An Analysis of Food Grain Consumption in Urban Jiangsu Province of China
Zheng, Zhihao, Henneberry, Shida Rastegari, Journal of Agricultural and Applied Economics
The impacts of economic and demographic variables on the demand for food grain commodities in urban Jiangsu province of China are estimated, using both the QUAIDS and the AIDS models. Results show that the demands for wheat flour and coarse grains are price-elastic while the demands for rice and grain products are price-inelastic. Certain demographic-variables show as having a significant impact on food grain demand. Finally, a decomposition of causes of changes in rice consumption over the period of 1995-2007 is performed.
Key Words: AIDS model, China food grain demand, China urban household demand
JEL Classifications: D12, Q18, Q13
Food grain consumption in urban China has decreased dramatically over the past decade. Per capita at-home consumption of food grains declined from an annual average of 93.5 kg during 1995-1997 to 79.1 kg during 2002-2004. The dramatic decrease in food grain consumption is a reflection of an overall change in consumers' diet in urban China toward more meats, fish, milk, and fruits. Not only has the per capita total volume decreased, the composition of food grain consumption of urban households has also changed significantly. Between the periods of 1995-1997 and 2002-2004, the average annual per capita rice consumption declined from 54.0 kg to 43.4 kg, a decrease of 19.6%; wheat flour consumption decreased from 20.9 kg to 12.5 kg, a decrease of 40.2%; while consumption of coarse grains increased from 2.3 kg to 3.0 kg, an increase of 27.0%; and consumption of grain products increased from 16.2 kg to 19.8 kg, an increase of 22.3% [China's National Bureau of Statistics (NBS), 1996-2005].
China is the world's largest producer and consumer of both wheat and rice. Prior to the mid 1990s, China was one of the largest wheat importers in the world. As China's government embarked on a self-sufficiency campaign to boost wheat production in 1995, China's wheat imports have remained low and erratic since 1997, mainly depending on the domestic production situation (Carter and Zhong, 1999; Lohmar, 2004). Contrary to wheat, China has been an exporter of rice in the global market. Considering that urban residents account for more than 40% of China's total population and this urban population share is expected to grow to 50% by 2020 (Hsu, Chern, and Gale, 2002), the changing food grain consumption patterns in urban China have the potential to significantly impact both the domestic and the world food and feed grain markets. For example, the continuous decrease in per capita consumption of wheat and rice since the early 1990s has caused China to grow less wheat but more corn in order to meet the feed needs of a growing livestock industry in China. Moreover, China has used some low-quality wheat and rice for livestock feed in recent years. These recent changes in the cropping structure and the usage of wheat and rice suggest that while China might retain its long-term self-sufficiency objective for wheat and rice until 2020, it will produce more feed grains than its current levels.1 This prospect has important repercussions for grain exporting country producers, such as those in the United States and Australia, who have viewed China as a potential large destination for wheat and feed grains.
There have been several studies that have focused on the demand for the broad category of food grains in China; however, only a few have analyzed the demand for individual commodities, namely rice, wheat, and coarse grains. Table 1 provides a summary of these studies (Carter and Zhong, 1999; Fan, Wailes, and Cramer, 1995; Gould and Villarreal, 2006; Huang, David, and Duff, 1991; Liu and Chern, 2003; Peterson, Jin, and Ito, 1991; Wu, Li, and Samuel, 1995; Zhuang and Abbott, 2007). Although all of the above studies are consistent in showing an inelastic demand response to ownprice and income changes, the elasticity figures vary considerably. Variations in estimation procedures, data (annual aggregate or household-level), and data period might explain the differences in elasticity estimates. …