2016
DOI: 10.1016/j.jinteco.2016.09.009
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Intermediate input imports and innovations: Evidence from Chinese firms' patent filings

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Cited by 318 publications
(160 citation statements)
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References 42 publications
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“…Liu et al (2015) used matched firm-level production data and firm innovation data, and found that a reduction in output tariffs reduces firms' invention innovation and utility model invention but increases firms' design innovation. Using the same matched firm and innovation dataset, Liu and Qiu (2016) found that input tariff reductions has a negative effect on firms' innovation. One possible mechanism underlying the negative effect is that when input tariffs decline, firms can replace their internal innovation with high-quality input imports.…”
Section: Firm Innovationmentioning
confidence: 99%
“…Liu et al (2015) used matched firm-level production data and firm innovation data, and found that a reduction in output tariffs reduces firms' invention innovation and utility model invention but increases firms' design innovation. Using the same matched firm and innovation dataset, Liu and Qiu (2016) found that input tariff reductions has a negative effect on firms' innovation. One possible mechanism underlying the negative effect is that when input tariffs decline, firms can replace their internal innovation with high-quality input imports.…”
Section: Firm Innovationmentioning
confidence: 99%
“…Therefore, we can treat the firms in industries with previously higher input tariff level as the treatment group, and the firms in industries with previously lower input tariff level as the control group, and then compare the change in markups of firms belong to treatment group before and after China's WTO accession to the corresponding change in firms belonging to the control group during the same period. In reality, Guadalupe and Wulf (), Lu and Yu () as well as Liu and Qiu () adopt a similar approach to empirically investigate the impacts of trade liberalisation on corporate hierarchies, markup dispersion and firm innovation, respectively.…”
Section: Empirical Strategy and Datamentioning
confidence: 99%
“…However, considering markup trends in different industries could be different as they may be affected by industry‐specific confounding factors, and if this is the case, the identifying assumption mentioned above would be violated. To examine whether unobserved industry‐specific factors would contaminate our estimates, we follow Liu and Qiu () by adding an industry‐specific linear time trend (i.e., αi·t) as an additional control in our DID specification (7). As shown in column (3) of Table , our coefficients of interest on the interaction term, InpTar 01 × PostWTO 02, remain robust and stable with a similar magnitude, as compared to the baseline specification (column (4) of Table ).…”
Section: Empirical Analysismentioning
confidence: 99%
“…In the robustness checks we use another log-like transformation (Liu and Qiu, 2016). In the robustness checks we use another log-like transformation (Liu and Qiu, 2016).…”
Section: Alternative Dependent Variablesmentioning
confidence: 99%