An increasing proportion of low-income women are experiencing periods of economic disconnection, defined as receiving no cash income from welfare or work.  Disconnection for any period of time may create challenges for families, but spell length is likely related to well-being.  Those who easily return to a state of disconnection and those who are chronically disconnected are arguably less able to maintain minimal living standards than those who return to connection promptly.  This study goes beyond a static perspective and adopts a dynamic view in investigating changes in individuals’ disconnected status. 

Additionally, approaches to assisting disconnected women must consider the barriers and opportunities facing them at a number of levels.  Although current research has added much to our knowledge of individual level risk factors, little is known about the influence of macro level factors, such as local socioeconomic characteristics and state policies, on one’s risk of economic disconnection.  This paper uniquely adds to our understanding of disconnected women by focusing on how socioeconomic county and state characteristics relate to the likelihood of moving into or out of economic disconnection. 

We use restricted-use, confidential, micro level Survey of Income and Program Participation (SIPP) data through the New York Census Research Data Center, operated in partnership with the U.S. Census Bureau’s Center for Economic Studies.  Unlike the public use SIPP data, the confidential data contain respondents’ state and county of residence, along with their economic and demographic information, which allows for analyses of the impact of regional attributes on disconnection.  This study specifically uses the SIPP data from the 1996, 2001, and 2004 panels as primary data and state and county data from a variety of public sources are merged with the SIPP data.  The sample is restricted to low-income single mothers (n = 5,263), who are categorized as always connected (n = 3,346), always disconnected (n = 198), and experiencing changes (n = 1,719).

This study utilizes discrete survival analysis to understand movements into and out of disconnection.  First, models estimate how likely a disconnected individual is to return to a connected status, how much time is necessary for her to return to that status, and what factors determine her level of resiliency.  After determining the probability of reconnection and the number of waves spent in the disconnected status, this analysis explores factors associated with the level of resilience through regression models.  As opposed to our first set of models, the second survival analysis measures how likely it is for a currently connected individual to become disconnected, how long it takes for her to change to the disconnected status, and what factors contribute to this type of change in status. 

Results from both sets of models identify regional characteristics with positive or negative impacts on the resiliency of low-income single mothers, along with statistically significant personal and state policy variables, providing a base for policy reforms aimed at helping this vulnerable population.