Intermediates Import and Gains from Trade Liberalization

Transcription

1 Intermediates Import and Gains from Trade Liberalization Ying Ge * Huiwen Lai Susan Chun Zhu May 2011 Abstract This paper uses matched Chinese firm and trade data to investigate the importing channels of productivity gains from trade liberalization. We show that input tariff reductions encourage the importers to increase the volume of imported inputs and capital goods, to expand product margin and source country margin, to access to more advance countries, and to import more expensive intermediates. Moreover, we find that the import response to trade liberalization significantly improve firm performance. These results provide direct evidences for the learning, variety and quality channels of intermediate imports. * School of International Trade and Economics, University of International Business and Economics,10 East Huixin Street, Chaoyang District, Beijing, China. yige60@vip.sina.com, geying@uibe.edu.cn. School of Accounting and Finance, The Hong Kong Polytechnic University, Li Ka Shing Tower, Hung Hom, Kowloon, Hong Kong. huiwen.lai@inet.polyu.edu.hk. Department of Economics, Michigan State University, Marshall-Adams Hall, East Lansing, MI zhuc@msu.edu. 1

2 1. Introduction There has been increasing interest in the productivity gains from trade liberalization through importing channels. One channel is the pro-competitive effect of trade liberalization: increasing foreign competition could result in both within-industry resource relocation (Melitz, 2003) and within-firm productivity improvement (see, e.g., Helpman and Krugman, 1995, Bernard et al., 2006, Aghion et al., 2005). Another channel is that trade liberalization increases access to foreign intermediate inputs and capital goods, and promotes international knowledge spillovers. Cross country studies have shown that import is an important vehicle of international technology diffusion and significantly contributes to domestic productivity growth (see, e.g., Coe and Helpman 1995, Coe, Helpman and Hoffmaister, 1997; Engelbrecht, 1997; Keller, 2004; Lumenga-Neso, Olarreaga, and Schiff, 2005; Madsen, 2007; Acharya and Keller, 2009). Due to access to high quality firm-level data, the micro-level studies on the link between intermediates import and firm performance have grown rapidly. For example, Kasahara and Rodrigue (2008) found that becoming an importer of foreign intermediates significantly improves productivity. Kasahara and Lapham (2010) introduce the imported intermediate goods into Melitz (2003) model and show that the use of foreign intermediates improves a firm s productivity, and productive firms select into intermediates import. Halpern, Koren and Szeidl (2009) found that imported inputs have large productivity effects, much of which is due to their complementarities with other goods in the production process. 2

3 Recently a few studies use both output tariff reduction and input tariff reduction to separate the pro-competition effect in final product market and the effect of access to superior foreign inputs. Amiti and Konings (2007) are the fist to provide the evidence that reducing intermediate inputs tariff leads to productivity gains for firms that import their inputs. They suggested that imports of intermediate inputs might be an important source of advanced technology for developing countries. Consistent with this story, Goldberg et al. (2009) show that Indian trade reform increase access to new imported inputs, which originated from more advanced countries and exhibited higher unit values relative to existing import. Goldberg et al. (2010) find that reduction of input tariffs gives more incentive to Indian firms to import more varieties and thus produce more varieties to the domestic market. Topalova and Khandelwal (2011) show the productivity gains of Indian trade reform come from both pro-competitive effect and access to better inputs. They also document heterogeneity in the impact of the reform across industries, firms and economic environments. However, the sources of productivity gains of importing firms from trade liberalization remain unclear. In response to trade liberalization, the importers might increase the imported inputs and capital goods from more advanced countries and learn from foreign technology incorporated in the imported intermediates the learning effect (Coe and Helpman 1995; Keller, 2004; Mendoza, 2010). Firms might improve efficiency through access to a broader range of imported intermediates (or new product variety) the variety effect (Goldberg et 3

4 al., 2010). Trade liberalization might lead firms to improve productivity by using higher quality imported inputs - the quality effect (Grossman and Helpman, 1991; Halpern, Koren and Szeidl, 2009). Jones (2010) discussed the contribution of intermediates inputs to economic development through the linkage and complementary in production chain. Beside these direct channels, input tariff reductions might have indirect effects on the performance of importers, through increasing competition in inputs import market or import spillovers within and across industry. Amiti and Konings (2007) concluded that it s a future research area to identify the direct benefit arising from higher-quality foreign inputs, more differentiated varieties of inputs, and/or learning effect (Page 1633). This paper attempts to fill the gap by examining how import behavior responds to trade liberalization, and then how the change of import behavior might affect firm performance. This paper uses matched Chinese firm and trade dataset to investigate the importing channels through which trade liberalization can improve firm performance. We link three sets of information: firm attributes, firm import behavior and import tariff reductions. The information of firm attributes comes from National Bureau of Statistics of China (NBSC) Enterprise Dataset in manufacturing sector. Key firm attribute variables, such as TFP and other performance measures, size, ownership, and capital intensity, are derived from the NBSC dataset. The information of firm import behavior comes from detailed firm-product-market level Customs trade record. Firm-level importing variables, 4

5 such as total import, intermediate and capital good imports, number of input varieties import, and those imported from advanced countries, are derived from the Customs dataset. After careful match between firm dataset and trade dataset, our dataset includes importing firms, accounting for about 70% of total import in manufacturing during This time period contained one of the most significant trade liberalization in China: WTO accession at the end of To measure trade liberalization, we follow Amiti and Konings (2007) and Goldberg et al. (2010) to construct 4-digit industry-level input tariff. However, industry level tariff might mask the large variations in trade liberalization within an industry. 2 In addition, a firm may not necessary import the 8-digit HS products exactly covered by the 4-digit CIC industry code it is designated to. To avoid aggregation bias and potential mismatch of tariff coverage, we instead construct a firm-level input tariff, using 8-digit HS product tariff and firm-level imported input bundle. This firm-level tariff, along with the detailed firm-level attributes and importing information, allow us to investigate importing channels of productivity gains from trade liberalization at the most disaggregated level possible. We adopt two-stage approach in this study. In first stage, we investigate how the import behavior responds to WTO accession. To separate the direct and indirect effects of input tariff reduction, we use processing importer as a counterpart for ordinary importer to identify the direct import response to input tariff reductions. One typical feature of China s trade policy is dual track in that imported 1 This 70% is inferred from exports reported in both Customs and NBSC datasets after matching. See Appendix A for more details. 2 On average, each 4-digit CIC covers 14 HS8 products and about 274 firms in our sample. 5

6 intermediates inputs for processing trade was duty-free during pre-wto accession period. Hence, decreasing formal import barrier has no direct but only indirect impacts on processing importers. We show that input tariff reductions encourage ordinary importers to increase the volume of imported inputs and capital goods, to expand product margin and source country margin, and to access to more advance countries. We also find that the firm-product level price of imported inputs and capital goods significantly increase in response to trade liberalization. For processing importers, input tariff reduction significantly reduced the import margins, volume, and the import from advanced countries, which suggests that the negative competition effect dominates. In second stage, we directly test how the changes in import behavior affect firm performance. To identify the causality between import and performance, we follow Goldberg et al. (2010) and use tariff reductions in trade reform as the instruments. The results suggest that trade reform-induced import changes have significant impact on firm performance. Increasing import volume, access to new varieties and new trade partners, and shifting to more advanced import sources significantly improve the productivity, output, and average wage of the importers. This paper makes important contributions to the literature by identifying the intermediate importing channels through which trade liberalization improve the firm performance. We offer the evidences of productivity gains from increasing import volume and access to inputs from advanced countries, which is consistent with the learning effect. We find significant gains from trade through access to 6

7 new imported inputs, capital goods and new trade partners, which is consistent with the variety effect. Consistent with the quality effect, we find that input tariff reductions encourage the importers shift to more expensive imported inputs and capital goods. While our work is motivated by Amiti and Konings (2007) and closely related to Goldberg et al. (2010), it differs in important ways largely due to the availability of firm attributes, trade behavior, and input tariff data all at the firm-levels. In Amiti and Konings (2007), a firm-level productivity gain is identified through the regression coefficient of the interaction between input tariff and an importing indicator. The input tariff used was constructed at industry-level, and the lack of more detailed trade information prevented them from further exploring the detailed channels of gains from trade liberalization. In Goldberg et al. (2010), two channels through which input tariff reduction increase the number of new varieties a firm produces are motivated. The two channels are conventional price index and import variety index in the spirit of Feenstra (1994) and Broda and Weinstein (2006). Both indexes and the input tariff are constructed at the industry-level. With the more detailed information on importing activities, we are able to extend Amiti and Konings (2007) and Goldberg et al. (2010) by exploring the mechanisms of gain of trade through input tariff reduction. In doing so, we use direct measures of input tariff and import margins at firm-levels, thus avoid potential biases due to aggregation and mismatch. Furthermore, we recognize the dual track feature of China s trade policy and separate the direct and indirect effects of input tariff 7

8 reduction through different treatments of processing importers and ordinary importers. This paper is organized as follows: Section 2 introduces the data and trade liberalization in China. Section 3 examines the firm-level import response to input tariff reductions. Section 4 estimates productivity gains from trade liberalization. Section 5 concludes the paper. 2. Data 2.1. Matched Firm Trade Data The data we use mainly come from two sources. The first is the disaggregated trade transaction data at the 8-digit HS level from Chinese Customs. The dataset covers monthly import and export for , we aggregate the dataset to annual frequency. The variables include trade type (e.g., processing trade or ordinary trade), value, quantity, and contact information about the firm (e.g., company name, telephone, zip code, contact person). The statistics are summarized in Chinese Customs Statistical Yearbooks. We derive eight firm-level trade margin variables and a firm-product-level unit value of import variable directly from the Customs dataset. This marks a key benefit of our dataset as compared with Amiti and Konings (2007), where more detailed import information are unavailable. The second source of data is the National Bureau of Statistics Enterprise Dataset for The National Bureau of Statistics of China (NBSC) obtains annual reports from all state enterprises and large- and medium-sized non-state enterprises (with sales above 5 million RMB) in the manufacturing sector. The annual reports contain 8

9 information on financial statement and nonfinancial variables such as contact information, age, location, industry, ownership structure and main products of the enterprises. The key variables include capital intensity, employment, gross output, intermediate inputs, value added and wage. Based on this dataset, the basic statistics on the aggregate manufacturing sector are summarized in China Statistical Yearbooks (NBSC, ), and the statistics on the 2-digit manufacturing industries are summarized in China Industry Economy Statistical Yearbooks (NBSC, ). We match firm-product-partner level trade transactions with firm data on the firm contact information. In total 86,835 firms are matched, among them firms import. About 94% are matched by company names exactly. Additional 4% of firms are matched by zip code and contact person exactly. And additional 2% of firms are matched by telephone number exactly. The matched trade and firm sample covers more than 70% of firm exports in the manufacturing sector, as inferred from exports of matched firms reported in both NBSC dataset and Customs dataset. We therefore expect similar coverage for the matched importing firms. The sample coverage of trade is comparable to the 75% reported in Bernard, Jensen and Schott (NBER, 2005) about their link between trade transactions and US firms. Because a higher proportion of domestic firms trade through the intermediary of trading companies, the firm-trade matched sample has a lower coverage for domestic firms than for other ownership types. See Appendix A for our matching procedures and quality of 9

10 the matching result Trade Liberalization in China Our sample period, , covers one of the most significant trade liberalization in China. China joined the WTO in December As the commitment to WTO accession, China agreed to lower its average tariff levels on industrial products to 8.9%, to eliminate all quotas, licenses, tendering requirements and other non-tariff barriers to imports of manufactured goods by The motivation of China s WTO accession is not only to integrate into global economic system, but more importantly, to advance the domestic reform agenda and speed up the transition into market economy (Branstetter and Lardy, 2006). One special feature of China s trade regime is a dual-track: an open trade regime for foreign firms and processing traders, and a restrictive regime for ordinary traders. Feenstra (1998) called it one country, two systems. This dualistic trade regime has been extensively discussed in Feenstra (1998), Lardy (2002), and Branstetter and Lardy (2006). Before the WTO accession, Chinese government provides special privileges to processing traders. The import of all intermediates inputs used in the production of goods for export is duty free. Capital goods import by foreign invested enterprises is also duty free. Since the processing importers already escape from the formal trade barrier, the import tariff reductions during the WTO accession have different impact on processing traders and ordinary traders. 3 NBSC dataset also reports an annual export value at firm-level but reports no import information. Since the firm-product-market import and export are reported in the same format in the Customs dataset, it is safe to conclude that import might have similar matched ratio as for export. 10

11 This type of special treatment toward processing importers is not unique to China, and other developing countries such as Vietnam, Mexico also provide favorable policy to promote processing export. We follow Amiti and Konings (2007) and Goldberg et al. (2010) to construct an industry-level panel of input tariff between 2000 and 2006 at 4-digit Chinese Industry Classification (CIC) level. 4 The challenging task is to link 8-digit HS product tariff with the 4-digit CIC code assigned to each firm in the NBSC dataset. Product level import tariff data at 8-digit HS code come from the WTO. The difficulty is that our sample period covers a revision of CIC system in 2003 and a major reclassification in the international HS 6-digit codes in First, we follow Brandt et al. (2011) to assign each firm a standardized 4-digit CIC code consistent before and the after Then we construct a link of 6-digit HS code between the before and the after 2002 periods. We further construct a crosswalk between the standardized 6-digit HS code and standardized CIC code based on Brandt et al. (2011). With these links in place, we assign each 8-digit HS product to the 6-digit HS code it belongs to, and then connect this 6-digits HS code with standardized 4-digit CIC code, for each year. Then we calculate an industry level input tariff as the average of all the 8-digit HS products fall into the standardized 4-digit CIC code for each year. 5 While previous studies usually use the industry-level input tariff reduction as 4 The CIC system resembles the old U.S. SIC system. 5 We use the 8-digit HS products imported in a constant year of 2003 (middle year of our sample) to calculate an average industry tariff. This is to alleviate a potential endogenous problem that the change of a tariff may induce a firm to shift their importing mixed. The empirical results are robust to alternative measures of input tariffs. 11

12 the measure of trade liberalization (Amiti and Konings, 2007, and Goldberg et al., 2010, Topalova and Khandelwal, 2011), this measure may not reflect actual tariff reduction faced by the importers. Although each firm is assigned a CIC code in the NSBC dataset, its import may not be restricted to the 8-digit HS products that fall into this industry. i.e., firms in all industries may import generic inputs such as computers and transportation equipments. Moreover, industry-level average ignores the variations of tariff within the industry, and possibly underestimates the tariff change. In this paper we develop a firm specific input tariff. Because the merged firm and trade data give us information on what inputs (8-digit HS) are imported by each importer, we can match the applied MFN tariffs (8-digit HS) to firm's imported intermediates to calculate a firm specific input tariff. The firm-level input tariff is G g =1 gt i measured byτ = τ / G, where τ gt is 8-digit HS tariff rate, and it i G i is the number of imported inputs by importer i over the sample period. For each importer, the bundle of inputs includes all imported inputs over the sample period and we give equal weights to each input. Since we fixed the import bundle during the sample period, the changes of firm-level tariffs will reflect the changes of tariff rates rather than the shift of input bundles. Besides being a direct measure of tariff faced by the firm, an additional advantage of this index is that its construction avoids the potential bias created by the multiple links procedure in the industry-level tariff construction as described above. [Figure 1 about here] 12

13 Figure 1 shows the trend of firm-level and industry-level input tariffs between 2000 and Both tariffs dropped about 30% in WTO accession and continually decreased during post-wto period. Figures 1 confirms that industry-level tariffs underestimate the variation of tariff change. In our sample, each 4-digit CIC covers about fourteen 8-digit HS codes and about 274 firms. While the sample means of the two input tariffs are very similar, the firm-level input tariff has double standard deviation than industry-level tariff. This higher variation implies that firm-level input tariff change may better capture the impact of trade liberalization on importers. [Figure 2 about here] Figure 2 shows growth of imported inputs and capital goods over the sample period. Total input, intermediate inputs and capital goods imports almost doubled from 2000 to This substantial growth of the imported inputs and capital goods is consistent with large input tariff reduction during the same period, suggesting that importers strongly respond to trade policy change. [Figure 3 about here] Figure 3 shows the change of average productivity of importers during trade liberalization. To measure total factor productivity, we follow Topalova and Khandelwal (2010) to use the Levinsohn-Petrin method. Levinsohn and Petrin (2003) offer an estimation technique that is very close in spirit to the Olley and Pakes (1996) approach. However, they suggest the use of intermediate inputs as a proxy rather than investment, since investment is often not directly reported in firm level datasets. 13

14 Due to the large number of firms in our dataset, we estimate the parameters of the production functions for each 4-digit CIC code. Real output, real input, and real value added are constructed by deflating their nominal values with corresponding industry output and input price deflators constructed by Brandt et al. (2011) 6. For robustness check, we also use value added per worker as the measure of labor productivity. The trend of TFP and labor productivity of importers is shown in Figure 3. The patterns of both measures are similar, and the productivity of importers kept on rising over time. This significant productivity growth of importers might be closely linked to the imported inputs expansion and trade liberalization during the same period. [Table 1 about here] Table 1 reports descriptive statistics of various attributes and import behaviors of importers 7. We separate total importers into two categories: processing importers and ordinary importers. The unconditional comparison between two categories of importers suggests that on average ordinary and processing importers have similar size and age, but ordinary importers are more productive, more capital intensive, and pay higher wage than processing importers. For import margin, on average, 6 Each firm reports the value of fixed capital stock at original purchase prices, and capital stock at original purchase prices less accumulated depreciation. So directly use book value in fixed capital stock may create bias. Hsieh and Klenow (2009) use this approach without any adjustment. Alternatively, Brandt et al. (2011) is careful in convert book values into real terms to derive a real fixed capital stock for TFP calculation by using a number of assumptions. While this approach is more logical from theoretical standpoint, empirically bias could be created if any assumptions are violated. In particular, we are concern with the assumptions of universe depreciation rate applied to all firms and all years. Thus both approaches have advantage and disadvantage. We experimented both, with the second approach following Brandt et al. We end up choosing first approach in TFP calculation due to large noise in our second approach implementation, and implement additional firm performance measure in our regressions for robustness. 7 All variables are measured in logarithm. 14

15 processing importers import more varieties, import from more countries, and import more intermediate inputs and capital goods than ordinary importers. 3. The Response of Importers to Trade Liberalization To identify the main channels on how trade liberalization improves the performance of importers, we first investigate how import behaviors change in response to input tariff reduction. The importers might directly responds to input tariff change by increasing the import volume of inputs and capital goods, access to a broader range of input variety, importing from more advanced countries, and using higher quality foreign inputs and capital goods. There are also potential indirect effects of input tariff reductions on imports, for example, through increasing intensive competition in intermediates importing market or the spillovers across importers. It s challenging task to distinguish the direct and indirect effects of input tariff reductions. The dual-track trade regimes in China provide an ideal control group, the processing importers, for our study. Since processing importers have special privilege to escape the formal trade barrier long before WTO accession, input tariff reductions have no direct impact on processing importers. Moreover, using processing importers as the counterparts, we are able to control the effects of other simultaneous industry policy changes which uniformly affect ordinary and processing traders. For example, Chinese government also provides favorable trade policy for high-tech industries, certain regions, foreign invested enterprise, and the firms which import high-tech equipment for technology upgrade. 15

16 We adopt the following specification to estimate the effect of input tariff reduction on import behavior: Import ft = α + α Ordinary 3 1Ordinaryft InputTariffft + α 2 Processin ft + Z f, t 1 λ+ D f + D t + ν ift g ft InputTariff ft (1) Where Importft is measured by (1) the number of import varieties; (2) the number of partners; (3) the value of total import; (4) the value of imported intermediate inputs (5) the value of imported capital goods (6) the number of varieties from advanced countries; (7) the number of partners from advanced countries (8) the value of import from advanced countries. All these variables are in the logarithm. Z include firm age, size and capital intensity. Pr ocessingft and Ordinary ft each is a dummy variable indicating whether the firm f is processing importer or ordinary importer. D t is a set of year dummies that control for possible variation in the macroeconomic environment over time; D f is included to control for the unobservable individual effect of firm f that could be correlated with the import behavior. [Table 2 about here] Table 2 present the results for the fixed effect estimation. We are most interested in the coefficients on the interaction term between input tariffs and ordinary importer, which captures the direct effect of trade liberalization on import behavior. Column 1 and 2 report the results for product margin and country margin. In response to tariff reductions, ordinary importers significantly increase import margin: declining input tariff encourage importers to access to a wider range of variety and import from more countries. This is consistent with Goldberg et al. 16

17 (2009) findings that trade reform increase access to new imported inputs. Column 3-5 shows the results of total import volume, intermediates input and capital goods, respectively. The results suggest that a 1% fall in input tariff increases total import value by 3.1%, increases intermediate inputs by 2.6%, and increase value of imported capital good by 4.3%. Column 6-8 report the results for import margin and volume from advanced countries. Imports from advanced countries have been recognized as one important channel of international technology diffusion. The results suggest that input tariff reductions have a significant and positive impact on imports from advanced countries in terms of the total value of imports, varieties and trade partners. In contrast, the estimated coefficients of the interaction term between processing importers and input tariff are significant and positive for all measures of import behavior. Since the imported intermediate inputs are duty-free for processing importers, input tariff reduction has no direct effect on processing import. These results suggest that the negative competition effect in intermediates import market dominates. Declining formal trade barrier significantly reduce the policy advantage of processing imports. The coefficients on other firm attributes are as expected. Larger firms and more capital intensive firms import more and access to a wider range of inputs and more advanced countries. The estimated coefficient on the age is significant and negative, which suggest that younger firms import more. [Table 3 about here] 17

18 One concern is that the importers might not fully respond to tariff reductions immediately. There might be both short run and long run effects of tariff reductions. For robustness check, we include four year-lagged input tariff into the regression to capture the long run impact of trade liberalization. The results, reported in Table 3, are consistent with the benchmark results in Table 2. Trade liberalization may encourage the importers to upgrade the quality of imported inputs. Goldberg et al. (2010) constructed an industry level import price index and decompose it into the conventional price index and the variety index which captures the extensive margin of imports. They found that product scope expansion is driven predominately by increasing access to new input varieties rather than by simply making existing imported inputs cheaper. Since we have accurate information on firm-product-source country import price, we directly estimate the response of unit value of imported intermediates in response to product-level input tariff reduction. The specification is following: Unitvalue i fct = α + αordinary 3 i 1Ordinaryft InputTarifft + α2 Processin ft + Z f, t 1 λ+ D if + D t + ν ift g ft InputTariff i t (2) Where i Unitvaluefct is import unit value of product i of firm f from country c in year t; i InputTariff t is the product-level input tariff instead of firm-level tariff. D if is unobserved product-firm fixed effect. The definitions of other variables are same as the ones in equation (1). [Table 4 about here] The results for total imported products, intermediates input and capital goods are reported in Column 1, 2 and 3 of Table 4, respectively. Consistent with quality 18

19 upgrading story, the ordinary importers significantly increase the unit value of imported inputs in responding to declining import tariff, controlling firm-product fixed effect. This effect is both statistically and economic significant: a 10 percentage point fall in input tariff increases import price by 5.8%. This import price response is consistent with previous results that ordinary importers significantly expand the product margin of imported inputs and shift to more advanced countries. In the study of Indian import, Goldberg et al. (2009) also found that new imported inputs mainly originated from more advanced countries and exhibited higher unit values relative to existing imports. In contrast, the import prices of the processing importers significantly decrease in respond to input tariff reductions. They import cheaper intermediate inputs but more expensive capital good. For other control variables, larger, younger, and more capital intensive firms tend to import more expensive inputs and capital goods. 4. The Productivity Gains from Trade Liberalization In the first stage, we show that input tariff reductions significantly change the import behavior: the ordinary importers import more, access to a wider range of variety and a larger number of source countries, shift to more advanced countries, and import more expensive inputs and capital goods. Processing importers, in contrast, reduce import margin, price and volume due to competition effect in trade liberalization. In the second stage, we further investigate how the changes in import behavior will affect firm performance. We adopt the fixed effect model to estimate the following equation: 19

20 TFP ft = α1ordinary Import + α 2 Proces sing Import + αordinary 3 ft ft + Z f, t 1 ft λ+ D f + D t + ν ift ft ft (3) Where TFPft is total factor productivity (TFP) of firm f at time t, which is estimated following the methodology of Levinsohn and Petrin (2003). Pr ocessingft and Ordinary ft each is a dummy variable indicating whether the firm f is processing importer or ordinary importer. Importft is measured by import product margin, country margin, import value, the margin and value of import from advanced countries. All these variables are in the logarithm. Z include firm age, size and capital intensity. D t is a set of year dummies, and D f captures the unobservable individual effect. [Table 5 about here] The fixed effect estimators are reported in Table 5 and these results suggest a positive link between intermediate import and productivity for all different measure of import behavior. The magnitudes of productivity gains are fairly close for both ordinary and processing importers. Column 1 and 2 show that expanding import margins are associated with high productivity. Double the product margin increase productivity by about 7 percent, and double the country margin increase productivity about 11 percent. The results in Column 3-5 show that the import value of intermediate inputs and capital goods has a significant and positive effect on firm productivity. A 10% increase in imported intermediate value is associated with 0.5% increase in TFP. A 10% increase in capital goods import increase productivity about 0.2%. Column 6-8 report the results for import margin and volume from advanced countries. The results show that increasing import from 20

21 advanced countries is positively associated with productivity. This is consistent with Mendoza s (2010) findings that more technologically advanced trading partners offer more scope for trade-induced learning. For other firm attributes, the results also suggest that larger firms and more capital intensive firms as well as younger firms tend to be more productive. An important issue is the potential endogeneity bias. The positive link between productivity and import might due to better importers choose to import more intermediates (or capital goods), import more variety of inputs, and import from more advanced countries. We deal with this endogeneity bias in three ways: first, we add several firm attributes and firm fixed effects to control for time-invariant firm attributes. Secondly, we use lagged import margins to alleviate this bias. Third, we follow Glodberg, et al. (2010) and use input tariff reductions in WTO accession as instruments for firm importing behaviors. One concern is the endogeneity of trade reform that less efficient industries lobby for the higher trade protection. Branstetter and Lardy (2006) discussed the motive for China s leadership to agree the commitments in WTO accession. They concluded that In short, China s top political leadership made extensive commitments to the WTO in order to advance their domestic reform agenda (Page 21). China joins WTO to speed up the domestic reform and facilitate the transition into the market economy. The input tariff reductions in WTO accession are unlikely due to the protection pressure from the interest groups in less efficient industries. In previous section we show that trade liberalization has significant and substantial impacts on import behavior. 21

22 Using input tariff reduction as IV, we are able to identify how the import change induced by trade liberalization can affect firm performance. [Table 6 about here] Table 6 presents the two stage least square results using current and lagged input tariffs as instruments. The results for ordinary importers are consistent with the ones in Table 5. Among all measures of import behaviors, only the coefficients of capital good import become insignificant. Import margins, value and sources have significantly positive impacts on TFP, and the magnitude of impacts is larger compared with the fixed effect estimators. For processing importers, most coefficients become insignificant. This is not surprising since decreasing formal trade barrier have no direct effect on processing import, and thus the effects of trade liberalization-induced import become weaker. [Table 7 about here] For robustness check, we first use the value added per worker as alternative measures of productivity. The results are reported in Panel A of Table 7. The pattern is consistent with the benchmark that import margin, volume and sources have positive impacts on labor productivity of importers. We further investigate the effect of import behavior on total output and average wage level, and the results are reported in Panel B and C, respectively. For ordinary importers, wider import margins, lager import value and more import from advanced countries are associated with better performance. For processing importers, the positive link between import and performance is robust for the fixed effect model while the IV 22

23 estimators are less significant. 5. Conclusion Previous studies show that reducing input import tariff significantly improves the importers performance. This paper uses matched Chinese manufacturing firms and trade data to explore the main sources for productivity gains of importers from trade liberalization. Our sample period, , covers one of the most significant trade liberalization in China: WTO accession at the end of In the first stage, we investigate how the importers respond to input tariff reduction in WTO accession. We use processing importers as control group to identify the direct effects of input tariff reduction. The results suggest that ordinary importers significantly increase the import volume, extend import margins, access to more advanced countries, and upgrade the quality of imported inputs and capital goods. For processing importers which are not directly affected by input tariff reduction, import margin, volume and quality are significantly reduced. This suggests a dominant negative competition effect in importing inputs market. In the second stage, we directly test the linkage between import behavior and firm performance, and use tariff reduction as instruments to identify the causality. The evidences suggest that larger import value of intermediate and capital goods, wider import margins and import from advanced countries significantly improve the performance of ordinary importers. The productivity gains are less significant for processing importers. This two-stage analysis provides direct evidences of the learning, variety and quality channels of intermediates imports. 23

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28 Appendix A. Matching Customs and NBSC Firms Datasets A.1. Customs dataset The trade dataset from Chinese Customs includes monthly product level data of all Chinese firms import and export with partner countries between 2000 and Each firm is identified by a unique firm id (firm_id_customs) and associated contact information: company name, address, zip code, contact person name, telephone number, and . In addition, each firm is characterized by its ownership type, i.e., if it is a state-owned, a privately-owned, or a foreign enterprise. Associated with a firm_id_customs for a specific month of a year, each trade is further identified by an 8-digit HS code, an import-export indicator, and partner country (source country for an import and destination country for an export). Each trade is measured by a quantity, a value, and a unit value as the ratio of the value over the quantity. Quantity is measured by a unit such as kilogram or item. Value and unit value are in current month (and year) US dollar. In addition, a trade regime variable identifies if the trade is an ordinary or processing trade, a transportation mode variable identifies if the trade is shipped by sea or air. To consist with the annual NBSC Dataset, we use monthly exchange rate between China and US to convert value and unit value to RMB, then we aggregate them to annual frequency. 8 After the aggregation, each observation in the Customs dataset is identified by a firm_id_customs, year, an import/export indicator, an 8-digit HS 8 For each firm-product-import (or export)-partner-year combination, the annual level quantity and value are calculated as the summation of their respective monthly quantity, and the annual level unit value is calculated as the annual value divided by the annual quantity. 28

29 code, and source/destination country. A.2. NBSC dataset NBSC dataset includes annual financial and nonfinancial variables of all state enterprises and large-and medium-sized non-state enterprises (with sales above 5 million RMB) in the manufacturing sector between 1998 and There are more than 100 variables of firm attributes. Each firm is identified by a unique firm id (firm_id_nbsc) and associated contact information similar to that of the Customs dataset, though in somewhat different format. Each firm is further characterized by its ownership type and an industry code represented by the 4-digit Chinese industry code (CIC). Additional variables used by this paper include capital stock, employment, gross output, value added, R&D expenditure, and export value. 9 A.3. Matching Customs and NBSC dataset To construct an integrated dataset of firm attributes, trading behaviors, and input tariff all at the firm-level, our first challenge is to link the Customs dataset with the NBSC dataset. Firm_id_customs and firm_id_nbsc are not related so there is no direct link between the two datasets. To proceed, we match them through the contact information shared by both datasets. The following are the steps of our matching algorithm: Step 1, Standardize contact information in both datasets 9 Note for the same firm the export value reported in the NBSC dataset may not necessary match the aggregate export value reported in the Customs dataset. First, a firm can import or export through an intermediary trading firm partially or completely. Second, the two export values could differ due to time lags of recording in two different systems. The first impact more likely dominates the second impact. Thus, the export value reported in Customs should not be less than the export value reported in NBSC dataset. 29

30 Both datasets include variables representing company name, contact person, zip code, telephone number, fax number, and . However, these variables can not be used directly in matching due to three major problems. First, for a typical contact variable the value format can be different for different companies in the same dataset or across two datasets for the potentially same company. For example, a telephone number can appear as either an area code plus a home phone number, several leading 0 s plus an area code and then a home phone number, a home phone number only, or a cell phone number (with a 11-digit number as compared to 7 or 8 digit home phone number depending on the city the company locates). Second, irregular characters, leading, or trailing spaces are often recorded in observations. In some cases, irregular characters are mixed with regular character values with identifiable patterns. Third, except for the company name, large number of missing values are observed in contact variables. We first identify the patterns of the irregularities in the contact information. Then we analyze these patterns and develop a generalized code to set missing for obviously wrong characters or extract any characters with useful information. We then create several standardized variables with consistent formats applied to both datasets: company name, zip code combined with contact person, and three versions of telephone numbers. Each of the standardized variables is used as a key variable in our matching. Step 2, Matching with the standardized contact information We then match these two datasets using the five standardized variables 30

31 sequentially. First we match two datasets by company name and year. We then match those unmatched (by company name and year) by zip code plus contact person and year. And continue the similar matching process using the three versions of standardized telephone numbers sequentially. We then pool all the matched observations. Each observation of the pooled dataset is uniquely identified by three variables, a firm_id_customs, a firm_id_nbsc, and year. Step 3, Consistence check To minimize the risk of wrong matches, we eliminate matched observations from step 2 if any inconsistency is found based on the following steps: First, notice each match between a firm_id_customs year combination and a firm_id_nbsc year combination is found based on one of the five matching keys separately. If for this matched observation (based on a specific key) there are any unmatched non-missing values related to any other matching keys from the two datasets, we treat this observation as suspicious, and set a flag for this observation. Second, for each matched observation, we compare the ownership types from the two datasets and set a flag if an inconsistency is found. Third, according to Brandt et al (2011), there exists potential inconsistence across firms in the NBSC dataset itself. We use similar strategy to identify the inconsistence and set a flag for each suspicious firm id. For example, the variable years of establishment in the NBSC dataset should hold identical values for observations related to the same firm_id_nbsc across different years. 31