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

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 2

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? 3

Upward Trend in Nonresponse 4

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

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 6

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 7

Income Per Person Comparisons 8

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 9

Poverty Trends:

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

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 12

Imputation Type and Poverty Status YearNoneItemWholeTotal Imputes Impute types sum to 100% 100% Poverty %28.3%9.7%38.0% %31.9%11.6%43.5% %24.4%9.3%33.7% %14.4%11.9%26.3% %13.4%9.2%22.6% 5 year average67.3%22.3%10.4%32.7% Not in Poverty 5 year average58.2%31.8%10.0%41.8% All Persons 5 year average59.1%30.9% % Percents are for number of persons with positive income 13

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. 14

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. 15 Income Type and Poverty Status (Con.)

Role of Imputes on Poverty Rates in 2007 No imputes only9.8% Item Imputes only6.1% Whole imputes only8.4% All of Above8.3% Whole plus no imputes 9.6% Item plus no imputes8.3% All, item imputes set to 035.1% Item imputes only set to % 16

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% to 35.1% % 17

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 18

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 19

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

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 21

Gender 22 Demographic category Official Poverty Lowest QuintileStandardized Official Poverty Standardized Lowest Quintile Not Imputed Male8.2%15.6%64.3%68.0% Female12.7%22.9%100.0% Item Imputed Male5.3%11.4%66.5%64.2% Female8.0%17.7%100.0% Whole Imputed Male7.6%14.9%66.8%67.6% Female11.4%22.1%100.0% All Males7.2%14.2%64.9%67.0% Females11.1%21.2%100.0%

Race 23

Age 24

Single and Two Parent Demographic category Official Poverty Lowest QuintileStandardized Official Poverty Standardized Lowest Quintile Not Imputed Two Parent7.3%7.0%19.7%13.8% Single Parent36.9%50.7%100.0% Item Imputed Two Parent3.8%3.6%16.4%9.7% Single Parent23.4%37.4%100.0% Whole Imputed Two Parent5.9%6.0%18.5%13.1% Single Parent32.1%46.0%100.0% All Two Parent6.2%5.9%18.4%12.5% Single Parent33.4%47.2%100.0% 25

Education 26

Education (cont.) 27

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 28

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 29

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? 30

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 31

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

Thank You!! Contact Dr. Joan Turek 33