Legacy of the Past: Evaluating the Long-Term Impact of Historical Trade Ports on Contemporary Industrial Agglomeration in China
-
Yuqin Sun
and
Zhao Yang
Abstract
Since its economic reforms and opening up, China has experienced rapid economic growth; however, regional disparities in economic development have continued to widen. In exploring the causes of regional economic disparities, historical factors have played a significant role. Grounded in the theory of New Economic Geography, this study examines the long-term impacts of trade port openings in the late Qing Dynasty on contemporary industrial agglomeration and regional economic imbalances and further investigates the mechanisms through which trade ports influence contemporary industrial agglomeration. By quantitatively analyzing historical and contemporary industrial indicators of counties and prefecture-level cities with and without trade ports, we find that the opening of these ports not only propelled the development of modern industry in the late Qing Dynasty but also significantly affects contemporary industrial agglomeration. Furthermore, areas with historical trade ports exhibit higher levels of foreign direct investment (FDI) and international trade today, indicating a greater degree of economic openness. Moreover, the interactions between foreign trade, FDI, and ports have historically stimulated modern industrial development and continue to foster industrial agglomeration today. This research highlights the importance of economic openness and underscores the necessity of considering historical factors when formulating regional development strategies.
1 Introduction
Since initiating economic reforms and opening up, China has achieved remarkable economic success. China’s gross domestic product (GDP) has expanded at an average annual rate of 9.5%, making it the world’s second-largest economy by 2010, trailing only the United States. Concurrently, China’s industrial sector has undergone rapid growth and expansion, establishing a comprehensive modern industrial system. This prolonged period of rapid industrial growth consistently increased China’s share of global manufacturing. Data from the National Bureau of Statistics show that in 1990, China’s manufacturing sector comprised 2.7% of the global total, ranked ninth worldwide; by 2000, it had risen to 6.0%, becoming the fourth largest; by 2007, it had reached 13.2%, ranking second globally; and by 2010, it had climbed to 19.8%, establishing China as the world’s leading manufacturer. However, as the opening up to the outside world intensified, the issue of regional imbalances in economic development became increasingly severe. Considering that the industrial sector represents more than one-third of the GDP, it significantly supports China’s GDP growth and plays a vital role in driving the development of other industries. Consequently, regional disparities in industrial development are a significant factor contributing to the economic gaps between regions (Fan & Zhu, 2002). Therefore, this study primarily focuses on the industrial sector to explore regional disparities in economic development.
The academic community has conducted extensive research into the causes of regional economic disparities. Démurger (2001) analyzed regional economic growth under the Economic Geography framework, finding that geographical location and infrastructure significantly influence inter-provincial growth disparities. Notably, communication infrastructure positively impacts growth; however, this study did not address industrial agglomeration. Gao (2002) confirmed the positive impact of foreign direct investment (FDI) and international trade on regional industrial growth. Additionally, factors including resource allocation (Capello & Cerisola, 2021), ownership structure (Liu & Liu, 2019), policy orientation (Chen & Groenewold, 2010; Fan et al., 2011), and human capital (Fleisher et al., 2010; Li & Qian, 2011) can contribute to disparities in regional economic development.
Over the past decade, New Economic Geography has significantly advanced our understanding of industrial agglomeration and regional disparities. The theory posits geographic location and historical advantages as the initial conditions for industrial clustering. Increasing returns to scale and positive feedback effects intensify this self-reinforcing agglomeration process, enabling advantaged regions to maintain their lead (Krugman, 1991). Using data from China’s manufacturing sector, Wen (2004) confirmed that the trend of industrial clustering since the economic reforms aligns with the theoretical framework of New Economic Geography. Jin et al. (2006), employing the New Economic Geography framework, observed that industrial clustering might commence in one of two regions with similar natural conditions due to certain random factors like historical events. Their study demonstrated that economic openness promotes industrial agglomeration, and economic openness is correlated with geographical and historical factors.
New economic geography emphasizes that even minor initial advantages stemming from historical events can trigger significant economic changes (Krugman, 1991). Additionally, historians’ research has further underscored the profound impact of historical factors on regional economic development, indicating that earlier historical events are fundamental to understanding contemporary regional economic disparities (Wu, 2007; Zhang, 1996). The concept of path dependence is crucial in analyzing the historical factors influencing regional economic disparities. Originally proposed by Paul A. David in 1994, this concept elucidates the historical dependencies in technological evolution and institutional changes. Path dependence theory asserts that historical events and initial decisions profoundly affect subsequent developments, regardless of whether these choices were accidental or suboptimal. According to this theory, once an economic pattern or institutional arrangement follows a specific trajectory, deviating from this path becomes increasingly challenging due to cumulative advantages and the reinforcing nature of established processes.
In the context of China’s economic development history, the establishment of trade ports in the late Qing Dynasty is undoubtedly a critical factor contributing to regional economic disparities. These ports were established due to specific historical events, and their locations were chosen somewhat randomly. This randomness makes them ideal proxy variables for economic policies, providing a natural experiment to examine the long-term impacts of historical policy factors on economic development (Jia, 2014; Li et al., 2019; Liang, 2015).
The opening of these trade ports triggered a series of economic activities that have profoundly shaped China’s current economic landscape (Jia, 2014; Zhang et al., 2021). Gradually, these areas evolved into centers of trade, foreign investment, and commercial activities, driven by their open policies and conducive institutional environments (Acemoglu, 2001). This advantage significantly lowered transaction costs and introduced advanced technologies and managerial expertise, thereby fostering the development and clustering of modern industries. According to new economic geography, such clustering promotes economies of scale and network effects, leading to further concentration of industries and populations. As economic activities clustered, the trade port areas generated positive externalities, including diversified labor markets, technological innovation, and improved commercial infrastructure. These externalities further attracted more businesses and talent, creating a positive feedback loop that fostered self-sustaining economic growth (Krugman, 1991). Thereby, historical economic policies have influenced contemporary industrial agglomeration and regional economic disparities through the theoretical framework of new economic geography, as illustrated in Figure 1. In this process, ongoing active foreign investment and trade in these regions, spurred by the opening of ports, have played a crucial role. They have generated the necessary momentum for sustained industrial clustering.
Pathways for the impact of trade ports on contemporary industrial agglomeration.
In recent years, although econometric methods have introduced new analytical tools and perspectives for understanding the historical event of trade port openings in late Qing China, they are still in a developmental stage, especially in quantifying the long-term economic impacts of these ports. Current research primarily focuses on the short-term effects of these port openings on China’s modern economy. Key focal areas include enhancing human capital (Lin, 2017), promoting industrialization processes (Liang, 2015), and accelerating domestic market integration (Li et al., 2019). However, research exploring how these historical events have shaped China’s economic structure, social organization, and development trajectories over a longer timeframe remains significantly limited.
This study aims to explore the long-term effects of the opening of trade ports on China’s economic development and identify the historical drivers behind contemporary industrial agglomeration and regional economic disparities. By integrating new economic datasets, employing Geographic Information Systems (GIS) technology, and applying econometric methods, this research seeks to conduct a precise and systematic quantitative analysis of the impact of trade ports opened since the late Qing Dynasty on contemporary industrial agglomeration and their functional pathways.
The findings reveal that prefecture-level cities with historical trade ports exhibit a higher degree of industrial clustering today. Utilizing various contemporary industrial indicators, we quantitatively assess how these trade port openings have shaped contemporary industrial agglomeration. We further conduct robustness checks of our results through several methods, including the Oster test. Grounded in the theoretical framework of New Economic Geography, we hypothesize that the establishment of trade ports catalyzed industrial development at the end of the Qing Dynasty. This early modern industrial pattern has influenced contemporary industrial agglomeration through network effects and path dependence.
Moreover, robust foreign trade and investment at these trade ports have played a pivotal role in sustaining industrial development. To validate our hypothesis, we initially analyze historical industrial data to confirm the early impact of trade port openings on industrialization. Subsequently, we apply contemporary data on foreign investment and trade to further explore how these ports supported ongoing industrial growth.
This research highlights the dynamic influence of historical trade port openings on contemporary industrial agglomeration through sustained economic activities. By rigorously analyzing both historical and current economic indicators, we enhance our theoretical understanding of economic openness and provide empirical evidence demonstrating how historical trade ports have shaped modern economic structures. These insights are crucial for developing effective regional strategies that leverage historical trade port openings to promote balanced economic growth across regions.
Despite these contributions, there are certain limitations to our study. Given the diverse economic contexts across different regions of China, we do not conduct a detailed regional analysis. Future research should explore how local conditions and historical backgrounds interact with trade port influences, allowing for a more nuanced understanding of regional disparities. Such investigations could lead to more tailored regional development strategies that consider specific local dynamics.
2 Historical Background
On the eve of the First Opium War in 1840, China’s foreign trade was confined to Guangzhou, reflecting the Qing government’s resistance to foreign influences and setting the stage for subsequent unequal treaties and foreign interventions. The First Opium War marked a pivotal moment in Chinese history, leading to the forced opening of additional ports and signaling a critical phase in China’s modernization. In the mid-nineteenth century, under military coercion from Western powers, China was compelled to sign a series of unequal treaties. The 1842 Treaty of Nanjing compelled China to open five ports, Guangzhou, Xiamen, Shanghai, Fuzhou, and Ningbo, to foreign trade, heralding the start of the modern treaty port era.
As foreign aggression intensified and unequal treaties were signed, China was progressively compelled to open additional coastal and inland cities, commonly known as “Treaty Ports.” The development of treaty ports began after the First Opium War, reached its peak following the Second Opium War from 1856 to 1860, and started to decline by the late 1890s. For instance, the 1851 Sino-Russian Treaty of Kulja enabled the inland cities of Yili and Tarbagatai in Xinjiang as treaty ports. Subsequently, the 1858 Treaty of Tianjin expanded the list of treaty ports to include Yingkou, Yantai, Tainan, Shantou, Qiongzhou, Hankou, Jiujiang, Zhenjiang, and Nanjing. The 1909 Sino-Japanese Treaty of Tumen River Affairs, the last of these treaties, opened northeastern inland towns like Baicaogou, Tou Dao Gou, and Longjing Village. From 1842 to 1909, through a series of unequal treaties, the number of treaty ports gradually increased to 77, including Qingdao and Weihaiwei, which continued as trade ports even after the return of the leased territories.
The strategy for opening ports evolved to include both treaty-based and self-initiated efforts after entering the twentieth century. The practice of self-opening ports, starting with Wusong, Yueyang, and Sandu’ao in 1898, continued until the late 1920s and experienced a notable rise in openings, particularly after 1904. After 1912, port openings became exclusively self-initiated, highlighted by the opening of 7 ports in 1914, culminating in a total of 33 by 1930. Although officially opened by the Qing government to promote domestic economic development, these self-opened ports were actually established in response to demands from foreign consuls and ministers, as well as suggestions from the Inspector General of Customs. This situation continued until the Qing Dynasty fell and the Republic of China was established in 1912.
In summary, between 1840 and 1930, China witnessed the emergence of 110 trade ports. Except for a few provinces like Guizhou, Shaanxi, Shanxi, Qinghai, and Ningxia, the majority of provinces, municipalities, and autonomous regions in China hosted several trade ports. Regarding the timing of openings, coastal ports opened first, followed by Yangtze River ports, border ports, and ultimately, inland ports. From 1840 to 1930, China transitioned from a single-port to a multi-port trade system, reflecting an outward-oriented transformation. Figure 2 offers an overview of the distribution of trade ports across China.
Distribution of trade ports in Late Qing and Early Republic of China. The information on trade ports and their locations is sourced from Yan Zhongping’s Selected Statistical Materials on Modern Chinese Economic History. This map excludes treaty ports located in Taiwan, China. Due to layout constraints, some port names are not displayed on the map.
Figure 2 clearly shows that trade ports are primarily concentrated in the southeastern coastal areas and along the Yangtze River, with fewer ports located in border regions. Major treaty ports including Shanghai, Tianjin, Hankou, Dalian, and Qingdao are predominantly situated in the eastern region. Although they cover half of China’s land area, the southwest and northwest regions together host only 16 ports including Mengzi, Simao, Hekou, Tengchong, Kunming, Yadong, Jiangzi, Gartok, Yili, Tarbatai, Urumqi, Hami, Gucheng, Turpan, Chongqing, and Wanxian. Beyond the southeastern coast and the Yangtze River, numerous inland ports are also located across the three northeastern provinces. Overall, the distribution of trade ports indicates a greater concentration in the southeast compared to the northwest, with a significant trend of self-opened ports located in the eastern region. This is highlighted by the fact that among the 16 ports in the southwest and northwest, only Kunming was self-initiated.
The initial drivers of industrialization in late Qing China primarily originated from the West, with treaty ports playing a crucial role in this process. Economically, on one hand, China’s local products were exported to international markets through treaty ports; on the other, imported goods were distributed nationwide through these ports, gradually integrating China into the global market system. Simultaneously, China’s industrialization process often began in treaty port cities and gradually expanded to inland areas. Treaty ports not only served as catalysts and promoters of industrialization but also frequently became its core areas. For instance, in Shanghai between 1911 and 1933, the number of factories with more than 30 workers increased from 48 to 3,485, representing 60% of the total capital in the 12 major cities nationwide. In terms of trade, in 1900, Shanghai’s direct foreign trade value constituted 55.16% of the national total, decreasing to 44.64% by 1930. By 1936, 94% of the industrial output value was contributed by seven cities: Shanghai, Tianjin, Qingdao, Guangzhou, Beiping (now Beijing), Nanjing, and Wuxi, all trade ports.
As presented in Figure 3, the trend indicates that before 1890, regardless of whether regions had trade ports or not, there was a minimal increase in the number of modern industrial enterprises, suggesting that modern industry was almost non-existent in China at that time. However, beginning in 1890, the number of modern industrial enterprises started to increase rapidly, particularly in regions with trade ports, where the growth of these enterprises significantly outpaced that in regions without trade ports. This change is closely linked to the increase in the number of trade ports.
Newly established enterprises in trade port and no-trade port areas.
The opening of trade ports has exerted a profound and lasting impact on China’s socio-economic development. Not only did these ports historically facilitate foreign investment and trade, thereby accelerating the country’s modern industrialization, but even decades later, they continue to promote the formation of industrial agglomeration and maintain a higher degree of openness in these cities. For instance, in 2020, the cities with the most large-scale industrial enterprises – Suzhou, Dongguan, Shenzhen, Shanghai, and Ningbo – all historic treaty ports, significantly underscore their role in economic advancement. Furthermore, the 2020 national map of industrial capital proportions (Figure 4) provides further tangible evidence of this legacy: provinces with higher shares of industrial capital are geographically aligned with the distribution of historic trade ports.
2020 provincial map of industrial capital proportions in China.
3 Data and Method
3.1 Data
This study employs both historical and contemporary datasets to investigate the impact of the opening of trade ports on contemporary industrial agglomeration. The historical data are primarily sourced from three major references: First, details on the historical trade ports are extracted from Selected Statistical Materials of Modern Chinese Economic History (Yan, 1955), which documents the locations of these ports during the late Qing Dynasty and the early Republic of China. Second, historical industrial data, including the number of enterprises, capital, and workforce, are gathered from Private Industry in the Late Qing and Early Republican Period (Zhang, 1989), which provides essential data for analyzing the industrialization process during the late Qing Dynasty. The data on foreign enterprises and investment during the late Qing Dynasty are sourced from the study titled “Foreign-funded Industry in Late Qing and Early Republican China” (Zhang, 1987). Finally, historical trade data are obtained from Statistics of China’s International Trade over Sixty-five Years (Yang & Hou, 1931), offering comprehensive data on international trade at various ports from 1868 to 1928.
Contemporary data for this study are sourced from the China City Statistical Yearbook (2022). This dataset comprises various indicators that assess industrial agglomeration and economic openness in prefecture-level cities, including the number of industrial enterprises, output value, circulating capital, foreign trade volume, number of foreign-funded enterprises and foreign investment contracts, actual foreign investment, and foreign enterprise output within the year.
This study covers 31 provinces and 296 cities in China, including 292 prefecture-level cities and four central municipalities: Beijing, Tianjin, Shanghai, and Chongqing. The data exclude the two Special Administrative Regions, Hong Kong and Macau, as well as Taiwan Province.
The historical information on the location of trade ports used in this study is based on archival records (Yan, 1955), in which the administrative affiliations of certain ports differ from present-day administrative divisions. To accurately position these historical trade ports within the current administrative framework, we adjust their geographical coordinates in accordance with the administrative division codes issued by the Ministry of Civil Affairs of the People’s Republic of China in 2004. Consequently, we identified 77 prefecture-level cities that previously hosted trade ports. By integrating historical data into modern geographic contexts, we can deepen our understanding and analysis of their significance in contemporary regional development.
Additionally, we employed both historical and contemporary economic indicators to thoroughly assess the industrial agglomeration and economic openness of China, spanning from historical to contemporary times. Industrial-related data assess the scale and quality of regional industrial clustering. The number of industrial enterprises indicates the density and scale of industry within a region, serving as a fundamental indicator of industrial agglomeration. Industrial output value serves as a key indicator for assessing the overall economic benefits and productivity of the industrial sector. The scale and efficiency of circulating capital reflect the operational efficiency and dynamics of industrial activities, with a higher concentration of circulating capital typically indicating higher production activities and enterprise density, which are direct signs of extensive industrial clustering. Additionally, the workforce size indicates a region’s production capacity and serves as an important metric of regional economic vitality. Labor aggregation facilitates knowledge sharing, skill complementarity, and innovation, thereby supporting broader industrial development and economic activity.
Data on foreign trade and investment not only reflect a city’s economic openness but also its participation and influence in the global economic system. The volume of import and export trade serves as a crucial indicator of a city’s economic openness. Higher trade volumes not only reflect a region’s integration with the global economy but also indicate its trading capacity and international economic influence. Similarly, the number of foreign-funded enterprises, foreign investment contracts, and the actual amount of FDI are key indicators of FDI’s impact and involvement in the regional economy. An increase in foreign enterprises not only demonstrates the local market’s attractiveness to international investors but also highlights the role of foreign capital in local industrial development and technological advancement. Moreover, the number of foreign investment contracts and the actual annual foreign investment clearly illustrate the scale and effectiveness of foreign investment, as well as the market’s response to and confidence in foreign investment policies.
Existing literature suggests that factors such as transportation locational advantages (Banerjee et al., 2020), geographical positioning (Bleakley & Lin, 2012; Rappaport & Sachs, 2003), and economic policies (Ge, 1999; Zeng, 2011) can influence economic performance. This implies that differences in contemporary industrial agglomeration and economic openness may not be solely attributed to the opening of trade ports. To precisely capture the specific effects of trade ports while controlling for these confounding factors, we employed GIS technology for data collection and analysis. Specifically, we collected city elevation data, including geographical coordinates and altitude. Additionally, we accurately identified cities located near the Yangtze River or coastlines, assuming that cities along Yangtze River and coastlines benefit from stronger transportation and trade advantages, particularly coastal cities, pivotal to China’s reform and opening-up, benefiting earlier from trade liberalization and economic incentives. These geographic traits make these cities crucial transportation hubs and commercial centers, significantly promoting regional economic growth and social progress. To further enhance the precision of our study, we also considered relevant policies affecting economic growth, such as economic development zones. In addition, we also consider the historical characteristics of each city, such as their distance from historical provincial capitals.
In the regression analysis of this study, all economic-related indicators are converted to logarithmic form, and descriptive statistics for these variables are presented in Table 1.
Descriptive statistics of variables
| Variables | Obs | Mean | sd | Min | Max |
|---|---|---|---|---|---|
| Variables used in equations ( 1 ), ( 2 ), ( 4 ), and ( 5 ) for the contemporary analysis (2020) | |||||
| Trade port (OLS) | 296 | 0.26 | 0.44 | 0.00 | 1.00 |
| Historical cumulative trade volume (ln) | 296 | 2.64 | 6.04 | 0.00 | 22.25 |
| Industrial enterprises (ln) | 296 | 6.51 | 1.25 | 1.61 | 9.38 |
| Industrial circulating capital (ln) | 294 | 16.06 | 1.29 | 10.91 | 19.52 |
| Industrial output value (ln) | 284 | 15.64 | 1.26 | 11.04 | 19.22 |
| Import volume (ln) | 292 | 12.78 | 2.57 | 4.51 | 19.17 |
| Export volume (ln) | 291 | 13.52 | 2.25 | 5.00 | 18.95 |
| Total trade volume (ln) | 291 | 14.12 | 2.16 | 5.50 | 19.67 |
| Foreign enterprises (ln) | 267 | 3.64 | 1.52 | 0.69 | 8.28 |
| Foreign contracts (ln) | 268 | 3.58 | 1.57 | 0.69 | 8.34 |
| Actual foreign investment (ln) | 272 | 11.23 | 1.82 | 5.08 | 15.53 |
| Per capita GDP (ln) | 288 | 1.74 | 0.47 | 0.56 | 2.90 |
| Coastal | 296 | 0.18 | 0.38 | 0.00 | 1.00 |
| Yangtze River | 296 | 0.13 | 0.34 | 0.00 | 1.00 |
| Population | 296 | 449.16 | 387.29 | 24 | 3209 |
| Longitude | 296 | 113.51 | 7.88 | 84.87 | 131.15 |
| Latitude | 296 | 32.94 | 6.71 | 18.26 | 50.25 |
| Altitude | 296 | 404.38 | 695.56 | −1.00 | 4505.00 |
| Distance from historical provincial capital | 288 | 265.94 | 321.21 | 0.00 | 1925.15 |
| Free trade zone | 296 | 0.17 | 0.37 | 0.00 | 1.00 |
| Economic development zone | 296 | 0.48 | 0.50 | 0.00 | 1.00 |
| Variables used in equation ( 3 ) for the late Qing Dynasty analysis (1840–1910) | |||||
| Trade port (DID) | 14,016 | 0.06 | 0.23 | 0.00 | 1.00 |
| Industrial enterprises (ln) | 14,016 | 0.0065 | 0.11 | 0.00 | 2.22 |
| Initial industrial investment (ln) | 14,016 | 0.20 | 1.39 | 0.00 | 14.41 |
| Employment in industry (ln) | 14,016 | 0.02 | 0.25 | 0.00 | 7.05 |
| Foreign enterprises (ln) | 14,016 | 0.0014 | 0.032 | 0.00 | 1.57 |
| Initial foreign investment (ln) | 14,016 | 0.014 | 0.30 | 0.00 | 10.52 |
We utilized cross-sectional data from 296 prefecture-level cities in 2020, which serves as the basis for our ordinary least squares (OLS) analysis examining the impact and mechanisms of historical trade ports on contemporary industrial agglomeration. Additionally, we employed county-level panel data spanning from 1840 to 1910, encompassing 14,016 observations across 8 periods with a 10-year interval (1840, 1850, 1860, etc.). This dataset is primarily used to investigate the initial stages of industrial clustering during the late Qing Dynasty, utilizing a staggered difference-in-differences method.
In this study, prefecture-level cities are selected as the basic unit for analyzing contemporary industrial agglomeration, while county-level analysis is utilized to examine industrialization in the late Qing Dynasty. This distinction takes into account dramatic changes in administrative divisions, economic management structures, data availability, and the evolution of geographic and economic agglomerations. During the late Qing period, counties functioned as the fundamental administrative units and focal points for local administration and economic activities. County-level data, with its capacity to uncover detailed industrial development differences and characteristics within a small area, is ideal for analyzing the initial stages of modern industry during that era. Most importantly, the trade areas specified in the treaties are also defined based on county-level administrative units. In contemporary times, following changes in administrative divisions, prefecture-level cities have emerged as more significant economic and administrative units. Furthermore, given the long-term radiative effects of trade ports on surrounding areas, employing prefecture-level cities as analytical units enables more precise observations of these impacts. Notably, many counties that were opened as trade ports during the late Qing Dynasty have since developed into prefecture-level cities over time. This analytical framework is effectively designed to adapt to the evolving economic and administrative structures and accommodates the complexity of temporal spans, ensuring comprehensive, in-depth, and practical research outcomes.
3.2 Method
In this study, we employ the OLS method to evaluate the long-term effects of trade ports openings in the late Qing Dynasty on contemporary economic development, as presented in the following equation:
In equation (1), the dependent variable Y jp represents the degree of industrial agglomeration for prefecture-level city j located in province p in the contemporary era in 2020. The degree of industrial agglomeration is measured by the number of industrial enterprises, output value, and circulating capita in city i in 2020. Additionally, this study introduces a dummy variable, Tradeport j , as the main explanatory variable to distinguish whether city j was historically a trade port. A value of 1 indicates that the city was historically a trade port, while a value of 0 indicates it was not. The design of this variable helps determine the influence of a city’s historical status on its current economic performance.
The model includes provincial fixed effects γ p to control for static factors such as geographical location, natural endowments, institutional environment, and cultural norms that vary between provinces but not over time. By controlling for provincial fixed effects, we can more effectively mitigate the impact of locational factors and other unobserved provincial-level influences on the outcomes.
Although provincial fixed effects are controlled, variations may still exist among prefecture-level cities within the same province. To ensure the robustness of the study’s conclusions, equation (1) also incorporates X j , a set of control variables, including a city’s proximity to the Yangtze River or coasts, latitude, longitude, altitude, population, its designation as an economic development zone, and the historical factor of distance from the historical provincial capital.
The model incorporates an error term ε j to capture other unobserved influences.
4 Results and Discussion
4.1 Baseline Regression Result
The study initially explores the long-term impacts of historical trade ports on contemporary industrial agglomeration. Regression results are presented in Table 2.
Trading port impact on industrial agglomeration (2020)
| Baseline regression | |||
|---|---|---|---|
| (1) | (2) | (3) | |
| Variables | Industrial enterprises | Industrial circulating capital | Industrial output |
| Tradeport j | 0.1744** | 0.3090*** | 0.3529*** |
| (0.0773) | (0.1099) | (0.1113) | |
| Population | 0.0016*** | 0.0017*** | 0.0015*** |
| (0.0002) | (0.0002) | (0.0002) | |
| Coastal | 0.0702 | 0.4703*** | 0.2853** |
| (0.1137) | (0.1485) | (0.1313) | |
| Yangtze River | 0.0129 | 0.4341*** | 0.3388** |
| (0.1233) | (0.1495) | (0.1656) | |
| Latitude | 0.0115 | 0.0808*** | 0.0570 |
| (0.0276) | (0.0294) | (0.0375) | |
| Longitude | −0.0026 | −0.0240 | −0.0142 |
| (0.0166) | (0.0224) | (0.0296) | |
| Altitude | −0.0002 | 0.0002 | −0.0003 |
| (0.0002) | (0.0003) | (0.0004) | |
| Distance from historical provincial capital | −0.0002 | −0.0007*** | −0.0006** |
| (0.0002) | (0.0003) | (0.0003) | |
| Constant | 5.8258*** | 15.3384*** | 14.7672*** |
| (1.9996) | (2.6221) | (3.1923) | |
| Observations | 288 | 286 | 280 |
| R-squared | 0.8463 | 0.7084 | 0.7128 |
| Province FE | Yes | Yes | Yes |
Note: Cluster-robust standard errors are computed at the prefecture level for all regressions based on the contemporary dataset unless otherwise specified; ***p < 0.01, **p < 0.05, *p < 0.1.
The results in Table 2 indicate that trade port regions outperform non-trade port areas by 17.4%, 30.9%, and 35.3% in terms of the number of industrial enterprises, industrial circulating capital, and output value, respectively. These findings highlight the significant positive impact of historical trade ports on contemporary regional industrial development, providing key insights into their enduring influence on current industrial agglomeration.
4.2 Robustness Test
The opening of trade ports in the late Qing Dynasty has a significant positive impact on current industrial agglomeration. To further validate this conclusion, we employed four methods to test the robustness of the baseline result.
4.2.1 Oster Test for Omitted Variables
Our baseline regression results, while controlling for variables such as population, geographical location, transportation accessibility, and historical factors at the prefectural level, still cannot fully account for all contemporary economic policies across different prefectures. This limitation may lead to the omission of certain unobservable variables. To address this issue, we adopted the method proposed by Oster (2019) to assess the impact of omitted variable bias on our baseline regression results. Oster’s method aims to quantify the potential impact of omitted variable bias on regression estimates, assisting researchers in understanding how sensitive their results are to unobserved factors. Oster provides two approaches:
First approach: This method involves assuming a ratio (δ) between the correlation of omitted variables with the dependent variable and the correlation of observable variables with the dependent variable. Typically, δ is set to 1. The maximum possible goodness-of-fit for the model with omitted variables is denoted as R max. Through simulation, the estimated coefficient of the independent variable (β*) can be derived. If β* falls within the 95% confidence interval of β from the baseline regression, it indicates robustness in the baseline regression results.
Second approach: In this method, R max is again established as the goodness-of-fit for the model with omitted variables; however, it is assumed that β = 0 to calculate δ. If δ > 1, it suggests that omitted variable bias does not significantly affect the model results; conversely, if δ ≤ 1, it indicates a potential significant impact.
By employing Oster’s methodology, we can better understand the robustness of our regression results concerning potential omitted variable bias and draw more informed conclusions about our findings.
We adopted Sun et al.’s (2024) approach to determine R max and incorporated economic development zones as an additional control variable. As a key component of China’s economic policy, economic development zones encompass various incentive measures, including tax benefits, land use support, technology transfer, and facilitation of foreign investment. Although economic development zones represent only one of many economic policies, their diverse policy combinations allow them to effectively capture a wide range of economic interventions, thereby serving as a significant upper bound for regional economic policies. Consequently, the R 2 value derived from the model with the expanded control variable serves as a reasonable estimate for R max.
As shown in Table 3, after including the economic development zone as an additional control variable, the new R² values for our three dependent variables are 0.8517, 0.7177, and 0.7212, all of which exceed the R² values from the baseline regression. The results of the Oster test are presented in Table 4.
Trade port impact on industrial agglomeration (2020)
| With expanded control variables | |||
|---|---|---|---|
| (1) | (2) | (3) | |
| Variables | Industrial enterprises | Industrial circulating capital | Industrial output |
| Tradeport j | 0.1504** | 0.2763** | 0.3164*** |
| (0.0763) | (0.1106) | (0.1125) | |
| Population | 0.0016*** | 0.0016*** | 0.0014*** |
| (0.0002) | (0.0002) | (0.0002) | |
| Coastal | −0.0049 | 0.3655** | 0.1850 |
| (0.1168) | (0.1543) | (0.1337) | |
| Yangtze River | 0.0013 | 0.4176*** | 0.3225** |
| (0.1211) | (0.1460) | (0.1621) | |
| Latitude | 0.0059 | 0.0730** | 0.0498 |
| (0.0282) | (0.0296) | (0.0380) | |
| Longitude | −0.0016 | −0.0227 | −0.0129 |
| (0.0161) | (0.0217) | (0.0294) | |
| Altitude | −0.0002 | 0.0002 | −0.0003 |
| (0.0002) | (0.0003) | (0.0004) | |
| Distance from historical provincial capital | −0.0001 | −0.0006** | −0.0005* |
| (0.0002) | (0.0003) | (0.0003) | |
| Economic development zone | 0.1962*** | 0.2724*** | 0.2715*** |
| (0.0567) | (0.0916) | (0.0941) | |
| Observations | 288 | 286 | 280 |
| R-squared | 0.8517 | 0.7177 | 0.7212 |
| Province FE | Yes | Yes | Yes |
Note: Unless otherwise specified, the control variables included in the subsequent tables for contemporary data regression are the same as those in this table. ***p < 0.01, **p < 0.05, *p < 0.1.
Oster test for omitted variables
| Dependent variable | R max | Test method | Criterion | Estimated result | Passed |
|---|---|---|---|---|---|
| Industrial enterprises | 0.8517 | Set δ = 1; solve for β | [0.0228, 0.3260] | 0.1706 | Yes |
| 0.8517 | Set β = 0; solve for δ | δ > 1 | 25.6607 | Yes | |
| Industrial circulating capital | 0.7177 | Set δ = 1; solve for β | [0.0936, 0.5244] | 0.2968 | Yes |
| 0.7177 | Set β = 0; solve for δ | δ > 1 | 12.1144 | Yes | |
| Industrial output | 0.7212 | Set δ = 1; solve for β | [0.1344, 0.9537] | 0.3393 | Yes |
| 0.7212 | Set β = 0; solve for δ | δ > 1 | 11.0575 | Yes |
In Table 4, the first, third, and fifth rows present the estimated β values under the assumption that δ = 1. The results indicate that all three estimated β values fall within the 95% confidence interval of the baseline regression, signifying that the regression results pass the Oster test. The second, fourth, and sixth rows display the δ values calculated under the assumption that β = 0, with results significantly greater than 1, indicating that the impact of omitted variable bias on the empirical results is minimal.
Moreover, the R max obtained after incorporating the economic development zone shows little difference from the R² values in the baseline regression. This further suggests that, despite the potential presence of other unobserved economic policy variables, their influence on the empirical results of this study is limited.
4.2.2 Exclusion of Initial Five Treaty Ports and Self-Opened Ports
Since the locations of the first five treaty ports established under the Treaty of Nanjing may not have been randomly selected, we follow Jia (2014) and exclude these initial five ports from the regression analysis. Additionally, since the self-opened ports were opened by the Qing government, their selection may have been influenced by certain economic policy plans. Therefore, we remove these self-opened ports from the dataset. The results are presented in Table 5.
Exclusion of the initial five treaty ports and all self-opened ports
| (1) | (2) | (3) | |
|---|---|---|---|
| OLS | OLS | OLS | |
| Variables | Industrial enterprises | Industrial circulating capital | Industrial output |
| Tradeport j | 0.2315** | 0.2644** | 0.3501** |
| (0.0985) | (0.1340) | (0.1418) | |
| (0.0613) | (0.0942) | (0.1030) | |
| Constant | 6.1926*** | 15.7329*** | 16.0094*** |
| (2.0594) | (2.5897) | (3.2186) | |
| Observations | 260 | 258 | 252 |
| R-squared | 0.8488 | 0.6992 | 0.7047 |
| Control Var | Yes | Yes | Yes |
| Province FE | Yes | Yes | Yes |
***p < 0.01, **p < 0.05, *p < 0.1.
4.2.3 Alternative Measurement of Explanatory Variables
During the late Qing Dynasty, the opening times of different trade ports varied significantly. Theoretically, all else being equal, trade ports with longer opening times should exhibit superior performance in today’s economic development. We replaced the original 0/1 dummy variable with the duration since the opening of the trade ports up to 2020 and re-estimated the regression. The regression results are presented in Table 6.
Impact of opening duration on industrial agglomeration (2020)
| (1) | (2) | (3) | |
|---|---|---|---|
| OLS | OLS | OLS | |
| Variables | Industrial enterprises | Industrial circulating capital | Industrial output |
| Open_duration | 0.0012* | 0.0023*** | 0.0028*** |
| (0.0006) | (0.0009) | (0.0009) | |
| Constant | 5.8585*** | 15.4037*** | 14.8315*** |
| (2.0094) | (2.5362) | (3.1808) | |
| Observations | 288 | 286 | 280 |
| R-squared | 0.8517 | 0.7181 | 0.7230 |
| Control Var | Yes | Yes | Yes |
| Province FE | Yes | Yes | Yes |
***p < 0.01, **p < 0.05, *p < 0.1.
We have also replaced the dummy variable “Tradeport j ” with the distance to the nearest historical trade port for each prefecture-level city. This change allows us to better capture the impact of proximity to historical trade infrastructure on contemporary economic outcomes. The results are presented in Table 7.
Distance to the nearest historical trade port (2020)
| (1) | (2) | (3) | |
|---|---|---|---|
| OLS | OLS | OLS | |
| Variables | Industrial enterprises | Industrial circulating capital | Industrial output |
| min_distance | −0.0009** | −0.0012*** | −0.0015*** |
| (0.0004) | (0.0004) | (0.0005) | |
| Constant | 6.0309*** | 15.5912*** | 14.9320*** |
| (2.1712) | (2.7330) | (3.3857) | |
| Observations | 288 | 286 | 280 |
| R-squared | 0.8534 | 0.7181 | 0.7230 |
| Control Var | Yes | Yes | Yes |
| Province FE | Yes | Yes | Yes |
***p < 0.01, **p < 0.05, *p < 0.1.
As illustrated in Table 7, the distance to the nearest historical trade port has a negative and statistically significant impact on contemporary industrial outcomes, suggesting that cities closer to historical trade ports experience more favorable industrial development compared to those further away. This finding can be attributed to the lasting economic effects of historical trade ports, which served as key infrastructure for foreign trade and industrial exchange. Historically, these ports reduced transportation costs and facilitated the flow of goods, capital, workforce etc., contributing to industrial development in their surrounding regions. Over time, this initial advantage may have compounded, leading to persistent industrial growth even in contemporary settings. Moreover, the promotional effects of these ports extend beyond the ports themselves, fostering industrial growth in neighboring areas, with potential spillover effects unfolding over time. These regions could benefit from the economic networks established by the ports, even if such benefits are realized with a temporal lag.
Following the opening of the trade ports, these regions evolved into international trade hubs. Theoretically, the longer a port has been open, the greater its accumulated trade volume, and, consequently, the more significant its impact. We incorporated the cumulative trade volume from 1868 to 1928 for each trade port as a new explanatory variable, and the regression results are presented in Table 8.
Impact of historical cumulative trade volume on industrial agglomeration (2020)
| (1) | (2) | (3) | |
|---|---|---|---|
| Variables | Industrial enterprises | Industrial circulating capital | Industrial output |
| Tradeport j | 0.2574** | 0.4503*** | 0.4443*** |
| (0.1264) | (0.1510) | (0.1460) | |
| Historical cumulative trade volume | 0.0217** | 0.0256** | 0.0249** |
| (0.0089) | (0.0112) | (0.0110) | |
| Constant | 6.3808*** | 15.8662*** | 15.4678*** |
| (0.0468) | (0.0613) | (0.0591) | |
| Observations | 296 | 294 | 282 |
| R-squared | 0.7446 | 0.5805 | 0.5712 |
| Control Var | Yes | Yes | Yes |
| Province FE | Yes | Yes | Yes |
***p < 0.01, **p < 0.05, *p < 0.1.
The results presented in Table 8 indicate a clear positive correlation between the level of historical trade activity and contemporary industrial development. These results not only reinforce the positive influence of the historical status of trade ports on contemporary industrial agglomeration but also reveal the significant role of cumulative historical trade volume as a critical economic factor in promoting modern industrial development, thereby emphasizing the close connection between trade and economic growth.
4.2.4 Spatial Lag Model (SLM)
The SLM is an econometric framework designed to analyze spatial data and capture the effect of spatial dependency. This model incorporates the values of the dependent variable from neighboring regions to account for spatial spillover effects, reflecting how economic activities in one area influence those in adjacent areas. When studying industrial agglomeration, it is insufficient to consider only internal factors within a region. Industrial agglomeration may be influenced by neighboring regions, and this influence manifests primarily through spatial spillover effects. In light of potential spatial dependencies, we introduced the SLM as a robustness check for the baseline regression results. The SLM serves as an effective tool for capturing spatial spillover effects, as economic interactions between cities can influence overall outcomes
We define a distance-based spatial weights matrix, ρWY, in equation (2) and set a distance threshold of 15 km to effectively capture economic interactions among adjacent cities. ρ is the spatial lag coefficient, representing the strength of spatial dependence. The specifications of the other variables are consistent with those in equation (1). The results are presented in Tables 9 and 10.
Results for the SLM
| (1) | (2) | (3) | |
|---|---|---|---|
| SLM | SLM | SLM | |
| Variables | Industrial enterprises | Industrial circulating capital | Industrial output |
| Tradeport j | 0.1496* | 0.2899*** | 0.3390*** |
| (0.0906) | (0.1102) | (0.1231) | |
| rho (ρ) | 0.0008*** | 0.0003*** | 0.0003*** |
| 0.0001 | (0.0001) | (0.0001) | |
| Constant | 3.6473*** | 13.0825*** | 12.8524*** |
| (0.9565) | (1.1129) | (1.8091) | |
| Control var | Yes | Yes | Yes |
| Observations | 288 | 286 | 281 |
Robust standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.
ρ (rho) test results for SLMs
| Dependent var | Test type | χ²(1) value (Chi²(1)) | Degrees of freedom | p-Value | Result |
|---|---|---|---|---|---|
| Industrial enterprises | Wald test (ρ = 0) | 35.917 | 1 | 0 | Pass |
| Lagrange multiplier test (ρ = 0) | 46.669 | 1 | 0 | Pass | |
| Industrial circulating capital | Wald test (ρ = 0) | 13.928 | 1 | 0 | Pass |
| Lagrange multiplier test (ρ = 0) | 17.260 | 1 | 0 | Pass | |
| Industrial output | Wald test (ρ = 0) | 8.831 | 1 | 0.003 | Pass |
| Lagrange multiplier test (ρ = 0) | 14.577 | 1 | 0 | Pass |
Table 9 presents the regression results from the SLM, indicating a positive and statistically significant spatial lag coefficient ρ across all results. This finding suggests the presence of a positive spatial spillover effect, implying that industrial agglomeration in neighboring regions may enhance industrial agglomeration in the region under consideration. Table 10 further supports these findings through the Wald test and Lagrange multiplier tests for ρ.
Although these coefficients are statistically significant, they are relatively small in magnitude, indicating that the actual impact of neighboring regions is comparatively weak. Furthermore, the coefficients for the Tradeport j are 0.1496, 0.2899, and 0.3390 from the respective regressions, all of which are significant at the 1% level.
The positive and statistically significant coefficient of ρ in our SLM confirms the existence of positive spillover effects from historical trade ports to adjacent regions. This aligns with theoretical expectations, as trade ports act as economic hubs, radiating benefits through labor mobility, technology diffusion, supply chain coordination, etc. In addition to this spatial dependency perspective, our earlier analysis indicates that the minimum distance to historical trade ports is negatively correlated with industrial agglomeration levels. This suggests that regions farther from ports exhibit lower levels of industrial agglomeration, implying that closer regions derive greater economic benefits from spatial spillover effects associated with historical trade ports. These two findings complement each other by providing evidence from different angles. The positive ρ coefficient highlights the spatial autocorrelation and interdependence among regions, while the negative distance parameter underscores the distance decay effect, where spillover benefits diminish as distance from historical trade ports increases.
The baseline regression results and robustness checks presented above provide empirical evidence that historical trade ports exert a lasting influence on contemporary economic structures, thereby promoting the agglomeration of modern industries.[1]
5 Mechanisms
This study suggests that the establishment of trade ports during the late Qing of China facilitated foreign investment and trade exchanges, which in turn promoted modern industrialization during those times. This industrial advantage, gained through the opening of ports, embedded a specific trajectory into regional economic development via network effects and path dependency, ultimately shaping the contemporary industrial landscape.
This study will empirically examine the pathways through which historical trade ports have influenced contemporary industrial agglomeration. In this chapter, to illustrate the effect of path dependence, our empirical research involves two parts: the late Qing Dynasty and contemporary times.
5.1 Empirical Research on the Late Qing Dynasty
We utilize county-level panel data on industrialization Du (1991) from the late Qing period, spanning 1840 to 1910, and employ the staggered difference-in-differences (DID) method to empirically test the hypothesis that the opening of trade ports facilitated industrialization during this era. Additionally, to address potential biases in the two-way fixed effects (TWFE) estimation method within the staggered DID design, this study introduces the Callaway and Sant’Anna (2021) method to conduct robustness checks on traditional TWFE estimates, aiming to control for and identify potential heterogeneous treatment effects, particularly those related to treatment timing and individual characteristics. The staggered DID model is described as follows:
Dependent Variable: The dependent variable y it represents the level of industrialization in county i at period t. This level is measured by three key indicators: the number of new industrial Enterprises, initial industrial investment, and employment in county i at period t. Since each period spans 10 years, we assess the level of industrialization by calculating the 10-year averages of the new additions for each of these indicators from 1840 to 1910. Additionally, we have applied natural logarithmic transformations to all the indicators.
Explanatory variable: we define the trade port variable at the county level as follows. The variable treat i is a binary dummy variable that indicates whether county i is a trade port, with a value of 1 if it is, and 0 otherwise. post it indicates whether county i was open for trade at period t. Consequently, treati × post it (did), the interaction term, serves as the model’s core explanatory variable, assigned a value of 1 if the county i is a trade port and was open at period t, and 0 in all other cases.
Equation (3) also incorporates county-level individual fixed effects (μ i ) and time fixed effects (η t ) to control for stable regional characteristics across counties, such as geographical location, natural resources, and cultural heritage, as well as the influence of global time trends, including macroeconomic changes. Additionally, the model introduces the interaction term of prefecture-time fixed effects (P jt ) to more accurately control for specific factors that vary over time at the prefecture level. The error term ε it captures the influence of other unobserved factors that are not accounted for by the model. The regression results are presented in Table 11.
Trade port impact on modern industrialization (1840–1910)
| (1) | (2) | (3) | (4) | (5) | (6) | |
|---|---|---|---|---|---|---|
| TWFE | TWFE | TWFE | Callaway and Sant’Anna (2021) | Callaway and Sant’Anna (2021) | Callaway and Sant’Anna (2021) | |
| Variables | Industrial enterprises | Initial industrial investment | Worker employment | Industrial enterprises | Initial industrial investment | Worker employment |
| did | 0.2140*** | 3.6634*** | 0.4721*** | 0.2861*** | 3.8211*** | 0.5541*** |
| (0.0519) | (0.4869) | (0.1045) | (0.0906) | (0.5043) | (0.1446) | |
| Constant | 0.0038*** | 0.1433*** | 0.0106*** | |||
| (0.0007) | (0.0063) | (0.0014) | ||||
| Observations | 13,512 | 13,512 | 13,512 | 14,016 | 14,016 | 14,016 |
| R-squared | 0.3981 | 0.4367 | 0.4113 | |||
| County FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
| City-Year FE | Yes | Yes | Yes |
Note: Cluster robust standard errors are computed at the county level for all regressions based on the late Qing dataset, unless otherwise specified. ***p < 0.01, **p < 0.05, *p < 0.1.
From the regression results in Table 11, it is evident that whether using the traditional TWFE method for staggered DID estimation or the method proposed by Callaway and Sant’Anna (2021) that controls for heterogeneous treatment effects, the opening of trade ports has a significantly positive impact on modern industrial development during the late Qing period.
Moreover, tests of the parallel trends assumption between the treatment group and the control group (Figures 5–7) reveal no significant differences in industrial development trends before the opening of trade ports. This confirmation of the parallel trends assumption, crucial for DID analysis, suggests that the industrial development in the late Qing Dynasty can be attributed to the opening of trade ports.
Parallel trend test for the number of newly established industiral enterprises.
Parallel trend test for newly added industrial capital.
Parallel trend test for newly added worker employment.
Employing the same method as in equation (3), we analyzed the impact of trade port openings on FDI from 1840 to 1910. In this analysis, the dependent variable was measured by the number of new foreign Enterprises and the amount of foreign initial investment in county i at period t, calculated as the 10-year average of new additions, similar to the industrial indicators. The results are presented in Table 12.
Impact of trade port openings on foreign investment (1840–1910)
| (1) | (2) | (3) | (4) | |
|---|---|---|---|---|
| TWFE | TWFE | Callaway and Sant’Anna (2021) | Callaway and Sant’Anna (2021) | |
| Variables | Foreign enterprises | Initial foreign investment | Foreign enterprises | Initial foreign investment |
| did | 0.2780*** | 1.8114*** | 0.3777*** | 2.5589*** |
| (0.0630) | (0.4992) | (0.1056) | (0.7499) | |
| Constant | 0.0022*** | 0.0127* | ||
| (0.0008) | (0.0065) | |||
| Observations | 13,512 | 13,512 | 14,016 | 14,016 |
| R-squared | 0.6759 | 0.5773 | ||
| County FE | Yes | Yes | Yes | Yes |
| City-year FE | Yes | Yes | ||
| Year FE | Yes | Yes | Yes | Yes |
***p < 0.01, **p < 0.05, *p < 0.1.
The result from Table 12 indicates that the opening of trade ports had a significant positive impact on the location choices of foreign enterprises. Following the opening of the ports, foreign enterprises concentrated in the trade port areas prior to domestic firms (Wu, 2007; Zhang, 1987). Furthermore, the opening of trade ports significantly boosted the development of foreign trade. Given that foreign trade during that period could only occur in trade ports, the opening of these ports became a natural catalyst for trade expansion.
Historically, the opening of trade ports significantly boosted foreign investment and international trade, which, either directly or through spillover effects, catalyzed the process of modern industrialization in China during the late Qing Dynasty (He, 2020; Liang, 2015; Liu, 2012).
5.2 Empirical Research in Contemporary Contexts
Building on this historical context and the fundamental effects of foreign trade and FDI, this study further investigates whether regions with trade ports still maintain high levels of foreign investment and trade activities today and evaluates their ongoing impact on promoting industrial development in contemporary times.
To thoroughly analyze the impact of historical trade ports on contemporary FDI and international trade, we continue to utilize equation (1) for empirical testing. We replace the dependent variables with metrics of contemporary foreign investment activities (including the number of foreign-funded enterprises, number of contracts, actual foreign investment amounts, and foreign enterprise output) and international trade activities (including imports, export, gross value of export and import) for prefecture-level city j .
The core explanatory variable continues to be the trade ports. Building on the existing control variables, we introduced per capita GDP and free trade zones as additional control variables. Per capita GDP reflects the level of regional economic development and residents’ consumption capacity. More economically developed regions typically have better market potential and consumption foundations, thereby attracting more foreign investment. Additionally, the establishment of free trade zones has a significant impact on import and export trade. By implementing policy innovations and trade facilitation measures, free trade zones can also attract foreign enterprises to settle in the area and promote external trade activities within the region. Specific regression results are presented in Table 13.
Trade port impact on contemporary foreign trade and FDI (2020)
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | |
|---|---|---|---|---|---|---|---|
| OLS | OLS | OLS | OLS | OLS | OLS | OLS | |
| Variables | Import | Export | Trade | Foreign enterprises | Foreign contracts | Actual foreign investment | Foreign output |
| Tradeport j | 0.5312** | 0.4397** | 0.3694** | 0.4116*** | 0.4218*** | 0.5062*** | 0.5253** |
| (0.2150) | (0.1869) | (0.1614) | (0.1316) | (0.1537) | (0.1681) | (0.2099) | |
| P-GDP | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Coastal | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Yangtze River | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Latitude | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Longitude | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Altitude | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Free trade zone | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Economic development zone | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Distance from historical provincial capital | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
| Constant | 6.6907 | 9.8792** | 9.3124** | −1.9629 | −10.3091*** | 7.1814* | 0.9406 |
| (5.6799) | (4.5125) | (4.1606) | (2.4452) | (3.0530) | (4.2369) | (4.6662) | |
| Observations | 278 | 278 | 278 | 257 | 259 | 263 | 252 |
| R-squared | 0.7065 | 0.7685 | 0.7946 | 0.8060 | 0.7262 | 0.7307 | 0.7301 |
| Province FE | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Note: Cluster-robust standard errors are computed at the prefecture level for all regressions based on the contemporary dataset. ***p < 0.01, **p < 0.05, *p < 0.1.
Table 13 illustrates the substantial impact of historical trade ports on contemporary FDI and international trade. In the regression results, the first three columns detail the effects of trade ports on international trade. Specifically, compared to regions without historical trade ports, trade port regions exhibit increases of 53.1% in import volume, 44% in export volume, and 36.9% in the total volume of trade. Furthermore, the fourth to sixth columns present regression results on the impact of historical trade ports on contemporary FDI. Specifically, the number of foreign enterprises, foreign contract counts, actual foreign investment amounts, and foreign enterprise outputs in historical trade port areas are 41.2, 42.2, 50.6, and 52.5% higher than those in non-trade port areas. These figures demonstrate the lasting influence of historical trade ports in attracting foreign capital and facilitating trade.
Empirical analysis has confirmed that regions with historical trade ports continue to exhibit active foreign investment and international trade activities today, aligning with the findings of Zhang et al. (2021). Subsequently, this study investigates the impact of these economic activities on contemporary industrial agglomeration and assess whether their historically facilitative role in industrialization persists. To this end, equations (4) and (5) have been designed for empirical testing:
Y jp represents the level of industrial agglomeration in prefecture-level city j located in province p, measured by the indicators previously described. The international trade activities, Trade j , are quantified by the total volume of imports and exports for city j, and the level of foreign investment, FDI j , by the actual amount of annual foreign investment in city j. The set of control variables, X j , remain as same as in Table 3; γ p denotes provincial fixed effects, and ε j represents the error term.
The interaction terms Tradeport j × Trade j , Tradeport j × FDI j of equations (4) and (5) are our core explanatory variables, and their coefficients are the focus of our attention. Detailed regression results are presented in Tables 14 and 15.
Interaction impact of foreign trade and trade ports on contemporary industrial agglomeration (2020)
| (1) | (2) | (3) | |
|---|---|---|---|
| Variables | Industrial enterprises | Industrial circulating capital | Industrial output |
| Tradeport j | 0.0435 | 0.0371 | 0.0182 |
| (0.0785) | (0.1004) | (0.1262) | |
| Trade | 0.1679*** | 0.3639*** | 0.3652*** |
| (0.0292) | (0.0373) | (0.0428) | |
| Tradeport j × Trade j | 0.0402* | 0.0795* | 0.1237** |
| (0.0395) | (0.0447) | (0.0529) | |
| Constant | 6.3980*** | 16.2603*** | 15.3747*** |
| (1.7610) | (2.1679) | (2.5039) | |
| Observations | 285 | 283 | 277 |
| R-squared | 0.8734 | 0.8111 | 0.8137 |
| Control var | Yes | Yes | Yes |
| Province FE | yes | yes | yes |
Cluster-robust standard errors at the prefecture level in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1.
Interaction impact of FDI and trade ports on contemporary industrial agglomeration (2020)
| (1) | (2) | (3) | |
|---|---|---|---|
| Variables | Industrial enterprises | Industrial circulating capital | Industrial output |
| Tradeport j | 0.0149 | −0.0033 | −0.0426 |
| (0.0707) | (0.1104) | (0.1153) | |
| FDI j | 0.1829*** | 0.2914*** | 0.3231*** |
| (0.0329) | (0.0468) | (0.0442) | |
| Tradeport j × FDI j | 0.0433* | 0.1453** | 0.1961*** |
| (0.0433) | (0.0642) | (0.0619) | |
| Constant | 7.4265*** | 16.9871*** | 14.3968*** |
| (1.7205) | (2.3525) | (2.7814) | |
| Observations | 268 | 266 | 268 |
| R-squared | 0.8629 | 0.7765 | 0.8052 |
| Control Var | Yes | Yes | Yes |
| Province FE | Yes | Yes | Yes |
***p < 0.01, **p < 0.05, *p < 0.1.
The regression results in Table 14 reflect the interaction between the level of international trade and trade ports and their interaction impact on industrial agglomeration. The coefficient and significance of the interaction term Tradeport j × Trade j indicate that the international trade activities of trade ports have a significant positive effect on industrial agglomeration.
The regression results from Table 15 further validate the significant positive interaction effects of foreign investment and trade ports on contemporary industrial agglomeration. The coefficient of the interaction term Tradeport j × FDI j indicates that historical trade ports, with their more active foreign investment, have significantly facilitated local industrial development.
The regression results in Tables 14 and 15 demonstrate that historical trade ports continue to promote local industrial development in contemporary times by facilitating foreign trade and foreign investment.
5.3 Discussion
In the early stages of modern industrialization, China’s indigenous modern industries were largely not transformed from existing handicraft workshops. Instead, they were established by directly importing foreign machinery and technology. Most of these enterprises were concentrated in treaty port areas, such as Shanghai and Guangzhou. This geographical choice facilitated the introduction of machinery and technology while also catering to the demand for raw material processing for export. For instance, factories for silk reeling, tea production, and cotton pressing were primarily set up to process raw materials for export. Additionally, some of these enterprises provide intermediate products and services to foreign companies.
The establishment of modern trade ports created an open economic and institutional environment that facilitated foreign investment and trade in these regions. The opening of these ports led to a substantial influx of foreign capital. The clustering of foreign enterprises not only brought direct investments but also introduced advanced technologies, management practices, and institutional innovations. These foreign firms played a crucial role in shaping China’s economic landscape, providing local enterprises with modernized models, particularly in technology and management. Moreover, the opening of trade ports significantly accelerated foreign trade, allowing Chinese products to enter international markets while facilitating the influx of foreign goods and technologies. This increase in trade activity generated new business opportunities and expanded economic openness. The growth of foreign trade supplied local industries with essential capital goods, such as machinery, while opening new markets for domestic products.
The interplay of foreign trade and investment in these port areas has been instrumental in China’s industrialization process. Furthermore, these regions exhibit a higher level of openness in contemporary times, as reflected in increased foreign investment and international trade. This open environment continues to exert a positive influence on regional economic development, further emphasizing the significance of openness.
6 Conclusion and Policy Recommendations
The process of opening up has been a crucial driver in China’s modernization, tracing back to the late Qing Dynasty when trade ports emerged as vital conduits for foreign engagement. These ports bridged traditional Chinese industries with Western modernity, igniting the country’s modern industrialization. The legacy of these trade ports continues to shape China’s economic landscape today, fostering a more open environment conducive to international exchange and cooperation. Consequently, these regions have transformed into some of the most dynamic and culturally diverse areas in China, significantly contributing to national economic growth.
The enduring significance of openness is evident across various epochs of China’s development, underscoring its role as a foundational principle that continues to shape national policies and strategies. To sustain and deepen its openness in the face of globalization, China should pursue several strategic measures:
Strengthen Multilateral Trade Systems: Actively engage in the reform of multilateral institutions such as the World Trade Organization. Uphold free trade principles and enhance global trade regulations to promote fair competition.
Promote Regional Economic Cooperation: Foster collaboration with regional economies through initiatives like the Belt and Road Initiative, which aims to facilitate trade and investment while advancing regional economic integration.
Furthermore, to maximize the benefits of openness while ensuring sustainable regional development, policymakers must implement comprehensive strategies that address both resource distribution and regional disparities:
Facilitate Access to Global Markets: Implement policies that enable inland regions to participate in international trade, such as improving logistics and transportation networks. This will help these areas benefit from global supply chains. Additionally, develop inclusive trade agreements that allow underdeveloped regions access to broader markets and resources, promoting economic integration.
Encourage Investment in Underdeveloped Regions: Provide incentives for businesses, including FDI, to invest in less developed areas through measures such as tax breaks or subsidies, which can stimulate economic activity and create jobs. Additionally, invest in training programs that equip the workforce in these regions with the skills necessary for industries that benefit from openness, particularly in technology and service sectors.
These measures will not only unlock the potential of various regions but also bolster overall economic resilience and vitality, laying a solid foundation for long-term national development. By embracing these recommendations, China can continue to evolve as a key player in the global economy while ensuring equitable growth across its diverse regions.
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Funding information: This research was funded by the Domestic and International Graduate Joint Training Program of the University of International Business and Economics.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal. All authors have read and agreed to the published version of the manuscript. Conceptualization, P.L.; methodology, P.L.; software, P.L.; validation, Y.S., P.L., and Z.Y.; formal analysis, P.L.; investigation, Z.Y.; resources, P.L.; data curation, P.L.; writing – original draft preparation, P.L.; writing – review and editing, P.L.; visualization, Z.Y.; supervision, Y.S.; project administration, Y.S. and P.L.; funding acquisition, P.L.
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Conflict of interest: Authors state no conflict of interest.
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Ethical approval: The conducted research is not related to either human or animal use.
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Data availability statement: The datasets generated during and/or analyzed during the current study are contained within the article.
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Article note: As part of the open assessment, reviews and the original submission are available as supplementary files on our website.
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