Our proposed discrete-time logit hazard estimation approach takes account of right-censored spells, while left-censored spells are more problematic. Whether including or excluding left-censored spells in an analysis produces misleading results depends on whether the analysis is trying to answer questions regarding poverty transitions or poverty duration. Iceland (1997a) looks at this exact topic in his paper "The Dynamics of Poverty Spells and Issues of Left-Censoring." He recommends that "when studying poverty transitions, using discrete-time logistic regression, all observations from left-censored spells should be included in [the] model to avoid selection bias." Iceland finds that omitting left-censored cases potentially introduces greater bias in poverty transitions than including them because it would systematically exclude individuals in the midst of long-term poverty.(22) Iceland (1997b) does not omit left-censored cases from his model because his focus is on how urban labor market characteristics affect transitions out of poverty, not the precise duration of poverty.(23) As our analysis focuses on poverty transitions, we incorporate left-censored spells. We do, however, identify left-censored spells in the model using a dummy variable. With this design, the model of poverty entries that includes left-censored spells, for example, examines "first observed poverty entry," not "first entry."
Summary: To summarize, we use the count method and the multivariate hazard model to answer our three research questions on the dynamics of poverty. We use the count method to examine the dynamics behind the poverty rate and the multivariate hazard model to examine events associated with poverty entries and exits. These methods are chosen because they are well-suited to answering the research questions.