Poverty Studies

Marieka Klawitter

Evans School of Public Affairs, U. of Washington

marieka@uw.edu

Region 8/10 ConferenceMay 13-15, 2014

Boise, ID

Agenda:

• Introductions

• Poverty Research• Poverty facts

• Asset-building

• Minimum wage policy

• Other research resources

• Building your case using research for your theory of change/logic model

• Scavenging Data and Research

I. Getting started:

• What kinds of research has helped you

do or communicate about your work?

• What kinds of research do you need now?

Past research? Need now?• Jlkjl• jlk

• Jkl• jkl

Past research? Need now?• Jlkjl• jlk

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II: Research on Poverty and Family Finances

Current Population Survey, American Community Survey, and Supplemental Poverty Measure Poverty Rates and Unemployment Rate 1959 to 2011

Poverty Measures over Time in the Northwest States

NWAF State Poverty rates: Rates for each state are higher over time but the patterns and levels vary.

National and State ACS Poverty Rates, 2004-2011

2004 2005 2006 2007 2008 2009 2010 20118%

9%

10%

11%

12%

13%

14%

15%

16%

17%

18%Po

vert

y Ra

te

Poverty Rates in the Northwest Area: A comparison of poverty measures

POVERTYThe overall poverty rate in the NWAF states was 12.3 percent. County poverty rates ranged from 3 percent to 54 percent. Seven of the 10 counties with the highest rates were in South Dakota. Nine of the 10 counties with the highest poverty rates have Native American populations over 50 percent.

National

IowaIdah

o

Minnesota

Montana

North Dak

ota

Oregon

South Dak

ota

Wash

ington

0%2%4%6%8%

10%12%14%16%

13.8%11.6%

13.6%

10.6%

14.5%12.3%

14.0% 13.7%12.1%

Percentage Below Poverty Level Counties With 10 Highest Rates of Poverty

Poverty Rate Population in Poverty

% Native Population

Shannon County, SD 54% 6,946 93%Buffalo County, SD 49% 949 89%Todd County, SD 49% 4,670 87%Sioux County, ND 47% 1,936 86%Ziebach County, SD 46% 1,260 74%Benson County, ND 36% 2,344 54%Corson County, SD 35% 1,418 67%Madison County, ID 32% 11,082 1%Bennett County, SD 32% 1,038 67%Rolette County, SD 32% 4,322 77%

Most states contain counties with very high and very low rates of poverty. Montana had the highest state poverty rate, followed by Oregon. Minnesota had the lowest rate, at just over 10.5 percent. Iowa, Washington, and North Dakota also had poverty rates below the national average.

Data source: 2006–2010 American Community Survey Estimates Poverty in the Northwest Area: An Index of Impoverished Counties

Impact of transfers and taxes on poverty over time (supplemental poverty measure)

1967 1977 1987 1997 2007 20120.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

22.5

41.337.3

45.743.7

47.5

Percent reduction in market poverty

Source: Fox, Garfinkel, Kaushal, Waldfogel & Wimer (2013)

Source: Survey of Consumer Finances, 2009

Percent of Families Holding Assets by Percentile of Income

Assets, Credit Use and Debt of Low-income Households

Source: Survey of Consumer Finances, 2009

Median Asset Value by Percentile of Income

Assets, Credit Use and Debt of Low-income Households

Source: Survey of Consumer Finances, 2009

Percent of Families Holding Any Debt by Percentile of Income

Assets, Credit Use and Debt of Low-income Households

Source: Survey of Consumer Finances, 2009

Median Value of Debt by Percentile of Income

Assets, Credit Use and Debt of Low-income Households

Arrive as immigrant or

refugee

Income Maximization

•Wages

•Access to Benefits

•Reduce cost of services

Asset Development - Building Financial Resiliency

Debt Reduction

•Stop outflow

•Debt management plan

•Financial ed. & personal counseling

Credit Building

•Financial ed.

•Increase credit score

•Secured credit instruments

•Timely bill payments

Savings

•Basic savings

•Purposed savings (person specific)

•Savings incentives

•Financial ed.

Asset Acquisition

•Small business investment

•Other assets TBD

•Retirement

•Financial ed.

Asset Retention

•Advance training

•Maximize asset value

Wage Progression Access To Capital

Needs Continuum

David Sieminski, Express Advantage

Credit, financial services, and debt Across the income spectrum

• Higher income families use mainstream financial services, pay low rates for credit, and carry manageable levels of debt

• Very low income families use few if any financial services, have little debt, and report they don’t need banks

• Low to Moderate income families are most likely to use very high cost financial services (payday loans, pawn shops, high cost credit cards with balances)

• Households without a bank account (about 8% of households) use alternative financial services at a significantly higher rate than do households with a bank account (64% versus 18%).

• Minority households use alternative financial services at rates that are 25 percentage points higher than those for whites, and are roughly 15 percentage points more likely to not have a bank account.

Alternative Financial Services Use Across Income, Race, And Ethnic Groups

LOW-INCOME HOUSEHOLDS AND INCOME VOLATILITY

Family income volatility has increased over the past three decades, though the trend for individual earnings volatility is less clear.

Low-income households have more volatile incomes than do higher income households and this gap has grown over time.

Job losses, reductions in employment hours, and loss of working adults in a household were the most common factors associated with large income drops. Additions in working adults and work hours, and increased wages were the most frequent causes of income increases.

Volatility means uncertainty which creates stress and possible cognative load (Mani et al 2013).

Low-income Households and Income Volatility

Behavioral Economic Model – Time preferences are inconsistent (need pre-commitment)– Social influence on decisions– Framing, anchors, habits, heuristics all affect decisions– Preferences can change over time– Information is incomplete, weighted by social factors, and can be wrong– Individuals can use pre-commitment and psychological strategies to “bind” to decisions

Social interactions, non-economic values, and institutions affect our decisions and we have to build programs and institutions that account for these.

Kruglaya, (2014). Behavioral Economics and Social Policy

• Increasing Incarcerated Noncustodial Parents’ Applications to Modify Their Child Support Payments (TX)

• Increasing TANF Client Engagement with Job Search: Asian Human Services in Illinois

• Increasing Willingness to Wait: The National Domestic Violence Hotline

Behavioral Economics and Social Policy Examples:

Kruglaya, (2014). Behavioral Economics and Social Policy

Kruglaya, (2014). Behavioral Economics and Social Policy

Who Would be Affected by an Increase in Seattle’s Minimum Wage?

What are the demographics of Seattle Workers? What do we know about Income and Poverty for Seattle workers What is the profile of Seattle Businesses? How might a change in the minimum wage affect Seattle workers and businesses?

What do we know about the cost of living in Seattle?

Klawitter, M., M. Long, and R. Plotnick. 2014

•About 42,000 people living in Seattle and about 38,000 people working in Seattle currently earn $9.32 or less.

•About 101,000 Seattle residents earn $15 or less (30%)

•About 102,000 people who work in Seattle earn $15 or less (24%)

Klawitter, M., M. Long, and R. Plotnick. 2014

•Median family Income is about $17,000 for minimum wage earners and about $90,000) for the highest wage group.

•Poverty rates are 40% for the lowest wage group and 2% for highest group.

Klawitter, M., M. Long, and R. Plotnick. 2014

• Minimum wage earners work a median of 44 weeks and 1,040 hours (0.5 FTE) per year.

• Workers with wages between the minimum and $15 work a median of 50 weeks and about 1,800 hours (0.9 FTE) per year.

Klawitter, M., M. Long, and R. Plotnick. 2014

Possible Earnings increases with minimum wage

Klawitter, M., M. Long, and R. Plotnick. 2014

Simple Poverty Simulations:

Poverty Rate Given an Increase of

Minimum Wage to:

Sample

Baseline Poverty

Rate

12.12 per hour

15.00 per hour

All Seattle residents 13.6% 11.5% 11.1% Seattle residents who earn wages 10.7% 8.5% 7.9% Seattle residents who earn wages in Seattle 7.6% 4.3% 3.4% Washington residents who earn wages in Seattle 5.7% 3.4% 2.9%

Klawitter, M., M. Long, and R. Plotnick. 2014

III: Building your Case:

• Theory of change model shows links• Identify critical links in model • Find research and data to support links• RE-ENGINEER YOUR STRATEGY IF YOU CAN’T

SUPPORT LINKS!!!

Evidence needs depend on stage of program:

Funder’s Guide To Using Evidence Of Program Effectiveness in Scale-up Decision

What’s needed? • Theory of change

• Participant tracking system

• Initial evidence program is feasible and has potential

• Gathering info on implementation and context

• Understanding of participants

• Functioning tracking system

• Evidence of participation

• Acceptable costs

• Evidence of impact

• Well-developed Theory of change

• Strong internal data systems

• Reasonable fidelity across sites

• Strong support for scale-up

• New rigor of evaluation scaled to investment size and adaptations of model

Example of theory of change from: “Theory of Change: A Practical Tool For Action, Results and Learning”

• http://www.aecf.org/upload/publicationfiles/cc2977k440.pdf

Where to get evidence on costs and impacts (from Hatry):

• Previous experience with similar changes

• Pilot study in your organization• Information from other organizations that implemented similar policies

(program evaluations)

• Academic or think tank studies • Modeled or “engineered” estimates

• Theories and logical inference about causal connections

WEAKEST!

H. Hatry, L. Blair, D. Fish, and W. Kimmel, Program Analysis for State and Local Governments 1987).

Does the evidence from elsewhere apply to your organization (external validity)?

• Is the policy or political context different in important ways?

• Are the economic conditions different?

• Is the target of the policy (e.g., client population or location) different in critical ways?

• Would the policy or program be implemented in the same way? To the same scale?

You must assess the severity of the differences and their impacts on your outcomes. Triangulate to support link!

Getting data:

• American Fact finder: Local poverty and demog facts: http://

factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml

• CAN needs assessment tool: Uses census data to get poverty rates by state or county

http://www.communityactioncna.org/

What evidence do you need?

• What are your key causal links?– Target population needs– Document implementation of intervention– Evidence of intervention short term impact– Evidence of long term changes associated with Short term impact

• Where can you gather evidence?

References: Northwest Area Foundation Research http://evans.uw.edu/centers-projects/economic-opportunity-applied-research-project: Colin Morgan-Cross and Marieka Klawitter, Evans School of Public Affairs • Poverty in the Northwest Area: An Index of Impoverished Counties • Poverty Rates in the Northwest Area: A comparison of poverty measures • Poverty Measures over Time in the Northwest States • Assets, Credit Use and Debt of Low-income Households • Low-income Households and Income Volatility • Alternative Financial Services Use Across Income, Race, And Ethnic Groups

Wimer, Christopher, Liana Fox, Irwin Garfinkel, Neeraj Kaushal, and Jane Waldfogel. 2013. "Trends in Poverty with an Anchored Supplemental Poverty Measure." CPRC Working Paper No. 13-01. http://cupop.columbia.edu/publications/2013

Kruglaya, I. (2014, April 29). Behavioral Economics and Social Policy. mdrc. Text. Retrieved May 8, 2014, from http://www.mdrc.org/publication/behavioral-economics-and-social-policy

Mani, A., Mullainathan, S., Shafir, E., & Zhao, J. (2013). Poverty Impedes Cognitive Function. Science, 341(6149), 976–980. doi:10.1126/science.1238041

Klawitter, M., M. Long, and R. Plotnick. 2014. “Who Would be Affected by an Increase in Seattle’s Minimum Wage?” http://murray.seattle.gov/wp-content/uploads/2014/03/Evans-report-3_21_14-+-appdx.pdf

H. Hatry, L. Blair, D. Fish, and W. Kimmel, Program Analysis for State and Local Governments, 2nd Edition. (Urban Institute, 1987). “Estimating Program Costs” pp. 49-62; and “Estimating Efficiency” pp. 63-74.

Bangser, M. (2014). Funder’s Guide To Using Evidence Of Program Effectiveness In Scale-up Decisions. Social Impact Exchange; MDRC. Retrieved from http://www.socialimpactexchange.org/webfm_send/900