Valuing housework time from willingness to spend time and money for environmental quality improvements

We develop a new approach to assessing the value of home production time based on willingness to spend time and money to obtain environmental improvements. When peoples’ choice is constrained by time as well as money, measures of willingness to pay can be defined with respect to either numeraire. In a model that explicitly allows for multiple shadow values of time, we show that the willingness to pay time and money measures are linked through the value of saving time. With survey information on peoples’ willingness to spend additional time on housework activities, as well as pay money, to obtain environmental quality improvements, joint estimation within a utility-consistent structure produces estimates of both willingness to pay and the value of saving housework time. From the value of saving housework time, the marginal value of housework time can be readily identified. When applied to Korean households’ valuation of water quality improvements in the Man Kyoung River, we find that the value of housework time is 70–80% of the market wage.

中国语言研究与地域文化——兼论韩汉音与上古音

韩国的中国语言学研究过于依靠权威的文献(如《说文解字》、《切韵》),忽视汉语的非文献资料(如当代方言以及语言的文化、社会及心理层面).文章试图把韩国的中国语言学研究的范围从文献研究扩大到非文献研究,并尝试证明今天的方言和对语言的文化层面的分析是如何同古代汉语和韩汉音的比较研究紧密相联的,希望借此来培养更多的中国语言文学方面的优秀青年学者.     

Comparison of data analysis procedures for real-time nanoparticle sampling data using classical regression and ARIMA models

Real-time monitoring is necessary for nanoparticle exposure assessment to characterize the exposure profile, but the data produced are autocorrelated. This study was conducted to compare three statistical methods used to analyze data, which constitute autocorrelated time series, and to investigate the effect of averaging time on the reduction of the autocorrelation using field data. First-order autoregressive (AR(1)) and autoregressive-integrated moving average (ARIMA) models are alternative methods that remove autocorrelation. The classical regression method was compared with AR(1) and ARIMA. Three data sets were used. Scanning mobility particle sizer data were used. We compared the results of regression, AR(1), and ARIMA with averaging times of 1, 5, and 10?min. AR(1) and ARIMA models had similar capacities to adjust autocorrelation of real-time data. Because of the non-stationary of real-time monitoring data, the ARIMA was more appropriate. When using the AR(1), transformation into stationary data was necessary. There was no difference with a longer averaging time. This study suggests that the ARIMA model could be used to process real-time monitoring data especially for non-stationary data, and averaging time setting is flexible depending on the data interval required to capture the effects of processes for occupational and environmental nano measurements.