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"The NBER Bulletin on Aging and Health provides summaries of publications like this. You can sign up to receive the NBER Bulletin on Aging and Health by email. In this paper, we take a structural approach to investigate the effects of wages and working hours on health behaviors of low-educated persons using variation in wages and hours caused by changes in economic activity. We find that increases in hours are associated with an increase in cigarette smoking, a reduction in physical activity, and fewer visits to physicians. More importantly, we find that most of the effects associated with changes in hours can be attributed to the changes in the extensive margin of employment. Increases in wages are associated with greater consumption of cigarettes"--National Bureau of Economic Research web site

Dr. Teh-wei Hu is a Professor Emeritus of Health Economics and served as Associate Dean and Department Chair in the School of Public Health at the University of California, Berkeley. Currently he serves as the Director for International Tobacco Control Policy Research and Evaluation at the Public Health Institute. He has been appointed by the U.S. Government as a member of the Interagency Committee on Smoking and Health. Professor Hu's areas of expertise are the application of econometrics to health care services research. He is the author of more than 200 publications. He contributed chapters in major WHO and World Bank tobacco control publications. During the past 20 years, he has been conducting research in economics of tobacco control in the US, China, Southeast Asia, and Estonia. He is the editor of Tobacco Control Policy Analysis in China: Economics and Health (2008). He co-authored a report on Tobacco Taxation and Its Potential Impact in China (Hu, Mao, Shi, and Chen, 2008) funded by the Bloomberg Philanthropies and the Gates Foundation. He has served as a consultant to the World Bank, the World Health Organization, and a senior policy advisor to the Ministry of Health, China.

Purpose: At the dynamic innovation market, it is very difficult for an enterprise to accomplish innovation individually; technology innovation is shifting towards collaborative R&D chain mode. Thus, supplier selection based on individually innovation efficiency of enterprise is inapplicable to construct collaborative R&D innovation chain. This study is seeking to address how to select R&D innovation chain supplier in manufacturing industry. Design/methodology/approach: Firstly, Delphi method and AHP method are applied to establish an index system evaluating the suppliers of innovation chain, and then each index is weighted by experts with AHP method. Thirdly, optimized PSO algorithm is put forwarded based on the optimal efficiency of innovation chain to discriminate ideal suppliers meeting realistic conditions. Fourthly, innovation chain construction at generator manufacturing industry was taken as empirical case study to testify the improved PSO model. Findings: The innovation chain is comprised up by several enterprises, innovation performance of a single enterprise is not always positively correlated to that of one innovation chain, and the proposed model is capable to find out the best combination to construct an innovation chain. Research limitations/implications: The relations between these constructs with other variables of interest to the academicals fields were analyzed by a precise and credible data with a clear and concise description of the supply chain integration measurement scales. Practical implications: providing scales that are valid as a diagnostic tool for best practices, as well as providing a benchmark with which to compare the score for each individual plant against a chain of industrial innovation from machinery. Originality/value: Innovation chain integration is an important factor in explaining the innovation performance of companies. The vast range of results obtained is due to the fact that there is no exactness to the group of scales used. An analysis of the measurement models nor clear benchmarks as to the variety of the scales used has not been published before. ; Peer Reviewed

Purpose: At the dynamic innovation market, it is very difficult for an enterprise to accomplish innovation individually; technology innovation is shifting towards collaborative R&D chain mode. Thus, supplier selection based on individually innovation efficiency of enterprise is inapplicable to construct collaborative R&D innovation chain. This study is seeking to address how to select R&D innovation chain supplier in manufacturing industry. Design/methodology/approach: Firstly, Delphi method and AHP method are applied to establish an index system evaluating the suppliers of innovation chain, and then each index is weighted by experts with AHP method. Thirdly, optimized PSO algorithm is put forwarded based on the optimal efficiency of innovation chain to discriminate ideal suppliers meeting realistic conditions. Fourthly, innovation chain construction at generator manufacturing industry was taken as empirical case study to testify the improved PSO model. Findings: The innovation chain is comprised up by several enterprises, innovation performance of a single enterprise is not always positively correlated to that of one innovation chain, and the proposed model is capable to find out the best combination to construct an innovation chain. Research limitations/implications: The relations between these constructs with other variables of interest to the academicals fields were analyzed by a precise and credible data with a clear and concise description of the supply chain integration measurement scales. Practical implications: providing scales that are valid as a diagnostic tool for best practices, as well as providing a benchmark with which to compare the score for each individual plant against a chain of industrial innovation from machinery. Originality/value: Innovation chain integration is an important factor in explaining the innovation performance of companies. The vast range of results obtained is due to the fact that there is no exactness to the group of scales used. An analysis of the measurement models nor clear benchmarks as to the variety of the scales used has not been published before. ; Peer Reviewed