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A cost-benefit analysis of Tulsa’s IDA program

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Published/Copyright: October 11, 2013
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Journal of Benefit-Cost Analysis
From the journal Journal of Benefit-Cost Analysis Volume 4 Issue 3

Abstract

This article presents findings from a cost-benefit analysis of the Tulsa Individual Development Account (IDA) program, a demonstration program that was initiated in the late 1990s and is being evaluated through random assignment. The program put particular emphasis on using savings subsidies to help participants accumulate housing assets. The key follow-up data used in the evaluation was collected around 10 years after random assignment, about 6 years after the program ended. The results imply that, during this 10-year observation period, program participants gained from the program and that the program resulted in net costs to the government and private donors, and that society as a whole was probably worse off as a consequence of the program. The article examines in some detail whether these findings are robust to a number of different considerations, including the assumptions upon which the results depend, uncertainly reflected by the standard errors of the impact estimates used to derive the benefits and costs, and omitted benefits and costs, and concludes that they are essentially robust. For example, a Monte Carlo analysis suggests that the probability that the societal net benefits of the Tulsa program were negative during the observation period is over 90% and that the probability that the loss to society exceeded $1000 is 80%. Further analysis considered benefits and costs that might occur beyond the observation period. Based on this analysis, it appeared plausible, although far from certain, that the societal net benefits of the Tulsa program could eventually become positive. This would occur if the program’s apparent positive net impact on educational attainment generates substantial positive effects on the earnings of program participants after the observation period ended. However, there was no evidence that the educational impacts had yet begun to produce positive effects on earnings by the end of the observation period.

Keywords: benefit-cost analysis; Individual Development Account (IDA); longterm follow-up; Monte Carlo analysis; random assignment field experiment
Corresponding author: David H. Greenberg, University of Maryland – Baltimore County – Economics 1000 Hilltop Circle, Baltimore, MD 21250, USA; and 5531 High Tor Hill, Columbia, MD 21045, USA, Tel.: +410-884-9620, e-mail:
  1. 1

    The Tulsa IDA was one of 14 IDA demonstration programs in the American Dream Demonstration and the only one evaluated through random assignment.

  2. 2

    Although they did not test IDA programs, there have been at least two other field experiments that tested using matching contributions to encourage saving (see Duflo, Gale, Liebman, Orzag, and Saez (2006) and Engelhardt, Dubnicki, Marks, and Rhodes (2011) for descriptions). Additional studies of savings behavior that rely on data from field experiments include Duflo and Saez (2003), Ashraf, Karlan, & Yin (2006), and Saez (2009).

  3. 3

    Greater detail on the experimental design and the estimation of program impacts can be found in Grinstein-Weiss et al. (2012a), which also summarizes findings from previous research on IDAs.

  4. 4

    The matching funds were actually paid directly to the sellers, rather than passing through the participants, a point to which I return later.

  5. 5

    Costs and benefits are estimated over a period from 1999 through 2008 and thus should not be very much affected by the collapse of housing markets that began in mid-summer 2008. In addition, the housing market in Tulsa was relatively less affected than housing markets elsewhere in the country (National Association of Realtors, 2012).

  6. 6

    See U.K. Treasury (2003); Boardman, Greenberg, Vining, and Weimer (2011); and Moore, Boardman, Vining, Weimer, and Greenberg (2004).

  7. 7

    Boardman et al., 2011.

  8. 8

    Because random assignment took place from October 1998 to December 1999, the exact calendar dates differ somewhat among members of the sample.

  9. 9

    However, they could potentially receive assistance from non-CAPTC sources such as the Housing Partners of Tulsa, which provided down-payment and closing-cost assistance equal to 5% of the purchase price of a home upon completion of a home buyer education program (Tulsa Housing Authority, 2012). Moreover, as discussed later, the bar seems to have been breached in some instances.

  10. 10

    I am indebted to Clinton Key for providing these calculations.

  11. 11

    For example, home repair expenses were capped at $10,000 over the 10-year observation period, although a few survey respondents reported much higher expenditures of up to $100,000. Because most of these outliers were in the control group, the winsorizing procedure reduced the absolute magnitude of the estimated program impact on home repair and maintenance expenditures from –$251 (649) to –$77 (249) [the standard errors are in parentheses].

  12. 12

    The estimated impact on home appreciation is statistically significant at the 10% level if a one-tail test is used and the impact on investment in business barely misses statistical significance at this level with a one-tail test. A one-tail test is arguably the appropriate test because both of these the estimated impacts are expected to be positive.

  13. 13

    For instance, at an alpha of 0.100 and a 5% point (or 10%) impact at a control mean of 0.5, the power is about 0.4. Thus, the ability of the data to detect even a moderate true impact is very weak at even a very low level of statistical significance of 10%.

  14. 14

    The returns per dollar invested were calculated as follows: $1950/3105 for participants and -$2126 – (–$2236)/$3105 for society. Note that because a negative value for the $2236 in operating cost is included in computing the net loss to society, it was necessary to net it out of the numerator of the ratio; otherwise, it would be included in both the numerator and the denominator.

  15. 15

    Possible start-up costs include: figuring out how to do an IDAs with some trial and error; putting the program infrastructure in place; and IDA policy engagement by CAPTC in Oklahoma and beyond.

  16. 16

    Engelhardt, Dubnicki, Marks, and Rhodes (2011) found that a matching plan intended to encourage parents to save for their children’s college “crowded out” 55% of other types of savings for children’s college.

  17. 17

    The 2.5 weight suggested by Fujiwara is applicable to typical low income participants in government transfer programs. Of course, participants in an IDA program may well differ from participants in government transfer programs and thus a higher or lower weight may be appropriate for them.

  18. 18

    For greater detail about Monte Carlo analysis in cost-benefit studies, see Boardman et al. (2011), pp. 183–187.

  19. 19

    One recent exception is Hendra et al. (2011).

  20. 20

    For example, see Nichol (2001); Weimer and Sager (2009); and Whittington, Lauria, Prabhu, and Cook (2004).

  21. 21

    In percentage terms, a given change in the discount rate has a larger effect on the gains of program participants than the losses of the government and private donors because expenditures by the latter on operating costs and matching funds occurred within a few years of random assignment, but many participant benefits were not received until considerably later.

  22. 22

    According to Grinstein-Weiss et al. (2012a), of the total of $774 in matching funds per IDA participant reported in Table 3 of this article, 49.5%, or $383, subsidized participant expenditures on housing repairs and maintenance, investments in business, and savings for retirement and 50.5% subsidized participant home purchase and educational expenditures.

  23. 23

    It is possible that some of these estimates are correlated. For example, the Tulsa program’s impact on income could have affected its impact on saving for retirement or making home repairs. Unfortunately, although these correlations are unlikely to be strong, it was not possible to estimate them in conducting the Monte Carlo analysis. Thus, for purposes of the Monte Carlo analysis, it was necessary to treat the impact estimates as if they are independent from one another. At least one recent study author found that his Monte Carlo findings were insensitive to this assumption (Jerome, 2012).

  24. 24

    For reasons discussed in the Appendix, there is no standard error for the Tulsa IDA’s impact on investment in education. Thus, in conducting the Monte Carlo, it was (somewhat arbitrarily) assumed the investments in education could have been as much as $20 higher or lower than the $89 value appearing in Table 3. It was further assumed that it is appropriate to specify a uniform distribution over this range. Because the impact on investments in education was so small, these assumptions have little influence over results from the Monte Carlo analysis.

  25. 25

    As mentioned in the footnote prior to the previous one, it was necessary to treat the estimates of the benefit and cost components as if they are independent from one another. As a result, to the extent the benefit and cost components are correlated, the estimates of the standard deviations of the means will be biased. Unfortunately, because the size and direction of these biases will depend on the size and direction of the correlations, which are unknown, they cannot be predicted.

  26. 26

    This statement is only valid to the extent the Tulsa IDA program did not result in non-financial benefits that exceed those resulting from simple transfer programs. As indicated in Section 5.3.1, the available evidence suggests that it did not.

  27. 27

    Some costs that result from the Tulsa IDA, such as some components of home closing costs, home repair and maintenance expenditures, some business expenditures, and expenditures on text books, are subject to Oklahoma’s 8.5% sales tax. It is difficult to determine the values of most of these expenditures and, thus, the sales taxes resulting from them are omitted from the formal cost-benefit analysis. However, they are likely small and the resulting sales tax revenues even smaller.

  28. 28

    These results differ from those in earlier studies based on non-experimental data. For example, DiPasquale and Glaeser (1999) found that home ownership is positively associated with citizenship and investments in social capital.

  29. 29

    Fujiwara (2010).

  30. 30

    The estimated impact is based on self-reported data about dedicated retirement accounts and, therefore, does not reflect savings intended for retirement, but saved in other ways, such as in general savings accounts.

  31. 31

    Program operating costs and matching fund expenditures are also relatively large. However, these costs obviously would not persist beyond the observation period. The Tulsa IDA was expected to increase business equity by subsidizing savings for business investments. If it had done so, then this would probably produce benefits that continued beyond the observation period. However, although Table 3 implies that the program increased investments in business, it also suggests that the impact on business equity was small and unexpectedly negative. Thus, future benefits from increases in business equity are not considered further.

  32. 32

    The estimate that appears in Table 3 of Tulsa program’s impact on home purchasing costs includes estimates of the program’s impact on down payments and closing costs, as well as its impact on monthly payments. Obviously, only the latter is likely to persist beyond the 10-year observation period.

  33. 33

    This was computed using the National Bureau of Economic Research’s (2012) Internet TAXSIM Model Version 9.0. Also see Marginal Tax Rate Calculator-Smart Money.com (2011), “What’s Your Marginal Tax Rate?” and The Tax Foundation (2011), “Marginal Tax Rates Calculator” for the marginal federal income tax faced by low-income households; and Government of the District of Columbia (2009), “Tax Rates and Tax Burdens in the District of Columbia – A Nationwide Comparison” for the state and local tax rate faced by low-income residents of Tulsa.

  34. 34

    See the previous footnote.

  35. 35

    A possibly superior measure is the impact on total payments over the entire observation period, but this measure is not available.

Appendix

This appendix describes how each of the measures of costs and benefits that were used in the cost-benefit analysis, other than operating costs and matching fund expenditures, was constructed. The sources of the estimates of expenditures on operating costs and matching funds are discussed in the main text.

1) Impact on income

Winsorized impact estimates of total monthly money income net of government transfer payments and of total monthly government transfer payments are available from the 18-month survey, the 4-year survey, and the 10-year survey. Thus, for each type of income, estimates are available for only three widely separated months. To obtain impacts for the remaining 117 months during the 10-year span covered by the cost-benefit analysis, it was necessary to interpolate between the 3 months for which estimates exist. In doing this, it was assumed the impact was zero during the month prior to random assignment. The 120 estimates that were available once the interpolation was completed were summed in order to obtain single measures of the impact on total monthly money income net of government transfer payments and the impact on total monthly government transfer payments.

2) Rental value of house for months of impact on ownership

This benefit is estimated as the product of the impact on the duration of homeownership and monthly rental value. Estimates of these two values are described next.

The estimated program impact on the duration of homeownership over the entire 10-year observation period is 0.180 years or 2.16 months (0.180×12).

Among the 184 control group members who were renters and who provided information on their monthly rent on the 10-year survey, the reported mean monthly rent was $484.13 per month.

3) Impact on appreciation on home

As shown in Table 2, the estimated winsorized impact on the appreciation rate (i.e., appreciation per year of homeownership) is $477.64. For many homeowners, appreciation of the home is a potential benefit; that is, it is not realized until the home is sold. According to the 2007 American Community Survey, the median single family home in the Midwest is owned for 17 years (Emrath, 2009). If the impact on homeownership occurred during the experimental period, 1999–2003, this would imply that the median home will not be sold until around 2018. This was taken into account in discounting.

The control group averaged 4.5 years or 54 months of homeownership between 1999 and 2009. Thus, the treatment group averaged 54+2.16=56.16 months (see item 2) or 4.68 years. This implies that the impact on appreciation over the observation period was $2,235.35 (4.68× $477.64).

4) Impact on equity in home

This benefit is computed as the sum of the Tulsa IDA program’s impact on down payments and its impact on the part of monthly home payments that accrues to principle. For many homeowners, this is a potential benefit; that is, it is not realized until the home is sold. Like appreciation on the home (see item 3), this was taken into account in discounting.

As reported in Table 2, the estimate of the treatment impact on homeownership is 2.9%. The product of this figure and the estimate of the average down payment amount provides an estimate of the treatment impact on down payments. For those in the treatment group purchasing a home, including those not making a down payment, the winsorized mean down payment is $3192. Therefore, the estimated impact on the down payment is $92.57 (0.029×$3192).

According to the 10-year survey, the average loan amount for homes purchased since random assignment was $72,620 for the treatment group and $73,802 for the control group. The mean length of the time the loan had existed at the time of the 10-year survey was 5.2 years for the treatment group and 4.7 years for the control group. The mean interest rate on the loan at the time of the 10-year survey was 6.51% for the treatment group and 6.36 for the control group. The mean length of the mortgage at the time of the 4-year survey (this information was not collected on the 10-year survey) was 25.4 years. Year 4 of the loan seems a reasonable point at which to compute the amount going to principal. That amount would be $1497 per year, or $125 per month, for a fixed interest 25-year loan of $72,620 at an interest rate of 6.51%. The part of the monthly payment that goes towards the principle is then multiplied by the Tulsa program’s estimated impact on housing ownership duration, 2.16 months (see Table 2) to derive the Tulsa program’s impact on the part of monthly home payments that accrues to principle. (This probably results in a small overstatement because about 75% of the respondents to the 10-year survey indicated that their mortgage amount included property taxes and insurance costs.)

5) Impact on income taxes due to impact on home ownership

This benefit, which is due to the property tax deduction, is very small given the Tulsa program’s small impact on the duration of homeownership and the low percentage of taxpayers who itemize. According to Gerald Prante of the Tax Foundation (“Fiscal Fact No. 95,” July 2007) only 16% of Oklahoma taxpayers with incomes under $50,000 in the 2005 tax year itemized (the average over all income groups in Oklahoma was 31%). The combined marginal federal, state, and local tax rate faced by the treatment group in 2004 was about 31%.33 The product of these two figures is multiplied by the impact on property taxes (see item 9) in order to determine amount of the deduction.

6) Impact on business equity

As indicated in Table 2, the estimated winsorized impact on the change in business equity is -$95.83. For many business owners, this is a potential (negative) benefit; that is, it is not realized until the business is sold (see item 3).

7) Taxes on the impact on income

This can be determined as product of the impact on income and the combined federal, state, and local marginal tax rate on income faced by the treatment group, which was about 31% (see item 5).34

8) Impact on home purchase expenditures

This cost is computed by summing program impacts on (a) down payments, (b) payments on the home loan, and (c) closing costs. The computation of each of these items is described next.

  1. As shown in Table 2, the estimate of the treatment impact on homeownership is 0.029. The product of this figure and the estimate of the average down payment amount provides an estimate of the treatment impact on down payments. For those in the treatment group purchasing a home, including those not making a down payment, the winsorized mean is $3192. Therefore, the estimated impact on the down payment is $92.6 (0.029×$3192).

  2. Payments on the loan are estimated as the product of the average amount of the monthly payment on the loan and the Tulsa program’s impact on housing ownership duration. According to the 10-year survey, the average amount of the monthly payment on the loan for currently outstanding mortgages on homes purchased during the program period at the time of the 10-year survey was $878. However, about 75% of the survey respondents included property taxes as part of their loan payment. Thus, I reduced the $878 amount by three-quarters of the amount computed under item (9) to prevent double counting.35

  3. Total closing costs in Oklahoma in 2010 on a loan amount of $200,000 were $4254 or 2.1% (http://www.bankrate.com/finance/mortgages/2010-closing-costs/oklahoma-closing-costs.aspx). This is probably higher than the downpayment for the IDA experiment sample because the experiment was run earlier than 2010 and the loan amounts were less than $200,000. Indeed, the 10-year survey found that the average loan amount for homes purchased since random assignment was $72,620 for the treatment group and $73,802 for the control group. Therefore, I assumed that closing costs were around $2000 for the treatment group. As shown in Table 2, the estimate of the treatment impact on homeownership is 0.029. Using the $2000 estimate for closing costs, this would imply that the Tulsa program’s impact on closing costs was $58 ($2000×0.029).

9) Impact on property taxes

This cost can be estimated as the product of the annual amount of property taxes paid by homeowners and the Tulsa program’s impact on housing ownership duration. However, the effect of purchasing a home on property taxes depends on the tax’s incidence on renters. To the extent renters pay property taxes as part of their rent, the amount of property taxes they pay when they purchase a home will not result in an increase in taxes. Studies of the incidence of property taxes vary considerably. However, a careful study by Carroll and Yinger (1994) on property taxes in communities in the Boston area found that landlords pay 85–90% of an increase in property taxes, but only 45% of existing property taxes. Renters pay the remainder. The latter figure is the relevant one for the cost-benefit analysis. However, estimates for the Boston area do not necessarily apply to Tulsa. Nonetheless, the 45% estimate is used for purposes of the cost-benefit study. However, the estimate of the impact on property taxes is sufficiently small that findings from the analysis would be little affected by an alternative value.

The annual amount of property taxes paid by homeowners at the time of the 10-year survey was $912. This figure is based on the 81 respondents to the 10-year survey who had non-zero values for the amount of property tax they paid at the time of the survey. The number of respondents is small because only current mortgage holders were asked the amount of their property tax (for instance, an owner with no outstanding mortgage was not asked).

10) Impact on home repair and maintenance expenses

As shown in Table 2, the estimated impact on the winsorized expense amount is –$77.04. This estimate pertains to the entire observation period, which is what is needed for the cost-benefit analysis. The negative point estimate implies that the Tulsa program’s substantial positive impact on home appreciation (see item 3) was not due to investments in home repairs and maintenance.

11) Impact on investments in business

As shown in Table 2, the estimated impact on initial investments in business is $219.34. Because only initial investments were asked about in the surveys, ongoing capital infusions were not captured.

12) Impact on investments in education

The survey data needed to estimate this impact do not exist. However, only 6.6% of the matching funds, an average of $48 per treatment group member ($721×0.066), were used for education. Given the 1:1 matching rate, if the product of $48 is multiplied by two, this provides an upper bound estimate of the impact on investments in education. The resulting $96 figure is too large because some of it would have been invested even in the absence of the IDA. However, most of the subsidy amount of $48 probably would not have been invested in the absence of the IDA. Therefore, $48 is a reasonable lower bound. The true figure may be closer to the upper than the lower bound because there is some evidence that the treatment had a non-trivial impact on educational investment, especially at the college level (see Grinstein-Weiss et al., 2012a, table 4.9). Consequently, in the cost-benefit analysis, I use $80 as the Tulsa program’s impact on educational investment. Even if the lower bound (or the upper bound) is the correct figure, the difference from $80 is so small that this would have a trivial effect on the cost-benefit findings.

13) Impact on savings for retirement

As shown in Table 2, the estimated impact of the Tulsa IDA program on the winsorized mean value of retirement savings is –$346. This unexpected negative, albeit statistically insignificant, estimate implies that the Tulsa program was unlikely to result in benefits at the time of retirement.

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Published Online: 2013-10-11
Published in Print: 2013-12-01

©2013 by Walter de Gruyter Berlin Boston

Articles in the same Issue

  1. Masthead
  2. Masthead
  4. A cost-benefit analysis of Tulsa’s IDA program
  5. Congressional modification of benefit-cost analysis as a vehicle for particularized benefits and a limitation on agency discretion: the case of the federal contract tower program
  6. Benefit-cost analysis of accelerated replacement of Hong Kong’s pre-Euro IV buses
  7. Accounting for systematic risk in benefit-cost analysis: a practical approach
  8. Benefit-cost analysis: government compensation vs. consumer tax model
  9. Comments
  10. The most appropriate discount rate
  11. The choice of the social discount rate and the opportunity cost of public funds
  12. Erratum
  13. Scitovsky reversals in benefit-cost analysis with normal goods
https://doi.org/10.1515/jbca-2013-0012
Keywords for this article
benefit-cost analysis; Individual Development Account (IDA); longterm follow-up; Monte Carlo analysis; random assignment field experiment

Articles in the same Issue

  1. Masthead
  2. Masthead
  4. A cost-benefit analysis of Tulsa’s IDA program
  5. Congressional modification of benefit-cost analysis as a vehicle for particularized benefits and a limitation on agency discretion: the case of the federal contract tower program
  6. Benefit-cost analysis of accelerated replacement of Hong Kong’s pre-Euro IV buses
  7. Accounting for systematic risk in benefit-cost analysis: a practical approach
  8. Benefit-cost analysis: government compensation vs. consumer tax model
  9. Comments
  10. The most appropriate discount rate
  11. The choice of the social discount rate and the opportunity cost of public funds
  12. Erratum
  13. Scitovsky reversals in benefit-cost analysis with normal goods
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