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Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

Despite rising incomes and reduction of extreme poverty, the feeling of being poor remains widespread. Support programs can improve well-being, but they first require identifying who are the households that judge their income is insufficient to meet their basic needs, and what factors are associated with subjective poverty.

Households report the income level they judge is sufficient to make ends meet. Then, they are classified as being subjectively poor if their own monetary income is inferior to the level they indicated. Second, the study compares the performance of three machine learning algorithms, the random forest, support vector machines and least absolute shrinkage and selection operator (LASSO) regression, applied to a set of socioeconomic variables to predict subjective poverty status.

The random forest generates 85.29% of correct predictions using a range of income and non-income predictors, closely followed by the other two techniques. For the middle-income group, the LASSO regression outperforms random forest. Subjective poverty is mostly associated with monetary income for low-income households. However, a combination of low income, low endowment (land, consumption assets) and unusual large expenditure (medical, gifts) constitutes the key predictors of feeling poor for the middle-income households.

To reduce the feeling of poverty, policy intervention should continue to focus on increasing incomes. However, improvements in nonincome domains such as health expenditure, education and family demographics can also relieve the feeling of income inadequacy. Methodologically, better performance of either algorithm depends on the data at hand.

For the first time, the authors show that prediction techniques are reliable to identify subjective poverty prevalence, with example from rural China. The analysis offers specific attention to the modest-income households, who may feel poor but not be identified as such by objective poverty lines, and is relevant when policy-makers seek to address the “next step” after ending extreme poverty. Prediction performance and mechanisms for three machine learning algorithms are compared.

The need for a universal rural pension system has been heightened by demographic changes in rural China, including the rapid aging of the nation’s rural population and a dramatic decline in fertility. In response to these changes, China’s Government introduced the New Rural Social Pension Program (NRSPP) in 2009, a voluntary and highly subsidized pension scheme. The purpose of this paper is to assess the participation of rural farmers in the NRSPP. Furthermore, the authors examine whether the NRSPP affects the labor supply of the elderly population in China.

This paper uses household-level data from a sample of 2,020 households originating from a survey conducted by the authors in five provinces, 25 counties, and 101 villages in rural China. Using a probit model and conducting correlation analysis, the authors demonstrate the factors affecting the participation and the impact of NRSPP on labor supply of the rural elderly.

The results show there are several factors that are correlated with participation, such as specific policy variant in force in the respective household's province, the size of the pension payout from government, the age of sample individuals, and the value of household durable assets. Specifically, different characteristics of NRSPP policy implementation increase participation in China’s social pension program. The results suggest that the introduction of the NRSPP has not affected the labor supply of the rural elderly, in general, although it has reduced participation for the elderly who were in poor health.

Several previous studies have covered the NRSPP. However, all previous studies were based on case studies or just focused on a small region, and for this reason the results cannot reflect the populations and heterogeneity of rural areas. Therefore, a data set with a large sample size is used in this paper to provide a new perspective to fully understand the participation of NRSPP and its impacts on rural households. This paper will make an update contribution to the literature in the area of pension programs in China.

Although access to safe drinking water is one of the most important health-related infrastructure programs in the world, drinking water remains a large problem in China today, especially in rural areas. Despite increased government investment in water resource protection and management, there is still an absence of academic studies that are able to document what path the investment has taken and whether it has had any tangible impact. The purpose of this paper is to analyze the impact of drinking water investment on drinking water in China.

The authors make use of nationally representative data from 2005 and 2012 to measure the impact of drinking water investment among 2,028 rural households in 101 villages across five provinces. Both ordinary least squares regression and probit regression are used to analyze the correlates and the impact of drinking water investment.

The authors demonstrate that water quality was likely a significant problem in 2004 but that China’s investment into drinking water appears to have resulted in initial improvements during the study period. The authors show that the most significant change came about in terms of hardware: villages that received more drinking water investment now have more piped tap water and more access to water treatment infrastructure (disinfecting and filtering facilities). High rates of rural resident satisfaction with drinking water suggest the effects of drinking water investment are being felt at the village level.

To the authors’ knowledge, this is the first empirical study on drinking water investment over time in rural China using nationally representative data.

Accompany with the development of economy, the environment becomes deteriorating, especially in developing countries. Many studies found that foreign direct investment (FDI) with the effect of technology spillover would be one of the best ways to solve the environmental problem. By using the model of trade theory distinguishing between environmental and productive technology and separately analyzing the technology spillover effect of these two technologies on reducing environmental pollution in host countries, we find that the pollution problem could be worse if more intensive pollution industrial structure is induced by the environmental technology development. Naturally, this did not mean restraining the development of technology, but rather emphasizing the importance of the regulation and the industrial structure.

The purpose of this paper is to estimate the returns to rural schooling in China, addressing both endogeneity in rural individuals’ schooling and self-selection into off-farm work.

This paper exploits geographical proximity to rural secondary schools to create instrumental variables (IV) for individuals’ years of schooling. It addresses both endogenous schooling and self-selection using the two-step procedure developed in Wooldridge (2002, p. 586).

The preferred IV estimate of schooling returns, 7.6 percent, is considerably higher than most previous estimates found in rural China.

This paper is among the few papers that examine returns to rural schooling in China while simultaneously addressing both endogeneity in individuals’ schooling and self-selection into off-farm work. Its findings suggest that rural education in China is potentially able to generate a respectable level of economic returns if policies are designed to provide greater school accessibility to rural individuals.

The purpose of this paper is to review the theoretical background, methodological extensions, and empirical applications of the Engel curve, which is applied to the research of the change in farmers’ welfare and food demand in China after the economic reform in 1978, compared with the statistics of income and food consumption.

This paper mainly uses the traditional method of Engel curve, which is compared with income growth and food consumption, to study farmers’ welfare improvement in rural China.

The Engel coefficients identify three different stages for farmers’ welfare change after 1978. The first stage is the period between 1978 and 1988, in which farmers’ welfare has been continuously enhanced due to the institutional bonus of the 1978 economic reform and increased government purchase price of agricultural products. The second stage is the period between 1989 and 1995, in which farmers’ welfare has been slightly deteriorated mainly due to the end of institutional reform bonus, suppressed food prices, relative high inflation, and instable political situation. The third stage is the period after 1995, in which farmers’ welfare returns to a growing path, as the dual price system was abolished, the transition from a planned economy to a market economy had been completed, and the government carried out protective policies for agriculture and started to heavily subsidize agriculture. The Engel coefficient still remained at a very high level at 0.59 in 1995, but it continuously decreased to 0.33 in 2015. The welfare enhancement for farmers mainly results from deepened market-oriented reform, protective policies for agriculture, and prevalent off-farm employment. The Engel coefficient is also linked to food demand elasticities. Along with the decreasing Engel coefficient in the past 40 years, income elasticities also continuously decrease from 0.55 in 1978 to 0.08 in 2015. Food demand is very inelastic now, and any further increase in income will not substantially increase food demand any more.

Inequality has not been analyzed.

This paper reviews the methodological advantages of the Engel curves, and uses it to identify different stages of welfare change and estimate income elasticities of food demand for farmers in China after the 1978 economic reform.

The purpose of this study is to explain the rapid growth of urban residents' sugar consumption in China from the perspective of habit formation.

Using the provincial panel data of Chinese urban households from 1995 to 2012, this study uses the two-step System Generalized Moment Method (GMM) to test the habit formation effect on residents' sugar expenditure in urban China. We also use system GMM and the recursive estimated method to explore the changes of the habit formation coefficients in different years.

We find a significant habit formation effect on overall residents' sugar expenditure and different types of sugary foods expenditure. The habit formation effect on total residents' sugar expenditure and different types of sugary foods is decreasing over the years. The patterns of the changes of the habit formation effect on different types of sugar foods are slightly different.

Due to data limitations, we are not able to do household-level analysis and to examine the heterogeneity of the habit formation effect.

This is the first study that examines changes in the habit formation effect on residents' sugar expenditure in urban China. Our findings provide a reasonable explanation for the rapid growth of residents' sugar consumption in urban China. The result helps to formulate targeted policies for future interventions to control the growth of sugar consumption.