Economic growth and nutrition transition: an empirical analysis comparing demand elasticities for foods in China and Russia

This study provides empirical evidence on the link between economic growth and nutrition transition in two emerging economies, China and Russia. Both countries have experienced rising average incomes, accompanied by an increasing rate of nutrition-related chronic diseases in recent years. Given the regional heterogeneity between these two countries, we analyze the extent to which income growth as a major driver of nutrition transition has a significant effect on the consumption of different food aggregates and how these effects differ between Chinese and Russian consumers. Our results indicate that with increasing household incomes over time the demand for carbohydrates decreases, while the demand for meat and dairy products, as well as fruits increases. This is a development generally known as nutrition transition. Further, we estimate a Quadratic Almost Ideal Demand System（QUAIDS） for nine different food aggregates for China and Russia. Our results indicate that in both countries all food aggregates have positive expenditure elasticities and are thus normal goods. Moreover, our results indicate that in 2008/2009 meat is still a luxury good in China yet a necessity good in Russia. For 2009, the highest own-price elasticities in China are found for non-meat protein sources and dairy products. Within the meat group, beef, poultry and mutton have the highest price elasticities in China. In Russia, the milk and dairy group, together with the vegetable group, is the most price-elastic food group in 2008. In line with the definition of a nutrition transition, our overall results underscore the finding that income growth in China and Russia tends to increase the demand for animal-based products much stronger than, for example, the demand for carbohydrates. Despite being a positive signal for problems of malnutrition in rural China, this trend of increasing meat consumption might further increase the incidence of chronic diseases in urban areas since there is convincing scientific evidence that increasing meat consumption, especially red and processed meat, is associated with an increased risk of chronic diseases.

Improving risk reduction potential of weather index insurance by spatially downscaling gridded climate data-a machine learning approach

Open-access gridded climate products have been suggested as a potential source of data for index insurance design and operation in data-limited regions.However,index insurance requires climate data with long historical records,global geographical coverage and fine spatial resolution at the same time,which is nearly impossible to satisfy,especially with open-access data.In this paper,we spatially downscaled gridded climate data(precipitation,temperature,and soil moisture)in coarse spatial resolution with globally available longterm historical records to finer spatial resolution,using satellite-based data and machine learning algorithms.We then investigated the effect of index insurance contracts based on downscaled climate data for hedging spring wheat yield.This study employed countylevel spring wheat yield data between 1982 and 2018 from 56 counties overall in Kazakhstan and Mongolia.The results showed that in the majority of cases(70%),hedging effectiveness of index insurances increases when climate data is spatially downscaled with a machine learning approach.These improvements are statistically significant(p≤0.05).Among other climate data,more improvements in hedging effectiveness were observed when the insurance design was based on downscaled temperature and precipitation data.Overall,this study highlights the reasonability and benefits of downscaling climate data for insurance design and operation.