Domestic Value Added in China’s Exports to the World and Its Partners

Abstract

This article uses the inter-country input-output tables with heterogeneous firms compiled by OECD (2015) to measure the ratio of domestic value added content in China’s exports (RDVAE) to the world and its major trading partners at the aggregate and sector level, respectively. Our empirical results indicate the following. (1) China’s RDVAE adjusted for processing trade is much lower than that computed without adjusting for processing trade; (2) The manufacture’s RDVAE for China to its major trading partners is typically lower than that for China’s major trading partners to China; and (3) The share of domestic value added generated by China’s exporting sector itself is declining, whereas that coming from the exporting sector’s upstream domestic suppliers is growing.

Keywords:

• domestic value added
• global value chains
• inter-country input-output (ICIO) tables

ACKNOWLEDGMENTS

This research project was conducted when Dongwei Wen was a visiting scholar at Cornell University. Dongwei Wen acknowledges professor Li and Professor Jia for their warm hosting. We are grateful to Heiwai Tang, Robert Koopman, Marcel Timmer, and Vries de Gaaitzen for their useful help.

Notes

Chen et al. (2008), (2012), Dean et al. (2011), and Koopman et al. (2012) all have the disadvantage of arbitrariness of dividing goods into intermediate inputs and other categories as pointed out by Hummels et al. (2001).

Johnson and Noguera (2012) and Koopman et al. (2012) both use the Global Trade Analysis Project Database (GTAP) developed by Purdue University to calculate the foreign and domestic content in China’s exports. Cheng (2014, 2015), Johnson (2014), Luo and Zhang (2014), and Wang et al. (2013, 2015 all use the 2013 version of World Input-Output Database (WIOD) developed by Dietzenbacher, Los, Stehrer, Timmer, and de Vries (2013) and Timmer (2012) to estimate the foreign and domestic value added in Chinese trade. GTAP and WIOD both distinguish a country’s intermediate inputs into imported and domestic intermediate inputs. So, they are both noncompetitive input-output tables. But China’s own input-output tables at the industry level do not distinguish the intermediate inputs by their origin. So, they are competitive input-output tables.

Kee and Tang (2016), Ma et al. (2015), Upward et al. (2013), and Zhang et al. (2013) all use China’s Annual Survey of Industrial Firms (CASIF) made by the National Bureau of Statistics of China (NBSC) and the Chinese Customs Trade Statistics (CCTS) compiled and maintained by the General Administration of Customs of China. They measure the foreign and domestic content in China’s exports by matching and merging the firm survey data and the customs transaction-level data.

Hummels et al. (2001) pointed out that the key advantage of using input-output tables is to avoid arbitrariness of classification schemes dividing goods into intermediate inputs and other categories.

Most of the literature only focuses on China’s domestic value added content in its exports to the world or to its major trading partners, and does not estimate China’s major trading partners’ domestic value added content embodied in its exports to China.

The OECD’s input-output database used in Hummels et al. (2001) only gives the intermediate inputs from the entire world imported by a country, and does not tell the specified source countries of imported intermediate inputs. But the inter-country input-output database developed by OECD (2015) gives the specified origin countries of intermediate inputs imported by a country.

In the case of China, processing exporters account for around 50% of China’s gross exports for the period from 1995 to 2011 according to China’s customs trade statistics.

By definition, the domestic sellers do not export, but the export from the domestic sellers is not zero according to the inter-country input-output tables compiled by OECD (2015). So we take the domestic sellers into Equation (6). According to our calculation, the domestic sellers contribute less than 1% of China’s gross exports. Therefore, the effect of the domestic sellers on the domestic content in China’s exports is negligible.

Johnson and Noguera (2012) assumes that all of the firms have the same domestic value added coefficient per unit output within the same sector. This assumption does not hold in the country where processing trade is pervasive, such as in Mexico and China. Usually, the processing exporters have much lower domestic value added coefficient per unit output than the normal exporters.

The indirect domestic content is the domestic content embodied in the imported intermediate inputs used to produce the domestic output of country c.

The indirect imported content means the foreign content embodied in the domestic intermediate inputs used to produce the domestic output of country c.

By merging the domestic seller’s processing and nonprocessing exporter’s inter-country input-output tables in China’s products sectors, we obtain the 2015 version of OECD’s ICIO database without splitting China’s products sectors. Similarly, by merging the global and nonglobal manufactures sector’s inter-country input-output tables in Mexico’s manufactures sectors, we obtain the 2015 version of OECD’s ICIO database without splitting Mexico’s manufactures sectors.

Kee and Tang (2016) find that the ratio of domestic value added in exports to gross exports (for short DVAR in their article) had been rising during the period of 2000–2007. But researches find that the domestic content in exports has been declining in many countries. Therefore, China has defied the declining trend. They argue that China’s rising domestic content in exports is mainly due to China’s trade and investment liberalization.

According to our calculations based on the 2015 OECD’s ICIO database, the share of processing exports is about 80% in China’s tech-intensive sectors and around 35% in China’s labor- and capital- intensive sectors for the period of from 1995–2011.

Additional information

Funding

We thank the National Social Science Foundation of China (NSSFC) for financial support under grant no. 12CJY083. Dongwei Wen thanks Nankai University for providing him the basic scientific research project under grant no. NKZXA1405. We also acknowledge financial support from the Collaborative Innovation Center for China Economy at Nankai University.