Fixed Export Costs and Trade Patterns: The Case of China

The World Economy The World Economy (2017) doi: 10.1111/twec.12425

Fixed Export Costs and Trade Patterns: The Case of China Yu Gao1, Yin He2 and Xiaopeng Yin2 1

Institute of International Business, Shanghai University of International Business and Economics, Shanghai, P. R. China and 2Department of International Trade, School of International Trade and Economics, University of International Business and Economics, Beijing, P. R. China 1. INTRODUCTION AND LITERATURE

A

S globalisation prevails and production processes are being segmented and distributed to different parts of the world, trade patterns, and mainly the choice between the processing trade and ordinary trade, have become an important issue in the international trade for developing countries. Processing exporters differ from ordinary exporters in that the business mode of this type of exporters is to import all or part of the raw and auxiliary materials, parts and components, accessories, and packaging materials from abroad in bond, and re-export as finished products after processing or assembly. The processing export trade is popular in China. In 2007, about half of China’s total exports (Figure 1) was consisted of processing exports. According to Baldwin and Lopez-Gonzalez (2015), on the supply side China is a factory economy with its comparative advantage in final assembly. However, processing exporters, who mostly engage in these final assembly activities, are often criticised for their low value added. For example, Xing and Detert (2010) find that the estimated wholesale cost of an iPhone shipped from China is $178.96, while the value added by Chinese workers at Hon Hai Precision Industry Corporation accounts for just 3.6 per cent of the total or $6.50. Due to the importance and uniqueness of China’s trade patterns, scholars have begun to examine how the two types of firms’ perform differently and the factors that affect a firm’s choices between China’s two different trade patterns. When making this decision, a firm needs to choose whether to export and which trade pattern to adopt. The theory of heterogeneous firms, developed by Melitz (2003) and many other subsequent researchers, tells us that firms only export when they pass a certain productivity threshold. This means exporters are usually more productive and profitable than non-exporters (Bernard et al., 2003; Eaton et al., 2004; Helpman et al., 2004, etc.). Castro et al. (2016) build up a fixed cost index and explore how fixed export costs and productivity jointly determine the export decisions of the firm. They also construct indices of fixed export costs for each industry region year triplet and match them to domestic (non-exporting) firms. Further, they demonstrate that increasing fixed export costs and productivity have opposite effects on the propensity to export and that they could substitute for each other. However, this substitution effect is weaker for high-productivity exporters. In addition, Castro et al. (2016) find that high-productivity non-exporters face greater fixed export costs than low-productivity exporters do. However, these studies have no implications for decisions on trade patterns.

We thank Keith Maskus, Edwin Lai, Albert Hu, Huiwen Lai, Mi Dai, Zhi Yu and participants of 2014 UIBE workshop on Technology Innovation and Intellectual Property Rights for their valuable comments. All remaining errors are our responsibility.

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© 2016 John Wiley & Sons Ltd

FIXED EXPORT COST AND TRADE PATTERNS

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FIGURE 1 Share of Processing Exports and Ordinary Exports in Total Exports in China (1999–2008) [Colour figure can be viewed at wileyonlinelibrary.com] 60 55 50 % 45 40 35 30 1999

2000

2001

2002

2003

Ordinary export

2004

2005

2006

2007

2008

Processing export

Studies on trade patterns in China are relatively scarce. Lu et al. (2010) finds that foreign affiliates engaging in exports are significantly less productive than foreign affiliates without exporting behavior in China, while Lu (2011) extends such phenomenon to general situation for all Chinese firms. On the other hand, Ahn et al. (2011) discuss the importance of trade media in China’s exports. Recently, Yu (2015) empirically demonstrates that low-productivity firms, large firms and foreign firms are more likely to engage in the processing trade. Manova and Yu (2012) find that value added, profits and profitability rise from pure assembly to processing, to ordinary trade. However, more profitable trade regimes require higher upfront costs. As a result, credit constraints are needed here to induce firms to conduct more processing trade and pure assembly. This paper, based on the idea developed by Castro et al. (2016), uses the firm-level data for China for the period 2000–06 to carry out a series of empirical analysis and shed more light on the role of productivity and fixed trade costs in co-determining a firm’s choice between the processing export and ordinary export in China. We argue that, given the characteristics of the processing trade, processing exporters can obtain foreign orders much easier than ordinary traders. The logic is that, compared with ordinary exporters, processing exporters can more easily obtain orders either directly from foreign downstream firms/industries or from intermediate trade agents who work for downstream firms/industries. Thus, the great savings in fixed trade costs give lower productivity or/and lower value-added firms a chance to survive in the foreign market as processing exporters. This paper is designed as follows. Section 2 describes the data. Section 3 presents the empirical results. Section 4 concludes this paper. 2. DATA

Two main data sets are used in this research. The first data set is the Chinese industrial enterprises database (firm data, hereafter) from the National Bureau of Statistics (NBS) for the period 1998–2008. These data account for all state-owned enterprises (SOEs) and other manufacturing firms in China with annual sales value, of more than RMB 5 million Yuan. © 2016 John Wiley & Sons Ltd

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Y. GAO, Y. HE AND X. YIN

This database includes firms’ basic information (such as firms’ name, ownership, location, property share and contact information), financial, employment and production information. The second data set is taken from the China Customs Statistics database for the period 2000–06. This database shows all transaction-level import and export data and includes firms’ basic information, trade patterns, trade volume, trade value and prices. To carry out the empirical analysis, it was necessary to merge the above two data sets. The firm-ID coding systems for these two data sets are different, so the method of Ge et al. (2011) was used to merge the firm data with the customs data for exporters only. To be more specific, we first matched the two data sets by company name in each sample year. Then, the postal code and telephone numbers were used to match the remaining firms. Some firms with the same postal codes had different telephone numbers, depending on the data set. Consequently, it was necessary to manually determine whether they were the same or two different firms. Table 1 shows the matched results. Columns (1), (3) and (5) are the number of firms in the firm, customs and matched data sets, respectively. Columns (2), (4), (6) and (7) show the total export value recorded by the firm data, custom data and the matched data, respectively. As shown in Table 1, the matched data set consists of 50–67 per cent of the firms listed in the firm data set and 30–40 per cent of the firms listed in the customs data set, depending on the year. Regarding the export value, the matched data set consists of 50–67 per cent of the total export value reported in the firm data set or 40–60 per cent of that reported in the customs data. That is, more than half of China’s SOEs and medium-to-large-size enterprises are engaged in exporting. In the meanwhile, more than 60 per cent of the firms in China that exported between 2000 and 2006 were small non-SOEs and they had relatively small average export values. The merged data set provides basic, financial and trade information of firms in China between 2000 and 2006. The total value of exports of each firm, in a specific year, is the sum of all its export transactions for that year. Before we get to the econometric analysis, let us take a look at some stylised facts of the difference between the two types of trade patterns in China (Table 2). Table 2 shows some interesting facts and differences between the two trade patterns in China. First, the total sales in both domestic and foreign markets of processing exporters were TABLE 1 Basic Information of Matched Data Set of China NBS Industry Data Set and China Custom Trade Data Set Year

2000 2001 2002 2003 2004 2005 2006

NBS Firm Data

Custom Data

Matched Data

(1) Number of Firms

(2) Export Value (Billion RMB)

(3) Number of Firms

(4) Export Value(Billion RMB)

(5) Number of Firms

(6) Export Value in Custom Data(Billion RMB)

(7) Export Value in Industry Data (Billion RMB)

37,200 40,804 45,306 50,906 76,990 75,624 79,310

1,458 1,625 2,006 2,694 4,048 4,774 6,055

62,771 68,487 78,612 95,688 120,590 144,030 171,205

2,063 2,405 2,695 3,629 4,914 6,234 7,714

19,182 20,828 23,910 28,176 46,463 47,770 52,747

742.95 903.71 1081.37 1509.95 2644.55 3183.24 3793.80

866.34 960.53 1266.23 1666.20 2951.48 3354.59 4201.06



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TABLE 2 Statistics of Main Characteristics of the Two Trade Patterns Variable

Explanation

log(Sales) log(Domestic Sales) log(Export) log(TFP)

Total sales Domestic sales Total export Total factor productivity log(Labour Labour Productivity) productivity log(Sale Sales expense Expense) log(Firm Age) Age of firm log(K/L) Capital-labour ratio log(Labour) Labour used log(Capital) Total capital

Full Samples

Processing Exporters

Ordinary Exporters

obs.

obs.

obs.

Mean SD

Mean SD

Mean

SD

100,177 10.57 1.29 23,684 10.62 1.37 76,493 10.56*** 1.27 100,177 7.08 4.57 23,684 4.21 4.75 76,493 7.97*** 4.13 100,177 100,177

9.49 1.70 23,684 10.22 1.59 76,493 7.19 1.30 23,684 6.93 1.34 76,493

9.27*** 1.66 7.26*** 1.28

100,177

3.84 1.09 23,684

3.51 1.18 76,493

3.95*** 1.04

100,177

6.75 1.74 23,684

6.35 1.78 76,493

6.88*** 1.71

100,177 100,177

2.00 0.74 23,684 3.77 1.34 23684

2.01 0.63 76,493 3.94 1.27 76,493

1.99*** 0.77 3.72*** 1.35

100,177 100,177

5.29 1.14 23,684 9.06 1.52 23,684

5.62 1.12 76,493 9.56 1.35 76,493

5.19*** 1.13 8.91*** 1.54

Notes: (i) TFP is calculated according to Levinsohn and Petrin (2003). (ii) We estimate the parameters of the production functions with each four-digit CIC code. (iii) All monetary values are real values using the code of Brandt et al. (2012) to transfer the book value into the real value. The bold values are bigger values when comparing the means of different characteristics of the two trade patterns. (iv) T-test significance of the two trade patterns are shown in the ‘Mean’ column of ‘Ordinary Exporters’. (v) ***p < 0.01

significantly larger than that of ordinary exporters over the period 2000–06. However, different trade patterns obviously have different advantages, depending on the market: processing exporters export significantly more than ordinary exporters, while ordinary exporters sell significantly more in the domestic market. Second, processing exporters in China are significantly more capital intensive compared with ordinary exporters. However, the latter have significantly higher labour productivity and total factor productivity. Again, these interesting facts lead to the question of what helps processing exporters to: (i) have significantly lower productivity, but export more; and (ii) sell more in total but spend less on sales expenses. As shown in the last row in Table 2, ordinary exporters have significantly higher sales expenses than processing traders. However, understanding the key mechanism behind these expenses would help. Although it is obvious that sales expenses cannot be equal to fixed trade costs, we are going to tackle this problem in the next section and then argue that the fixed trade costs for processing exporters might be the key factor in helping them save money and sell more products, especially in foreign markets. Exporters can be divided into three groups: (i) firms doing only ordinary trade; (ii) firms doing only processing trade; and (iii) firms doing both processing and ordinary trade.1 As this

1

There are some exporting firms that cannot be matched in China Custom’s database. They are not included in our empirical analysis in this paper.


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paper focuses on differences in trade costs, productivity and profit between processing and ordinary trade exporters, while firms in the third group could have a more complex and/or mixed production mode with possible cost sharing/transferring between the two choices of exporting patterns, only the first two groups are included in our empirical analysis. 3. EMPIRICAL RESULTS

a. Determinants of Trade Pattern Choices First, we examine the determinants of a firm’s choice between the two trade patterns in China. There are many factors affecting this choice, such as the productivity (Melitz, 2003), a firm’s ownership (Yu, 2015) and location. We adopt a logit model to examine a firm’s trade pattern decision between the processing export and ordinary export as follows: PrðProcessingit ¼ 1Þ ¼ U½a0 þ a1 logðprodit Þ þ a2 logðsales expenseit Þ X X X X Zit þ c Dj þ g Dr þ d Dt þ eit ; þb i

j

r

(1)

t

where Processingit is a dummy variable for firm i’s trade pattern in year t, which takes the value of one if the firm is a processing exporter and zero if it is an ordinary exporter; Prodit is the productivity of firm i in year t, which is measured by either its labour productivity (i.e. value added per worker) or total factor productivity (TFP); Sale expenset is the total sales expenses of firm i in year t. Sales expenses is a rough measure for the key variable, fixed export costs, the focus of this paper. It is obvious that a firm’s total sales expenses include expenses for both domestic and foreign sales, and variable and fixed sales costs. Thus, to ensure that total sales expenses are the right measure for fixed export costs, the contamination of domestic sales and variable costs needs to be dealt with later in this section. We also control the firm’s other characteristics, such as capital stock, amount of labour employed, and firm age, and also industry-specific effects (Dj), provincial-level regional-specific effects (Dr) and year-specific effects (Dt). The marginal production cost for the two trade patterns might also be different and affect the firm’s choice of trade pattern. To be more specific, processing traders are often just low value-added assemblers. So firms with high marginal production costs might have a lower tendency to be processing exporters. Unfortunately, we do not have a variable that captures marginal production costs. To some degree, the variable for productivity, ‘value added per worker’, used in the regression, can be treated as a rough proxy for this, as high value-added industries and firms are often industries and firms that also have high marginal production costs. Again, our focus in this section was to first examine whether processing exporters have a significantly lower productivity than ordinary exporters, after controlling for all other factors. We then explore how a firm’s fixed trade costs affect its choice of the trade patterns, after controlling for productivity. Table 3 presents the results. Column (1) in Table 3 uses TFP to measure a firm’s productivity, while column (2) uses labour productivity (value added per worker). Regardless of the measure used for a firm’s productivity, the results show that, after controlling for the industry, location and year-specific effects and other firm characteristics (capital stock, labour employed, firm age, and ownership), an exporter with lower productivity and lower total sales expenses has a significantly



0.171*** (0.0137) 0.288*** (0.00947) 0.339*** (0.0118) 0.353*** (0.0167) 0.155*** (0.0194) 7.219*** (0.788) Yes

Yes Yes Yes 98,069

0.287*** (0.00946) 0.323*** (0.0116) 0.489*** (0.0162) 0.156*** (0.0194) 7.147*** (0.788) Yes

Yes Yes Yes 98,069

0.175*** (0.0136)

Yes Yes Yes 25,407

0.211*** (0.0208) 0.674*** (0.0262) 0.598*** (0.0345) 0.551*** (0.0399) 21.00 (662.7) Yes

0.268*** (0.0301)

Notes: (i) We control the industry fixed effect at the four-digit CIC code-levelled effect. (ii) Robust standard errors in parentheses. (iii) ***p < 0.01, **p < 0.05.

Ownership fixed effect Industry fixed effect Province fixed effect Year fixed effect Observations

Constant

log(Firm Age)

log(Labour)

log(Capital)

log(Labour Productivity) log(Sales Expense)

log(TFP)

(3)

(1)

(2)

Pure Exporters

Full Sample

Yes Yes Yes 25,407

0.271*** (0.0301) 0.210*** (0.0208) 0.698*** (0.0265) 0.389*** (0.0358) 0.550*** (0.0399) 21.07 (661.0) Yes

(4)

Yes Yes Yes 8,352

0.236*** (0.0291) 0.203*** (0.0366) 0.440*** (0.0525) 0.108** (0.0478) 4.983*** (1.497) Yes

0.183*** (0.0423)

(5)

Yes Yes Yes 8,352

0.177*** (0.0423) 0.237*** (0.0291) 0.220*** (0.0372) 0.299*** (0.0535) 0.110** (0.0478) 5.067*** (1.497) Yes

(6)

First Year Exporters

No Yes Yes 1,358

0.130** (0.0605) 0.658*** (0.0726) 0.414*** (0.0873) 0.00871 (0.0869) 7.803*** (2.063) Yes

0.276*** (0.0813)

(7)

No Yes Yes 1,358

0.212** (0.0875) 0.146** (0.0606) 0.688*** (0.0735) 0.249** (0.0988) 0.000843 (0.0866) 8.151*** (2.047) Yes

(8)

First-time Pure Exporters

TABLE 3 Choice of Trade Patterns (Dependent Variable: Processing = 1 if the Firm is a Processing Exporter/0 if the Firm is an Ordinary Exporter)

FIXED EXPORT COST AND TRADE PATTERNS 1619

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higher probability of being a processing exporter. In addition, an exporter with a larger size of capital stock, a larger number of workers and a longer history is also more likely to be a processing exporter. These results are interesting in that processing exporters in China are larger in size in terms of both capital and labour, but they also have a significantly lower productivity. The typical observations made on heterogeneous firms in Melitz (2003) and other studies predict that firms need to pass a certain threshold before beginning to export. The fact that lower productivity firms have a significantly larger probability of choosing processing as their trade pattern might imply that this trade pattern provides firms with some advantage in terms of substituting for their low productivity. Here, we adopt the idea of Castro et al. (2016) that fixed export costs might be a substitute for productivity. We argue that the processing trade has its natural advantage in saving the lower productivity firm fixed export costs, so this type of firm will usually choose the processing trade as their export pattern. Columns (3) and (4) in Table 3 use a subsample of pure exporters to carry out the analyses. Using this subsample means, the total number of firms becomes much smaller, decreasing from 98,096 to only 25,407 firms. This is to say, about 75 per cent of China’s exporters also sell in the domestic market and only 25 per cent serve the foreign market. The purpose of composing this subsample is to eliminate the possibility of domestic sales contaminating the data and to ensure that sales expenses only come from exporting behaviour. The results show that, all the previous results remain unchanged, for pure exporters with no domestic sales and it is true that lower sales expenses are a key determinant for firms in China becoming processing exporters. The heterogeneous firms in Melitz (2003) and many subsequent theoretical and empirical studies also predict that exporting would reversely affect the productivity of a firm. To tackle this issue, we use an even smaller subsample of first-time exporters2 to avoid the impact of exporting on productivity. The results are shown in Table 3, columns (5) and (6). All of the previous results remain unchanged. Finally, in order to make sales expenses as close as possible to the firm’s fixed export costs, we repeat the analysis, this time using first-time exporters who do not sell in the domestic market, that is first-time pure exporters. This method would not only eliminate the possibility of domestic sales data contaminating fixed export costs, but it would also alleviate the impact of a productivity change caused by the trade pattern. As a result, although the total sales expenses used in this regression would still not be a perfect measure for fixed export costs, they would be a close enough proxy. The subsample now contains only 1,358 firms, and the results are shown in Table 3, columns (7) and (8). All previous results remain unchanged: firms with lower productivity and lower sales expenses have a higher probability of choosing to be processing exporters. Based on Yu (2015), some trade economists suspect that the difference in the tariff rates of intermediate input imports could contribute the difference in production costs between processing and ordinary trade, which might affect the choice between the two patterns. These are not the same as the fixed export costs we focus on in this paper. However, because we do observe that processing exporters import more intermediate goods and China has export-

2

‘First-time exporters’ refers to firms that export in year t and do not have any exports in previous years. In the empirical analysis, we only use the data at year t (firms’ first exporting year) for these firms. This makes the analysis in this part a cross-sectional one. © 2016 John Wiley & Sons Ltd


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tax-rebate policies, we still want to eliminate any possible impact from intermediate input tariff rates on a firm’s choice between the two trade patterns. To identify this potential argument, we include a dummy variable for whether the firm has intermediate inputs from overseas and the tariff rate of the firm’s imported intermediate products into our empirical model. Due to space limitations, we are unable to include these results in this paper,3 but our key results still hold after controlling for this aspect. Our argument confirms that lower productivity exporters have a significantly higher probability of becoming processing exporters. An important reason for this is that the processing trade pattern gives firms a chance to save on fixed export costs. In this sense, in the determinants of export patterns, productivity and fixed trade costs are substitutes for each other. This is similar to the results of Castro et al. (2016). b. Difference in Fixed Export Costs The hypothesis in the previous sections stated that the fixed export costs between the processing and ordinary exporters should be significantly different because processing exporters can save a great deal, in exploring and penetrating foreign market, by taking orders directly from related downstream industries or trade intermediaries that work for these industries. However, ordinary traders usually need to open up a new foreign market for their products and maintain this market by themselves. Unfortunately, our database does not provide a direct measure that captures fixed export costs or export expenses. Only total sales expenses are available. These include total sales expenses for domestic and foreign trade, and fixed and variable expenses. Fortunately, rather than calculating the precise value or rank of fixed export costs, as in Castro et al. (2016), the main focus of this paper was to see how fixed export costs differ between the two trade patterns in China and how this difference affects a firm’s trade pattern choice. The following steps were taken to separate fixed export costs from the other above-mentioned factors. First, we included in the regression total exports, so that the variable for sales expenses in exporting is controlled for to some degree. For a similar purpose, we also controlled for domestic sales in the regression to reduce the possibility of contamination by domestic sales costs data. The basic regression equation in the section is as follows: logðSales expenseit Þ ¼ a0 þ a1 Processingit þ a2 log ðExportit Þ X Zit þ a3 logðDomestic salesit Þ þ b þc

X j

Dj þ g

X r

Dr þ d

X

i

(2)

Dt þ eit :

t

The regression results are shown in Table 4. As was expected, column (1) in Table 4 shows that the higher value of total exports corresponds with significantly higher levels of sales expenses. This controls for the variable costs of exporting. In addition, firms with more domestic sales also have significantly higher levels of sales expenses. Thus, controlling for domestic sales could alleviate its contamination on trade fixed costs. After controlling for these factors, and other firm characteristics and industry fixed effects, province fixed effects 3

The results are available upon request.


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Y. GAO, Y. HE AND X. YIN TABLE 4 Difference of Fixed Export Costs (Dependent Variable: log(Sales Expense))

Variables

(1) Full Sample

(2) Pure Exporters

(3) First Year Exporters

(4) First-time Pure Exporters

Processing

0.360*** (0.0159) 0.417*** (0.00338) 0.131*** (0.00124) 0.000167** (6.88e-05) 0.171*** (0.00656) 1.999*** (0.155) Yes Yes Yes Yes 100,177 0.406

0.220*** (0.0258) 0.817*** (0.00793)

0.240*** (0.0590) 0.272*** (0.00929) 0.160*** (0.00511) 0.000293*** (6.59e-05) 0.223*** (0.0183) 2.711*** (0.509) Yes Yes Yes Yes 10,085 0.375

0.245** (0.0953) 0.825*** (0.0417)

log(Export) log(Domestic Sales) Log(K/L) log(Firm Age) Constant Ownership fixed effect Industry fixed effect Province fixed effect Year fixed effect Observations R2

2.39e-05 (9.15e-05) 0.0302** (0.0125) 2.529*** (0.479) Yes Yes Yes Yes 26,064 0.430

0.000101 (0.000243) 0.0626* (0.0372) 2.942*** (0.586) Yes No Yes Yes 1,393 0.359

Notes: (i) We control the industry fixed effect at the four-digit CIC code-levelled effect. (ii) Robust standard errors in parentheses. (iii) ***p < 0.01, **p < 0.05, *p < 0.1.

and year fixed effects, as in Castro et al. (2016), we found that China’s processing exporter have significantly lower fixed export costs compared with ordinary exporters. In addition, we excluded exporters with domestic sales and used the subsample of pure exporters to conduct the regression again. As previously stated, this should completely eliminate the contamination impact of domestic sales on fixed trade costs. Column (2) shows the results, and the previous conclusion that processing exporters are associated with significantly lower trade fixed trade costs remains unchanged. Similar to the previous section, we used the subsample of first-time exporters to repeat the analysis. The results are shown in Table 4, column (3), and our hypothesis is proven. That is, after controlling for their other characteristics, processing exporters in China have a significantly lower level of fixed export costs than ordinary exporters. Finally, to more accurately capture the fixed export costs, we used the subsample of only first-time pure exporters to repeat the analysis performed in Section 3a. The results are shown in Table 4, column (4). Again, they support our key argument. 4. CONCLUDING REMARKS AND FURTHER RESEARCH

Castro et al. (2016) predict that when firms are deciding whether to export, there is a substitution effect between productivity and trade fixed costs. Our study shows that a similar story exists in the choice between two different trade patterns in developing countries. This © 2016 John Wiley & Sons Ltd


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paper also provides an explanation of the existence and growth of low-productivity processing firms in China. Our results are fully consistent with the results of the ‘new-new trade theory’ of Melitz (2003) and many other researchers. This investigation enriches the literature by deepening our understanding of firms’ exporting behaviour. In the future, we expect to continue our study. We would like to determine whether and how firms change their decisions between the two trade patterns. Moreover, adding an intermediate goods sector into the theoretical framework could increase our understanding of such a change. Market heterogeneity could also be considered in a future study. REFERENCES Ahn, J. B., A. K. Khandelwal and S. J. Wei (2011), ‘The Role of Intermediaries in Facilitating Trade’, Journal of International Economics, 84, 1, 73–85. Baldwin, R. and J. Lopez-Gonzalez (2015), ‘Supply-chain Trade: A Portrait of Global Patterns and Several Testable Hypotheses’, The World Economy, 38, 11, 1682–721. Bernard, A. B., J. Eaton, J. B. Jensen and S. Kortum (2003), ‘Plants and Productivity in International Trade’, American Economic Review, 93, 4, 1268–90. Brandt, L., J. Van Biesebroeck and Y. Zhang (2012), ‘Creative Accounting or Creative Destruction? Firm-level Productivity Growth in Chinese Manufacturing’, Journal of Development Economics, 97, 2, 339–51. Castro, L., B. Li, K. Maskus and Y. Xie (2016), ‘Fixed Export Costs and Firm-Level Export Behavior’, Southern Economics Journal, 83, 1, 300–20. Eaton, J., S. Kortum and F. Kramarz (2004), ‘Dissecting Trade: Firms, Industries, and Export Destinations’, American Economic Review, 94, 2, 150–54. Ge, Y., H. Lai and S. Zhu (2011), ‘Intermediates Import and Gains From Trade Liberalization’, mimeo (East Lansing, MI: Michigan State University). Helpman, E., M. J. Melitz and S. R. Yeaple (2004), ‘Export Versus FDI With Heterogeneous Firms’, American Economic Review, 94, 1, 300–16. Levinsohn, J. and A. Petrin (2003), ‘Estimating Production Functions Using Inputs to Control for Unobservables’, Review of Economic Studies, 70, 2, 317–41. Lu, D. (2011), ‘Exceptional Exporter Performance? Evidence From Chinese Manufacturing Firms’ mimeo (Chicago, IL: University of Chicago). Lu, J., Y. Lu and Z. Tao (2010), ‘Exporting Behavior of Foreign Affiliates: Theory and Evidence’, Journal of International Economics, 81, 2, 197–205. Manova, K. and Z. Yu (2012), ‘How Firms Export: Processing vs. Ordinary Trade With Financial Frictions’, NBER Working Paper 18561 (Cambridge, MA: NBER). Melitz, M. J. (2003), ‘The Impact of Trade on Intra-industry Reallocations and Aggregate Industry Productivity’, Econometrica, 71, 6, 1695–725. Xing, Y. and N. Detert (2010), ‘How the IPhone Widens the United States Trade Deficit With the People’s Republic of China’, ADBI Working Paper 257 (Tokyo: Asian Development Bank Institute). Yu, M. (2015), ‘Processing Trade, Tariff Reductions and Firm Productivity: Evidence From Chinese Firms’, Economic Journal, 125, 585, 943–88.
