Characteristics and Determinants of the Proportion of China’s Trade by Air
LuJian(逯建),ZhangLong(张龙)andYangHaoqing(杨昊擎).....................................................................
Abstract:
Based on the China Customs Database and the China Industrial Enterprises Database, this paper estimates the proportions of imports and exports by air of Chinese firms and variables that may influence such proportions. Through OLS regression and seemingly unrelated regression (SUR), this paper analyzes the possible determinants of the share of trade by air. Our findings suggest that the TFP of firms is positively correlated with the share of trade by air. The ratio of value-added in exports is positively correlated with the share of imports by air and negatively correlated with the share of exports by air; the average distance of transport is significantly positively correlated with the share of trade by air in full-sample and grouped regressions. Rising TFP increases the share of imports and exports by air the most for processing trade firms, particularly for firms in the eastern region and foreign-funded firms. An increase in the ratio of value-added in exports increases the share of imports by air the most for general trade firms, and also significantly influences the use of air transport by firms in the eastern region and foreign-funded firms. These conclusions offer valuable policy references for promoting trade in various parts of China and especially the inland regions.
Keywords:
air transport, trade, SUR ( seemingly unrelated regression )
JEL Classification Codes: F19, D29, C13
DOI:1 0.19602/j .chinaeconomist.2019.3.0819602/ j .chinaeconomist.2018.09.02
1. Introduction and Literature Review
In international trade, most goods are transported by sea. While large cargoes can be transported by sea at relatively low costs, maritime transport also has many disadvantages. Maritime transport is slow and involves long distances. Most maritime routes are not straight lines but winding lines along coasts. For instance, the maritime routes between China and countries in Western Europe are more than double the straight-line distances (Lu and Shi, 2014), making it time-consuming for goods to be transported to their destinations by sea. Long transport time incurs high costs of storage and depreciation. For goods with a short lifecycle and high demand volatility, excessive delivery time may cause a supply and demand mismatch. For inland countries and regions, it is costly to move goods from inland to coastal regions before shipped by sea.
Huang and Xu (2012) consider that the cost of inland transport in China is high and the cost of domestic road transport accounts for at least 2% of China’s GDP. Given the rapid upgrades of high-tech products and the long-distance of China’s central and western regions from seaports, it is both necessary and feasible for China to develop air transport. Rodrik (2003) believes that the real distance between two countries differs due to different modes of transport. With its rapid speed, air transport has shortened physical and psychological distances between countries and can meet time-critical market demand and promote international trade. As discovered by Freund & Rocha (2010) based on Africa’s trade data, the shortening of transport time by each day will lead to an increase in export by 0.7%.
In China, each province, municipality and autonomous region has its airport, and the distance of inland transport by air is relatively short. Therefore, air transport can avoid the disadvantages of maritime transport and play a key role in international trade. The popularity of air transport is growing. Hummuls’ (2007) calculation suggests the proportion of air transport increased by 11.7% on average each year globally from 1965 to 2004 in contrast to 4.4% annual increase in the share of maritime transport. Cristea’s (2013) estimate shows that in 2000, air cargo represented 36% of imports and 58% of exports of the United States. According to the latest statistics of the International Air Transport Association (IATA), the International Civil Aviation Organization (ICAO) and the Airport Council International (ACI), in 2015, global air cargo reached 51.3 million tons, and trade by air totaled 5.71 trillion US dollars, accounting for 35% of total world trade. Although maritime transport still dominates international trade, the rising status of air transport warrants our attention.
International scholars ascribe the rising share of air transport to the following reasons: First, the cost of air transport has reduced drastically. Hummuls’ (2007) systematic research on the cost of transport shows that from 1955 to 2005, the cost of air transport reduced by more than ten times, which significantly promoted trade by air. Second, firms are willing to pay a premium for timely transport to operate just-in-time or swiftly respond to market demand. Jams Harrign (2006) notes that the late delivery of critical components may cripple an entire factory. Hummuls (2010) believes air transport is the key to matching market demand with supply and should be used more broadly to meet the urgent demand for certain products. Third, the cost of late delivery is exorbitant for certain types of products. Hummuls (2013) believes the rapid upgrades of high-tech products make it necessary for such products to be transported by air to their destinations in order to gain an early foothold in the market.
Compared with extensive international quantitative research, existing research by Chinese scholars remains insufficient. Chinese scholars have focused on the effects of air transport on growth and trade. Gu and He (2003) contend that air transport has increased specialization and growth. Jiang Hua (2005) believes modern logistics and particularly aviation logistics provide the necessary conditions for the operation of a regional economy. Chu and Wang (2010) indicate that there is a mutual causality between aviation logistics and international trade. Based on data from North America, Japan, and Europe, Yang (2007) discovers mutual causality between modern logistics and economic development. Based on an analysis of China’s Zhejiang Province, Liu and Li (2007) also arrive at a similar conclusion. Han (2009) believes GDP has direct and indirect effects on air transport. Research by Chinese scholars proves that air transport and economic development promote each other. However, the question is whether firms are willing to use air transport more broadly. Qi and Fan (2015) indicate that greater uncertainties in product demand and more scattered international production activities lead to a greater share and possibility of air transport in international trade. Compared with general trade in goods, a higher percentage of parts and components are traded by air.
In a word, air transport is increasingly common in international trade, but the cost of air transport is high, and the volume of trade by air remains limited. Currently, there is a limited literature of empirical analysis on the pros and cons of maritime and air transport in China’s foreign trade. This paper attempts to explore the determinants of the share of trade by air with data from the customs database and the database of industrial enterprises at a micro level of firms and products, focusing on how firm TFP, ratio
of value added in exports and average distance of cargo transport influence the percentage of trade by air. This paper selects TFP and ratio of value added in exports because firm and product attributes may influence trade by air. Selection of average distance of transport references the conclusion of the gravity model of trade, i.e. trade volume is inversely proportional to the distance between two countries, while air transport is suitable for small-scale transport.
The second part of this paper introduces data source and treatment method and makes a series of descriptive analyses. Part 3 is empirical analysis, in which we create import and export models, offer a brief description of the regression methods, estimate relevant indicators, and offer economic explanations on the regression result. The final part is conclusions and policy recommendations.
2. Data Source, Treatment and Descriptive Analysis 2.1 Data Source and Treatment
This paper primarily uses the following two databases: the China Customs Database and China Industrial Enterprises Database. The specific process of data treatment is as follows.
2.1.1 Treatment of China’s customs database
This paper uses China’s customs database of 2000- 2006, which records each entry of import and export information on customs clearance firms. Since such a database is monthly data, this paper firstly consolidates the monthly data of each year into annual data. We only consider two modes of transport for the customs database: maritime transport, which is the most common mode of transport in international trade, and air transport, which is the subject of our research. Uncommon modes of transport are deleted. It should be noted that many firms engage in both processing and general trade, which are the two primary modes of trade in the customs database, in the same year. In the empirical analysis, this paper will focus on an explanatory variable: the ratio of value-added in exports, the calculation of which will be discussed according to the mode of trade. After initially screening processing and general trade firms, we consolidate these two types of firms and then identify firms that simultaneously engaged in processing and general trade, referring to them as mixed trade firms. Finally, we identify firms that only engaged in processing trade and those that only engaged in general trade in a given year after consolidating mixed trade firms with the initially screened processing and general trade firms.
2.1.2 Treatment of the China industrial enterprises database
The China Industrial Enterprises Database includes the survey data of SOEs and large (revenue from primary business exceeds 5 million yuan) non-SOEs. Our data treatment is as follows: First, we deleted firms with negative or zero total fixed assets or net worth of fixed assets. Second, we deleted firms with fewer than eight employees. Third, we deleted firms with negative or zero value-added of output. However, there is no such an indicator as value-added of output in the industrial enterprises database of 2004. To obtain the value-added of industrial output, this paper references Nie’s et al. (2012) equation: value-added of output = revenue from primary business - stock at the beginning of period + stock at the end of period - company intermediate inputs + VAT payable by a firm. Fourth, we deleted firms with abnormal business operations. Fifth, we controlled for the industry attributes and two-digit code of province/municipality of firms.
After the above treatment of the customs database and the industrial enterprises database, this paper consolidates data by the Chinese names of firms. After consolidation, firms possess the following attributes: (1) SOEs or large non-SOEs; (2) Firms that directly participate in imports and exports.
2.2 Stylized Facts of the Proportion of Trade by Air for China
In this paper, the proportion of trade by air refers to trade in goods transported by air between
Chinese trading firms and other countries and is the explained variable. Using imports and exports by air as a share in total trade, we may calculate the proportion of trade by air in the imports and exports of each firm or country. This paper describes the stylized facts of the proportion of trade by air for China from the following three aspects:
2.2.1 Quarterly changes in the share of corporate trade by air
Based on the calculated proportions of imports and exports by air, this paper estimates the proportions of corporate imports and exports by air for each quarter of 2000-2006. In the following chart, each scale of the horizontal axis denotes the last quarter of the previous year.
As can be seen from Figure 1, the share of corporate imports by air is higher than that of exports by air for every single quarter, which reveals China’s domestic firms’ relatively high reliance on and urgent demand for time-critical imports. The percentage of exports by air had been fluctuating around 10%, which reflects a low level in the use of air transport for Chinese exports. In other words, most of the Chinese exports were transported by sea. Given the significant difference between the percentages of imports and exports by air, it is necessary to investigate the determinants of the percentages of imports and exports by air. The models created later in this paper are specified based on import and export respectively.
2.2.2 Change in the percentages of China’s trade with trading partners by air
This paper divides China trading partners into developed and developing countries. While the former includes countries like the United States, Japan, the UK, Canada, Singapore and Australia, the latter refers to countries like South Africa, Angola, Nigeria, Egypt and Algeria. The percentages of
China’s imports and exports with these two groups of trading partners are illustrated in the following charts:
As Figure 2 shows, the percentages of both imports and exports by air between domestic Chinese firms and developed countries had been increasing year by year. The percentage of imports by air increased from 36.3% to 42.9%, and the percentage of exports by air rose from 12.1% to 14.6%. Apparently, the percentage of imports by air is far higher than that of exports by air. The implication is that Chinese firms are highly dependent on time-critical imports from developed countries, which are the prerequisites for domestic trading firms to operate normally. Figure 3 shows that for trade between domestic firms and developing countries, the percentages of imports and exports by air are both smaller than those of trade with developed countries, but still exhibit a rising tendency over time.
2.2.3 Changes in the percentages of trade by air for different types of firms
There are two ways to differentiate the type of firms. The first way is to determine the nature of firms by the share of paid-in capital (≥50%). However, since there is no paid-in capital statistics in the customs database, the customs database and the industrial enterprises database would have to be consolidated if this method is to be followed, which would cause a great loss of data. Here, this paper tries to demonstrate changes in the shares of imports and exports by air for different types of firms in China as accurately as possible. A considerable loss of data will inevitably cause significant errors in descriptive statistics. Therefore, this paper opts for the second method: to recognize different types of
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firms based on original information registered at customs database, which roughly divides importing
and exporting firms into SOEs, private enterprises and foreign-funded enterprises. Among them, private enterprises include collective enterprises, privately owned enterprises, and individual businesses. Foreign-funded enterprises include wholly foreign-owned enterprises and Sino-foreign joint ventures. After differentiation of the types of firms, the percentages of imports and exports by air for the three types of firms are calculated respectively, as illustrated below:
As shown by the above charts, the share of imports by air has increased for all the three types of firms and is higher than the share of exports by air without exception. Regarding exports, we have found something special, i.e. the share of exports by air for foreign-funded enterprises increased from 13.5% to 15%. This percentage is higher than those for the other two types of enterprises. The reason is that the products of foreign-funded enterprises have high technology content, and their sales would be significantly affected if the time of transport is too long. Time to market is essential to ensure that products are not obsolete in an ever-changing market. Besides, high-tech products are small in size, lightweight and of high-value, which make air transport an appropriate option.
3. Empirical Analysis
As mentioned before, this paper specifies two models to discuss the determinants of the shares of corporate imports and exports by air. There could be a contemporaneous correlation between the random disturbance terms of these two models. Namely, there could be an unknown factor that simultaneously affects the percentages of imports and exports by air. Therefore, estimation using OLS may not be the most efficient. To make our estimation more efficient, we adopt the seemingly unrelated regression (SUR) method for full-sample and grouped regressions in this paper. After considering the factors that may affect the share of imports and exports by air, this paper carries out OLS and SUR regressions
respectively, and OLS regression is used as a method for robustness test. The models created are as follows: Where the explained variables are the share of imports (VRI) by air and the share of exports (VRE) by air for firm i from country j in year t. Three explanatory variables include corporate total factor productivity (TFP), the ratio of value-added in exports (evar) and the average distance of cargo transport 2
(lndis). We will explain the reasons for the selection of explanatory variables later. Z includes control variables: (1) average weight of goods imported and exported by the firm (lnwei); (2) average time of transport for imports and exports (time); (3) age of firm (age); (4) convenience of access to an airport in the province/municipality in which the firm is located (airport); (5) distance to the nearest port in the province/municipality in which the firm is located (Indis); (6) subsidy received by the firm (subsidy).
3.1 Estimation of Explanatory Variables
This paper involves the following explanatory variables: corporate total factor productivity (TFP), the ratio of value-added in exports (evar) and the average distance of transport (lndisj for imports and lndisc for exports). Reasons for the selection of variables and measurement method are elaborated as follows:
3.1.1 TFP estimation
TFP reflects technology progress. Melitz (2003) believes more productive firms can afford higher costs of trade. In this sense, TFP may affect the use of air transport. Lu and Liu (2010) note that China’s high-TFP sectors are mostly high-tech sectors, for which trade by air is more suitable. Before estimating TFP, we also need to specify the production function. Using a common CD utility function and referencing Chen and Lian (2012) and Mahmut (2008), this paper employs an OP semi-parametric threestep estimation method. Where the state variables are lnk and age; lnk is the logarithm of a firm’s fixed asset, and age is firm age expressed by the difference between the year of firm business opening and the year of the observation period. The control variable is year; proxy variable is corporate intermediate input; labor input is a free variable; exit variable is generated according to firm survival and operational status.
3.1.2 Estimation of the ratio of value-added in exports (evar)
By definition, all imported goods are registered as processing imports under processing trade and are used for the production of goods for export, which are registered under processing exports. Therefore, imported intermediate inputs under processing trade can be identified based on the mode of trade. Then, the above equation can be used to calculate the ratio of value-added in exports under processing trade.
In the above equation, i stands for firm, t for year, IMP for the total import of processing trade firm in year t, and EXP for total corporate export volume.
For general trade, the identification of imported intermediate inputs must follow the Broad Economic Categories (BEC) introduced by the UN. Based on the BEC classification table, we may
divide imported goods into consumer goods, imported intermediate inputs and capital goods. The customs database reports the HS8 code of each imported product. By consolidating the processed classification tables with the customs database, we may identify the intermediate inputs imported by general trade firms and calculate their values. Then, we will be able to estimate the ratio of value-added in exports of firms under general trade:
IMP| is the value of identified imported intermediate inputs of firm i.
BEC
When a firm engages in mixed trade, there are two ways to identify intermediate inputs: Processing imports are identified by the mode of import, while intermediate inputs imported under general trade should be identified according to the BEC classification table. With this approach, the ratio of valueadded in exports under mixed trade is calculated as follows:
In this equation, IMP is the value of intermediate inputs imported by processing trade firms, and IMP| is the value of intermediate inputs under general trade that have been differentiated.
BEC
3.1.3 Average distance of cargo transport
on Although two-way According maritime trade to the flow. transport gravity The longer is model far less the of trade, distance, costly the than the distance air smaller transport, between the size with two of increasing countries trade between has distance, an both inverse it countries. becomes effect this slower too high problem. and due more to When the time-consuming the limited size quantity of the for two-way of goods cargo. trade to Apparently, be is shipped relatively by the sea. small, average However, the distance cost air of air transport of transport transport can will will overcome not affect be the air and choice maritime of the mode transport of transport modes. by The firms. distance In calculating of air transport the distance is denoted of transport, by the this shortest paper spherical involves surface distance, which is commonly used in literature. Specifically, the province or municipality in which a firm is located is a representative point for measuring the shortest spherical surface distance between the firm and the representative cities (capitals) of trading partners. In estimating the distance of maritime transport, we referenced Lu and Shi (2014), i.e. the actual distance of maritime transport between Chinese ports and the largest ports of its major trading partners. For inland countries, the distance between their capitals and the nearest ports should also be included. By averaging the distances of maritime and air transport, we arrive at a firm’s average distance of transport. Following the approach mentioned before, we also calculate imports and exports respectively and include the logarithms into the models.
3.1.4 Explanatory variables
(1) average weight of cargo (lnwei), which affects the cost of transport and the choice of air transport by firms. The customs database only contains the values of HS8 products for each import or export cargo. To calculate the weight of each import or export cargo, we need to use the BACI database in CEPII. The BACI database reports the value of import and export products under the same HS6 code (in 1,000 US dollars) and corresponding rate (in tons). In the BACI database, weight is divided by value and averaged by HS6 code to arrive at the average weight for each dollar of product denoted by the HS6 code. All products in the customs database are denoted by HS8 codes. For the convenience of consolidation, we first consolidate the consolidated HS6 codes with the transcoding table to find the HS8 codes that correspond to the HS6 codes. Then, these codes are consolidated with the customs database.
After consolidation, the value that corresponds to each HS8 code product is multiplied by the weight denoted by one US dollar of product, and the result is averaged by firms to estimate the average weight of each cargo. Since explained variables include the percentages of imports by air and exports by air, we respectively estimate the average weight of cargo and introduce its logarithm into the models.
(2) The average time of transport (time). The distances of air transport and maritime transport from a firm to different countries, which was previously calculated, are divided by the speed of aircraft and
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ship, and the result of time is averaged.
(3) Age of firm (age). Firms of different age may have different considerations in deciding to use less or more air transport. This variable is denoted by the difference between the year of a firm’s business opening and the year of the observation period.
(4) The convenience of access to an airport. Access to air transport may also influence a firm’s choice between air transport and maritime transport. The number of annual aircraft movements in the province (municipality) in which a firm is located is divided by the local population of the current year. The result of the number of aircraft movements per one-hundred-person goes into the model.
(5) The shortest distance of transport between the province (municipality) in which a firm is located to the nearest ports (lndisl), i.e. the distance of inland transport. Naturally, a longer distance of inland transport will increase the overall cost of maritime transport, thus encouraging firms to use more air transport.
(6) Subsidies received by a firm (subsidy), which may reduce the pressures of transport cost for firms. Referencing Zheng (2014), the amount of subsidies received by a firm divided by its sales revenue goes into the models.
3.1.5 Three dummy variables
The three dummy variables are the industry characteristic of a firm (two-digit sector), the mode of trade and the type of firm. The modes of trade primarily include processing trade, general trade and mixed trade. Guariglia (2011) notes that it would be more accurate to divide the types of firms by the share of paid-in capital (≥50%): SOEs, private enterprises and foreign-funded enterprises.
3.2 Empirical Results and Economic Explanation
3.2.1 Full-sample regression
Since the models of this paper involve imports and exports and the error term has a contemporaneous correlation, the full-sample regression and grouped regression in this paper both adopt SUR regression to increase efficiency. In order to ensure the robustness of regression result, this paper follows two different test methods: The first method is to carry out SUR regression and OLS regression for total samples, and the result is reported in Table 1:
As Table 1 shows, under the two types of regression, there is not much change in the significance of coefficients of most explanatory variables. However, the values of coefficients are somewhat different under the two types of regression since SUR regression may raise the efficiency of estimation.
The second type of robustness test is SUR regression of total samples after changing the method of TFP calculation. The fixed effect method and approximate TFP method are adopted to corporate TFP. Regression results are shown in Table 2:
As Table 2 shows, no matter the fixed effect method or the approximate TFP method is used to calculate TFP, there is no noticeable change in the sign and significance of coefficients of most variables after substituted into the models for SUR regression. Also, there is not much change in the values of
coefficients of most variables. Thus, the regression result can be known as robust.
The economic explanation of full- sample regression: According to the result of full- sample regression, corporate TFP is positively correlated with both the percentages of imports and exports by air. This conclusion is consistent with Melitz’s (2003) theory, i.e. firms with higher TFP are more able to afford the high cost of transport. Lu and Lian (2012) suggest that Chinese firms with high TFP are concentrated in high-tech sectors. In today’s world of ever-changing technology, the manufacturing of high-tech goods requires rapid transport of critical imported intermediate inputs, and finished products also need to be swiftly transported to overseas markets. Firms with higher TFP and products with more sophisticated technology, therefore, use more air transport for the international transport of goods. The reasons are twofold: While such firms can afford higher costs of transport, high-tech goods also have a stronger demand for air transport.
As can be seen from overall SUR regression result, the ratio of value-added in exports (evar) is positively correlated with the percentage of imports by air but negatively correlated with the percentage of exports by air. A possible explanation is that most of China’s export merchandise are low-technology labor-intensive goods that need to be shipped in large quantities. Due to limited capacity and high cost, it would be too expensive to transport such large quantities of goods by air than by sea. In this case, it is
more advantageous to transport goods by sea in large quantities and at a lower cost.
For this reason, the ratio of value-added in exports is negatively correlated with the percentage of exports by air. Since products with a high value-added ratio have a smaller demand for imported intermediate inputs, thus involving a smaller quantity of goods to be imported, and the value-added ratio of finished goods is high, it is easier for firms to transport imported goods by air to save time and expedite
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production. Therefore, the ratio of value-added in exports is positively correlated with imports by air.
3.2.2 Result of grouped regression for different modes of trade
Based on this paper’s calculation of the ratio of value-added in products under different modes of trade, it can be found that the ratio of value-added in exports is the lowest under processing trade, followed by that of mixed trade, and the highest for general trade. Since the ratio of value-added in exports has an impact on the percentage of trade by air, it is necessary to conduct grouped regression for different modes of trade and make further analysis. Table 3 offers grouped regression for different modes of trade:
As Table 3 shows, the sign of TFP’s coefficient is the same as mentioned before. But when TFP increases by 1%, the percentage of imports by air under processing trade will increase by 0.038%, and the percentage of exports by air will increase by 0.021%. These increases are more significant than those under general trade and mixed trade. The reason is that among the three modes of trade, the ratio of value-added in exports is the lowest under processing trade. In other words, China’s processing trade firms are the most dependent on overseas intermediate inputs. Since the ratio of value-added in products is the highest under general trade, general trade firms are the least dependent on overseas intermediate inputs with the smallest demand for such imports. As can be learned from the prior analysis, air transport is the most suitable for general trade firms. The increasing ratio of value-added in exports further reduces the imports of general trade firms. When the ratio of value-added in exports increases, therefore, general trade firms will use more air transport. Also, when the ratio of value-added in exports increases, the percentage of trade by air reduces the least under processing trade. The implication is that exports have the highest overall technology content under processing trade. Moreover, general trade firms have the smallest demand for imported intermediate inputs. When the average distance of transport for imports reduces, firms will import still less from overseas. Hence, a shorter average distance of transport for imports will prompt general trade firms to increase air transport more substantially.
3.2.3 Result of grouped regression for different regions
China is a country with extremely uneven development across regions. According to the locations and economic development levels across regions, China can be divided into eastern, central and western
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regions. Most prosperous coastal provinces and cities are located in the eastern region, and many provinces and cities in the western region are less developed. There is a great deal of heterogeneity in the products made by firms in different regions, and their distances to ports vary a lot as well. Generally, most parts of the eastern region are closer to seaports, and most coastal regions are located in the eastern region. Central and western regions, in comparison, are more distant from seaports. Since airports are scattered across China, however, the western region also has geographical advantages from an air transport perspective despite being distant from seaports. In this sense, changes in the determinants of trade by air have differentiated effects on the proportions of trade by air across different parts of China. Table 4 shows the result of grouped regression for different regions:
As Table 4 shows, TFP only has a significant effect on the proportions of imports and exports by air for firms in the eastern region. The reason is that as China’s most developed region with the highest concentration of the most educated workforce, products made in the eastern region are the most technologically sophisticated. When TFP rises, high-tech firms in the eastern region must use more air transport when they import in order to shorten the time to market. They also need to use more air transport for exports in order to stay competitive and attractive to consumers in the overseas market. When the ratio of value-added in export increases by 1%, the proportion of exports by air reduces by 0.069% for the western region; this reduction is higher than that for the eastern region. The reason is that goods manufactured in the western region are more labor-intensive and less sophisticated in technology, making mass transport by sea more suitable. As can be learned from prior analysis, when the ratio of value-added in exports increases, the ratio of exports by air reduces the most for the western region. Besides, since most of China’s foreign trade takes place in its eastern region, when the average distance of transport for imports and exports increases, central and western regions will see their relatively limited trade volumes further shrink and increase their use of air transport more suitable for smaller quantities of cargo. Without substantially increasing the cost of transport, air transport will expedite the delivery of goods in smaller quantities to satisfy just-in-time production and deliver goods in a shorter time.
3.2.4 Result of grouped regression for different types of firms
Different types of firms vary in size and strength. Besides, uneven development in China will also affect the distribution of firms. For instance, the eastern region has the highest number of foreign-funded
enterprises with easy access to air and maritime transport. Thus, it is necessary to conduct a grouped regression analysis based on different types of firms. Table 5 shows the result of grouped regression by the types of firms:
As Table 5 shows, TFP’s effect on the percentages of imports and exports by air passes significance test at 1% only for the regression of foreign-funded enterprises, which have the highest concentration of high-tech firms. As can be learned from the prior analysis, TFP’s increase will bring firms closer to the high-tech sector. Among the three types of firms, when TFP increases, the overall level of technical sophistication is the highest for products made by foreign-funded enterprises, which therefore have the highest demand for air transport. They need to import goods by air to expedite production and export high-tech goods by air to shorten the time to market and stay competitive in the face of ever-changing technology. Given China’s high dependence on overseas intermediate inputs for the manufacturing of high-tech products, i.e. the relatively low ratio of value-added in exports, the technology sophistication of products made by firms will increase when the ratio of value-added in exports reduces. Since foreignfunded enterprises are closer to high-tech industry, we analyze their relationship with the share of air transport under the scenario of a falling ratio of value-added in exports: the falling ratio of value-added in exports means an increase in imports. At this moment, the percentage of trade by air will diminish. However, given the high-tech attribute of their goods, the reduction in the share of imports by air would be limited for foreign-funded enterprises in order to ensure just-in-time manufacturing of products. In the export process, given the stronger high-tech attribute of their products, foreign-funded enterprises are more likely to transport finished products by air. Finally, when the average distance of transport increases, all three types of firms significantly increase their use of air transport for imports and exports, which highlights the air transport’s advantage for small quantities of cargo.
4. Concluding Remarks and Policy Recommendations
This paper carries out an empirical analysis of the characteristics and determinants of the proportion of China’s trade by air and finds that an increasing TFP of firms will effectively increase imports and exports by air. The ratio of value-added in exports of firms is positively correlated with the share of trade by air, and negatively correlated with the share of exports by air. A longer average distance of international cargo transport will also lead to an increase in the share of trade by air. In this case, the share of imports by air will increase more significantly than the share of exports by air, which reflects Chinese firms’ relatively strong dependence on foreign goods.
Through SUR regression, this paper carries out a regression analysis for different types of trade, regions and firms. The result shows that TFP has the most significant positive effect on processing trade; the ratio of value-added in exports is the highest for products made by general trade firms, which have the smallest demand for imported intermediate inputs. Therefore, the ratio of value-added in exports has the most significant impact on imports by air for this type of firms. China’s eastern region is home to a large number of high-tech manufacturers, and the effect of TFP and the ratio of value-added in exports on the share of trade by air for firms in the eastern region all pass the significance test at 1%. Firms in the western region, which primarily manufacture less technologically sophisticated labor-intensive products, will substantially reduce the use of air transport when the ratio of value-added in exports increases. In the regression for different types of firms, since most high-tech firms are foreign-funded enterprises, TFP and the ratio of value-added in exports have the most considerable influence on this type of firms.
The relatively significant share of imports by air reveals the strong dependence of Chinese firms on imports from foreign countries. In order to reduce their reliance on foreign goods, Chinese firms must enhance their independent R&D capabilities. The small and slow increase in the share of exports by air shows that most Chinese exports are less technologically sophisticated labor-intensive goods. This structure is unfavorable to the survival of Chinese firms in a fiercely competitive international market.
Therefore, Chinese companies must speed up the structural upgrade of their export products. Most parts of central and western regions are interior regions and distant from seaports. With higher costs of domestic transport, firms located in these regions have a stronger demand for air transport compared with their peers in the eastern region. To facilitate their development, China should enhance airport construction in its central and western regions, which is currently far behind the eastern region, and open more international air routes to promote trade.