In this article we analyze the outcomes of nearly 3200 awards issued in employment disputes settled by arbitration in the securities industry over the period 1986–2008. The large amount of litigation in the securities industry alleging discrimination by securities firms against the women they employ led us to hypothesize that women would do less well than men in these arbitration cases. Regression analysis reveals that the gender of the complainant and the complainant's attorney (but not the gender of the respondent's attorney or the arbitrator) had significant effects on the size of the awards. Regardless of the definition of the dependent variable, female complainants did less well than male complainants in these employment arbitration cases. In most estimates, the gender of the attorney representing the complainant also affected the size of the award: male attorneys obtained larger awards than female attorneys. We conclude that these gender differentials are more likely to be the consequence of employment conditions in the securities industry rather than biases in the arbitration process.

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today we are living in the world of data science, where we have to handle bulk amount of data to run any organization. To accomplish the goal of any organization it's mandatory to take right decision at the right time. For this data is maintained in the form of data ware housing and for the extract, transform and load majorly informatics power center tool is used by organization. So in this paper we have shared the complete informatics power center logics, that will be useful not only for organization's but also be useful for data scientists as a complete reference.

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