Transportation Mode and Exporter Heterogeneity

Transcription

1 Transportation Mode and Exporter Heterogeneity Ying Ge Huiwen Lai Chia-Hui Lu Susan Chun Zhu July 2014 Abstract Using the matched firm-trade data from China, in this paper we examine the effect of firm productivity on firms choice of transportation mode air freight or ocean freight. We find that within the same product-destination market, high productivity firms are more likely to use air mode; and the productivity effect on the propensity of using air freight is more significant when goods are exported to richer markets, when firms are engaged in global production networks, and when products are more R&D intensive. These results imply that high productivity firms are more likely to be specialized in higher value and more time sensitive varieties. In addition, transportation costs incurred by individual firms depend on firm characteristics and on their interaction with the characteristics of product and destination markets. School of International Trade and Economics, University of International Business and Economics, 10 East Huixin Street, Chaoyang District, Beijing, China. School of Accounting and Finance, The Hong Kong Polytechnic University, Li Ka Shing Tower, Hung Hom, Kowloon, Hong Kong. Department of Economics and Finance, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong. Department of Economics, Michigan State University, Marshall-Adams Hall, East Lansing, MI 48824, USA.

2 Transportation costs play an important role in reshaping global trade and production. The patterns of transportation costs on the macro level have been well documented in the previous literature (Hummels, 2007). In contrast, firm-level studies of transportation costs are scant. In this paper we aim to fill this gap. Since transportation costs incurred by individual firms depend on their choice of transportation mode, it is crucial to have a good understanding of the factors that may influence transportation mode decisions. It is shown that the choice of transportation mode hinges on product characteristics such as size, weight, sensitivity to timeliness, and on destination characteristics such as geographic distance (e.g., see Hummels, 2007; Hummels and Schaur, 2010; Harrigan, 2010). However, because these studies use product-level trade data, so far there is little evidence on how the transportation mode choice varies across heterogeneous firms. In this paper we focus on transportation mode choices made by heterogeneous exporters. Our empirical analysis employs the matched firm-trade data from China, which provide rich information on firms, products, destination markets, and transportation mode used for each trade transaction. Several new patterns emerge from our data, as displayed by Figure 1. We divide firms into deciles based on their total factor productivity (TFP) relative to other firms in the same industry. 1 We then compute the number of air shipments as a share in the total number of trade transactions by firms in each decile. We find that more productive firms have a higher propensity of using air freight: the share of air shipments is more than 20 percent for firms in the highest decile and about 14 percent for firms in the lowest decile. Furthermore, in each productivity decile, exports to high-income countries have a higher share of air shipments than do those to low-income countries. That is, given firm productivity, exports to rich markets are more likely to be air shipped. Looking across TFP deciles, we also find that the difference between high- and low-productivity firms in the share of air shipments is even larger for exports to high-income countries. To understand these data patterns, we extend the Melitz (2003) model by endogenizing 1 Details about the TFP measure are given in Appendix C. 1

3 a firm s decision about product quality and transportation mode (by air or by ocean). Similar to Hummels and Schaur (2013), firms in our model face the trade-off between cost of shipping and monetary value of time. However, differing from Hummels and Schaur (2013), we endogenize the choice of product quality in order to explain the patterns revealed by our data. A key assumption in our model is that fast delivery enhances utility from consuming high-quality products. That is, the quality of transportation services (speed of delivery) and the quality of products are complementary. In equilibrium, more productive firms find it optimal to improve product quality, which is consistent with the recent quality-and-trade literature (e.g., see Bastos and Silva, 2010; Kugler and Verhoogen, 2012). Given the complementary between the quality of transportation services and the quality of products, high productivity firms are more likely to use air freight. We further examine how the relationship between firm productivity and transportation mode varies across different destination markets. We assume that consumers in high-income countries attach a bigger value to fast delivery than do consumers in low-income countries, which implies that given firm productivity, using air freight gives a higher payoff when exporting to rich markets than to poor markets. As a result, exports to high-income markets are more likely to be air shipped than exports to low-income markets. In addition, compared to low productivity firms, high productivity firms would gain more from air shipping goods to rich markets. Our theoretical framework further suggests that the relationship between firm productivity and transportation mode can be affected by a firm s engagement in global production networks and by its product R&D intensity. Guided by the theoretical model, we estimate how the probability of using air freight depends on firm productivity, and how the relationship between productivity and the use of air shipping varies across export markets of different income levels, across firms with different degree of engagement in global supply chains, and across industries with different R&D intensities. Because we are interested in exporter heterogeneity, we fully control for the demand side by including product-destination-year fixed effects. Our key empirical results can be summarized as follows. First, the probability of shipping by air is 2

4 on average 5.9 percentage points higher for firms in the 75 th TFP percentile than for firms in the 25 th percentile. Second, the positive relationship between the use of air freight and firm productivity is stronger for exports to high-income markets. The difference in the probability of using air freight between firms in the 75 th TFP percentile and those in the 25 th percentile is 7.6 percentage points for high-income markets and 2.1 percentage points for low-income markets. Thus, the patterns revealed by Figure 1 are confirmed by our detailed empirical analysis that compares firms in the same product-destination market in the same year. Finally, consistent with theoretical predictions, we also find that the gap between high- and low-productivity firms in the likelihood of using air freight is larger when firms are engaged in global production chains, and when products are more R&D intensive. These results can help deepen our understanding of transportation costs. Because air freight is typically priced times that of ocean shipping (The World Bank, 2009), our estimates imply that transportation costs can differ substantially between high- and low-productivity firms even when they export similar products to the same destination. This transportation cost difference between high- and low-productivity firms is greater when they sell to high-income markets. Thus, our results suggest that transportation costs incurred by individual firms should be viewed as an endogenous variable, depending on firm characteristics and on their interaction with the characteristics of destination markets. These results further imply that transportation costs may be only imperfectly correlated with geographic distance. Related to the transportation cost difference between high- and low-productivity firms is the product differentiation between these two types of firms. Our estimates suggest that compared to low productivity firms, high productivity firms are more likely to be specialized in time sensitive varieties. Related literature Our study builds on the growing literature in the choice of transportation mode for international trade. In particular, our paper extends the seminal work by Hummels and 3

5 Schaur (2013). They examine a trade-off between the speed and cost of delivery using product-level data on U.S. imports. Given the level of aggregation in their data, it is plausible that the same HS6 product may be produced by multiple firms of different characteristics. Thus, due to data limitations, their study cannot shed light on how the choice of transportation mode varies across firms. By contrast, we focus on the variation across heterogeneous firms within the same product-destination markets. To our best knowledge, we are the first to use detailed data at the firm-product-destination-year level to investigate the relationship between firm characteristics and transportation mode decisions. We show that firm productivity, along with its interactions with destination and product characteristics, plays a key role in determining a firm s transportation mode. We contribute to the literature by identifying the sorting pattern that high-productivity exporters are more likely to incur the higher cost of air shipping in order to serve overseas customers through fast delivery. Existing studies have also shown that the choice of transportation mode depends on product characteristics such as size, weight, sensitivity to timeliness, and on destination characteristics such as geographic distance. For example, Evans and Harrigan (2005), Aizenman (2004), and Hummels and Schaur (2010) document that the use of costly air shipment is a response to demand uncertainty. Hummels (2007) shows that products with high value per weight are more likely to be delivered by air shipping. Harrigan (2010) finds that the U.S. imports heavier goods from nearby countries, and lighter goods from faraway countries. In contrast to these studies, we are mainly interested in how the choice of transportation mode depends on firm characteristics. We account for the impact of product and destination characteristics by including product-destination-year fixed effects. Furthermore, our paper is closely related to the recent literature on quality and trade. Much of the quality-and-trade literature proxies product quality using product prices or a residual of quantity demanded controlling for prices. By contrast, the quality of transportation services is observed in our data. More productive firms incur not only 4

6 higher marginal costs of production to produce higher quality goods (as in the qualityand-trade literature), but also higher transportation costs to deliver their goods at faster speed (which is our new finding). Faster delivery can enhance consumption experience for final goods consumers. More importantly, faster delivery is more reliable and saves time. Given the estimates of time costs by Hummels and Schaur (2013), we can see that although high productivity firms pay more on delivery goods, they save money on time. The increased revenues generated from high quality goods and faster delivery can outweigh the higher costs of production and transportation. Our results complement the results in the quality-and-trade literature by looking at the quality of transportation services. Finally, the recent literature suggests global production fragmentation and trade in tasks increase the demand for fast delivery (Baldwin, 2011; Hummels and Schaur, 2013). Our work provides supporting evidence that the likelihood of using air freight is significantly higher when exporters are engaged in global production chains. This paper is organized as follows. Section 1 outlines the theoretical framework for a firm s choice of transportation mode. Section 2 provides details about the merged firm and trade dataset and the empirical specification. Sections 3 presents the main empirical results and robustness checks. Section 4 concludes the paper. 1 Theoretical Framework In order to understand the patterns revealed by Figure 1, we extend the Melitz (2003) model by endogenizing firm decisions about product quality and transportation mode. Similar to Hummels and Schaur (2013), we consider the trade-off between the speed and cost of delivery. However, differing from them, product quality is endogenously determined in our model. Endogenizing the quality choice decision allows us to explain several key patterns displayed in our data. For example, without endogenous quality, we will be unable to explain why high-productivity firms are more likely to use air freight. 5

7 Preferences We are interested in how firm characteristics affect their transportation mode decisions. Thus, we fix products and destinations. To simplify notation, the indices for product and destination are dropped. It is assumed that a representative consumer has the constantelasticity-of-substitution (CES) utility function U = q (ω) T σ 1 [ ( ) σ 1 ] σ σ m(ω) x (ω) dω ω Ω, where ω indexes varieties in the product set Ω, σ represents the elasticity of substitution between varieties (σ > 1), q (ω) is the quality of variety ω (q 1), x (ω) is the quantity demanded, m represents transportation mode (m = S for ocean shipping, or m = A for air freight), and T m(ω) captures how the delivery speed affects the utility from consuming quality products. Specifically, T m(ω) = T A T S if ω is shipped by air freight if ω is shipped by ocean freight where T A > T S > 0, which implies that consumers attach a higher value to faster delivery service. In our setup of consumer preferences, the quality of transportation services (speed of delivery) and the quality of products are complementary, implying that fast delivery increases the utility from consuming a high quality product more than from consuming a low quality product. Our complementarity assumption is based on the observation that high quality goods typically require higher quality transportation services such as greater care in handling and more rapid delivery. This feature of consumer preferences together with the endogenous choice of product quality plays a key role in delivering the main results of our theory. Consumer optimization yields the following demand for variety ω where p (ω) is the price of variety ω, and Y E/ ω Ω x (ω) = q (ω) (σ 1)T m(ω) p (ω) σ Y, (1) 6 ( p (ω) /q (ω) T m(ω)) 1 σ dω is the

8 market size and E is the expenditure. Ceteris Paribus, high quality varieties that are delivered by air generate higher demand. Firm s Choice of Transportation Mode Each variety is produced by one firm. Firms differ in their productivity φ. We assume that the marginal cost of production is q λ /φ, where λ > 0 denotes the cost elasticity of quality upgrading. This means that quality is expensive to produce, which is common to the recent quality-and-trade literature. Exporters also incur a per-unit shipping cost τ m. Air freight has a higher unit shipping cost than ocean freight, i.e., τ A > τ S > 0. Thus, the marginal cost of producing and shipping the product is q λ /φ+τ m. 2 In addition, exporters incur fixed costs of entering a foreign market, denoted by F. The profit from foreign sales can be written as π m (φ) = [ p ( q λ /φ + τ m )] x F. Plugging the demand function (1) into it yields π m (φ) = ) (p qλ φ τ m q (σ 1)Tm p σ Y F. (2) Firms choose price, quality, and transportation mode to maximize their profits. Conditional on product quality q and transportation mode m, the optimal price is p (φ; q, m) = σ ( ) q λ σ 1 φ + τ m. (3) It is the standard result of constant markup in a monopolistic competition model with CES preferences. Combined with this result, the first-order condition of profit maximization with respect to product quality yields the optimal level of quality (conditional on transportation mode m): ( ) 1 q φtm τ λ m (φ; m) =. (4) λ T m 2 Note that we follow Hummels and Schaur (2013) by assuming that the shipping charge is proportional to the quantity, not the value shipped. Also see Hummels and Skiba (2004) for empirical evidence that rejects the iceberg assumption on transportation costs. 7

9 To ensure that the optimal quality level is an interior solution, we make the following two assumptions. First, we assume φ φ 0 (λ T S ) /T S τ S so that all firms choose quality above the minimum requirement q 1. Second, we assume λ > T A > T S to ensure that the optimal quality, regardless of the transportation mode choice, is finite. To see the intuition, let us refer to equations (1) and (3). Equation (1) suggests that quality upgrading helps shift the demand upward; equation (3) implies that quality upgrading increases the unit profit, which together improve a firm profit from producing high-quality products. Then it can be seen from equation (2) that T m captures the profit increase from quality grading. On the other hand, firms need to incur bigger costs to produce higher quality, and λ represents the cost increase from upgrading quality. If T A λ or T S λ, the marginal benefit of quality upgrading would be greater than the marginal cost of quality upgrading, leading to infinitely high quality. Equation (4) implies that more productive firms produce higher quality, which is consistent with the quality-and-trade literature. Equation (4) also suggests that goods shipped by air (i.e., with a higher T and τ) tend to have higher quality (i.e., a higher q). Intuitively, high-quality products have a higher price (see equation (3)). Given the assumption that the shipping charge is proportional to the quantity, not the value shipped, shipping costs will be a smaller share of the final price for higher value products. Hence, goods shipped by air are likely to be associated with a higher quality/higher price. Now we turn to firms decision about transportation mode. Plugging equations (3) and (4) into the profit function (2) yields [ (λ ) ] σ 1 λ Tm λ ( ) σ 1 Tm σ 1 π m (φ) = (φt m ) Tm σ σ Y F. (5) λ τ m Thus, given the transportation mode, profits increase in firm productivity. Let φ be the productivity at which firms are indifferent between air mode and ocean mode, i.e. π A ( φ) = π S ( φ). Also let φ m denote the productivity cutoff (given the transportation mode) between exporters and non-exporters as endogenously determined by the Melitz 8

10 (2003) model, i.e., φ m satisfies π m ( φ m ) = 0. Given our focus on the transportation mode decision by exporters, we limit our analysis to the case when φ > φ m. The profit profiles against firm productivity are displayed in Figure 2. Let R (φ) be the ratio between gross profits from air shipping and those from ocean shipping: ( R (φ) π A (φ) + F π S (φ) + F = ( λ T A τ A λ T S τ S ) λ TA (φta ) T A ) λ TS (φts ) T S σ 1 λ, (6) ) where by definition, R ( φ = 1. We note that this profit ratio R (φ) increases in φ, implying that high productivity firms are more likely to choose air shipping. More precisely, higher productivity firms with φ > φ choose air freight, while lower productivity firms with φ < φ choose ocean shipping. The results are summarized in Proposition 1. Proposition 1 (Transportation mode and firm productivity) Under assumptions λ > T A > T S and φ φ 0 (λ T S ) /T S τ S, we have (i) R (φ) / φ > 0; and (ii) 2 R (φ) / φ 2 > 0 if (T A T S )(σ 1) / λ > 1. These imply that the probability of using air freight increases in firm productivity φ and φ 2. Proof. See appendix A. Predictions of Proposition 1 are consistent with the patterns revealed by Figure 1. We note that without the assumption of complementarity between speedy delivery and product quality, firms would always prefer to use ocean mode. This point can be seen from equation (6). Suppose the utility from consuming quality goods is independent of delivery speed, that is, T A = T S = 1. Then firms would always find it more profitable to use ocean mode because of its lower shipping cost (τ A > τ S ). We also note that without endogenizing product quality, high productivity firms (with a high φ) would have a lower production cost and a lower product price. Under the assumption that the shipping charge is proportional to the quantity, not the value shipped, shipping costs would be a larger share of the final price. Thus, high productivity firms would be more likely to use ocean shipping to avoid higher costs of air freight, which is 9

11 against the key pattern revealed by our data that high productivity firms are more likely to use air freight. 3 Now we investigate how a firm s transportation mode decision may vary across foreign markets it serves. In high-income countries, consumers are more willing to pay for precise product characteristics, which has triggered the replacement of mass production by flexible manufacturing that emphasizes quality and speedy response to market conditions (Milgrom and Roberts, 1990). These suggest that consumers in high-income countries are likely to place a higher value on speedy delivery. Thus, we assume that T A is higher for rich countries and lower for poor countries. For simplicity, we also assume that all countries apply the same discount factor to ocean shipping, that is, T S is the same across countries. 4 Then it is straightforward to show that given firm productivity, shipping by air to rich countries generates a higher profit than shipping by air to poor countries. As shown in Figure 3, the productivity at which firms are indifferent between air freight and ocean freight is lower for exports to high-income countries. This further implies that given firm productivity, the probability of using air freight is higher for exports to high-income countries. The results are summarized in Proposition 2. Proposition 2 (Transportation mode and income level of destination market) Under assumptions λ > T A > T S and φ φ 0 (λ T S ) /T S τ S, we have (i) R (φ) / T A > 0; (ii) 2 R (φ) / φ T A > 0; and (iii) 3 R (φ) / φ 2 T A > 0 if (T A T S )(σ 1) / λ > 1. These results imply that the probability of using air freight is higher for exports to highincome markets (i.e., those with a higher T A ). In addition, higher productivity firms are more likely to choose air freight to serve high-income markets. Proof. See appendix A. 3 More precisely, this point can be seen by treating q as a given parameter. Without loss of generality, set q = 1. Plugging equation (3) into (2), we can then rewrite (6) as R (φ) = [(1/φ + τ S ) / (1/φ + τ A )] σ 1. It is easy to show that R (φ) decreases in φ. 4 We note that the results hold when T A T S is bigger for high-income countries, but smaller for lowincome countries. That is, consumers in high-income countries are more willing to pay for fast delivery than do consumers in low-income countries. 10

12 Predictions of Proposition 2 are consistent with the patterns revealed by Figure 1: (i) given the same firm productivity, exports to high-income countries are more likely to be shipped by air; and (ii) high productivity firms are more likely to use air freight and the difference from low-productivity firms in the propensity of choosing air freight is larger for high-income markets than for low-income markets. Furthermore, our model can shed light on how the choice of transportation mode may be related to a firm s engagement in global production chains. When firms are engaged in global production sharing, the management of just-in-time delivery becomes more complex and challenging. Any delay in the middle stages of the production chain may cause domino effects, disrupting the entire production chain and thus damaging the sales of final goods. This implies that for firms that are more engaged in a global production network, using ocean freight could lead to a larger penalty when delays occur. Thus, we assume firms in a global production chain are likely to have a smaller T S than do firms that are not in a global production chain. 5 That is, firms in a global production chain face a higher discount factor for slow delivery than those that are not in a global production chain. Then we can show that given firm productivity, the profit from ocean shipping is lower for firms that are engaged in a global production chain and higher for firms that are not engaged in a global production chain. As displayed in Figure 4, the productivity at which firms are indifferent between air freight and ocean shipping is lower for firms that are engaged in a global production chain (i.e., with a smaller T S ), suggesting that these firms are more likely to use air freight given the same productivity and destination markets. The results are summarized in Proposition 3. Proposition 3 (Transportation mode and engagement in global production chains) Under assumptions λ > T A > T S and φ φ 0 (λ T S ) /T S τ S, we have (i) R (φ) / T S < 0; (ii) 2 R (φ) / φ T S < 0; (iii) 3 R (φ) / φ 2 T S < 0 if (T A T S )(σ 1) / λ > 1. These results imply that the probability of using air freight is higher for firms that are engaged in 5 We note that the results hold when T A T S is bigger for firms that are engaged in a global production chain, but smaller for those that are not engaged in a global production chain. 11

13 a global production chain (i.e., those with a smaller T S ). In addition, higher productivity firms are more likely to use air freight if they are engaged in global production sharing. Proof. See appendix A. Finally, we are interested in how the transportation mode decision may vary with products of different R&D intensities. We note that λ measures the cost elasticity of quality upgrading and is a product-specific parameter. As shown in Sutton (1998, chap. 3), under weak assumptions, in equilibrium industries with a lower λ are featured with high R&D intensities. The parameter λ is thus inversely related to an industry s R&D intensity. It is straightforward to derive that for high R&D intensity products (with a lower λ), the profit differential between air freight and ocean freight is larger. As shown in Figure 5, the productivity cutoff at which firms are indifferent between air freight and ocean freight is lower for high R&D intensity products, suggesting that firms are more likely to use air freight for these products. The results are summarized in Proposition 4: Proposition 4 (Transportation mode and industry R&D intensity) Under assumptions λ > T A > T S and φ φ 0 (λ T S ) /T S τ S, we have (i) R (φ) / λ < 0; (ii) 2 R (φ) / φ λ < 0; (iii) 3 R (φ) / φ 2 λ < 0 if (T A T S )(σ 1) / λ > 1. These results imply that the probability of using air freight is higher for R&D-intensive products (i.e., those with a lower λ). In addition, higher productivity firms are more likely to choose air mode for R&D-intensive exports. Proof. See appendix A. In the following we examine the predictions of Propositions 1-4 empirically. 2 Empirical Model and Data 2.1 Empirical Model A firm chooses air mode if the profit from using air freight is bigger than from using ocean freight, i.e., π A (φ) > π S (φ). As seen from equation (5), the profit is determined by firm, 12

14 product, and destination market characteristics. Thus, we parameterize a firm s profit as a function of firm characteristics (including productivity), and product-destination market characteristics. The probability of choosing air mode can be specified using the following linear probability model: prob(a fgct = 1 φ f,t 1, x f,t 1 ) = β 1 φ f,t 1 + β 2 φ 2 f,t 1 + x f,t 1 γ + D gct (7) where the dependent variable A fgct = 1 if firm f chooses to ship product g by air to destination c in time t, and A fgct = 0 if the firm chooses ocean mode; φ f,t 1 is firm productivity at a one-year lag; x f,t 1 is a vector of other firm characteristics at a oneyear lag, including firm size (measured by the log of employment), capital-labor ratio (measured by a ratio of fixed assets over employment), and foreign ownership. These additional firm controls could be correlated with firm productivity, and influence a firm s decision on transportation mode. In addition, the product-destination-year fixed effects, represented by D gct, are included to control for any product-, destination-, or year-specific factors that could affect a firm s transportation mode decisions. For example, Hummels (2007) documents that products with high value per weight are more likely to be shipped by air. Aizenman (2004) and Hummels and Schaur (2010) focus on models in which there is uncertainty in the quantity demanded in foreign markets. They show that fast delivery by air can smooth demand uncertainty because firms can wait until demand shocks are realized. This means that exports to markets with higher demand uncertainty are more likely to be air shipped. In addition, the air transportation industry is highly sensitive to fuel prices which can fluctuate over time and across markets. The inclusion of D gct can fully control for all these product-, destination-, or year-specific factors that may influence a firm s choice of transportation mode. Therefore, departing from the existing literature, we are mainly interested in the role of firm heterogeneity in determining a firm s choice of transportation mode. The 13

15 empirical relationship between transportation mode and firm heterogeneity is identified using the variation of transportation choices and firm characteristics within the same product-destination market for the same year. Specification (7) and its extension will be used to examine the hypotheses summarized in Propositions 1-4. Propositions 1 suggests that β 1 > 0 and β 2 > 0. To examine how the relationship between transportation mode and firm productivity may vary across destination markets, across firms with different degree of global production engagement, or across products with different R&D intensities, we will expand specification (7) by adding interaction terms of firm productivity (φ and φ 2 ) with destination market characteristics, the degree of a firm s engagement in global production sharing, and industry R&D intensities. 2.2 Data Our empirical analysis uses a matched firm-trade dataset from China. The data on international trade transactions come from China Customs and cover monthly imports and exports for We aggregate the monthly trade data within each year. Products are classified at the eight-digit HS level. Key variables include the value and quantity of trade, trading partners, transportation mode used for each trade transaction, and contact information for firms (e.g., company name, telephone, zip code, contact person). A shipment is defined as export of an eight-digit HS product by a firm to a country using a particular transportation mode in a particular year. The panel data of manufacturing firms for come from the annual surveys conducted by the National Bureau of Statistics (NBS). The survey contains information on financial statement and non-financial variables for all manufacturing firms that are either state owned, or are non-state enterprises with sales above 5 million RMB ( abovescale firms). Key variables include ownership, employment, capital stock, gross output, value added, wages, industry affi liation, and contact information for firms (e.g., company name, telephone, zip code, contact person). 14

16 We match trade transactions to manufacturing firms based on firms contact information. 6 The matched sample includes 76,642 Chinese exporters and covers more than 70 percent of total manufacturing exports. 7 The sample coverage of trade is comparable to the 75 percent reported in Bernard et al. (2005) about their link of trade transactions to U.S. firms. This matched dataset excludes non-manufacturing firms that purely serve as trade intermediaries. Trade between China and adjacent countries/regions is dominated by land transportation, which includes road, rail, and post. By contrast, merchandise trade with nonadjacent countries is nearly all transported by air or ocean. Because China is far away from its major markets in North America and in Western/Northern Europe, air freight is needed for time sensitive products. China s portion of air transportation among developing countries is significant. Based on the World Bank report (Bofinger, 2009), China represented above one third of all global air cargo traffi c for the entire developing world in Because we are interested in the choice between air mode and ocean mode for international trade, our following empirical analysis will exclude trade with adjacent countries/regions which is dominated by other transportation modes such as road, rail. 8 Table 1 provides detailed information on air shipping over the sample period ( ). Columns 1-4 show statistics for the full sample, while columns 5-8 report statistics of the smaller sample where trade with bordering countries/regions is excluded. Using our data for 2006, the number of shipments involved in trade with nonadjacent countries was 82 percent of the total number of shipments in the whole sample, and the value of exports to nonadjacent countries was 71 percent of the total value of exports in the whole sample. Since trade with adjacent countries/regions is mainly by land transportation, we observe 6 Unfortunately, the trade transaction data and the panel data of manufacturing firms use different ID numbers to identify firms. As a result, we need to match the two datasets using firms contact information. 7 The panel data of manufacturing firms report export values without any information on destinations or products. The trade coverage rate of 70 percent is calculated as a ratio of the total export value by exporters in the merged firm-trade sample to the total export value by all exporters in the panel data of manufacturing firms. 8 Exports to Hong Kong constitute the majority share of Chinese trade with adjacent countries/regions. Trade with Hong Kong is nearly all transported by land given the proximity of Hong Kong and Guangdong province a hub of manufacturing export in Southern China. 15

17 a slightly higher share of air shipments for the smaller sample. In addition, the value share of air shipments is higher than the number share of air shipments, indicating that goods shipped by air tend to have higher values. Using the smaller sample, air cargo as a share of export value was 28 percent in 2006, and the number of air shipments as a share of total number of shipments was 16 percent. 9 Finally, as shown in columns 2 and 6, the value share of air shipping increases over the sample period. By contrast, the number share of air shipping appears to be more stable, as illustrated by columns 1 and 5. See Appendix B for more detail about the data. 3 Empirical Results In this section we report empirical evidence supporting the hypotheses summarized in Propositions Firm Productivity Proposition 1 predicts a positive relationship between the propensity of using air freight and firm productivity. We measure firm productivity by total factor productivity (TFP) estimated using the Levinsohn and Petrin (2003) method. Details about the TFP measure are given in Appendix C. In addition to TFP, we use labor productivity as robustness checks. Column 1 of Table 2 reports the basic results. The coeffi cient on firm productivity is significantly positive, indicating that high productivity firms are more likely to use air freight than low productivity firms. The estimated coeffi cient on TFP implies that the probability of using air freight is 2.5 percentage points higher for firms in the 75 th TFP percentile than for firms in the 25 th TFP percentile. 9 Cristea et al. (2013) document that in 2004, air cargo as a share of export value was 29 percent for the UK, 42 percent for Ireland, and 51 percent for Singapore. Thus, the use of air shipping by Chinese firms is comparable to UK firms. 16

18 In column 2, we add controls for firm size, capital-labor ratio, and foreign ownership, which are correlated with firm productivity and affect firms transportation mode decisions. We find that more capital intensive firms and foreign owned firms are more likely to use air freight. Because these firms are more likely to produce higher quality and higher valued products, the increased propensity of using air freight is consistent with the theoretical framework outlined above. 10 On the other hand, the implied effect of productivity on the probability of using air mode reduces slightly, reflecting the fact that TFP is positively correlated with capital-labor ratio and foreign ownership in our sample. In column 3, we add TFP 2 to capture the nonlinear relationship between firm productivity and the propensity of using air freight. Consistent with Proposition 1, the coeffi cients on TFP and TFP 2 are significantly positive. The estimated coeffi cients imply that the probability of air shipping is 5.9 percentage points higher for firms in the 75 th TFP percentile than for firms in the 25 th TFP percentile. Given the number share of air shipments was about percent in our sample (see Table 1), the estimated difference between high and low productivity firms in the likelihood of using air freight is big. In addition, compared to the specification in column 2, adding TFP 2 can better capture the increased propensity of using air freight by high productivity firms. The results in column 3 are also consistent with the nonlinear pattern revealed by Figure 1. Because air freight is typically priced times that of ocean transport (The World Bank, 2009), our estimates imply that transportation costs can differ substantially between high and low productivity firms that export similar products to the same destination, depending on which transport mode is used. 11 On the other hand, although more productive firms incur larger shipping costs, they may save money in inventory costs. More importantly, the time saved by using air freight can be substantially large for trade between China and its major markets in the North America or West/North Europe. For 10 Using the matched firm-trade dataset from China, Ge et al. (2014) find that within the same productdestination markets, foreign owned firms, more productive and capital intensive firms have higher export prices. 11 Unfortunately, our dataset does not contain information on freight costs. 17

19 example, the distance from Guangzhou terminal in Guangdong province (a hub for manufacturing and export in Southern China) to Los Angles terminal in the United States is 11,923 kilometers. The transit time for ocean cargo between the two cities is estimated to be about 19 days. 12 In contrast, air shipments may take just 1-2 days. Thus, our results suggest that compared to low productivity firms, high productivity firms are more likely to produce higher valued and more time sensitive varieties within the same product category. 3.2 Income Level of Destination Countries The relationship between productivity and the likelihood of using air freight may vary across export destination countries with different income levels, as predicted by Proposition 2. We measure the income level of destination countries using GDP per capita drawn from the World Development Indicators database. To examine Proposition 2, we expand specification (7) by adding destination income level and its interaction with firm productivity. In columns 1-3 of Table 3, destination markets are divided into two groups: the highincome group includes countries with GDP per capita above the sample mean ($9,484 for 2006), and the low-income group includes those with GDP per capita below the sample mean. The coeffi cients on TFP and TFP 2 capture the impact of firm productivity on the transportation mode choice for exports to low-income destinations, while the interactions between firm productivity and the high-income destination indicator reflect the differential effect of productivity between high- and low-income destinations. Column 1 includes destination country specific variables: the high-income destination indicator and the log of geographic distance from China. Accordingly, the productdestination-year fixed effects are replaced with product-year fixed effects. Consistent with the predictions of Proposition 2, exports to high-income destinations are more likely to be 12 The information on distances and estimated transit time for ocean cargo can be obtained from the website of SeaRate at 18

20 air shipped than exports to low-income markets. 13 The coeffi cient on distance is significantly positive, indicating that exports to distant markets are more likely to be shipped by air. This is because the gains from using air rather than ocean shipping are more pronounced on longer routes (Hummels, 2007). For shorter distances air freight rates per kilometer are higher because a greater part of the trip is spent on the ground and more time in the air is spent climbing and descending. Column 1 further shows that higher productivity firms are more likely to use air freight and the propensity of using air mode is even greater when exporting to high-income destination markets. In column 2, we include product-destination-year fixed effects to fully control for the demand side. Both bilateral distance and the high-income destination indicator are now subsumed in the fixed effects. The coeffi cients on TFP and its interaction with the highincome destination indicator remain positive and are not significantly different from the estimates in column 1. In column 3, we further add TFP 2 and its interaction with the high-income destination dummy variable. The coeffi cient for TFP 2 is insignificant, suggesting that the nonlinear effect of TFP is statistically insignificant for exports to low-income markets. By contrast, the nonlinear effect of TFP is stronger for exports to high-income markets, as seen from the coeffi cient on the interaction between TFP 2 and the high-income destination indicator. These estimates imply that the difference in the probability of choosing air mode between high and low productivity firms (i.e., in the 75 th versus 25 th TFP percentile) is 2.1 percentage points for exports to low-income markets and 7.6 percentage points for high-income markets. In columns 4-6 we measure the income level of destination markets by the log of GDP per capita. The results are similar to those reported in columns 1-3. In order to understand the economic magnitude of the estimated coeffi cients, we compare the TFP effect on the use of air freight for exports to markets with per capita income in the 75 th 13 To facilitate interpretation, TFP is expressed as relative to its sample mean. So the coeffi cient on the high-income destination indicator measures the difference between high- and low-income destination markets for a representative firm with average TFP. 19

21 percentile ($8,784 for 2006) with that for markets with per capita income in the 25 th percentile ($900 for 2006). As reported at the bottom of column 6, the difference between high- and low-productivity firms in the likelihood of using air freight is 3.6 percentage points for markets with per capita income in the 25 th percentile and 5.9 percentage points for markets in the 75 th percentile. Therefore, the results in Table 3 provide strong support for Proposition 2, and are consistent with the patterns displayed in Figure 1. We note that our results are not simply driven by the fact that high-income countries tend to import higher priced products (Hallak, 2006). Without a higher value placed on speedy delivery, ocean freight is always preferred because of its lower shipping cost. Furthermore, our results change little after the unit value of exports is included in the regressions. 14 The higher value placed on fast delivery by high-income countries can come from two sources. First, consumers are more willing to pay for precise product characteristics when their income rises. At the same time, in high-income countries the mass production model is replaced by the flexible manufacturing model that emphasizes quality and speedy response to market conditions (Milgrom and Roberts, 1990). Thus, the demand for fast delivery is higher in rich markets. Second, with reduced transportation and communication costs, firms from advanced countries have increasingly outsourced their non-core activities to overseas suppliers according to comparative advantage. Faster transportation can meet the growing industrial demand for timeliness that is coincided with a growing dispersal of operations around the globe (Hummels, 2007). In the following we are going to examine the impact of global production sharing on the propensity of using air freight. 14 These results are available upon request. However, we would prefer not to include the unit value of exports in our main specifications because the export price and transportation mode are jointly determined in the theoretical model. In particular, as seen from equations (3) and (4), the equilibrium price is a function of φ, T m, τ m and λ. 20

22 3.3 Engagement in Global Production Chains Increased global production sharing has been well documented in the literature (Hummels et al., 2001). As noted by Hummels and Schaur (2013), time costs may be magnified in the presence of multi-stage global supply chains. Thus, when countries specialize in different stages of production, the demand for timely delivery increases. In this section we examine how a firm s engagement in global production chains could influence its transportation mode decisions. Our matched firm-trade dataset has detailed information on both exports and imports of a firm, allowing us to identify whether the traded goods are intermediates or not. In columns 1-2 of Table 4 we measure whether a firm engages in global production sharing using a dummy variable indicating whether the exported good is an intermediate or not. Suppliers of intermediate inputs to foreign producers are in the upstream of global production chains. Since the measure is product specific, it is subsumed into the product-destination-year fixed effects. The coeffi cient on the interaction between TFP and the intermediate export dummy is significantly positive, suggesting that the TFP effect on the use of air freight is stronger for firms that export intermediates than for firms that export final goods. In column 2, we add TFP 2 and its interaction with the intermediate export dummy. The estimates imply that the difference between high- and low-productivity firms in the propensity of using air freight is 4.1 percentage points if firms export final goods, but 7.4 percentage points if firms export intermediate inputs. In columns 3-4 we use an indicator for intermediate imports to capture whether a firm engages in global production networks. Since many of these firms purchase imported inputs, process, and export finished goods, the measure can be viewed as an empirical counterpart of the concept importing to export (I2E) coined by Baldwin and Lopez- Gonzalez (2013) to describe the case when foreign intermediates are used to produce goods that are subsequently exported. Unlike the product-specific measure used in columns 1-2, the indicator for intermediate imports is firm specific. 15 The coeffi cient on the intermedi- 15 Unfortunately, we do not observe which imported inputs are used to produce the specific product 21

23 ate import dummy is significantly positive, indicating that given the same productivity, exporters that use imported intermediates are more likely to use air freight. For firms with average TFP, the probability of using air freight is about 1.3 percentage points higher for exporters that use imported inputs than for exporters that use domestic inputs only. 16 Furthermore, the interaction between TFP and the intermediate import dummy is significantly positive, suggesting that the difference between high- and low-productivity firms in the propensity of air shipping is bigger for exporters that use imported inputs than for exporters that use domestic inputs only. In columns 5-6 we use the share of imported inputs in total intermediate inputs to capture the different extent of engagement in global production sharing. The results are consistent with columns 3-4. Given firm productivity, firms that use more imported inputs are significantly more likely to use air shipping than those that use more domestic inputs. The estimate suggests that a 10 percentage points increase in the use of imported inputs translates into an increase in the probability of using air freight by nearly 1 percentage point. In addition, the interaction between TFP and the share of imported inputs is significantly positive. In order to understand the magnitude of the TFP effects, we compare exporters in the 25 th percentile for the share of imported intermediates with exporters in the 75 th percentile. As reported at the bottom of column 6, the difference between highand low-productivity firms in the propensity of using air shipping is 2.1 percentage points for firms that use less imported inputs, and 6.1 percentage points for firms that use more imported inputs. In sum, Table 4 provides strong evidence for Proposition 3. We find that (i) given the same productivity, engaging in a global production network is positively correlated with the likelihood of using air shipping; (ii) more productive firms are more likely to use air freight if they are engaged in a global production chain (as implied by the interaction that is exported. 16 To facilitate interpretation, TFP is expressed as relative to its sample mean. So the coeffi cient on the indicator of global production engagement in columns 3-4 measures the difference between exporters that use imported inputs and those that only use domestic inputs when both types of exporters have average TFP. 22