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Griliches (1988) and Coe and Moghadam (1993) show that there is sufficient empirical evidence to support the idea that cumulative domestic R&D is important for productivity. Coe and Helpman (1993) go even further by proposing that in the context of the entire international trade construct, which encompasses trade in goods and services, FDI, the exchange of information, and dissemination of knowledge, a country’s productivity depends on its own R&D as well as the R&D efforts of its trade partners. They argue that own R&D enhances a country’s benefits from overseas technical advances which lead to productivity increases.
METHODOLOGYModel Design This paper’s attempt to examine the extent to which productivity increases in China are linked to its trade with the U.S. embraces the ideas presented by Coe and Helpman (1993, 1995) in their study of international R&D spillovers among OECD countries and Israel, and adapts them to a bi-lateral trade scenario. Their approach is predicated on new theories of growth which emphasize links between investments in R&D and TFP increases. In it, they develop a framework for examining how a countries investment in R&D affects TFP of its trade partners.
This paper’s empirical framework is a follows the Coe and Helpman version of empirical equations based on theoretical models of innovation-driven growth, which we have broadly discussed in or literature review.
However, there are a few modifications suited to the objective of the study. A more elaborate description of the full model can be found in Coe and Helpman (1995).A simplification assumes an economy manufactures final output Y from an assortment of intermediate inputs.
Our simplest equation has the following specification:
Where L stands for labor inputs, K for capital inputs, and β for share of capital inputs.
Sd in (1) represents the domestic R&D capital stock, and Sf represents the foreign R&D capital stock defined as the import-share-weighted average of the domestic R&D capital stocks of trade partners. The specification (1) allows the coefficients α to vary across respective countries; in our case China and the U.S.
Some reasons offered in Coe and Helpman (1995) for varying the constant α0 are: there may be countryspecific effects on productivity that are not captured by the variables used in the equations; and, variables TFP is measured in country specific currencies whereas both R&D capital stocks are in US dollars. In our case, for TFP estimates, an index approach is used instead of currencies. Details are discussed in the section that follows.
The specification of (1) can be thought of as an extension of models relating TFP to only the domestic R&D capital stock, to include foreign R&D efforts (αf ≠ 0). Coe and Helpman (1995) acknowledge that the specification of may not capture fully the role of international trade. They explain that although the foreign stock of knowledge Sf consists of import-weighted foreign R&D capital stocks, these weights are fractions that add up to one and therefore do not properly reflect the level of imports. For these reason a modified specification of (1) that accounts for the interaction between foreign R&D capital stocks and the level of international trade seems preferable in the case of this study, along with other plausible arguments presented
by Coe and Helpman. A modified version of (1) follows:
Where m represents the fraction of imports as a share of the GDP. In this equation the elasticity of TFP with respect to the domestic R&D capital stock equals αd while the elasticity of TFP with respect to the foreign R&D capital stock equals αfm. It follows that whenever αf is the same for both China and the U.S. the latter elasticity will vary in both countries in proportion to their import shares.
Preliminary Data & Empirical Testing
A preliminary empirical analysis is conducted using appropriate data for years 1996-2014. Sources used include the World Bank and WTO Statistical databases, and the U.S. Census Bureau. TFP estimates obtained were only available as percentage growth changes for the period covered. An index estimate was used, with a base index of 1000. R&D spending estimates for both China and the U.S. used were extrapolated from respective current dollar value GDPs (Data obtained from databases represented these as a percentage of the GDP). The import-weighted R&D spending data for China utilizes U.S. export data to China for physical goods. The selection of goods data is an attempt to approximate the role of intermediate goods imports and minimize the challenges associated with accurate estimation of cross-border trade in services.
The data obtained (see appendix section) is applied to equation (2), modified to accommodate to accommodate TFP estimates otherwise obtained as changes in (TFP) growth rates. A regression analysis follows, using appropriate statistical software. Preliminary outcomes indicate that the model equation is only partially effective in explaining the link between changes in China’s productivity, own R&D stock and R&D stock in the U.S. The regression output indicates the model is significant and 95% confidence level. Even though all explanatory variables show significance, the intercept and domestic R&D variables had large standard errors. Table 1 is a partial representation of the regression results.
GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 478 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Table 1: Select Regression Results of Equation (3)
Dependent variable: TFP growth Index. Sample (adjusted): 1996-2014. Included observations: 18 Number of cross sections: 2. Total panel observations (balanced): 36 Discussion of Limited Results Forbearing limited size of the data and scope of the paper, the results give us useful insights in the formulation of a more complete study in the dispensation of trading in U.S. and China trade. From the results, we can obtain that domestic R&D spending likely plays a larger role in productivity increases in China compared to foreign R&D stock of capital in the U.S. However, while theory does not necessarily quantitatively define the impact of the latter, most of those that we have examined suggest a positive connection between productivity increases and both domestic and foreign R&D expenditures among trade partners. In this study, this variable is estimated by the import-share weighted R&D expenditure. The results indicate a negative correlation in the case of U.S.-China trade. The problem may indicate some problems with model specification or choice of estimates. While China’s productivity increases may not be solely explained by its trade relations with the U.S. (several other countries traded and invested directly in China over the same study period), there is evidence of transfers of knowledge and technical know-how.
Additional statistical testing and theoretical support for the variable choices may be necessary in developing the study. The large standard errors for both the explanatory variables seem to endorse. Given that China traded with several other technologically advanced countries which also invested directly in China, we estimate that even with improvements in data and model framework, the R-square values will likely improve only modestly.
In this paper, variable estimates used are not necessarily specific to sectors that have a propensity for technology-based products and innovative ideas. In this regard, a focus on variables estimates from to technology-driven sectors such as capital equipment manufacturing, electronics, and telecommunication among others would better serve the objective of the study. Also, a narrower focus is likely to offer more meaningful insights that can form the foundation for similar studies across multi-sectors. We also anticipate that sector specific data for China may not be easily accessible and may make the case for a narrower scope that may hold more realistic data options. In addition, future research will seek to effectively isolate the influence of trade and R&D expenditures with trading partners other than the U.S. Finally, the size of the data set will be expanded to include the immediate year’s following China’s economic reforms. The U.S.GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 479 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 China trade relationship is one of the most important in the world and deserves a better understanding as an important gauge for global trade dynamics.
APPENDIXTable A: Data Variables for China
GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 480 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Table B: Data Variables for the U.S.
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3–22 GCBF ♦ Vol. 11 ♦ No. 1 ♦ 2016 ♦ ISSN 1941-9589 ONLINE & ISSN 2168-0612 USB Flash Drive 482 Global Conference on Business and Finance Proceedings ♦ Volume 11 ♦ Number 1 Shaukat, A., Wei G., “Determinants of FDI in China,” Journal of Global Business and Technology, Vol.
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BIOGRAPHYTony Mutsune is an Associate Professor of Management and Economics at Luther College. He can be reached at Luther College, 700 College Drive, Decorah, Iowa 52101, USA.
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