Policy Uses of Federal Statistics

Rebecca M. BlankDepartment of Commerce

Data provides information about us as a society from which we can tell stories about how our world and our lives are changing

Chart: Age and Gender Distribution of U.S. Population: 1970, 1990, 2008

A. 1970 B. 1990

C. 2008

8 6 4 2 0 2 4 6 8

0-4 yrs.10-14 yrs.20-24 yrs30-34 yrs.40-44 yrs.50-54 yrs.60-64 yrs.70-74 yrs.80-84 yrs.

Age

Male Female

8 6 4 2 0 2 4 6 8

0-4 yrs.10-14 yrs.20-24 yrs30-34 yrs.40-44 yrs.50-54 yrs.60-64 yrs.70-74 yrs.80-84 yrs.

8 6 4 2 0 2 4 6 8

0-4 yrs.10-14 yrs.20-24 yrs30-34 yrs.40-44 yrs.50-54 yrs.60-64 yrs.70-74 yrs.80-84 yrs.

Age

PercentSource: Statistical Abstract of the United States,1996, Tables 14 & 47. Statistical Abstract of the United States,1992, Table 40.U.S. Census Bureau, Table 1: Annual Estimates of the Resident Population by Sex and Five-Year Age Groups for the United States:April 1, 2000 to July 1, 2008 (NC-EST2008-01)

Percent Percent

Official Statistics

Statistics imbedded in tradition and law, produced and released by a data agency of the government

• Concept is important to policymakers• Have a consistent definition over time(although it may be updated/improved)• Often drive data collection efforts

1. How Do Official Statistics Originate?

Combination of• Private analysis• Public analysis• Legislative push• Executive push

Examples of origin stories

• National Income and Product Accounts• Unemployment rates

In each case:• Data developed by researchers• Legislative & administrative interest• Formal definition provided within data

agency

Contrasting example:Poverty Measurement

1963 calculation, requested by WH

Poverty threshold= 3 * food budget(based on 1955 Household expenditure data)

Family resource definition: Cash income

Updated since then with price adjustments to the threshold.

Chart: Official Poverty Rate, 1959-2007

0%

5%

10%

15%

20%

25%

Percent of Persons in Families Below the Official Poverty Line

Data Source: U.S. Census Bureau. Historical Poverty Tables,

22.4%

12.5%

2007 Poverty Line for Family of 4: $21,203

Contrasting example:Poverty Measurement

The result?

Changing and updating the poverty measure has been impossible.

Any change must be approved within the White House…and no president has an incentive to announce major changes in the poverty statistic.

2. Official Statistics Require Judgment

No statistic is a ‘simple statistic’

Marriage Example

Percent married:But what’s the definition of marriage?• Legally sanctioned by the state?• Religiously sanctioned?• Self-declared?

Marriage Example

This is a significant issue for Census data around same-sex marriage

Numbers show a much larger number than is credible.

• Is this self-reporting relative to legal marriage?

• Does this reflect reporting errors?What should our official count of

‘percent married’ be reporting?

Poverty Example

Poverty measurement requires a poverty threshold and a resource definition.

Poverty threshold: could be based on • Expenditure shares• Percent of median income• Bottom –up budget calculation• Self-reports

Chart: Alternate Poverty Measures in Current Dollars, 1947-2007 (for a family of four)

Data Sources: Gallup data from Jones (2007) and Vaughan (1993). Poverty thresholds and median income levelsfrom U.S. Census Bureau historical tables.Notes: Gallup polls ask about the minimum amount of money a family of four would need to "get along in your local community." Gallup estimates are response means, except for 1967, 1987, and 2007, which are medians. Mean and median Gallupresponses track together closely across the years for which both numbers are available. 50 percent median incomefigures and poverty thresholds are for a family of four.

0

10000

20000

30000

40000

50000

1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007

Pov

erty

Thr

esho

ld

Mean Median

$21,203

Gallup Poll Responses

$45,000

$37,838

Official Poverty Threshold

50% of Median Income

Poverty Example

Equally complex: What resources do you count?

• Cash• After tax?• In-Kind? Including health care?

Do you adjust for cost of living differences between areas?

3. Alternative Numbers Can Help Interpret Official Statistics

...Sometimes

Statistics are always better interpreted when placed in context…in comparison to history, to other countries, or to other closely-related statistics

Useful examples:• Alternative Unemployment measures • Satellite GDP accounts

Chart: Unemployment Rate - Alternative Measures

0

3

6

9

12

15

18

1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009

Per

cent

Total unemployed plus all marginally attached workers plus persons employed part-time for economic reasons

Official

Persons unemployed 15 weeks and over

Source: U.S. Bureau of Labor Statistics, Table A-12: Alternative Measures of Labor Underutilization, www.bls.gov/webapps/legacy/cpsatab12.htm

Not useful examplesAlternative measures of health insurance coverage

Uninsured________ Survey Year for full year @time of surveyCPS 2007 45.7 m N/A

(15.3%) MEPS 2006 37.1 m 47.3 m (14.5%) (16.6%)NHIS 2007 30.6 m 43.1 m (10.3%) (14.5%)SIPP 2001 18.9 m 38.7 m (6.8%) (14.0%)

Alternatives need to be coherent

Most useful when they vary along an understandable dimension:

• Different definitions within the same data; or

• Same definitions but measured in different populations or surveys.

4. Official Statistics are Different from Program Eligibility Criteria

Most statistics are aggregate, telling us something about the group

But programs need individual eligibility information

Poverty measurement has been used for both purposes

• This has limited the ability to developed a more nuanced poverty measure

• Makes changes almost impossible to contemplate

Conclusions

The process by which official statistics are created matters.

It’s particularly important to leave their definition and updating to a data agency over time; don’t try to set it in law or regulation.

(Of course, for credibility, the release of official statistics also belongs in the data agency.)

Conclusions

All definitions require some degree of judgment.

Nothing wrong with responding to political and public debate on these issues. Indeed, that debate typically reflects valid concerns about existing statistics.

Conclusions

Sometimes two statistics (or more) are better than one.

These should not be arbitrary ‘add-ons’ but developed as part of a coherent data strategy.

Conclusions

Statistics are developed to tell us something about aggregate well-being. This may not be appropriately used to determine individual well-being.

Thank you!