Whose problem? Disability narratives and available identities

In this article, the author demonstrates that contemporary cultural disability discourses offer few positive resources for people with impairments to draw upon in constructing positive personal and social identities. Examining the emergence of the Disability Arts Movement in Britain, consideration is given to alternative discourses developed by disabled people who have resisted the passive roles expected of them and developed a disability identity rooted in notions of power, respect and control. It is suggested that these alternative discourses provide an empowering rather than a disabling basis for community development and community arts practice and should be embraced by workers in these fields

Categorical Data

A very brief survey of regression for categorical data. Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest lies. For binary data (two categories) probit and logit models or semiparametric methods are used. For multinomial data (more than two categories) that are unordered, common models are multinomial and conditional logit, nested logit, multinomial probit, and random parameters logit. The last two models are estimated using simulation or Bayesian methods. For ordered data, standard multinomial models are ordered logit and probit, or count models are used if ordered discrete data are actually a count.binary data, multinomial, logit, probit, count data

A Survival Analysis of Australian Equity Mutual Funds

Determining which types of mutual (or managed) investment funds are good financial investments is complicated by potential surbivorship biases. This project adds to a small recent international literature on the patterns and determinants of mutual fund survivorship. We use statistical techniques for survival data that are rarely applied in finance. Of specific interest is the hazard rate of fund closure, which gives the variation over time in the conditional probability of fund closure given fund survival to date. For a sample of 251 retail investment funds in Australia from 1980 to 1999 we identify a hump-shaped hazard function that reaches its maximum after about five or six years, a pattern similar to the UK findings of Lunde, Timmermann and Blake (1999). We also consider the impact of monthly and annual fund performance (gross and relative to a market benchmark). Returns relative to the benckmark are much more important than gross returns, with hgiher relative returns associated with lower hazard of fund closure. There appears to be an asymmetric response to performance, with positive shocks having a larger impact on the hazard rate than negative shocks.mutual funds; survivorship bias; duration analysis; cox regression

Robust Inference with Clustered Data

In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical significance, as emphasized most notably in empirical studies by Moulton (1990) and Bertrand, Duflo and Mullainathan (2004). We emphasize OLS estimation with statistical inference based on minimal assumptions regarding the error correlation process. Complications we consider include cluster-specific fixed effects, few clusters, multi-way clustering, more efficient feasible GLS estimation, and adaptation to nonlinear and instrumental variables estimators.Cluster robust, random eects, xed eects, dierences in dierences, cluster bootstrap, few clusters, multi-way clusters.

Robust Inference with Clustered Data

In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical significance, as emphasized most notably in empirical studies by Moulton (1990) and Bertrand, Duflo and Mullainathan (2004). We emphasize OLS estimation with statistical inference based on minimal assumptions regarding the error correlation process. Complications we consider include cluster-specific fixed effects, few clusters, multi-way clustering, more efficient feasible GLS estimation, and adaptation to nonlinear and instrumental variables estimators.Cluster robust, random effects, fixed effects, differences in differences, cluster bootstrap, few clusters, multi-way clusters.

Panel data methods for microeconometrics using Stata

This presentation provides an overview of the subset of methods for panel data and the associated Stata xt commands most commonly used by microeconometricians. First, attention is focused on a short panel, meaning data on many individual units and few time periods. Examples include longitudinal surveys of many individuals and panel datasets on many firms. Then the data can be viewed as being clustered on the individual unit and panel methods used are also applicable to other forms of clustered data such as cross-section data from individual-level surveys conducted at many villages with clustering at the village level. Second, emphasis is placed on using the repeated measures aspect of panel data to estimate key marginal effects that can be interpreted as measuring causation rather than mere correlation. The leading methods assume time-invariant individual-specific effects (or “fixed effects”). Instrumental variables (IV) methods can also be used, with data from periods other than the current year potentially serving as instruments. Third, some analyses use dynamic models rather than static models. Particular interest lies in fitting models with both lagged dependent variables and fixed effects. The paper additionally surveys other panel methods used in econometrics, such as those for nonlinear models and those for dynamic panels with many periods of data.

The Rhetoric of Blame: A Rhetorical Framing Analysis of Othering and Blame in Historical Health Crises

The United States’ response to the COVID-19 pandemic was hallmarked by blame rhetoric and fluid social and political expedience. However, the pervasiveness of othering and blame in contemporary pandemic discourse is perhaps consistent with the blame rhetoric of health crises throughout history. Using a rhetorical framing analysis approach, this study aims to explore the various elements of blame rhetoric embedded in newsprint media frames regarding historic infectious disease outbreaks. In doing so, this study investigates three case studies: the San Francisco smallpox outbreak of 1876, the Spanish Flu pandemic of 1918, and the AIDS crisis of the 1980s – 1990s. Through this investigation, I demonstrate how the elements of othering and blame in these historic health crises consistently mirror the political rhetoric surrounding the current COVID-19 pandemic. I argue that the practices of othering and blame defining the contemporary pandemic rhetoric are not a new phenomenon, but rather a continuation of an ongoing problem. Lastly, I argue that it is not the intention of this study to establish an origin for these practices. Rather, the purpose of this study is to use these historic case studies to showcase how the past occupies the present and better illuminate the consequences of medical scapegoating as they occur in our current moment

Bootstrap-Based Improvements for Inference with Clustered Errors

Microeconometrics researchers have increasingly realized the essential need to account for any within-group dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate cluster-robust or sandwich standard errors that permit quite general heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. In applications with few (5-30) clusters, standard asymptotic tests can over-reject considerably. We investigate more accurate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the much-cited differences-in-differences example of Bertrand, Mullainathan and Duflo (2004). In situations where standard methods lead to rejection rates in excess of ten percent (or more) for tests of nominal size 0.05, our methods can reduce this to five percent. In principle a pairs cluster bootstrap should work well, but in practice a Wild cluster bootstrap performs better.clustered errors; random effects; cluster robust; sandwich; bootstrap; bootstrap-t; clustered bootstrap; pairs bootstrap; wild bootstrap.

Bootstrap-Based Improvements for Inference with Clustered Errors

Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (5-30) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo and Mullainathan (2004). Rejection rates of ten percent using standard methods can be reduced to the nominal size of five percent using our methods.