Factors Affecting Textiles Products Exports of Major Producers: A Gravity Model Approach

Abstract

This study employed the gravity model approach to explore the predictors of textile product exports of the top 10 major textile producers. A specific dataset was generated and used to estimate the effects of 14 textile commodities selected in specific countries using Poisson regression and panel data from 1990 to 2021. The 10 major textile export countries hold significance in the global industry, providing comprehensive insights into export factors. Their diverse representation aids in understanding the challenges faced by exporters across continents. These countries possess substantial export markets and showcase various strategies and trade policies. Reliable data availability enables accurate analysis of factors affecting textile exports. Studying these countries enhances export performance and informs global industry strategies. Estimated results indicate that the real exchange rate, real GDP of the reporting country, preferential trade agreements, and the common border are significant positive predictors for textile exports of all commodities. The results further show that distance, average weighted tariff, and language have negative effects on the textile exports of all selected commodities. The transportation cost, measured by distance, is a barrier to textile commodity exports. The findings highlight the significant factors influencing textile exports and provide insights for exporters, policymakers, and researchers in the industry. Notably, this study addresses the challenges encountered by textile product export companies. These challenges include trade barriers, regulatory compliance, fluctuating exchange rates, intense competition, rising production costs, and market access barriers. By analyzing these challenges within the gravity model framework, the study aims to offer comprehensive insights into the complexities faced by textile exporters and their impact on the industry. The outcome of this study will be beneficial for the world’s major textile producers in setting their export targets and strategies to promote textile and cotton exports. The study suggests that the major exporters should benefit from moving exports to rich markets, which are situated at a nearby distance. The results of this study can be valuable for exporters in setting export targets, devising strategies, and formulating trade policies to overcome these challenges and enhance the competitiveness of the textile industry. The corresponding practical implications include the need to strategically manage exchange rates, focus on promoting exports to rich economies, reduce transportation costs, leverage preferential trade agreements, manage average weighted tariffs, address language barriers, and develop strategic export objectives and strategies. This research offers valuable insights for policymakers and society regarding factors influencing textile exports by major producers. Policymakers can utilize this information to inform trade regulations, exchange rate management, and the promotion of preferential trade agreements, enhancing export competitiveness and fostering sustainable practices. The research promotes international cooperation by encouraging collaboration and the sharing of best practices among reporting and partner countries. The findings contribute to economic growth, job creation, and improved standards of living, benefiting society as a whole.

Plain Language Summary

This study employed the gravity model approach to examine the factors affecting the textile product exports of the top 10 major textile producers. A specific dataset was generated and used to estimate the effects of 14 textile commodities selected in specific countries using Poisson regression and panel data from 1990 to 2021. The 10 major textile export countries hold significance in the global industry, providing comprehensive insights into export factors. Their diverse representation aids in understanding the challenges faced by exporters across continents. These countries possess substantial export markets and showcase various strategies and trade policies. Reliable data availability enables accurate analysis of factors affecting textile exports. Studying these countries enhances export performance and informs global industry strategies. Estimated results show that the real exchange rate, real GDP of the reporting country, preferential trade agreements, and the common border are positive and statistically significant factors for textile exports of all commodities. The results further show that distance, average weighted tariff, and language have negative effects on the textile exports of all selected commodities. The transportation cost, proxied by distance, is found to have a negative influence on textile commodity exports. The findings highlight the significant factors influencing textile exports and provide insights for exporters, policymakers, and researchers in the industry.

Keywords

textile exports international trade exchange rate gravity model

Introduction

Exports are seen as a significant driver to economic development (Lewis, 1980; Zhang et al., 2023). Firms can consider exporting when they face a high cost of global production and other circumstances (Cherunilam, 2005). The textile industry produces lots of competitive goods that are helpful to empower exports (Sharma & Dhiman, 2014). This industry, also known as conventional industry and regarded as the backbone of many economies, holding a significant position in many economies in terms of industrial output, wages, and exports. It is one of the world’s promising sectors, as well as one of the largest and most diverse, with its products used by almost everyone (Dhiman & Sharma, 2016).

Global trade in textiles also went through tarification, with much of the textile trade regulated under the Multi-Fiber Agreement (MFA). During this period, import quotas dominated the international trade of textile products. However, it is followed by the Agreement on Textiles and Clothing (ATC), and after 1995, most of the quotas are replaced by tariffs, a process referred to as tarification. The ATC agreement does not exist anymore, understanding the role of tariffs in the global textile product trade becomes important. The MFA allowed for a selective quantitative restriction, which regulates the majority of trade in textiles and clothes and is a departure from GATT. It attempts to protect domestic producers in developed countries against market disruption (Ramaswamy & Gereffi, 2000). Mausmi found that the historically more productive exporters of textile commodities of the MFA phase-out countries, which suffered as a result of the revoke of MFA. Most researchers conclude that MFA is extremely discriminatory and that the constraints are stricter and stricter (Goto, 1989). On 1955, WTO settlements replaced MFA, which presented a provisional approach for the removal of those quotas.

The cancel of the quotas in 2005 markedly boosted textile and clothing industries in Asian developing countries, which began losing comparative advantages and experiencing weaker export results. This reform leads to the competitiveness of nations in world trade for the greatest labor surplus countries such as China, Pakistan, and India, is possible to justify this situation (Abraham & Sasikumar, 2011; Zhang & Hathcote, 2008). In this sense, the abolition of these quotas in January 2005 led to a major re-allocation of production at the detriment of other less competitive developed economies and, to a lesser degree, a boost in global trade for the good of the most successful countries (China, which joined the WTO in the first place).

Based on the statistics from WTO, the value of world textile (SITC 65) exports totaled $315 billion in 2018, a rise of 6.4 from the year before. This was the fastest growth seen in 6 years. The textile export trade in the world grew from US$258.4 billion in 2016 to US$288.06 billion in 2021. On the basis of rapid growth in textile trade, the influencing factors of bilateral textile trade have become a hot topic of researches (Abu-Qdais et al., 2021; Bhuyan & Oh, 2021; Wong & Ngai, 2022). Using the gravity model, previous literature have explored on the roles of GDP, geographical distance, exchange rate (ER), and labor burden in textile trade (Gendron, 2021; Rotimi et al., 2021; Wong & Ngai, 2022; Youn & Jung, 2021). However, there are still some institutional and cultural factors that haven’t been explored yet. For instance, preferential trade agreements, average weighted tariff, common border, and language. Examining how these institutional and cultural factors affect textile trade is of great importance because it enlightens policymakers to make full use of institutions and cultural integration to improve textile trade and eventually drive economic development (Aman et al., 2022; Coulibaly, 2021; Richter et al., 2021). Thus, this study is designed to check the roles of the ER, GDP, distance, common border, language, and average weighted tariff in textile product exports of the top 10 major textile producers in a gravity model. A specific data set was generated and used to estimate 14 textile commodities selected in specific countries, discussed in more detail later. This research study used recent available data taken from UN Commerce, WDI, WITS, and so on. This study used data from the period 1990 to 2021 to investigate the determinants of textile product exports by major producers around the world. On the basis of the current information, this will help in determining whether the exchange rate, GDP, common border, language, distance, and average weighted tariff affect textile product exports by major producers. This is a unique and new study on these macroeconomic predictors on textile product exports by major producers.

This research study makes significant contributions to both theoretical and practical aspects of the literature. Theoretically, it expands upon the existing literature by incorporating additional variables within the gravity model framework for textile exports. By including variables such as preferential trade agreements, average weighted tariff, common border, and language, the study provides a more comprehensive analysis of the factors influencing textile exports. This theoretical contribution enhances our understanding of the dynamics of textile trade and offers a refined framework for future research in the field. Moreover, the study offers practical implications for policymakers and exporters involved in the textile industry. The recommendations, such as targeting exports to wealthy economies in close proximity and reducing transportation costs through infrastructure improvements and logistics system enhancements, provide actionable insights to enhance the performance of textile exports. Additionally, the recognition of the positive relationship between depreciated reporting country currencies and textile exports helps exporters leverage currency dynamics to their advantage. By addressing the research gap of a comprehensive analysis of the factors affecting textile exports, particularly the role of exchange rates, this research bridges the gap by incorporating additional variables and providing more nuanced findings. The study fills a knowledge gap regarding the specific factors influencing textile exports by including preferential trade agreements, average weighted tariffs, common borders, and language as explanatory variables. This study also bridges the gap by offering a more holistic understanding of the dynamics of textile trade and its relationship with exchange rates.

The remaining sections are: Section 2 provides a literature review, which serves as the basis for constructing conceptual and empirical models to estimate the factors that influence the exports of textile products for major producers. The methodology is presented in Section 3, while the results and subsequent discussion of the estimated models are presented in Section 4. The final part is conclusions and implications.

Literature Review

On the predictors of exports, existing literature uniformly focus on exchange rates and its volatility. Previous studies found that the real ER is a negative factor of exports (Chani et al., 2017), but its effect on disaggregated levels was mixed. Focusing on ER’s volatility and exports, previous literatures have not reached a consistent conclusion. Most of them find it is negative relationship, while some hold it is positive. To sum up, although ER uncertainty is not helpful to trade (Haile & Pugh, 2013), it may also stimuli risk-averse exporters to increase their marginal utility in exports (Hsu & Chiang, 2011). During this process, the third country also plays a negative role (Farhana et al., 2022).

Further, researchers also investigated other different factors in the textile industry, for instance, labor costs, GDP, and institutions. Surrounding labor costs, they claim that higher labor costs pose an obstacle to export performance (Wang, 2013). Surrounding GDP, it is found that GDP, especially importers’ per capita GDP, is an important deterrents to textile exports. Surrounding institution predictors of the textile industry, some studies focus on the MFA in South Asia, such as Kar (2010). They find that countries such as China, India, and Pakistan were traditionally the more efficient exporters of textile merchandise, but those who are not necessarily efficient producers are damaged because of the revoke of MFA. If these nations’ textile businesses fulfill consumer expectations well, they have the potential to increase their worldwide market share. China is currently the global market leader, accounting for roughly 30% of textile-related exports in recent years (Gautam & Lal, 2020).

The top 10 textile exporters are important because they represent major players in the global textile industry and have significant contributions to textile production, export volumes, and market influence. Focusing on textile exports allows for a targeted analysis of the specific dynamics and obstacles faced by the textile industry. While the characteristics of textile exports may vary among the 10 countries, studying them collectively provides a comprehensive understanding of the factors influencing textile exports in different contexts and regions. This addresses an existing knowledge gap by focusing on the effects of exchange rates, GDP, common borders, language, distance, and average weighted tariffs on the exports of textile products by major producers. This study offers a unique and novel analysis of the macroeconomic variables’ impact on textile exports. This study also analyzes the effects of multiple macroeconomic variables on textile exports for major producers. What sets it apart from previous research is its comprehensive approach, considering factors such as GDP, common borders, language, distance, and average weighted tariffs. By focusing on the top 10 textile exporters, the study offers unique insights specific to the global textile industry. This research fills a knowledge gap and provides valuable guidance for textile producers aiming to optimize their export strategies in a competitive market.

The review of the literature suggested that lots of studies have used the gravity model and estimated the impact of the rate of ER on exports (or trade). This research used recent available data taken from UN Commerce, WDI, WITS, and so on. This study used data from the period 1990 to 2021 to investigate the determinants of textile product exports by major producers around the world. On the basis of the current information, the roles of the ER, distance, GDP, language, and other variables in the trade flow can be determined with the support of literature. This will help in determining whether the exchange rate, GDP, common border, language, distance, and average weighted tariff affect textile product exports by major producers. This is a unique and new study on the effects of these macroeconomic variables on textile product exports by major producers.

Methods

Data

The Data has been gathered from the Pakistan Economics Survey (various issues), World Development Indicators (WDI), International Monetary Fund (IMF), UN Comtrade, the World Integrated Trade Solution (WITS), and the World Bank database. This study has used the top 10 textile exporters in the world. The top 10 textile exporters are Bangladesh, China, Germany, India, Pakistan, the Republic of Korea, Turkey, the United Kingdom, the United States, and Vietnam. Considering the availability of data, the time spans from 1990 to 2021. The data in this study was then analyzed using Stata software. The factors influencing textile product exports of the major producers were assessed using panel data collected annually from 1990 to 2021.

Collecting data from 10 textile export countries serves multiple purposes in research studies. It ensures representation of major players in the global textile industry, allows for comparative analysis, enhances the generalizability of findings, provides industry insight, and addresses existing knowledge gaps. By examining trends, factors, and strategies within these countries, researchers gain a comprehensive understanding of the textile industry and can inform policy decisions, industry strategies, and future research endeavors. The data collection from these countries contributes to a more robust and holistic understanding of the dynamics and challenges in the global textile market.

Empirical Model

The gravity model has been widely used in the field of trade performance (Abbas et al., 2015; Abdullahi, Huo et al., 2021). Apart from the basic variables (GDP and distance) (Abdullahi, Shahriar et al., 2021; Abdullahi et al., 2022), this study also includes additional variables such as trade agreements, exchange rates, language, and common borders. The model’s specifications include logarithmic transformations of variables and dummy variables to account for specific characteristics, which are factors influencing textile

Targeting the research purposes, this study specifies the following model:

\begin{matrix} lnEX P_{ij} = β_{0} + β_{1} \ln (GDPit) + β_{2} \ln (IGDPjt) \\ + β_{3} (TARij) + β_{4} (EXijt) + β_{5} \ln (DISij) \\ + β_{6} (PTAij) + β_{7} (Languag e_{ij}) \\ + β_{8} \ln (Common Bij) + (μ_{ijt}) \end{matrix}

3.4

where lnEXPit is logarithm of total exports; lnGDPit is logarithm of GDP; $TARij$ is logarithm of country $i$ and country $j$ ; $(EXijt)$ is the bilateral exchange rate; $lnDISij$ is logarithm of distance between country $i$ capital to each importing countries $j$ biggest economic center; $PTAij$ is a dummy of having any form of PTA. The $Languag e_{ij}$ and $Common Bit$ is also a dummy variables; $μ_{it}$ the error term. The specifications of variables are shown in Table 1. By focusing specifically on the determinants of textile exports, the model sheds light on the factors that impact these exports. This study makes a unique contribution by including variables such as distance, average weighted tariff, language, and common borders. These factors, which are often overlooked in existing literature that has a broader trade focus, provide a more comprehensive understanding of the complexities within the textile industry.

Table 1.

The Specification of Variables.

Variable	Data source	Website source	Definition
Textiles products	UN Comtrade	https://comtradeplus.un.org/	Textile products are goods made from fabrics or fibers, including clothing, accessories, home textiles, and technical textiles.
Real exchange rate	IMF	https://www.imf.org/en/Data	The real exchange rate reflects the purchasing power of a currency relative to another, considering inflation adjustments.
Real GDP	WDI	https://databank.worldbank.org/source/world-development-indicators	Real GDP measures a country’s economic output by valuing all goods and services produced within its borders, while accounting for inflation.
Preferential trade agreements	WTO If two countries have a PTA between them, we indicate it as 1; otherwise, we indicate it as 0.	https://rtais.wto.org/UI/PublicMaintainRTAHome.aspx	Preferential trade agreements are deals between countries that offer preferential treatment, like lower tariffs or trade barriers, to each other’s goods. They reflect the presence and influence of trade agreements on textile exports.
Common border	Common border, 1; otherwise, 0.		Common border: The common border variable indicates if two countries share a land or maritime border, representing their geographic proximity and potential trade facilitation.
Distance	Indo	www.indo.com/distance	Distance reflects the physical separation between countries and serves as a proxy for transportation costs.
Average weighted tariff	WITS	https://wits.worldbank.org/	Average weighted tariff signifies the average import duty imposed by a country on its imports and indicates the extent of trade barriers affecting textile products.
Language	Common language, 1; otherwise, 0.		The language variable indicates if two countries have a common language, highlighting the potential for smooth communication and cultural similarities that can impact trade relationships.

Result and Discussion

Descriptive Summary and Statistics

This section presents a descriptive analysis of the structure of trade and its change. The time span of the analysis, which is from 1990 to 2021, is divided into five intervals: 1990 to 1995, 1996 to 2000, 2001 to 2005, 2006 to 2010, and 2011 to 2021. The findings of this research study address the research question by demonstrating that exchange rates have a significant impact on the exports of textile products from major producers. Being identical to gravity model, this study finds increasing in proportion to the GDP of the reporting and importing countries, while decreasing with greater distance between trading partners. Additionally, the study found that preferential trade agreements, lower tariffs, a common language, and a depreciated reporting country currency all positively affect textiles exports. These findings provide valuable insights for reporting and partner countries in setting export objectives and strategies, highlighting the importance of reducing transportation costs and considering currency actions.

The findings of this research study could surprise a knowledgeable reader in the main literature by offering new insights and confirming certain aspects of previous research while also challenging others. The study confirms the basic gravity model framework for textiles exports, which aligns with previous research. However, it contributes to the literature by incorporating additional variables and using the Poisson method to estimate the gravity model, providing more nuanced findings. The study reveals the significant positive relationship between preferential trade agreements (PTAs) and textiles exports, emphasizing the high trade gains from PTAs. Furthermore, the study highlights the positive relationship between a depreciated reporting country currency and textiles exports, which contradicts previous research that may have suggested a negative relationship. This surprising result suggests that major textiles exporting countries can leverage currency depreciation to boost exports. By conducting this research, the study aims to refine and enhance the existing knowledge in the field, provide a more updated analysis, and offer policy implications for reporting and partner countries in their export strategies.

The trade presents the aggregate value of all the sub-sectors of textile products, the number are code (see Table 4 for detail sub-sectors). Table 2 provides insights into the value of exports for selected countries in different time periods, as well as the changes in their share of total textile product trade. Bangladesh’s exports grew significantly from $7.1 billion in 1990 to 1995 to $83.8 billion in 2011 to 2021, with a sudden decline to $3.7 billion in 2021. In comparison, China’s exports showed remarkable growth, starting at $30.6 billion in 1990 to 1995 and reaching $650.8 billion in 2011 to 2021. However, China faced challenges due to substantial tariff hikes imposed by the US. Pakistan’s exports remained relatively lower, reaching $61.1 billion in the same time period. Disparities in exports can be attributed to various factors such as infrastructure, technology, trade policies, and market demand. Notably, India, the US, the UK, and Turkey had similar exports in 2009, but their export performances diverged, with India reaching $16.4 billion, the UK reaching $3.4 billion, and Turkey reaching $7.9 billion in 2021. These findings highlight significant differences among the top 10 global textile product exporters (Table 2). It is shown that the nominal value of China’s cotton and textiles export sectors increased. China accounted for 21.7% of the textile trade in 1990 to 1995, and it increased to 54.7% in the 2011 to 2021 time periods. Hence, China has been accounting for half of the textile products trade, a remarkable transformation from US$ 0.3 billion to US$ 100 billion between 2009 and 2021.

Table 2.

Nominal Value of Cotton and Textile Exports (Billion US$).

Country	1990–1995	1996–2000	2001–2005	2006–2010	2011–2021	All
Bangladesh	7.1	10.2	18.5	37.5	83.8	157.1
China	30.6	40.0	90.8	256.9	650.8	1069.0
Germany	12.8	13.0	13.7	15.5	25.5	80.4
India	16.7	21.1	27.9	48.2	105.2	219.1
Pakistan	7.5	14.3	26.4	39.2	61.1	148.6
Rep. of Korea	25.8	22.7	23.1	17.9	34.5	124.1
Turkey	17.8	24.6	35.0	37.1	56.0	170.6
United Kingdom	9.8	10.0	10.1	11.0	21.9	62.7
United States	11.9	10.4	14.8	24.8	44.3	106.0
Viet Nam	1.0	2.3	10.7	31.8	107.7	153.5
All	141.1	168.5	271.0	520.0	1190.8	2291.3
	Percent share
Bangladesh	5.1	6.1	6.8	7.2	7.0	6.9
China	21.7	23.7	33.5	49.4	54.7	46.7
Germany	9.1	7.7	5.1	3.0	2.1	3.5
India	11.8	12.5	10.3	9.3	8.8	9.6
Pakistan	5.3	8.5	9.8	7.5	5.1	6.5
Rep. of Korea	18.3	13.5	8.5	3.5	2.9	5.4
Turkey	12.6	14.6	12.9	7.1	4.7	7.4
United Kingdom	6.9	5.9	3.7	2.1	1.8	2.7
United States	8.4	6.1	5.5	4.8	3.7	4.6
Viet Nam	0.7	1.4	3.9	6.1	9.0	6.7

Source. Author’s derivation.

Almost all the countries have lost their portion of the textile export market to China. The biggest loser in textile product trade is the Republic of Korea, as its exports declined from 18.3 to 2.9%. Germany lost its share from 9.1 to 2.1, Turkey from 12.6 to 4.7, the UK from 6.9 to 1.8, and the US from 8.3 to 2.7. When every country was losing its share to China, Vietnam gained and improved its textile export share from 0.7 to about 9%. Pakistan kept its share intact, while India marginally gained.

Table 3 shows descriptive statistics. The textile exports among the countries are basically increasing. The real GDP, the number of PTA, the number of common border and common language of countries are increasing, whereas the average weighted tariff is decreasing. This result preliminarily confirms the positive impact of real GDP, the number of PTA, common border, and common language on textile exports and the negative impact of the average weighted tariff on textile exports.

Table 3.

Descriptive Statistics.

Variable	Mean	Std	Min	Max	Unit
Textiles products	62.4611	426.4075	0	17,573.0200	Billion US$
Real exchange rate	156.7202	538.3721	0.7492	3,417.4829	Per US$
Real GDP	2,359.5720	5,936.5647	6.4717	23,390.1830	Billion US$
Preferential trade agreements	0.1334	0.3407	0	1
Common border	0.1116	0.3149	0	1
Distance	6,818.0862	4,327.2740	653	15,554	Kilometer
Average weighted tariff	15.4065	17.2209	0	47.1493	Percentage
Language	0.0669		0	1

Econometric Analysis

The correlation matrix indicates a negative correlation between the exchange rate, average weighted tariff, common border preferential, as well as language, and trade value. Further, the result indicates a positive correlation between trade value, distance, and the real GDP of the reporting country. Overall, there are 36 estimates in the correlation matrix, but it is important to understand that correlation has the disadvantage that partial correlation does not account for the effect of one variable on another variable. It just estimates the strength of the association without controlling for the effect of one variable on other variables. Table 4 shows estimates of the gravity models and results of the panel data regression estimation for selected fourteen textile commodities in selected countries. The Poisson regression result shows that distance, average weighted tariff, and language have negative effects on textile and all commodity exports. Estimated results suggest that the real ER, GDP, preferential trade agreement, and common border are positive and statistically significant factors that affect textile and all commodity exports. Our findings supported by Hatab et al. (2010). The cotton commodity export relationship is negative with distance and positive between ER and GDP.

Table 4.

Gravity Model of the Selected Textile Products Estimated Using Poisson Regression.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)
	All Commodities	50Silk	51Wool	52Cotton	53V-textile	54Man made-filaments	55Staple fibers	56Wadding, felt	57Carpets	58Special woven	59Impregnated	60Knitted	61Apparel knit	62Apparel	63Clothing
Distance	−0.0000107 (0.098)	0.0000111 (0.635)	−0.00010*** (0.000)	−0.00011*** (0.000)	−0.00015*** (0.000)	−0.000046*** (0.000)	−0.000083*** (0.000)	−0.000094*** (0.000)	−0.000036* (0.044)	−0.000063*** (0.000)	−0.000061*** (0.000)	0.00000685 (0.623)	−0.00015*** (0.000)	−0.000075*** (0.000)	−0.000045*** (0.000)
Common border	0.398*** (0.000)	1.490*** (0.000)	0.109 (0.576)	0.0658 (0.696)	0.0478 (0.778)	1.532*** (0.000)	0.757*** (0.000)	0.334* (0.025)	−0.884** (0.008)	1.054*** (0.000)	0.988*** (0.000)	1.444*** (0.000)	1.620*** (0.000)	0.479 (0.159)	−1.524*** (0.000)
Preferential trade agreement	1.245*** (0.000)	1.962*** (0.000)	0.721*** (0.000)	0.578*** (0.000)	−0.126 (0.470)	1.216*** (0.000)	1.166*** (0.000)	1.513*** (0.000)	0.822*** (0.000)	1.327*** (0.000)	1.822*** (0.000)	2.259*** (0.000)	1.835*** (0.000)	1.508*** (0.000)	1.430*** (0.000)
Average weighted tariff	−0.0055* (0.042)	−0.00034 (0.955)	0.0052 (0.444)	0.0028 (0.550)	−0.0178*** (0.000)	0.0110** (0.005)	−0.0020 (0.690)	−0.0133* (0.011)	−0.0290*** (0.001)	0.0138** (0.002)	−0.0091 (0.053)	0.0074 (0.091)	0.0107 (0.073)	−0.0091* (0.048)	−0.0254*** (0.000)
Real GDP of reporting country	1.580*** (0.000)	1.754*** (0.000)	1.406*** (0.000)	1.771*** (0.000)	1.172*** (0.000)	2.364*** (0.000)	1.876*** (0.000)	2.759*** (0.000)	0.032 (0.061)	2.0547*** (0.000)	2.157*** (0.000)	2.593*** (0.000)	1.862*** (0.000)	1.234*** (0.000)	1.426*** (0.000)
Language	−0.187* (0.012)	1.476*** (0.000)	0.757*** (0.000)	0.405* (0.016)	0.763*** (0.001)	−0.270* (0.038)	−0.502*** (0.001)	−0.593*** (0.000)	0.303* (0.022)	−0.282* (0.014)	−0.243* (0.036)	−0.293 (0.117)	0.216 (0.107)	−0.523*** (0.000)	0.497*** (0.000)
Real exchange rate	0.0522*** (0.000)	0.0494*** (0.000)	0.0302* (0.031)	0.0916*** (0.000)	0.0296*** (0.000)	0.0795*** (0.000)	0.0424*** (0.000)	0.0391*** (0.000)	−0.0014 (0.892)	0.0681*** (0.000)	0.0844*** (0.000)	0.0522* (0.013)	0.0562*** (0.000)	0.0423*** (0.000)	0.0601*** (0.000)
Constant	7.273*** (0.000)	0.200 (0.873)	1.992** (0.006)	11.07*** (0.000)	12.19*** (0.000)	5.786*** (0.000)	6.447*** (0.000)	4.865*** (0.000)	3.002*** (0.000)	7.347*** (0.000)	1.247* (0.019)	4.891*** (0.000)	7.156*** (0.000)	8.682*** (0.000)	4.791*** (0.000)
Fixed effects
Year	262.19***	94.41***	50.83**	43.94**	53.34**	61.01***	45.16**	89.52***	28.74	141.38***	185.56***	169.44***	151.15***	138.47***	477.64***
Country	2709.84***	1207.75***	1939.30***	653.03***	1017.70***	746.74***	908.00***	557.08***	1882.61***	1077.01***	901.78***	735.20***	1623.90***	1027.79***	3491.08***
Partner	2607.66***	321.87***	374.68***	694.98***	305.11***	612.53***	665.76***	1202.75***	1000.07***	1087.51***	810.99***	1420.84***	829.07***	1558.89***	2001.22***
Number of observations	36288	2592	2592	2592	2592	2592	2592	2592	2592	2592	2592	2592	2592	2592	2592
Pseudo R²	0.7174	0.8515***	0.7489***	0.6997***	0.6782***	0.7253***	0.7054***	0.8200***	0.8602***	0.7742***	0.7862***	0.8668***	0.8795***	0.8904***	0.9413***

Source. Author estimation.

***

, **, and * show that estimates are statistically significant at 1, 5, and 10%.

The estimates of the gravity model of the selected textile products were estimated using Poisson regression for m-filaments, staple fibers, wadding-felt, carpets, special woven, impregnated, knitted, articles of apparel, and clothing commodities, and the independent variables were distance, common border, preferential trade agreement, average weight tariff, real GDP, language, and real exchange rate. The distance has a significantly negative relationship with m-filament, staple fibers, wadding carpets, special woven, impregnated, knitted, articles of apparel, and clothing. The above finding is identical to the fundamental gravity model (Frankel & Rose, 2002). Similarly, language has a negative relationship with these commodity exports. The real exchange rate, preferential trade agreement, and real GDP have a positive relationship with these textile commodity exports.

Table 4 provides comparison of textiles commodities exports. The findings reveal that distance, average weighted tariff, and language have negative effects on overall textiles exports. Conversely, the real exchange rate, real GDP, preferential trade agreement, and common border exhibit positive impacts on exports. Examining specific commodities, cotton exports are negatively influenced by distance but positively affected by ER and GDP. The gravity model analysis across various textiles commodities confirms the negative relationship between distance and exports, while language also shows a negative association. In contrast, the real exchange rate, preferential trade agreement, and real GDP of reporting country positively impact the exports of m-filaments, staple fibers, wadding-felt, carpets, special woven, impregnated, knitted, articles of apparel knit, articles apparel, and clothing. These findings shed light on the complex dynamics influencing trade patterns in the textiles sector, with distance and language acting as barriers and the real exchange rate, preferential trade agreements, and GDP playing pivotal roles in facilitating export growth.

Table 5 shows the average marginal effects of the selected commodities estimated using the Poisson method (elasticity dy/dx) of textile exports and their standard error. In model 1, an average marginal effect of all commodities exports with respect to distance of −0.072, which shows that every 1 km increase in distance drops all commodities exports by −0.072. Similarly, the average weighted tariff and language have negative effects on all commodity exports. If a 1-unit increase in average weighted tariff decreases all commodity exports by −0.041, which is significant at the 5% level, Chen et al. (2017) support these findings. The preferential trade agreement, common border, real GDP, and ER have a positive impact on all commodity exports. If one unit increases in the real GDP, textiles and all commodities exports increase by 0.427. Model-2 presents the silk (50) commodity, model-3 presents wool (51) commodity, model-4 presents cotton (52) commodity, and model-5 presents vegetable textiles (53) commodity exports. These models present estimation results with distance, common border, preferential trade agreement, average weighted tariff, real GDP, language, and real exchange rate.

Table 5.

Average Marginal Effects.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)
Variables	All Commodities	50Silk	51Wool	52Cotton	53V-textile	54Man-made filaments	55Staple fibers	56Wadding, felt	57Carpets	58Special woven	59Impregnated	60Knitted	61Apparel Knit	62Apparel	63Clothing
Distance	−0.072* (0.044)	0.075 (0.159)	−0.697*** (0.075)	−0.767*** (0.083)	−1.040*** (0.143)	−0.316*** (0.082)	−0.565*** (0.078)	−0.639*** (0.057)	−0.245** (0.122)	−0.428*** (0.078)	−0.415*** (0.075)	0.046 (0.094)	−1.058*** (0.172)	−0.509*** (0.106)	−0.304*** (0.081)
Common border	0.034*** (0.008)	0.130*** (0.020)	0.009 (0.017)	0.005 (0.015)	0.0041 (0.014)	0.132*** (0.013)	0.065*** (0.013)	0.0288** (0.012)	−0.075** (0.028)	0.091*** (0.017)	0.085*** (0.013)	0.124*** (0.015)	0.139*** (0.029)	0.041 (0.029)	−0.131*** (0.017)
Preferential trade agreement	0.491*** (0.025)	0.780*** (0.113)	0.284*** (0.040)	0.228*** (0.052)	−0.049 (0.070)	0.480*** (0.037)	0.460*** (0.036)	0.597*** (0.030)	0.323*** (0.045)	0.524*** (0.039)	0.719*** (0.041)	0.891*** (0.057)	0.725*** (0.064)	0.595*** (0.049)	0.564*** (0.034)
Average weighted tariff	−0.041** (0.020)	−0.002*** (0.046)	0.039 (0.050)	0.021 (0.036)	−0.134*** (0.038)	0.081** (0.029)	−0.015 (0.039)	−0.100** (0.039)	−0.214*** (0.063)	0.103** (0.032)	−0.068* (0.035)	0.055* (0.032)	0.080* (0.045)	−0.067** (0.034)	−0.190*** (0.038)
Real GDP of reporting country	0.427*** (0.027)	0.471*** (0.089)	0.378*** (0.048)	0.476*** (0.062)	0.314*** (0.070)	0.636** (0.053)	0.502*** (0.056)	0.741*** (0.036)	0.088* (0.047)	0.553*** (0.047)	0.580*** (0.046)	0.698*** (0.062)	0.501*** (0.047)	0.332*** (0.039)	0.382*** (0.026)
Language	−0.013** (0.005)	0.108*** (0.021)	0.055*** (0.009)	0.030** (0.013)	0.056*** (0.016)	−0.019** (0.009)	−0.037*** (0.011)	−0.043*** (0.008)	0.022** (0.009)	−0.020** (0.008)	−0.017** (0.008)	−0.021 (0.013)	0.015** (0.009)	−0.038*** (0.007)	0.036*** (0.006)
Real exchange rate	1.077*** (0.132)	1.019*** (0.267)	0.622** (0.289)	1.890*** (0.263)	0.612*** (0.165)	1.640*** (0.181)	0.875*** (0.170)	0.806*** (0.151)	−0.030 (0.226)	1.405*** (0.279)	1.741*** (0.262)	1.077** (0.434)	1.159*** (0.230)	0.873*** (0.133)	1.240*** (0.139)
Constant	−0.072* (0.044)	0.075 (0.159)	−0.697*** (0.075)	−0.767*** (0.083)	−1.040*** (0.143)	−0.316*** (0.082)	−0.565*** (0.078)	−0.639*** (0.057)	−0.245** (0.122)	−0.428*** (0.078)	−0.415*** (0.075)	0.046 (0.094)	−1.058*** (0.172)	−0.509*** (0.106)	−0.304*** (0.081)

Source. Author estimation.

***

, **, and * show that estimates are statistically significant at 1, 5, and 10%.

The results are 0.471 for the real GDP, 0.780 for preferential trade agreements, −0.002 for average weighted tariffs, and 1.077 for real exchange rates. Turning to the real exchange rate, its effect on wool, cotton, and vegetable commodities is significantly positive and is equal to 0.6224, 1.8900, and 0.612 in our most preferred specification. The coefficient implies that if the real exchange rate increases by 1 percentage point, wool, cotton, and v-textiles rise by 0.622, 1.890, and 0.612%, respectively. The real GDP is also a positive predictor of wool, cotton, and v-textiles exports. This coefficient shows that a 1% increase in reporting country GDP leads to an increase in wool, cotton, and v-textiles exports of 0.378, 0.476, and 0.314%, respectively. These findings are identical to those of Chen et al. (2017) and Chi (2010). The dependent variables Textile exports have a statistical relationship with seven independent variables (models 6–10): distance, common border, preferential trade agreement, average weighted tariff, real GDP, language, and real exchange rate, because significant levels are 1% and 5%. However, it has no statistical relationship with model-7 staple fibers and average weighted tariff, model-9 carpets, or the real exchange rate. The average marginal effects (elasticity dy/dx) of textile exports and their standard error in model 6 show an average marginal effect of manmade filaments (54) exports with respect to distance of −0.316 (significant at the 10% level) and staple fibers (55), wadding, felt (56), carpets (57), and special woven (58) is a negative relationship, respectively, that every one kilometer increase in distance will decrease by −0.565, −0.6398, −0.245, −0.428 significant at the 1 and 5% level. Similarly, the average weighted tariff and language have negative effects on the commodity staple fibers (55), wadding, felt (56), and carpets (57) exports if a 1-unit increase in the average weighted tariff causes a decrease of −0.015, −0.100, and −0.2148, which is significant at the 1 and 5% levels.

The real GDP is positive for commodities 54, 55, 56, and 57 and significant at 1, 5, and 10%. If unit percent increases the real GDP of the reporting country, the commodity 54 to 57 exports rise by 0.636, 0.502, 0.741, 0.088, and 0.553%. This is identical to prior studies on textile exports by Chan and Au (2007) and Chen et al. (2017). Similarly, ER and preferential trade agreement positively affect commodity exports. Only the carpet (57) commodity is negatively associated with ER. ER is positive in models 6, 7, 8, and 10, but has a negative relationship with carpet model 10.

The estimated results show that, as expected, distance, common border, preferential trade agreement, average weighted tariff, real GDP of the reporting country, language, and RER have a significant effect on impregnated, knitted, and knitted articles, apparel, and clothing commodities exports. The calculated value of the model-11 commodity impregnated coefficient is −0.415. It has raised the exports of knitted, clothing, and impregnated commodities. Khan and Saqib also support the findings that real GDP and real exports are positive and significant. The coefficient of average weighted tariff is found to have a negative relationship with impregnated (59) model 11, articles of apparel model 14, and clothing model 15. This indicates that a one percent increase in the average weighted tariff causes an approximate −0.068 impregnated, −0.067 articles, and −0.190 decrease in its exports.

There is a negative relationship between distance and impregnated product exports; there is a negative relationship with knit, apparel, and clothing exports. The real GDP of the reporting country is significant at 1%. This variable has a coefficient of 0.580 for impregnated in model 11, 0.698 for knitted in model 12, and 0.382 for clothing in model 63. This shows a one-unit rise in the GDP of the reporting country. The preferential trade agreement is also statistically significant because it affects impregnated, knitted, and knitted articles, apparel, and clothing commodities exports at the 1% level. The ER has a significantly positive impact on impregnated (59), knitted (60), knitted (61), articles of apparel (62), and clothing (63) commodities exports; in other words, a devaluation of the Pakistan rupee against importing countries or US dollar’s currencies’ causes a rise in these commodities exports.

To verify the robustness of above conclusions, this study conducts three robustness checks. Firstly, this study divides the whole sample into two subsamples based on the average value of textile exports. It is found that the basic conclusions have no change in all subsamples. Secondly, this study changes the real GDP into GDP per capita and then re-regresses. The conclusions stay no change. Finally, this study winsorizes the dependent variable (textile exports) based on 1 and 99 quantiles to weaken the impacts of outliers. All results from robustness checks verify the robustness of the conclusions. Due to space limitations, the regression results will not be presented.

Table 5 provides a comprehensive comparison across all commodities, revealing the significant effects of various variables on exports. The results indicate that distance has a negative impact on all commodities exports and its elasticity is −0.072. Average weighted tariff and language also negatively affect all commodities exports, with a 1-unit increase in average weighted tariff causing a decrease of −0.041. Conversely, variables such as preferential trade agreement, common border, real GDP of reporting country, and real exchange rate have positive relationships with all commodities exports.

For specific commodities, such as silk, wool, cotton, and textile exports, the variables exhibit varying effects. Real GDP of reporting country, preferential trade agreement, average weighted tariff, and real exchange rate have significant positive relationships with these commodities’ exports. Similar findings are supported by prior studies. Additionally, distance, average weighted tariff, and real GDP of reporting country show negative effects on staple fibers, wadding, felt, and carpets exports. ER and preferential trade agreement are positively associated with most commodities exports, except for carpets. Impregnated, knitted, knit, articles apparel, and clothing commodities exports are significantly affected by distance, common border, preferential trade agreement, and average weighted tariff, real GDP of reporting country, language, and real exchange rate. Average weighted tariff exhibits a negative relationship with impregnated, articles apparel, and clothing exports. Real GDP of reporting country has positive effects on impregnated, knitted, and clothing exports. The preferential trade agreement is also significant for these commodities, while the real exchange rate positively influences exports except for carpets. These findings contribute to understanding the factors influencing commodity exports and align with prior research.

The comparison reveals the significant impact of various variables on exports across all commodities. Distance and average weighted tariff generally have negative effects, while variables such as preferential trade agreement, real GDP of reporting country, language, and real exchange rate have positive effects. The specific relationships and magnitudes vary for different commodities, highlighting the nuanced nature of their influence on exports.

Conclusion and Policy Suggestions

This study investigates whether the exchange rate affects the exports of textile products by major producers. The exports of textiles have increased other achievements and become more significant to the country’s economy, which is designed to investigate and find out the aspects that affect the major textile producer’s exports. Using data from 1990 to 2021, the gravity model was estimated through the Poisson method to find and explore major causes that influence exports of textile commodities. Besides the basic variables of the gravity model, which are the real GDP of the reporting country, partner country, and distance, additional variables, including the real exchange rate, preferential trade agreements, average weighted tariff, common border, and language. This research study shows that textile commodity exports are positively affected by GDP and negatively affected by distance. Therefore, countries should focus on promoting exports to near and rich economies. Moreover, transportation cost is a barrier that discourages trade between countries, finding ways to reduce the transportation costs is important, such as making improvements to transportation infrastructure as well as logistics systems.

The preferential trade agreement (PTA) is a positive predictor of its textiles and commodities exports. The result of PTA indicates the trade gained from PTA is very high, which explains the textiles and all commodities exports. It is not amazing that the average weighted tariff has a negative relationship with textiles exports. In other words, an increase in average weighted tariff causes a decrease in textiles and all commodities exports, similar to how the language variable has a negative relationship with textiles exports. ER is reported to have a positive relationship with textiles and all commodity exports, which means a depreciation of the reporting country’s currency helps commodity exports. This outcome shows that, so as to achieve its textile export objectives, major textile exporting countries can take suitable actions. Therefore, the consequences of this research study with respect to the exchange rate effects of textile product exports by major producers may be helpful and stable for both reporting and partner countries in defining their export objectives and strategies. In any case, referring to the investigation alone isn’t sufficient. The outcome of this research study is that the textile exporters should conduct research on several different facets identified with textile exports so as to have a superior implication, such as improving transportation infrastructure. Furthermore, improving the performance of reporting countries exports in the international market is also important. The cost and benefits of each action should also be taken into consideration. For example, a devaluation of the reporting country’s currency might increase textile exports, but it also increases the burden of external obligations on both the reporting and other textile partner countries.

The research findings have important implications for textile producers and offer practical applications for the industry. Producers can utilize the knowledge about the effect of exchange rates on textiles exports to strategically manage their pricing and marketing strategies, taking advantage of depreciated currencies and adjusting their approach in the face of appreciation. The study emphasizes the significance of market proximity and trade agreements, highlighting the need for producers to prioritize exports to geographically close countries and actively engage in preferential trade agreements to access markets with reduced trade barriers. Additionally, investing in infrastructure and logistics improvements can help lower costs and enhance supply chain efficiency, while understanding customer preferences and market demand enables producers to tailor products and strategies to specific target markets. Policymakers can also benefit from the research by formulating export promotion policies that reduce trade barriers, enhance export incentives, and support market diversification efforts. By considering these implications, textile producers can enhance their competitiveness and navigate the global market more effectively.

Limitation

Several restrictions were encountered by the authors throughout the data collection phase. Because some variable data for certain nations was unavailable, we compiled data from various sources. Future studies may also consider additional factors affecting textile product exports. For a better outcome, future researchers may expand the number of nations and explanatory factors. In-depth studies should be conducted in future studies to discover microeconomic and macroeconomic factors influencing textile product exports. Furthermore, data from textiles and 14 other goods may be analyzed to offer more valuable information.

Besides, this study takes average weighted tariff as an influencing factor of textile exports. With the escalation of trade barriers, trade restriction and barriers came in form of non-technical barriers. Compared to technical trade barriers, non-technical trade barriers may have a stronger impact on export trade. However, existing studies have not yet formed an indicator system for measuring non-technical trade barriers, and most of the relevant data is also unavailable. Therefore, when these data is available, future studies may further explore the impact of non-technical barriers on textile exports.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Fundamental Research Funds for the Central Universities in University of International Business and Economics (Grant No. 23YB02).

Ethics Statement

It is not applicable.

ORCID iDs

Fuzhong Chen

Shakil Ahmad

Data Availability Statement

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

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