Investigating the diversity in income level and education level of household sector innovation according to geographical area

innovation find that higher individual incomes are strongly associated with increased frequency of both household sector innovation and innovation diffusion. When we combine personal income effects with the positive impact of educational levels and technical training (both competence-related antecedents), it appears that increases in national development are associated with increases in household sector innovation - a very useful public policy finding. Second, in this survey we included household sector innovations motivated by personal need and additional motivations related to learning, fun, helping others and selling/commercialization. This has a major impact on estimated household sector innovation frequencies - raising them by a factor of approximately 1.4. Reanalysis of data obtained in two earlier national surveys suggests that similar adjustment factors hold in those nations too. This finding shows that prior surveys have significantly underestimated household innovation. For many research purposes, such as national accounting, the total amount and value of household sector innovation is what is of interest, independent of motivations that may drive the activity. We surveyed 5000 consumers in China aged 18 and over. Variation in income and education levels in China is substantial, making the country a good context for our research on the impact of these variables on levels of household sector innovation. In some geographical regions (e.g., Beijing, Shanghai) levels of income and education match those of the most prosperous regions across the globe. In other areas of China many have very low levels of income and education, similar to those encountered in much less developed parts of the world.To identify the sampling frame for our survey, and also to carry out data collection, we collaborated with Dataway, a marketing research company based in Beijing. Because an exhaustive sampling frame with details of all Chinese citizens was not available, the initial sample was obtained with a random number generator covering both cell phones and landlines. To minimize the probability that we would get in touch with businesses, generated phone numbers were first filtered with a public database of all companies and public organizations. Also, data collection was done in the evenings to diminish the probability of contacting businesses, and the introduction to the survey explicitly mentioned our interest in consumer behavior, not business-related innovation. Our research approach insures that each citizen has an equal chance of being sampled.