National Poverty Center Working Paper Series  

#12 – 02 

January 2012 

Are Interstate Cases Still the ‘Black Hole’ of Child Support 

Enforcement?  Effects of the Uniform Interstate Enforcement Act 

Elizabeth T. Powers 

University of Illinois, Urbana ‐ Champaign 

 

 

 

 

 

 

 

 

 

 This paper is available online at the National Poverty Center Working Paper Series index at: 

http://www.npc.umich.edu/publications/working_papers/  

 

This project was supported by the National Poverty Center using funds received from the U.S. 

Census Bureau, Housing and Household Economics Statistics Division.  The opinions and 

conclusions expressed herein are solely those of the author(s) and should not be construed as 

representing the opinions or policy of any agency of the Federal government. 

Are Interstate Cases Still the ‘Black Hole’ of Child Support Enforcement?

Effects of the Uniform Interstate Enforcement Act

ELIZABETH T. POWERS

University of Illinois at Urbana-Champaign

epowers@illinois.edu

January 2012

ABSTRACT: So long as child support enforcement was entirely the legal domain of the states, it

was nearly impossible to pursue claims across state lines, and interstate claims were

characterized as the “black hole” of child support enforcement. The Uniform Interstate Family

Support Act (UIFSA) clarified lines of authority, opened state IV-D agencies and courts to

interstate claimants, and invented powerful new tools for pursuing cross-state claims. This paper

uses Survey of Income and Program Participation data spanning the reform era to assess the

success of this policy. The potential endogeneity of interstate moves with the policy regime may

bias conventional regression estimates. A conditional difference-in-difference matching

estimator is implemented instead. The findings indicate greatly increased administrative

enforcement activity for interstate cases subsequent to UIFSA. This activity increased formal

support agreements and identified greater amounts of support owed. There is also evidence of

increased interstate collections and a closing of the ‘black hole’. Support collections increased

especially for welfare-receiving households, but nonwhite households and households with

nonmarital births do not appear to be helped by UIFSA.

KEYWORDS: Child support policy, child support enforcement, single parent families, welfare,

matching estimators

ACKNOWLEDGMENTS: Development of this project was supported by a grant from the

National Poverty Center at the University of Michigan with funds provided by the U.S. Census

Bureau, Housing and Household Economics Statistics Division. The opinions and conclusions

expressed herein are solely those of the author and should not be construed as representing the

opinions or policy of the National Poverty Center or of any agency of the Federal Government.

Maghaisvarei Sellakumaran and Emilie Bagby provided able research assistance. I am grateful to

Yunhee Chang, Austin Nichols and other participants at a joint Census-National Poverty Center,

Don Fullerton, Petra Todd and economics department seminar participants at Purdue University

for comments on various iterations of this research.

1

1. Introduction

Policymakers have long hoped that child support would serve as a major income source for

families with children. Over the past several decades, the federal government has attempted to

correct widely acknowledged weaknesses in the establishment and enforcement of child support

claims through a series of system reforms. One of the first major enforcement reform initiatives,

a requirement that states garnish the wages of uncooperative obligors, ocurred in the mid-1980s.

From the mid-1990s there was a marked shift in responsibility from the courts to administrative

agencies that was integrated with the introduction of important new enforcement tools (see

Huang, 2002, Sorensen and Hill (2004), and Pirog and Ziol-Guest, 2006, for overviews).

The near-impossibility of pursuing claims through legal or administrative means across state

lines prior to the mid-1990s was a major exception to a trend of continually more coercive child

support policy. Prior to reform, interstate cases were characterized as the “black hole” of child

support enforcement (Lerman, 1993; Haynes, 1996). Improvement of interstate collection was a

consequential issue, as interstate cases were estimated to comprise as much as one-quarter to

one-third of all support cases in the early 1990s (U.S. GAO, 1992). The Uniform Interstate

Family Support Act (UIFSA) was intended to not only put interstate and in-state cases on an

equal footing by clarifying lines of authority and priority in both the legal and administrative

realms but also to create powerful new tools for pursuing interstate claims (see Haynes, 1996, for

details). The 1996 Federal welfare reform essentially mandated states’ universal adoption of

UIFSA, and all states did so by 1998.

This study uses rich, nationally representative data from multiple panels of the Survey of Income

and Program Participation (SIPP) spanning the decade from 1993 to 2003 on child support

claims, collections, and claimant contacts with the enforcement system in order to assess how

well the objective of closing the black hole of interstate cases was met. UIFSA removed the legal

and administrative obstacles to interstate case establishment and enforcement. If UIFSA worked

as intended, outcomes of interstate claims should have converged to patterns more typical of in-

state claims after 1998.

There is little research on interstate child support enforcement and none that is nationally

representative (for research using administrative data for Illinois, see Chang, 2003). The SIPP is

2

an unusually rich data source but little used for child support research. Peters, Argys, Howard,

and Butler (2004) use the SIPP to study whether improved child support collections increase

paternal contact with non-resident children. However, their work focuses exclusively on

nonmarital cases and does not contain a specific test of UIFSA’s impact on interstate case

enforcement. Rather, the UIFSA adoption status of the obligee’s state of residence appeared as

one of several instrumental variables for child support collections.

In fact, much of the

enforcement literature relies on a small number of cases in the National Longitudinal Survey of

Youth (NLSY) that are tracked into the early 1990s, prior to the most significant reform efforts.

Research on the effect of UIFSA on interstate cases faces a methodological challenge. If moving

behavior is endogenous with UIFSA adoption, estimates of the effectiveness of UIFSA derived

from a standard difference-in-differences (DID) analysis comparing movers’ and non-movers’

outcomes before and after implementation are biased. The direction of the bias likely depends on

the identity of the mover; that is, whether the interstate case status was created by the move of

the parent who owed or was owed support (obligee or obligor).1

Specifically, prior to the implementation of UIFSA, uncooperative obligors may move across a

state line to evade enforcement. If so, the pre-UIFSA-implementation sample of interstate cases

is negatively selected with respect to the latent propensity to cooperate with child support. Self-

selection of the least compliant or cooperative obligor-parents as ‘movers’ associates poor

enforcement outcomes to the pre-UIFSA period. UIFSA implementation reduces or eliminates

the advantage of an out-of-state move to an uncooperative obligor. If moving is strategic, UIFSA

implementation reduces negative selection of interstate movers, creating the appearance of

improving enforcement under UIFSA in the data. A standard difference-in-difference analysis

would overstate the effectiveness of UIFSA policy.2

1 Self-selection of movers generally is not necessarily problematic. For example, movers often have better economic

opportunities. If this is not fully captured with the controls, then estimates of the interstate gap prior to UIFSA are

biased downward, but the estimated policy effects of UIFSA are unbiased so long as the process governing the

beneficial selection of movers is stable over the sample period. 2 Interstate cases associated with obligee parents who move across a state line prior to UIFSA are likely beneficially

selected. Only obligees without some confidence that the obligor parent will honor his obligation would risk an

interstate move prior to UIFSA. If UIFSA is effective, obligee parents with uncooperative former partners are made

no worse off by a move. Therefore, in a sample of interstate cases originating with the obligee parent’s move, the

standard DID approach understates the effectiveness of UIFSA implementation.

3

In the absence of compelling instrumental variables or natural experiments to identify the

moving decision, I employ a conditional difference-in-differences matching estimator. Under the

assumption that the counterfactual difference in outcomes between movers and nonmovers in the

absence of UIFSA is time-invariant, this method provides an unbiased estimate of the effect of

UIFSA policy on child support outcomes. Matching is performed separately within the

subsamples of both interstate and within-state cases, based on the relationships of outcomes to

observables in the pre-UIFSA-implementation era. The pre-post differences that are constructed

for the interstate and within-state cases are then contrasted, as in a conventional DID analysis.

To preview the findings, there is strong evidence that UIFSA implementation increases interstate

obligees’ access to administrative enforcement. After UIFSA implementation, interstate obligees

who approach state IV-D agencies for help report much higher rates of satisfaction. IV-D

intermediation of interstate payments also increases subsequent to UIFSA implementation. There

is also strong evidence that UIFSA implementation increases the incidence of formal support

arrangements for interstate cases and increases the amount of back support identified as owed.

The incidence of payments to households potentially needing child support in interstate

situations rises after UIFSA implementation. Under reasonable sample restrictions, the estimated

effects of the policy on interstate payments are large enough to completely close the pre-UIFSA-

era interstate payment black hole.

The next section provides essential background on the child support enforcement system and

details the problems UIFSA addresses. Section 3 presents the predicted effects of the universal

adoption of UIFSA by the states on access and enforcement outcomes. Section 4 explains the

empirical strategy for identifying the effects of UIFSA implementation. Section 5 discusses the

data source and construction of the variables. The main findings are presented in section 6, while

the robustness of the main findings to various changes in samples and specifications is discussed

in section 7. Conclusions and a discussion of the findings are presented in section 8.

2. Background on Child Support Enforcement

4

Although some parents are able to amicably and privately agree on support and self-enforce their

agreements, obtaining and enforcing child support is more often a protracted, multi-step process

involving third parties.3 The necessary steps are roughly as follows. First, legal paternity is

established at birth or conceded later. A claim for financial support is made on the potential

obligor and an award amount is established by a local court, following guidelines laid out in state

law. The basis for the award amount is often a formula that takes into account the numbers and

ages of the children whose support is in question, parents’ incomes, custody arrangements, and

other factors (see Neelakantan, 2009, Argys, Peters, and Waldman, 2001, and Argys, and Peters,

2003, for discussions of guidelines). The court then issues an order to pay. If the obligor ignores

this order, the obligee can seek enforcement, although if the obligor is unemployed or

incarcerated, he may be excused from payment.4 At many stages of the process, it is extremely

difficult for the case to progress if the obligor’s whereabouts are unknown.

Obligees may pursue paternity establishment, awards, and payments with the aid of public and

private entities. Welfare recipients are required to assign their child support rights to their state’s

child support enforcement (IV-D) agency with the aim of recouping the state’s welfare and

medical expenses for the child. Obligees outside the welfare system can either voluntarily obtain

assistance from their state’s IV-D agency, possibly incurring a modest fee, or use a private

attorney. In addition to the value of having the IV-D agency act as an intermediary in the court

system, administrative enforcement authority permits the IV-D agency to take action without

consent of the courts or other state entities. Over time IV-D agencies have gained the authority to

order paternity tests; intercept obligor’s unemployment insurance payments, income tax refunds,

and lottery winnings; suspend driver’s and professional licenses; and deny passport applications.

Another important feature of IV-D operations is the requirement since 1990 to collect child

support for new or modified support orders through wage withholding. Pirog and Ziol-Guest

(2006) provide an overview of IV-D services and characteristics of the IV-D caseload.

3 It is not known whether support arrangements and payments are voluntary in the SIPP. Argys and Peters (2003)

estimate that 21 percent of child support agreements in a small NLSY sample are set by parents alone. These

agreements appear largely self-enforcing; almost 80 percent of child support amounts promised are reported

received when the parents work exclusively together. Another 35 percent of cases use an attorney to arrive at an

agreement. It is not possible to determine the share of those agreements that are still essentially cooperative, but the

share of promised payments received drops to 61 percent in this group. The remaining 44 percent of agreements

come through a court. 4 Throughout, I simplify language by assuming that the father is obliged to pay child support and that the child lives

in the mother’s household. While this is not always the case, it is by far the typical case in these data.

5

The difficulty of enforcing child support when parents live in different states has deep roots in

the development of U.S. family law, traditionally the jurisdiction of state and local governments.

The laws and court orders of each state are inapplicable and unenforceable in another’s. As a

consequence, prior to UIFSA, a parent residing in state A who sought to enforce the child

support order of state A’s court faced insurmountable obstacles if the obligor moved to state B.

State A had no jurisdiction in state B, and was therefore powerless to enforce its order. Even if

state B were willing to assist with enforcement, it was unclear whether the award should conform

to the laws of State A or State B. While the parent residing in state A would benefit enormously

from access to the services of state B’s IV-D agency, as a nonresident she had no claim to these

services whatsoever. Even had they been willing to entertain the obligee’s claim in principle,

State B’s courts and IV-D agency might have been reluctant to voluntarily enforce state A’s

court order on behalf of a nonresident for other reasons like cost.

UIFSA was created in 1992 to directly address and resolve conflicting jurisdictional issues and

the lack of cooperation and coordination in states’ enforcement efforts.5 UIFSA resolved

jurisdictional problems by requiring all states to defer to the child support order originating in the

child’s state of residence. This state was assigned a continuing exclusive jurisdiction over the

order, prohibiting its alteration by other states’ courts.6 Beyond demarcating the authority for

establishing and modifying awards, UIFSA obliged not only state courts, but also state IV-D

agencies, to fully recognize the authority of another state’s orders. As a result, Courts and IV-D

agencies were compelled to give out-of-state and in-state cases equal priority (see DeMaria,

1999, for a detailed analysis of jurisdictional issues under UIFSA).

UIFSA actually grants preferential status to out-of-state cases, because it permits the obligee’s

attorney to send a child support order directly to the obligor’s employer, bypassing the court and

administrative systems entirely, immediately triggering wage withholding and health insurance

5 Title IV-D of the Social Security Act of 1975 provided federal funding for child support enforcement and paternity

establishment but was careful to preserve state autonomy in child support enforcement program design. Prior

attempts at coordination of state systems were ineffective, with so-called “uniform” laws applied differently from

state to state (particularly with respect to arrears and medical support; OCSE, 2008), and states permitted to adopt

only parts of the Acts. 6 During the transition to UIFSA involved states were directed to cooperatively determine a single controlling order

for cases with multiple orders.

6

coverage.7 “Enrollment of the child in the health care plan at the employee-obligor’s expense is

not dependent on the obligor’s consent, any more than withholding a sum certain from the

obligor’s wages is subject to a veto. It is up to the obligor to assert any defense to prevent the

employer from abiding by the medical support order (UIFSA, 1996, p.50).”

Finally, locating the obligor is a common problem in interstate cases. UIFSA mandates that law

enforcement officers in the obligor’s state (usually either the state’s attorney or state attorney

general) seek the obligor’s location from an exhaustive list of sources, including state and federal

records. Use of the obligor’s Social Security number, as listed in the Act, reveals not only the

obligor’s address but also the identity of his employer, expediting collection through wage

garnishment. Under welfare reform, the IV-D agencies are mandated to provide locator services.

UIFSA was drafted by the National Conference of Commissioners on Uniform State Laws,

enacted in 1992 with revisions enacted in 1996 and 2001. The Personal Responsibility and Work

Opportunity Reconciliation Act of 1996 (“welfare reform”) mandated the adoption and

implementation of UIFSA by all states by January 1, 1998. Universal adoption was promptly

achieved under the threat of a complete loss of federal child support enforcement funding. In

1993, fewer than 10 percent of at-risk households (those containing a child whose other parent is

absent from the household) were under UIFSA. By 1995, 27 percent of these households were

covered by voluntary state UIFSA adoption. By 1997, two-thirds of at-risk households were

covered. All at-risk households have been covered by UIFSA since 1999.8

3. Predicted Effects of UIFSA Adoption

UIFSA’s focus is on legal and administrative systems for child support enforcement, and it is

possible that important causes of non-payment are unrelated to these systems. For example, the

most common reasons given for the absence of a child support agreement in the SIPP are that the

7 UIFSA requires medical support arrangements to be specified in the child support order. They can be provided

either through a cash payment or enrollment as a dependent in a health plan. 8 Author’s computations using 27,865 observations on households eligible for the child support edit (i.e., respondent

is parent of child under age 21 whose other parent is absent from the household) from the 1992, 1993, 1996 and

2001 SIPP panels. Statistics are weighted by wave 1 household weights, as recommended by Census Bureau staff.

7

potential obligee did not try to obtain support, that the potential obligee did not want support, and

that the potential obligor was impoverished and unable to pay. UIFSA implementation may have

little or no effect on these causes of nonsupport. Self-reported reasons are difficult to interpret,

and this research focuses on objective measures of activity and outcomes.

By opening access to state IV-D agencies, UIFSA dramatically reduces obligee’s transaction

costs for pursuing interstate claims. By establishing the authority of the originating state’s order

both with the obligor’s state’s own IV-D system and his employer, UIFSA implementation also

greatly increases the potential for involuntarily collection from an interstate obligor. Clarification

of state lines of authority, access to IV-D services in an era of heightened administrative

enforcement powers, and a prioritization of locate services (a problem disproportionately

plaguing interstate cases) are all predicted to contribute to a positive overall effect of UIFSA on

the incidence and amount of established awards and collections made for all at-risk households.

In contrast, the overall impact of UIFSA adoption on the average awards and payment amounts

from interstate obligors, conditional on owing any payment, is theoretically ambiguous. By

lowering transaction costs, UIFSA rationalizes the pursuit of smaller claims. The average effect

on award amounts and payments also depends on how the cases pursued under UIFSA are

weighted between “deadbeats” and “turnips” (Mincy and Sorenson, 1998). As one can’t get

blood from a turnip, one can’t garnish earnings where there are none. If the problem of pre-

UIFSA interstate enforcement is largely that better-off obligors who can afford to pay do not (the

“deadbeats” problem), average awards and payments rise as it becomes possible to pursue these

obligors effectively under UIFSA. Alternatively, if the powers of the new interstate enforcement

regime are largely directed at low-income “turnips”—those unable to pay –average awards and

payments for those identified to provide support are predicted to fall.

Mandating that the courts and administrative agencies undertake interstate enforcement,

providing easier and faster methods of collection from uncooperative obligors, and lowering

barriers to using IV-D agencies by interstate claimants should all increase third-party

intermediation in interstate cases. General equilibrium effects of changes to the legal

environment also suggest that intermediation increases with UIFSA implementation. Argys and

Peters (2003) lay out models of cooperative and noncooperative parental bargaining over child

8

support and show that when state enforcement effectiveness increases, obligees negotiating for

support are attracted to the enhanced ‘default’ position of state enforcement under state award

guidelines. UIFSA implementation is predicted to increase intermediation by increasing the

equilibrium level of non-cooperative, state-enforced, interstate agreements. Finally, given the

increase in administrative enforcement and the advantages it offers to obligees around the era of

UIFSA adoption, it is seems plausible that intermediation through IV-D agencies dominates

intermediation by the courts subsequent to UIFSA adoption.

If state IV-D agencies implemented UIFSA as envisioned, obligees should have turned to out-of-

state IV-D agencies (either directly or through the intercession of their own state of residence’s

IV-D agency) for help with claims and enforcement. If information about improved IV-D

assistance was disseminated to interstate obligees, UIFSA implementation might have generated

more help requests in out-of-state cases. Conditional on requesting help, the equal priority of out-

of-state obligees under UIFSA ought to have increased the frequency of successful resolutions to

interstate enforcement problems.

Finally, UIFSA requires arrangements for medical support and introduces a powerful new tool

for its collection. If effective, these policies should lead to more frequently specified

arrangements for medical support in agreements and orders and increase the collection of

medical support conditional on its inclusion in the order for interstate cases.

4. Empirical strategy

Endogenous self-selection of movers with respect to UIFSA’s nationwide implementation may

severely bias simple D-in-D estimates of UIFSA’s effectiveness. A matching approach is an

alternative that solves this problem under specific assumptions.

4.1 The conditional differences-in-differences matching estimator

It is convenient to introduce some simple notation. Designating the average child support

outcome as Y –assumed throughout the discussion to be conditioned on X, a set of observables—

child support outcomes in the pre-UIFSA-implementation era are specified asYN = Y and YM =

9

Y + 𝛾 + η. The subscript M indicates a situation where the parents live in different states, while

N indicates ‘within-state’ status. The parameter 𝛾 is the influence of sample selection on the

expected outcome due to strategic interstate moves.9 In other words, 𝛾 is the influence of the

preponderance of ‘uncooperative’ cases in the subsample on the expected outcome Y. The

parameter η is the unobservable difference between interstate movers and nonmovers that exists

independent of the UIFSA regime.

Designating the post-UIFSA-implementation era with prime superscripts, outcomes in the post-

implementation era are specified as = Y+ τ and

𝛾 . The key parameter

is ’, the change in interstate case outcomes due to the policy change. The parameter τ is a

secular time trend, while the parameter 𝛾 is the post-UIFSA-implementation selection bias in

the sample of movers. The crux of the identification problem with respect to the endogeneity of

moving behavior and UIFSA policy is that 𝛾 and 𝛾 may differ. This poses a problem if

differences between movers and nonmovers are changing over time for reasons other than

UIFSA policy; i.e., might differ from zero.

A within-group matching estimator is implemented by matching interstate cases in the post-

UIFSA-implementation regime with interstate cases in the pre-UIFSA-implementation regime,

using the estimated propensity scores derived from a matching function. This matching function

is estimated from the sample of pre-UIFSA-implementation interstate cases (alternatively, in-

state cases). Let outcomes for the matched sample be indicated . Without further assumptions,

+ τ and

τ. The difference 𝛾 𝛾) –the change in the

selection bias due to strategic moving—is eliminated by the matching method, because the

selection process is fixed at the pre-UIFSA-implementation regime. Taking the difference-in-

difference eliminates the secular time trend, τ. The policy effect of UIFSA is clearly not

identified unless is zero.

The assumption that is zero is readily interpreted within the framework of the matching

literature, which emphasizes how the data at hand relate to the unobserved counterfactuals

(treatment of untreated groups or non-treatment of treated groups). The difference-in-difference

9 To avoid excessive notation, it is assumed that the share of movers is small (as is empirically true), so that

selection bias is an inconsequential issue in the sample of nonmovers.

10

(DID) matching estimator identifies under the assumption that the counterfactual difference

between movers’ and nonmovers’ outcomes if UIFSA had never been implemented is

independent of the UIFSA implementation regime. If strong differential time trends affect the

movers and nonmovers—that is if (η’- η) is not zero—knowledge of UIFSA regime helps predict

if an observation belongs to the mover or nonmover group, violating the assumption

underpinning matching techniques.10

Note that the assumption =0 is no stronger than

those that typically support matching estimators in the literature.

This DID conditional matching estimator differs importantly from the one described in

Heckman, Ichimura, and Todd (1998), which cannot identify θ in this case. Following Heckman

et al.’s (1998) procedure, one would match nonmovers to movers within each (pre-

implementation/post-implementation) period, using a matching function estimated from each

period’s sample of movers. Under the assumption that the counterfactual change in the outcome

in the absence of any treatment is orthogonal to treatment group membership (conditional on X),

this method controls for unobserved differences in movers and nonmovers that are not

attributable to the policy change. However, Heckman et al.’s (1998) method does not control the

movers’ sample composition changes due to UIFSA implementation, because the policy change

is reflected in each period’s matching function. Matching according to Heckman, et al. (1998)

yields, in effect, 𝛾 and

𝛾 and a DID estimator 𝛾 𝛾 . This

DID estimate of the policy effect is contaminated by the UIFSA-driven composition effect.

What this estimator does have in common with other conditional DID estimators is the

advantage, touted by Heckman, et al. (1998), of the feasibility of matching on outcomes, as

opposed to, say, the propensity to make an interstate move. This is due to the fact that the sample

mean of Y is expected to change over time, even conditional on matching, due to random shocks

and trends unrelated to the treatment.

4.2 Implementation of the matching estimator and evidence on match quality

10

Note changing selection of the samples due to UIFSA itself does not violate the assumption; the assumption is

based on the (counterfactual) difference in outcomes in a world without UIFSA implementation.

11

There are many options for deriving the propensity score used to match the subsamples.

Matching on outcomes is appealing because outcomes incorporate the quality of

‘noncooperation’ that drives the endogenous negative selection of interstate moves prior to

UIFSA implementation. In quite a few cases, however, the number of positive observations of

the outcome are sparse, preventing reliable estimation of a propensity score. Throughout this

paper, a variable indicating whether a payment has been made in the past 12 months serves as the

basis for the propensity score. This variable captures the degree of cooperation that potentially

underlies the troublesome sample selection issues associated with UIFSA implementation. In

effect, the matching estimator strives to compare cases that are equally ‘uncooperative’ in the pre

and post-UIFSA implementation eras.

The exact matching technique implemented uses local linear regression method and restriction

over common support. Local linear regression uses a tricube kernel weighting scheme (i.e.,

locally weighted polynomial regression, also known as LOWESS) to assign weights to all

observations based on their similarity according to the matching criterion.

Appendix A presents the raw difference-in-difference calculations for the explanatory variables

used in the estimation. D-in-D statistics that are insignificantly different from zero indicate that

the samples are balanced over the two periods with respect to that factor.11

In general, there is

more evidence of balance as the samples are more tightly restricted on observed outcomes, as

one expects. Table A also presents recalculated DID statistics employing the weights from the

conditional DID matching estimator. There is considerable improvement in balance, although

evidence of imbalance is largely but not entirely eliminated by the matching process for the least

restricted sample. When households with a written agreement are considered, the weighting

scheme is quite successful at rebalancing the sample. For those with a written agreement who are

owed a payment in the past 12 months, reweighting is moderately successful at rebalancing; no

DID statistics are significantly different from zero at the 95 percent confidence level or above.

Finally, when the sample is restricted to those seeking help with child support from a state

11

Under the DID conditional matching strategy, it is innocuous for the mover sample to be imbalanced between

periods, so long as the nonmover sample is imbalanced in a similar fashion. Intuitively, similar imbalance across

treatment and control groups suggests that the imbalance does not spring from UIFSA implementation policy. What

matters is how well the matching process balances trends in the variables.

12

agency, the DID PPM weighting scheme balances the sample quite well. Appendix A provides

further details and discussion.

5. Data source and variables

The SIPP is a nationally representative survey of U.S. households. The sample for this

analysis draws from the 1992, 1993, 1996, 2001, and 2004 panels and spans the calendar years

1993 through 2003. Each panel of households is interviewed every 4 months over an

approximately 2-year period. Respondents with a child under age 21 are eligible for a survey

module covering matters related to child support (the “child support edit”) if that child has a

parent living outside the household.12

This criterion defines the group of households ‘at risk’ for

enforcement services. It is unknown whether the surveyed parent is the primary caregiver of the

child, who is usually the parent most appropriately awarded financial support. SIPP respondents

are asked if a child who “lived with” them had another parent outside the household. Lin,

Schaeffer, Seltzer, and Tuschen (2004) provide evidence that parent-respondents interpret the

term “live with” (as opposed to ‘stay with’) as a permanent arrangement, meaning it is likely that

most SIPP household parents answering the child support questionnaire are the child’s primary

caregiver. The child support topical module is usually administered twice during the course of

each panel, at a one-year interval.

There are four major samples used in the primary analysis, and they are the sample of 22,544

households at risk for child support, a sample of 12,539 at-risk households with a written child-

support agreement, a sample of 10,593 at-risk households with a written child support agreement

who are owed a payment over the prior 12 months and a sample of 6,098 at-risk households with

a written child-support agreement that request help with child support from a state IV-D or

welfare agency. Appendix Table B provides the sample statistics for these variables. Interstate

cases created by the move of an obligee are excluded from all samples. The share of interstate

cases created by the move of a potential obligor ranges from 16 to 22 percent of the samples.

5.1 Information on child support

12

If the other parent’s absence is military or job-related, the household is ineligible for the child support edit.

13

The SIPP child support module is complex and highly detailed. Parents are asked about the

existence of written or verbal agreements for each and every at-risk child. The empirical analysis

often focuses on child support cases with a written agreement, a status that restricts the universe

of many important questions.13

Most information is not available for each and every at-risk child

in a household. Support questions sometimes pertain to either the youngest or oldest child

eligible for the child support edit, but most important questions refer to “any or all” agreements

for any or all of a household’s children in the child-support edit. The dependent variables

reference either any at-risk child in the household or all at-risk children in the household.

The key areas of child support information are award establishment, payments owed and made,

help-seeking for various problems from IV-D agencies, payment intermediation, and health care

arrangements. The key establishment variables are having a written agreement for any child or

having a court-ordered agreement for any child. The major award and payment variables used

are whether a payment was due during the 12 months preceding the interview date, the amount

owed over that period, whether at least one payment was received in the past 12 months, and the

amount received over that period (amounts are cumulated over all children in the family with a

payment due in the past 12 months under a written agreement). I use the ratio of amounts paid

and owed over the past year as a payment yield variable. Arguably, this measures the efficiency

of the child support system in harvesting money that is owed. Argys and Peters (2003) also argue

that controlling for support owed may be important, and this variable implicitly does that.

However, since both support and awards can change, it is also reasonable to evaluate the

“success” of enforcement by effects on these variables alone as well. In addition, there is some

information on the total amount of back-support owed prior to the previous year, cumulated over

an indefinite period, for all households with a payment due in the past 12 months under a written

agreement. Households at-risk for enforcement who do not have a written agreement are

assigned zeros for payments owed and made and for the payment ratio.

Questions about help sought and obtained from a state IV-D agency are put to those who report

they were due a payment in the previous 12 months pursuant to a written agreement for any

child. While specific questions are also asked about the specific areas of enforcement, order,

13

Paternity establishment questions are only asked of potential obligees with verbal agreements and are not

analyzed.

14

locate, modify, paternity establishment, medical support establishment, and other, the universe

restriction is unfortunate in this instance, as those who have a written order and are owed support

have already achieved a large measure of success in child support establishment.

Information is provided on the extent to which payments are intermediated. Respondents in the

child support edit who are due a payment in the past 12 months for any child under a written

agreement are asked about the source of the payment (the entity from which the payment was

received). Permissible responses are directly from the parent, through a court, through a welfare

or child support (IV-D) agency, or no payment received.

In addition to detailed questions about payments owed and made, the SIPP asks about

arrangements for children’s health care. Respondents due a payment in the past 12 months under

a written agreement for any child are asked which parent is to provide health insurance, whether

there is another provision for medical care, or whether health care is not specified in the support

agreement. There is no information on whether these agreements are successfully enforced.

State’s UIFSA implementation dates are matched to the SIPP households by state of residence.

The key policy variable indicates the in-sample UIFSA regime (pre- or post-implementation).

Since most of the child support questions refer to written agreements with a payment due in the

past year, implementation of UIFSA at the survey date is the relevant policy variable. Estimates

using alternative UIFSA policy variables matched to the date of case origination and the most

recent date of modification of the child support agreement differ little and are not discussed.

5.2 Other information from the SIPP

The SIPP collects extensive information on all household members. Three broad categories of

exogenous variables are defined: characteristics of the youngest child covered in the child

support edit, characteristics of the responding parent, and household-level characteristics.

Characteristics of the youngest child covered in the child support edit are a binary variable

indicating sex, binary variables for the age categories 0-5, 6-12, and 13-17, and a binary variable

for race (white or other).14

Characteristics of the responding parent are age (years), a binary

14

Demographic information focuses on the youngest child under the assumption that, in the presence of multiple

children covered by the child support edit, the youngest child is likely to have the most activity on its case.

15

variable indicating sex, a binary variable for whether the responding parent was younger than 18

years of age at the birth of their oldest child covered by the child support edit (i.e., early

motherhood), a binary variable for education (no high school diploma), and a binary variable for

never married. Household characteristics include binary variables indicating the household

parent’s total number of own children at home (defined as one or two children and three or more

children), binary variables for the number of children 0-17 covered in the child support edit

(defined as one, two, three, and more than three), an indicator for the household’s food stamps

receipt, an indicator for household cash welfare receipt, a binary variable indicating the parent is

presently married, and a binary variable indicating the household’s second appearance in a panel.

The SIPP longitudinal framework is short and there is scant information on potential obligor-

parents. Household parents are asked about the location of the absent parent for all children for

whom there is a child support agreement, written or verbal. For children without agreements, the

question is asked about the absent parents of both the youngest and oldest household children in

the child support edit. I combine these two sources of information to construct an indicator of the

absent parent’s location for the entire at-risk sample. If the absent parent of any child in the

household is resides in another state, the household is coded as having an interstate case.

5.3 Empirical evidence on the ‘Black Hole’

Cross-sectional difference regression estimates give a sense of the black hole of interstate child

support prior to UIFSA. The coefficients on the ‘interstate case’ variable, restricting the sample

to households that have not yet been exposed to UIFSA, are presented in Table 1 (the regression

specifications also include all the explanators described in section 5.2). In general, these

estimates are biased because unobserved differences between movers and nonmovers are

uncontrolled. Nevertheless, they may give a rough idea of the magnitude of the problem faced.

They are an appropriate benchmark for the conditional matching DID estimates, because the

conditional DID matching estimate adjusts the post-UIFSA-implementation samples to match the

degree of ‘noncooperation’ of pre-UIFSA-implementation interstate movers.

Panel A covers help requested and reports of help received. The first row indicates that at-risk

households with interstate cases request help at higher rates than other at-risk households.

Although interstate at-risk households report more help received, this is due to their higher

16

incidence of help requests, as the findings in the next row reveal. Conditional on requesting help,

interstate-case households are much less likely to report that the IV-D agency helps them with

their problem. The findings on specific help requested (row 2) indicate that interstate-cases’ help

requests are especially focused on locate services, at a rate double that of other households.15

The

last row of panel A conditions the sample on the type of help requested and presents estimates of

the rates of help received for specific problems. In the case of locate services, interstate-case

households are more likely to report receiving help with their problem. Findings for other

individual help categories are not estimated to be significantly different from zero.

Panel B presents findings on payment intermediation. Interstate households are more likely to

receive payment through an institution (the court or IV-D agency) and less likely to receive

payment directly from the child’s absent parent. The estimated difference in the incidence of

direct-from-parent payments in interstate cases is large, amounting to more than one-quarter of

the sample mean of parent payment incidence for all households.

Panel C presents findings for variables that describe whether there is a written agreement and the

specific features of existing written agreements. The findings in the first row indicate that

interstate-case, at-risk households are less likely to have a written agreement of any kind

(including a non-court-ordered written agreement, which may include ‘voluntary’ agreements).16

Among all at-risk households, total liability for child support over the past calendar year is

somewhat lower for interstate-case households (the point estimate of -$73.26 is around 6 percent

of the sample mean), but the total amount of support owed that has accumulated from preceding

years is considerably higher, with a point estimate of $250.07 that approaches 50 percent of the

sample mean. Conditional on a written agreement, there is evidence of a lower incidence of

voluntary agreements for interstate cases (this is also true when the sample is restricted to those

owed a payment last year; the point estimate is around 16 percent of the sample mean in this

case). There is not a significant difference in support owed over the past calendar year according

to interstate status conditional on a written agreement, but back support identified as owed from

past years is much greater for interstate cases. This is consistent with interstate cases not being

15

The point estimate is a 0.131 differential in locate requests for interstate cases in contrast to a rate of locate

requests among all help-requesting households of 0.149. 16

These variables are coded to zero for households with no written agreement.

17

enforced over longer periods than other cases, as this liability has time to build. The estimated

differences for the samples of those with a written agreement and those owed a payment amount

to two-thirds or more of the mean back support liability estimated for the entire sample. The last

three columns of Panel C indicate health care arrangements. Interstate-case child support

agreements are significantly less likely to specify that the obligor is responsible for the child’s

health care expenses (the ‘interstate gap’ relative to the mean for the entire sample is almost 15

percent), and more likely to specify that the obligee is responsible or that an unspecified

arrangement has been made.

Panel D indicates the payment gaps between in-state and interstate cases. Households with

interstate cases are much less likely to report any payment was received in the past year (the

point estimate is over 20 percent of the sample mean in absolute magnitude), although the gap in

payments amounts over the past year conditional on having a written agreement or being owed a

payment is not significantly different from zero. This suggests that the interstate payment gap of

$111.48 reported for the entire at-risk sample (approximately 13 percent of the sample mean) is

largely driven by the absence of written agreements for interstate cases, rather than nonpayment

or underpayment on established agreements. However, if payments are conditioned on award

amounts, there is evidence of underpayment in interstate cases, even for those with an award.

The payment ‘yield’ (i.e., the ratio of payments made to payments owed during the last year) is

significantly lower for interstate cases across all samples. While this is strong evidence of for

ineffective collection in interstate cases, the magnitude of the interstate gap in the payment ratio

is modest, at about 10 percent of the sample mean.

6. Major Findings

This section presents and contrasts the conditional DID matching model estimates with raw DID

and DID estimates from simple linear regressions.17

The findings are organized into four

domains; (1) help sought and received from state IV-D agencies; (2) payment intermediation; (3)

agreements on payments and health care; and (4) cash support received.

17

Bertrand, Duflo, and Mullainathan (2004) criticize D-in-D regressions in longer repeated cross sections and

demonstrate that estimated policy effects have an upward bias with downward-biased estimated standard errors.

Since the analysis is not focused on DID regression estimates, I choose to present ‘naïve’ versions.

18

6.1 Help from states’ IV-D agencies

Table 2 provides estimates of the impact of UIFSA implementation on help asked for and help

received from state IV-D agencies. There is no evidence that UIFSA implementation increases

the incidence of help-seeking by those with interstate cases (panel A). For particular types of

help (panel B), there is some weak evidence of increased requests for help with paternity

establishment in interstate cases, and none of the estimates, including the conditional DID

matching estimates, for specific help requests are estimated to be significantly different from

zero. In sharp contrast, UIFSA implementation raises the probability that help is received by

requestors in interstate cases. The point estimates of 0.086 is large enough to close the prior

interstate gap in receipt of help conditional on making a request.18

6.2 Payment intermediation

The increasing use of IV-D services for interstate cases is hypothesized to increase

intermediation of interstate payments and the findings in Table 3 indicate that this is clearly the

case. The increase in interstate cases receiving payment through the IV-D system relative to in-

state cases is highly significant across all estimators. The preferred estimate of a 7.5 percentage-

point increase is an increase of 25% above the mean of the data. There is a corresponding 8.5

percentage-point decline in payments received directly from parents (a one-third decline relative

to the mean) and no difference in the pattern of payments made through the courts. In

comparison with estimates of the interstate gap in intermediation, findings indicate that UIFSA

implementation greatly and further increases the prior disproportionate payment intermediation

of interstate cases by institutions, by increasing IV-D agency intermediation.

6.3 Awards and agreements for financial support and health care

The preferred estimate (Table 4) indicates that UIFSA implementation raises the share of

interstate cases with a written agreement a modest 5 percent above the sample mean. This closes

about half of the interstate gap in agreements for all at-risk households that exists prior to UIFSA

18

The improved success of interstate cases in receiving help from IV-D is likely driven by improved locate services.

A linear DID regression indicates a 22 percentage-point increase in help received with locate for interstate cases,

conditional on requesting this service (not reported in a table; estimates for other types of help received are all

insignificant). Unfortunately, the DID matching estimates cannot be obtained for these very small samples.

19

implementation. There is no estimated impact on non-court-ordered (‘voluntary’) agreements,

consistent with the hypothesis that UIFSA’s impact works largely through improving access to

IV-D services. This finding also suggests that UIFSA implementation does not change the

bargaining stance of parents in interstate cases (i.e., voluntary agreements do not decline).

UIFSA implementation has little impact on support amounts owed over the past year (rows 3 and

4 of panel A). These estimates of the amount owed last year are negative in sign, suggesting that

UIFSA may have encouraged the pursuit of smaller-than-average claims by lowering transaction

costs, in the case of samples conditioned on a written agreement, but all are imprecisely

estimated. However, UIFSA implementation has a very large positive effect on the total amount

of pending ‘back’ support identified in interstate cases. For example, the preferred estimate of

$279.69 for the at-risk household sample is 53 percent of the sample mean. The findings are

quite consistent across all households, those with a written agreement, and those owed a

payment. In all cases, the preferred estimate suggests that UIFSA implementation roughly

doubles the existing interstate gap in back support liability.

Table 4 also provides evidence on health care arrangements (see Panel B). There is little

evidence that UIFSA implementation affects medical support arrangements for children in

interstate cases. The magnitudes suggest a possible shift from obligee responsibility to the ‘other’

category but coefficient estimates are not estimated to differ significantly from zero.

6.4 Payment Patterns

Table 5 provides the estimates of the effects of UIFSA on actual child support payments made

and total amounts collected in the past year. There is consistent evidence that UIFSA increases

the probability that any payment is received in the sample of all at-risk households. The

underlying mechanism for this appears to be the increase in written agreements under UISFA,

since there is no increase in the probability of receiving a payment, conditional on having a

written agreement, or conditional on being owed a payment under a written agreement. There is

marginal evidence of increases in the amount of payments received over the past year in

interstate cases under UIFSA in some samples. There is consistent evidence that the 12-month

payment ‘yield’ (the ratio of payments made to payments owed over the past 12 months) on

20

interstate cases for the group of at-risk households rises by about 4 percentage points (about 10

percent of the sample mean) under UIFSA. Coupled with the estimates from the restricted

subsamples, the evidence is consistent with a hypothesis that UIFSA implementation increases

payments by establishing ‘new’ interstate support claims, which obligees then pay on, and not by

increasing payments when support orders are ignored. Recall the evidence from table 3 that the

average amount identified as owed is constant or even declines somewhat (although the point

estimates are insignificant at standard confidence levels). The small estimated preferred increase

in payments of about 10 percent is enough to increase the overall yield of payments on interstate

support owed under UIFSA.

Many of the point estimates in Table 5 are insignificant and because of their imprecision, it is

difficult to reject the hypothesis that UIFSA implementation closes the interstate payment gap in

many cases. The (significant) point estimate for ‘any payment’ amounts to half of the magnitude

of the estimated interstate payment gap in (see Table 1), while the hypotheses that UIFSA

implementation closes the interstate gaps in the amount paid in the past year and the payment

yield for all at-risk households cannot be rejected at standard confidence levels. When restricting

consideration to households with written agreements (whether owing a payment or not), UIFSA

implementation arguably falls short of closing the interstate gap in the incidence of payments

made. Again, this is consistent with a story of UIFSA implementation having most of its effect

through the increased establishment of orders, rather than through more vigorous enforcement of

established orders. While one cannot reject the hypotheses that UIFSA implementation closes

existing interstate payment gaps as measured by any payment made and the payment yield, the

insignificant conditional DID point estimates preclude a definitive answer. The robustness

analysis, presented next, provides more conclusive evidence on this point.

7. Robustness of the Findings

This section presents findings from important changes to the specifications and samples made in

order to explore the robustness of the main findings and also presents an examination of the

heterogeneity of findings for important sub-populations. Issues addressed are the treatment of

duplicate observations, the concentration of child support cases in large states, the treatment of

likely (as opposed to actual) interstate movers, and the treatment of transitional years in which

21

some but not all states adopt UIFSA. In order to place the findings in the context of the wider

child support literature, I also discuss findings for the subpopulations of nonwhites, welfare

recipients, and the never married. A table summarizing the original and alternative findings is

provided as Appendix C.

7.1 Duplicate observations

To this point, the estimation samples include multiple appearances of the same household.19

To

investigate the sensitivity of the findings to their inclusion, I drop all second observations of a

child support case from the estimation.20

The major findings are overall quite robust with respect

to this change (see the second column of Appendix Table C). Some key findings for at-risk

households—the probability of a written agreement, the amount of support received in the past

year, and the amount of back support identified for those owed a payment—grow larger in

magnitude with this modification and are precisely estimated. An exception is that reports of

help received by help seekers is no longer estimated to be significantly different from zero.

7.2 Concentration of child support cases in large states

One third of the sample households reside in California, Texas, New York, Florida, or Illinois. It

is possible that states with very large child support caseloads differ in characteristics, practices

and policies. If so, the findings may be dominated by the actions of larger states and may not be

broadly applicable across the country.

The findings are in fact very robust to removing households in these states from the samples, and

some key findings grow sharper. For example, the effect of UIFSA on the probability that an at-

risk household with an interstate case has a written agreement rises from 0.029 to 0.051,

effectively closing the interstate gap, and the estimated effect on the payment yield for all

households rises from 0.043 to 0.070 (eliminating the interstate gap). While the effect on the

total amount of support owed for interstate cases under UIFSA is smaller in this subsample

(about $80 below the prior estimate), this is likely due to cost-of-living differences among states.

19

In principle, including duplicates could be advantageous, as the matching technique increases the likelihood of a

household in the pre-implementation period being matched with itself in the post period. Unfortunately, very few

observations straddle a UIFSA implementation in their state, so even this limited longitudinal aspect does not

contribute much to the estimation. 20

Two-thirds of the 2,144 affected observations are from 1999 and 2003, the other from 1994 and 1995.

22

If anything, the evidence suggests that UIFSA implementation may have been more effective in

lower-population states.

7.3 Treatment of likely interstate movers

The difference strategies presented so far rely on the implicit assumption that relatively few

households are prone to experience an obligor’s interstate move. However, it is possible that

there are households in the nonmovers’ sample who are marginal to making an interstate move,

and whose behavior and outcomes might be affected by UIFSA. I explore whether this is a

potential problem by estimating a moving propensity based on the pre-UIFSA implementation

distribution of interstate cases created by an obligor’s move and dropping households with

propensity scores in the top quintile from the group of ‘nonmovers’ prior to estimating the

conditional DID matching model.

Conditional DID matching estimates, presented in the fourth column of the table, are quite robust

with respect to this alternative estimation strategy. UIFSA’s effect on the probability of an at-risk

household with an interstate case receiving a payment in the past year drops from an estimate of

0.043 to 0.031 and is significant. The effect on the probability that a help-requesting household

receives help from IV-D also drops somewhat (from 0.086 to 0.076) but remains significant.

7.4 Transitional years of UIFSA policy

In the early years of UIFSA adoption, the effect of the policy is likely weak, because UIFSA’s

initiatives are only in full force if both parents reside in states that have adopted. The SIPP does

not reveal where the absent parent lives, so cases cannot be classified according to specific state-

pair UIFSA adoption policies. As an alternative, ‘pre’ and ‘post’ UIFSA observations are

identified more cleanly by dropping the transition years 1995-1997 from the analysis, which is

hypothesized to generate sharper estimated effects of UIFSA implementation.

Overall, the findings are stable with respect to this change, with some more pronounced

estimated effects of UIFSA, as hypothesized. For example, the effect of UIFSA implementation

on the payment yield for at-risk households jumps from 0.043 to 0.067 (large enough to offset

the interstate gap prior to implementation), and the payment yield conditional on being owed a

payment becomes significant and very large (at 0.108), providing some evidence that UIFSA

23

increases payments by improving collections conditional on orders (and not entirely through

establishing new orders, as the main findings suggest). The estimated effects on payment

intermediation are also larger for the ‘sharper’ sample; the coefficient associated with IV-D

intermediation rises 0.075 to 0.097.

7.5 Heterogeneity of key findings by race, welfare receipt, and marital history

The child support literature frequently finds varying levels of child support enforcement

effectiveness according to race, welfare receipt, and marital history. White parents typically have

better child support outcomes than African-Americans (Graham and Beller, 1996). Welfare

recipients often face disincentives to cooperate with child support enforcement because of

benefit offset policies, particularly prior to welfare reform, and this may explains their inferior

outcomes (Cancian, Meyer, and Caspar, 2008; Roff, 2010). Finally, when cases originate with

nonmarital births, the enforcement process can be particularly difficult (Graham and Beller,

1996; Freeman and Waldfogel, 2001).

While SIPP samples of nonwhite households are fairly small, there is convincing evidence that

the effects of UIFSA implementation are far different for this group. Most notably perhaps,

nonwhites with interstate cases are not estimated to experience any improvement in help

received from IV-D, conditional on a help request, subsequent to UIFSA implementation. Also

in stark contrast to the unrestricted sample, while UIFSA implementation has an extremely large

impact on payment intermediation for nonwhites (the absolute magnitude of the decline is more

than double the original estimate), the decline in payments from parents is offset by increased

payments from courts (for whites this decline is entirely accounted for by a shift into IV-D).

These two pieces of evidence suggest that UIFSA implementation does not improve

administrative access for nonwhites with interstate cases.

There is no evidence that UIFSA implementation improved agreements or payments for

interstate cases in the nonwhite sample. In fact, for nonwhites with interstate cases, less support

is identified and collected after UIFSA implementation.

Few estimates are significant for the never-married group and it was infeasible to estimate the

DID conditional matching model for many dependent variables. In contrast with prior findings,

24

there is a reduction in help requests from IV-D for those with interstate cases subsequent to

UIFSA implementation.

In contrast with the findings for nonwhites, estimates for welfare-receiving households suggest

that UIFSA implementation is very effective at improving agreements, support identified, and

payments for interstate cases. First, note that the estimated effect of UIFSA on help received

with a problem from IV-D rises to 0.133 (from 0.086) for welfare households, and while

intermediation estimates are imprecise for this smaller sample, they are at least as large in

magnitude as the initial estimates and follow a pattern of substitution of IV-D intermediation for

parent contact. For at-risk welfare recipients with interstate cases, UIFSA’s impact on

establishment of agreements for welfare cases (0.089) is nearly three times as large as the

estimate for the entire sample. There are large increases in the amount of support owed over the

past year ($237.41), the probability of receiving any payment (0.093), and in the amount actually

received in the past year ($213.62). While the payment yield is unchanged, it appears that UIFSA

has a large beneficial impact on both total support identified and collections for at-risk, welfare-

receiving households. For this group, there are huge effects of UISFA on payments, conditional

on being owed a payment. The estimated effect on any payment being made for this group is

0.213, while the estimated payment increase exceeds $500.

Finally, estimates for health care agreements are occasionally significant. In several cases 9when

duplicates are omitted, when large states are omitted, and for nonwhites), UIFSA implementation

significantly increase the share of households with “other” arrangements for health care.

8. Conclusion

8.1 Summary

This paper has analyzed the impact of a sweeping reform to the legal and administrative

treatment of interstate child support cases on help sought and received from state child support

(IV-D) agencies, payment intermediation, agreements on payments and health care, and

payments received. Conditional DID matching estimates adjusted for the influence of UIFSA

25

implementation on incentives to move out of state and were shown to substantially improve the

balance of the analysis samples with respect to the exogenous variables.

The findings from the unrestricted sample indicated no help-seeking response to UIFSA, but a

much higher rate of satisfaction with help from IV-D conditional on requesting help. For those

owed support, payment mode shifted strongly away from parents to IV-D agencies. UIFSA was

estimated to raise the incidence of written agreements, the amount of back support identified but

there was little evidence of an impact on health care arrangements. UIFSA was estimated to

increase payments received in the past year and the ratio of payments to support owed among all

at-risk households with interstate cases. In the general sample, UIFSA appeared to increase

support received in large part by addressing a deficit in award establishment for interstate cases.

Most findings were robust with respect to sample modifications that addressed the treatment of

duplicates, the dominance of large states, and omission of likely interstate movers from the

control group. In fact, these sample modifications frequently resulted in estimates suggesting that

UIFSA implementation successfully closed the black hole of interstate enforcement.

There was evidence of heterogeneity in the responses of various groups to UIFSA

implementation. There was a strong effect of UIFSA on payment intermediation on nonwhites,

but in contrast with all other groups, the shift was from parent-to-parent to court (not IV-D)

intermediation. Nonwhites who requested help from the IV-D agency showed no improvement in

help received subsequent to UIFSA implementation. Nonwhites with interstate cases also

showed no improvement in the amount of back support identified, payments received, and

payment yield subsequent to UIFSA. Like nonwhites, those who never married showed no

improvement in the amount of back support identified, payments received, and payment yield

(many estimates could not be obtained for this group due to sample size issues).

In contrast, welfare recipients with interstate cases appear to have experienced the greatest

benefits of UIFSA implementation, with huge increases in the incidence of written agreements,

the amount of back support identified, payments made, and payment yield.

8.2 Discussion

26

The overall pattern of findings suggests an explanation of UIFSA’s success. UIFSA ‘leveled’

access of interstate obligees to IV-D services—services that had become quite effective by the

welfare reform era due to enhanced administrative enforcement powers. It appears that the

greatest area of effectiveness was awards establishment. Significantly more support orders were

established for at-risk interstate cases after UIFSA implementation, and these orders were then

paid on, very likely on an involuntarily basis.

Although the findings are consistent with administrative reforms and initiatives ‘causing’

improved outcomes, caution is needed because award and payment outcomes cannot be tied

directly to institutional changes using the data. It is possible to affect intermediation without

affecting ‘real’ enforcement (i.e., actual payments) if reforms mostly succeed in formalizing

previously voluntary agreements. Since payment information is only available on a consistent

basis for cases with a written agreement, it is not possible to estimate the extent to which the

increased payments received by the at-risk sample with interstate cases after UIFSA

implementation merely substitute for voluntary payments. However, the reputation of the pre-

UIFSA interstate child support arena as a black hole, coupled with research on the negative

relationship of support and geographic distance of the obligor from the children (e.g., Seltzer,

1991), suggests that crowd-out of voluntary payments is minimal in this case. The fact that help-

seekers report improved help with problems under UIFSA provides further independent evidence

that the activities of IV-D agencies go beyond formalization to actual enforcement.

That nonwhites with interstate cases failed to experience the improved administrative

enforcement effects of UIFSA merits further research. The findings for both help received from

IV-D and IV-D agency intermediation suggest the failure stems from lack of contact with IV-D.

While there is a strong trend of declining welfare participation and hence IV-D contact (the

sample proportion of nonwhites receiving cash welfare plummets from almost half in 1993 to

just 20 percent by 2005), this trend is similar for households with inter- and in-state cases.

Greater difficulty enforcing interstate cases may mean that the loss of contact with IV-D was

more damaging to the interstate group.

Enforcement for the subsample of welfare recipients with interstate cases was dramatically

improved by UIFSA. The major changes to welfare in this era (increases in the child support

27

pass through and increased negative selection of recipients as reform proceeds) are not expected

to necessarily have a differential impact on individuals with interstate situations on welfare. As

with nonwhites, a reasonable hypothesis consistent with this strong evidence of the increasing

value of IV-D contact over this era is that IV-D contact has greater value for those with interstate

cases, post-UIFSA. It is also the case that interstate cases may require, on average, more

resources to pursue, in which case welfare recipients with interstate cases in an era of rapidly

declining caseloads might benefit from receiving more intense services.

8.3 Conclusions and Directions for Further Research

The findings suggest that UIFSA was very successful at leveling the playing field for interstate

and in-state child support enforcement, as intended. Its success was manifested in increased

awards stablishment in interstate cases. Once awards were established, administrative

enforcement (e.g., wage garnishment) was evidently highly effective at collecting payments.

There is less evidence that UIFSA was successful at addressing underpayment on existing

awards. Why this is the case is an interesting topic for further research and likely requires a more

detailed understanding of the child support enforcement process at IV-D offices. The findings for

subgroups also indicate that direct contact with IV-D agencies was a very important determinant

of UIFSA’s success. Understanding how welfare reform affected access to IV-D services as well

as the quality of services received is also an important area for future research.

Larger policy questions related to this research merit further study. UIFSA may have encouraged

interstate mobility of obligees and discouraged inefficient cross-state moves of obligors. The

welfare improvement from increased mobility due to UIFSA to obligees, especially, could

plausibly be quite large. Second, UIFSA and other initiatives brought a new focus on health care

arrangements to child support enforcement policy, and the importance of health insurance and

health care coverage for children have only grown in the intervening years. The effectiveness of

the various initiatives to improve health care coverage of children at risk for child support,

including tradeoffs with Medicaid and SCHIP coverage, would be an interesting area for further

research. Finally, UIFSA is also part of an important trend of ever-more-coercive child support

policy. More research is needed on the longer-run effects of such a strategy on child

development, family relationships, work effort, and job mobility.

28

Appendix A

Table A presents DID statistics computed for the entire sample of at-risk households used in the

regressions in the first two columns. In this case, there are significant differences between

movers and nonmovers in the interperiod changes in the sex of the youngest child, the incidence

of very young children at home, low education of the obligee, and the share of cash-welfare-

receiving households. In the case of the sample restricted to households with a written

agreement, interperiod changes in the sex of the youngest child, the presence of a very large

number of children at-risk for child support, and the share of cash-welfare-receiving households

for interstate movers are all relatively larger than the corresponding changes in the nonmovers’

sample, while the interperiod changes in the obligee’s age and the appearance of the household

for a second time in the survey for interstate movers are relatively smaller than the corresponding

changes in the nonmovers’ sample. When the sample is restricted further to those who are also

owed a payment in the past 12 months, interperiod changes in the sex of the youngest child, the

presence of a large number of children at-risk for child support, and the share of cash-welfare-

receiving households are relatively larger for interstate movers than the corresponding changes in

the nonmovers’ sample. Balance is much improved by restricting the sample to households

seeking help from a IV-D agency. The interperiod change in the sex of the youngest child is

relatively larger than the corresponding change in the nonmovers’ sample, while the DID statistic

for very young children is only marginally significantly different from zero.

Table A also presents the DID statistics for the explanatory variables when the calculations are

re-weighted with the weights generated by the conditional DID matching estimator. The

reweighted unrestricted sample is balanced with respect to the presence of young children, cash

welfare participation and obligee education (although the latter two are only marginally

imbalanced in the first place), although the significant DID statistic associated with the sex of the

youngest ‘at-risk’ child persists . There is a marginally significant statistic associated with never

married status of the obligee. While there is improvement, not all evidence of imbalance is

eliminated.

When only those with a written agreement are considered, the weighting scheme is quite

successful at rebalancing the sample, although weighting introduces a marginally significant DID

29

statistic for the ‘married’ variable. For those owed a payment in the past 12 months under a

written agreement, reweighting is moderately successful at rebalancing; there are no DID

statistics significantly different from zero at the 95 percent confidence level or above. Finally,

when the sample is restricted to those seeking help with child support from a state agency, the

DID PPM weighting scheme balances the sample quite well on the exogenous explanators.

Appendix Table A: Difference-in-difference statistics for explanators

At-risk

N=22,544

With a written

agreement

N= 12,539

Owed payment in the

past 12 months

N= 10,593

Sought help from a IV-

D agency

N=6,098

Un-

weighted

Weighted

Un-

weighted

Weighted

Un-

weighted

Weighted

Un-

weighted

Weighted

Youngest

child in edit

is male

0.048***

(0.018)

0.071**

(0.029)

0.056**

(0.027)

0.047

(0.045)

0.058**

(0.029)

0.049

(0.044)

0.073**

(0.034)

0.043

(0.045)

Youngest

child in edit

is white

0.012

(0.018)

.012

(0.019)

0.008

(0.023)

0.033

(0.028)

0.011

(0.026)

0.033

(0.039)

0.013

(0.030

0.037

(0.049)

2+ at-risk

children

-0.031

(0.021)

-.008

(0.028)

-0.011

(0.030)

-0.012

(0.037)

-0.027

(0.028)

-0.015

(0.038)

-0.039

(0.034

-0.076*

(0.046)

3+ at-risk

children

-0.006

(0.015)

-.020

(0.017)

0.020

(0.020)

0.005

(0.025)

0.019

(0.022)

0.011

(0.031)

-0.002

(0 .028

-0.015

(0.030)

4+ at-risk

children

0.000

(0.007)

-.004

(0.008)

0.020**

(0.009)

0.0193

(0.014)

0.023**

(0.010)

0.022*

(0.012)

0.005

(0.016)

0.015

(0.017)

Any

children 0-5

0.036***

(0.012)

0.012

(0.020)

0.022

(0.017)

0.007

(0.025)

0.023

(0.018)

0.008

(0.032)

0.046*

(0.026)

0.027

(0.038)

Any

children 5-

12

-0.011

(0.017)

-.017

(0.025)

-0.011

(0.030)

-0.007

(0.038)

-0.002

(0.029)

0.005

(0.036)

-0.022

(0.031)

0.002

(0.039)

Lacks high

school

diploma

0.025*

(0.015)

.018

(0.020)

0.025

(0.020)

0.024

(0.024)

0.026

0.022

0.021

(0.028)

-0.005

(0.030)

-0.033

(0.033)

Age -0.534

(0.332)

-.029

(0.474)

-0.622*

(0.374)

-0.241

(0.473

-0.512

(0.452)

0.273

(0.652)

-0.216

(0.539)

-0.189

0.706

Young

mother

0.004

(0.011)

-0.009

(0.014)

0.001

(0.013)

-0.000

(0.019)

-0.005

(0.015)

-0.006

(0.023)

-0.026

(0.019)

-0.043

(0.027)

1 child 0.004

(0.012)

0.002

(0.015)

-0.005

(0.014)

-0.016

(0.020)

0.013

(0.012)

0.018

(0.018)

0.005

(0.012)

0.005

(0.015)

2 children -0.014

(0.017)

0.000

(0.020)

-0.024

(0.032)

-0.014

(0.036)

-0.033

(0 .031)

-0.051

(0.038)

-0.020

(0.033)

0.014

(0.039)

30

Appendix Table A (continued): Difference-in-difference statistics for explanators

At-risk

N=22,544

With a written

agreement

N= 12,539

Owed payment in the

past 12 months

N= 10,593

Sought help from a IV-

D agency

N=6,098

Un-

weighted

Weighted

Un-

weighted

Weighted

Un-

weighted

Weighted

Un-

weighted

Weighted

Sex -0.010

(0.012)

-0.001

(0.017)

0.012

(0.014)

0.002

(0.027)

0.002

(0.018)

0.002

(0.022)

0.008

(0.015)

0.004

(0.019)

Never

married

0.010

(0.018)

0.035*

(0.019)

-0.001

(0.016)

0.003

(0.024)

.008

(0.019)

-0.001

(0.0315

-0.028

(0.030)

-0.032

(0.032)

Currently

married

-0.004

(0.020)

-0.019

(0.030)

-0.021

(0.025)

-0.066*

(0.037)

-0.022

(0.029

-0.037

(0.045)

0.014

(0.033)

-0.002

(0.033)

Receives

cash

welfare

0.028*

(0.016)

-0.008

(0.021)

0.049**

(0.023)

0.035

(0.029)

0.054**

(0.022)

0.063*

(0.035)

0.016

(0.040)

0.013

(0.036)

Receives

Food

Stamps

0.012

(0.016)

0.005

(0.023)

0.025

(0.021)

0.020

(0.025)

0.023

(0.026)

0.037

(0.034)

-0.007

(0.028)

0.004

(0.047)

Repeats in

panel

-0.007

(0.020)

-0.040

(0.025)

-0.053**

(0.025)

-0.052

(0.037)

-0.031

(0.029)

-0.024

(0.036)

-0.024

(0.027)

-0.026

(0.043)

Notes: Each cell contains the difference-in-difference statistic for the variable with bootstrapped standard error in

parentheses beneath.

*/**/*** indicates significantly different from zero at a confidence level exceeding 90%/95%/99%.

31

Appendix B: Table of sample statistics accompanying estimates

At-risk

N=22,544

With a

written

agreement

N=12,539

Owed

payment,

past 12

months

N=10,593

Sought help

from a IV-D

agency

N=6,098

Dependent variables

Requested help from IV-D (0/1) 0.270

(0.444)

0.356

(0.479)

0.369

(0.483)

1.000

(0.000)

Received help from IV-D (0/1) 0.140

(0.347)

0.208

(0.406)

0.218

(0.413)

0.516

(0.500)

Written child support agreement (0/1) 0.556

(0.497)

1

0

1.00

(0.00)

0.732

(0.443)

Written child support agreement that is not court-ordered

(0/1)

0.134

(0.340)

0.240

(0 .427)

0.233

(0.423)

0.089

(0.285)

Request help with enforcement (0/1) 0.145

(0.353)

0.225

(0.418)

0.237

(0.425)

0.538

(0.499)

Request help establishing order (0/1) 0.107

(0.310)

0.116

(0.321)

0.118

(0.322)

0.397

(0.489)

Request help with locate (0/1) 0.040

(0.197)

0.041

(0.199)

0.043

(0.203)

0.149

(0.356)

Request help with modifying order (0/1) 0.016

(0.126)

0.025

(0.157)

0.026

(0.159)

0.060

(0.237)

Request help with medical support (0/1) 0.016

(0124)

0.0183

(0.134)

0.019

(0.136)

0.058

(0.233)

Request help with other (0/1) 0.015

(0.123)

0.019

(0.138)

0.019

(0.138)

0.057

(0.231)

Request help with paternity establishment (0/1) 0.013

(0.113)

0.011

(0.105)

0.012

(0.107)

0.048

(0.213)

Obligor responsible for health care (0/1) 0.218

(0.413)

0.392

(0.488)

0.412

(0.492)

0.276

(0.447)

Obligee responsible for health care (0/1) 0.118

(0.322)

0.212

(0.409)

0.212

(0.409)

0.126

(0.332)

Order does not specify health care responsibilities (0/1) 0.157

(0.364)

0.282

(0.450)

0.290

(0.454)

0.243

(0.429)

Other arrangements have been made for health care (0/1) 0.041

(0.198)

0.074

(0.261)

0.076

(0.265)

0.057

(0.232)

Amount of child support owed over previous 12 months

($82-84=100)

1149.57

(1825.10)

2066.82

(2023.13)

2340.99

(2005.70)

1404.24

(1685.74)

Any payment made over previous 12 months (0/1) 0.391

(0.488)

0.703

(0.457)

0.797

(0.403)

0.494

(0.500)

32

Appendix B (continued): Table of sample statistics accompanying estimates

At-risk

N=22,544

With a

written

agreement

N=12,539

Owed

payment,

past 12

months

N=10,593

Sought help

from a IV-D

agency

N=6,098

Amount of child support paid over previous 12 months

($82-84=100)

854.38

(1716.86)

1536.10

(2062.14)

1738.30

(2116.76)

836.40

(1521.65)

Amount of child support owed over prior 12 months

($82-84=100)

1149.57

(1825.10)

2066.82

(2023.13)

2340.99

(2005.70)

1404.24

(1685.74)

Ratio of child support paid to child support owed over

previous 12 months

0.351

(0.620)

0.634

(0.718)

0.719

(0.720)

0.594

(0.742)

Total amount of back support owed (excluding previous

12 months) ($82-84=100)

527.53

(2725.34)

941.19

(3586.48)

1025.00

(3729.95)

1023.19

(3849.50)

Payment received directly from parent (0/1) 0.153

(0.360)

0.275

(0.447)

0.306

(0.461)

0.100

(0.300)

Payment received directly from court (0/1) 0.196

(0.397)

0.352

(0.478)

0.402

(0.490)

0.295

(0.456)

Payment received directly from IV-D (0/1) 0.125

(0.331)

0.225

(0.418)

0.257

(0.437)

0.256

(0.437)

Interstate case, post-UIFSA implementation (0/1) 0.135

(0.341)

0.124

(0.329)

0.125

(0.331)

0.155

(0.362)

Post-UIFSA implementation (0/1) 0.744

(0.437)

0.748

(0.434)

0.758

(0.429)

0.710

(0.454)

Obligor’s move created interstate case (0/1) 0.181

(0.385)

0.163

(0.370)

0.162

(0.368)

0.218

(0.413)

Explanators

Youngest at-risk child is male (0/1) 0.512

(0.500)

0.504

(0.500)

0.504

(0.500)

0.509

(0.500)

Youngest at-risk child is white (0/1) 0.719

(0.450)

0.780

(0.414)

0.778

(0.416)

0.689

(0.463)

Two or more ‘at-risk’ children (0/1) 0.453

(0.498)

0.493

(0.500)

0.512

(0.500)

0.529

(0.499)

Three or more ‘at-risk’ children (0/1) 0.146

(0.353)

0.153

(0.360)

0.160

(0.367)

0.196

(0.397)

Four or more ‘at-risk’ children (0/1) 0.040

(0.197)

0.040

(0.197)

0.041

(0.199)

0.060

(0.237)

Any child 0-5 (0/1) 0.169

(0.375)

0.131

(0.338)

0.132

(0.339)

0.186

(0.389)

Any child 5-12 (0/1) 0.334

(0.472)

0.357

(0.479)

0.371

(0.483)

0.380

(0.485)

33

Appendix B (continued): Table of sample statistics accompanying estimates

At-risk

N=22,544

With a

written

agreement

N=12,539

Owed

payment,

past 12

months

N=10,593

Sought help

from a IV-D

agency

N=6,098

Obligee does not have high school diploma (0/1) 0.164

(0.370)

0.132

(0.338)

0.133

(0.339)

0.179

(0.383)

Obligee age (years) 36.5

(8.78)

36.72

8.08

36.35

(7.92)

34.453

(8.151)

Obligee under 18 at first birth (0/1) 0.088

(0.283)

0.079

(0.270)

0.082

(0.274)

0.115

(0.319)

One child present (0/1) 0.083

(0.276)

0.073

(0.260)

0.054

(0.225)

0.053

(0.225)

Two children present (0/1) 0.682

(0.466)

0.693

(0.461)

0.704

(0.457)

0.660

(0.474)

Three children present (0/1) 0.235

(0.424)

0.234

(0.400)

0.243

(0.429)

0.286

(0.452)

Obligee is male (0/1) 0.158

(0.365)

0.100

(0.300)

0.089

(0.284)

0.064

(0.245)

Obligee never married (0/1) 0.236

(0.424)

0.173

(0.378)

0.176

(0.381)

0.286

(0.452)

Obligee currently married (0/1) 0.312

(0.463)

0.331

(0.471)

0.331

(0.471)

0.287

(0.453)

Household receives cash welfare (0/1) 0.164

(0.371)

0.135

(0.342)

0.135

(0.342)

0.237

(0.425)

Household receives Food Stamps (0/1) 0.218

(0.413)

0.195

(0.396)

0.197

(0.398)

0.325

(0.469)

Second appearance of household in panel (0/1) 0.398

(0.489)

0.407

(0.491)

0.384

(0.486)

0.397

(0.489)

Appendix C: Estimated effects of UIFSA implementation on interstate cases for alternative subsamples Panel A: At-risk households

Original

sample

Drop

duplicates

Lower-

population states

Delete likely

movers

Delete

transition years

Nonwhite

Receives

welfare

Never

married

N=22,544 N=14,880 N=15,508 N=18,854 N=17,471 N=5,975 N=5,886 N=5,282

Requested help -0.006

(0.015)

0.022

(0.018)

0.018

(0.017)

-0.009

(0.014)

-0.012

(0.018)

-0.015

(0.041)

-0.008

(0.034)

-0.079*

(0.043)

Written agreement 0.029**

(0.011)

0.054***

(0 .020)

0.051**

(0.023)

0.026**

(0.013)

0.027

(0.019)

-0.022

(0.029)

0.089***

(0.028)

0.016

(0.050)

Written, not court-

ordered

0.002

(0.009)

0.009

(0.014)

0.009

(0.012)

0.003

(0.010)

0.004

(0.012)

-0.016

(0.025)

0.012

(0.016)

-0.036

(0.028)

Amount owed in past

year

-16.65

(85.90)

132.75*

(72.16)

53.23

(76.81)

21.74

(64.04)

-54.22

(89.23)

-299.65**

(139.14)

237.41***

(77.54)

-57.26

(120.28)

Total amount of back

support owed

279.69***

(78.00)

281.07***

(81.31)

196.65***

(63.84)

235.60***

(53.35)

251.74***

(55.68)

239.75*

(145.49)

37.22

(110.34)

170.23

(104.64)

Any payment made 0.043**

(0.019)

0.055***

(0.015)

0.063***

(0.018)

0.031**

(0.014)

0.037*

(0.019)

-0.026

(0.034)

0.093***

(0.025)

-0.014

(0.040)

Amount paid in past

year

125.81*

(72.47)

169.06***

(58.36)

152.38***

(47.96)

123.68**

(52.76)

144.94**

(70.44)

-239.72*

(127.02)

213.62***

(62.06)

-158.92

(114.58)

Payment yield, past

year

0.043**

(0.022)

0.043

(0.029)

0.070***

(0.025)

0.041**

(0.019)

0.067***

(0.025)

-0.042

(0.035)

0.036

(0.050)

-0.130

(0.093)

Panel B: Households owed a payment

N=10,593 N=6,804 N= 7,641 N=8,832 N=8,522 N= 2,213 N= 2511

Amount owed in past

year

-173.38

(125.04)

0.945

(167.53)

-148.74

(120.36)

-140.77

(146.92)

-210.04

(165.50)

-602.49

(430.70)

122.42

(188.49)

NAa

Total amount of back

support owed

648.70***

(132.15)

775.66***

(182.40)

533.05***

(149.42)

654.01***

(133.84)

670.57***

(124.29)

778.04**

(340.44)

304.92

(332.86)

NAa

Amount paid in past

year

193.93*

(105.61)

220.90

(153.87)

176.28

(129.71)

217.52

(149.33)

219.99

(147.49)

-119.62

(292.09)

518.94***

(127.63)

NAa

35

Appendix C (continued): Estimated effects of UIFSA implementation on interstate cases for alternative subsamples Original

sample

Drop

duplicates

Lower-

population states

Delete likely

movers

Delete

transition years

Nonwhite

Receives

welfare

Never

married

Payment yield, past

year

0.056

(0.050)

0.023

(0.062)

0.064

(0.047)

0.057

(0.049)

0.108**

(0.049)

-0.066

(0.182)

0.069

(0.189)

NAa

Payment from IV-D 0.075***

(0.024)

0.078**

(0.036)

0.073**

(0.031

0.076***

(0.021)

0.097***

(0.029)

0.048

.0707

0.117

(0.086)

NA a

Payment from court 0.018

(0.027)

0.016

(0.034)

0.023

(0.029)

0.019

(0.025)

0.008

(0.031)

0.161**

(0.0745

-0.054

(0.072)

NAa

Payment from parent -0.085***

(0.020)

-0.093***

(0.032)

-0.084***

(0.028)

-0.089***

(0.029)

-0.094***

(0.028)

-0.193***

.0691

-0.092

(0.058)

NAa

Obligor provides

health care

0.009

(0.026)

0.029

(0.033)

-0.025

(0.025)

0.005

(0.029)

-0.009

(0.030)

-0.025

(0.070)

-0.048

(0.068)

NAa

Obligee provides

health care

-0.019

(0.017)

-0.045

(0.030)

0.004

(0.028)

-0.007

(0.028)

-0.005

(0.028)

-0.049

(0.085)

-0.034

(0.047)

NAa

Other arrangement for

health care

0.020

(0.014)

0.034**

(0.016)

0.026**

(0.013)

0.017

(0.012)

0.010

(0.016)

0.049***

(0.016)

-0.026

(0.038)

NAa

No arrangement for

health care

-0.007

(0.022)

-0.016

(0.031)

-0.007

(0.028)

-0.011

(0.025)

0.001

v0.034)

0.018

(0.079)

0.103

(0.076)

NAa

Panel C: Households asking IV-D for help

N=6,098 N=4,100 N=4,405 N=5,288 N=4,676 N= 1,783 N=2,277 N=1,735

Received help 0.086***

(0.026)

0.050

(0.032)

0.083**

(0.035)

0.076***

(0.026)

0.111**

(0.043)

0.039

(0.059)

0.133***

(0.046)

0.092

(0.074)

Notes: Each cell contains the estimated effect of UIFSA implementation on interstate cases, using a DID calculation, DID regression or conditional matching

DID estimator as indicated. Bootstrapped standard errors are reported in parentheses beneath the estimate.

*/**/*** indicates significantly different from zero at a confidence level exceeding 90%/95%/99%. a Subsample sizes of interstate cases are too small to obtain the conditional matching DID estimator.

References

Argys Laura and H. Elizabeth Peters, 2003. “Can Adequate Child Support Be Legislated?

Responses to Guidelines and Enforcement.” Economic Inquiry 41(3, July):463-479.

Argys, Laura M., H. Elizabeth Peters, and Donald M. Waldman. 2001. "Can the Family Support

Act Put Some Life Back into Deadbeat Dads? An Analysis of Child-Support Guidelines, Award

Rates, and Levels." Journal Of Human Resources 36, no. 2: 224-252.

Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan, 2004. “How Much Should we

Trust Differences-in-Differences Estimates?” The Quarterly Journal of Economics 119

(1,February): 249-275.

Cancian, Maria; Meyer, Daniel R.; Caspar, Emma, 2008. “Welfare and Child Support:

Complements, Not Substitutes.” Journal of Policy Analysis and Management 27(2, Spring):354-

75

Chang, Yunhee. 2003. "Essays on Welfare Reform and Child Support Enforcement: Evidence

from State Administrative Data." Dissertation. University of Illinois.

DeMaria,Mechelene, 1999. “Comment, Jurisdictional Issues under the Uniform Interstate Family

Support Act.” Journal of the American Academy of Matrimonial Lawyers. 16(1): 243- 257.

Freeman, Richard B., and Jane Waldfogel, 2001. “Dunning Delinquent Dads: The Effects of

Child Support Enforcement Policy on Child Support Receipt by Never Married Women.”

Journal of Human Resources 36(2, Spring): 207-25.

Graham, John W., and Andrea H. Beller, 1996. “Child support in Black and White: Racial

Differentials in the Award and Receipt of Child Support during the 1980s.” Social Science

Quarterly 77(3, September): 528-42.

Haynes, Margaret Campbell, 1996. Child Support across State Lines: The Uniform Interstate

Family Support Act: Curriculum for Judicial Educators. Washington, DC: American Bar

Association Center on Children and Law. 286 pages.

Heckman, James J., Hidehiko Ichimura, and Petra Todd. 1998. "Matching as an Econometric

Evaluation Estimator." Review of Economic Studies 65(2): 261-294.

Huang, Chien-Chung, 2002. “The Impact of Child Support Enforcement on Nonmarital and

Marital Births: Does it Differ by Racial and Age Groups?” Social Service Review 76 (2, June):

275-301.

Lerman, R.I., 1993. “Policy Watch: Child Support Policies.” Journal of Economic Perspectives

7: 171-182.

37

Lin, I-Fen, Nora Cate Schaeffer, Judith A. Seltzer, and Kay L. Tuschen, 2004. “Divorced

Parents’ Qualitative and Quantitative Reports of Children’s Living Arrangements,” Journal of

Marriage and the Family 66(May): 385-97.

Neelakantan, Urvi. 2009. "The Impact of Changes in Child Support Policy." Journal Of

Population Economics 22(3): 641-663.

Office of Child Support Enforcement, 2008b. Essentials for Attorneys in Child Support

Enforcement. U.S. Department of Health and Human Services, 3rd

edition. Retrieved September

12, 2008 from http://www.acf.hhs.gov/programs/cse/pubs/2002/reports/essentials/index.html .

Peters H., Argys L., Howard H., Butler J. Legislating Love: The Effect of Child Support and

Welfare Policies on Father-Child Contact. 2004. Review Of Economics Of The Household 2(3,

September):255-274.

Pirog, Maureen A., and Kathleen M. Ziol-Guest. “Child Support Enforcement: Programs and

Policies, Impacts and Questions.” Journal of Policy Analysis and Management 25 (4, Fall): 943-

990.

Roff, Jennifer, 2010. “Welfare, Child Support, and Strategic Behavior: Do High Orders and Low

Disregards Discourage Child Support Awards?” Journal of Human Resources 45(1, Winter): 59-

86.

Seltzer, Judy, 1991. “Relationships between Fathers and Children who Live Apart: The Father’s

Role after Separation.” Journal of Marriage and the Family 53(1, February): 79-101.

Sorensen, E., and Hill, A. 2004. “Single Mothers and their Child-Support Receipt: How Well is

Child-Support Enforcement Doing?” Journal of Human Resources 39: 135-154.

Uniform Interstate Family Support Act (1992; Revised 1996, 2001). National Conference of

Commissioners on Uniform State Laws.

U.S. General Accounting Office, 1992. Interstate Child Support: Mothers Report Receiving Less

Support from Out-of-State Fathers. GAO/HRD-92-39FS.

38

Table 1: Estimated differences between interstate and other cases in help-seeking, child support awards,

and enforcement prior to UIFSA implementation Panel A: Help requested and received

Request Receipt Enforce Establish Locate Paternity Modify Medical

At-risk

households

N = 16,764

0.078***

(0.009)

0.0164**

(0.007)

Help

requested

N= 4,330

NA -0.084***

(0.019)

-0.037**

(0.019)

0.017

(0.018

0.131***

(0.015)

0.023***

(0.008)

-0.014*

(0.008)

0.017**

(0.009)

Help

received

NA NA -0.018

(0.027)

N=1,337

-0.012

(0.041)

N=832

0.120*

(0.062)

N=223

-0.123

(0.120)

N=95

0.038

(0.086)

N=182

0.041

(0.117)

N=118

Panel B: Intermediation

Payment from

IV-D

Payment from

court

Payment from

parent

Households with a written agreement and owed a payment

in the past 12 months (N=8025)

0.048***

(0.014)

0.034**

(0.015)

-0.081***

(0.013)

Panel C: Payment & health care agreements

Written

agreement

Written,

not court-

ordered

Amount

owed in

past year

Back

support

owed

Obligor

responsible

Obligee

responsible

Unspecified

At-risk

households

N = 16,764

-0.071***

(0.010)

-0.036***

(0.006)

-73.62**

(33.27)

250.07***

(67.64)

NA NA NA

Written

agreement

N= 9,385

NA -0.043***

(0.011)

78.98

(50.70)

603.10***

(121.82)

Written

agreement &

owed a

payment

N=8,025

NA -0.037***

(0.012)

45.77

(53.40)

703.62***

(136.94)

-0.061***

(0.015)

0.021*

(0.013)

0.047***

(0.014)

Panel D: Payments

Any payment

made

Amount paid in

past year

Payment yield,

past year

At-risk

N = 16,764

-0.085***

(0.009)

-111.48***

(31.73)

-0.068***

(0.012)

Written agreement

N= 9,385

-0.082***

(0.014)

-54.45

(52.51)

-0.055***

(0.019)

Written agreement & owed a payment

(N=8,025)

-0.106***

(0.014)

-84.86

( 58.00)

-0.074***

(0.021)

Notes: Each cell contains the difference in the outcome between interstate and other cases as estimated from a

regression that also includes a full set of explanators, described in the narrative. The standard error of the estimate is

reported in parentheses beneath the estimate. */**/*** indicates significantly different from zero at a confidence

level exceeding 90%/95%/99%. In Panel A, findings of “other” category not reported to economize on space. The

coefficient estimates for ‘other help’ are all zero. In Panel C, findings of “other” category not reported to economize

on space. The coefficient estimates for ‘other help’ are all zero.

39

Table 2: Estimated effects of UIFSA implementation on help sought and received for interstate cases Unadjusted DID DID Regression Conditional matching DID

Panel A: At-risk households, N=22,544

Requested help 0.000

(0.020)

0.002

(0.017)

-0.006

(0.015)

Panel B: Help requesters N=6,098

Received help 0.101***

(0.033)

0.106***

(0.034)

0.086***

(0.026)

Enforcement 0.003

(0.0318)

0.002

(0.035)

0.005

(0.025)

Establish order -0.043

(0.032)

-0.045

(0.033)

-0.026

(0.032)

Locate 0.014

(0.028)

0.021

(0.023)

0 .004

(0.023)

Modify order 0.013

(0.013)

0.027

(0.023)

0.013

(0.010)

Medical support 0.026

(0.018)

0.026

(0.020)

0.019

(0.012)

Other 0.013

(0.018)

0.014

(0.018)

0.010

(0.012)

Paternity establishment 0.027

(0.017)

0.024*

(0.016)

0.018

(0.011)

Notes: Each cell contains the estimated effect of UIFSA implementation on interstate cases, using a DID

calculation, DID regression or conditional matching DID estimator as indicated. Bootstrapped standard errors are

reported in parentheses beneath the estimate.

*/**/*** indicates significantly different from zero at a confidence level exceeding 90%/95%/99%. a The sample size is too small to obtain the conditional matching DID estimator.

40

Table 3: Estimated effects of UIFSA implementation on payment intermediation for interstate cases.

Unadjusted DID

calculation Regression-adjusted

DID Conditional matching

DID Payment from IV-D 0.097

***

(0.024) 0.098

***

(0.032) 0.075

***

(0.024)

Payment from court 0.018

(0.035) 0.019

(0.031) 0.018

(0.027)

Payment from parent -0.104***

(0.032) -0.094

***

(0.025) -0.085

***

(0.020)

Notes: Each cell contains the estimated effect of UIFSA implementation on interstate cases, using a DID

calculation, DID regression or conditional matching DID estimator as indicated. Bootstrapped standard errors are

reported in parentheses beneath the estimate.

Sample is at-risk households with a written agreement who were owed a payment in the past 12 months. N=10,593.

*/**/*** indicates significantly different from zero at a confidence level exceeding 90%/95%/99%. a The sample size is too small to obtain the conditional matching DID estimator.

Table 4: Estimated effects of UIFSA implementation on agreements and awards for interstate cases.

At-risk

N=22,544

Written agreement

N=12,539

Written agreement and

owed a payment last year

N=10,593

Unadjusted

DID

Regression-

adjusted DID

Conditional

matching

DID

Unadjusted

DID

Regression-

adjusted DID

Conditional

matching

DID

Unadjusted

DID

Regression-

adjusted DID

Conditional

matching

DID

Panel A: Payment agreements

Written 0.035*

(0.019)

0.045**

(0.019)

0.029**

(0.011)

NA NA NA NA NA NA

Written, not

court-ordered

0.004

(0.013)

0.003

(0.014)

0.002

(0.009)

NA NA NA NA NA NA

Amount owed

in past year

-16.65

(78.74)

22.10

(67.49)

-16.65

(85.90)

-222.67

(148.61)

-166.49

(106.67)

-103.41

(113.44)

-275.96

(187.15)

-212.14*

(117.41)

-173.38

(125.04)

Total amount

of back support

owed

279.69***

(72.43)

(N=18,771)

287.90**

(120.48)

(N=18,771)

279.69***

(78.00)

(N18,771)

646.87***

(132.29)

(N=10,521)

640.61***

(220.05)

(N =10,521)

547.09***

(129.53)

(N=10,521)

755.29***

(166.92)

(N=8,802)

754.89***

257.96

(N=8,802)

648.70***

132.15

(N=8,802)

Panel B: Health care agreements

Obligor

responsible

NA NA NA NA NA NA 0.005

(0.035)

0.007

(0.031)

0.009

(0.026)

Obligee

responsible

NA NA NA NA NA NA -0.024

(0.019)

-0.019

(0.024)

-0.019

(0.017)

Unspecified NA NA NA NA NA NA 0.003

(0.026)

-0.006

(0.028)

-0.007

(0.022)

Other NA NA NA NA NA NA 0.019

(0.016)

0.018

(0.019)

0.020

(0.014)

Notes: Each cell contains the estimated effect of UIFSA implementation on interstate cases, using a DID calculation, DID regression or conditional matching

DID estimator as indicated. Bootstrapped standard errors are reported in parentheses beneath the estimate.

*/**/*** indicates significantly different from zero at a confidence level exceeding 90%/95%/99%.

42

Table 5: Estimated effects of UIFSA implementation on child support collections for interstate cases.

At-risk

N=22,544

Written agreement

N=12,539

Written agreement and

owed a payment last year

N=10,593

Unadjusted

DID

Regression-

adjusted DID

Conditional

matching DID

Unadjusted

DID

Regression-

adjusted DID

Conditional

matching DID

Unadjusted

DID

Regression-

adjusted DID

Conditional

matching DID

Any

payment

made

0.043**

(0.019)

0.051***

(0.018)

0.043**

(0.019)

0.033

(0.024)

0.052**

(0.024)

0.034

(0.026)

0.033

(0.027)

0.047**

(0.021)

0.031

(0.023)

Amount

paid in past

year

125.81**

(60.92)

162.21**

(64.01)

125.81*

(72.47)

115.14

(123.29)

182.75*

(109.13)

157.05

(101.34)

179.92

(141.77)

258.67**

(124.60)

193.93*

(105.61)

Payment

yield, past

year

0.052**

(0.026)

0.060**

(0.024)

0.043**

(0.022)

0.049

(0.048)

0.060

(0.040)

0.050

(0.046)

0.053

(0 .051)

0.067

(0.045)

0.056

(0.050)

Notes: Each cell contains the estimated effect of UIFSA implementation on interstate cases, using a DID calculation, DID regression or conditional matching

DID estimator as indicated. Bootstrapped standard errors are reported in parentheses beneath the estimate.

*/**/*** indicates significantly different from zero at a confidence level exceeding 90%/95%/99%.