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Trade liberalization, credit constraints, and export quality upgrading

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Abstract

This paper provides evidence that external financial status is an important determinant of firms’ responses to trade liberalization. Based on the difference-in-differences (DID) estimation strategy and data from Chinese firms, we find that input tariff reduction has a significantly positive effect on export quality for firms with high credit constraints but has no significant impact on firms with low credit constraints. This finding suggests that trade liberalization leads to the upgrading of export quality by firms that face binding credit constraints. We also find that the quality upgrading of intermediate inputs and the enhancement of productivity can plausibly explain the upgrading of export quality by firms with high credit constraints. Our paper has some important implications for trade and financial policies.

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Availability of data and material

The datasets of this paper are available from the authors upon reasonable request.

Code availability

The computer code of this paper is available from the authors upon reasonable request.

Notes

  1. In the literature, input tariff reduction is a popular measure of trade liberalization, not just in this case study of China. For example, Bas (2012) examined the effects of input trade liberalization on export decisions of Argentinean firms.

  2. For example, large firms or interest groups may intervene with the input tariff policy of their governments, which may lead to the endogenous problem.

  3. Output tariff of an industry is calculated as the simple average of output tariffs of the HS 6-digit products included in that industry (Sect. 3.2.3).

  4. The data is from Manova et al. (2015).

  5. Noting that all of those three measures are constructed based on U.S. data, and thus the order of these indicators rather than their absolute values has economic meaning for the case study of China. Therefore, we mainly identify the effect of credit constraints on the relationship between trade liberalization and export quality by dividing the sample according to the order of firms’ credit constraints rather than using the triple differences (DDD) strategy (i.e., regressing the triple term, \(Tangible_{f} \times Input \, tariff2001_{i} \times Post_{t}\), on firms’ export sophistication).

  6. Since the substantial input tariff reduction is concentrated over a period of 2002–2005 (See Fig. 1), the time frame of our sample is from 2000 to 2006 (e.g., Liu and Qiu, 2016).

  7. To avoid measurement errors, we remove firms with sales revenues less than RMB 5 million, fewer than eight employees, or non-positive total assets.

  8. The registration type of firms includes state-owned firm, private-owned firm, joint state-private firm, and Hong Kong, Macao or Taiwan firm, and others.

  9. After controlling the firm- and year-fixed effects, there are only 137,292 observations used in estimations.

  10. Liu and Qiu (2016) argue that the schedule of tariff reductions of China’s WTO accession was released in 2002, and the schedule may be endogenous; thus, the phase-out process could be exploited by firms.

  11. We define \(D(Input \, tariff)_{i} { = }Input \, tariff_{i,2000 - 2001} - Input \, tariff_{i,2002 - 2006}\), where \(Input \, tariff_{i,2000 - 2001}\) is the average input tariff from 2000 to 2001, and \(Input \, tariff_{i,2002 - 2006}\) is the average input tariff from 2002 to 2006.

  12. We also use the triple differences (DDD) technique which replacing the triple term \(Tangible_{f} \times Input tariff2001_{i} \times Post_{t}\) with \(Input tariff2001_{i} \times Post_{t}\) to estimate Eq. (1) rather than taking regressions in sub-groups. The results of triple differences (DDD) estimations are consistent with our baseline results in Columns (2) and (3) of Table 3.

  13. The magnitude of coefficient of the factor \(Input \, tariff2001_{i} \times Post_{t}\) in Column (2) of Table A1 is larger than that in Column (3) of Table 3, which confirms the lagged effects of trade liberalization.

  14. The availability of internal sources of finance (\((CF/K)_{i}\)) is not available from U.S. data, and we calculate it by using the data from ASIF. To mitigate the endogenous problem, we use the industry-level CF/K in the initial year to denote the internal financial constraints faced by firms in that industry. And the industry-level CF/K is the CF/K of the median firm in each industry. We also measure the industry-level CF/K as the average CF/K over 2000–2006 for the median firm in each industry (Manova, 2013; Manova et al., 2015), and find the similar results as those in Columns (1) and (2) of Table 6.

  15. Actually, all firms in the ASIF database are above scale firms (with annual sales revenue above 500 million RMB), and thus we just consider the relative size rather than the absolute size of firms in our sample.

  16. Previous studies have confirmed that there is a positive relationship between productivity and product quality (e.g., Fan, 2005; Verhoogen, 2008; Cruzi and Olper, 2012; Zhang et al., 2020).

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Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. School of Securities and Futures, Southwestern University of Finance and Economics, No.55 Guanghuacun Street, Chengdu, 610074, Sichuan, China

    Teng Zhang

  2. School of Economics, Southwestern University of Finance and Economics, No.55 Guanghuacun Street, Chengdu, 610074, Sichuan, China

    Qiuyao Fu

  3. School of Marxism, South China Normal University, Tianhe District, No.55 Zhongshan Street, Guangzhou, 510631, Guangdong, China

    Chunhui Zhu

Authors
  1. Teng Zhang
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  2. Qiuyao Fu
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  3. Chunhui Zhu
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Contributions

T.Z. contributed to conceptualization, methodology, writing—original draft, and supervision. Q.F. contributed to investigation, methodology, software, and writing—reviewing. C.Z. contributed to methodology, software, and writing—reviewing and editing.

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Correspondence to Chunhui Zhu.

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Appendix

Appendix

1.1 The lagged effect of trade liberalization

Table A1 The two year-lagged effects of input trade liberalization
Full size table

1.2 Firm TFP

In the literature, there are mainly three methods to estimate TFP, including OP (Olley, Pakes), LP (Levinsohn, Petrin) and ACF (Ackerberg, Caves, Frazer). For the OP method (Olley and Pakes 1996), a nonparametrically inverted investment equation is used to instrument productivity shocks in the production function, and one estimates the labor coefficient by regressing output on the labor input and this nonparametric function in the first stage. Levinsohn and Petrin (2003) use a similar approach in which intermediates rather than investments are adopted as the proxy of unobserved productivity shocks. However, both OP and LP methods face the issue of functional dependence in the first step of estimation. Ackerberg et al. (2015) propose an alternative estimation procedure that uses moment conditions very similar to those used by LP (OP) method, but that avoid this functional dependence problem. Therefore, in this paper, we use the ACF’s approach in the LP context to estimate the firm-level TFP for each 2-digit CIC industry. The summary statistic of firm TFP is reported in Table A2.

Table A2 Firm-level TFP
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Zhang, T., Fu, Q. & Zhu, C. Trade liberalization, credit constraints, and export quality upgrading. Empir Econ 63, 499–524 (2022). https://doi.org/10.1007/s00181-021-02138-9

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  • DOI: https://doi.org/10.1007/s00181-021-02138-9

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