How Do Child Support Order Amounts Affect Payments and Compliance?

2. Optimizing Reliability and Collections Finding the ‘Win-Win’ for both parents How Do Child Support Order Amounts Affect Payments and Compliance?

3. Orange County CSS Steven Eldred Director Mark Takayesu Manager, Research Team Ruth Garcia Staff Specialist, Research Team

4. Is it possible to determine an “ideal” child support guideline amount? Custodial parents want the maximum amount of sustainable support, and want it to be reliable – something they can count on. Non-custodial parents want to support their children financially, but want an order they can meet without going into debt each month.

5. Yes!! • Analysis of research done nationally over the past twenty years, • Analysis of over 100,000 California cases – matching income, expenses and orders with the corresponding performance of the cases. • There is a ‘sweet spot’ of reliability (regular payments) and collections, where both are maximized.

6. Outline Requirements – (a little history here) Research – what’s been done up to this point Data - California’s dataset and how it was reduced to manageable information Analysis – What the data tells us So What? How does that affect the Operational Manager? So What, Part Two – What do policy-makers do with this information?

7. Requirements • Congress decreed in 1988 that all States participating in the AFDC (now TANF) program have a predictable, set formula or guideline to establish child support obligations. • Prior to that time, local courts often used arbitrary factors or methods for setting support.

8. Models of Guidelines • Income-Shares Model: • Both parents’ income combined, adjusted for time-share of custody. • Obligor-Income Model: • Flat percentage of Obligor’s income, with some adjustments • Melson Method: • Income-shares model with self-sufficiency reserve built-in

9. Models of Guidelines

10. Research – What do we know? • Arrears are bad • Show that current support was not collected at the time it was needed. • Have a strong negative effect on continued efforts for obligor to pay or participate • Nationally, over $110 billion in unpaid arrears; • California, over $19 billion

11. Research Findings • Dr. Elaine Sorenson, Urban Institute, (2007) • Non-Compliance of a current support order is another major factor is arrears growth • Majority of arrears owed by a small percentage of obligors • 11% of obligors owed 54% of the arrears • Of those obligors, 3/4 had no reported income, or income less than $10,000 per year. • Interest on support arrears is responsible for a large portion of arrears growth

12. Research Recommendations • Dr. Elaine Sorenson, Urban Institute, (2007) • Set realistic orders • Increase obligor participation in order establishment • Reduce or eliminate setting retroactive support • Modify orders promptly when appropriate • Institute arrears compromise programs

13. Setting Appropriate Orders • Setting Appropriate Orders is effective in reducing arrears growth: • Turetsky, Vicki (2000) Center for Law and Social Policy • Sorensen, Elaine, et al (2007) Urban Institute • Meyer, Daniel (2003,2008) Institute for Research on Poverty • Formoso, Carl (2003, 2010) Washington State • Specific link seen at 20% ‘tax rate’ – when support was over 20% of the obligor’s income, arrears grew

14. Prior Research and Limitations • While many researchers have done great work on child support collectability and arrears growth, many have had troubles with data. • Use of large government databases – Employment Department or census data against child support databases – not linked person-to-circumstances.

15. The Potential of the Child Support Enforcement Program to Avoid Costs to Public Programs: A Review and Synthesis of the Literature (2000) Barnow, et al (Johns Hopkins and The Lewin Group) “Data limitations. Many of the published studies suffer from data limitations including small sample size, incomplete data, and lack of longitudinal data. The small sample sizes . . . analyzed in previous studies reduce the reliability of the . . . findings and the ability to generalize the results from the sample to the U.S. population.”

16. Guideline Calculator Analysis • California has an advantage in research as it carries all guideline input data in CSE, and that data can be linked to case payment behavior. • Since all cases require a calculation be recorded in support of an order, we can see what income/expense factors led to the order. We can than see how that case paid.

17. Data Set 102,332 cases, representing 142,730 children All cases with orders established since December 2008. Current Assistance: 36,198 cases Former Assistance: 32,307 cases Never Assisted: 33,827 cases

18. Data Set • Compliance Rates: • Current: 40.8% • Former: 61.6% • Never: 70.6% • Median NCP Income : $1504/month • Average Visitation: 9.8%

19. What we Expected: We expected to find a “Laffer Curve” of increasing rates bringing increasing collections, up to a point – after that point, as the order rises, collections actually decrease.

20. What we saw: Compliance By Percentage of Income – One Child

21. What we saw: Payments Per Child By Percentage of Income – One Child

22. Compliance by 10% Category and Percent of Months Paid *N/S = Non Significant Finding

23. Compliance and Percent of Months Paid By ROTW 1% Categories:

24. Compliance, Percent of Months Paid and Payments Per Child By NCP Income Category

25. Percent of Cases Paying $0 Minimum Wage vs. Non-Minimum Wage

26. What’s Up with Minimum Wage Orders? The drops indicate negative compliance and Minimum Wage cases represent: • 62 % in the 1 child calculation • 66% in the 2 children calculation • 66% in the 3 children calculation

27. What’s Up with Minimum Wage Orders? When is it used? • Used in defaults • Presumed Orders – Family Code 17400(d)(2) • Imputed orders – ’40 hours per week’ when obligor is really working 24 What are the results? • Lower overall compliance • Lower collections

28. Operational Impact of Guideline Research That’s nice – but so what? How does this affect me, the Operational Manager?

29. Establishing Orders Using this data, we can see that income ‘fiction’ is very dangerous – imputing income, or using presumed income, may get a higher order, but does not increase collections. Payment behavior is very closely linked to ‘provable’ income – not ‘should be’ income.

30. Establishing Orders • Lessons: • Take time to research real income – the extra time at the front end will be repaid by better compliance later. • Income less than minimum wage floors is OK – and will result in higher compliance AND more dollars collected, than using artificial income.

31. What if you could predict how well a case is going to pay, before you file the court order? Based just on the ‘tax rate’, or percentage of gross income ordered for support, you can predict payment behavior. Adding in variables such as education, criminal history, language issues, etc., gives you a very good predictive ability.

32. What do I do with the prediction? If your order isn’t appropriate, don’t get it – which variable is wrong? If your order is right, but the predictor tells you that compliance is low, identify this case for extra support, early intervention strategies, etc.

33. Compliance Predictor Green Square = 60% or better; Yellow Square = 40%-60%; Red Square = less than 40%

34. But my order is already set! • It’s a mod, mod, mod world….. • Modifying orders will either: • Upward: Increase collections at a small FPM3 cost; • Down: Increase FPM3 at a small collections cost; • No change – Increase FPM3 and collections by 11%

35. Find the orders with poor collectability Get a list of the high ‘tax-rate’ cases and intervene – until you can get them modified, call early and often. Consider connecting these obligors with services – workforce panel, etc.

36. What to do with the Information? Policy Implications of Guideline Data

37. The Big Picture Provide policy support and data to policymakers in the legislature Be able to show advocates at what level support orders become counter-productive

38. National Implications Regardless of the guideline method used, this analysis provides policymakers and child support professionals with hard data for predicting compliance and collections. The methodology will work with any state’s dataset, and should encourage further research into compliance and collections behavior.

39. Next Steps? • To what extent do the following affect payment behavior? • Education Level • Obligor age when 1st child born • Criminal history • Language • Parental relationship with child • If ‘tax rate’ is the most important, to what degree to the factors above affect payments, and how can the CSS Program assist?

40. Think ‘Bubbles’!