocks in each sector. This method can be implemented in any market with available data on prices and quantities.
In SVAR models with sign restrictions, structural shocks are identified by imposing constraints on the signs of impulse responses (e.g., a positive demand shock increases both output and prices, while a positive supply shock increases output but decreases prices). Thus, structural decomposition is performed based on the applied sign restrictions. The process is as follows: first, a reduced-form VAR model is fitted to the data to obtain reduced-form parameters (coefficients and errors). Then, sign restrictions are applied to the estimated coefficients to enable their use in identifying structural shocks. This involves sampling rotation matrices that satisfy the sign restrictions on impulse responses. Finally, the time series of structural shocks are recovered by transforming the reduced-form errors using the identified structural transformation matrix. Labor market shocks were decomposed into supply and demand components using the sign restriction method within an SVAR framework, with parameter distribution updates conducted through Bayesian estimation. Subsequently, the real effect of money supply growth on the decomposed supply and demand labor shocks was analyzed using a panel model, with the exchange rate included as a control variable. The results of the model indicate that an increase in money supply has a positive effect on labor demand in the industrial sector across seasonal lags, while it has a negative effect in the agricultural sector.
Using the Household Expenditure and Income Survey, monthly household-level wages from 1388 to 1401 (2009–2022) were extracted, applying the current weighting aligned with the sampling weights of the Statistical Centre of Iran. Subsequently, wage data were adjusted for inflation using the Consumer Price Index (CPI) to obtain real wages. Since wages are collected monthly in this survey, the wage data were aggregated quarterly and classified into three economic sectors: agriculture, industry, and services, based on ISIC codes. Additionally, the number of working hours in the private sector was extracted quarterly from the Labor Force Survey data of the Statistical Centre of Iran at the household level for the years 1388 to 1401 (2009–2022). The data were then aggregated and integrated at the quarterly level, categorized by economic sectors according to ISIC codes. The growth rate of real wages and working hours has been calculated on a quarterly basis, and the analyses have been conducted using private sector data. Money supply data were extracted on a quarterly basis from the time series data of the Central Bank, and exchange rate data were obtained from the Gold, Coin, and Currency Information Network for the period from 1388 to 1401 (2009–2022). For the exchange rate, the data from the middle month of each quarter were used as a representative of the quarterly data.
Results and Discussion
In summary, a 1% increase in money supply leads to a 0.21% reduction in labor demand and a 0.15% reduction in labor supply in the agricultural sector. Additionally, a 1% increase in money supply results in a 0.09% increase in labor demand and a 0.16% increase in labor supply in the industrial sector. These findings collectively suggest that the interest rate reduction channel has a greater impact on increasing employment in industrial firms. In contrast, the inflationary effect dominates labor demand in the agricultural and service sectors. Concurrently, the exchange rate has the most significant negative impact on labor demand in the industrial sector and the least negative impact in the agricultural sector, indicating that the industrial sector is more affected by exchange rate fluctuations. On the labor supply side, only in the agricultural sector, due to low wage levels and despite the inflationary increase resulting from the money supply shock, individuals’ willingness to work decreases, leading to their migration to other economic sectors, such as industry and services.
The results of this study indicate the impact of inflation on the labor market, particularly in the agricultural sector. Accordingly, policymakers are recommended to focus on reducing inflation as a tool to improve labor market conditions on the supply side. Given the evident exodus of labor from the agricultural sector, as clearly observed in the results of this study, it is suggested that governing institutions prioritize implementing more supportive policies for this sector. Furthermore, considering the effectiveness of monetary policy through the interest rate reduction channel in the industrial sector, it is necessary to adopt appropriate policies to strengthen lending capacity in this sector to improve demand-side labor market conditions. The use of credit data and the impact of government fiscal policies for a more precise analysis of decomposed supply and demand labor shocks could be considered in future research.
Azin Kiani Rad; Ali Asghar Banouei; Parisa Mohajeri; Somayeh Shahhosseini
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) ...
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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, respectively, were examined using econometric methods. 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.
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.
Maryam Mohammadi; Mostafa Karimzadeh; Ahmad Seifi; MARYAM Mohammadi
Abstract
Electricity trade has emerged as a crucial element within the global energy market, necessitating strategic optimization of Iran's role within this system. This study endeavors to design a mathematical model for optimizing Iran’s electricity exchanges by analyzing the balance between direct ...
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Electricity trade has emerged as a crucial element within the global energy market, necessitating strategic optimization of Iran's role within this system. This study endeavors to design a mathematical model for optimizing Iran’s electricity exchanges by analyzing the balance between direct electricity exports and indirect exports, particularly through product groups such as steel and cement. The research employs an applied quantitative design, utilizing supply and demand modeling, and integrating elements of trade network analysis and competitiveness theory. The statistical population encompassed Iran's electricity-intensive export sectors, with key products demonstrating high energy consumption and export relevance selected via a purposive sampling method. The study modeled decision-making scenarios related to electricity production, import, and export structures, employing a mathematical framework rooted in competitiveness indices and trade gravity models. Data analysis was performed using sensitivity analysis and network trade indicators. The results indicate that a moderate increase in electricity production capacity significantly supports enhanced profitability and regional competitiveness, while excessive expansion leads to diminishing returns. Steel was identified as a high-potential long-term export product, whereas cement proved to be a more viable short-term option. The study concludes that reforms in the supply structure and the implementation of targeted export strategies are essential for improving Iran’s electricity trade, noting that increased bargaining power alone will not guarantee higher profitability. The developed model serves as a strategic planning tool to strengthen Iran’s position in both regional and international electricity trade.
Introduction
Electricity is vital to modern infrastructure and economic development, and Iran—endowed with ample energy resources and strong generation capacity—has the potential to lead regional electricity trade. However, rising domestic demand, resource limitations, and regional competition require a strategic reassessment of export and import policies. While earlier studies mostly emphasized direct electricity trade, this study introduces a dynamic multi-objective optimization model that also includes indirect exports through energy-intensive goods like aluminum and steel. The model positions Iran as the central node in a regional trade network, with its electricity flows treated as decision variables and external flows assumed stable. It aims to optimize electricity allocation among domestic use, direct exports, and indirect exports, using criteria such as trade competitiveness, profitability, gravity index, and infrastructure constraints. The study evaluates trade with key partners like Turkey, Iraq, and Pakistan, and supports policymakers in identifying optimal strategies and understanding the economic rationale behind Iran’s electricity trade. The paper provides a structured analysis across five sections, offering practical insights and policy tools.
Methodology
This study develops a decision-making framework to optimize Iran's electricity trade, covering both direct electricity exchanges and indirect trade through electricity-intensive goods. The model uses a multi-objective optimization approach to enhance competitiveness and profitability while reducing costs. It assumes a fixed trade network with constant flows among other countries, treating Iran’s electricity inflows and outflows as decision variables. The mathematical formulation defines key sets for countries (V), electricity-intensive commodities (K), and domestic allocation scenarios (S).
Key Constraints:
Electricity Supply Constraint: This constraint ensures that domestic consumption, direct exports, and indirect trade do not exceed the total electricity capacity
Export Capacity Constraints: Exports cannot exceed the domestic production limits for energy-intensive goods. Demand Constraints: Trade must align with market demand limits. Minimum Electricity Allocation for Industry: To prevent the collapse of energy-intensive sectors, a minimum electricity allocation for substitute commodity exports is ensured, and also Total Trade Volume Calculation: Iran’s total trade accounts for both direct and indirect electricity flows.
Objective Functions:
Maximize Iran’s Trade Competitiveness:
Maximize Profitability:
This methodological framework enables policymakers to optimize Iran’s electricity trade position, balancing domestic electricity allocation, direct exports, and indirect exports.
Results and Discussion
This study introduces a mathematical model for optimizing Iran’s electricity trade by incorporating both direct exports and indirect trade through electricity-intensive goods like steel and cement. Using real-world data, the model identifies effective trade strategies to enhance competitiveness and profitability. Key findings show that steel is a valuable long-term export, while cement offers short-term gains. A moderate (10–15%) increase in domestic electricity production boosts trade performance, but returns diminish beyond that. Political efforts to increase demand are not financially effective under current conditions.
Policy Recommendations
Based on the study's findings, the following policy recommendations are proposed:
Prioritize Energy-Intensive Exports: Focus on industries like steel and cement over direct electricity exports to maximize economic gains.
Control Generation Growth: Keep electricity production increases within 10–15% to avoid diminishing returns.
Support Export-Oriented Plants: Promote private investment in power plants dedicated to exports to balance domestic supply and boost revenue.
Improve Trade Infrastructure: Strengthen legal and technical frameworks to reduce dependence on political negotiations and enhance regional trade efficiency.
Fatemeh Izadi Yazdanabadi; Tahereh Ashtiani
Abstract
Tourism is one of the largest economic activities worldwide, with a significant share in employment and countries’ GDP, attracting policymakers’ attention in recent decades. According to global reports, in 2019, this industry directly and indirectly supported over 10 percent of global ...
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Tourism is one of the largest economic activities worldwide, with a significant share in employment and countries’ GDP, attracting policymakers’ attention in recent decades. According to global reports, in 2019, this industry directly and indirectly supported over 10 percent of global jobs. Iran, with its climatic diversity and rich cultural-historical heritage, has high potential to benefit from tourism. However, the development of inbound tourism is influenced by various factors whose identification can assist effective policymaking. This study, using the gravity model and panel data, examines the economic and non-economic factors affecting foreign
tourist attraction to Iran from 2000 to 2020. Key variables include per capita GDP, population density, geographical distance, relative pollution, exchange rate, and linguistic and religious commonalities. Results show that per capita GDP, population density, exchange rate, and shared religion have a significant positive impact on tourist attraction with 99% confidence, and common language with 90% confidence. Geographical distance has a significant negative effect with 99% confidence, indicating that as distance increases, tourists’ willingness to travel decreases. These findings are consistent with the gravity theory of tourism. Therefore, focusing on neighboring countries with greater cultural and religious similarities and shorter distances, along with developing pilgrimage tourism, can be an effective strategy. Overall, a combination of economic and non-economic factors shapes the pattern of inbound tourism demand to Iran, and based on this, policy recommendations for sustainable development are proposed.
Introduction
After the COVID-19 pandemic, the tourism industry has witnessed remarkable growth and plays a vital role in the global GDP. Besides its economic importance, tourism significantly contributes to promoting peace, cultural exchange, and sustainable development. Despite Iran’s rich cultural, historical, and natural diversity, the country has not fully utilized its potential in attracting international tourists. Data from 2024 show an increase in inbound tourists, mainly from neighboring countries. This study aims to investigate the factors influencing inbound tourism development in Iran using the gravity model.
According to this model, factors such as GDP per capita, population density, geographical distance, air pollution levels, exchange rates, shared language, and shared religion are key determinants in attracting tourists. These variables impact tourism flows through economic, cultural, and geographical dimensions. Analyzing these factors can help policymakers design more effective strategies to boost tourism development and improve Iran’s position in the international tourism market.
Methods and Material
This study aims to identify and analyze factors affecting inbound tourism development in Iran using a panel gravity model. The sample includes 19 countries: Afghanistan, Armenia, Azerbaijan, Bahrain, China, Germany, Georgia, India, Italy, Iraq, Kuwait, Lebanon, Oman, Pakistan, Russia, Saudi Arabia, Turkey, and Uzbekistan, considering Iran as the destination country. Based on previous research, the explanatory variables are GDP per capita, population density, geographical distance, relative pollution (CO₂ emissions), exchange rate, common language, and shared religion, with inbound tourist arrivals as the dependent variable. Data from 2000 to 2020 are used. The study employs a panel data approach, combining cross-sectional and time-series data, which offers advantages like higher variability, more degrees of freedom, and efficiency.
Gravity models are widely used in trade economics to assess bilateral flows, which is applicable here given tourism’s service-trade nature. The econometric modeling involves testing fixed effects and random effects through the F-test and the Hausman test. The gravity model equation is log-linearized, using GDP per capita and population size instead of GDP levels.
Geographical distance reflects transport costs between countries. The final model structure is based on Anderson (2011) and other studies, considering economic mass and distance as key determinants. This methodological framework allows a dynamic analysis of tourism inflow factors and offers robust insights into policy-making for enhancing Iran’s tourism sector.
Results and Discussion
The study analyzed the factors affecting the development of inbound tourism in Iran using a panel gravity model. Descriptive statistics for the variables under study were presented in Table 1. According to Table 2, the average number of foreign tourist arrivals in Iran was 1,874,896, with a minimum of 28,000 and a maximum of 2,635,488. The average per capita GDP for the available data was $10,982, with a minimum of $0 and a maximum of $55,494.
The average population density was 215.11 people per square kilometer, with a minimum of 7.32 and a maximum of 2,181.51 people per square kilometer. The average geographic distance from Iran was 2,298.39 km, ranging from 0 km (for the reference country, Iran) to 6,410 km.
Regarding shared language, countries like Afghanistan, Pakistan, and Uzbekistan share a language with Iran, while 16 other countries have minimal language overlap. Countries such as Afghanistan, Azerbaijan, Bahrain, Iraq, Kuwait, Lebanon, Oman, Pakistan, and Uzbekistan share religious beliefs with Iran. The Chow test confirmed that the data follow a panel structure, with a significant F-statistic of 261.97, supporting the rejection of pooled effects (OLS) and accepting fixed effects.
The Hausman test further supported random effects. Tests for heteroscedasticity and autocorrelation showed that variance homogeneity and lack of serial correlation were valid assumptions. The VIF results indicated no multicollinearity between explanatory variables.
The final estimation results, based on random effects, showed that variables such as GDP per capita, population density, exchange rates, and shared religion had a significant positive impact on inbound tourism, while geographic distance negatively affected tourism. Specifically, a 1% increase in GDP per capita could increase tourist arrivals by 125.52%, a 1% increase in population density could increase arrivals by 0.369%, and a 1% decrease in geographic distance could lead to an 82.96% increase in tourism. On the other hand, air pollution (CO2 emissions) was not significant. Overall, the findings suggest that improving economic factors (e.g., GDP per capita), population density, favorable exchange rates, and religious and language similarities can increase tourism, while greater geographic distance poses a barrier.
Conclusion
This study examines the factors influencing inbound tourism development in Iran using the gravity panel approach. Key variables such as GDP per capita, population density, geographic distance, relative pollution, exchange rates, common language, and common religion from 2000 to 2020 for 19 countries were considered.
The results revealed that GDP per capita, population density, exchange rates, and common religion positively impact foreign tourist attraction to Iran. Geographic distance had a negative effect on tourism, confirming the gravity model in the tourism industry. Iran, with its rich history and civilization, has the potential to attract tourists. Major obstacles to tourism development in Iran include economic instability, lack of tourism infrastructure, and inadequate facilities. The study suggests focusing on neighboring countries and organizing religious and tourism events to boost tourist inflows.
