Enhancing Child Support Enforcement Efforts Through Improved Use of Information on Debtor Income



  1. Background
  2. Summary of Findings
  3. Implications for State Child Support Enforcement Agencies


The recently released National Child Support Enforcement Strategic Plan for Fiscal Years 2005 to 2009 recognizes that the Federal-state child support enforcement program, operated under the authority of Title IV-D of the Social Security Act (IV-D) is no longer primarily a welfare reimbursement and revenue-producing program. Rather, it is an important component of Federal, state, community and faith-based efforts to help families attain self-sufficiency by making child support a more reliable source of income.(1) The plan addresses getting money to families, preventing the accumulation of child support arrearages, and improving program accountability through performance measurement.

The Office of Child Support Enforcement (OCSE) within the Department of Health and Human Services(HHS)/Administration for Children and Families(ACF) has a wealth of information resources that can be used to help national and state policy makers and program managers develop strategies to ensure that the IV-D program is meeting the goals set forth in the strategic plan. These include:

  • The Federal Parent Locator Service (FPLS), which helps state child support programs locate non-custodial parents and putative fathers so that they can establish paternity and child support orders, and enforce and modify orders. The FPLS includes two databases: The National Directory of New Hires and the Federal Case Registry.
  • The Federal Offset Program Case Master File, which includes person-level data for cases submitted for Federal income tax refund offsets, administrative offsets, passport denials, and the multi-state financial institution data match.
  • State performance data, which is aggregate data collected annually from the states on paternity establishment, order establishment, collections, arrears, and expenditures. Data are used to calculate incentive payments and to develop annual statistical reports.

This report examines how some of the information available to OCSE and the states' IV-D programs through the Federal Parent Locator Service can be used to enhance enforcement efforts. Specifically, this report explores the potential income sources of non-custodial parents with arrearages. It examines alternative income sources for those obligors who have no reported income in the Unemployment Insurance (UI) Quarterly Wage data and for all obligors who have arrearages. It also analyzes how arrearages for individual obligors change over time, and how those changes are related to type and amount of income. By helping Federal and state policy makers and managers understand obligor income streams and debt patterns, it is hoped that this report will contribute to the development of additional data-driven solutions for enhancing child support collections. This report also summarizes Federal administrative data, including how it can be used to answer program management questions.

The data used for this study come from three primary sources:

  1. Arrearage data from the OCSE Federal Offset Program Case Master File. Data were provided for debtors who, in March 2003, had no wages based on a match with quarterly wage files for the four quarters ending December 2002 and for all non-custodial parents who owed debt as of February 2005.
  2. Internal Revenue Service data from IRS Information Returns Master File (IRMF). Data were provided for Tax Year 2003.
  3. Social Security Administration (SSA) data from the State Verification and Exchange System (SVES), which provides information on confinement in prisons and correctional facilities, as well as benefit amounts paid under Title II (Social Security Old-Age, Survivors, and Disability Insurance) and Title XVI (Supplemental Security Income).

Data were provided by OCSE. All individual identifiers (such as names, addresses, and Social Security numbers) were removed to protect the confidentiality of the individuals included in the data set. However, encrypted identifiers allowed matching of records among the various data sets. As an added privacy precaution, income from each source was top-coded at $100,000.

Two populations were defined for analyses:

  • No-wage debtors: These individuals are a subset of child support debtors; they had arrears in March 2003 and did not have quarterly wages reported in the prior four quarters, based on data from the National Directory of New Hires (NDNH). This group, referred to throughout this report as "no-wage debtors" consists of about 1.7 million individuals. This group represents about 34 percent of all debtors.
  • All debtors: These are all individuals who had child support arrears in February 2005. This group includes about 5.3 million individuals. It is estimated that this file contains approximately 85 percent of all individuals with child support obligations in the IV-D system.

Exhibit ES.1 indicates the income sources that were considered for the analyses.


Exhibit ES.1:
Description of Income Sources
Income Source Description
Wages Wages, tips, and other compensation
Gambling Gross winnings
Stocks, Bonds, Etc. Proceeds from stocks and bonds, bartering, and aggregate profit/loss
Social Security Total benefits paid minus any repayments from the four prior years
Certain Government Payments Taxable grants (from federal, state, or local government), UI compensation, agricultural subsidies, and prior year refund of state and local taxes
  • UI Compensation
Unemployment Insurance compensation
  • Prior Year Refund of State and Local Taxes
Prior year refund of state and local taxes
  • Other Government Payments
Taxable grants and agricultural subsidies
Dividends Capital gains, cash liquid distribution, noncash liquid distribution, and ordinary dividends
Interest Interest and savings bonds
Miscellaneous Non-employee compensation,(2) medical payments, fishing income, rents, royalties, other income, substitute payments for dividends in excess of Golden Parachute, crop insurance
  • Non-employee compensation
Non-employee compensation
  • Other Miscellaneous
All other miscellaneous income fields
Pensions Primarily gross distributions(3)

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Summary of Findings

States should actively pursue collections from debtors with no indicated wages. Of the 1.7 million debtors in the "no wage" file, almost half (46 percent) had an income source in the following year. The median income amount of these debtors, however, was low  about $7,500 (compared to about $13,200 for all debtors). Because the median debt amount for no-wage debtors (about $9,800) exceeds their median income, identifying and applying various enforcement mechanisms likely will not eliminate large amounts of debt for the median debtor. However, almost one in ten no-wage debtors had income in excess of $20,000 per year. State child support agencies could identify these debtors and take appropriate enforcement actions.

The primary income for no-wage debtors were wages and wage-like sources. About one-fourth of all "no-wage" debtors had wages in the following year. These individuals might be in jobs not covered by the Unemployment Insurance system (thus would not appear in the NDNH quarterly wage file) or could have cycled out of unemployment between the somewhat different time periods captured by the different data sets. The median amount of this income source, however, was very low  about $4,500. About half as many no-wage debtors (one in eight) had what the IRS terms "miscellaneous income," including non-employee compensation for jobs not performed as official employees. The median income was considerably higher for those with this income source ($8,100). About one in nine debtors had Social Security income (either Old Age Survivors and Disability Insurance or Supplemental Security Income); the median amount was the highest of the three primary sources ($8,900).

By way of comparison, debtors in the "all debtor" file were also most likely to have wage-like income. Like no-wage debtors, the most common income source among all debtors was wages. Fully two-thirds of the debtors in this file had wage income. The median amount, though, was about three times larger ($12,500). Miscellaneous income was also common; about one in ten debtors had this source. However, government payments, including Unemployment Insurance, were more common among all debtors (about one-fourth had income from this source). Social Security was not a common income source among all debtors (fewer than 1 in 20).

Higher arrears are associated with lower incomes. The study explored the relationship between debtors' arrearages and income amounts. For most income sources, the median income of no-wage debtors was highest at the lowest arrearage levels. Wages is a prime example. The median wages of debtors were highest ($8,900) at the lowest arrearage level ($1 to $149) and declined by half ($4,470) at the highest arrearage level ($100,000 and up). However, the median income amount for two sources  Social Security and government payments  increased with debt amount. The patterns were the same for debtors in the all debtor file, with the exception of miscellaneous income; the median amount increased as debt rose.

Many no-wage debtors appear to be available to be in the labor force. About one in ten no-wage debtors were receiving Social Security benefits, thus were unlikely to work due to age or disability. Further, one in ten no-wage debtors have a history of confinement in a prison, jail, or other correctional facility.(4) The analysis only showed if there was a match and not whether the debtors were in such a facility at the time of the data match. If one assumed that all the 10 percent of debtors with a history of confinement were in correctional facilities at the time of the data match and that the 10 percent of debtors who were Social Security beneficiaries were not in the labor force, the majority of debtors  eight in ten  may have been available to work. (The data provided no information about other barriers to work such as homelessness, substance abuse, or lack of job skills.) Similar data for all debtors were not available.

Many debtors do reduce their arrears over time. The study explored changes in debt amounts of those individuals in the no-wage file between March 2003, August 2004, and February 2005. Between the first two time periods, total debt among the population of no-wage debtors fell by $2 billion  a decline of about 8 percent. Although arrearage amounts rose between 2004 and 2005, the aggregate change in debt between 2003 and 2005 was a reduction of $920 million, or about 3 percent. Debtors who owed less than $5,000 were most likely to have a complete elimination of arrears between 2003 and 2004 (almost one in three). An additional one in five had a partial reduction. Thus, almost half of those that owed less than $5,000 had a reduction in debt. Among those who owed more than $20,000, the pattern was reversed.

The data do not identify the reason(s) for the change in debt. It is possible that debtors are paying off their arrears. However, it is also possible that state policies (e.g., debt forgiveness, closing cases) or technical issues (e.g., corrections to incorrect arrearages amounts for some individuals) play a role. Data for all debtors were not available.

Debt reductions appear associated with income and arrearage type. The income sources most associated with total reduction in arrears were stocks/bonds, dividends, interest, prior year tax refunds, and government payments other than Unemployment Insurance. About one-third of debtors with these sources had no arrears in 2004. Debtors with income from wages, Unemployment Insurance, and non-employee compensation were most likely to owe more debt in 2004 than 2003 (about half of debtors with these sources had increases).

The type of debt also seems to make a difference. Debtors were far more likely to have had a complete or partial reduction in arrears if they owed non-TANF debt only (almost half of debtors in this category). By way of comparison, less than four in ten debtors who owed TANF arrears only and about one-third of those who owed both types of debt had reductions (either full or partial) between 2003 and 2004.

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Implications for State Child Support Enforcement Agencies

The findings reported above indicate that federal administrative data can be used to help states and OCSE manage the child support enforcement program. These findings involve a match between the Federal Offset Program (FOP) Case Master File, the Quarterly Wage File of the NDNH, and IRS data. Other data sources (e.g., the Federal Case Registry, the NDNH) could answer additional management questions at both the state and national level. The findings from the FOP/IRS match have a number of implications for child support enforcement programs. They are grouped into three areas: income sources and amounts, debt type, and confinement in prisons or other correctional facilities.

Income sources and amounts. Median total income, for no-wage and all debtors with at least one source of income, was low  about $7,500 for no-wage debtors and $13,200 for all debtors-particularly in comparison to the median U.S. household income (over $43,000) and per capita income (over $23,000).(5)

  • The low median amount of the most common source of income  wages  suggests that for no-wage debtors and all debtors, wage withholding will not in itself eliminate large arrearages, particularly if the debtor also has a current support payment.
  • State child support agencies may want to look more closely at no-wage and all debtors receiving non-employee compensation. This income is not generally captured by state employment agencies and may represent an additional enforcement opportunity for states, especially considering the relatively high median income amounts.
  • Social Security was one of the more common sources of income for no-wage debtors. Social Security receipt generally is indicative of detachment from the labor force due to age or disability. Modifications may be warranted for this group of debtors to prevent further accumulation of debt. This may especially be the case if the initial order was set at a time when the debtor was working or had other sources of income. Retirement and disability payments under Title II of the Social Security Act (Old Age, Survivors, and Disability Insurance) can be attached, and the children may be eligible for benefits.
  • Government income, including UI compensation, is a prevalent income source for all debtors and UI income can be attached. Child support enforcement agencies might want to target these individuals for order modification because orders were likely set according to pre-job loss wages. Thus, there is a danger of arrearages building. Orders might be considered for short-term modifications (e.g., set to return to the original order amount after the standard 26-week UI benefits period). Also, the provision of employment supports could hasten re-employment and reduce the amount of time that the debtor is accumulating arrearages.
  • Income associated with assets, such as interest, dividends, and proceeds from stock and bond transactions was less common. However, no-wage debtors with these types of income were the most likely to eliminate their arrears from one year to the next. Thus, these income sources may be suggestive of additional assets that child support enforcement agencies can seize or intercept, which many state programs may already be doing.

Debt type. The no-wage debtors, who owe both TANF and non-TANF child support debt, appear to have the highest debt level and least likelihood of reducing their debt from one year to the next.

  • Child support agencies might want to explore the characteristics of these cases, including:
    • Are debtors who owe both types of debt more likely to be involved with more than one case?
    • Are they more likely to have been in the child support system for a longer time, thus accumulating more debt?
    • Are they more likely to owe both current support and arrears (as opposed to debt only)?
    • What is the order amount and was it set by default (and if so, what income assumptions were used)?
  • Additional information might suggest a modification that would increase the chances of more reliable collections or indicate the need for referrals to employment programs.

Prisoner data. Matching state child support data to SVES data on confinement in prisons or other correctional facilities could be instructive for a number of reasons.

  • It indicates the proportion of the caseload that is unable to earn income, at least for a specified period of time. This proportion of the caseload will accumulate arrearages if action is not taken to modify the order amount.
  • The match would be especially valuable if states can ascertain the length of confinement. For example, debtors with relatively short confinements, combined with past payment history, will likely have different payment and debt profiles than those who accumulated large arrearages prior to confinement.
  • States may want to examine how to better collaborate with corrections officials to ensure that child support obligors in the corrections system are quickly identified and that appropriate actions are taken in accordance with state guidelines and policies.

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1. Administration for Children and Families Office of Child Support Enforcement (2005). National Child Support Enforcement Strategic Plan FY 2005-2009. Available on line at: http://www.acf.hhs.gov/programs/cse/pubs/2004/Strategic_Plan_FY2005-2009.pdf

2. Examples of non-employee earnings include fees, commissions, prizes, awards, and other compensation for services performed as a non-employee (e.g., individual contractor). The amounts reported as non-employee compensation are subject to self-employment tax.

3. This includes pensions for public servants.

4. This figure is based on SVES data provided by the Social Security Administration. The data identify individuals confined in a number of different types of related facilities, including mental facilities that hold individuals found not-guilty-by-reason-of-insanity or other mental condition. See pages 6-7 for more discussion of this data source. For the sake of simplicity, this paper often refers to the whole range of these facilities as "prisons" or "correctional facilities."

5. Carmen DeNavas-Walt, Bernadette D. Proctor; and Robert J. Mills. 2004. Income, Poverty, and Health Insurance Coverage in the United States: 2003. Washington, D.C.: U.S. Census Bureau (http://www.census.gov/prod/2004pubs/p60-226.pdf).


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