Split or straight? Evidence of the effects of work schedules on workers’ well-being, time use, and productivity

About half of all employees in Spain are on a daytime split work schedule, i.e. they typically work for 5 h in the morning, take a 2-hour break at lunch time, and work for another 3 h in the afternoon/evening. This paper studies the effects of split work schedule on workers’ psychological well-being, daily time use, and productivity. Using cross-sectional data from the 2002 to 2003 Spanish Time Use Survey, I find that female split-shifters experience an increased feeling of being at least sometimes overwhelmed by tasks and not having enough time to complete them. On working days, a split work schedule is positively related to time spent on the job, sleeping, and eating and drinking, and negatively associated with time spent on housework, parental child care, and leisure activities. Most of the time-use effects are similar across the sexes, and only a few of the time reductions are partly made up on days off. I also find that the split work schedule is associated with lower hourly wages

Fast Statistical Alignment

We describe a new program for the alignment of multiple biological sequences that is both statistically motivated and fast enough for problem sizes that arise in practice. Our Fast Statistical Alignment program is based on pair hidden Markov models which approximate an insertion/deletion process on a tree and uses a sequence annealing algorithm to combine the posterior probabilities estimated from these models into a multiple alignment. FSA uses its explicit statistical model to produce multiple alignments which are accompanied by estimates of the alignment accuracy and uncertainty for every column and character of the alignment—previously available only with alignment programs which use computationally-expensive Markov Chain Monte Carlo approaches—yet can align thousands of long sequences. Moreover, FSA utilizes an unsupervised query-specific learning procedure for parameter estimation which leads to improved accuracy on benchmark reference alignments in comparison to existing programs. The centroid alignment approach taken by FSA, in combination with its learning procedure, drastically reduces the amount of false-positive alignment on biological data in comparison to that given by other methods. The FSA program and a companion visualization tool for exploring uncertainty in alignments can be used via a web interface at http://orangutan.math.berkeley.edu/fsa/, and the source code is available at http://fsa.sourceforge.net/