The role of drug use in welfare to work: Evidence from Houston, Texas

The purpose of this study is to examine the prevalence of drug abuse among welfare recipients in Houston, TX and compare the work activities and employment barriers of drug abusers in order to better understand the potential effects of welfare reform for this population. Four hypotheses were tested comparing the work activities and employment barriers of drug abusers to others on welfare and the relative importance of drug abuse and employment barriers in predicting work activity. ^ This cross-sectional study examined the characteristics and work activities of 447 welfare recipients (81 drug abusers and 366 non-abusers) who were surveyed between October 1998 and April 1999 in Houston, TX. Subjects were introduced and recruited to participate in the study through a flyer, door to door visits, and peer driven recruitment/referral. ^ About 18% were found to be drug abusers, which is consistent with the national average (10–33%) among welfare recipients. Compared to others on welfare, drug abusers were less involved in work activities, and had more employment barriers. Employment barriers were found to be more predictive of welfare to work activities than drug abuse. The results suggest that alleviating employment barriers should be stressed in programs aimed at welfare recipients with drug abuse problems. ^

The optimization principle in phylogenetic analysis tends to give incorrect topologies when the number of nucleotides or amino acids used is small

In the maximum parsimony (MP) and minimum evolution (ME) methods of phylogenetic inference, evolutionary trees are constructed by searching for the topology that shows the minimum number of mutational changes required (M) and the smallest sum of branch lengths (S), respectively, whereas in the maximum likelihood (ML) method the topology showing the highest maximum likelihood (A) of observing a given data set is chosen. However, the theoretical basis of the optimization principle remains unclear. We therefore examined the relationships of M, S, and A for the MP, ME, and ML trees with those for the true tree by using computer simulation. The results show that M and S are generally greater for the true tree than for the MP and ME trees when the number of nucleotides examined (n) is relatively small, whereas A is generally lower for the true tree than for the ML tree. This finding indicates that the optimization principle tends to give incorrect topologies when n is small. To deal with this disturbing property of the optimization principle, we suggest that more attention should be given to testing the statistical reliability of an estimated tree rather than to finding the optimal tree with excessive efforts. When a reliability test is conducted, simplified MP, ME, and ML algorithms such as the neighbor-joining method generally give conclusions about phylogenetic inference very similar to those obtained by the more extensive tree search algorithms.