Transcript Poverty Studies with Marieka Klawitter, University of Washington

Poverty Studies
Marieka Klawitter
Evans School of Public Affairs, U. of Washington
[email protected]
Region 8/10 Conference
May 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?
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Need now?
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Past research?
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Need now?
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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.
18%
17%
16%
Poverty Rate
15%
14%
13%
12%
11%
10%
9%
8%
2004
2005
2006
2007
2008
2009
National and State ACS Poverty Rates, 2004-2011
Poverty Rates in the Northwest Area: A comparison of poverty measures
2010
2011
POVERTY
The 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.
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.
Percentage Below Poverty Level
16%
14%
12%
10%
8%
6%
4%
2%
0%
13.8%
14.5%
13.6%
11.6%
14.0%
12.3%
10.6%
13.7%
12.1%
Poverty in the Northwest Area: An Index of Impoverished Counties
Counties With 10 Highest Rates
of Poverty
Shannon County, SD
Buffalo County, SD
Todd County, SD
Sioux County, ND
Ziebach County, SD
Benson County, ND
Corson County, SD
Madison County, ID
Bennett County, SD
Rolette County, SD
Poverty Rate
54%
49%
49%
47%
46%
36%
35%
32%
32%
32%
Data source: 2006–2010 American Community Survey Estimates
Population in
Poverty
6,946
949
4,670
1,936
1,260
2,344
1,418
11,082
1,038
4,322
% Native
Population
93%
89%
87%
86%
74%
54%
67%
1%
67%
77%
Impact of transfers and taxes on poverty over time (supplemental poverty
measure)
Percent reduction in market poverty
50.0
45.7
45.0
41.3
40.0
47.5
43.7
37.3
35.0
30.0
25.0
22.5
20.0
15.0
10.0
5.0
0.0
1967
1977
1987
Source: Fox, Garfinkel, Kaushal, Waldfogel & Wimer (2013)
1997
2007
2012
Percent of Families Holding Assets by Percentile of Income
Source: Survey of Consumer Finances, 2009
Assets, Credit Use and Debt of Low-income Households
Median Asset Value by Percentile of Income
Source: Survey of Consumer Finances, 2009
Assets, Credit Use and Debt of Low-income Households
Percent of Families Holding Any Debt by Percentile of Income
Source: Survey of Consumer Finances, 2009
Assets, Credit Use and Debt of Low-income Households
Median Value of Debt by Percentile of Income
Source: Survey of Consumer Finances, 2009
Assets, Credit Use and Debt of Low-income Households
Needs Continuum
Asset Development - Building Financial Resiliency
Arrive as
immigrant or
refugee
Income Maximization
Debt Reduction
•Stop outflow
•Wages
•Access to Benefits
•Reduce cost of services
•Debt management plan
•Financial ed. &
personal counseling
Wage Progression
Credit Building
Savings
Asset Acquisition
•Financial ed.
•Basic savings
•Increase credit score
•Secured credit
instruments
•Purposed savings
(person specific)
•Small business
investment
•Other assets TBD
•Savings incentives
•Retirement
•Timely bill payments
•Financial ed.
•Financial ed.
Access To Capital
David Sieminski, Express Advantage
Asset Retention
•Advance training
•Maximize asset value
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
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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
Behavioral Economics and Social Policy Examples:
• 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
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:
Baseline
Poverty
Rate
Sample
All Seattle residents
Seattle residents who earn wages
Seattle residents who earn wages in Seattle
Washington residents who earn wages in
Seattle
Klawitter, M., M. Long, and R. Plotnick. 2014
Poverty Rate Given
an Increase of
Minimum Wage to:
12.12 per 15.00 per
hour
hour
13.6%
11.5%
11.1%
10.7%
8.5%
7.9%
7.6%
4.3%
3.4%
5.7%
3.4%
2.9%
III: Building your Case:
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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:
What’s needed? • Theory of change • Gathering info on
implementation and
• Participant
context
tracking system
• Understanding of
• Initial evidence
participants
program is
feasible and has • Functioning tracking
system
potential
• 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
Funder’s Guide To Using Evidence Of Program Effectiveness in Scale-up Decision
• 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/behavioraleconomics-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/wpcontent/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