Improvement effect of import and export product diversification on terms of trade: empirical evidence from China's manufacturing industry

CHEN Rong1,2 XU Peiyuan1

(1.School of Economics and Finance of Huaqiao University)
(2.Business School of Minnan Normal University)

【Abstract】This paper measures the terms of trade index of Chinese manufacturing sub-sectors by UNCOMTRADE HS96 six digit trade data, finding that the terms of trade of most of Chinese manufacturing sub-sectors deteriorated from 2001 to 2013, no matter the product variety is changed or not. The empirical analysis based on the dynamic panel system GMM method shows that export product diversification and human capital accumulation effectively improve the terms of trade of the manufacturing sector, but import product diversification worsen the terms of trade. In addition, there is a close relationship between the changes of terms of trade and the substitution elasticity of import and export products.

【Keywords】 import and export product diversification; terms of trade; manufacture;

【DOI】

【Funds】 Supporting Plan of Ministry of Education for Excellent Talents in the New Cantury (NCET-13-0806) Key Project of Maritime Silk Road Research Fund of Huaqiao University

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(Translated by ZHANG Ning)

    Footnote

    [1]. ① If there is only one six-digit code product under HS four digit code, we reject the HS four digit code because relevant results cannot be calculated with only one six-digit code product. [^Back]

    [2]. ② Price of product is its value divided by number. The six-digit code trade data of UNCOMTRADE HS96 at the same time report on the trade volume and trade quantity of each product category with the majority of the units being "kg," but about 15% of the data use other units (such as meter and piece). In order to ensure the uniformity of measurement results, the paper removes the six-digit code products whose statistical units of "quantity" are not "kg." [^Back]

    [3]. ⑦ Economic Industrial Classification (GB/T4754-2002) will divide manufacturing industry into 30 sub-sectors. This article excludes crafts and other manufacturing, waste resources and waste materials recycling and processing industry and selects 28 sub-sectors as a sample. 28 manufacturing sub-sectors are: 13 agro-food processing industry, 14 food manufacturing, 15 beverage manufacturing, 16 tobacco industry, 17 textiles, 18 textile and clothing and shoes manufacturing, 19 leather, fur, feather (down) and relevant products, 20 timber processing and wood, bamboo, cane, palm fiber and straw products, 21 furniture manufacturing, 22 paper and paper products manufacturing, 23 printing and copying of recorded media, 24 stationery and sporting products manufacturing, 25 oil processing, coking and nuclear fuel processing industry, 26 chemical materials and chemical products manufacturing, 27 pharmaceutical manufacturing, 28 chemical fiber manufacturing, 29 rubber product manufacturing, 30 plastic product manufacturing, 31 non-metallic mineral product manufacturing, 32 ferrous metal smelting and rolling processing, 33 non-ferrous metal smelting and rolling processing, 34 metal products, 35 general equipment manufacturing, 36 special equipment manufacturing, 37 transport equipment manufacturing, 39 electrical machinery and equipment manufacturing, 40 communications equipment, computers and other electronic equipment manufacturing, 41 instrumentation and stationery and office machinery manufacturing. [^Back]

    [4]. ⑧ Bond et al. (2001) believe that mixed OLS estimation tend to overestimate the coefficients of lagged explained variables, while fixed effects estimation tend to underestimate the coefficients of lagged explained variables. If the GMM estimates of the lagged explained variables are between fixed effect estimates and mixed OLS estimates, the System GMM estimates are valid. [^Back]

    [5]. ⑨ According to estimation results of System GMM, mixed OLS and fixed effects, when the explained variable is the terms of trade index with fixed product category (FTT), the System GMM estimate (0.854) of lagged term of the explained variable is between mixed OLS estimate (0.889) and fixed effects estimate (0.572). When the explained variable is the terms of trade index with variable product category (VTT1), the System GMM estimate (0.842) of the lagged term of the explained variable is between mixed OLS estimate (0.870) and fixed effects estimate (0.524). When the explained variable is the terms of trade index with fixed product category (VTT2), the System GMM estimate (0.851) of the lagged term of the explained variable is between mixed OLS estimate (0.878) and fixed effects estimate (0.560). When the explained variable is the terms of trade index with fixed product category (VTT3), the System GMM estimate (0.856) of the lagged term of the explained variable is between mixed OLS estimate (0.880) and fixed effects estimate (0.560). [^Back]

    [6]. ⑩ Zong (2012) examined the impact of the dualistic margin of China's manufacturing exports growth on the terms of trade, finding that extensive margin has a positive effect on the terms of trade, while intensive margin has a negative effect. [^Back]

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This Article

ISSN:1002-4670

CN: 11-1692/F

Vol , No. 12, Pages 133-144

December 2015

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Article Outline

Abstract

  • Introduction
  • 1 Metrics and result of analysis on the terms of trade in sub-sectors of China's manufacturing industry
  • 2 Model specification, variables and data description
  • 3 Measurement test result and analysis
  • 4 Conclusion and enlightenment
  • Footnote

    References