Effects of Income Imputation on Traditional Poverty Estimates 1987-2007

1. Effects of Income Imputation on Traditional Poverty Estimates1987-2007 The views expressed here are the authors and do not represent the official positions of their organizations.

2. Authors • Joan Turek, Brian Sinclair James and Bula Ghose, Department of Health and Human Services • Charles Nelson and Edward Welniak, Bureau of the Census • Fritz Scheuren, NORC

3. Outline of Talk • Handling nonresponse on the CPS • Effects of imputation on income and poverty estimates • Official poverty vs. first quintile measure -- demographic characteristics • Summary of findings • Implications for new measure?

4. Upward Trend in Nonresponse

5. Handling CPS Nonresponse Uses “Hot Deck” procedures Imputation occurs at the person level by income source Assigns amounts from reporters with similar characteristics Imputation method consistent over time 5

6. Types of Imputation Two types of non-response in ASEC: item and whole imputes Item impute: sample person or other household member fails to respond to a specific question Item imputes are based on responses to both the basic monthly survey and on the ASEC supplement

7. Types of Imputes (Con.) • Whole impute: Sample persons only responded to the basic labor force questions in the monthly survey -- entire supplement is imputed using the monthly survey • More limited data on monthly survey—have labor force experience for last month and not last year

8. Income Per PersonComparisons

9. What Comparisons Tell Us Imputation has greatest effect at lower per person income levels Predictable consequences for poverty rates Shown for 2007, but generally true over time

10. Poverty Trends: 1987-2007

11. Poverty Trends Summary No imputes -- highest poverty rates Item imputes -- lowest poverty rates Growth in imputation rates has not really changed the poverty distribution

12. Income Type and Poverty Status Next look at the percent of the total population with positive income: • below the official poverty line by type of imputation at five year intervals and for 5-year average • Compares this 5 year average to those not in poverty and to all persons with positive income

13. Imputation Type and Poverty Status

14. Income Type and Poverty Status (Con.) Percentage of all persons with positive income who are item imputed falling below the poverty line grew, but trend seemed to reverse in recent years • Whole imputes are relatively stable -- ranging between 9 and 12 percent.

15. Income Type and Poverty Status (Con.) • Overall trend has been toward more imputing – • On average, less imputation for poverty population • Not sure what recent reversal between 2002 and 2007 means for the long term.

16. Role of Imputes on Poverty Rates in 2007 • No imputes only 9.8% • Item Imputes only 6.1% • Whole imputes only 8.4% • All of Above 8.3% • Whole plus no imputes 9.6% • Item plus no imputes 8.3% • All, item imputes set to 0 35.1% • Item imputes only set to 0 51.7%

17. What Imputation Does? • In 2007, the poverty rate including no, item and whole imputes is 8.26% • Without whole imputes the poverty rate is 8.25% • When all item imputed amounts are set to zero, the poverty rate increases from 8.3% to35.1%%

18. What Imputation Does? • When looking only at persons with item imputes and setting these imputes equal to zero, the poverty rate increases from 6.1% to 51.7% • Most persons with item imputes are workers • O’Hara finds more persons have item imputed rather than reported or whole imputed earnings up to approx. $30,000

19. Official Poverty vs. Lowest Quintile • Approximately 50% of the worst off 20% of the population are in official poverty estimate • Only 2% of the population in the next quintile are in the official poverty estimate • Family income is used in putting persons into a quintile – but counts are number of persons

20. Official Poverty vs Lowest Quintile (Con.) • How are the demographics of the poor affected by the poverty measure selected • Averages were constructed for selected demographic characteristics from annual estimates at five year intervals for: 2007, 2002, 1997, 1992 and 1987

21. Official Poverty vs. Lowest Quintile (cont.) • Comparisons made by gender, race, family type, age, and education • First chart shows poverty rates for males and females separately by official poverty measure and by lowest quintile

22. Gender

23. Race

24. Age

25. Single and Two Parent

26. Education

27. Education (cont.)

28. Summary of Findings Who are viewed as poor, is influenced by measure used: • In one instance (Gender) no difference in impact of non-response • In another instance (Race) large differences are found

29. Summary of Findings • In still other cases, (age, family type, education) results are mixed: • More poor elderly in poverty when use lowest quintile • fewer two parent families in poverty using lowest quintile • more persons with education below high school graduate in poverty using official poverty

30. The Supplemental Measures?? • Money income, with many additional adjustments, will also be used to construct supplemental poverty measures • How does the addition of these new elements, such as near money income, expenditure and tax estimates affect the overall pattern of nonresponse • Will poverty trends remain stable over a long period of time?

31. Next Steps • ASPE and Census are jointly sponsoring a project that will match SSA, TANF and SSI records to the 2008 ASEC • We will compare the incomes reported on these files to those on the ASEC by imputation type and other characteristics • This will add an additional dimension to the retooling of CPS Poverty measures

32. Sources • Amy O’Hara, Allocated Values in Linked Files, Housing and Household Economic Statistics Division, U.S. Census Bureau. amy.b.ohara@census.gov • Joan Turek, Fritz Scheuren, Charles Nelson, Edward Welniak Jr., Brian Sinclair-James, and Bula Ghose, -  Effects of Imputation on CPS Income and Poverty Series: 1981-2007,  Papers and Proceedings of the American Statistical Association, August 2009 - Effects of Imputation on CPS Poverty Series: 1987 – 2007, Papers and Proceedings of the Federal Committee on Statistical Methodology, November 2009.

33. Thank You!! Contact Dr. Joan Turek Joan.Turek@HHS.GOV