IDA and Asset-Building Strategies: Lessons and Directions

Center for Social DevelopmentWashington University in St. Louis

IDA and Asset-Building Strategies:Lessons and Directions

Michael SherradenYoungdahl Professor of Social Development

Director, Center for Social DevelopmentWashington University in St. Louis, USA

Conference on Access, Assets, and PovertyUniversity of Michigan and Brookings Institution

October 11-12, 2007


Overview of IDA Research:Results to Date, Lessons, Directions

On-going body of work on IDAs and inclusive asset building.

Asked by the organizers to cover several IDA research methods: non-experimental, cost assessment, experimental. Will attempt to do that, and put the work in context of research directions.

This is an opportunity for a mid-course assessment. Yourcomments, questions, and suggestions are very welcome.


Motivation: Why Assets? Key to Household Development

Basic income and consumption are essential, and in any wealthy and decent society basic income and consumption should be supported.

But development of households and families occurs over the long term through asset accumulation and investment -- in education, experience, careers, homes, businesses, and financial instruments.

This applies to all families, rich and poor alike.


Why Assets? Millions in the US Have Little or None

About 20% of US households have zero or negativenet worth (Mischel et al., 2007).

Between 1983 and 2001, the average net worth of the poorest 40% of US household declined by 44%, falling to $2,900 in 2001 (Wolff, 2004).


Why Assets?Basis of Racial Inequality

(Oliver & Shapiro, 2006; Kochhar, 2004; Shapiro, 2004)

Ratio of white to non-white income is about 1.5:1Ratio of white to non-white net worth is about 10.0:1(Nonwhite here refers to the largest groups of color:Blacks and Hispanics)

Asset inequality by race is much greater than income inequality, and has different meaning. If assets are a foundation for household development, then assetinequality may be more fundamental in the long term.


Increasing Questioning of Income asSole Definition of Poverty and Well-Being

Welfare States have used primarily an income definition of well-being in social policy.

Amartya Sen (1993, 1999) and others are looking toward a broader definition of capabilities.

Assets can be seen as part of this discussion: one aspect ofof long-term capabilities.


Can Policy Aim for Asset Accumulation?

In US, over $300 billion annually in tax expendituresfor assets (homes, investments, retirement accounts)

Over 90 percent of this goes to householdswith incomes over $50,000 per year

(Sherraden, 1991; Howard, 1997; Seidman, 2001;Corporation for Enterprise Development, 2004)


The Poor Do Not Have the SameOpportunities and Subsidies

for Asset Accumulation

The poor are less likely to own homes, have investments,or have retirement accounts, where most asset-basedpolicies are targeted (Haveman and Wolff, 2005).

The poor have little or no tax incentives, or otherincentives, for asset accumulation (Seidman, 2001).


One Policy Strategy for Asset Building:Individual Development Accounts (IDAs)

(Sherraden, 1988, 1991)

• Special savings accounts

• Started as early as birth

• Savings are matched for the poor, up to a cap

• Multiple sources of matching deposits

• With financial education

• For homes, education, business capitalization


IDAs in Demonstration Phase

IDAs proposed as a universal policy (everyone has an account from birth), with greater subsidies for the poor in the form ofmatches for deposits.

But implemented as short-term (3 to 5 year) demonstrationstargeting low-income households, in community-basedprojects.


Research on IDAs:American Dream Demonstration (ADD)

• First major demonstration of IDAs• Fourteen IDA programs around the country • ADD from 1997 through 2001, research through 2005• Organized by Corporation for Enterprise Development• Research plan by Center for Social Development• Experiment designed and led by Abt Associates• Funded by twelve foundations (Ford has been the major

supporter, with partnerships from CS Mott, Citigroup, Fannie Mae, EM Kauffman, MetLife, FB Heron, Levi Strauss, Rockefeller, and others)


ADD Research Agenda:Multiple Methods

Research methods in ADD have been multiple, each makingunique contributions. In this presentation, we look at fourresearch methods:

• Account monitoring research• In-depth interviews• Cost assessment• Experiment


Account Monitoring Research

Data set from MIS IDA, created by CSD for this purpose.Record of all savings transactions for all participants.

Very accurate data on saving, coming from bank records.Perhaps the most thorough existing data set on saving in low-income households.

Analysis using two-step regression. First identifies factorsassociated with being a “saver” (net savings of over $100), then among “savers”, identifies factors associated with averagemonthly net deposit (Schreiner & Sherraden, 2007).


Account Monitoring:Limitations and Usefulness

All participants in ADD are self-selected and program selected.

This data set from all 14 ADD sites does not have a counterfactual, and cannot address impacts.

This data set can address IDA savings patterns andoutcomes, as well as participant and programcharacteristics associated with IDA savings, controllingfor many other variables.


Account Monitoring Research:Overall Savings Outcomes

(Schreiner & Sherraden, 2007)

Number of IDA participants in ADD = 2,364Average monthly net deposit (AMND) = $16.60About half of IDA participants (52%) were “savers”“Savers” had AMND of $32.44Match rates varied, with 2:1 most typicalOf every $1.00 that could be matched, $0.42 was saved.Home purchase is the most popular use of IDAs (with

positive impact in experimental conditions).


Uses of IDA Savings in ADD:Matched Withdrawals

Of matched withdrawals taken by the end of ADD:26% for microenterprise22% for home repair21% for home purchase21% for post-secondary education7% for retirement2% for job training

Home purchase and repair continue to be popular (48%).

When offered (to 35% of participants) retirement savingis also a popular option.


Account Monitoring Research:Individual Variables

Controlling for many other factors, income (both recurrent and intermittent) is weakly associated with savings outcomes.The poorest ADD participants saved a higher proportion of theirincome in IDAs

Employment and welfare receipt (past or current) was not associated with saving outcomes in IDAs.

These results suggest that something other than the observed individual characteristics may be leading to savings outcomesin IDAs.


Account Monitoring Research:Program Variables

We look briefly at:

Match rateMatch capDirect depositFinancial education


Match Rate

Matching seems to attract and hold participants in IDAs.

Controlling for many participant and program variables, higher match rates are associated with the probability of beinga “saver”.

But among “savers”, higher match rates are associated with lower AMND. It could be that a higher match substitutes foran individual’s own saving to reach a saving goal.


Match “Cap”

Controlling for many other factors, the amount that canbe matched (“match cap”) is strongly associated with netmonthly savings.

Each additional dollar of match cap is related to anadditional $0.57 in net monthly savings (a large effect).

Participants appear to turn the match cap into a savingstarget.


Automatic Deposit

Controlling for many other factors, automatic monthly deposit is associated with participants being “savers”

But among “savers”, direct deposit has no statistical relationship with AMND. This may be due to savingbeing on “autopilot” with direct deposit.


Financial Education

Among “savers” the first 10 hours of financial education ispositively associated with AMND, with no relationship after 10 hours. This is important, because financial education isquite expensive to deliver, and perhaps the full benefit is in the first 10 hours.

Each of the first 10 hours is associated with an increase of$1.16 in AMND. Therefore 10 hours is associated with an increase of $11.60 per month or $139 per year, which wouldbe $418 if matched 2:1, and $1,670 over four years.


Summary Observations from Account Monitoring Research

Overall, it may be that program characteristics areas or more important than individual characteristics inexplaining savings outcomes in IDAs.

Regarding program characteristics, more than incentives(in the form of matches) are related to savings outcomes. The positive effect of incentives may be in attractingand keeping people saving in the program, and not in savingamount by those who participate (see also Engen, Gale, &Scholz, 1996).


• Access (eligible, available, default enrollment)• Information (financial education, feedback)• Incentives (match rates, other inducements)• Facilitation (direct deposit, personal assistance)• Expectations (match cap, saving targets)• Restrictions (pre-commitment, restricted uses)• Security (money safe, dependable access)• Simplicity (simple products, limited choices)

Beyond Program Characteristics: Institutional Constructs that May Affect

Saving and Asset Accumulation


Toward Policy: An Inclusive Savings Plan

Policy implications are for a saving plan with desirable institutional features, similar to a 401(k) Plan, Federal ThriftSavings Plan, or 529 College Savings Plan.

Desirable features might include: automatic enrollment, low initial deposits, simple investment options, low annual fees, financial education, matched savings for the poor, and targetsavings amounts (Clancy et al., 2003, 2004, 2005).

As particular application, “Save More Tomorrow” illustratespotential of a plan structure (Thaler & Benartzi, 2003).


Insights from In-depth Interviews(Margaret Sherraden et al., 2005; Margaret

Sherraden & Mcbride, forthcoming)

Subsample of ADD experiment, 60 IDAs and 30 controls

One key finding: IDA funds are not perfectly fungible.Participants have mental understanding of short-term and long-term accounts and value this distinction, e.g., glad that IDA savings are “out of reach”.

Another key finding: IDA participants understand the match-cap as a target saving amount. The institutional constructof “expectations” emerged from in-depth interviews.


IDAs and Future Orientation

IDA participants say they can “see more clearly”and better “visualize a future” than before IDAs.

IDA program is said to “create goals and purpose.”IDA program is said to provide “way to reach goals.”

With these changes in outlook and capability, IDA participants say they are “more able to save”, “look forward to saving”, and “plan to save for the future”.

These findings from in-depth interviews may support a cognitive approach to understanding the influence of institutions on savings outcomes.


How Much Do IDAs Cost?(Schreiner 2002, 2006)

Thorough cost assessments published by CSD. Has caused our policy colleagues some challenges, but necessary.

Program costs for administration (not counting matching funds)are about $61 per month.

In part, high costs are due to demonstration (e.g., start-up inefficiency, communications, policy involvement), but IDAs are unlikely in this community-based model to get much below $30 per month.

Compare to 401(k) administration of about $10 per month, and intensive family intervention that may reach $400 per month.


Demonstration Model:Cost Is a Barrier to Scale

We do not yet know if IDAs are cost beneficial. ADD Wave 4will include a cost-benefit analysis. All things considered, it seems unlikely that monetizable benefits in ADD will exceed costs.

Even if the cost-benefit outcome is positive (possible), high administrative costs will be a barrier to large-scale policyexpansion.

The most scalable form of IDAs or other progressive savingswill be a large, centralized, efficient plan structure (discussedabove).


The ADD Experiment:What Are the Measured Impacts?

(Mills et al., 2004, 2006, forthcoming)

Experiment in Tulsa, OK, with 1103 participants randomlyassigned to treatment and control groups, three waves, 1998to 2002.

ADD implemented by CFED, IDA program run by CAPTC, with Abt Associates leading the experiment. CSD provided Coordination. Abt was funded directly and did not report to CSD. CSD asked to step back so that Abt can report the basicexperimental results, which we have been glad to do.

Key financial impacts are reviewed in this paper.


Impact on Homeownership

Mills et al. (2004):For treatment group, + 6.2 percentage points

Mills et al. (2006):For black renters, +10% percentage points

Mills et al. (forthcoming):For renters, +7 to 11% percentage points


Interpreting Home Ownership Impact

It seems likely that research results in home ownership, which is dichotomous, may have less measurement error thanfinancial variables, as these stable results may indicate.

A 6 to 11% positive impact in home ownership is large, andcould be a very positive outcome in ADD, especially if moreAfrican Americans have become homeowners.

But perhaps IDAs only rushed people into homeownership whowould have done so otherwise. Or perhaps IDAs encouraged home ownership among people who cannot sustain it over the long term. All of this remains to be seen.


Impact on Net Worth

Mills et al. (2004):+$29 (NS)

Mills et al. (2006):+$2,118 (NS)

Mills et al. (forthcoming):+$1,339 (NS)


Interpreting Net Worth Impact

The most apparent explanation is that there is no increase in networth due to IDAs during this period.

It could be that closing costs are not yet recovered in home equitygrowth. In other words, home ownership may take longer to show up as a change in net worth (Mills et al., all three versions).

It could be that extreme values in ADD data, leading to large standard errors, make it impossible to identify a modestunderlying impact on net worth if it existed. One analysis byAbt, addressing extreme values by trimming 3% of net worthvalues and setting out-of-range controls to the mean, finds apositive impact on net worth ($5,390, p<.01).


Interpreting New Worth Impact (cont.)

Overall, Bill Gale’s assessment is that it is not possible to saywith this data set whether net worth increased on not due tolarge variance in changes in net worth and relatively smallsample size (Mills et al., forthcoming).

The analysis by Abt cited above may suggest that variance is agreater challenge than sample size, including variance in bothnet worth and controls.

CSD will offer additional analyses that attempt to deal withextreme values and possible measurement error (APPAMin November). Regardless of these findings, we agree thatconclusions on net worth may be unwarranted at this time.


Selected Impacts:Other Assets & Liabilities

Mills et al. (2004), for all IDA group:Real assets +$6,310 (p<.10) (+ p<.05 for black and 36+)Total assets +$4,186 (NS) (+ p<.10 for black and 36+)Total liabilities +4,157 (NS) (+p<.10 for black and 36+)

Mills et al. (2006):For all IDA group, +computer purchase (p<.05)For black renters, +$4,073 home equity (p<.05)For black renters, -$1,348 financial assets (p<.10)For black renters, -business ownership (p<.10)For white renters, +$1,747 business equity (p<.05)

Mills et al. (forthcoming):For all IDA group, financial assets -$1,925 (p<.05)


Interpreting Asset & Liability Impacts

Overall, some indication that both assets and financial liabilitiesincreased.

It could be that increased asset ownership, even if offset by liabilities, represents a positive (or negative) impact.

Having greater assets and greater liabilities could represent ahigher level of economic functioning with greater well being, asmay occur in owning a home with mortgage debt.

Or conversely, taking on greater liabilities to purchase assetscould lead to debt problems that limit future well being.


Next Step: ADD Experiment, Wave 4

As indicated above, many questions remain unanswered. In key respects, the time period for 3 waves of ADD was short, and a follow-up Wave 4 may be informative.

ADD Experiment Wave 4 is now in planning and should be inthe field in 2008. Supported by the MacArthur and F.B. HeronFoundations (so far).

The research team is led by University of North Carolina, with Brookings Institution and CSD as partners, and surveydata collection by RTI International.


Influence of IDA Research in US

IDAs included as a state option in 1996 “welfare reform” Act (Boshara, 2003)

Federal Assets for Independence Act in 1998, first public IDA demonstration (Boshara, 2003).

Over 40 US states now have some type of IDA policy (Edwards & Mason, 2003; Warren & Edwards, 2005)

United Way of American in 2007 announced a $1.5 billion Financial Stability Partnership focused on increasing income,savings, IDAs, and protecting assets.


Influence of ADD Research Internationally

Saving Gateway and Child Trust Fund in the United Kingdom (Kelly and Lissauer, 2000; Blair, 2001; Sherraden,2002, H.M. Treasury, 2003; Paxton, 2003; Kempson et al., 2003, 2006)

Family Development Accounts by Taipei City (Cheng, 2003)

IDAs and “Learn$ave” demonstration in Canada (Kingwell et al., 2003)

Matched saving in Australia (ANZ Bank), Uganda (CSD’s AssetsAfrica initiative), Colombia and Peru (International Fund for Agricultural Development), Hungary (OSI).


Example: Matched Savings withHIV/AIDS Orphans in Uganda

(Ssewamala and Curley, 2005)

High risk population, including likely HIV infection laterAiming for US$600 for secondary schoolingSavings of $200 matched with $400Pilot successfulNow larger project with NIH funding:• One of few NIH grants outside of US• One of first NIH grants to test economic intervention on health

outcomes (preventing HIV infection as children grow older)So far, evidence of positive impact (vs. comparison group) on HIVprevention attitudes and educational planning (Ssewamala et al.,2007).


Average children’s allowance in Western Europe is 1.8% of GDP. US has no children’s allowance and under-invests in children. Even 0.1% of US GDP would be enough for a $2,500start in life account for every newborn (Curley & Sherraden, 2000).

Many proposals now in the US Congress for CDAs (NewAmerica Foundation, 2004, 2006).

Recently Sen. Clinton proposed an account for every USNewborn, with initial $5,000 deposit.

Children’s Development Accounts:Potential in the United States


Next Step: Testing CDAs in the US:The SEED Demonstration

Twelve SEED demonstration sites around the country, multipleresearch methods.

Partners are CFED, New America Foundation, University ofKansas, Research Triangle Institute, Aspen Institute, and CSD.

Research funding for SEED from Ford, CS Mott, MetLife,Lumina, and Smith Richardson (pending) foundations.

CSD is organizing an experiment (random assignment at birth)in one state (Oklahoma), using the 529 College Savings Plan:1,490 participants and 1,490 controls, $1,000 deposits at birth and matching savings, seven-year project to 2014.


Last Comment: Payoffs ofDemonstration Research

Field demonstrations over an extended period can be challenging to implement and carry out. They depend oncooperation among many different partners. But payoffs ofdemonstration research include:

• Provides opportunity to test and improve an innovation• Builds practitioner and field capacity • Creates examples that can influence policy• Yields research that can inform knowledge and policy• Trains new scholars for future research and analysis


References

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Blair, T. (2001). Savings and assets for all, speech. London: 10 Downing Street,April 26.

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References (cont.)Clancy, M, Orszag, P., & Sherraden, M. (2004). College savings plans: A

platform for inclusive savings policy? St. Louis: Center for SocialDevelopment, Washington University.

Clancy, M., & Sherraden, M. (2003). The potential for inclusion in 529 savings plans: Report of a survey of states. St. Louis: Center for Social Development, Washington University in St. Louis.

Corporation for Enterprise Development (2004). Hidden in plain site: A look at the $335 billion federal asset-building budget. Washington: Corporation forEnterprise Development.

Curley, J., & Sherraden, M. (2000). Policy lessons from children’s allowances for children’s savings accounts, Child Welfare, 79(6), 661-687.

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References (cont.)


References (cont.)

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References (cont.)

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References (cont.)


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References (cont.)


References (cont.)

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References (cont.)

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IDA and Asset-Building Strategies: Lessons and Directions

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