Nola du Toit Cathy Haggerty Instability Overlooked: Evidence of the Importance of Household Roster Data Collection and Matching Over Time

2 Background: Instability Relationship Instability Divorce or dissolution of cohabiting union Children wellbeing (Amato and Sobolewski 2001) School performance (Frisco et al. 2007) Life satisfaction (Hans-Jurgen and Brockel 2007) Household instability Change in number of children/adults Household debt (Disney et al. 2008) Depression (Heflin and Iceland 2009) Outdoor play (Handy et al. 2008)

3 Measuring Instability: Relationship Instability Relationship instability Change in marriage or cohabiting relationship over time TIME 1TIME 2OUTCOME Are you married/cohabiting? Instability YES 0 NO 1

4 Measuring Instability: Change in Number of Adults/Children Household instability Change in number of adults or children Are we missing anything? TIME 1TIME 2OUTCOME Number of children Instability

5 1959: Are you married? YES 1964: Are you married? YES Instability = NO But…. 1959: Married to Eddie Fisher 1964: Married to Richard Burton Instability? Example1: Elizabeth Taylor YES

6 3 children in the household Lisa, Maggie, and Bart Episode children in the household Lisa, Maggie, and “BART” Still 3 children But not the same 3 children Instability? Example 2: The Simpsons YES

7 Research Questions Is there another way to measure instability? How much instability is overlooked by current measures? Does it matter?

8 Data Making Connections Survey Annie E. Casey Foundation Low income households at 10 sites Longitudinal Baseline ( ) Wave 2 ( ) Wave 3 ( ) Information on variety of topics People in household, age, gender, employment, relationships to one another, children, economic wellbeing, etc.

9 Data Household roster data Matched across waves Link Plus, eyeballing Unique identifiers for everyone in household Waves 2 and 3 for 6 sites 2242 cases‏ PERSON 1PERSON 2PERSON 3 WAVE A C WAVE A B.

10 Methodology Compare “old way” to “new way” Relationship Instability Old way= Is there a spouse or partner present? New way= Is the Wave 2 spouse or partner id# present in Wave 3? Household Instability in Number of Children and Adults Old way = count of adults and children New way = count of same id# at each wave Compare old way and new way Age, gender, log of income, education, and employment

11 Findings: Relationship Instability ***Significantly different p<.001

12 Findings: Relationship Instability 6% is not the same person ***Significantly different p<.001

13 Findings: Household Instability ***Significantly different p<.001 Change in number of children Change in number of adults

14 Findings: Household Instability ***Significantly different p<.001 Change in number of children Change in number of adults

15 Does it matter? Wave 2 Characteristics Relationship Instability OLD WAYNEW WAY Unweighted n(141)(176) Age mean Household income mean ($)$35,499.08$31, Household income (log) mean Percent Female87%83% Education - Less than high school37%42% Education - High school/GED32%30% Education - More than high school31%28% Employed respondent56%52% NOTE: *p<.05, **p<.01; ***p<.001

16 Does it matter? WAVE 2 CHARACTERISTICS RELATIONSHIP INSTABILITY OLD WAYNEW WAY Unweighted n(141)(176) Age mean38.30*40.81 Household income mean ($)$35,499.08$31, Household income (log) mean Percent Female87%***83% Education - Less than high school37%***42% Education - High school/GED32%**30% Education - More than high school31%***28% Employed respondent56%***52% NOTE: *p<.05, **p<.01; ***p<.001

17 Does it matter? Wave 2 Characteristics Change in Number of Adults OLD WAYNEW WAY Unweighted n(670)(786) Age mean Household income mean ($)$30,426.91$29, Household income (log) mean Percent Female80%78% Education - Less than high school33%34% Education - High school/GED35% Education - More than high school31% Employed respondent58% NOTE: *p<.05, **p<.01; ***p<.001

18 Does it matter? Wave 2 Characteristics Change in Number of Adults OLD WAYNEW WAY Unweighted n(670)(786) Age mean Household income mean ($)$30,426.91$29, Household income (log) mean Percent Female80%***78% Education - Less than high school33%*34% Education - High school/GED35% Education - More than high school31% Employed respondent58% NOTE: *p<.05, **p<.01; ***p<.001

19 Does it matter? Wave 2 Characteristics Change in Number of Adults OLD WAYNEW WAY Unweighted n(511)(544) Age mean Household income mean ($)$26,994.62$26, Household income (log) mean Percent Female85% Education - Less than high school38% Education - High school/GED33%34% Education - More than high school28%27% Employed respondent59%60% NOTE: *p<.05, **p<.01; ***p<.001

20 Does it matter? Wave 2 Characteristics Change in Number of Adults OLD WAYNEW WAY Unweighted n(511)(544) Age mean Household income mean ($)$26,994.62$26, Household income (log) mean Percent Female85% Education - Less than high school38% Education - High school/GED33%*34% Education - More than high school28%27% Employed respondent59%*60% NOTE: *p<.05, **p<.01; ***p<.001

21 Conclusions Is there another way to measure instability? Roster matching and unique person identifiers add depth How much instability is overlooked by current measures? Old way overlooks significant amount of instability Does it matter? Characteristics are significantly different

22 Conclusions Study is important Household rosters are important part of questionnaire Roster matching and unique identifiers add depth Substantive topics are enhanced by the added details Limitations to roster matching and unique identifiers Burdensome process Ideal for longitudinal surveys But can be incorporated into cross-sectional surveys Very beneficial to capturing the whole picture

Thank You! Nola du Toit: Cathy Haggerty: