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The U.S. Poverty Measure and the Politics of Statistics Rebecca M. Blank U.S. Department of Commerce.

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Presentation on theme: "The U.S. Poverty Measure and the Politics of Statistics Rebecca M. Blank U.S. Department of Commerce."— Presentation transcript:

1 The U.S. Poverty Measure and the Politics of Statistics Rebecca M. Blank U.S. Department of Commerce

2 Much of today is about “How to measure poverty” I want to talk about “How to implement and institutionalize a poverty measure”

3 Why haven’t we changed the U.S. poverty measure in 45 years? Larger question: What lessons can we learn about effective statistical policy from the statistical politics that have swirled around the poverty measure and prevented changes?

4 What isn’t unique about poverty measurement: The question of how to define and measure poverty is not obviously any more complex or politically sensitive than many other statistical questions that we regularly answer

5 Political economy lessons from the endless U.S. discussion about the poverty measure: A. What we learned about implementation by doing it badly B. What we learned about advocating for a new measure by doing it badly

6 What we learned about implementation by doing it badly 1. Do not define a statistic through specific regulations that require political action to change

7 What we learned about implementation by doing it badly 2. Do not set regulations inside an office that is part of the Executive Office of the President

8 What we learned about implementation by doing it badly 3. The less a statistical definition is used in program eligibility and benefit determination, the easier it is to improve and change that statistic

9 What we learned about advocating for a new measure by doing it badly 1. Do not assume that change is a rational process requiring appropriate statistical arguments and a reasonable alternative measure

10 What we learned about advocating for a new measure by doing it badly 2. Do not ignore how the definition is utilized in program eligibility, thus creating disincentives toward change

11 What we learned about advocating for a new measure by doing it badly 3. Don’t make the perfect the enemy of the good (and don’t want too much from a single statistic)

12 What we learned about advocating for a new measure by doing it badly 4. Don’t think there’s only one way to accomplish what you want

13 What do we need for a good long- term statistical measure? 1. Agreement that the problem/issue is important enough to justify the effort of producing a statistic

14 What do we need for a good long- term statistical measure? 1. Agreement that the problem/issue is important enough 2. A quasi-independent statistical agency charged with developing, defining and producing the statistic

15 What do we need for a good long- term statistical measure? 1. Agreement that the problem/issue is important enough 2. A quasi-independent statistical agency 3. Data and definitions that are ‘good enough’

16 What do we need for a good long- term statistical measure? 1. Agreement that the problem/issue is important enough 2. A quasi-independent statistical agency 3. Data and definitions that are ‘good enough’ 4. A statistical agency that can, over time, balance the need for updating against the costs of changes


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