This Was Not on the Syllabus!: An Examination of First-Year Urban Teachers' Self-Efficacy

Many first-year teachers find it difficult to reach the needs of all their students in part, because they feel their college coursework left them ill-prepared for the complexity they face in the classroom. This is particularly true among urban teachers who often face crowded classrooms of diverse students with a wide range of instructional needs. This study is a comparative case study of two University of Connecticut graduates during their first year teaching in urban schools. Using mixed-methods, the study draws on interviews, questionnaires, and videotape data shared as a part of a monthly teacher study group of similar graduates. I also draw on group conversations in which teachers discussed their ability to reach the needs of all of their students as this was related to their preservice coursework. My findings suggest that many first-year teachers feel university coursework failed to help them. One teacher felt it did not help her at all, while the other felt it helped her but she still could not meet all of her students' needs. Many first-year, urban teachers do not feel confident in the classroom as a result of their preparation from coursework. With this lack in confidence, the teachers may be more likely to leave their urban position, and this may contribute to the high turnover of teachers in urban placements.

Some Identification Issues in Nonparametric Linear Models with Endogenous Regressors

In applied work economists often seek to relate a given response variable y to some causal parameter mu* associated with it. This parameter usually represents a summarization based on some explanatory variables of the distribution of y, such as a regression function, and treating it as a conditional expectation is central to its identification and estimation. However, the interpretation of mu* as a conditional expectation breaks down if some or all of the explanatory variables are endogenous. This is not a problem when mu* is modelled as a parametric function of explanatory variables because it is well known how instrumental variables techniques can be used to identify and estimate mu*. In contrast, handling endogenous regressors in nonparametric models, where mu* is regarded as fully unknown, presents di±cult theoretical and practical challenges. In this paper we consider an endogenous nonparametric model based on a conditional moment restriction. We investigate identification related properties of this model when the unknown function mu* belongs to a linear space. We also investigate underidentification of mu* along with the identification of its linear functionals. Several examples are provided in order to develop intuition about identification and estimation for endogenous nonparametric regression and related models.