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It was very early in the morning. She was looking at the yard from behind a window. The tree was in full bloom. She removed a lock of white hair from her forehead. It was the forty-first time that she was watching the tree blossom. She was standing in front of the window in her thick white nightgown with long sleeves and closed collar. She was a bit cold. The wind made the blossoms shiver. It seemed that the flowers never felt cold. The woman recalled the very first time she watched them.She has awakened to the sound of her husband's voice, "Wake up! Come and see! The tree has blossomed!" They have stood watching together at the window. Her long white nightgown was sleeveless and thin, with lace around its open collar. She wasn't cold, or perhaps it was her sense of decency that made her forget the cold.

This paper challenges the notion that on-the-job training investments are quantitatively important for workers' welfare and argues that on-the-job training may not increase lifetime income by more than 1 percent. I argue that it is very difficult to reconcile the slowdown in wage growth late in a worker's career with optimizing behavior unless the technology for learning on the job is such that it generates very low gains from training. The analysis is based on a nonparametric methodology for estimating the learning technology from wage profiles; the results are arrived at by comparing the lifetime income when the worker optimally invests in his human capital to the one where he does not make any investments.

Is lifetime inequality mainly due to differences across people established early in life or to differences in luck experienced over the working lifetime? We answer this question within a model that features idiosyncratic shocks to human capital, estimated directly from data, as well as heterogeneity in ability to learn, initial human capital, and initial wealth. We find that, as of age 23, differences in initial conditions account for more of the variation in lifetime earnings, lifetime wealth, and lifetime utility than do differences in shocks received over the working lifetime. (JEL D31, D91, J24, J31)

As modern economies become predominantly service-based, companies increasingly derive revenue from the creation and sustenance of long-term relationships with their customers. In such an environment, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company's customers. This article reviews a number of implementable CLV models that are useful for market segmentation and the allocation of marketing resources for acquisition, retention, and cross-selling. The authors review several empirical insights that were obtained from these models and conclude with an agenda of areas that are in need of further research.