The Effect of Food Safety and Quality on the Consumption and Price of Meat in Beijing, China

China's economic success has helped it become one of the largest markets in the world. As a result, the demand for agricultural commodities in China has experienced a significant increase. Increasingly affluent Chinese people are paying increasing attention to food safety and quality instead of just quantity. Understanding how meat demands and prices are related to food safety and quality in Beijing will provide guidance for industry and policymakers interested in the Chinese meat market. The purpose of this study is to develop two models to analyze meat demand and prices associated with food safety and quality respectively. First, An Almost Ideal Demand System (AIDS) is used to investigate the effects of food safety on meat consumption. To address the potential bias of zero consumption in the estimation procedures, a simulated maximum likelihood (SML) estimation is applied in the regression. Second, we analyze the implicit price of meat with the intrinsic and extrinsic attributes using a hedonic price model. Five meat categories are regressed on several intrinsic and extrinsic attributes in the model using household survey data collected in Beijing in 2007. The key results of this research have two major outcomes. First, food safety has a significant and positive influence on meat consumption for Beijing residents. Second, the quality-related attributes or characteristics such as meat appearance, supermarket, meat brand, and processed meat as well as demographic variables such as household head's income have a significantly positive influence on the price of meat, which suggest that the consumers in Beijing are willing to pay a price premium to guarantee the quality and safety of meat

Bayesian jackknife empirical likelihood with complex surveys

We introduce a novel approach called the Bayesian Jackknife empirical likelihood method for analyzing survey data obtained from various unequal probability sampling designs. This method is particularly applicable to parameters described by U-statistics. Theoretical proofs establish that under a non-informative prior, the Bayesian Jackknife pseudo-empirical likelihood ratio statistic converges asymptotically to a normal distribution. This statistic can be effectively employed to construct confidence intervals for complex survey samples. In this paper, we investigate various scenarios, including the presence or absence of auxiliary information and the use of design weights or calibration weights. We conduct numerical studies to assess the performance of the Bayesian Jackknife pseudo-empirical likelihood ratio confidence intervals, focusing on coverage probability and tail error rates. Our findings demonstrate that the proposed methods outperform those based solely on the jackknife pseudo-empirical likelihood, addressing its limitations

Jackknife empirical likelihood with complex surveys

We propose the so-called jackknife empirical likelihood approach for the survey data of general unequal probability sampling designs, and analyze parameters defined according to U-statistics. We prove theoretically that jackknife pseudo-empirical likelihood ratio statistic is asymptotically distributed as a chi-square random variable, and can be used to construct confidence intervals for complex survey samples. In the process of research, we consider with or without auxiliary information, utilizing design weights or calibration weights. Simulation studies are included to examine that in terms of coverage probability and tail error rates, the jackknife pseudo-empirical likelihood ratio confidence intervals are superior to those based on the normal approximation